Association between Mediterranean diet and dementia and Alzheimer disease: a systematic review with meta-analysis.

BACKGROUND: Dementia affects 5-8% of the population aged over 65 years (~50 million worldwide). Several factors are associated with increased risk, including diet. The Mediterranean diet (MedDiet) has shown potential protective effects against several chronic diseases. AIMS: This systematic review with meta-analysis aim was to assess the association between adherence to the MedDiet and the risk of dementia in the elderly. METHODS: PRISMA-2020 guidelines were followed. PubMed/MEDLINE and Scopus were searched on 17 July 2023. The Newcastle-Ottawa Scale tool was used to assess the risk of bias. The protocol was pre-registered in PROSPERO (registration number: CRD 42023444368). Heterogeneity was assessed using the I2 test. Publication bias was assessed by visual inspection of the funnel plot and by Egger's regression asymmetry test. The final effect size was reported as OR or HR, depending on the study design of the included studies. RESULTS: Out of 682 records, 21 were included in the analysis. The pooled OR was 0.89 (95% CI = 0.84-0.94) based on 65,955 participants (I2 = 69.94). When only cohort studies were included, HR was 0.84 (95% CI = 0.76-0.94) based on 55,205 participants (I2 = 89.70). When only Alzheimer Disease was considered OR was 0.73 (95% CI = 0.62-0.85) based on 38,292 participants (I2 = 63.85). DISCUSSION: Despite the relatively low risk reduction associated with higher adherence to MedDiet among elderly, it should be considered that this population is the most affected. CONCLUSIONS: Adherence to MedDiet could be an effective non-pharmacological measure to reduce the burden of dementia, even among elderly.

A pilot study for investigating the feasibility of supervised machine learning approaches for the classification of pedestrians struck by vehicles.

This research focuses on the application of Artificial Intelligence (AI) methodologies to the problem of classifying vehicles involved in lethal pedestrian collisions. Specifically, the vehicle type is predicted on the basis of traumatic injury suffered by casualties, exploiting machine learning algorithms. In the present study, AI-assisted diagnosis was shown to have correct prediction about 70% of the time. In pedestrians struck by trucks, more severe injuries were appreciated in the facial skeleton, lungs, major airways, liver, and spleen as well as in the sternum/clavicle/rib complex, whereas the lower extremities were more affected by fractures in pedestrians struck by cars. Although the distinction of the striking vehicle should develop beyond autopsy evidence alone, the presented approach which is novel in the realm of forensic science, is shown to be effective in building automated decision support systems. Outcomes from this system can provide valuable information after the execution of autoptic examinations supporting the forensic investigation. Preliminary results from the application of machine learning algorithms with real-world datasets seem to highlight the efficacy of the proposed approach, which could be used for further studies concerning this topic.