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Li Y, Wang Y, Wu Q, Qi R, Li L, Xu L, Yuan H. High-throughput fluorescence sensing array based on tetraphenylethylene derivatives for detecting and distinguishing pathogenic microbes. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 318:124435. [PMID: 38796890 DOI: 10.1016/j.saa.2024.124435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 05/05/2024] [Accepted: 05/08/2024] [Indexed: 05/29/2024]
Abstract
Infections induced by pathogenic microorganisms will bring negative effects such as diseases that damage health and result in heavy economic burden. Therefore, it is very important to detect and identify the pathogens in time. Moreover, traditional clinical diagnosis or food testing often faces the problem of dealing with a large number of samples. Here, we designed a high-throughput fluorescent sensor array based on the different binding ability of five tetraphenylethylene derivatives (TPEs) with various side chains to different kinds of pathogenic microbes, which is used to detect and distinguish various species, so as to realize rapid mass diagnosis, and hopefully provide guidance for further determination of microbial infections and clinical treatment.
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Affiliation(s)
- Yutong Li
- Department of Chemistry, Beijing Technology and Business University, Beijing 100048, China
| | - Yi Wang
- Department of Chemistry, Beijing Technology and Business University, Beijing 100048, China
| | - Qiaoyue Wu
- Department of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430065, China
| | - Ruilian Qi
- Department of Chemistry, Beijing Technology and Business University, Beijing 100048, China.
| | - Li Li
- Department of Chemistry, Beijing Technology and Business University, Beijing 100048, China
| | - Li Xu
- Department of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430065, China.
| | - Huanxiang Yuan
- Department of Chemistry, Beijing Technology and Business University, Beijing 100048, China.
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2
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Ligda P, Mittas N, Kyzas GZ, Claerebout E, Sotiraki S. Machine learning and explainable artificial intelligence for the prevention of waterborne cryptosporidiosis and giardiosis. WATER RESEARCH 2024; 262:122110. [PMID: 39042970 DOI: 10.1016/j.watres.2024.122110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/21/2024] [Accepted: 07/15/2024] [Indexed: 07/25/2024]
Abstract
Cryptosporidium and Giardia are important parasitic protozoa due to their zoonotic potential and impact on human health, and have often caused waterborne outbreaks of disease. Detection of (oo)cysts in water matrices is challenging and extremely costly, thus only few countries have legislated for regular monitoring of drinking water for their presence. Several attempts have been made trying to investigate the association between the presence of such (oo)cysts in waters with other biotic or abiotic factors, with inconclusive findings. In this regard, the aim of this study was the development of an holistic approach leveraging Machine Learning (ML) and eXplainable Artificial Intelligence (XAI) techniques, in order to provide empirical evidence related to the presence and prediction of Cryptosporidium oocysts and Giardia cysts in water samples. To meet this objective, we initially modelled the complex relationship between Cryptosporidium and Giardia (oo)cysts and a set of parasitological, microbiological, physicochemical and meteorological parameters via a model-agnostic meta-learner algorithm that provides flexibility regarding the selection of the ML model executing the fitting task. Based on this generic approach, a set of four well-known ML candidates were, empirically, evaluated in terms of their predictive capabilities. Then, the best-performed algorithms, were further examined through XAI techniques for gaining meaningful insights related to the explainability and interpretability of the derived solutions. The findings reveal that the Random Forest achieves the highest prediction performance when the objective is the prediction of both contamination and contamination intensity with Cryptosporidium oocysts in a given water sample, with meteorological/physicochemical and microbiological markers being informative, respectively. For the prediction of contamination with Giardia, the eXtreme Gradient Boosting with physicochemical parameters was the most efficient algorithm, while, the Support Vector Regression that takes into consideration both microbiological and meteorological markers was more efficient for evaluating the contamination intensity with cysts. The results of the study designate that the adoption of ML and XAI approaches can be considered as a valuable tool for unveiling the complicated correlation of the presence and contamination intensity with these zoonotic parasites that could constitute, in turn, a basis for the development of monitoring platforms and early warning systems for the prevention of waterborne disease outbreaks.
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Affiliation(s)
- Panagiota Ligda
- Laboratory of Parasitology, Veterinary Research Institute, Hellenic Agricultural Organization - DIMITRA, Thermi, Thessaloniki 57001, Greece.
| | - Nikolaos Mittas
- Hephaestus Laboratory, School of Chemistry, Faculty of Sciences, Democritus University of Thrace, Kavala GR-65404, Greece
| | - George Z Kyzas
- Hephaestus Laboratory, School of Chemistry, Faculty of Sciences, Democritus University of Thrace, Kavala GR-65404, Greece
| | - Edwin Claerebout
- Laboratory of Parasitology, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, Merelbeke B-9820, Belgium
| | - Smaragda Sotiraki
- Laboratory of Parasitology, Veterinary Research Institute, Hellenic Agricultural Organization - DIMITRA, Thermi, Thessaloniki 57001, Greece
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3
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Chin SY, Dong J, Hasikin K, Ngui R, Lai KW, Yeoh PSQ, Wu X. Bacterial image analysis using multi-task deep learning approaches for clinical microscopy. PeerJ Comput Sci 2024; 10:e2180. [PMID: 39145215 PMCID: PMC11323154 DOI: 10.7717/peerj-cs.2180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 06/17/2024] [Indexed: 08/16/2024]
Abstract
Background Bacterial image analysis plays a vital role in various fields, providing valuable information and insights for studying bacterial structural biology, diagnosing and treating infectious diseases caused by pathogenic bacteria, discovering and developing drugs that can combat bacterial infections, etc. As a result, it has prompted efforts to automate bacterial image analysis tasks. By automating analysis tasks and leveraging more advanced computational techniques, such as deep learning (DL) algorithms, bacterial image analysis can contribute to rapid, more accurate, efficient, reliable, and standardised analysis, leading to enhanced understanding, diagnosis, and control of bacterial-related phenomena. Methods Three object detection networks of DL algorithms, namely SSD-MobileNetV2, EfficientDet, and YOLOv4, were developed to automatically detect Escherichia coli (E. coli) bacteria from microscopic images. The multi-task DL framework is developed to classify the bacteria according to their respective growth stages, which include rod-shaped cells, dividing cells, and microcolonies. Data preprocessing steps were carried out before training the object detection models, including image augmentation, image annotation, and data splitting. The performance of the DL techniques is evaluated using the quantitative assessment method based on mean average precision (mAP), precision, recall, and F1-score. The performance metrics of the models were compared and analysed. The best DL model was then selected to perform multi-task object detections in identifying rod-shaped cells, dividing cells, and microcolonies. Results The output of the test images generated from the three proposed DL models displayed high detection accuracy, with YOLOv4 achieving the highest confidence score range of detection and being able to create different coloured bounding boxes for different growth stages of E. coli bacteria. In terms of statistical analysis, among the three proposed models, YOLOv4 demonstrates superior performance, achieving the highest mAP of 98% with the highest precision, recall, and F1-score of 86%, 97%, and 91%, respectively. Conclusions This study has demonstrated the effectiveness, potential, and applicability of DL approaches in multi-task bacterial image analysis, focusing on automating the detection and classification of bacteria from microscopic images. The proposed models can output images with bounding boxes surrounding each detected E. coli bacteria, labelled with their growth stage and confidence level of detection. All proposed object detection models have achieved promising results, with YOLOv4 outperforming the other models.
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Affiliation(s)
- Shuang Yee Chin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Jian Dong
- China Electronics Standardization Institute, Beijing, China
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Centre of Intelligent Systems for Emerging Technology (CISET), Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Romano Ngui
- Malaria Research Centre, Faculty of Medicine and Health Sciences, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Pauline Shan Qing Yeoh
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Xiang Wu
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Institute of Medical Information Security, Xuzhou Medical University, Xuzhou, China
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4
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Zhang X, Zhang D, Zhang X, Zhang X. Artificial intelligence applications in the diagnosis and treatment of bacterial infections. Front Microbiol 2024; 15:1449844. [PMID: 39165576 PMCID: PMC11334354 DOI: 10.3389/fmicb.2024.1449844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2024] [Accepted: 07/04/2024] [Indexed: 08/22/2024] Open
Abstract
The diagnosis and treatment of bacterial infections in the medical and public health field in the 21st century remain significantly challenging. Artificial Intelligence (AI) has emerged as a powerful new tool in diagnosing and treating bacterial infections. AI is rapidly revolutionizing epidemiological studies of infectious diseases, providing effective early warning, prevention, and control of outbreaks. Machine learning models provide a highly flexible way to simulate and predict the complex mechanisms of pathogen-host interactions, which is crucial for a comprehensive understanding of the nature of diseases. Machine learning-based pathogen identification technology and antimicrobial drug susceptibility testing break through the limitations of traditional methods, significantly shorten the time from sample collection to the determination of result, and greatly improve the speed and accuracy of laboratory testing. In addition, AI technology application in treating bacterial infections, particularly in the research and development of drugs and vaccines, and the application of innovative therapies such as bacteriophage, provides new strategies for improving therapy and curbing bacterial resistance. Although AI has a broad application prospect in diagnosing and treating bacterial infections, significant challenges remain in data quality and quantity, model interpretability, clinical integration, and patient privacy protection. To overcome these challenges and, realize widespread application in clinical practice, interdisciplinary cooperation, technology innovation, and policy support are essential components of the joint efforts required. In summary, with continuous advancements and in-depth application of AI technology, AI will enable doctors to more effectivelyaddress the challenge of bacterial infection, promoting the development of medical practice toward precision, efficiency, and personalization; optimizing the best nursing and treatment plans for patients; and providing strong support for public health safety.
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Affiliation(s)
- Xiaoyu Zhang
- First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Deng Zhang
- Department of Infectious Diseases, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Xifan Zhang
- First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Xin Zhang
- First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China
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5
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Kieran TJ, Sun X, Maines TR, Belser JA. Machine learning approaches for influenza A virus risk assessment identifies predictive correlates using ferret model in vivo data. Commun Biol 2024; 7:927. [PMID: 39090358 PMCID: PMC11294530 DOI: 10.1038/s42003-024-06629-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 07/24/2024] [Indexed: 08/04/2024] Open
Abstract
In vivo assessments of influenza A virus (IAV) pathogenicity and transmissibility in ferrets represent a crucial component of many pandemic risk assessment rubrics, but few systematic efforts to identify which data from in vivo experimentation are most useful for predicting pathogenesis and transmission outcomes have been conducted. To this aim, we aggregated viral and molecular data from 125 contemporary IAV (H1, H2, H3, H5, H7, and H9 subtypes) evaluated in ferrets under a consistent protocol. Three overarching predictive classification outcomes (lethality, morbidity, transmissibility) were constructed using machine learning (ML) techniques, employing datasets emphasizing virological and clinical parameters from inoculated ferrets, limited to viral sequence-based information, or combining both data types. Among 11 different ML algorithms tested and assessed, gradient boosting machines and random forest algorithms yielded the highest performance, with models for lethality and transmission consistently better performing than models predicting morbidity. Comparisons of feature selection among models was performed, and highest performing models were validated with results from external risk assessment studies. Our findings show that ML algorithms can be used to summarize complex in vivo experimental work into succinct summaries that inform and enhance risk assessment criteria for pandemic preparedness that take in vivo data into account.
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Affiliation(s)
- Troy J Kieran
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA.
| | - Xiangjie Sun
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Taronna R Maines
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jessica A Belser
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA.
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6
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Yu D, Andersson-Li M, Maes S, Andersson-Li L, Neumann NF, Odlare M, Jonsson A. Development of a logic regression-based approach for the discovery of host- and niche-informative biomarkers in Escherichia coli and their application for microbial source tracking. Appl Environ Microbiol 2024; 90:e0022724. [PMID: 38940567 PMCID: PMC11267920 DOI: 10.1128/aem.00227-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 06/07/2024] [Indexed: 06/29/2024] Open
Abstract
Microbial source tracking leverages a wide range of approaches designed to trace the origins of fecal contamination in aquatic environments. Although source tracking methods are typically employed within the laboratory setting, computational techniques can be leveraged to advance microbial source tracking methodology. Herein, we present a logic regression-based supervised learning approach for the discovery of source-informative genetic markers within intergenic regions across the Escherichia coli genome that can be used for source tracking. With just single intergenic loci, logic regression was able to identify highly source-specific (i.e., exceeding 97.00%) biomarkers for a wide range of host and niche sources, with sensitivities reaching as high as 30.00%-50.00% for certain source categories, including pig, sheep, mouse, and wastewater, depending on the specific intergenic locus analyzed. Restricting the source range to reflect the most prominent zoonotic sources of E. coli transmission (i.e., bovine, chicken, human, and pig) allowed for the generation of informative biomarkers for all host categories, with specificities of at least 90.00% and sensitivities between 12.50% and 70.00%, using the sequence data from key intergenic regions, including emrKY-evgAS, ibsB-(mdtABCD-baeSR), ompC-rcsDB, and yedS-yedR, that appear to be involved in antibiotic resistance. Remarkably, we were able to use this approach to classify 48 out of 113 river water E. coli isolates collected in Northwestern Sweden as either beaver, human, or reindeer in origin with a high degree of consensus-thus highlighting the potential of logic regression modeling as a novel approach for augmenting current source tracking efforts.IMPORTANCEThe presence of microbial contaminants, particularly from fecal sources, within water poses a serious risk to public health. The health and economic burden of waterborne pathogens can be substantial-as such, the ability to detect and identify the sources of fecal contamination in environmental waters is crucial for the control of waterborne diseases. This can be accomplished through microbial source tracking, which involves the use of various laboratory techniques to trace the origins of microbial pollution in the environment. Building on current source tracking methodology, we describe a novel workflow that uses logic regression, a supervised machine learning method, to discover genetic markers in Escherichia coli, a common fecal indicator bacterium, that can be used for source tracking efforts. Importantly, our research provides an example of how the rise in prominence of machine learning algorithms can be applied to improve upon current microbial source tracking methodology.
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Affiliation(s)
- Daniel Yu
- School of Public Health, University of Alberta, Edmonton, Alberta, Canada
| | | | - Sharon Maes
- Department of Natural Sciences, Design and Sustainable Development, Mid Sweden University, Östersund, Sweden
| | - Lili Andersson-Li
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Solna, Sweden
| | - Norman F. Neumann
- School of Public Health, University of Alberta, Edmonton, Alberta, Canada
| | - Monica Odlare
- Department of Natural Sciences, Design and Sustainable Development, Mid Sweden University, Östersund, Sweden
| | - Anders Jonsson
- Department of Natural Sciences, Design and Sustainable Development, Mid Sweden University, Östersund, Sweden
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7
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Wang L, Lu W, Song Y, Liu S, Fu YV. Using machine learning to identify environmental factors that collectively determine microbial community structure of activated sludge. ENVIRONMENTAL RESEARCH 2024; 260:119635. [PMID: 39025351 DOI: 10.1016/j.envres.2024.119635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 07/12/2024] [Accepted: 07/15/2024] [Indexed: 07/20/2024]
Abstract
Activated sludge (AS) microbial communities are influenced by various environmental variables. However, a comprehensive analysis of how these variables jointly and nonlinearly shape the AS microbial community remains challenging. In this study, we employed advanced machine learning techniques to elucidate the collective effects of environmental variables on the structure and function of AS microbial communities. Applying Dirichlet multinomial mixtures analysis to 311 global AS samples, we identified four distinct microbial community types (AS-types), each characterized by unique microbial compositions and metabolic profiles. We used 14 classical linear and nonlinear machine learning methods to select a baseline model. The extremely randomized trees demonstrated optimal performance in learning the relationship between environmental factors and AS types (with an accuracy of 71.43%). Feature selection identified critical environmental factors and their importance rankings, including latitude (Lat), longitude (Long), precipitation during sampling (Precip), solids retention time (SRT), effluent total nitrogen (Effluent TN), average temperature during sampling month (Avg Temp), mixed liquor temperature (Mixed Temp), influent biochemical oxygen demand (Influent BOD), and annual precipitation (Annual Precip). Significantly, Lat, Long, Precip, Avg Temp, and Annual Precip, influenced metabolic variations among AS types. These findings emphasize the pivotal role of environmental variables in shaping microbial community structures and enhancing metabolic pathways within activated sludge. Our study encourages the application of machine learning techniques to design artificial activated sludge microbial communities for specific environmental purposes.
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Affiliation(s)
- Lu Wang
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Weilai Lu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Yang Song
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Shuangjiang Liu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Yu Vincent Fu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China; Savaid Medical School, University of Chinese Academy of Sciences, Beijing, 100049, China.
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Giacobbe DR, Marelli C, Mora S, Cappello A, Signori A, Vena A, Guastavino S, Rosso N, Campi C, Giacomini M, Bassetti M. Prediction of candidemia with machine learning techniques: state of the art. Future Microbiol 2024; 19:931-940. [PMID: 39072500 PMCID: PMC11290752 DOI: 10.2217/fmb-2023-0269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 03/06/2024] [Indexed: 07/30/2024] Open
Abstract
In this narrative review, we discuss studies assessing the use of machine learning (ML) models for the early diagnosis of candidemia, focusing on employed models and the related implications. There are currently few studies evaluating ML techniques for the early diagnosis of candidemia as a prediction task based on clinical and laboratory features. The use of ML tools holds promise to provide highly accurate and real-time support to clinicians for relevant therapeutic decisions at the bedside of patients with suspected candidemia. However, further research is needed in terms of sample size, data quality, recognition of biases and interpretation of model outputs by clinicians to better understand if and how these techniques could be safely adopted in daily clinical practice.
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Affiliation(s)
- Daniele Roberto Giacobbe
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
- UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cristina Marelli
- UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Sara Mora
- UO Information & Communication Technologies (ICT), IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Alice Cappello
- UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Alessio Signori
- Section of Biostatistics, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Antonio Vena
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
- UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | | | - Nicola Rosso
- UO Information & Communication Technologies (ICT), IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cristina Campi
- Department of Mathematics (DIMA), University of Genoa, Genoa, Italy
- Life Science Computational Laboratory (LISCOMP), IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Mauro Giacomini
- Department of Informatics, Bioengineering, Robotics & System Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - Matteo Bassetti
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
- UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
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9
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Shi Y, Zhou M, Chang C, Jiang P, Wei K, Zhao J, Shan Y, Zheng Y, Zhao F, Lv X, Guo S, Wang F, He D. Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management. Front Immunol 2024; 15:1409555. [PMID: 38915408 PMCID: PMC11194317 DOI: 10.3389/fimmu.2024.1409555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 05/24/2024] [Indexed: 06/26/2024] Open
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA.
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Affiliation(s)
- Yiming Shi
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Mi Zhou
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Cen Chang
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Ping Jiang
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Kai Wei
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Jianan Zhao
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Yu Shan
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Yixin Zheng
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Fuyu Zhao
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Xinliang Lv
- Traditional Chinese Medicine Hospital of Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia Autonomous Region, China
| | - Shicheng Guo
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Fubo Wang
- Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
- Department of Urology, Affiliated Tumor Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, Guangxi, China
| | - Dongyi He
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
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10
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Stamate E, Piraianu AI, Ciobotaru OR, Crassas R, Duca O, Fulga A, Grigore I, Vintila V, Fulga I, Ciobotaru OC. Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years. Diagnostics (Basel) 2024; 14:1103. [PMID: 38893630 PMCID: PMC11172021 DOI: 10.3390/diagnostics14111103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 05/12/2024] [Accepted: 05/23/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. RESULTS We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. CONCLUSIONS The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.
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Affiliation(s)
- Elena Stamate
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Alin-Ionut Piraianu
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Oana Roxana Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
| | - Rodica Crassas
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Oana Duca
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Ionica Grigore
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Vlad Vintila
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Clinical Department of Cardio-Thoracic Pathology, University of Medicine and Pharmacy “Carol Davila” Bucharest, 37 Dionisie Lupu Street, 4192910 Bucharest, Romania
| | - Iuliu Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Octavian Catalin Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
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Taiwo OR, Onyeaka H, Oladipo EK, Oloke JK, Chukwugozie DC. Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models. Int J Microbiol 2024; 2024:6612162. [PMID: 38799770 PMCID: PMC11126350 DOI: 10.1155/2024/6612162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 04/01/2024] [Accepted: 04/23/2024] [Indexed: 05/29/2024] Open
Abstract
Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.
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Affiliation(s)
| | - Helen Onyeaka
- School of Chemical Engineering, University of Birmingham, Edgbaston B15 2TT, Birmingham, UK
| | - Elijah K. Oladipo
- Genomics Unit, Helix Biogen Institute, Ogbomosho, Oyo, Nigeria
- Department of Microbiology, Laboratory of Molecular Biology, Immunology and Bioinformatics, Adeleke University, Ede, Osun, Nigeria
| | - Julius Kola Oloke
- Department of Natural Science, Microbiology Unit, Precious Cornerstone University, Ibadan, Oyo, Nigeria
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Ranjan J, Ahmad A, Subudhi M, Kumar A. Assessment of Artificial Intelligence Platforms With Regard to Medical Microbiology Knowledge: An Analysis of ChatGPT and Gemini. Cureus 2024; 16:e60675. [PMID: 38770053 PMCID: PMC11104281 DOI: 10.7759/cureus.60675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2024] [Indexed: 05/22/2024] Open
Abstract
The performance of two artificial intelligence (AI) platforms, ChatGPT 3.5 (OpenAI, California, United States) and Gemini (Google AI, California, United States) was assessed by answering 200 questions of microbiology drawn from validated sources. The questions were selected from topics such as General Microbiology, Immunology, and Microbiology Applied to Infectious Diseases. The study was conducted from December 2023 to March 2024, and the responses of the different AI platforms were compared with an answer key. Statistical analysis was performed to assess accuracy. ChatGPT 3.5 and Gemini had comparable accuracy with correct response scores of 71% and 70.5%, respectively. Their performance varied across different sections. Gemini performed better in General Microbiology and Immunology, and ChatGPT 3.5 had a better score in the Applied Microbiology section. The study's findings highlight that AI platforms such as ChatGPT and Gemini can be utilized in microbiology and medical education. The evolution and continuous updating of AI platforms are required to improve their performance.
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Affiliation(s)
- Jai Ranjan
- Microbiology, All India Institute of Medical Sciences, Bathinda, Bathinda, IND
| | - Absar Ahmad
- Animal Genetics and Breeding, Faculty of Veterinary Science and Animal Husbandry, Birsa Agricultural University, Ranchi, IND
| | - Monalisa Subudhi
- Microbiology, Institute of Medical Sciences and SUM-II Hospital, Bhubaneswar, IND
| | - Ajay Kumar
- Microbiology, Manipal Tata Medical College, Manipal Academy of Higher Education, Manipal, IND
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13
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Cao S, Hu Y. Creating machine learning models that interpretably link systemic inflammatory index, sex steroid hormones, and dietary antioxidants to identify gout using the SHAP (SHapley Additive exPlanations) method. Front Immunol 2024; 15:1367340. [PMID: 38751428 PMCID: PMC11094226 DOI: 10.3389/fimmu.2024.1367340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 04/22/2024] [Indexed: 05/18/2024] Open
Abstract
Background The relationship between systemic inflammatory index (SII), sex steroid hormones, dietary antioxidants (DA), and gout has not been determined. We aim to develop a reliable and interpretable machine learning (ML) model that links SII, sex steroid hormones, and DA to gout identification. Methods The dataset we used to study the relationship between SII, sex steroid hormones, DA, and gout was from the National Health and Nutrition Examination Survey (NHANES). Six ML models were developed to identify gout by SII, sex steroid hormones, and DA. The seven performance discriminative features of each model were summarized, and the eXtreme Gradient Boosting (XGBoost) model with the best overall performance was selected to identify gout. We used the SHapley Additive exPlanation (SHAP) method to explain the XGBoost model and its decision-making process. Results An initial survey of 20,146 participants resulted in 8,550 being included in the study. Selecting the best performing XGBoost model associated with SII, sex steroid hormones, and DA to identify gout (male: AUC: 0.795, 95% CI: 0.746- 0.843, accuracy: 98.7%; female: AUC: 0.822, 95% CI: 0.754- 0.883, accuracy: 99.2%). In the male group, The SHAP values showed that the lower feature values of lutein + zeaxanthin (LZ), vitamin C (VitC), lycopene, zinc, total testosterone (TT), vitamin E (VitE), and vitamin A (VitA), the greater the positive effect on the model output. In the female group, SHAP values showed that lower feature values of E2, zinc, lycopene, LZ, TT, and selenium had a greater positive effect on model output. Conclusion The interpretable XGBoost model demonstrated accuracy, efficiency, and robustness in identifying associations between SII, sex steroid hormones, DA, and gout in participants. Decreased TT in males and decreased E2 in females may be associated with gout, and increased DA intake and decreased SII may reduce the potential risk of gout.
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Affiliation(s)
- Shunshun Cao
- Pediatric Endocrinology, Genetics and Metabolism, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yangyang Hu
- Reproductive Medicine Center, Obstetrics and Gynecology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
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14
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Rusic D, Kumric M, Seselja Perisin A, Leskur D, Bukic J, Modun D, Vilovic M, Vrdoljak J, Martinovic D, Grahovac M, Bozic J. Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence. Microorganisms 2024; 12:842. [PMID: 38792673 PMCID: PMC11123121 DOI: 10.3390/microorganisms12050842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 05/26/2024] Open
Abstract
Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.
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Affiliation(s)
- Doris Rusic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marko Kumric
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Ana Seselja Perisin
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Dario Leskur
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Josipa Bukic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Darko Modun
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marino Vilovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Josip Vrdoljak
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Dinko Martinovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Department of Maxillofacial Surgery, University Hospital of Split, Spinciceva 1, 21000 Split, Croatia
| | - Marko Grahovac
- Department of Pharmacology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia;
| | - Josko Bozic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
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Asnicar F, Thomas AM, Passerini A, Waldron L, Segata N. Machine learning for microbiologists. Nat Rev Microbiol 2024; 22:191-205. [PMID: 37968359 DOI: 10.1038/s41579-023-00984-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/03/2023] [Indexed: 11/17/2023]
Abstract
Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. The applications in microbiology are quickly expanding and the machine learning tools frequently used in basic and clinical research range from classification and regression to clustering and dimensionality reduction. In this Review, we examine the main machine learning concepts, tasks and applications that are relevant for experimental and clinical microbiologists. We provide the minimal toolbox for a microbiologist to be able to understand, interpret and use machine learning in their experimental and translational activities.
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Affiliation(s)
- Francesco Asnicar
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy
| | - Andrew Maltez Thomas
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy
| | - Andrea Passerini
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Levi Waldron
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy.
- Department of Epidemiology and Biostatistics, City University of New York, New York, NY, USA.
| | - Nicola Segata
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy.
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy.
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Wang Z, Dai H, Chen B, Cheng S, Sun Y, Zhao J, Guo Z, Cai X, Wang X, Li B, Geng H. Effluent quality prediction of the sewage treatment based on a hybrid neural network model: Comparison and application. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 351:119900. [PMID: 38157580 DOI: 10.1016/j.jenvman.2023.119900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 12/17/2023] [Accepted: 12/19/2023] [Indexed: 01/03/2024]
Abstract
The accurate prediction and assessment of effluent quality in wastewater treatment plants (WWTPs) are paramount for the efficacy of sewage treatment processes. Neural network models have exhibited promise in enhancing prediction accuracy by simulating and analyzing diverse influent parameters. In this study, a back propagation neural network hybrid model based on a tent chaotic map and sparrow search algorithm (Tent_BP_SSA) was developed to predict the effluent quality of sewage treatment processes. The prediction performance of the propose hybrid model was compared with traditional neural network models using five performance indicators (MAE, RMSE, SSE, MAPE and R2). Specifically, in comparison with the prior Tent_BP_SSA, Tent_BP_SSA2 demonstrated notable enhancements, with the R2 increasing from 0.9512 to 0.9672, while MAE, RMSE, SSE, and MAPE decreased by 9.62%, 18.84%, 24.80%, and 47.10%, respectively. These indicators collectively affirm that the utilization of higher-order input parameters ensures improved accuracy of the Tent_BP_SSA2 hybrid model in predicting effluent quality. Moreover, the Tent_BP_SSA2 model exhibited robust prediction ability (R2 of 0.9246) when applied to assess the effluent quality of an actual sewage treatment plant. The incorporation of integrated models comprising the sparrow search optimizing algorithm, tent chaotic mapping, and higher-order magnitude decomposition of input parameters has demonstrated the capacity to enhance the accuracy of effluent quality prediction. This study illuminates novel perspectives on the prediction of effluent quality and the assessment of effluent warnings in WWTPs.
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Affiliation(s)
- Zeyu Wang
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212100, China.
| | - Hongliang Dai
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212100, China.
| | - Beiyue Chen
- College of Electronics Engineering, Nanjing Xiaozhuang University, Nanjing, 211171, China.
| | - Sichao Cheng
- Hangzhou City Planning and Design Academy, Hangzhou, 310012, China.
| | - Yang Sun
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212100, China.
| | - Jinkun Zhao
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212100, China.
| | - Zechong Guo
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212100, China; School of Environmental and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
| | - Xingwei Cai
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212100, China.
| | - Xingang Wang
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212100, China.
| | - Bing Li
- Jiangsu Zhongchuang Qingyuan Technology Co., Ltd., Yancheng, 224000, China.
| | - Hongya Geng
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518075, China.
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Cao L, Zeng L, Wang Y, Cao J, Han Z, Chen Y, Wang Y, Zhong G, Qiao S. U 2-Net and ResNet50-Based Automatic Pipeline for Bacterial Colony Counting. Microorganisms 2024; 12:201. [PMID: 38258027 PMCID: PMC10820204 DOI: 10.3390/microorganisms12010201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 01/24/2024] Open
Abstract
In this paper, an automatic colony counting system based on an improved image preprocessing algorithm and convolutional neural network (CNN)-assisted automatic counting method was developed. Firstly, we assembled an LED backlighting illumination platform as an image capturing system to obtain photographs of laboratory cultures. Consequently, a dataset was introduced consisting of 390 photos of agar plate cultures, which included 8 microorganisms. Secondly, we implemented a new algorithm for image preprocessing based on light intensity correction, which facilitated clearer differentiation between colony and media areas. Thirdly, a U2-Net was used to predict the probability distribution of the edge of the Petri dish in images to locate region of interest (ROI), and then threshold segmentation was applied to separate it. This U2-Net achieved an F1 score of 99.5% and a mean absolute error (MAE) of 0.0033 on the validation set. Then, another U2-Net was used to separate the colony region within the ROI. This U2-Net achieved an F1 score of 96.5% and an MAE of 0.005 on the validation set. After that, the colony area was segmented into multiple components containing single or adhesive colonies. Finally, the colony components (CC) were innovatively rotated and the image crops were resized as the input (with 14,921 image crops in the training set and 4281 image crops in the validation set) for the ResNet50 network to automatically count the number of colonies. Our method achieved an overall recovery of 97.82% for colony counting and exhibited excellent performance in adhesion classification. To the best of our knowledge, the proposed "light intensity correction-based image preprocessing→U2-Net segmentation for Petri dish edge→U2-Net segmentation for colony region→ResNet50-based counting" scheme represents a new attempt and demonstrates a high degree of automation and accuracy in recognizing and counting single-colony and multi-colony targets.
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Affiliation(s)
- Libo Cao
- Center for Global Health, Nanjing Medical University, Nanjing 211166, China (Y.W.)
| | - Liping Zeng
- Department of Pathogen Biology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing 211166, China;
| | - Yaoxuan Wang
- Center for Global Health, Nanjing Medical University, Nanjing 211166, China (Y.W.)
| | - Jiayi Cao
- Center for Global Health, Nanjing Medical University, Nanjing 211166, China (Y.W.)
| | - Ziyu Han
- Center for Global Health, Nanjing Medical University, Nanjing 211166, China (Y.W.)
| | - Yang Chen
- Center for Global Health, Nanjing Medical University, Nanjing 211166, China (Y.W.)
| | - Yuxi Wang
- Center for Global Health, Nanjing Medical University, Nanjing 211166, China (Y.W.)
| | - Guowei Zhong
- Center for Global Health, Nanjing Medical University, Nanjing 211166, China (Y.W.)
| | - Shanlei Qiao
- Center for Global Health, Nanjing Medical University, Nanjing 211166, China (Y.W.)
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Visonà G, Duroux D, Miranda L, Sükei E, Li Y, Borgwardt K, Oliver C. Multimodal learning in clinical proteomics: enhancing antimicrobial resistance prediction models with chemical information. Bioinformatics 2023; 39:btad717. [PMID: 38001023 PMCID: PMC10724849 DOI: 10.1093/bioinformatics/btad717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 11/08/2023] [Accepted: 11/23/2023] [Indexed: 11/26/2023] Open
Abstract
MOTIVATION Large-scale clinical proteomics datasets of infectious pathogens, combined with antimicrobial resistance outcomes, have recently opened the door for machine learning models which aim to improve clinical treatment by predicting resistance early. However, existing prediction frameworks typically train a separate model for each antimicrobial and species in order to predict a pathogen's resistance outcome, resulting in missed opportunities for chemical knowledge transfer and generalizability. RESULTS We demonstrate the effectiveness of multimodal learning over proteomic and chemical features by exploring two clinically relevant tasks for our proposed deep learning models: drug recommendation and generalized resistance prediction. By adopting this multi-view representation of the pathogenic samples and leveraging the scale of the available datasets, our models outperformed the previous single-drug and single-species predictive models by statistically significant margins. We extensively validated the multi-drug setting, highlighting the challenges in generalizing beyond the training data distribution, and quantitatively demonstrate how suitable representations of antimicrobial drugs constitute a crucial tool in the development of clinically relevant predictive models. AVAILABILITY AND IMPLEMENTATION The code used to produce the results presented in this article is available at https://github.com/BorgwardtLab/MultimodalAMR.
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Affiliation(s)
- Giovanni Visonà
- Department of Empirical Inference, Max Planck Institute for Intelligent Systems, Max-Planck-Ring 4, Tübingen 72076, Germany
| | - Diane Duroux
- BIO3—GIGA-R Medical Genomics, University of Liège, Avenue de l’Hôpital 11, Liège 4000, Belgium
- ETH AI Center, ETH Zürich, Andreasstrasse 5, Zürich 8092, Switzerland
| | - Lucas Miranda
- Research Group Statistical Genetics, Max Planck Institute of Psychiatry, Kraepelinstraße 10, München 80804, Germany
| | - Emese Sükei
- Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Leganés 28911, Spain
| | - Yiran Li
- Department of Biosystems Science and Engineering, ETH Zürich, Basel 4058, Switzerland
| | - Karsten Borgwardt
- Department of Biosystems Science and Engineering, ETH Zürich, Basel 4058, Switzerland
- Swiss Institute for Bioinformatics (SIB), Amphipôle, Quartier UNIL-Sorge, Lausanne 1015, Switzerland
- Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried 82152, Germany
| | - Carlos Oliver
- Department of Biosystems Science and Engineering, ETH Zürich, Basel 4058, Switzerland
- Swiss Institute for Bioinformatics (SIB), Amphipôle, Quartier UNIL-Sorge, Lausanne 1015, Switzerland
- Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried 82152, Germany
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Giacobbe DR, Zhang Y, de la Fuente J. Explainable artificial intelligence and machine learning: novel approaches to face infectious diseases challenges. Ann Med 2023; 55:2286336. [PMID: 38010090 PMCID: PMC10836268 DOI: 10.1080/07853890.2023.2286336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 11/16/2023] [Indexed: 11/29/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) are revolutionizing human activities in various fields, with medicine and infectious diseases being not exempt from their rapid and exponential growth. Furthermore, the field of explainable AI and ML has gained particular relevance and is attracting increasing interest. Infectious diseases have already started to benefit from explainable AI/ML models. For example, they have been employed or proposed to better understand complex models aimed at improving the diagnosis and management of coronavirus disease 2019, in the field of antimicrobial resistance prediction and in quantum vaccine algorithms. Although some issues concerning the dichotomy between explainability and interpretability still require careful attention, an in-depth understanding of how complex AI/ML models arrive at their predictions or recommendations is becoming increasingly essential to properly face the growing challenges of infectious diseases in the present century.
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Affiliation(s)
- Daniele Roberto Giacobbe
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
- Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Italy
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK
- School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - José de la Fuente
- SaBio (Health and Biotechnology), Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM, Ciudad Real, Spain
- Department of Veterinary Pathobiology, Center for Veterinary Health Sciences, Oklahoma State University, Stillwater, OK, USA
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20
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Abdrabou AMM, Sy I, Bischoff M, Arroyo MJ, Becker SL, Mellmann A, von Müller L, Gärtner B, Berger FK. Discrimination between hypervirulent and non-hypervirulent ribotypes of Clostridioides difficile by MALDI-TOF mass spectrometry and machine learning. Eur J Clin Microbiol Infect Dis 2023; 42:1373-1381. [PMID: 37721704 PMCID: PMC10587247 DOI: 10.1007/s10096-023-04665-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 09/03/2023] [Indexed: 09/19/2023]
Abstract
Hypervirulent ribotypes (HVRTs) of Clostridioides difficile such as ribotype (RT) 027 are epidemiologically important. This study evaluated whether MALDI-TOF can distinguish between strains of HVRTs and non-HVRTs commonly found in Europe. Obtained spectra of clinical C. difficile isolates (training set, 157 isolates) covering epidemiologically relevant HVRTs and non-HVRTs found in Europe were used as an input for different machine learning (ML) models. Another 83 isolates were used as a validation set. Direct comparison of MALDI-TOF spectra obtained from HVRTs and non-HVRTs did not allow to discriminate between these two groups, while using these spectra with certain ML models could differentiate HVRTs from non-HVRTs with an accuracy >95% and allowed for a sub-clustering of three HVRT subgroups (RT027/RT176, RT023, RT045/078/126/127). MALDI-TOF combined with ML represents a reliable tool for rapid identification of major European HVRTs.
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Affiliation(s)
- Ahmed Mohamed Mostafa Abdrabou
- Institute of Medical Microbiology and Hygiene, Saarland University, Kirrberger Straße 100, Building 43, D-66421, Homburg, Saar, Germany.
- Medical Microbiology and Immunology Department, Faculty of Medicine, Mansoura University, El Gomhouria Street, Mansoura, 35516, Egypt.
- National Reference Center for Clostridioides (Clostridium) difficile, Homburg-Münster-Coesfeld, Germany.
| | - Issa Sy
- Institute of Medical Microbiology and Hygiene, Saarland University, Kirrberger Straße 100, Building 43, D-66421, Homburg, Saar, Germany
| | - Markus Bischoff
- Institute of Medical Microbiology and Hygiene, Saarland University, Kirrberger Straße 100, Building 43, D-66421, Homburg, Saar, Germany
- National Reference Center for Clostridioides (Clostridium) difficile, Homburg-Münster-Coesfeld, Germany
| | - Manuel J Arroyo
- Clover Bioanalytical Software, Av. del Conocimiento, 41, 18016, Granada, Spain
| | - Sören L Becker
- Institute of Medical Microbiology and Hygiene, Saarland University, Kirrberger Straße 100, Building 43, D-66421, Homburg, Saar, Germany
| | - Alexander Mellmann
- National Reference Center for Clostridioides (Clostridium) difficile, Homburg-Münster-Coesfeld, Germany
- Institute of Hygiene, University of Münster, Robert-Koch-Straße 41, 48149, Münster, Germany
| | - Lutz von Müller
- National Reference Center for Clostridioides (Clostridium) difficile, Homburg-Münster-Coesfeld, Germany
- Christophorus Kliniken Coesfeld, Coesfeld, Germany
| | - Barbara Gärtner
- Institute of Medical Microbiology and Hygiene, Saarland University, Kirrberger Straße 100, Building 43, D-66421, Homburg, Saar, Germany
- National Reference Center for Clostridioides (Clostridium) difficile, Homburg-Münster-Coesfeld, Germany
| | - Fabian K Berger
- Institute of Medical Microbiology and Hygiene, Saarland University, Kirrberger Straße 100, Building 43, D-66421, Homburg, Saar, Germany
- National Reference Center for Clostridioides (Clostridium) difficile, Homburg-Münster-Coesfeld, Germany
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21
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Introducing AI-supported Research Highlights. Nat Rev Microbiol 2023; 21:701. [PMID: 37828087 DOI: 10.1038/s41579-023-00978-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
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22
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Shelke YP, Badge AK, Bankar NJ. Applications of Artificial Intelligence in Microbial Diagnosis. Cureus 2023; 15:e49366. [PMID: 38146579 PMCID: PMC10749263 DOI: 10.7759/cureus.49366] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 11/24/2023] [Indexed: 12/27/2023] Open
Abstract
The diagnosis is an important factor in healthcare care, and it is essential to identify microorganisms that cause infections and diseases. The application of artificial intelligence (AI) systems can improve disease management, drug development, antibiotic resistance prediction, and epidemiological monitoring in the field of microbial diagnosis. AI systems can quickly and accurately detect infections, including new and drug-resistant strains, and enable early detection of antibiotic resistance and improved diagnostic techniques. The application of AI in bacterial diagnosis focuses on the speed, precision, and identification of pathogens and the ability to predict antibiotic resistance.
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Affiliation(s)
- Yogendra P Shelke
- Microbiology, Bhaktshreshtha Kamalakarpant Laxmanrao Walawalkar Rural Medical College, Ratnagiri, IND
| | - Ankit K Badge
- Microbiology, Datta Meghe Medical College, Datta Meghe Institute of Higher Education and Research (Deemed to Be University), Wardha, IND
| | - Nandkishor J Bankar
- Microbiology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research (Deemed to Be University), Wardha, IND
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23
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Yang C, Zhang H. A review on machine learning-powered fluorescent and colorimetric sensor arrays for bacteria identification. Mikrochim Acta 2023; 190:451. [PMID: 37880465 DOI: 10.1007/s00604-023-06021-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 09/27/2023] [Indexed: 10/27/2023]
Abstract
Biosensors have been widely used for bacteria determination with great success. However, the "lock-and-key" methodology used by biosensors to identify bacteria has a significant limitation: it can only detect one species of bacteria. In recent years, optical (fluorescent and colorimetric) sensor arrays are gradually gaining attention from researchers as a new type of biosensor. They can acquire multiple features of a target simultaneously, form a feature pattern, and determine the bacteria species with the help of pattern recognition/machine learning algorithms. Previous reviews in this area have focused on the interaction between the sensor array and bacteria or the materials used to make the sensors. This review, on the other hand, will provide researchers with a better understanding of the field by discussing fluorescent and colorimetric sensor arrays based on the mechanism of optical signal generation. These sensor arrays will be compared based on the identified species. Finally, we will discuss the limitations of these sensor arrays and explore possible solutions.
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Affiliation(s)
- Changmao Yang
- Department of Biotechnology, College of Life Science and Technology, Huazhong University of Science and Technology, MOE Key Laboratory of Molecular Biophysics, Wuhan, 430074, China
| | - Houjin Zhang
- Department of Biotechnology, College of Life Science and Technology, Huazhong University of Science and Technology, MOE Key Laboratory of Molecular Biophysics, Wuhan, 430074, China.
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24
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Feng J, Yang K, Liu X, Song M, Zhan P, Zhang M, Chen J, Liu J. Machine learning: a powerful tool for identifying key microbial agents associated with specific cancer types. PeerJ 2023; 11:e16304. [PMID: 37901464 PMCID: PMC10601900 DOI: 10.7717/peerj.16304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 09/26/2023] [Indexed: 10/31/2023] Open
Abstract
Machine learning (ML) includes a broad class of computer programs that improve with experience and shows unique strengths in performing tasks such as clustering, classification and regression. Over the past decade, microbial communities have been implicated in influencing the onset, progression, metastasis, and therapeutic response of multiple cancers. Host-microbe interaction may be a physiological pathway contributing to cancer development. With the accumulation of a large number of high-throughput data, ML has been successfully applied to the study of human cancer microbiomics in an attempt to reveal the complex mechanism behind cancer. In this review, we begin with a brief overview of the data sources included in cancer microbiomics studies. Then, the characteristics of the ML algorithm are briefly introduced. Secondly, the application progress of ML in cancer microbiomics is also reviewed. Finally, we highlight the challenges and future prospects facing ML in cancer microbiomics. On this basis, we conclude that the development of cancer microbiomics can not be achieved without ML, and that ML can be used to develop tumor-targeting microbial therapies, ultimately contributing to personalized and precision medicine.
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Affiliation(s)
- Jia Feng
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Kailan Yang
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Xuexue Liu
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Min Song
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Ping Zhan
- Department of Obstetrics, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Mi Zhang
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Jinsong Chen
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Jinbo Liu
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
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25
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Xu Z, Wang X, Meng J, Zhang L, Song B. m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features. Front Microbiol 2023; 14:1277099. [PMID: 37937221 PMCID: PMC10627201 DOI: 10.3389/fmicb.2023.1277099] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 10/02/2023] [Indexed: 11/09/2023] Open
Abstract
5-Methyluridine (m5U) is one of the most common post-transcriptional RNA modifications, which is involved in a variety of important biological processes and disease development. The precise identification of the m5U sites allows for a better understanding of the biological processes of RNA and contributes to the discovery of new RNA functional and therapeutic targets. Here, we present m5U-GEPred, a prediction framework, to combine sequence characteristics and graph embedding-based information for m5U identification. The graph embedding approach was introduced to extract the global information of training data that complemented the local information represented by conventional sequence features, thereby enhancing the prediction performance of m5U identification. m5U-GEPred outperformed the state-of-the-art m5U predictors built on two independent species, with an average AUROC of 0.984 and 0.985 tested on human and yeast transcriptomes, respectively. To further validate the performance of our newly proposed framework, the experimentally validated m5U sites identified from Oxford Nanopore Technology (ONT) were collected as independent testing data, and in this project, m5U-GEPred achieved reasonable prediction performance with ACC of 91.84%. We hope that m5U-GEPred should make a useful computational alternative for m5U identification.
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Affiliation(s)
- Zhongxing Xu
- Department of Public Health, School of Medicine and Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China
- School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Xuan Wang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Jia Meng
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Lin Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Bowen Song
- Department of Public Health, School of Medicine and Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China
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26
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Oehm AW, Leinmueller M, Zablotski Y, Campe A, Hoedemaker M, Springer A, Jordan D, Strube C, Knubben-Schweizer G. Multinomial logistic regression based on neural networks reveals inherent differences among dairy farms depending on the differential exposure to Fasciola hepatica and Ostertagia ostertagi. Int J Parasitol 2023; 53:687-697. [PMID: 37355196 DOI: 10.1016/j.ijpara.2023.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/17/2023] [Accepted: 05/18/2023] [Indexed: 06/26/2023]
Abstract
Fasciola hepatica and Ostertagia ostertagi are cattle parasites with worldwide relevance for economic outcome as well as animal health and welfare. The on-farm exposure of cattle to both parasites is a function of host-associated, intrinsic, as well as environmental and farm-specific, extrinsic, factors. Even though knowledge on the biology of both parasites exists, sophisticated and innovative modelling approaches can help to deepen our understanding of key aspects fostering the exposure of dairy cows to these pathogens. In the present study, multiple multinomial logistic regression models were fitted via neural networks to describe the differences among farms where cattle were not exposed to either F. hepatica or O. ostertagi, to one parasite, or to both, respectively. Farm-specific production and management characteristics were used as covariates to portray these differences. This elucidated inherent farm characteristics associated with parasite exposure. In both studied regions, pasture access for cows, farm-level milk yield, and lameness prevalence were identified as relevant factors. In region 'South', adherence to organic farming principles was a further covariate of importance. In region 'North', the prevalence of cows with a low body condition score, herd size, hock lesion prevalence, farm-level somatic cell count, and study year appeared to be of relevance. The present study broadens our understanding of the complex epidemiological scenarios that could predict differential farm-level parasite status. The analyses have revealed the importance of awareness of dissimilarities between farms in regard to the differential exposure to F. hepatica and O. ostertagi. This provides solid evidence that dynamics and relevant factors differ depending on whether or not cows are exposed to F. hepatica, O. ostertagi, or to both.
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Affiliation(s)
- Andreas W Oehm
- Institute of Parasitology, Vetsuisse Faculty of Zurich, University of Zurich, Zurich, Switzerland; Clinic for Ruminants with Ambulatory and Herd Health Services, Ludwig-Maximilians-Universität Munich, Oberschleissheim, Germany.
| | - Markus Leinmueller
- Clinic for Ruminants with Ambulatory and Herd Health Services, Ludwig-Maximilians-Universität Munich, Oberschleissheim, Germany
| | - Yury Zablotski
- Clinic for Ruminants with Ambulatory and Herd Health Services, Ludwig-Maximilians-Universität Munich, Oberschleissheim, Germany
| | - Amely Campe
- Department of Biometry, Epidemiology and Information Processing, WHO Collaborating Center for Research and Training for Health at the Human-Animal-Environment Interface, University of Veterinary Medicine, Foundation, Hannover, Germany
| | - Martina Hoedemaker
- Clinic for Cattle, University of Veterinary Medicine Hannover Foundation, Hannover, Germany
| | - Andrea Springer
- Institute for Parasitology, Centre for Infection Medicine, University of Veterinary Medicine Hannover, Hannover, Germany
| | - Daniela Jordan
- Institute for Parasitology, Centre for Infection Medicine, University of Veterinary Medicine Hannover, Hannover, Germany
| | - Christina Strube
- Institute for Parasitology, Centre for Infection Medicine, University of Veterinary Medicine Hannover, Hannover, Germany
| | - Gabriela Knubben-Schweizer
- Clinic for Ruminants with Ambulatory and Herd Health Services, Ludwig-Maximilians-Universität Munich, Oberschleissheim, Germany
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27
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Gao P, Yang M, Chen Y, Yan J, Han M, Deng H, Qian K, Yang J, Lu Y, Zhou L, Huang A, Li X, Deng W, Long Q. A spacer design strategy for CRISPR-Cas12f1 with single-nucleotide polymorphism mutation resolution capability and its application in the mutations diagnosis of pathogens. J Med Virol 2023; 95:e29189. [PMID: 37855689 DOI: 10.1002/jmv.29189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 09/19/2023] [Accepted: 10/11/2023] [Indexed: 10/20/2023]
Abstract
Infectious diseases remain a major global issue in public health. It is important to develop rapid, sensitive, and accurate diagnostic methods to detect pathogens and their mutations. Cas12f1 is an exceptionally compact RNA-guided nuclease and have the potential to fulfill the clinical needs. Based on the interaction between crRNA-SSDNA binary sequence and Cas12f1, here, we addressed the essential features that determine the recognition ability of CRISPR-Cas12f1 single-nucleotide polymorphism (SNP), such as the length of spacer region and the base pairing region that determines the trans-cleavage of ssDNA. A fine-tuning spacer design strategy is also proposed to enhance the SNP recognition capability of CRISPR-Cas12f1. The optimized spacer confers the Cas12f1 system a strong SNP identification capability for viral or bacterial drug-resistance mutations, with a specificity ratio ranging from 19.63 to 110.20 and an admirable sensitivity up to 100 copy/μL. Together, the spacer screening and CRISPR-Cas12f1 based SNP identification method, is sensitive and versatile, and will have a wide application prospect in pathogen DNA mutation diagnosis and other mutation profiling.
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Affiliation(s)
- Panqi Gao
- Key Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China
- School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Maoyi Yang
- Key Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China
| | - Yi Chen
- Key Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China
| | - Jun Yan
- Key Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China
| | - Miaomiao Han
- Key Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China
| | - Haijun Deng
- Key Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China
| | - Keli Qian
- Department of Infection Control, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jiandong Yang
- Urumqi Municipal Centre for Disease Control and Prevention, Xinjiang, China
| | - Yaoqin Lu
- Urumqi Municipal Centre for Disease Control and Prevention, Xinjiang, China
| | - Ling Zhou
- Key Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China
| | - Ailong Huang
- Key Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China
| | - Xiaosong Li
- Key Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China
| | - Wanyan Deng
- School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China
| | - Quanxin Long
- Key Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China
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28
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Ruparell A, Gibbs M, Colyer A, Wallis C, Harris S, Holcombe LJ. Developing diagnostic tools for canine periodontitis: combining molecular techniques and machine learning models. BMC Vet Res 2023; 19:163. [PMID: 37723566 PMCID: PMC10507867 DOI: 10.1186/s12917-023-03668-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 07/19/2023] [Indexed: 09/20/2023] Open
Abstract
BACKGROUND Dental plaque microbes play a key role in the development of periodontal disease. Numerous high-throughput sequencing studies have generated understanding of the bacterial species associated with both canine periodontal health and disease. Opportunities therefore exist to utilise these bacterial biomarkers to improve disease diagnosis in conscious-based veterinary oral health checks. Here, we demonstrate that molecular techniques, specifically quantitative polymerase chain reaction (qPCR) can be utilised for the detection of microbial biomarkers associated with canine periodontal health and disease. RESULTS Over 40 qPCR assays targeting single microbial species associated with canine periodontal health, gingivitis and early periodontitis were developed and validated. These were used to quantify levels of the respective taxa in canine subgingival plaque samples collected across periodontal health (PD0), gingivitis (PD1) and early periodontitis (PD2). When qPCR outputs were compared to the corresponding high-throughput sequencing data there were strong correlations, including a periodontal health associated taxa, Capnocytophaga sp. COT-339 (rs =0.805), and two periodontal disease associated taxa, Peptostreptococcaceae XI [G-4] sp. COT-019 (rs=0.902) and Clostridiales sp. COT-028 (rs=0.802). The best performing models, from five machine learning approaches applied to the qPCR data for these taxa, estimated 85.7% sensitivity and 27.5% specificity for Capnocytophaga sp. COT-339, 74.3% sensitivity and 67.5% specificity for Peptostreptococcaceae XI [G-4] sp. COT-019, and 60.0% sensitivity and 80.0% specificity for Clostridiales sp. COT-028. CONCLUSIONS A qPCR-based approach is an accurate, sensitive, and cost-effective method for detection of microbial biomarkers associated with periodontal health and disease. Taken together, the correlation between qPCR and high-throughput sequencing outputs, and early accuracy insights, indicate the strategy offers a prospective route to the development of diagnostic tools for canine periodontal disease.
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Affiliation(s)
- Avika Ruparell
- Waltham Petcare Science Institute, Melton Mowbray, Leicestershire, UK.
| | - Matthew Gibbs
- Waltham Petcare Science Institute, Melton Mowbray, Leicestershire, UK
| | - Alison Colyer
- Waltham Petcare Science Institute, Melton Mowbray, Leicestershire, UK
| | - Corrin Wallis
- Waltham Petcare Science Institute, Melton Mowbray, Leicestershire, UK
| | - Stephen Harris
- Waltham Petcare Science Institute, Melton Mowbray, Leicestershire, UK
| | - Lucy J Holcombe
- Waltham Petcare Science Institute, Melton Mowbray, Leicestershire, UK
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29
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Knisz J, Eckert R, Gieg LM, Koerdt A, Lee JS, Silva ER, Skovhus TL, An Stepec BA, Wade SA. Microbiologically influenced corrosion-more than just microorganisms. FEMS Microbiol Rev 2023; 47:fuad041. [PMID: 37437902 PMCID: PMC10479746 DOI: 10.1093/femsre/fuad041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 06/29/2023] [Accepted: 07/11/2023] [Indexed: 07/14/2023] Open
Abstract
Microbiologically influenced corrosion (MIC) is a phenomenon of increasing concern that affects various materials and sectors of society. MIC describes the effects, often negative, that a material can experience due to the presence of microorganisms. Unfortunately, although several research groups and industrial actors worldwide have already addressed MIC, discussions are fragmented, while information sharing and willingness to reach out to other disciplines are limited. A truly interdisciplinary approach, which would be logical for this material/biology/chemistry-related challenge, is rarely taken. In this review, we highlight critical non-biological aspects of MIC that can sometimes be overlooked by microbiologists working on MIC but are highly relevant for an overall understanding of this phenomenon. Here, we identify gaps, methods, and approaches to help solve MIC-related challenges, with an emphasis on the MIC of metals. We also discuss the application of existing tools and approaches for managing MIC and propose ideas to promote an improved understanding of MIC. Furthermore, we highlight areas where the insights and expertise of microbiologists are needed to help progress this field.
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Affiliation(s)
- J Knisz
- Department of Water Supply and Sewerage, Faculty of Water Sciences, University of Public Service, 6500, Baja, Hungary
| | - R Eckert
- Microbial Corrosion Consulting, LLC, Commerce Township, 48382, MI, USA
| | - L M Gieg
- Petroleum Microbiology Research Group, Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada
| | - A Koerdt
- Federal Institute for Materials Research and Testing (BAM), 12205, Berlin, Germany
| | - J S Lee
- Naval Research Laboratory, Ocean Sciences Division, Stennis Space Center, 39529, MS, USA
| | - E R Silva
- BioISI—Biosystems and Integrative Sciences Institute, Faculty of Sciences, University of Lisboa, Campo Grande, C8 bdg, 1749-016, Lisboa, Portugal
- CERENA - Centre for Natural Resources and the Environment, Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais, 1, 1049-001, Lisboa, Portugal
| | - T L Skovhus
- Research Center for Built Environment, Energy, Water and Climate, VIA, University College, 8700, Horsens, Denmark
| | - B A An Stepec
- Department of Energy and Technology, NORCE Norwegian Research Centre AS, Nygårdsgaten 112, 5008 Bergen, Norway
| | - S A Wade
- Bioengineering Research Group, Swinburne University of Technology, 3122, Melbourne, Australia
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30
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Bendriss G, MacDonald R, McVeigh C. Microbial Reprogramming in Obsessive-Compulsive Disorders: A Review of Gut-Brain Communication and Emerging Evidence. Int J Mol Sci 2023; 24:11978. [PMID: 37569349 PMCID: PMC10419219 DOI: 10.3390/ijms241511978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 07/15/2023] [Accepted: 07/17/2023] [Indexed: 08/13/2023] Open
Abstract
Obsessive-compulsive disorder (OCD) is a debilitating mental health disorder characterized by intrusive thoughts (obsessions) and repetitive behaviors (compulsions). Dysbiosis, an imbalance in the gut microbial composition, has been associated with various health conditions, including mental health disorders, autism, and inflammatory diseases. While the exact mechanisms underlying OCD remain unclear, this review presents a growing body of evidence suggesting a potential link between dysbiosis and the multifaceted etiology of OCD, interacting with genetic, neurobiological, immunological, and environmental factors. This review highlights the emerging evidence implicating the gut microbiota in the pathophysiology of OCD and its potential as a target for novel therapeutic approaches. We propose a model that positions dysbiosis as the central unifying element in the neurochemical, immunological, genetic, and environmental factors leading to OCD. The potential and challenges of microbial reprogramming strategies, such as probiotics and fecal transplants in OCD therapeutics, are discussed. This review raises awareness of the importance of adopting a holistic approach that considers the interplay between the gut and the brain to develop interventions that account for the multifaceted nature of OCD and contribute to the advancement of more personalized approaches.
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Sun X, Yin Y, Yang Q, Huo T. Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives. Eur J Med Res 2023; 28:242. [PMID: 37475050 PMCID: PMC10360247 DOI: 10.1186/s40001-023-01065-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 02/15/2023] [Indexed: 07/22/2023] Open
Abstract
Artificial intelligence (AI), the technique of extracting information from complex database using sophisticated computer algorithms, has incorporated itself in medical field. AI techniques have shown the potential to accelerate the progression of diagnosis and treatment of cardiovascular diseases (CVDs), including heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease and so on. In clinical scenario, AI have been proved to apply well in CVD diagnosis, enhance effectiveness of auxiliary tools, disease stratification and typing, and outcome prediction. Deeply developed to capture subtle connections from massive amounts of healthcare data, recent AI algorithms are expected to handle even more complex tasks than traditional methods. The aim of this review is to introduce current applications of AI in CVDs, which may allow clinicians who have limited expertise of computer science to better understand the frontier of the subject and put AI algorithms into clinical practice.
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Affiliation(s)
- Xiaoyu Sun
- National Institute of Hospital Administration, National Health Commission, Beijing, China
| | - Yuzhe Yin
- The Sixth Clinical Medical School, Capital Medical University, Beijing, China
| | - Qiwei Yang
- Department of Thorax, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Tianqi Huo
- National Institute of Hospital Administration, National Health Commission, Beijing, China.
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Karlsen ST, Rau MH, Sánchez BJ, Jensen K, Zeidan AA. From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry. FEMS Microbiol Rev 2023; 47:fuad030. [PMID: 37286882 PMCID: PMC10337747 DOI: 10.1093/femsre/fuad030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/31/2023] [Accepted: 06/06/2023] [Indexed: 06/09/2023] Open
Abstract
When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.
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Affiliation(s)
- Signe T Karlsen
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Martin H Rau
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Benjamín J Sánchez
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Kristian Jensen
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Ahmad A Zeidan
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
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Olmo R, Wetzels SU, Berg G, Cocolin L, Hartmann M, Hugas M, Kostic T, Rattei T, Ruthsatz M, Rybakova D, Sessitsch A, Shortt C, Timmis K, Selberherr E, Wagner M. Food systems microbiome-related educational needs. Microb Biotechnol 2023; 16:1412-1422. [PMID: 37338855 DOI: 10.1111/1751-7915.14263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 03/20/2023] [Accepted: 03/30/2023] [Indexed: 06/21/2023] Open
Abstract
Within the European-funded Coordination and Support Action MicrobiomeSupport (https://www.microbiomesupport.eu/), the Workshop 'Education in Food Systems Microbiome Related Sciences: Needs for Universities, Industry and Public Health Systems' brought together over 70 researchers, public health and industry partners from all over the world to work on elaborating microbiome-related educational needs in food systems. This publication provides a summary of discussions held during and after the workshop and the resulting recommendations.
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Affiliation(s)
- Rocío Olmo
- FFoQSI GmbH - Austrian Competence Centre for Feed and Food Quality, Safety and Innovation, Tulln, Austria
- Unit of Food Microbiology, Institute of Food Safety, Food Technology and Veterinary Public Health, University of Veterinary Medicine, Vienna, Austria
| | - Stefanie Urimare Wetzels
- FFoQSI GmbH - Austrian Competence Centre for Feed and Food Quality, Safety and Innovation, Tulln, Austria
- Unit of Food Microbiology, Institute of Food Safety, Food Technology and Veterinary Public Health, University of Veterinary Medicine, Vienna, Austria
| | - Gabriele Berg
- Institute of Environmental Biotechnology, Graz University of Technology, Graz, Austria
- Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Potsdam, Germany
- Institute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Luca Cocolin
- Department of Agricultural, Forest and Food Sciences, University of Turin, Turin, Italy
| | - Moritz Hartmann
- Unit of Food Microbiology, Institute of Food Safety, Food Technology and Veterinary Public Health, University of Veterinary Medicine, Vienna, Austria
| | - Marta Hugas
- European Food Safety Authority (EFSA), EU, Parma, Italy
| | - Tanja Kostic
- Bioresouces Unit, Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Tulln, Austria
| | - Thomas Rattei
- Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria
| | | | - Daria Rybakova
- Institute of Environmental Biotechnology, Graz University of Technology, Graz, Austria
| | - Angela Sessitsch
- Bioresouces Unit, Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Tulln, Austria
| | | | - Kenneth Timmis
- Institute of Microbiology, Technical University of Braunschweig, Braunschweig, Germany
| | - Evelyne Selberherr
- FFoQSI GmbH - Austrian Competence Centre for Feed and Food Quality, Safety and Innovation, Tulln, Austria
- Unit of Food Microbiology, Institute of Food Safety, Food Technology and Veterinary Public Health, University of Veterinary Medicine, Vienna, Austria
| | - Martin Wagner
- FFoQSI GmbH - Austrian Competence Centre for Feed and Food Quality, Safety and Innovation, Tulln, Austria
- Unit of Food Microbiology, Institute of Food Safety, Food Technology and Veterinary Public Health, University of Veterinary Medicine, Vienna, Austria
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Ramasamy KP, Mahawar L, Rajasabapathy R, Rajeshwari K, Miceli C, Pucciarelli S. Comprehensive insights on environmental adaptation strategies in Antarctic bacteria and biotechnological applications of cold adapted molecules. Front Microbiol 2023; 14:1197797. [PMID: 37396361 PMCID: PMC10312091 DOI: 10.3389/fmicb.2023.1197797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/31/2023] [Indexed: 07/04/2023] Open
Abstract
Climate change and the induced environmental disturbances is one of the major threats that have a strong impact on bacterial communities in the Antarctic environment. To cope with the persistent extreme environment and inhospitable conditions, psychrophilic bacteria are thriving and displaying striking adaptive characteristics towards severe external factors including freezing temperature, sea ice, high radiation and salinity which indicates their potential in regulating climate change's environmental impacts. The review illustrates the different adaptation strategies of Antarctic microbes to changing climate factors at the structural, physiological and molecular level. Moreover, we discuss the recent developments in "omics" approaches to reveal polar "blackbox" of psychrophiles in order to gain a comprehensive picture of bacterial communities. The psychrophilic bacteria synthesize distinctive cold-adapted enzymes and molecules that have many more industrial applications than mesophilic ones in biotechnological industries. Hence, the review also emphasizes on the biotechnological potential of psychrophilic enzymes in different sectors and suggests the machine learning approach to study cold-adapted bacteria and engineering the industrially important enzymes for sustainable bioeconomy.
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Affiliation(s)
| | - Lovely Mahawar
- Department of Plant Physiology, Faculty of Agrobiology and Food Resources, Slovak University of Agriculture, Nitra, Slovakia
| | - Raju Rajasabapathy
- Department of Marine Science, Bharathidasan University, Tiruchirappalli, Tamilnadu, India
| | | | - Cristina Miceli
- School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy
| | - Sandra Pucciarelli
- School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy
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Adenaike O, Olabanjo OE, Adedeji AA. Integrating computational skills in undergraduate Microbiology curricula in developing countries. Biol Methods Protoc 2023; 8:bpad008. [PMID: 37396465 PMCID: PMC10310463 DOI: 10.1093/biomethods/bpad008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 05/19/2023] [Accepted: 05/21/2023] [Indexed: 07/04/2023] Open
Abstract
The employability of young graduates has gained increasing significance in the labour market of the 21st century. Universities turn out millions of graduates annually, but at the same time, employers highlight their lack of the requisite skills for sustainable employment. We live today in a world of data, and therefore courses that feature numerical and computational tools to gather and analyse data are to be sourced for and integrated into life sciences' curricula as they provide a number of benefits for both the students and faculty members that are engaged in teaching the courses. The lack of this teaching in undergraduate Microbiology curricula is devastating and leaves a knowledge gap in the graduates that are turned out. This results in an inability of the emerging graduates to compete favourably with their counterparts from other parts of the world. There is a necessity on the part of life science educators to adapt their teaching strategies to best support students' curricula that prepare them for careers in science. Bioinformatics, Statistics and Programming are key computational skills to embrace by life scientists and the need for training beginning at undergraduate level cannot be overemphasized. This article reviews the need to integrate computational skills in undergraduate Microbiology curricula in developing countries with emphasis on Nigeria.
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Affiliation(s)
- Omolara Adenaike
- Correspondence address. Department of Biological Sciences (Microbiology Unit), Oduduwa University, Ipetumodu, Nigeria. Tel: +2348061278100; E-mail:
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Manolitsis I, Feretzakis G, Katsimperis S, Angelopoulos P, Loupelis E, Skarmoutsou N, Tzelves L, Skolarikos A. A 2-Year Audit on Antibiotic Resistance Patterns from a Urology Department in Greece. J Clin Med 2023; 12:jcm12093180. [PMID: 37176622 PMCID: PMC10178956 DOI: 10.3390/jcm12093180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 04/21/2023] [Accepted: 04/27/2023] [Indexed: 05/15/2023] Open
Abstract
PURPOSE The high incidence of urinary tract infections (UTIs), often in nosocomial environments, is a major cause of antimicrobial resistance (AMR). The dissemination of antibiotic-resistant infections results in very high health and economic burdens for patients and healthcare systems, respectively. This study aims to determine and present the antibiotic resistance profiles of the most common pathogens in a urology department in Greece. METHODS During the period 2019-2020, we included 12,215 clinical samples of blood and urine specimens that tested positive for the following pathogens: Escherichia coli, Enterococcus faecium, Enterococcus faecalis, Proteus mirabilis, Klebsiella pneumoniae, or Pseudomonas aeruginosa, as these are the most commonly encountered microbes in a urology department. RESULTS The analysis revealed a 22.30% mean resistance rate of E. coli strains with a 76.42% resistance to ampicillin and a 54.76% resistance rate to ciprofloxacin in the two-year period. It also showed an approximately 19% resistance rate of P. mirabilis strains and a mean resistance rate of 46.205% of K. pneumoniae strains, with a decreasing trend during the four semesters (p-value < 0.001), which presented an 80% resistance rate to ampicillin/sulbactam and 73.33% to ciprofloxacin. The resistance to carbapenems was reported to be 39.82%. The analysis revealed a 24.17% mean resistance rate of P. aeruginosa with a declining rate over the two-year period (p-value < 0.001). The P. aeruginosa strains were 38% resistant to fluoroquinolones and presented varying resistance against carbapenems (31.58% against doripenem and 19.79% against meropenem). Regarding the Enteroccocal strains, a 46.91% mean resistance was noted for E. faecium with 100% resistance to ampicillin, and a 24.247% mean resistance rate for E. faecalis strains that were 41% resistant to ciprofloxacin. Both types showed 100% sensitivity to linezolid. CONCLUSIONS The dissemination of antibiotic-resistant pathogens poses the need to implement surveillance programs and, consequently, to develop strategies to prevent the emergence of such pathogens in order to optimize patient outcomes.
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Affiliation(s)
- Ioannis Manolitsis
- Second Department of Urology, Sismanogleio General Hospital, 15126 Marousi, Greece
| | - Georgios Feretzakis
- School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
- Department of Quality Control, Research and Continuing Education, Sismanogleio General Hospital, 15126 Marousi, Greece
| | | | | | | | | | - Lazaros Tzelves
- Second Department of Urology, Sismanogleio General Hospital, 15126 Marousi, Greece
| | - Andreas Skolarikos
- Second Department of Urology, Sismanogleio General Hospital, 15126 Marousi, Greece
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Caldrer S, Deotto N, Pertile G, Bellisola G, Guidi MC. Infrared analysis in the aqueous humor of patients with uveitis: Preliminary results. JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY. B, BIOLOGY 2023; 243:112715. [PMID: 37126864 DOI: 10.1016/j.jphotobiol.2023.112715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 03/23/2023] [Accepted: 04/17/2023] [Indexed: 05/03/2023]
Abstract
Inflammatory processes affecting the uvea result in a temporary o permanent blurred vision and represent an important cause of visual impairment worldwide. It is often hard to make a precise diagnosis which is dependent on the clinical expertise, diagnostic tests, laboratory investigations in blood and sometimes in the aqueous humor (AH). With the aim of obtaining proof of principle Fourier Transformed Infrared (FT-IR) absorbance spectroscopy was applied to study the molecular composition of 72 AH samples collected in 26 patients with uveitis and in 44 controls. The unsupervised exploration of the internal structure of the dataset by principal component analysis reduced hundreds IR variables to those most representative allowing to obtain the predictive model that distinguished the AH spectra of patients with uveitis from controls. The same result was obtained by unsupervised agglomerative cluster analysis. After labeling the spectra with some clinical information it was observed that most severe uveitis with active processes were grouped separately from chronic and relapsing uveitis and controls. The consistence of prediction models is discussed in the light of supporting etiological diagnosis by machine learning processes. In conclusion, proof of principle has been obtained that the IR spectral pattern of AH may reflect particular uveal diseases.
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Affiliation(s)
- Sara Caldrer
- Department of Infectious - Tropical Diseases and Microbiology, IRCCS Sacro Cuore - Don Calabria Hospital, Via Don A. Sempreboni, 5, Negrar di Valpolicella (Verona) 37024, Italy.
| | - Niccolò Deotto
- Department of Ophthalmology, IRCCS Sacro Cuore Don Calabria Hospital, Via Don A. Sempreboni, 5, Negrar di Valpolicella (Verona) 37024, Italy.
| | - Grazia Pertile
- Department of Ophthalmology, IRCCS Sacro Cuore Don Calabria Hospital, Via Don A. Sempreboni, 5, Negrar di Valpolicella (Verona) 37024, Italy.
| | - Giuseppe Bellisola
- INFN - Laboratori Nazionali di Frascati, Via E. Fermi, 54, Frascati (Rome) 00044, Italy.
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Kumar R, Yadav G, Kuddus M, Ashraf GM, Singh R. Unlocking the microbial studies through computational approaches: how far have we reached? ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:48929-48947. [PMID: 36920617 PMCID: PMC10016191 DOI: 10.1007/s11356-023-26220-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 02/24/2023] [Indexed: 04/16/2023]
Abstract
The metagenomics approach accelerated the study of genetic information from uncultured microbes and complex microbial communities. In silico research also facilitated an understanding of protein-DNA interactions, protein-protein interactions, docking between proteins and phyto/biochemicals for drug design, and modeling of the 3D structure of proteins. These in silico approaches provided insight into analyzing pathogenic and nonpathogenic strains that helped in the identification of probable genes for vaccines and antimicrobial agents and comparing whole-genome sequences to microbial evolution. Artificial intelligence, more precisely machine learning (ML) and deep learning (DL), has proven to be a promising approach in the field of microbiology to handle, analyze, and utilize large data that are generated through nucleic acid sequencing and proteomics. This enabled the understanding of the functional and taxonomic diversity of microorganisms. ML and DL have been used in the prediction and forecasting of diseases and applied to trace environmental contaminants and environmental quality. This review presents an in-depth analysis of the recent application of silico approaches in microbial genomics, proteomics, functional diversity, vaccine development, and drug design.
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Affiliation(s)
- Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India
- Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, MO, USA
| | - Garima Yadav
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India
| | - Mohammed Kuddus
- Department of Biochemistry, College of Medicine, University of Hail, Hail, Saudi Arabia
| | - Ghulam Md Ashraf
- Department of Medical Laboratory Sciences, College of Health Sciences, and Sharjah Institute for Medical Research, University of Sharjah, Sharjah , 27272, United Arab Emirates
| | - Rachana Singh
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India.
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Giuffrè M, Moretti R, Tiribelli C. Gut Microbes Meet Machine Learning: The Next Step towards Advancing Our Understanding of the Gut Microbiome in Health and Disease. Int J Mol Sci 2023; 24:ijms24065229. [PMID: 36982303 PMCID: PMC10049444 DOI: 10.3390/ijms24065229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/27/2023] [Accepted: 03/08/2023] [Indexed: 03/11/2023] Open
Abstract
The human gut microbiome plays a crucial role in human health and has been a focus of increasing research in recent years. Omics-based methods, such as metagenomics, metatranscriptomics, and metabolomics, are commonly used to study the gut microbiome because they provide high-throughput and high-resolution data. The vast amount of data generated by these methods has led to the development of computational methods for data processing and analysis, with machine learning becoming a powerful and widely used tool in this field. Despite the promising results of machine learning-based approaches for analyzing the association between microbiota and disease, there are several unmet challenges. Small sample sizes, disproportionate label distribution, inconsistent experimental protocols, or a lack of access to relevant metadata can all contribute to a lack of reproducibility and translational application into everyday clinical practice. These pitfalls can lead to false models, resulting in misinterpretation biases for microbe–disease correlations. Recent efforts to address these challenges include the construction of human gut microbiota data repositories, improved data transparency guidelines, and more accessible machine learning frameworks; implementation of these efforts has facilitated a shift in the field from observational association studies to experimental causal inference and clinical intervention.
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Affiliation(s)
- Mauro Giuffrè
- Department of Medical, Surgical and Health Sciences, University of Trieste, 34149 Trieste, Italy
- Department of Internal Medicine, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
| | - Rita Moretti
- Department of Medical, Surgical and Health Sciences, University of Trieste, 34149 Trieste, Italy
- Fondazione Italiana Fegato-Onlus, The Liver-Brain Unit “Rita Moretti”, 34149 Trieste, Italy
| | - Claudio Tiribelli
- Fondazione Italiana Fegato-Onlus, The Liver-Brain Unit “Rita Moretti”, 34149 Trieste, Italy
- Correspondence:
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Chong JWR, Khoo KS, Chew KW, Ting HY, Show PL. Trends in digital image processing of isolated microalgae by incorporating classification algorithm. Biotechnol Adv 2023; 63:108095. [PMID: 36608745 DOI: 10.1016/j.biotechadv.2023.108095] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 12/17/2022] [Accepted: 01/01/2023] [Indexed: 01/05/2023]
Abstract
Identification of microalgae species is of importance due to the uprising of harmful algae blooms affecting both the aquatic habitat and human health. Despite this occurence, microalgae have been identified as a green biomass and alternative source due to its promising bioactive compounds accumulation that play a significant role in many industrial applications. Recently, microalgae species identification has been conducted through DNA analysis and various microscopy techniques such as light, scanning electron, transmission electron, and atomic force -microscopy. The aforementioned procedures have encouraged researchers to consider alternate ways due to limitations such as costly validation, requiring skilled taxonomists, prolonged analysis, and low accuracy. This review highlights the potential innovations in digital microscopy with the incorporation of both hardware and software that can produce a reliable recognition, detection, enumeration, and real-time acquisition of microalgae species. Several steps such as image acquisition, processing, feature extraction, and selection are discussed, for the purpose of generating high image quality by removing unwanted artifacts and noise from the background. These steps of identification of microalgae species is performed by reliable image classification through machine learning as well as deep learning algorithms such as artificial neural networks, support vector machines, and convolutional neural networks. Overall, this review provides comprehensive insights into numerous possibilities of microalgae image identification, image pre-processing, and machine learning techniques to address the challenges in developing a robust digital classification tool for the future.
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Affiliation(s)
- Jun Wei Roy Chong
- Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia
| | - Kuan Shiong Khoo
- Department of Chemical Engineering and Materials Science, Yuan Ze University, Taoyuan, Taiwan.
| | - Kit Wayne Chew
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore, 637459 Singapore
| | - Huong-Yong Ting
- Drone Research and Application Centre, University of Technology Sarawak, No.1, Jalan Universiti, 96000 Sibu, Sarawak, Malaysia
| | - Pau Loke Show
- Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia; Department of Sustainable Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India.
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Li C, Feng C, Xu R, Jiang B, Li L, He Y, Tu C, Li Z. The emerging applications and advancements of Raman spectroscopy in pediatric cancers. Front Oncol 2023; 13:1044177. [PMID: 36814817 PMCID: PMC9939836 DOI: 10.3389/fonc.2023.1044177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 01/18/2023] [Indexed: 02/09/2023] Open
Abstract
Although the survival rate of pediatric cancer has significantly improved, it is still an important cause of death among children. New technologies have been developed to improve the diagnosis, treatment, and prognosis of pediatric cancers. Raman spectroscopy (RS) is a non-destructive analytical technique that uses different frequencies of scattering light to characterize biological specimens. It can provide information on biological components, activities, and molecular structures. This review summarizes studies on the potential of RS in pediatric cancers. Currently, studies on the application of RS in pediatric cancers mainly focus on early diagnosis, prognosis prediction, and treatment improvement. The results of these studies showed high accuracy and specificity. In addition, the combination of RS and deep learning is discussed as a future application of RS in pediatric cancer. Studies applying RS in pediatric cancer illustrated good prospects. This review collected and analyzed the potential clinical applications of RS in pediatric cancers.
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Affiliation(s)
- Chenbei Li
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Central South University, Changsha, China,Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Chengyao Feng
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Central South University, Changsha, China,Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Ruiling Xu
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Central South University, Changsha, China,Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Buchan Jiang
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Central South University, Changsha, China,Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Lan Li
- Department of Pathology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yu He
- Department of Radiology, Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Chao Tu
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Central South University, Changsha, China,Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Chao Tu, ; Zhihong Li,
| | - Zhihong Li
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Central South University, Changsha, China,Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Chao Tu, ; Zhihong Li,
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Sun T, Niu X, He Q, Chen F, Qi RQ. Artificial Intelligence in microbiomes analysis: A review of applications in dermatology. Front Microbiol 2023; 14:1112010. [PMID: 36819026 PMCID: PMC9929457 DOI: 10.3389/fmicb.2023.1112010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 01/05/2023] [Indexed: 02/04/2023] Open
Abstract
Microorganisms are closely related to skin diseases, and microbiological imbalances or invasions of exogenous pathogens can be a source of various skin diseases. The development and prognosis of such skin diseases are also closely related to the type and composition ratio of microorganisms present. Therefore, through detection of the characteristics and changes in microorganisms, the possibility for diagnosis and prediction of skin diseases can be markedly improved. The abundance of microorganisms and an understanding of the vast amount of biological information associated with these microorganisms has been a formidable task. However, with advances in large-scale sequencing, artificial intelligence (AI)-related machine learning can serve as a means to analyze large-scales of data related to microorganisms along with determinations regarding the type and status of diseases. In this review, we describe some uses of this exciting, new emerging field. In specific, we described the recognition of fungi with convolutional neural networks (CNN), the combined application of microbial genome sequencing and machine learning and applications of AI in the diagnosis of skin diseases as related to the gut-skin axis.
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Affiliation(s)
- Te Sun
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, China,Key Laboratory of Immunodermatology, Ministry of Education and NHC, National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, Shenyang, China
| | - Xueli Niu
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, China,Key Laboratory of Immunodermatology, Ministry of Education and NHC, National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, Shenyang, China
| | - Qing He
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, China,Key Laboratory of Immunodermatology, Ministry of Education and NHC, National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, Shenyang, China
| | - Fujun Chen
- Liaoning Center for Drug Evaluation and Inspection, Shenyang, China,*Correspondence: Fujun Chen,
| | - Rui-Qun Qi
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, China,Key Laboratory of Immunodermatology, Ministry of Education and NHC, National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, Shenyang, China,Rui-Qun Qi,
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Salim F, Mizutani S, Zolfo M, Yamada T. Recent advances of machine learning applications in human gut microbiota study: from observational analysis toward causal inference and clinical intervention. Curr Opin Biotechnol 2023; 79:102884. [PMID: 36623442 DOI: 10.1016/j.copbio.2022.102884] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 02/24/2022] [Accepted: 12/09/2022] [Indexed: 01/08/2023]
Abstract
Statistical methods, especially machine learning, learning(ML), are pivotal for the analyses of large data generated by multiomics human gut microbiota study. These analyses lead to the discovery of microbe-disease associations. Furthermore, recent efforts for more data transparency and accessible analytical tools improved data availability and study reproducibility. Our recent accumulated knowledge on microbe-disease associations brings light to the next questions: what is the role of microbes in disease progression and how can we apply our knowledge of microbiome in clinical settings? Here, we introduce recent studies that implemented ML to answer the questions of causal inference and clinical translation.
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Affiliation(s)
- Felix Salim
- School of Life Science and Technology, Tokyo Institute of Technology
| | - Sayaka Mizutani
- School of Life Science and Technology, Tokyo Institute of Technology; Japan Society for the Promotion of Science
| | - Moreno Zolfo
- School of Life Science and Technology, Tokyo Institute of Technology
| | - Takuji Yamada
- School of Life Science and Technology, Tokyo Institute of Technology; Metagen, Inc.; Metagen Therapeutics, Inc.; digzyme, Inc..
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Ebersbach JC, Sato MO, de Araújo MP, Sato M, Becker SL, Sy I. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry for differential identification of adult Schistosoma worms. Parasit Vectors 2023; 16:20. [PMID: 36658630 PMCID: PMC9854196 DOI: 10.1186/s13071-022-05604-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 11/30/2022] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Schistosomiasis is a major neglected tropical disease that affects up to 250 million individuals worldwide. The diagnosis of human schistosomiasis is mainly based on the microscopic detection of the parasite's eggs in the feces (i.e., for Schistosoma mansoni or Schistosoma japonicum) or urine (i.e., for Schistosoma haematobium) samples. However, these techniques have limited sensitivity, and microscopic expertise is waning outside endemic areas. Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has become the gold standard diagnostic method for the identification of bacteria and fungi in many microbiological laboratories. Preliminary studies have recently shown promising results for parasite identification using this method. The aims of this study were to develop and validate a species-specific database for adult Schistosoma identification, and to evaluate the effects of different storage solutions (ethanol and RNAlater) on spectra profiles. METHODS Adult worms (males and females) of S. mansoni and S. japonicum were obtained from experimentally infected mice. Species identification was carried out morphologically and by cytochrome oxidase 1 gene sequencing. Reference protein spectra for the creation of an in-house MALDI-TOF MS database were generated, and the database evaluated using new samples. We employed unsupervised (principal component analysis) and supervised (support vector machine, k-nearest neighbor, Random Forest, and partial least squares discriminant analysis) machine learning algorithms for the identification and differentiation of the Schistosoma species. RESULTS All the spectra were correctly identified by internal validation. For external validation, 58 new Schistosoma samples were analyzed, of which 100% (58/58) were correctly identified to genus level (log score values ≥ 1.7) and 81% (47/58) were reliably identified to species level (log score values ≥ 2). The spectra profiles showed some differences depending on the storage solution used. All the machine learning algorithms classified the samples correctly. CONCLUSIONS MALDI-TOF MS can reliably distinguish adult S. mansoni from S. japonicum.
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Affiliation(s)
- Jurena Christiane Ebersbach
- grid.11749.3a0000 0001 2167 7588Institute of Medical Microbiology and Hygiene, Saarland University, Homburg, Germany
| | - Marcello Otake Sato
- grid.255137.70000 0001 0702 8004Laboratory of Tropical Medicine and Parasitology, Dokkyo Medical University, Mibu, Tochigi Japan
| | - Matheus Pereira de Araújo
- grid.255137.70000 0001 0702 8004Laboratory of Tropical Medicine and Parasitology, Dokkyo Medical University, Mibu, Tochigi Japan
| | - Megumi Sato
- grid.260975.f0000 0001 0671 5144Graduate School of Health Sciences, Niigata University, Niigata, Japan
| | - Sören L. Becker
- grid.11749.3a0000 0001 2167 7588Institute of Medical Microbiology and Hygiene, Saarland University, Homburg, Germany ,grid.416786.a0000 0004 0587 0574Swiss Tropical and Public Health Institute, Allschwil, Switzerland ,grid.6612.30000 0004 1937 0642University of Basel, Basel, Switzerland
| | - Issa Sy
- grid.11749.3a0000 0001 2167 7588Institute of Medical Microbiology and Hygiene, Saarland University, Homburg, Germany
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He X, Hanusch M, Ruiz-Hernández V, Junker RR. Accuracy of mutual predictions of plant and microbial communities vary along a successional gradient in an alpine glacier forefield. FRONTIERS IN PLANT SCIENCE 2023; 13:1017847. [PMID: 36714711 PMCID: PMC9880484 DOI: 10.3389/fpls.2022.1017847] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 12/23/2022] [Indexed: 06/18/2023]
Abstract
Receding glaciers create virtually uninhabited substrates waiting for initial colonization of bacteria, fungi and plants. These glacier forefields serve as an ideal ecosystem for studying transformations in community composition and diversity over time and the interactions between taxonomic groups in a dynamic landscape. In this study, we investigated the relationships between the composition and diversity of bacteria, fungi, and plant communities as well as environmental factors along a successional gradient. We used random forest analysis assessing how well the composition and diversity of taxonomic groups and environmental factors mutually predict each other. We did not identify a single best indicator for all taxonomic and environmental properties, but found specific predictors to be most accurate for each taxon and environmental factor. The accuracy of prediction varied considerably along the successional gradient, highlighting the dynamic environmental conditions along the successional gradient that may also affect biotic interactions across taxa. This was also reflected by the high accuracy of predictions of plot age by all taxa. Next to plot age, our results indicate a strong importance of pH and temperature in structuring microbial and plant community composition. In addition, taxonomic groups predicted the community composition of each other more accurately than environmental factors, which may either suggest that these groups similarly respond to other not measured environmental factors or that direct interactions between taxa shape the composition of their communities. In contrast, diversity of taxa was not well predicted, suggesting that community composition of one taxonomic group is not a strong driver of the diversity of another group. Our study provides insights into the successional development of multidiverse communities shaped by complex interactions between taxonomic groups and the environment.
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Affiliation(s)
- Xie He
- Department of Environment and Biodiversity, Paris Lodron University of Salzburg, Salzburg, Austria
| | - Maximilian Hanusch
- Department of Environment and Biodiversity, Paris Lodron University of Salzburg, Salzburg, Austria
| | - Victoria Ruiz-Hernández
- Department of Environment and Biodiversity, Paris Lodron University of Salzburg, Salzburg, Austria
| | - Robert R. Junker
- Department of Environment and Biodiversity, Paris Lodron University of Salzburg, Salzburg, Austria
- Evolutionary Ecology of Plants, Department of Biology, Philipps University of Marburg, Marburg, Germany
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Tu DY, Cao J, Zhou J, Su BB, Wang SY, Jiang GQ, Jin SJ, Zhang C, Peng R, Bai DS. Identification of the mitophagy-related diagnostic biomarkers in hepatocellular carcinoma based on machine learning algorithm and construction of prognostic model. Front Oncol 2023; 13:1132559. [PMID: 36937391 PMCID: PMC10014545 DOI: 10.3389/fonc.2023.1132559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 02/15/2023] [Indexed: 03/05/2023] Open
Abstract
Background and aims As a result of increasing numbers of studies most recently, mitophagy plays a vital function in the genesis of cancer. However, research on the predictive potential and clinical importance of mitophagy-related genes (MRGs) in hepatocellular carcinoma (HCC) is currently lacking. This study aimed to uncover and analyze the mitophagy-related diagnostic biomarkers in HCC using machine learning (ML), as well as to investigate its biological role, immune infiltration, and clinical significance. Methods In our research, by using Least absolute shrinkage and selection operator (LASSO) regression and support vector machine- (SVM-) recursive feature elimination (RFE) algorithm, six mitophagy genes (ATG12, CSNK2B, MTERF3, TOMM20, TOMM22, and TOMM40) were identified from twenty-nine mitophagy genes, next, the algorithm of non-negative matrix factorization (NMF) was used to separate the HCC patients into cluster A and B based on the six mitophagy genes. And there was evidence from multi-analysis that cluster A and B were associated with tumor immune microenvironment (TIME), clinicopathological features, and prognosis. After then, based on the DEGs (differentially expressed genes) between cluster A and cluster B, the prognostic model (riskScore) of mitophagy was constructed, including ten mitophagy-related genes (G6PD, KIF20A, SLC1A5, TPX2, ANXA10, TRNP1, ADH4, CYP2C9, CFHR3, and SPP1). Results This study uncovered and analyzed the mitophagy-related diagnostic biomarkers in HCC using machine learning (ML), as well as to investigate its biological role, immune infiltration, and clinical significance. Based on the mitophagy-related diagnostic biomarkers, we constructed a prognostic model(riskScore). Furthermore, we discovered that the riskScore was associated with somatic mutation, TIME, chemotherapy efficacy, TACE and immunotherapy effectiveness in HCC patients. Conclusion Mitophagy may play an important role in the development of HCC, and further research on this issue is necessary. Furthermore, the riskScore performed well as a standalone prognostic marker in terms of accuracy and stability. It can provide some guidance for the diagnosis and treatment of HCC patients.
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Affiliation(s)
| | | | | | | | | | | | | | - Chi Zhang
- *Correspondence: Dou-sheng Bai, ; Rui Peng, ; Chi Zhang,
| | - Rui Peng
- *Correspondence: Dou-sheng Bai, ; Rui Peng, ; Chi Zhang,
| | - Dou-sheng Bai
- *Correspondence: Dou-sheng Bai, ; Rui Peng, ; Chi Zhang,
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He X, Wang C, Wang Y, Yu J, Zhao Y, Li J, Hussain M, Liu B. Rapid classification of micro-particles using multi-angle dynamic light scatting and machine learning approach. Front Bioeng Biotechnol 2022; 10:1097363. [PMID: 36588961 PMCID: PMC9800508 DOI: 10.3389/fbioe.2022.1097363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 12/06/2022] [Indexed: 12/23/2022] Open
Abstract
The rapid classification of micro-particles has a vast range of applications in biomedical sciences and technology. In the given study, a prototype has been developed for the rapid detection of particle size using multi-angle dynamic light scattering and a machine learning approach by applying a support vector machine. The device consisted of three major parts: a laser light, an assembly of twelve sensors, and a data acquisition system. The laser light with a wavelength of 660 nm was directed towards the prepared sample. The twelve different photosensors were arranged symmetrically surrounding the testing sample to acquire the scattered light. The position of the photosensor was based on the Mie scattering theory to detect the maximum light scattering. In this study, three different spherical microparticles with sizes of 1, 2, and 4 μm were analyzed for the classification. The real-time light scattering signals were collected from each sample for 30 min. The power spectrum feature was evaluated from the acquired waveforms, and then recursive feature elimination was utilized to filter the features with the highest correlation. The machine learning classifiers were trained using the features with optimum conditions and the classification accuracies were evaluated. The results showed higher classification accuracies of 94.41%, 94.20%, and 96.12% for the particle sizes of 1, 2, and 4 μm, respectively. The given method depicted an overall classification accuracy of 95.38%. The acquired results showed that the developed system can detect microparticles within the range of 1-4 μm, with detection limit of 0.025 mg/ml. Therefore, the current study validated the performance of the device, and the given technique can be further applied in clinical applications for the detection of microbial particles.
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Affiliation(s)
- Xu He
- Jiangsu Province Engineering Research Center of Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Chao Wang
- Jiangsu Province Engineering Research Center of Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Yichuan Wang
- Jiangsu Province Engineering Research Center of Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Junxiao Yu
- Jiangsu Province Engineering Research Center of Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Yanfeng Zhao
- Jiangsu Province Engineering Research Center of Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Jianqing Li
- Jiangsu Province Engineering Research Center of Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China,The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Mubashir Hussain
- Jiangsu Province Engineering Research Center of Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China,Changzhou Medical Center, The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University, Changzhou Second People’s Hospital, Nanjing Medical University, Changzhou, China,*Correspondence: Mubashir Hussain, ; Bin Liu,
| | - Bin Liu
- Jiangsu Province Engineering Research Center of Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China,*Correspondence: Mubashir Hussain, ; Bin Liu,
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Yan MY, Zheng D, Li SS, Ding XY, Wang CL, Guo XP, Zhan L, Jin Q, Yang J, Sun YC. Application of combined CRISPR screening for genetic and chemical-genetic interaction profiling in Mycobacterium tuberculosis. SCIENCE ADVANCES 2022; 8:eadd5907. [PMID: 36417506 PMCID: PMC9683719 DOI: 10.1126/sciadv.add5907] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 10/05/2022] [Indexed: 05/30/2023]
Abstract
CRISPR screening, including CRISPR interference (CRISPRi) and CRISPR-knockout (CRISPR-KO) screening, has become a powerful technology in the genetic screening of eukaryotes. In contrast with eukaryotes, CRISPR-KO screening has not yet been applied to functional genomics studies in bacteria. Here, we constructed genome-scale CRISPR-KO and also CRISPRi libraries in Mycobacterium tuberculosis (Mtb). We first examined these libraries to identify genes essential for Mtb viability. Subsequent screening identified dozens of genes associated with resistance/susceptibility to the antitubercular drug bedaquiline (BDQ). Genetic and chemical validation of the screening results suggested that it provided a valuable resource to investigate mechanisms of action underlying the effects of BDQ and to identify chemical-genetic synergies that can be used to optimize tuberculosis therapy. In summary, our results demonstrate the potential for efficient genome-wide CRISPR-KO screening in bacteria and establish a combined CRISPR screening approach for high-throughput investigation of genetic and chemical-genetic interactions in Mtb.
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Affiliation(s)
- Mei-Yi Yan
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
| | - Dandan Zheng
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
| | - Si-Shang Li
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
| | - Xin-Yuan Ding
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
| | - Chun-Liang Wang
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
| | - Xiao-Peng Guo
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
| | - Lingjun Zhan
- NHC Key Laboratory of Human Disease Comparative Medicine, Institute of Laboratory Animal Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
| | - Qi Jin
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
| | - Jian Yang
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
| | - Yi-Cheng Sun
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
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Lactic Acid Bacteria as Potential Agents for Biocontrol of Aflatoxigenic and Ochratoxigenic Fungi. Toxins (Basel) 2022; 14:toxins14110807. [PMID: 36422981 PMCID: PMC9699002 DOI: 10.3390/toxins14110807] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/16/2022] [Accepted: 11/17/2022] [Indexed: 11/22/2022] Open
Abstract
Aflatoxins (AF) and ochratoxin A (OTA) are fungal metabolites that have carcinogenic, teratogenic, embryotoxic, genotoxic, neurotoxic, and immunosuppressive effects in humans and animals. The increased consumption of plant-based foods and environmental conditions associated with climate change have intensified the risk of mycotoxin intoxication. This study aimed to investigate the abilities of eleven selected LAB strains to reduce/inhibit the growth of Aspergillus flavus, Aspergillus parasiticus, Aspergillus carbonarius, Aspergillus niger, Aspergillus welwitschiae, Aspergillus steynii, Aspergillus westerdijkiae, and Penicillium verrucosum and AF and OTA production under different temperature regiments. Data were treated by ANOVA, and machine learning (ML) models able to predict the growth inhibition percentage were built, and their performance was compared. All factors LAB strain, fungal species, and temperature significantly affected fungal growth and mycotoxin production. The fungal growth inhibition range was 0-100%. Overall, the most sensitive fungi to LAB treatments were P. verrucosum and A. steynii, while the least sensitive were A. niger and A. welwitschiae. The LAB strains with the highest antifungal activity were Pediococcus pentosaceus (strains S11sMM and M9MM5b). The reduction range for AF was 19.0% (aflatoxin B1)-60.8% (aflatoxin B2) and for OTA, 7.3-100%, depending on the bacterial and fungal strains and temperatures. The LAB strains with the highest anti-AF activity were the three strains of P. pentosaceus and Leuconostoc mesenteroides ssp. dextranicum (T2MM3), and those with the highest anti-OTA activity were Leuconostoc paracasei ssp. paracasei (3T3R1) and L. mesenteroides ssp. dextranicum (T2MM3). The best ML methods in predicting fungal growth inhibition were multilayer perceptron neural networks, followed by random forest. Due to anti-fungal and anti-mycotoxin capacity, the LABs strains used in this study could be good candidates as biocontrol agents against aflatoxigenic and ochratoxigenic fungi and AFL and OTA accumulation.
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Development of a Prediction Method of Cell Density in Autotrophic/Heterotrophic Microorganism Mixtures by Machine Learning Using Absorbance Spectrum Data. BIOTECH (BASEL (SWITZERLAND)) 2022; 11:biotech11040046. [PMID: 36278558 PMCID: PMC9624369 DOI: 10.3390/biotech11040046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 10/09/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022]
Abstract
Microflora is actively used to produce value-added materials in industry, and each cell density should be controlled for stable microflora use. In this study, a simple system evaluating the cell density was constructed with artificial intelligence (AI) using the absorbance spectra data of microflora. To set up the system, the prediction system for cell density based on machine learning was constructed using the spectra data as the feature from the mixture of Saccharomyces cerevisiae and Chlamydomonas reinhardtii. As the results of predicting cell density by extremely randomized trees, when the cell densities of S. cerevisiae and C. reinhardtii were shifted and fixed, the coefficient of determination (R2) was 0.8495; on the other hand, when the cell densities of S. cerevisiae and C. reinhardtii were fixed and shifted, the R2 was 0.9232. To explain the prediction system, the randomized trees regressor of the decision tree-based ensemble learning method as the machine learning algorithm and Shapley additive explanations (SHAPs) as the explainable AI (XAI) to interpret the features contributing to the prediction results were used. As a result of the SHAP analyses, not only the optical density, but also the absorbance of the Soret and Q bands derived from the chloroplasts of C. reinhardtii could contribute to the prediction as the features. The simple cell density evaluating system could have an industrial impact.
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