Ferroptosis: A new way to intervene in the game between Mycobacterium tuberculosis and macrophages.

Mycobacterium tuberculosis (Mtb), the main pathogen responsible for the high mortality and morbidity of tuberculosis (TB) worldwide, primarily targets and invades macrophages. Infected macrophages activate a series of immune mechanisms to clear Mtb, however, Mtb evades host immune surveillance through subtle immune escape strategies to create a microenvironment conducive to its own proliferation, growth, and dissemination, while inducing immune cell death. The course of TB is strongly correlated with the form of cell death, including apoptosis, pyroptosis, and necrosis.

Construction and validation of a prognostic model for esophageal cancer based on prognostic-related RNA-binding protein.

Background: Construction of a prognostic model for esophageal cancer (ESCA) based on prognostic RNA-binding proteins (RBPs) and preliminary evaluation of RBP function.

Methods: RNA-seq data of ESCA was downloaded from The Cancer Genome Atlas database and mRNA was extracted to screen differentially expressed genes using R. After screening RBPs in differentially expressed genes, R packages clusterProfiler and pathview were used to analyze the RBPs for Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway.

An abnormal metabolism-related gene, ALG3, is a potential diagnostic and prognostic biomarker for lung adenocarcinoma.

Background: To explore the abnormal metabolism-related genes that affect the prognosis of patients with lung adenocarcinoma (LUAD), and analyze the relationship with immune infiltration and competing endogenous RNA (ceRNA) network.

Methods: Transcriptome data of LUAD were downloaded from the Cancer Genome Atlas database. Abnormal metabolism-related differentially expressed genes in LUAD were screened by the R language.

Predicting hospitalization costs for pulmonary tuberculosis patients based on machine learning.

Background: Pulmonary tuberculosis (PTB) is a prevalent chronic disease associated with a significant economic burden on patients. Using machine learning to predict hospitalization costs can allocate medical resources effectively and optimize the cost structure rationally, so as to control the hospitalization costs of patients better.

Methods: This research analyzed data (2020-2022) from a Kashgar pulmonary hospital's information system, involving 9570 eligible PTB patients.

A deep learning radiomics model based on CT images for predicting the biological activity of hepatic cystic echinococcosis.

Hepatic cystic echinococcosis (HCE) is a widely seen parasitic infection. Biological activity is crucial for treatment planning. This work aims to explore the potential applications of a deep learning radiomics (DLR) model, based on CT images, in predicting the biological activity grading of hepatic cystic echinococcosis.

Lipopolysaccharide-induced bacterial infection model: microRNA-370-3p participates in the anti-infection response by targeting the macrophage TLR4-NLRP3 caspase-1 cellular pyroptosis pathway.

Objective: To explore the effect of miR-370-3p on LPS triggering, in particular its involvement in disease progression by targeting the TLR4-NLRP3-caspase-1 cellular pyroptosis pathway in macrophages.

Methods: Human macrophage RAW264.7 was divided into 6 groups: control, LPS, LPS + inhibitor-NC, LPS + miR-370-3p inhibitor, LPS + mimics-NC and LPS + miR-370-3p mimics.

Credibility of the evidence on green space and human health: an overview of meta-analyses using evidence grading approaches.

Background: Green space is an important part of the human living environment, with many epidemiological studies estimating its impact on human health. However, no study has quantitatively assessed the credibility of the existing evidence, impeding their translations into policy decisions and hindering researchers from identifying new research gaps. This overview aims to evaluate and rank such evidence credibility.

Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study.

Rationale And Objectives: Medulloblastoma (MB) and Ependymoma (EM) in children, share similarities in age group, tumor location, and clinical presentation. Distinguishing between them through clinical diagnosis is challenging. This study aims to explore the effectiveness of using radiomics and machine learning on multiparametric magnetic resonance imaging (MRI) to differentiate between MB and EM and validate its diagnostic ability with an external set.

Deep learning on longitudinal CT scans: automated prediction of treatment outcomes in hospitalized tuberculosis patients.

Three deep learning (DL)-based prediction models (PMs) using longitudinal CT images were developed to predict tuberculosis (TB) treatment outcomes. The internal dataset consists of 493 bacteriologically confirmed TB patients who completed the anti-tuberculosis treatment with three-time CT scans, including a pretreatment CT scan and two follow-up CT scans. PM1 was trained using only pretreatment CT scans, and PM2 and PM3 were developed by adding follow-up scans.

Deep learning and radiomics of longitudinal CT scans for early prediction of tuberculosis treatment outcomes.

Background: To predict tuberculosis (TB) treatment outcomes at an early stage, prevent poor outcomes ofdrug-resistant tuberculosis(DR-TB) and interrupt transmission.

Methods: An internal cohort for model development consists of 204 bacteriologically-confirmed TB patients who completed anti-tuberculosis treatment, with one pretreatment and two follow-up CT images (612 scans). Three radiomics feature-based models (RM) with multiple classifiers of Bagging, Random forest and Gradient boosting and two deep-learning-based models (i.

Outdoor environmental exposome and the burden of tuberculosis: Findings from nearly two million adults in northwestern China.

We simultaneously assessed the associations for a range of outdoor environmental exposures with prevalent tuberculosis (TB) cases in a population-based health program with 1940,622 participants ≥ 15 years of age. TB status was confirmed through bacteriological and clinical assessment. We measured 14 outdoor environmental exposures at residential addresses.

Clinical symptoms and immune injury reflected by low CD4/CD8 ratio should increase the suspicion of HIV coinfection with tuberculosis.

Background: Patients who are coinfected with human immunodeficiency virus 1 (HIV) and (TB) benefit from timely diagnosis and treatment. In the present study frequencies of CD3, CD4, and CD8 T cells among peripheral blood mononuclear cells (PBMCs) of patients in the Kashi region of China infected with HIV, TB, and both HIV and TB (HIV-TB) were investigated to provide a basis for rapid identification of coinfected patients.

Methods: A total of 62 patients with HIV, TB, or HIV-TB who were first hospitalized at our institution were included in the study, as were 30 controls.

Protective effect of astaxanthin on tuberculosis-associated inflammatory lung injury.

Mycobacterium tuberculosis (MTB) invades the lungs and is the key cause of tuberculosis (TB). MTB induces immune overreaction and inflammatory damage to lung tissue. There is a lack of protective drugs against pulmonary inflammatory damage.

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.

Astaxanthin Prevents Tuberculosis-Associated Inflammatory Injury by Inhibiting the Caspase 4/11-Gasdermin-Pyroptosis Pathway.

Pyroptosis is a programmed cell death caused by inflammation. Multiple studies have suggested that Mycobacterium tuberculosis infection causes tissue pyroptosis. However, there are currently no protective drugs against the inflammatory damage caused by pyroptosis.

A symmetric prior knowledge based deep learning model for intracerebral hemorrhage lesion segmentation.

Accurate localization and classification of intracerebral hemorrhage (ICH) lesions are of great significance for the treatment and prognosis of patients with ICH. The purpose of this study is to develop a symmetric prior knowledge based deep learning model to segment ICH lesions in computed tomography (CT). A novel symmetric Transformer network (Sym-TransNet) is designed to segment ICH lesions in CT images.

Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS).

Background: Identifying factors associated with cardiovascular disease (CVD) is critical for its prevention, but this topic is scarcely investigated in Kashgar prefecture, Xinjiang, northwestern China. We thus explored the CVD epidemiology and identified prominent factors associated with CVD in this region.

Methods: A total of 1,887,710 adults at baseline (in 2017) of the Kashgar Prospective Cohort Study were included in the analysis.

Human umbilical cord-derived mesenchymal stem cells ameliorate experimental colitis by normalizing the gut microbiota.

Background: Crohn's disease (CD) is a chronic non-specific inflammatory bowel disease. Current CD therapeutics cannot fundamentally change the natural course of CD. Therefore, it is of great significance to find new treatment strategies for CD.

Segmentation only uses sparse annotations: Unified weakly and semi-supervised learning in medical images.

Since segmentation labeling is usually time-consuming and annotating medical images requires professional expertise, it is laborious to obtain a large-scale, high-quality annotated segmentation dataset. We propose a novel weakly- and semi-supervised framework named SOUSA (Segmentation Only Uses Sparse Annotations), aiming at learning from a small set of sparse annotated data and a large amount of unlabeled data. The proposed framework contains a teacher model and a student model.

Short and long-term prognosis of admission hyperglycemia in patients with and without diabetes after acute myocardial infarction: a retrospective cohort study.

Objective: Admission hyperglycemia is associated with poor prognosis in patients with acute myocardial infarction (AMI), but the effects of baseline diabetes status on this association remain elusive. We aim to investigate the impact of admission hyperglycemia on short and long-term outcomes in diabetic and non-diabetic AMI patients.

Methods: In this retrospective cohort study, 3330 patients with regard to first-time AMI between July 2012 and July 2020 were identified.

Extracellular vesicles from bone mesenchymal stem cells transport microRNA-206 into osteosarcoma cells and target NRSN2 to block the ERK1/2-Bcl-xL signaling pathway.

Osteosarcoma (OS) is a kind of malignant tumor originating from mesenchymal tissue Bone mesenchymal stem cells-derived extracellular vesicles (BMSCs-EVs) can play important roles in OS. This study investigated the mechanism of BMSCs-EVs on OS. BMSC surface antigens and adipogenic and osteogenic differentiation were detected by flow cytometry, and oil red O and alizarin red staining.

Prediction and Diagnosis of Respiratory Disease by Combining Convolutional Neural Network and Bi-directional Long Short-Term Memory Methods.

Objective: Based on the respiratory disease big data platform in southern Xinjiang, we established a model that predicted and diagnosed chronic obstructive pulmonary disease, bronchiectasis, pulmonary embolism and pulmonary tuberculosis, and provided assistance for primary physicians.

Methods: The method combined convolutional neural network (CNN) and long-short-term memory network (LSTM) for prediction and diagnosis of respiratory diseases. We collected the medical records of inpatients in the respiratory department, including: chief complaint, history of present illness, and chest computed tomography.

Bioartificial livers: a review of their design and manufacture.

Acute liver failure (ALF) is a rapidly progressive disease with high morbidity and mortality rates. Liver transplantation and artificial liver (AL) support systems, such as ALs and bioartificial livers (BALs), are the two major therapies for ALF. Compared to ALs, BALs are composed of functional hepatocytes that provide essential liver functions, including detoxification, metabolite synthesis, and biotransformation.