Motoric Cognitive Risk Syndrome, Subtypes and 8-Year All-Cause Mortality in Aging Phenotypes: The Salus in Apulia Study

BackgroundThis study aims to establish the key clinical features of different motoric cognitive risk (MCR) subtypes based on individual quantitative measures of cognitive impairment and to compare their predictive power on survival over an 8-year observation time.MethodsWe analyzed data from a population-based study of 1138 subjects aged 65 years and older in south Italy. These individuals were targeted and allocated to subtypes of the MCR phenotype according to the slowness criterion plus one other different cognitive domain for each characterized phenotype (Subjective Cognitive Complaint [SCC]; Global Function [Mini Mental State Examination (MMSE) < 24]; or a combination of both). Clinical evaluation and laboratory assays, along with a comprehensive battery of neuropsychological and physical tests, completed the sample investigation.ResultsMCR prevalence was found to be 9.8% (n = 112), 3.6% (n = 41), 3.4% (n = 39) and 1.8% (n = 21) for the MCR, MCR-GlobalFunction, MCR-StructuredSCC and MCR-SCC and GlobalFunction, respectively. Univariate Cox survival analysis showed an association only of the MCR-GlobalFunction subtype with an almost three-fold increased risk of overall death as compared to the other counterparts (HR 2.53, 95%CI 1.28 to 4.99) over an 8-year observation period. Using Generalized Estimating Equations (GEE) for clustered survival data, we found that MCR males had an increased and significant mortality risk with respect to MCR female subjects.ConclusionsMCR phenotypes assigned to the MMSE cognitive domain are more likely to have an increased risk of overall mortality, and gender showed a huge effect on the risk of death for MCR subjects over the 8-year observation

Role of Dietary Carotenoids in Frailty Syndrome: A Systematic Review

Unbalanced diets and altered micronutrient intake are prevalent in the aging adult population. We conducted a systematic review to appraise the evidence regarding the association between single (α-carotene, β-carotene, lutein, lycopene, β-cryptoxanthin) or total carotenoids and frailty syndrome in the adult population. The literature was screened from study inception to December 2021, using six different electronic databases. After establishing inclusion criteria, two independent researchers assessed the eligibility of 180 retrieved articles. Only 11 fit the eligibility requirements, reporting five carotenoid entries. No exclusion criteria were applied to outcomes, assessment tools, i.e., frailty constructs or surrogates, recruitment setting, general health status, country, and study type (cohort or cross-sectional). Carotenoid exposure was taken as either dietary intake or serum concentrations. Cross-sectional design was more common than longitudinal design (n = 8). Higher dietary and plasma levels of carotenoids, taken individually or cumulatively, were found to reduce the odds of physical frailty markedly, and the evidence showed consistency in the direction of association across all selected studies. Overall, the methodological quality was rated from moderate (27%) to high (73%). Prevention of micronutrient deficiencies has some potential to counteract physical decline. Considering carotenoids as biological markers, when monitoring micronutrient status, stressing increased fruit and vegetable intake may be part of potential multilevel interventions to prevent or better manage disability

Dietary Patterns Associated with Diabetes in an Older Population from Southern Italy Using an Unsupervised Learning Approach

Dietary behaviour is a core element in diabetes self-management. There are no remarkable differences between nutritional guidelines for people with type 2 diabetes and healthy eating recommendations for the general public. This study aimed to evaluate dietary differences between subjects with and without diabetes and to describe any emerging dietary patterns characterizing diabetic subjects. In this cross-sectional study conducted on older adults from Southern Italy, eating habits in the “Diabetic” and “Not Diabetic” groups were assessed with FFQ, and dietary patterns were derived using an unsupervised learning algorithm: principal component analysis. Diabetic subjects (n = 187) were more likely to be male, slightly older, and with a slightly lower level of education than subjects without diabetes. The diet of diabetic subjects reflected a high-frequency intake of dairy products, eggs, vegetables and greens, fresh fruit and nuts, and olive oil. On the other hand, the consumption of sweets and sugary foods was reduced compared to non-diabetics (23.74 ± 35.81 vs. 16.52 ± 22.87; 11.08 ± 21.85 vs. 7.22 ± 15.96). The subjects without diabetes had a higher consumption of red meat, processed meat, ready-to-eat dishes, alcoholic drinks, and lower vegetable consumption. The present study demonstrated that, in areas around the Mediterranean Sea, older subjects with diabetes had a healthier diet than their non-diabetic counterparts

Unveiling the Diagnostic Potential of Linguistic Markers in Identifying Individuals with Parkinson’s Disease through Artificial Intelligence: A Systematic Review

While extensive research has documented the cognitive changes associated with Parkinson’s disease (PD), a relatively small portion of the empirical literature investigated the language abilities of individuals with PD. Recently, artificial intelligence applied to linguistic data has shown promising results in predicting the clinical diagnosis of neurodegenerative disorders, but a deeper investigation of the current literature available on PD is lacking. This systematic review investigates the nature of language disorders in PD by assessing the contribution of machine learning (ML) to the classification of patients with PD. A total of 10 studies published between 2016 and 2023 were included in this review. Tasks used to elicit language were mainly structured or unstructured narrative discourse. Transcriptions were mostly analyzed using Natural Language Processing (NLP) techniques. The classification accuracy (%) ranged from 43 to 94, sensitivity (%) ranged from 8 to 95, specificity (%) ranged from 3 to 100, AUC (%) ranged from 32 to 97. The most frequent optimal linguistic measures were lexico-semantic (40%), followed by NLP-extracted features (26%) and morphological consistency features (20%). Artificial intelligence applied to linguistic markers provides valuable insights into PD. However, analyzing measures derived from narrative discourse can be time-consuming, and utilizing ML requires specialized expertise. Moving forward, it is important to focus on facilitating the integration of both narrative discourse analysis and artificial intelligence into clinical practice

Combining Biomechanical Features and Machine Learning Approaches to Identify Fencers' Levels for Training Support

Nowadays, modern technology is widespread in sports; therefore, finding an excellent approach to extracting knowledge from data is necessary. Machine Learning (ML) algorithms can be beneficial in biomechanical data management because they can handle a large amount of data. A fencing lunge represents an exciting scenario since it necessitates neuromuscular coordination, strength, and proper execution to succeed in a competition. However, to investigate and analyze a sports movement, it is necessary to understand its nature and goal and to identify the factors that affect its performance. The present work aims to define the best model to screen élite and novice fencers to develop further a tool to support athletes’ and trainers’ activity. We conducted a cross-sectional study in a fencing club to collect anthropometric and biomechanical data from élite and novice fencers. Wearable sensors were used to collect biomechanical data, including a wireless inertial system and four surface electromyographic (sEMG) probes. Four different ML algorithms were trained for each dataset, and the most accurate was further trained with hyperparameter tuning. The best Machine Learning algorithm was Multilayer Perceptron (MLP), which had 96.0% accuracy and 90% precision, recall, and F1-score when predicting class novice (0); and 93% precision, recall, and F1-score when predicting class élite (1). Interestingly, the MLP model has a slightly higher capacity to recognize élite fencers than novices; this is important to determine which training planning and execution are the best to achieve good performances.</jats:p

Profili giuridici della discriminazione razziale nel mondo di oggi

Lo scritto affronta aspetti delle discriminazioni razziali legati alle migrazioni, scavando nella relazione tra società, economia e diritto (in occasione del Convegno nazionale MEIC 2016). Le discriminazioni razziali e altre forme di razzismo son affrontate nelle ricadute sulla protezione giurisdizionale civile e penale, tra diritto comunitario e nazionale. Per superare le rinvigorite forme di discriminazione razziale occorron forti spinte culturali, capaci di incidere anche sul diritto

Dietary profiling of physical frailty in older age phenotypes using a machine learning approach: the Salus in Apulia Study

Abstract Purpose Growing awareness of the biological and clinical value of nutrition in frailty settings calls for further efforts to investigate dietary gaps to act sooner to achieve focused management of aging populations. We cross-sectionally examined the eating habits of an older Mediterranean population to profile dietary features most associated with physical frailty. Methods Clinical and physical examination, routine biomarkers, medical history, and anthropometry were analyzed in 1502 older adults (65 +). CHS criteria were applied to classify physical frailty, and a validated Food Frequency Questionnaire to assess diet. The population was subdivided by physical frailty status (frail or non-frail). Raw and adjusted logistic regression models were applied to three clusters of dietary variables (food groups, macronutrients, and micronutrients), previously selected by a LASSO approach to better predict diet-related frailty determinants. Results A lower consumption of wine (OR 0.998, 95% CI 0.997–0.999) and coffee (OR 0.994, 95% CI 0.989–0.999), as well as a cluster of macro and micronutrients led by PUFAs (OR 0.939, 95% CI 0.896–0.991), zinc (OR 0.977, 95% CI 0.952–0.998), and coumarins (OR 0.631, 95% CI 0.431–0.971), was predictive of non-frailty, but higher legumes intake (OR 1.005, 95%CI 1.000–1.009) of physical frailty, regardless of age, gender, and education level. Conclusions Higher consumption of coffee and wine, as well as PUFAs, zinc, and coumarins, as opposed to legumes, may work well in protecting against a physical frailty profile of aging in a Mediterranean setting. Longitudinal investigations are needed to better understand the causal potential of diet as a modifiable contributor to frailty during aging. </jats:sec