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Target sequence of single cells captured by a polymeric microfluidic device

Correction: Non-analgesic effects of opioids: Topical application of Eucerin-based ointment containing opium on the healing process of thermal skin damage in rats

Sleep patterns, plasma metabolites, and risk of incident osteoarthritis: a prospective cohort study

Abstract Although previous studies have assessed the effect of sleep traits on osteoarthritis (OA) risk, the association between the complex interplay of multiple sleep patterns and OA risk remains uncertain. We included participants who were free of OA at baseline based the UK Biobank. We evaluated the associations of five sleep behaviors with the risk of OA using Cox proportional hazard regression models. To explore the metabolic profile of sleep patterns, we regressed the sleep score on 167 standardized metabolites using ten iterations of LASSO model with ten-fold cross-validation. Restricted cubic splines (RCS) with four knots were used in the fully adjusted model to explore the potential non-linear association of sleep score and metabolic profile with OA risk. We discovered that individuals with poor sleep patterns experienced a notably higher incidence of OA (HR, 1.23, 95% CI, 1.18 to 1.28, P = 2.69 × 10–23). Furthermore, the risk of hand OA specifically was 1.29 times higher among those with poor sleep patterns compared to those with healthy sleep patterns (HR, 1.29, 95% CI, 1.12 to 1.49, P = 4.23 × 10–4). Individuals belonging to the highest quintile of metabolic score exhibited a 1.14-fold elevated risk of OA compared to those in the lowest quintile (HR, 1.14; 95% CI, 1.08 to 1.20; P = 3.09 × 10–6). Our findings have important public health implications as we provide an objective and more comprehensive evaluation of sleep patterns, and novel insights into the mechanisms linking sleep patterns and OA through metabolic profile.

Real-world clinical practice of Diabetic Foot Ulcer prevention and care in Singapore: A qualitative inquiry with healthcare professionals

Aim People living with diabetes are at risk of developing diabetic foot ulcers (DFU). While international and local clinical care guidelines and pathways have been formulated to optimize the prevention and treatment of DFUs, a continuous audit of real-world adherence among healthcare professionals (HCPs) is needed to ensure care quality, safety, and efficacy. Methods A qualitative study design involving focus group discussions was used to explore practices in the prevention and treatment of DFUs. Verbatim transcripts from eight discussions involving 19 HCPs, purposively sampled, were analyzed using the framework method. Coding was guided by a DFU model of care developed from international and local clinical guidelines. Results Clinical practices for DFU prevention and care management in Singapore generally adhere to existing guidelines, though risk stratification and DFU classification are not commonly performed. During clinical visits, HCPs perform foot assessments that encompass mainly visual inspection, evaluation of vascular status and neurological status. Education on DFU prevention and management is extensive across all diabetes care. Referrals to podiatrists include cases beyond active wounds and high-risk issues. Conclusion Implications for practice are considered and highlight the need for a clearer delineation of roles among HCPs in DFU clinical care guidelines. This study provides a guide for further studies in the area of patient management.

System dynamics modeling and simulation of exercise-based health promotion in the context of population aging

Enhancing meningioma tumor classification accuracy through multi-task learning approach and image analysis of MRI images

Background Accurate classification of meningioma brain tumors is crucial for determining the appropriate treatment plan and improving patient outcomes. However, this task is challenging due to the slow-growing nature of these tumors and the potential for misdiagnosis. Additionally, deep learning models for tumor classification often require large amounts of labeled data, which can be costly and time-consuming to obtain, especially in the medical domain. Objective Our main aim is to enhance Meningioma Tumor Classification Accuracy. Method This study proposes a multi-task learning (MTL) approach to enhance the accuracy of meningioma tumor classification while mitigating the need for excessive labeled data. The primary task involves classifying meningioma tumors based on MRI imaging data, while auxiliary tasks leverage patient demographic information, such as age and gender. By incorporating these additional data sources into the learning process, the proposed MTL framework leverages the interdependencies among multiple tasks to improve overall prediction accuracy. The study evaluates the performance of the MTL approach using a dataset of 2218 brain MRI images from 34 patients diagnosed with meningioma, obtained from the Mahdia Imaging Center in Hamadan, Iran. Results Results demonstrate that the MTL model significantly outperforms single-task learning baselines, achieving 99.6% ± 0.2 accuracy on the test data in 95% confidence interval. Discussion This highlights the efficacy of the proposed approach in enhancing meningioma tumor classification and its potential for aiding clinical decision-making and personalized treatment planning. Conclusion Our proposed method can be used in computer-aided diagnosis systems.

An innovative mathematical model for integrated traffic flow optimization in organized industrial zones

Interventions addressing impacts of climate change on sexual and reproductive health and rights in sub-Saharan Africa: A scoping review

Sub-Saharan Africa is faced with triple challenges of high vulnerability to climate change impacts, high levels of inequality, and poor sexual and reproductive health and rights (SRHR) outcomes. Climate change impacts can worsen the SRHR situation for high-risk groups such as women, children, adolescent girls, and people living with Human Immunodeficiency Virus (HIV). This scoping review examined interventions addressing the impacts of climate change on SRHR in the region to identify barriers to and facilitators of effective integration. The review followed Arksey and O’Malley’s framework for scoping reviews. Data search was conducted in peer-reviewed journal databases and from grey literature on the official websites of selected organizations. Data charting was conducted using the Population, Intervention, Comparator, Outcome tool in Covidence. There is limited evidence on interventions at the intersection of climate change and SRHR, with seven (7) documents included in the review. Maternal and Child Health, HIV prevention, and a combination of maternal and child health and family planning were the SRHR components addressed. Other components like Gender-based violence, harmful practices, and abortion care do not have targeted interventions. A siloed approach to SRHR and climate change programming impedes intervention integration. Documented interventions are implicit about climate risks, focus on impact pathways, and do not directly target SRHR. There are no interventions targeting vulnerable and marginalized groups. Limited policy integration, financial constraints, and poor SRHR recognition deter intervention integration. Effective and equitable integration requires that population growth impacts and SRHR issues be recognized and deliberate investments (research, policies, programs, interventions, and financing) put in place to address critical SRHR gaps and climate vulnerabilities to enhance resilience.

Association between hearing loss and insulin resistance as measured by metabolic score for insulin resistance in NHANES 1999 to 2018

Atomistic study on mechanical properties of Al matrix composite with different combining forms of reinforcements

Models of matrix composite (MMC) are built through molecular dynamic (MD) simulation to study the effect of different combining forms of reinforcements. Diamond particle and graphene nanoplate (GNP) are selected as the two kinds of reinforcements, forming six combinations by changing the location and orientation of them. Then, the same sintering processes are conducted to achieve sintered composites. Bulk volume and Al volume of sintered composite reveal that a compacter structure can be produced in the model with two GNPs those are not parallel, or in the model where the diamond particle is out of the GNP plane. Structural analysis indicates that the ratio of arranged atoms rather than nanopore has a greater impact on Al volume. Tensile results show that the model reinforced by both GNP and diamond in a same plane gives the best performance in both strength and ductility, regardless of its low ratio of arranged atoms that may lead to a further improvement at the larger scale. In other words, GNP can play its role very well along the GNP plane, and diamond particle can improve the property vertical to GNP. This combined strengthening mechanism can be well presented by evolution of atomic configurations.

Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and multi feature analysis

Abstract Recent innovations in medical imaging have markedly improved brain tumor identification, surpassing conventional diagnostic approaches that suffer from low resolution, radiation exposure, and limited contrast. Magnetic Resonance Imaging (MRI) is pivotal in precise and accurate tumor characterization owing to its high-resolution, non-invasive nature. This study investigates the synergy among multiple feature representation schemes such as local Binary Patterns (LBP), Gabor filters, Discrete Wavelet Transform, Fast Fourier Transform, Convolutional Neural Networks (CNN), and Gray-Level Run Length Matrix alongside five learning algorithms namely: k-nearest Neighbor, Random Forest, Support Vector Classifier (SVC), and probabilistic neural network (PNN), and CNN. Empirical findings indicate that LBP in conjunction with SVC and CNN obtained high specificity and accuracy, rendering it a promising method for MRI-based tumor diagnosis. Further to investigate the contribution of LBP, Statistical analysis chi-square and p-value tests are used to confirm the significant impact of LBP feature space for identification of brain Tumor. In addition, The SHAP analysis was used to identify the most important features in classification. In a small dataset, CNN obtained 97.8% accuracy while SVC yielded 98.06% accuracy. In subsequent analysis, a large benchmark dataset is also utilized to evaluate the performance of learning algorithms in order to investigate the generalization power of the proposed model. CNN achieves the highest accuracy of 98.9%, followed by SVC at 96.7%. These results highlight CNN’s effectiveness in automated, high-precision tumor diagnosis. This achievement is ascribed with MRI–based feature extraction by combining high resolution, non-invasive imaging capabilities with the powerful analytical abilities of CNN. CNN demonstrates superiority in medical imaging owing to its ability to learn intricate spatial patterns and generalize effectively. This interaction enhances the accuracy, speed, and consistency of brain tumor detection, ultimately leading to better patient outcomes and more efficient healthcare delivery. https://github.com/asifrahman557/BrainTumorDetection.

Population aging and corporate tax avoidance: Suppression or promotion?

Addressing population aging has emerged as a paramount national strategic priority. Against the backdrop of a continual rise in population aging and considerable escalation in labor costs within China, this study aims to investigate whether firms, confronted with the burden of labor costs, intensify their tax avoidance motives to generate a “stimulating effect” or whether they are compelled to adapt traditional factors, thereby reducing tax avoidance and exerting a “restraining effect”. To address this inquiry, we empirically examine the impact of population aging on corporate tax avoidance and its underlying mechanisms utilizing a sample of non-financial listed companies in China’s A-share market spanning from 2008 to 2023. Our findings substantiate that the level of population aging significantly diminishes firms’ tax avoidance motives, confirming the presence of a “restraining effect”. Mechanism tests unveil that population aging, through capital-labor substitution, fosters research and development innovation and improves production efficiency, thus curbing firms’ tax avoidance behavior and affirming the existence of the “factor substitution effect” “innovation capacity effect” and “resource allocation effect”. Heterogeneity analysis reveals that the inhibitory impact of population aging on corporate tax avoidance is more pronounced in labor-intensive industries, entities with limited financing capacity, weak financial conditions, and adverse external financial conditions. By examining the economic ramifications of population aging from a micro-level perspective on corporate financial decision-making and enriching the existing literature on corporate tax avoidance through a population economics lens, this study provides valuable insights for firms undergoing transformation and informs policy formulation in response to the challenges posed by population aging.

Long wavelength infrared sensor array using VO2 microstructures fabricated on visible GaN LED

Correction: Effects of KRAS, STK11, KEAP1, and TP53 mutations on the clinical outcomes of immune checkpoint inhibitors among patients with lung adenocarcinoma

Elman and feedforward neural network based models for predicting mechanical properties of flow formed AA6082 tubes

Correction: Pediatric Diabetic Ketoacidosis (PDKA) among newly diagnosed diabetic patients at Dilla University Hospital, Dilla, Ethiopia: Prevalence and predictors

Cross-species comparison of ultramafic rock bio-accelerated weathering performance

Correction: Cost-utility analysis of proton beam therapy for locally advanced esophageal cancer in Japan

Social support and anxiety, a moderated mediating model

Low birth weight and adverse pregnancy outcomes among women living with HIV and HIV-uninfected in Rwanda

Introduction In utero exposure to HIV and/or antiretroviral therapy (ART) has been shown to be associated with stillbirth, preterm births, and low birth weight (LBW), but data from low-resource, high- HIV-burden settings remain limited. This study describes adverse pregnancy outcomes among Rwandan women living with HIV (WLWH) and HIV-uninfected women and examines associations between HIV, ART timing, and LBW. Methods This retrospective cohort study used antenatal care, delivery, and PMTCT registry data from the Central Africa International Epidemiology Databases to Evaluate AIDS (CA-IeDEA). Women with documented HIV status and recorded birth weights were included. Adverse outcomes were defined as LBW (<2,500 g), stillbirth, and preterm birth (<37 weeks gestation). Logistic regression was used to assess associations between maternal HIV status, ART timing, and LBW, adjusting for relevant covariates. Results and discussion Among 10,191 women with known HIV status and babies’ birth weights, 12.7% (n = 1,293) were WLWH. There were 47 stillbirths (0.5%) and 70 preterm births (0.7%). Among 10,037 term births, 366 (3.6%) were LBW. WLWH had significantly higher rates of stillbirth (0.6% vs. 0.4%, p < 0.05) and LBW (6.5% vs. 2.9%, p < 0.001) compared to HIV-uninfected women; preterm birth rates did not differ significantly. The adjusted odds of LBW among WLWH were 1.61 (95% CI: 1.08, 2.39), controlling for marital status, primigravidae, and maternal weight at admission. Among WLWH (n = 1,274), ART initiation prior to pregnancy was associated with 50% lower odds of LBW after adjusting for age and WHO stage. Conclusions Even among relatively healthy uncomplicated pregnancies in low-risk delivery settings and universal ART, WLWH experienced significantly higher rates of stillbirth and LBW. Among WLWH, initiation of ART prior to current pregnancy had a protective effect against LBW. This underscores the importance of early HIV diagnosis and initiation of ART.