Malignant progression of liver cancer progenitors requires lysine acetyltransferase 7-acetylated and cytoplasm-translocated G protein GαS.

BACKGROUND AND AIMS: Hepatocarcinogenesis goes through HCC progenitor cells (HcPCs) to fully established HCC, and the mechanisms driving the development of HcPCs are still largely unknown. APPROACH AND RESULTS: Proteomic analysis in nonaggregated hepatocytes and aggregates containing HcPCs from a diethylnitrosamine-induced HCC mouse model was screened using a quantitative mass spectrometry-based approach to elucidate the dysregulated proteins in HcPCs. The heterotrimeric G stimulating protein α subunit (GαS) protein level was significantly increased in liver cancer progenitor HcPCs, which promotes their response to oncogenic and proinflammatory cytokine IL-6 and drives premalignant HcPCs to fully established HCC. Mechanistically, GαS was located at the membrane inside of hepatocytes and acetylated at K28 by acetyltransferase lysine acetyltransferase 7 (KAT7) under IL-6 in HcPCs, causing the acyl protein thioesterase 1-mediated depalmitoylation of GαS and its cytoplasmic translocation, which were determined by GαS K28A mimicking deacetylation or K28Q mimicking acetylation mutant mice and hepatic Kat7 knockout mouse. Then, cytoplasmic acetylated GαS associated with signal transducer and activator of transcription 3 (STAT3) to impede its interaction with suppressor of cytokine signaling 3, thus promoting in a feedforward manner STAT3 phosphorylation and the response to IL-6 in HcPCs. Clinically, GαS, especially K28-acetylated GαS, was determined to be increased in human hepatic premalignant dysplastic nodules and positively correlated with the enhanced STAT3 phosphorylation, which were in accordance with the data obtained in mouse models. CONCLUSIONS: Malignant progression of HcPCs requires increased K28-acetylated and cytoplasm-translocated GαS, causing enhanced response to IL-6 and driving premalignant HcPCs to fully established HCC, which provides mechanistic insight and a potential target for preventing hepatocarcinogenesis.

Diagnostic Accuracy of Computerized Bowel Sound Analysis with Non-Invasive Devices for Irritable Bowel Syndrome: A Systematic Review and Meta-Analysis.

Objective To assess the diagnostic accuracy of bowel sound analysis for irritable bowel syndrome (IBS) with a systematic review and meta-analysis. Methods We searched MEDLINE, EMBASE, the Cochrane Library, Web of Science, and IEEE Xplore databases until September 2023. Cross-sectional and case-control studies on diagnostic accuracy of bowel sound analysis for IBS were identified. We estimated the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio with a 95% confidence interval (CI), and plotted a summary receiver operating characteristic curve and evaluated the area under the curve. Results Four studies were included. The pooled diagnostic sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.94 (95% CI, 0.87－0.97), 0.89 (95% CI, 0.81－0.94), 8.43 (95% CI, 4.81－14.78), 0.07 (95% CI, 0.03－0.15), and 118.86 (95% CI, 44.18－319.75), respectively, with an area under the curve of 0.97 (95% CI, 0.95－0.98). Conclusions Computerized bowel sound analysis is a promising tool for IBS. However, limited high-quality data make the results' validity and applicability questionable. There is a need for more diagnostic test accuracy studies and better wearable devices for monitoring and analysis.

Building a trustworthy AI differential diagnosis application for Crohn's disease and intestinal tuberculosis.

BACKGROUND: Differentiating between Crohn's disease (CD) and intestinal tuberculosis (ITB) with endoscopy is challenging. We aim to perform more accurate endoscopic diagnosis between CD and ITB by building a trustworthy AI differential diagnosis application. METHODS: A total of 1271 electronic health record (EHR) patients who had undergone colonoscopies at Peking Union Medical College Hospital (PUMCH) and were clinically diagnosed with CD (n = 875) or ITB (n = 396) were used in this study. We build a workflow to make diagnoses with EHRs and mine differential diagnosis features; this involves finetuning the pretrained language models, distilling them into a light and efficient TextCNN model, interpreting the neural network and selecting differential attribution features, and then adopting manual feature checking and carrying out debias training. RESULTS: The accuracy of debiased TextCNN on differential diagnosis between CD and ITB is 0.83 (CR F1: 0.87, ITB F1: 0.77), which is the best among the baselines. On the noisy validation set, its accuracy was 0.70 (CR F1: 0.87, ITB: 0.69), which was significantly higher than that of models without debias. We also find that the debiased model more easily mines the diagnostically significant features. The debiased TextCNN unearthed 39 diagnostic features in the form of phrases, 17 of which were key diagnostic features recognized by the guidelines. CONCLUSION: We build a trustworthy AI differential diagnosis application for differentiating between CD and ITB focusing on accuracy, interpretability and robustness. The classifiers perform well, and the features which had statistical significance were in agreement with clinical guidelines.

Can natural language processing help differentiate inflammatory intestinal diseases in China? Models applying random forest and convolutional neural network approaches.

BACKGROUND: Differentiating between ulcerative colitis (UC), Crohn's disease (CD) and intestinal tuberculosis (ITB) using endoscopy is challenging. We aimed to realize automatic differential diagnosis among these diseases through machine learning algorithms. METHODS: A total of 6399 consecutive patients (5128 UC, 875 CD and 396 ITB) who had undergone colonoscopy examinations in the Peking Union Medical College Hospital from January 2008 to November 2018 were enrolled. The input was the description of the endoscopic image in the form of free text. Word segmentation and key word filtering were conducted as data preprocessing. Random forest (RF) and convolutional neural network (CNN) approaches were applied to different disease entities. Three two-class classifiers (UC and CD, UC and ITB, and CD and ITB) and a three-class classifier (UC, CD and ITB) were built. RESULTS: The classifiers built in this research performed well, and the CNN had better performance in general. The RF sensitivities/specificities of UC-CD, UC-ITB, and CD-ITB were 0.89/0.84, 0.83/0.82, and 0.72/0.77, respectively, while the values for the CNN of CD-ITB were 0.90/0.77. The precisions/recalls of UC-CD-ITB when employing RF were 0.97/0.97, 0.65/0.53, and 0.68/0.76, respectively, and when employing the CNN were 0.99/0.97, 0.87/0.83, and 0.52/0.81, respectively. CONCLUSIONS: Classifiers built by RF and CNN approaches had excellent performance when classifying UC with CD or ITB. For the differentiation of CD and ITB, high specificity and sensitivity were achieved as well. Artificial intelligence through machine learning is very promising in helping unexperienced endoscopists differentiate inflammatory intestinal diseases. CONFERENCE: The abstract of this article has won the first prize of the Young Investigator Award during the Asian Pacific Digestive Week (APDW) 2019 held in Kolkata, India.

A literature review and case report of severe and refractory post-colectomy enteritis.

BACKGROUND: Ulcerative colitis (UC)-related post-colectomy enteritis is a very rare condition that is characterized by diffuse small-bowel mucosal inflammation following colectomy and could be very dangerous. In previously reported cases, corticosteroid therapy seemed to be the optimal choice for inducing remission; however, the patient studied herein presented with severe diarrhoea and hypovolemic shock and failed to achieve full remission with corticosteroid therapy. CASE PRESENTATION: We describe the case of a patient with severe pan-enteritis presenting with life-threatening diarrhoea complicated with hypovolemic shock and acute kidney injury after colectomy and ileal pouch anal anastomosis (IPAA) for UC; this patient was successfully treated by ileostomy closure after failing to achieve full remission with corticosteroid therapy. Next, we review other cases of post-colectomy enteritis reported in the literature and propose a flow-chart for its diagnosis and initial treatment. CONCLUSION: Post-colectomy enteritis can be dangerous, and the early awareness of this condition plays a vital role. Additionally, in patients who do not respond well to corticosteroid or immunosuppressant therapy, early closure of the ileostomy and re-establishment of the natural faecal stream could be important considerations.

Hepatic IFIT3 predicts interferon-α therapeutic response in patients of hepatocellular carcinoma.

Adjuvant interferon-α (IFN-α) therapy is used to control certain types of cancer in clinics. For hepatocellular carcinoma (HCC), IFN-α therapy is effective in only a subgroup of patients; therefore, identifying biomarkers to predict the response to IFN-α therapy is of high significance and clinical utility. As the induced IFN-stimulated gene expression following IFN-α treatment plays pivotal roles in IFN-α effects, we screened IFN-stimulated gene expression in HCC tissues and found that several IFN-stimulated genes were significantly decreased in HCC. Interestingly, expression of IFN-induced protein with tetratricopeptide repeats (IFIT) family members, including IFIT1, IFIT2, IFIT3, and IFIT5, was decreased in HCC tissues. We further analyzed the expression of IFIT family members in HCC and their roles in patients' responses to IFN-α therapy in two independent randomized controlled IFN-α therapy clinical trials of HCC patients. We found that higher expression of IFIT3, but not other IFITs, in HCC tissues predicts better response to IFN-α therapy, suggesting that IFIT3 may be a useful predictor of the response to IFN-α therapy in HCC patients. Mechanistically, IFIT3 enhanced the antitumor effects of IFN-α by promoting IFN-α effector responses both in vitro and in vivo. IFIT3 could bind signal transducer and activator of transcription 1 (STAT1) and STAT2 to enhance STAT1-STAT2 heterodimerization and nuclear translocation upon IFN-α treatment, thus promoting IFN-α effector signaling. CONCLUSION: Higher IFIT3 expression in HCC tissues predicts better response to IFN-α therapy in HCC patients; IFIT3 promotes IFN-α effector responses and therapeutic effects by strengthening IFN-α effector signaling in HCC. (Hepatology 2017;66:152-166).

Type I IFN-Inducible Downregulation of MicroRNA-27a Feedback Inhibits Antiviral Innate Response by Upregulating Siglec1/TRIM27.

Upon recognition of viral components by pattern recognition receptors, including TLRs and retinoic acid-inducible gene I-like helicases, cells are activated to produce type I IFN, which plays key roles in host antiviral innate immune response. However, excessive IFN production may induce immune disorders, and the mechanisms responsible for the regulation of type I IFN production have attracted much attention. Furthermore, type I IFN activates the downstream IFN/JAK/STAT pathway to modulate expression of a set of genes against viral infection, but whether these genes can feedback regulate type I IFN production is poorly understood. In this study, by screening the microRNAs modulated by viral infection in macrophages, we identified that microRNA (miR)-27a was significantly downregulated via the IFN/JAK/STAT1/runt-related transcription factor 1 pathway. Inducible downregulation of miR-27a, in turn, negatively regulated vesicular stomatitis virus-triggered type I IFN production, thus promoting vesicular stomatitis virus replication in macrophages. Mechanistically, we found that miR-27a directly targeted sialic acid-binding Ig-like lectin (Siglec)1 and E3 ubiquitin ligase tripartite motif-containing protein 27 (TRIM27), both of which were previously verified as negative regulators of type I IFN production. Furthermore, we constructed "Sponge" transgenic mice against miR-27a expression and found that Siglec1 and TRIM27 expression were elevated whereas type I IFN production was inhibited and viral replication was aggregated in vivo. Therefore, type I IFN-induced downregulation of miR-27a can upregulate Siglec1 and TRIM27 expression, feedback inhibiting type I IFN production in antiviral innate response. Our study outlines a new negative way to feedback regulate type I IFN production.

Efficacy and safety of rituximab induction therapy and effect of rituximab maintenance for IgG4-related disease: a systematic review and meta-analysis.

BACKGROUND: Previous studies have reported that rituximab (RTX) therapy might be beneficial in reducing relapse rates in patients with IgG4-related disease (IgG4-RD). Therefore, we aimed to systematically assess the efficacy and safety of RTX induction treatment and the effect of RTX maintenance in patients with IgG4-RD. METHODS: The protocol was registered in the PROSPERO (CRD42023427352). PubMed, Embase, the Cochrane database, Scopus, and the Web of Science were interrogated to identify studies that evaluated the impact of RTX on prognosis in IgG4-RD. We explored the impact of various subgroups of factors on relapse outcomes and focused on the possible role of maintenance therapy in reducing relapse rates. The pooled incidence of adverse events of RTX therapy and the influencing factors have also been evaluated. RESULTS: Eighteen studies comprising 374 patients (mean age 56.0 ± 8.7 years; male 73.7 %) with a mean follow-up duration of 23.4 ± 16.3 months were included. The pooled estimate of the response rate, complete remission rate, overall relapse rate, adverse event rate, and serious adverse event rate of RTX induction therapy were 97.3 % (95 % CI, 94.7 %-99.1 %), 55.8 % (95 % CI, 39.6 %-71.3 %), 16.9 % (95 % CI, 8.7 %-27.1 %), 31.6 % (95 % CI, 16.7 %-48.9 %) and 3.9 % (95 % CI, 0.8 %-8.9 %), respectively. In subgroup analysis, the pooled relapse rate was significantly lower in studies with maintenance than without maintenance (2.8% vs 21.5 %, p < 0.01). Pooled Kaplan-Meier relapse curves also demonstrated that RTX maintenance therapy provided a better prognosis. CONCLUSIONS: RTX induction therapy appears to have satisfactory efficacy in the induction of remission in IgG4-RD. In addition, prophylactic RTX maintenance therapy after induction may be beneficial in preventing relapse of IgG4-RD.

PIEZO1 mechanically regulates the antitumour cytotoxicity of T lymphocytes.

The killing function of cytotoxic T cells can be enhanced biochemically. Here we show that blocking the mechanical sensor PIEZO1 in T cells strengthens their traction forces and augments their cytotoxicity against tumour cells. By leveraging cytotoxic T cells collected from tumour models in mice and from patients with cancers, we show that PIEZO1 upregulates the transcriptional factor GRHL3, which in turn induces the expression of the E3 ubiquitin ligase RNF114. RNF114 binds to filamentous actin, causing its downregulation and rearrangement, which depresses traction forces in the T cells. In mice with tumours, the injection of cytotoxic T cells collected from the animals and treated with a PIEZO1 antagonist promoted their infiltration into the tumour and attenuated tumour growth. As an immunomechanical regulator, PIEZO1 could be targeted to enhance the outcomes of cancer immunotherapies.

Clinical manifestation, lifestyle, and treatment patterns of chronic erosive gastritis: A multicenter real-world study in China.

BACKGROUND: Although chronic erosive gastritis (CEG) is common, its clinical characteristics have not been fully elucidated. The lack of consensus regarding its treatment has resulted in varied treatment regimens. AIM: To explore the clinical characteristics, treatment patterns, and short-term outcomes in CEG patients in China. METHODS: We recruited patients with chronic non-atrophic or mild-to-moderate atrophic gastritis with erosion based on endoscopy and pathology. Patients and treating physicians completed a questionnaire regarding history, endoscopic findings, and treatment plans as well as a follow-up questionnaire to investigate changes in symptoms after 4 wk of treatment. RESULTS: Three thousand five hundred sixty-three patients from 42 centers across 24 cities in China were included. Epigastric pain (68.0%), abdominal distension (62.6%), and postprandial fullness (47.5%) were the most common presenting symptoms. Gastritis was classified as chronic non-atrophic in 69.9% of patients. Among those with erosive lesions, 72.1% of patients had lesions in the antrum, 51.0% had multiple lesions, and 67.3% had superficial flat lesions. In patients with epigastric pain, the combination of a mucosal protective agent (MPA) and proton pump inhibitor was more effective. For those with postprandial fullness, acid regurgitation, early satiety, or nausea, a MPA appeared more promising. CONCLUSION: CEG is a multifactorial disease which is common in Asian patients and has non-specific symptoms. Gastroscopy may play a major role in its detection and diagnosis. Treatment should be individualized based on symptom profile.

Interferon-α stimulates DExH-box helicase 58 to prevent hepatocyte ferroptosis.

BACKGROUND: Liver ischemia/reperfusion (I/R) injury is usually caused by hepatic inflow occlusion during liver surgery, and is frequently observed during war wounds and trauma. Hepatocyte ferroptosis plays a critical role in liver I/R injury, however, it remains unclear whether this process is controlled or regulated by members of the DEAD/DExH-box helicase (DDX/DHX) family. METHODS: The expression of DDX/DHX family members during liver I/R injury was screened using transcriptome analysis. Hepatocyte-specific Dhx58 knockout mice were constructed, and a partial liver I/R operation was performed. Single-cell RNA sequencing (scRNA-seq) in the liver post I/R suggested enhanced ferroptosis by Dhx58hep-/-. The mRNAs and proteins associated with DExH-box helicase 58 (DHX58) were screened using RNA immunoprecipitation-sequencing (RIP-seq) and IP-mass spectrometry (IP-MS). RESULTS: Excessive production of reactive oxygen species (ROS) decreased the expression of the IFN-stimulated gene Dhx58 in hepatocytes and promoted hepatic ferroptosis, while treatment using IFN-α increased DHX58 expression and prevented ferroptosis during liver I/R injury. Mechanistically, DHX58 with RNA-binding activity constitutively associates with the mRNA of glutathione peroxidase 4 (GPX4), a central ferroptosis suppressor, and recruits the m6A reader YT521-B homology domain containing 2 (YTHDC2) to promote the translation of Gpx4 mRNA in an m6A-dependent manner, thus enhancing GPX4 protein levels and preventing hepatic ferroptosis. CONCLUSIONS: This study provides mechanistic evidence that IFN-α stimulates DHX58 to promote the translation of m6A-modified Gpx4 mRNA, suggesting the potential clinical application of IFN-α in the prevention of hepatic ferroptosis during liver I/R injury.

[Parameter optimization for non-contrast-enhanced selective magnetic resonance portography imaging].

OBJECTIVE: To evaluate subtraction images acquired with 3D true steady-state free-precession(SSFP)sequence combined with time-spatial labeling inversion pulse(T-SLIP)for selective and non-contrast-enhanced(non-CE)visualization of the portal venous system,and explore the optimization of this protocol. METHODS: Totally 13 healthy volunteers were recruited.Respiratory-triggered 3D true SSFP sequences on a 1.5T MRI system combined with T-SLIP placed on the spleen and mesenteric area were performed.The portographic images were generated from the subtraction between the pulse on and off images.According to the difference in inversion time(TI)of T-SLIP,four image groups group A(TI of 1300 ms),group B(TI of 1100 ms),group C(TI of 900 ms)and group D(TI of 700 ms),were assigned and compared to detect the optimal TI for portography.For quantitative analysis,the signal intensity(SI)of left and right liver lobe,the large vessels as main,right and left portal vein(MPV,RPV and LPV,respectively)and small vessels as branches of segments four(P4),six(P6)and eight(P8)were measured.The relative SI of MPV,RPV and LPV,as well as P4,P6 and P8 were also compared.For qualitative evaluation,the quality score of visualization was also evaluated using a 4-point scale.One-Way ANOVA and LSD test were used for comparison of quantitative data,and Friedman signed rank test was used for comparison of qualitative scores. RESULTS: In 52 sequences of 13 volunteers,the selective visualization of the portal vein was all successfully conducted.Quantitative evaluation showed significant increased SI at the left lobe between C and D groups and A and B groups(comparison of group C to group A and BP=0.004,0.011;comparison of group D to group A and BP=0.001,0.004),while relative SI of LPV of groups C and D were lower than groups A and B(comparison of group C to group A and BP=0.015,0.015;comparison of group D to group A and BP=0.000,0.000).The relative SI of MPV in group D were decreased than groups A(P=0.000),B(P=0.000),and C(P=0.019).There was no significant difference in relative SI of small vessels among four groups(P>0.05).The image score of portal vessels in four groups also showed no differences(P>0.05). CONCLUSIONS: 3D true SSFP scan with T-SLIP enabled selective non-CE visualization of the portal vein with digital subtraction method.A fixed TI of both 1300 and 1100 ms can be preferable.

Lightweight deep learning model incorporating an attention mechanism and feature fusion for automatic classification of gastric lesions in gastroscopic images.

Accurate diagnosis of various lesions in the formation stage of gastric cancer is an important problem for doctors. Automatic diagnosis tools based on deep learning can help doctors improve the accuracy of gastric lesion diagnosis. Most of the existing deep learning-based methods have been used to detect a limited number of lesions in the formation stage of gastric cancer, and the classification accuracy needs to be improved. To this end, this study proposed an attention mechanism feature fusion deep learning model with only 14 million (M) parameters. Based on that model, the automatic classification of a wide range of lesions covering the stage of gastric cancer formation was investigated, including non-neoplasm(including gastritis and intestinal metaplasia), low-grade intraepithelial neoplasia, and early gastric cancer (including high-grade intraepithelial neoplasia and early gastric cancer). 4455 magnification endoscopy with narrow-band imaging(ME-NBI) images from 1188 patients were collected to train and test the proposed method. The results of the test dataset showed that compared with the advanced gastric lesions classification method with the best performance (overall accuracy = 94.3%, parameters = 23.9 M), the proposed method achieved both higher overall accuracy and a relatively lightweight model (overall accuracy =95.6%, parameter = 14 M). The accuracy, sensitivity, and specificity of low-grade intraepithelial neoplasia were 94.5%, 93.0%, and 96.5%, respectively, achieving state-of-the-art classification performance. In conclusion, our method has demonstrated its potential in diagnosing various lesions at the stage of gastric cancer formation.

Clinical assistant decision-making model of tuberculosis based on electronic health records.

BACKGROUND: Tuberculosis is a dangerous infectious disease with the largest number of reported cases in China every year. Preventing missed diagnosis has an important impact on the prevention, treatment, and recovery of tuberculosis. The earliest pulmonary tuberculosis prediction models mainly used traditional image data combined with neural network models. However, a single data source tends to miss important information, such as primary symptoms and laboratory test results, that is available in multi-source data like medical records and tests. In this study, we propose a multi-stream integrated pulmonary tuberculosis diagnosis model based on structured and unstructured multi-source data from electronic health records. With the limited number of lung specialists and the high prevalence of tuberculosis, the application of this auxiliary diagnosis model can make substantial contributions to clinical settings. METHODS: The subjects were patients at the respiratory department and infectious cases department of a large comprehensive hospital in China between 2015 to 2020. A total of 95,294 medical records were selected through a quality control process. Each record contains structured and unstructured data. First, numerical expressions of features for structured data were created. Then, feature engineering was performed through decision tree model, random forest, and GBDT. Features were included in the feature exclusion set as per their weights in descending order. When the importance of the set was higher than 0.7, this process was concluded. Finally, the contained features were used for model training. In addition, the unstructured free-text data was segmented at the character level and input into the model after indexing. Tuberculosis prediction was conducted through a multi-stream integration tuberculosis diagnosis model (MSI-PTDM), and the evaluation indices of accuracy, AUC, sensitivity, and specificity were compared against the prediction results of XGBoost, Text-CNN, Random Forest, SVM, and so on. RESULTS: Through a variety of characteristic engineering methods, 20 characteristic factors, such as main complaint hemoptysis, cough, and test erythrocyte sedimentation rate, were selected, and the influencing factors were analyzed using the Chinese diagnostic standard of pulmonary tuberculosis. The area under the curve values for MSI-PTDM, XGBoost, Text-CNN, RF, and SVM were 0.9858, 0.9571, 0.9486, 0.9428, and 0.9429, respectively. The sensitivity, specificity, and accuracy of MSI-PTDM were 93.18%, 96.96%, and 96.96%, respectively. The MSI-PTDM prediction model was installed at a doctor workstation and operated in a real clinic environment for 4 months. A total of 692,949 patients were monitored, including 484 patients with confirmed pulmonary tuberculosis. The model predicted 440 cases of pulmonary tuberculosis. The positive sample recognition rate was 90.91%, the false-positive rate was 9.09%, the negative sample recognition rate was 96.17%, and the false-negative rate was 3.83%. CONCLUSIONS: MSI-PTDM can process sparse data, dense data, and unstructured text data concurrently. The model adds a feature domain vector embedding the medical sparse features, and the single-valued sparse vectors are represented by multi-dimensional dense hidden vectors, which not only enhances the feature expression but also alleviates the side effects of sparsity on the model training. However, there may be information loss when features are extracted from text, and adding the processing of original unstructured text makes up for the error within the above process to a certain extent, so that the model can learn data more comprehensively and effectively. In addition, MSI-PTDM also allows interaction between features, considers the combination effect between patient features, adds more complex nonlinear calculation considerations, and improves the learning ability of the model. It has been verified using a test set and via deployment within an actual outpatient environment.

Emerging infectious disease surveillance using a hierarchical diagnosis model and the Knox algorithm.

Emerging infectious diseases are a critical public health challenge in the twenty-first century. The recent proliferation of such diseases has raised major social and economic concerns. Therefore, early detection of emerging infectious diseases is essential. Subjects from five medical institutions in Beijing, China, which met the spatial-specific requirements, were analyzed. A quality control process was used to select 37,422 medical records of infectious diseases and 56,133 cases of non-infectious diseases. An emerging infectious disease detection model (EIDDM), a two-layer model that divides the problem into two sub-problems, i.e., whether a case is an infectious disease, and if so, whether it is a known infectious disease, was proposed. The first layer model adopts the binary classification model TextCNN-Attention. The second layer is a multi-classification model of LightGBM based on the one-vs-rest strategy. Based on the experimental results, a threshold of 0.5 is selected. The model results were compared with those of other models such as XGBoost and Random Forest using the following evaluation indicators: accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. The prediction performance of the first-layer TextCNN is better than that of other comparison models. Its average specificity for non-infectious diseases is 97.57%, with an average negative predictive value of 82.63%, indicating a low risk of misdiagnosing non-infectious diseases as infectious (i.e., a low false positive rate). Its average positive predictive value for eight selected infectious diseases is 95.07%, demonstrating the model's ability to avoid misdiagnoses. The overall average accuracy of the model is 86.11%. The average prediction accuracy of the second-layer LightGBM model for emerging infectious diseases reaches 90.44%. Furthermore, the response time of a single online reasoning using the LightGBM model is approximately 27 ms, which makes it suitable for analyzing clinical records in real time. Using the Knox method, we found that all the infectious diseases were within 2000 m in our case, and a clustering feature of spatiotemporal interactions (P < 0.05) was observed as well. Performance testing and model comparison results indicated that the EIDDM is fast and accurate and can be used to monitor the onset/outbreak of emerging infectious diseases in real-world hospitals.

Impact of Helicobacter pylori on the gastric microbiome in patients with chronic gastritis: a systematic review and meta-analysis protocol.

INTRODUCTION: Chronic gastritis is a common disease worldwide. Studies have consistently shown that chronic gastritis is usually associated with gastric microbial dysbiosis, especially the infection of Helicobacter pylori. However, the interaction between H. pylori and non-H. pylori bacteria in patients with chronic gastritis has not been clearly identified yet. Consequently, we designed a protocol for a systematic review and meta-analysis, which focused on identifying the changes in gastrointestinal microbiota composition between patients with H. pylori-infective and non-infective chronic gastritis. METHOD AND ANALYSIS: We will search PubMed, EMBASE and Cochrane Library databases to retrieve observational studies on humans. The eligible studies must include data about the relative abundance of the gastrointestinal microbiome in patients with H. pylori-infective or non-infective chronic gastritis. Only the data of adults aged over 18 years will be analysed. Two researchers will extract the data independently, and Newcastle-Ottawa Scale will be used to assess the risk of bias. Random-effects model will be performed in quantitative analyses. Correlation analysis, bioinformatics analysis and function analysis will be performed. DISCUSSION: Currently, numerous studies have revealed the role of H. pylori in chronic gastritis. However, the alterations of non-H. pylori bacteria in patients with chronic gastritis remain an open question. The results of our study might provide new insights into future diagnosis and treatments. ETHICS AND DISSEMINATION: This study is based on published documents, unrelated to personal data, so ethical approval is not in need. The results of this study are expected to be published in journals or conference proceedings. PROSPERO REGISTRATION NUMBER: CRD42020205260; Pre-results.

Mitochondrial IRG1 traps MCL-1 to induce hepatocyte apoptosis and promote carcinogenesis.

Hepatocarcinogenesis is initiated by repeated hepatocyte death and liver damage, and the underlying mechanisms mediating cell death and the subsequent carcinogenesis remain to be fully investigated. Immunoresponsive gene 1 (IRG1) and its enzymatic metabolite itaconate are known to suppress inflammation in myeloid cells, and its expression in liver parenchymal hepatocytes is currently determined. However, the potential roles of IRG1 in hepatocarcinogenesis are still unknown. Here, using the diethylnitrosamine (DEN)-induced hepatocarcinogenesis mouse model, we found that IRG1 expression in hepatocytes was markedly induced upon DEN administration. The DEN-induced IRG1 was then determined to promote the intrinsic mitochondrial apoptosis of hepatocytes and liver damage, thus enhancing the subsequent hepatocarcinogenesis. Mechanistically, the mitochondrial IRG1 could associate and trap anti-apoptotic MCL-1 to inhibit the interaction between MCL-1 and pro-apoptotic Bim, thus promoting Bim activation and downstream Bax mitochondrial translocation, and then releasing cytochrome c and initiating apoptosis. Thus, the inducible mitochondrial IRG1 promotes hepatocyte apoptosis and the following hepatocarcinogenesis, which provides mechanistic insight and a potential target for preventing liver injury and HCC.

RNF19a inhibits antiviral immune response to RNA viruses through degradation of TBK1.

TANK-binding kinase 1 (TBK1) plays a pivotal role in antiviral innate immunity. TBK1 mediates the activation of interferon regulatory factor (IRF) 3, leading to the induction of type I IFNs (IFN-α/ß) and of NF-κB signal transduction following viral infections. TBK1 must be tightly regulated to effectively control viral infections and maintain immune homeostasis. Here, we found that E3 ubiquitin ligase RNF19a mediated K48-linked ubiquitination and proteasomal degradation of TBK1. Specifically, the silence of RNF19a enhanced the production of type I interferons and suppressed RNA viral replication. Our results uncover that RNF19a acts as a negative mediator in the RIG-I signaling pathway to attenuate antiviral immune responses and suggest RNF19a as a potential therapy target in clinical infectious and inflammatory diseases.

Early Warning of Infectious Diseases in Hospitals Based on Multi-Self-Regression Deep Neural Network.

Objective: Infectious diseases usually spread rapidly. This study aims to develop a model that can provide fine-grained early warnings of infectious diseases using real hospital data combined with disease transmission characteristics, weather, and other multi-source data. Methods: Based on daily data reported for infectious diseases collected from several large general hospitals in China between 2012 and 2020, seven common infectious diseases in medical institutions were screened and a multi self-regression deep (MSRD) neural network was constructed. Using a recurrent neural network as the basic structure, the model can effectively model the epidemiological trend of infectious diseases by considering the current influencing conditions while taking into account the historical development characteristics in time-series data. The fitting and prediction accuracy of the model were evaluated using mean absolute error (MAE) and root mean squared error. Results: The proposed approach is significantly better than the existing infectious disease dynamics model, susceptible-exposed-infected-removed (SEIR), as it addresses the concerns of difficult-to-obtain quantitative data such as latent population, overfitting of long time series, and considering only a single series of the number of sick people without considering the epidemiological characteristics of infectious diseases. We also compare certain machine learning methods in this study. Experimental results demonstrate that the proposed approach achieves an MAE of 0.6928 and 1.3782 for hand, foot, and mouth disease and influenza, respectively. Conclusion: The MRSD-based infectious disease prediction model proposed in this paper can provide daily and instantaneous updates and accurate predictions for epidemic trends.

Flexible Dual-Channel Digital Auscultation Patch With Active Noise Reduction for Bowel Sound Monitoring and Application.

Bowel sounds (BSs) have important clinical value in the auxiliary diagnosis of digestive diseases, but due to the inconvenience of long-term monitoring and too much interference from environmental noise, they have not been well studied. Most of the current electronic stethoscopes are hard and bulky without the function of noise reduction, and their application for long-term wearable monitoring of BS in noisy clinical environments is very limited. In this paper, a flexible dual-channel digital auscultation patch with active noise reduction is designed and developed, which is wireless, wearable, and conformably attached to abdominal skin to record BS more accurately. The ambient noise can be greatly reduced through active noise reduction based on the adaptive filter. At the same time, some nonstationary noises appearing intermittently (e.g., frictional noise) can also be removed from BS by the cross validation of multichannel simultaneous acquisition. Then, two kinds of typical BS signals are taken as examples, and the feature parameters of the BS in the time domain and frequency domain are extracted through the time-frequency analysis algorithm. Furthermore, based on the short-term energy ratio between the four channels of dual patches, the two-dimensional localization of BS on the abdomen mapping plane is realized. Finally, the continuous wearable monitoring of BS for patients with postoperative ileus (POI) in the noisy ward from pre-operation (POD0) to postoperative Day 7 (POD7) was carried out. The obtained change curve of the occurrence frequency of BS provides guidance for doctors to choose a reasonable feeding time for patients after surgery and accelerate their recovery. Therefore, flexible dual-channel digital auscultation patches with active noise reduction will have promising applications in the clinical auxiliary diagnosis of digestive diseases.