Subarachnoid hemorrhage is associated with severe neurological deficits for survivors. Among survivors of the initial bleeding, secondary brain injury leads to additional brain damage. Apart from cerebral vasospasm, secondary brain injury mainly results from cerebral inflammation taking place in the brain parenchyma after bleeding. The brain’s innate immune system is activated, which leads to disturbances in brain homeostasis, cleavage of inflammatory cytokines and, subsequently, neuronal cell death. The toll-like receptor (TLR)4 signaling pathway has been found to play an essential role in the pathophysiology of acute brain injuries such as subarachnoid hemorrhage (SAH). TLR4 is expressed on the cell surface of microglia, which are key players in the cellular immune responses of the brain. The participants in the signaling pathway, such as TLR4-pathway-like ligands, the receptor itself, and inflammatory cytokines, can act as biomarkers, serving as clues regarding the inflammatory status after SAH. Moreover, protein complexes such as the NLRP3 inflammasome or receptors such as TREM1 frame the TLR4 pathway and are indicative of inflammation. In this review, we focus on the activity of the TLR4 pathway and its contributors, which can act as biomarkers of neuroinflammation or even offer potential new treatment targets for secondary neuronal cell death after SAH.

Surgical flow augmentation for treatment of cerebral hemodynamic impairment remains controversial. Here, we investigated the benefit of endothelial progenitor cell (EPC) treatment in a rat model of chronic cerebral hypoperfusion. At repeated time points after 3-vessel occlusion (3-VO), animals were treated with 1 × 10 ⁶ Dil-labeled (a) ex vivo-expanded embryonic-EPC (e-EPC), (b) cyclic AMP-differentiated embryonic-endothelial progenitor-derived cells (e-EPDC as biologic control) or, (c) saline. The cerebrovascular reserve capacity (CVRC) was assessed immediately before and on days 7 and 21 after 3-VO. Structural effects were assessed by latex perfusion, immunohistochemistry, and intravital fluorescence video microscopy on day 21. Three-vessel occlusion resulted in a significant impairment of the CVRC with better functional recovery after treatment with e-EPC (16.4 ± 8%) compared with e-EPDC (3.7 ± 8%) or saline (6.4 ± 9%) by day 21 ( P<0.05), which was paralleled by a significant increase in the vessel diameters of the anterior Circle of Willis, a significantly higher number of leptomeningeal anastomoses and higher parenchymal capillary density in e-EPC-treated animals. Interestingly, despite in vivo interaction of e-EPC with the cerebral endothelium, e-EPC incorporation into the cerebral vasculature was not observed. Our results suggest that EPC may serve as a novel therapeutic agent in clinical trials for nonsurgical treatment of chronic cerebral hemodynamic impairment.

Although relatively little attention has been paid to the question how acute alcohol withdrawal might affect cognitive functions, this factor remains of particular interest because it influences psychotherapeutic treatment during detoxification. The clinical outcome and neuropsychological state of 37 inpatients with alcohol withdrawal was investigated in a randomized single‐blind approach. Two different medical strategies [chlormethiazole (CMZ) vs. carbamazepine (CBZ)] in the treatment of inpatients with alcohol withdrawal syndrome were compared. Among comparable groups (related to gender, age, initial alcohol level, severity of abuses, severity of initial withdrawal symptoms such as tremor, perspiration, psychomotor agitation, hallucinations, orientation, intelligence, patient demographics), CBZ is just as potent as CMZ in therapy of withdrawal symptoms (circulatory function, vegetative function, psychomotor activity). Patients in both groups showed initial impairments in some neuropsychological tests (d2, Zahlen‐Verbundings test, Beck Depression Inventory, Anxiety Sensitivity Index) with significant improvement during detoxification. Additionally, CBZ‐treated patients showed significantly better verbal memory performance during the first days of treatment. Without any addictive potential, CBZ therapy could be very supportive in alcohol detoxification. In addition a higher verbal memory performance state could be favourable for a psychotherapeutic approach.

Background: Intraoperative neurophysiological monitoring (IONM) guides the surgeon in ensuring motor pathway integrity during high-risk neurosurgical and orthopedic procedures. Although motor-evoked potentials (MEPs) are valuable for predicting motor outcomes, the key features of predictive signals are not well understood, and standardized warning criteria are lacking. Developing a muscle identification prediction model could increase patient safety while allowing the exploration of relevant features for the task. Objective: The aim of this study is to expand the development of machine learning (ML) methods for muscle classification and evaluate them in a bicentric setup. Further, we aim to identify key features of MEP signals that contribute to accurate muscle classification using explainable artificial intelligence (XAI) techniques. Methods: This study used ML and deep learning models, specifically random forest (RF) classifiers and convolutional neural networks (CNNs), to classify MEP signals from routine supratentorial neurosurgical procedures from two medical centers according to muscle identity of four muscles (extensor digitorum, abductor pollicis brevis, tibialis anterior, and abductor hallucis). The algorithms were trained and validated on a total of 36,992 MEPs from 151 surgeries in one center, and they were tested on 24,298 MEPs from 58 surgeries from the other center. Depending on the algorithm, time-series, feature-engineered, and time-frequency representations of the MEP data were used. XAI techniques, specifically Shapley Additive Explanation (SHAP) values and gradient class activation maps (Grad-CAM), were implemented to identify important signal features. Results: High classification accuracy was achieved with the RF classifier, reaching 87.9% accuracy on the validation set and 80% accuracy on the test set. The 1D- and 2D-CNNs demonstrated comparably strong performance. Our XAI findings indicate that frequency components and peak latencies are crucial for accurate MEP classification, providing insights that could inform intraoperative warning criteria. Conclusions: This study demonstrates the effectiveness of ML techniques and the importance of XAI in enhancing trust in and reliability of artificial intelligence–driven IONM applications. Further, it may help to identify new intrinsic features of MEP signals so far overlooked in conventional warning criteria. By reducing the risk of muscle mislabeling and by providing the basis for possible new warning criteria, this study may help to increase patient safety during surgical procedures.

Objective: To longitudinally analyze smartphone-based real-life activity data and compare it with established clinical outcome measures in patients undergoing lumbar spine surgery for sciatica, focusing on identifying divergence in recovery trajectories.Methods: Fifty patients were assessed preoperatively and at 6 weeks (6W), 3 months (3M), and 6 months (6M). Outcomes included smartphone-derived daily Step Count, objective capacity (6-minute walking test [6WT]), and subjective disability (visual analogue scale [VAS] leg/back, Core Outcome Measures Index [COMI] back, and Oswestry Disability Index [ODI]). All metrics were standardized into z-scores relative to baseline. Piecewise linear mixed-effects (LME) models compared recovery slopes across 2 segments: phase I (early: 0–6 weeks) and phase II (late: 6 weeks–6 months).Results: The cohort (mean age, 50.7 years; 24 females) included 33 patients with lumbar disc herniation and 17 with lateral recess stenosis. All measures improved significantly during phase I (all p<0.05). However, LME modeling revealed a significant interaction between time segment and measurement type in phase II. Daily Step Count was the only metric maintaining a significant, linear upward recovery slope during the late phase (β=0.31 Z/mo). Conversely, slopes for 6WT, ODI, and COMI were significantly flatter (p<0.001 vs. Step Count), indicating a statistical plateau or “ceiling effect.” Spearman correlations between Step Count and traditional metrics weakened from strong at baseline to weak at 6 months.Conclusion: Smartphone-derived real-life activity data detect continuous functional improvement up 6 months postoperatively, whereas conventional objective and subjective measures plateau by 6 weeks. Real-world activity monitoring provides a more sensitive assessment of long-term surgical success.