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Abstract MicroRNAs (miRNAs) are involved in gene regulation and may affect secondary brain injury and recovery in patients with disorders of consciousness (DoC). This study investigated the role of five miRNAs (150-5p, 132-3p, 23b-3p, 451a, and 16-5p) in prolonged DoC. miRNA levels were assessed in serum samples from 30 patients with unresponsive wakefulness syndrome or minimally conscious state due to traumatic or hypoxic-ischemic brain injury (TBI, HIBI) at baseline (1–3 months) and 6 months post-injury. Patients’ diagnoses were determined using the Coma Recovery Scale revised, and functional outcomes were evaluated 6 months after injury with the Glasgow Outcome Scale Extended (GOSE) and the Functional Independence Measure (FIM). Compared to healthy controls, patients with TBI had lower levels of miRNAs 150-5p, 132-3p, and 23b-3p at baseline, while patients with HIBI had lower levels of miRNA 150-5p at baseline and 6 months post-injury and a reduction of miRNA 451a at baseline. Higher levels of miRNAs 132-3p and 23b-3p were associated with better outcomes in TBI patients as indicated by GOSE and FIM scores. This study highlights distinct miRNA dysregulated patterns in patients with prolonged DoC, dependent on etiology and post-injury time, and suggests that miRNAs 132-3p and 23b-3p may serve as prognostic biomarkers.

Abstract This study describes a dynamic non-linear mathematical approach for modeling the course of disease in acquired brain injury (ABI) patients. Data from a multicentric study were used to evaluate the reliability of the Michaelis–Menten (MM) model applied to well-known clinical variables that assess the outcome of ABI patients. The sample consisted of 156 ABI patients admitted to eight neurorehabilitation subacute units and evaluated at baseline (T0), 4 months after the event (T1) and at discharge (T2). The MM model was used to characterize the trend of the first Principal Component Analysis (PCA) dimension (represented by the variables: feeding modality, RLAS, ERBI-A, Tracheostomy, CRS-r and ERBI-B) in order to predict the most plausible outcome, in terms of positive or negative Glasgow outcome score (GOS) at discharge. Exploring the evolution of the PCA dimension 1 over time, after day 86 the MM model better differentiated between the time course for individuals with a positive and negative GOS (accuracy: 85%; sensitivity: 90.6%; specificity: 62.5%). The non-linear dynamic mathematical model can be used to provide more comprehensive trajectories of the clinical evolution of ABI patients during the rehabilitation period. Our model can be used to address patients for interventions designed for a specific outcome trajectory.