Background: Patients undergoing liver transplantation (LT) are at risk of perioperative neurocognitive dysfunction (PND), which significantly affects the patients’ prognosis. Objective: This study used machine learning (ML) algorithms with an aim to extract critical predictors and develop an ML model to predict PND among LT recipients. Methods: In this retrospective study, data from 958 patients who underwent LT between January 2015 and January 2020 were extracted from the Third Affiliated Hospital of Sun Yat-sen University. Six ML algorithms were used to predict post-LT PND, and model performance was evaluated using area under the receiver operating curve (AUC), accuracy, sensitivity, specificity, and F1-scores. The best-performing model was additionally validated using a temporal external dataset including 309 LT cases from February 2020 to August 2022, and an independent external dataset extracted from the Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database including 325 patients. Results: In the development cohort, 201 out of 751 (33.5%) patients were diagnosed with PND. The logistic regression model achieved the highest AUC (0.799) in the internal validation set, with comparable AUC in the temporal external (0.826) and MIMIC-Ⅳ validation sets (0.72). The top 3 features contributing to post-LT PND diagnosis were the preoperative overt hepatic encephalopathy, platelet level, and postoperative sequential organ failure assessment score, as revealed by the Shapley additive explanations method. Conclusions: A real-time logistic regression model-based online predictor of post-LT PND was developed, providing a highly interoperable tool for use across medical institutions to support early risk stratification and decision making for the LT recipients.

Background: Patients with severe lumbar disc herniation (LDH), particularly those complicated by spinal stenosis or vertebral instability, frequently require posterior lumbar interbody fusion to alleviate nerve compression and reconstruct spinal biomechanical stability. Aiming to optimize individualized surgical planning, it is necessary to establish accurate predictive models derived from multidimensional clinical data. Methods: In this retrospective, multi-center study, the data utilized in this study were sourced from the Degenerative Spine Diseases in China (DSDC2024, NCT05867732). The model was trained on 3055 cases and externally validated across four geographically distinct cohorts ( n = 3186). Leveraging a two-stage ensemble framework, we first applied Lasso regression to select target predictive variables from 38 clinical accessibility features (demographics, comorbidities, surgical parameters, and laboratory indices), then integrated XGBoost, random forest, and logistic regression through stacked generalization. Bayesian optimization with 10-fold cross-validation refined hyperparameters, while decision curve analysis quantified clinical utility against traditional risk assessment methods. Shapley Additive exPlanations analysis quantified feature contributions and interaction effects. Results: Amongst the 70 algorithmic combinations evaluated, the integration of Lasso with Stack emerged as the most predictive, achieving an impressive average area under the receiver operating characteristic curve of 0.884. The top five significant predictors were the fusion levels, clinical course duration, preoperative hospitalization, preoperative hemoglobin, and preoperative albumin. Conclusion: The IBLED-LDH model provides a valuable tool for preoperative intraoperative blood loss risk stratification, balancing predictive accuracy with interpretability through advanced ensemble learning.

Objective: The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms. Methods: The Surveillance, Epidemiology, and End Results (SEER) database was queried for 4,727 patients with NM based on the inclusion/exclusion criteria. Their clinicopathological characteristics were retrospectively reviewed, and logistic regression analysis was utilized to identify risk factors for metastasis. This was followed by employing Multilayer Perceptron (MLP), Adaptive Boosting (AB), Bagging (BAG), logistic regression (LR), Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting (XGB) algorithms to develop metastasis models. The performance of the six models was evaluated and compared, leading to the selection and visualization of the optimal model. Through integrating the prognostic factors of Cox regression analysis with the optimal models, the prognostic prediction model was constructed, validated, and assessed. Results: Logistic regression analyses identified that marital status, gender, primary site, surgery, radiation, chemotherapy, system management, and N stage were all independent risk factors for NM metastasis. MLP emerged as the optimal model among the six models (AUC = 0.932, F1 = 0.855, Accuracy = 0.856, Sensitivity = 0.878), and the corresponding network calculator (https://shimunana-nm-distant-m-nm-m-distant-8z8k54.streamlit.app/) was developed. The following were examined as independent prognostic factors: MLP, age, marital status, sequence number, laterality, surgery, radiation, chemotherapy, system management, T stage, and N stage. System management and surgery emerged as protective factors (HR < 1). To predict 1-, 3-, and 5-year overall survival (OS), a nomogram was created. The validation results demonstrated that the model exhibited good discrimination and consistency, as well as high clinical usefulness. Conclusion: The developed prediction model more effectively reflects the prognosis of patients with NM and differentiates between the risk level of patients, serving as a useful supplement to the classical American Joint Committee on Cancer (AJCC) staging system and offering a reference for clinically stratified individualized treatment and prognosis prediction. Furthermore, the model enables clinicians to quantify the risk of metastasis in NM patients, assess patient survival, and administer precise treatments.

Objective: This study aimed to investigate the effects of electroacupuncture (EA) on postoperative cognitive dysfunction (POCD) and gut microbiota in aged rats anesthetized with propofol. Methods: Forty 18-months-old male SD rats were randomly divided into four groups: Sham, Model (MD), Sham Electroacupuncture (JE), and Electroacupuncture (EA), with 10 rats in each group. The Sham group underwent a skin incision without surgery, while the MD, JE, and EA groups received propofol anesthesia followed by right tibial surgery. The EA group received electroacupuncture at Baihui, Shenmen, and bilateral Zusanli points for 5 days prior to surgery, while the JE group received acupuncture without electrical stimulation. Behavioral tests, including the Morris water maze and open field tests, were conducted at 1-, 2-, and 3-weeks post-surgery to assess cognitive function. Gut microbiota composition was analyzed using second-generation sequencing. Results: At 1-week post-surgery, the MD, JE, and EA groups showed longer latencies and fewer crossings in the behavioral tests. However, at 3- and 4-weeks post-surgery, the EA group exhibited significantly reduced latency and increased crossing times compared to the MD and JE groups. Gut microbiome analysis revealed that the EA group had a higher relative abundance of Bacteroidetes and Proteobacteria, and a reduced relative abundance of Unclassified Lactobacillaceae compared to the MD and JE groups. Conclusion: Electroacupuncture was associated with improved postoperative cognitive function in aged rats after propofol anesthesia and tibial surgery. These effects were accompanied by alterations in gut microbiota composition, suggesting a possible link with the gut–brain axis, although causality remains to be established.

Diagnosis of hydrocephalus involves a careful check of the patient’s history and thorough neurological assessment. The traditional diagnosis has predominantly depended on the professional judgment of physicians based on clinical experience, but with the advancement of precision medicine and individualized treatment, such experience-based methods are no longer sufficient to keep pace with current clinical requirements. To fit this adjustment, the medical community actively devotes itself to data-driven intelligent diagnostic solutions. Building a prognosis prediction model for hydrocephalus has thus become a new focus, among which intelligent prediction systems supported by deep learning offer new technical advantages for clinical diagnosis and treatment decisions. Over the past several years, algorithms of deep learning have demonstrated conspicuous advantages in medical image analysis. Studies revealed that the accuracy rate of the diagnosis of hydrocephalus by magnetic resonance imaging can reach 90% through convolutional neural networks, while their sensitivity and specificity are also better than these of traditional methods. With the extensive use of medical technology in terms of deep learning, its successful use in modeling hydrocephalus prognosis has also drawn extensive attention and recognition from scholars. This review explores the application of deep learning in hydrocephalus diagnosis and prognosis, focusing on image-based, biochemical, and structured data models. Highlighting recent advancements, challenges, and future trajectories, the study emphasizes deep learning’s potential to enhance personalized treatment and improve outcomes.