MyMedR

Displaying publications 61 - 80 of 909 in total

Abstract:

Sort:

Fulltext EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features

Hag A, Handayani D, Pillai T, Mantoro T, Kit MH, Al-Shargie F

Sensors (Basel), 2021 Sep 20;21(18).
PMID: 34577505 DOI: 10.3390/s21186300

Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute or episodic stress. In this study, we developed an experimental protocol to induce two different levels of stress by utilizing a mental arithmetic task with time pressure and negative feedback as the stressors. We assessed the levels of stress on 22 healthy subjects using frontal electroencephalogram (EEG) signals, salivary alpha-amylase level (AAL), and multiple machine learning (ML) classifiers. The EEG signals were analyzed using a fusion of functional connectivity networks estimated by the Phase Locking Value (PLV) and temporal and spectral domain features. A total of 210 different features were extracted from all domains. Only the optimum multi-domain features were used for classification. We then quantified stress levels using statistical analysis and seven ML classifiers. Our result showed that the AAL level was significantly increased (p < 0.01) under stress condition in all subjects. Likewise, the functional connectivity network demonstrated a significant decrease under stress, p < 0.05. Moreover, we achieved the highest stress classification accuracy of 93.2% using the Support Vector Machine (SVM) classifier. Other classifiers produced relatively similar results.

Matched MeSH terms: Machine Learning
Fulltext Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review

Farook TH, Jamayet NB, Abdullah JY, Alam MK

Pain Res Manag, 2021;2021:6659133.
PMID: 33986900 DOI: 10.1155/2021/6659133

Purpose: The study explored the clinical influence, effectiveness, limitations, and human comparison outcomes of machine learning in diagnosing (1) dental diseases, (2) periodontal diseases, (3) trauma and neuralgias, (4) cysts and tumors, (5) glandular disorders, and (6) bone and temporomandibular joint as possible causes of dental and orofacial pain.
Method: Scopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29th October 2020. Articles were screened and narratively synthesized according to PRISMA-DTA guidelines based on predefined eligibility criteria. Articles that made direct reference test comparisons to human clinicians were evaluated using the MI-CLAIM checklist. The risk of bias was assessed by JBI-DTA critical appraisal, and certainty of the evidence was evaluated using the GRADE approach. Information regarding the quantification method of dental pain and disease, the conditional characteristics of both training and test data cohort in the machine learning, diagnostic outcomes, and diagnostic test comparisons with clinicians, where applicable, were extracted.
Results: 34 eligible articles were found for data synthesis, of which 8 articles made direct reference comparisons to human clinicians. 7 papers scored over 13 (out of the evaluated 15 points) in the MI-CLAIM approach with all papers scoring 5+ (out of 7) in JBI-DTA appraisals. GRADE approach revealed serious risks of bias and inconsistencies with most studies containing more positive cases than their true prevalence in order to facilitate machine learning. Patient-perceived symptoms and clinical history were generally found to be less reliable than radiographs or histology for training accurate machine learning models. A low agreement level between clinicians training the models was suggested to have a negative impact on the prediction accuracy. Reference comparisons found nonspecialized clinicians with less than 3 years of experience to be disadvantaged against trained models.
Conclusion: Machine learning in dental and orofacial healthcare has shown respectable results in diagnosing diseases with symptomatic pain and with improved future iterations and can be used as a diagnostic aid in the clinics. The current review did not internally analyze the machine learning models and their respective algorithms, nor consider the confounding variables and factors responsible for shaping the orofacial disorders responsible for eliciting pain.

Matched MeSH terms: Machine Learning/statistics & numerical data*
Fulltext Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry-A Systematic Review

Ahmed N, Abbasi MS, Zuberi F, Qamar W, Halim MSB, Maqsood A, et al.

Biomed Res Int, 2021;2021:9751564.
PMID: 34258283 DOI: 10.1155/2021/9751564

Objective: The objective of this systematic review was to investigate the quality and outcome of studies into artificial intelligence techniques, analysis, and effect in dentistry.
Materials and Methods: Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted.
Results: The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics.
Conclusion: The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.

Matched MeSH terms: Machine Learning
Fulltext Developing a bank of faculty-authored, valid and reliable objective questions for institutional use: sharing the experience

Rukhsana Hussain Malik, Alam Sher Malik

Malaysian Journal of Medicine and Health Sciences, 2020;16(107):28-35.
MyJurnal

Introduction: With the increasing number of institutions implementing competency-based education which demands to provide feedback to students at regular intervals, there is an increase in the frequency of assessments. For this purpose, the written examinations using multiple choice questions (MCQs) are the most feasible form of assessment. However, constructing MCQs is an arduous task and significantly adds to the work-load of the academ- ic staff members. To ease this burden, the institutions may consider to develop banks of valid and reliable MCQs. Methods: Based and built on our experience and literature review, the steps – relating to the process of constructing valid and reliable questions and development of question banks (QBs) – are the actions needed to develop new QBs or improve on the existing ones. Results: We have described ten practical steps for developing and banking of MCQs. The first five steps relate to the development of quality items and the remaining steps relate to the development of QBs, their maintenance, growth and safety and security. We have also established the criteria for selection and the frequency of reuse of questions. Conclusion: Using QBs will alleviate some of the burden of constructing novel quality questions needed for frequent assessments of students using 21st century teaching/learning approaches. The use of banked questions with known psychometric properties would allow the authorities to take charge and control of items’ quality and overall examination standards.

Matched MeSH terms: Learning
Long lead time drought forecasting using lagged climate variables and a stacked long short-term memory model

Dikshit A, Pradhan B, Alamri AM

Sci Total Environ, 2021 Feb 10;755(Pt 2):142638.
PMID: 33049536 DOI: 10.1016/j.scitotenv.2020.142638

Drought forecasting with a long lead time is essential for early warning systems and risk management strategies. The use of machine learning algorithms has been proven to be beneficial in forecasting droughts. However, forecasting at long lead times remains a challenge due to the effects of climate change and the complexities involved in drought assessment. The rise of deep learning techniques can solve this issue, and the present work aims to use a stacked long short-term memory (LSTM) architecture to forecast a commonly used drought measure, namely, the Standard Precipitation Evaporation Index. The model was then applied to the New South Wales region of Australia, with hydrometeorological and climatic variables as predictors. The multivariate interpolated grid of the Climatic Research Unit was used to compute the index at monthly scales, with meteorological variables as predictors. The architecture was trained using data from the period of 1901-2000 and tested on data from the period of 2001-2018. The results were then forecasted at lead times ranging from 1 month to 12 months. The forecasted results were analysed in terms of drought characteristics, such as drought intensity, drought onset, spatial extent and number of drought months, to elucidate how these characteristics improve the understanding of drought forecasting. The drought intensity forecasting capability of the model used two statistical metrics, namely, the coefficient of determination (R2) and root-mean-square error. The variation in the number of drought months was examined using the threat score technique. The results of this study showed that the stacked LSTM model can forecast effectively at short-term and long-term lead times. Such findings will be essential for government agencies and can be further tested to understand the forecasting capability of the presented architecture at shorter temporal scales, which can range from days to weeks.

Matched MeSH terms: Machine Learning
Attitude, knowledge and ethical perception toward precision medicine among junior and senior medical students: Findings from one Malaysian medical school

Koh, Kwee Choy, Shanmugan Goonasakaren, Ng, Lam Kean, Chua, Yi Lin, Lee, Jia Ying, Alaric Ding Tian Ang

International e-Journal of Science, Medicine & Education, 2017;11(3):10-19.
MyJurnal

Background: Medical schools are escalating changes
to meet the need for doctors competent to work in the
era of precision medicine. Information on the current
level of awareness of precision medicine among medical
students can help effect the necessary changes in the
medical curriculum. A cross-sectional comparative
study was done to assess the knowledge, attitude and
perception toward the practice of precision medicine
among junior and senior medical students in a medical
school in Malaysia.

Materials and Method: A survey instrument measuring
attitude toward precision medicine, perceived
knowledge of genomic testing concepts, and perception
toward ethical consideration related to precision
medicine, was distributed to junior and senior medical
students. Comparisons were made between senior and
junior medical students.

Results: Only about one-third of the 356 respondents
had heard of precision medicine although 92.7%
expressed interest to learn more about precision
medicine. Overall, junior and senior medical students
had positive attitude toward the adoption of genomeguided
prescribing and precision medicine but were
uncomfortable with their knowledge of genomic testing
concepts. Both junior and senior students were largely
well grounded in their understanding of ethical issues
related to precision medicine.

Conclusions: Knowledge of precision medicine was low
among junior and senior medical students. Although
the students supported the use of precision medicine,
they did not feel adequately prepared to apply genomics
to clinical practice. Their perceptions on ethical issues
related to precision medicine were sound. Seniority did
not appear to influence the perceptions of the students.

Matched MeSH terms: Learning
Fulltext A comparative analysis of reflection in a primary care outpatient setting in two learning environments

Abdul Rahman NF, Albualy R

Education in Medicine Journal, 2018;10(2):41-47.
MyJurnal

Situated learning characterises the learning that takes place in the clinical environment. Learning in the workplace is characterised by transferring classroom knowledge into performing tasks and this may take various forms. In the medical education field, the cognitive apprenticeship instructional model developed by Collins (2016) supported this learning in the workplace setting due to its common characteristics of apprenticeship. This paper analysed two concrete learning situations in a Malaysian undergraduate and an Omani postgraduate learning environment. Both learning situations occurred in the primary healthcare outpatient setting. The cognitive apprenticeship model was used to identify characteristics of the individual learning environments and discusses factors that stimulate learning. Attention was paid to the role of reflection in stimulating learning in the described settings. The paper provided the context in both institutes, described the learning situation and provided an analysis based on the theoretical framework. Based on the analysis of the situations, solutions to problems in the two settings were suggested.

Matched MeSH terms: Learning; Problem-Based Learning
Learning Psychodynamic Psychotherapy in Southeast Asia

Loo JL, Ang JK, Subhas N, Ho BK, Zakaria H, Alfonso CA

Psychodyn Psychiatry, 2017;45(1):45-57.
PMID: 28248565 DOI: 10.1521/pdps.2017.45.1.45

The subjective nature of psychodynamic psychotherapy (PP) makes training and supervision more abstract compared to other forms of psychotherapy. The issues encountered in the learning and supervision process of PP of Malaysian psychiatry trainees are discussed in this article. Issues of preparation before starting PP, case selection, assessment of patients, dynamic formulations, supervision, anxieties in the therapy, countertransference, termination of therapy, the treatment alliance, transfer of care, the therapeutic setting, and bioethical considerations are explored. Everyone's experience of learning PP is unique and there is no algorithmic approach to its practice. With creative thinking, effort, and "good enough" supervision, a trainee can improve PP skills, even in underserved areas of the world.

Matched MeSH terms: Learning
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
Problem-based learning (PBL): making an impact in education

Azila NMA, Sim SM, Tan CPL, Alhady SF

JUMMEC, 1999;4<I> </I>:94-98.

Problem-based learning (PBL) i s an educational reform that is now becoming a household word in higher education, particularly in medical schools. Many medical schools have implemented a full problem-based learning curriculum (PBLC) whiIe some have included PBL into selected units of the course in an otherwise conventional cumculum (embedded PBL) and others run their tutorials in a PBL manner within a modified conventional curriculum (hybrid curriculum). Yet there are others who claim that small components of PBL in a conventional curriculum are not PBL at all. Thus amateurs in the subject matter find difficulty in evaluating the logistics and outcome of these variations. This article focuses or, the general characteristics of PBL and how this learning method can help enhance independent learning and critical thinking, whether in a full, embedded or hybrid curriculum. The extent of PBL to be included and which of the three types is to be adopted depends on the objective of the undergraduate medical course as determined by the faculty, resources available, limitations, feedback on the existing curriculum and various other factors. KEYWORDS: Problem-based Learning (PBL); Embedded PBL; Hybrid PBL; New Integrated Curriculum (NIC).

Matched MeSH terms: Learning; Problem-Based Learning
Fulltext Extreme learning machine based optimal embedding location finder for image steganography

Atee HA, Ahmad R, Noor NM, Rahma AM, Aljeroudi Y

PLoS One, 2017;12(2):e0170329.
PMID: 28196080 DOI: 10.1371/journal.pone.0170329

In image steganography, determining the optimum location for embedding the secret message precisely with minimum distortion of the host medium remains a challenging issue. Yet, an effective approach for the selection of the best embedding location with least deformation is far from being achieved. To attain this goal, we propose a novel approach for image steganography with high-performance, where extreme learning machine (ELM) algorithm is modified to create a supervised mathematical model. This ELM is first trained on a part of an image or any host medium before being tested in the regression mode. This allowed us to choose the optimal location for embedding the message with best values of the predicted evaluation metrics. Contrast, homogeneity, and other texture features are used for training on a new metric. Furthermore, the developed ELM is exploited for counter over-fitting while training. The performance of the proposed steganography approach is evaluated by computing the correlation, structural similarity (SSIM) index, fusion matrices, and mean square error (MSE). The modified ELM is found to outperform the existing approaches in terms of imperceptibility. Excellent features of the experimental results demonstrate that the proposed steganographic approach is greatly proficient for preserving the visual information of an image. An improvement in the imperceptibility as much as 28% is achieved compared to the existing state of the art methods.

Matched MeSH terms: Machine Learning*
Fulltext A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things

Khan ZA, Naz S, Khan R, Teo J, Ghani A, Almaiah MA

Comput Intell Neurosci, 2022;2022:5112375.
PMID: 35449734 DOI: 10.1155/2022/5112375

Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). To resolve this issue, numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is used to decrease the size of the collected data and, thus, improve the performance of the underlined IoTs in terms of congestion control, data accuracy, and lifetime. However, these approaches do not consider neighborhood information of the devices (cluster head in this case) in the data refinement phase. In this paper, a smart and intelligent neighborhood-enabled data aggregation scheme is presented where every device (cluster head) is bounded to refine the collected data before sending it to the concerned server module. For this purpose, the proposed data aggregation scheme is divided into two phases: (i) identification of neighboring nodes, which is based on the MAC address and location, and (ii) data aggregation using k-mean clustering algorithm and Support Vector Machine (SVM). Furthermore, every CH is smart enough to compare data sets of neighboring nodes only; that is, data of nonneighbor is not compared at all. These algorithms were implemented in Network Simulator 2 (NS-2) and were evaluated in terms of various performance metrics, such as the ratio of data redundancy, lifetime, and energy efficiency. Simulation results have verified that the proposed scheme performance is better than the existing approaches.

Matched MeSH terms: Machine Learning
Fulltext Evaluating critical success factors in implementing E-learning system using multi-criteria decision-making

Naveed QN, Qureshi MRN, Tairan N, Mohammad A, Shaikh A, Alsayed AO, et al.

PLoS One, 2020;15(5):e0231465.
PMID: 32365123 DOI: 10.1371/journal.pone.0231465

Learning using the Internet or training through E-Learning is growing rapidly and is increasingly favored over the traditional methods of learning and teaching. This radical shift is directly linked to the revolution in digital computer technology. The revolution propelled by innovation in computer technology has widened the scope of E-Learning and teaching, whereby the process of exchanging information has been made simple, transparent, and effective. The E-Learning system depends on different success factors from diverse points of view such as system, support from the institution, instructor, and student. Thus, the effect of critical success factors (CSFs) on the E-Learning system must be critically analyzed to make it more effective and successful. This current paper employed the analytic hierarchy process (AHP) with group decision-making (GDM) and Fuzzy AHP (FAHP) to study the diversified factors from different dimensions of the web-based E-Learning system. The present paper quantified the CSFs along with its dimensions. Five different dimensions and 25 factors associated with the web-based E-Learning system were revealed through the literature review and were analyzed further. Furthermore, the influence of each factor was derived successfully. Knowing the impact of each E-Learning factor will help stakeholders to construct education policies, manage the E-Learning system, perform asset management, and keep pace with global changes in knowledge acquisition and management.

Matched MeSH terms: Learning/physiology*
Fulltext Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Hasan RI, Yusuf SM, Alzubaidi L

Plants (Basel), 2020 Oct 01;9(10).
PMID: 33019765 DOI: 10.3390/plants9101302

Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

Matched MeSH terms: Machine Learning
Evaluation of the neurotoxic effects of chronic embryonic exposure with inorganic mercury on motor and anxiety-like responses in zebrafish (Danio rerio) larvae

Abu Bakar N, Mohd Sata NS, Ramlan NF, Wan Ibrahim WN, Zulkifli SZ, Che Abdullah CA, et al.

Neurotoxicol Teratol, 2017 Jan-Feb;59:53-61.
PMID: 27919701 DOI: 10.1016/j.ntt.2016.11.008

Chronic exposure to mercury (Hg) can lead to cumulative impairments in motor and cognitive functions including alteration in anxiety responses. Although several risk factors have been identified in recent year, little is known about the environmental factors that either due exposure toward low level of inorganic mercury that may led to the developmental disorders. The present study investigated the effects of embryonic exposure of mercury chloride on motor function and anxiety-like behavior. The embryo exposed to 6 different concentrations of HgCl2 (7.5, 15, 30, 100, 125, 250nM) at 5hpf until hatching (72hpf) in a semi-static condition. The mortality rate increased in a dose dependent manner where the chronic embryonic exposure to 100nM decreased the number of tail coiling, heartbeat, and swimming activity. Aversive stimulus was used to examine the effects of 100nM interferes with the development of anxiety-related behavior. No elevation in both thigmotaxis and avoidance response of 6dpf larvae exposed with 100nM were found. Biochemical analysis showed HgCl2 exposure affects proteins, lipids, carbohydrates and nucleic acids of the zebrafish larvae. These results showed that implication of HgCl2 on locomotor and biochemical defects affects motor performance and anxiety-like responses. Yet, the potential underlying mechanisms these responses need to be further investigated which is crucial to prevent potential hazards on the developing organism due to neurotoxicant exposure.

Matched MeSH terms: Avoidance Learning/drug effects*
Fulltext Knowledge-Based Methods To Train and Optimize Virtual Screening Ensembles

Swift RV, Jusoh SA, Offutt TL, Li ES, Amaro RE

J Chem Inf Model, 2016 05 23;56(5):830-42.
PMID: 27097522 DOI: 10.1021/acs.jcim.5b00684

Ensemble docking can be a successful virtual screening technique that addresses the innate conformational heterogeneity of macromolecular drug targets. Yet, lacking a method to identify a subset of conformational states that effectively segregates active and inactive small molecules, ensemble docking may result in the recommendation of a large number of false positives. Here, three knowledge-based methods that construct structural ensembles for virtual screening are presented. Each method selects ensembles by optimizing an objective function calculated using the receiver operating characteristic (ROC) curve: either the area under the ROC curve (AUC) or a ROC enrichment factor (EF). As the number of receptor conformations, N, becomes large, the methods differ in their asymptotic scaling. Given a set of small molecules with known activities and a collection of target conformations, the most resource intense method is guaranteed to find the optimal ensemble but scales as O(2(N)). A recursive approximation to the optimal solution scales as O(N(2)), and a more severe approximation leads to a faster method that scales linearly, O(N). The techniques are generally applicable to any system, and we demonstrate their effectiveness on the androgen nuclear hormone receptor (AR), cyclin-dependent kinase 2 (CDK2), and the peroxisome proliferator-activated receptor δ (PPAR-δ) drug targets. Conformations that consisted of a crystal structure and molecular dynamics simulation cluster centroids were used to form AR and CDK2 ensembles. Multiple available crystal structures were used to form PPAR-δ ensembles. For each target, we show that the three methods perform similarly to one another on both the training and test sets.

Matched MeSH terms: Machine Learning*
Fulltext Identification of Planck's constant by light emitting diodes (LEDS)

Nurain Azmi, Sabirin Mustafa, Nur Hazirah Mohd Yunos, Wan Nor Azlin Wan Mohd Sakri, Muhammad Nazzim Abdul Halim, Amin Aadenan

Journal of Academia Universiti Teknologi MARA Negeri Sembilan, 2020;8(2):33-38.
MyJurnal

In this paper, a simple analysis yet a straight forward method of determining the Planck’s constant by
evaluating the stopping potential of five different colors of light emitting diodes (LEDs) is presented.
The study aimed to identify the Planck’s constant based on the relationship between the potential
difference of LEDs to their respective frequencies under room temperature with low illumination of
ambient light by applying a simple theoretical analysis. The experiment was performed by connecting
the circuit in series connection and the voltage reading of LEDs were recorded and then presented in a
graph of frequency, f versus stopping voltage, Vo. To determine the Planck’s constant, the best fit line
was analyzed and the centroid was also identified in order to find the minimum and maximum errors
due the gradient of the graph. From the analysis, results showed that the Planck constant value was
(5.997 ± 1.520) × 10–34 J.s with approximately 10% of deviation from the actual value. This
demonstrates that a simple analysis can be utilized to determine the Planck’s constant for the purpose
of the laboratory teaching and learning at the undergraduate level and can be served as a starting point
for the students to understand the concept of quantization of energy in Modern Physics more
effectively. This is to further suggest that the Planck’s constant can be identified via a low-cost and
unsophisticated experimental setup.

Matched MeSH terms: Learning
Fulltext Augmented Reality Interface for Complex Anatomy Learning in the Central Nervous System: A Systematic Review

Wirza R, Nazir S, Khan HU, García-Magariño I, Amin R

J Healthc Eng, 2020;2020:8835544.
PMID: 32963749 DOI: 10.1155/2020/8835544

The medical system is facing the transformations with augmentation in the use of medical information systems, electronic records, smart, wearable devices, and handheld. The central nervous system function is to control the activities of the mind and the human body. Modern speedy development in medical and computational growth in the field of the central nervous system enables practitioners and researchers to extract and visualize insight from these systems. The function of augmented reality is to incorporate virtual and real objects, interactively running in a real-time and real environment. The role of augmented reality in the central nervous system becomes a thought-provoking task. Gesture interaction approach-based augmented reality in the central nervous system has enormous impending for reducing the care cost, quality refining of care, and waste and error reducing. To make this process smooth, it would be effective to present a comprehensive study report of the available state-of-the-art-work for enabling doctors and practitioners to easily use it in the decision making process. This comprehensive study will finally summarise the outputs of the published materials associate to gesture interaction-based augmented reality approach in the central nervous system. This research uses the protocol of systematic literature which systematically collects, analyses, and derives facts from the collected papers. The data collected range from the published materials for 10 years. 78 papers were selected and included papers based on the predefined inclusion, exclusion, and quality criteria. The study supports to identify the studies related to augmented reality in the nervous system, application of augmented reality in the nervous system, technique of augmented reality in the nervous system, and the gesture interaction approaches in the nervous system. The derivations from the studies show that there is certain amount of rise-up in yearly wise articles, and numerous studies exist, related to augmented reality and gestures interaction approaches to different systems of the human body, specifically to the nervous system. This research organises and summarises the existing associated work, which is in the form of published materials, and are related to augmented reality. This research will help the practitioners and researchers to sight most of the existing studies subjected to augmented reality-based gestures interaction approaches for the nervous system and then can eventually be followed as support in future for complex anatomy learning.

Matched MeSH terms: Machine Learning; Learning*
Lingzhi or Reishi Medicinal Mushroom, Ganoderma lucidum (Agaricomycetes) Ameliorates Spatial Learning and Memory Deficits in Rats with Hypercholesterolemia and Alzheimer's Disease

Rahman MA, Hossain S, Abdullah N, Aminudin N

Int J Med Mushrooms, 2020;22(1):93-103.
PMID: 32464001 DOI: 10.1615/IntJMedMushrooms.2020033383

Hypercholesterolemia has been implicated as one of the pathomechanistic factors of Alzheimer's disease (AD), the most common neurodegenerative disorder affecting memory and learning abilities. In the present study, ameliorative effect of hot water extract (HWE) of mushroom Ganoderma lucidum to the memory and learning related behavioral performance of hypercholesterolemic and AD rats was investigated using Morris water maze (MWM). Male Wistar rats were randomly grouped into control, extract fed control, hypercholesterolemic, extract fed hypercholesterolemic, AD, and extract fed AD groups, each group containing 8 animals. Hypercholesterolemia was induced in rats by adding 1% cholesterol and 1% cholic acid with the basal diet of the respective group. Alzheimer's disease model rats were prepared through infusion of amyloid β(1-42) to the right ventricle. Memory and learning related performance of all the rats was tested for 6 consecutive days that included time taken to reach the submerged platform (sec) and distance traveled (m). G. lucidum HWE fed rats took less time and traveled less distance to find the submerged platform, which indicates the spatial learning and memory related behavioral amelioration of the extract fed rats compared with their non-fed counterparts. Thus, usage of G. lucidum seems promising in withstanding hypercholesterolemia-induced Alzheimer's disease pathogenesis.

Matched MeSH terms: Spatial Learning*
Lingzhi or Reishi Medicinal Mushroom, Ganoderma lucidum (Agaricomycetes), Ameliorates Nonspatial Learning and Memory Deficits in Rats with Hypercholesterolemia and Alzheimer's Disease

Rahman MA, Hossain S, Abdullah N, Aminudin N

Int J Med Mushrooms, 2020;22(11):1067-1078.
PMID: 33426838 DOI: 10.1615/IntJMedMushrooms.2020036354

Alzheimer's disease (AD) is the leading neurodegenerative disorder affecting memory and learning of aged people. Hypercholesterolemia had been implicated as one of the stark hallmarks of AD. Recent AD control guidelines have suggested lifestyle modification to slow down the progression of AD. In this regard, medicinal mushroom Ganoderma lucidum seems apt. In the present study, hot water extract of G. lucidum (200 mg/kg body weight) was fed to the hypercholesterolemic and AD model rats for 8 weeks. Nonspatial memory and learning abilities of the model animals was assessed using novel object recognition (NOR) test, rotarod test, and locomotor/open-field test. Then, the animals were sacrificed and transmission electron micrograph (TEM) view of the hippocampal neurons was assessed. In all the nonspatial memory and learning tests, the G. lucidum HWE fed rats performed better indicating improved memory and learning abilities. TEM view showed regular arrangement of the neurons in the G. lucidum HWE fed rats compared with those of the deranged arrangement of the AD rats. G. lucidum might have aided in restoring the memory and learning abilities of the AD model animals through maintaining neuronal structure and function. Thus, G. lucidum could be suggested as a medicotherapeutic agent against AD.

Matched MeSH terms: Learning/drug effects

Filters

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links