General issues encountered while diagnosing mild cognitive impairment in Romanian patients

Recent advances in the therapeutic potential of emodin for human health

Emodin (1,3,8-trihydroxy-6-methylanthraquinone) is a bioactive compound, a natural anthraquinone aglycone, present mainly in herbaceous species of the families Fabaceae, Polygonaceae and Rhamnaceae, with a physiological role in protection against abiotic stress in vegetative tissues. Emodin is mainly used in traditional Chinese medicine to treat sore throats, carbuncles, sores, blood stasis, and damp-heat jaundice. Pharmacological research in the last decade has revealed other potential therapeutic applications such as anticancer, neuroprotective, antidiabetic, antioxidant and anti-inflammatory. The present study aimed to summarize recent studies on bioavailability, preclinical pharmacological effects with evidence of molecular mechanisms, clinical trials and clinical pitfalls, respectively the therapeutic limitations of emodin. For this purpose, extensive searches were performed using the PubMed/Medline, Scopus, Google scholar, TRIP database, Springer link, Wiley and SciFinder databases as a search engines. The in vitro and in vivo studies included in this updated review highlighted the signaling pathways and molecular mechanisms of emodin. Because its bioavailability is low, there are limitations in clinical therapeutic use. In conclusion, for an increase in pharmacotherapeutic efficacy, future studies with carrier molecules to the target, thus opening up new therapeutic perspectives.

Weighted Random Support Vector Machine Clusters Analysis of Resting-State fMRI in Mild Cognitive Impairment

The identification of abnormal cognitive decline at an early stage becomes an increasingly significant conundrum to physicians and is of major interest in the studies of mild cognitive impairment (MCI). Support vector machine (SVM) as a high-dimensional pattern classification technique is widely employed in neuroimaging research. However, the application of a single SVM classifier may be difficult to achieve the excellent classification performance because of the small-sample size and noise of imaging data. To address this issue, we propose a novel method of the weighted random support vector machine cluster (WRSVMC) in which multiple SVMs were built and different weights were given to corresponding SVMs with different classification performances. We evaluated our algorithm on resting state functional magnetic resonance imaging (RS-fMRI) data of 93 MCI patients and 105 healthy controls (HC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The maximum accuracy given by the WRSVMC is 87.67%, demonstrating excellent diagnostic power. Furthermore, the most discriminative brain areas have been found out as follows: gyrus rectus (REC.L), precentral gyrus (PreCG.R), olfactory cortex (OLF.L), and middle occipital gyrus (MOG.R). These findings of the paper provide a new perspective for the clinical diagnosis of MCI.