Displaying publications 81 - 100 of 909 in total

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  1. Perspectives on opportunities and challenges in a predominantly flipped classroom-based pharmacy curriculum: A qualitative study
    Wong WJ, Lee SWH, White PJ, Efendie B, Lee RFS
    Curr Pharm Teach Learn, 2023 Mar;15(3):242-251.
    PMID: 37055316 DOI: 10.1016/j.cptl.2023.03.004
    INTRODUCTION: To adapt to flipped classroom pedagogy in universities, factors such as the amount of the program that is flipped, students' pre-existing educational experiences, and cultural background may influence adjusting to the approach. We investigated students' perspectives across four years of a predominantly flipped classroom-based pharmacy curriculum in a low to middle income country.

    METHODS: We conducted five semi-structured focus groups with 18 pharmacy students from years one to four of the bachelor of pharmacy program at Monash University Malaysia where students came from different pre-university backgrounds. Focus group recordings were transcribed verbatim and thematically analysed. Interrater reliability was performed to ascertain reliability of themes.

    RESULTS: Three major themes were identified. Firstly, students cited issues moving past the initial barrier when starting flipped classrooms in terms of education background impacting adaptability and how/why they eventually adapted. Another theme was how flipped classrooms helped development of life skills such as adaptability, communication, teamwork, self-reflection, and time management. The final theme was on requiring a sufficient safety net and support system in flipped classrooms that included well designed pre-classroom materials and well-implemented feedback mechanisms.

    CONCLUSIONS: We have identified students' perspectives on the benefits and challenges associated with a predominantly flipped classroom pharmacy curriculum in a low to middle income country setting. We suggest using scaffolding and effective feedback approaches to guide the implementation of flipped classrooms successfully. This work can aid future educational designers in preparation and supporting a more equitable learning experience regardless of student background.

    Matched MeSH terms: Learning
  2. Fulltext A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention
    Majeed MA, Shafri HZM, Zulkafli Z, Wayayok A
    Int J Environ Res Public Health, 2023 Feb 25;20(5).
    PMID: 36901139 DOI: 10.3390/ijerph20054130
    This research aims to predict dengue fever cases in Malaysia using machine learning techniques. A dataset consisting of weekly dengue cases at the state level in Malaysia from 2010 to 2016 was obtained from the Malaysia Open Data website and includes variables such as climate, geography, and demographics. Six different long short-term memory (LSTM) models were developed and compared for dengue prediction in Malaysia: LSTM, stacked LSTM (S-LSTM), LSTM with temporal attention (TA-LSTM), S-LSTM with temporal attention (STA-LSTM), LSTM with spatial attention (SA-LSTM), and S-LSTM with spatial attention (SSA-LSTM). The models were trained and evaluated on a dataset of monthly dengue cases in Malaysia from 2010 to 2016, with the task of predicting the number of dengue cases based on various climate, topographic, demographic, and land-use variables. The SSA-LSTM model, which used both stacked LSTM layers and spatial attention, performed the best, with an average root mean squared error (RMSE) of 3.17 across all lookback periods. When compared to three benchmark models (SVM, DT, ANN), the SSA-LSTM model had a significantly lower average RMSE. The SSA-LSTM model also performed well in different states in Malaysia, with RMSE values ranging from 2.91 to 4.55. When comparing temporal and spatial attention models, the spatial models generally performed better at predicting dengue cases. The SSA-LSTM model was also found to perform well at different prediction horizons, with the lowest RMSE at 4- and 5-month lookback periods. Overall, the results suggest that the SSA-LSTM model is effective at predicting dengue cases in Malaysia.
    Matched MeSH terms: Machine Learning
  3. Fulltext The effect of professional portfolio learning on nursing students' professional self-concepts in geriatric adult internship: a- quasi-experimental study
    Mohajer S, Li Yoong T, Chan CM, Danaee M, Mazlum SR, Bagheri N
    BMC Med Educ, 2023 Feb 15;23(1):114.
    PMID: 36793032 DOI: 10.1186/s12909-023-04097-4
    BACKGROUND: Professional self-concept is one of the important outcomes of nursing professionalism. There is a lack of adequately planned curriculum may limit nursing students' practical knowledge, skills and professional self-concept in providing comprehensive geriatric-adult care and promoting nursing professionalism. Professional portfolio learning strategy has allowed nursing students to continue professional development and enhance nursing professionalism in professional clinical practice. However, there is little empirical evidence in nursing education to support the use of professional portfolios in blended learning modality among internship nursing students. Therefore, this study aims to examine the effect of the blended professional portfolio learning on professional self-concept among undergraduate nursing students during Geriatric-Adult internship.

    METHODS: A quasi-experimental study two-group pre-test post-test design. A total of 153 eligible senior undergraduate students completed the study (76 in the intervention group and 77 in the control group). They were recruited from two Bachelor of Sciences in Nursing (BSN) cohorts from nursing schools at Mashhad University of Medical Sciences (MUMS), in Iran, in January 2020. Randomization was undertaken at the level of school via a simple lottery method. The intervention group received the professional portfolio learning program as a holistic blended learning modality, though the control group received conventional learning during professional clinical practice. A demographic questionnaire and the Nurse Professional Self-concept questionnaire were used for data collection.

    RESULTS: The findings imply the effectiveness of the blended PPL program. Results of Generalized Estimating Equation (GEE) analysis was indicated significantly improved professional self-concept development and its dimensions (self-esteem, caring, staff relation, communication, knowledge, leadership) with high effect size. The results of the between-group comparison for professional self-concept and its dimensions at different time points (pre, post and follow up test) showed a significant difference between groups at post-test and follow up test (p  0.05).The results of within-group comparison for both control and intervention showed that there were significant differences in professional self-concept and for all its dimensions across the time from pre-test to post-test and follow-up (p 

    Matched MeSH terms: Learning
  4. Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems
    Singh NK, Yadav M, Singh V, Padhiyar H, Kumar V, Bhatia SK, et al.
    Bioresour Technol, 2023 Feb;369:128486.
    PMID: 36528177 DOI: 10.1016/j.biortech.2022.128486
    Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available information is reviewed and presented in terms of bibliometric analysis, model's description, specific applications, and major findings for investigated WWTS. Among the applied models, artificial neural network (ANN), fuzzy logic (FL) algorithms, random forest (RF), and long short-term memory (LSTM) were predominantly used in the biological wastewater treatment. These models are tested by predictive control of effluent parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), nutrient parameters, solids, and metallic substances. Following model performance indicators were mainly used for the accuracy analysis in most of the studies: root mean squared error (RMSE), mean square error (MSE), and determination coefficient (DC). Besides, outcomes of various models are also summarized in this study.
    Matched MeSH terms: Machine Learning
  5. Nitrate Classification Based on Optical Absorbance Data Using Machine Learning Algorithms for a Hydroponics System
    Sulaiman R, Azeman NH, Abu Bakar MH, Ahmad Nazri NA, Masran AS, Ashrif A Bakar A
    Appl Spectrosc, 2023 Feb;77(2):210-219.
    PMID: 36348500 DOI: 10.1177/00037028221140924
    Nutrient solution plays an essential role in providing macronutrients to hydroponic plants. Determining nitrogen in the form of nitrate is crucial, as either a deficient or excessive supply of nitrate ions may reduce the plant yield or lead to environmental pollution. This work aims to evaluate the performance of feature reduction techniques and conventional machine learning (ML) algorithms in determining nitrate concentration levels. Two features reduction techniques, linear discriminant analysis (LDA) and principal component analysis (PCA), and seven ML algorithms, for example, k-nearest neighbors (KNN), support vector machine, decision trees, naïve bayes, random forest (RF), gradient boosting, and extreme gradient boosting, were evaluated using a high-dimensional spectroscopic dataset containing measured nitrate-nitrite mixed solution absorbance data. Despite the limited and uneven number of samples per class, this study demonstrated that PCA outperformed LDA on the high-dimensional spectroscopic dataset. The classification accuracy of ML algorithms combined with PCA ranged from 92.7% to 99.8%, whereas the classification accuracy of ML algorithms combined with LDA ranged from 80.7% to 87.6%. The PCA with the RF algorithm exhibited the best performance with 99.8% accuracy.
    Matched MeSH terms: Machine Learning
  6. Early diagnosis of Parkinson's disease: A combined method using deep learning and neuro-fuzzy techniques
    Nilashi M, Abumalloh RA, Yusuf SYM, Thi HH, Alsulami M, Abosaq H, et al.
    Comput Biol Chem, 2023 Feb;102:107788.
    PMID: 36410240 DOI: 10.1016/j.compbiolchem.2022.107788
    Predicting Unified Parkinson's Disease Rating Scale (UPDRS) in Total- UPDRS and Motor-UPDRS clinical scales is an important part of controlling PD. Computational intelligence approaches have been used effectively in the early diagnosis of PD by predicting UPDRS. In this research, we target to present a combined approach for PD diagnosis using an ensemble learning approach with the ability of online learning from clinical large datasets. The method is developed using Deep Belief Network (DBN) and Neuro-Fuzzy approaches. A clustering approach, Expectation-Maximization (EM), is used to handle large datasets. The Principle Component Analysis (PCA) technique is employed for noise removal from the data. The UPDRS prediction models are constructed for PD diagnosis. To handle the missing data, K-NN is used in the proposed method. We use incremental machine learning approaches to improve the efficiency of the proposed method. We assess our approach on a real-world PD dataset and the findings are assessed compared to other PD diagnosis approaches developed by machine learning techniques. The findings revealed that the approach can improve the UPDRS prediction accuracy and the time complexity of previous methods in handling large datasets.
    Matched MeSH terms: Machine Learning
  7. Review on machine learning-based bioprocess optimization, monitoring, and control systems
    Mondal PP, Galodha A, Verma VK, Singh V, Show PL, Awasthi MK, et al.
    Bioresour Technol, 2023 Feb;370:128523.
    PMID: 36565820 DOI: 10.1016/j.biortech.2022.128523
    Machine Learning is quickly becoming an impending game changer for transforming big data thrust from the bioprocessing industry into actionable output. However, the complex data set from bioprocess, lagging cyber-integrated sensor system, and issues with storage scalability limit machine learning real-time application. Hence, it is imperative to know the state of technology to address prevailing issues. This review first gives an insight into the basic understanding of the machine learning domain and discusses its complexities for more comprehensive applications. Followed by an outline of how relevant machine learning models are for statistical and logical analysis of the enormous datasets generated to control bioprocess operations. Then this review critically discusses the current knowledge, its limitations, and future aspects in different subfields of the bioprocessing industry. Further, this review discusses the prospects of adopting a hybrid method to dovetail different modeling strategies, cyber-networking, and integrated sensors to develop new digital biotechnologies.
    Matched MeSH terms: Machine Learning*
  8. Application of machine learning on understanding biomolecule interactions in cellular machinery
    Dixit R, Khambhati K, Supraja KV, Singh V, Lederer F, Show PL, et al.
    Bioresour Technol, 2023 Feb;370:128522.
    PMID: 36565819 DOI: 10.1016/j.biortech.2022.128522
    Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.
    Matched MeSH terms: Machine Learning*
  9. Fulltext Machine learning assisted hepta band THz metamaterial absorber for biomedical applications
    Jain P, Chhabra H, Chauhan U, Prakash K, Gupta A, Soliman MS, et al.
    Sci Rep, 2023 Jan 31;13(1):1792.
    PMID: 36720922 DOI: 10.1038/s41598-023-29024-x
    A hepta-band terahertz metamaterial absorber (MMA) with modified dual T-shaped resonators deposited on polyimide is presented for sensing applications. The proposed polarization sensitive MMA is ultra-thin (0.061 λ) and compact (0.21 λ) at its lowest operational frequency, with multiple absorption peaks at 1.89, 4.15, 5.32, 5.84, 7.04, 8.02, and 8.13 THz. The impedance matching theory and electric field distribution are investigated to understand the physical mechanism of hepta-band absorption. The sensing functionality is evaluated using a surrounding medium with a refractive index between 1 and 1.1, resulting in good Quality factor (Q) value of 117. The proposed sensor has the highest sensitivity of 4.72 THz/RIU for glucose detection. Extreme randomized tree (ERT) model is utilized to predict absorptivities for intermediate frequencies with unit cell dimensions, substrate thickness, angle variation, and refractive index values to reduce simulation time. The effectiveness of the ERT model in predicting absorption values is evaluated using the Adjusted R2 score, which is close to 1.0 for nmin = 2, demonstrating the prediction efficiency in various test cases. The experimental results show that 60% of simulation time and resources can be saved by simulating absorber design using the ERT model. The proposed MMA sensor with an ERT model has potential applications in biomedical fields such as bacterial infections, malaria, and other diseases.
    Matched MeSH terms: Machine Learning*
  10. Fulltext Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques
    ELKarazle K, Raman V, Then P, Chua C
    Sensors (Basel), 2023 Jan 20;23(3).
    PMID: 36772263 DOI: 10.3390/s23031225
    Given the increased interest in utilizing artificial intelligence as an assistive tool in the medical sector, colorectal polyp detection and classification using deep learning techniques has been an active area of research in recent years. The motivation for researching this topic is that physicians miss polyps from time to time due to fatigue and lack of experience carrying out the procedure. Unidentified polyps can cause further complications and ultimately lead to colorectal cancer (CRC), one of the leading causes of cancer mortality. Although various techniques have been presented recently, several key issues, such as the lack of enough training data, white light reflection, and blur affect the performance of such methods. This paper presents a survey on recently proposed methods for detecting polyps from colonoscopy. The survey covers benchmark dataset analysis, evaluation metrics, common challenges, standard methods of building polyp detectors and a review of the latest work in the literature. We conclude this paper by providing a precise analysis of the gaps and trends discovered in the reviewed literature for future work.
    Matched MeSH terms: Machine Learning
  11. Cardiovascular complications in a diabetes prediction model using machine learning: a systematic review
    Kee OT, Harun H, Mustafa N, Abdul Murad NA, Chin SF, Jaafar R, et al.
    Cardiovasc Diabetol, 2023 Jan 19;22(1):13.
    PMID: 36658644 DOI: 10.1186/s12933-023-01741-7
    Prediction model has been the focus of studies since the last century in the diagnosis and prognosis of various diseases. With the advancement in computational technology, machine learning (ML) has become the widely used tool to develop a prediction model. This review is to investigate the current development of prediction model for the risk of cardiovascular disease (CVD) among type 2 diabetes (T2DM) patients using machine learning. A systematic search on Scopus and Web of Science (WoS) was conducted to look for relevant articles based on the research question. The risk of bias (ROB) for all articles were assessed based on the Prediction model Risk of Bias Assessment Tool (PROBAST) statement. Neural network with 76.6% precision, 88.06% sensitivity, and area under the curve (AUC) of 0.91 was found to be the most reliable algorithm in developing prediction model for cardiovascular disease among type 2 diabetes patients. The overall concern of applicability of all included studies is low. While two out of 10 studies were shown to have high ROB, another studies ROB are unknown due to the lack of information. The adherence to reporting standards was conducted based on the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) standard where the overall score is 53.75%. It is highly recommended that future model development should adhere to the PROBAST and TRIPOD assessment to reduce the risk of bias and ensure its applicability in clinical settings. Potential lipid peroxidation marker is also recommended in future cardiovascular disease prediction model to improve overall model applicability.
    Matched MeSH terms: Machine Learning
  12. Fulltext A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction
    Adnan MSG, Siam ZS, Kabir I, Kabir Z, Ahmed MR, Hassan QK, et al.
    J Environ Manage, 2023 Jan 15;326(Pt B):116813.
    PMID: 36435143 DOI: 10.1016/j.jenvman.2022.116813
    Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flood susceptibility models (FSMs) may produce varying spatial predictions. However, there have not been many attempts to address these uncertainties because identifying spatial agreement in flood projections is a complex process. This study presents a framework for reducing spatial disagreement among four standalone and hybridized ML-based FSMs: random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), and hybridized genetic algorithm-gaussian radial basis function-support vector regression (GA-RBF-SVR). Besides, an optimized model was developed combining the outcomes of those four models. The southwest coastal region of Bangladesh was selected as the case area. A comparable percentage of flood potential area (approximately 60% of the total land areas) was produced by all ML-based models. Despite achieving high prediction accuracy, spatial discrepancy in the model outcomes was observed, with pixel-wise correlation coefficients across different models ranging from 0.62 to 0.91. The optimized model exhibited high prediction accuracy and improved spatial agreement by reducing the number of classification errors. The framework presented in this study might aid in the formulation of risk-based development plans and enhancement of current early warning systems.
    Matched MeSH terms: Machine Learning*
  13. Fulltext Predicting Return to Work after Cardiac Rehabilitation using Machine Learning Models
    Yuan CJ, Varathan KD, Suhaimi A, Ling LW
    J Rehabil Med, 2023 Jan 09;55:jrm00348.
    PMID: 36306152 DOI: 10.2340/jrm.v54.2432
    OBJECTIVE: To explore machine learning models for predicting return to work after cardiac rehabilitation.

    SUBJECTS: Patients who were admitted to the University of Malaya Medical Centre due to cardiac events.

    METHODS: Eight different machine learning models were evaluated. The models included 3 different sets of features: full features; significant features from multiple logistic regression; and features selected from recursive feature extraction technique. The performance of the prediction models with each set of features was compared.

    RESULTS: The AdaBoost model with the top 20 features obtained the highest performance score of 92.4% (area under the curve; AUC) compared with other prediction models.

    CONCLUSION: The findings showed the potential of using machine learning models to predict return to work after cardiac rehabilitation.

    Matched MeSH terms: Machine Learning
  14. An explainable predictive model for suicide attempt risk using an ensemble learning and Shapley Additive Explanations (SHAP) approach
    Nordin N, Zainol Z, Mohd Noor MH, Chan LF
    Asian J Psychiatr, 2023 Jan;79:103316.
    PMID: 36395702 DOI: 10.1016/j.ajp.2022.103316
    Machine learning approaches have been used to develop suicide attempt predictive models recently and have been shown to have a good performance. However, those proposed models have difficulty interpreting and understanding why an individual has suicidal attempts. To overcome this issue, the identification of features such as risk factors in predicting suicide attempts is important for clinicians to make decisions. Therefore, the aim of this study is to propose an explainable predictive model to predict and analyse the importance of features for suicide attempts. This model can also provide explanations to improve the clinical understanding of suicide attempts. Two complex ensemble learning models, namely Random Forest and Gradient Boosting with an explanatory model (SHapley Additive exPlanations (SHAP)) have been constructed. The models are used for predictive interpretation and understanding of the importance of the features. The experiment shows that both models with SHAP are able to interpret and understand the nature of an individual's predictions with suicide attempts. However, compared with Random Forest, the results show that Gradient Boosting with SHAP achieves higher accuracy and the analyses found that history of suicide attempts, suicidal ideation, and ethnicity as the main predictors for suicide attempts.
    Matched MeSH terms: Machine Learning
  15. Team-based self-directed learning enhanced students' learning experience in undergraduate surgical teaching
    Sim SK, Myo N, Sohail M
    Med J Malaysia, 2023 Jan;78(1):61-67.
    PMID: 36715193
    INTRODUCTION: To evaluate the effectiveness of team-based self-directed learning (SDL) in the teaching of the undergraduate Year 5 surgical posting.

    MATERIALS AND METHODS: A quasi-experimental study was conducted to develop and administer a team-based SDL versus a conventional SDL to teach undergraduate surgical topics. One hundred and seventy-four medical students who underwent the Year 5 surgical posting were recruited. They were assigned to two groups receiving either the teambased SDL or the conventional SDL. Pre- and post-SDL assessments were conducted to determine students' understanding of selected surgical topics. A selfadministered questionnaire was used to collect student feedback on the team-based SDL.

    RESULTS: The team-based SDL group scored significantly higher than the conventional SDL group in the post-SDL assessment (74.70 ± 6.81 vs. 63.77 ± 4.18, t = -12.72, p < 0.01). The students agreed that the team-based SDL method facilitated their learning process.

    CONCLUSION: The study demonstrated that the use of a teambased SDL is an effective learning strategy for teaching the Year 5 surgical posting. This method encouraged peer discussion and promoted teamwork in completing task assignments to achieve the learning objectives.

    Matched MeSH terms: Learning
  16. Fulltext A digital module-based experiential learning in protein biochemistry during the COVID-19 pandemic paradigm
    Yap MKK
    Biochem Mol Biol Educ, 2023 Jan;51(1):77-80.
    PMID: 36194083 DOI: 10.1002/bmb.21680
    Experiential learning is compromised in meeting the educational demands of our students during the challenging time of the COVID-19 pandemic. A more inclusive, flexible, and objective-oriented experiential learning environment is required. In this context, module-based experiential learning that is executable on a digital platform was designed. The learning module focused on protein biochemistry, contained a combination of asynchronous and synchronous activities categorized into 'Knowledge Hub' and 'Lab-based Movie', across 5 weeks. Digital and module-based experiential learning provides equitable, inclusive, and flexible access to students at remote locations. Furthermore, it is an objective-oriented and highly organized experiential learning framework that encourages students to engage and participate more in the learning process.
    Matched MeSH terms: Learning; Problem-Based Learning*
  17. Fulltext Blended learning in physical education: A systematic review
    Wang C, Omar Dev RD, Soh KG, Mohd Nasirudddin NJ, Yuan Y, Ji X
    Front Public Health, 2023;11:1073423.
    PMID: 36969628 DOI: 10.3389/fpubh.2023.1073423
    This review aims to provide a detailed overview of the current status and development trends of blended learning in physical education by reviewing journal articles from the Web of Science (WOS) database. Several dimensions of blended learning were observed, including research trends, participants, online learning tools, theoretical frameworks, evaluation methods, application domains, Research Topics, and challenges. Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), a total of 22 journal articles were included in the current review. The findings of this review reveal that the number of blended learning articles in physical education has increased since 2018, proving that the incorporation of online learning tools into physical education courses has grown in popularity. From the reviewed journal articles, most attention is given to undergraduates, emphasizing that attention in the future should be placed on K-12 students, teachers, and educational institutions. The theoretical framework applied by journal articles is also limited to a few articles and the assessment method is relatively homogeneous, consisting mostly of questionnaires. This review also discovers the trends in blended learning in physical education as most of the studies focus on the topic centered on dynamic physical education. In terms of Research Topics, most journal articles focus on perceptions, learning outcomes, satisfaction, and motivation, which are preliminary aspects of blended learning research. Although the benefits of blended learning are evident, this review identifies five challenges of blended learning: instructional design challenges, technological literacy and competency challenges, self-regulation challenges, alienation and isolation challenges, and belief challenges. Finally, a number of recommendations for future research are presented.
    Matched MeSH terms: Learning*
  18. Fulltext Mpropred: A machine learning (ML) driven Web-App for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists
    Ferdous N, Reza MN, Hossain MU, Mahmud S, Napis S, Chowdhury K, et al.
    PLoS One, 2023;18(6):e0287179.
    PMID: 37352252 DOI: 10.1371/journal.pone.0287179
    The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic emerged in 2019 and still requiring treatments with fast clinical translatability. Frequent occurrence of mutations in spike glycoprotein of SARS-CoV-2 led the consideration of an alternative therapeutic target to combat the ongoing pandemic. The main protease (Mpro) is such an attractive drug target due to its importance in maturating several polyproteins during the replication process. In the present study, we used a classification structure-activity relationship (CSAR) model to find substructures that leads to to anti-Mpro activities among 758 non-redundant compounds. A set of 12 fingerprints were used to describe Mpro inhibitors, and the random forest approach was used to build prediction models from 100 distinct data splits. The data set's modelability (MODI index) was found to be robust, with a value of 0.79 above the 0.65 threshold. The accuracy (89%), sensitivity (89%), specificity (73%), and Matthews correlation coefficient (79%) used to calculate the prediction performance, was also found to be statistically robust. An extensive analysis of the top significant descriptors unveiled the significance of methyl side chains, aromatic ring and halogen groups for Mpro inhibition. Finally, the predictive model is made publicly accessible as a web-app named Mpropred in order to allow users to predict the bioactivity of compounds against SARS-CoV-2 Mpro. Later, CMNPD, a marine compound database was screened by our app to predict bioactivity of all the compounds and results revealed significant correlation with their binding affinity to Mpro. Molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) analysis showed improved properties of the complexes. Thus, the knowledge and web-app shown herein can be used to develop more effective and specific inhibitors against the SARS-CoV-2 Mpro. The web-app can be accessed from https://share.streamlit.io/nadimfrds/mpropred/Mpropred_app.py.
    Matched MeSH terms: Machine Learning
  19. Fulltext On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data
    Ng GYL, Tan SC, Ong CS
    PLoS One, 2023;18(10):e0292961.
    PMID: 37856458 DOI: 10.1371/journal.pone.0292961
    Cell type identification is one of the fundamental tasks in single-cell RNA sequencing (scRNA-seq) studies. It is a key step to facilitate downstream interpretations such as differential expression, trajectory inference, etc. scRNA-seq data contains technical variations that could affect the interpretation of the cell types. Therefore, gene selection, also known as feature selection in data science, plays an important role in selecting informative genes for scRNA-seq cell type identification. Generally speaking, feature selection methods are categorized into filter-, wrapper-, and embedded-based approaches. From the existing literature, methods from filter- and embedded-based approaches are widely applied in scRNA-seq gene selection tasks. The wrapper-based method that gives promising results in other fields has yet been extensively utilized for selecting gene features from scRNA-seq data; in addition, most of the existing wrapper methods used in this field are clustering instead of classification-based. With a large number of annotated data available today, this study applied a classification-based approach as an alternative to the clustering-based wrapper method. In our work, a quantum-inspired differential evolution (QDE) wrapped with a classification method was introduced to select a subset of genes from twelve well-known scRNA-seq transcriptomic datasets to identify cell types. In particular, the QDE was combined with different machine-learning (ML) classifiers namely logistic regression, decision tree, support vector machine (SVM) with linear and radial basis function kernels, as well as extreme learning machine. The linear SVM wrapped with QDE, namely QDE-SVM, was chosen by referring to the feature selection results from the experiment. QDE-SVM showed a superior cell type classification performance among QDE wrapping with other ML classifiers as well as the recent wrapper methods (i.e., FSCAM, SSD-LAHC, MA-HS, and BSF). QDE-SVM achieved an average accuracy of 0.9559, while the other wrapper methods achieved average accuracies in the range of 0.8292 to 0.8872.
    Matched MeSH terms: Machine Learning
  20. Fulltext LEARNING CURVE IN ESOPHAGEAL ENDOSCOPIC SUBMUCOSAL DISSECTION BY WESTERN ENDOSCOSPISTS TRAINED IN JAPAN: EXPERIENCE IN LATIN AMERICA
    Aliaga Ramos J, Yoshida N, Abdul Rani R, Arantes VN
    Arq Gastroenterol, 2023;60(2):208-216.
    PMID: 37556747 DOI: 10.1590/S0004-2803.20230222-168
    •This study aimed to assess the learning curve effect on patient's clinical outcome for EESD. Retrospective observational study, enrolling patients that underwent EESD from 2009 to 2021, divided in 2 groups. Mean procedure time was 111.8 min and 103.6 min for T1 and T2, respectively (P=0.004). The learning curve in esophageal ESD could be overcomed effectively and safely by an adequately trained Western endoscopist. Background - Esophageal endoscopic submucosal dissection (EESD) is a complex and time-consuming procedure at which training are mainly available in Japan. There is a paucity of data concerning the learning curve to master EESD by Western endoscopists. Objective - This study aimed to assess the learning curve effect on patient's clinical outcome for EESD. Methods - This is a retrospective observational study. Enrolling patients that underwent EESD from 2009 to 2021. The analysis was divided into two periods; T1: case 1 to 49 and T2: case 50 to 98. The following features were analyzed for each group: patients and tumors characteristics, en-bloc, complete and curative resection rates, procedure duration and adverse events rate. Results - Ninety-eight EESD procedures were performed. Mean procedure time was 111.8 min and 103.6 min for T1 and T2, respectively (P=0.004). En bloc resection rate was 93.8% and 97.9% for T1 and T2, respectively (P=0.307). Complete resection rate was 79.5% and 85.7% for T1 and T2, respectively (P=0.424). Curative resection rate was 65.3% and 71.4% for T1 and T2, respectively (P=0.258). Four patients had complications; three during T1 period and one during T2 period. Overall mortality rate: 0%. Conclusion - The esophageal endoscopic submucosal dissection could be performed effectively and safely by an adequately trained Western endoscopist.
    Matched MeSH terms: Learning Curve*
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