Detection of human adenoviruses in influenza-negative patients with respiratory tract infections in Nanning, China.

BACKGROUND: Human adenoviruses (HAdV) have been known to cause a range of diseases, including respiratory tract infections (RTIs). However, there is limited information available regarding the genotype diversity and epidemiology of HAdV associated with RTIs in Nanning. METHODS: Between June 2019 and December 2021, throat swab, nasal swab, or nasopharyngeal swab samples were obtained from individuals hospitalized with respiratory tract infections (RTIs). Statistical software was used to analyze the epidemiological data. The highly conserved 132-bp gene region of the HAdV hexon was targeted for the detection of HAdV using a qPCR assay. An 875-bp hexon gene fragment was subjected to phylogenetic analysis. RESULTS: Significant variations were observed in the age and gender distribution of HAdV-positive patients (P = 0.004 and P = 0.025, respectively). The age distribution of HAdV-positive patients showed that 67.89% of those who tested positive were the age group of 0-6 years. Furthermore, the prevalence of HAdV detection was highest during spring and autumn, with a peak in February. Additionally, genotyping of the 36 HAdV-positive samples with 875-bp fragments identified the presence of circulating HAdV species B, C, and E in Nanning between 2019 and 2021. CONCLUSIONS: This study identified an association between HAdV prevalence and age as well as season. Among hospitalized patients with RTIs in Nanning, HAdV-B, HAdV-C, and HAdV-E were found to be co-circulating. The most commonly detected genotypes were HAdV-C1, HAdV-C6, and HAdV-E4.

Prediction of the risk of cytopenia in hospitalized HIV/AIDS patients using machine learning methods based on electronic medical records.

Background: Cytopenia is a frequent complication among HIV-infected patients who require hospitalization. It can have a negative impact on the treatment outcomes for these patients. However, by leveraging machine learning techniques and electronic medical records, a predictive model can be developed to evaluate the risk of cytopenia during hospitalization in HIV patients. Such a model is crucial for designing a more individualized and evidence-based treatment strategy for HIV patients. Method: The present study was conducted on HIV patients who were admitted to Guangxi Chest Hospital between June 2016 and October 2021. We extracted a total of 66 clinical features from the electronic medical records and employed them to train five machine learning prediction models (artificial neural network [ANN], adaptive boosting [AdaBoost], k-nearest neighbour [KNN] and support vector machine [SVM], decision tree [DT]). The models were tested using 20% of the data. The performance of the models was evaluated using indicators such as the area under the receiver operating characteristic curve (AUC). The best predictive models were interpreted using the shapley additive explanation (SHAP). Result: The ANN models have better predictive power. According to the SHAP interpretation of the ANN model, hypoproteinemia and cancer were the most important predictive features of cytopenia in HIV hospitalized patients. Meanwhile, the lower hemoglobin-to-RDW ratio (HGB/RDW), low-density lipoprotein cholesterol (LDL-C) levels, CD4+ T cell counts, and creatinine clearance (Ccr) levels increase the risk of cytopenia in HIV hospitalized patients. Conclusion: The present study constructed a risk prediction model for cytopenia in HIV patients during hospitalization with machine learning and electronic medical record information. The prediction model is important for the rational management of HIV hospitalized patients and the personalized treatment plan setting.

Plasma gp96 is a Novel Predictive Biomarker for Severe COVID-19.

Early and effective identification of severe coronavirus disease 2019 (COVID-19) may allow us to improve the outcomes of associated severe acute respiratory illness with fever and respiratory symptoms. This study analyzed plasma concentrations of heat shock protein gp96 in nonsevere (including mild and typical) and severe (including severe and critical) patients with COVID-19 to evaluate its potential as a predictive and prognostic biomarker for disease severity. Plasma gp96 levels that were positively correlated with interleukin-6 (IL-6) levels were significantly elevated in COVID-19 patients admitted to the hospital but not in non-COVID-19 patients with less severe respiratory impairment. Meanwhile, significantly higher gp96 levels were observed in severe than nonsevere patients. Moreover, the continuous decline of plasma gp96 levels predicted disease remission and recovery, whereas its persistently high levels indicated poor prognosis in COVID-19 patients during hospitalization. Finally, monocytes were identified as the major IL-6 producers under exogenous gp96 stimulation. Our results demonstrate that plasma gp96 may be a useful predictive and prognostic biomarker for disease severity and outcome of COVID-19. IMPORTANCE Early and effective identification of severe COVID-19 may allow us to improve the outcomes of associated severe acute respiratory illness with fever and respiratory symptoms. Some heat shock proteins (Hsps) are released during oxidative stress, cytotoxic injury, and viral infection and behave as danger-associated molecular patterns (DAMPs). This study analyzed plasma concentrations of Hsp gp96 in nonsevere and severe patients with COVID-19. Significantly higher plasma gp96 levels were observed in severe than those in nonsevere patients, and its persistently high levels indicated poor prognosis in COVID-19 patients. The results demonstrate that plasma gp96 may be a useful predictive and prognostic biomarker for disease severity and outcome of COVID-19.