Human milk bank: The breastfeeding counseling and the duration of exclusive breastfeeding

Made available in DSpace on 2019-09-12T16:32:38Z (GMT). No. of bitstreams: 0 Previous issue date: 2015Introduction: To identify which sociodemographic factors are associated with early weaning and compare the duration of exclusive breast feeding between mothers which received counseling about maternal feeding and mothers which was not guided for these practices. Methods: A cross-sectional study with 25 mothers that received counseling about the benefits of maternal feeding in the human milk bank and 25 from a university hospital that were not counseled. Data were collected using a structured questionnaire addressing mother-related data, the infant and the breastfeeding in the first semester. Comparison and odds ratio were the statistical analyses adopted. Results: There was not a significant difference of the duration of exclusive breast-feeding between the two groups (p = 0,524). Among mothers in the human milk bank group that discontinued early exclusive breast-feeding, fewer children (p=0,034) and a higher frequency of maternal work (p = 0,022) were observed. Wile, in the university hospital group low education (p < 0.001) and lower income (p = 0.009) were prevalent. In the totality of the sample, the interruption of exclusive breast-feeding was associated with <1 children (OR = 0.21, p = 0.030), the presence of a partner (OR = 0.046, p = 0.001) and the use of bottles or pacifiers (OR = 87.5, p <0.001). In both groups, the most cited motivation for exclusive breast-feeding discontinuation was the medical assistance. It was observed the absence of a specific standard guideline provided in the Human Milk Bank, and less than the recommended number of consultations in the literature. Conclusions: Socioeconomic factors and inadequate incentives negatively influence the duration of exclusive breast feeding. It was verified the need to provide standardized and more frequent counseling for effective reduction of early weaning.Figueiredo, M.C.D., Departamento de Enfermagem e Nutrição, Universidade de Taubaté (Unitau), Taubaté, BrazilBueno, M.P., Departamento de Enfermagem e Nutrição, Universidade de Taubaté (Unitau), Taubaté, BrazilRibeiro, C.C., Departamento de Enfermagem e Nutrição, Universidade de Taubaté (Unitau), Taubaté, BrazilLima, P.A., Departamento de Nutrição, Faculdade de Saúde Pública (FSP), Universidade de São Paulo (USP), São Paulo, BrazilSilva, Í.T., Departamento de Nutrição, Faculdade de Saúde Pública (FSP), Universidade de São Paulo (USP), São Paulo, Brazi

Alzheimer's disease diagnosis by blood plasma molecular fluorescence spectroscopy (EEM)

Despite tremendous research advances in detecting Alzheimer's disease (AD), traditional diagnostic tests remain expensive, time-consuming or invasive. The search for a low-cost, rapid, and minimally invasive test has marked a new era of research and technological developments toward establishing blood-based AD biomarkers. The current study has employed excitation-emission matrices (EEM) of fluorescence spectroscopy combined with machine learning to diagnose AD using blood plasma samples from 230 individuals (83 AD patients from 147 healthy controls). To evaluate the performance of the classification algorithms, we calculated the commonly used figures of merit (accuracy, sensitivity and specificity) and figures of merit that take into account the samples unbalance and the discrimination power of the models, as F2-score (F2), Matthews correlation coefficient (MCC) and test effectiveness ([Formula: see text]). The classification models achieved satisfactory results: Parallel Factor Analysis with Quadratic Discriminant Analysis (PARAFAC-QDA) with 83.33% sensitivity, 100% specificity, 86.21% F2; and Tucker3-QDA with 91.67% sensitivity, 95.45% specificity and 91.67% F2. In addition, the classifiers show high overall performance with 94.12% accuracy and 0.87 MCC. Regarding the discrimination power between healthy and AD patients, the classification algorithms showed high effectiveness with the mean scores separated by three or more standard deviations. The PARAFAC's spectral profiles and the wavelength values from both models loading profiles can be used in future research to relate this information to plasma AD biomarkers. Our results point to a rapid, low-cost and minimally invasive blood-based method for AD diagnosis. © 2022. The Author(s)