Background: Prediction of the functional outcome for patients with convulsive status epilepticus (CSE) has been a challenge. The aim of this study was to characterize the prognostic factors and functional outcomes of patients after CSE in order to develop a practicable scoring system for outcome prediction. Methods: We performed a retrospective explorative analysis on consecutive patients diagnosed with CSE between March, 2008 and November, 2014 in a tertiary academic medical center in northwest China. The modified Rankin Scale (mRS) was used to measure the functional outcome at three months post discharge. Results: A total of 132 CSE patients was included, with a median age of 25.5 years and 60.6 % were male. Three months post discharge, an unfavorable outcome with mRS of 3–6 was seen in 62 (47.0 %) patients, 25 (18.9 %) of whom died. Logistic regression analysis revealed that encephalitis (p = 0.029), nonconvulsive SE (p = 0.018), diazepam resistance (p = 0.005), image abnormalities (unilateral lesions, p = 0.027; bilateral lesions or diffuse cerebral edema, p < 0.001) and tracheal intubation (p = 0.032) were significant independent predictors for unfavorable outcomes. Based on the coefficients in the model, these predictors were assigned a value of 1 point each, with the exception of the image, creating a 6-point scoring system, which we refer to as END-IT, for the outcome prediction of CSE. The area under the receiver operating characteristic curve for the END-IT score was 0.833 and using a cut-off point of 3 produced the highest sum sensitivity (83.9 %) and specificity (68.6 %). Compared with status epilepticus severity score (STESS) and Epidemiology-based Mortality score in SE (EMSE), END-IT score showed better discriminative power and predictive accuracy for the outcome prediction. Conclusions: We developed an END-IT score with a strong discriminative power for predicting the functional outcome of CSE patients. External prospective validation in different cohorts is needed for END-IT score.

Microglia, as resident macrophages in the central nervous system, play a multifunctional role in the pathogenesis of Alzheimer’s disease (AD). Their clustering around amyloid-β (Aβ) deposits is a core pathological feature of AD. Recent advances in single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) have revealed dynamic changes in microglial phenotypes over time and across different brain regions during aging and AD progression. As AD advances, microglia primarily exhibit impaired phagocytosis of Aβ and tau, along with the release of pro-inflammatory cytokines that damage synapses and neurons. Targeting microglia has emerged as a potential therapeutic approach for AD. Treatment strategies involving microglia can be broadly categorized into two aspects: (1) enhancing microglial function: This involves augmenting their phagocytic ability against Aβ and cellular debris and (2) mitigating neuroinflammation: Strategies include inhibiting TNF-α signaling to reduce the neuroinflammatory response triggered by microglia. Clinical trials exploring microglia-related approaches for AD treatment have garnered attention. Additionally, natural products show promise in enhancing beneficial effects and suppressing inflammatory responses. Clarifying microglial dynamics, understanding their roles, and exploring novel therapeutic approaches will advance our fight against AD.

A reliable predictor for early recovery of consciousness in comatose patients is of great clinical significance. Here we aimed to investigate the potentially prognostic value of electroencephalogram-reactivity (EEG-R) in combination with sleep spindles, termed EEG-awakening, for behavioral awakening in etiologically diverse comatose patients. We performed a prospectively observational study on a sample of patients, all of whom were in coma lasting longer than 3 days. Continuous EEG monitoring was performed for at least 24 h to detect the presence of EEG-R and sleep spindles. We then followed patients for 1 month to determine their subsequent level of consciousness, classifying them as either awakened or non-awakened. Finally, Univariate and multivariate analyses were employed to assess the association of predictors with consciousness recovery. One hundred and six patients with different etiologies leading to coma were included in the study. Of these, 48 patients (45.3 %) awoke and 58 patients (54.7 %) did not awake in the month after the onset of the study. Of note, 26 patients (24.5 %) had a good neurological outcome, and 31 patients (29.3 %) died. Univariate analysis revealed that the Glasgow Coma Scale (GCS) score, EEG-R, sleep spindles, and EEG-awakening were all associated with one-month awakening. Comparisons of the area under the receiving operator characteristic curve (AUC) showed that EEG-awakening (0.839; 0.757–0.921) was superior to all of the following: EEG-R (0.798; 0.710–0.886), sleep spindles (0.772; 0.680–0.864), and GCS scores (0.720; 0.623–0.818). However, age, gender, etiology, and pupillary light reflex did not correlate significantly with one-month awakening. Further logistic regression analysis showed that only EEG-awakening and GCS scores at study entry were significant independent predictors of awakening and that the prognostic model containing these two variables yielded an outstanding predictive performance with an AUC of 0.903. EEG-awakening incorporates both EEG-R and sleep spindles and is an excellent predictor for early behavioral awakening in comatose patients. The prognostic model combining EEG-awakening and GCS scores shows an outstanding discriminative power for awakening.