A robust benchmark for detecting low-frequency variants in the HG002 Genome In A Bottle NIST reference material.

Somatic mosaicism is an important cause of disease, but mosaic and somatic variants are often challenging to detect because they exist in only a fraction of cells. To address the need for benchmarking subclonal variants in normal cell populations, we developed a benchmark containing mosaic variants in the Genome in a Bottle Consortium (GIAB) HG002 reference material DNA from a large batch of a normal lymphoblastoid cell line. First, we used a somatic variant caller with high coverage (300x) Illumina whole genome sequencing data from the Ashkenazi Jewish trio to detect variants in HG002 not detected in at least 5% of cells from the combined parental data.

EFFECT OF ELECTRONIC SHORT MESSAGE SERVICE REMINDERS ON MEDICATION ADHERENCE AMONG HYPERTENSIVE PATIENTS IN KATSINA STATE: A QUASIEXPERIMENTAL STUDY.

Introduction: Globally, hypertension is considered a public health problem affecting over 1.3 billion individuals of which 60% need reminders to take medication to achieve adherence, optimal blood pressure and avert complications. Literature shows electronic short message reminders (eSMS) improved medication adherence.

Relationship between MRI brain-age heterogeneity, cognition, genetics and Alzheimer's disease neuropathology.

Background: Brain ageing is highly heterogeneous, as it is driven by a variety of normal and neuropathological processes. These processes may differentially affect structural and functional brain ageing across individuals, with more pronounced ageing (older brain age) during midlife being indicative of later development of dementia. Here, we examined whether brain-ageing heterogeneity in unimpaired older adults related to neurodegeneration, different cognitive trajectories, genetic and amyloid-beta (Aβ) profiles, and to predicted progression to Alzheimer's disease (AD).

Alcohol-exacerbates post-traumatic stress psychiatric behavior and its neuropathological sequalae in experimental mice: preventive effects of morin.

Posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) are very prevalent and co-occurring. It is unclear how alcohol exacerbates PTSD predicaments owing to less characterized pathophysiological mechanisms. Also, studies on pharmacological agents that can effectively reverse PTSD-AUD comorbidity have, to date, been scarce.

Introducing second-trimester anatomic scanning for improved detection and management of congenital anomalies: Experience from a novice center in East Africa.

Objectives: The aim of the current study was to describe the magnitude and pattern of congenital anomalies on routine second-trimester ultrasound and its practical implication in the management of pregnant women with fetal anomalies at a novice center in East Africa.

Methods: This cross-sectional study was conducted from September 2021 to May 2022 among women who had second-trimester anatomic scanning. Data were collected using a structured questionnaire and analyzed using SPSS version 23.

Dietary exposure to polycyclic aromatic hydrocarbons-contaminated Talinum Triangulare grown along polluted river.

The research aims to evaluate the source, dispersion, and human health risk assessment of PAH-contaminated Talinum Triangulare grown along the polluted Ikpoba River. The freeze-dried vegetables were sonicated with dichloromethane for an hour and then concentrated using a rotary evaporator. The extract was purified using a glass column with anhydrous NaSO and silica gel and analyzed using gas chromatography-mass spectrometry (GC-MS).

Deep learning-based cell segmentation for rapid optical cytopathology of thyroid cancer.

Fluorescence polarization (Fpol) imaging of methylene blue (MB) is a promising quantitative approach to thyroid cancer detection. Clinical translation of MB Fpol technology requires reduction of the data analysis time that can be achieved via deep learning-based automated cell segmentation with a 2D U-Net convolutional neural network. The model was trained and tested using images of pathologically diverse human thyroid cells and evaluated by comparing the number of cells selected, segmented areas, and Fpol values obtained using automated (AU) and manual (MA) data processing methods.

Unknown spa types, spa repeats, and relatedness of MRSA isolated from horses, dogs, cats, and their human handlers.

Methicillin resistant Staphylococcus aureus (MRSA) represents a worrying example of antimicrobial resistance, and it is essential to acquire new information to monitor the spread and limit it further diffusion. This study aimed to characterise 22 MRSA isolates from horses, dogs, cats, and their human handlers focusing on spa typing. In the analysis of the sequences obtained, the spa type is "unknown" (unidentified) and all the sequences except one had repeats previously not known in all databases potentially indicating new spa-repeats.

Regional and Demographic Disparities in Atrial Fibrillation Mortality in the USA.

Background: Despite the burden of atrial fibrillation/flutter (AF/AFL) in the USA, an assessment of contemporary mortality trends is scarce in the literature. This study aimed to assess the temporal trends in AF/AFL deaths among US adults by age, sex, race/ethnicity, and census region from 1999 to 2020.

Methods: National mortality data was abstracted from the National Center for Health Statistics to identify decedents whose underlying cause of death was cardiovascular disease and multiple cause of death, AF/AFL.

Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures.

Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have been instrumental in understanding the neuroanatomical changes that occur during aging in large and diverse populations. However, the multiplicity and mutual overlap of both pathologic processes and affected brain regions make it difficult to precisely characterize the underlying neurodegenerative profile of an individual from an MRI scan.

Genomic loci influence patterns of structural covariance in the human brain.

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size.

Assessment of dietary exposure and levels of mycotoxins in sorghum from Niger State of Nigeria.

This study reports levels of mycotoxins in sorghum from Niger State, Nigeria, and provides a comprehensive assessment of their potential health risks by combining mycotoxin levels and dietary exposure assessment. A total of 240 samples of red and white sorghum were collected from both stores and markets across four microclimatic zones. Fungal species were identified using a dilution plate method.

Imaging phenotypes predict overall survival in glioma more accurate than basic demographic and cell mutation profiles.

Background: Magnetic resonance imaging (MRI), digital pathology imaging (PATH), demographics, and IDH mutation status predict overall survival (OS) in glioma. Identifying and characterizing predictive features in the different modalities may improve OS prediction accuracy.

Purpose: To evaluate the OS prediction accuracy of combinations of prognostic markers in glioma patients.

Paucity of Health Data in Africa: An Obstacle to Digital Health Implementation and Evidence-Based Practice.

Among the numerous challenges that Africa faces in improving its healthcare systems, the paucity of health data stands out as paramount. This study aims to examine the challenges related to the paucity of health data in Africa and its impact on the implementation of digital health and evidence-based practice. The findings of the study reveal that health data availability in Africa is both limited and frequently of poor quality.

What is the margin of error of surgeons and radiological imaging in diagnosing acute appendicitis?

Background: Acute appendicitis is the most common emergency requiring surgical intervention in general surgery. Negative appendectomy is defined as the removal of a pathologically normal appendix.

Aim: In this study, we aimed to show our negative appendectomy rate.

From Paper Files to Web-Based Application for Data-Driven Monitoring of HIV Programs: Nigeria's Journey to a National Data Repository for Decision-Making and Patient Care.

Background: Timely and reliable data are crucial for clinical, epidemiologic, and program management decision making. Electronic health information systems provide platforms for managing large longitudinal patient records. Nigeria implemented the National Data Repository (NDR) to create a central data warehouse of all people living with human immunodeficiency virus (PLHIV) while providing useful functionalities to aid decision making at different levels of program implementation.

Developing an Implementation Blueprint for the NIH HEAL Initiative GRACE Trial: Perspectives on Acupuncture and Guided Relaxation for Chronic Sickle Cell Disease Pain.

This study aimed to explore perspectives of people living with sickle cell disease (SCD) and SCD clinic providers and staff about the use of acupuncture and guided relaxation for treating chronic SCD pain. Data obtained were to inform an implementation blueprint for an effectiveness implementation clinical trial (GRACE Trial) testing whether acupuncture or guided relaxation reduces chronic pain when compared with usual care. Qualitative research design.

Adaptive data-driven selection of sequences of biological and cognitive markers in pre-clinical diagnosis of dementia.

Effective clinical decision procedures must balance multiple competing objectives such as time-to-decision, acquisition costs, and accuracy. We describe and evaluate POSEIDON, a data-driven method for PrOspective SEquentIal DiagnOsis with Neutral zones to individualize clinical classifications. We evaluated the framework with an application in which the algorithm sequentially proposes to include cognitive, imaging, or molecular markers if a sufficiently more accurate prognosis of clinical decline to manifest Alzheimer's disease is expected.

Harmonization of multi-site functional connectivity measures in tangent space improves brain age prediction.

Brain age prediction based on functional magnetic resonance imaging (fMRI) data has the potential to serve as a biomarker for quantifying brain health. To predict the brain age based on fMRI data robustly and accurately, we curated a large dataset (n = 4259) of fMRI scans from seven different data acquisition sites and computed personalized functional connectivity measures at multiple scales from each subject's fMRI scan. Particularly, we computed personalized large-scale functional networks and generated functional connectivity measures at multiple scales to characterize each fMRI scan.

Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering.

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors.

Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study.

To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4186 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and the data harmonization in the functional connectivity measures' tangent space worked better than in their original space.