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.

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.

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.

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.

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.

Automated Multimodal Machine Learning for Esophageal Variceal Bleeding Prediction Based on Endoscopy and Structured Data.

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.

C9orf72 knockdown alleviates hepatic insulin resistance by promoting lipophagy.

Insulin resistance (IR) attributed by the deficiency of lipophagy, is an abnormal state of downregulation of insulin-mediated glucose uptake and use into the liver. Chromosome 9 open reading frame 72 (C9orf72) variously modulates autophagy. We investigated the role and the downstream pathway of C9orf72 in hepatic IR. We found that C9orf72 knockdown alleviated hepatic IR by lipophagy promotion in T2DM mice and in IR-challenged hepatocytes in vitro. C9orf72 interacted with and activated cell division cycle 42 (Cdc42) protein in IR-challenged hepatocytes, Which in turn, inhibits lipophagy by promoting neural Wiskott-Aldrich syndrome protein (N-WASP) expression and activation. C9orf72 inhibited lipophagy by activating the Cdc42/N-WASP axis to facilitate hepatic IR; therefore, the knockdown of C9orf72 may be potentially therapeutic for the treatment of IR.

Children's exposure to BC and PM pollution, and respiratory tract deposits during commuting trips to school.

Although children have been identified as a vulnerable group highly susceptible to traffic-related air pollution, their exposure during school commutes to traffic-related pollutants and the relevant health impact is rarely studied. In this study, we measured black carbon (BC) and particulate matter (PM: PM1, PM2.5, and PM10) concentrations that children are exposed to during their multi-modal (walking, private cars, and e-bikes) commuting trips to schools in Xi'an, China. A multi-parameter inhalation rate assessment model was developed in combination with the Multi-Path Particle Dosimetry (MPPD) model to quantify the deposition dose in different parts of children's respiratory system (head, tracheobronchial (TB), pulmonary (PUL)). Results show that walking to school exposed children to the lowest PM1, PM2.5, and BC concentrations, whereas riding an e-bike led to significantly elevated exposure to PM1 and BC than the other two modes. This is due to children's closer proximity to vehicle tail pipe emissions when they bike to school on road or roadside. The PM and BC concentrations showed remarkable increases in comparison to background concentrations during children's school commutes. Urban background (UB) concentration, traffic volume (TV), time of day, and meteorological parameters could influence a child's personal exposure, and the impact of each factor vary across different transportation modes. Particle size of the pollutant affects its deposition site in the respiratory system. Deposition fractions (DFs) and deposition doses in the head region (DF > 50%) were the highest for PM and BC, for which fine particles (BC, PM1, and PM2.5) were then most easily deposited in the PUL region while coarse particles rarely reach PUL. Children inhaled higher doses of polluted air during active commuting (walking) than passive commuting (private cars, e-bikes), due to longer times of exposure coupled with more active breathing.

Edaphic niche characterization of four Proteaceae reveals unique calcicole physiology linked to hyper-endemism of Grevillea thelemanniana.

Endemism and rarity have long intrigued scientists. We focused on a rare endemic and critically-endangered species in a global biodiversity hotspot, Grevillea thelemanniana (Proteaceae). We carried out plant and soil analyses of four Proteaceae, including G. thelemanniana, and combined these with glasshouse studies. The analyses related to hydrology and plant water relations as well as soil nutrient concentrations and plant nutrition, with an emphasis on sodium (Na) and calcium (Ca). The local hydrology and matching plant traits related to water relations partially accounted for the distribution of the four Proteaceae. What determined the rarity of G. thelemanniana, however, was its accumulation of Ca. Despite much higher total Ca concentrations in the leaves of the rare G. thelemanniana than in the common Proteaceae, very few Ca crystals were detected in epidermal or mesophyll cells. Instead of crystals, G. thelemanniana epidermal cell vacuoles contained exceptionally high concentrations of noncrystalline Ca. Calcium ameliorated the negative effects of Na on the very salt-sensitive G. thelemanniana. Most importantly, G. thelemanniana required high concentrations of Ca to balance a massively accumulated feeding-deterrent carboxylate, trans-aconitate. This is the first example of a calcicole species accumulating and using Ca to balance accumulation of an antimetabolite.

Construction of an ultrasensitive electrochemiluminescent aptasensor for ractopamine detection.

In this study, an ultrasensitive electrochemiluminescence (ECL) aptasensor based on Ru(bpy)32+-doped silica nanoparticles (Ru@SiO2 NPs) coupled with gold nanoparticles (Au NPs) was developed for the determination of ractopamine (Rac). TPrA is used as the coreactant, the Ru@SiO2 NPs serve as the ECL luminophores, and the Au NPs work as a catalyzer in the redox reaction as well as the carrier that immobilizes the aptamer. Meanwhile, the complete incorporation of the Ru@SiO2 NPs and Au NPs increases the localized surface plasmon resonance (LSPR) probability, thus promoting ECL emission. The ractopamine (Rac) target molecules are specifically captured on the electrode surface by aptamer recognition. The ECL signal is quenched by energy transfer from the luminophore to benzoquinone compounds, which are oxidative products of Rac from the electrochemical scanning process. The proposed ECL aptasensor exhibits ultrahigh sensitivity and excellent selectivity for Rac detection. The linear response ranged over Rac concentrations from 1.5 × 10-12 M to 1.5 × 10-8 M with a detection limit of 4.1 × 10-14 M (S/N = 3). The detection recovery of Rac in real meat samples confirmed the satisfactory performance of the method. This study describes a versatile ECL aptasensor based on the combined functions of luminous nanospheres and Au NPs, indicating its potential application for the ultrasensitive analysis of targets in diverse systems.

Impaired electron transfer accounts for the photosynthesis inhibition in wheat seedlings (Triticum aestivum L.) subjected to ammonium stress.

No single mechanism can provide an adequate explanation for the inhibition of photosynthesis when plants are supplied with ammonium (NH4 + ) as the sole nitrogen (N) source. We performed a hydroponic experiment using two N sources [5 mM NH4 + and 5 mM nitrate (NO3 - )] to investigate the effects of NH4 + stress on the photosynthetic capacities of two wheat cultivars (NH4 + -sensitive AK58 and NH4 + -tolerant XM25). NH4 + significantly inhibited the growth and light-saturated photosynthesis (Asat ) of both cultivars, but the extent of such inhibition was greater in the NH4 + -sensitive AK58. The CO2 concentration did not limit CO2 assimilation under NH4 + nutrition; though both stomatal and mesophyll conductance were significantly suppressed. Carboxylation efficiency (CE), light-saturated potential rate of electron transport (Jmax ), the quantum efficiency of PSII (ΦPSII ), electron transport rate through PSII [Je(PSII)], and Fv /Fm were significantly reduced by NH4 + . As a result, NH4 + nutrition resulted in a significant increase in the production of hydrogen peroxide (H2 O2 ) and superoxide anion radicals (O2 •- ), but these symptoms were less severe in the NH4 + -tolerant XM25, which had a higher capacity of removing elevated reactive oxygen species (ROS). Thus, NH4 + N sources might decreased electron transport efficiency and increased the production of ROS, exacerbating damage to the electron transport chain, leading to a reduced plant photosynthetic capacity.

Enhanced Rubisco activation associated with maintenance of electron transport alleviates inhibition of photosynthesis under low nitrogen conditions in winter wheat seedlings.

Studying the response of photosynthesis to low nitrogen (N) and the underlying physiological mechanism can provide a theoretical basis for breeding N-efficient cultivars and optimizing N management. We conducted hydroponic experiments using two wheat (Triticum aestivum) cultivars, Zaoyangmai (low N sensitive) and Yangmai158 (low N tolerant), with either 0.25 mM N as a low N (LN) treatment or 5 mM N as a control. Under LN, a decrease in net photosynthetic rate (Pn) was attributed to reduction in the maximum Rubisco carboxylation rate, which then accelerated a reduction in the maximum ribulose-1,5-bisphosphate regeneration rate, and the reduction in Pn was 5-35% less in Yangmai158 than in Zaoyangmai. Yangmai158 maintained a 10-25% higher Rubisco concentration, especially in the upper leaves, and up-regulated Rubisco activase activity compared with Zaoyangmai to increase the Rubisco activation to sustain Rubisco carboxylation under LN conditions. In addition, Yangmai158 increased electron flux to the photorespiratory carbon oxidation cycle and alternative electron flux to maintain a faster electron transport rate and avoid photodamage. In conclusion, the LN-tolerant cultivar showed enhanced Rubisco activation and sustained electron transport to maintain a greater photosynthetic capacity under LN conditions.

Pre-drought priming sustains grain development under post-anthesis drought stress by regulating the growth hormones in winter wheat (Triticum aestivum L.).

MAIN CONCLUSION: Drought stress during grain filling is the most yield-damaging to wheat. Pre-drought priming facilitated the wheat plants to sustain grain development against the post-anthesis drought stress by modulating the levels of growth hormones. Post-anthesis drought stress substantially reduces grain yield in wheat (Triticum aestivum L.) due to impaired grain development associated with imbalanced levels of growth hormones. To investigate whether pre-drought priming could sustain grain development in wheat by regulating favorable levels of growth hormones under post-anthesis drought conditions, the plants of a drought-sensitive (Yangmai-16) and drought-tolerant (Luhan-7) wheat cultivar were exposed to a moderate drought stress during tillering (Feekes 2 stage) for priming, and then, a subsequent severe drought stress was applied from 7 to 14 days after anthesis. The results showed that drought-stressed plants of both cultivars showed a decline in flag leaf water potential, chlorophyll contents, photosynthetic rate, grain size initiation, and grain filling as compared to well-watered plants; however, decline in these traits was less in pre-drought primed (PD) plants than in nonprimed (ND) plants. Under drought stress, the PD plants regulated higher concentrations of zeatin and zeatin riboside, indole-3-acetic acid, gibberellins, and lower abscisic acid content in grains, resulting in higher endosperm cell division and expansion, grain size initiation, grain-filling rate and duration, and finally higher grain dry weights as compared to ND plants. The PD plants of both cultivars showed higher potential to tolerate the post-anthesis drought stress, but more effect was displayed by drought-tolerant cultivar. From the achieved results, it was concluded that pre-drought priming facilitated the wheat plants to sustain higher grain development and yield against the most yield-damaging post-anthesis drought stress by modulating the levels of growth hormones.

Sanitizer efficacy in preventing cross-contamination of heads of lettuce during retail crisping.

This study was conducted to provide information regarding mitigation of cross-contamination through the use of sanitizer during crisping at retail outlets. Seven non-inoculated heads and one inoculated head (≈5 log CFU/g) of lettuce were placed into commercial sink filled with 76 L of tap water (TW), electrolyzed water (EW, free chlorine: 43 ± 6 ppm), lactic acid and phosphoric acid-based sanitizer (LPA, pH 2.89), or citric acid-based sanitizer (CA, pH 2.78) and soaked for 5 min. Two subsequent batches (eight non-inoculated heads per batch) were soaked in the same solution. Soaking with EW significantly reduced the population of S. enterica (2.8 ± 1.5 log CFU/g), E. coli O157:H7 (3.4 ± 1.1 log CFU/g), and L. monocytogenes (2.6 ± 0.7 log CFU/g) inoculated on Romaine lettuce compared to TW, LPA, and CA (p < 0.05). On Red leaf lettuce, EW significantly reduced populations of S. enterica and E. coli O157:H7, but not L. monocytogenes compared to other treatments. No significant difference was noted between TW, LPA, and CA in reducing foodborne pathogens (p > 0.05) or preventing cross-contamination. Soaking with EW prevented cross-contamination among lettuce heads and controlled bacterial populations in crisping water for three consecutive batches. EW may be an effective option as a sanitizer to minimizing the cross-contamination of leafy greens during the retail crisping.

Inhibitory effect of Aristolochia fruit on Cytochrome P450 isozymes in vitro and in vivo.

The mature fruits of Aristolochia debilis, known in China by the name, "Madouling" has been popularly prescribed in Asia, particularly in China, to treat a range of conditions including gynaecological problems, arthritis and wound healing. This study was aimed to evaluate the potential effect of Madouling on the cytochrome P450 (CYP) isozymes in vitro in microsomal fractions and in vivo in rats. The influence of Madouling on CYPs activity was first explored by an in vitro method of estimating levels of four respective metabolites in rat liver microsomes. The results were re-examined in vivo in rats by using a cocktail approach involving the probe drugs theophylline, tolbutamide, chlorzoxazone and dapsone. Pharmacokinetics of the four substrates was used to analyze the activities of the targeting isozymes. In vitro study revealed that Madouling decreased the activity of CYP1A2, 3A1 and 2E1. However, no significant influence on CYP2C6 was found. These results coincided with those of in vivo study to a great degree except that in vivo estimation the herb didn't inhibit CYP1A2 significantly. From the data obtained, Madouling is suggested as a candidate for clinically significant CYP interactions. Drug co-administrated with Madouling may need dose adjustment.

Determination of newly synthesized lipoic acid-niacin dimer in rat plasma by UPLC/electrospray ionization tandem mass spectrometry: assay development, validation and application to a pharmacokinetic study.

A simple, sensitive and specific ultra-performance liquid chromatography/tandem mass spectrometry (UPLC-MS/MS) method was developed to determine the newly synthesized compound lipoic acid-niacin dimer (N2L) in plasma. Plasma samples were precipitated by methanol using tetrahydropalmatine as internal standard. Chromatographic separation was achieved on an Acquity BEH C18 (2.1 × 50 mm i.d., 1.7 µm) column; the mobile phase contains methanol and buffer solution (water with 0.5% formic acid and 10 mmol/L ammonium acetate). Multiple reaction monitoring (m/z 353.9 → 148.6 for N2L and m/z 356.0 → 192.0 for internal standard) was performed for detection and quantification. The method was validated to be rapid, specific, accurate and precise over the concentration range of 1-750 ng/mL; N2L was not stable on the bench-top or during freeze-freeze-thaw cycles in plasma, but was stable in the stock solution and after preparation in the autosampler for 24 h. The utility of the assay was confirmed by pharmacokinetic study of N2L in rats.

Effect of raw Radix Rehmanniae on the pharmacokinetics of pioglitazone in rats.

Raw Radix Rehmanniae (RRR) is a frequently used traditional Chinese medicine in the treatment of diabetes mellitus according to the statistics on all of the anti-diabetic formulas recorded in New National Traditional Chinese Medicine. Pioglitazone and RRR may be co-administrated for presumably enhanced therapeutic effects because of the common indications. Therefore, the aim of the study was to evaluate the effect of RRR on the pharmacokinetics of pioglitazone in healthy rats and type 2 diabetic rats. The pharmacokinetic effect of RRR on pioglitazone was studied in healthy rats and type 2 diabetic rats. A validated UPLC-MS/MS method was used to analyze the concentration of pioglitazone in blood samples. The pharmacokinetic parameters were calculated using non-compartmental analyses by Winnonlin 5.0.1. In healthy group, the pre-treatment of RRR significantly (P<0.05) reduced the C(max) but enhanced the V/F of pioglitazone; whereas in T2DM group, significant increase of C(max) and decrease of V/F and T½ were found after the rats were pre-treated with RRR. However, AUC(0-t) and CL/F remained unchanged in both healthy group and T2DM group. In conclusion, co-administration with RRR could alter the pharmacokinetic profiles of pioglitazone to statistically significant levels.

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.

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.

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.