Background: Cryptococcal meningitis (CM) is a significant global health issue, particularly affecting individuals with HIV. Amphotericin B (AmB) serves as the cornerstone treatment for CM; however, its clinical application is restricted due to limited penetration of the blood–brain barrier and associated nephrotoxicity.Objective: This study investigates the use of exosomes derived from induced pluripotent stem cells (iPSC-Exos) as carriers for AmB in treating CM, aiming to enhance therapeutic efficacy and safety and reduce AmB toxicity.Methods: Exosomes were extracted from iPSC culture supernatants using ultrafiltration and ultracentrifugation. Their morphology and size were analyzed using transmission electron microscopy (TEM) and nanoparticle flow cytometry (nFCM). Purity was confirmed by Western blotting for markers CD9, CD63, and TSG101. AmB was loaded into iPSC-Exos using a co-incubation method. The cytotoxicity of the iPSC-Exo/AmB complex was evaluated on HEK 293 T and RAW264.7 cells using the CCK-8 assay, while apoptosis was assessed using live/dead cell staining and flow cytometry. The hemolytic effects were tested using rabbit red blood cells. In a C57BL/6 J mouse model of cryptococcal infection, treatment groups (AmB, iPSC-Exo/AmB, and iPSC-Exo) were administered corresponding drugs, with blood and brain samples collected for analysis. The minimum inhibitory concentration (MIC) of iPSC-Exo/AmB and conventional AmB against Cryptococcus was determined.Results: The iPSC-Exo/AmB complex exhibited reduced cytotoxicity in vitro and decreased AmB-induced renal and hepatic toxicity in vivo. Its MIC against Cryptococcus was over eight times lower than conventional AmB, significantly reducing fungal burden in the mouse brain and lowering serum inflammatory factors.Conclusion: The iPSC-Exo/AmB complex is a promising therapeutic strategy that enhances AmB efficacy while reducing toxicity, offering new hope for treating CM and other refractory fungal infections of the central nervous system.

Postoperative pulmonary complications (PPCs) are a significant concern following lung resection due to prolonged hospital stays and increased morbidity and mortality among patients. This study aims to develop and validate a risk prediction model for PPCs after lung resection using the random forest (RF) algorithm to enhance early detection and intervention. Data from 180 patients who underwent lung resections at the Third Affiliated Hospital of the Naval Medical University between September 2022 and February 2024 were retrospectively analyzed. The patients were randomly allocated into a training set and a test set in an 8:2 ratio. An RF model was constructed using Python, with feature importance ranked based on the mean Gini index. The predictive performance of the model was evaluated through analyses of the receiver operating characteristic curve, calibration curve, and decision curve. Among the 180 patients included, 47 (26.1%) developed PPCs. The top 5 predictive factors identified by the RF model were blood loss, maximal length of resection, number of lymph nodes removed, forced expiratory volume in the first second as a percentage of predicted value, and age. The receiver operating characteristic curve and calibration curve analyses demonstrated favorable discrimination and calibration capabilities of the model, while decision curve analysis indicated its clinical applicability. The RF algorithm is effective in predicting PPCs following lung resection and holds promise for clinical application.

Background. Herein, we tried to develop a prognostic prediction model for patients with LUAD based on the expression profiles of lipid metabolism-related genes (LMRGs). Methods. Molecular subtypes were identified by non-negative matrix factorization (NMF) clustering. The overall survival (OS) predictive gene signature was developed and validated internally and externally based on online data sets. Time-dependent receiver operating characteristic (ROC) curve, KaplanMeier curve, nomogram, restricted mean survival time (EMST), and decision curve analysis (DCA) were used to assess the performance of the gene signature. Results. We identified three molecular subtypes in LUAD with distinct characteristics on immune cells infiltration and clinical outcomes. Moreover, we confirmed a seven-gene signature as an independent prognostic factor for patients with LUAD. Calibration and DCA analysis plots indicated the excellent predictive performance of the prognostic nomogram constructed based on the gene signature. In addition, the nomogram showed higher robustness and clinical usability compared with four previously reported prognostic gene signatures. Conclusions. Findings in the present study shed new light on the characteristics of lipid metabolism within LUAD, and the established seven-gene signature can be utilized as a new prognostic marker for predicting survival in patients with LUAD.

Objective: Chronic lung diseases (CLDs) are a major global health concern, characterized by a progressive decline in pulmonary function that severely impacts quality of life. It is essential to identify and predict the primary risk factors for CLDs. This study aims to establish a predictive model to assist healthcare providers in the early identification of high-risk patients and timely interventions and treatment options. Methods: This study utilized questionnaire data from the China Health and Retirement Longitudinal Study (CHARLS) collected in 2011, 2013, and 2015. A latent class growth model (LCGM) was established using CLDs as the baseline sample. This model stratified the patients based on the extent of the decline in Δpeak expiratory flow (ΔPEF), which served as the target variable. Independent variables included age, gender, smoking status, body mass index, education level, and comorbidities. A random forest model was developed using Python, and the importance of the feature was visualized through the SHAP method. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis. Results: After screening, a total of 553 patients with CLDs were included in the study. The random forest model pinpointed grip strength, age, education level, gender, and asthma as the top five risk factors for pulmonary function decline. Specifically, the model demonstrated robust predictive performance with an area under the ROC curve (AUC) value of 0.77, affirming its accuracy and clinical applicability. Both calibration and decision curves further substantiated the reliability of the model in identifying patients at increased risk for pulmonary function decline. Conclusion: The predictive model developed in this study serves as a valuable tool for clinicians to target early interventions and optimize treatment strategies to enhance the quality of care and patient outcomes in the management of CLDs.

Background: Depressive symptoms are prevalent among patients with chronic lung diseases (CLDs) and adversely impact their quality of life. This study aims to explore the association of grip strength (GS), sleep duration, and comorbidities with depressive symptoms in patients with CLDs, with an in-depth analysis of the underlying mechanisms.Methods: Based on data from the China Health and Retirement Longitudinal Study (CHARLS), this study included participants aged 45 and above with diagnosed CLDs. Depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10). Binary logistic regression, subgroup analysis, and network analysis were conducted to examine the intricate relationships between GS, sleep duration, comorbidities, and specific depressive symptoms.Results: Among the 1,427 participants, 39.8% exhibited depressive symptoms. Adjusted analyses revealed that GS (OR = 0.964, p < 0.001) and sleep duration (OR = 0.808, p < 0.001) were negatively associated with depressive symptoms, whereas the number of comorbid chronic diseases (OR = 1.189, p < 0.001) showed a significant positive correlation with depressive symptoms. Subgroup analyses demonstrated variations in these relationships across different demographic characteristics and lifestyle factors. Network analysis pinpointed “depressed” as the core symptom, with “annoyed,” “exhausted,” and “lonely” as secondary core symptoms. The robust associations between specific factors and depressive symptoms (GS with “annoyed,” sleep and comorbid chronic diseases with “depressed”) suggested potential targets for interventions.Conclusion: This study underscores the complex interplay of GS, sleep duration, and comorbidities with depressive symptoms in patients with CLDs. These findings offer new perspectives for improving the mental health of this vulnerable population.

Non-small cell lung cancer (NSCLC) poses significant therapeutic challenges, highlighting the urgent need for novel and effective therapeutic strategies. T