AST to ALT Ratio is elevated in disseminated histoplasmosis as compared to localized pulmonary disease and other endemic mycoses

Machine learning-based in-hospital mortality prediction of HIV/AIDS patients with Talaromyces marneffei infection in Guangxi, China

Objective

Talaromycosis is a serious regional disease endemic in Southeast Asia. In China, Talaromyces marneffei (T. marneffei) infections is mainly concentrated in the southern region, especially in Guangxi, and cause considerable in-hospital mortality in HIV-infected individuals. Currently, the factors that influence in-hospital death of HIV/AIDS patients with T. marneffei infection are not completely clear. Existing machine learning techniques can be used to develop a predictive model to identify relevant prognostic factors to predict death and appears to be essential to reducing in-hospital mortality.

Methods

We prospectively enrolled HIV/AIDS patients with talaromycosis in the Fourth People’s Hospital of Nanning, Guangxi, from January 2012 to June 2019. Clinical features were selected and used to train four different machine learning models (logistic regression, XGBoost, KNN, and SVM) to predict the treatment outcome of hospitalized patients, and 30% internal validation was used to evaluate the performance of models. Machine learning model performance was assessed according to a range of learning metrics, including area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) tool was used to explain the model.

Results

A total of 1927 HIV/AIDS patients with T. marneffei infection were included. The average in-hospital mortality rate was 13.3% (256/1927) from 2012 to 2019. The most common complications/coinfections were pneumonia (68.9%), followed by oral candida (47.5%), and tuberculosis (40.6%). Deceased patients showed higher CD4/CD8 ratios, aspartate aminotransferase (AST) levels, creatinine levels, urea levels, uric acid (UA) levels, lactate dehydrogenase (LDH) levels, total bilirubin levels, creatine kinase levels, white blood-cell counts (WBC) counts, neutrophil counts, procaicltonin levels and C-reactive protein (CRP) levels and lower CD3⁺ T-cell count, CD8⁺ T-cell count, and lymphocyte counts, platelet (PLT), high-density lipoprotein cholesterol (HDL), hemoglobin (Hb) levels than those of surviving patients. The predictive XGBoost model exhibited 0.71 sensitivity, 0.99 specificity, and 0.97 AUC in the training dataset, and our outcome prediction model provided robust discrimination in the testing dataset, showing an AUC of 0.90 with 0.69 sensitivity and 0.96 specificity. The other three models were ruled out due to poor performance. Septic shock and respiratory failure were the most important predictive features, followed by uric acid, urea, platelets, and the AST/ALT ratios.

Conclusion

The XGBoost machine learning model is a good predictor in the hospitalization outcome of HIV/AIDS patients with T. marneffei infection. The model may have potential application in mortality prediction and high-risk factor identification in the talaromycosis population.

Talaromyces marneffei can cause a fatal deeply disseminated fungal infection- talaromycosis. It is widely distributed in Southeast Asia and spreading globally, the disease is insidious and responsible for significant deaths. Clinicians need easy-to-use tools to make decisions on which patients are at a higher risk of dying after infecting T. marneffei. In this study, conducted in Southern China, we have evolved XGBoost machine learning model. 15 clinical indicators and laboratory measures were used to estimate a patient’s risk of dying in the hospital due to the T. marneffei infection. The study showed that the machine learning model has good predictive ability when tested in an internal testing population of patients. We expect that the model could help clinicians assess a patient’s risk of death in just the time of admission to help decide on early treatment timing of high-risk patients who are likely to die.

Biapenem reduces sepsis mortality via barrier protective pathways against HMGB1-mediated septic responses

BACKGROUND

As a late mediator of sepsis, the role of high mobility group box 1 (HMGB1) has been recognized as important, and suppression of HMGB1 release and restoration of vascular barrier integrity are regarded as potentially promising therapeutic strategies for sepsis. For repositioning of previously FDA-approved drugs to develop new therapies for human diseases, screening of chemical compound libraries, biological active, is an efficient method. Our study illustrates an example of drug repositioning of Biapenem (BIPM), a carbapenem antibiotic, for the modulation of HMGB1-induced septic responses.

METHODS

We tested our hypothesis that BIPM inhibits HMGB1-induced vascular hyperpermeability and thereby increases the survival of septic mouse model from suppression of HMGB1 release upon lipopolysaccharide (LPS)-stimulation. In LPS-activated human umbilical vein endothelial cells (HUVECs) and a cecal ligation and puncture (CLP)-induced sepsis mouse model, antiseptic activity of BIPM was investigated from suppression of vascular permeability, pro-inflammatory proteins, and markers for tissue injury.

RESULTS

BIPM significantly suppressed release of HMGB1 both in LPS-activated HUVECs (upto 60%) and the CLP-induced sepsis mouse model (upto 54%). BIPM inhibited hyperpermeability (upto 59%) and reduced HMGB1-mediated vascular disruptions (upto 62%), mortality (upto 50%), and also tissue injury including lung, liver, and kidney in mice.

CONCLUSION

Reduction of HMGB1 release and septic mortality by BIPM (in vitro, from 5 to 15 μM for 6 h; in vivo, from 0.37 to 1.1 mg/kg, 24 h) indicate a possibility of successful repositioning of BIPM for the treatment of sepsis.

Concomitant onset of systemic lupus erythematosus and disseminated histoplasmosis: a case-based review

INTRODUCTION

Concomitant infections during the debut or relapse of systemic lupus erythematosus are a common scenario, due to multiple mechanisms including the use of immunosuppressive drugs and autoimmunity per se. Invasive fungal infections are rare in systemic lupus erythematosus and are associated with profound immunosuppressed states. Disseminated histoplasmosis in patients with lupus has rarely been reported and the concomitant presentation of both entities is exceptional.

METHODS

We describe a case and performed a literature review in order to identify all case reports. A literature search was carried out using in PubMed/MEDLINE, EMBASE and Google Scholar (the first 200 relevant references) bibliographic databases. All available inclusion studies from January 1968 through July 2020. All data were tabulated, and outcomes were cumulatively analyzed.

RESULTS

Thirty-one additional cases were identified. Disseminated histoplasmosis was the most common clinical presentation and most cases have been reported in patients with a prior diagnosis of lupus in the setting of moderate to high steroid dose use, usually in combination with some other immunosuppressant. Description at systemic lupus disease onset was only reported in 3 cases with a high associated mortality. In our patient, severe disease activity, significant immunosuppression, malnutrition and multi-organ compromise conditioned the patient's fatal outcome.

CONCLUSION

Histoplasmosis can closely mimic activity of lupus. Thus, early clinical recognition is important since a delay in diagnosis and treatment can lead to fatal outcomes.