Development of a predictive machine learning model for pathogen profiles in patients with secondary immunodeficiency.

BACKGROUND: Secondary immunodeficiency can arise from various clinical conditions that include HIV infection, chronic diseases, malignancy and long-term use of immunosuppressives, which makes the suffering patients susceptible to all types of pathogenic infections. Other than HIV infection, the possible pathogen profiles in other aetiology-induced secondary immunodeficiency are largely unknown. METHODS: Medical records of the patients with secondary immunodeficiency caused by various aetiologies were collected from the First Affiliated Hospital of Nanchang University, China. Based on these records, models were developed with the machine learning method to predict the potential infectious pathogens that may inflict the patients with secondary immunodeficiency caused by various disease conditions other than HIV infection. RESULTS: Several metrics were used to evaluate the models' performance. A consistent conclusion can be drawn from all the metrics that Gradient Boosting Machine had the best performance with the highest accuracy at 91.01%, exceeding other models by 13.48, 7.14, and 4.49% respectively. CONCLUSIONS: The models developed in our study enable the prediction of potential infectious pathogens that may affect the patients with secondary immunodeficiency caused by various aetiologies except for HIV infection, which will help clinicians make a timely decision on antibiotic use before microorganism culture results return.

SGLT2 inhibitors: a novel therapy for cognitive impairment via multifaceted effects on the nervous system.

The rising prevalence of diabetes mellitus has casted a spotlight on one of its significant sequelae: cognitive impairment. Sodium-glucose cotransporter-2 (SGLT2) inhibitors, originally developed for diabetes management, are increasingly studied for their cognitive benefits. These benefits may include reduction of oxidative stress and neuroinflammation, decrease of amyloid burdens, enhancement of neuronal plasticity, and improved cerebral glucose utilization. The multifaceted effects and the relatively favorable side-effect profile of SGLT2 inhibitors render them a promising therapeutic candidate for cognitive disorders. Nonetheless, the application of SGLT2 inhibitors for cognitive impairment is not without its limitations, necessitating more comprehensive research to fully determine their therapeutic potential for cognitive treatment. In this review, we discuss the role of SGLT2 in neural function, elucidate the diabetes-cognition nexus, and synthesize current knowledge on the cognitive effects of SGLT2 inhibitors based on animal studies and clinical evidence. Research gaps are proposed to spur further investigation.

Efficacy and safety of camrelizumab combined with albumin-bound paclitaxel as third- or later-line regimen in patients with advanced non-small cell lung cancer.

Background: The clinical efficacy and safety of camrelizumab as a third- or later-line regimen in patients with advanced non-small cell lung cancer (NSCLC) have not been determined in large clinical trials. Objective: This study aimed to evaluate the clinical efficacy and safety of camrelizumab in combination with albumin-bound paclitaxel as a third- or later-line treatment for patients with advanced NSCLC. Methods: A total of 257 patients with advanced NSCLC who were histopathologically confirmed and failed in clinical second-line therapy regimens at Jiangxi Province Cancer hospital from January 2018 to December 2021 were retrospectively selected. Patients with advanced NSCLC were divided into the single treatment group (STG) of camrelizumab, and the combined treatment group (CTG) of camrelizumab in combination with albumin-bound paclitaxel according to the treatment regimen. The primary outcomes of interest were clinical efficacy[objective response rate (ORR) and disease control rate (DCR)], progression-free survival (PFS), and overall survival (OS). Survival data were analyzed using the Kaplan-Meier method, and the log-rank test was performed. Additionally, Cox proportional hazard regression was used to analyze the correlation of prognosis and baseline characteristics between subgroups, to identify the potential independent risk factors for PFS and OS. Furthermore, the occurrence of side effects was assessed according to the Common Terminology Criteria for Adverse Events (CTCAE 4.03). Results: Of the 257 patients with advanced NSCLC included in the research, 135 patients received camrelizumab, and 122 patients received camrelizumab plus albumin-bound paclitaxel. The ORR of CTG and STG was 59.84% and 50.38%, and the DCR was 77.05% and 65.93%, respectively. The median PFS in CTG was higher than that in the STG (5.27 vs. 3.57 months, P = 0.0074), and the median OS was longer (7.09 vs. 6.47 months, P < 0.01). The lines of treatment, metastases, and PD-L1 expression levels were independent risk factors for the mPFS and mOS of patients with advanced NSCLC. The occurrence of adverse events was similar between camrelizumab and camrelizumab plus albumin-bound paclitaxel groups. Conclusion: Camrelizumab combined with albumin-bound paclitaxel as the third- or later-line regimen greatly prolonged PFS and OS of advanced NSCLC patients. A prospective clinical trial is warranted.