Tuberous sclerosis complex (TSC) is an autosomal dominant syndrome that presents with diverse and complex clinical features and involves multiple human systems. TSC-related neurological abnormalities and organ dysfunction greatly affect the quality of life and can even result in death in patients with TSC. It is widely accepted that most TSC-related clinical manifestations are associated with hyperactivation of the mammalian target of rapamycin (mTOR) pathway caused by loss‑of‑function mutations in TSC1 or TSC2. Remarkable progress in basic and translational research has led to encouraging clinical advances. Although mTOR inhibitors (rapamycin/everolimus) demonstrate great potential in TSC management, two major concerns hamper their generalized application. One is the frequent manifestation of adverse events, such as stomatitis, infections, and menstrual disorders; and the other is the poor response in certain patients. Thus, indicators are required to effectively predict the efficacy of mTOR inhibitors. Herein, we have summarized the current utilization of mTOR inhibitors in the treatment of TSC and focused on their efficacy and safety, in an attempt to provide a reference to guide the treatment of TSC.

The gut microbiota is a large symbiotic community of anaerobic and facultative aerobic bacteria inhabiting the human intestinal tract, and its activities significantly affect human health. Increasing evidence has suggested that the gut microbiome plays an important role in tumor-related immune regulation. In the tumor microenvironment (TME), the gut microbiome and its metabolites affect the differentiation and function of immune cells regulating the immune evasion of tumors. The gut microbiome can indirectly influence individual responses to various classical tumor immunotherapies, including immune checkpoint inhibitor therapy and adoptive immunotherapy. Microbial regulation through antibiotics, prebiotics, and fecal microbiota transplantation (FMT) optimize the composition of the gut microbiome, improving the efficacy of immunotherapy and bringing a new perspective and hope for tumor treatment.

Bladder cancer (BLCA) is a prevalent malignancy characterized by high recurrence and poor prognosis, particularly muscle-invasive bladder cancer (MIBC). Histopathology, the gold standard for assessing muscle invasion, often suffers from sampling errors and operator dependency, underscoring the need for non-invasive, accurate preoperative assessment methods. This study aimed to develop and validate a hybrid artificial intelligence (AI) model based on computed tomography (CT) radiomics and deep learning (DL) to predict MIBC and overall survival (OS) preoperatively in BLCA patients. A total of 1370 patients from 6 academic medical centers were retrospectively included. Preoperative contrast-enhanced CT scans were analyzed to extract handcrafted radiomic features using PyRadiomics and DL features using ResNet101, followed by machine learning (ML)-based modeling for prediction. A hybrid model combining radiomic and DL features was constructed and validated in internal and external cohorts. Model performance was evaluated using metrics such as the area under the curve (AUC) and Cox proportional hazards analysis for OS prediction. The DL radiomics nomogram (DLRN) model demonstrated superior diagnostic performance, achieving an AUC of 0.807 in the internal validation cohort and 0.783 in the external multi-center validation cohort for predicting muscle invasion. The DLRN generated an imaging-derived risk score (DLRN score), which was subsequently incorporated as one covariate into a multivariable Cox proportional hazards model together with clinicopathological variables to evaluate OS. Using this approach, patients were effectively stratified into high- and low-risk groups for OS, showing robust generalizability across diverse clinical settings. AI-assisted diagnostics significantly improved the sensitivity and accuracy of urologists, particularly among less experienced clinicians. The DLRN model provides a reliable, non-invasive tool for preoperative assessment of muscle invasion and prognosis in BLCA. Addressing histopathology limitations, it offers valuable insights for personalized treatment strategies, paving the way for precision oncology in real-world clinical applications.