The first diagnosis of Severe Fever with Thrombocytopenia Syndrome caused by tick-borne Severe Fever with Thrombocytopenia Syndrome virus in Chongqing, China: A case report and literature review.

BACKGROUND: Severe Fever with Thrombocytopenia Syndrome (SFTS) is a tick-borne disease caused by the SFTS virus (SFTSV) which has the potential to become a pandemic and is currently a major public health concern. CASE PRESENTATION: We present the case of a 74-year-old female from an urban area of Chongqing, with leukocytopenia, thrombocytopenia, organ function, inflammatory, blood coagulation, and immune abnormalities. SFTSV infection was confirmed through molecular detection and metagenomic next-generation sequencing (mNGS) analysis, indicating a diagnosis of SFTS due to the patient's history of tick bites. The patient received symptomatic and supportive therapy, including antibiotics, antiviral treatment, and antifungal therapy, and finally discharged from the hospital on day 18. CONCLUSIONS: This study highlights the need for increased awareness, early diagnosis, and prompt treatment for tick-borne SFTS. It also provides a comprehensive understanding of the disease's characteristics, pathogenesis, detection methods, and available treatments.

An exploration on the machine-learning-based stroke prediction model.

Introduction: With the rapid development of artificial intelligence technology, machine learning algorithms have been widely applied at various stages of stroke diagnosis, treatment, and prognosis, demonstrating significant potential. A correlation between stroke and cytokine levels in the human body has recently been reported. Our study aimed to establish machine-learning models based on cytokine features to enhance the decision-making capabilities of clinical physicians. Methods: This study recruited 2346 stroke patients and 2128 healthy control subjects from Chongqing University Central Hospital. A predictive model was established through clinical experiments and collection of clinical laboratory tests and demographic variables at admission. Three classification algorithms, namely Random Forest, Gradient Boosting, and Support Vector Machine, were employed. The models were evaluated using methods such as ROC curves, AUC values, and calibration curves. Results: Through univariate feature selection, we selected 14 features and constructed three machine-learning models: Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machine (GBM). Our results indicated that in the training set, the RF model outperformed the GBM and SVM models in terms of both the AUC value and sensitivity. We ranked the features using the RF algorithm, and the results showed that IL-6, IL-5, IL-10, and IL-2 had high importance scores and ranked at the top. In the test set, the stroke model demonstrated a good generalization ability, as evidenced by the ROC curve, confusion matrix, and calibration curve, confirming its reliability as a predictive model for stroke. Discussion: We focused on utilizing cytokines as features to establish stroke prediction models. Analyses of the ROC curve, confusion matrix, and calibration curve of the test set demonstrated that our models exhibited a strong generalization ability, which could be applied in stroke prediction.