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Ajmal A, Alkhatabi HA, Alreemi RM, Alamri MA, Khalid A, Abdalla AN, Alotaibi BS, Wadood A. Prospective virtual screening combined with bio-molecular simulation enabled identification of new inhibitors for the KRAS drug target. BMC Chem 2024; 18:57. [PMID: 38528576 DOI: 10.1186/s13065-024-01152-z] [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: 10/26/2023] [Accepted: 02/26/2024] [Indexed: 03/27/2024] Open
Abstract
Lung cancer is a disease with a high mortality rate and it is the number one cause of cancer death globally. Approximately 12-14% of non-small cell lung cancers are caused by mutations in KRASG12C. The KRASG12C is one of the most prevalent mutants in lung cancer patients. KRAS was first considered undruggable. The sotorasib and adagrasib are the recently approved drugs that selectively target KRASG12C, and offer new treatment approaches to enhance patient outcomes however drug resistance frequently arises. Drug development is a challenging, expensive, and time-consuming process. Recently, machine-learning-based virtual screening are used for the development of new drugs. In this study, we performed machine-learning-based virtual screening followed by molecular docking, all atoms molecular dynamics simulation, and binding energy calculations for the identifications of new inhibitors against the KRASG12C mutant. In this study, four machine learning models including, random forest, k-nearest neighbors, Gaussian naïve Bayes, and support vector machine were used. By using an external dataset and 5-fold cross-validation, the developed models were validated. Among all the models the performance of the random forest (RF) model was best on the train/test dataset and external dataset. The random forest model was further used for the virtual screening of the ZINC15 database, in-house database, Pakistani phytochemicals, and South African Natural Products database. A total of 100 ns MD simulation was performed for the four best docking score complexes as well as the standard compound in complex with KRASG12C. Furthermore, the top four hits revealed greater stability and greater binding affinities for KRASG12C compared to the standard drug. These new hits have the potential to inhibit KRASG12C and may help to prevent KRAS-associated lung cancer. All the datasets used in this study can be freely available at ( https://github.com/Amar-Ajmal/Datasets-for-KRAS ).
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Affiliation(s)
- Amar Ajmal
- Department of Biochemistry, Abdul Wali Khan University Mardan, Mardan, 23200, Pakistan
| | - Hind A Alkhatabi
- Department of Biochemistry, College of Science, University of Jeddah, Jeddah, 21959, Saudi Arabia
| | - Roaa M Alreemi
- Department of Biochemistry, College of Science, University of Jeddah, Jeddah, 21959, Saudi Arabia
| | - Mubarak A Alamri
- Department of Pharmaceutical Chemistry, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
| | - Asaad Khalid
- Substance Abuse and Toxicology Research Center, Jazan University, P.O. Box: 114, Jazan, 45142, Saudi Arabia.
| | - Ashraf N Abdalla
- Department of Pharmacology and Toxicology, College of Pharmacy, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
| | - Bader S Alotaibi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Shaqra Univesity, Al- Quwayiyah, Riyadh, Saudi Arabia
| | - Abdul Wadood
- Department of Biochemistry, Abdul Wali Khan University Mardan, Mardan, 23200, Pakistan.
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Tang H, Miri Rekavandi A, Rooprai D, Dwivedi G, Sanfilippo FM, Boussaid F, Bennamoun M. Analysis and evaluation of explainable artificial intelligence on suicide risk assessment. Sci Rep 2024; 14:6163. [PMID: 38485985 PMCID: PMC10940617 DOI: 10.1038/s41598-024-53426-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 01/31/2024] [Indexed: 03/18/2024] Open
Abstract
This study explores the effectiveness of Explainable Artificial Intelligence (XAI) for predicting suicide risk from medical tabular data. Given the common challenge of limited datasets in health-related Machine Learning (ML) applications, we use data augmentation in tandem with ML to enhance the identification of individuals at high risk of suicide. We use SHapley Additive exPlanations (SHAP) for XAI and traditional correlation analysis to rank feature importance, pinpointing primary factors influencing suicide risk and preventive measures. Experimental results show the Random Forest (RF) model is excelling in accuracy, F1 score, and AUC (>97% across metrics). According to SHAP, anger issues, depression, and social isolation emerge as top predictors of suicide risk, while individuals with high incomes, esteemed professions, and higher education present the lowest risk. Our findings underscore the effectiveness of ML and XAI in suicide risk assessment, offering valuable insights for psychiatrists and facilitating informed clinical decisions.
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Affiliation(s)
- Hao Tang
- Department of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia
| | - Aref Miri Rekavandi
- Department of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia
| | - Dharjinder Rooprai
- Armadale Mental Health Service, Perth, Australia.
- Bethesda Clinic, Perth, Australia.
| | - Girish Dwivedi
- Advanced Clinical and Translational Cardiovascular Imaging, Harry Perkins Institute of Medical Research, The University of Western Australia, Perth, Australia
- Department of Cardiology, Fiona Stanley Hospital, Murdoch, WA, Australia
| | - Frank M Sanfilippo
- School of Population and Global Health, University of Western Australia, Perth, Australia
| | - Farid Boussaid
- Department of Electrical, Electronic and Computer Engineering, The University of Western Australia, Perth, Australia
| | - Mohammed Bennamoun
- Department of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia.
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Küppers L, Göbel J, Aretz B, Rieger MA, Weltermann B. Associations between COVID-19 Pandemic-Related Overtime, Perceived Chronic Stress and Burnout Symptoms in German General Practitioners and Practice Personnel-A Prospective Study. Healthcare (Basel) 2024; 12:479. [PMID: 38391854 PMCID: PMC10888352 DOI: 10.3390/healthcare12040479] [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: 11/22/2023] [Revised: 02/07/2024] [Accepted: 02/08/2024] [Indexed: 02/24/2024] Open
Abstract
BACKGROUND The mental burdens of general practitioners (GPs) and practice assistants (PrAs) during the COVID-19 pandemic are well investigated. Work-related conditions like overtime are known to contribute to perceived chronic stress and burnout symptoms. However, there is limited evidence regarding the specific mechanisms, which link pandemic-related overtime, chronic stress and burnout symptoms. This study used data from the IMPROVEjob trial to improve psychological well-being in general practice personnel. METHODS This prospective study with 226 German GPs and PrAs used the baseline (pre-pandemic: October 2019 to March 2020) and follow-up data (pandemic: October 2020 to April 2021) of the IMPROVEjob trial. Overtime was self-reported as hours above the regular work time. Perceived chronic stress was assessed using the Trier Inventory for the Assessment of Chronic Stress Screening Scale (TICS-SSCS), while burnout symptoms were evaluated using a short version of the Maslach Burnout Inventory (MBI). A mediation analysis investigated the differences of the three main variables between pre-pandemic and pandemic periods. RESULTS Burnout symptoms increased significantly from baseline to follow-up (p = 0.003). Overtime correlated positively with burnout symptoms (Total Effect: 0.13; 95% CI: 0.03, 0.23). Decomposition of the total effect revealed a significant indirect effect over perceived chronic stress (0.11; 95% CI: 0.06, 0.18) and no significant direct effect (0.02; 95% CI: -0.08, 0.12), indicating a full mediation. CONCLUSION In this large longitudinal study, pandemic-related overtime led to significantly higher levels of burnout symptoms, linked by a pathway through perceived chronic stress. Future prevention strategies need to aim at reducing the likelihood of overtime to ensure the mental well-being of practice personnel.
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Affiliation(s)
- Lucas Küppers
- Institute of General Practice and Family Medicine, University Hospital Bonn, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Julian Göbel
- Institute of General Practice and Family Medicine, University Hospital Bonn, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Benjamin Aretz
- Institute of General Practice and Family Medicine, University Hospital Bonn, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Monika A Rieger
- Institute of Occupational and Social Medicine and Health Services Research, University Hospital Tuebingen, 72074 Tuebingen, Germany
| | - Birgitta Weltermann
- Institute of General Practice and Family Medicine, University Hospital Bonn, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
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Burnazovic E, Yee A, Levy J, Gore G, Abbasgholizadeh Rahimi S. Application of Artificial intelligence in COVID-19-related geriatric care: A scoping review. Arch Gerontol Geriatr 2024; 116:105129. [PMID: 37542917 DOI: 10.1016/j.archger.2023.105129] [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: 12/20/2022] [Revised: 07/11/2023] [Accepted: 07/13/2023] [Indexed: 08/07/2023]
Abstract
BACKGROUND Older adults have been disproportionately affected by the COVID-19 pandemic. This scoping review aimed to summarize the current evidence of artificial intelligence (AI) use in the screening/monitoring, diagnosis, and/or treatment of COVID-19 among older adults. METHOD The review followed the Joanna Briggs Institute and Arksey and O'Malley frameworks. An information specialist performed a comprehensive search from the date of inception until May 2021, in six bibliographic databases. The selected studies considered all populations, and all AI interventions that had been used in COVID-19-related geriatric care. We focused on patient, healthcare provider, and healthcare system-related outcomes. The studies were restricted to peer-reviewed English publications. Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. RESULTS Six databases were searched , yielding 3,228 articles, of which 10 were included. The majority of articles used a single AI model to assess the association between patients' comorbidities and COVID-19 outcomes. Articles were mainly conducted in high-income countries, with limited representation of females in study participants, and insufficient reporting of participants' race and ethnicity. DISCUSSION This review highlighted how the COVID-19 pandemic has accelerated the application of AI to protect older populations, with most interventions in the pilot testing stage. Further work is required to measure effectiveness of these technologies in a larger scale, use more representative datasets for training of AI models, and expand AI applications to low-income countries.
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Affiliation(s)
- Emina Burnazovic
- Integrated Biomedical Engineering and Health Sciences, Department of Computing and Software, Faculty of Engineering, McMaster University, Hamilton, ON, Canada
| | - Amanda Yee
- Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada
| | - Joshua Levy
- Department of Pharmacology and Therapeutics, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada
| | - Genevieve Gore
- Schulich Library of Physical Sciences, Life Sciences and Engineering, McGill University, Montreal, QC, Canada
| | - Samira Abbasgholizadeh Rahimi
- Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada; Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada; Mila-Quebec Artificial Intelligence Institute, Montreal, QC, Canada; Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, QC, Canada.
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Bozorgmehr A, Weltermann B. Prediction of Chronic Stress and Protective Factors in Adults: Development of an Interpretable Prediction Model Based on XGBoost and SHAP Using National Cross-sectional DEGS1 Data. JMIR AI 2023; 2:e41868. [PMID: 38875576 PMCID: PMC11041452 DOI: 10.2196/41868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 01/06/2023] [Accepted: 03/03/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND Chronic stress is highly prevalent in the German population. It has known adverse effects on mental health, such as burnout and depression. Known long-term effects of chronic stress are cardiovascular disease, diabetes, and cancer. OBJECTIVE This study aims to derive an interpretable multiclass machine learning model for predicting chronic stress levels and factors protecting against chronic stress based on representative nationwide data from the German Health Interview and Examination Survey for Adults, which is part of the national health monitoring program. METHODS A data set from the German Health Interview and Examination Survey for Adults study including demographic, clinical, and laboratory data from 5801 participants was analyzed. A multiclass eXtreme Gradient Boosting (XGBoost) model was constructed to classify participants into 3 categories including low, middle, and high chronic stress levels. The model's performance was evaluated using the area under the receiver operating characteristic curve, precision, recall, specificity, and the F1-score. Additionally, SHapley Additive exPlanations was used to interpret the prediction XGBoost model and to identify factors protecting against chronic stress. RESULTS The multiclass XGBoost model exhibited the macroaverage scores, with an area under the receiver operating characteristic curve of 81%, precision of 63%, recall of 52%, specificity of 78%, and F1-score of 54%. The most important features for low-level chronic stress were male gender, very good general health, high satisfaction with living space, and strong social support. CONCLUSIONS This study presents a multiclass interpretable prediction model for chronic stress in adults in Germany. The explainable artificial intelligence technique SHapley Additive exPlanations identified relevant protective factors for chronic stress, which need to be considered when developing interventions to reduce chronic stress.
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Affiliation(s)
- Arezoo Bozorgmehr
- Institute of General Practice and Family Medicine, University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Birgitta Weltermann
- Institute of General Practice and Family Medicine, University Hospital Bonn, University of Bonn, Bonn, Germany
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The Influence of Family Social Status on Farmer Entrepreneurship: Empirical Analysis Based on Thousand Villages Survey in China. SUSTAINABILITY 2022. [DOI: 10.3390/su14148450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Researchers in the fields of psychology and sociology have demonstrated the profound influence of social status on people’s behavior. Although existing studies show that social status matters, scholars have devoted little attention to how family social status affects individuals’ risk-taking entrepreneurial behavior. In Chinese rural areas, where the idea of “family” is deeply embedded, how family social status affects farmers’ entrepreneurial behavior is still an unsolved question. In this paper, we analyze the impact of family social status on farmer entrepreneurship and investigate the moderating effects of external institutional factors, such as regional policy uncertainty and local family culture. Our findings show that family social status has an important impact on farmers’ risk preferences, therefore affecting their motivation to undertake entrepreneurial behaviors. By inspecting the role of social status at the family level, our study offers important implications for social class and entrepreneurial theorizing.
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Sadeghi M, McDonald AD, Sasangohar F. Posttraumatic stress disorder hyperarousal event detection using smartwatch physiological and activity data. PLoS One 2022; 17:e0267749. [PMID: 35584096 PMCID: PMC9116643 DOI: 10.1371/journal.pone.0267749] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 04/16/2022] [Indexed: 12/26/2022] Open
Abstract
Posttraumatic Stress Disorder (PTSD) is a psychiatric condition affecting nearly a quarter of the United States war veterans who return from war zones. Treatment for PTSD typically consists of a combination of in-session therapy and medication. However; patients often experience their most severe PTSD symptoms outside of therapy sessions. Mobile health applications may address this gap, but their effectiveness is limited by the current gap in continuous monitoring and detection capabilities enabling timely intervention. The goal of this article is to develop a novel method to detect hyperarousal events using physiological and activity-based machine learning algorithms. Physiological data including heart rate and body acceleration as well as self-reported hyperarousal events were collected using a tool developed for commercial off-the-shelf wearable devices from 99 United States veterans diagnosed with PTSD over several days. The data were used to develop four machine learning algorithms: Random Forest, Support Vector Machine, Logistic Regression and XGBoost. The XGBoost model had the best performance in detecting onset of PTSD symptoms with over 83% accuracy and an AUC of 0.70. Post-hoc SHapley Additive exPlanations (SHAP) additive explanation analysis showed that algorithm predictions were correlated with average heart rate, minimum heart rate and average body acceleration. Findings show promise in detecting onset of PTSD symptoms which could be the basis for developing remote and continuous monitoring systems for PTSD. Such systems may address a vital gap in just-in-time interventions for PTSD self-management outside of scheduled clinical appointments.
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Affiliation(s)
- Mahnoosh Sadeghi
- Department of Industrial and / Systems Engineering, Texas A&M University, College Station, Texas, United States of America
| | - Anthony D. McDonald
- Department of Industrial and / Systems Engineering, Texas A&M University, College Station, Texas, United States of America
| | - Farzan Sasangohar
- Department of Industrial and / Systems Engineering, Texas A&M University, College Station, Texas, United States of America
- * E-mail:
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Xu S, Arnetz JE, Arnetz BB. Applying machine learning to explore the association between biological stress and near misses in emergency medicine residents. PLoS One 2022; 17:e0264957. [PMID: 35259166 PMCID: PMC8903283 DOI: 10.1371/journal.pone.0264957] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 02/19/2022] [Indexed: 01/22/2023] Open
Abstract
Physician stress is associated with near misses and adverse medical events. However, little is known about physiological mechanisms linking stress to such events. We explored the utility of machine learning to determine whether the catabolic stress hormone cortisol and the anabolic, anti-stress hormone dehydroepiandrosterone sulfate (DHEA-S), as well as the cortisol to DHEA-S ratio relate to near misses in emergency medicine residents during active duty in a trauma 1 emergency department. Compared to statistical models better suited for inference, machine learning models allow for prediction in situations that have not yet occurred, and thus better suited for clinical applications. This exploratory study used multiple machine learning models to determine possible relationships between biomarkers and near misses. Of the various models tested, support vector machine with radial bias function kernels and support vector machine with linear kernels performed the best, with training accuracies of 85% and 79% respectively. When evaluated on a test dataset, both models had prediction accuracies of around 80%. The pre-shift cortisol to DHEA-S ratio was shown to be the most important predictor in interpretable models tested. Results suggest that interventions that help emergency room physicians relax before they begin their shift could reduce risk of errors and improve patient and physician outcomes. This pilot demonstrates promising results regarding using machine learning to better understand the stress biology of near misses. Future studies should use larger groups and relate these variables to information in electronic medical records, such as objective and patient-reported quality measures.
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Affiliation(s)
- Sonnet Xu
- Troy High School, Troy, Michigan, United States of America
- * E-mail:
| | - Judith E. Arnetz
- Department of Family Medicine, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, United States of America
| | - Bengt B. Arnetz
- Department of Family Medicine, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, United States of America
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