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Background and purpose Acute ischaemic stroke (AIS) is a vital cause of mortality and morbidity in China. Many AIS patients develop early neurological deterioration (END). This study aimed to construct a nomogram to predict END in AIS patients. Methods Acute ischaemic stroke patients in Nanjing First Hospital were recruited as the training cohort. Additional patients in Nantong Third People’s Hospital were enrolled as the validation cohort. Multivariate logistic regression was utilized to establish the nomogram. Discrimination and calibration performance of the nomogram were tested by concordance index and calibration plots. Decision curve analysis was employed to assess the utility of the nomogram. Results In all, 1889 and 818 patients were recruited in the training and validation cohorts, respectively. Age [odds ratio (OR) 1.075; 95% confidence interval (CI) 1.059–1.091], diabetes mellitus (OR 1.673; 95% CI 1.181–2.370), atrial fibrillation (OR 3.297; 95% CI 2.005–5.421), previous antiplatelet medication (OR 0.473; 95% CI 0.301–0.744), hyper‐sensitive C‐reactive protein (OR 1.049; 95% CI 1.036–1.063) and baseline National Institutes of Health Stroke Scale (OR 1.071; 95% CI 1.045–1.098) were associated with END and incorporated in the nomogram. The concordance index was 0.826 (95% CI 0.785–0.885) and 0.798 (95% CI 0.749–0.847) in the training and validation cohorts. By decision curve analysis, the model was relevant between thresholds of 0.06 and 0.90 in the training cohort and 0.08 and 0.77 in the validation cohort. Conclusions The nomogram composed of hyper‐sensitive C‐reactive protein, age, diabetes mellitus, atrial fibrillation, previous antiplatelet medication and baseline National Institutes of Health Stroke Scale may predict the risk of END in AIS patients.

Growing evidence suggests that the gut microbiome (GM) plays a critical role in health and disease. However, the contribution of GM to psychiatric disorders, especially anxiety, remains unclear. We used the Collaborative Cross (CC) mouse population-based model to identify anxiety associated host genetic and GM factors. Anxiety-like behavior of 445 mice across 30 CC strains was measured using the light/dark box assay and documented by video. A custom tracking system was developed to quantify seven anxiety-related phenotypes based on video. Mice were assigned to a low or high anxiety group by consensus clustering using seven anxiety-related phenotypes. Genome-wide association analysis (GWAS) identified 141 genes (264 SNPs) significantly enriched for anxiety and depression related functions. In the same CC cohort, we measured GM composition and identified five families that differ between high and low anxiety mice. Anxiety level was predicted with 79% accuracy and an AUC of 0.81. Mediation analyses revealed that the genetic contribution to anxiety was partially mediated by the GM. Our findings indicate that GM partially mediates and coordinates the effects of genetics on anxiety.