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"en": "Abstract Aim This study aimed to evaluate the effects of using the teach-back method among women with limited maternal health literacy (LMHL) on maternal health literacy(MHL), postpartum health behaviours and maternal-infant health outcomes. Methods A randomized controlled study was conducted in the obstetrics department of Anhui Provincial Hospital, China. A total of 258 pregnant women with LMHL were recruited at the point of admission to the hospital for birth and randomly assigned to the control group (n = 130), where women received routine education sessions, and the teach-back group (n = 128), where women received routine education sessions plus a teach-back intervention. The two groups were assessed in terms of MHL before and after the intervention, breastfeeding execution, uptake of 42-day postpartum check-ups, complete uptake of one-time recommended vaccines, and physical health outcomes. Statistical tests were employed for data analysis. Results There was no significant difference between the two groups in terms of MHL and other social, demographic, and medical status at baseline. After the intervention, the teach-back group had a higher level of MHL (p < 0.001), better postpartum health behaviours in terms of exclusive breastfeeding within 24 hours postpartum (x2 = 22.853, p"
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"en": "Abstract Background DNA methylation plays an important role in studying the epigenetics of various biological processes including many diseases. Although differential methylation of individual cytosines can be informative, given that methylation of neighboring CpGs are typically correlated, analysis of differentially methylated regions is often of more interest. Results We have developed a probabilistic method and software, LuxHMM, that uses hidden Markov model (HMM) to segment the genome into regions and a Bayesian regression model, which allows handling of multiple covariates, to infer differential methylation of regions. Moreover, our model includes experimental parameters that describe the underlying biochemistry in bisulfite sequencing and model inference is done using either variational inference for efficient genome-scale analysis or Hamiltonian Monte Carlo (HMC). Conclusions Analyses of real and simulated bisulfite sequencing data demonstrate the competitive performance of LuxHMM compared with other published differential methylation analysis methods."
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