Anquan Shang

Background: Prolonged hospitalization after endoscopic endonasal transsphenoidal surgery for non-functioning pituitary adenomas increases costs and complications. Early identification of high-risk patients is crucial for optimizing perioperative management. Methods: In this dual-center retrospective study of 368 patients, a predictive model was developed using a training cohort (n = 268). Prolonged length of stay was defined as ≥75th percentile (≥16 days). LASSO regression selected features from clinical, radiological, and perioperative variables. Independent predictors from multivariable logistic regression were dichotomized via ROC analysis and integrated into a nomogram. Model performance was assessed internally and validated externally (n = 100). Results: Six independent predictors were identified: age > 50 years, vertical tumor diameter > 17.8 mm, anteroposterior diameter > 20.5 mm, transverse diameter > 17.8 mm, anesthesia duration > 194 min, and systolic blood pressure > 119 mmHg. The nomogram showed moderate but reproducible discrimination (AUC = 0.762 in training; 0.750 in validation). Calibration and decision curve analysis confirmed good fit and clinical utility. Conclusion: We developed and validated a practical nomogram predicting prolonged hospitalization risk using readily available perioperative variables. This tool may assist individualized risk stratification and perioperative planning in comparable clinical settings, with potential implications for patient flow and resource utilization.

This study aims to analyze clinical and laboratory data from patients to identify indicators associated with lymph node metastasis (LNM) in papillary thyro

Colorectal cancer (CRC) remains a leading cause of cancer-related morbidity and mortality worldwide. The clinical treatment faces multiple challenges of significant tumor heterogeneity, prevalence of chemo-resistance, low response rate to immunotherapy, and the impact of the patient’s intestinal microenvironment. Recent studies have shown that extracellular vesicles (EVs), as important information transfer carriers for regulating tumorigenesis and development, play a key role in mediating the complex regulatory network of the gut microbiota-tumor microenvironment (TME). Based on current research advances, our review systematically elucidates how CRC-derived EVs function as dynamic molecular messengers, mediating bidirectional interactions between the TME and the gut microbiota. It also provides a comprehensive outline of EV biogenesis and the key signaling pathways regulated by their diverse molecular cargo. It further delineates how these pathways act in concert to promote the formation of an immunosuppressive microenvironment, drive tumor metastasis, and confer therapy resistance. This review aims to provide a coherent theoretical framework for understanding CRC progression and drug resistance, to offer a scientific rationale for novel therapies targeting CRC-derived EVs, and to highlight future research directions essential for overcoming methodological bottlenecks, deciphering complex interaction networks, and advancing clinical translation.

Microbial biofilms are increasingly recognized as critical modulators of chronic airway inflammation and immune dysregulation in pulmonary diseases. This review summarizes current evidence on how biofilm formation and persistence alter host immune responses, contributing to the pathogenesis of chronic lung disorders. We first outline the characteristics of respiratory biofilms and the major pathogens involved. We then discuss how biofilms reshape innate and adaptive immunity—impairing pattern recognition receptor signaling, promoting neutrophil extracellular trap (NET) formation, altering macrophage polarization, and skewing T-cell differentiation. These immune alterations sustain low-grade inflammation, tissue remodeling, and immune tolerance, driving disease progression in chronic obstructive pulmonary disease, bronchiectasis, severe asthma, and even lung cancer. We further highlight emerging diagnostic biomarkers and therapeutic approaches targeting biofilm-associated immune pathways, including combined antibiofilm and immunomodulatory strategies. Finally, we identify key knowledge gaps and propose future research directions, emphasizing multi-omics approaches and personalized interventions to better define and target biofilm-driven immune dysregulation in chronic respiratory diseases.

Artificial intelligence has facilitated the automated selection of embryos and the prediction of pregnancy outcomes during in vitro fertilization (IVF), yet multimodal approaches remain underexplored–particularly for predicting multiple specific outcomes such as singleton pregnancy vs. multiple pregnancy, and miscarriage vs. live birth. In this study, we propose VaTEP (Video and Table model for Embryo Prediction), a multimodal embryo prediction model integrating time-lapse system (TLS) videos and tabular clinical variables. VaTEP is first pretrained on two pre-tasks (TLS video reconstruction and embryo developmental phase prediction) to fully capture the rich spatiotemporal dynamics and developmental information contained in the video, and further improved by a multiple frame sampling strategy and multitask learning framework. These designs enable VaTEP to estimate the probabilities of fetal heartbeat, singleton vs. multiple pregnancy, and miscarriage vs. live birth, promoting more informed embryo selection and outcome precognition. This helps reduce the risk of implantation failure by minimizing the chances of non-viable pregnancies, multiple gestations, and miscarriages. VaTEP offers a comprehensive and data-driven tool for personalized IVF decision-making, supporting safer and more effective reproductive treatment.

Immune checkpoint inhibitors (ICIs) have improved outcomes for patients with non-small cell lung cancer (NSCLC), yet their efficacy is hindered by the immu

Osteosarcoma (OS) is the most common primary malignant bone tumor in children and adolescents, with an annual incidence of approximately 3–4 cases pe