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Plasma S100β is a predictor for pathology and cognitive decline in Alzheimer's disease.

Fluids Barriers CNS

January 2025

Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, 760 Press Ave, 124 HKRB, Lexington, KY, 40536-0679, USA.

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.

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Objective: The occurrence of hypofibrinogenemia after tocilizumab treatment has attracted increasing attention, which may cause bleeding and even life-threatening. This study aims to explore the risk factors for tocilizumab-induced hypofibrinogenemia (T-HFIB) and construct a risk prediction model.

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Background: Urinary tract infection (UTI) is a frequent health-threatening condition. Early reliable diagnosis of UTI helps to prevent misuse or overuse of antibiotics and hence prevent antibiotic resistance. The gold standard for UTI diagnosis is urine culture which is a time-consuming and also an error prone method.

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Background And Objective: In clinical practice, CK19 can be an important predictor for the prognosis of HCC. Due to the high incidence and mortality rates of HCC, more effective and practical prognostic prediction models need to be developed urgently.

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Introduction: COVID-19 has caused tremendous hardships and challenges around the globe. Due to the prevalence of asymptomatic and pre-symptomatic carriers, relying solely on disease testing to screen for infections is not entirely reliable, which may affect the accuracy of predictions about the pandemic trends. This study is dedicated to developing a predictive model aimed at estimating of the dynamics of COVID-19 at an early stage based on wastewater data, to assist in establishing an effective early warning system for disease control.

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