Objective: Spinal cord injury (SCI), which is characterized by motor and/or sensory dysfunction, presents a significant health challenge resulting from mechanical trauma. Secondary injury, which follows the mechanical trauma and is driven by factors such as inflammation, plays a critical role in the SCI pathophysiology. Scientific evidence indicates that treatment strategies aimed at modulating inflammation during the acute phase of SCI alleviate the seconder injury. In this regard, the present study seeks to evaluate the effectiveness of certolizumab, a monoclonal antibody targeting TNF-α that is widely used in the treatment of various inflammatory diseases, in a SCI model. Methods: In this study, Control, Trauma, and Trauma + Certolizumab groups were established, each comprising eight male rats. One hour after SCI induction, rats in the Trauma + Certolizumab group were administered 10 µg Certolizumab dissolved in saline intraperitoneally, while rats in the Control and Trauma groups received an equivalent volume of saline. After Modified Tarlov Scoring was performed on the seventh day of the experiment, all rats were sacrificed. The effects of certolizumab on neuroinflammation and apoptosis in the SCI model were evaluated using histological, biochemical, and molecular analyses of blood and tissue samples obtained from the rats. Results: Certolizumab downregulated the expression of TNF-α, NF-κB, and IL-6. In addition, as evidenced by the TUNEL assay, Caspase-3 expression (an apoptotic marker), and Modified Tarlov Score results, certolizumab effectively suppressed inflammation-induced neural apoptosis and alleviated locomotor deficits. Conclusion: Certolizumab treatment exerts a neuroprotective effect against secondary damage in SCI through the inhibition of neuroinflammation and apoptosis.

Background Recent studies suggest that large language models (LLMs) such as ChatGPT are useful tools for medical students or residents when preparing for examinations. These studies, especially those conducted with multiple-choice questions, emphasize that the level of knowledge and response consistency of the LLMs are generally acceptable; however, further optimization is needed in areas such as case discussion, interpretation, and language proficiency. Therefore, this study aimed to evaluate the performance of six distinct LLMs for Turkish and English neurosurgery multiple-choice questions and assess their accuracy and consistency in a specialized medical context. Methods A total of 599 multiple-choice questions drawn from Turkish Board examinations and an English neurosurgery question bank were presented to six LLMs (ChatGPT-o1pro, ChatGPT-4, AtlasGPT, Gemini, Copilot, and ChatGPT-3.5). Correctness rates were compared using the proportion z-test, and inter-model consistency was examined using Cohen’s kappa. Results ChatGPT-o1pro, ChatGPT-4, and AtlasGPT demonstrated relatively high accuracy for Single Best Answer–Recall of Knowledge (SBA-R), Single Best Answer–Interpretative Application of Knowledge (SBA-I), and True/False question types; however, performance notably decreased for questions with images, with some models leaving many unanswered items. Conclusion Our findings suggest that GPT-4-based models and AtlasGPT can handle specialized neurosurgery questions at a near-expert level for SBA-R, SBA-I, and True/False formats. Nevertheless, all models exhibit notable limitations in questions with images, indicating that these tools remain supplementary rather than definitive solutions for neurosurgical training and decision-making.