Deep regression analysis for enhanced thermal control in photovoltaic energy systems.

Efficient cooling systems are critical for maximizing the electrical efficiency of Photovoltaic (PV) solar panels. However, conventional temperature probes often fail to capture the spatial variability in thermal patterns across panels, impeding accurate assessment of cooling system performance. Existing methods for quantifying cooling efficiency lack precision, hindering the optimization of PV system maintenance and renewable energy output.

Outcomes of CD19 CAR T in Transformed Indolent Lymphoma Compared to De Novo Aggressive Large B-Cell Lymphoma.

Chimeric antigen receptor (CAR) T-cell therapy has revolutionized treatment of aggressive large B-cell lymphoma (aLBCL). Patients with transformed indolent non-Hodgkin lymphoma (tiNHL) were included in key CAR trials, but outcomes of CAR for this distinct, historically high-risk group are poorly understood. We conducted a multicenter retrospective study of 1182 patients with aLBCL receiving standard-of-care CAR T between 2017 and 2022, including 338 (29%) with tiNHL.

The Economic Burden of Myasthenia Gravis (MG): A Survey of Affected People and Their Families.

Introduction/aims: Claims-based analyses have demonstrated high medical costs associated with myasthenia gravis (MG). We examined the economic burden of MG from the perspective of affected people and their families.

Methods: The Muscular Dystrophy Association developed and conducted an online survey of people with MG and their caregivers between October 26 and December 6, 2021.

Susceptibility formulation of density matrix perturbation theory.

Density matrix perturbation theory based on recursive Fermi-operator expansions provides a computationally efficient framework for time-independent response calculations in quantum chemistry and materials science. From a perturbation in the Hamiltonian, we can calculate the first-order perturbation in the density matrix, which then gives us the linear response in the expectation values for some chosen set of observables. We present an alternative, dual formulation, where we instead calculate the static susceptibility of an observable, which then gives us the linear response in the expectation values for any number of different Hamiltonian perturbations.

Circulating biomarkers in high-grade gliomas: current insights and future perspectives.

Purpose: High-grade gliomas (HGG) represent a challenging subset of brain tumors characterized by aggressive nature and poor prognosis. Histopathology remains to be the standard for diagnosis, however, it is invasive, prone to sampling errors, and may not capture the full tumor heterogeneity and evolution over time. In recent years, there has been a growing interest in the potential utility of circulating biomarkers, obtained through minimally-invasive liquid biopsies, providing an opportunity for diagnosis, prognostication, monitoring treatment response and developing targeted therapies.

Leveraging generative neural networks for accurate, diverse, and robust nanoparticle design.

Tandem neural networks for inverse design can only make single predictions, which limits the diversity of predicted structures. Here, we use conditional variational autoencoder (cVAE) for the inverse design of core-shell nanoparticles. cVAE is a type of generative neural network that generates multiple valid solutions for the same input condition.

Superior mesenteric artery syndrome misdiagnosed and preceded by -induced gastritis: a rare diagnosis with misleading features.

Introduction And Importance: Superior mesenteric artery (SMA) syndrome is a rare morbid vascular disorder that is defined as the decrease in the aortomesenteric angle and the compression of the third part of the duodenum. This disorder mainly affects young adult females, and severe weight loss represents a leading predisposing factor.

Case Presentation: The authors report the case of a 19-year-old male who was admitted to our hospital with a 3-month history of epigastric pain, nausea, and weight loss.

The Neuromusculoskeletal Modeling Pipeline: MATLAB-based Model Personalization and Treatment Optimization Functionality for OpenSim.

Neuromusculoskeletal injuries including osteoarthritis, stroke, spinal cord injury, and traumatic brain injury affect roughly 19% of the U.S. adult population.

Data Generation for Machine Learning Interatomic Potentials and Beyond.

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved the way for accurate modeling of diverse chemical and structural properties at the atomic level. The key determinant defining MLIP reliability remains the quality of the training data.

Subsurface damage identification and localization in PZT ceramics using point contact excitation and detection: An image processing framework.

Piezoelectric sensors hold immense potential in structural health monitoring (SHM) applications. However, their performance can be deteriorated by defects and extreme weathering. Therefore, diagnosing the sensor before implementation is very crucial.

Prognostic factors in localized pancreatic ductal adenocarcinoma after neoadjuvant therapy and resection: a systematic review and Meta-Analysis.

Introduction: Prognostic markers for overall survival (OS) in resected pancreatic ductal adenocarcinoma (PDAC) are well-established but remain unclear following neoadjuvant therapy (NAT). This systematic review and meta-analysis aimed to determine factors associated with OS following NAT in resected PDAC.

Methods: The PubMed, Embase, Scopus, Web of Science, and Cochrane CENTRAL databases were systematically searched from inception till May 2024.

VX-548 in the treatment of acute pain.

Acute pain management requires balancing analgesia with adverse effects risk. The voltage-gated sodium channel NaV1.8 plays an important role in pain physiology, and its inhibition was shown to have analgesic effects.

Challenges and prospects of clinical trials in Nigeria - Lessons from the WHO COVID-19 solidarity plus trial.

Conducting randomized clinical trials (RCTs) during a pandemic is essential for obtaining timely evidence on safe and effective treatments, but it presents numerous obstacles. During the COVID-19 pandemic, the World Health Organization (WHO) sponsored multinational RCTs investigating repurposed drugs among hospitalized COVID-19 patients across countries with varying clinical research capacities. In Nigeria, several challenges hindered our progress, including inadequate infrastructure, initial insufficient investigator certification and expertise, and delays in securing regulatory approvals due to bureaucratic hurdles.

PATTERN OF MORBIDITY OF PATIENTS ADMITTED TO NIGERIAN TERTIARY-LEVEL HOSPITALS: INSIGHTS FROM THE MOHOPA STUDY.

Background: There is a paucity of nationally representative data on the pattern of morbidity of hospitalised medical patients in Nigeria. We hereby provide insights from the MOHOPA (Pattern of Morbidities, Mortality and Healthcare Financing of Hospitalised Medical Patients in Hospitals) study on the pattern of morbidity among patients admitted to the medical wards of Nigerian tertiary-level hospitals.

Methods: At least 100 patients admitted to the medical and emergency wards of 7 tertiary-level hospitals, spread across the 6 geopolitical zones of Nigeria and the Capital City of Abuja, were consecutively recruited to join the study after obtaining written informed consent.

Exploration of an intrinsically explainable self-attention based model for prototype generation on single-channel EEG sleep stage classification.

Prototype-based methods in deep learning offer interpretable explanations for decisions by comparing inputs to typical representatives in the data. This study explores the adaptation of SESM, a self-attention-based prototype method successful in electrocardiogram (ECG) tasks, for electroencephalogram (EEG) signals. The architecture is evaluated on sleep stage classification, exploring its efficacy in predicting stages with single-channel EEG.

Combined spinal-epidural vs. dural puncture epidural techniques for labour analgesia: a randomised controlled trial.

Background: The dural puncture epidural technique is a modification of the combined spinal-epidural technique. Data comparing the two techniques are limited. We performed this randomised study to compare the quality of labour analgesia following initiation of analgesia with the dural puncture epidural vs.

Development and Validation of a Predictive Model for Maternal Cardiovascular Morbidity Events in Patients With Hypertensive Disorders of Pregnancy.

Background: Hypertensive disorders of pregnancy (HDP) are a major contributor to maternal morbidity, mortality, and accelerated cardiovascular (CV) disease. Comorbid conditions are likely important predictors of CV risk in pregnant people. Currently, there is no way to predict which people with HDP are at risk of acute CV complications.

Acetylsalicylic acid dosed at bedtime vs. dosed in the morning for circadian rhythm of blood pressure- a systematic review and meta-analysis.

Introduction: Cardiovascular disease (CVD) is a leading global cause of morbidity and mortality, with high systolic blood pressure (SBP) identified as a major risk factor. Aspirin (Acetylsalicylic acid-ASA) has been considered for CVD prevention, prompting questions about its optimal use in primary and secondary prevention and the ideal dosing time to maximize its impact on circadian blood pressure rhythms. Previous research suggests a potential benefit of bedtime aspirin dosing in reducing blood pressure, attributed to its effects on the renin-angiotensin-aldosterone system and nitric oxide production.

Agrarian transformation and rural community food security in the lower Gangetic basin: A household survey dataset.

This data article describes a dataset derived from a comprehensive household survey aimed at understanding the dynamics of agrarian transformation and its impacts on rural community food security within the Lower Gangetic Basin, specifically along the Arial Khan River, Bangladesh. The survey was conducted as part of a doctoral research project and encompasses data collected from 250 households across a defined Mouza within the basin. The dataset provides detailed insights into various dimensions such as household demographics, land ownership and use, agricultural practices and transformations, socio-cultural and environmental factors, power dynamics, food security status, and the strategies employed to enhance food security.