Management of Scapula Fractures at a Level 1 Trauma Centre in the United Kingdom.

Background Scapular fractures, an uncommon injury that can be brought on by a high-energy mechanism because of its proximity to the pectoral and shoulder muscles, are frequently linked to fatal injuries. This study aimed to compare surgical versus conservative treatment of scapular fractures and the results of treated patients. Methods The traumatic scapular fracture patients in this cross-sectional study (n = 391) were treated at a major trauma centre (level 1) in the United Kingdom between 2012 and 2018.

Silent threats of lead-based paints in toys and households to children's health and development.

Lead (Pb), a highly toxic heavy metal, poses a significant global health risk, particularly to children. Widely used in paint manufacturing for its remarkable corrosion-resistance properties Pb exposure has been linked to severe health issues, including reduced neurotransmitter levels, organ damage, potentially leading to death in extreme cases. Children Are particularly vulnerable, with Pb toxicity primarily affecting the brain, reproductive, kidneys, and cardiovascular systems.

Risk thresholds for the frequency of cannabis use during pregnancy and adverse neonatal outcomes: protocol for a systematic review and dose-response meta-analysis.

Background: Cannabis use during pregnancy has been increasing and is associated with adverse neonatal outcomes, such as low birth weight (LBW) and preterm birth (PTB). It remains largely unknown whether the association between cannabis use in pregnancy and increased risk of adverse neonatal outcomes is impacted by the frequency of cannabis use and whether thresholds exist below which risk is not significantly increased. The objective of this systematic review is to assess whether the association between cannabis use during pregnancy and the risk of adverse neonatal outcomes is dependent on the frequency of use and whether risk thresholds exist.

Impact of postpartum exercise on pelvic floor disorders and diastasis recti abdominis: a systematic review and meta-analysis.

Objective: To examine the effect of exercise during the first year postpartum on pelvic floor disorders and diastasis recti abdominis.

Design: Systematic review with random effects meta-analysis.

Data Sources: MEDLINE, EMBASE, CINAHL, SPORTDiscuss, Evidence-Based Medicine Reviews (Ovid), Scopus, Web of Science and ClinicalTrials.

AppleLeafNet: a lightweight and efficient deep learning framework for diagnosing apple leaf diseases.

Accurately identifying apple diseases is essential to control their spread and support the industry. Timely and precise detection is crucial for managing the spread of diseases, thereby improving the production and quality of apples. However, the development of algorithms for analyzing complex leaf images remains a significant challenge.

MRI-Driven Alzheimer's Disease Diagnosis Using Deep Network Fusion and Optimal Selection of Feature.

Alzheimer's disease (AD) is a degenerative neurological condition characterized by cognitive decline, memory loss, and reduced everyday function, which eventually causes dementia. Symptoms develop years after the disease begins, making early detection difficult. While AD remains incurable, timely detection and prompt treatment can substantially slow its progression.

GraFMRI: A graph-based fusion framework for robust multi-modal MRI reconstruction.

Purpose: This study introduces GraFMRI, a novel framework designed to address the challenges of reconstructing high-quality MRI images from undersampled k-space data. Traditional methods often suffer from noise amplification and loss of structural detail, leading to suboptimal image quality. GraFMRI leverages Graph Neural Networks (GNNs) to transform multi-modal MRI data (T1, T2, PD) into a graph-based representation, enabling the model to capture intricate spatial relationships and inter-modality dependencies.

Impact of postpartum physical activity on maternal depression and anxiety: a systematic review and meta-analysis.

Objective: To examine the influence of postpartum exercise on maternal depression and anxiety.

Design: Systematic review with random effects meta-analysis and meta-regression.

Data Sources: Online databases up to 12 January 2024, reference lists, recommended studies and hand searches.

Understanding the Impact of SAM Fermi Levels on High Efficiency p-i-n Perovskite Solar Cells.

Completing the picture of the underlying physics of perovskite solar cell interfaces that incorporate self-assembled molecular layers (SAMs) will accelerate further progress in p-i-n devices. In this work, we modified the Fermi level of a nickel oxide-perovskite interface by utilizing SAM layers with a range of dipole strengths to establish the link between the resulting shift of the built-in potential of the solar cell and the device parameters. To achieve this, we fabricated a series of high-efficiency perovskite solar cells with no hysteresis and characterized them through stabilize and pulse (SaP), JV curve, and time-resolved photoluminescence (TRPL) measurements.

Impact of postpartum physical activity on cardiometabolic health, breastfeeding, injury and infant growth and development: a systematic review and meta-analysis.

Objective: To examine the relationship between postpartum physical activity and maternal postnatal cardiometabolic health, breastfeeding, injury, and infant growth and development.

Design: Systematic review with random-effects meta-analysis and meta-regression.

Data Sources: Eight online databases were searched up until 12 January 2024.

Enhancing Alzheimer's disease diagnosis and staging: a multistage CNN framework using MRI.

This study addresses the pervasive and debilitating impact of Alzheimer's disease (AD) on individuals and society, emphasizing the crucial need for timely diagnosis. We present a multistage convolutional neural network (CNN)-based framework for AD detection and sub-classification using brain magnetic resonance imaging (MRI). After preprocessing, a 26-layer CNN model was designed to differentiate between healthy individuals and patients with dementia.

The effects of diabetes self-management programs on clinical and patient reported outcomes in older adults: a systematic review and meta-analysis.

Objectives: With diabetes self-management continuing to become more complex for older adults, self-management programs have been shown to support this population in meeting their multifaceted medical needs. Building on our previous systematic review and meta-analysis, we aimed to update the literature on the effectiveness of diabetes self-management programs and investigate the impact of specific self-management interventions on clinical and patient-reported outcomes.

Methods: We updated our literature search in the following databases: Medline, EMBASE, PsychINFO, CINAHL and Cochrane Database of Randomized Controlled Trials from November 2013 to July 2023 for studies that may fit our inclusion criteria.

Comparison of the Efficacy of Clascoterone, Trifarotene, and Tazarotene for the Treatment of Acne: A Systematic Literature Review and Meta-Analysis.

Introduction: Acne vulgaris, a chronic inflammatory condition, is associated with significant physical and psychosocial burden. Since 2019, three new topical agents for acne vulgaris have been approved in the USA and Canada. We performed a systematic review and meta-analysis to compare the efficacy between twice-daily clascoterone cream 1%, once-daily trifarotene 0.

Heavy metals pollution from smelting activities: A threat to soil and groundwater.

Throughout the literature, the word "heavy metal" (HM) has been utilized to describe soil contamination; in this context, we characterize it as those elements with a density greater than 5 g per cubic centimeter. Contamination is one of the major global health concerns, especially in China. China's rapid urbanization over the past decades has caused widespread urban water, air, and soil degradation.

Artificial neural network models: implementation of functional near-infrared spectroscopy-based spontaneous lie detection in an interactive scenario.

Deception is an inevitable occurrence in daily life. Various methods have been used to understand the mechanisms underlying brain deception. Moreover, numerous efforts have been undertaken to detect deception and truth-telling.

Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework.

The early identification and treatment of various dermatological conditions depend on the detection of skin lesions. Due to advancements in computer-aided diagnosis and machine learning approaches, learning-based skin lesion analysis methods have attracted much interest recently. Employing the concept of transfer learning, this research proposes a deep convolutional neural network (CNN)-based multistage and multiclass framework to categorize seven types of skin lesions.

Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN.

Lumbar spine stenosis (LSS) is caused by low back pain that exerts pressure on the nerves in the spine. Detecting LSS is a significantly important yet difficult task. It is detected by analyzing the area of the anteroposterior diameter of the patient's lumbar spine.

Human biomonitoring of heavy metals exposure in different age- and gender-groups based on fish consumption patterns in typical coastal cities of China.

The study aimed to investigate heavy metals (HMs) concentrations in human hair based on fish consumption patterns in Qingdao, Xiamen, and Zhoushan. The (HMs) concentrations were determined using acid digestion and an inductively coupled plasma mass spectrometer (ICP-MS, NexION 300X, PerkinElmer). The associated health risks were investigated using risk assessment models described by the United States Environmental Protection Agency (USEPA).

BU-DLNet: Breast Ultrasonography-Based Cancer Detection Using Deep-Learning Network Selection and Feature Optimization.

Early detection of breast lesions and distinguishing between malignant and benign lesions are critical for breast cancer (BC) prognosis. Breast ultrasonography (BU) is an important radiological imaging modality for the diagnosis of BC. This study proposes a BU image-based framework for the diagnosis of BC in women.

An Isolated CNN Architecture for Classification of Finger-Tapping Tasks Using Initial Dip Images: A Functional Near-Infrared Spectroscopy Study.

This work investigates the classification of finger-tapping task images constructed for the initial dip duration of hemodynamics (HR) associated with the small brain area of the left motor cortex using functional near-infrared spectroscopy (fNIRS). Different layers (i.e.

Correlation-Filter-Based Channel and Feature Selection Framework for Hybrid EEG-fNIRS BCI Applications.

The proposed study is based on a feature and channel selection strategy that uses correlation filters for brain-computer interface (BCI) applications using electroencephalography (EEG)-functional near-infrared spectroscopy (fNIRS) brain imaging modalities. The proposed approach fuses the complementary information of the two modalities to train the classifier. The channels most closely correlated with brain activity are extracted using a correlation-based connectivity matrix for fNIRS and EEG separately.

OptEF-BCI: An Optimization-Based Hybrid EEG and fNIRS-Brain Computer Interface.

Multimodal data fusion (electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS)) has been developed as an important neuroimaging research field in order to circumvent the inherent limitations of individual modalities by combining complementary information from other modalities. This study employed an optimization-based feature selection algorithm to systematically investigate the complementary nature of multimodal fused features. After preprocessing the acquired data of both modalities (i.