Dose-dependent toxicological profiling of large-sized ZnO and Co-ZnO nanoparticles: Renal and hepatic implications in Balb/c mice.

Objective: The current study investigated the nephrotoxic and hepatotoxic effects of large-sized zinc oxide nanoparticles [(ZnO) (93 nm)] and cobalt-doped zinc oxide [(Co-ZnO) (90 nm)] in Balb/c mice.

Method: Both the nanoparticles were administered intraperitoneally at 50, 100, and 200 mg/kg, respectively, for 14 days, followed by biochemical and histopathological evaluations.

Results: Co-ZnO at 200 mg/kg induced significant hepatic toxicity, as evidenced by elevated ALP, AST, and ALT levels (p < 0.

Applications of Computer Intelligence in Hydrogen Production.

In response to environmental degradation and diminishing fossil fuel reserves, there is an urgent global shift toward sustainable and cleaner energy solutions. Hydrogen has gained importance as an alternative fuel due to its low carbon emissions and high combustion energy, in addition to its capacity for efficient renewable energy storage and transport. This paper presents a comprehensive review of various hydrogen production methods, including water splitting, hydrocarbon reforming, and biological decomposition, and evaluates the integration of machine learning techniques into these processes.

Prevalence of and Antimicrobial Drug Residues in Chicken Liver its Antibiogram Analysis and Implications on Public Health.

Chicken is a primary source of food worldwide. Ensuring food safety and preserving public health is crucial, and chicken meat inspection plays a critical role in achieving this goal. The transfer of pathogenic bacteria from animal intestines to carcasses during processing, and subsequently from meat products to consumers, is a significant contributor to the growing incidence of resistance and foodborne illnesses.

Machine learning driven dashboard for chronic myeloid leukemia prediction using protein sequences.

The prevalence of Leukaemia, a malignant blood cancer that originates from hematopoietic progenitor cells, is increasing in Southeast Asia, with a worrisome fatality rate of 54%. Predicting outcomes in the early stages is vital for improving the chances of patient recovery. The aim of this research is to enhance early-stage prediction systems in a substantial manner.

Physicochemical Properties and Molecular Insights of Favipiravir and Roflumilast Solid Dispersions for COVID-19 Treatment.

Fixed-dose combinations (FDCs) offer significant advantages for patients and healthcare systems by improving adherence and reducing pill burden. However, developing multi-drug formulations remains challenging due to complexities in drug compatibility, stability, and dissolution behavior. The COVID-19 pandemic has necessitated innovative therapeutic approaches.

Adaptive non-parametric kernel density estimation for under-frequency load shedding with electric vehicles and renewable power uncertainty.

As power systems around the world shift to incorporate more renewable energy sources, particularly wind power, maintaining grid stability becomes increasingly challenging due to the inherent variability of these sources. This paper introduces a novel bi-level robust optimization framework that enhances the capabilities of adaptive Under-Frequency Load Shedding (AUFLS) in managing the uncertainties brought by high penetration of wind energy and dynamic participation of electric vehicles (EVs). Central to this framework is an innovative adaptive non-parametric Kernel Density Estimation (AAKDE) technique, which sharpens the accuracy of wind power fluctuation predictions.

Compliance With Iron and Folic Acid Supplements Among Pregnant Women Attending Tertiary Care Hospital in the District of Peshawar.

Objectives This study aimed to determine the level of compliance with iron and folic acid supplementation (IFAS) and identify factors influencing compliance among pregnant women attending tertiary care hospitals in District Peshawar. Methods This cross-sectional study was conducted over a period of two months, from January 15, 2022, to April 30, 2022, among pregnant women visiting tertiary care hospitals in district Peshawar for antenatal checkups. A nonprobability-purposive sampling technique was used to collect the data.

Enhancing pediatric congenital heart disease detection using customized 1D CNN algorithm and phonocardiogram signals.

Congenital heart disease (CHD), impacting around 1 % of infants worldwide, constitutes a significant healthcare challenge. Early detection is crucial, however constrained by the intricacies of conventional diagnostic techniques such as auscultation and echocardiography. This research presents a tailored one-dimensional convolutional neural network (1D-CNN) for the classification of phonocardiogram (PCG) signals into normal or abnormal categories, providing an automated and efficient solution for congenital heart disease (CHD) diagnosis.

Advanced AI-driven techniques for fault and transient analysis in high-voltage power systems.

Each substation is critically essential to the overall operation of the electrical power system. Potential dangers include thermal stress, noise, slip, trip, fall hazards, animal waste, and nonionizing radiation. These are the causes of joint failures of cables and overhead lines, failure of one or more phases of circuit breakers, and melting of fuses or conductors in one or more phases.

6-Hydroxyflavanone treats anxiety and chemotherapy-induced neuropathy in Sprague-Dawley rats.

Background: Cancer remains a predominant cause of death worldwide. The advent of effective chemotherapy has enormously decreased the mortality rate and increased the life expectancy of cancer patients. However, the adverse effects allied with chemotherapy contribute to the development of neurotoxicity, anxiety, and depression.

Baicalein Long-Circulating Liposomes Improve Histological and Functional Outcomes After Traumatic Brain Injury.

High mortality and disability have been principally linked with traumatic brain injury (TBI) with this condition being a principal issue in public health. Currently, there is no reliable pharmaceutical agent to salvage the damage caused by TBI. Baicalein (BCL), a traditional Chinese medicine active ingredient, has preliminarily shown repair activity on brain injury.

Long short term memory networks for predicting resilient Modulus of stabilized base material subject to wet-dry cycles.

The resilient modulus (M) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the M, although it is expensive, time-consuming, and requires specialized lab facilities. The purpose of this study is to establish a new model based on Long Short-Term Memory (LSTM) networks for predicting the M of stabilized base materials with various additives during wet-dry cycles (WDC).

Effect of Bacillus clausii in attenuating symptoms of DSS-induced ulcerative colitis by modulating NFkB pathway and oxidative stress in mice.

Ulcerative colitis (UC) is a condition characterized by inflammation and ulcer formation in the colon and rectum due to genetic and environmental factors. It is a common condition, with a global prevalence rate exceeding 0.3%.

Evaluation of the Ameliorative Potential of 3,5-(2-hydroxyethyl)-1,3,5-thiadiazinane-2-thione against Scopolamine-Induced Alzheimer's Disease.

Alzheimer's disease (AD) is the most common neurodegenerative disorder, marked by cognitive impairment. Currently, the available treatment provides only symptomatic relief and there is a great need to design and formulate new drugs to stabilize AD. In the search for a new anti-Alzheimer's drug, 3,5-(2-hydroxyethyl)-1,3,5-thiadiazinane-2-thione (THTT), a tetrahydro-2H-1,3,5-thiadiazine-2-thione derivative, was investigated against a scopolamine-induced Alzheimer's model.

Co-targeting CDK 4/6 and C-MYC/STAT3/CCND1 axis and inhibition of tumorigenesis and epithelial-mesenchymal-transition in triple negative breast cancer by Pt(II) complexes bearing NH as trans-co-ligand.

In search of potential anticancer agents, we synthesized SNO-donor salicylaldimine main ligand-based Pt(II) complexes bearing NH as co-ligand at trans-position (C1-C6). These complexes showed similarity in structure with transplatin as the two N donor atoms of the main ligand and NH co-ligand were coordinated to Pt in trans position to each other. Each complex with different substituents on the main ligand was characterized thoroughly by detailed spectroscopic and spectrophotometric methods.

Correction to "Ion-Exchange Resin/Carrageenan-Copper-Based Nanocomposite: Artificial Neural Network, Advanced Thermodynamic Profiling, and Anticoagulant Studies".

[This corrects the article DOI: 10.1021/acsomega.4c01540.

Knowledge, attitudes, and practices of healthcare professionals regarding rabies in tertiary care hospitals: A cross-sectional study in Peshawar, Pakistan.

Background: Rabies, caused by the rhabdovirus, is a fatal zoonotic disease with over 59,000 annual deaths globally. Asia and Africa account for 95%, with India leading, followed by China. In Pakistan, where it's endemic, over 50,000 cases are reported yearly.

Ion-Exchange Resin/Carrageenan-Copper-Based Nanocomposite: Artificial Neural Network, Advanced Thermodynamic Profiling, and Anticoagulant Studies.

Carrageenan (CG) and ion exchange resins (IERs) are better metal chelators. Kappa (κ) CG and IERs were synthesized and subjected to copper ion (Cu) adsorption to obtain DMSCH/κ-Cu, DC20H/κ-Cu, and IRP69H/κ-Cu nanocomposites (NCs). The NCs were studied using statistical physics formalism (SPF) at 315-375 K and a multilayer perceptron with five input nodes.

Advanced detection of coronary artery disease via deep learning analysis of plasma cytokine data.

The 2017 World Health Organization Fact Sheet highlights that coronary artery disease is the leading cause of death globally, responsible for approximately 30% of all deaths. In this context, machine learning (ML) technology is crucial in identifying coronary artery disease, thereby saving lives. ML algorithms can potentially analyze complex patterns and correlations within medical data, enabling early detection and accurate diagnosis of CAD.

Anomaly detection in IoT-based healthcare: machine learning for enhanced security.

Internet of Things (IoT) integration in healthcare improves patient care while also making healthcare delivery systems more effective and economical. To fully realize the advantages of IoT in healthcare, it is imperative to overcome issues with data security, interoperability, and ethical considerations. IoT sensors periodically measure the health-related data of the patients and share it with a server for further evaluation.

Effects of eccentric loading on performance of concrete columns reinforced with glass fiber-reinforced polymer bars.

Glass fiber-reinforced polymer (GFRP) reinforcements are superior to traditional steel bars in concrete structures, particularly in vertical elements like columns, and offer significant advantages over conventional steel bars when subjected to axial and eccentric loadings. However, there is limited experimental and numerical research on the behavior of GFRP-reinforced concrete (RC) columns under eccentric loading having different spacing of stirrups. In this study, six specimens were cast under three different values of eccentricities (25 mm, 50 mm, and 75 mm) with two groups of stirrups spacing (50 mm and 100 mm).

Self-emulsifying micelles as a drug nanocarrier system for itraconazole oral bioavailability enhancement; and assessment.

Itraconazole (ITZ) is a renowned antifungal medication, however its therapeutic efficacy is limited by low solubility and oral bioavailability. The current research work attempted to augment the oral bioavailability of ITZ by incorporating into self-emulsifying micelles (SEMCs). To fabricate the SEMCs, various preparation techniques including physical mixture, melt-emulsification, solvent evaporation and kneading, were opted by using different weight ratio of drug and solubilizers i.

Exploring transfer learning in chest radiographic images within the interplay between COVID-19 and diabetes.

The intricate relationship between COVID-19 and diabetes has garnered increasing attention within the medical community. Emerging evidence suggests that individuals with diabetes may experience heightened vulnerability to COVID-19 and, in some cases, develop diabetes as a post-complication following the viral infection. Additionally, it has been observed that patients taking cough medicine containing steroids may face an elevated risk of developing diabetes, further underscoring the complex interplay between these health factors.

Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection.

Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study.