Search Results (4)

Background: Depression is a heterogeneous and recurrent condition, whose underlying biological mechanisms remain poorly understood. MicroRNAs (miRNAs), small non-coding RNAs that regulate post-transcriptional gene expression, are increasingly implicated in cross sectional miRNA studies of depression and depressive symptoms; however, longitudinal studies capturing miRNA changes over time in relation to depression are scarce. Methods: We conducted small RNA sequencing of leukocyte-derived miRNAs at two timepoints in a prospective community-based cohort (n = 185) to assess associations between within-person changes in depressive symptom severity (ΔPHQ-9) and longitudinal miRNA expression. Differential expression analyses were performed using a paired limma-voom framework, adjusting for covariates (baseline PHQ-9, sex, age, DNAm-derived immune cell covariates and ancestry components derived from matched blood samples) and within-subject correlation. Results: Although no miRNAs survived multiple-testing correction, 68 mature unique miRNAs showed nominal associations (p < 0.05) with depressive symptom severity change (ΔPHQ-9). Several top candidates, including miR-493-3p, miR-409-3p, and miR-323a-3p, displayed expression patterns aligning with prior reports implicating these miRNAs in stress responsivity, synaptic plasticity, and neurodevelopmental regulation. Exploratory follow-up of predicted targets of the nominally symptom-associated miRNAs converged on genes in pathways central to depression biology, including neurotransmission, HPA axis/inflammatory signaling, neuroplasticity, and circadian regulation. Enrichment analyses highlighted receptor tyrosine kinase and intracellular signaling cascades, hypothalamic–pituitary–adrenal axis feedback, and inflammatory pathways. Conclusions: These findings provide preliminary evidence that peripheral miRNA expression changes may reflect depressive symptom trajectories, highlighting potential molecular pathways involved in depression. Further studies with larger samples and broader symptom severity are warranted to validate these dynamic miRNA signatures. Full article

Breakfast consumption among youth is associated with improved diet quality, weight, cognition, and behavior. However, not all youth in the United States consume breakfast. Participation in the School Breakfast Program (SBP) is also low relative to the lunch program. Universal free breakfast (UFB) policies have been implemented to increase breakfast participation by reducing cost and stigma associated with the SBP. This study examined whether a UFB policy implemented in a school district in the Southeast US was associated with changes in breakfast participation, school attendance, and student weight. A longitudinal study of secondary data was conducted, and a mixed modeling approach was used to assess patterns of change in SBP participation. General linear models were used to assess attendance and student weight change. On average, across schools in the district, there was an increase in breakfast participation of 4.1 percentage points following the implementation of the policy. The change in breakfast participation in schools differed by the percent of students in the school who received school meals for free or at a reduced price, the percent of students of color, and the grade level of the school. Increases in SBP participation were not associated with significant changes in attendance or weight. UFB policies may be effective in increasing participation in the SBP. Full article

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings. Full article

One function of skin microbiota is to resist colonization and infection by external microorganisms. We sought to detect whether the structure of the hand microbiota of 34 healthcare workers (HCW) in a surgical intensive care unit mediates or modifies the relationship between demographic and behavioral factors and potential pathogen carriage on hands after accounting for pathogen exposure. We used a taxonomic screen (16S rRNA) to characterize the bacterial community, and qPCR to detect presence of Staphylococcus aureus, Enterococcus spp., methicillin-resistant Staphylococcus aureus (MRSA), and Candida albicans on their dominant hands. Hands were sampled weekly over a 3-week period. Age, hand hygiene, and work shift were significantly associated with potential pathogen carriage and the associations were pathogen dependent. Additionally, the overall hand microbiota structure was associated with the carriage of potential pathogens. Hand microbiota community structure may act as a biomarker of pathogen carriage, and modifying that structure may potentially limit pathogen carriage among HCW. Full article