Wenle Li

Accurate quantification of glutathione (GSH), a key regulator of intracellular redox homeostasis, and its correlation with the early therapeutic response to cuproptosis‐based treatment in animal models are crucial for developing effective glutathione‐depletion therapeutics, yet in vivo GSH quantification remains challenging. Here, we developed a GSH‐responsive Cu nanoplatform (Cu‐CDs) for early and noninvasive assessment of cuproptosis by exploiting the thiol chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) signal of glutathione. In vivo studies in 4T1 tumor‐bearing mice demonstrated that thiol CEST MRI enabled quantitative detection of GSH as low as 0.39 µmol/g in tumors (R ² = 0.944). Notably, no correlation was observed between the levels of GSH and the MRI signals (R ² = 0.302 or 0.117) from the exogenous analog, indicating that assessments using exogenous probes at a single time point were inaccurate. Remarkably, dynamic changes in the thiol CEST signals within only 8 h after administration of the Cu‐CDs strongly correlated with the tumor inhibition rate measured (R ² = 0.993), enabling early prediction of the long‐term cuproptosis therapeutic efficacy. Thus, CEST MRI provides a non‐invasive and quantitative approach for monitoring GSH in vivo, with potential for assessing therapeutic agent accumulation and predicting early therapeutic response of GSH‐related therapies.

Operative duration reflects surgical complexity and is valuable for perioperative planning. To predict prolonged operative time in posterior spinal deformity correction, we developed a preoperative machine‐learning model. Data from 162 patients across 11 hospitals were analyzed. Ten algorithms were trained and compared, and the best‐performing model was implemented as a web calculator. Boruta identified seven key predictors: interval from illness to surgery, fibrinogen, white blood cell count, activated partial thromboplastin time, American Society of Anesthesiologists class, albumin, and age. LightGBM showed the most consistent performance, achieving an AUC of approximately 0.70. SHAP analyses improved interpretability, and calibration and decision‐curve analyses indicated good calibration and net clinical benefit. We developed an interpretable, internally validated model to estimate operative duration in posterior spinal deformity surgery. The web calculator may support individualized planning and more efficient operating‐room resource allocation. Clinical Trial Registration: NCT05867732

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.

The diffuse and infiltrative nature of high-grade gliomas poses ongoing challenges in treatment and management. Radiotherapy is an important glioma treatment, with a standard radiotherapy dose of 60 gray. However, high-dose radiotherapy is associated with radiation-induced side effects on normal tissue. Scintillator-mediated low-dose x-ray–induced photodynamic therapy using kilovoltage x-rays has been shown to be effective for multiple tumor types without causing damage to healthy tissue. However, x-ray–induced photodynamic therapy is yet to be explored in glioma because of the inability of the photosensitizers to cross the blood-brain barrier. Here, we present an integrated gold clustoluminogen containing protein-protected gold nanoclusters conjugated to a photosensitizer and a cell-penetrating peptide. Using intravital imaging, we showed that gold clustoluminogen crossed the intact blood-brain barrier in healthy animals and accumulated in tumors in two murine intracranial orthotopic glioma models. Gold clustoluminogen efficiently suppressed glioma growth and prolonged animal survival under ultralow-dose x-ray treatment (total 2 gray, megavoltage x-ray) using the same protocol as that used for clinical megavoltage radiotherapy. Moreover, gold clustoluminogen potently inhibited tumor growth in an orthotopic patient-derived xenograft glioma model with prolonged animal survival under ultralow-dose x-ray treatment. Gold clustoluminogen was eliminated through hepatic and renal excretion, with no observed toxicity. These results highlight new opportunities to develop clinically relevant glioma therapies with reduced side effects.

This Special Issue, titled ‘Vaccination and Global Health,’ compiles 11 broad-ranging papers, each exploring critical facets of vaccination, public health, and global healthcare systems

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.

Infections and neurodegenerative diseases induce neuroinflammation, but affected individuals often show nonneural symptoms including muscle pain and muscle fatigue. The molecular pathways by which neuroinflammation causes pathologies outside the central nervous system (CNS) are poorly understood. We developed multiple models to investigate the impact of CNS stressors on motor function and found that Escherichia coli infections and SARS-CoV-2 protein expression caused reactive oxygen species (ROS) to accumulate in the brain. ROS induced expression of the cytokine Unpaired 3 (Upd3) in Drosophila and its ortholog, IL-6, in mice. CNS-derived Upd3/IL-6 activated the JAK-STAT pathway in skeletal muscle, which caused muscle mitochondrial dysfunction and impaired motor function. We observed similar phenotypes after expressing toxic amyloid-β (Aβ42) in the CNS. Infection and chronic disease therefore activate a systemic brain-muscle signaling axis in which CNS-derived cytokines bypass the connectome and directly regulate muscle physiology, highlighting IL-6 as a therapeutic target to treat disease-associated muscle dysfunction.