OXIDIZED ISOFORMS AS DIAGNOSTIC BIOMARKERS OF ALZHEIMER'S DISEASE

Gene Families ◽  
2001 ◽  
pp. 29-38
Author(s):  
ROBERT W. GRACY ◽  
JOHN M. TALENT ◽  
CHRISTINA MALAKOWSKY ◽  
RACHEL DAWSON ◽  
PAM MARSHALL ◽  
...  
2021 ◽  
pp. 1-30
Author(s):  
Claudio Babiloni ◽  
Raffaele Ferri ◽  
Giuseppe Noce ◽  
Roberta Lizio ◽  
Susanna Lopez ◽  
...  

Background: In relaxed adults, staying in quiet wakefulness at eyes closed is related to the so-called resting state electroencephalographic (rsEEG) rhythms, showing the highest amplitude in posterior areas at alpha frequencies (8–13 Hz). Objective: Here we tested the hypothesis that age may affect rsEEG alpha (8–12 Hz) rhythms recorded in normal elderly (Nold) seniors and patients with mild cognitive impairment due to Alzheimer’s disease (ADMCI). Methods: Clinical and rsEEG datasets in 63 ADMCI and 60 Nold individuals (matched for demography, education, and gender) were taken from an international archive. The rsEEG rhythms were investigated at individual delta, theta, and alpha frequency bands, as well as fixed beta (14–30 Hz) and gamma (30–40 Hz) bands. Each group was stratified into three subgroups based on age ranges (i.e., tertiles). Results: As compared to the younger Nold subgroups, the older one showed greater reductions in the rsEEG alpha rhythms with major topographical effects in posterior regions. On the contrary, in relation to the younger ADMCI subgroups, the older one displayed a lesser reduction in those rhythms. Notably, the ADMCI subgroups pointed to similar cerebrospinal fluid AD diagnostic biomarkers, gray and white matter brain lesions revealed by neuroimaging, and clinical and neuropsychological scores. Conclusion: The present results suggest that age may represent a deranging factor for dominant rsEEG alpha rhythms in Nold seniors, while rsEEG alpha rhythms in ADMCI patients may be more affected by the disease variants related to earlier versus later onset of the AD.


2012 ◽  
Vol 8 (4S_Part_7) ◽  
pp. P274-P274
Author(s):  
Andrea F.N. Rosenberger ◽  
Jeroen Hoozemans ◽  
Riet Hilhorst ◽  
Philip Scheltens ◽  
Wiesje Van der Flier ◽  
...  

2021 ◽  
Vol 3 (1) ◽  
pp. 12-18
Author(s):  
Shyamasri Biswas ◽  

The emergence of biomarkers in biologic fluids is considered an important milestone in the field of Alzheimer’s disease (AD) research. Biomarkers are widely considered critically important for the diagnosis and therapeutic intervention of the disease. It is believed that an early diagnosis of AD at a presymptomatic stage could provide the key for a successful intervention and treatment of AD. It is due to the reason that preventative and therapeutic strategies that are known to be AD stage-dependent can have a better chance of clinical success at a very early stage of the disease when critical neurons are not lost. To this end, current clinical trials are extensively being employed by taking advantage of different diagnostic biomarkers. While there has been notable progress in biomarkers for AD, the current research emphasis has been on exploring non-invasive biomarkers due to the advantages of cost-effectiveness, rapid diagnosis and significantly less medical procedural complexities that make these biomarkers potential game changer in AD diagnostics. Here, we present a bird eye view on the subject and discuss the progress made in important non-invasive biomarkers for AD.


2021 ◽  
Vol 15 ◽  
Author(s):  
Yuanyuan Liang ◽  
Lin Wang

Alzheimer’s disease (AD) is the most common cause of senile dementia. Although AD research has made important breakthroughs, the pathogenesis of this disease remains unclear, and specific AD diagnostic biomarkers and therapeutic strategies are still lacking. Recent studies have demonstrated that neuroinflammation is involved in AD pathogenesis and is closely related to other health effects. MicroRNAs (miRNAs) are a class of endogenous short sequence non-coding RNAs that indirectly inhibit translation or directly degrade messenger RNA (mRNA) by specifically binding to its 3′ untranslated region (UTR). Several broadly expressed miRNAs including miR-21, miR-146a, and miR-155, have now been shown to regulate microglia/astrocytes activation. Other miRNAs, including miR-126 and miR-132, show a progressive link to the neuroinflammatory signaling. Therefore, further studies on these inflamma-miRNAs may shed light on the pathological mechanisms of AD. The differential expression of inflamma-miRNAs (such as miR-29a, miR-125b, and miR-126-5p) in the peripheral circulation may respond to AD progression, similar to inflammation, and therefore may become potential diagnostic biomarkers for AD. Moreover, inflamma-miRNAs could also be promising therapeutic targets for AD treatment. This review provides insights into the role of inflamma-miRNAs in AD, as well as an overview of general inflamma-miRNA biology, their implications in pathophysiology, and their potential roles as biomarkers and therapeutic targets.


Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 476
Author(s):  
Qi Ge ◽  
Zhuo-Chen Lin ◽  
Yong-Xiang Gao ◽  
Jin-Xin Zhang

(1) Background: Growing evidence suggests that electroencephalography (EEG), recording the brain’s electrical activity, can be a promising diagnostic tool for Alzheimer’s disease (AD). The diagnostic biomarkers based on quantitative EEG (qEEG) have been extensively explored, but few of them helped clinicians in their everyday practice, and reliable qEEG markers are still lacking. The study aims to find robust EEG biomarkers and propose a systematic discrimination framework based on signal processing and computer-aided techniques to distinguish AD patients from normal elderly controls (NC). (2) Methods: In the proposed study, EEG signals were preprocessed firstly and Maximal overlap discrete wavelet transform (MODWT) was applied to the preprocessed signals. Variance, Pearson correlation coefficient, interquartile range, Hoeffding’s D measure, and Permutation entropy were extracted as the input of the candidate classifiers. The AD vs. NC discriminant performance of each model was evaluated and an automatic diagnostic framework was eventually developed. (3) Results: A classification procedure based on the extracted EEG features and linear discriminant analysis based classifier achieved the accuracy of 93.18 ± 3.65 (%), the AUC of 97.92 ± 1.66 (%), the F-measure of 94.06 ± 4.04 (%), separately. (4) Conclusions: The developed discrimination framework can identify AD from NC with high performance in a systematic routine.


PLoS ONE ◽  
2016 ◽  
Vol 11 (6) ◽  
pp. e0158000 ◽  
Author(s):  
Gergő Kalló ◽  
Miklós Emri ◽  
Zsófia Varga ◽  
Bernadett Ujhelyi ◽  
József Tőzsér ◽  
...  

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