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The benefits of breastfeeding for the health and wellbeing of both infants and mothers are well documented, yet global breastfeeding rates are low. One factor associated with low breast feeding is maternal body mass index (BMI), which is used as a measure of obesity. The negative relationship between maternal obesity and breastfeeding is likely caused by a variety of social, psychological, and physiological factors. Maternal obesity may also have a direct biological association with breastfeeding through changes in maternal DNA methylation. Here, we investigate this potential biological association using data from a UK-based cohort study, the Avon Longitudinal Study of Parents and Children (ALSPAC). We find that pre-pregnancy body mass index (BMI) is associated with lower initiation to breastfeed and shorter breastfeeding duration. We conduct epigenome-wide association studies (EWAS) of pre-pregnancy BMI and breastfeeding outcomes, and run candidate-gene analysis of methylation sites associated with BMI identified via previous meta-EWAS. We find that DNA methylation at cg11453712, annotated to PHTP1, is associated with pre-pregnancy BMI. From our results, neither this association nor those at candidate-gene sites are likely to mediate the link between pre-pregnancy BMI and breastfeeding., (© 2024. The Author(s).)

The COVID-19 pandemic drastically affected higher education and higher education students around the world, but few studies of college students' experiences during the COVID-19 pandemic have been conducted in Latin America. This study describes the COVID-19-related experiences and perspectives of Peruvian college students. We surveyed 3,427 full-time college students (average age: 23 years) attending a multi-campus Peruvian university in fall 2020. Participants were recruited through the digital platform of the learning management system at their university, email, and social media. We asked participants how they were managing risks related to COVID-19; the continuity of social, educational, and work activities; and the psychological and economic impacts of the pandemic on their lives. Since March 2020, 73.0% of participants reported COVID-19-related symptoms, but only 33.9% were tested for COVID-19. During the national quarantine imposed by the Peruvian government (March 15-June 30, 2020), 64.3% of participants remained in their house. Furthermore, while 44.0% of participants were working in February 2020 (95% CI: [41.7%, 46.4%]), only 23.6% (95% CI: [21.7%, 25.7%]) were working immediately after the pandemic began (i.e., at the end of April 2020). Participants were more stressed about the health and educational implications of COVID-19 for Peruvian society and their families than about themselves. The public health, economic, and educational implications of COVID-19 on college students are continuing to unfold. This study informed Peruvian higher education institutions' continued response to the COVID-19 pandemic, the progressive return to postpandemic activities, as well as other future pandemics and other crises., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: AI models have shown promise in performing many medical imaging tasks. However, our ability to explain what signals these models have learned is severely lacking. Explanations are needed in order to increase the trust of doctors in AI-based models, especially in domains where AI prediction capabilities surpass those of humans. Moreover, such explanations could enable novel scientific discovery by uncovering signals in the data that aren't yet known to experts., Methods: In this paper, we present a workflow for generating hypotheses to understand which visual signals in images are correlated with a classification model's predictions for a given task. This approach leverages an automatic visual explanation algorithm followed by interdisciplinary expert review. We propose the following 4 steps: (i) Train a classifier to perform a given task to assess whether the imagery indeed contains signals relevant to the task; (ii) Train a StyleGAN-based image generator with an architecture that enables guidance by the classifier ("StylEx"); (iii) Automatically detect, extract, and visualize the top visual attributes that the classifier is sensitive towards. For visualization, we independently modify each of these attributes to generate counterfactual visualizations for a set of images (i.e., what the image would look like with the attribute increased or decreased); (iv) Formulate hypotheses for the underlying mechanisms, to stimulate future research. Specifically, present the discovered attributes and corresponding counterfactual visualizations to an interdisciplinary panel of experts so that hypotheses can account for social and structural determinants of health (e.g., whether the attributes correspond to known patho-physiological or socio-cultural phenomena, or could be novel discoveries)., Findings: To demonstrate the broad applicability of our approach, we present results on eight prediction tasks across three medical imaging modalities-retinal fundus photographs, external eye photographs, and chest radiographs. We showcase examples where many of the automatically-learned attributes clearly capture clinically known features (e.g., types of cataract, enlarged heart), and demonstrate automatically-learned confounders that arise from factors beyond physiological mechanisms (e.g., chest X-ray underexposure is correlated with the classifier predicting abnormality, and eye makeup is correlated with the classifier predicting low hemoglobin levels). We further show that our method reveals a number of physiologically plausible, previously-unknown attributes based on the literature (e.g., differences in the fundus associated with self-reported sex, which were previously unknown)., Interpretation: Our approach enables hypotheses generation via attribute visualizations and has the potential to enable researchers to better understand, improve their assessment, and extract new knowledge from AI-based models, as well as debug and design better datasets. Though not designed to infer causality, importantly, we highlight that attributes generated by our framework can capture phenomena beyond physiology or pathophysiology, reflecting the real world nature of healthcare delivery and socio-cultural factors, and hence interdisciplinary perspectives are critical in these investigations. Finally, we will release code to help researchers train their own StylEx models and analyze their predictive tasks of interest, and use the methodology presented in this paper for responsible interpretation of the revealed attributes., Funding: Google., Competing Interests: Declaration of interests OL, DYS, HCL, CS, DRW, GSC, AH, YM, YL, NH and BB are current or past Google employees and may own Alphabet stock. IT, CRB, and CL are paid consultants to Google. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Care homes enable people with advanced physical and cognitive impairment to live well with 24-h support from staff. They are a feature of care systems in most countries. They have proved pivotal to the coronavirus disease 2019 (COVID-19) response. We searched Age and Ageing for care-home articles published since 2015. From these we collated 42 into the Age and Ageing care-home collection. This collection draws together important papers that show how Age and Ageing is helping to shape and grow care-home research. The  collection outlines the technical issues that researchers face by grouping together important feasibility trials conducted in the sector. It looks at the challenges of measuring quality of life and working with routine data in care homes. It brings together observational studies considering loneliness, functional dependency, stroke outcomes, prescribing and acute deterioration. Health services research in care homes is represented by two studies that demonstrate realist evaluation as a way to make sense of service innovations. Papers are included that consider: non-pharmacological strategies for residents with dementia, end-of-life care, sexuality and intimacy and the care-home workforce. Given the importance of the COVID-19 pandemic in care homes, all of the care home COVID-19 papers published in Age and Ageing to date are included. Finally, a group of papers that present innovative approaches to research in care homes, each of which give voice to residents and/or staff, are collated and presented as a way of moving towards a more resident and care home centred research agenda., (© The Author(s) 2021. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Sexual traits convey information about individual quality to potential mates. Environmental and genetic factors affect sexual trait expression and perception via effects on animal condition and health. High fat diet (HFD) is one environmental factor that adversely affects Drosophila melanogaster health, and its effects on animal health are mediated through conserved metabolic signaling pathways. HFD decreases female attractiveness, resulting in reduced male mating behaviors toward HFD females. HFD also affects the ability of males to judge mate attractiveness and likely alters fly condition and sexual traits to impact mating behavior. Here we show that HFD affects both visual (body size) and non-visual (pheromone profiles) sexual traits, which likely contribute to decreased fly attractiveness. We also demonstrate that adult-specific HFD effects on male mate preference can be rescued by changing metabolic signaling. These results demonstrate that HFD alters Drosophila sexual cues to reflect concurrent effects on condition and that less severe behavioral defects can be reversed by genetic manipulations that rescue fly health. This work expands on current knowledge of the role that metabolic signaling pathways play in linking animal health, sexual traits, and mating behavior, and provides a robust assay in a genetically tractable system to continue examining these processes.

Since the initial reports that a group of small RNAs, now known as microRNAs (miRNAs), regulates gene expression without being translated into proteins, there has been an explosion of studies on these important expression modulators. Drosophila melanogaster has proven to be one of the most amenable animal models for investigations of miRNA biogenesis and gene regulatory activities. Here, we highlight the publicly available genetic tools and strategies for in vivo functional studies of miRNA activity in D. melanogaster. By coupling genetic approaches using available strain libraries with technologies for miRNA expression analysis and target and pathway prediction, researchers' ability to test functional activities of miRNAs in vivo is now greatly enhanced. We also comment on the tools that need to be developed to aid in comprehensive evaluation of Drosophila miRNA activities that impact traits of interest.