Microinfarcts occur commonly in the aging brain as a consequence of diffuse embolic events and are associated with the development of vascular dementia and Alzheimer's disease. However, the manner in which disperse microscopic lesions reduce global cognitive function and increase the risk for Alzheimer's disease is unclear. The glymphatic system, which is a brain-wide perivascular network that supports the recirculation of CSF through the brain parenchyma, facilitates the clearance of interstitial solutes including amyloid ␤ and tau. We investigated whether glymphatic pathway function is impaired in a murine model of multiple microinfarcts induced by intraarterial injection of cholesterol crystals. The analysis showed that multiple microinfarcts markedly impaired global influx of CSF along the glymphatic pathway. Although suppression of global glymphatic function was transient, resolving within 2 weeks of injury, CSF tracers also accumulated within tissue associated with microinfarcts. The effect of diffuse microinfarcts on global glymphatic pathway function was exacerbated in the mice aged 12 months compared with the 2-to 3-month-old mice. These findings indicate that glymphatic function is focally disrupted around microinfarcts and that the aging brain is more vulnerable to this disruption than the young brain. These observations suggest that microlesions may trap proteins and other interstitial solutes within the brain parenchyma, increasing the risk of amyloid plaque formation.

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Background The existing comorbidity indexes, like Charlson Comorbidity Index (CCI) and the Elixhauser Comorbidity Index (ECI), do not take infection factors into account for critically ill patients with immunocompromise, bringing about a decrease of prediction accuracy. Therefore, we attempted to incorporate infection location into the analysis to construct a rapid comorbidity scoring system independent of laboratory tests. Methods Data were extracted from the Multiparameter Intelligent Monitoring in Intensive Care III database. A total of 3904 critically ill patients with immunocompromise admitted to ICU were enrolled and assigned into training or validation sets according to the date of ICU admission. The predictive nomogram was constructed in the training set based on logistic regression analysis and then undergone validation in the validation set in comparison with SOFA, CCI and ECI. Results Factors eligible for the nomogram included patient’s age, gender, ethnicity, underlying disease of immunocompromise like metastatic cancer and leukemia, possible infection on admission including pulmonary infection, urinary tract infection and blood infection, and one comorbidity, coagulopathy. The nomogram we developed exhibited better discrimination than SOFA, CCI and ECI with an area under the receiver operating characteristic curve (AUC) of 0.739 (95% CI 0.707–0.771) and 0.746 (95% CI 0.713–0.779) in the training and validation sets, respectively. Combining the nomogram and SOFA could bring a new prediction model with a superior predictive effect in both sets (training set AUC = 0.803 95% CI 0.777–0.828, validation set AUC = 0.818 95% CI 0.783–0.854). The calibration curve exhibited coherence between the nomogram and ideal observation for two cohorts (p>0.05). Decision curve analysis revealed the clinical usefulness of the nomogram in both sets. Conclusion We established a nomogram that could provide an accurate assessment of 30 days ICU mortality in critically ill patients with immunocompromise, which can be employed to evaluate the short-term prognosis of those patients and bring more clinical benefits without dependence on laboratory tests.