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Background: An increasing number of HIV-negative cryptococcal meningitis (CM) patients have been reported with fatality approaching 30%.At present, HIV-negative CM patients are stratified according to clinical guidelines and clinical experience for individualized treatment, but the effect seems to be not ideal in clinical practice. Therefore, an accurate model that predict the prognosis for HIV-negative CM patients is needed to provide reference for precision treatment. Methods: This retrospective study involved 490 HIV-negative CM patients diagnosed between January 1, 1998, and March 31, 2022, by neurologists from 3 tertiary Chinese centres. Prognosis was evaluated at 10 weeks after the initiation of antifungal therapy. We used least absolute shrinkage and selection operator (LASSO) for feature filtering and developed a machine learning (ML) model to predict the prognosis in HIV-negative CM patients. Fifty-six patients from 2 other hospitals were analysed for external validation. An artificial intelligence (AI)-based detection model was also developed to automate the rapid counting of microscopic cryptococcal counts. Results:The final prediction model for HIV-negative CM patients comprised 8 variables: CSF cryptococcal count, CSF white blood cell (WBC), altered mental status, hearing impairment, CSF chloride levels, CSF opening pressure (OP), aspartate aminotransferase levels at admission and decreased rate of CSF cryptococcal count within 2 weeks after admission. The areas under the curve (AUCs) in the internal and external validation sets were 0.87 (95% CI 0.794-0.944) and 0.86 (95% CI 0.744-0.975), respectively. An AI model was trained to detect and count cryptococci, and the mean average precision (mAP) was 0.993. Additionally, an online and freely available platform for predicting prognosis and detecting and counting cryptococci in HIV-negative CM patients was established. Conclusions:A ML model for predicting prognosis in HIV-negative CM patients was built and validated, and the model might provide a reference for personalized treatment of HIV-negative CM patients. The change in the CSF cryptococcal count in the early phase of HIV-negative CM treatment can reflect the prognosis of the disease. In addition, utilizing AI to detect and count CSF cryptococci in HIV-negative CM patients can eliminate the interference of human factors in detecting cryptococci in CSF samples and reduce the workload of the examiner.

Background: Chronic hepatitis B patients with severe acute exacerbation is at progression stage of acute-on-chronic liver failure (ACLF). We aimed to develop and validate an accurate risk model to early identify the patients at high-risk of liver failure and predict patient’s survival. Methods: We selected the best variable combination using recursive feature elimination algorithm to develop and validate a multivariate logistic regression risk model as well as online application on cloud server from the training cohort with a total of 342 patients and two external cohorts with a sample size of 96 and 65 patients, respectively. Findings: An excellent prediction model called PATA model including four predictors, prothrombin time, age, total bilirubin and alanine aminotransferase could achieve AUROC of 0.959 (95% CI 0.941-0.977) in the development set and AUROC of 0.932 (95% CI 0.876-0.987) and 0.905 (95% CI 0.826-0.984) in two external validation cohorts, respectively. The C-index of the model was 0.720 (95% CI 0.675-0.765) in prognostic stratification of 90-days mortality for training set patients, while the end-stage liver disease score model had a C-index of 0.549 (95% CI 0.506-0.592). Interpretation: The highly-predictive risk model and easy-to-use online application can accurately predict the risk of ACLF and patient survival, which is expected to become a widely accepted prediction model and guide therapeutic options. Funding This work was supported by a grant for National Key RD National Natural Science Foundation of China; Major Science and Technology Projects in 13th Five-Year and Natural Science Foundation of Guangdong. Declaration of Interest: The authors declare that they have no competing interests. Ethical Approval: This study was approved by the Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University ([2015]2-206 No.1). This study was conducted according to the Declaration of Helsinki. All adult participants provided written informed consent.

Background: Chronic patients with flare of hepatitis B virus (HBV) may progress to acute-on-chronic liver failure (ACLF), which is characterized by high mortality. Uniform models to predict risk of ACLF are lacking. We aimed to present an accurate risk prediction model that incorporates clinical manifestations and laboratory results in chronic HBV-infected patients with flare of hepatitis. Methods: We selected the best variable combination using recursive feature elimination (RFE) from a perspective cohort including 360 patients with hepatitis flare at the Third Affiliated Hospital of Sun Yat-sen University to develop a risk prediction model and nomogram scoring system, then validated the model using two external independent sets consisting of 96 and 65 individuals, and compared its performance with End-Stage Liver Disease (MELD) score model. Findings: An excellent prediction model called the PAT model using the five final identified predictors including prothrombin time (PT), total bilirubin (TBil), ascites, pneumonia and hemoglobin (HGB) was constructed. The model could achieve an Area under the Receiver Operating Characteristic curve (AUROC) of 0.964 (95% confidence interval [CI]: 0.949-0.980) in the development set and performed well in external validation cohorts (validation cohort 1, AUROC = 0.909, 95% CI 0.840-0.977; validation cohort 2, AUROC = 0.898, 95% CI 0.813-0.983, both of which were significantly higher than the MELD score model (P

Background: Breakthroughs have been made in cancer immunotherapy using antibodies against immune checkpoints. But successful immune checkpoint blockade (ICB) responses only occur in a fraction of people, and we have no idea which patients will be the lucky one before treatment. Picking out the effective population accurately can improve the cure rate significantly. We set out to design a novel immunophenotyping approaches for kidney renal clear cell carcinoma (KIRC) based on the tumor immune microenvironment(TIME), which target to distinguish immunotherapy responders. Methods: Five hundred and thirty KIRC samples from The Cancer Genome Atlas (TCGA) were analyzed as the training cohort. Non-negative matrix factorization (NMF) was used to extract immune-related features from complex tumor bulks. We associated the expression patterns with a series of immune-related gene signatures and clinicopathological features. GSE73731 and GSE40435 validation datasets were analyzed to further confirm our findings. Findings: We found that almost 40% of KIRC patients in the training cohort (209/530) were designated as the Immune Class with a high enrichment score for inflammatory response, cytolytic activity, and CD8 T cells (all P