The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency.

Background And Aims: Artificial Intelligence (AI) beginning to integrate in healthcare, is ushering in a transformative era, impacting diagnostics, altering personalized treatment, and significantly improving operational efficiency. The study aims to describe AI in healthcare, including important technologies like robotics, machine learning (ML), deep learning (DL), and natural language processing (NLP), and to investigate how these technologies are used in patient interaction, predictive analytics, and remote monitoring. The goal of this review is to present a thorough analysis of AI's effects on healthcare while providing stakeholders with a road map for navigating this changing environment.

Characterization and distribution of brominated flame retardants in soils from informal E-waste recycling facilities: insights from Pakistan.

E-waste, a global environmental concern, particularly affects developing nations due to the rise in informal recycling practices. This leads to contamination of environmental matrices, posing threats to both ecosystems and human health. To assess this issue, we monitored brominated flame retardants (BFRs) in 164 samples (soil) from 32 informal e-waste operational locations and 9 background locations across nine mega cities of Pakistan from September 2020 to December 2021.

Association between vascular endothelial growth factor and atrial fibrillation: a systematic review.

Background: Atrial fibrillation (AF) is the most prevalent form of sustained cardiac arrhythmia, with vascular endothelial growth factor (VEGF) increasingly recognized for its potential role in the pathogenesis of AF through mechanisms involving atrial remodeling, inflammation, and fibrosis. This systematic review aims to synthesize available evidence on the association between VEGF and AF, exploring the implications of VEGF as a biomarker and therapeutic target.

Methods: We conducted a comprehensive search across PubMed, Embase, and Web of Science until November 10 2024, selecting studies based on pre-defined criteria that involve adults with AF and measurements of VEGF levels.

PPA1, TRIM68 and FBXO46: Potential Therapeutic Targets for Triple Negative Breast Cancer.

Background: Triple-negative breast cancer (TNBC) is an aggressive type of breast cancer with a high recurrence rate. A new therapeutic intervention is urgently needed to combat this lethal subtype. The identification of biomarkers is also crucial for improving outcomes in TNBC.

Alpha-Glucosidase Inhibitors in Aging and Aging-Related Diseases: Clinical Applications and Relevant Mechanisms.

Aging is a complex and universal process marked by gradual functional declines at the cellular and tissue levels, often leading to a range of aging-related diseases such as diabetes, cardiovascular diseases, and cancer. Delaying the aging process can help prevent, slow down, and alleviate the severity of these various conditions, enhancing overall health and well-being. Alpha-glucosidase inhibitors (AGIs) are a class of widely used antidiabetic drugs that inhibit alpha-glucosidase in the small intestinal mucosa, delaying carbohydrate absorption and reducing postprandial hyperglycemia.

Predicting Microvascular Invasion in Liver Transplant Recipients for Hepatocellular Carcinoma.

Background: Among primary liver tumors, hepatocellular carcinoma (HCC) is considered the most common hepatic tumor. Liver transplantation is one of the curative treatment options for HCC. However, the risk of HCC recurrence after liver transplantation varies and is influenced by various factors.

Deep-learning prediction of cardiovascular outcomes from routine retinal images in individuals with type 2 diabetes.

Background: Prior studies have demonstrated an association between retinal vascular features and cardiovascular disease (CVD), however most studies have only evaluated a few simple parameters at a time. Our aim was to determine whether a deep-learning artificial intelligence (AI) model could be used to predict CVD outcomes from routinely obtained diabetic retinal screening photographs and to compare its performance to a traditional clinical CVD risk score.

Methods: We included 6127 individuals with type 2 diabetes without myocardial infarction or stroke prior to study entry.

Implementing a Novel "Frontiers in Neoplasia" Curriculum to Engage Fourth-Year Medical Students in Evidence-Based, Multidisciplinary Oncology Care.

Few medical students are exposed to evidence-based, multidisciplinary oncology care, and few studies in oncology education reflect consolidated pre-clinical curricula. We developed a four-week curriculum, "Frontiers in Neoplasia," for fourth-year medical students, which included didactic lectures, interactive site visits, and team-based simulations of tumor boards and clinical trial design. A mixed methods approach was utilized to investigate the course's impact on students' understanding and interest in oncology, involving pre- and post-course responses to Likert-scale and open-ended questions.

From light sensing to adaptive learning: hafnium diselenide reconfigurable memcapacitive devices in neuromorphic computing.

Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior, dynamic responses, and energy efficiency characteristics. Although charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity, replicating the key functionality of neurons-integrating diverse presynaptic inputs to fire electrical impulses-has remained challenging. In this study, we developed reconfigurable metal-oxide-semiconductor capacitors (MOSCaps) based on hafnium diselenide (HfSe).

Outcomes of Standard-Risk Multiple Myeloma Patients Who Undergo Upfront Autologous Hematopoietic Stem Cell Transplantation.

Patients with multiple myeloma (MM) without high-risk cytogenetic abnormalities are classified as having standard-risk MM (SRMM), and data focusing on their outcomes after autologous hematopoietic stem cell transplantation (autoHCT) are limited. To evaluate survival outcomes for patients with SRMM receiving autoHCT, and to elucidate factors that impact these outcomes. Single-center retrospective analysis that included consecutive MM patients who received upfront autoHCT between 2013 and 2021, had available cytogenetic information and had no high-risk chromosomal abnormalities on fluorescence in situ hybridization, defined as t(4;14), t(14;16), del(17p) or 1q21 gain or amplification.

Incidence, Clinicopathological Features, and Outcomes of Signet Ring Colorectal Carcinoma: A Retrospective Study.

Background Signet ring cell carcinoma (SRCC) is a rare subtype of colorectal cancer with significant variations in clinical characteristics and poor prognosis. However, there is limited data available in Pakistan. Therefore, we analyzed to examine the incidence, clinicopathological features, treatments, and outcomes of SRCC in colorectal cancer cases in Pakistan.

Psychometric properties of the English and Hindi versions of the Brief Inventory of Thriving for use among Indian adolescents.

The Brief Inventory of Thriving (BIT) provides a holistic measure of well-being, but has only been validated for adults, and does not have a Hindi version. The present study investigated the unidimensional structure, internal consistency, convergent/discriminant, and criterion validity of both the original English version of the BIT (BIT-E) and its Hindi-translated version (BIT-H) among adolescents in India. Further, we tested measurement invariance across these two language versions, gender, and academic disciplines.

Neuro-protective and redox potential of troxerutin against cypermethrin-induced neurotoxicity and oxidative stress in mice.

The present study was designed to evaluate the protective efficacy of troxerutin against cypermethrin-induced behavioral defects, motor function abnormalities, and oxidative stress in mice. Twenty-four adult female albino mice were randomly divided into four equal groups. The first group served as control, the second group was treated with cypermethrin (20 mg/kg b.

Detection of Vitiligo Through Machine Learning and Computer-Aided Techniques: A Systematic Review.

Vitiligo is a chronic skin damage disease, triggered by differential melanocyte death. Vitiligo (0.5%-1% of the population) is one of the most severe skin conditions.

Assessment of myocardial infarctions knowledge, attitudes and beliefs among adults living in Riyadh Saudi Arabia - insights from cross-sectional study.

Globally, the prevalence of coronary artery disease (CAD) is increasing, accounting for a third of all deaths worldwide including myocardial infarctions (MIs) which represent the most severe clinical manifestation of CAD and are among the most dangerous coronary events. Therefore, this study aims to assess the knowledge of symptoms and risk factors of MIs, as well as attitudes and beliefs regarding MIs and confidence in recognizing CAD symptoms in Riyadh, Saudi Arabia. A cross-sectional study was conducted among individuals living in Riyadh, Saudi Arabia between November 2023 and April 2024 to assess their knowledge and beliefs about CAD and MIs.

Assessing availability and trainees' perceptions of simulation and augmented reality in prosthodontics postgraduate education in Pakistan: a cross-sectional study.

Background: Simulation-based learning (SBL) and augmented reality (AR) /virtual reality (VR) are increasingly adapted and investigated globally to aid traditional teaching methods of clinical skills in several fields of clinical dentistry. This cross-sectional study was, therefore, aimed to assess the availability of such technology to Prosthodontics postgraduate trainees in Pakistan, as well as their introspective views regarding the effectiveness of adapting to simulation-based learning methods.

Method: Total population sampling yielded a sample of 200 participants.

Explainable AI-Based Skin Cancer Detection Using CNN, Particle Swarm Optimization and Machine Learning.

Skin cancer is among the most prevalent cancers globally, emphasizing the need for early detection and accurate diagnosis to improve outcomes. Traditional diagnostic methods, based on visual examination, are subjective, time-intensive, and require specialized expertise. Current artificial intelligence (AI) approaches for skin cancer detection face challenges such as computational inefficiency, lack of interpretability, and reliance on standalone CNN architectures.

X-Ray Image-Based Real-Time COVID-19 Diagnosis Using Deep Neural Networks (CXR-DNNs).

On 11 February 2020, the prevalent outbreak of COVID-19, a coronavirus illness, was declared a global pandemic. Since then, nearly seven million people have died and over 765 million confirmed cases of COVID-19 have been reported. The goal of this study is to develop a diagnostic tool for detecting COVID-19 infections more efficiently.

MediScan: A Framework of U-Health and Prognostic AI Assessment on Medical Imaging.

With technological advancements, remarkable progress has been made with the convergence of health sciences and Artificial Intelligence (AI). Modern health systems are proposed to ease patient diagnostics. However, the challenge is to provide AI-based precautions to patients and doctors for more accurate risk assessment.

FISPGHAN statement on the global public health impact of metabolic dysfunction-associated steatotic liver disease.

As rates of obesity rise worldwide, incidence of metabolic dysfunction-associated steatotic liver disease (MASLD), formerly referred to as nonalcoholic fatty liver disease, is increasing, worsening the burden of healthcare systems. The council of the Federation of International Societies for Pediatric Gastroenterology, Hepatology, and Nutrition (FISPGHAN) identified the topic of MASLD epidemiology, treatment, and prevention as a global priority issue to be addressed by an expert team, with the goal to describe feasible and evidence-based actions that may contribute to reducing MASLD risk. The FISPGHAN member societies nominated experts in the field.

The measurement and insight of bacterial community structure succession in cyanobacteria biochar co-composting based on basic carbon and nitrogen indices.

The effects of cyanobacteria biochar (CB) amendment on microbial community succession (MCS) during pig manure co-composting was evaluated. Conventional composting (T1) and different concentrations of CB co-composting were set up here (T2: 2.5% CB, T3: 5% CB, T4: 7.

Preferences, Perceptions, and Environmental Considerations of Natural and Synthetic Psychedelic Substances: Findings from the Global Psychedelic Survey.

Although several studies have well described the characteristics of people who use psychedelics alongside their motivations and beliefs, little research has examined the preferences surrounding the source of psychedelic substances. In an anonymous online survey, we collected data from 6,379 consumers of 11 different psychedelic substances from 85 different countries, exploring their preferences and perceptions on natural and synthetic psychedelics. There was a strong preference of natural sources over synthetic alternatives for psilocybin (75%), DMT (56%), and mescaline (56%).

Unveiling the role of sintering temperatures in the physical properties of Cu-Mg ferrite nanoparticles for photocatalytic application.

This research presents an explicit analysis of the effects of sintering temperature (T) on the structural, morphological, magnetic, and optical properties of CuMgFeO nanoferrites synthesized via the sol-gel method. To accomplish it, Cu-Mg ferrite NPs were sintered at temperatures ranging from 300 to 800 °C in increments of 100 with a constant holding duration of 5 h. Thermogravimetric analysis was used to observe the degradation of organic components and the thermally stable zone of the material.