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.

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.

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.

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.

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.

The Development of a Prediction Model Based on Random Survival Forest for the Postoperative Prognosis of Pancreatic Cancer: A SEER-Based Study.

Accurate prediction for the prognosis of patients with pancreatic cancer (PC) is a emerge task nowadays. We aimed to develop survival models for postoperative PC patients, based on a novel algorithm, random survival forest (RSF), traditional Cox regression and neural networks (Deepsurv), using the Surveillance, Epidemiology, and End Results Program (SEER) database. A total of 3988 patients were included in this study. Eight clinicopathological features were selected using least absolute shrinkage and selection operator (LASSO) regression analysis and were utilized to develop the RSF model. The model was evaluated based on three dimensions: discrimination, calibration, and clinical benefit. It found that the RSF model predicted the cancer-specific survival (CSS) of the postoperative PC patients with a c-index of 0.723, which was higher than the models built by Cox regression (0.670) and Deepsurv (0.700). The Brier scores at 1, 3, and 5 years (0.188, 0.177, and 0.131) of the RSF model demonstrated the model's favorable calibration and the decision curve analysis illustrated the model's value of clinical implement. Moreover, the roles of the key variables were visualized in the Shapley Additive Explanations plotting. Lastly, the prediction model demonstrates value in risk stratification and individual prognosis. In this study, a high-performance prediction model for PC postoperative prognosis was developed, based on RSF The model presented significant strengths in the risk stratification and individual prognosis prediction.

The relationship between Helicobacter pylori and pancreatic cancer: a meta-analysis.

Background: The relationship between Helicobacter pylori (H. pylori, HP) infection and pancreatic cancer would be investigated in this article. Methods: All cohort studies and case-control studies about H. pylori infection and pancreatic cancer up to October 2021 were searched in the databases of PubMed, Embase and Cochrane. The combined odds ratio (OR) and 95% confidence interval (CI) were calculated by R 4.1.0 software. Funnel plot and Egger test were used to evaluate publication bias. Results: A total of 17 studies which included 8 case-control studies, 5 nested case-control studies, and 4 cohort studies were included in this study, and the results of this article have confirmed that the H. pylori infection was significantly correlated with the occurrence of pancreatic cancer (OR =1.30, 95% CI: 1.02-1.64), especially in economically underdeveloped areas (OR =2.10, 95% CI: 1.44-3.05). However, negative results were obtained in the relationship between CagA + H. pylori and pancreatic cancer. Similarly, we also did not find an association between vacuolating cytotoxin gene A-positive strains (VacA-positive H. pylori) and pancreatic cancer. The heterogeneity of this study was significant. Through a sensitivity analysis by the leave-one-out method, we found the results remained unchanged on the whole but the correlation between H. pylori infection and the occurrence of pancreatic cancer in the Asian population was significant. The tests for funnel plot asymmetry indicated that there might be obvious publication bias in this study. After carrying out the Egger test, we proved the existence of the publication bias in this study, which could have a certain impact on the results. Discussion: Based on the currently available data, we confirm that H. pylori infection can increase the incidence of pancreatic cancer in general. CagA/VacA-positive H. pylori infection is not associated with the incidence of pancreatic cancer. H. pylori infection is significantly associated with the incidence of pancreatic cancer in economically underdeveloped areas, while the relationship between H. pylori infection and the incidence of pancreatic cancer in the Asian population is uncertain. In addition, more high-quality studies are needed to be included to confirm this conclusion.

Development of a Deep Learning Model for Malignant Small Bowel Tumors Survival: A SEER-Based Study.

Background This study aims to explore a deep learning (DL) algorithm for developing a prognostic model and perform survival analyses in SBT patients. Methods The demographic and clinical features of patients with SBTs were extracted from the Surveillance, Epidemiology and End Results (SEER) database. We randomly split the samples into the training set and the validation set at 7:3. Cox proportional hazards (Cox-PH) analysis and the DeepSurv algorithm were used to develop models. The performance of the Cox-PH and DeepSurv models was evaluated using receiver operating characteristic curves, calibration curves, C-statistics and decision-curve analysis (DCA). A Kaplan−Meier (K−M) survival analysis was performed for further explanation on prognostic effect of the Cox-PH model. Results The multivariate analysis demonstrated that seven variables were associated with cancer-specific survival (CSS) (all p < 0.05). The DeepSurv model showed better performance than the Cox-PH model (C-index: 0.871 vs. 0.866). The calibration curves and DCA revealed that the two models had good discrimination and calibration. Moreover, patients with ileac malignancy and N2 stage disease were not responding to surgery according to the K−M analysis. Conclusions This study reported a DeepSurv model that performed well in CSS in SBT patients. It might offer insights into future research to explore more DL algorithms in cohort studies.

SPARCL1 Is a Novel Prognostic Biomarker and Correlates with Tumor Microenvironment in Colorectal Cancer.

BACKGROUND: Secreted protein acidic and rich in cysteine-like 1 (SPARCL1) plays an important role in tumor pathogenesis. We aim to evaluate the clinical significance and potential biological roles of SPARCL1 in colorectal cancer (CRC). METHODS: Datasets from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were downloaded to evaluate the expression levels of SPARCL1 in CRC. Receiver operating characteristic (ROC) curve was constructed to evaluate the diagnostic value of SPARCL1. Then, comprehensive database search was conducted for published clinical studies to explore clinical significance of SPARCL1. In addition, coexpression genes of SPARCL1 were identified through the cBioPortal database and enrichment analysis of SPARCL1 and its coexpression genes were performed by the "clusterProfiler" R package. Finally, the correlations between SPARCL1 and tumor microenvironment scores, tumor-infiltrating immune cells in CRC were determined by "ESTIMATE" and "GSVA" R packages. RESULTS: SPARCL1 was significantly downregulated in CRC tissues, and SPARCL1 showed high accuracy for diagnosis of primary CRC in both GEO and TCGA datasets. Pooled results from published clinical studies showed SPARCL1 expression was associated with differentiation (OR = 1.89, 95% CI: 1.38-2.59), tumor stage (OR = 0.47, 95% CI: 0.29-0.77), distant metastasis (OR = 0.53, 95% CI: 0.33-0.84), and overall survival (HR = 0.56, 95% CI: 0.43-0.74). SPARCL1 and its top 300 coexpression genes were involved in several KEGG pathways, such as focal adhesion, cell adhesion molecules, PI3K-Akt signaling pathway, cGMP-PKG signaling pathway, and ECM-receptor interaction. Besides, the SPARCL1 expression was significantly correlated with stromal score, immune score, ESTIMATE score, and diverse immune cells. CONCLUSION: SPARCL1 significantly correlated with clinicopathological features and tumor microenvironment in CRC.

Au-Luminol-decorated porous carbon nanospheres for the electrochemiluminescence biosensing of MUC1.

Electrochemiluminescence (ECL) nanomaterials are usually deposited compactly on the surface of electrodes, which may cause poor mass transfer of reactants, thereby resulting in low ECL efficiency. In this work, we developed a novel kind of luminescent material denoted as C-Au-luminol nanospheres (C-Au-Lum NSs) by high dispersion of luminophores on porous carbon nanospheres (PCNSs). C-Au-Lum NSs were facilely prepared by the in situ reduction of chloroauric acid with the luminescent reagent luminol (Lum) on the nano-pores of PCNSs. Plenty of luminescent Au-Lum NPs were dispersedly concentrated inside the numerous pores and hollow interiors of PCNSs, effectively increasing the mass transfer of reagents and accelerating the electron transport inside the porous nanospheres. This greatly improved the availability of luminophores and endowed C-Au-Lum NSs with excellent ECL emission. After further integrating with enzymatic circulation and strand displacement, an ultrasensitive ECL biosensor was achieved for the ultrasensitive detection of an important tumor biomarker, mucin1. The logarithmically linear range from 0.1 pg mL-1 to 1 ng mL-1 with the detection limit of 47.6 fg mL-1 (S/N = 3) was achieved, demonstrating the superior performance of C-Au-Lum NSs. This work would provide new ideas for the construction of high-performance ECL sensing platforms for diverse applications.

Hollow copper sulfide nanocubes as multifunctional nanozymes for colorimetric detection of dopamine and electrochemical detection of glucose.

Nanozymes have fascinated increasing attention in the field of artificial enzyme. Designing an ideal nanozyme usually requires a synergic advantage of reasonable nanostructures and large specific surface area for ensuring excellent mimicking-enzyme catalytic activity. Here we report a CuS nanozyme with hollow nanocube structure (h-CuS NCs), which has a large surface area of 57.84 m2 g-1, and thus realizes excellent mimicking-enzyme catalytic activity. Expectedly, our directed design of h-CuS NCs nanozymes has an affinity for H2O2 of 0.94 mM, which is outstanding among the state-of-the-art Cu-based nanozymes. Furthermore, this nanozyme acts as a multifunctional catalyst to induce luminol chemiluminescence and oxide 3, 3', 5, 5'-tetramethylbenzidine (TMB) in the presence of H2O2, and displays distinguished electrocatalytic activity to glucose oxidation. More intriguingly, the nanozyme can produce a promising photothermal effect under the illumination of near-infrared light. This work will provide a prototype for rational design of distinct nanostructures as multifunctional nanozymes in the area of electrochemical sensing, mimicking-enzyme catalytic biosening and cancer therapy.

Direct Synthesis of Water-Soluble Aptamer-Ag2 S Quantum Dots at Ambient Temperature for Specific Imaging and Photothermal Therapy of Cancer.

Water-soluble Ag2 S near-infrared (NIR) fluorescent quantum dots (QDs) are directly synthesized at ambient temperature for specific cancer imaging and photothermal therapy (PTT) using a designed aptamer (Apt43) as template, which consists of the following two fragments: an aptamer S2.2 sequence for specifically recognizing the cancer cells and an 18-cytosine (18-C) extending spacer for growing Ag2 S QDs. The synthesized Ag2 S QDs (Apt43-Ag2 S QDs), which exhibit strong absorption and fluorescence emission in the NIR region and high photothermal conversion capabilities, can specifically recognize MCF-7 cells (human breast cancer cells) and are usable as a highly intensified imaging agent for cancer diagnosis. Moreover, they can be applied as photothermal agents for the in vitro killing of MCF-7 cells and the in vivo ablation of tumors, which were constructed on the bodies of nude mice. MCF-7 cells almost quantitatively die after they are incubated with the QDs (at 100 µg mL(-1) ) for 2 h and irradiated under an 808 nm laser at a power density of 1.0 W cm(-2) for 10 min. The tumors on the nude mice can also be effectively ablated without regrowth during the period of observation (at least 20 d) after PTT.

Tubelike Gold Sphere-Attapulgite Nanocomposites with a High Photothermal Conversion Ability in the Near-Infrared Region for Enhanced Cancer Photothermal Therapy.

Near-infrared (NIR)-induced photothermal therapy (PTT) is now considered to be a promising and highly efficient method for tumor therapy. Photothermal agents play a crucial role in PTT, and they are required to possess the ability to harvest NIR light and transform the photon energy into heat energy. This work reports a facile method to synthesize a new PTT agent, which is based on the electrostatic binding of the Au nanospheres (Au NSs, ∼15 nm) to the surface of a nanometer-sized mineral, attapulgite, to form tubelike Au-attapulgite nanocomposites. These nanocomposites consist of numerous Au NSs, which are linked to each other along the attapulgite surface. The nanocomposites exhibit similar localized surface plasmon resonance absorption characteristics to those of Au nanorods with a longitudinal absorption mode that shifts to the NIR region (∼670 nm). Moreover, the nanocomposites have a high Cabs/Csca ratio (cross section of absorption to scattering) and photothermal conversion efficiency of 25.6%. Their photothermal therapy effect is studied using A549 cells and A549 cell-bearing nude mice as examples. The results indicate that the nanocomposites can be effectively taken up by the cells, and the nanocomposites show good biocompatibility. The A549 cells almost died after they were incubated with the nanocomposites (at 100 µg mL(-1)) for 12 h and irradiated by an 808 nm laser with a power density of 0.5 W cm(-2) for 15 min. The tumors of nude mice can also be effectively ablated without regrowth during the period of observation (at least 10 d) after photothermal therapy.