Inhibition of ROS/caspase-3/GSDME-mediated pyroptosis alleviates high glucose-induced injury in AML-12 cells.

Diabetic liver injury (DLI) is a chronic complication of the liver caused by diabetes, and its has become one of the main causes of nonalcoholic fatty liver disease (NAFLD). The gasdermin E (GSDME)-dependent pyroptosis signaling pathway is involved in various physiological and pathological processes; however, its role and mechanism in DLI are still unknown. This study was performed to investigate the role of GSDME-mediated pyroptosis in AML-12 cell injury induced by high glucose and to evaluate the therapeutic potential of caspase-3 inhibition for DLI. The results showed that high glucose activated apoptosis by regulating the apoptotic protein levels including Bax, Bcl-2, and enhanced cleavage of caspase-3 and PARP. Notably, some of the hepatocytes treated with high glucose became swollen, accompanied by GSDME-N generation, indicating that pyroptosis was further induced by active caspase-3. Moreover, the effects of high glucose on AML-12 cells could be partly reversed by a reactive oxygen scavenger (NAC) and caspase-3 specific inhibitor (Z-DEVD-FMK), which suggests high glucose induced GSDME-dependent pyroptosis in AML-12 cells through increasing ROS levels and activating caspase-3. In conclusion, our results show that high glucose can induce pyroptosis in AML-12 cells, at least in part, through the ROS/caspase-3/GSDME pathway，and inhibition of caspase-3 can ameliorate high glucose-induced hepatocyte injury, providing an important basis for clarifying the pathogenesis and treatment of DLI.

The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction.

Background: Lymph node metastasis (LNM) is considered an essential prognosis factor for adenocarcinoma of the esophagogastric junction (AEG), which also affects the treatment strategies of AEG. We aimed to evaluate automated machine learning (AutoML) algorithms for predicting LNM in Siewert type II T1 AEG. Methods: A total of 878 patients with Siewert type II T1 AEG were selected from the Surveillance, Epidemiology, and End Results (SEER) database to develop the LNM predictive models. The patients from two hospitals in Suzhou were collected as the test set. We applied five machine learning algorithms to develop the LNM prediction models. The performance of predictive models was assessed using various metrics including accuracy, sensitivity, specificity, the area under the curve (AUC), and receiver operating characteristic (ROC) curve. Results: Patients with LNM exhibited a higher proportion of male individuals, a poor degree of differentiation, and submucosal infiltration, with statistical differences. The deep learning (DL) model demonstrated relatively good accuracy (0.713) and sensitivity (0.868) among the five models. Moreover, the DL model achieved the highest AUC (0.781) and sensitivity (1.000) in the test set. Conclusion: The DL model showed good predictive performance among five AutoML models, indicating the advantage of AutoML in modeling LNM prediction in patients with Siewert type II T1 AEG.

Effects of Trehalose Preconditioning on H9C2 Cell Viability and Autophagy Activation in a Model of Donation after Circulatory Death for Heart Transplantation.

Donation after circulatory death (DCD) is a promising strategy for alleviating donor shortage in heart transplantation. Trehalose, an autophagy inducer, has been shown to be cardioprotective in an ischemia-reperfusion (IR) model; however, its role in IR injury in DCD remains unknown. In the present study, we evaluated the effects of trehalose on cardiomyocyte viability and autophagy activation in a DCD model. In the DCD model, cardiomyocytes (H9C2) were exposed to 1 h warm ischemia, 1 h cold ischemia, and 1 h reperfusion. Trehalose was administered before cold ischemia (preconditioning), during cold ischemia, or during reperfusion. Cell viability was measured using the Cell Counting Kit-8 after treatment with trehalose. Autophagy activation was evaluated by measuring autophagy flux using an autophagy inhibitor, chloroquine, and microtubule-associated protein 1A/1B light chain 3 B (LC3)-II by western blotting. Trehalose administered before the ischemic period (trehalose preconditioning) increased cell viability. The protective effects of trehalose preconditioning on cell viability were negated by chloroquine treatment. Furthermore, trehalose preconditioning increased autophagy flux. Trehalose preconditioning increased cardiomyocyte viability through the activation of autophagy in a DCD model, which could be a promising strategy for the prevention of cardiomyocyte damage in DCD transplantation.

A Semi-Supervised Learning Framework for Classifying Colorectal Neoplasia Based on the NICE Classification.

Labelling medical images is an arduous and costly task that necessitates clinical expertise and large numbers of qualified images. Insufficient samples can lead to underfitting during training and poor performance of supervised learning models. In this study, we aim to develop a SimCLR-based semi-supervised learning framework to classify colorectal neoplasia based on the NICE classification. First, the proposed framework was trained under self-supervised learning using a large unlabelled dataset; subsequently, it was fine-tuned on a limited labelled dataset based on the NICE classification. The model was evaluated on an independent dataset and compared with models based on supervised transfer learning and endoscopists using accuracy, Matthew's correlation coefficient (MCC), and Cohen's kappa. Finally, Grad-CAM and t-SNE were applied to visualize the models' interpretations. A ResNet-backboned SimCLR model (accuracy of 0.908, MCC of 0.862, and Cohen's kappa of 0.896) outperformed supervised transfer learning-based models (means: 0.803, 0.698, and 0.742) and junior endoscopists (0.816, 0.724, and 0.863), while performing only slightly worse than senior endoscopists (0.916, 0.875, and 0.944). Moreover, t-SNE showed a better clustering of ternary samples through self-supervised learning in SimCLR than through supervised transfer learning. Compared with traditional supervised learning, semi-supervised learning enables deep learning models to achieve improved performance with limited labelled endoscopic images.

Association of the fat mass index with hepatic steatosis and fibrosis: evidence from NHANES 2017-2018.

Limited population-based studies discuss the association between fat mass index (FMI) and the risk of liver diseases. This investigation utilized data from the National Health and Nutrition Examination Survey (NHANES) to examine the linkage between the FMI and liver conditions, specifically steatosis and fibrosis. The study leveraged data from NHANES's 2017-2018 cross-sectional study, employing an oversampling technique to deal with sample imbalance. Hepatic steatosis and fibrosis were identified by vibration-controlled transient elastography. Receiver operating curve was used to assess the relationship of anthropometric indicators, e.g., the FMI, body mass index (BMI), weight-adjusted-waist index (WWI), percentage of body fat (BF%), waist-to-hip ratio (WHR), and appendicular skeletal muscle index (ASMI), with hepatic steatosis and fibrosis. In this study, which included 2260 participants, multivariate logistic regression models, stratified analyses, restricted cubic spline (RCS), and sharp regression discontinuity analyses were utilized. The results indicated that the WHR and the FMI achieved the highest area under the curve for identifying hepatic steatosis and fibrosis, respectively (0.720 and 0.726). Notably, the FMI presented the highest adjusted odds ratio for both hepatic steatosis (6.40 [4.91-8.38], p = 2.34e-42) and fibrosis (6.06 [5.00, 7.37], p = 5.88e-74). Additionally, potential interaction effects were observed between the FMI and variables such as the family income-to-poverty ratio, smoking status, and hypertension, all of which correlated with the presence of liver fibrosis (p for interaction < 0.05). The RCS models further confirmed a significant positive correlation of the FMI with the controlled attenuation parameter and liver stiffness measurements. Overall, the findings underscore the strong link between the FMI and liver conditions, proposing the FMI as a potential straightforward marker for identifying liver diseases.

Development and Validation of Multimodal Models to Predict the 30-Day Mortality of ICU Patients Based on Clinical Parameters and Chest X-Rays.

We aimed to develop and validate multimodal ICU patient prognosis models that combine clinical parameters data and chest X-ray (CXR) images. A total of 3798 subjects with clinical parameters and CXR images were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and an external hospital (the test set). The primary outcome was 30-day mortality after ICU admission. Automated machine learning (AutoML) and convolutional neural networks (CNNs) were used to construct single-modal models based on clinical parameters and CXR separately. An early fusion approach was used to integrate both modalities (clinical parameters and CXR) into a multimodal model named PrismICU. Compared to the single-modal models, i.e., the clinical parameter model (AUC = 0.80, F1-score = 0.43) and the CXR model (AUC = 0.76, F1-score = 0.45) and the scoring system APACHE II (AUC = 0.83, F1-score = 0.77), PrismICU (AUC = 0.95, F1 score = 0.95) showed improved performance in predicting the 30-day mortality in the validation set. In the test set, PrismICU (AUC = 0.82, F1-score = 0.61) was also better than the clinical parameters model (AUC = 0.72, F1-score = 0.50), CXR model (AUC = 0.71, F1-score = 0.36), and APACHE II (AUC = 0.62, F1-score = 0.50). PrismICU, which integrated clinical parameters data and CXR images, performed better than single-modal models and the existing scoring system. It supports the potential of multimodal models based on structured data and imaging in clinical management.

Terazosin attenuates abdominal aortic aneurysm formation by downregulating Peg3 expression to inhibit vascular smooth muscle cell apoptosis and senescence.

Abdominal aortic aneurysm (AAA), a vascular degenerative disease, is a potentially life-threatening condition characterised by the loss of vascular smooth muscle cells (VSMCs), degradation of extracellular matrix (ECM), inflammation, and oxidative stress. Despite the severity of AAA, effective drugs for treatment are scarce. At low doses, terazosin (TZ) exerts antiapoptotic and anti-inflammatory effects in several diseases, but its potential to protect against AAA remains unexplored. Herein, we investigated the effects of TZ in two AAA animal models: Angiotensin II (Ang II) infusion in Apoe-/- mice and calcium chloride application in C57BL/6J mice. Mice were orally administered with TZ (100 or 1000 µg/kg/day). The in vivo results indicated that low-dose TZ alleviated AAA formation in both models. Low-dose TZ significantly reduced aortic pulse wave velocity without exerting an apparent antihypertensive effect in the Ang II-induced AAA model. Paternally expressed gene 3 (Peg3) was identified via RNA sequencing as a novel TZ target. PEG3 expression was significantly elevated in both mouse and human AAA tissues. TZ suppressed PEG3 expression and reduced the abundance of matrix metalloproteinases (MMP2/MMP9) in the tunica media. Functional experiments and molecular analyses revealed that TZ (10 nM) treatment and Peg3 knockdown effectively prevented Ang II-induced VSMC senescence and apoptosis in vitro. Thus, Peg3, a novel target of TZ, mediates inflammation-induced VSMC apoptosis and senescence. Low-dose TZ downregulates Peg3 expression to attenuate AAA formation and ECM degradation, suggesting a promising therapeutic strategy for AAA.

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images.

BACKGROUND: Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) to develop a prediction model based on CT characteristics for the identification of asymptomatic carriers. METHODS: Asymptomatic carriers were from Yangzhou Third People's Hospital from August 1st, 2020, to March 31st, 2021, and the control group included a healthy population from a nonepizootic area with two negative RT‒PCR results within 48 h. All CT images were preprocessed using MATLAB. Model development and validation were conducted in R with the H2O package. The models were built based on six algorithms, e.g., random forest and deep neural network (DNN), and a training set (n = 691). The models were improved by automatically adjusting hyperparameters for an internal validation set (n = 306). The performance of the obtained models was evaluated based on a dataset from Suzhou (n = 178) using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score. RESULTS: A total of 1,175 images were preprocessed with high stability. Six models were developed, and the performance of the DNN model ranked first, with an AUC value of 0.898 for the test set. The sensitivity, specificity, PPV, NPV, F1 score and accuracy of the DNN model were 0.820, 0.854, 0.849, 0.826, 0.834 and 0.837, respectively. A plot of a local interpretable model-agnostic explanation demonstrated how different variables worked in identifying asymptomatic carriers. CONCLUSIONS: Our study demonstrates that AutoML models based on CT images can be used to identify asymptomatic carriers. The most promising model for clinical implementation is the DNN-algorithm-based model.

Targeting cAMP in D1-MSNs in the nucleus accumbens, a new rapid antidepressant strategy.

Studies have suggested that the nucleus accumbens (NAc) is implicated in the pathophysiology of major depression; however, the regulatory strategy that targets the NAc to achieve an exclusive and outstanding anti-depression benefit has not been elucidated. Here, we identified a specific reduction of cyclic adenosine monophosphate (cAMP) in the subset of dopamine D1 receptor medium spiny neurons (D1-MSNs) in the NAc that promoted stress susceptibility, while the stimulation of cAMP production in NAc D1-MSNs efficiently rescued depression-like behaviors. Ketamine treatment enhanced cAMP both in D1-MSNs and dopamine D2 receptor medium spiny neurons (D2-MSNs) of depressed mice, however, the rapid antidepressant effect of ketamine solely depended on elevating cAMP in NAc D1-MSNs. We discovered that a higher dose of crocin markedly increased cAMP in the NAc and consistently relieved depression 24 h after oral administration, but not a lower dose. The fast onset property of crocin was verified through multicenter studies. Moreover, crocin specifically targeted at D1-MSN cAMP signaling in the NAc to relieve depression and had no effect on D2-MSN. These findings characterize a new strategy to achieve an exclusive and outstanding anti-depression benefit by elevating cAMP in D1-MSNs in the NAc, and provide a potential rapid antidepressant drug candidate, crocin.

Development and validation of a multimodal model in predicting severe acute pancreatitis based on radiomics and deep learning.

OBJECTIVE: Aim to establish a multimodal model for predicting severe acute pancreatitis (SAP) using machine learning (ML) and deep learning (DL). METHODS: In this multicentre retrospective study, patients diagnosed with acute pancreatitis at admission were enrolled from January 2017 to December 2021. Clinical information within 24 h and CT scans within 72 h of admission were collected. First, we trained Model α based on clinical features selected by least absolute shrinkage and selection operator analysis. Second, radiomics features were extracted from 3D-CT scans and Model ß was developed on the features after dimensionality reduction using principal component analysis. Third, Model γ was trained on 2D-CT images. Lastly, a multimodal model, namely PrismSAP, was constructed based on aforementioned features in the training set. The predictive accuracy of PrismSAP was verified in the validation and internal test sets and further validated in the external test set. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, recall, precision and F1-score. RESULTS: A total of 1,221 eligible patients were randomly split into a training set (n = 864), a validation set (n = 209) and an internal test set (n = 148). Data of 266 patients were for external testing. In the external test set, PrismSAP performed best with the highest AUC of 0.916 (0.873-0.960) among all models [Model α: 0.709 (0.618-0.800); Model ß: 0.749 (0.675-0.824); Model γ: 0.687 (0.592-0.782); MCTSI: 0.778 (0.698-0.857); RANSON: 0.642 (0.559-0.725); BISAP: 0.751 (0.668-0.833); SABP: 0.710 (0.621-0.798)]. CONCLUSION: The proposed multimodal model outperformed any single-modality models and traditional scoring systems.

3D Printed Conformal Strain and Humidity Sensors for Human Motion Prediction and Health Monitoring via Machine Learning.

Wearable sensors have garnered considerable attention due to their flexibility and lightweight characteristics in the realm of healthcare applications. However, developing robust wearable sensors with facile fabrication and good conformity remains a challenge. In this study, a conductive graphene nanoplate-carbon nanotube (GC) ink is synthesized for multi jet fusion (MJF) printing. The layer-by-layer fabrication process of MJF not only improves the mechanical and flame-retardant properties of the printed GC sensor but also bolsters its robustness and sensitivity. The direction of sensor bending significantly impacts the relative resistance changes, allowing for precise investigations of joint motions in the human body, such as those of the fingers, wrists, elbows, necks, and knees. Furthermore, the data of resistance changes collected by the GC sensor are utilized to train a support vector machine with a 95.83% accuracy rate for predicting human motions. Due to its stable humidity sensitivity, the sensor also demonstrates excellent performance in monitoring human breath and predicting breath modes (normal, fast, and deep breath), thereby expanding its potential applications in healthcare. This work opens up new avenues for using MJF-printed wearable sensors for a variety of healthcare applications.

Towards Personalized Medicine for Chronic Liver Disease.

Chronic liver disease is a progressive deterioration of hepatic functions and a continuous process of inflammation, destruction, and regeneration of liver parenchyma, resulting in fibrosis and cirrhosis [...].

Deep learning to predict esophageal variceal bleeding based on endoscopic images.

OBJECTIVE: Esophageal varix (EV) bleeding is a particularly serious complications of cirrhosis. Prediction of EV bleeding requires extensive endoscopy experience; it remains unreliable and inefficient. This retrospective cohort study evaluated the feasibility of using deep learning (DL) to predict the 12-month risk of EV bleeding based on endoscopic images. METHODS: Six DL models were trained to perform binary classification of endoscopic images of EV bleeding. The models were subsequently validated using an external test dataset, then compared with classifications performed by two endoscopists. RESULTS: In the validation dataset, EfficientNet had the highest accuracy (0.910), followed by ConvMixer (0.898) and Xception (0.875). In the test dataset, EfficientNet maintained the highest accuracy (0.893), which was better than the endoscopists (0.800 and 0.763). Notably, one endoscopist displayed higher recall (0.905), compared with EfficientNet (0.870). When their predictions were assisted by artificial intelligence, the accuracies of the two endoscopists increased by 17.3% and 19.0%. Moreover, statistical agreement among the models was dependent on model architecture. CONCLUSIONS: This study demonstrated the feasibility of using DL to predict the 12-month risk of EV bleeding based on endoscopic images. The findings suggest that artificial intelligence-aided diagnosis will be a useful addition to cirrhosis management.

Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review.

With the advances in endoscopic technologies and artificial intelligence, a large number of endoscopic imaging datasets have been made public to researchers around the world. This study aims to review and introduce these datasets. An extensive literature search was conducted to identify appropriate datasets in PubMed, and other targeted searches were conducted in GitHub, Kaggle, and Simula to identify datasets directly. We provided a brief introduction to each dataset and evaluated the characteristics of the datasets included. Moreover, two national datasets in progress were discussed. A total of 40 datasets of endoscopic images were included, of which 34 were accessible for use. Basic and detailed information on each dataset was reported. Of all the datasets, 16 focus on polyps, and 6 focus on small bowel lesions. Most datasets (n = 16) were constructed by colonoscopy only, followed by normal gastrointestinal endoscopy and capsule endoscopy (n = 9). This review may facilitate the usage of public dataset resources in endoscopic research.

Predicting the Recurrence of Common Bile Duct Stones After ERCP Treatment with Automated Machine Learning Algorithms.

BACKGROUND: Recurrence of common bile duct stones (CBDs) commonly happens after endoscopic retrograde cholangiopancreatography (ERCP). The clinical prediction models for the recurrence of CBDs after ERCP are lacking. AIMS: We aim to develop high-performance prediction models for the recurrence of CBDS after ERCP treatment using automated machine learning (AutoML) and to assess the AutoML models versus the traditional regression models. METHODS: 473 patients with CBDs undergoing ERCP were recruited in the single-center retrospective cohort study. Samples were divided into Training Set (65%) and Validation Set (35%) randomly. Three modeling approaches, including fully automated machine learning (Fully automated), semi-automated machine learning (Semi-automated), and traditional regression were applied to fit prediction models. Models' discrimination, calibration, and clinical benefits were examined. The Shapley additive explanations (SHAP), partial dependence plot (PDP), and SHAP local explanation (SHAPLE) were proposed for the interpretation of the best model. RESULTS: The area under roc curve (AUROC) of semi-automated gradient boost machine (GBM) model was 0.749 in Validation Set, better than the other fully/semi-automated models and the traditional regression models (highest AUROC = 0.736). The calibration and clinical application of AutoML models were adequate. Through the SHAP-PDP-SHAPLE pipeline, the roles of key variables of the semi-automated GBM model were visualized. Lastly, the best model was deployed online for clinical practitioners. CONCLUSION: The GBM model based on semi-AutoML is an optimal model to predict the recurrence of CBDs after ERCP treatment. In comparison with traditional regressions, AutoML algorithms present significant strengths in modeling, which show promise in future clinical practices.

Activation of Pgk1 Results in Reduced Protein Aggregation in Diverse Neurodegenerative Conditions.

The prevention of protein condensates has emerged as a new drug target to treat diverse neurodegenerative disorders. We previously reported that terazosin (TZ), a prescribed antagonist of the α1 adrenergic receptor, is an activator of phosphoglycerate kinase 1 (Pgk1) and Hsp90. In this study, we aimed to determine whether TZ prevents the formation of diverse pathological condensates in cell cultures and animal disease models. In primary neuron culture, TZ treatment reduced both the protein density and abundance of fused in sarcoma (FUS)-P525L-GFP, a disease-associated mutant form of FUS. Regarding the mechanism, we found that increased intracellular ATP levels were critical for the reduction in protein aggregate density. In addition, Hsp90 activation by TZ enhanced Hsp90 interaction with ULK1, a master regulator of autophagy. Through in vivo studies, we examined neuron-specific overexpression of tau in Drosophila, mouse models of APP/PS1 Alzheimer's disease (AD), and a rat model of multiple system atrophy (MSA) via the viral expression of α-synuclein in the striatum. TZ prevented and reversed the formation of pathological protein condensates. Together, our results suggest that activation of Pgk1 in cytosol may dissolve pathological protein aggregates via increased ATP levels and degrade these proteins via autophagy; the FUS-P525L degradation pathway in nucleus is unclear.

Deep learning for pancreatic diseases based on endoscopic ultrasound: A systematic review.

BACKGROUND AND AIMS: Endoscopic ultrasonography (EUS) is one of the main examinations in pancreatic diseases. A series of the studies reported the application of deep learning (DL)-assisted EUS in the diagnosis of pancreatic diseases. This systematic review is to evaluate the role of DL algorithms in assisting EUS diagnosis of pancreatic diseases. METHODS: Literature search were conducted in PubMed and Semantic Scholar databases. Studies that developed DL models for pancreatic diseases based on EUS were eligible for inclusion. This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and quality assessment of the included studies was performed according to the IJMEDI checklist. RESULTS: A total of 23 studies were enrolled into this systematic review, which could be categorized into three groups according to computer vision tasks: classification, detection and segmentation. Seventeen studies focused on the classification task, among which five studies developed simple neural network (NN) models while twelve studies constructed convolutional NN (CNN) models. Three studies were concerned the detection task and five studies were the segmentation task, all based on CNN architectures. All models presented in the studies performed well based on EUS images, videos or voice. According to the IJMEDI checklist, six studies were recognized as high-grade quality, with scores beyond 35 points. CONCLUSIONS: DL algorithms show great potential in EUS images/videos/voice for pancreatic diseases. However, there is room for improvement such as sample sizes, multi-center cooperation, data preprocessing, model interpretability, and code sharing.

Identification of Asymptomatic COVID-19 Patients on Chest CT Images Using Transformer-Based or Convolutional Neural Network-Based Deep Learning Models.

Novel coronavirus disease 2019 (COVID-19) has rapidly spread throughout the world; however, it is difficult for clinicians to make early diagnoses. This study is to evaluate the feasibility of using deep learning (DL) models to identify asymptomatic COVID-19 patients based on chest CT images. In this retrospective study, six DL models (Xception, NASNet, ResNet, EfficientNet, ViT, and Swin), based on convolutional neural networks (CNNs) or transformer architectures, were trained to identify asymptomatic patients with COVID-19 on chest CT images. Data from Yangzhou were randomly split into a training set (n = 2140) and an internal-validation set (n = 360). Data from Suzhou was the external-test set (n = 200). Model performance was assessed by the metrics accuracy, recall, and specificity and was compared with the assessments of two radiologists. A total of 2700 chest CT images were collected in this study. In the validation dataset, the Swin model achieved the highest accuracy of 0.994, followed by the EfficientNet model (0.954). The recall and the precision of the Swin model were 0.989 and 1.000, respectively. In the test dataset, the Swin model was still the best and achieved the highest accuracy (0.980). All the DL models performed remarkably better than the two experts. Last, the time on the test set diagnosis spent by two experts-42 min, 17 s (junior); and 29 min, 43 s (senior)-was significantly higher than those of the DL models (all below 2 min). This study evaluated the feasibility of multiple DL models in distinguishing asymptomatic patients with COVID-19 from healthy subjects on chest CT images. It found that a transformer-based model, the Swin model, performed best.

Intestinal Barrier Function in the Pathogenesis of Nonalcoholic Fatty Liver Disease.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide. The mechanisms involved in NAFLD onset are complicated and multifactorial. Recent literature has indicated that altered intestinal barrier function is related to the occurrence and progression of liver disease. The intestinal barrier is important for absorbing nutrients and electrolytes and for defending against toxins and antigens in the enteric environment. Major mechanisms by which the intestinal barrier influences the development of NAFLD involve the altered epithelial layer, decreased intracellular junction integrity, and increased intestinal barrier permeability. Increased intestinal permeability leads to luminal dysbiosis and allows the translocation of pathogenic bacteria and metabolites into the liver, inducing inflammation, immune response, and hepatocyte injury in NAFLD. Although research has been directed to NAFLD in recent decades, the pathophysiological changes in NAFLD initiation and progression are still not completely understood, and the therapeutic targets remain limited. A deeper understanding on the correlation between NAFLD pathogenesis and intestinal barrier regulation must be attained. Therefore, in this review, the components of the intestinal barrier and their respective functions and disruptions during the progression of NAFLD are discussed.

Influences of photosensitizer curcumin on microbial survival and physicochemical properties of chicken during storage.

Curcumin is a natural plant derived antimicrobial, which was shown to inactivate or inhibit the growth of a broad spectrum of microorganisms through photodynamic inactivation. The purpose of the present study is to evaluate the influence of curcumin against commensal spoilage bacteria on chicken, foodborne pathogens, and the chicken skin pH and color. Chicken skin samples were immersed into water, photosensitizer curcumin (PSC), or peracetic acid (PAA). PSC samples were subsequently subjected to illumination by LEDs (430 nm). The PSC treatments did not inhibit the outgrowth of the four groups of spoilage bacteria evaluated. PSC treatment resulted in 2.9 and 1.5 log CFU/cm2 reduction of L. monocytogenes and Salmonella, respectively. Over a 10-d period, population of Salmonella remained significantly lower on PSC treated samples compared to other treatments. PSC treatment resulted in no significant changes in pH or color as compared to water treated samples. This research suggests PSC effectively controlled pathogen outgrowth on chicken without negatively influencing quality; and may be suitable for use in commercial chicken processing.