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. Participants were divided into two groups according to diabetes status (1060 diabetic patients and 2270 non-diabetic patients). Thereafter, they were divided into four groups according to diabetes status-specific cutoff values of fasting blood glucose (FBG) identified by restricted cubic spline. Short-term outcomes included in-hospital death and cardiac complications. Long-term outcomes were all-cause mortality and major adverse cardiovascular events (MACE). Inverse probability of treatment weighting (IPTW) was conducted to adjust for baseline differences among the groups, followed by a weighted Cox proportional hazards regression analysis to calculate hazard ratios and 95% confidence intervals for all-cause mortality associated with each FBG category. Subgroup analysis and sensitivity analysis were performed to test the robustness of our findings. RESULTS: During a median follow-up of 3.2 years, 837 patients died. There was a significant interaction between diabetes status and FBG levels for all-cause mortality during long-term follow-up (p-interaction < 0.001). Moreover, restricted cubic spline curves for the association between FBG and all-cause mortality followed a J shape in patients with diabetes and a non-linear in patients without diabetes. Kaplan-Meier analysis demonstrated greater survival in non-hyperglycemia patients compared to hyperglycemia patients for both diabetic and non-diabetic patients groups. Survival of hyperglycemia patients without diabetes greater than in hyperglycemia patients with diabetes. In the weighted Multivariable cox analysis, admission hyperglycemia predicted higher short and long-term mortality. Subgroup analysis and sensitivity analysis showed the robustness of the results. CONCLUSIONS: The inflection points of FBG level for poor prognosis were 5.60 mmol/L for patients without diabetes and 10.60 mmol/L for patients with diabetes. Admission hyperglycemia was identified as an independent predictor of worse short and long-term outcomes in AMI patients, with or without diabetes. These findings should be explored further.

Distribution and identification of Mycobacterium tuberculosis lineage in Kashgar prefecture.

OBJECTIVES: Kashgar prefecture is an important transportation and trade hub with a high incidence of tuberculosis. The following study analyzed the composition and differences in Mycobacterium tuberculosis (M.tb) lineage and specific tags to distinguish the lineage of the M.tb in Kashgar prefecture, thus providing a basis for the classification and diagnosis of tuberculosis in this area. METHODS: Whole-genome sequencing (WGS) of 161 M.tb clinical strains was performed. The phylogenetic tree was constructed using Maximum Likelihood (ML) based on single nucleotide polymorphisms (SNPs) and verified through principal component analysis (PCA). The composition structure of M.tb in different regions was analyzed by combining geographic information. RESULTS: M.tb clinical strains were composed of lineage 2 (73/161, 45.34%), lineage 3 (52/161, 32.30%) and lineage 4 (36/161, 22.36%). Moreover, the 3 lineages were subdivided into 11 sublineages, among which lineage 2 included lineage 2.2.2/Asia Ancestral 1 (9/73, 12.33%), lineage 2.2.1-Asia Ancestral 2 (9/73, 12.33%), lineage 2.2.1-Asia Ancestral 3 (18/73, 24.66%), and lineage 2.2.1-Modern Beijing (39/73, 53.42%). Lineage 3 included lineage 3.2 (14/52, 26.92%) and lineage 3.3 (38/52, 73.08%), while lineage 4 included lineage 4.1 (3/36, 8.33%), lineage 4.2 (2/36, 5.66%), lineage 4.4.2 (1/36, 2.78%), lineage 4.5 (28/36, 77.78%) and lineage 4.8 (2/36, 5.66%), all of which were consistent with the PCA results. One hundred thirty-six markers were proposed for discriminating known circulating strains. Reconstruction of a phylogenetic tree using the 136 SNPs resulted in a tree with the same number of delineated clades. Based on geographical location analysis, the composition of Lineage 2 in Kashgar prefecture (45.34%) was lower compared to other regions in China (54.35%-90.27%), while the composition of Lineage 3 (32.30%) was much higher than in other regions of China (0.92%-2.01%), but lower compared to the bordering Pakistan (70.40%). CONCLUSION: Three lineages were identified in M.tb clinical strains from Kashgar prefecture, with 136 branch-specific SNP. Kashgar borders with countries that have a high incidence of tuberculosis, such as Pakistan and India, which results in a large difference between the M.tb lineage and sublineage distribution in this region and other provinces of China.

A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning.

Urolithiasis is a common urological disease, and treatment strategy options vary between different stone types. However, accurate detection of stone composition can only be performed in vitro. The management of infection stones is particularly challenging with yet no effective approach to pre-operatively identify infection stones from non-infection stones. Therefore, we aimed to develop a radiomic model for preoperatively identifying infection stones with multicenter validation. In total, 1198 eligible patients with urolithiasis from three centers were divided into a training set, an internal validation set, and two external validation sets. Stone composition was determined by Fourier transform infrared spectroscopy. A total of 1316 radiomic features were extracted from the pre-treatment Computer Tomography images of each patient. Using the least absolute shrinkage and selection operator algorithm, we identified a radiomic signature that achieved favorable discrimination in the training set, which was confirmed in the validation sets. Moreover, we then developed a radiomic model incorporating the radiomic signature, urease-producing bacteria in urine, and urine pH based on multivariate logistic regression analysis. The nomogram showed favorable calibration and discrimination in the training and three validation sets (area under the curve [95% confidence interval], 0.898 [0.840-0.956], 0.832 [0.742-0.923], 0.825 [0.783-0.866], and 0.812 [0.710-0.914], respectively). Decision curve analysis demonstrated the clinical utility of the radiomic model. Thus, our proposed radiomic model can serve as a non-invasive tool to identify urinary infection stones in vivo, which may optimize disease management in urolithiasis and improve patient prognosis.

Deep learning assistance for tuberculosis diagnosis with chest radiography in low-resource settings.

Tuberculosis (TB) is a major health issue with high mortality rates worldwide. Recently, tremendous researches of artificial intelligence (AI) have been conducted targeting at TB to reduce the diagnostic burden. However, most researches are conducted in the developed urban areas. The feasibility of applying AI in low-resource settings remains unexplored. In this study, we apply an automated detection (AI) system to screen a large population in an underdeveloped area and evaluate feasibility and contribution of applying AI to help local radiologists detect and diagnose TB using chest X-ray (CXR) images. First, we divide image data into one training dataset including 2627 TB-positive cases and 7375 TB-negative cases and one testing dataset containing 276 TB-positive cases and 619 TB-negative cases, respectively. Next, in building AI system, the experiment includes image labeling and preprocessing, model training and testing. A segmentation model named TB-UNet is also built to detect diseased regions, which uses ResNeXt as the encoder of U-Net. We use AI-generated confidence score to predict the likelihood of each testing case being TB-positive. Then, we conduct two experiments to compare results between the AI system and radiologists with and without AI assistance. Study results show that AI system yields TB detection accuracy of 85%, which is much higher than detection accuracy of radiologists (62%) without AI assistance. In addition, with AI assistance, the TB diagnostic sensitivity of local radiologists is improved by 11.8%. Therefore, this study demonstrates that AI has great potential to help detection, prevention, and control of TB in low-resource settings, particularly in areas with more scant doctors and higher rates of the infected population.

Advances in Cardiac Computed Tomography Functional Imaging Technology.

Cardiovascular disease (CVD) is the leading cause of death among patients in China, and cardiac computed tomography (CT) is one of the most commonly used examination methods for CVD. Coronary artery CT angiography can be used for the morphologic evaluation of the coronary artery. At present, cardiac CT functional imaging has become an important direction of development of CT. At present, common CT functional imaging technologies include transluminal attenuation gradient, stress dynamic CT myocardial perfusion imaging, and CT-fractional flow reserve. These three imaging modes are introduced and analyzed in this review.

SERPINE2 rs16865421 polymorphism is associated with a lower risk of chronic obstructive pulmonary disease in the Uygur population: A case-control study.

BACKGROUND: The present study aimed to investigate the relationship between seven polymorphisms of the serine protease inhibitor-2 (SERPINE2) gene and the risk of chronic obstructive pulmonary disease (COPD) in the Uygur population via a case-control study. METHODS: In total, 440 Uygur patients with COPD were included in the patient group and 384 healthy individuals were recruited in the matched control group. Data on demographic variables, smoking status, occupational dust exposure history and living conditions were collected. Polymorphism analysis was performed for seven loci of the SERPINE2 gene by mass spectrometry. RESULTS: The genotype distribution of rs16865421 showed a significant difference between the patient and control groups (p < 0.05). Participants carrying the rs16865421-AG heterozygous mutant genotype had a lower risk of COPD compared to those with the rs16865421-A allele (odds ratio = 0.68, 95% confidence interval = 0.47-0.98, p = 0.041). However, no such association was found for rs1438831, rs6734100, rs6748795, rs7583463, rs840088 and rs975278. No significant interaction was observed between the genotypes and risk factors. CONCLUSIONS: Polymorphisms of rs16865421-AG carried by the Uygur population may be protective against COPD.

Analysis of the CYP2C19 genetic polymorphism in Han and Uyghur patients with cardiovascular and cerebrovascular diseases in the Kashi area of Xinjiang.

BACKGROUND: The aim of this study was to analyze the CYP2C19 genetic polymorphism among Han and Uyghur patients with cardiovascular and cerebrovascular diseases in the Kashi area of Xinjiang. MATERIAL/METHODS: We enrolled 1020 patients with cardiovascular and cerebrovascular diseases, including 220 Han subjects and 800 Uyghur subjects. We used the gene chip method to detect polymorphisms in CYP2C19. The allele frequencies of CYP2C19 and the metabolic phenotype frequencies were then compared between the 2 ethnic groups. RESULTS: The frequency of CYP2C19 *1 was 0.6454 in Han subjects and 0.7869 in Uyghur subjects, and the difference was statistically significant (P<0.05). The frequency of CYP2C19 *2 was 0.3273 in Han subjects and 0.1837 in Uyghur subjects (P<0.05). The frequency of the homozygous extensive metabolizer phenotype was 42.72% and 62.13% in Han and Uyghur subjects, respectively (P<0.01). The frequency of the heterozygous extensive metabolizer phenotype was 43.64% and 33.13% in Han and Uyghur subjects, respectively (P<0.01). The frequency of poor metabolizers in Han and Uyghur subjects was 13.64% and 4.76%, respectively (P<0.01). CONCLUSIONS: Among patients with cardiovascular and cerebrovascular diseases located in the Kashgar Prefecture of Xinjiang, there is a differential distribution of CYP2C19 genotypes between the Han and Uyghur populations. Uyghur patients showed higher frequencies of extensive metabolizer genotypes than Han patients, while Han patients showed higher frequencies of poor metabolizer genotypes than Uyghur patients.

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. MATERIALS AND METHODS: Axial T2 weighted image (T2WI) and contrast-enhanced T1weighted image (CE-T1WI) MRI sequences of 135 patients from two centers were collected as train/test sets. Volume of interest (VOI) was manually delineated by an experienced neuroradiologist, supervised by a senior. Feature selection analysis and the least absolute shrinkage and selection operator (LASSO) algorithm identified valuable features, and Shapley additive explanations (SHAP) evaluated their significance. Five machine-learning classifiers-extreme gradient boosting (XGBoost), Bernoulli naive Bayes (Bernoulli NB), Logistic Regression (LR), support vector machine (SVM), linear support vector machine (Linear SVC) classifiers were built based on T2WI (T2 model), CE-T1WI (T1 model), and T1 + T2WI (T1 + T2 model). A human expert diagnosis was developed and corrected by senior radiologists. External validation was performed at Sun Yat-Sen University Cancer Center. RESULTS: 31 valuable features were extracted from T2WI and CE-T1WI. XGBoost demonstrated the highest performance with an area under the curve (AUC) of 0.92 on the test set and maintained an AUC of 0.80 during external validation. For the T1 model, XGBoost achieved the highest AUC of 0.85 on the test set and the highest accuracy of 0.71 on the external validation set. In the T2 model, XGBoost achieved the highest AUC of 0.86 on the test set and the highest accuracy of 0.82 on the external validation set. The human expert diagnosis had an AUC of 0.66 on the test set and 0.69 on the external validation set. The integrated T1 + T2 model achieved an AUC of 0.92 on the test set, 0.80 on the external validation set, achieved the best performance. Overall, XGBoost consistently outperformed in different classification models. CONCLUSION: The combination of radiomics and machine learning on multiparametric MRI effectively distinguishes between MB and EM in childhood, surpassing human expert diagnosis in training and testing sets.

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 Mycobacterium tuberculosis (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. PBMCs were isolated, and the frequencies of CD3+, CD4+, and CD8+ T cells were determined via flow cytometry. Results: The frequency of CD4+ T cells and the CD4/CD8 ratio were significantly lower in the HIV-TB group than in the other three groups. In fever patients the frequency of CD4+ T cells and the CD4/CD8 ratio were significantly lower in the HIV-TB group than in the HIV group and the TB group. In patients who exhibited rapid weight loss there were no significant differences in the frequency of CD4+ T cells or the CD4/CD8 ratio between the groups. The results of treatment were compared in the HIV, TB, and HIV-TB groups after 7 days, and there were obvious improvements in the frequency of CD4+ T cells and the CD4/CD8 ratio. Conclusion: Clinical symptoms and the degree of immune injury can heighten suspicion for HIV-TB coinfection.

Key genes associated with non-alcoholic fatty liver disease and hepatocellular carcinoma with metabolic risk factors.

Background: Hepatocellular carcinoma (HCC) has become the world's primary cause of cancer death. Obesity, hyperglycemia, and dyslipidemia are all illnesses that are part of the metabolic syndrome. In recent years, this risk factor has become increasingly recognized as a contributing factor to HCC. Around the world, non-alcoholic fatty liver disease (NAFLD) is on the rise, especially in western countries. In the past, the exact pathogenesis of NAFLD that progressed to metabolic risk factors (MFRs)-associated HCC has not been fully understood. Methods: Two groups of the GEO dataset (including normal/NAFLD and HCC with MFRs) were used to analyze differential expression. Differentially expressed genes of HCC were verified by overlapping in TCGA. In addition, functional enrichment analysis, modular analysis, Receiver Operating Characteristic (ROC) analysis, LASSO analysis, and Genes with key survival characteristics were analyzed. Results: We identified six hub genes (FABP5, SCD, CCL20, AGPAT9(GPAT3), PLIN1, and IL1RN) that may be closely related to NAFLD and HCC with MFRs. We constructed survival and prognosis gene markers based on FABP5, CCL20, AGPAT9(GPAT3), PLIN1, and IL1RN.This gene signature has shown good diagnostic accuracy in both NAFLD and HCC and in predicting HCC overall survival rates. Conclusion: As a result of the findings of this study, there is some guiding significance for the diagnosis and treatment of liver disease associated with NAFLD progression.

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. Herein, the protective roles and mechanisms of Astaxanthin (ASTA), a natural compound, in inflammatory injured lung epithelial cells were investigated. Lipopolysaccharide (LPS) was used to establish inflammatory injury model in the murine lung epithelial (MLE)-12 cells. Cell counting kit-8 was used for screening of compound concentrations. Cell proliferation was observed real-time with a high content analysis system. Flow cytometry assessed apoptosis. The changes of apoptotic proteins and key proteins in nuclear factor kappa-B (NF-κB) pathway were measured with the western blot. LPS was used to establish an animal model of pulmonary injury. The pathological changes and degree of inflammatory injury in lung tissue were observed with hematoxylin and eosin (HE) staining. The levels of inflammatory mediators were detected with enzyme-linked immunosorbent assay. The results showed that ASTA reduced lung inflammation and attenuated inflammatory damage in lung tissues. ASTA reduced apoptosis stimulated by LPS through suppressing the NF-κB pathway in MLE-12 cells. We believe that ASTA may have great potential for protection against inflammatory damage to lung tissue.

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. An independent testing was performed on external dataset comprising 86 TB patients. The area under the curve for classifying success and drug-resistant (DR)-TB was improved on both internal (0.609 vs. 0.625 vs. 0.815) and external (0.627 vs. 0.705 vs. 0.735) dataset by adding follow-up scans. The accuracy and F1-score also showed an increasing tendency in the external test. Regular follow-up CT scans can aid in the treatment prediction, and special attention should be given to early intensive phase of treatment to identify high-risk DR-TB patients.

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.e., supervised deep-learning model, SDLM; weakly supervised deep-learning model, WSDLM) are developed independently. Prediction scores of RM and deep-learning models with respectively highest performance are fused to create new fusion models under different fusion strategies. An additional independent validation was conducted on the external cohort comprising 80 patients (160 scans). RESULTS: For RM scheme, 16 optimal radiomics features are finally selected using longitudinal scans. The AUCs of RM for Bagging, Random forest and Gradient boosting were 0.789, 0.773 and 0.764 in the internal cohort and 0.840, 0.834 and 0.816 in the external cohort, respectively. For deep learning-based scheme, AUCs of SDLM and WSDLM were 0.767 and 0.661 in the internal cohort, and 0.823 and 0.651 in the external. The fusion model yields AUCs from 0.767 to 0.802 in the internal cohort, and from 0.831 to 0.857 in the external cohort. CONCLUSIONS: Fusion of radiomics features and deep-learning model may have the potential to predict early failure outcome of DR-TB, which may be combined to help prevent poor TB treatment outcomes.

Short-Term Clinical Response and Changes in the Fecal Microbiota and Metabolite Levels in Patients with Crohn's Disease After Stem Cell Infusions.

Recent studies have shown a close relationship between the gut microbiota and Crohn's disease (CD). This study aimed to determine whether mesenchymal stem cell (MSC) treatment alters the gut microbiota and fecal metabolite pathways and to establish the relationship between the gut microbiota and fecal metabolites. Patients with refractory CD were enrolled and received 8 intravenous infusions of MSCs at a dose of 1.0 × 106 cells/kg. The MSC efficacy and safety were evaluated. Fecal samples were collected, and their microbiomes were analyzed by 16S rDNA sequencing. The fecal metabolites at baseline and after 4 and 8 MSC infusions were identified by liquid chromatography-mass spectrometry (LC--MS). A bioinformatics analysis was conducted using the sequencing data. No serious adverse effects were observed. The clinical symptoms and signs of patients with CD were substantially relieved after 8 MSC infusions, as revealed by changes in weight, the CD activity index (CDAI) score, C-reactive protein (CRP) level, and erythrocyte sedimentation rate (ESR). Endoscopic improvement was observed in 2 patients. A comparison of the gut microbiome after 8 MSC treatments with that at baseline showed that the genus Cetobacterium was significantly enriched. Linoleic acid was depleted after 8 MSC treatments. A possible link between the altered Cetobacterium abundance and linoleic acid metabolite levels was observed in patients with CD who received MSCs. This study enabled an understanding of both the gut microbiota response and bacterial metabolites to obtain more information about host-gut microbiota metabolic interactions in the short-term response to MSC treatment.

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. An exposome-wide association study (ExWAS) approach was used to estimate cross-sectional associations between environmental exposures and prevalent TB, an adaptive elastic net model (AENET) was implemented to select important exposure(s), and the Extreme Gradient Boosting algorithm was subsequently applied to assess their relative importance. In ExWAS analysis, 12 exposures were significantly associated with prevalent TB. Eight of the exposures were selected as predictors by the AENET model: particulate matter ≤ 2.5 µm (odds ratio [OR]=1.01, p = 0.3295), nitrogen dioxide (OR=1.09, p < 0.0001), carbon monoxide (OR=1.19, p < 0.0001), and wind speed (OR=1.08, p < 0.0001) were positively associated with the odds of prevalent TB while sulfur dioxide (OR=0.95, p = 0.0017), altitude (OR=0.97, p < 0.0001), artificial light at night (OR=0.98, p = 0.0001), and proportion of forests, shrublands, and grasslands (OR=0.95, p < 0.0001) were negatively associated with the odds of prevalent TB. Air pollutants had higher relative importance than meteorological and geographical factors, and the outdoor environment collectively explained 11% of TB prevalence.

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. EVs were isolated from BMSCs by differential centrifugation and identified by transmission electron microscopy, nanoparticle tracking analysis, and Western blot (WB). miR-206 and neurensin-2 (NRSN2) levels in human osteoblast hFOB 1.19 or OS cells (143B, MG-63, Saos2, HOS) were detected by RT-qPCR. Human OS cells with lower miR-206 levels were selected and treated with BMSCs-EVs or pSUPER-NRSN2. The uptake of EVs by 143B cells, cell proliferation, apoptosis, invasion, and migration were detected by immunofluorescence, 5-ethynyl-2'-deoxyuridine (EdU) and colony formation assays, flow cytometry, scratch test, and transwell assays. The binding sites between miR-206 and NRSN2 were predicted by Starbase database and verified by dual-luciferase assay. The OS xenograft model was established and treated by BMSCs-EVs. Tumor growth rate and volume, cell proliferation, and p-ERK1/2, ERK1/2, and Bcl-xL levels were detected by vernier caliper, immunohistochemistry, and WB. BMSCs-EVs were successfully extracted. miR-206 was diminished and NRSN2 was promoted in OS cells. BMSCs-EVs inhibited proliferation, migration, and invasion, and promoted apoptosis of OS cells. BMSCs-EVs carried miR-206 into OS cells. Inhibition of miR-206 in EVs partially reversed the inhibitory effect of EVs on malignant behaviors of OS cells. miR-206 targeted NRSN2. Overexpression of NRSN2 reversed the inhibitory effect of EVs on OS cells. NRSN2 activated the ERK1/2-Bcl-xL pathway. BMSC-EVs inhibited OS growth in vivo. In summary, BMSC-EVs targeted NRSN2 and inhibited the ERK1/2-Bcl-xL pathway by carrying miR-206 into OS cells, thus inhibiting OS progression.

Artificial Intelligence Assisting the Early Detection of Active Pulmonary Tuberculosis From Chest X-Rays: A Population-Based Study.

As a major infectious disease, tuberculosis (TB) still poses a threat to people's health in China. As a triage test for TB, reading chest radiography with traditional approach ends up with high inter-radiologist and intra-radiologist variability, moderate specificity and a waste of time and medical resources. Thus, this study established a deep convolutional neural network (DCNN) based artificial intelligence (AI) algorithm, aiming at diagnosing TB on posteroanterior chest X-ray photographs in an effective and accurate way. Altogether, 5,000 patients with TB and 4,628 patients without TB were included in the study, totaling to 9,628 chest X-ray photographs analyzed. Splitting the radiographs into a training set (80.4%) and a testing set (19.6%), three different DCNN algorithms, including ResNet, VGG, and AlexNet, were trained to classify the chest radiographs as images of pulmonary TB or without TB. Both the diagnostic accuracy and the area under the receiver operating characteristic curve were used to evaluate the performance of the three AI diagnosis models. Reaching an accuracy of 96.73% and marking the precise TB regions on the radiographs, ResNet algorithm-based AI outperformed the rest models and showed excellent diagnostic ability in different clinical subgroups in the stratification analysis. In summary, the ResNet algorithm-based AI diagnosis system provided accurate TB diagnosis, which could have broad prospects in clinical application for TB diagnosis, especially in poor regions with high TB incidence.

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. Pre-processing of clinical records with "jieba" word segmentation module, and the Bidirectional Encoder Representation from Transformers (BERT) model was used to perform word vectorization on the text. The partial and total information of the fused feature set was encoded by convolutional layers, while LSTM layers decoded the encoded information. Results: The precisions of traditional machine-learning, deep-learning methods and our proposed method were 0.6, 0.81, 0.89, and F1 scores were 0.6, 0.81, 0.88, respectively. Conclusion: Compared with traditional machine learning and deep-learning methods that our proposed method had a significantly higher performance, and provided precise identification of respiratory disease.

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

Background: 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). Methods: A novel symmetric Transformer network (Sym-TransNet) is designed to segment ICH lesions in CT images. A cohort of 1,157 patients diagnosed with ICH is established to train (n = 857), validate (n = 100), and test (n = 200) the Sym-TransNet. A healthy cohort of 200 subjects is added, establishing a test set with balanced positive and negative cases (n = 400), to further evaluate the accuracy, sensitivity, and specificity of the diagnosis of ICH. The segmentation results are obtained after data pre-processing and Sym-TransNet. The DICE coefficient is used to evaluate the similarity between the segmentation results and the segmentation gold standard. Furthermore, some recent deep learning methods are reproduced to compare with Sym-TransNet, and statistical analysis is performed to prove the statistical significance of the proposed method. Ablation experiments are conducted to prove that each component in Sym-TransNet could effectively improve the DICE coefficient of ICH lesions. Results: For the segmentation of ICH lesions, the DICE coefficient of Sym-TransNet is 0.716 ± 0.031 in the test set which contains 200 CT images of ICH. The DICE coefficients of five subtypes of ICH, including intraparenchymal hemorrhage (IPH), intraventricular hemorrhage (IVH), extradural hemorrhage (EDH), subdural hemorrhage (SDH), and subarachnoid hemorrhage (SAH), are 0.784 ± 0.039, 0.680 ± 0.049, 0.359 ± 0.186, 0.534 ± 0.455, and 0.337 ± 0.044, respectively. Statistical results show that the proposed Sym-TransNet can significantly improve the DICE coefficient of ICH lesions in most cases. In addition, the accuracy, sensitivity, and specificity of Sym-TransNet in the diagnosis of ICH in 400 CT images are 91.25%, 98.50%, and 84.00%, respectively. Conclusion: Compared with recent mainstream deep learning methods, the proposed Sym-TransNet can segment and identify different types of lesions from CT images of ICH patients more effectively. Moreover, the Sym-TransNet can diagnose ICH more stably and efficiently, which has clinical application prospects.