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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

JMJD4-demethylated RIG-I prevents hepatic steatosis and carcinogenesis.

BACKGROUND: Hepatocarcinogenesis is driven by necroinflammation or metabolic disorders, and the underlying mechanisms remain largely elusive. We previously found that retinoic acid-inducible gene-I (RIG-I), a sensor for recognizing RNA virus in innate immune cells, is mainly expressed by parenchymal hepatocytes in the liver. However, its roles in hepatocarcinogenesis are unknown, which is intensively investigated in this study. METHODS: DEN-induced necroinflammation-driven hepatocarcinogenesis and STAM NASH-hepatocarcinogenesis were carried out in hepatocyte-specific RIG-I knockout mice. The post-translational modification of RIG-I was determined by mass spectrometry, and specific antibodies against methylated lysine sites and the RIG-I lysine mutant mice were constructed to identify the functions of RIG-I methylation. RESULTS: We interestingly found that DEN-induced hepatocarcinogenesis was enhanced, while NASH-induced hepatocarcinogenesis was suppressed by hepatocyte-specific RIG-I deficiency. Further, IL-6 decreased RIG-I expression in HCC progenitor cells (HcPCs), which then viciously promoted IL-6 effector signaling and drove HcPCs to fully established HCC. RIG-I expression was increased by HFD, which then enhanced cholesterol synthesis and steatosis, and the in-turn NASH and NASH-induced hepatocarcinogenesis. Mechanistically, RIG-I was constitutively mono-methylated at K18 and K146, and demethylase JMJD4-mediated RIG-I demethylation suppressed IL-6-STAT3 signaling. The constitutive methylated RIG-I associated with AMPKα to inhibit HMGCR phosphorylation, thus promoting HMGCR enzymatic activity and cholesterol synthesis. Clinically, RIG-I was decreased in human hepatic precancerous dysplastic nodules while increased in NAFLD livers, which were in accordance with the data in mouse models. CONCLUSIONS: Decreased RIG-I in HcPCs promotes necroinflammation-induced hepatocarcinogenesis, while increased constitutive methylated RIG-I enhances steatosis and NASH-induced hepatocarcinogenesis. JMJD4-demethylated RIG-I prevents both necroinflammation and NASH-induced hepatocarcinogenesis, which provides mechanistic insight and potential target for preventing HCC.

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.

Peripancreatic vascular involvement in patients with type 1 autoimmune pancreatitis.

Background: Type 1 autoimmune pancreatitis (AIP) is the pancreatic manifestation of IgG4-related disease. However, this benign disease can result in the peripancreatic vascular involvement (PVI) on occasion, which increases the difficulty of diagnosis and treatment of this clinical entity as well as for differentiating it from pancreatic malignancies. Methods: We retrospectively reviewed the information on demographics, clinical presentation, laboratory, imaging and endoscopic findings of 101 hospitalized patients with type 1 AIP treated in our department. All the patients were divided into non-PVI and PVI groups according to the first hospitalized medical data. Univariate and multivariate analyses were performed to analyse the potential predictive parameter(s) of PVI in AIP patients. Results: Among the 101 type 1 AIP patients, 52 (51.5%) exhibited PVI, with a male/female ratio 5.5:1. Their average age was 58.37±8.68 years old. Univariate analysis revealed that the location of pancreatitis lesions, including the pancreatic tail (P=0.010), the presence of splenomegaly (P=0.001) and the white blood cell (WBC) number in peripheral blood (P=0.020), were significantly associated with PVI. The location of pancreatitis lesions, including the pancreatic tail (P=0.023), and the presence of splenomegaly (P=0.010) were found to be independent predictors of the development of PVI by a multivariable regression analysis. A total of 18 out of 25 patients in PVI group who underwent corticosteroid treatment and no less than 6 months radiological follow-up showed improvement in vascular lesions, and no case exhibited exacerbation of PVI lesions during follow-up. Of 36 patients in non-PVI group who were followed up for no less than 6 months, only one case exhibited PVI. Conclusions: This retrospective study demonstrated that type 1 AIP was associated with a high proportion of PVI. Pancreatic tail involvement and splenomegaly may predict the PVI in type 1 AIP. PVI lesions are reversible in a subset of patients.

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.

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.

Alterations in Gastric Mucosal Microbiota in Gastric Carcinogenesis: A Systematic Review and Meta-Analysis.

Background: The gastric microbiota profile alters during gastric carcinogenesis. We aimed to identify the alterations in the alpha diversity and relative abundance of bacterial phyla and genera of gastric microbiota in the development of gastric cancer (GC). Methods: The systematic review was performed based on a published protocol with the registration number CRD42020206973. We searched through PubMed, EMBASE and Cochrane databases, as well as conference proceedings and references of review articles (May 2021) for observational studies reporting either the relative abundance of bacterial phyla or genera, or alpha diversity indexes in both GC and non-cancer groups. Selection of studies and data extraction were performed independently by two researchers, with disagreements resolved through discussion. Risk of bias was assessed using the self-modified Newcastle-Ottawa Scale. Results of random-effects meta-analyses were presented as mean differences (MD). Results: Our systematic review included 751 GC patients and 792 non-cancer patients from 14 case-control studies. Gastric cancer group had fewer operational taxonomic units (OTUs) (MD = -68.52, 95%CI: -126.65 to -10.39) and a lower Simpson index (MD = -0.13, 95%CI: -0.20 to -0.07) compared with non-cancer group. At the phylum level, gastric cancer group had a higher abundance of Firmicutes (MD = 7.11, 95%CI: 1.76 to 12.46). At the genus level, Streptococcus (MD = 3.03, 95%CI: 0.07 to 6.00) and Lactobacillus (MD = 5.15, 95%CI: 1.27 to 9.04) were found to be enriched in GCgroup. The relative abundance of the rest bacterial phyla or genera analyzed in our study did not significantly differ between two groups. Subgroup analyses indicated that the source of samples was the major source of interstudy heterogeneity. Conclusion: This systematic review suggested that gastric microbiota dysbiosis occurred in gastric carcinogenesis, with alpha diversity declined and microbiota composition altered.

Educating Outpatients for Bowel Preparation Before Colonoscopy Using Conventional Methods vs Virtual Reality Videos Plus Conventional Methods: A Randomized Clinical Trial.

Importance: Adequate bowel preparation is essential for diagnostic, screening, and surveillance colonoscopy. Virtual reality (VR) has the characteristics of immersion, interaction, and imagination and has been widely used in medicine for training and teaching, indicating that it could be used in the education of outpatients for bowel preparation before colonoscopy. Objective: To investigate whether using VR videos for patient education before colonoscopy could improve bowel preparation. Design, Setting, and Participants: A prospective, single-blinded, randomized clinical trial of 346 patients undergoing colonoscopy with local anesthesia in a tertiary care hospital was conducted between October 1, 2018, and November 1, 2020. Outpatients who had indications for colonoscopy and had not received one before were enrolled. Statistical analysis was performed from November 1 to December 31, 2020. All data were analyzed according to the intention-to-treat approach. Exposures: Conventional bowel preparation education (oral instructions and written materials that had the same contents) or conventional education plus VR videos. Main Outcomes and Measures: The primary outcome was the quality of bowel preparation measured by the Boston Bowel Preparation Scale score (range, 0-9, where 0 indicates extremely unsatisfactory bowel preparation and 9 indicates complete bowel preparation). Secondary outcomes included polyp and adenoma detection rates, compliance with complete bowel cleansing, preprocedure anxiety, overall satisfaction, and willingness to undergo a follow-up colonoscopy. Results: A total of 346 outpatients were enrolled in the trial, with 173 patients randomly assigned to each group (control group: 87 women [50.3%]; mean [SD] age, 50.5 [12.5] years; VR video group: 84 women [48.6%]; mean [SD] age, 52.6 [11.4] years). Baseline characteristics, including demographic information, medical history, lifestyle, and the characteristics of stool, were comparable between the VR video group and the control group. The mean (SD) Boston Bowel Preparation Scale score was significantly higher in the VR video group than in the control group (7.61 [1.65] vs 7.04 [1.70]; P = .002). The detection rate of polyps (72 of 172 [41.9%] vs 46 of 172 [26.7%]; P = .003) and the detection rate of adenomas (56 of 172 [32.6%] vs 38 of 172 [22.1%]; P = .03) were also higher in the VR video group. Patients who received VR education had better compliance (119 [68.8%] vs 87 [50.3%]; P < .001) and higher mean (SD) overall satisfaction (8.68 [1.70] vs 8.16 [2.15]; P = .01) with bowel preparation. Conclusions and Relevance: Patients who received VR video education before colonoscopy had better bowel preparation, higher polyp and adenoma detection rates, and improved compliance and satisfaction. Trial Registration: ClinicalTrials.gov Identifier: NCT03667911.

Predicting Malignancy of Biliary Stricture with a Nomogram in Patients with a Non-Malignant Endoscopic Tissue Diagnosis: A Retrospective Study.

PURPOSE: The accurate differentiation between benign and malignant biliary stricture is significant but challenging. Tissue diagnosis of biliary stricture by endoscopy sampling can provide excellent specificity but insufficient sensitivity. For patients with suspected malignant biliary stricture (MBS) but non-malignant was reported in endoscopy tissue samples, we constructed a nomogram to predict malignancy and improve the overall diagnostic performance. PATIENTS AND METHODS: 232 patients with suspected MBS and underwent endoscopy tissue sampling from January 2017 to December 2019 were included, among which 123 patients' endoscopy tissue samples were classified as non-malignant (including atypical, negative for malignancy, and nondiagnostic). Demographics, serum markers, radiological and sampling results of these 123 patients were collected to construct a nomogram using multivariate analysis. RESULTS: The nomogram was developed based on bilirubin, CA19-9, radiological result, and atypical sampling results and provided an AUC of 0.863 (95% CI 0.795-0.930) for predicting MBS. The specificity, sensitivity, and accuracy of endoscopy tissue diagnosis were 100.00%, 59.90%, and 68.53%, respectively. With the nomogram added, the overall diagnosis specificity, sensitivity, and accuracy were 95.24%, 89.20%, and 90.23%, respectively. CONCLUSION: The nomogram can predict malignancy in patients whose endoscopy tissue diagnoses were non-malignant. The overall diagnostic performance was improved with the nomogram added.

Flexible Doppler ultrasound device for the monitoring of blood flow velocity.

Thrombosis and restenosis after vascular reconstruction procedures may cause complications such as stroke, but a clinical means to continuously monitor vascular conditions is lacking. Conventional ultrasound probes are rigid, particularly for postoperative patients with fragile skin. Techniques based on photoplethysmography or thermal analysis provide only relative changes in flow volume and have a shallow detection depth. Here, we introduce a flexible Doppler ultrasound device for the continuous monitoring of the absolute velocity of blood flow in deeply embedded arteries based on the Doppler effect. The device is thin (1 mm), lightweight (0.75 g), and skin conforming. When the dual-beam Doppler method is used, the influence of the Doppler angle on the velocity measurement is avoided. Experimental studies on ultrasound phantoms and human subjects demonstrate accurate measurement of the flow velocity. The wearable Doppler device has the potential to enhance the quality of care of patients after reconstruction surgery.