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. Recent studies have revealed that ferroptosis, a novel type of programmed cell death characterized by iron-dependent lipid peroxidation, is closely linked to the regulatory mechanisms of TB. The central role of ferroptosis in the pathologic process of TB is increasingly becoming a focal point for exploring new therapeutic targets in this field. This paper will delve into the dynamic game between Mtb and host immune cells, especially the role of ferroptosis in the pathogenesis of TB. At the same time, this paper will analyze the regulatory pathways of ferroptosis and provide unique insights and innovative perspectives for TB therapeutic strategies based on the ferroptosis mechanism. This study not only expands the theoretical basis of TB treatment, but also points out the direction of future drug development, providing new possibilities for overcoming this global health problem.

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. SPSS 26.0 was used for multiple regression analysis, while Python 3.7 was used for random forest regression (RFR) and MLP. The training set included data from 2020 and 2021, while the test set included data from 2022. The models predicted seven various costs related to PTB patients, including diagnostic cost, medical service cost, material cost, treatment cost, drug cost, other cost, and total hospitalization cost. The model's predictive performance was evaluated using R-square (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) metrics. RESULTS: Among the 9570 PTB patients included in the study, the median and quartile of total hospitalization cost were 13,150.45 (9891.34, 19,648.48) yuan. Nine factors, including age, marital status, admission condition, length of hospital stay, initial treatment, presence of other diseases, transfer, drug resistance, and admission department, significantly influenced hospitalization costs for PTB patients. Overall, MLP demonstrated superior performance in most cost predictions, outperforming RFR and multiple regression; The performance of RFR is between MLP and multiple regression; The predictive performance of multiple regression is the lowest, but it shows the best results for Other costs. CONCLUSION: The MLP can effectively leverage patient information and accurately predict various hospitalization costs, achieving a rationalized structure of hospitalization costs by adjusting higher-cost inpatient items and balancing different cost categories. The insights of this predictive model also hold relevance for research in other medical conditions.

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.

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. Based on the prognosis-related RBPs, COX regression was used to establish the prognostic risk model of ESCA. Risk model predictive ability was assessed using calibration analysis, receiver operating characteristic curves, Kaplan-Meier curves, decision curve analysis, and Harrell consistency index (C-index). A nomogram was established by combining the risk model with clinicopathological features. RESULTS: A total of 105 RBPs were screened from ESCA. A prognostic risk model consisting of 6 prognostic RBPs (ARHGEF28, BOLL, CIRBP, DKC1, SNRPB, and TRIT1) was constructed by COX regression analysis. The prognosis was worse in the high-risk group, and the receiver operating characteristic curve showed (area under the curve = 0.90) that the model better predicted patients' 5-year survival. In addition, 6 prognostic RBPs had good diagnostic power for ESCA. In addition, a total of 39 mRNAs were identified as predicted target molecules for DKC1. CONCLUSION: ARHGEF28, BOLL, CIRBP, DKC1, SNRPB, and TRIT1, as RBPs, are associated with the prognosis of ESCA, which may provide new ideas for targeted therapy of ESCA.

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. RT-qPCR was used to detect the expression level of miR-370-3p and analyzed comparatively. CCK-8 and flow cytometry assays were used to detect cell viability and apoptosis. ELISA assay was used to detect the levels of IL-1ß and TNF-α in the supernatant of the cells. The WB assay was used to detect TLR4, NLRP3, Caspase-1 and GSDMD levels. RESULTS: After LPS induction, macrophage miR-370-3p levels decreased, cell viability decreased, and apoptosis increased. At the same time, the levels of TLR4, NLRP3, Caspase-1 and GSDMD increased in the cells, and the levels of IL-1ß and TNF-α increased in the cell supernatant. Compared with the LPS group, the significantly higher expression level of miR-370-3p in the cells of the LPS + miR-370-3p mimics group was accompanied by significantly higher cell viability, significantly lower apoptosis rate, significantly lower levels of TLR4, NLRP3, Caspase-1, and GSDMD in the cells, and significantly lower levels of IL-1ß and TNF-α in the cell supernatant. CONCLUSION: MiR-370-3p may be involved in anti-infective immune responses by targeting and inhibiting the macrophage TLR4-NLRP3-caspase-1 cellular pyroptosis pathway.

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

Introduction: 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. Methods: A retrospective analysis of 160 patients with hepatic echinococcosis was performed (127 and 33 in training and validation sets). Volume of interests (VOIs) were drawn, and radiomics features and deep neural network features were extracted. Feature selection was performed on the training set, and radiomics score (Rad Score) and deep learning score (Deep Score) were calculated. Seven diagnostics models (based on logistic regression algorithm) for the biological activity grading were constructed using the selected radiomics features and two deep model features respectively. All models were evaluated using the receiver operating characteristic curve, and the area under the curve (AUC) was calculated. A nomogram was constructed using the combined model, and its calibration, discriminatory ability, and clinical utility were assessed. Results: 12, 6 and 10 optimal radiomics features, deep learning features were selected from two deep learning network (DLN) features, respectively. For biological activity grading of hepatic cystic echinococcosis, the combined model demonstrated strong diagnostic performance, with an AUC value of 0.888 (95% CI: 0.837-0.936) in the training set and 0.876 (0.761-0.964) in the validation set. The clinical decision analysis curve indicated promising results, while the calibration curve revealed that the nomogram's prediction result was highly compatible with the actual result. Conclusion: The DLR model can be used for predicting the biological activity grading of hepatic echinococcosis.

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. Cox analysis was used to construct LUAD prognostic risk model. Kaplan-Meier test, ROC curve and nomograms were used to evaluate the predictive ability of metabolic related gene prognostic model. CIBERSORT algorithm was used to analyze the relationship between risk score and immune infiltration. The starBase database constructed a regulatory network consistent with the ceRNA hypothesis. IHC experiments were performed to verify the differential expression of ALG3 in LUAD and paracancerous samples. RESULTS: In this study, 42 abnormal metabolism-related differential genes were screened. After survival analysis, the final 5 metabolism-related genes were used as the construction of prognosis model, including ALG3, COL7A1, KL, MST1, and SLC52A1. In the model, the survival rate of LUAD patients in the high-risk subgroup was lower than that in the low-risk group. In addition, the risk score of the constructed LUAD prognostic model can be used as an independent prognostic factor for patients. According to the analysis of CIBERSORT algorithm, the risk score is related to the infiltration of multiple immune cells. The potential ceRNA network of model genes in LUAD was constructed through the starBase database. IHC experiments revealed that ALG3 expression was upregulated in LUAD. CONCLUSION: The prognostic model of LUAD reveals the relationship between metabolism and prognosis of LUAD, and provides a novel perspective for diagnosis and research of LUAD.

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.

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. METHODS: Following the PRISMA guideline, we systematically searched PubMed, Web of Science, and Embase databases for systematic reviews with meta-analyses concerning green spaces and health outcomes published up to January 15, 2024. We categorized the credibility of meta-analytical evidence from interventional studies into four levels (i.e., high, moderate, low, and very low) using the Grading of Recommendation, Assessment, Development and Evaluations framework, based on five domains including risk of bias, inconsistency, indirectness, imprecision, and publication bias. Further, we recalculated all the meta-analyses from observational studies and classified evidence into five levels (i.e., convincing, highly suggestive, suggestive, weak, and non-significant) by considering stringent thresholds for P-values, sample size, robustness, heterogeneity, and testing for biases. FINDINGS: In total, 154 meta-analysed associations (interventional = 44, observational = 110) between green spaces and health outcomes were graded. Among meta-analyses from interventional studies, zero, four (wellbeing, systolic blood pressure, negative affect, and positive affect), 20, and 20 associations between green spaces and health outcomes were graded as high, moderate, low, and very low credibility evidence, respectively. Among meta-analyses from observational studies, one (cardiovascular disease mortality), four (prevalence/incidence of diabetes mellitus, preterm birth, and small for gestational age infant, and all-cause mortality), 12, 22, and 71 associations were categorized as convincing, highly suggestive, suggestive, weak, and non-significant evidence, respectively. INTERPRETATION: The current evidence largely confirms beneficial associations between green spaces and human health. However, only a small subset of these associations can be deemed to have a high or convincing credibility. Hence, future better designed primary studies and meta-analyses are still needed to provide higher quality evidence for informing health promotion strategies. FUNDING: The National Natural Science Foundation of China of China; the Guangzhou Science and Technology Program; the Guangdong Medical Science and Technology Research Fund; the Research Grant Council of the Hong Kong SAR; and Sino-German mobility program.

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.

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.

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.