Conv-MTD: A CNN Based Multi-Label Medical Tubes Detection and Classification Model to Facilitate Resource-constrained Point-of-care Devices.

Computer-aided detection through deep learning is becoming a prevalent approach across various fields, including detection of anomalies in medical procedures. One such medical procedure involves the placement of medical tubes to provide nutrition or other medical procedures in critically ill patients. Medical tube placement can be highly complex and prone to subjective errors.

PEX1 remains functional in peroxisome biogenesis but is rapidly degraded by the proteasome.

The PEX1/PEX6 AAA-ATPase is required for the biogenesis and maintenance of peroxisomes. Mutations in and disrupt peroxisomal matrix protein import and are the leading cause of Peroxisome Biogenesis Disorders (PBDs). The most common disease-causing mutation in PEX1 is the PEX1 allele, which results in a reduction of peroxisomal protein import.

Identifying COVID-19 survivors living with post-traumatic stress disorder through machine learning on Twitter.

The COVID-19 pandemic has disrupted people's lives and caused significant economic damage around the world, but its impact on people's mental health has not been paid due attention by the research community. According to anecdotal data, the pandemic has raised serious concerns related to mental health among the masses. However, no systematic investigations have been conducted previously on mental health monitoring and, in particular, detection of post-traumatic stress disorder (PTSD).

NPSV-deep: a deep learning method for genotyping structural variants in short read genome sequencing data.

Motivation: Structural variants (SVs) play a causal role in numerous diseases but can be difficult to detect and accurately genotype (determine zygosity) with short-read genome sequencing data (SRS). Improving SV genotyping accuracy in SRS data, particularly for the many SVs first detected with long-read sequencing, will improve our understanding of genetic variation.

Results: NPSV-deep is a deep learning-based approach for genotyping previously reported insertion and deletion SVs that recasts this task as an image similarity problem.

Clindamycin: An Effective Treatment for Granulomatous Mastitis Caused by in a Pregnant Patient.

Unlabelled: Corynebacterium spp. are Gram-positive bacteria, and recent studies have proposed a potential link between granulomatous mastitis and infections, posing a challenge in selecting appropriate antibiotics, particularly in pregnant women. A young pregnant woman presented with a palpable lump in her left breast.

Novel approach for quantitative and qualitative authors research profiling using feature fusion and tree-based learning approach.

Article citation creates a link between the cited and citing articles and is used as a basis for several parameters like author and journal impact factor, H-index, i10 index, ., for scientific achievements. Citations also include self-citation which refers to article citation by the author himself.

Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes.

Despite a worldwide decline in maternal mortality over the past two decades, a significant gap persists between low- and high-income countries, with 94% of maternal mortality concentrated in low and middle-income nations. Ultrasound serves as a prevalent diagnostic tool in prenatal care for monitoring fetal growth and development. Nevertheless, acquiring standard fetal ultrasound planes with accurate anatomical structures proves challenging and time-intensive, even for skilled sonographers.

A foundational atlas of autism protein interactions reveals molecular convergence.

Translating high-confidence (hc) autism spectrum disorder (ASD) genes into viable treatment targets remains elusive. We constructed a foundational protein-protein interaction (PPI) network in HEK293T cells involving 100 hcASD risk genes, revealing over 1,800 PPIs (87% novel). Interactors, expressed in the human brain and enriched for ASD but not schizophrenia genetic risk, converged on protein complexes involved in neurogenesis, tubulin biology, transcriptional regulation, and chromatin modification.

The N1 domain of the peroxisomal AAA-ATPase Pex6 is required for Pex15 binding and proper assembly with Pex1.

The heterohexameric ATPases associated with diverse cellular activities (AAA)-ATPase Pex1/Pex6 is essential for the formation and maintenance of peroxisomes. Pex1/Pex6, similar to other AAA-ATPases, uses the energy from ATP hydrolysis to mechanically thread substrate proteins through its central pore, thereby unfolding them. In related AAA-ATPase motors, substrates are recruited through binding to the motor's N-terminal domains or N terminally bound cofactors.

Heart failure patients monitoring using IoT-based remote monitoring system.

Intelligent health monitoring systems are becoming more important and popular as technology advances. Nowadays, online services are replacing physical infrastructure in several domains including medical services as well. The COVID-19 pandemic has also changed the way medical services are delivered.

The Pex6 N1 domain is required for Pex15 binding and proper assembly with Pex1.

The heterohexameric AAA-ATPase Pex1/Pex6 is essential for the formation and maintenance of peroxisomes. Pex1/Pex6, similar to other AAA-ATPases, uses the energy from ATP hydrolysis to mechanically thread substrate proteins through its central pore, thereby unfolding them. In related AAA-ATPase motors, substrates are recruited through binding to the motor's N-terminal domains or N-terminally bound co-factors.

Low-Latency Federated Learning via Dynamic Model Partitioning for Healthcare IoT.

Federated learning (FL) is receiving much attention in the Healthcare Internet of Things (H-IoT) to support various instantaneous E-health services. Today, the deployment of FL suffers from several challenges, such as high training latency and data privacy leakage risks, especially for resource-constrained medical devices. In this article, we develop a three-layer FL architecture to decrease training latency by introducing split learning into FL.

Millimeter-wave channel modeling in a VANETs using coding techniques.

The Vehicular ad-Hoc Network (VANET) is envisioned to ensure wireless transmission with ultra-high reliability. In the presence of fading and mobility of vehicles, error-free information between Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) requires extensive investigation. The current literature lacks in designing an ultra-reliable comprehensive tractable model for VANET using millimeter wave.

A Critical Cybersecurity Analysis and Future Research Directions for the Internet of Things: A Comprehensive Review.

The emergence of the Internet of Things (IoT) technology has brought about tremendous possibilities, but at the same time, it has opened up new vulnerabilities and attack vectors that could compromise the confidentiality, integrity, and availability of connected systems. Developing a secure IoT ecosystem is a daunting challenge that requires a systematic and holistic approach to identify and mitigate potential security threats. Cybersecurity research considerations play a critical role in this regard, as they provide the foundation for designing and implementing security measures that can address emerging risks.

Longitudinal fundus imaging and its genome-wide association analysis provide evidence for a human retinal aging clock.

Biological age, distinct from an individual's chronological age, has been studied extensively through predictive aging clocks. However, these clocks have limited accuracy in short time-scales. Here we trained deep learning models on fundus images from the EyePACS dataset to predict individuals' chronological age.

Neisseria elongata -mediated peritonitis in an end-stage renal disease patient on automated peritoneal dialysis, A case report and literature review.

Introduction: Peritoneal dialysis (PD) can result in peritonitis, which frequently causes severe and near-fatal clinical implications if left untreated. Usually, Gram-positive bacteria are the most common organisms involved. Uncommonly recognized as the cause of peritonitis in PD patients, Neisseria Elongata is a gram-negative nasal and oropharyngeal normal flora organism.

Characterization of the immunoglobulin lambda chain locus from diverse populations reveals extensive genetic variation.

Immunoglobulins (IGs), crucial components of the adaptive immune system, are encoded by three genomic loci. However, the complexity of the IG loci severely limits the effective use of short read sequencing, limiting our knowledge of population diversity in these loci. We leveraged existing long read whole-genome sequencing (WGS) data, fosmid technology, and IG targeted single-molecule, real-time (SMRT) long-read sequencing (IG-Cap) to create haplotype-resolved assemblies of the IG Lambda (IGL) locus from 6 ethnically diverse individuals.

Microbiological and clinical characteristics of invasive Group B Streptococcal blood stream infections in children and adults from Qatar.

Introduction: Group B Streptococci (GBS) colonize almost one third of human gastrointestinal and genitourinary tracts, particularly in females. The aim of this study is to evaluate the epidemiology, microbiological characteristics, and clinical outcomes of invasive GBS disease in Qatar from all age groups.

Methods: A retrospective study was conducted on patients with confirmed GBS blood stream infections during the period between January 2015 and March 2019.

Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19.

All witnessed the terrible effects of the COVID-19 pandemic on the health and work lives of the population across the world. It is hard to diagnose all infected people in real time since the conventional medical diagnosis of COVID-19 patients takes a couple of days for accurate diagnosis results. In this paper, a novel learning framework is proposed for the early diagnosis of COVID-19 patients using hybrid deep fusion learning models.

The Outcome of Fatherhood in Patients With Philadelphia-Negative Myeloproliferative Neoplasms: A Single-Institution Experience.

Background Fertility is a highly complex subject; it involves more than one individual and has profound psychological and economic implications. Moreover, it is affected by several factors, including age, significant systemic illness in either partner, exposure to environmental toxins, medications, or radiation. In patients with malignancy, fertility is more complicated.

Conditional Anonymous Remote Healthcare Data Sharing Over Blockchain.

As an important carrier of healthcare data, Electronic Medical Records (EMRs) generated from various sensors, i.e., wearable, implantable, are extremely valuable research materials for artificial intelligence and machine learning.

Mid- and long-term outcomes of thoracic endovascular aortic repair in acute and subacute uncomplicated type B aortic dissection.

Background: Uncomplicated type B aortic dissection (un-TBAD) has been managed conservatively with medical therapy to control the heart rate and blood pressure to limit disease progression, in addition to radiological follow-up. However, several trials and observational studies have investigated the use of thoracic endovascular aortic repair (TEVAR) in un-TBAD and suggested that TEVAR provides a survival benefit over medical therapy. Outcomes of TEVAR have also been linked with the timing of intervention.