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Immature Citrus unshiu fruit extracts inhibit adipogenesis in 3T3-L1 adipocytes via AMPK and MAPK signaling pathways
In Korea, immature citrus fruits have been extensively explored for their potential utility as functional bio-health materials owing to their various bioactive properties. However, the specific mechanisms by which they exert inhibitory effects on adipogenesis remain unclear. Therefore, this study aimed to examine the anti-obesity effects of 70% ethanol extracts of immature Citrus unshiu fruits and their solvent fractions (n-hexane, ethyl acetate, n-butanol, and water) on 3T3-L1 cells, as well as to explore the underlying molecular mechanisms. Additionally, this study was conducted to identify the bioactive components responsible for the anti-obesity effects. Among the fractions, the hexane fraction exhibited the most potent inhibitory effect on lipid accumulation in 3T3-L1 cells without inducing cytotoxicity. Notably, this effect was concentration-dependent. This fraction also inhibited adipogenesis during the differentiation of 3T3-L1 preadipocytes by downregulating the expression of CCAAT/enhancer-binding proteins (C/EBP), peroxisome proliferator-activated receptor-γ (PPARγ), sterol regulatory element-binding protein (SREBP), fatty acid synthase (FAS), and fatty acid binding protein 4 (FABP4). Moreover, the hexane fraction modulated the activity of AMP-activated protein kinase (AMPK) and mitogen-activated protein kinase (MAPK), both of which play critical roles in lipid metabolism. Specifically, it induced AMPK activation while downregulating MAPK signaling. Phytochemical analysis identified phytol, hexatriacontane, tangeretin, and nobiletin as the main bioactive components responsible for the observed anti-obesity effects of ICE. Overall, our results revealed that ICE exhibited notable anti-obesity activity by targeting the AMPK and MAPK signaling pathways, highlighting its potential as a natural therapeutic agent for obesity management.
A comparative study of the perception of traditional villages between different media
Could the shingles vaccine help to prevent dementia?
Climatic factors driving influenza transmission in Sahelian area: A twelve-year retrospective study in Niger (2010–2021)
The relationship between influenza transmission and climate has many public health implications, particularly on the occurrence of epidemics and disease severity. Environmental factors such as temperature, wind and humidity can influence transmission, particularly in this time of climate change. This study aims to use statistical modelling to decipher the impact of climate factors on influenza transmission in Niger. The reference center of respiratory disease (CERMES) collected samples from patients with acute respiratory illness in eight sentinel sites over a period of twelve years. Detection of respiratory virus was conducted on each sample using molecular approaches. Meteorological parameters were recorded on a weekly basis at the National Meteorological Station in Niamey. Climatic and virological data were plotted over the weeks of the years. A multivariate approach was used to identify clusters of weeks with homogeneous climatic conditions, independent of the season. The impact of the predictor variables was determined using generalized additive modelling (GAM). During this study, 9836 suspected influenza cases were PCR tested, of which 982 (9.98%) were confirmed positive for either influenza A or B. 631 (64.25%) of the influenza A/B positive cases were detected during the low temperature periods (December to February). Using clustering analysis, six distinct periods can be identified, with the most favorable conditions for influenza occurring in conjunction with dry, cold and windy weather patterns. Of greater importance, however, are the conditions that predominate in the weeks preceding the detection of clinical cases. The final GAM model accounts for 77% of the variability in the occurrence of influenza cases, indicating that the epidemic can be anticipated weeks before clinical detection in dispensaries using wind and minimum temperature as indicators. Clustering and GAM models can be considered as an efficient and simple approach to analyze the impact of climatic conditions on the transmission of infectious diseases.
Thermal performance, entropy generation, and machine learning insights of Al₂O₃-TiO₂ hybrid nanofluids in turbulent flow
MADNet: Marine Animal Detection Network using the YOLO platform
It is necessary to overcome the real-life challenges encountered in detecting marine animals in underwater bodies through computer vision for monitoring their populations and biological data. At the same time, the detectors for such tasks are limited by large parameters, high computation costs, low accuracy, low speed, and unfriendly deployment in low-power computing devices due to their large size. To tackle these problems, MADNet was developed using the YOLO framework, incorporating both anchor-based and anchor-free techniques. The structure of MADNet includes CBS, C3b, Bottleneck, SPPFr, and C3 modules, and it was evaluated against YOLOv5n, YOLOv6n, YOLOv7-tiny, and YOLOv8n with consistent application methods on various open-source underwater image datasets. Using the computation cost, trained time, loss, accuracy, speed, and mean absolute error (MAE) as performance evaluation metrics, the anchor-free methods performed better than the anchor-based methods. Similarly, the overall performance score for MADNet was analyzed at 27.8%, which is higher than 20% for YOLOv8n, 18.9% for YOLOv6n, 17.8% for YOLOv5n, and 15.6% for YOLOv7-tiny. As a result, MADNet is lightweight and effective for detecting marine animals in challenging underwater scenarios.
Association of sleep disturbance and sleep apnea with the size of the thoracic aorta and the main pulmonary artery
5′-Nucleotidase for Predicting Snakebite Outcomes: A Biomarker Breakthrough?
The RAD52 double-ring remodels replication forks restricting fork reversal
Damping factor in magnetorheological shear stiffening gel: Fabrication and formulation of a fractional constitutive model
A variety of magnetorheological shear stiffening gels (MRSSGs) were synthesised by incorporating varying amounts of carbonyl iron powder (CIP) into a shear stiffening gel (SSG) matrix. The dynamic damping performance of the MRSSG was initially evaluated using a rheometer. The damping factor of MRSSGs demonstrates magnetic-sensitive characteristics and can autonomously respond to external stimuli due to B-O cross-linked bonds. The examination of the effects of applied frequency and magnetic field on the damping factor included the delineation of viscous damping from the SSG matrix, magneto-induced damping, and interfacial damping, leading to the development of an innovative fractional constitutive model. This model explicitly illustrates the relationship among the damping factor, shear angular frequency, and magnetic field strength. Theoretical results of the damping factor, derived from modelling calculations and analyses, closely align with experimental findings as excitation angular frequencies vary across different magnetic induction intensities, exhibiting a high fitting accuracy with a correlation coefficient exceeding 0.999. Furthermore, an augmentation in magnetic induction strength correlates with a reduction in the fractional order value, which declines from 0.96 to 0.49.
Predicting onward care needs at admission to reduce discharge delay using explainable machine learning
Abstract Early identification of patients who require onward referral to social care can prevent delays to discharge from hospital. We introduce an explainable machine learning (ML) model to identify potential social care needs at the first point of admission. This model was trained using routinely collected data on patient admissions, hospital spells and discharge at a large tertiary hospital in the UK between 2017 and 2023. The model performance (one-vs-rest AUROC = 0.915 [0.907 0.924] (95% confidence interval), is comparable to clinician’s predictions of discharge care needs, despite working with only a subset of the information available to the clinician. We find that ML and clinicians perform better for identifying different types of care needs, highlighting the added value of a potential system supporting decision making. We also demonstrate the ability for ML to provide automated initial discharge need assessments, in the instance where initial clinical assessment is delayed and provide reasoning for the decision. Finally, we demonstrate that combining clinician and machine predictions, in a hybrid model, provides even more accurate early predictions of onward social care requirements (OVR AUROC = 0.936 [0.928 0.943]) and demonstrates the potential for human-in-the-loop decision support systems in clinical practice.
The Quality and Significance of a Study on the Validity of the CAM-ICU Depends Heavily on their Inclusion and Exclusion Criteria
Identifying primary-care features associated with complex mental health difficulties
Aim The coded prevalence of complex mental health difficulties in electronic health records, such as personality disorder and dysthymia,is much lower than expected from population surveys. We aimed to identify features in primary care records that might be useful in promoting greater recognition of complex mental health difficulties. Methods and Findings We analysed Connected Bradford, an anonymised primary care database of approximately 1.15M citizens. We used multiple approaches to generate a large set of features representing multi-level collections of patient attributes across time and dimensions of healthcare. Feature sets included antecedent and concurrent problems (psychiatric, social and medical), patterns of prescription and service use and temporal stability of attendance. These were tested individually and in combination. We analysed the relationship between features and diagnostic codes using scaled mutual information. We identified 3,040 records satisfying our definition of complex mental health difficulties. This was 0.3% of the population compared to an expected prevalence of 3–5%. We generated >500,000 features. The most informative feature was count of unique psychiatric diagnoses. Other features were identified, including binary features (e.g., presence or absence of prescription for antipsychotic medication), continuous features (e.g., entropy of non-attendance) and counts of features (e.g., concerning behaviours such as self-harm & substance misuse). Several of these showed odds ratios >=5 or <=0.2 but low positive predictive value. We suggest this is due to the large number of “cases” being uncoded and, thus appearing as “controls”. Conclusion Complex mental health difficulties are poorly coded. We demonstrated the feasibility of using information theoretic approaches to develop a large set of novel features in electronic health records. While these are currently insufficient for diagnosis, several can act as prompts to consider further diagnostic assessment.
Investigation of the air permeability of fabric weaves to increase the wearing comfort of firefighter clothing and improve stab and cut protection
Abstract Firefighter protective clothing is composed of multiple layers, each serving distinct functions. The outer layer shields the user from fire, chemicals, cuts, body fluids, and water, while also permitting water vapour to escape. The middle membrane layer acts as a thermal and moisture barrier, preventing heat and liquid penetration but allowing vapour diffusion. The inner layer enhances thermal protection and wearer comfort. A nationwide German survey and risk analysis with different fire brigades identified a need for enhanced comfort, reduced physiological heat load, and improved protection against stabs and cuts. Enhanced tear resistance is one proposed method for increased stab and cut protection. Wearer comfort parameters include water vapour permeability, breathability, air permeability, efficient cooling and increased breathability of the protective clothing are crucial for comfort. Sweat is diffused through the jacket due to differing water vapour partial pressures inside and outside the jacket. Enhancing air permeability of the outer layer and reducing the water vapour transmission resistance across the entire layer structure improve cooling by lowering the external water vapour partial pressure, thus facilitating better sweat transport and heat dissipation. To increase breathability and stab- and cut protection, different fabric weaves for the outer layer of a firefighter´s jacket are produced and compared with each other. The Honeycomb and the Huck-a-back fabric achieve better properties than Twill 2/2 fabric used as standard.
Effect of High Protein Normocaloric Nutrition on Skeletal Muscle Wasting in Critically Ill Mechanically Ventilated Patients: A Randomized Double-blind Study
The effect of social participation on depressive symptoms in older women adults in China: A tracking survey database
Background Under the context of active aging, older women face prominent mental health risks due to their dual vulnerability stemming from biological characteristics and social roles. Given the existing practical bottlenecks in current research namely, unclear mechanisms regarding how social participation alleviates geriatric depression and insufficient studies on population heterogeneity. It is important to analyze the impact of social participation on depressive symptoms of older women in China and to implement corresponding policy systems. Methods: Based on the 2018 health and old-age care tracking survey database in China, 3047 subjects older female were included (mean age = 69.46 years).Depressive symptoms were measured using the CESD-10 depression scale from the database, and 11 categories of social activity questionnaires were utilized to reflect the level of social participation. The impact of social participation on the depressive symptoms of older women in China was empirically analyzed using the common least squares (OLS) and two-stage least squares (2SLS) methods, and tested the endogenous and robustness of the subjects, the heterogeneity of age and urban-rural areas, and the mediating effect of self-rated health were tested using the sobel method. Results: Social participation improved the depressive symptoms of older women(β=-0.789, P < 0.01), and the variable method confirmed the estimated results through the endogeneity and robustness tests. At the same time, there was age and urban-rural heterogeneity. Social participation has a greater impact on depression symptoms in older adults 60–80 + than 80 + , and in urban areas than in rural areas. And social participation indirectly affected depression through self-assessment of health, and the indirect effect accounted for 21.4% (β = -0.139, P < 0.01). Conclusion: In the process of actively dealing with the aging of the population, the older women should be actively encouraged to participate in social activities such as community volunteering and hobby activities. According to the physical and mental development characteristics of different older groups, the government should formulate targeted social participation policies and improve relevant facilities for the elderly and the old-age security system.
Accelerating multi-objective optimization of concrete thin shell structures using graph-constrained GANs and NSGA-II
Impact of Noninvasive Ventilation on Quality of Sleep among Patients Admitted to the Critical Care Unit
The effect of supply chain risks management practices on operational performance of pharmaceutical manufacturing companies in Addis Ababa, Ethiopia: Analytical cross-sectional study
Background The efficient movement of pharmaceuticals through various stakeholders to reach customers in the appropriate quantity and at the right time is achieved through supply chain management. However, the intricate nature of supply chain processes poses tremendous supply chain risks and jeopardizing pharmaceutical manufacturing company’s ability. Failure to address these risks can hinder the provision of high-quality health services. However, effective supply chain risk management can foster more resilient and efficient global supply chains. Therefore, this study aimed to investigate the effect of pharmaceutical supply chain risks management practices on operational performance of pharmaceutical manufacturing companies in Addis Ababa, Ethiopia. Methods Analytical cross-sectional study complemented with qualitative method was conducted at pharmaceutical manufacturing companies in Addis Ababa between May-August 2023. One hundred seventy two staffs working in four manufacturing companies included in the study. For quantitative part, pretested a self-administered five-point Likert scale questionnaire was used and analyzed using SPSS® -version 26. Assumptions of linear multivariate regression were checked and the level of significance determined at a 95% CI and p-value <0.05. Nine face to face in-depth interviews with key informants were conducted to gather the qualitative data, and the data were analyzed using thematic analysis technique. Result The study included 172 employees from four manufacturing companies, with a response rate of 97%. The regression analysis revealed that demand (β=-0.191, t = -4.162, p < 0.05) and supply side risks (β=-0.131, t = -2.015, p < 0.05) have a negative effect on the operational performance of manufacturing companies. Holding other variables constant, a one-unit increase in demand and supply side risks results in 19.1% and 13.1% lead to decrease in operational performance of manufacturing companies, respectively. Increasing costs of freight, shortage and fluctuating foreign exchange rate for currency, lack of logistics expertise and organized risks mitigation team were the major challenges for manufacturing company’s operational performance. Conclusion Demand and supply side risks affected the supply chain performance of the manufacturing companies. Furthermore, the increasing costs of freight, shortage of foreign currency, lack of logistics expertise and organized risk mitigation team were the main bottleneck for pharmaceutical manufacturing company’s performance. The study result suggests demand and supply side risks, increasing costs of freight; shortage foreign currency exchange rate and lack of logistics expertise in companies should be given attention by stakeholders to improve operational performance. Furthermore, understanding these risk factors can improve the operational performance of the pharmaceutical industry by influencing policy and industry practices in the larger framework of global supply chain risk management not only one country.