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Buican IL, Gheorman V, Udriştoiu I, Olteanu M, Rădulescu D, Calafeteanu DM, Nemeş AF, Călăraşu C, Rădulescu PM, Streba CT. Interactions between Cognitive, Affective, and Respiratory Profiles in Chronic Respiratory Disorders: A Cluster Analysis Approach. Diagnostics (Basel) 2024; 14:1153. [PMID: 38893678 PMCID: PMC11171769 DOI: 10.3390/diagnostics14111153] [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: 05/04/2024] [Revised: 05/25/2024] [Accepted: 05/28/2024] [Indexed: 06/21/2024] Open
Abstract
This study conducted at Leamna Pulmonology Hospital investigated the interrelations among cognitive, affective, and respiratory variables within a cohort of 100 patients diagnosed with chronic respiratory conditions, utilizing sophisticated machine learning-based clustering techniques. Spanning from October 2022 to February 2023, hospitalized individuals confirmed to have asthma or COPD underwent extensive evaluations using standardized instruments such as the mMRC scale, the CAT test, and spirometry. Complementary cognitive and affective assessments were performed employing the MMSE, MoCA, and the Hamilton Anxiety and Depression Scale, furnishing a holistic view of patient health statuses. The analysis delineated three distinct clusters: Moderate Cognitive Respiratory, Severe Cognitive Respiratory, and Stable Cognitive Respiratory, each characterized by unique profiles that underscore the necessity for tailored therapeutic strategies. These clusters exhibited significant correlations between the severity of respiratory symptoms and their effects on cognitive and affective conditions. The results highlight the benefits of an integrated treatment approach for COPD and asthma, which is personalized based on the intricate patterns identified through clustering. Such a strategy promises to enhance the management of these diseases, potentially elevating the quality of life and everyday functionality of the patients. These findings advocate for treatment customization according to the specific interplays among cognitive, affective, and respiratory dimensions, presenting substantial prospects for clinical advancement and pioneering new avenues for research in the domain of chronic respiratory disease management.
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Affiliation(s)
- Iulian-Laurențiu Buican
- U.M.F. Doctoral School Craiova, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania;
- Leamna Pulmonology Hospital, 207129 Leamna, Romania; (C.C.); (P.-M.R.); (C.-T.S.)
| | - Victor Gheorman
- Department of Psychiatry, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania; (V.G.); (I.U.)
| | - Ion Udriştoiu
- Department of Psychiatry, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania; (V.G.); (I.U.)
| | - Mădălina Olteanu
- Department of Orthodontics, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania;
| | - Dumitru Rădulescu
- Department of Surgery, The Military Emergency Clinical Hospital ‘Dr. Stefan Odobleja’ Craiova, 200749 Craiova, Romania
| | - Dan Marian Calafeteanu
- Department of Surgery, The Military Emergency Clinical Hospital ‘Dr. Stefan Odobleja’ Craiova, 200749 Craiova, Romania
| | - Alexandra Floriana Nemeş
- Department of Neonatology, ‘Louis Ţurcanu’ Clinical Emergency Hospital for Children, 300011 Timişoara, Romania;
| | - Cristina Călăraşu
- Leamna Pulmonology Hospital, 207129 Leamna, Romania; (C.C.); (P.-M.R.); (C.-T.S.)
- Department of Pulmonology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | | | - Costin-Teodor Streba
- Leamna Pulmonology Hospital, 207129 Leamna, Romania; (C.C.); (P.-M.R.); (C.-T.S.)
- Department of Pulmonology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
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Vilain M, Aris-Brosou S. Machine Learning Algorithms Associate Case Numbers with SARS-CoV-2 Variants Rather Than with Impactful Mutations. Viruses 2023; 15:1226. [PMID: 37376526 DOI: 10.3390/v15061226] [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: 04/03/2023] [Revised: 05/12/2023] [Accepted: 05/13/2023] [Indexed: 06/29/2023] Open
Abstract
During the SARS-CoV-2 pandemic, much effort has been geared towards creating models to predict case numbers. These models typically rely on epidemiological data, and as such overlook viral genomic information, which could be assumed to improve predictions, as different variants show varying levels of virulence. To test this hypothesis, we implemented simple models to predict future case numbers based on the genomic sequences of the Alpha and Delta variants, which were co-circulating in Texas and Minnesota early during the pandemic. Sequences were encoded, matched with case numbers at a future time based on collection date, and used to train two algorithms: one based on random forests and one based on a feed-forward neural network. While prediction accuracies were ≥93%, explainability analyses showed that the models were not associating case numbers with mutations known to have an impact on virulence, but with individual variants. This work highlights the necessity of gaining a better understanding of the data used for training and of conducting explainability analysis to assess whether model predictions are misleading.
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Affiliation(s)
- Matthieu Vilain
- Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Stéphane Aris-Brosou
- Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada
- Department of Mathematics and Statistics, University of Ottawa, Ottawa, ON K1N 6N5, Canada
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Borkenhagen LK, Allen MW, Runstadler JA. Influenza virus genotype to phenotype predictions through machine learning: a systematic review. Emerg Microbes Infect 2021; 10:1896-1907. [PMID: 34498543 PMCID: PMC8462836 DOI: 10.1080/22221751.2021.1978824] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background: There is great interest in understanding the viral genomic predictors of phenotypic traits that allow influenza A viruses to adapt to or become more virulent in different hosts. Machine learning techniques have demonstrated promise in addressing this critical need for other pathogens because the underlying algorithms are especially well equipped to uncover complex patterns in large datasets and produce generalizable predictions for new data. As the body of research where these techniques are applied for influenza A virus phenotype prediction continues to grow, it is useful to consider the strengths and weaknesses of these approaches to understand what has prevented these models from seeing widespread use by surveillance laboratories and to identify gaps that are underexplored with this technology. Methods and Results: We present a systematic review of English literature published through 15 April 2021 of studies employing machine learning methods to generate predictions of influenza A virus phenotypes from genomic or proteomic input. Forty-nine studies were included in this review, spanning the topics of host discrimination, human adaptability, subtype and clade assignment, pandemic lineage assignment, characteristics of infection, and antiviral drug resistance. Conclusions: Our findings suggest that biases in model design and a dearth of wet laboratory follow-up may explain why these models often go underused. We, therefore, offer guidance to overcome these limitations, aid in improving predictive models of previously studied influenza A virus phenotypes, and extend those models to unexplored phenotypes in the ultimate pursuit of tools to enable the characterization of virus isolates across surveillance laboratories.
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Affiliation(s)
- Laura K Borkenhagen
- Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine, Tufts University, North Grafton, MA, USA
| | - Martin W Allen
- Department of Computer Science, School of Engineering, Tufts University, Medford, MA, USA
| | - Jonathan A Runstadler
- Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine, Tufts University, North Grafton, MA, USA
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Tarasova O, Poroikov V. Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy. Curr Med Chem 2021; 28:7840-7861. [PMID: 33949929 DOI: 10.2174/0929867328666210504114351] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/13/2021] [Accepted: 02/24/2021] [Indexed: 11/22/2022]
Abstract
Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.
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Affiliation(s)
- Olga Tarasova
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow. Russian Federation
| | - Vladimir Poroikov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow. Russian Federation
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Rubin DHF, Ross JDC, Grad YH. The frontiers of addressing antibiotic resistance in Neisseria gonorrhoeae. Transl Res 2020; 220:122-137. [PMID: 32119845 PMCID: PMC7293957 DOI: 10.1016/j.trsl.2020.02.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Revised: 02/08/2020] [Accepted: 02/10/2020] [Indexed: 12/21/2022]
Abstract
The sexually transmitted infection gonorrhea, caused by the Gram-negative bacterium Neisseria gonorrhoeae, can cause urethritis, cervicitis, and systemic disease, among other manifestations. N. gonorrhoeae has rapidly rising incidence along with increasing levels of antibiotic resistance to a broad range of drugs including first-line treatments. The rise in resistance has led to fears of untreatable gonorrhea causing substantial disease globally. In this review, we will describe multiple approaches being undertaken to slow and control this spread of resistance. First, a number of old drugs have been repurposed and new drugs are being developed with activity against Neisseria gonorrhoeae. Second, vaccine development, long an important goal, is advancing. Third, new diagnostics promise rapid detection of antibiotic resistance and a shift from empiric to tailored treatment. The deployment of these new tools for addressing the challenge of antibiotic resistance will require careful consideration to provide optimal care for all patients while extending the lifespan of treatment regimens.
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Affiliation(s)
- Daniel H F Rubin
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Jonathan D C Ross
- Department of Sexual Health and HIV, Birmingham University Hospitals NHS Foundation Trust, Birmingham, UK
| | - Yonatan H Grad
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, Massachusetts; Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts; Division of Infectious Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
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Dench J, Hinz A, Aris‐Brosou S, Kassen R. Identifying the drivers of computationally detected correlated evolution among sites under antibiotic selection. Evol Appl 2020; 13:781-793. [PMID: 32211067 PMCID: PMC7086105 DOI: 10.1111/eva.12900] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 10/02/2019] [Accepted: 11/14/2019] [Indexed: 11/29/2022] Open
Abstract
The ultimate causes of correlated evolution among sites in a genome remain difficult to tease apart. To address this problem directly, we performed a high-throughput search for correlated evolution among sites associated with resistance to a fluoroquinolone antibiotic using whole-genome data from clinical strains of Pseudomonas aeruginosa, before validating our computational predictions experimentally. We show that for at least two sites, this correlation is underlain by epistasis. Our analysis also revealed eight additional pairs of synonymous substitutions displaying correlated evolution underlain by physical linkage, rather than selection associated with antibiotic resistance. Our results provide direct evidence that both epistasis and physical linkage among sites can drive the correlated evolution identified by high-throughput computational tools. In other words, the observation of correlated evolution is not by itself sufficient evidence to guarantee that the sites in question are epistatic; such a claim requires additional evidence, ideally coming from direct estimates of epistasis, based on experimental evidence.
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Affiliation(s)
- Jonathan Dench
- Department of BiologyUniversity of OttawaOttawaOntarioCanada
| | - Aaron Hinz
- Department of BiologyUniversity of OttawaOttawaOntarioCanada
| | - Stéphane Aris‐Brosou
- Department of BiologyUniversity of OttawaOttawaOntarioCanada
- Department of Mathematics and StatisticsUniversity of OttawaOttawaOntarioCanada
| | - Rees Kassen
- Department of BiologyUniversity of OttawaOttawaOntarioCanada
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