Explore the vast range of titles available.

Non-alcoholic fatty liver disease (NAFLD) is a prevalent liver disorder characterized by excessive hepatic fat accumulation without alcohol intake. It can progress to non-alcoholic steatohepatitis, increasing the risk of cirrhosis and liver failure. This study aims to evaluate the efficacy of omega-3 polyunsaturated fatty acids (n-3 PUFAs) in treating NAFLD. A systematic review and meta-analysis was conducted including studies published from January 2018 to June 2023. Databases searched included PubMed, Embase, Cochrane Library, and ClinicalTrials.gov. Inclusion criteria comprised randomized controlled trials and cohort studies involving human subjects or animal models with NAFLD. Data were extracted and analyzed to assess the impact of omega-3 PUFAs on liver fat, hepatic enzymes, and serum lipid profiles using RevMan 5.4. A total of 15 studies met the inclusion criteria. Omega-3 supplementation significantly decreased alanine aminotransferase (ALT) (mean difference = -2.12, 95% confidence interval (CI) = -3.36, -0.87) and aspartate aminotransferase (AST) (mean difference = -1.50, 95% CI = -2.59, -0.42). Gamma-glutamyl transferase levels showed a trend toward reduction (mean difference = -0.82, 95% CI = -1.66, 0.02). Serum lipid profiles improved significantly with reductions in triglycerides, low-density lipoprotein, and total cholesterol along with significant reductions in AST, ALT, and alkaline phosphatase in animal models. Omega-3 PUFAs appear to offer beneficial effects on liver enzymes, serum lipid profiles, and anthropometric indices in NAFLD patients. While their impact on liver fat content remains uncertain, omega-3 supplementation could serve as a valuable adjunct treatment for enhancing metabolic profiles and liver function in NAFLD patients.

Background: Recent studies reveal an association between increased cell-free fetal (cff) nucleic acid in maternal blood and pregnancy challenges like loss, pre-eclampsia, growth restriction, and preterm labor. Objective: This article assesses the role of cff nucleic acids as potential diagnostic markers for the prediction and monitoring progression of severe pregnancy-related complications. Materials and Methods: In this systematic review and meta-analysis, various databases were searched. Original articles reporting on the role of cff nucleic acids in predicting the complications of pregnancy were included. I square test and funnel plot were used to analyze heterogeneity and publication bias, respectively. The quality of studies was assessed using the critical appraisal checklists for studies created by the Joanna Briggs Institute. Results: 70 publications were selected for the final qualitative analysis. Articles were published between 2010 and 2023, and most studies were conducted in the USA and China. The majority of studies were conducted on the quantity of cff-DNA (n = 40), and the remaining on microRNA (n = 18), messenger RNA (n = 11), and cell-free RNA (n = 1). The pooled sensitivity of cff nucleic acids for detecting trisomy was found to be 90.9 (95% CI: 80.9–100%). MicroRNA levels were significantly increased in participants with gestational diabetes mellitus, with a standardized mean difference of 1.22 (95% CI: -0.90–3.34). Conclusion: Fetal nucleic acids can serve as accurate noninvasive diagnostic tools for predicting serious complications during pregnancy.

Alzheimer's Disease (AD) is a special class of neurodegenerative diseases demarcated as a progressive disorder affecting especially older adults globally. The AD-infected brain shows declination in cognitive functions, memory loss, and other exhausting symptoms. In this study, we focused on using advanced bioinformatics and next-generation sequencing to explore essential clusters of genes from various diversified Alzheimer's, Parkinson and Frontotemporal Dementia diseased cases. The significant differential expression analysis of genes (p-value ≤ 0.05, log fold change ≤ 0.05) was carried out, followed by meta-analysis, which resulted in the identification of 20 conserved genes across variable case studies. Out of 20 conserved genes, CASP8 and PTPN11 were observed to show essential regulatory mechanisms in AD metabolic pathways and proceeded further for docking analysis. Moreover, the natural compounds were screened for ligand library preparation based on extensive scientific literature and (ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity)) property check. Molecular docking was carried out with screened ligands and target receptors, resulting in the identification of Rosmarinic acid (RA) with CASP8 having docked score (∆G = -8.0 kcal/mol); Donepezil (FDA drug) dock score (∆G = -7.3 kcal/mol) (control). PTPN11 receptor with Carnosol ligand resulted in docking score (∆G = -9.1 kcal/mol) w.r.t Tacrine (FDA drug) docked score (∆G = -8.0 kcal/mol) followed by MD simulation. This research will aid in the identification of potential natural compounds that future researchers can use for further validation as a potential candidate drug in combating various neurodegenerative diseases highlighting AD.

The identification accuracy of low‐voltage distribution consumer–transformer relationship and phase are crucial to three‐phase unbalanced regulation and error correction in consumer–transformer relationships. However, owing to the rapid increase in the number of consumers and the upgrade of the feed lines for low‐voltage distribution systems, the timely update of the consumer‐transformer relationship and phase information of consumers is challenging. This influences the accuracy of the basic information of the power grid. Thus, this study proposes a low‐voltage distribution network consumer–transformer relationship and phase identification method based on anomaly detection and the clustering algorithm. First, the improved fast dynamic time warping distance based on the filter search between voltage sequences is used to measure the similarity between voltage curves. Subsequently, an abnormal consumer detection method based on the local outlier factor is used to identify consumers with mismatched consumer‐transformer relationships by determining the local outlier factor scores of voltage curves. Furthermore, the phase information of normal consumers is identified through clustering by fast search and find of density peaks. Finally, the proposed method is validated using case studies of practical low‐voltage distribution systems in China. The proposed method can effectively improve phase identification accuracy and maintain high adaptability in various data environments.

ABSTRACT Objective: To compare platelet rich plasma against local steroid injection in patients with chronic plantar fasciitis in terms of mean pain and functional scores. Study Design: Quasi-experimental study. Place and Duration of study: Armed Forces Institute of Rehabilitation Medicine(AFIRM) Rawalpindi, from May 2016 to Apr 2018. Methodology: A total of 120 patients having chronic plantar fasciitis were included in the study and were split into 2 groups. The group “A”(n=60) patients were injected with a single dose of autologous platelet rich plasma. The group “B”(n=60) patients received a single dose of methylprednisolone added with a local anesthetic agent. Functional and symptomatic evaluation was done using the American foot and ankle score and the visual analog scale respectively at baseline and at 6 months follow-up. Results: Mean visual analogue score was 7.83 ± 0.99 at baseline and 3.43 ± 1.30 at 6 months follow-up in group “A” and 7.90 ± 1.06 and 4.97 ± 1.16, respectively, in group “B”(p<0.001). Mean American Foot and Ankle Score was 39.37 ± 5.93 at baseline and 88.73 ± 5.02 at 6 months follow-up in group “A” and 39.03 ± 5.97 and 80.30 ± 8.03, respectively, in group “B”(p<0.001). Changes in the scores of both the evaluation tools were significantly higher in the group “A”(p<0.001). Conclusion: Platelet rich plasma turns out to be more efficacious compared to steroid injection in terms of pain relief and functional outcome in the management of chronic plantar fasciitis in long term.

Quetta Valley is an integral part of the Pishin Lora Basin (PLB), a prominently water-deficient basin within the Balochistan province of Pakistan. The Pishin Lora River (PLR) flowing through the basin supports agriculture and domestic water use. However, the available scientific research on hydrological modeling in the study area is either limited or outdated. The influence of climate change on the basin's hydrology has intensified the demand for advanced approaches in flow prediction studies. Hydrological modeling is complex, particularly in a basin like Quetta with complicated terrain, including high elevations and varied slopes with diverse rainfall distribution. A machine learning (ML) data-driven approach is a viable alternative to conceptual hydrological models, mitigating the dependency on extensive data that may not always be readily available. This study employs Artificial Neural Network Multilayer Perceptron (ANN-MLP), Random Forest (RF), and Multiple Linear Regression (MLR) models for runoff prediction. Remotely sensed meteorological datasets spanning over thirty-two years (1990–2022) were obtained and compiled at three distinct timescales: daily, weekly, and monthly. The dataset comprises rainfall, humidity, and temperature records from the Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) satellite. These variables serve as model inputs for predicting the runoff of the watershed. Simulated runoff data using the SCS-CN method were used in ML models in the absence of actual runoff records. Statistical tests were performed for the performance evaluation of the model, including coefficient of determination (R 2 ), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), and Willmott index (WI). Two input combinations (M1 and M2) were used to predict the daily, weekly, and monthly runoff timescales. The results of the all models were promising for both input combinations, with a slightly superior predictive performance observed in M2. In M2, the R 2 values for ANN, RF, and MLR at the daily timescale were 0.998, 0.996, and 0.994, respectively. At the weekly timescales, these values were 0.994, 0.998, and 0.995, while at the monthly timescales, they were 0.995, 0.994, and 0.997. This study establishes improved water management strategies for data-scarce regions, exploiting the power of cutting-edge machine learning tools to drive innovation in hydrological research within the region.

Metal alloy matrix composites are generally lightweight structural materials with a high strength-to-weight ratio. They can be extensively used in various fields of modern engineering applications, such as aerospace and automotive components and biomedical engineering. This study focuses on the development and characterization of lightweight metal alloy matrix composites for industrial applications, with a particular emphasis on magnesium (Mg) alloys as a replacement for aluminum-based alloys. Mg alloys offer significant weight advantages, being 33% lighter than aluminum and 75% lighter than steel, making them highly desirable for use in various engineering fields. In the present study, Mg (AZ91) alloy reinforced with x-Si3N4 composites (x = 0, 1, 3, 5, 7, 9 wt.%) were fabricated using a liquid state process. The AZ91/x-Si3N4 composites were evaluated through physical, mechanical, wear, and microstructural characterization. The experimental results, supported by statistical analysis, demonstrated that the incorporation of Si3N4 particles amplified the mechanical properties, wear resistance, and porosity of the composites. However, the presence of the reinforced particles resulted in reduced forgeability and elongation, limiting certain deformation characteristics. The existence of the reinforced particles within the composites was confirmed through SEM analysis, providing visual evidence of their distribution and interaction within the Mg alloy matrix. Finally, it was concluded that the implication of the study could be sought for the light structural parts of aerospace, automotive, biomedical, and prosthetic applications.

This paper presents an experimental and numerical investigation of pultruded composite glass fibre-reinforced polymer (pGFRP) cross-arms subjected to flexural creep behaviour to assess their performance and sustainability in composite cross-arm structure applications. The primary objective of this study was to investigate the failure creep behaviour of pGFRP cross-arms with different stacking sequences. Specifically, the study aimed to understand the variations in strain rate exhibited during different stages of the creep process. Therefore, this study emphasizes a simplified approach within the experiment, numerical analysis, and mathematical modelling of three different pGFRP composites to estimate the stiffness reduction factors that determine the prediction of failure. The findings show that Findley’s power law and the Burger model projected very different strains and diverged noticeably outside the testing period. Findley’s model estimated a minimal increase in total strain over 50 years, while the Burger model anticipated PS-1 and PS-2 composites would fail within about 11 and 33 years, respectively. The Burger model’s forecasts might be more reasonable due to the harsh environment the cross-arms are expected to withstand. The endurance and long-term performance of composite materials used in overhead power transmission lines may be predicted mathematically, and this insight into material property factors can help with design and maintenance.

Air pollution, driven by rapid urbanization and industrialization, poses serious health risks, particularly in densely populated cities where individuals spend most of their time indoors. This study assessed indoor and outdoor air quality in residential houses in Okhla, South Delhi—a recognized high-pollution hotspot—during spring 2023 (February–April) using calibrated low-cost sensors. A total of 24 houses were monitored and categorized as well-ventilated or poorly ventilated based on the presence of mechanical ventilation systems. Concentrations of particulate matter (PM 2.5 and PM 10 ) were measured indoors and outdoors, and indoor/outdoor (I/O) ratios were calculated to evaluate the role of ventilation. Results revealed that indoor PM 10 levels in poorly ventilated houses reached approximately 320 µg/m 3 , far exceeding the WHO guideline of 45 µg/m 3 , while indoor PM 2.5 levels were nearly five times above permissible limits. PM 2.5 and PM 10 exhibited similar temporal patterns, with I/O ratios consistently greater than one, indicating substantial indoor contributions to exposure. Poorly ventilated houses demonstrated higher ratios, underscoring the influence of inadequate ventilation in exacerbating indoor air quality. These findings highlight the urgent need for improved ventilation practices and sustainable urban planning to mitigate health risks. Beyond providing evidence of high indoor exposures in a critical urban hotspot, the study demonstrates the utility of low-cost sensors for monitoring. It contributes to policy discussions on scalable, affordable tools for managing indoor air quality in rapidly urbanizing regions.

Metal alloy matrix composites are generally lightweight structural materials with a high strength-to-weight ratio. They can be extensively used in various fields of modern engineering applications, such as aerospace and automotive components and biomedical engineering. This study focuses on the development and characterization of lightweight metal alloy matrix composites for industrial applications, with a particular emphasis on magnesium (Mg) alloys as a replacement for aluminum-based alloys. Mg alloys offer significant weight advantages, being 33% lighter than aluminum and 75% lighter than steel, making them highly desirable for use in various engineering fields. In the present study, Mg (AZ91) alloy reinforced with x-Si[sub.3]N[sub.4] composites (x = 0, 1, 3, 5, 7, 9 wt.%) were fabricated using a liquid state process. The AZ91/x-Si[sub.3]N[sub.4] composites were evaluated through physical, mechanical, wear, and microstructural characterization. The experimental results, supported by statistical analysis, demonstrated that the incorporation of Si[sub.3]N[sub.4] particles amplified the mechanical properties, wear resistance, and porosity of the composites. However, the presence of the reinforced particles resulted in reduced forgeability and elongation, limiting certain deformation characteristics. The existence of the reinforced particles within the composites was confirmed through SEM analysis, providing visual evidence of their distribution and interaction within the Mg alloy matrix. Finally, it was concluded that the implication of the study could be sought for the light structural parts of aerospace, automotive, biomedical, and prosthetic applications.