Increasing cellular NAD protects hepatocytes against palmitate-induced lipotoxicity by preventing PARP-1 inhibition and the mTORC1-p300 pathway activation.

Hepatic lipotoxicity, resulting from excessive lipid accumulation in hepatocytes, plays a central role in the pathogenesis of various metabolic liver diseases. Despite recent progress, the precise mechanisms remain incompletely understood. Employing excessive exposure to palmitate in hepatocytes as our primary experimental model and mice studies, we aimed to uncover the mechanisms behind hepatic lipotoxicity, thereby developing potential treatments.

Impact of cariprazine on body weight and blood pressure among adults with bipolar I disorder, schizophrenia, or major depressive disorder in a real-world setting.

Background: Atypical antipsychotics are a common treatment for serious mental illness, but many are associated with adverse effects, including weight gain and cardiovascular issues, and real-world experience may differ from clinical trial data. Cariprazine has previously demonstrated a favorable safety and tolerability profile in clinical trials. Here, we evaluated the effects of cariprazine on body weight and blood pressure for bipolar I disorder (BP-I), schizophrenia, or as adjunctive treatment for major depressive disorder (MDD) using real-world data.

NIRS-BIDS: Brain Imaging Data Structure Extended to Near-Infrared Spectroscopy.

Functional near-infrared spectroscopy (fNIRS) is an increasingly popular neuroimaging technique that measures cortical hemodynamic activity in a non-invasive and portable fashion. Although the fNIRS community has been successful in disseminating open-source processing tools and a standard file format (SNIRF), reproducible research and sharing of fNIRS data amongst researchers has been hindered by a lack of standards and clarity over how study data should be organized and stored. This problem is not new in neuroimaging, and it became evident years ago with the proliferation of publicly available neuroimaging datasets.

Propidium Monoazide is Unreliable for Quantitative Live-Dead Molecular Assays.

Propidium monoazide (PMA) is a dye that distinguishes between live and dead cells in molecular assays like the Polymerase Chain Reaction (PCR). It works by cross-linking to the DNA of cells that have compromised membranes or extracellular DNA upon photoactivation, making the DNA inaccessible for amplification. Currently, PMA is used to detect viable pathogens and alleviate systemic bias in the microbiome analysis of samples using 16S rRNA gene sequencing.

Selective pressure of various levels of erythromycin on the development of antibiotic resistance.

This study evaluated microbial fitness under selective pressure of various erythromycin concentrations and the development of resistance genes in Escherichia coli (E. coli) and Enterococcus faecalis (E. faecalis).

Deep Learning of CYP450 Binding of Small Molecules by Quantum Information.

Drug-drug interaction can lead to diminished therapeutic effects or increased toxicity, posing significant risks, especially in polypharmacy, and cytochrome P450 plays an indispensable role in this interaction. Cytochrome P450, responsible for the metabolism and detoxification of most drugs, metabolizes about 90% of Food and Drug Administration-approved drugs, making early detection of potential drug-drug interactions. Over the years, in-silico modeling has become a valuable tool for predicting drug-drug interactions.

Rationalizing protein-ligand interactions via the effective fragment potential method and structural data from classical molecular dynamics.

The Effective Fragment Potential (EFP) method, a polarizable quantum mechanics-based force field for describing non-covalent interactions, is utilized to calculate protein-ligand interactions in seven inactive cyclin-dependent kinase 2-ligand complexes, employing structural data from molecular dynamics simulations to assess dynamic and solvent effects. Our results reveal high correlations between experimental binding affinities and EFP interaction energies across all the structural data considered. Using representative structures found by clustering analysis and excluding water molecules yields the highest correlation (R2 of 0.

A Food Pantry Nutrition Education Program RCT: Food Choices and Diet-Quality.

Purpose: Food pantries have provided nutrition education to promote healthy food choices with mixed outcomes. This study assessed the impact of Guided Stars food quality rating system to promote healthy food choices among food pantry clients.

Design: Randomized parallel-group study with balanced randomization.

Family Functioning, Life Satisfaction, and Depressive Symptoms in Chinese Adults With Diabetes and Hypertension: A Cross-Lagged Model.

This study aims to evaluate the bidirectional relationship between family functioning and depressive symptoms, considering life satisfaction as a potential mediator and gender, age and income as a moderator of these associations. A longitudinal study was conducted with 708 Chinese adults with diabetes and hypertension (51.6% women; mean age: 64.

An Extended Langevinized Ensemble Kalman Filter for non-Gaussian Dynamic Systems.

State estimation for large-scale non-Gaussian dynamic systems remains an unresolved issue, given nonscalability of the existing particle filter algorithms. To address this issue, this paper extends the Langevinized ensemble Kalman filter (LEnKF) algorithm to non-Gaussian dynamic systems by introducing a latent Gaussian measurement variable to the dynamic system. The extended LEnKF algorithm can converge to the right filtering distribution as the number of stages become large, while inheriting the scalability of the LEnKF algorithm with respect to the sample size and state dimension.

Modification of Non-photochemical Quenching Pathways in the C Model Plant Revealed Shared and Unique Photoprotection Mechanisms as Compared to C Plants.

Light is essential for photosynthesis; however, excess light can increase the accumulation of photoinhibitory reactive oxygen species that reduce photosynthetic efficiency. Plants have evolved photoprotective non-photochemical quenching (NPQ) pathways to dissipate excess light energy. In tobacco and soybean (C plants), overexpression of three NPQ genes, e (VDE), (PsbS), and (ZEP), hereafter VPZ, resulted in faster NPQ induction and relaxation kinetics, and increased crop yields in field conditions.

Infiltrating lipid-rich macrophage subpopulations identified as a regulator of increasing prostate size in human benign prostatic hyperplasia.

Introduction: Macrophages exhibit marked phenotypic heterogeneity within and across disease states, with lipid metabolic reprogramming contributing to macrophage activation and heterogeneity. Chronic inflammation has been observed in human benign prostatic hyperplasia (BPH) tissues, however macrophage activation states and their contributions to this hyperplastic disease have not been defined. We postulated that a shift in macrophage phenotypes with increasing prostate size could involve metabolic alterations resulting in prostatic epithelial or stromal hyperplasia.

Coconut ( L.) and Carob ( L.) Flours Dietary Fibers Differentially Impact Fecal Microbiota Composition and Metabolic Outputs In Vitro.

Alternative flours can reveal beneficial health effects. The aim of this study was to evaluate and compare the effects of dietary fibers (DFs) of coconut and carob flours on colonic microbiota compositions and function. Coconut flour DFs were found to be dominated by mannose-containing polysaccharides by gas chromatography (GC)/MS and spectrophotometer, whereas glucose and uronic acid were the main monosaccharide moieties in carob flour DFs.

Integrating deep learning and machine learning for improved CKD-related cortical bone assessment in HRpQCT images: A pilot study.

High resolution peripheral quantitative computed tomography (HRpQCT) offers detailed bone geometry and microarchitecture assessment, including cortical porosity, but assessing chronic kidney disease (CKD) bone images remains challenging. This proof-of-concept study merges deep learning and machine learning to 1) improve automatic segmentation, particularly in cases with severe cortical porosity and trabeculated endosteal surfaces, and 2) maximize image information using machine learning feature extraction to classify CKD-related skeletal abnormalities, surpassing conventional DXA and CT measures. We included 30 individuals (20 non-CKD, 10 stage 3 to 5D CKD) who underwent HRpQCT of the distal and diaphyseal radius and tibia and contributed data to develop and validate four different AI models for each anatomical site.

Bayesian Analysis of Exponential Random Graph Models Using Stochastic Gradient Markov Chain Monte Carlo.

The exponential random graph model (ERGM) is a popular model for social networks, which is known to have an intractable likelihood function. Sampling from the posterior for such a model is a long-standing problem in statistical research. We analyze the performance of the stochastic gradient Langevin dynamics (SGLD) algorithm (also known as noisy Longevin Monte Carlo) in tackling this problem, where the stochastic gradient is calculated via running a short Markov chain (the so-called inner Markov chain in this paper) at each iteration.

Ways to Measure Metals: From ICP-MS to XRF.

Purpose Of Review: This review explores the use of Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and X-ray Fluorescence (XRF) for quantifying metals and metalloids in biological matrices such as hair, nails, blood, bone, and tissue. It provides a comprehensive overview of these methodologies, detailing their technological limitations, application scopes, and practical considerations for selection in both laboratory and field settings. By examining traditional and novel aspects of each method, this review aims to guide researchers and clinical practitioners in choosing the most suitable analytical tool based on their specific needs for sensitivity, precision, speed, and sample preparation.

Analysis of multi-class unregulated organic compounds in soil and biosolids using LC-MS/MS.

Numerous unregulated organic compounds (UOCs) including pharmaceuticals, opioids, and personal care products (PCPs) end up in wastewater. UOC presence in biosolids (a wastewater treatment byproduct), which are applied to soil for different reasons raises environmental and health risk concerns. In this study, two multi-class extraction methods were developed and validated to target 111 UOCs from 8 different major families simultaneously in biosolids and biosolids-impacted soil.

AI-based methods for biomolecular structure modeling for Cryo-EM.

Cryo-electron microscopy (Cryo-EM) has revolutionized structural biology by enabling the determination of macromolecular structures that were challenging to study with conventional methods. Processing cryo-EM data involves several computational steps to derive three-dimensional structures from raw projections. Recent advancements in artificial intelligence (AI) including deep learning have significantly improved the performance of these processes.

Mechanical Wear of Degraded Articular Cartilage.

Purpose: To evaluate the mechanical wear of cartilage with different types of degradation.

Methods: Bovine osteochondral explants were treated with interleukin-1β (IL-1β) to mimic inflammatory conditions, with chondroitinase ABC (ChABC) to specifically remove glycosaminoglycans (GAGs), or with collagenase to degrade the collagen network during 5 days of culture. Viscoelastic properties of cartilage were characterized via indentation.

Asymptomatic female softball pitchers have altered hip morphology and cartilage composition.

Few studies have explored hip morphology and cartilage composition in female athletes or the impact of asymmetric repetitive loading, such as occurs during softball pitching. The current cross-sectional study assessed bilateral bony hip morphology on computed tomography imaging in collegiate-level softball pitchers ('Pitch1', n = 25) and cross-country runners ('Run', n = 13). Magnetic resonance imaging was used to assess cartilage relaxation times in a second cohort of pitchers ('Pitch2', n = 10) and non-athletic controls ('Con', n = 4).

Ultra high density imaging arrays in diffuse optical tomography for human brain mapping improve image quality and decoding performance.

Functional magnetic resonance imaging (fMRI) has dramatically advanced non-invasive human brain mapping and decoding. Functional near-infrared spectroscopy (fNIRS) and high-density diffuse optical tomography (HD-DOT) non-invasively measure blood oxygen fluctuations related to brain activity, like fMRI, at the brain surface, using more-lightweight equipment that circumvents ergonomic and logistical limitations of fMRI. HD-DOT grids have smaller inter-optode spacing (~ 13 mm) than sparse fNIRS (~ 30 mm) and therefore provide higher image quality, with spatial resolution ~ 1/2 that of fMRI, when using the several source-detector distances (13-40 mm) afforded by the HD-DOT grid.

The combination of hyperspectral imaging, untargeted metabolomics and lipidomics highlights a coordinated stress-related biochemical reprogramming triggered by polyethylene nanoparticles in lettuce.

Polyethylene nanoplastics (NPs) are widely diffused in terrestrial environments, including soil ecosystems, but the stress mechanisms in plants are not well understood. This study aimed to investigate the effects of two increasing concentrations of NPs (20 and 200 mg kg of soil) in lettuce. To this aim, high-throughput hyperspectral imaging was combined with metabolomics, covering both primary (using NMR) and secondary metabolism (using LC-HRMS), along with lipidomics profiling (using ion-mobility-LC-HRMS) and plant performance.

SEPO-FI: Deep-learning based software to calculate fusion index of muscle cells.

The fusion index is a critical metric for quantitatively assessing the transformation of in vitro muscle cells into myotubes in the biological and medical fields. Traditional methods for calculating this index manually involve the labor-intensive counting of numerous muscle cell nuclei in images, which necessitates determining whether each nucleus is located inside or outside the myotubes, leading to significant inter-observer variation. To address these challenges, this study proposes a three-stage process that integrates the strengths of pattern recognition and deep-learning to automatically calculate the fusion index.

Inclusion of health equity language in recruitment materials for postgraduate pharmacy residencies.

Disclaimer: In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

A Cross-Sectional Study Exploring the Role of Social Isolation in the Relationship Between Food Insecurity, Depressive Symptoms, and Resource Use Among Midwestern Rural Veterans in the U.S.

Background/objectives: The study's objective was to determine whether social isolation serves as a mediator in the cross-sectional relationship between food insecurity, both as a short-term and longer-term situation, with resource use and depressive symptoms as outcomes.

Methods: This cross-sectional design study utilized secondary survey data, including 30-day and 12-month food security measured by the U.S.