The invisible threat: A 30-year review of air pollution's impact on diarrhoea from the global burden of disease study 2021.

Background: Diarrhoea remains a major contributor to global morbidity and mortality among children younger than 5 years. This study aims to provide an updated assessment of diarrhoea deaths and disability-adjusted life years (DALYs) attributable to air pollution from 1990 to 2021.

Methods: The deaths and DALYs data for diarrhoea attributable to air pollution were derived from the 2021 Global Burden of Diseases Study (GBD).

The role of canopy family proteins: biological mechanism and disease function.

Canopy family proteins are highly sequence-conserved proteins with an N-terminal hydrophobic signal sequence, a unique pattern of six cysteine residues characteristic of the saposin-like proteins, and a C-terminal putative endoplasmic reticulum retention signal sequence. At present, the known canopy family proteins are canopy fibroblast growth factor signaling regulator 1 (CNPY1), CNPY2, CNPY3, and CNPY4. Despite similar structures, canopy family proteins regulate complex signal networks to participate in various biological processes.

Automated machine learning models for nonalcoholic fatty liver disease assessed by controlled attenuation parameter from the NHANES 2017-2020.

Background: Nonalcoholic fatty liver disease (NAFLD) is recognized as one of the most common chronic liver diseases worldwide. This study aims to assess the efficacy of automated machine learning (AutoML) in the identification of NAFLD using a population-based cross-sectional database.

Methods: All data, including laboratory examinations, anthropometric measurements, and demographic variables, were obtained from the National Health and Nutrition Examination Survey (NHANES).

Early Drain Removal Versus Routine Drain Removal After Pancreaticoduodenectomy and/or Distal Pancreatectomy: A Meta-Analysis and Systematic Review.

Background: Early drain removal (EDR) has been widely accepted, but not been routinely used in patients after pancreaticoduodenectomy (PD) and distal pancreatectomy (DP). This study aimed to evaluate the safety and benefits of EDR versus routine drain removal (RDR) after PD or DP.

Methods: A systematic search was conducted on medical search engines from January 1, 2008 to November 1, 2023, for articles that compared EDR versus RDR after PD or DP.

The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction.

Background: Lymph node metastasis (LNM) is considered an essential prognosis factor for adenocarcinoma of the esophagogastric junction (AEG), which also affects the treatment strategies of AEG. We aimed to evaluate automated machine learning (AutoML) algorithms for predicting LNM in Siewert type II T1 AEG.

Methods: A total of 878 patients with Siewert type II T1 AEG were selected from the Surveillance, Epidemiology, and End Results (SEER) database to develop the LNM predictive models.

Association of the fat mass index with hepatic steatosis and fibrosis: evidence from NHANES 2017-2018.

Limited population-based studies discuss the association between fat mass index (FMI) and the risk of liver diseases. This investigation utilized data from the National Health and Nutrition Examination Survey (NHANES) to examine the linkage between the FMI and liver conditions, specifically steatosis and fibrosis. The study leveraged data from NHANES's 2017-2018 cross-sectional study, employing an oversampling technique to deal with sample imbalance.

Development and Validation of Multimodal Models to Predict the 30-Day Mortality of ICU Patients Based on Clinical Parameters and Chest X-Rays.

We aimed to develop and validate multimodal ICU patient prognosis models that combine clinical parameters data and chest X-ray (CXR) images. A total of 3798 subjects with clinical parameters and CXR images were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and an external hospital (the test set). The primary outcome was 30-day mortality after ICU admission.

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images.

Background: Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) to develop a prediction model based on CT characteristics for the identification of asymptomatic carriers.

Methods: Asymptomatic carriers were from Yangzhou Third People's Hospital from August 1st, 2020, to March 31st, 2021, and the control group included a healthy population from a nonepizootic area with two negative RT‒PCR results within 48 h.

Few-shot learning for the classification of intestinal tuberculosis and Crohn's disease on endoscopic images: A novel learn-to-learn framework.

Background And Aim: Standard deep learning methods have been found inadequate in distinguishing between intestinal tuberculosis (ITB) and Crohn's disease (CD), a shortcoming largely attributed to the scarcity of available samples. In light of this limitation, our objective is to develop an innovative few-shot learning (FSL) system, specifically tailored for the efficient categorization and differential diagnosis of CD and ITB, using endoscopic image data with minimal sample requirements.

Methods: A total of 122 white-light endoscopic images (99 CD images and 23 ITB images) were collected (one ileum image from each patient).

Development and validation of a multimodal model in predicting severe acute pancreatitis based on radiomics and deep learning.

Objective: Aim to establish a multimodal model for predicting severe acute pancreatitis (SAP) using machine learning (ML) and deep learning (DL).

Methods: In this multicentre retrospective study, patients diagnosed with acute pancreatitis at admission were enrolled from January 2017 to December 2021. Clinical information within 24 h and CT scans within 72 h of admission were collected.

Automated machine learning for early prediction of acute kidney injury in acute pancreatitis.

Background: Acute kidney injury (AKI) represents a frequent and grave complication associated with acute pancreatitis (AP), substantially elevating both mortality rates and the financial burden of hospitalization. The aim of our study is to construct a predictive model utilizing automated machine learning (AutoML) algorithms for the early prediction of AKI in patients with AP.

Methods: We retrospectively analyzed patients who were diagnosed with AP in our hospital from January 2017 to December 2021.

CT-based radiomics nomogram for overall survival prediction in patients with cervical cancer treated with concurrent chemoradiotherapy.

Background And Purpose: To establish and validate a hybrid radiomics model to predict overall survival in cervical cancer patients receiving concurrent chemoradiotherapy (CCRT).

Methods: We retrospectively collected 367 cervical cancer patients receiving chemoradiotherapy from the First Affiliated Hospital of Soochow University in China and divided them into a training set and a test set in a ratio of 7:3. Handcrafted and deep learning (DL)-based radiomics features were extracted from the contrast-enhanced computed tomography (CT), and the two types of radiomics signatures were calculated based on the features selected using the least absolute shrinkage and selection operator (LASSO) Cox regression.

Deep learning to predict esophageal variceal bleeding based on endoscopic images.

Objective: Esophageal varix (EV) bleeding is a particularly serious complications of cirrhosis. Prediction of EV bleeding requires extensive endoscopy experience; it remains unreliable and inefficient. This retrospective cohort study evaluated the feasibility of using deep learning (DL) to predict the 12-month risk of EV bleeding based on endoscopic images.

Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review.

With the advances in endoscopic technologies and artificial intelligence, a large number of endoscopic imaging datasets have been made public to researchers around the world. This study aims to review and introduce these datasets. An extensive literature search was conducted to identify appropriate datasets in PubMed, and other targeted searches were conducted in GitHub, Kaggle, and Simula to identify datasets directly.

A nomogram based on platelet-to-lymphocyte ratio for predicting lymph node metastasis in patients with early gastric cancer.

Background: Preoperative assessment of the presence of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) remains difficult. We aimed to develop a practical prediction model based on preoperative pathological data and inflammatory or nutrition-related indicators.

Methods: This study retrospectively analyzed the clinicopathological characteristics of 1,061 patients with EGC who were randomly divided into the training set and validation set at a ratio of 7:3.

Application Value of the Automated Machine Learning Model Based on Modified Computed Tomography Severity Index Combined With Serological Indicators in the Early Prediction of Severe Acute Pancreatitis.

Background And Aims: Machine learning (ML) algorithms are widely applied in building models of medicine due to their powerful studying and generalizing ability. To assess the value of the Modified Computed Tomography Severity Index (MCTSI) combined with serological indicators for early prediction of severe acute pancreatitis (SAP) by automated ML (AutoML).

Patients And Methods: The clinical data, of the patients with acute pancreatitis (AP) hospitalized in Hospital 1 and hospital 2 from January 2017 to December 2021, were retrospectively analyzed.

Development of a scoring system for predicting primary resistance to venetoclax plus hypomethylating agents (HMAs) in acute myeloid leukemia patients.

In recent years, one of the most promising advances in the treatment of acute myeloid leukemia (AML) is the combination of a hypomethylating agent (HMA) with the BCL2 inhibitor venetoclax (VEN). To better understand the key factors associated with the response of VEN plus HMA, 212 consecutive AML patients were retrospectively recruited to establish and validate a scoring system for predicting the primary resistance to VEN-based induced therapy. All AML patients were divided randomly into a training set (n = 155) and a validation set (n = 57).

Identification of gastric signet ring cell carcinoma based on endoscopic images using few-shot learning.

Background: Deep learning (DL) models perform poorly when there are limited gastric signet ring cell carcinoma (SRCC) samples. Few-shot learning (FSL) can address the small sample problem.

Methods: EfficientNetV2-S was first pretrained on ImageNet and then pretrained on esophageal endoscopic images (i.

[Application of machine learning model based on XGBoost algorithm in early prediction of patients with acute severe pancreatitis].

Objective: To establish a machine learning model based on extreme gradient boosting (XGBoost) algorithm for early prediction of severe acute pancreatitis (SAP), and explore its predictive efficiency.

Methods: A retrospective cohort study was conducted. The patients with acute pancreatitis (AP) who admitted to the First Affiliated Hospital of Soochow University, the Second Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University from January 1, 2020 to December 31, 2021 were enrolled.

Predicting the Recurrence of Common Bile Duct Stones After ERCP Treatment with Automated Machine Learning Algorithms.

Background: Recurrence of common bile duct stones (CBDs) commonly happens after endoscopic retrograde cholangiopancreatography (ERCP). The clinical prediction models for the recurrence of CBDs after ERCP are lacking.

Aims: We aim to develop high-performance prediction models for the recurrence of CBDS after ERCP treatment using automated machine learning (AutoML) and to assess the AutoML models versus the traditional regression models.

Deep learning for pancreatic diseases based on endoscopic ultrasound: A systematic review.

Background And Aims: Endoscopic ultrasonography (EUS) is one of the main examinations in pancreatic diseases. A series of the studies reported the application of deep learning (DL)-assisted EUS in the diagnosis of pancreatic diseases. This systematic review is to evaluate the role of DL algorithms in assisting EUS diagnosis of pancreatic diseases.

Intestinal Barrier Function in the Pathogenesis of Nonalcoholic Fatty Liver Disease.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide. The mechanisms involved in NAFLD onset are complicated and multifactorial. Recent literature has indicated that altered intestinal barrier function is related to the occurrence and progression of liver disease.

Identification of Asymptomatic COVID-19 Patients on Chest CT Images Using Transformer-Based or Convolutional Neural Network-Based Deep Learning Models.

Novel coronavirus disease 2019 (COVID-19) has rapidly spread throughout the world; however, it is difficult for clinicians to make early diagnoses. This study is to evaluate the feasibility of using deep learning (DL) models to identify asymptomatic COVID-19 patients based on chest CT images. In this retrospective study, six DL models (Xception, NASNet, ResNet, EfficientNet, ViT, and Swin), based on convolutional neural networks (CNNs) or transformer architectures, were trained to identify asymptomatic patients with COVID-19 on chest CT images.

Automated Machine Learning in Predicting 30-Day Mortality in Patients with Non-Cholestatic Cirrhosis.

Objective: To evaluate the feasibility of automated machine learning (AutoML) in predicting 30-day mortality in non-cholestatic cirrhosis.

Methods: A total of 932 cirrhotic patients were included from the First Affiliated Hospital of Soochow University between 2014 and 2020. Participants were divided into training and validation datasets at a ratio of 8.