Multi-Stage Cascaded Deep Learning-Based Model for Acute Aortic Syndrome Detection: A Multisite Validation Study.

: Acute Aortic Syndrome (AAS), encompassing aortic dissection (AD), intramural hematoma (IMH), and penetrating atherosclerotic ulcer (PAU), presents diagnostic challenges due to its varied manifestations and the critical need for rapid assessment. : We developed a multi-stage deep learning model trained on chest computed tomography angiography (CTA) scans. The model utilizes a U-Net architecture for aortic segmentation, followed by a cascaded classification approach for detecting AD and IMH, and a multiscale CNN for identifying PAU.

Maternal diesel exhaust exposure exacerbates neonatal hyperoxia-induced lung injury in rats.

Objectives: Prenatal exposure to air pollution is associated with an increased risk of lung disease in offspring. However, animal studies on the effects of intrauterine exposure on fetal lung development are limited and yield inconsistent results. This study aimed to investigate the effects of maternal exposure to diesel exhaust particles (DEP) during pregnancy on offspring lung development and whether it would exacerbate neonatal hyperoxia-induced lung injury.

Diagnosis of early glaucoma likely combined with high myopia by integrating OCT thickness map and standard automated and Pulsar perimetries.

Early-stage glaucoma diagnosis is crucial for preventing permanent structural damage and irreversible vision loss. While various machine-learning approaches have been developed for glaucoma diagnosis, only a few specifically address early-stage detection. Moreover, existing early-stage detection methods rely on unimodal information and exclude subjects with high myopia, which contradicts clinical practice and overlooks the adverse effect of high myopia on prediction performance.

Cross-institutional validation of a polar map-free 3D deep learning model for obstructive coronary artery disease prediction using myocardial perfusion imaging: insights into generalizability and bias.

Purpose: Deep learning (DL) models for predicting obstructive coronary artery disease (CAD) using myocardial perfusion imaging (MPI) have shown potential for enhancing diagnostic accuracy. However, their ability to maintain consistent performance across institutions and demographics remains uncertain. This study aimed to investigate the generalizability and potential biases of an in-house MPI DL model between two hospital-based cohorts.

Validating Machine Learning Models Against the Saline Test Gold Standard for Primary Aldosteronism Diagnosis.

Background: In this study, we developed and validated machine learning models to predict primary aldosteronism (PA) in hypertensive East-Asian patients, comparing their performance against the traditional saline infusion test. The motivation for this development arises from the need to provide a more efficient and standardized diagnostic approach, because the saline infusion test, although considered a gold standard, is often cumbersome, is time-consuming, and lacks uniform protocols. By offering an alternative diagnostic method, this study seeks to enhance patient care through quicker and potentially more reliable PA detection.

Maternal Lactobacillus johnsonii supplementation attenuates hyperoxia-induced lung injury in neonatal mice through microbiota regulation.

Background: Supplemental oxygen impairs lung development in premature infants with respiratory distress. This study investigated the effects of maternal Lactobacillus johnsonii supplementation on hyperoxia-induced lung injury in neonatal mice.

Methods: Pregnant C57BL/6 mice received L.

Maternal aspartame exposure alters lung Th1/Th2 cytokine balance in offspring through nuclear factor-κB activation.

Background: Epidemiological evidence suggests that maternal intake of nonnutritive sweeteners is positively associated with early childhood asthma incidence. We investigated the effects of maternal aspartame exposure during pregnancy and lactation on lung Th1/Th2 cytokine balance and intestinal microbiota in offspring and explored the mechanisms that mediate these effects.

Method: Pregnant BALB/c mice were randomly divided on gestational day 7 into two dietary intervention groups: control (drinking water only) and aspartame (drinking water +0.

GAPPA: Enhancing prognosis prediction in primary aldosteronism post-adrenalectomy using graph-based modeling.

Background And Objective: Predicting postoperative prognosis is vital for clinical decision making in patients undergoing adrenalectomy (ADX). This study introduced GAPPA, a novel GNN-based approach, to predict post-ADX outcomes in patients with unilateral primary aldosteronism (UPA). The objective was to leverage the intricate dependencies between clinico-biochemical features and clinical outcomes using GNNs integrated into a bipartite graph structure to enhance prognostic prediction accuracy.

Longitudinal registration of thoracic CT images with radiation-induced lung diseases: A divide-and-conquer approach based on component structure wise registration using coherent point drift.

Background And Objective: Registration of pulmonary computed tomography (CT) images with radiation-induced lung diseases (RILD) was essential to investigate the voxel-wise relationship between the formation of RILD and the radiation dose received by different tissues. Although various approaches had been developed for the registration of lung CTs, their performances remained clinically unsatisfactory for registration of lung CT images with RILD. The main difficulties arose from the longitudinal change in lung parenchyma, including RILD and volumetric change of lung cancers, after radiation therapy, leading to inaccurate registration and artifacts caused by erroneous matching of the RILD tissues.

Altered purine and pentose phosphate pathway metabolism in uteroplacental insufficiency-induced intrauterine growth restriction offspring rats impair intestinal function.

This study aimed to identify metabolic alterations in the small intestine of newborn rats with intrauterine growth restriction (IUGR), a condition linked to intestinal dysfunction. Pregnant Sprague Dawley rats underwent bilateral uterine artery ligation on gestational day 17 to induce intrauterine growth restriction or sham surgery. Rat pups were delivered spontaneously on gestational day 22.

The clinical impacts of lung microbiome in bronchiectasis with fixed airflow obstruction: a prospective cohort study.

Background: Airflow obstruction is a hallmark of disease severity and prognosis in bronchiectasis. The relationship between lung microbiota, airway inflammation, and outcomes in bronchiectasis with fixed airflow obstruction (FAO) remains unclear. This study explores these interactions in bronchiectasis patients, with and without FAO, and compares them to those diagnosed with chronic obstructive pulmonary disease (COPD).

Long-term effects on growth in preterm and small for gestational age infants: A national birth cohort study.

Background: Premature and small-for-gestational-age (SGA) infants tend to have long-term growth morbidities such as short stature, failure to thrive, and obesity. Although most of these infants show catch-up growth at 2-4 years of age, they are still more susceptible to childhood obesity and related metabolic disorders. Those who fail to achieve catch-up will suffer from pathological short stature and neurodevelopmental impairment through adulthood.

Utilizing ChatGPT for Curriculum Learning in Developing a Clinical Grade Pneumothorax Detection Model: A Multisite Validation Study.

: Pneumothorax detection is often challenging, particularly when radiographic features are subtle. This study introduces a deep learning model that integrates curriculum learning and ChatGPT to enhance the detection of pneumothorax in chest X-rays. : The model training began with large, easily detectable pneumothoraces, gradually incorporating smaller, more complex cases to prevent performance plateauing.

Quantification of Resection Margin following Sublobar Resection in Lung Cancer Patients through Pre- and Post-Operative CT Image Comparison: Utilizing a CT-Based 3D Reconstruction Algorithm.

Sublobar resection has emerged as a standard treatment option for early-stage peripheral non-small cell lung cancer. Achieving an adequate resection margin is crucial to prevent local tumor recurrence. However, gross measurement of the resection margin may lack accuracy due to the elasticity of lung tissue and interobserver variability.

Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images.

The presence of spread through air spaces (STASs) in early-stage lung adenocarcinoma is a significant prognostic factor associated with disease recurrence and poor outcomes. Although current STAS detection methods rely on pathological examinations, the advent of artificial intelligence (AI) offers opportunities for automated histopathological image analysis. This study developed a deep learning (DL) model for STAS prediction and investigated the correlation between the prediction results and patient outcomes.

Deep learning model for pleural effusion detection via active learning and pseudo-labeling: a multisite study.

Background: The study aimed to develop and validate a deep learning-based Computer Aided Triage (CADt) algorithm for detecting pleural effusion in chest radiographs using an active learning (AL) framework. This is aimed at addressing the critical need for a clinical grade algorithm that can timely diagnose pleural effusion, which affects approximately 1.5 million people annually in the United States.

Uteroplacental insufficiency decreases leptin expression and impairs lung development in growth-restricted newborn rats.

Background: The study aimed to analyze the effect of uteroplacental insufficiency (UPI) on leptin expression and lung development of intrauterine growth restriction (IUGR) rats.

Methods: On day 17 of pregnancy, time-dated Sprague-Dawley rats were randomly divided into either an IUGR group or a control group. Uteroplacental insufficiency surgery (IUGR) and sham surgery (control) were conducted.

Intrauterine growth restriction alters kidney metabolism at the end of nephrogenesis.

Background: This study investigated the effect of uteroplacental insufficiency (UPI) on renal development by detecting metabolic alterations in the kidneys of rats with intrauterine growth restriction (IUGR).

Methods: On gestational day 17, pregnant Sprague Dawley rats were selected and allocated randomly to either the IUGR group or the control group. The IUGR group received a protocol involving the closure of bilateral uterine vessels, while the control group underwent a sham surgery.

CT-Based Deep-Learning Model for Spread-Through-Air-Spaces Prediction in Ground Glass-Predominant Lung Adenocarcinoma.

Background: Sublobar resection is strongly associated with poor prognosis in early-stage lung adenocarcinoma, with the presence of tumor spread through air spaces (STAS). Thus, preoperative prediction of STAS is important for surgical planning. This study aimed to develop a STAS deep-learning (STAS-DL) prediction model in lung adenocarcinoma with tumor smaller than 3 cm and a consolidation-to-tumor (C/T) ratio less than 0.