Bacterial infections and antimicrobial resistance patterns: a comprehensive analysis of health dynamics across regions in Pakistan (2013-2023).

Antimicrobial resistance (AMR) is a significant public health concern globally, and Pakistan is no exception. The misuse and overuse of antibiotics, inadequate regulation of their sale, and a lack of awareness contribute to the rising levels of AMR in the country. study presents a detailed analysis of blood and urine samples collected in Pakistan over various periods, focusing on pathogen prevalence, gender distribution, and age-wise patterns.

Unveiling the dual-pathway effect of CSR perception environmental on employee pro-environmental behavior: evidence from Chinese marine enterprises.

To achieve the sustainable development goals and in response to the green development policies, many enterprises have actively incorporated corporate social responsibility (CSR) into their strategic plans in order to enhance environmental sustainability. This study explores the cognitive and emotional pathways through which perceived environmental CSR (PECSR) influences pro-environmental behavior (PEB) among employees in China's marine enterprises, based on the Cognitive-Affective Personality System theory. The research was conducted through data collection and verified through the structural equation model.

Scenario simulation analysis of urbanization spatial pattern optimization promoting regional coordinated development: A case study of Guizhou, China.

This research delves into the optimization of urbanization spatial patterns in Guizhou Province, China. The findings reveal that with regional coordinated development as the central objective and the optimization of urbanization spatial patterns as the strategic focus, a research framework encompassing "temporal and spatial evolution of urbanization - identification and summation of pain points and difficulties - scenario simulation and optimization - strategic goal selection" is utilized to specifically tackle issues pertaining to urbanization spatial patterns. Through the construction of diverse scenarios and rigorous research analysis, an implementation pathway is derived, advocating for "strengthening the central region of Guizhou, fostering urban agglomeration development, reinforcing developmental support points, and promoting regional coordinated development.

Prevalence and magnitude of perceived dual-task difficulties in people with multiple sclerosis: an international study.

Objective: To investigate the prevalence and magnitude of dual-task (DT) difficulties and the discriminative ability of three questionnaires evaluating perceived DT difficulties: the Dual-Tasking Questionnaire (DTQ), Dual-Task Screening-List (DTSL), and Dual-Task-Impact on Daily-life Activities Questionnaire (DIDA-Q).

Design: Multicenter, cross-sectional study SETTING: Persons with multiple sclerosis (pwMS) and healthy controls (HC) were recruited from 7 multiple sclerosis centers across 6 countries (Belgium, Chile, Italy, Israel, Spain, and Turkey).

Participants: A total of 540 participants: 175 with mild disability (mean EDSS: 2.

An empirical comparative study on the diagnostic index system for techniques and tactics in men's table tennis during the ABS plastic ball Era.

In order to accurately identify the diagnostic index system that can best reflect the technical and tactical performance of table tennis after the implementation of the new ABS material ball, and then more accurately and efficiently diagnose and analyze the technical and tactical ability of players. This paper uses the methods of match observation, mathematical statistics, literature and other methods to carry out an empirical comparative study on the representative segmented diagnostic indicator system constructed by predecessors in the past 20 years. Research suggests that: 1) in the New Ball Era, each segmented diagnostic indicator system has a certain degree of rationality, but in comparison, the five-segment diagnostic indicator system is the most optimal and the most accurate to reveal the technical and tactical performance in the new era.

Persistent progression independent of relapse activity in multiple sclerosis.

Patients with relapsing-remitting multiple sclerosis (RRMS) may experience disability progression independent of relapse activity (PIRA), which can be an early sign of secondary progressive MS (SPMS). We defined persistent PIRA as ongoing sustained disability over the entire available follow-up period. However, PIRA events can regress over time.

Exploring Risk Factors and Predictive Modeling of Child Malnutrition in Pakistan Using Machine Learning.

Objective: This study aims to identify risk factors and develop predictive models of child malnutrition (stunting, wasting, and underweight) in Pakistani children under five using machine learning approaches.

Study Design: This cross-sectional design utilized data from the Pakistan Demographic and Health Survey 2017-2018 (PDHS).

Methods: Logistic regression was employed to identify significant socio-demographic and health-related risk factors.

The nonlinear impact of ESG performance on audit pricing: Evidence from China.

The substantial impact of corporate Environmental, Social, and Governance (ESG) performance, predominantly measured by non-financial metrics, on external auditors is investigated in this paper. Using a sample of Chinese-listed companies from 2011 to 2021, we examine how auditors respond to corporate ESG performance from the perspective of audit pricing. The findings reveal a U-shaped, non-linear relationship between corporate ESG performance and audit pricing.

The Discursive Construction of the Concept of LGBTQ People in Hungarian Online Political Communication.

Anti-LGBTQ measures and political discourse have significantly intensified in Hungary since 2020, with the initiation and unanimous support of Fidesz and KDNP, the governing parties since 2010. To understand the political context in which these took place, this study investigates how prominent Hungarian politicians-government and opposition alike-discursively constructed the concept of LGBTQ people in their social media communication prior to this wave of anti-LGBTQ legislation. Specifically, it turns to the 2019 social media communication of 44 prominent Hungarian politicians from 12 political parties and groups.

Deep learning approach for automatic assessment of schizophrenia and bipolar disorder in patients using R-R intervals.

Schizophrenia and bipolar disorder are severe mental illnesses that significantly impact quality of life. These disorders are associated with autonomic nervous system dysfunction, which can be assessed through heart activity analysis. Heart rate variability (HRV) has shown promise as a potential biomarker for diagnostic support and early screening of those conditions.

Cooperative Catalytic Role of Co and Mn Sites on LaCo Mn O Perovskite Nanoparticles in CO and NO Oxidation.

Perovskites have significant potential to improve efficiency, reduce the costs of conventional oxidation catalysts, and contribute to cleaner and more sustainable energy solutions. However, numerous structural factors influencing their catalytic performance are still a subject to debate. In this study, simple perovskite nanoparticles in the form of LaCoO (LC) and LaMnO (LM), as well as LaCo Mn O (LCM)-mixed B-site perovskites with different B-site cations, were synthesized and their performances in CO oxidation and NO oxidation reactions were examined.

Research on strategies for enhancing system security resilience of prefabricated building engineering.

With the construction industry's deepening development towards digitalization, intelligentization, greening, and industrialization, prefabricated building is poised for significant promotion due to its characteristics of energy efficiency, environmental friendliness, rapid progress, and high quality. Current safety management models for prefabricated construction primarily focus on preventing safety accidents, lacking a holistic, systematic analysis of the system's comprehensive capability when subjected to risk disturbances. This study adopts a resilience perspective, focusing on the safety resilience of prefabricated construction engineering systems.

Beyond the neuron: Unveiling the role of reactive astrocytes in epileptic seizure dynamics through self-organized bistability.

During epileptic seizures, brain activity and connectivity undergo dramatic changes. Brain networks transition from a balanced resting state to a hyperactive and hypersynchronous state. However, the mechanisms driving these state transitions remain unclear.

Resistance of mature and elderly bodybuilders to anaerobic energy supply load.

Background: This study aims to assess the resistance level of beginner bodybuilders of mature and elderly age to strength training performed under anaerobic energy supply modes (creatine phosphokinase and glycolysis).

Methods: The study involved 60 men who had been engaged in bodybuilding for only 12 months. Group A included men aged 24 ± 1.

Subjective socioeconomic status moderates depression's impact on fairness perception in the ultimatum game: A moderated mediation model.

Individuals with depression often exhibit cognitive distortions in socioeconomic decision-making, particularly in interpreting fairness. However, the role of subjective socioeconomic status in shaping these distortions remains underexplored. This study investigates how depression influences fairness perception and rejection behavior in the Ultimatum Game.

Ionizing radiation-induced testicular oxidative stress and apoptosis: the role of small GTPase RhoA.

Purpose: The effects of ionizing radiation on living organisms are mainly known as the generation of reactive oxygen species (ROS), apoptosis, and DNA damage. Small GTPases (RhoA, Rac1, Cdc42) are known to have roles in the regulation of oxidative stress and apoptosis. The aim of this study was to investigate the role of the RhoA molecule in testicular tissue damage due to oxidative stress and apoptosis induced by ionizing radiation.

Combining knowledge distillation and neural networks to predict protein secondary structure.

The secondary structure of a protein serves as the foundation for constructing its three-dimensional (3D) structure, which in turn is critical for determining its function and role in biological processes. Therefore, accurately predicting secondary structure not only facilitates the understanding of a protein's 3D conformation but also provides essential insights into its interactions, functional mechanisms, and potential applications in biomedical research. Deep learning models are particularly effective in protein secondary structure prediction because of their ability to process complex sequence data and extract meaningful patterns, thereby increasing prediction accuracy and efficiency.

Cross-sectional study of the association between nutrition and depression in older people living in nursing homes.

Background: Cognitive dysfunction and depressive symptoms are common and closely related mental health issues in older people, significantly affecting their quality of life and social functioning. Nutritional factors play a key role in preventing and managing these conditions. This study aimed to examine the relationship between nutritional status and depressive symptoms and cognitive dysfunction in older people.

Electrospun poly(3-hydroxybutyrate) fibers containing pheophorbide derivatives: Structural, photophysical, and photodynamic properties for anticancer applications.

The study explores novel electrospun poly(3-hydroxybutyrate) (PHB) systems for pheophorbide derivative (PheoD) photosensitizers delivery in anticancer photodynamic therapy (PDT). The photophysical properties of PheoD within PHB matrix and the resulting impact on polymer morphology (SEM, AFM), supramolecular structure (XRD, EPR), mechanical behavior, in vitro release, cytotoxicity assay on A431 cells, and antimicrobial properties were investigated. It was shown, that the difference in the PheoD's radical does not affect PHB's semicrystalline nature and amorphous region's structure but causes significant changes in the morphological characteristics of the surface.

Prescreening of Mango L.) Leaves as a Potential Functional Food Ingredient: Techno-Functional and Antioxidative Characteristics.

Mango ( L.) is cultivated in tropical and subtropical regions, with all parts of the tree-including leaves-used traditionally to treat diabetes, infections, pain, and other conditions. Mango leaves contain proteins, minerals, vitamins, and phenolic compounds, including mangiferin, quercetin, and kaempferol, whose content varies by cultivar.

Structure-Forming Properties of : A Promising Resource for Edible 3D Printing Applications.

Approximately 20-30% of cultivated oyster mushrooms () are classified as low grade due to morphological and visual imperfections or mechanical damage, representing significant waste in mushroom production systems. This review examines the structural and biochemical properties of , particularly focusing on cell wall components including chitin, β-glucans, and mannogalactans, which provide crucial rheological characteristics for 3D printing. The literature results demonstrate that these natural polysaccharides contribute essential viscosity, water-binding capacity, and mechanical stability required for printable edible inks.

SHAP-Based Identification of Potential Acoustic Biomarkers in Patients with Post-Thyroidectomy Voice Disorder.

The objective of this study was to identify potential robust acoustic biomarkers for functional post-thyroidectomy voice disorder (PTVD) that may support early diagnosis and personalized treatment strategies, using acoustic analysis and explainable machine learning methods. Spectral and cepstral features were extracted from /a/ and /i/ voice recordings collected preoperatively and 4-6 weeks postoperatively from a total of 126 patients. Various Support Vector Machine (SVM) and Boosting models were trained.

Diffusion Model-Based Augmentation Using Asymmetric Attention Mechanisms for Cardiac MRI Images.

The limited availability of cardiac MRI data significantly constrains deep learning applications in cardiovascular imaging, necessitating innovative approaches to address data scarcity while preserving critical cardiac anatomical features. We developed a specialized denoising diffusion probabilistic model incorporating an attention-enhanced UNet architecture with strategically placed attention blocks across five hierarchical levels. The model was trained and evaluated on the OCMR dataset and compared against state-of-the-art generative approaches including StyleGAN2-ADA, WGAN-GP, and VAE baselines.