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Parkinson's disease (PD) is a degenerative nervous system disorder involving motor disturbances. Motor alterations affect the gait according to the progression of PD and can be used by experts in movement disorders to rate the severity of the disease. However, this rating depends on the expertise of the clinical specialist. Therefore, the diagnosis may be inaccurate, particularly in the early stages of PD where abnormal gait patterns can result from normal aging or other medical conditions. Consequently, several classification systems have been developed to enhance PD diagnosis. In this paper, a PD gait severity classification algorithm was developed using vertical ground reaction force (VGRF) signals. The VGRF records used are from a public database that includes 93 PD patients and 72 healthy controls adults. The work presented here focuses on modeling each foot's gait stance phase signals using a modified convolutional long deep neural network (CLDNN) architecture. Subsequently, the results of each model are combined to predict PD severity. The classifier performance was evaluated using ten-fold cross-validation. The best-weighted accuracies obtained were 99.296(0.128)% and 99.343(0.182)%, with the Hoehn-Yahr and UPDRS scales, respectively, outperforming previous results presented in the literature. The classifier proposed here can effectively differentiate gait patterns of different PD severity levels based on gait signals of the stance phase., (© 2024. International Federation for Medical and Biological Engineering.)

Background/objectives: Fat-mass (FM) assessment since birth using valid methodologies is crucial since excessive adiposity represents a risk factor for adverse metabolic outcomes., Aim: To develop infant FM prediction equations using anthropometry and validate them against air-displacement plethysmography (ADP)., Subjects/methods: Clinical, anthropometric (weight, length, body-mass index -BMI-, circumferences, and skinfolds), and FM (ADP) data were collected from healthy-term infants at 1 (n = 133), 3 (n = 105), and 6 (n = 101) months enrolled in the OBESO perinatal cohort (Mexico City). FM prediction models were developed in 3 steps: 1) Variable Selection (LASSO regression), 2) Model behavior evaluation (12-fold cross-validation, using Theil-Sen regressions), and 3) Final model evaluation (Bland-Altman plots, Deming regression)., Results: Relevant variables in the FM prediction models included BMI, circumferences (waist, thigh, and calf), and skinfolds (waist, triceps, subscapular, thigh, and calf). The R 2 of each model was 1 M: 0.54, 3 M: 0.69, 6 M: 0.63. Predicted FM showed high correlation values (r ≥ 0.73, p < 0.001) with FM measured with ADP. There were no significant differences between predicted vs measured FM (1 M: 0.62 vs 0.6; 3 M: 1.2 vs 1.35; 6 M: 1.65 vs 1.76 kg; p > 0.05). Bias were: 1 M -0.021 (95%CI: -0.050 to 0.008), 3 M: 0.014 (95%CI: 0.090-0.195), 6 M: 0.108 (95%CI: 0.046-0.169)., Conclusion: Anthropometry-based prediction equations are inexpensive and represent a more accessible method to estimate body composition. The proposed equations are useful for evaluating FM in Mexican infants., (© 2023. The Author(s).)

The identification of brain dynamical changes under different cognitive conditions with noninvasive techniques such as electroencephalography (EEG) is relevant for the understanding of their underlying neural mechanisms. The comprehension of these mechanisms has applications in the early diagnosis of neurological disorders and asynchronous brain computer interfaces. In both cases, there are no reported features that could describe intersubject and intra subject dynamics behavior accurately enough to be applied on a daily basis. The present work proposes the use of three nonlinear features (recurrence rate, determinism, and recurrence times) extracted from recurrence quantification analysis (RQA) to describe central and parietal EEG power series complexity in continuous alternating episodes of mental calculation and rest state. Our results demonstrate a consistent mean directional change of determinism, recurrence rate, and recurrence times between conditions. Increasing values of determinism and recurrence rate were present from the rest state to mental calculation, whereas recurrence times showed the opposite pattern. The analyzed features in the present study showed statistically significant changes between rest and mental calculation states in both individual and population analysis. In general, our study described mental calculation EEG power series as less complex systems in comparison to the rest state. Moreover, ANOVA showed stability of RQA features along time., (© 2023 Author(s). Published under an exclusive license by AIP Publishing.)

Ultra-processed food (UPF) consumption during gestation may lead to increased oxidative stress (OS) and could affect pregnancy outcomes. This study aims to evaluate the association of UPF consumption during pregnancy with circulating levels of OS markers. Diet was assessed (average of three assessments) in 119 pregnant women enrolled in the OBESO perinatal cohort (Mexico), obtaining quantitative data and the percentage of energy that UPFs (NOVA) contributed to the total diet. Sociodemographic, clinical (pregestational body-mass index and gestational weight gain) and lifestyle data were collected. Maternal circulating levels of OS markers (malondialdehyde (MDA), protein carbonylation (PC), and total antioxidant capacity (TAC)) were determined at the third trimester of pregnancy. Adjusted linear regression models were performed to analyze the association between UPFs and OS markers. UPFs represented 27.99% of the total energy intake. Women with a lower UPF consumption (<75 percentile°) presented a higher intake of fiber, ω-3, ω-6, and a lower ω-6/3 ratio. Linear regression models showed that UPFs were inversely associated with TAC and MDA. Fiber intake was associated with PC. UPF intake during pregnancy may result in an increase in oxidative stress. When providing nutrition care, limiting or avoiding UPFs may be an intervention strategy that could promote a better antioxidant capacity in the body.