Background: Disorders of consciousness (DoC) have limited effective therapies. Implantable vagus nerve stimulation (VNS) is established for epilepsy but remains underexplored for DoC with comorbid epilepsy. Objectives: To evaluate whether VNS is associated with improved consciousness recovery in DoC patients with epilepsy and to explore factors related to response. Design: Retrospective propensity score-matched cohort study. Methods: Ninety DoC patients with epilepsy received implantable VNS or conservative management. The primary outcome was clinically meaningful improvement at 1 year (Coma Recovery Scale-Revised (CRS-R) increase ⩾3 points). Propensity score matching (1:1) balanced baseline characteristics (23 per group). Longitudinal CRS-R trajectories were assessed using linear mixed-effects models with Type III ANOVA. Logistic regression within the VNS cohort explored factors associated with responder status; adverse events were extracted from operative and follow-up records. Results: The 1-year responder rate was higher with VNS than with conservative management (34.78% vs 4.35%; two-sided Fisher’s exact test, p = 0.022). Longitudinal analyses showed a significant group × time interaction (χ2 = 43.535, p < 0.001) with greater CRS-R gains from 3 months onward in the VNS group. Within the VNS cohort, responder status was associated with baseline minimally conscious state (aOR = 9.750, 95% confidence interval (CI) 1.592–59.695; p = 0.014) and better seizure control (McHugh classification; aOR = 22.667, 95% CI 3.140–163.629; p = 0.002). Traumatic etiology was not associated with 12-month net CRS-R improvement after adjustment for baseline CRS-R (β = 0.893, 95% CI −1.583 to 3.369; p = 0.466). Five patients reported stimulation-related hoarseness/dysphonia, and two had surgical-site complications; no device removal occurred. Conclusion: Implantable VNS was associated with higher 1-year clinically meaningful improvement and greater longitudinal CRS-R gains than conservative management in DoC patients with epilepsy. Prospective controlled studies are warranted.
J. Zuo, K. Ma, Zizhang Cheng et al.· Therapeutic Advances in Neur...· 0 citations
Raman spectroscopy and mass spectrometry-based proteomics offer deeply complementary yet largely disconnected views of cancer biology: the former provides a label-free, real-time biochemical phenotype, while the latter delivers a quantitative inventory of specific protein effectors. Bridging this gap remains a fundamental challenge in analytical biomedicine. Here, we introduce Spectral-Proteomic Integration Analysis (SPIA)─a novel, data-driven integrative framework that systematically links Raman spectroscopic phenotypes with quantitative proteomic profiles through machine learning and statistical correlation. Using a DMBA-induced rat breast cancer model with and without Toremifene (TOR) intervention, SPIA dynamically maps tumor microenvironment remodeling, capturing progressive collagen deposition and lipid metabolic reprogramming. An SVM classifier trained on Raman spectra achieves exceptional diagnostic accuracy (AUC ≥ 99.0%) and successfully predicts TOR therapeutic response. Proteomic analysis identifies 1,350 differentially expressed proteins, with convergent machine learning feature selection (LASSO, Random Forest, XGBoost) pinpointing core regulators including Luc7l2, Nucb1, Cbx3, and Csnk2a1. Crucially, Spearman correlation analysis between key Raman bands and core DEPs reveals strong, statistically robust associations (median ρ ∼ 0.75 in the 1533-1669 cm-1 region), empirically validating SPIA's core integrative logic. Leveraging this multimodal map, we elucidate a multitarget mechanism for TOR involving concurrent suppression of collagen deposition and correction of aberrant lipid metabolism. SPIA establishes a powerful, generalizable paradigm for integrating phenotypic and molecular data, with broad implications for biomarker discovery, drug mechanism elucidation, and precision oncology.