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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study aimed to explore the effect and mechanism of Bacillus coagulans TL3 (B. coagulansTL3) on ileal inflammatory injury induced by lipopolysaccharide (LPS). Animal models were established wherein male Wistar rats were randomly divided into four groups: a control group, an LPS group, a high-concentration B. coagulans (HBC) group, and a low-concentration B. coagulans (LBC) group. The results showed that the biochemical indices changed, significant pathological changes were found, the number of apoptotic cells increased in the ileal tissue of the LPS group rats; the protein expressions of NFκB, MYD88, TLR4, TNF-α, Il-6, IL-1β, Claudin-1, Occludin, and ZO-1 in the LPS group were significantly decreased. The biochemical indices, pathological changes, and protein expressions in rats subjected to intragastric administration with high or low concentrations of B. coagulans TL3, were significantly reversed compared with the LPS group. These results indicated that TL3 strain could protect rats against ileal oxidative stress and inflammation induced by LPS and the protective mechanism was related to inhibition of the toll-like receptor 4 (TLR4) / myeloid differentiation factor-88 (MyD88) signaling pathway. [ABSTRACT FROM AUTHOR]

Esophageal variceal (EV) bleeding is a severe medical emergency related to cirrhosis. Early identification of cirrhotic patients with at a high risk of EV bleeding is key to improving outcomes and optimizing medical resources. This study aimed to evaluate the feasibility of automated multimodal machine learning (MMML) for predicting EV bleeding by integrating endoscopic images and clinical structured data. This study mainly includes three steps: step 1, developing deep learning (DL) models using EV images by 12-month bleeding on TensorFlow (backbones include ResNet, Xception, EfficientNet, ViT and ConvMixer); step 2, training and internally validating MMML models integrating clinical structured data and DL model outputs to predict 12-month EV bleeding on an H2O-automated machine learning platform (algorithms include DL, XGBoost, GLM, GBM, RF, and stacking); and step 3, externally testing MMML models. Furthermore, existing clinical indices, e.g., the MELD score, Child‒Pugh score, APRI, and FIB-4, were also examined. Five DL models were transfer learning to the binary classification of EV endoscopic images at admission based on the occurrence or absence of bleeding events during the 12-month follow-up. An EfficientNet model achieved the highest accuracy of 0.868 in the validation set. Then, a series of MMML models, integrating clinical structured data and the output of the EfficientNet model, were automatedly trained to predict 12-month EV bleeding. A stacking model showed the highest accuracy (0.932), sensitivity (0.952), and F1-score (0.879) in the test dataset, which was also better than the existing indices. This study is the first to evaluate the feasibility of automated MMML in predicting 12-month EV bleeding based on endoscopic images and clinical variables. [ABSTRACT FROM AUTHOR]

Conflict between cell growth and product accumulation is frequently encountered in the biosynthesis of secondary metabolites. To address the growth-production conflict in yeast strains harboring the isoprene synthetic pathway in the mitochondria, the dynamic control of isoprene biosynthesis was explored. A dual temperature regulation system was developed through engineering and expression regulation of the transcriptional activator Gal4p. A cold-sensitive mutant, Gal4ep19, was created by directed evolution of Gal4p based on an internally developed growth-based high-throughput screening method and expressed under the heat-shock promoter PSSA4 to control the expression of PGAL-driven pathway genes in the mitochondria. Compared to the control strain with constitutively expressed wild-type Gal4p, the dual temperature regulation strategy led to 34.5% and 72% improvements in cell growth and isoprene production, respectively. This study reports the creation of the first cold-sensitive variants of Gal4p by directed evolution and provides a dual temperature control system for yeast engineering that may also be conducive to the biosynthesis of other high-value natural products. [ABSTRACT FROM AUTHOR]

We examined the long-term effectiveness of a group-based psychological intervention ("MoVo-LISA") to promote physical activity in patients with coronary heart disease. In this randomized controlled trial, N = 202 inactive patients with coronary heart disease were assigned to the control group (n = 102; treatment as usual) or the intervention group (n = 100; treatment as usual plus MoVo-LISA). Physical activity was assessed at baseline, 6 weeks (post-treatment), 6 months, and 12 months after discharge. ANCOVA for repeated measures revealed a significant interaction effect [p <.001; ηp2=.214] indicating a large effect [d = 1.03] of the intervention on behavior change post-treatment. At 12-month follow-up, the level of physical activity in the intervention group was still 94 min per week higher than in the control group (p <.001; d = 0.57). Results of this RCT indicate that the MoVo-LISA intervention substantially improves the level of physical activity among initially inactive patients with coronary heart disease up to 1 year after the intervention. [ABSTRACT FROM AUTHOR]

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Progesterone has nongenomic effects on inducible nitric oxide synthase (iNOS), which is mediated by mitogen activated protein kinase (MAPK) pathways. This effect is supposed to have some potential association with asymptomatic gonococcal infections in women by immunological depression. In this study, polymorphonuclear leukocytes (PMNs) challenged by gonococci were used to study the nongenomic effects of progesterone. The activation of iNOS was assessed by measuring [3H] L-arginine converses to [3H] L-citrulline, and the activity of MAPK was detected by Western blot. It was found that the activity of iNOS and the yields of NO were enhanced significantly in gonococci-challenged PMNs compared with the controls ( P<0.01). Progesterone could repress the activation of iNOS through P38MAPK pathway within PMNs ( P<0.05), which could be blocked by SB203580 ( P<0.01), but not by actinomycin D ( P>0.05). It was also found subsequently that in the serum specimens collected from gonococci-infected but asymptomatic women, the progesterone level was higher than that in women with severe symptoms ( P<0.01). Moreover, the yield of NO had an inverse correlation with progesterone. With these results it suggested that the rapid nongenomic effects of progesterone may inhibit iNOS activation and NO yields mediated by P38MAPK pathways, which were supposed to be concerned with asymptomatic women infected with gonococci. [ABSTRACT FROM AUTHOR]

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., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

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., (© 2024. The Author(s).)