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

BACKGROUND: 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. METHODS: 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. Small intestine tissues were collected on postnatal days 0 and 7 from offspring. Liquid chromatography-mass spectrometry analysis was performed to investigate untargeted metabolomic profiles. Western blot analysis assessed protein expression of key regulators. RESULTS: Newborn rats with intrauterine growth restriction exhibited distinct small intestine metabolic profiles compared to controls on postnatal day 0. Notably, significant alterations were observed in purine metabolism, the pentose phosphate pathway, and related pathways. Western blot analysis revealed a decrease expression in transketolase, a key enzyme of the pentose phosphate pathway, suggesting impaired activity of the pentose phosphate pathway. Additionally, decreased expression of tight junction proteins ZO-1 and occludin indicated compromised intestinal barrier function in rats with intrauterine growth restriction. Similar metabolic disruptions persisted on postnatal day 7, with further reductions in tricarboxylic acid cycle intermediates and folate biosynthesis precursors. Interestingly, lysyl-glycine, a protein synthesis marker, was elevated in rats with intrauterine growth restriction. CONCLUSIONS: Our findings reveal a distinct metabolic signature in the small intestine of neonatal rats with intrauterine growth restriction, characterized by disruptions in the pentose phosphate pathway, purine metabolism, and energy production pathways. These novel insights suggest potential mechanisms underlying IUGR-associated intestinal dysfunction and impaired growth.

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. This study aims to depict the growth pattern of premature or SGA infants and their growth morbidities in Taiwan. METHODS: Data were obtained from a nationally representative cohort of 24,200 pairs of postpartum women and newborns in the Taiwan Birth Cohort Study (TBCS), using structured questionnaire interviews. A total of 16,358 infants were included and three follow-up surveys were completed at 6, 18, and 36 months after the deliveries. We constructed growth curves to conduct an in-depth investigation into anthropometric data, applying a linear mixed model. Logistic regression was used to model the relevant outcomes, with adjustment for various potential confounding factors. RESULTS: Despite being born shorter and lighter, preterm and SGA infants generally showed catch-up growth and had no higher odds ratios (ORs) of developing short stature or failure to thrive compared to appropriate-for-gestational-age (AGA) term infants before 3 years of age. Preterm SGA infants, particularly females, had higher ORs for obesity at the 36-month follow-up. CONCLUSION: This is the first nationwide population-based study depicting the growth of SGA infants in Taiwan. The growth patterns of preterm and term SGA infants are different from those of preterm and term AGA infants. Further research is necessary to understand the growth trajectories of preterm and SGA infants and their associations with later diseases.

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). METHODS: This prospective observational study in Taiwan enrolled patients with either bronchiectasis or COPD. To analyze the lung microbiome and assess inflammatory markers, bronchoalveolar lavage (BAL) samples were collected for 16S rRNA gene sequencing. The study cohort comprised 181 patients: 86 with COPD, 46 with bronchiectasis, and 49 with bronchiectasis and FAO, as confirmed by spirometry. RESULTS: Patients with bronchiectasis, with or without FAO, had similar microbiome profiles characterized by reduced alpha diversity and a predominance of Proteobacteria, distinctly different from COPD patients who exhibited more Firmicutes, greater diversity, and more commensal taxa. Furthermore, compared to COPD and bronchiectasis without FAO, bronchiectasis with FAO showed more severe disease and a higher risk of exacerbations. A significant correlation was found between the presence of Pseudomonas aeruginosa and increased airway neutrophilic inflammation such as Interleukin [IL]-1ß, IL-8, and tumor necrosis factor-alpha [TNF]-α, as well as with higher bronchiectasis severity, which might contribute to an increased risk of exacerbations. Moreover, in bronchiectasis patients with FAO, the ROSE (Radiology, Obstruction, Symptoms, and Exposure) criteria were employed to classify individuals as either ROSE (+) or ROSE (-), based on smoking history. This classification highlighted differences in clinical features, inflammatory profiles, and slight microbiome variations between ROSE (-) and ROSE (+) patients, suggesting diverse endotypes within the bronchiectasis with FAO group. CONCLUSION: Bronchiectasis patients with FAO may exhibit two distinct endotypes, as defined by ROSE criteria, characterized by greater disease severity and a lung microbiome more similar to bronchiectasis without FAO than to COPD. The significant correlation between Pseudomonas aeruginosa colonization and increased airway neutrophilic inflammation, as well as disease severity, underscores the clinical relevance of microbial patterns. This finding reinforces the potential role of these patterns in the progression and exacerbations of bronchiectasis with FAO.

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

Background: 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. Methods: The model training began with large, easily detectable pneumothoraces, gradually incorporating smaller, more complex cases to prevent performance plateauing. The training dataset comprised 6445 anonymized radiographs, validated across multiple sites, and further tested for generalizability in diverse clinical subgroups. Performance metrics were analyzed using descriptive statistics. Results: The model achieved a sensitivity of 0.97 and a specificity of 0.97, with an area under the curve (AUC) of 0.98, demonstrating a performance comparable to that of many FDA-approved devices. Conclusions: This study suggests that a structured approach to training deep learning models, through curriculum learning and enhanced data extraction via natural language processing, can facilitate and improve the training of AI models for pneumothorax detection.

Natural sweetener glycyrrhizin protects against precocious puberty by modulating the gut microbiome.

AIMS: Precocious puberty (PP) may lead to many adverse outcomes. Recent evidence suggests that PP is a gut-brain disease. On the other hand, the use of glycyrrhizin, a natural sweetener, has become popular in the past decade. Glycyrrhizin possesses various health benefits, but its impact on PP has yet to be investigated. We aimed to explore the protective effects of glycyrrhizin against PP in both humans (observational) and animals (interventional). MATERIALS AND METHODS: In the human cohort, we investigated the association between glycyrrhizin consumption and risk of PP. In the animal experiment, we observed puberty onset after feeding danazol-induced PP rats with glycyrrizin. Blood, fecal, and hypothalamic samples were harvested to evaluate potential mechanistic pathways. We also performed a fecal microbiota transplantation to confirm to causal relationship between glycyrrhizin and PP risk. KEY FINDINGS: Glycyrrhizin exhibited a protective effect against PP in children (OR 0.60, 95%CI: 0.39-0.89, p = 0.013), primarily driven by its significance in girls, while no significant effect was observed in boys. This effect was consistent with findings in rodents. These benefits were achieved through the modulation of the gut microbiome, which functionally suppressed the hypothalamic-pituitary-gonadal axis and prevented PP progression. A fecal microbiota transplantation indicated that the causal correlation between glycyrrhizin intake and PP is mediated by the gut microbiome alterations. SIGNIFICANCE: Our findings suggest that glycyrrhizin can protect against PP by altering the gut microbiome. Long term use of glycyrrhizin is safe and tolerable. Therefore, glycyrrhizin can serve as a safe and affordable complementary therapy for PP.

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. Therefore, this study aimed to develop an objective measurement method, the CT-based 3D reconstruction algorithm, to quantify the resection margin following sublobar resection in lung cancer patients through pre- and post-operative CT image comparison. An automated subvascular matching technique was first developed to ensure accuracy and reproducibility in the matching process. Following the extraction of matched feature points, another key technique involves calculating the displacement field within the image. This is particularly important for mapping discontinuous deformation fields around the surgical resection area. A transformation based on thin-plate spline is used for medical image registration. Upon completing the final step of image registration, the distance at the resection margin was measured. After developing the CT-based 3D reconstruction algorithm, we included 12 cases for resection margin distance measurement, comprising 4 right middle lobectomies, 6 segmentectomies, and 2 wedge resections. The outcomes obtained with our method revealed that the target registration error for all cases was less than 2.5 mm. Our method demonstrated the feasibility of measuring the resection margin following sublobar resection in lung cancer patients through pre- and post-operative CT image comparison. Further validation with a multicenter, large cohort, and analysis of clinical outcome correlation is necessary in future studies.

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. To develop the DL-based STAS prediction model, 1053 digital pathology whole-slide images (WSIs) from the competition dataset were enrolled in the training set, and 227 WSIs from the National Taiwan University Hospital were enrolled for external validation. A YOLOv5-based framework comprising preprocessing, candidate detection, false-positive reduction, and patient-based prediction was proposed for STAS prediction. The model achieved an area under the curve (AUC) of 0.83 in predicting STAS presence, with 72% accuracy, 81% sensitivity, and 63% specificity. Additionally, the DL model demonstrated a prognostic value in disease-free survival compared to that of pathological evaluation. These findings suggest that DL-based STAS prediction could serve as an adjunctive screening tool and facilitate clinical decision-making in patients with early-stage lung adenocarcinoma.

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. METHODS: In this multisite study, 10,599 chest radiographs from 2006 to 2018 were retrospectively collected from an institution in Taiwan to train the deep learning algorithm. The AL framework utilized significantly reduced the need for expert annotations. For external validation, the algorithm was tested on a multisite dataset of 600 chest radiographs from 22 clinical sites in the United States and Taiwan, which were annotated by three U.S. board-certified radiologists. RESULTS: The CADt algorithm demonstrated high effectiveness in identifying pleural effusion, achieving a sensitivity of 0.95 (95% CI: [0.92, 0.97]) and a specificity of 0.97 (95% CI: [0.95, 0.99]). The area under the receiver operating characteristic curve (AUC) was 0.97 (95% DeLong's CI: [0.95, 0.99]). Subgroup analyses showed that the algorithm maintained robust performance across various demographics and clinical settings. CONCLUSION: This study presents a novel approach in developing clinical grade CADt solutions for the diagnosis of pleural effusion. The AL-based CADt algorithm not only achieved high accuracy in detecting pleural effusion but also significantly reduced the workload required for clinical experts in annotating medical data. This method enhances the feasibility of employing advanced technological solutions for prompt and accurate diagnosis in medical settings.

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 0.5. METHODS: The study retrospectively enrolled of 581 patients from two institutions between 2015 and 2019. The STAS-DL model was developed to extract the feature of solid components through solid components gated (SCG) for predicting STAS. The STAS-DL model was assessed with external validation in the testing sets and compared with the deep-learning model without SCG (STAS-DLwoSCG), the radiomics-based model, the C/T ratio, and five thoracic surgeons. The performance of the models was evaluated using area under the curve (AUC), accuracy and standardized net benefit of the decision curve analysis. RESULTS: The study evaluated 458 patients (institute 1) in the training set and 123 patients (institute 2) in the testing set. The proposed STAS-DL yielded the best performance compared with the other methods in the testing set, with an AUC of 0.82 and an accuracy of 74%, outperformed the STAS-DLwoSCG with an accuracy of 70%, and was superior to the physicians with an AUC of 0.68. Moreover, STAS-DL achieved the highest standardized net benefit compared with the other methods. CONCLUSION: The proposed STAS-DL model has great potential for the preoperative prediction of STAS and may support decision-making for surgical planning in early-stage, ground glass-predominant lung adenocarcinoma.

Machine Learning Model with Computed Tomography Radiomics and Clinicobiochemical Characteristics Predict the Subtypes of Patients with Primary Aldosteronism.

RATIONALE AND OBJECTIVES: Adrenal venous sampling (AVS) is the primary method for differentiating between primary aldosterone (PA) subtypes. The aim of study is to develop prediction models for subtyping of patients with PA using computed tomography (CT) radiomics and clinicobiochemical characteristics associated with PA. MATERIALS AND METHODS: This study retrospectively enrolled 158 patients with PA who underwent AVS between January 2014 and March 2021. Neural network machine learning models were developed using a two-stage analysis of triple-phase abdominal CT and clinicobiochemical characteristics. In the first stage, the models were constructed to classify unilateral or bilateral PA; in the second stage, they were designed to determine the predominant side in patients with unilateral PA. The final proposed model combined the best-performing models from both stages. The model's performance was evaluated using repeated stratified five-fold cross-validation. We employed paired t-tests to compare its performance with the conventional imaging evaluations made by radiologists, which categorize patients as either having bilateral PA or unilateral PA on one side. RESULTS: In the first stage, the integrated model that combines CT radiomic and clinicobiochemical characteristics exhibited the highest performance, surpassing both the radiomic-alone and clinicobiochemical-alone models. It achieved an accuracy and F1 score of 80.6% ± 3.0% and 74.8% ± 5.2% (area under the receiver operating curve [AUC] = 0.778 ± 0.050). In the second stage, the accuracy and F1 score of the radiomic-based model were 88% ± 4.9% and 81.9% ± 6.2% (AUC=0.831 ± 0.087). The proposed model achieved an accuracy and F1 score of 77.5% ± 3.9% and 70.5% ± 7.1% (AUC=0.771 ± 0.046) in subtype diagnosis and lateralization, surpassing the accuracy and F1 score achieved by radiologists' evaluation (p < .05). CONCLUSION: The proposed machine learning model can predict the subtypes and lateralization of PA. It yields superior results compared to conventional imaging evaluation and has potential to supplement the diagnostic process in PA.

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. Offspring rats were spontaneously delivered on day 22 of pregnancy. On postnatal days 0 and 7, rats' pups were selected at random from the control and IUGR groups. Blood was withdrawn from the heart to determine leptin levels. The right lung was obtained for leptin and leptin receptor levels, immunohistochemistry, proliferating cell nuclear antigen (PCNA), western blot, and metabolomic analyses. RESULTS: UPI-induced IUGR decreased leptin expression and impaired lung development, causing decreased surface area and volume in offspring. This results in lower body weight, decreased serum leptin levels, lung leptin and leptin receptor levels, alveolar space, PCNA, and increased alveolar wall volume fraction in IUGR offspring rats. The IUGR group found significant relationships between serum leptin, radial alveolar count, von Willebrand Factor, and metabolites. CONCLUSION: Leptin may contribute to UPI-induced lung development during the postnatal period, suggesting supplementation as a potential treatment. IMPACT: The neonatal rats with intrauterine growth restriction (IUGR) caused by uteroplacental insufficiency (UPI) showed decreased leptin expression and impaired lung development. UPI-induced IUGR significantly decreased surface area and volume in lung offspring. This is a novel study that investigates leptin expression and lung development in neonatal rats with IUGR caused by UPI. If our findings translate to IUGR infants, leptin may contribute to UPI-induced lung development during the postnatal period, suggesting supplementation as a potential treatment.

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. The rat pups were delivered on gestational day 22 by natural means. Pups were randomly recruited from both the control and IUGR groups on the seventh day after birth. The kidneys were surgically removed to conduct Western blot and metabolomic analyses. RESULTS: IUGR was produced by UPI, as evidenced by the significantly lower body weights of the pups with IUGR compared to the control pups on postnatal day 7. UPI significantly increased the levels of cleaved caspase-3 (p < 0.05) and BAX/Bcl-2 (p < 0.01) in the pups with IUGR. Ten metabolites exhibited statistically significant differences between the groups (q < 0.05). Metabolic pathway enrichment analysis demonstrated statistically significant variations between the groups in the metabolism related to fructose and mannose, amino and nucleotide sugars, and inositol phosphate. CONCLUSIONS: UPI alters kidney metabolism in growth-restricted newborn rats and induces renal apoptosis. The results of our study have the potential to provide new insights into biomarkers and metabolic pathways that are involved in the kidney changes generated by IUGR.

Automated marker-free longitudinal infrared breast image registration by GA-PSO.

The automated marker-free longitudinal Infrared (IR) breast image registration overcomes several challenges like no anatomic fiducial markers on the body surface, blurry boundaries, heat pattern variation by environmental and physiological factors, nonrigid deformation, etc, has the ability of quantitative pixel-wise analysis with the heat energy and patterns change in a time course study. To achieve the goal, scale-invariant feature transform, Harris corner, and Hessian matrix were employed to generate the feature points as anatomic fiducial markers, and hybrid genetic algorithm and particle swarm optimization minimizing the matching errors was used to find the appropriate corresponding pairs between the 1st IR image and thenth IR image. Moreover, the mechanism of the IR spectrogram hardware system has a high level of reproducibility. The performance of the proposed longitudinal image registration system was evaluated by the simulated experiments and the clinical trial. In the simulated experiments, the mean difference of our system is 1.64 mm, which increases 57.58% accuracy than manual determination and makes a 17.4% improvement than the previous study. In the clinical trial, 80 patients were captured several times of IR breast images during chemotherapy. Most of them were well aligned in the spatiotemporal domain. In the few cases with evident heat pattern dissipation and spatial deviation, it still provided a reliable comparison of vascular variation. Therefore, the proposed system is accurate and robust, which could be considered as a reliable tool for longitudinal approaches to breast cancer diagnosis.

Intranasal administration of Lactobacillus johnsonii attenuates hyperoxia-induced lung injury by modulating gut microbiota in neonatal mice.

BACKGROUND: Supplemental oxygen impairs lung development in newborn infants with respiratory distress. Lactobacillus johnsonii supplementation attenuates respiratory viral infection in mice and exhibits anti-inflammatory effects. This study investigated the protective effects of intranasal administration of L. johnsonii on lung development in hyperoxia-exposed neonatal mice. METHODS: Neonatal C57BL/6N mice were reared in either room air (RA) or hyperoxia condition (85% O2). From postnatal days 0 to 6, they were administered intranasal 10 µL L. johnsonii at a dose of 1 × 105 colony-forming units. Control mice received an equal volume of normal saline (NS). We evaluated the following four study groups: RA + NS, RA + probiotic, O2 + NS, and O2 + probiotic. On postnatal day 7, lung and intestinal microbiota were sampled from the left lung and lower gastrointestinal tract, respectively. The right lung of each mouse was harvested for Western blot, cytokine, and histology analyses. RESULTS: The O2 + NS group exhibited significantly lower body weight and vascular density and significantly higher mean linear intercept (MLI) and lung cytokine levels compared with the RA + NS and RA + probiotic groups. At the genus level of the gut microbiota, the O2 + NS group exhibited significantly higher Staphylococcus and Enterobacter abundance and significantly lower Lactobacillus abundance compared with the RA + NS and RA + probiotic groups. Intranasal L. johnsonii treatment increased the vascular density, decreased the MLI and cytokine levels, and restored the gut microbiota in hyperoxia-exposed neonatal mice. CONCLUSIONS: Intranasal administration of L. johnsonii protects against hyperoxia-induced lung injury and modulates the gut microbiota.

Molecular Mechanisms of Hyperoxia-Induced Neonatal Intestinal Injury.

Oxygen therapy is important for newborns. However, hyperoxia can cause intestinal inflammation and injury. Hyperoxia-induced oxidative stress is mediated by multiple molecular factors and leads to intestinal damage. Histological changes include ileal mucosal thickness, intestinal barrier damage, and fewer Paneth cells, goblet cells, and villi, effects which decrease the protection from pathogens and increase the risk of necrotizing enterocolitis (NEC). It also causes vascular changes with microbiota influence. Hyperoxia-induced intestinal injuries are influenced by several molecular factors, including excessive nitric oxide, the nuclear factor-κB (NF-κB) pathway, reactive oxygen species, toll-like receptor-4, CXC motif ligand-1, and interleukin-6. Nuclear factor erythroid 2-related factor 2 (Nrf2) pathways and some antioxidant cytokines or molecules including interleukin-17D, n-acetylcysteine, arginyl-glutamine, deoxyribonucleic acid, cathelicidin, and health microbiota play a role in preventing cell apoptosis and tissue inflammation from oxidative stress. NF-κB and Nrf2 pathways are essential to maintain the balance of oxidative stress and antioxidants and prevent cell apoptosis and tissue inflammation. Intestinal inflammation can lead to intestinal damage and death of the intestinal tissue, such as in NEC. This review focuses on histologic changes and molecular pathways of hyperoxia-induced intestinal injuries to establish a framework for potential interventions.

Alteration in branching morphogenesis via YAP/TAZ in fibroblasts of fetal lungs in an LPS-induced inflammation model.

BACKGROUND: Chorioamnionitis is a common cause of preterm birth and leads to serious complications in newborns. The objective of this study was to investigate the role of the Hippo signaling pathway in lung branching morphogenesis under a lipopolysaccharide (LPS)-induced inflammation model. MATERIALS AND METHODS: IMR-90 cells and ex vivo fetal lungs were treated with 0, 10, 30, or 50 µg/ml LPS for 24 and 72 h. Supernatant levels of lactate dehydrogenase (LDH), interleukin (IL)-6, IL-8, Chemokine (C-X-C motif) ligand 1(CXCL1), branching and the surface area ratio, Yes-associated protein (YAP), transcription coactivator with PDZ-binding motif (TAZ), fibroblast growth factor 10 (FGF10), fibroblast growth factor receptor II (FGFR2), SRY-box transcription factor 2 (SOX2), SOX9, and sirtuin 1 (SIRT1) levels were examined. Differentially expressed genes in fetal lungs after LPS treatment were identified by RNA-sequencing. RESULTS: LPS at 50 µg/ml increased IL-6 and IL-8 in IMR-90 cells and increased IL-6, CXCL1 and LDH in fetal lungs. The branching ratio significantly increased by LPS at 30 µg/ml compared to the control but the increased level had decreased by 50 µg/ml LPS exposure. Exposure to 50 µg/ml LPS increased phosphorylated (p)-YAP, p-YAP/YAP, and p-TAZ/TAZ in IMR-90 cells, whereas 50 µg/ml LPS decreased FGF10 and SOX2. Consistently, p-YAP/YAP and p-TAZ/TAZ were increased in fibronectin+ cells of fetal lungs. Moreover, results of RNA-sequencing in fetal lungs showed that SMAD, FGF, IκB phosphorylation, tissue remodeling and homeostasis was involved in branching morphogenesis following exposure to 50 µg/ml LPS. The p-SIRT1/SIRT1 ratio increased in IMR-90 cells by LPS treatment. CONCLUSIONS: This study showed that regulation of the Hippo pathway in fibroblasts of fetal lungs was involved in branching morphogenesis under an inflammatory disease such as chorioamnionitis.

Diesel exhaust particles inhibit lung branching morphogenesis via the YAP/TAZ pathway.

Prenatal exposure to air pollution may associated with inhibition of lung development in the child, however the possible mechanism is unclear. We investigated the effects of traffic-related diesel exhaust particle (DEP) exposure on fetal lung branching morphogenesis and elucidate the possible mechanism. Ex vivo fetal lungs collected from ICR mice at an age of 11.5 embryonic (E) days were exposed to DEPs at 0 (control), 10, and 50 µg/mL and branching morphogenesis was measured for 3 days. Normal IMR-90 human fetal lung fibroblast cells were exposed to DEPs at 0 (control), 10, and 50 µg/mL for 24 h. We observed that DEP exposure significantly inhibited lung branching morphogenesis with reduced lung branching ratios and surface areas on day 3. RNA sequencing (RNA-Seq) showed that DEP increased the inflammatory response and impaired lung development-related gene expressions. DEPs significantly decreased Yes-associated protein (YAP), phosphorylated (p)-YAP, transcriptional coactivator with a PDZ-binding motif (TAZ), and p-TAZ in IMR-90 cells at 10 and 50 µg/mL. Treatment of fetal lungs with the YAP inhibitor, PFI-2, also demonstrated restricted lung branching development similar to that of DEP exposure, with a significantly decreased lung branching ratio on day 3. DEP exposure significantly decreased the lung branching modulators fibroblast growth factor receptor 2 (FGFR2), sex-determining region Y-box 2 (SOX2), and SOX9 in IMR-90 cells at 10 and 50 µg/mL. Fetal lung immunofluorescence staining showed that DEP decreased SOX2 expression in fibronectin+ fibroblasts. DEP exposure decreased the cellular senescence regulator, p-sirtuin 1 (SIRT1)/SIRT1 in IMR-90 cells, with RNA-Seq showing impaired telomere maintenance. DEP exposure impaired fetal lung growth during the pseudoglandular stage through dysregulating the Hippo signaling pathway, causing fibroblast lung branching restriction and early senescence. Prenatal exposure to traffic-related air pollution has adverse effects on fetal lung development.

Polar map-free 3D deep learning algorithm to predict obstructive coronary artery disease with myocardial perfusion CZT-SPECT.

PURPOSE: Deep learning (DL) models have been shown to outperform total perfusion deficit (TPD) quantification in predicting obstructive coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, previously published methods have depended on polar maps, required manual correction, and normal database. In this study, we propose a polar map-free 3D DL algorithm to predict obstructive disease. METHODS: We included 1861 subjects who underwent MPI using cadmium-zinc-telluride camera and subsequent coronary angiography. The subjects were divided into parameterization and external validation groups. We implemented a fully automatic algorithm to segment myocardium, perform registration, and apply normalization. We further flattened the image based on spherical coordinate system transformation. The proposed model consisted of a component to predict patent arteries and a component to predict disease in each vessel. The model was cross-validated in the parameterization group, and then further tested using the external validation group. The performance was assessed by area under receiver operating characteristic curves (AUCs) and compared with TPD. RESULTS: Our algorithm preprocessed all images accurately as confirmed by visual inspection. In patient-based analysis, the AUC of the proposed model was significantly higher than that for stress-TPD (0.84 vs 0.76, p < 0.01). In vessel-based analysis, the proposed model also outperformed regional stress-TPD (AUC = 0.80 vs 0.72, p < 0.01). The addition of quantitative images did not improve the performance. CONCLUSIONS: Our proposed polar map-free 3D DL algorithm to predict obstructive CAD from MPI outperformed TPD and did not require manual correction or a normal database.

Effects of uteroplacental insufficiency on cardiac development in growth-restricted newborn rats.

Fetal growth restriction (FGR) is associated with reduced cardiac function in neonates. Uteroplacental insufficiency (UPI) is the most common cause of FGR. The mechanisms underlying these alterations remain unknown. We hypothesized that UPI would influence cardiac development in offspring rats. Through this study, we evaluated the effects of UPI during pregnancy on heart histology and pulmonary hypertension in growth-restricted newborn rats. On gestation Day 18, either UPI was induced through bilateral uterine vessel ligation (FGR group) or sham surgery (control group) was performed. The right middle lobe of the lung and the heart were harvested for histological and immunohistochemical evaluation on postnatal days 0 and 7. The FGR group exhibited significantly lower body weight, hypertrophy and degeneration of cardiomyocytes, increased intercellular spaces between the cardiomyocytes and collagen deposition, and decreased glycogen deposition and HNK-1 expression compared with the control group on postnatal days 0 and 7. These results suggest that neonates with FGR may have inadequate myocardial reserves, which may cause subsequent cardiovascular compromise in future life. Further studies are required to evaluate the hemodynamic changes in these growth-restricted neonates.