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Shen J, Sun H, Zhou S, Wang L, Dong C, Ren K, Du Q, Cao J, Wang Y, Sun J. Development of a screening system of gene sets for estimating the time of early skeletal muscle injury based on second-generation sequencing technology. Int J Legal Med 2024:10.1007/s00414-024-03210-6. [PMID: 38532207 DOI: 10.1007/s00414-024-03210-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 03/13/2024] [Indexed: 03/28/2024]
Abstract
The present study is aimed to address the challenge of wound age estimation in forensic science by identifying reliable genetic markers using low-cost and high-precision second-generation sequencing technology. A total of 54 Sprague-Dawley rats were randomly assigned to a control group or injury groups, with injury groups being further divided into time points (4 h, 8 h, 12 h, 16 h, 20 h, 24 h, 28 h, and 32 h after injury, n = 6) to establish rat skeletal muscle contusion models. Gene expression data were obtained using second-generation sequencing technology, and differential gene expression analysis, weighted gene co-expression network analysis (WGCNA) and time-dependent expression trend analysis were performed. A total of six sets of biomarkers were obtained: differentially expressed genes at adjacent time points (127 genes), co-expressed genes most associated with wound age (213 genes), hub genes exhibiting time-dependent expression (264 genes), and sets of transcription factors (TF) corresponding to the above sets of genes (74, 87, and 99 genes, respectively). Then, random forest (RF), support vector machine (SVM) and multilayer perceptron (MLP), were constructed for wound age estimation from the above gene sets. The results estimated by transcription factors were all superior to the corresponding hub genes, with the transcription factor group of WGCNA performed the best, with average accuracy rates of 96% for three models' internal testing, and 91.7% for the highest external validation. This study demonstrates the advantages of the indicator screening system based on second-generation sequencing technology and transcription factor level for wound age estimation.
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Affiliation(s)
- Junyi Shen
- Department of Forensic Medicine, Shanxi Medical University, Jinzhong, China
- Institute of Forensic Science Public Security Department of Shanxi, Taiyuan, China
| | - Hao Sun
- Department of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Shidong Zhou
- Department of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Liangliang Wang
- Department of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Chaoxiu Dong
- Institute of Forensic Science Public Security Department of Shanxi, Taiyuan, China
| | - Kang Ren
- Department of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Qiuxiang Du
- Department of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Jie Cao
- Department of Forensic Medicine, Shanxi Medical University, Jinzhong, China
| | - Yingyuan Wang
- Department of Forensic Medicine, Shanxi Medical University, Jinzhong, China.
| | - Junhong Sun
- Department of Forensic Medicine, Shanxi Medical University, Jinzhong, China.
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Farzaneh F, Jafari Ashtiani A, Hashemi M, Hosseini MS, Arab M, Ashrafganjoei T, Hooshmand Chayjan S. Endometrial cancer in women with abnormal uterine bleeding: Data mining classification methods. Caspian J Intern Med 2023; 14:526-533. [PMID: 37520874 PMCID: PMC10379791 DOI: 10.22088/cjim.14.3.526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/16/2022] [Accepted: 08/14/2022] [Indexed: 08/01/2023]
Abstract
Background Over the last decade, artificial intelligence in medicine has been growing. Since endometrial cancer can be treated with early diagnosis, finding a non-invasive method for screening patients, especially high-risk ones, could have a particular value. Regarding the importance of this issue, we aimed to investigate the risk factors related to endometrial cancer and find a tool to predict it using machine learning. Methods In this cross-sectional study, 972 patients with abnormal uterine bleeding from January 2016 to January 2021 were studied, and the essential characteristics of each patient, along with the findings of curettage pathology, were analyzed using statistical methods and machine learning algorithms, including artificial neural networks, classification and regression trees, support vector machine, and logistic regression. Results Out of 972 patients with a mean age of 45.77 ± 10.70 years, 920 patients had benign pathology, and 52 patients had endometrial cancer. In terms of endometrial cancer prediction, the logistic regression model had the best performance (sensitivity of 100% and 98%, specificity of 98.83% and 98.7%, for trained and test data sets respectively,) followed by the classification and regression trees model. Conclusion Based on the results, artificial intelligence-based algorithms can be applied as a non-invasive screening method for predicting endometrial cancer.
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Affiliation(s)
- Farah Farzaneh
- Preventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Azadeh Jafari Ashtiani
- Preventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Hashemi
- Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
| | - Maryam Sadat Hosseini
- Preventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Maliheh Arab
- Preventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Tahereh Ashrafganjoei
- Preventative Gynecology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Kumar R, Sharma A, Alexiou A, Ashraf GM. Artificial Intelligence in De novo Drug Design: Are We Still There? Curr Top Med Chem 2022; 22:2483-2492. [PMID: 36263480 DOI: 10.2174/1568026623666221017143244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 09/06/2022] [Accepted: 09/15/2022] [Indexed: 01/20/2023]
Abstract
BACKGROUND The artificial intelligence (AI)-assisted design of drug candidates with novel structures and desired properties has received significant attention in the recent past, so related areas of forward prediction that aim to discover chemical matters worth synthesizing and further experimental investigation. OBJECTIVES The purpose behind developing AI-driven models is to explore the broader chemical space and suggest new drug candidate scaffolds with promising therapeutic value. Moreover, it is anticipated that such AI-based models may not only significantly reduce the cost and time but also decrease the attrition rate of drug candidates that fail to reach the desirable endpoints at the final stages of drug development. In an attempt to develop AI-based models for de novo drug design, numerous methods have been proposed by various study groups by applying machine learning and deep learning algorithms to chemical datasets. However, there are many challenges in obtaining accurate predictions, and real breakthroughs in de novo drug design are still scarce. METHODS In this review, we explore the recent trends in developing AI-based models for de novo drug design to assess the current status, challenges, and opportunities in the field. CONCLUSION The consistently improved AI algorithms and the abundance of curated training chemical data indicate that AI-based de novo drug design should perform better than the current models. Improvements in the performance are warranted to obtain better outcomes in the form of potential drug candidates, which can perform well in in vivo conditions, especially in the case of more complex diseases.
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Affiliation(s)
- Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Uttar Pradesh, India
| | - Anju Sharma
- Department of Applied Science, Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India
| | - Athanasios Alexiou
- Novel Global Community Educational Foundation, Hebersham, 2770 NSW, Australia.,AFNP Med Austria, 1010 Wien, Austria
| | - Ghulam Md Ashraf
- Pre-Clinical Research Unit (PCRU), King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia.,Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
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Shi R, Bao X, Unger K, Sun J, Lu S, Manapov F, Wang X, Belka C, Li M. Identification and validation of hypoxia-derived gene signatures to predict clinical outcomes and therapeutic responses in stage I lung adenocarcinoma patients. Am J Cancer Res 2021; 11:5061-5076. [PMID: 33754044 PMCID: PMC7978303 DOI: 10.7150/thno.56202] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 01/23/2021] [Indexed: 12/18/2022] Open
Abstract
Rationale: The current tumour-node-metastasis (TNM) staging system is insufficient for precise treatment decision-making and accurate survival prediction for patients with stage I lung adenocarcinoma (LUAD). Therefore, more reliable biomarkers are urgently needed to identify the high-risk subset in stage I patients to guide adjuvant therapy. Methods: This study retrospectively analysed the transcriptome profiles and clinical parameters of 1,400 stage I LUAD patients from 14 public datasets, including 13 microarray datasets from different platforms and 1 RNA-Seq dataset from The Cancer Genome Atlas (TCGA). A series of bioinformatic and machine learning approaches were combined to establish hypoxia-derived signatures to predict overall survival (OS) and immune checkpoint blockade (ICB) therapy response in stage I patients. In addition, enriched pathways, genomic and copy number alterations were analysed in different risk subgroups and compared to each other. Results: Among various hallmarks of cancer, hypoxia was identified as a dominant risk factor for overall survival in stage I LUAD patients. The hypoxia-related prognostic risk score (HPRS) exhibited more powerful capacity of survival prediction compared to traditional clinicopathological features, and the hypoxia-related immunotherapeutic response score (HIRS) outperformed conventional biomarkers for ICB therapy. An integrated decision tree and nomogram were generated to optimize risk stratification and quantify risk assessment. Conclusions: In summary, the proposed hypoxia-derived signatures are promising biomarkers to predict clinical outcomes and therapeutic responses in stage I LUAD patients.
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Blanc D, Racine V, Khalil A, Deloche M, Broyelle JA, Hammouamri I, Sinitambirivoutin E, Fiammante M, Verdier E, Besson T, Sadate A, Lederlin M, Laurent F, Chassagnon G, Ferretti G, Diascorn Y, Brillet PY, Cassagnes L, Caramella C, Loubet A, Abassebay N, Cuingnet P, Ohana M, Behr J, Ginzac A, Veyssiere H, Durando X, Bousaïd I, Lassau N, Brehant J. Artificial intelligence solution to classify pulmonary nodules on CT. Diagn Interv Imaging 2020; 101:803-810. [PMID: 33168496 DOI: 10.1016/j.diii.2020.10.004] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/12/2020] [Accepted: 10/13/2020] [Indexed: 12/20/2022]
Abstract
PURPOSE The purpose of this study was to create an algorithm to detect and classify pulmonary nodules in two categories based on their volume greater than 100 mm3 or not, using machine learning and deep learning techniques. MATERIALS AND METHOD The dataset used to train the model was provided by the organization team of the SFR (French Radiological Society) Data Challenge 2019. An asynchronous and parallel 3-stages pipeline was developed to process all the data (a data "pre-processing" stage; a "nodule detection" stage; a "classifier" stage). Lung segmentation was achieved using 3D U-NET algorithm; nodule detection was done using 3D Retina-UNET and classifier stage with a support vector machine algorithm on selected features. Performances were assessed using area under receiver operating characteristics curve (AUROC). RESULTS The pipeline showed good performance for pathological nodule detection and patient diagnosis. With the preparation dataset, an AUROC of 0.9058 (95% confidence interval [CI]: 0.8746-0.9362) was obtained, 87% yielding accuracy (95% CI: 84.83%-91.03%) for the "nodule detection" stage, corresponding to 86% specificity (95% CI: 82%-92%) and 89% sensitivity (95% CI: 84.83%-91.03%). CONCLUSION A fully functional pipeline using 3D U-NET, 3D Retina-UNET and classifier stage with a support vector machine algorithm was developed, resulting in high capabilities for pulmonary nodule classification.
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Affiliation(s)
- D Blanc
- QuantaCell, IRMB, Hôpital Saint-Eloi, 34090 Montpellier, France
| | - V Racine
- QuantaCell, IRMB, Hôpital Saint-Eloi, 34090 Montpellier, France
| | - A Khalil
- Department of Radiology, Neuroradiology unit, Assistance Publique-Hôpitaux de Paris, Hôpital Bichat Claude Bernard, 75018 Paris, France; Université de Paris, 75010, Paris, France
| | - M Deloche
- >IBM Cognitive Systems Lab, 34000 Montpellier, France
| | - J-A Broyelle
- >IBM Cognitive Systems Lab, 34000 Montpellier, France
| | - I Hammouamri
- >IBM Cognitive Systems Lab, 34000 Montpellier, France
| | | | - M Fiammante
- IBM Cognitive Systems France, 92270 Bois-Colombes, France
| | - E Verdier
- IBM Cognitive Systems France, 92270 Bois-Colombes, France
| | - T Besson
- IBM Cognitive Systems France, 92270 Bois-Colombes, France
| | - A Sadate
- Department of Radiology and Medical Imaging, CHU Nîmes, University Montpellier, EA2415, 30029 Nîmes, France
| | - M Lederlin
- Department of Radiology, Hôpital Universitaire Pontchaillou, 35000 Rennes, France
| | - F Laurent
- Department of thoracic and cardiovascular Imaging, Respiratory Diseases Service, Respiratory Functional Exploration Service, Hôpital universitaire de Bordeaux, CIC 1401, 33600 Pessac, France
| | - G Chassagnon
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, 75014, Paris, France & Université de Paris, 75006 Paris, France
| | - G Ferretti
- Department of Radiology and Medical Imaging, CHU Grenoble Alpes, 38700 Grenoble, France
| | - Y Diascorn
- Department of Radiology, Hôpital Universitaire Pasteur, Nice, France
| | - P-Y Brillet
- Inserm UMR 1272, Université Sorbonne Paris Nord, Assistance Publique-Hôpitaux de Paris, Department of Radiology, Hôpital Avicenne, 93430 Bobigny, France
| | - Lucie Cassagnes
- Department of radiology B, CHU Gabriel Montpied, 63003 Clermont-Ferrand, France
| | - C Caramella
- Department of Radiology, Institut Gustave Roussy, 94800 Villejuif, France
| | - A Loubet
- Department of Neuroradiology, Hôpital Gui-de-Chauliac, CHRU de Montpellier, 34000 Montpellier, France
| | - N Abassebay
- Department of Radiology, CH Douai, 59507 Douai, France
| | - P Cuingnet
- Department of Radiology, CH Douai, 59507 Douai, France
| | - M Ohana
- Department of Radiology, Nouvel Hôpital Civil, 67000 Strasbourg, France
| | - J Behr
- Department of Radiology, CHRU de Jean-Minjoz Besançon, 25030 Besançon, France
| | - A Ginzac
- Clinical Research Unit, Clinical Research and Innovation Delegation, Centre de Lutte contre le Cancer, Centre Jean Perrin, 63011 Clermont-Ferrand Cedex 1, France; Université Clermont Auvergne,INSERM, U1240 Imagerie Moléculaire et Stratégies Théranostiques, Centre Jean Perrin, 63011 Clermont-Ferrand, France; Clinical Investigation Center, UMR501, 63011 Clermont-Ferrand, France
| | - H Veyssiere
- Clinical Research Unit, Clinical Research and Innovation Delegation, Centre de Lutte contre le Cancer, Centre Jean Perrin, 63011 Clermont-Ferrand Cedex 1, France; Université Clermont Auvergne,INSERM, U1240 Imagerie Moléculaire et Stratégies Théranostiques, Centre Jean Perrin, 63011 Clermont-Ferrand, France; Clinical Investigation Center, UMR501, 63011 Clermont-Ferrand, France
| | - X Durando
- Clinical Research Unit, Clinical Research and Innovation Delegation, Centre de Lutte contre le Cancer, Centre Jean Perrin, 63011 Clermont-Ferrand Cedex 1, France; Université Clermont Auvergne,INSERM, U1240 Imagerie Moléculaire et Stratégies Théranostiques, Centre Jean Perrin, 63011 Clermont-Ferrand, France; Clinical Investigation Center, UMR501, 63011 Clermont-Ferrand, France; Department of Medical Oncology, Centre Jean Perrin, 63011 Clermont-Ferrand, France
| | - I Bousaïd
- Digital Transformation and Information Systems Division, Gustave Roussy, 94800 Villejuif, France
| | - N Lassau
- Multimodal Biomedical Imaging Laboratory Paris-Saclay, BIOMAPS, UMR 1281, Université Paris-Saclay, Inserm, CNRS, CEA, Department of Radiology, Institut Gustave Roussy, 94800, Villejuif, France
| | - J Brehant
- Department of Radiology, Centre Jean Perrin, 63011 Clermont-Ferrand, France.
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