Displaying publications 1 - 20 of 906 in total

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  1. Spooner M, Reinhardt C, Boland F, McConkey S, Pawlikowska T
    Med Educ Online, 2024 Dec 31;29(1):2330259.
    PMID: 38529848 DOI: 10.1080/10872981.2024.2330259
    There are differing views on how learners' feedback-seeking behaviours (FSB) develop during training. With globalisation has come medical student migration and programme internationalisation. Western-derived educational practices may prove challenging for diverse learner populations. Exploring undergraduate activity using a model of FSB may give insight into how FSB evolves and the influence of situational factors, such as nationality and site of study. Our findings seek to inform medical school processes that support feedback literacy. Using a mixed methods approach, we collected questionnaire and interview data from final-year medical students in Ireland, Bahrain, and Malaysia. A validated questionnaire investigated relationships with FSB and goal orientation, leadership style preference, and perceived costs and benefits. Interviews with the same student population explored their FSB experiences in clinical practice, qualitatively, enriching this data. The data were integrated using the 'following the thread' technique. Three hundred and twenty-five of a total of 514 completed questionnaires and 57 interviews were analysed. Learning goal orientation (LGO), instrumental leadership and supportive leadership related positively to perceived feedback benefits (0.23, 0.2, and 0.31, respectively, p 
    Matched MeSH terms: Learning
  2. Basri KN, Yazid F, Mohd Zain MN, Md Yusof Z, Abdul Rani R, Zoolfakar AS
    PMID: 38394882 DOI: 10.1016/j.saa.2024.124063
    Dental caries has high prevalence among kids and adults thus it has become one of the global health concerns. The current modern dentistry focused on the preventives measures to reduce the number of dental caries cases. The employment of machine learning coupled with UV spectroscopy plays a crucial role to detect the early stage of caries. Artificial neural network with hyperparameter tuning was employed to train spectral data for the classification based on the International Caries Detection and Assesment System (ICDAS). Spectra preprocessing namely mean center (MC), autoscale (AS) and Savitzky Golay smoothing (SG) were applied on the data for spectra correction. The best performance of ANN model obtained has accuracy of 0.85 with precision of 1.00. Convolutional neural network (CNN) combined with Savitzky Golay smoothing performed on the spectral data has accuracy, precision, sensitivity and specificity for validation data of 1.00 respectively. The result obtained shows that the application of ANN and CNN capable to produce robust model to be used as an early screening of dental caries.
    Matched MeSH terms: Machine Learning
  3. Su G, Jiang P
    Bioresour Technol, 2024 May;399:130519.
    PMID: 38437964 DOI: 10.1016/j.biortech.2024.130519
    This study developed six machine learning models to predict the biochar properties from the dry torrefaction of lignocellulosic biomass by using biomass characteristics and torrefaction conditions as input variables. After optimization, gradient boosting machines were the optimal model, with the highest coefficient of determination ranging from 0.89 to 0.94. Torrefaction conditions exhibited a higher relative contribution to the yield and higher heating value (HHV) of biochar than biomass characteristics. Temperature was the dominant contributor to the elemental and proximate composition and the yield and HHV of biochar. Feature importance and SHapley Additive exPlanations revealed the effect of each influential factor on the target variables and the interactions between these factors in torrefaction. Software that can accurately predict the element, yield, and HHV of biochar was developed. These findings provide a comprehensive understanding of the key factors and their interactions influencing the torrefaction process and biochar properties.
    Matched MeSH terms: Machine Learning*
  4. Alymann AA, Alymann IA, Ong SQ, Rusli MU, Ahmad AH, Salim H
    Sci Data, 2024 Apr 05;11(1):337.
    PMID: 38580692 DOI: 10.1038/s41597-024-03172-9
    Reliable sex identification in Varanus salvator traditionally relied on invasive methods like genetic analysis or dissection, as less invasive techniques such as hemipenes inversion are unreliable. Given the ecological importance of this species and skewed sex ratios in disturbed habitats, a dataset that allows ecologists or zoologists to study the sex determination of the lizard is crucial. We present a new dataset containing morphometric measurements of V. salvator individuals from the skin trade, with sex confirmed by dissection post- measurement. The dataset consists of a mixture of primary and secondary data such as weight, skull size, tail length, condition etc. and can be used in modelling studies for ecological and conservation research to monitor the sex ratio of this species. Validity was demonstrated by training and testing six machine learning models. This dataset has the potential to streamline sex determination, offering a non-invasive alternative to complement existing methods in V. salvator research, mitigating the need for invasive procedures.
    Matched MeSH terms: Machine Learning
  5. Ali M, Wahab IBA, Huri HZ, Yusoff MS
    Syst Rev, 2024 Apr 02;13(1):99.
    PMID: 38566190 DOI: 10.1186/s13643-024-02478-4
    BACKGROUND: Personalised learning, an educational approach that tailors teaching and learning to individual needs and preferences, has gained attention in recent years, particularly in higher education. Advances in educational technology have facilitated the implementation of personalised learning in various contexts. Despite its potential benefits, the literature on personalised learning in health sciences higher education remains scattered and heterogeneous. This scoping review aims to identify and map the current literature on personalised learning in health sciences higher education and its definition, implementation strategies, benefits, and limitations.

    METHODS: A comprehensive search of electronic databases, PubMed, Scopus, Google Scholar, Educational Research Complete, and Journal Storage (JSTOR), will be conducted to identify relevant articles. The search will be limited to articles published in the English language between 2000 and 2023. The search strategy will be designed and adapted for each database using a combination of keywords and subject headings related to personalised learning and health sciences higher education. Eligibility criteria will be applied to screen and select articles. Data extraction and quality assessment will be performed, and thematic synthesis will be used to analyse the extracted data.

    DISCUSSION: The results of the scoping review will present a comprehensive and coherent overview of the literature on personalised learning in health sciences higher education. Key themes and topics related to personalised learning, its definitions, models, implementation strategies, benefits, and limitations, will be identified. The geographical and temporal distribution of research on personalised learning in health sciences higher education will also be described. This scoping review will provide a structured synthesis of the available evidence on personalised learning in health sciences higher education, highlighting potential gaps and areas for future research. The findings will contribute to ongoing scholarly and policy debates on personalised learning in higher education, informing the development of best practices, guidelines, and future research agendas.

    Matched MeSH terms: Learning*
  6. Zhang J, Xiao W, Soh KG, Yao G, Anuar MABM, Bai X, et al.
    BMC Public Health, 2024 Apr 02;24(1):949.
    PMID: 38566018 DOI: 10.1186/s12889-024-18243-0
    BACKGROUND: Evidence indicates that the Sport Education Model (SEM) has demonstrated effectiveness in enhancing students' athletic capabilities and fostering their enthusiasm for sports. Nevertheless, there remains a dearth of comprehensive reviews examining the impact of the SEM on students' attitudes toward physical education learning.

    PURPOSE: The purpose of this review is to elucidate the influence of the SEM on students' attitudes toward physical education learning.

    METHODS: Employing the preferred reporting items of the Systematic Review and Meta-analysis (PRISMA) statement guidelines, a systematic search of PubMed, SCOPUS, EBSCOhost (SPORTDiscus and CINAHL Plus), and Web of Science databases was conducted in mid-January 2023. A set of keywords associated with the SEM, attitudes toward physical education learning, and students were employed to identify relevant studies. Out of 477 studies, only 13 articles fulfilled all the eligibility criteria and were consequently incorporated into this systematic review. The validated checklist of Downs and Black (1998) was employed for the assessment, and the included studies achieved quality scores ranging from 11 to 13. The ROBINS-I tool was utilized to evaluate the risk of bias in the literature, whereby only one paper exhibited a moderate risk of bias, while the remainder were deemed to have a high risk.

    RESULTS: The findings unveiled significant disparities in cognitive aspects (n = 8) and affective components (n = 12) between the SEM intervention and the Traditional Teaching (TT) comparison. Existing evidence suggests that the majority of scholars concur that the SEM yields significantly superior effects in terms of students' affective and cognitive aspects compared to the TT.

    CONCLUSIONS: Nonetheless, several issues persist, including a lack of data regarding junior high school students and gender differences, insufficient frequency of weekly interventions, inadequate control of inter-group atmosphere disparities resulting from the same teaching setting, lack of reasonable testing, model fidelity check and consideration for regulating variables, of course, learning content, and unsuitable tools for measuring learning attitudes. In contrast, the SEM proves more effective than the TT in enhancing students' attitudes toward physical learning.

    SYSTEMATIC REVIEW REGISTRATION: ( https://inplasy.com/ ) (INPLASY2022100040).

    Matched MeSH terms: Learning
  7. Rajasegaran S, Nooraziz AN, Abdullah A, Sanmugam A, Singaravel S, Gan CS, et al.
    J Pediatr Surg, 2024 Apr;59(4):577-582.
    PMID: 38160184 DOI: 10.1016/j.jpedsurg.2023.12.007
    BACKGROUND: Congenital diaphragmatic hernia (CDH) survivors often experience long-term CDH-associated morbidities, including musculoskeletal, gastrointestinal and respiratory issues. This study evaluates parent-reported health-related quality of life (HRQOL) and family impact of the disease.

    METHODS: Electronic medical records (EMR) were reviewed and phone surveys performed with parents of CDH survivors who underwent repair at our institution from 2010 to 2019. They completed the following Pediatric Quality of Life Inventory™ (PedsQL™) questionnaires: Generic Core Scales 4.0 (parent-proxy report) and Family Impact (FI) Module 2.0. Age-matched and gender-matched healthy controls from an existing database were used for comparison. Subgroup analysis of CDH patients alone was also performed. Appropriate statistical analysis was used with p 

    Matched MeSH terms: Learning Disorders*
  8. Ahad MA, Chear NJ, Abdullah MH, Ching-Ga TAF, Liao P, Wei S, et al.
    Ageing Res Rev, 2024 Apr;96:102252.
    PMID: 38442748 DOI: 10.1016/j.arr.2024.102252
    Chronic cerebral hypoperfusion (CCH) is a common mechanism of acute brain injury due to impairment of blood flow to the brain. Moreover, a prolonged lack of oxygen supply may result in cerebral infarction or global ischemia, which subsequently causes long-term memory impairment. Research on using Clitoria ternatea root extract for treating long-term memory has been studied extensively. However, the bioactive compound contributing to its neuroprotective effects remains uncertain. In the present study, we investigate the effects of clitorienolactone A (CLA) and B (CLB) from the roots of Clitoria ternatea extract on hippocampal neuroplasticity in rats induced by CCH. CLA and CLB were obtained using column chromatography. The rat model of CCH was induced using two-vessel occlusion surgery (2VO). The 2VO rats were given 10 mg/kg of CLA and CLB orally, followed by hippocampal neuroplasticity recording using in vivo electrophysiological. Rats received CLA and CLB (10 mg/kg) significantly reversed the impairment of long-term potentiation following 2VO surgery. Furthermore, we investigate the effect of CLA and CLB on the calcium channel using the calcium imaging technique. During hypoxia, CLA and CLB sustain the increase in intracellular calcium levels. We next predict the binding interactions of CLA and CLB against NMDA receptors containing GluN2A and GluN2B subunits using in silico molecular docking. Our result found that both CLA and CLB exhibited lower binding affinity against GluN2A and GluN2B subunits. Our findings demonstrated that bioactive compounds from Clitoria ternatea improved long-term memory deficits in the chronic cerebral hypoperfusion rat model via calcium uptake. Hence, CLA and CLB could be potential therapeutic tools for treating cognitive dysfunction.
    Matched MeSH terms: Maze Learning/physiology
  9. Boo KBW, El-Shafie A, Othman F, Khan MMH, Birima AH, Ahmed AN
    Water Res, 2024 Mar 15;252:121249.
    PMID: 38330715 DOI: 10.1016/j.watres.2024.121249
    Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent years, there has been a notable increase in the use of machine learning (ML) techniques to model GWL, with many studies reporting exceptional results. In this paper, we present a comprehensive review of 142 relevant articles indexed by the Web of Science from 2017 to 2023, focusing on key ML models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), evolutionary computing (EC), deep learning (DL), ensemble learning (EN), and hybrid-modeling (HM). We also discussed key modeling concepts such as dataset size, data splitting, input variable selection, forecasting time-step, performance metrics (PM), study zones, and aquifers, highlighting best practices for optimal GWL forecasting with ML. This review provides valuable insights and recommendations for researchers and water management agencies working in the field of groundwater management and hydrology.
    Matched MeSH terms: Machine Learning
  10. Modi S, Kasmiran KA, Mohd Sharef N, Sharum MY
    J Biomed Inform, 2024 Mar;151:104603.
    PMID: 38331081 DOI: 10.1016/j.jbi.2024.104603
    BACKGROUND: An adverse drug event (ADE) is any unfavorable effect that occurs due to the use of a drug. Extracting ADEs from unstructured clinical notes is essential to biomedical text extraction research because it helps with pharmacovigilance and patient medication studies.

    OBJECTIVE: From the considerable amount of clinical narrative text, natural language processing (NLP) researchers have developed methods for extracting ADEs and their related attributes. This work presents a systematic review of current methods.

    METHODOLOGY: Two biomedical databases have been searched from June 2022 until December 2023 for relevant publications regarding this review, namely the databases PubMed and Medline. Similarly, we searched the multi-disciplinary databases IEEE Xplore, Scopus, ScienceDirect, and the ACL Anthology. We adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement guidelines and recommendations for reporting systematic reviews in conducting this review. Initially, we obtained 5,537 articles from the search results from the various databases between 2015 and 2023. Based on predefined inclusion and exclusion criteria for article selection, 100 publications have undergone full-text review, of which we consider 82 for our analysis.

    RESULTS: We determined the general pattern for extracting ADEs from clinical notes, with named entity recognition (NER) and relation extraction (RE) being the dual tasks considered. Researchers that tackled both NER and RE simultaneously have approached ADE extraction as a "pipeline extraction" problem (n = 22), as a "joint task extraction" problem (n = 7), and as a "multi-task learning" problem (n = 6), while others have tackled only NER (n = 27) or RE (n = 20). We further grouped the reviews based on the approaches for data extraction, namely rule-based (n = 8), machine learning (n = 11), deep learning (n = 32), comparison of two or more approaches (n = 11), hybrid (n = 12) and large language models (n = 8). The most used datasets are MADE 1.0, TAC 2017 and n2c2 2018.

    CONCLUSION: Extracting ADEs is crucial, especially for pharmacovigilance studies and patient medications. This survey showcases advances in ADE extraction research, approaches, datasets, and state-of-the-art performance in them. Challenges and future research directions are highlighted. We hope this review will guide researchers in gaining background knowledge and developing more innovative ways to address the challenges.

    Matched MeSH terms: Machine Learning*
  11. Sharif-Nia H, Arslan G, Reardon J, Allen KA, Ma L, She L, et al.
    Nurs Open, 2024 Mar;11(3):e2130.
    PMID: 38486130 DOI: 10.1002/nop2.2130
    AIM: This study explored the influence of student computer competency on e-learning outcomes among Iranian nursing students and examined its mediating role in the relationship between virtual learning infrastructure, student collaboration, access to electronic facilities, and e-learning outcomes.

    DESIGN: A cross sectional study.

    METHOD: A self-administered online survey was used from August to October 2022, with a sample size of 417 nursing students selected through convenience sampling. Descriptive statistics, correlation analyses, and PROCESS macro v4.1 (Model 4) were used for data analysis.

    RESULTS: The results revealed that virtual learning infrastructure, access to electronic facilities, and student collaboration, significantly predict student computer competency and e-learning outcomes. Virtual learning infrastructure and access to electronic facilities were found to be the strongest predictors of student computer competency, while student collaboration had a smaller but still significant effect. Student computer competency was found to mediate the relationship between virtual learning infrastructure, access to electronic facilities, student collaboration, and e-learning outcomes.

    Matched MeSH terms: Learning
  12. Ngugi HN, Ezugwu AE, Akinyelu AA, Abualigah L
    Environ Monit Assess, 2024 Feb 24;196(3):302.
    PMID: 38401024 DOI: 10.1007/s10661-024-12454-z
    Digital image processing has witnessed a significant transformation, owing to the adoption of deep learning (DL) algorithms, which have proven to be vastly superior to conventional methods for crop detection. These DL algorithms have recently found successful applications across various domains, translating input data, such as images of afflicted plants, into valuable insights, like the identification of specific crop diseases. This innovation has spurred the development of cutting-edge techniques for early detection and diagnosis of crop diseases, leveraging tools such as convolutional neural networks (CNN), K-nearest neighbour (KNN), support vector machines (SVM), and artificial neural networks (ANN). This paper offers an all-encompassing exploration of the contemporary literature on methods for diagnosing, categorizing, and gauging the severity of crop diseases. The review examines the performance analysis of the latest machine learning (ML) and DL techniques outlined in these studies. It also scrutinizes the methodologies and datasets and outlines the prevalent recommendations and identified gaps within different research investigations. As a conclusion, the review offers insights into potential solutions and outlines the direction for future research in this field. The review underscores that while most studies have concentrated on traditional ML algorithms and CNN, there has been a noticeable dearth of focus on emerging DL algorithms like capsule neural networks and vision transformers. Furthermore, it sheds light on the fact that several datasets employed for training and evaluating DL models have been tailored to suit specific crop types, emphasizing the pressing need for a comprehensive and expansive image dataset encompassing a wider array of crop varieties. Moreover, the survey draws attention to the prevailing trend where the majority of research endeavours have concentrated on individual plant diseases, ML, or DL algorithms. In light of this, it advocates for the development of a unified framework that harnesses an ensemble of ML and DL algorithms to address the complexities of multiple plant diseases effectively.
    Matched MeSH terms: Machine Learning
  13. Mahmud SMH, Goh KOM, Hosen MF, Nandi D, Shoombuatong W
    Sci Rep, 2024 Feb 05;14(1):2961.
    PMID: 38316843 DOI: 10.1038/s41598-024-52653-9
    DNA-binding proteins (DBPs) play a significant role in all phases of genetic processes, including DNA recombination, repair, and modification. They are often utilized in drug discovery as fundamental elements of steroids, antibiotics, and anticancer drugs. Predicting them poses the most challenging task in proteomics research. Conventional experimental methods for DBP identification are costly and sometimes biased toward prediction. Therefore, developing powerful computational methods that can accurately and rapidly identify DBPs from sequence information is an urgent need. In this study, we propose a novel deep learning-based method called Deep-WET to accurately identify DBPs from primary sequence information. In Deep-WET, we employed three powerful feature encoding schemes containing Global Vectors, Word2Vec, and fastText to encode the protein sequence. Subsequently, these three features were sequentially combined and weighted using the weights obtained from the elements learned through the differential evolution (DE) algorithm. To enhance the predictive performance of Deep-WET, we applied the SHapley Additive exPlanations approach to remove irrelevant features. Finally, the optimal feature subset was input into convolutional neural networks to construct the Deep-WET predictor. Both cross-validation and independent tests indicated that Deep-WET achieved superior predictive performance compared to conventional machine learning classifiers. In addition, in extensive independent test, Deep-WET was effective and outperformed than several state-of-the-art methods for DBP prediction, with accuracy of 78.08%, MCC of 0.559, and AUC of 0.805. This superior performance shows that Deep-WET has a tremendous predictive capacity to predict DBPs. The web server of Deep-WET and curated datasets in this study are available at https://deepwet-dna.monarcatechnical.com/ . The proposed Deep-WET is anticipated to serve the community-wide effort for large-scale identification of potential DBPs.
    Matched MeSH terms: Machine Learning
  14. Zain E, Talreja N, Hesarghatta Ramamurthy P, Muzaffar D, Rehman K, Khan AA, et al.
    Eur J Dent Educ, 2024 Feb;28(1):358-369.
    PMID: 37864324 DOI: 10.1111/eje.12957
    INTRODUCTION: Simulation-based education is of paramount importance in a dental pre-clinical setting. Hence, continuous quality improvement is crucial to optimize students' knowledge and clinical skills. This study aimed to evaluate the impact of evidence-based simulation learning (EBSL) compared with traditional-based simulation learning (TBSL) using Plan-Do-Study-Act (PDSA) model.

    MATERIALS AND METHODS: This quality improvement project was undertaken at a private university. Guided by the PDSA model, rubber dam application tasks were conducted in the simulation lab in 2 phases. Phase 1 included TBSL and phase 2 included EBSL comprising of 2 PDSA cycles. 'Plan' stage involved obtaining feedback from students and the concerned staff. 'Do' stage included implementation of EBSL in eight steps adopted from Higgins's framework. 'Study' stage evaluated the outcomes and in 'Act' stage amendments were made to the first EBSL cycle. In the second PDSA cycle re-implementation and evaluation of the rubber dam application exercises were carried out. Descriptive data were presented as percentages and mean scores were compared using paired t-test.

    RESULTS: Thirty-seven year 2 students participated in this study. A significant improvement in the mean scores was observed between TBSL and EBSL (3.02 + 0.16 and 3.91 + 0.27, respectively, p 

    Matched MeSH terms: Learning
  15. Xu M, Abdullah NA, Md Sabri AQ
    Comput Biol Chem, 2024 Feb;108:107997.
    PMID: 38154318 DOI: 10.1016/j.compbiolchem.2023.107997
    This work focuses on data sampling in cancer-gene association prediction. Currently, researchers are using machine learning methods to predict genes that are more likely to produce cancer-causing mutations. To improve the performance of machine learning models, methods have been proposed, one of which is to improve the quality of the training data. Existing methods focus mainly on positive data, i.e. cancer driver genes, for screening selection. This paper proposes a low-cancer-related gene screening method based on gene network and graph theory algorithms to improve the negative samples selection. Genetic data with low cancer correlation is used as negative training samples. After experimental verification, using the negative samples screened by this method to train the cancer gene classification model can improve prediction performance. The biggest advantage of this method is that it can be easily combined with other methods that focus on enhancing the quality of positive training samples. It has been demonstrated that significant improvement is achieved by combining this method with three state-of-the-arts cancer gene prediction methods.
    Matched MeSH terms: Machine Learning
  16. Sultan G, Zubair S
    Comput Biol Chem, 2024 Feb;108:107999.
    PMID: 38070457 DOI: 10.1016/j.compbiolchem.2023.107999
    Breast cancer continues to be a prominent cause for substantial loss of life among women globally. Despite established treatment approaches, the rising prevalence of breast cancer is a concerning trend regardless of geographical location. This highlights the need to identify common key genes and explore their biological significance across diverse populations. Our research centered on establishing a correlation between common key genes identified in breast cancer patients. While previous studies have reported many of the genes independently, our study delved into the unexplored realm of their mutual interactions, that may establish a foundational network contributing to breast cancer development. Machine learning algorithms were employed for sample classification and key gene selection. The best performance model further selected the candidate genes through expression pattern recognition. Subsequently, the genes common in all the breast cancer patients from India, China, Czech Republic, Germany, Malaysia and Saudi Arabia were selected for further study. We found that among ten classifiers, Catboost exhibited superior performance with an average accuracy of 92%. Functional enrichment analysis and pathway analysis revealed that calcium signaling pathway, regulation of actin cytoskeleton pathway and other cancer-associated pathways were highly enriched with our identified genes. Notably, we observed that these genes regulate each other, forming a complex network. Additionally, we identified PALMD gene as a novel potential biomarker for breast cancer progression. Our study revealed key gene modules forming a complex network that were consistently expressed in different populations, affirming their critical role and biological significance in breast cancer. The identified genes hold promise as prospective biomarkers of breast cancer prognosis irrespective of country of origin or ethnicity. Future investigations will expand upon these genes in a larger population and validate their biological functions through in vivo analysis.
    Matched MeSH terms: Machine Learning
  17. Mohd Sahini SN, Mohd Nor Hazalin NA, Srikumar BN, Jayasingh Chellammal HS, Surindar Singh GK
    Neurobiol Learn Mem, 2024 Feb;208:107880.
    PMID: 38103676 DOI: 10.1016/j.nlm.2023.107880
    Environmental enrichment (EE) is a process of brain stimulation by modifying the surroundings, for example, by changing the sensory, social, or physical conditions. Rodents have been used in such experimental strategies through exposure to diverse physical, social, and exploration conditions. The present study conducted an extensive analysis of the existing literature surrounding the impact of EE on dementia rodent models. The review emphasised the two principal aspects that are very closely related to dementia: cognitive function (learning and memory) as well as psychological factors (anxiety-related behaviours such as phobias and unrealistic worries). Also highlighted were the mechanisms involved in the rodent models of dementia showing EE effects. Two search engines, PubMed and Science Direct, were used for data collection using the following keywords: environmental enrichment, dementia, rodent model, cognitive performance, and anxiety-related behaviour. Fifty-five articles were chosen depending on the criteria for inclusion and exclusion. The rodent models with dementia demonstrated improved learning and memory in the form of hampered inflammatory responses, enhanced neuronal plasticity, and sustained neuronal activity. EE housing also prevented memory impairment through the prevention of amyloid beta (Aβ) seeding formation, an early stage of Aβ plaque formation. The rodents subjected to EE were observed to present increased exploratory activity and exert less anxiety-related behaviour, compared to those in standard housing. However, some studies have proposed that EE intervention through exercise would be too mild to counteract the anxiety-related behaviour and risk assessment behaviour deficits in the Alzheimer's disease rodent model. Future studies should be conducted on old-aged rodents and the duration of EE exposure that would elicit the greatest benefits since the existing studies have been conducted on a range of ages and EE durations. In summary, EE had a considerable effect on dementia rodent models, with the most evident being improved cognitive function.
    Matched MeSH terms: Maze Learning/physiology
  18. Caliph SM, Lee CY
    Curr Pharm Teach Learn, 2024 Feb;16(2):119-123.
    PMID: 38158334 DOI: 10.1016/j.cptl.2023.12.017
    BACKGROUND AND PURPOSE: Pharmacy students' perception of the effectiveness of remote online learning experienced during the pandemic, and their learning expectations post-pandemic were unknown. The main purpose of this study was to examine students' perceived effectiveness of online teaching and learning activities developed for active learning and pharmacy professional skills development, and the feasibility of online assessments.

    EDUCATIONAL ACTIVITY AND SETTING: A cross-sectional online survey involving second-year pharmacy students of Monash Malaysia (MA) and Monash Australia (PA) campuses was conducted. The survey consisted of 15 Likert-scale multiple-choice questions and an open-ended question. Data were analysed statistically.

    FINDINGS: Students at both MA and PA campuses were satisfied with the remote online learning experienced during the pandemic but indicated a preference for a blended learning approach. Students at the MA campus felt that on-campus face-to-face classes were more engaging and advantageous for their learning and skills development (P 

    Matched MeSH terms: Problem-Based Learning
  19. Alkhamis MA, Al Jarallah M, Attur S, Zubaid M
    Sci Rep, 2024 Jan 12;14(1):1243.
    PMID: 38216605 DOI: 10.1038/s41598-024-51604-8
    The relationships between acute coronary syndromes (ACS) adverse events and the associated risk factors are typically complicated and nonlinear, which poses significant challenges to clinicians' attempts at risk stratification. Here, we aim to explore the implementation of modern risk stratification tools to untangle how these complex factors shape the risk of adverse events in patients with ACS. We used an interpretable multi-algorithm machine learning (ML) approach and clinical features to fit predictive models to 1,976 patients with ACS in Kuwait. We demonstrated that random forest (RF) and extreme gradient boosting (XGB) algorithms, remarkably outperform traditional logistic regression model (AUCs = 0.84 & 0.79 for RF and XGB, respectively). Our in-hospital adverse events model identified left ventricular ejection fraction as the most important predictor with the highest interaction strength with other factors. However, using the 30-days adverse events model, we found that performing an urgent coronary artery bypass graft was the most important predictor, with creatinine levels having the strongest overall interaction with other related factors. Our ML models not only untangled the non-linear relationships that shape the clinical epidemiology of ACS adverse events but also elucidated their risk in individual patients based on their unique features.
    Matched MeSH terms: Machine Learning
  20. Singh H, Mohammed AH, Stokes E, Malone D, Turner J, Hassan BAR, et al.
    Curr Pharm Teach Learn, 2024 Jan;16(1):69-76.
    PMID: 38158327 DOI: 10.1016/j.cptl.2023.12.007
    BACKGROUND AND PURPOSE: This study aimed to evaluate an accelerated dispensing course for graduate entry (GE) pharmacy students with prior science-related degrees to join undergraduate (UG) students in year three of the Monash Pharmacy degree.

    EDUCATIONAL ACTIVITY AND SETTING: A one day accelerated dispensing course using MyDispense software was delivered to 59 GE students. The accelerated dispensing course was identical to the standard three-week dispensing course delivered to UG students. The same assessment of dispensing skills was conducted after course completion for both UG and GE students and included dispensing four prescriptions of varying difficulty. The assessment scores of the UG and GE students were compared. Perception data from the accelerated course were also collected.

    FINDINGS: The accelerated dispensing curriculum was well received by students. They found the simulation relevant to practice, easy to navigate, and helpful for preparing them for assessment. Overall, 5.1% of GE students failed the assessment, which was lower than the 32.6% failure rate in the UG cohort. Comparison of assessment grades between UG and GE students showed no notable disadvantage to attainment of learning outcomes with the accelerated curriculum. However, UG students were more likely to provide unsafe instructions compared to GE students in their labeling for three out of four prescriptions.

    SUMMARY: An accelerated dispensing curriculum can be effectively delivered to mature learners with a prior science-related degree as no notable deficiencies were identified when comparing the assessment results of GE students against UG students when both student cohorts undertook the same dispensing assessment.

    Matched MeSH terms: Learning
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