Spinal cord injury can have a range of debilitating effects, permanently impacting a patient's quality of life. Initially thought to be an immune privileged site, the spinal cord is able to mount a timely and well organized inflammatory response to injury. Intricate immune cell interactions are triggered, typically consisting of a staggered multiphasic immune cell response, which can become deregulated if left unchecked. Although several immunomodulatory compounds have yielded success in experimental rodent spinal cord injury models, their translation to human clinical studies needs further consideration. Because temporal differences between rodent and human inflammatory responses to spinal cord injury do exist, drug delivery timing will be a crucial component in recovery from spinal cord injury. Given too early, immunomodulatory therapies may impede beneficial inflammatory reactions to the injured spinal cord or even miss the opportunity to dampen delayed harmful autoimmune processes. Therefore, this review aims to summarize the temporal inflammatory response to spinal cord injury, as well as detailing specific immune cell functions. By clearly defining the chronological order of inflammatory events after trauma, immunomodulatory drug delivery timing can be better optimized. Further, we compare spinal cord injury-induced inflammatory responses in rodent and human studies, enabling clinicians to consider these differences when initiating clinical trials. Improved understanding of the cellular immune response after spinal cord injury would enhance the efficacy of immunomodulatory agents, enabling combined therapies to be considered.

Microglia are activated after spinal cord injury (SCI), but their phagocytic mechanisms and link to neuroprotection remain incompletely characterized. Docosahexaenoic acid (DHA) has been shown to have significant neuroprotective effects after hemisection and compression SCI and can directly affect microglia in these injury models. In rodent contusion SCI, we demonstrate that DHA (500 nmol/kg) administered acutely post-injury confers neuroprotection and enhances locomotor recovery, and also exerts a complex modulation of the microglial response to injury. In rodents, at 7 days after SCI, the level of phagocytosed myelin within Iba1-positive or P2Y12-positive cells was significantly lower after DHA treatment, and this occurred in parallel with an increase in intracellular miR-124 expression. Furthermore, intraspinal administration of a miR-124 inhibitor significantly reduced the DHA-induced decrease in myelin phagocytosis in mice at 7 days post-SCI. In rat spinal primary microglia cultures, DHA reduced the phagocytic response to myelin, which was associated with an increase in miR-124, but not miR-155. A similar response was observed in a microglia cell line (BV2) treated with DHA, and the effect was blocked by a miR-124 inhibitor. Furthermore, the phagocytic response of BV2 cells to stressed neurones was also reduced in the presence of DHA. In peripheral monocyte-derived macrophages, the expression of the M1, but not the M0 or M2 phenotype, was reduced by DHA, but the phagocytic activation was not altered. These findings show that DHA induces neuroprotection in contusion injury. Furthermore, the improved outcome is via a miR-124-dependent reduction in the phagocytic response of microglia.

The present study suggests that raised cerebrospinal fluid tumor necrosis factor -α, interleukin-1β, and interleukin-8 in a temporal manner may indicate early bacterial meningitis development in neurosurgical patients, enabling earlier diagnostic certainty and improved patient outcomes.

e13579 Background: Cutaneous squamous cell carcinoma (cSCC) are the most common form of metastasising skin cancer. Whilst rates of metastatic cSCC are low, they account for a significant proportion of skin cancer related morbidity and mortality, particularly within elderly cohorts, which poses a significant burden to healthcare services. Established cSCC tumour staging systems perform poorly at predicting metastatic risk. Additionally, we lack clinically validated prognostic biomarkers – highlighting the unmet need for novel risk stratification tools to guide clinical practice and improve outcomes for patients with advanced disease. We aimed to train four recognised machine learning (ML) algorithms on a large clinic-pathological dataset of primary cSCC, with the objective of optimising an ML strategy and developing a reliable and clinically useful risk stratification tool capable of accurately predicting metastatic events following primary cSCC. Methods: A dataset of primary cSCC registrations was derived from the Northern Cancer Registry, UK. This identified 7003 histologically confirmed primary cSCC registered between 2010–2020; providing a minimum of 2 years clinical follow-up. We conducted a retrospective analysis of standardised pathology datasets, recording clinical-pathological features. Primary outcome measure was regional and/or distant metastasis. Four machine learning algorithms, were trained based on these features, including: a Logistic Regression Trainer, a Decision Tree Classifier, a Random Forest Classifier and a fully connected artificial neural network (ANN). The algorithms were optimised on training data using five-fold cross validation. Subgroup analysis was performed using mean Shapley additive explanations (SHAP). Results: Accuracy scoring identified the ANN as the optimal predictor of metastasis (0.94), followed by: Logistic Regression Trainer (0.82), Random Forest Classifier (0.80), and Decision Tree Classifier (0.71). Preliminary subgroup analysis identified immunosuppression as most sensitive risk factor for developing metastatic disease (SHAP = 0.122). Conclusions: Significant heterogeneity in current morbidity and mortality data has limited the capacity of traditional statistical models and tumour staging systems to identify very high-risk cSSC. Our findings demonstrate that ML algorithms can accurately predict metastatic events in cSSC populations. Further development of a model user-interface is necessary to support the development of a useful risk stratification tool to guide clinical practice.