Higher Scores Research Articles

Association Between Uveal Melanoma and Allostatic Load.

Allostatic load (AL) is a measure of chronic stress that is associated with worse cancer outcomes. The purpose of this retrospective cohort study was to investigate the relationship between AL and uveal melanoma (UM) clinical features. AL score was calculated as a composite of ten biomarkers in 111 patients with UM from the University of Illinois Hospital. One point was assigned to an AL score for each biomarker based on predetermined cutoff values. Linear and logistic regression analyses evaluated the relationship between AL score and several tumor clinical characteristics. High AL score had a significant relationship with extraocular extension (p=0.015). There was also a significant difference in mean blood glucose levels between the different tumor size groups (p=0.029). Higher AL scores also had a trend of being associated with a smaller tumor size (p=0.069). AL score was significantly associated with the presence of extraocular extension for uveal melanoma, while the smallest tumor size group was associated with the highest blood glucose level. No other significant correlations were found between AL and other clinical features of UM. The relationship between AL score and extraocular extension warrants further investigation. Additional research is needed to evaluate socioeconomic factors and their effect on the relationship between chronic stress and the clinical features of UM.

Expanding molecular diagnostic coverage for tuberculosis by combining computer-aided chest radiography and sputum specimen pooling: a modeling study from four high-burden countries

BackgroundIn 2022, fewer than half of persons with tuberculosis (TB) had access to molecular diagnostic tests for TB due to their high costs. Studies have found that the use of artificial intelligence (AI) software for chest X-ray (CXR) interpretation and sputum specimen pooling can each reduce the cost of testing. We modeled the combination of both strategies to estimate potential savings in consumables that could be used to expand access to molecular diagnostics.MethodsWe obtained Xpert testing and positivity data segmented into deciles by AI probability scores for TB from the community- and healthcare facility-based active case finding conducted in Bangladesh, Nigeria, Viet Nam, and Zambia. AI scores in the model were based on CAD4TB version 7 (Zambia) and qXR (all other countries). We modeled four ordinal screening and testing approaches involving AI-aided CXR interpretation to indicate individual and pooled testing. Setting a false negative rate of 5%, for each approach we calculated additional and cumulative savings over the baseline of universal Xpert testing, as well as the theoretical expansion in diagnostic coverage.ResultsIn each country, the optimal screening and testing approach was to use AI to rule out testing in deciles with low AI scores and to guide pooled vs individual testing in persons with moderate and high AI scores, respectively. This approach yielded cumulative savings in Xpert tests over baseline ranging from 50.8% in Zambia to 57.5% in Nigeria and 61.5% in Bangladesh and Viet Nam. Using these savings, diagnostic coverage theoretically could be expanded by 34% to 160% across the different approaches and countries.ConclusionsUsing AI software data generated during CXR interpretation to inform a differentiated pooled testing strategy may optimize TB diagnostic test use, and could extend molecular tests to more people who need them. The optimal AI thresholds and pooled testing strategy varied across countries, which suggests that bespoke screening and testing approaches may be needed for differing populations and settings.

Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) Score As a Predictor of Prognosis After Hepatectomy.

The hemoglobin, albumin, lymphocyte, and platelet (HALP) score is an immune-nutritional assessment score that is a prognostic indicator for several malignant tumors. This study aimed to investigate its prognostic value in patients who underwent hepatectomy for hepatocellular carcinoma. Data of 685 patients with hepatocellular carcinoma who underwent hepatectomy at Kurume University between 2006 and 2021 were retrospectively analyzed. The patients were classified into high and low HALP score groups based on a cut-off HALP score determined using a receiver operating characteristic curve. To minimize bias, 1:1 propensity score matching was performed. Kaplan-Meier curves were used to estimate survival time, and data were evaluated using the log-rank test. Univariate and multivariate analyses were performed using Cox hazard or logistic regression models for assessing survival time and postoperative outcomes, respectively. Low HALP scores were significantly associated with poor overall survival (p=0.0066). Univariate and multivariate analyses revealed that the HALP score independently predicted overall survival (p=0.005). However, the HALP score was not significantly related to recurrence-free survival or postoperative outcomes. The HALP score is a simple inexpensive tool for predicting prognosis after hepatectomy for hepatocellular carcinoma.

Intensive Care Unit Sleep Promotion Bundle: Impact on Sleep Quality, Delirium, and Other Patient Outcomes.

High-quality sleep is important for optimal patient recovery. Sleep deprivation during hospitalization may lead to poor patient outcomes. To examine whether implementation of a sleep promotion bundle in the intensive care unit affects rates of delirium and agitation, restraint use, and length of stay. An evidence-based sleep promotion bundle was developed and implemented in 2 intensive care units in a 1025-bed level I trauma teaching hospital. Deidentified data from the electronic health record were obtained for patients hospitalized before and during the intervention. Data included scores on the Confusion Assessment Method for the Intensive Care Unit, Richmond Agitation-Sedation Scale, and Glasgow Coma Scale; restraint use; and hospital and intensive care unit length of stay. A total of 137 patients during the preintervention period and 149 patients during the intervention period were hospitalized in the intensive care units and met inclusion criteria. A 9-percentage-point decrease in the incidence of delirium from before to during the intervention was found, although it was not statistically significant (P = .07). Significant reductions were found in both intensive care unit (P = .04) and hospital (P = .03) length of stay. A significant decrease was found in Richmond Agitation-Sedation Scale high scores for patients requiring mechanical ventilation (P = .03). No significant differences were found in Richmond Agitation-Sedation Scale low scores, Glasgow Coma Scale scores, or restraint use. Critical care nurses are in an optimal position to implement evidence-based sleep promotion measures. Further research on sleep promotion bundles is needed.

An attribution graph-based interpretable method for CNNs

Convolutional Neural Networks (CNNs) have demonstrated outstanding performance in various domains, such as face recognition, object detection, and image segmentation. However, the lack of transparency and limited interpretability inherent in CNNs pose challenges in fields such as medical diagnosis, autonomous driving, finance, and military applications. Several studies have explored the interpretability of CNNs and proposed various post-hoc interpretable methods. The majority of these methods are feature-based, focusing on the influence of input variables on outputs. Few methods undertake the analysis of parameters in CNNs and their overall structure. To explore the structure of CNNs and intuitively comprehend the role of their internal parameters, we propose an Attribution Graph-based Interpretable method for CNNs (AGIC) which models the overall structure of CNNs as graphs and provides interpretability from global and local perspectives. The runtime parameters of CNNs and feature maps of each image sample are applied to construct attribution graphs (At-GCs), where the convolutional kernels are represented as nodes and the SHAP values between kernel outputs are assigned as edges. These At-GCs are then employed to pretrain a newly designed heterogeneous graph encoder based on Deep Graph Infomax (DGI). To comprehensively delve into the overall structure of CNNs, the pretrained encoder is used for two types of interpretable tasks: (1) a classifier is attached to the pretrained encoder for the classification of At-GCs, revealing the dependency of At-GC’s topological characteristics on the image sample categories, and (2) a scoring aggregation (SA) network is constructed to assess the importance of each node in At-GCs, thus reflecting the relative importance of kernels in CNNs. The experimental results indicate that the topological characteristics of At-GC exhibit a dependency on the sample category used in its construction, which reveals that kernels in CNNs show distinct combined activation patterns for processing different image categories, meanwhile, the kernels that receive high scores from SA network are crucial for feature extraction, whereas low-scoring kernels can be pruned without affecting model performance, thereby enhancing the interpretability of CNNs.

Relevance of farm-scale indicators and tools for farmers to assess sustainability of their mixed crop-ruminant livestock systems

Ensuring the sustainability and circularity of mixed crop-ruminant livestock systems is essential if they are to deliver on the enhancement of long-term productivity and profitability with a smaller footprint. The objectives of this study were to select indicators in the environmental, economic and social dimensions of sustainability of crop-livestock systems, to assess if these indicators are relevant in the operational schedule of farmers, and to score the indicators in these farm systems. The scoring system was based on relevance to farmers, data availability, frequency of use, and policy. The study was successful in the assemblage of a suite of indicators comprising three dimensions of sustainability and the development of criteria to assess the usefulness of these indicators in crop-ruminant livestock systems in distinct agro-climatic regions across the globe. Except for ammonia emissions, indicators within the Emissions to air theme obtained high scores, as expected from mixed crop-ruminant systems in countries transitioning towards low emission production systems. Despite the inherent association between nutrient losses and water quality, the sum of scores was numerically greater for the former, attributed to a mix of economic and policy incentives. The sum of indicator scores within the Profitability theme (farm net income, expenditure and revenue) received the highest scores in the economic dimension. The Workforce theme (diversity, education, succession) stood out within the social dimension, reflecting the need for an engaged labor force that requires knowledge and skills in both crop and livestock husbandry. The development of surveys with farmers/stakeholders to assess the relevance of farm-scale indicators and tools is important to support direct actions and policies in support of sustainable mixed crop-ruminant livestock farm systems.

Prognostic Value of the Controlling Nutritional Status (CONUT) Score in Patients Who Underwent Cytoreductive Surgery Combined with Hyperthermic Intraperitoneal Chemotherapy

The Controlling Nutritional Status (CONUT) score is a novel nutritional index that integrates the serum albumin level, peripheral blood lymphocyte count, and total cholesterol level. This retrospective study explores its prognostic significance in patients undergoing cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC). We included 436 patients who underwent CRS-HIPEC, categorized into low (0–3) and high (4–12) CONUT score groups, and performed logistic regression analysis to predict one-year mortality and postoperative morbidity. Our findings revealed that high CONUT scores correlate with increased one-year mortality (47.1% vs. 20.3%, p < 0.001) and morbidity (39.2% vs. 18.2%, p < 0.001) compared to low CONUT scores. Multivariable regression analysis confirmed high CONUT scores as independent predictors of one-year mortality (odds ratio: 2.253, 95% CI: 1.014–5.005, p = 0.046) and postoperative morbidity (odds ratio: 2.201, 95% CI: 1.066–4.547, p = 0.033). These results underscore the CONUT score’s effectiveness as an independent marker for evaluating risks associated with CRS-HIPEC, emphasizing its potential to improve risk stratification.

Coalbed methane reservoir properties assessment and 3D static modeling for sweet-spot prediction in Dahebian block, Liupanshui Coal field, Guizhou Province, southwestern China

Guizhou Province has many multi-layer, thin-thick coal seams; however, complex geology, incomplete reservoir characterization, and sweet-spot selection technology prevent large-scale coalbed methane (CBM) development. This study evaluates the CBM reservoir properties within the Dahebian block using logging data, coal sample analysis, and well-testing data and develops a 3D static reservoir properties model to analyze their spatial and vertical propagation. A sweet spot evaluation model was established using a multi-level fuzzy method based on 9 parameters extracted from a 3D static reservoir properties model. The coal measure has 22 coal seams, and seams >2 m thick have 2 or 3 thin non-coal layers intercalated. Coal seams 1#, 7#, and 11# are thin to thick, deeply buried, widely distributed, and have high gas content and saturation. Undeformed and cataclastic coal predominates the coal seam 1# and 7#, whereas coal seam 11# is dominated by cataclastic and granulated coal. The southern and central parts of coal seam 7# and 11# have less tectonically deformed coal (TDC). Coal seams 1# and 7# have low permeability relative to seam 11# and are localized, while coal seams 11# have high permeability, are extensively distributed, and contain substantial gas concentrations. Comparative analysis of evaluation scores and CBM production statistics shows that high scores indicate sweet spots for CBM development. Sweet-spot potential was classified as high, medium, and low. Scattered sweet spots are found in single layers, while combined development (1# + 7#+11#) reveals a wider high-potential area in the south-central region. This area, featuring deep, thick coal seams, high permeability, gas saturation, reservoir pressure, and low TDC proportion, indicates significant development potential. This study validates CBM development statistics, identifies future development areas, and guides the development of geologically complex Guizhou CBM.

Psychological resilience, athletic experience, and competitive level of judokas. A transversal study

IntroductionWhile there is agreement on the positive link between psychological resilience and athletic performance, conclusive findings regarding the association between psychological resilience and other variables of interest (for example, age, gender, type of sport, or competitive level) remain elusive.ObjectiveThe study aimed to assess psychological resilience levels among judokas and explore potential associations with demographic factors, judo experience and competitive level.MethodsA total of 702 judokas (469 men and 233 women) participated in the study, of whom 194 (27.6%) were classified as TOP by their competitive level. Psychological resilience was evaluated using the Spanish version of the 10 item Connor-Davidson Resilience Scale (CD-RISC 10), with a score range from 0 to 40. Independent T-test and Pearson’s coefficient were used for bivariate analysis. A two-way non-parametric ANCOVA was carried out to analyse the impact of gender and competitive level on psychological resilience.ResultsThe judokas showed total mean scores in the CD-RISC 10 of 33.08 points (SD = 4.79), considered high. Levels of psychological resilience were significantly higher among men (33.36 ± 4.76) than women (32.53 ± 4.80) and were positively correlated with age and number of years practicing and competing in Judo (p = 0.019). Judokas with a higher competitive level (TOP judokas) showed significantly higher levels of resilience than the others (non-TOP judokas) (p < 0.001). These differences in resilience according to competitive level persisted, among both men and women, when adjusting the model of analysis (two-way ANCOVA) for all variables considered in the study, although with a small effect size.ConclusionThe results suggest that the practice of Judo, especially over long periods of time, is associated with high scores in psychological resilience. Furthermore, psychological resilience appears to be a differentiating variable among judokas at a high-competitive level, and its evaluation and development using different strategies based on age and gender should be considered by trainers and psychologists.

Machine learning developed an intratumor heterogeneity signature for predicting clinical outcome and immunotherapy benefit in bladder cancer.

Bladder cancer is a common malignancy with high invasion and poor clinical outcome. Intratumor heterogeneity (ITH) is linked to cancer progression and metastasis and high ITH can accelerate tumor evolution. Our objective is to develop an ITH-related signature (IRS) for predicting clinical outcome and immunotherapy benefit in bladder cancer. Integrative procedure containing ten machine learning methods was applied to develop an IRS with The Cancer Genome Atlas (TCGA), gene series expression (GSE)13507, GSE31684, GSE32984 and GSE48276 datasets. To evaluate the performance of IRS in predicting the immunotherapy benefit, we also used several predicting scores and three immunotherapy datasets, including GSE91061, GSE78220 and IMvigor210. The predicting model constructed with Enet (alpha =0.2) algorithm had a highest average C-index of 0.69, which was suggested as the optimal IRS. As an independent risk factor for bladder cancer, IRS had a powerful performance in predicting the overall survival (OS) rate of patients, with an area under curve of 1-, 3- and 5-year receiver operating characteristic (ROC) curve being 0.744, 0.791 and 0.816 in TCGA dataset. Bladder cancer patients with low IRS score presented with a higher level of immune-activated cells, cytolytic function and T cell co-stimulation. We also found a lower tumor immune dysfunction and exclusion (TIDE) score, lower immune escape score, higher programmed cell death protein 1 (PD-1) & cytotoxic T-lymphocyte associated protein 4 immunophenoscore, higher tumor mutation burden (TMB) score, higher response rate and better prognosis in bladder cancer with low IRS score. Bladder cancer cases with high IRS score had a higher half maximal inhibitory concentration value of common chemotherapy and targeted therapy regimens. The current study developed an optimal IRS for bladder cancer patients, which acted as an indicator for predicting prognosis, stratifying risk and guiding treatment for bladder cancer patients. Further analysis should be focused on the exploration the differentially expressed genes (DEGs) and related underlying mechanism mediating the development of bladder cancer in different IRS score group.

Immune infiltration and prognosis in gastric cancer: role of NAD+ metabolism-related markers.

This study endeavored to develop a nicotinamide adenine dinucleotide (NAD+) metabolism-related biomarkers in gastric cancer (GC), which could provide a theoretical foundation for prognosis and therapy of GC patients. In this study, differentially expressed genes (DEGs1) between GC and paraneoplastic tissues were overlapped with NAD+ metabolism-related genes (NMRGs) to identify differentially expressed NMRGs (DE-NMRGs). Then, GC patients were divided into high and low score groups by gene set variation analysis (GSVA) algorithm for differential expression analysis to obtain DEGs2, which was overlapped with DEGs1 for identification of intersection genes. These genes were further analyzed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses to obtain prognostic genes for constructing a risk model. Enrichment and immune infiltration analyses further investigated investigate the different risk groups, and qRT-PCR validated the prognostic genes. Initially, we identified DE-NMRGs involved in NAD biosynthesis, with seven (DNAJB13, CST2, THPO, CIDEA, ONECUT1, UPK1B and SNCG) showing prognostic significance in GC. Subsequent, a prognostic model was constructed in which the risk score, derived from the expression profiles of these genes, along with gender, emerged as robust independent predictors of patient outcomes in GC. Enrichment analysis linked high-risk patients to synaptic membrane pathways and low-risk to the CMG complex pathway. Tumor immune infiltration analysis revealed correlations between risk scores and immune cell abundance, suggesting a relationship between NAD+ metabolism and immune response in GC. The prognostic significance of our identified genes was validated by qRT-PCR, which confirmed their upregulated expression in GC tissue samples. In this study, seven NAD+ metabolism-related markers were established, which is of great significance for the development of prognostic molecular biomarkers and clinical prognosis prediction for gastric cancer patients.

Genomics and proteomics to determine novel molecular subtypes and predict the response to immunotherapy and the effect of bevacizumab in glioblastoma

Glioblastoma (GBM) is a highly aggressive, infiltrative malignancy that cannot be completely cured by current treatment modalities, and therefore requires more precise molecular subtype signatures to predict treatment response for personalized precision therapy. Expression subtypes of GBM samples from the Cancer Genome Atlas (TCGA) were identified using BayesNM and compared with existing molecular subtypes of GBM. Biological features of the subtypes were determined by single-sample gene set enrichment analysis. Genomic and proteomic data from GBM samples were combined and Genomic Identification of Significant Targets in Cancer analysis was used to screen genes with recurrent somatic copy-number alterations phenomenon. The immune environment among subtypes was compared by assessing the expression of immune molecules and the infiltration of immune cells. Molecular subtypes adapted to immunotherapy were identified based on Tumor Immune Dysfunction and Exclusion (TIDE) score. Finally, least absolute shrinkage and selection operator (LASSO) logistic regression was performed on the expression profiles of S2, S3 and S4 in TCGA-GBM and RPPA to determine the respective corresponding best predictive model. Four novel molecular subtypes were classified. Specifically, S1 exhibited a low proliferative profile; S2 exhibited the profile of high proliferation, IDH1 mutation, TP53 mutation and deletion; S3 was characterized by high immune scores, innate immunity and adaptive immune infiltration scores, with the lowest TIDE score and was most likely to benefit from immunotherapy; S4 was characterized by high proliferation, EGFR amplification, and high protein abundance, and was the most suitable subtype for bevacizumab. LASSO analysis constructed the best prediction model composed of 13 genes in S2 with an accuracy of 96.7%, and the prediction model consisting of 17 genes in S3 with an accuracy of 86.7%, and screened 14 genes as components of the best prediction model in S4 with an accuracy of 93%. To conclude, our study classified reproducible and robust molecular subtypes of GBM, and these findings might contribute to the identification of patients responding to immunotherapy, thereby improving GBM prognosis.

CHARTING THE LANDSCAPE OF CORPORATE PHILANTHROPY: A BIBLIOMETRIC EXPLORATION OF TRENDS AND THEMES

Purpose: This study identifies the main area and current dynamics of Corporate Philanthropy (CP) and offers recommendations for future research directions. Design/methodology/approach: Using bibliometrics, we examined a sample of 558 studies from Scopus, an online abstract and citation repository, to examine the research activities relating to CP between 1955 and 2022. Articles and authors with high citation impact scores are identified based on the publication’s position and significance within the field. We then explore contemporary issues, identify possible limitations towards the development of the literature, and propose new lines of inquiry for future studies. Findings: In addition to providing an in-depth analysis, this study discusses recent discoveries and shifts in mindsets pertaining to Corporate Philanthropy, taking into account previously documented findings which regarded CP disadvantageous to many companies mainly due to money outflow. The ‘agency theory’ considers the relationship between a principal and its hired representative. Past reports cited managers leveraging on CP as a way to justify unnecessary expenses. Meanwhile, the latest studies discovered that CP brings in various advantages to the companies such as additional profit avenues, enhanced visibility, and stronger market presence, benefiting its stakeholders and securing the sustainability of those companies. Future research on CP can be expanded through our suggested research directions. Research limitations/implications: This study focuses on using the Scopus database as the primary source of the journals. Hence, our findings may differ from the studies conducted using other databases. Originality/value: This is among the pioneer studies using Harzing and VOSviewer software to analyse the development of CP studies. Several future research agendas are generated due to the review, which focuses on annual publication trends, the most prolific authors, the most cited papers, the most prolific countries, the most prolific institutions and the most abundant sources. Keywords: Corporate philanthropy, bibliometric analysis, citation network, Scopus, systematic literature review. Cite as: Aziz, N. F., Ramli, R., Mohd Amir A., & Wahid, H. (2024). Charting the landscape of corporate philanthropy: A bibliometric exploration of trends and themes. Journal of Nusantara Studies, 9(2), 160-184. http://dx.doi.org/10.24200/jonus.vol9iss2pp160-184

Sustainable Development Perspective: Analysis of Capture Fisheries in Muncar District Banyuwangi

Capture fisheries resources in Muncar Regency have enormous potential to support the lives of the community, especially fishermen. However, in these conditions, the volume of capture fisheries production in Muncar Regency has decreased. This research aims to direct sustainable development efforts in the capture fisheries subsector so that it can improve the welfare of fishermen. This is done by optimizing the use of fishery resources without ignoring economic, ecological (environmental), and social aspects. This research uses descriptive analysis methods. The results of measuring the level of sustainability of capture fisheries resources in Muncar District show that the average value is still low. From each dimension (economic, social, and environmental) several criteria have medium (M) and high (H) scores. This means that each dimension requires alternative recommendations to support sustainable capture fisheries development. It takes synergy from all levels of society to maintain the balance of the marine ecosystem. Increasing awareness of coastal communities is needed to maintain the balance of the capture fisheries resource ecosystem. Apart from that improving the quality of human resources in fishing communities is an important thing that the government can do to support the sustainable development of capture fisheries resources. Keywords: sustainable development, capture fisheries, Muncar

Quality of life and work-life balance among Nurse Educators in the Schools of Nursing in Sri Lanka

Objective: The study aimed to assess the quality of life and work-life balance (WLB) among Nurse Educators in the Schools of Nursing in Sri Lanka. Material and Methods: A descriptive cross-sectional study was conducted among 257 Nurse educators in all schools of Nursing in Sri Lanka. Data were collected by using validated and pretested self-administered questionnaires which include Socio-demographic details, a validated quality of life questionnaire (WHO QOL – BREF), and a Work Life Balance assessment scale. Data were analyzed using descriptive and inferential statistics. Results: The overall mean perceived QOL and health of the participants were 3.82 (±0.66) and 3.82 (±0.69) respectively. Participants obtained a high score for the social relationship domain (98.83/100) and a low score for the environmental domain (19.96/100) of the QOL. Findings of the work=life balance revealed an overall mean of 2.87 (SD=1.5) which indicates neither agree nor disagree with work-life interference with personal life and an overall mean of 2.53 (SD=0.96) which indicates neither agree nor disagree with personal life interference with work (PLIW). Conclusions: Nurse educators perceive a moderate level of personal life interference with work, but overall, they acknowledge work/personal life enhancement. These results highlight the complex and multifaceted nature of work-life balance among nurse educators.

A Geospatial Drug Abuse Risk Assessment and Monitoring Dashboard Tailored for School Students: Development Study With Requirement Analysis and Acceptance Evaluation.

The enormous consequences of drugs include suicides, traffic accidents, and violence, affecting the individual, family, society, and country. Therefore, it is necessary to constantly identify and monitor the drug abuse rate among school-going youth. A geospatial dashboard is vital for the monitoring of drug abuse and related crime incidence in a decision support system. This paper mainly focuses on developing MyAsriGeo, a geospatial drug abuse risk assessment and monitoring dashboard tailored for school students. It introduces innovative functionality, seamlessly orchestrating the assessment of drug abuse usage patterns and risks using multivariate student data. A geospatial drug abuse dashboard for monitoring and analysis was designed and developed in this study based on agile methodology and prototyping. Using focus group and interviews, we first examined and gathered the requirements, feedback, and user approval of the MyAsriGeo dashboard. Experts and stakeholders such as the National Anti-Drugs Agency, police, the Federal Department of Town and Country Planning, school instructors, students, and researchers were among those who responded. A total of 20 specialists were involved in the requirement analysis and acceptance evaluation of the pilot and final version of the dashboard. The evaluation sought to identify various user acceptance aspects, such as ease of use and usefulness, for both the pilot and final versions, and 2 additional factors based on the Post-Study System Usability Questionnaire and Task-Technology Fit models were enlisted to assess the interface quality and dashboard sufficiency for the final version. The MyAsriGeo geospatial dashboard was designed to meet the needs of all user types, as identified through a requirement gathering process. It includes several key functions, such as a geospatial map that shows the locations of high-risk areas for drug abuse, data on drug abuse among students, tools for assessing the risk of drug abuse in different areas, demographic information, and a self-problem test. It also includes the Alcohol, Smoking, and Substance Involvement Screening Test and its risk assessment to help users understand and interpret the results of student risk. The initial prototype and final version of the dashboard were evaluated by 20 experts, which revealed a significant improvement in the ease of use (P=.047) and usefulness (P=.02) factors and showed a high acceptance mean scores for ease of use (4.2), usefulness (4.46), interface quality (4.29), and sufficiency (4.13). The MyAsriGeo geospatial dashboard is useful for monitoring and analyzing drug abuse among school-going youth in Malaysia. It was developed based on the needs of various stakeholders and includes a range of functions. The dashboard was evaluated by a group of experts. Overall, the MyAsriGeo geospatial dashboard is a valuable resource for helping stakeholders understand and respond to the issue of drug abuse among youth.

Mask region-based convolutional neural network and VGG-16 inspired brain tumor segmentation

The process of brain tumour segmentation entails locating the tumour precisely in images. Magnetic Resonance Imaging (MRI) is typically used by doctors to find any brain tumours or tissue abnormalities. With the use of region-based Convolutional Neural Network (R-CNN) masks, Grad-CAM and transfer learning, this work offers an effective method for the detection of brain tumours. Helping doctors make extremely accurate diagnoses is the goal. A transfer learning-based model has been suggested that offers high sensitivity and accuracy scores for brain tumour detection when segmentation is done using R-CNN masks. To train the model, the Inception V3, VGG-16, and ResNet-50 architectures were utilised. The Brain MRI Images for Brain Tumour Detection dataset was utilised to develop this method. This work's performance is evaluated and reported in terms of recall, specificity, sensitivity, accuracy, precision, and F1 score. A thorough analysis has been done comparing the proposed model operating with three distinct architectures: VGG-16, Inception V3, and Resnet-50. Comparing the proposed model, which was influenced by the VGG-16, to related works also revealed its performance. Achieving high sensitivity and accuracy percentages was the main goal. Using this approach, an accuracy and sensitivity of around 99% were obtained, which was much greater than current efforts.

Factors affecting nutritional status among children aged below five years in Rwanda’s Western and Southern Provinces

BackgroundThe state of a child’s nutrition is a critical indicator of their overall health and wellbeing. Public health still faces challenges from undernutrition, especially in developing nations across the globe. In Rwanda, around 33% of children aged under five years suffer from chronic undernutrition. Many factors, such as poverty, illiteracy, poor WASH practices, improper child feeding practices, and insufficient healthcare, are the leading causes of undernutrition. The study aims to assess infant and young child feeding practices, WASH, food security, and their association with the nutritional status of children under five years in Rwanda’s Western and Southern provinces.MethodsA community-based cross-sectional study design was applied to study factors affecting the nutritional status of children under five years in 439 households in the Karongi, Nyabihu, and Nyamagabe districts of Rwanda. The study assessed anemia, stunting, underweight, and wasting indicators, and collected data was analyzed using SPSS version 25.ResultsThe study findings indicate that among the children surveyed, 29.2% (128) were identified as stunted, 5.9% (26) were underweight, 2.3% (10) suffered from wasting, and 20.9% (31) had anemia. Factors associated with these conditions included larger household size [AOR = 2.108; 95% CI (1.016–4.371)], positively associated with stunting. Additionally, children from households where the head was above 60 years old were more likely to exhibit stunting [AOR = 4.809; 95% CI (1.513, 15.283)]. Furthermore, a high household dietary diversity score was positively linked to being underweight [AOR = 6.061; 95% CI (1.535,23.942)].ConclusionHousehold characteristics like size, dietary diversity, and the age of the household head affect children’s nutritional status. Improving these conditions would enhance children’s nutritional status.

APPLICATION OF POWTOON-BASED AI TECHNOLOGY IN MAKING ENTREPRENEURSHIP TEACHING MATERIALS

Technological advances with various other implications also require a greater role for the world of education, especially for teachers who use different technologies, methods and approaches in conveying material and values ​​to students. A teacher's ability to adapt to technological developments has become an inevitability that cannot be avoided. Based on the research obtained, the results show that the development of powtoon learning media has a feasibility percentage of 83,75% (feasible). Student learning outcomes were proven to have increased in obtaining a minimum score of 70 and a maximum score of 100. The gain test showed that 18 students obtained high score criteria and 9 students obtained medium score criteria. In the student response questionnaire, the percentage was 93.4% (Very Eligible). Thus, it can be stated that the Powtoon learning media is suitable for use in entrepreneurship learning. It is hoped that this research can help students and the instruments involved in the process of teaching and learning activities.

An analysis of protective health behavior and polypharmacy among older patients: a nationwide cohort study

BackgroundThis study analyzed the relationship between protective health behaviors and polypharmacy in individuals aged 65 years and older.MethodsWe used data from a nationwide survey (KNHANES) from 2012 to 2016 in conjunction with the health insurance claims databases. A total of 3297 adults aged 65 or older were included in the study. Polypharmacy was defined as more than 30 prescription days in 6 months with five or more different drugs. Health-related behaviors (BMI, smoking, drinking, regular walking, and living alone) were extracted for 6 months before measuring polypharmacy. We used multivariable logistic regression on polypharmacy for each protective health behavior, as well as a composite score of protective health behavior. Subgroup analysis was also conducted by age and sex.ResultsAmong protective health behaviors, BMI < 25 (OR, 0.76; 95% CI, 0.66–0.88) and never smoking (OR, 0.78; 95% CI, 0.62–0.98) were associated with a lower risk of polypharmacy. Polypharmacy was significantly associated with BMI < 25 in both sex subgroups (male: OR, 0.71; 95% CI, 0.56–0.88; female: OR, 0.81; 95% CI, 0.67–0.99) and 65–79 subgroup (OR, 0.74; 95% CI, 0.63–0.86). The association between never smoking and polypharmacy was only significant in the 65–79 subgroup (OR, 0.71; 95% CI, 0.55–0.91). Participants with five protective health behaviors had a lower risk of polypharmacy than participants with zero or one health behavior, which was only statistically significant in the subgroup analysis of participants aged 65–79 years (OR, 0.52; 95% CI, 0.29–0.94).ConclusionsThis study finds that health behaviors such as obesity and smoking are associated with a higher risk of polypharmacy. Furthermore, we confirm that a high score of protective health behaviors is associated with a lower risk of polypharmacy. Our findings indicate the need for geriatric-centered management of protective health behaviors to prevent polypharmacy.