RÉSUMÉ
The cognitive impairment known as dementia affects millions of individuals throughout the globe. The use of machine learning (ML) and deep learning (DL) algorithms has shown great promise as a means of early identification and treatment of dementia. Dementias such as Alzheimer's Dementia, frontotemporal dementia, Lewy body dementia, and vascular dementia are all discussed in this article, along with a literature review on using ML algorithms in their diagnosis. Different ML algorithms, such as support vector machines, artificial neural networks, decision trees, and random forests, are compared and contrasted, along with their benefits and drawbacks. As discussed in this article, accurate ML models may be achieved by carefully considering feature selection and data preparation. We also discuss how ML algorithms can predict disease progression and patient responses to therapy. However, overreliance on ML and DL technologies should be avoided without further proof. It's important to note that these technologies are meant to assist in diagnosis but should not be used as the sole criteria for a final diagnosis. The research implies that ML algorithms may help increase the precision with which dementia is diagnosed, especially in its early stages. The efficacy of ML and DL algorithms in clinical contexts must be verified, and ethical issues around the use of personal data must be addressed, but this requires more study.
RÉSUMÉ
BACKGROUND: Recent studies have suggested that the hippocampus (HC) is involved in cognitive and behavioral functions beyond memory. We aimed to investigate how the volume of each subfield of the HC is associated with distinct patterns of coping strategies, emotion regulation, and impulsivity in a healthy population. METHODS: We studied a total of 218 healthy subjects using the Leipzig mind-brain-body dataset. Participants were assessed for coping strategies, emotion regulation, and impulsivity using the Cognitive Emotion Regulation Questionnaire (CERQ), Coping Orientations to Problems Experienced (COPE), Impulsive Behavior Scale (UPPS), and Behavioral Activation and Inhibition System (BAS/BIS). The associations between HC subfield volumes including CA1, CA2/3, CA4/DG, SR-SL-SM, and subiculum, and behavioral scores were examined using multiple linear regression models adjusted for possible confounders, including age, sex, years of education, handedness, total intracranial volume (ICV), and HC volume. RESULTS: The use of emotional support, venting, and positive reframing coping strategies were significantly and positively correlated with total, total right, and total left HC volumes. Venting was significantly associated with CA1 after adjusting for age, sex, handedness, and education (P=0.001, B = 0.265, P-FDR = 0.005). No significant association was observed between CERQ subscales and HC subfield volumes after controlling for confounders and multiple analyses. However, sensation-seeking subscale of the UPPS-P was positively correlated with total and right CA2-CA3 volumes after adjustments for age, sex, handedness, ICV, and HC volumes (P=0.002, B = 0.266, P-FDR = 0.035). BAS and BIS subscales did not show significant relationship with HC subfield volumes. CONCLUSION: Patterns of HC subfields volumes are associated with coping strategies, impulsivity, and emotion regulation. In particular, using emotional support, positive reframing, venting, and sensation seeking are significantly associated with certain HC subfield volumes. These findings suggest that the hippocampus may play a crucial role in modulating emotional responses and behavioral adaptations, offering potential targets for therapeutic interventions.
RÉSUMÉ
Age-related neurological disorders [AND] include neurodegenerative diseases [NDDs] such as Alzheimer's disease [AD] and Parkinson's disease [PD], which are the most prevalent types of dementia in the elderly. It also includes other illnesses such as migraine and epilepsy. ANDs are multifactorial, but aging is their major risk factor. The most frequent and vital pathological features of AND are oxidative stress, inflammation, and accumulation of misfolded proteins. As AND brain damage is a significant public health burden and its incidence is increasing, much has been done to overcome it. Pomegranate (Punica granatum L.) is one of the polyphenol-rich fruits that is widely mentioned in medical folklore. Pomegranate is commonly used to treat common disorders such as diarrhea, abdominal pain, wound healing, bleeding, dysentery, acidosis, microbial infections, infectious and noninfectious respiratory diseases, and neurological disorders. In the current review article, we aimed to summarize the data on the pharmacotherapeutic potentials of pomegranate in ANDs.