Radiomics nomogram based on optimal VOI of multi-sequence MRI for predicting microvascular invasion in intrahepatic cholangiocarcinoma.

OBJECTIVE: Microvascular invasion (MVI) is a significant adverse prognostic indicator of intrahepatic cholangiocarcinoma (ICC) and affects the selection of individualized treatment regimens. This study sought to establish a radiomics nomogram based on the optimal VOI of multi-sequence MRI for predicting MVI in ICC tumors. METHODS: 160 single ICC lesions with MRI scanning confirmed by postoperative pathology were randomly separated into training and validation cohorts (TC and VC). Multivariate analysis identified independent clinical and imaging MVI predictors. Radiomics features were obtained from images of 6 MRI sequences at 4 different VOIs. The least absolute shrinkage and selection operator algorithm was performed to enable the derivation of robust and effective radiomics features. Then, the best three sequences and the optimal VOI were obtained through comparison. The MVI prediction nomogram combined the independent predictors and optimal radiomics features, and its performance was evaluated via the receiver operating characteristics, calibration, and decision curves. RESULTS: Tumor size and intrahepatic ductal dilatation are independent MVI predictors. Radiomics features extracted from the best three sequences (T1WI-D, T1WI, DWI) with VOI10mm (including tumor and 10 mm peritumoral region) showed the best predictive performance, with AUCTC = 0.987 and AUCVC = 0.859. The MVI prediction nomogram obtained excellent prediction efficacy in both TC (AUC = 0.995, 95%CI 0.987-1.000) and VC (AUC = 0.867, 95%CI 0.798-0.921) and its clinical significance was further confirmed by the decision curves. CONCLUSION: A nomogram combining tumor size, intrahepatic ductal dilatation, and the radiomics model of MRI multi-sequence fusion at VOI10mm may be a predictor of preoperative MVI status in ICC patients.

SND1 acts as an anti-apoptotic factor via regulating the expression of lncRNA UCA1 in hepatocellular carcinoma.

Multifunctional SND1 (staphylococcal nuclease and tudor domain containing 1) protein is reportedly associated with different types of RNA molecules, including mRNA, miRNA, pre-miRNA, and dsRNA. SND1 has been implicated in a number of biological processes in eukaryotic cells, including cell cycle, DNA damage repair, proliferation, and apoptosis. However, the specific molecular mechanism regarding the anti-apoptotic role of SND1 in mammalian cells remains largely elusive. In this study, the analysis of the online HPA (human protein atlas) and TCGA (the cancer genome atlas) databases showed the significantly high expression of SND1 in liver cancer patients. We found that the downregulation or complete depletion of SND1 enhanced the apoptosis levels of HepG2 and SMMC-7721 cells upon stimulation with 5-Fu (5-fluorouracil), a chemotherapeutic drug for HCC (hepatocellular carcinoma). SND1 affected the 5-Fu-induced apoptosis levels of HCC cells by modulating the expression of UCA1 (urothelial cancer associated 1), which is a lncRNA (long non-coding RNA). Moreover, MYB (MYB proto-oncogene, transcription factor) may be involved in the regulation of SND1 in UCA1 expression. In summary, our study identified SND1 as an anti-apoptotic factor in hepatocellular carcinoma cells via the modulation of lncRNA UCA1, which sheds new light on the relationship between SND1 protein and lncRNA.

Automatic grading of knee osteoarthritis with a plain radiograph radiomics model: combining anteroposterior and lateral images.

OBJECTIVES: To establish a radiomics-based automatic grading model for knee osteoarthritis (OA) and evaluate the influence of different body positions on the model's effectiveness. MATERIALS AND METHODS: Plain radiographs of a total of 473 pairs of knee joints from 473 patients (May 2020 to July 2021) were retrospectively analyzed. Each knee joint included anteroposterior (AP) and lateral (LAT) images which were randomly assigned to the training cohort and the testing cohort at a ratio of 7:3. First, an assessment of knee OA severity was done by two independent radiologists with Kallgren-Lawrence grading scale. Then, another two radiologists independently delineated the region of interest for radiomic feature extraction and selection. The radiomic classification features were dimensionally reduced and a machine model was conducted using logistic regression (LR). Finally, the classification efficiency of the model was evaluated using receiver operating characteristic curves and the area under the curve (AUC). RESULTS: The AUC (macro/micro) of the model using a combination of AP and LAT (AP&LAT) images were 0.772/0.778, 0.818/0.799, and 0.864/0.879, respectively. The radiomic features from the combined images achieved better classification performance than the individual position image (p < 0.05). The overall accuracy of the radiomic model with AP&LAT images was 0.727 compared to 0.712 and 0.417 for radiologists with 4 years and 2 years of musculoskeletal diagnostic experience. CONCLUSIONS: A radiomic model constructed by combining the AP&LAT images of the knee joint can better grade knee OA and assist clinicians in accurate diagnosis and treatment. CRITICAL RELEVANCE STATEMENT: A radiomic model based on plain radiographs accurately grades knee OA severity. By utilizing the LR classifier and combining AP&LAT images, it improves accuracy and consistency in grading, aiding clinical decision-making, and treatment planning. KEY POINTS: Radiomic model performed more accurately in K/L grading of knee OA than junior radiologists. Radiomic features from the combined images achieved better classification performance than the individual position image. A radiomic model can improve the grading of knee OA and assist in diagnosis and treatment.

Predicting overall survival and prophylactic cranial irradiation benefit in small-cell lung cancer with CT-based deep learning: A retrospective multicenter study.

BACKGROUND AND PURPOSE: To develop a computed tomography (CT)-based deep learning model to predict overall survival (OS) among small-cell lung cancer (SCLC) patients and identify patients who could benefit from prophylactic cranial irradiation (PCI) based on OS signature risk stratification. MATERIALS AND METHODS: This study retrospectively included 556 SCLC patients from three medical centers. The training, internal validation, and external validation cohorts comprised 309, 133, and 114 patients, respectively. The OS signature was built using a unified fully connected neural network. A deep learning model was developed based on the OS signature. Clinical and combined models were developed and compared with a deep learning model. Additionally, the benefits of PCI were evaluated after stratification using an OS signature. RESULTS: Within the internal and external validation cohorts, the deep learning model (concordance index [C-index] 0.745, 0.733) was far superior to the clinical model (C-index: 0.635, 0.630) in predicting OS, but slightly worse than the combined model (C-index: 0.771, 0.770). Additionally, the deep learning model had excellent calibration, clinical usefulness, and improved accuracy in classifying survival outcomes. Remarkably, patients at high risk had a survival benefit from PCI in both the limited and extensive stages (all P < 0.05), whereas no significant association was observed in patients at low risk. CONCLUSIONS: The CT-based deep learning model exhibited promising performance in predicting the OS of SCLC patients. The OS signature may aid in individualized treatment planning to select patients who may benefit from PCI.

Screening for osteoporosis based on IQon spectral CT virtual low monoenergetic images: Comparison with conventional 120 kVp images.

Objectives: To explore the differences between low kiloelectron volt (keV) virtual monoenergetic images (VMIs) using IQon spectral CT and conventional CT (120 kVp) in the diagnosis of osteoporosis. Methods: This retrospective study included 317 patients who underwent IQon spectral CT and dual-energy X-ray absorptiometry (DXA) examination. Commercial deep learning-based software was used for the fully automated extraction of the CT values of the first to fourth lumbar vertebrae (L1-L4) from two different low-keV levels (including 40/70 keV) VMIs and conventional 120 kVp images. The DXA examination results served as the standard of reference (normal [T-score ≥ -1], osteopenia [-2.5 < T-score < -1], and osteoporosis [T-score < -2.5]). Osteoporosis diagnosis models were constructed using machine learning classifiers (logistic regression, support vector machine, random forest, XGBoost, and multilayer perceptron) based on the average CT values of L1-L4. The area under the receiver operating characteristic curve (AUC) and DeLong test were performed to compare differences in the performance of the osteoporosis diagnosis model between virtual low-keV VMIs and standard 120 kVp images. Results: Random forest-based prediction model obtained good overall performance among all classifiers, and macro/micro average AUC values of 0.820/840, 0.834/853, and 0.831/852 were obtained based on 40/70 keV and 120 kVp images, respectively. The model presented no significant difference between low-keV VMIs and standard 120 kVp images for the diagnosis of osteoporosis (p > 0.05). Conclusions: The performance of the osteoporosis diagnosis model using IQon spectral CT simulating the low tube voltage scanning condition (less than 120 kVp) was also satisfactory. Bone density screening evaluation can be performed with a combination of low-dose lung scanning CT, greatly reducing the radiation dose without affecting the diagnosis.

Albumin­bilirubin grade and INR for the prediction of esophagogastric variceal rebleeding after endoscopic treatment in cirrhosis.

Rebleeding following endoscopic treatment in patients with cirrhosis is a serious life-threatening complication. In the present study, a novel, reliable and non-invasive score for prediction of rebleeding following endoscopic therapy for esophagogastric variceal bleeding (EGVB) was developed. The present retrospective study recruited cirrhotic patients with EGVB (n=596) who underwent endoscopic therapy. Patients hospitalized from January 2015 to January 2020 were grouped into a training (n=437) cohort to develop the new score and those hospitalized from February 2020 to February 2022 were grouped into a validation (n=159) cohort to validate the score. The international normalized ratio (INR) and albumin-bilirubin (ALBI) grade were used to develop the INR-ALBI (IALBI) score to predict risk of rebleeding. In the training cohort, the prognostic performance of the IALBI score and other ALBI-associated scores (modified ALBI, platelet-ALBI and ALBI-fibrosis-4) at 1, 3 and 12 months was assessed using receiver operating characteristic (ROC) curve and Kaplan-Meier analysis. At each time point, most areas under the ROC curve of IALBI were higher than those of other ALBI-associated scores, particularly for prediction of early rebleeding. At 1 month, the rebleeding rates of patients with IALBI grade 2 and 3 were ~10.0- and 19.5-times higher than those of patients with grade 1, respectively. The negative predictive value (NPV) of IALBI for the training and validation cohort at 1 month was 100.0 and 97.8%, respectively. For viral and non-viral patients in the training cohort, IALBI showed good predictive ability and NPV for early rebleeding. The IALBI grading system successfully assessed rebleeding, particularly early rebleeding, in cirrhotic patients with EGVB following endoscopic therapy IALBI grade 1, predicted low risk of rebleeding and may not require endoscopic treatment again in the short-term.

Radiomics Nomogram Model Based on TOF-MRA Images: A New Effective Method for Predicting Microaneurysms.

Objective: To develop a radiomics nomogram model based on time-of-flight magnetic resonance angiography (TOF-MRA) images for preoperative prediction of true microaneurysms. Methods: 118 patients with Intracranial Aneurysm Sac (40 positive and 78 negative) were enrolled and allocated to training and validation groups (8:2 ratio). Findings of clinical characteristics and MRA features were analyzed. A radiomics signature was built on the basis of reproducible features by using the least absolute shrinkage and selection operator (LASSO) regression algorithm in the training group. The radiomics nomogram model was constructed by combining clinical risk factors and radiomics signature. In order to compare the classification performance of clinical models, radiomics model and radiomics nomogram model, AUC was used to evaluate them. The performance of the radiomics nomogram model was evaluated by calibration curve and decision curve analysis. Results: Eleven features were selected to develop radiomics model with AUC of 0.875 (95% CI 0.78-0.97), sensitivity of 0.84, and specificity of 0.68. The radiomics model achieved a better diagnostic performance than the clinic model (AUC = 0.75, 95% CI: 0.53-0.97) and even radiologists. The radiomics nomogram model, which combines radiomics signature and clinical risk factors, is effective too (AUC = 0.913, 95% CI: 0.87-0.96). Furthermore, the decision curve analysis demonstrated significantly better net benefit in the radiomics nomogram model. Conclusion: Radiomics features derived from TOF-MRA can reliably be used to build a radiomics nomogram model for effectively differentiating between pseudo microaneurysms and true microaneurysms, and it can provide an objective basis for the selection of clinical treatment plans.

Nomogram model based on radiomics signatures and age to assist in the diagnosis of knee osteoarthritis.

BACKGROUND: Knee osteoarthritis (KOA) is a common disease in the elderly. An effective method for accurate diagnosis could affect the management and prognosis of patients. OBJECTIVES: To develop a nomogram model based on X-ray imaging data and age, and to evaluate its effectiveness in the diagnosis of KOA. METHODS: A total of 4403 knee X-rays from 1174 patients (July 2017 to November 2018) were retrospectively analyzed. Radiomics features were extracted and selected from the X-ray image data to quantify the phenotypic characteristics of the lesion region. Feature selection was performed in three steps to enable the derivation of robust and effective radiomics signatures. Then, logistic regression (LR), support vector machine (SVM) AdaBoost, gradient boosting decision tree (GBDT), and multi-layer perceptron (MLP) was adopted to verify the performance of radiomics signatures. In addition, a nomogram model combining age with radiomics signatures was constructed. At last, receiver operating characteristic (ROC) curve, calibration and decision curves were used to evaluate the discriminative performance. RESULTS: The LR model has the best classification performance among the four radiomics models in testing cohort (LR AUC vs. SVM AUC: 0.843 vs. 0.818, DeLong test P = 0.0024; LR AUC vs. GBDT AUC: 0.843 vs. 0.821, P = 0.0028; LR AUC vs. MLP AUC: 0.843 vs. 0.822, P = 0.0019). The nomogram model achieved better predictive efficacy than the radiomics model in testing cohort compared to radiomics models although the statistical difference was not significant (Nomogram AUC vs. Radiomics AUC: 0.847 vs. 0.843, P = 0.06). The decision curve analysis revealed that the constructed nomogram had clinical usefulness. CONCLUSION: The nomogram model combining radiomics signatures with age has good performance for the accurate diagnosis of KOA and may help to improve clinical decision-making.

Deep learning-assisted knee osteoarthritis automatic grading on plain radiographs: the value of multiview X-ray images and prior knowledge.

Background: Knee osteoarthritis (OA) is harmful to people's health. Effective treatment depends on accurate diagnosis and grading. This study aimed to assess the performance of a deep learning (DL) algorithm based on plain radiographs in detecting knee OA and to investigate the effect of multiview images and prior knowledge on diagnostic performance. Methods: In total, 4,200 paired knee joint X-ray images from 1,846 patients (July 2017 to July 2020) were retrospectively analyzed. Kellgren-Lawrence (K-L) grading was used as the gold standard for knee OA evaluation by expert radiologists. The DL method was used to analyze the performance of anteroposterior and lateral plain radiographs combined with prior zonal segmentation to diagnose knee OA. Four groups of DL models were established according to whether they adopted multiview images and automatic zonal segmentation as the DL prior knowledge. Receiver operating curve analysis was used to assess the diagnostic performance of 4 different DL models. Results: The DL model with multiview images and prior knowledge obtained the best classification performance among the 4 DL models in the testing cohort, with a microaverage area under the receiver operating curve (AUC) and macroaverage AUC of 0.96 and 0.95, respectively. The overall accuracy of the DL model with multiview images and prior knowledge was 0.96 compared to 0.86 for an experienced radiologist. The combined use of anteroposterior and lateral images and prior zonal segmentation affected diagnostic performance. Conclusions: The DL model accurately detected and classified the K-L grading of knee OA. Additionally, multiview X-ray images and prior knowledge improved classification efficacy.

CTE-Based Radiomics Models Can Identify Mucosal Healing in Patients with Crohn's Disease.

RATIONALE AND OBJECTIVES: To develop computed tomography enterography (CTE)-based radiomics models to assess mucosal healing (MH) in patients with Crohn's disease (CD). MATERIALS AND METHODS: CTE images were retrospectively collected from 92 confirmed cases of CD at the post-treatment review. Patients were randomly divided into developing (n = 73) and testing (n = 19) groups. Radiomics features were extracted from the enteric phase images, and the least absolute shrinkage and selection operator (LASSO) logistic regression was applied for feature selection using 5-fold cross-validation on the developing group. The selected features were further identified from the top-ranked features and used to create improved radiomics models. Machine learning models were constructed to compare radiomics models with different radiomics features. The area under the ROC curve (AUC) was calculated to assess the predictive performance for identifying MH in CD. RESULTS: Among the 92 CD patients included in our study, 36 patients achieved MH. The AUC of the radiomics model 1, which was based on the 26 selected radiomics features, was 0.976 for evaluating MH in the testing cohort. The AUCs of radiomics models 2 and 4, based on the top 10 and top 5 positive and negative radiomics features, were 0.974 and 0.952 in the testing cohort, respectively. The AUC of the radiomics model 3, built by removing features with r > 0.5, was 0.956 in the testing cohort. The clinical utility of the clinical radiomics nomogram was confirmed by the decision curve analysis (DCA). CONCLUSION: The CTE-based radiomics models have demonstrated favorable performance in assessing MH in patients with CD. Radiomics features can be used as a promising imaging biomarker for MH.

Isolated gastric variceal bleeding related to non-cirrhotic portal hypertension following oxaliplatin-based chemotherapy: A case report.

BACKGROUND: Sinusoidal obstruction syndrome has been reported after oxaliplatin-based chemotherapy, but liver fibrosis and non-cirrhotic portal hypertension (NCPH) are rarely reported. CASE SUMMARY: Here, we describe the case of a 64-year-old woman who developed isolated gastric variceal bleeding 16 mo after completing eight cycles of oxaliplatin combined with capecitabine chemotherapy after colon cancer resection. Surprisingly, splenomegaly and thrombocytopenia were not accompanied by variceal bleeding, which has been reported to have predictive value for gastric variceal formation. However, a liver biopsy showed fibrosis in the portal area, suggesting NCPH. The patient underwent endoscopic treatment and experienced no further symptoms. CONCLUSION: It is necessary to guard against long-term complications after oxaliplatin-based chemotherapy. Sometimes splenic size and platelet level may not always accurately predict the occurrence of portal hypertension.

Prophylactic antibiotics on patients with cirrhosis and upper gastrointestinal bleeding: A meta-analysis.

OBJECTIVE: To evaluate the effect of different prophylactic antibiotic treatments for cirrhosis patients with upper gastrointestinal bleeding (UGIB) and to investigate whether prophylactic antibiotics are equally beneficial to reducing the risk of adverse outcomes in A/B with low Child-Pugh scores. METHODS: Relevant studies were searched via PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Internet (CNKI), Wanfang, and VIP databases up to July 16, 2021. The heterogeneity test was conducted for each outcome measuring by I2 statistics. Subgroup analysis was performed regarding antibiotic types. Relative risk (RR) and 95% confidence interval (CI) were used to evaluate prophylactic antibiotics on the risk of adverse outcomes in cirrhosis patients with UGIB. RESULTS: Twenty-six studies involving 12,440 participants fulfilled our inclusion criteria. Antibiotic prophylaxis was associated with a reduced overall mortality (RR: 0.691, 95%CI: 0.518 to 0.923), mortality due to bacterial infections (RR: 0.329, 95%CI: 0.144 to 0.754), bacterial infections (RR: 0.389, 95%CI: 0.340 to 0.444), rebleeding (RR: 0.577, 95%CI: 0.433 to 0.767) and length of hospitalization [weighted mean difference (WMD): -3.854, 95%CI: -6.165 to -1.543] among patients with UGIB. Nevertheless, prophylactic antibiotics may not benefit to A/B population with low Child-Pugh scores. In our subgroup analysis, quinolone, beta-lactams alone or in combination reduced adverse outcomes in cirrhosis patients with UGIB. CONCLUSION: Administration of antibiotics was associated with a reduction in mortality, bacterial infections, rebleeding, and length of hospitalization. Quinolone, beta-lactams alone or in combination can be used in cirrhosis patients with UGIB. Nevertheless, targeted efforts are needed to promote the appropriate use of antibiotics among patients with cirrhosis and UGIB.

Performance of Deep Learning-Based Algorithm for Detection of Pediatric Intussusception on Abdominal Ultrasound Images.

Background and Aims: Diagnosing pediatric intussusception from ultrasound images can be a difficult task in many primary care hospitals that lack experienced radiologists. To address this challenge, this study developed an artificial intelligence- (AI-) based system for automatic detection of "concentric circles" signs on ultrasound images, thereby improving the efficiency and accuracy of pediatric intussusception diagnosis. Methods: A total of 440 cases (373 pediatric intussusception and 67 normal cases) were retrospectively collected from Children's Hospital affiliated to Zhejiang University School of Medicine from January 2020 to December 2020. An improved Faster RCNN deep learning framework was used to detect "concentric circle" signs. Finally, independent validation set was used to evaluate the performance of the developed AI tool. Results: The data of pediatric intussusception were divided into a training set and validation set according to the ratio of 8 : 2, with training set (298 pediatric intussusception) and validation set (75 pediatric intussusception and 67 normal cases). In the "concentric circle" detection model, the detection rate, recall, specificity, and F1 score assessed by the validation set were 92.8%, 95.0%, 92.2%, and 86.4%, respectively. Pediatric intussusception was classified by "concentric circle" signs, and the accuracy, recall, specificity, and F1 score were 93.0%, 92.0%, 94.1%, and 93.2% on the validation set, respectively. Conclusion: The model established in this paper can realize the automatic detection of "concentric circle" signs in the ultrasound images of abdominal intussusception in children; the AI tool can improve the diagnosis speed of pediatric intussusception. It is necessary to further develop an artificial intelligence system for real-time detection of "concentric circles" in ultrasound images for the judgment of children with intussusception.

Deep learning based CT images automatic analysis model for active/non-active pulmonary tuberculosis differential diagnosis.

Active pulmonary tuberculosis (ATB), which is more infectious and has a higher mortality rate compared with non-active pulmonary tuberculosis (non-ATB), needs to be diagnosed accurately and timely to prevent the tuberculosis from spreading and causing deaths. However, traditional differential diagnosis methods of active pulmonary tuberculosis involve bacteriological testing, sputum culturing and radiological images reading, which is time consuming and labour intensive. Therefore, an artificial intelligence model for ATB differential diagnosis would offer great assistance in clinical practice. In this study, computer tomography (CT) scans images and corresponding clinical information of 1160 ATB patients and 1131 patients with non-ATB were collected and divided into training, validation, and testing sets. A 3-dimension (3D) Nested UNet model was utilized to delineate lung field regions in the CT images, and three different pre-trained deep learning models including 3D VGG-16, 3D EfficientNet and 3D ResNet-50 were used for classification and differential diagnosis task. We also collected an external testing set with 100 ATB cases and 100 Non-ATB cases for further validation of the model. In the internal and external testing set, the 3D ResNet-50 model outperformed other models, reaching an AUC of 0.961 and 0.946, respectively. The 3D ResNet-50 model reached even higher levels of diagnostic accuracy than experienced radiologists, while the CT images reading and diagnosing speed was 10 times faster than human experts. The model was also capable of visualizing clinician interpretable lung lesion regions important for differential diagnosis, making it a powerful tool assisting ATB diagnosis. In conclusion, we developed an auxiliary tool to differentiate active and non-active pulmonary tuberculosis, which would have broad prospects in the bedside.

Impact of hepatocyte-specific deletion of staphylococcal nuclease and tudor domain containing 1 (SND1) on liver insulin resistance and acute liver failure of mice.

Although our previous research shows an ameliorated high-fat diet (HFD)-induced hepatic steatosis and insulin resistance in global SND1 transgenic mice, the involvement of SND1 loss-of-function in hepatic metabolism remains elusive. Herein, we aim to explore the potential impact of hepatocyte-specific SND1 deletion on insulin-resistant mice. As SND1 is reported to be linked to inflammatory response, the pathobiological feature of acute liver failure (ALF) is also investigated. Hence, we construct the conditional liver knockout (LKO) mice of SND1 for the first time. Under the condition of HFD, the absence of hepatic SND1 affects the weight of white adipose tissue, but not the gross morphology, body weight, cholesterol level, liver weight, and hepatic steatosis of mice. Furthermore, we fail to observe significant differences in either HFD-induced insulin resistance or lipopolysaccharide/D-galactosamine-induced (LPS/D-GaIN) ALF between LKO and wild type (WT) mice in terms of inflammation and tissue damage. Compared with negative controls, there is no differential SND1 expression in various species of sample with insulin resistance or ALF, based on several gene expression omnibus datasets, including GSE23343, GSE160646, GSE120243, GSE48794, GSE13271, GSE151268, GSE62026, GSE120652, and GSE38941. Enrichment result of SND1-binding partners or related genes indicates a sequence of issues related to RNA or lipid metabolism, but not glucose homeostasis or hepatic failure. Overall, hepatic SND1 is insufficient to alter the phenotypes of hepatic insulin resistance and acute liver failure in mice. The SND1 in various organs is likely to cooperate in regulating glucose homeostasis by affecting the expression of lipid metabolism-related RNA transcripts during stress.

Deglycosylated Azithromycin Targets Transgelin to Enhance Intestinal Smooth Muscle Function.

Azithromycin (AZM) has been widely used as an antibacterial drug for many years. It has also been used to treat delayed gastric emptying. However, it exerts several side effects. We found that deglycosylated AZM (Deg-AZM or CP0119), an AZM metabolite, is a positively strong intestinal agonist that may result in the intestinal mobility experienced by patients after AZM administration. We confirmed that Deg-AZM can function strongly on intestinal peristalsis and identified transgelin as its potential molecular target. Furthermore, our pharmacological studies showed that the binding of Deg-AZM to transgelin enhanced the contractility of intestinal smooth muscle cells by facilitating the assembly of actin filaments into tight bundles and stress fibers. Specifically, Deg-AZM promoted intestinal peristaltic activity in wild-type mice but not in transgelin (-/-) mice. Moreover, Deg-AZM did not exert antibacterial activity and did not disrupt intestinal flora. Thus, Deg-AZM may become a potential drug for slow-transit constipation treatment.

USP5 promotes epithelial-mesenchymal transition by stabilizing SLUG in hepatocellular carcinoma.

Rationale: The role of SLUG in epithelial-mesenchymal transition during tumor progression has been thoroughly studied, but its precise regulation remains poorly explored. Methods: The affinity purification, mass spectrometry and CO-IP were performed to identify the interaction between SLUG and ubiquitin-specific protease 5 (USP5). Cycloheximide chase assays and deubiquitination assays confirmed that the effect of USP5 on the deubiquitin of SLUG. The dual-luciferase reporter and chromatin immunoprecipitation assays were employed to observe the direct transcriptional regulation of E-cadherin by SLUG effected by USP5. EMT related markers was detected by western blotting and immunofluorescence. Molecular docking, SPR sensor (biacore) and co-location were detected to prove Formononetin targets USP5. Bioinformatics analysis was used to study the relation of USP5 and SLUG to malignancy degree of HCC. Cell migration, invasion in HCC cells and xenografts model in nude mouse were conducted to detect the promotion of USP5 and the inhibition of Formononetin on EMT. Results: USP5 interacts with and stabilizes SLUG to regulate its abundance through USP5 deubiquitination activities in epithelial-mesenchymal transition (EMT) of hepatocellular carcinoma (HCC). USP5 is highly expressed and positively correlated with SLUG expression in HCC with high malignancy. Knockdown of USP5 inhibits SLUG deubiquitination and inhibits HCC cells proliferation, metastasis, and invasion, while overexpression of USP5 promotes SLUG stability and EMT in vitro and in vivo. Through virtual screening, we found that Formononetin exhibits excellent binding to USP5. Moreover, Formononetin inhibits deubiquitinating activities of USP5 to SLUG and consequently impedes the EMT and malignant progression of HCC. Conclusion: Our findings reveal that USP5 serve as a potential target for tumor intervention and provide a preliminary antitumor therapy for inhibit EMT by targeting USP5 or its interaction with SLUG in HCC.

Meta-Analysis of the Relation Between IL10 Promoter Polymorphisms and Autoimmune Liver Disease Risk.

BACKGROUND: Single nucleotide polymorphisms of the IL10 gene have been linked to the occurrence of autoimmune liver disease. METHODS: We performed a meta-analysis to assess the association between three IL10 promoter polymorphisms (rs1800896, rs1800871, and rs1800872) and the risk of autoimmune hepatitis, primary biliary cholangitis, and primary sclerosing cholangitis. RESULTS: In total, 1420 articles were initially identified through database retrieval. After screening, seven eligible articles were ultimately included in the meta-analysis. A fixed-effect model was used for all Mantel-Haenszel statistics due to the absence of large between-study heterogeneity (all I2 < 50%, p > 0.1). No association between any of the studied polymorphisms and risk of autoimmune liver disease was detected in the allele, homozygote, heterozygote, dominant, recessive, or carrier genetic models (passociation > 0.05). Potential publication bias was excluded using Begg's and Egger's tests. Similar negative results were observed in subgroup analyses and in an analysis of the three haplotypes of rs1800896/rs1800871/rs1800872 (G/C/C, A/C/C, and A/T/A). CONCLUSION: Our meta-analysis strongly suggests that the IL10 rs1800896, rs1800871, and rs1800872 polymorphisms are not associated with the risk of autoimmune liver disease.