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Background: Blood–brain barrier dysfunction is one characteristic of Alzheimer's disease (AD) and is recognized as both a cause and consequence of the pathological cascade leading to cognitive decline. The goal of this study was to assess markers for barrier dysfunction in postmortem tissue samples from research participants who were either cognitively normal individuals (CNI) or diagnosed with AD at the time of autopsy and determine to what extent these markers are associated with AD neuropathologic changes (ADNC) and cognitive impairment. Methods: We used postmortem brain tissue and plasma samples from 19 participants: 9 CNI and 10 AD dementia patients who had come to autopsy from the University of Kentucky AD Research Center (UK-ADRC) community-based cohort; all cases with dementia had confirmed severe ADNC. Plasma samples were obtained within 2 years of autopsy. Aβ40, Aβ42, and tau levels in brain tissue samples were quantified by ELISA. Cortical brain sections were cleared using the X-CLARITY™ system and immunostained for neurovascular unit-related proteins. Brain slices were then imaged using confocal microscopy and analyzed for microvascular diameters and immunoreactivity coverage using Fiji/ImageJ. Isolated human brain microvessels were assayed for tight-junction protein expression using the JESS™ automated Western blot system. S100 calcium-binding protein B (S100β), matrix metalloproteinase (MMP)-2, MMP-9, and neuron-specific enolase (NSE) levels in plasma were quantified by ELISA. All outcomes were assessed for linear associations with global cognitive function (MMSE, CDR) and cerebral atrophy scores by Pearson, polyserial, or polychoric correlation, as appropriate, along with generalized linear modeling or generalized linear mixed-level modeling. Results: As expected, we detected elevated Aβ and tau pathology in brain tissue sections from AD patients compared to CNI. However, we found no differences in microvascular diameters in cleared AD and CNI brain tissue sections. We also observed no differences in claudin-5 protein levels in capillaries isolated from AD and CNI tissue samples. Plasma biomarker analysis showed that AD patients had 12.4-fold higher S100β plasma levels, twofold lower NSE plasma levels, 2.4-fold higher MMP-9 plasma levels, and 1.2-fold lower MMP-2 plasma levels than CNI. Data analysis revealed that elevated S100β plasma levels were predictive of AD pathology and cognitive impairment. Conclusion: Our data suggest that among different markers relevant to barrier dysfunction, plasma S100β is the most promising diagnostic biomarker for ADNC. Further investigation is necessary to assess how plasma S100β levels relate to these changes and whether they may predict clinical outcomes, particularly in the prodromal and early stages of AD. [ABSTRACT FROM AUTHOR]

Objective: This study aims to investigate the clinical application value of the central venous-arterial carbon dioxide partial pressure difference (Pv-aCO2) in postoperative cognitive dysfunction(POCD) in patients with acute aortic dissection. Methods: A retrospective analysis was conducted on the general data of 236 patients. Blood gas samples were collected from the arterial and venous lines at various time points during the surgery, including before and after the initiation of cardiopulmonary bypass (CPB), immediately after CPB initiation, before and after deep hypothermic circulatory arrest, 30 min after rewarming, and 5 min before weaning from CPB. The partial PV-aCO2 was calculated. Based on the average PV-aCO2 value, patients were divided into an observation group (PV-aCO2 > 6 mmHg, n = 112) and a control group (PV-aCO2 < 6 mmHg, n = 124). The perioperative data and Mini-Mental State Examination (MMSE) scores were compared between the two groups to assess the incidence and severity of POCD. Additionally, the expression levels of peripheral serum S100β in the two groups were compared 6 h postoperatively. Results: The incidence of POCD was higher in the observation group compared to the control group, while MMSE scores and serum S100β levels were lower in the observation group. Additionally, the observation group patients with POCD had lower MMSE scores and serum S100β levels compared to the control group patients. In addition, logistic regression analysis revealed that advanced age, serum S100β levels, female gender, CPB time, unilateral brain perfusion time, hyperlipidemia, diabetes, and smoking history were all independent risk factors for postoperative POCD (all P < 0.05). Conclusion: Pv-aCO2 can effectively reflect the intraoperative cerebral metabolic level in patients with acute aortic dissection and can serve as an intraoperative warning indicator for cognitive dysfunction. Its clinical recommendation for use is warranted. [ABSTRACT FROM AUTHOR]

• The variety of methods adopted to perform the automatic analysis, ranging from traditional morphosyntactic analysis based on statistical methods, transformers-based language models, sentiment and emotions analysis, and perplexity metrics. • The types of information which is automatically retrieved from the dialogue transcripts that regards lexical and morphosyntactic choices as well as speaker's emotions. • The analysis of highly ecological and large corpus, that is the Anchise Corpus. Automatic linguistic analysis can provide cost-effective, valuable clues to the diagnosis of cognitive difficulties and to therapeutic practice, and hence impact positively on wellbeing. In this work, we analyzed transcribed conversations between elderly individuals living with dementia and healthcare professionals. The material came from the Anchise 2022 Corpus, a large collection of transcripts of conversations in Italian recorded in naturalistic conditions. The aim of the work was to test the effectiveness of a number of automatic analyzes in finding correlations with the progression of dementia in individuals with cognitive decline as measured by the Mini-Mental State Examination (MMSE) score, which is the only psychometric-clinical information available on the participants in the conversations. Healthy controls (HC) were not considered in this study, nor does the corpus itself include HCs. The main innovation and strength of the work consists in the high ecological validity of the language analyzed (most of the literature to date concerns controlled language experiments); in the use of Italian (there is little corpora for Italian); in the size of the analyzed data (more than 200 conversations were considered); in the adoption of a wide range of NLP methods, that span from traditional morphosyntactic investigation to deep linguistic models for conducting analyzes such as through perplexity, sentiment (polarity) and emotions. Analyzing real-world interactions not designed with computational analysis in mind, such as is the case of the Anchise Corpus, is particularly challenging. To achieve the research goals, a wide variety of tools were employed. These included traditional morphosyntactic analysis based on digital linguistic biomarkers (DLBs), transformer-based language models, sentiment and emotion analysis, and perplexity metrics. Analyzes were conducted both on the continuous range of MMSE values and on the severe/moderate/mild categorization suggested by AIFA (Italian Medicines Agency) guidelines, based on MMSE threshold values. Correlations between MMSE and individual DLBs were weak, up to 0.19 for positive, and -0.21 for negative correlation values. Nevertheless, some correlations were statistically significant and consistent with the literature, suggesting that people with a greater degree of impairment tend to show a reduced vocabulary, to have anomia, to adopt a more informal linguist register, and to display a simplified use of verbs, with a decrease in the use of participles, gerunds, subjunctive moods, modal verbs, as well as a flattening in the use of the tenses towards the present to the detriment of the past. The -0.26 inverse correlation between perplexity and MMSE suggests that perplexity captures slightly more specific linguistic information, which can complement the MMSE scores. In the categorization tasks, the classifier based on DLBs achieved an F1 score of 0.79 for binary classification between SEVERE and MILD, and 0.61 for multi-label categorization. Sentiment and emotion analyzes showed inverse trends for joy while MMSE scores suggested that less impaired individuals were less joyful, or more "negative", than others. Considering the real-world context, this is consistent with the hypothesis of a gradual reduction in awareness in individuals affected by dementia. Finally, integrating various profiles of analysis has been proved to be effective in offering a wider picture of linguistic and communication deficits, as well as more precise data regarding the progression of dementia. [ABSTRACT FROM AUTHOR]