Bridging the Vaccination Equity Gap: A Community-Driven Approach to Reduce Vaccine Inequities in Polio High-Risk Areas of Pakistan.

Immunization saves millions of lives, and globally, vaccines have significantly contributed to reducing mortality and morbidity due to more than 20 life-threatening illnesses. However, there are considerable disparities in vaccination coverage among countries and within populations. This study evaluates the reduction in disparities in vaccination coverage across various socio-economic groups by adopting an integrated community-engagement approach combined with maternal and child health services through mobile health camps.

Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI.

Fetal health holds paramount importance in prenatal care and obstetrics, as it directly impacts the wellbeing of mother and fetus. Monitoring fetal health through pregnancy is crucial for identifying and addressing potential risks and complications that may arise. Early detection of abnormalities and deviations in fetal health can facilitate timely interventions to mitigate risks and improve outcomes for the mother and fetus.

Mapping global evidence of caregiver experiences in bipolar disorder in low and middle-income countries: A scoping review.

Background: Bipolar disorder (BD) imposes significant social, psychological, and economic burdens on individuals and their caregivers. While developing treatments for BD patients is crucial, supportive interventions for caregivers in low- and middle-income countries (LMICs) are equally important, given the limited resources and healthcare infrastructure. Understanding caregiver experiences in these settings is essential for creating effective interventions.

FacialNet: facial emotion recognition for mental health analysis using UNet segmentation with transfer learning model.

Facial emotion recognition (FER) can serve as a valuable tool for assessing emotional states, which are often linked to mental health. However, mental health encompasses a broad range of factors that go beyond facial expressions. While FER provides insights into certain aspects of emotional well-being, it can be used in conjunction with other assessments to form a more comprehensive understanding of an individual's mental health.

Radiological Outcomes According to the Matta Score After the Surgical Fixation of Acetabular Fractures.

Background Acetabular fractures, a rising concern in developing countries, pose a significant challenge due to their complexity and association with post-operative complications. Often caused by high-energy mechanisms like falls and motor vehicle accidents, these fractures require accurate reduction to prevent long-term issues and the potential need for hip replacement. This study investigates the radiological outcomes of acetabular fracture surgery at six months, focusing on the effectiveness of achieving anatomical reduction using the Matta criteria in a low-and middle-income country (LMIC) setting.

Thirty Days Outcomes of Limb Salvage Surgery in Pediatric Patients Treated at a Tertiary Care Hospital.

Background Managing primary bone and soft tissue sarcomas in pediatric patients poses significant challenges, with surgical resection remaining essential for cure. While limb salvage surgery has emerged as the standard approach, concerns persist regarding post-operative complications. Our study aims to evaluate the 30-day morbidity and mortality of limb salvage surgery in pediatric tumor patients, bridging critical knowledge gaps and contributing to enhancing the standard of care in low- and middle-income countries (LMICs).

Incidence of postoperative cerebrospinal fluid leaks in endoscopic endonasal transsphenoidal surgery for pituitary adenomas without sellar floor reconstruction: A retrospective observational study from a low-middle-income country.

Objectives: To determine the incidence of postoperative cerebrospinal fluid (CSF) leaks after endoscopic endonasal transsphenoidal surgery (EETS) for pituitary adenomas without sellar floor reconstruction (SFR).

Methods: This retrospective observational study was conducted at Department of Neurosurgery, Punjab Institute of Neurosciences (PINS), Lahore, Pakistan from January, 2018 to December, 2022. It is a non-probability based consecutive case series.

Novel approach for Arabic fake news classification using embedding from large language features with CNN-LSTM ensemble model and explainable AI.

The widespread fake news challenges the management of low-quality information, making effective detection strategies necessary. This study addresses this critical issue by advancing fake news detection in Arabic and overcoming limitations in existing approaches. Deep learning models, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), EfficientNetB4, Inception, Xception, ResNet, ConvLSTM and a novel voting ensemble framework combining CNN and LSTM are employed for text classification.

Implications of the Gut Microbiome in Alzheimer's Disease: A Narrative Review.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder, with its prevalence doubling approximately every decade. It is a significant contributor to disability-adjusted life-years in individuals aged 50 and older, impacting a substantial portion of this population globally. The pathophysiology of AD is primarily explained by two hypotheses: the amyloid cascade hypothesis and the tau hypothesis.

TRI-POSE-Net: Adaptive 3D human pose estimation through selective kernel networks and self-supervision with trifocal tensors.

Accurate and flexible 3D pose estimation for virtual entities is a strenuous task in computer vision applications. Conventional methods struggle to capture realistic movements; thus, creative solutions that can handle the complexities of genuine avatar interactions in dynamic virtual environments are imperative. In order to tackle the problem of precise 3D pose estimation, this work introduces TRI-POSE-Net, a model intended for scenarios with limited supervision.

Case 331: Cardiac Sarcoidosis.

A 43-year-old male patient with no known past medical history presented to the emergency department with new-onset bitemporal headache, dizziness, and bilateral lower extremity weakness for 1 day. The patient denied chest pain, shortness of breath, cough, or recent exposure to sick individuals. He was not on any medications and denied alcohol or illicit drug use.

Early detection of autism spectrum disorder using explainable AI and optimized teaching strategies.

Autism spectrum disorder (ASD) is defined by the deficits of social relating, language, object use and understanding, intelligence and learning, and verbal and nonverbal communication. Most of the individuals with ASD have genetic conditions; however, early identification and intervention reduce the use of health services and other diagnostic procedures. The varied nature of ASD is widely acknowledged, with each affected individual displaying distinct traits.

BrainNet: an automated approach for brain stress prediction utilizing electrodermal activity signal with XLNet model.

Brain stress monitoring has emerged as a critical research area for understanding and managing stress and neurological health issues. This burgeoning field aims to provide accurate information and prediction about individuals' stress levels by analyzing behavioral data and physiological signals. To address this emerging problem, this research study proposes an innovative approach that uses an attention mechanism-based XLNet model (called BrainNet) for continuous stress monitoring and stress level prediction.

Data-centric automated approach to predict autism spectrum disorder based on selective features and explainable artificial intelligence.

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by notable challenges in cognitive function, understanding language, recognizing objects, interacting with others, and communicating effectively. Its origins are mainly genetic, and identifying it early and intervening promptly can reduce the necessity for extensive medical treatments and lengthy diagnostic procedures for those impacted by ASD. This research is designed with two types of experimentation for ASD analysis.

LesionNet: an automated approach for skin lesion classification using SIFT features with customized convolutional neural network.

Accurate detection of skin lesions through computer-aided diagnosis has emerged as a critical advancement in dermatology, addressing the inefficiencies and errors inherent in manual visual analysis. Despite the promise of automated diagnostic approaches, challenges such as image size variability, hair artifacts, color inconsistencies, ruler markers, low contrast, lesion dimension differences, and gel bubbles must be overcome. Researchers have made significant strides in binary classification problems, particularly in distinguishing melanocytic lesions from normal skin conditions.

Violet light transmission through eyeglasses and its effects on myopic children: A systematic review and meta-analysis.

Purpose: Myopia is a refractive error that impairs visual function and leads to visual blurring. This study aims to investigate the effect of violet light (VL) on controlling myopia, specifically in terms of axial length (AL), spherical equivalent refraction (SER), and visual acuity (VA).

Methods: A systematic review was conducted to compare VL and single-vision spectacles (SVSs) for treating childhood myopia.

Mutational analysis of cytoplasmic domain of integrin subunit alpha-1 and its association with periapical wound healing after surgical endodontic treatment.

Background: Numerous studies reported that the healing after surgical endodontic retreatment is influenced by multiple factors which include the genetic profile of the patient, epigenetics, and immune responses. The genes which are primarily responsible for the healing potential in different individuals are those which are involved in the regulation of the cytoskeleton and cellular adhesion which subsequently affects bone deposition and healing. Integrins are cell-surface molecules, possess a key role in the cytoskeleton and cellular adhesion.

Artificial intelligence-driven predictive framework for early detection of still birth.

Predictive modeling is becoming increasingly popular in the context of early disease detection. The use of machine learning approaches for predictive modeling can help early detection of diseases thereby enabling medical experts to appropriate medical treatments. Stillbirth prediction is a similar domain where artificial intelligence-based predictive modeling can alleviate this significant global health challenge.

Multiple recurrences of a left ventricular pseudoaneurysm: a case report.

Background: Takotsubo syndrome (TTS) is being diagnosed more often with its increased recognition over the past 2 decades and with the availability of imaging such as point-of-care echocardiography and tissue characterization by cardiovascular magnetic resonance (CMR).

Case Summary: A young man developed pericarditis and was treated with steroids. A few weeks later, he suffered classic TTS and then presented a week later with the rare complication of apical myocardial rupture and a left ventricular (LV) pseudoaneurysm.

Genome-wide association study reveals the genetic basis of amino acids contents variations in Peanut (Arachis hypogaea L.).

Peanut is a significant source of protein for human consumption. One of the primary objectives in peanut breeding is the development of new cultivars with enhanced nutritional values. To further this goal, a genome-wide association study (GWAS) was conducted to analyze seed amino acids contents in 390 diverse peanut accessions collected worldwide, mainly from China, India, and the United States, in 2017 and 2018.