In adult brain, new neurons are generated throughout adulthood in the subventricular zone and the dentate gyrus; this process is commonly known as adult neurogenesis. The regulation or modulation of adult neurogenesis includes various intrinsic pathways (signal transduction pathway and epigenetic or genetic modulation pathways) or extrinsic pathways (metabolic growth factor modulation, vascular, and immune system pathways). Altered neurogenesis has been identified in Alzheimer’s disease (AD), in both human AD brains and AD rodent models. The exact mechanism of the dysregulation of adult neurogenesis in AD has not been completely elucidated. However, neuroinflammation has been demonstrated to alter adult neurogenesis. The presence of various inflammatory components, such as immune cells, cytokines, or chemokines, plays a role in regulating the survival, proliferation, and maturation of neural stem cells. Neuroinflammation has also been considered as a hallmark neuropathological feature of AD. In this review, we summarize current, state-of-the art perspectives on adult neurogenesis, neuroinflammation, and the relationship between these two phenomena in AD. Furthermore, we discuss the potential therapeutic approaches, focusing on the anti-inflammatory and proneurogenic interventions that have been reported in this field.

Ascertaining stroke severity in claims data-based studies is difficult because clinical information is unavailable. We assessed the predictive validity of a claims-based stroke severity index (SSI) and determined whether it improves case-mix adjustment. We analyzed patients with acute ischemic stroke (AIS) from hospital-based stroke registries linked with a nationwide claims database. We estimated the SSI according to patient claims data. Actual stroke severity measured with the National Institutes of Health Stroke Scale (NIHSS) and functional outcomes measured with the modified Rankin Scale (mRS) were retrieved from stroke registries. Predictive validity was tested by correlating SSI with mRS. Logistic regression models were used to predict mortality. The SSI correlated with mRS at 3 months (Spearman rho = 0.578; 95 % confidence interval [CI], 0.556–0.600), 6 months (rho = 0.551; 95 % CI, 0.528–0.574), and 1 year (rho = 0.532; 95 % CI 0.504–0.560). Mortality models with the SSI demonstrated superior discrimination to those without. The AUCs of models including the SSI and models with the NIHSS did not differ significantly. The SSI correlated with functional outcomes after AIS and improved the case-mix adjustment of mortality models. It can act as a valid proxy for stroke severity in claims data-based studies.

Background and Purpose—: Various risk score models have been developed to predict symptomatic intracerebral hemorrhage (SICH) after intravenous thrombolysis for acute ischemic stroke. In this study, we aimed to determine the prediction performance of these risk scores in a Taiwanese population Methods—: Prospectively collected data from 4 hospitals were used to calculate probability of SICH with the scores developed by Cucchiara et al, the Hemorrhage After Thrombolysis (HAT) score, the Safe Implementation of Thrombolysis in Stroke-SICH risk score, the Glucose Race Age Sex Pressure Stroke Severity score, and the Stroke Prognostication using Age and National Institutes of Health Stroke Scale-100 index. We used logistic regression to evaluate the effectiveness of each risk model in predicting SICH and the c statistic to assess performance. Results—: A total of 548 patients were included. The rates of SICH were 7.3% by the National Institute of Neurological Diseases and Stroke definition, 5.3% by the European-Australasian Cooperative Acute Stroke Study II definition, and 3.5% by the Safe Implementation of Thrombolysis in Stroke-Monitoring Study definition. The Cucchiara score, the HAT score, and the Safe Implementation of Thrombolysis in Stroke-SICH risk score were significant predictors of SICH for all 3 definitions, whereas the Glucose Race Age Sex Pressure Stroke Severity score and the Stroke Prognostication using Age and National Institutes of Health Stroke Scale-100 index predicted well only for 1 or 2 definitions of SICH. The c statistic was highest for the HAT score (range, 0.69–0.73) across the definitions of SICH. Conclusions—: The Cucchiara score, the HAT score, and the Safe Implementation of Thrombolysis in Stroke-SICH risk score predicted SICH reasonably well regardless of which SICH definition was used. However, only the HAT score had an acceptable discriminatory ability.

Background: Timely and accurate outcome prediction plays a vital role in guiding clinical decisions on acute ischemic stroke. Early condition deterioration and severity after the acute stage are determinants for long-term outcomes. Therefore, predicting early outcomes is crucial in acute stroke management. However, interpreting the predictions and transforming them into clinically explainable concepts are as important as the predictions themselves. Objective: This work focused on machine learning model analysis in predicting the early outcomes of ischemic stroke and used model explanation skills in interpreting the results. Methods: Acute ischemic stroke patients registered on the Stroke Registry of the Chang Gung Healthcare System (SRICHS) in 2009 were enrolled for machine learning predictions of the two primary outcomes: modified Rankin Scale (mRS) at hospital discharge and in-hospital deterioration. We compared 4 machine learning models, namely support vector machine (SVM), random forest (RF), light gradient boosting machine (LGBM), and deep neural network (DNN), with the area under the curve (AUC) of the receiver operating characteristic curve. Further, 3 resampling methods, random under sampling (RUS), random over sampling, and the synthetic minority over-sampling technique, dealt with the imbalanced data. The models were explained based on the ranking of feature importance and the SHapley Additive exPlanations (SHAP). Results: RF performed well in both outcomes (discharge mRS: mean AUC 0.829, SD 0.018; in-hospital deterioration: mean AUC 0.710, SD 0.023 on original data and 0.728, SD 0.036 on resampled data with RUS for imbalanced data). In addition, DNN outperformed other models in predicting in-hospital deterioration on data without resampling (mean AUC 0.732, SD 0.064). In general, resampling contributed to the limited improvement of model performance in predicting in-hospital deterioration using imbalanced data. The features obtained from the National Institutes of Health Stroke Scale (NIHSS), white blood cell differential counts, and age were the key features for predicting discharge mRS. In contrast, the NIHSS total score, initial blood pressure, having diabetes mellitus, and features from hemograms were the most important features in predicting in-hospital deterioration. The SHAP summary described the impacts of the feature values on each outcome prediction. Conclusions: Machine learning models are feasible in predicting early stroke outcomes. An enriched feature bank could improve model performance. Initial neurological levels and age determined the activity independence at hospital discharge. In addition, physiological and laboratory surveillance aided in predicting in-hospital deterioration. The use of the SHAP explanatory method successfully transformed machine learning predictions into clinically meaningful results.

Background and aim: Peroxisome proliferator-activated receptor-γ (PPAR-γ) modulating treatment may have cardiovascular benefits in type 2 diabetes mellitus (T2DM) patients after ischemic stroke (IS). However, whether there are additional benefits from intensive PPAR-γ modulating treatments in Asian patients with T2DM and hypertension (HTN) after IS remains unknown. Methods: Between 2001 and 2013, patients admitted due to IS were identified from the National Health Insurance Research Database of Taiwan. Patients with T2DM and HTN using angiotensin receptor blockers were further included. Eligible patients were divided into two groups: (1) pioglitazone and (2) non-pioglitazone oral anti-diabetic agent groups. Propensity score matching (1:2) was used to balance the distribution of baseline characteristics, stroke severity and medications. The primary outcome was recurrent IS. Subgroup analysis for recurrent IS in pioglitazone and/or telmisartan users, the trend of IS risks across different PPAR-γ intensity treatments, and dose-dependent outcomes across different pioglitazone possession ratios were further studied. Statistical significance was set at p < 0.05 and p < 0.1 for clinical outcomes and interaction of subgroup analyses, respectively. Results: There were 3190 and 32,645 patients in the pioglitazone and non-pioglitazone groups. Patients of the pioglitazone group had a lower risk of recurrent IS (subdistribution hazard ratio, 0.91; 95% confidence interval 0.84–0.99). Pioglitazone was also associated with reduced recurrent IS in patients who also used telmisartan (p for interaction = 0.071). A graded correlation was found a borderline significant trend between the intensity of PPAR-γ therapy and following IS (p = 0.076). The dose-dependent outcome also showed that a borderline significant trend that higher pioglitazone possession ratio was associated with a lower risk of recurrent IS (p = 0.068). Conclusions: The current study suggests that the use of pioglitazone in type 2 diabetic and hypertensive IS patients is associated with fewer recurrent IS events in an Asian population. Concurrent telmisartan use or a higher pioglitazone possession ratio may have a trend of increased pleiotropic effects, which could possibly be related to higher PPAR-γ effects. Future studies are warranted to confirm or refute the clinical effects and the possible mechanism of more intensive PPAR-γ-modulating treatments.