Enhancing generalizability and performance in drug-target interaction identification by integrating pharmacophore and pre-trained models.

MOTIVATION: In drug discovery, it is crucial to assess the drug-target binding affinity (DTA). Although molecular docking is widely used, computational efficiency limits its application in large-scale virtual screening. Deep learning-based methods learn virtual scoring functions from labeled datasets and can quickly predict affinity. However, there are three limitations. First, existing methods only consider the atom-bond graph or one-dimensional sequence representations of compounds, ignoring the information about functional groups (pharmacophores) with specific biological activities. Second, relying on limited labeled datasets fails to learn comprehensive embedding representations of compounds and proteins, resulting in poor generalization performance in complex scenarios. Third, existing feature fusion methods cannot adequately capture contextual interaction information. RESULTS: Therefore, we propose a novel DTA prediction method named HeteroDTA. Specifically, a multi-view compound feature extraction module is constructed to model the atom-bond graph and pharmacophore graph. The residue concat graph and protein sequence are also utilized to model protein structure and function. Moreover, to enhance the generalization capability and reduce the dependence on task-specific labeled data, pre-trained models are utilized to initialize the atomic features of the compounds and the embedding representations of the protein sequence. A context-aware nonlinear feature fusion method is also proposed to learn interaction patterns between compounds and proteins. Experimental results on public benchmark datasets show that HeteroDTA significantly outperforms existing methods. In addition, HeteroDTA shows excellent generalization performance in cold-start experiments and superiority in the representation learning ability of drug-target pairs. Finally, the effectiveness of HeteroDTA is demonstrated in a real-world drug discovery study. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/daydayupzzl/HeteroDTA.

MetDIT: Transforming and Analyzing Clinical Metabolomics Data with Convolutional Neural Networks.

Clinical metabolomics is growing as an essential tool for precision medicine. However, classical machine learning algorithms struggle to comprehensively encode and analyze the metabolomics data due to their high dimensionality and complex intercorrelations. This article introduces a new method called MetDIT, designed to analyze intricate metabolomics data effectively using deep convolutional neural networks (CNN). MetDIT comprises two components: TransOmics and NetOmics. Since CNN models have difficulty in processing one-dimensional (1D) sequence data efficiently, we developed TransOmics, a framework that transforms sequence data into two-dimensional (2D) images while maintaining a one-to-one correspondence between the sequences and images. NetOmics, the second component, leverages a CNN architecture to extract more discriminative representations from the transformed samples. To overcome the overfitting due to the small sample size and class imbalance, we introduced a feature augmentation module (FAM) and a loss function to improve the model performance. Furthermore, we systematically optimized the model backbone and image resolution to balance the model parameters and computational costs. To demonstrate the performance of the proposed MetDIT, we conducted extensive experiments using three different clinical metabolomics data sets and achieved better classification performance than classical machine learning methods used in metabolomics, including Random Forest, SVM, XGBoost, and LightGBM. The source code is available at the GitHub repository at https://github.com/Li-OmicsLab/MetDIT, and the WebApp can be found at http://metdit.bioinformatics.vip/.

Syntrophic microbes involved in the oxidation of short-chain fatty acids in continuous-flow anaerobic digesters treating waste activated sludge with hydrochar.

The rapid degradation of short-chain fatty acids (SCFAs) is an essential issue of anaerobic digestion (AD), in which SCFA oxidizers could generally metabolize in syntrophy with methanogens. The dynamic responses of active metagenome-assembled genomes to low concentrations of propionate and acetate were analyzed to identify specific syntrophic SCFA oxidizers and their metabolic characteristics in continuous-flow AD systems treating waste activated sludge with and without hydrochar. In this study, hydrochar increased methane production by 19%, possibly due to hydrochar enhancing acidification and methanogenesis processes. A putative syntrophic propionate oxidizer and two acetate oxidizers contributed substantially to the syntrophic degradation of SCFAs, and hydrochar positively regulated their functional gene expressions. A significant relationship was established between the replication rate of SCFA oxidizers and their stimulation-related transcriptional activity. Acetate was degraded in the hydrochar group, which might be mainly through the syntrophic acetate oxidizer from the genus Desulfallas and methanogens from the genus Methanosarcina.IMPORTANCEShort-chain fatty acid (SCFA) degradation is an important process in the methanogenic ecosystem. However, current knowledge of this microbial mechanism is mainly based on studies on a few model organisms incubated as mono- or co-cultures or in enrichments, which cannot provide appropriate evidence in complex environments. Here, this study revealed the microbial mechanism of a hydrochar-mediated anaerobic digestion (AD) system promoting SCFA degradation at the species level and identified key SCFA oxidizing bacteria. Our analysis provided new insights into the SCFA oxidizers involved in the AD of waste activated sludge facilitated by hydrochar.

Neutrophil extracellular traps promote proliferation of pulmonary smooth muscle cells mediated by CCDC25 in pulmonary arterial hypertension.

BACKGROUND: Previous studies have indicated that neutrophil extracellular traps (NETs) play a pivotal role in pathogenesis of pulmonary arterial hypertension (PAH). However, the specific mechanism underlying the impact of NETs on pulmonary artery smooth muscle cells (PASMCs) has not been determined. The objective of this study was to elucidate underlying mechanisms through which NETs contribute to progression of PAH. METHODS: Bioinformatics analysis was employed in this study to screen for potential molecules and mechanisms associated with occurrence and development of PAH. These findings were subsequently validated in human samples, coiled-coil domain containing 25 (CCDC25) knockdown PASMCs, as well as monocrotaline-induced PAH rat model. RESULTS: NETs promoted proliferation of PASMCs, thereby facilitating pathogenesis of PAH. This phenomenon was mediated by the activation of transmembrane receptor CCDC25 on PASMCs, which subsequently activated ILK/ß-parvin/RAC1 pathway. Consequently, cytoskeletal remodeling and phenotypic transformation occur in PASMCs. Furthermore, the level of NETs could serve as an indicator of PAH severity and as potential therapeutic target for alleviating PAH. CONCLUSION: This study elucidated the involvement of NETs in pathogenesis of PAH through their influence on the function of PASMCs, thereby highlighting their potential as promising targets for the evaluation and treatment of PAH.

Viral and Bacterial Community Dynamics in Food Waste and Digestate from Full-Scale Biogas Plants.

Anaerobic digestion (AD) is commonly used in food waste treatment. Prokaryotic microbial communities in AD of food waste have been comprehensively studied. The role of viruses, known to affect microbial dynamics and metabolism, remains largely unexplored. This study employed metagenomic analysis and recovered 967 high-quality viral bins within food waste and digestate derived from 8 full-scale biogas plants. The diversity of viral communities was higher in digestate. In silico predictions linked 20.8% of viruses to microbial host populations, highlighting possible virus predators of key functional microbes. Lineage-specific virus-host ratio varied, indicating that viral infection dynamics might differentially affect microbial responses to the varying process parameters. Evidence for virus-mediated gene transfer was identified, emphasizing the potential role of viruses in controlling the microbiome. AD altered the specific process parameters, potentially promoting a shift in viral lifestyle from lysogenic to lytic. Viruses encoding auxiliary metabolic genes (AMGs) were involved in microbial carbon and nutrient cycling, and most AMGs were transcriptionally expressed in digestate, meaning that viruses with active functional states were likely actively involved in AD. These findings provided a comprehensive profile of viral and bacterial communities and expanded knowledge of the interactions between viruses and hosts in food waste and digestate.

Hepatocyte growth factor is protective in early stage but bone-destructive in late stage of experimental periodontitis.

BACKGROUND AND OBJECTIVE: Clinical studies found high levels of hepatocyte growth factor (HGF) expression in patients with periodontitis. Studies suggest that HGF plays an important role in periodontitis, is involved in inflammation, and modulates alveolar bone integrity in periodontitis. This study aims to investigate the effects and mechanisms of HGF in the progression of experimental periodontitis. METHODS: We used silk thread ligation to induce periodontitis in HGF-overexpressing transgenic (HGF-Tg) and wild-type C57BL/6J mice. The effects of HGF overexpression on alveolar bone destruction were assessed by microcomputed tomography imaging at baseline and on days 7, 14, 21, and 28. We analyzed the cytokines (IL-6 and TNF-α) and lymphocytes in periodontitis tissues by enzyme-linked immunosorbent assay and flow cytometry. The effects of HGF on alveolar bone destruction were further tested by quantifying the systemic bone metabolism markers CTXI and PINP and by RNA sequencing for the signaling pathways involved in bone destruction. Western blotting and immunohistochemistry were performed to further elucidate the involved signaling pathways. RESULTS: We found that experimental periodontitis increased HGF production in periodontitis tissues; however, the effects of HGF overexpression were inconsistent with disease progression. In the early stage of periodontitis, periodontal inflammation and alveolar bone destruction were significantly lower in HGF-Tg mice than in wild-type mice. In the late stage, HGF-Tg mice showed higher inflammatory responses and progressively aggravated bone destruction with continued stimulation of inflammation. We identified the IL-17/RANKL/TRAF6 pathway as a signaling pathway involved in the HGF effects on the progression of periodontitis. CONCLUSION: HGF plays divergent effects in the progression of experimental periodontitis and accelerates osteoclastic activity and bone destruction in the late stage of inflammation.

Syntrophic propionate degradation in anaerobic digestion facilitated by hydrochar: Microbial insights as revealed by genome-centric metatranscriptomics.

Propionate is a model substrate for studying energy-limited syntrophic communities in anaerobic digestion, and syntrophic bacteria usually catalyze its degradation in syntrophy with methanogens. In the present study, metagenomics and metatranscriptomics were used to study the effect of the supportive material (e.g., hydrochar) on the key members of propionate degradation and their cooperation mechanism. The results showed that hydrochar increased the methane production rate (up to 57.1%) from propionate. The general transcriptional behavior of the microbiome showed that both interspecies H2 transfer (IHT) and direct interspecies electron transfer (DIET) played essential roles in the hydrochar-mediated methanation of propionate. Five highly active syntrophic propionate-oxidizing bacteria were identified by genome-centric metatranscriptomics. H85pel, a member of the family Pelotomaculaceae, was specifically enriched by hydrochar. Hydrochar enhanced the expression of the flagellum subunit, which interacted with methanogens and hydrogenases in H85pel, indicating that IHT was one of the essential factors promoting propionate degradation. Hydrochar also enriched H162tha belonging to the genus of Thauera. Hydrochar induced the expression of genes related to the complete propionate oxidation pathway, which did not produce acetate. Hydrochar and e-pili-mediated DIET were enhanced, which was another factor promoting propionate degradation. These findings improved the understanding of metabolic traits and cooperation between syntrophic propionate oxidizing bacteria (SPOB) and co-metabolizing partners and provided comprehensive transcriptional insights on function in propionate methanogenic systems.

HerbMet: Enhancing metabolomics data analysis for accurate identification of Chinese herbal medicines using deep learning.

INTRODUCTION: Chinese herbal medicines have been utilized for thousands of years to prevent and treat diseases. Accurate identification is crucial since their medicinal effects vary between species and varieties. Metabolomics is a promising approach to distinguish herbs. However, current metabolomics data analysis and modeling in Chinese herbal medicines are limited by small sample sizes, high dimensionality, and overfitting. OBJECTIVES: This study aims to use metabolomics data to develop HerbMet, a high-performance artificial intelligence system for accurately identifying Chinese herbal medicines, particularly those from different species of the same genus. METHODS: We propose HerbMet, an AI-based system for accurately identifying Chinese herbal medicines. HerbMet employs a 1D-ResNet architecture to extract discriminative features from input samples and uses a multilayer perceptron for classification. Additionally, we design the double dropout regularization module to alleviate overfitting and improve model's performance. RESULTS: Compared to 10 commonly used machine learning and deep learning methods, HerbMet achieves superior accuracy and robustness, with an accuracy of 0.9571 and an F1-score of 0.9542 for distinguishing seven similar Panax ginseng species. After feature selection by 25 different feature ranking techniques in combination with prior knowledge, we obtained 100% accuracy and an F1-score for discriminating P. ginseng species. Furthermore, HerbMet exhibits acceptable inference speed and computational costs compared to existing approaches on both CPU and GPU. CONCLUSIONS: HerbMet surpasses existing solutions for identifying Chinese herbal medicines species. It is simple to use in real-world scenarios, eliminating the need for feature ranking and selection in classical machine learning-based methods.

Quercetin Attenuates KLF4-Mediated Phenotypic Switch of VSMCs to Macrophage-like Cells in Atherosclerosis: A Critical Role for the JAK2/STAT3 Pathway.

The objective of this study was to elucidate the protective role of quercetin in atherosclerosis by examining its effect on the phenotypic switch of vascular smooth muscle cells (VSMCs) to macrophage-like cells and the underlying regulatory pathways. Aorta tissues from apolipoprotein E-deficient (ApoE KO) mice fed a high-fat diet (HFD), treated with or without 100 mg/kg/day quercetin, were analyzed for histopathological changes and molecular mechanisms. Quercetin was found to decrease the size of atherosclerotic lesions and mitigate lipid accumulation induced by HFD. Fluorescence co-localization analysis revealed a higher presence of macrophage-like vascular smooth muscle cells (VSMCs) co-localizing with phospho-Janus kinase 2 (p-JAK2), phospho-signal transducer and activator of transcription 3 (p-STAT3), and Krüppel-like factor 4 (KLF4) in regions of foam cell aggregation within aortic plaques. However, this co-localization was reduced following treatment with quercetin. Quercetin treatment effectively inhibited the KLF4-mediated phenotypic switch in oxidized low-density lipoprotein (ox-LDL)-loaded mouse aortic vascular smooth muscle cells (MOVAS), as indicated by decreased expressions of KLF4, LGALS3, CD68, and F4/80, increased expression of alpha smooth muscle actin (α-SMA), reduced intracellular fluorescence Dil-ox-LDL uptake, and decreased lipid accumulation. In contrast, APTO-253, a KLF4 activator, was found to reverse the effects of quercetin. Furthermore, AG490, a JAK2 inhibitor, effectively counteracted the ox-LDL-induced JAK2/STAT3 pathway-dependent switch to a macrophage-like phenotype and lipid accumulation in MOVAS cells. These effects were significantly mitigated by quercetin but exacerbated by coumermycin A1, a JAK2 activator. Our research illustrates that quercetin inhibits the KLF4-mediated phenotypic switch of VSMCs to macrophage-like cells and reduces atherosclerosis by suppressing the JAK2/STAT3 pathway.

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OBJECTIVES: To explore the role and potential mechanisms of chitinase-3-like protein 1 (CHI3L1) in coronary artery lesions in a mouse model of Kawasaki disease (KD)-like vasculitis. METHODS: Four-week-old male SPF-grade C57BL/6 mice were randomly divided into a control group and a model group, with 10 mice in each group. The model group mice were intraperitoneally injected with 0.5 mL of lactobacillus casei cell wall extract (LCWE) to establish a mouse model of KD-like vasculitis, while the control group mice were injected with an equal volume of normal saline. The general conditions of the mice were observed on the 3rd, 7th, and 14th day after injection. Changes in coronary artery tissue pathology were observed using hematoxylin-eosin staining. The level of CHI3L1 in mouse serum was measured by enzyme-linked immunosorbent assay. Immunofluorescence staining was used to detect the expression and localization of CHI3L1, von Willebrand factor (vWF), and α-smooth muscle actin (α-SMA) in coronary artery tissue. Western blot analysis was used to detect the expression of CHI3L1, vWF, vascular endothelial cadherin (VE cadherin), Caspase-3, B cell lymphoma-2 (Bcl-2), Bcl-2 associated X protein (Bax), nuclear factor κB (NF-κB), and phosphorylated NF-κB (p-NF-κB) in coronary artery tissue. RESULTS: The serum level of CHI3L1 in the model group was significantly higher than that in the control group (P<0.05). Compared to the control group, the expression of CHI3L1 in the coronary artery tissue was higher, while the expression of vWF was lower in the model group. The relative expression levels of CHI3L1, Bax, Caspase-3, NF-κB, and p-NF-κB were significantly higher in the model group than in the control group (P<0.05). The relative expression levels of vWF, VE cadherin, and Bcl-2 were lower in the model group than in the control group (P<0.05). CONCLUSIONS: In the LCWE-induced mouse model of KD-like vasculitis, the expression levels of CHI3L1 in serum and coronary arteries increase, and it may play a role in coronary artery lesions through endothelial cell apoptosis mediated by inflammatory reactions.

Using Smartphones to Enhance Vision Screening in Rural Areas: Pilot Study.

BACKGROUND: While it is treatable, uncorrected refractive error is the number one cause of visual impairment worldwide. This eye condition alone, or together with ocular misalignment, can also cause amblyopia, which is also treatable if detected early but still occurs in about 4% of the population. Mass vision screening is the first and most critical step to address these issues, but due to limited resources, vision screening in many rural areas remains a major challenge. OBJECTIVE: We aimed to pilot-test the feasibility of using smartphone apps to enhance vision screening in areas where access to eye care is limited. METHODS: A vision screening program was piggybacked on a charity summer camp program in a rural county in Sichuan, China. A total of 73 fourth and fifth graders were tested for visual acuity using a standard eye chart and were then tested for refractive error and heterophoria using 2 smartphone apps (a refraction app and a strabismus app, respectively) by nonprofessional personnel. RESULTS: A total of 5 of 73 (6.8%, 95% CI 2.3%-15.3%) students were found to have visual acuity worse than 20/20 (logarithm of minimal angle of resolution [logMAR] 0) in at least one eye. Among the 5 students, 3 primarily had refractive error according to the refraction app. The other 2 students had manifest strabismus (one with 72-prism diopter [PD] esotropia and one with 33-PD exotropia) according to the strabismus app. Students without manifest strabismus were also measured for phoria using the strabismus app in cover/uncover mode. The median phoria was 0.0-PD (IQR 2.9-PD esophoria to 2.2-PD exophoria). CONCLUSIONS: The results from this vision screening study are consistent with findings from other population-based vision screening studies in which conventional tools were used by ophthalmic professionals. The smartphone apps are promising and have the potential to be used in mass vision screenings for identifying risk factors for amblyopia and for myopia control. The smartphone apps may have significant implications for the future of low-cost vision care, particularly in resource-constrained and geographically remote areas.

Field Evaluation of a Mobile App for Assisting Blind and Visually Impaired Travelers to Find Bus Stops.

Purpose: GPS location-based navigation apps are insufficient to aid blind and visually impaired (BVI) travelers for micro-navigation tasks, such as finding the exact location of bus stops. The resulting large gaps could lead to BVI travelers missing their bus. We evaluated the ability of a signage detection mobile app, All_Aboard, to guide BVI travelers precisely to the bus stops compared to Google Maps alone. Methods: The All_Aboard app detected bus stop signs in real-time via smartphone camera using a deep neural network model, and provided distance coded audio feedback to help localize the detected sign. BVI individuals used the All_Aboard and Google Maps app to localize 10 bus stops each in downtown and suburban Boston, Massachusetts. For each bus stop, the subjects used both apps to navigate as close as possible to the physical bus stop sign, starting from 30 to 50 meters away. The outcome measures were success rate and gap distance between the app-indicated location and the actual physical location of the bus stop. Results: The study involved 24 legally blind participants (mean age [SD] = 51 [14] years; 11 [46%] women). The success rate of the All_Aboard app (91%) was significantly higher than the Google Maps (52%, P < 0.001). The gap distance when using the All_Aboard app was significantly lower (mean = 1.8, 95% confidence interval [CI] = 1.2-2.3 meters) compared to the Google Maps alone (mean = 7, 95% CI = 6.5-7.5 meters, P < 0.001). Conclusions: All_Aboard micro-navigation app guided BVI travelers to bus stops more accurately and reliably than a location-based macro-navigation app alone. Translational Relevance: The All_Aboard app together with a macro-navigation app can potentially help BVI individuals independently access public transportation.

A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection.

Inhaled corticosteroid (ICS) is a mainstay treatment for controlling asthma and preventing exacerbations in patients with persistent asthma. Many types of ICS drugs are used, either alone or in combination with other controller medications. Despite the widespread use of ICSs, asthma control remains suboptimal in many people with asthma. Suboptimal control leads to recurrent exacerbations, causes frequent ER visits and inpatient stays, and is due to multiple factors. One such factor is the inappropriate ICS choice for the patient. While many interventions targeting other factors exist, less attention is given to inappropriate ICS choice. Asthma is a heterogeneous disease with variable underlying inflammations and biomarkers. Up to 50% of people with asthma exhibit some degree of resistance or insensitivity to certain ICSs due to genetic variations in ICS metabolizing enzymes, leading to variable responses to ICSs. Yet, ICS choice, especially in the primary care setting, is often not tailored to the patient's characteristics. Instead, ICS choice is largely by trial and error and often dictated by insurance reimbursement, organizational prescribing policies, or cost, leading to a one-size-fits-all approach with many patients not achieving optimal control. There is a pressing need for a decision support tool that can predict an effective ICS at the point of care and guide providers to select the ICS that will most likely and quickly ease patient symptoms and improve asthma control. To date, no such tool exists. Predicting which patient will respond well to which ICS is the first step toward developing such a tool. However, no study has predicted ICS response, forming a gap. While the biologic heterogeneity of asthma is vast, few, if any, biomarkers and genotypes can be used to systematically profile all patients with asthma and predict ICS response. As endotyping or genotyping all patients is infeasible, readily available electronic health record data collected during clinical care offer a low-cost, reliable, and more holistic way to profile all patients. In this paper, we point out the need for developing a decision support tool to guide ICS selection and the gap in fulfilling the need. Then we outline an approach to close this gap via creating a machine learning model and applying causal inference to predict a patient's ICS response in the next year based on the patient's characteristics. The model uses electronic health record data to characterize all patients and extract patterns that could mirror endotype or genotype. This paper supplies a roadmap for future research, with the eventual goal of shifting asthma care from one-size-fits-all to personalized care, improve outcomes, and save health care resources.

Azilsartan improves urinary albumin excretion in hypertension mice.

Hypertension is one of the most important risk factors for chronic kidney diseases, leading to hypertensive nephrosclerosis, including excessive albuminuria. Azilsartan, an angiotensin II type 1 receptor blocker, has been widely used for the treatment of hypertension. However, the effects of Azilsartan on urinary albumin excretion in hypertension haven't been reported before. In this study, we investigated whether Azilsartan possesses a beneficial property against albuminuria in mice treated with angiotensin II and a high-salt diet (ANG/HS). Compared to the control group, the ANG/HS group had higher blood pressure, oxidative stress, and inflammatory response, all of which were rescued by Azilsartan dose-dependently. Importantly, the ANG/HS-induced increase in urinary albumin excretion and decrease in the expression of occludin were reversed by Azilsartan. Additionally, it was shown that increased fluorescence intensity of FITC-dextran, declined trans-endothelial electrical resistance (TEER) values, and reduction of occludin and krüppel-like factor 2 (KLF2) were observed in ANG/HS-treated human renal glomerular endothelial cells (HrGECs), then prevented by Azilsartan. Moreover, the regulatory effect of Azilsartan on endothelial monolayer permeability in ANG/HS-treated HrGECs was abolished by the knockdown of KLF2, indicating KLF2 is required for the effect of Azilsartan. We concluded that Azilsartan alleviated diabetic nephropathy-induced increase in Uterine artery embolization (UAE) mediated by the KLF2/occludin axis.

A case report of pulmonary atresia with intact ventricular septum: an extraordinary finding of subsystemic right ventricle.

Background: Massive tricuspid regurgitation (TR) is the most common feature of pulmonary atresia with intact ventricular septum (PA/IVS), and mild or absent TR is observed in severe right ventricular (RV) dysplasia or RV-to-coronary fistulous connections, resulting in non-biventricular (BV) outcomes postnatally. Case summary: We report a case of fetal severe pulmonary stenosis with IVS diagnosed at 26 weeks of gestation. The severity of RV hypoplasia did not worsen or reach indications for intrauterine intervention, while the jet velocity of TR decreased significantly during pregnancy. The fetus was definitely diagnosed with PA/IVS with mild RV dysplasia after birth. Unusually, the fetus did not experience severe TR and myocardial sinusoids, the TR jet velocity was maintained at 2.0 m/s, and the coronary artery was almost normal. The incapable RV cannot pump blood into pulmonary circulation after RV decompression from valvular perforation and balloon dilation. It may be an extraordinary finding of subsystemic RV. Conclusion: PA/IVS is a heterogeneous disease with various degrees of RV dysplasia. Mild or no baseline TR is a reliable indicator with non-BV outcomes for fetal PA/IVS, even with acceptable dysplasia RV structures.

Explanatory deep learning to predict elevated pulmonary artery pressure in children with ventricular septal defects using standard chest x-rays: a novel approach.

Objective: Early risk assessment of pulmonary arterial hypertension (PAH) in patients with congenital heart disease (CHD) is crucial to ensure timely treatment. We hypothesize that applying artificial intelligence (AI) to chest x-rays (CXRs) could identify the future risk of PAH in patients with ventricular septal defect (VSD). Methods: A total of 831 VSD patients (161 PAH-VSD, 670 nonPAH-VSD) was retrospectively included. A residual neural networks (ResNet) was trained for classify VSD patients with different outcomes based on chest radiographs. The endpoint of this study was the occurrence of PAH in VSD children before or after surgery. Results: In the validation set, the AI algorithm achieved an area under the curve (AUC) of 0.82. In an independent test set, the AI algorithm significantly outperformed human observers in terms of AUC (0.81 vs. 0.65). Class Activation Mapping (CAM) images demonstrated the model's attention focused on the pulmonary artery segment. Conclusion: The preliminary findings of this study suggest that the application of artificial intelligence to chest x-rays in VSD patients can effectively identify the risk of PAH.

Fundamental properties and phosphorus transformation mechanism of soybean straw during microwave hydrothermal conversion process.

Microwave hydrothermal (MHT) conversion is emerging as a promising technology for the disposal and reutilization of biowastes. This study investigated the fundamental properties and phosphorus transformation mechanism of soybean straw during the MHT conversion process. The oxygen-containing functional groups in soybean straw were stripped, and a trend of dehydration was observed as the temperature increased during the MHT process. Cellulose was identified as the major component of the MHT solid products at high temperature. Glucose and glucuronic acid in the MHT liquid products were gradually converted to formic acid and acetic acid with increasing temperature and holding time. The characteristics of the MHT products directly affected the changes in P speciation and transformation. Most of the P was distributed in liquid products and the impact of holding time was not significant on P distribution at low MHT temperature. With the increase in temperature and holding time, P gradually transferred into the solid products. The proportion of organic phosphorus and soluble inorganic phosphorus in soybean straw was high, and it decreased noticeably after the MHT process. The increase in MHT temperature promoted the conversion of OP and AP into IP and NAIP respectively. P K-edge X-ray absorption near edge structure analysis reveals that Ca5(PO4)3(OH) was the major component of soybean straw and more Ca5(PO4)3(OH) was formed at lower MHT temperature. This study provides fundamental knowledge on the property changes of soybean straw and the transformation of phosphorus during MHT conversion process, which is essential for its disposal and further utilization.

Association of Dietary Retinol Intake and Serum Neurofilament Light Chain Levels: Results from NHANES 2013-2014.

BACKGROUND: There is increasing evidence suggesting that serum neurofilament light chain (sNfL) levels can be used as biomarkers for axonal injury. Retinol is recognized for its significant involvement in nervous system function, but the precise connection between dietary retinol and sNfL levels remains uncertain. OBJECTIVE: Our objective was to investigate the relationship between dietary retinol intake and sNfL, and to find an optimal retinol intake level for neurological health. METHODS: In the National Health and Nutrition Examination Survey (NHANES), conducted from 2013 to 2014, a cohort of 1684 participants who met the criteria were selected for the study. sNfL levels were measured from stored serum samples using a novel high-throughput immunoassay platform from Siemens Healthineers. Assessment of dietary retinol intake was performed by a uniformly trained interviewer through a 24 h dietary recall method. A generalized linear model was evaluated to assess the correlation between dietary retinol intake and sNfL concentrations. Furthermore, the nonlinear association between the two is further explored using restricted cubic spline (RCS) analysis. RESULTS: Upon adjusting for potential confounders, a 10% increase in dietary retinol intake was associated with a 3.47% increase in sNfL levels (95% CI: 0.54%, 6.49%) across all participants. This relationship was more pronounced in specific subgroups, including those under 60 years of age, non-obese, impaired estimated glomerular filtration rate (eGFR), and non-diabetic. In subgroup analysis, among those younger than 60 years of age (percent change: 3.80%; 95% CI: 0.43%, 7.28%), changes were found in non-obese participants (percent change: 6.28%; 95% CI: 2.66%, 10.02%), those with impaired eGFR (percent change: 6.90%; 95% CI: 1.44%, 12.65%), and non-diabetic patients (percentage change: 4.17%; 95% CI: 1.08%, 7.36%). RCS analysis showed a linear relationship between dietary retinol intake and sNfL levels. Furthermore, the positive correlation between the two was more significant after the inflection point, according to piecewise linear analysis. CONCLUSION: This current investigation uncovered a J-shaped relationship between dietary retinol and sNfL levels, suggesting that axonal damage can occur when dietary retinol intake increases more than a specific threshold. These findings need to be further confirmed in future prospective studies to determine the precise intake level that may trigger axonal injury.

LncRNA MEG3 Inhibits the Epithelial-mesenchymal Transition of Bladder Cancer Cells through the Snail/E-cadherin Axis.

OBJECTIVE: This study aimed to investigate the role of the long noncoding RNA (lncRNA) maternally expressed gene 3 (MEG3) in the epithelial-mesenchymal transition (EMT) of bladder cancer cells and the potential mechanisms. METHODS: Cell invasion, migration, and wound healing assays were conducted to assess the effects of MEG3 on the invasive and migratory capabilities of bladder cancer cells. The expression levels of E-cadherin were measured using Western blotting, RT-qPCR, and dual luciferase reporter assays. RNA immunoprecipitation and pull-down assays were performed to investigate the interactions between MEG3 and its downstream targets. RESULTS: MEG3 suppressed the invasion and migration of bladder cancer cells and modulated the transcription of E-cadherin. The binding of MEG3 to the zinc finger region of the transcription factor Snail prevented its ability to transcriptionally repress E-cadherin. Additionally, MEG3 suppressed the phosphorylation of extracellular regulated protein kinase (ERK), c-Jun N-terminal kinase (JNK), and P38, thereby decreasing the expression of Snail and stimulating the expression of E-cadherin. CONCLUSION: MEG3 plays a vital role in suppressing the EMT in bladder cancer cells, indicating its potential as a promising therapeutic target for the treatment of bladder cancer.