METHOD: A retrospective record review study using positive COVID-19 cases and contact-tracing data from an area in Malaysia was performed and analysed using the SNA method through R software and visualised by Gephi software. The justification for utilizing SNA is its capability to pinpoint the individuals with the highest impact and accountability for the transmission of COVID-19 within the area, as determined through SNA.
RESULT: Analysis revealed 76 (4.5%) people tested positive for COVID-19 from 1,683 people, with 51 (67.1%) of the positive ones being male. Outdegrees for 38 positive people were between 1 and 12, while 41 people had 1-13 indegree. Older males have a higher outdegree, while younger females have a higher outdegree than other age groups among same-sex groups. Betweenness was between 0.09 and 34.5 for 15 people. We identified 15 people as super-spreaders from the 42 communities detected.
CONCLUSION: Women play a major role in bridging COVID-19 transmission, while older men may transmit COVID-19 through direct connections. Thus, health education on face mask usage and hand hygiene is important for both groups. Working women should be given priority for the work-from-home policy compared to others. A large gathering should not be allowed to operate, or if needed, with strict adherence to specific standard operating procedures, as it contributes to the spread of COVID-19 in the district. The SNA allows the identification of key personnel within the network. Therefore, SNA can help healthcare authorities recognise evolving clusters and identify potential super-spreaders; hence, precise and timely action can be taken to prevent further spread of the disease.
METHODS: We used pre-COVID-19 pulmonary tuberculosis (PTB) data (2007-2018) to fit SARIMA, Prophet, and LSTM models, assessing their ability to predict PTB incidence trends. These models were then applied to compare the predicted PTB incidence patterns with actual reported cases during the COVID-19 pandemic (2020-2023), using deviations between predicted and actual values to reflect the impact of COVID-19 countermeasures on PTB incidence.
RESULTS: Prior to the COVID-19 outbreak, PTB incidence in China exhibited a steady decline with strong seasonal fluctuations, characterized by two annual peaks-one in March and another in December. These seasonal trends persisted until 2019. During the COVID-19 pandemic, there was a significant reduction in PTB cases, with actual reported cases falling below the predicted values. The disruption in PTB incidence appears to be temporary, as 2023 data indicate a gradual return to pre-pandemic trends, though the incidence rate remains slightly lower than pre-COVID levels. Additionally, we compared the fitting and forecasting performance of the SARIMA, Prophet, and LSTM models using RMSE (root mean squared error), MAE (mean absolute error), and MAPE (mean absolute percentage error) indexes prior to the COVID-19 outbreak. We found that the Prophet model had the lowest values for all three indexes, demonstrating the best fitting and prediction performance.
CONCLUSIONS: The COVID-19 pandemic has had a temporary but significant impact on PTB incidence in China, leading to a reduction in reported cases during the pandemic. However, as pandemic control measures relax and the healthcare system stabilizes, PTB incidence patterns are expected to return to pre-COVID-19 levels. The Prophet model demonstrated the best predictive performance and proves to be a valuable tool for analyzing PTB trends and guiding public health planning in the post-pandemic era.
METHODS: Guided by the PRISMA framework, we conducted a rigorous search through the PubMed, Web of Science and Scopus databases, analyzing 254 articles. Each article was scrutinized against pre-defined inclusion criteria, yielding a refined selection of 14 studies worthy of in-depth analysis.
RESULTS: The trends in using morphological approaches were identified for analyzing osteoblast and osteoclast differentiation. The three most used techniques for osteoblasts were Alizarin Red S (mineralization; six articles), von Kossa (mineralization; three articles) and alkaline phosphatase (ALP; two articles) followed by one article on Giemsa staining (cell morphology) and finally immunochemistry (three articles involved Vinculin, F-actin and Col1 biomarkers). For osteoclasts, tartrate-resistant acid phosphatase (TRAP staining) has the highest number of articles (six articles), followed by two articles on DAPI staining (cell morphology), and immunochemistry (two articles with VNR, Cathepsin K and TROP2. The study involved four stem cell types: peripheral blood monocyte, mesenchymal, dental pulp, and periodontal ligament.
CONCLUSION: This review offers a valuable resource for researchers, with Alizarin Red S and TRAP staining being the most utilized morphological procedures for osteoblasts and osteoclasts, respectively. This understanding provides a foundation for future research in this rapidly changing field.
METHODOLOGY: A systematic search was conducted across Web of Science, PubMed, and Google Scholar. Studies were selected based on strict inclusion and exclusion criteria: peer-reviewed; published between 2000 and 2024 (in English); focused on adults; investigated mind-reading (mental state decoding, brain-computer interfaces) or related processes; and employed various mind-reading techniques (pattern classification, multivariate analysis, decoding algorithms).
RESULTS: This review highlights the critical role of fMRI in uncovering the neural mechanisms of mind-reading. Key brain regions involved include the superior temporal sulcus (STS), medial prefrontal cortex (mPFC), and temporoparietal junction (TPJ), all crucial for mentalizing (understanding others' mental states).
CONCLUSIONS: This review emphasizes the importance of fMRI in advancing our knowledge of how the brain interprets and processes mental states. It offers valuable insights into the current state of mind-reading research in adults and paves the way for future exploration in this field.
METHOD: Three databases were searched, namely PubMed, Web of Science, and Scopus. The Arksey and O'Malley (2005) framework was used as a guide in conducting this scoping review. The reporting was carried out based on the Preferred Reporting Items for Systematic Reviews and the Meta-Analyses Extension for Scoping Reviews (PRISMA). The literature search retrieved 552 articles and 29 articles were included in the final review.
RESULTS: As high as 83% of the 29 included studies followed an observational study design while the rest were experimental animal studies. Among the observational studies, two-thirds (66%) were cross-sectional studies while the rest were case-control studies (31%) and cohort studies (n = 1, 3%). Few number of studies in this review reported a significant association between Cr, As, Cd, Hg, and Pb with noncancerous thyroid diseases (2, 3, 16, 8, and 12) while another few (5, 8, 9, 5, and 11) did not show any significant association.
CONCLUSION: A heterogeneous and diverse sample population in the included studies could have potentially led to mixed findings about the association between toxic heavy metals and thyroid diseases in this review. Therefore, future research should prioritize longitudinal studies and controlled clinical trials to better elucidate the causative mechanisms and long-term impact of heavy metal exposure on thyroid health.
OBJECTIVE: This study aims to conduct a comprehensive analysis of machine learning (ML) methods for MRI-based biomarker selection and classification to investigate early cognitive decline in AD. The focus to discriminate between classifying healthy control (HC) participants who remained stable and those who developed mild cognitive impairment (MCI) within five years (unstable HC or uHC).
METHODS: 3-Tesla (3T) MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies 3 (OASIS-3) were used, focusing on HC and uHC groups. Freesurfer's recon-all and other tools were used to extract anatomical biomarkers from subcortical and cortical brain regions. ML techniques were applied for feature selection and classification, using the MATLAB Classification Learner (MCL) app for initial analysis, followed by advanced methods such as nested cross-validation and Bayesian optimization, which were evaluated within a Monte Carlo replication analysis as implemented in our customized pipeline. Additionally, polynomial regression-based data harmonization techniques were used to enhance ML and statistical analysis. In our study, ML classifiers were evaluated using performance metrics such as Accuracy (Acc), area under the receiver operating characteristic curve (AROC), F1-score, and a normalized Matthew's correlation coefficient (MCC').
RESULTS: Feature selection consistently identified biomarkers across ADNI and OASIS-3, with the entorhinal, hippocampus, lateral ventricle, and lateral orbitofrontal regions being the most affected. Classification results varied between balanced and imbalanced datasets and between ADNI and OASIS-3. For ADNI balanced datasets, the naíve Bayes model using z-score harmonization and ReliefF feature selection performed best (Acc = 69.17%, AROC = 77.73%, F1 = 69.21%, MCC' = 69.28%). For OASIS-3 balanced datasets, SVM with zscore-corrected data outperformed others (Acc = 66.58%, AROC = 72.01%, MCC' = 66.78%), while logistic regression had the best F1-score (66.68%). In imbalanced data, RUSBoost showed the strongest overall performance on ADNI (F1 = 50.60%, AROC = 81.54%) and OASIS-3 (MCC' = 63.31%). Support vector machine (SVM) excelled on ADNI in terms of Acc (82.93%) and MCC' (70.21%), while naïve Bayes performed best on OASIS-3 by F1 (42.54%) and AROC (70.33%).
CONCLUSION: Data harmonization significantly improved the consistency and performance of feature selection and ML classification, with z-score harmonization yielding the best results. This study also highlights the importance of nested cross-validation (CV) to control overfitting and the potential of a semi-automatic pipeline for early AD detection using MRI, with future applications integrating other neuroimaging data to enhance prediction.
METHODOLOGY: A triethylene glycol dimethacrylate (TEGDMA) and urethane dimethacrylate (UDMA)-based experimental resin infiltrate was prepared. Initial mixing was done manually for 1 h at room temperature, followed by another mix for 30 min on a magnetic stirrer. This prepared resin, called "PURE RESIN" was then further incorporated with three different types of bioactive glasses, i.e., Bioglass (45S5), boron-substituted (B-BG), and fluoride-substituted (F-BG). Initial manual mixing for 1 h, followed by ultrasonic mixing for 3 min and then proceeded for the final mixing on a magnetic stirrer for 24 h in a dark room at ambient temperature. Human-extracted teeth were demineralized, and the experimental resins were infiltrated on the demineralized surface. The surface area, pore size, and volume of the demineralized surface were measured. The microleakage and penetration depth were analyzed with the stereomicroscope and micro-CT, respectively. The samples were challenged with the pH cycle for 14 days, followed by a scanning electron microscope (SEM). Thermocycling (5,000 cycles) and chemical aging (4 weeks) were conducted, followed by microhardness, surface roughness, and SEM analyses. Statistical analyses were conducted after each test.
RESULTS: The F-BG group achieved the highest initial and day 14 penetration coefficients. There was a superior dye penetration with the microleakage analysis in the F-BG group. The 45S5 group had the highest average penetration depth via micro-CT analysis. After thermocycling and chemical aging, the micro-hardness was reduced (non-significantly) among all samples except the F-BG group in post-chemical aging analysis, whereas the surface roughness was significantly increased. SEM images showed the presence of micro-pits on the surfaces after the thermal and chemical aging.
CONCLUSION: The F-BG group achieved the highest initial and day 14 penetration coefficients. There was a superior dye penetration with the microleakage analysis in the F-BG group. The 45S5 group had the highest average penetration depth via micro-CT analysis. After thermocycling and chemical aging, the micro-hardness was reduced (non-significantly) among all samples except the F-BG group in post-chemical aging analysis, whereas the surface roughness was significantly increased. SEM images showed the presence of micro-pits on the surfaces after the thermal and chemical aging.
METHOD: Twenty-two healthy adult participants walked along an indoor walkway whilst eight video cameras recorded their gait in either tight- or loose-fitting clothing. A commercial markerless motion capture system (Theia3D) provided gait kinematics for evaluation.
RESULTS: Reliability results showed average inter-trial variation of <2°, inter-session variation of <3° and inter-session-clothing variation <3.5°. Root mean square differences (RMSD) between clothing conditions were <2°.
DISCUSSION: Pelvis variations were smaller than those at the hip, knee and ankle. Our results showed smaller variation than in previous studies which may be due to updates to software. The demonstration of the reliability of markerless motion capture for gait analysis in healthy adults should prompt further evaluation in clinical conditions and reconsideration of multi-assessor marker-based gait analysis protocols, where variation is highest.
METHODS: The frequency and length of primary cilia were determined in OKF6-TERT2 cells, HSC-2 cells, and HSC-3 cells using immunofluorescence. Additionally, primary cilia presence in non-proliferating OSCC cells was examined. OSCC cells were treated with either small interfering RNA (siRNA) negative control or siRNA targeting IFT20 for functional analysis. mRNA expression levels of IFT20 and MMP-9 were quantified using quantitative reverse transcription polymerase chain reaction (qRT-PCR).
RESULTS: Results showed that HSC-2 cells exhibit abundant primary cilia when cultured in low serum media (2% serum) for 48 h, followed by serum starvation for over 72 h. No significant changes in cilia expression were observed in HSC-3 cells compared to OKF6-TERT2 cells. Ciliated cells were found in non-proliferating HSC-2 and HSC-3 cells. OSCC cells showed longer cilia than OKF6-TERT2 cells, indicating ciliary abnormalities. Changes in ciliation and cilium length of OSCC cells were accompanied by increased expression of IFT20, an intraflagellar transport protein crucial for the primary cilia assembly. However, IFT20 knockdown did not affect MMP-9 at the mRNA level in these cells.
CONCLUSIONS: This study reveals the differences in primary cilia expression among OSCC cells. Furthermore, the increased abundance and elongation of primary cilia in OSCC cells are accompanied by elevated expression of IFT20. Nonetheless, IFT20 did not affect MMP-9 mRNA expression in OSCC cells.
METHODS: The search was conducted in accordance with the PRISMA guidelines and utilized the following databases: Scopus, Web of Science, ProQuest, and Google Scholar. Inclusion and exclusion criteria: population, research methods, keywords, and time limit were described for this study. This article predominantly includes cross-sectional studies, so we have used the AXIS risk assessment methodology.
RESULTS: The study included ten articles, seven of which (70%) were quantitative. Three key findings emerged from this review: first, the studies on self-efficacy were more noteworthy than the studies on burnout. Second, female teachers were more expressive in their digital teaching, while male teachers had higher levels of self-efficacy in their digital teaching. Finally, the study explored various factors affecting self-efficacy and burnout in relation to digital teaching. The study demonstrated that professional development has a higher impact on physical education teachers' self-efficacy, and in turn, self-efficacy reduces burnout. Additionally, burnout had a significant impact on professional development.
CONCLUSION: This study describes the limitations of risk assessment and uses the AXIS tool to assess the methodological quality of this review report instead of using the risk of bias tool. The use of digital teaching methods can increase self-efficacy and alleviate burnout among physical education teachers. This review analyses the effects of digital technology, self-efficacy, and burnout on the career progression of physical education instructors and examines the implications for future developments.
METHODS: This cross-sectional study involved 271 stroke survivors and was conducted at the Department of Neurology, Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, China, from September 2023 to January 2024. Participants independently completed the Fatigue Severity Scale (FSS), Patient Health Questionnaire-9 (PHQ-9), and the Short Version of the Stroke-Specific Quality of Life Scale (SV-SS-QoL) as part of a convenience sampling method, while medical professionals assessed the Barthel Index (BI) using the same sampling framework. Multivariable linear regression analyses were employed to determine the factors associated with the persistence of PSF.
RESULTS: The mean FSS score was 35.04 ± 11.60, while the average score for the SV-SS-QoL was 34.28 ± 9.51, and the BI score averaged 77.79 ± 25.90. Approximately 45.8% of participants (n = 124) experienced PSF. The mean score on the PHQ-9 was 7.63 ± 6.13. A significant negative correlation was identified between fatigue and both QoL and ADLs (P
METHODS: This ecological cross-sectional study utilised a geographic information system (GIS) and remote sensing techniques to analyse the spatiotemporal distribution of leptospirosis in Selangor from 2011 to 2019. Laboratory-confirmed leptospirosis cases (n = 1,045) were obtained from the Selangor State Health Department. Using ArcGIS Pro, spatial autocorrelation analysis (Moran's I) and Getis-Ord Gi* (hotspot analysis) was conducted to identify hotspots based on the monthly aggregated cases for each subdistrict. Satellite-derived rainfall and land surface temperature (LST) data were acquired from NASA's Giovanni EarthData website and processed into monthly averages. These data were integrated into ArcGIS Pro as thematic layers. Machine learning algorithms, including support vector machine (SVM), Random Forest (RF), and light gradient boosting machine (LGBM) were employed to develop predictive models for leptospirosis hotspot areas. Model performance was then evaluated using cross-validation and metrics such as accuracy, precision, sensitivity, and F1-score.
RESULTS: Moran's I analysis revealed a primarily random distribution of cases across Selangor, with only 20 out of 103 observed having a clustered distribution. Meanwhile, hotspot areas were mainly scattered in subdistricts throughout Selangor with clustering in the central region. Machine learning analysis revealed that the LGBM algorithm had the best performance scores compared to having a cross-validation score of 0.61, a precision score of 0.16, and an F1-score of 0.23. The feature importance score indicated river water level and rainfall contributes most to the model.
CONCLUSIONS: This GIS-based study identified a primarily sporadic occurrence of leptospirosis in Selangor with minimal spatial clustering. The LGBM algorithm effectively predicted leptospirosis hotspots based on the analysed hydroclimatic factors. The integration of GIS and machine learning offers a promising framework for disease surveillance, facilitating targeted public health interventions in areas at high risk for leptospirosis.