Browse Articles

Microwave assisted starch stabilized green synthesis of zinc oxide nanoparticles for antibacterial and photocatalytic applications

RNA sequencing and immunohistochemistry jointly improve tumor biomarker interpretation

Design, synthesis and bioactivity evaluation of phosphinanes as potential anticancer agents

Global motion coherent deficits in individuals with autism spectrum disorder and their family members are associated with retinal function

Abstract This study aims to evaluate if the reduced sensitivity to global motion observed in some individuals with autism spectrum disorder (ASD) is associated with altered retinal processing. Motion coherence thresholds were measured from individuals with ASD and their family members and compared to the test reference limits derived from control participants. The light adapted electroretinogram (ERG) a- and b-wave amplitudes and peak-times, and photopic negative response (PhNR) parameters were measured from the ASD individuals and their families and compared to those of controls. Abnormally high motion coherence thresholds were found in ASD probands and their family members compared to that in controls, particularly mothers. Altered retinal functions were found in ASD probands and their parents. The PhNR, a- and b-wave time-to-peak were significantly correlated with motion coherence thresholds. The altered retinal function was associated with the age, intelligence and autism severity of the ASD family members. There were associations between the motion coherence and ERG parameters, including smaller amplitudes of the PhNR, and longer time-to-peak of the a- and b-waves and time to the PhNR, compared to those with abnormal motion coherence thresholds. The results showed that global motion coherence deficits were associated with altered retinal function in ASD and their family members. The findings suggest that motion perception deficits follow a familial pattern and that affected mothers may have an increased risk of a child with ASD.

Validation of the age, neutrophil to lymphocyte ratio, C reactive protein score on 28 day mortality in the National COVID cohort collaborative

Experimental study on the influence of ground temperature and pore water pressure on the mechanics of excavation unloading sandstone

Association between post-stroke depressiveness and the utilization of healthcare services three months after the stroke

Abstract Post-stroke depression affects approximately one-third of stroke survivors and can result in various adverse outcomes. With the rising prevalence of strokes, understanding the impact of post-stroke depression on healthcare utilization is crucial. The study examined the association between patient-reported post-stroke depressiveness and the healthcare service utilization three months post-stroke. The prospective Stroke Cohort Augsburg (SCHANA) study assessed post-stroke depressiveness using the Patient Health Questionnaire (PHQ-9) during hospital stay. Participants reported their utilization of medical and therapeutic services, rehabilitation, and inpatient hospital stays three months post-discharge. Multivariable adjusted linear and binary logistic regression analyses examined the association between post-stroke depressiveness and healthcare service utilization. Among 546 participants included into the analyses, 57.1% had no depression, 28.8% had mild depression and 14.9% had moderate to severe depression. Higher PHQ-9 scores were associated with a significantly increased likelihood of inpatient hospital treatment (OR = 1.07; 95% CI: 1.01–1.14; p = 0.016), attendance at rehabilitation programs (OR = 1.09; 95% CI: 1.03–1.15; p = 0.003), and more frequent utilization of general practitioners/internists (beta = 0.10; 95% CI: 0.01–0.19; p = 0.036). In conclusion, post-stroke depressiveness was associated with increased healthcare service utilization. Further studies are required to gain a more comprehensive understanding.

Enhancing shear strength predictions of UHPC beams through hybrid machine learning approaches

Abstract Ultra-high-performance concrete (UHPC) beam shear strength prediction is a complicated process due to the involvement of numerous parameters. The accuracy needed for precise predictions is frequently lacking in current empirical equations and traditional machine learning (ML) techniques. This study proposes hybrid ML models that integrate three nature inspired metaheuristic algorithms—Giant Armadillo Optimization (GOA), Spotted Hyena Optimization (SHO) and Leopard seal optimization (LSA)- Extreme Gradient Boosting (XGB) to predict the shear strength of UHPC beams. A comprehensive dataset was created from extensive literature reviews and trained and tested on the models using multiple input parameters that affect UHPC’s shear capacity. For model assessment, performance metrics, such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and variance accounted for (VAF), were utilized. Results showcased high accuracy, with R2 values approaching 0.9912 in training and 0.9802 in testing phases using the LSA-XGB algorithm, indicating excellent model fit and predictive reliability. To improve the model’s transparency and interpretability, the study also incorporates shapely additive explanations (SHAP), which reveal how each dataset attribute affects the predictive results. The LSA-XGB algorithm performed better than prior studies and empirical equations in predicting the shear strength of UHPC beams. More sophisticated machine learning techniques that improve the precision of predicting the shear capacity of UHPC beams are demonstrated in the study. Further, the use of a graphical user interface (GUI) helps researchers and engineers to make quick, well-informed decisions in real-time. These findings offer a reliable, interpretable, and accessible approach to predicting shear strength in UHPC beams, contributing to safer structural engineering practices.

Cross sectional analysis of bone mineral density and diet balance index in preschool children in Bengbu, Anhui, China

Investigating the effects of pcDNA3 polybia-MP1 on the apoptosis induction in lung cancer cell line

Methodic aspects of influenza and respiratory syncytial virus detection in raw wastewater and presence in treatment plants in southeastern Germany

Abstract During the COVID-19 pandemic, wastewater-based epidemiology (WBE) has been developed as an additional tool to follow epidemiological trends in the catchment area of treatment plants. Meanwhile, further viral agents of respiratory infections were included in monitoring programs. However, differences in sample processing may impair the results comparison among studies. With identical virus strains, we investigated different concentration methods, RNA isolation kits and primer/probe combinations for detection of influenza virus (IV) A/B and respiratory syncytial virus (RSV) A/B. For procedure optimization, virus enrichment and RNA extraction methods were found to be of relevance. Conversely, the detection step was identified to have a relatively low influence. Using standardized protocols, 24 h composite samples from eight wastewater treatment plants (WWTP) in southeast Germany (2x/week) were analyzed (January 2024 to December 2024) for IVA/B and RSV-A/B. The following rates of virus-positive samples were determined: 20.5% (IVA including 2.2% H1N1pdm09), 4.5% (IVB), 32.6% (RSV-A) and 2.4% (RSV-B). IV and RSV detections showed a matching trend when compared to reported cases. This study contributes to a better understanding of factors influencing the detection of IV and RSV in wastewater as well as in the epidemiological significance of virus monitoring in WWTPs.

Assessing the location potential for tourism development of traditional villages in Sichuan province, China

Introducing the kernel descent optimizer for variational quantum algorithms

Lipidomics analysis of phospholipid profiles and oxidative stability in pan-fried beef patties incorporating sacha inchi leaf extracts

Abstract High-temperature cooking methods, such as pan-frying, often accelerate lipid oxidation in meat products, negatively affecting their nutritional quality and safety; thus, natural antioxidants, such as those found in sacha inchi (Plukenetia volubilis) leaves, could be beneficial in mitigating this issue. Beef patties were additioned with sacha inchi leaf extracts at concentrations of 0.5%, 1.0%, and 1.5%, focusing on their antioxidant effects, lipid oxidation, and lipid profiles. The results showed that the addition of sacha inchi leaf extracts to pan-fried beef patties significantly reduced lipid oxidation. Higher concentrations of extracts correlated with increased antioxidant activity. Lipid profiling had distinct differences between samples, with the 1.5% of sacha inchi leaf extracts concentration showing the most promising results. Specifically, this concentration was characterized by elevated levels of LPC (Lysophosphatidylcholine)(18:2), LPE(Lysophosphatidylethanolamine)(18:2), and PA (Phosphatidic Acid)(27:2/8:0). These findings suggest that the sacha inchi leaf extracts not only mitigate lipid oxidation but also enhance the nutritional value of beef patties by influencing their lipid composition.

Harnessing prebiotic formamide chemistry: a novel platform for antiviral exploration

Abstract Viruses and host cells are intricately connected through a shared “chemical language” that may trace back to the prebiotic chemistry of early Earth. In this study, we present an innovative platform for antiviral exploration inspired by this primordial chemical framework. By “doping” the formamide-based prebiotic chemistry model with orotic acid derivatives, we generated complex, non-natural chemical mixtures capable of disrupting the replication of multiple viruses with minimal or no toxicity for eukaryotic cells. This strategy underscores the potential of an evolution-inspired approach in antiviral discovery, offering a novel avenue for identifying new agents with unconventional mechanisms that might elude traditional discovery methods.

A novel ultra-steep subthreshold swing iTFET with control gate and control source biasing

Neural correlates and dynamical brain states of creative insight in a spatial problem task

Convolutional transform learning based fusion framework for scale invariant long term target detection and tracking in unmanned aerial vehicles

Establishment of THTT derivatives as potential antileishmanial and anti-inflammatory agents through in vitro and in silico investigations

Multiscale performance analysis and optimization of a composite clamp plate for leaf spring assembly considering fiber orientation distribution

Abstract The growing demand for lightweight solutions in automotive engineering has propelled the adoption of fiber-reinforced thermoplastic composites, necessitating precise characterization of their process-induced mechanical properties. This study develops an integrated multiscale methodology addressing injection-molding-induced fiber orientation heterogeneity in structural components. Through synergistic integration of injection molding simulation, mesoscopic constitutive modeling, and macroscopic structural analysis, we systematically investigated failure mechanisms in automotive leaf spring clamp plates. The proposed framework successfully identifies gravitational segregation during vertical molding as the root cause of terminal fracture under operational loads. Subsequent design optimization implements (1) reorientation of the injection direction to horizontal and (2) localized wall thickness reduction from 37 mm to 19.86 mm. These interventions collectively reduce the maximum principal stress by 19% (from 231 MPa to 187 MPa) while achieving a 12.8% mass reduction (from 780 g to 680 g), demonstrating the concurrent enhancement of structural reliability and lightweighting efficacy.