Displaying publications 1 - 20 of 908 in total

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  1. Loh BCS, Then PHH
    Mhealth, 2017;3:45.
    PMID: 29184897 DOI: 10.21037/mhealth.2017.09.01
    Cardiovascular diseases are one of the top causes of deaths worldwide. In developing nations and rural areas, difficulties with diagnosis and treatment are made worse due to the deficiency of healthcare facilities. A viable solution to this issue is telemedicine, which involves delivering health care and sharing medical knowledge at a distance. Additionally, mHealth, the utilization of mobile devices for medical care, has also proven to be a feasible choice. The integration of telemedicine, mHealth and computer-aided diagnosis systems with the fields of machine and deep learning has enabled the creation of effective services that are adaptable to a multitude of scenarios. The objective of this review is to provide an overview of heart disease diagnosis and management, especially within the context of rural healthcare, as well as discuss the benefits, issues and solutions of implementing deep learning algorithms to improve the efficacy of relevant medical applications.
    Matched MeSH terms: Machine Learning
  2. Loh SY, Jahans-Price T, Greenwood MP, Greenwood M, Hoe SZ, Konopacka A, et al.
    eNeuro, 2017 12 21;4(6).
    PMID: 29279858 DOI: 10.1523/ENEURO.0243-17.2017
    The supraoptic nucleus (SON) is a group of neurons in the hypothalamus responsible for the synthesis and secretion of the peptide hormones vasopressin and oxytocin. Following physiological cues, such as dehydration, salt-loading and lactation, the SON undergoes a function related plasticity that we have previously described in the rat at the transcriptome level. Using the unsupervised graphical lasso (Glasso) algorithm, we reconstructed a putative network from 500 plastic SON genes in which genes are the nodes and the edges are the inferred interactions. The most active nodal gene identified within the network was Caprin2. Caprin2 encodes an RNA-binding protein that we have previously shown to be vital for the functioning of osmoregulatory neuroendocrine neurons in the SON of the rat hypothalamus. To test the validity of the Glasso network, we either overexpressed or knocked down Caprin2 transcripts in differentiated rat pheochromocytoma PC12 cells and showed that these manipulations had significant opposite effects on the levels of putative target mRNAs. These studies suggest that the predicative power of the Glasso algorithm within an in vivo system is accurate, and identifies biological targets that may be important to the functional plasticity of the SON.
    Matched MeSH terms: Unsupervised Machine Learning*
  3. Lim ZN, Ng WJ, Lee CC
    Z Evid Fortbild Qual Gesundhwes, 2023 Aug;180:103-106.
    PMID: 37357108 DOI: 10.1016/j.zefq.2023.05.019
    BACKGROUND: In Malaysia, advance care planning is still in its infancy. There is no national implementation of Advance Care Planning.

    AIMS: To describe the national state of advance care planning development in Malaysia METHODS: Review of relevant advance care planning literature locally and internationally was undertaken.

    RESULTS: Positive development in Malaysia includes implementation of advance care planning at institutional level, initiatives to develop educational programmes as well as research activities to understand the attitude and perception of patients on advance care planning. However, there remain challenges, including lack of knowledge and awareness, lack of legislative framework to guide advance care planning implementation and lack of strong initiatives at a national level.

    CONCLUSIONS: It is evident that there is much to learn nationally and internationally about ACP before any decision on implementation of ACP is made in Malaysia. ACP is a public health issue and requires concerted effort of all stakeholders, including Government agencies, academic institutions, and non-government organizations to raise public awareness. More research is needed to shape the future direction of ACP development in Malaysia.

    Matched MeSH terms: Learning
  4. Hossain R, Ibrahim RB, Hashim HB
    World Neurosurg, 2023 Jul;175:57-68.
    PMID: 37019303 DOI: 10.1016/j.wneu.2023.03.115
    To develop a research overview of brain tumor classification using machine learning, we conducted a systematic review with a bibliometric analysis. Our systematic review and bibliometric analysis included 1747 studies of automated brain tumor detection using machine learning reported in the previous 5 years (2019-2023) from 679 different sources and authored by 6632 investigators. Bibliographic data were collected from the Scopus database, and a comprehensive bibliometric analysis was conducted using Biblioshiny and the R platform. The most productive and collaborative institutes, reports, journals, and countries were determined using citation analysis. In addition, various collaboration metrics were determined at the institute, country, and author level. Lotka's law was tested using the authors' performance. Analysis showed that the authors' publication trends followed Lotka's inverse square law. An annual publication analysis showed that 36.46% of the studies had been reported in 2022, with steady growth from previous years. Most of the cited authors had focused on multiclass classification and novel convolutional neural network models that are efficient for small training sets. A keyword analysis showed that "deep learning," "magnetic resonance imaging," "nuclear magnetic resonance imaging," and "glioma" appeared most often, proving that of the several brain tumor types, most studies had focused on glioma. India, China, and the United States were among the highest collaborative countries in terms of both authors and institutes. The University of Toronto and Harvard Medical School had the highest number of affiliations with 132 and 87 publications, respectively.
    Matched MeSH terms: Machine Learning
  5. Tao H, Rahman MA, Jing W, Li Y, Li J, Al-Saffar A, et al.
    Work, 2021;68(3):903-912.
    PMID: 33720867 DOI: 10.3233/WOR-203424
    BACKGROUND: Human-robot interaction (HRI) is becoming a current research field for providing granular real-time applications and services through physical observation. Robotic systems are designed to handle the roles of humans and assist them through intrinsic sensing and commutative interactions. These systems handle inputs from multiple sources, process them, and deliver reliable responses to the users without delay. Input analysis and processing is the prime concern for the robotic systems to understand and resolve the queries of the users.

    OBJECTIVES: In this manuscript, the Interaction Modeling and Classification Scheme (IMCS) is introduced to improve the accuracy of HRI. This scheme consists of two phases, namely error classification and input mapping. In the error classification process, the input is analyzed for its events and conditional discrepancies to assign appropriate responses in the input mapping phase. The joint process is aided by a linear learning model to analyze the different conditions in the event and input detection.

    RESULTS: The performance of the proposed scheme shows that it is capable of improving the interaction accuracy by reducing the ratio of errors and interaction response by leveraging the information extraction from the discrete and successive human inputs.

    CONCLUSION: The fetched data are analyzed by classifying the errors at the initial stage to achieve reliable responses.

    Matched MeSH terms: Learning
  6. Miraj M, Chuntian L, Rehman RU, Osei-Bonsu R, Mohd Said R, Ali R, et al.
    Work, 2022;73(4):1365-1378.
    PMID: 36093656 DOI: 10.3233/WOR-205237
    BACKGROUND: Research is essential and necessary for those who love learning, whether they belong to a research institution or not. Numerous elements influence researchers' attitudes towards good research work, but in this study we focus on the most significant ones: advisor support, intrinsic motivation, timing, and planning.

    OBJECTIVES: The current study aims at motivating readers to help improve students' attitudes towards research work within the university context.

    METHOD: The target demographic of the current research comprises masters and doctoral students from three major public institutions in Xi'an, China. We aimed to examine the effects of the variables and the study employed correlation and stepwise regression.

    RESULTS: The results show that advisor support influences attitudes towards research positively and significantly (β= 0.20, p 

    Matched MeSH terms: Learning
  7. 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
  8. Concessao P, Bairy LK, Raghavendra AP
    Vet World, 2020 Aug;13(8):1555-1566.
    PMID: 33061227 DOI: 10.14202/vetworld.2020.1555-1566
    Background and Aim: Intoxication of arsenic in rats is known to result in neurological effects as well as liver and kidney dysfunction. Mucuna pruriens has been identified for its medicinal properties. The aim of the study was to investigate the protective effect of aqueous seed extract of M. pruriens on sodium arsenite-induced memory impairment, liver, and kidney functions in rats.

    Materials and Methods: The experiment was divided into short-term treatment (45 days) and long-term treatment (90 days), with each group divided into nine sub-groups consisting of six animals each. Sub-groups 1 and 2 served as normal, and N-acetylcysteine (NAC) controls, respectively. Sub-groups 3-9 received sodium arsenite in drinking water (50 mg/L). In addition, sub-group 4 received NAC (210 mg/kg b.wt) orally once daily, sub-groups 5-7 received aqueous seed extract of M. pruriens (350 mg/kg b.wt, 530 mg/kg b.wt, and 700 mg/kg b.wt) orally once daily and sub-groups 8 and 9 received a combination of NAC and aqueous seed extract of M. pruriens (350 mg/kg b.wt and 530 mg/kg b.wt) orally once daily. Following the treatment, the blood was drawn retro-orbitally to assess the liver (serum alanine transaminase [ALT], serum aspartate transaminase, and serum alkaline phosphatase) and kidney (serum urea and serum creatinine) functions. Learning and memory were assessed by passive avoidance test. Animals were sacrificed by an overdose of ketamine, and their Nissl stained hippocampal sections were analyzed for alterations in neural cell numbers in CA1 and CA3 regions.

    Results: In the short-term treatment, groups administered with M. pruriens 530 mg/kg b.wt alone and combination of NAC + M. pruriens 350 mg/kg b.wt exhibited a significant improvement in memory retention, less severe neurodegeneration, and decrease in serum ALT levels. In long-term treatment, groups administered with M. pruriens 700 mg/kg b.wt alone and combination of NAC+M. pruriens 350 mg/kg b.wt, respectively, showed better memory retention, decreased neural deficits, and reduced levels of kidney and liver enzymes.

    Conclusion: The seed extract of M. pruriens showed significant enhancement in memory and learning. The number of surviving neurons in the CA1 and CA3 regions also increased on treatment with M. pruriens. Serum ALT, serum urea, and serum creatinine levels showed significant improvement on long-term treatment with M. pruriens.

    Matched MeSH terms: Learning
  9. Putra TA, Hezmee MN, Farhana NB, Hassim HA, Intan-Shameha AR, Lokman IH, et al.
    Vet World, 2016 Sep;9(9):955-959.
    PMID: 27733795
    The One Health (OH) approach, which seeks to bring together human and animal health, is particularly suited to the effective management of zoonotic diseases across both sectors. To overcome professional silos, OH needs to be taught at the undergraduate level. Here, we describe a problem-based learning activity using the OH approach that was conducted outdoors for 3(rd)-year veterinary students in Malaysia.
    Matched MeSH terms: Problem-Based Learning
  10. Narayanan SN, Kumar RS, Potu BK, Nayak S, Bhat PG, Mailankot M
    Ups. J. Med. Sci., 2010 May;115(2):91-6.
    PMID: 20095879 DOI: 10.3109/03009730903552661
    The interaction of mobile phone radio-frequency electromagnetic radiation (RF-EMR) with the brain is a serious concern of our society.
    Matched MeSH terms: Avoidance Learning/radiation effects*
  11. Alguri KS, Chia CC, Harley JB
    Ultrasonics, 2021 Mar;111:106338.
    PMID: 33338729 DOI: 10.1016/j.ultras.2020.106338
    Wavefield imaging is a powerful visualization tool in nondestructive evaluation for studying ultrasonic wave propagation and its interactions with damage. To isolate and study damage scattering, damage-free baseline data is often subtracted from a wavefield. This is often necessary because the damage wavefield can be orders of magnitude weaker than the incident waves. Yet, baselines are not always accessible. When the baselines are accessible, the experimental conditions for the baseline and test data must be extremely similar. Researchers have created several baseline-free approaches for isolating damage wavefields, but these often rely on specific experimental setups. In this paper, we discuss a flexible approach based on ultrasonic guided wave digital surrogates (i.e., numerical simulations of incident waves) and transfer learning. We demonstrate this approach with two setups. We first isolate reflections from a circular, 2 mm diameter half-thickness hole on a 10 × 10 cm steel plate. We then isolate 8 circular, half-thickness holes of various diameters from 1 mm to 40 mm on a 60 × 60 cm steel plate. The second plate has a non-square geometry and the data has multi-path reflections. With both data sets, we isolate damage reflections without explicit experimental baselines. We also briefly illustrate the comparison of our dictionary learning method with wavenumber filtering technique which is often used to enhance the defect wavefields.
    Matched MeSH terms: Machine Learning
  12. NURSHAFIKAH SHAFFIE, ROSLIZA MAT ZIN, SHAHNAZ ISMAIL
    MyJurnal
    Learning preferences among undergraduate accounting students might vary considerably and are still largely unexplored although their findings might be useful for lecturers to improve learning and teaching strategies. Students’ preferences in selecting the appropriate learning strategies can help improve their understanding and lead to improved competency for better academic achievement. This study examined students’ preferences towards learning strategies and the differences in learning strategies among accounting students in Universiti Malaysia Terengganu, between genders. The data were collected using online survey completed by accounting undergraduate students from Year 1 until Year 3 for the academic session 2018/2019. 150 students responded to the online survey, with 32% response rate. Using a revised two-factor version of the Study Process Questionnaire, the survey assessed deep and surface approaches in learning preferred by the students. The results showed that deep learning approach scored a higher mean of 3.36 compared to surface learning and gender was found insignificantly related to the preferred learning approach. This finding suggests that the use of deep approach (for example, active learning or student-centered learning) is to encourage better learning process that would contribute to better academic performance and teaching strategy practices among accounting students.
    Matched MeSH terms: Problem-Based Learning
  13. MUHAMMAD IQBAL NORDIN, NOOR HAFHIZAH ABD RAHIM
    MyJurnal
    Parser is aprocess of classifying sentence structuresof a language. Parser receives a sentence and breaks it up into correct phrases. The purpose of this research is to develop a Malay single sentence parser that can help primary school studentsto learn Malay language according to the correct phrases. Thisis because research in Malay sentenceparsinghasnot gottenenough attention from researchers tothe extent ofbuildingparserprototypes. This research used top-down parsing technique,and grammar chosen was context-free grammar (CFG) for Malay language. However, to parse a sentence with correct phrase was a difficult task due to lack of resourcesfor obtainingMalay lexicon. Malay lexicon is a database that storesthousands of words with their correct phrases. Therefore, this research developeda Malay lexicon based on an articlefrom Dewan Masyarakatmagazine. In conclusion, this research can providehelpto the primaryschoolstudentsto organize correct Malay single sentences.
    Matched MeSH terms: Learning
  14. Ohn MH, Souza U, Ohn KM
    Tzu Chi Med J, 2020 08 02;32(4):392-397.
    PMID: 33163387 DOI: 10.4103/tcmj.tcmj_91_19
    Objective: Negative affect state toward learning has a substantial impact on the learning process, academic performance, and practice of a particular subject, but such attitude toward electrocardiogram (ECG) learning has still received relatively little attention in medical education research. In spite of the significant emphasis in investigating ECG teaching method, the educators would not be able to address ECG incompetency without understanding the negative perception and attitude toward ECG learning. The purpose of this study was to assess the undergraduate students' difficulties in ECG learning and hence help educators design appropriate ECG learning curriculum to instill competent skill in ECG interpretation based on this outcome.

    Materials and Methods: A total of 324 undergraduate preclinical (year 2) and clinical (year 3-5) medical students participated in this study. The research design used thematic analysis of an open-ended questionnaire to analyze the qualitative data.

    Results: The thematic analysis detected five major emergent themes: lack of remembering (18.2%), lack of understanding (28.4%), difficulty in applying (3.6%), difficulty in analysis (15.1%), and difficulty in interpretation (17.8%), of which addressing these challenges could be taken as a foundation step upon which medical educators put an emphasis on in order to improve ECG teaching and learning.

    Conclusion: Negative attitude toward ECG learning poses a serious threat to acquire competency in ECG interpretation skill. The concept of student's memorizing ECG is not a correct approach; instead, understanding the concept and vector analysis is an elementary key for mastering ECG interpretation skill. The finding of this study sheds light into a better understanding of medical students' deficient points of ECG learning in parallel with taxonomy of cognitive domain and enables the medical teachers to come up with effective and innovative strategies for innovative ECG learning in an undergraduate medical curriculum.

    Matched MeSH terms: Learning
  15. Oktiansyah R, Juliandi B, Widayati KA, Juniantito V
    Trop Life Sci Res, 2018 Jul;29(2):1-11.
    PMID: 30112137 DOI: 10.21315/tlsr2018.29.2.1
    Neuronal cell death can occur in a tissue or organ, including the brain, which affects memory. The objectives of this study were to determine the dose of bee venom that causes neuronal death and analyse the alteration of mouse behaviour, focusing in particular on spatial memory. Fifteen male mice of Deutsche Denken Yoken (DDY) strain were divided into control and treatment groups. Bee venom was injected six times for two weeks intraperitoneally with 1.88 mg/kg, 3.76 mg/kg, 5.6 mg/kg, and 7.48 mg/kg doses of venom. Brain histology was studied using haematoxylin-eosin stained paraffin embedded 5 μm coronal sections. A Y maze test was used to assay behaviour. Parameters observed were the number of dead neurons and the percentage of mice with altered behaviour. ANOVA showed that the effects of bee venom were significantly different in the case of the neuronal death parameter but were not significantly different in the case of the mice behaviour parameter. Duncan's Multiple Range Test (DMRT) demonstrated that P4 (7.48 mg/kg) gave the highest effect of bee venom to promote neuronal death.
    Matched MeSH terms: Maze Learning
  16. Townsend D
    Trop Doct, 2001 Jan;31(1):8-10.
    PMID: 11205619
    Rapid participatory research and project development is possible within a tightly controlled social context such as a prison. Having gained access, based on trust and mutual respect, external agents may then facilitate significant change. Given adequate support, incarcerated people with HIV/AIDS and limited medical access may be able to develop mutual care, social support and income-generating activities. In the Malaysian context, we estimated in 1998 that up to one-quarter of prisoners with HIV had indicators of significant disease. We estimated that significant indicators remained unrevealed among between one-half and two-thirds of these. Given prevailing conditions, these would probably only be amenable to peer-based care.
    Matched MeSH terms: Learning
  17. Mani MS, Joshi MB, Shetty RR, DSouza VL, Swathi M, Kabekkodu SP, et al.
    Toxicol Lett, 2020 Dec 15;335:11-27.
    PMID: 32949623 DOI: 10.1016/j.toxlet.2020.09.010
    Lead is a toxin of great public health concern affecting the young and aging population. Several factors such as age, gender, lifestyle, dose, and genetic makeup result in interindividual variations to lead toxicity mainly due to variations in metabolic consequences. Hence, the present study aimed to examine dose-dependent lead-induced systemic changes in metabolism using rat model by administering specific doses of lead such as 10 (low lead; L-Pb), 50 (moderate lead; M-Pb), and 100 mg/kg (high lead; H-Pb) body weight for a period of one month. Biochemical and haematological analysis revealed that H-Pb was associated with low body weight and feed efficiency, low total protein levels (p ≤ 0.05), high blood lead (Pb-B) levels (p ≤ 0.001), low ALAD (δ-aminolevulinate dehydratase) activity (p ≤ 0.0001), high creatinine (p ≤ 0.0001) and blood urea nitrogen (BUN) (p ≤ 0.01) levels, elevated RBC and WBC counts, reduced haemoglobin and blood cell indices compared to control. Spatial learning and memory test revealed that H-Pb exposed animals presented high latency to the target quadrant and escape platform compared to other groups indicating H-Pb alters cognition function in rats. Histopathological changes were observed in liver and kidney as they are the main target organs of lead toxicity. LC-MS analysis further revealed that Butyryl-L-carnitine (p ≤ 0.01) and Ganglioside GD2 (d18:0/20:0) (p ≤ 0.05) levels were significantly reduced in H-Pb group compared to all groups. Further, pathway enrichment analysis revealed abundance and significantly modulated metabolites associated with oxidative stress pathways. The present study is the first in vivo model of dose-dependent lead exposure for serum metabolite profiling.
    Matched MeSH terms: Spatial Learning
  18. Kheirollahpour MM, Danaee MM, Merican AFAF, Shariff AAAA
    ScientificWorldJournal, 2020;2020:4194293.
    PMID: 32508538 DOI: 10.1155/2020/4194293
    The importance of eating behavior risk factors in the primary prevention of obesity has been established. Researchers mostly use the linear model to determine associations among these risk factors. However, in reality, the presence of nonlinearity among these factors causes a bias in the prediction models. The aim of this study was to explore the potential of a hybrid model to predict the eating behaviors. The hybrid model of structural equation modelling (SEM) and artificial neural networks (ANN) was applied to evaluate the prediction model. The SEM analysis was used to check the relationship of the emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) and their effect on different categories of eating behavior patterns (EBP). In the second step, the input and output required for ANN analysis were obtained from SEM analysis and were applied in the neural network model. 340 university students participated in this study. The hybrid model (SEM-ANN) was conducted using multilayer perceptron (MLP) with feed-forward network topology. Moreover, Levenberg-Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The tangent/sigmoid function was used for the input layer, while the linear function was applied for the output layer. The coefficient of determination (R2) and mean square error (MSE) were calculated. Using the hybrid model, the optimal network happened at MLP 3-17-8. It was proved that the hybrid model was superior to SEM methods because the R2 of the model was increased by 27%, while the MSE was decreased by 9.6%. Moreover, it was found that BSC, BAS, and EES significantly affected healthy and unhealthy eating behavior patterns. Thus, a hybrid approach could be suggested as a significant methodological contribution from a machine learning standpoint, and it can be implemented as software to predict models with the highest accuracy.
    Matched MeSH terms: Machine Learning
  19. Chan CYW, Chiu CK, Ch'ng PY, Lee SY, Chung WH, Hasan MS, et al.
    Spine J, 2021 07;21(7):1049-1058.
    PMID: 33610804 DOI: 10.1016/j.spinee.2021.02.009
    BACKGROUND CONTEXT: The implementation of a dual attending surgeon strategy had improved perioperative outcomes of idiopathic scoliosis (IS) patients. Nevertheless, the learning curve of a dual attending surgeon practice in single-staged posterior spinal fusion (PSF) surgery has not been established.

    OBJECTIVE: To evaluate the surgical learning curve of a dual attending surgeon strategy in IS patients.

    STUDY DESIGN: Retrospective study.

    PATIENT SAMPLE: 415 IS patients (Cobb angle <90°) who underwent PSF using a dual attending surgeon strategy OUTCOME MEASURES: Primary outcomes included operative time, total blood loss, allogenic blood transfusion requirement, length of hospital stay and perioperative complication rate.

    METHODS: Regression analysis using Locally Weighted Scatterplot Smoothing (LOWESS) method was applied to create the best-fit-curve between case number versus operative time and total blood loss in identifying cut-off points for the learning curve.

    RESULTS: The mean Cobb angle was 60.8±10.8°. Mean operative time was 134.4±32.1 minutes and mean total blood loss was 886.0±450.6 mL. The mean length of hospital stay was 3.0±1.6 days. The learning curves of a dual attending surgeon strategy in this study were established at the 115th case (operative time) and 196th case (total blood loss) respectively (p

    Matched MeSH terms: Learning Curve
  20. Barling PM, Ramasamy P
    Clin Teach, 2011 Mar;8(1):37-42.
    PMID: 21324071 DOI: 10.1111/j.1743-498X.2010.00419.x
    This paper presents our experience of running a special study module (SSM) in the second semester of the first year of our 5-year medical programme, worth 10 per cent of that semester's assessment, in which each student constructs an individually selected model illustrating a specific aspect of the teaching course.
    Matched MeSH terms: Learning*
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