The factors influencing the occurrence of post-ischemic stroke depression and its relationship with the burden score of cerebral small vessel disease.

OBJECTIVE: This study explored the determinants of post-stroke depression (PSD) in ischemic stroke (AIS) patients and its association with the burden score of cerebral small vessel disease (CSVD). PATIENTS AND METHODS: We analyzed 374 AIS patients treated between January 2020 and January 2022. Patients were categorized into 90 with PSD and 284 without PSD, enabling an investigation into PSD risk factors and the CSVD-PSD relationship. RESULTS: There was no significant difference in health factors between PSD and non-PSD patients (p>0.05). However, significant disparities were noted in age, gender, initial Barthel Index (BI), Mini-Mental State Examination (MMSE) score, plasma fibrinogen, homocysteine, red cell distribution width, National Institutes of Health Stroke Scale (NIHSS) score, and CSVD burden score (p<0.05). Regression analysis indicated that these variables were pivotal PSD predictors (OR>1, p<0.05). Surprisingly, a positive correlation with PSD occurrence was found for age, NIHSS score, plasma fibrinogen, homocysteine levels, red cell distribution width, CSVD burden score (r=0.565, 0.615, 0.482, 0.514, 0.572, 0.608, respectively; p<0.05). Meanwhile, the MMSE score and BI index were inversely related to PSD onset (r=-0.604, -0.590; p<0.05). The ROC curve analysis of the combination model based on MMSE, NIHSS and CSVD score revealed an AUC of 0.926 and Youden's index of 0.744. CONCLUSIONS: Age, MMSE score, BI index, NIHSS score, plasma fibrinogen concentration, homocysteine level, red blood cell distribution width, and CSVD burden score are all major influencing factors in the occurrence of PSD. The combination model based on MMSE, NIHSS, and CSVD scores presented a valuable approach to predicting PSD.

Relating Nanoparticle Properties to Biological Outcomes in Exposure Escalation Experiments.

A fundamental goal in nano-toxicology is that of identifying particle physical and chemical properties, which are likely to explain biological hazard. The first line of screening for potentially adverse outcomes often consists of exposure escalation experiments, involving the exposure of micro-organisms or cell lines to a library of nanomaterials. We discuss a modeling strategy, that relates the outcome of an exposure escalation experiment to nanoparticle properties. Our approach makes use of a hierarchical decision process, where we jointly identify particles that initiate adverse biological outcomes and explain the probability of this event in terms of the particle physicochemical descriptors. The proposed inferential framework results in summaries that are easily interpretable as simple probability statements. We present the application of the proposed method to a data set on 24 metal oxides nanoparticles, characterized in relation to their electrical, crystal and dissolution properties.