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This research investigates whether and how two fundamental environmental factors-harshness and unpredictability-interact in regulating child and adolescent development, informed by life-history theory and drawing on data from the National Institute of Child Health & Human Development Study of Early Child Care and Youth Development (N = 1,364). Early life harshness was operationalized as the typical level of family income-to-needs based on six repeated measurements across the first 4.5 years of life and early life unpredictability as random variation using the same family income measurements. Results revealed that children functioned most competently in the social and academic domain as kindergarteners when exposed to low environmental harshness and low unpredictability and least competently when they experienced high harshness and low unpredictability. The same interaction pattern emerged in adolescence in forecasting cognitive-academic competence and sexual behavior. Findings are discussed in terms of how reliable and unreliable environmental cues shape developmental trajectories. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Understanding the genetic basis of disease is a fundamental aspect of medical research, as genes are the classic units of heredity and play a crucial role in biological function. Identifying associations between genes and diseases is critical for diagnosis, prevention, prognosis, and drug development. Genes that encode proteins with similar sequences are often implicated in related diseases, as proteins causing identical or similar diseases tend to show limited variation in their sequences. Predicting gene–disease association (GDA) requires time-consuming and expensive experiments on a large number of potential candidate genes. Although methods have been proposed to predict associations between genes and diseases using traditional machine learning algorithms and graph neural networks, these approaches struggle to capture the deep semantic information within the genes and diseases and are dependent on training data. To alleviate this issue, we propose a novel GDA prediction model named FusionGDA, which utilizes a pre-training phase with a fusion module to enrich the gene and disease semantic representations encoded by pre-trained language models. Multi-modal representations are generated by the fusion module, which includes rich semantic information about two heterogeneous biomedical entities: protein sequences and disease descriptions. Subsequently, the pooling aggregation strategy is adopted to compress the dimensions of the multi-modal representation. In addition, FusionGDA employs a pre-training phase leveraging a contrastive learning loss to extract potential gene and disease features by training on a large public GDA dataset. To rigorously evaluate the effectiveness of the FusionGDA model, we conduct comprehensive experiments on five datasets and compare our proposed model with five competitive baseline models on the DisGeNet-Eval dataset. Notably, our case study further demonstrates the ability of FusionGDA to discover hidden associations effectively. The complete code and datasets of our experiments are available at https://github.com/ZhaohanM/FusionGDA. [ABSTRACT FROM AUTHOR]

A biochemical pathway consists of a series of interconnected biochemical reactions to accomplish specific life activities. The participating reactants and resultant products of a pathway, including gene fragments, proteins, and small molecules, coalesce to form a complex reaction network. Biochemical pathways play a critical role in the biochemical domain as they can reveal the flow of biochemical reactions in living organisms, making them essential for understanding life processes. Existing studies of biochemical pathway networks are mainly based on experimentation and pathway database analysis methods, which are plagued by substantial cost constraints. Inspired by the success of representation learning approaches in biomedicine, we develop the biochemical pathway prediction (BPP) platform, which is an automatic BPP platform to identify potential links or attributes within biochemical pathway networks. Our BPP platform incorporates a variety of representation learning models, including the latest hypergraph neural networks technology to model biochemical reactions in pathways. In particular, BPP contains the latest biochemical pathway-based datasets and enables the prediction of potential participants or products of biochemical reactions in biochemical pathways. Additionally, BPP is equipped with an SHAP explainer to explain the predicted results and to calculate the contributions of each participating element. We conduct extensive experiments on our collected biochemical pathway dataset to benchmark the effectiveness of all models available on BPP. Furthermore, our detailed case studies based on the chronological pattern of our dataset demonstrate the effectiveness of our platform. Our BPP web portal, source code and datasets are freely accessible at https://github.com/Glasgow-AI4BioMed/BPP. [ABSTRACT FROM AUTHOR]

The properties of nanomaterials make them promising and advantageous for use in drug delivery systems, but challenges arise from the immune system's recognition of exogenous nanoparticles, leading to their clearance and reduced targeting efficiency. Drawing inspiration from nature, this paper explores biomimetic strategies to transform recognizable nanomaterials into a "camouflaged state." The focal point of this paper is the exploration of bionic nanoparticles, with a focus on cell membrane-coated nanoparticles. These biomimetic structures, particularly those mimicking red blood cells (RBCs), white blood cells (WBCs), platelets, and cancer cells, demonstrate enhanced drug delivery efficiency and prolonged circulation. This article underscores the versatility of these biomimetic structures across diverse diseases and explores the use of hybrid cell membrane-coated nanoparticles as a contemporary trend. This review also investigated exosomes and protein bionic nanoparticles, emphasizing their potential for specific targeting, immune evasion, and improved therapeutic outcomes. We expect that this continued development based on biomimetic nanomaterials will contribute to the efficiency and safety of disease treatment. [ABSTRACT FROM AUTHOR]

ABSTRACTBackground:Low- and middle-income countries such as Vietnam are home to a majority of the world's population with dementia, yet little is known regarding how individuals in these countries perceive memory problems that might be indicative of cognitive impairment. This study examined the prevalence and correlates of subjective memory complaints (SMCs) in Vietnamese adults in Da Nang, Vietnam.MethodsA stratified sample of 600 adults (aged ≥ 55 years) living in Da Nang, Vietnam, and surrounding areas were recruited to participate in a cross-sectional study. Students and faculty from the National Technical Medical College Number 2 administered questionnaires in participants' homes regarding socio-demographic characteristics, functional health, social support, cognitive and mental health, and SMCs. Descriptive and stepwise regression analyses examined the prevalence and correlates of SMCs.ResultsApproximately 64% of the sample reported at least poor memory and 39% said that memory interfered with their daily life at least somewhat. Multivariate regression analyses (adjusted for all covariates) showed that depressive symptoms, cognitive impairment, self-rated health and pain, and material hardship were associated with SMCs.ConclusionsPrevalence of SMCs as well as depressive symptoms was high in this Vietnamese population. Although future research using more detailed measures of subjective memory and which include longitudinal data are required, the need for physicians to routinely assess Vietnamese patients for depression, SMCs, and cognitive impairment may be warranted.

This longitudinal study examined the role of narrative skills in English reading comprehension, after controlling for vocabulary and decoding, with a sample of 112 dual language learners (DLLs), including both Spanish-English and Cantonese-English children. Decoding, vocabulary, and narrative samples were collected in the winter of first grade and reading comprehension skills were assessed on the same children one year later in second grade. Spanish-English DLLs had significantly lower English receptive vocabulary but higher L1 receptive vocabulary than their Cantonese peers. At the same time, Spanish-English DLLs scored lower than Cantonese-English DLLs on English reading comprehension. There were no differences in English reading comprehension between DLL children in bilingual programs and those in mainstream English programs after controlling for L1. Multiple regression results show that English decoding and English vocabulary explain a significant portion of the variance in English reading comprehension. Regression results also revealed a significant, albeit small, effect of narrative quality (both within- and cross-language) on English reading comprehension one year later, after controlling for English decoding and English vocabulary. Implications and directions for future research are discussed.

Most family-system research on triangulation procebes has been undertaken in the West, with little known about this family dynamic in the East. The present crob-sectional study analysed 1,073 Chinese 3rd-12th-graders' self-reported exposure to three kinds of triangulation-crob-generation coalition, scapegoating, and parentification-in relation to family and child factors and with respect to children's school and social adjustment. Age-related analyses generally indicated that older children were leb frequently exposed to all three dimensions of triangulation than younger ones. Children residing with only their parents experienced more scapegoating than those living in extended families; and boys were exposed to crob-generation coalition and scapegoating more than were girls. Higher levels of coalition and scapegoating exposure were related to poorer school adjustment and greater deprebion of children. Higher levels of parentification exposure, however, were abociated with better school adjustment and social functioning. Findings are discubed in terms of theory and research on parent-child triangulation and cultural differences between East and West.

This research investigates whether and how two fundamental environmental factors--harshness and unpredictability--interact in regulating child and adolescent development, informed by life-history theory and drawing on data from the National Institute of Child Health & Human Development Study of Early Child Care and Youth Development (N = 1,364). Early life harshness was operationalized as the typical level of family income-to-needs based on six repeated measurements across the first 4.5 years of life and early life unpredictability as random variation using the same family income measurements. Results revealed that children functioned most competently in the social and academic domain as kindergarteners when exposed to low environmental harshness and low unpredictability and least competently when they experienced high harshness and low unpredictability. The same interaction pattern emerged in adolescence in forecasting cognitive-academic competence and sexual behavior. Findings are discussed in terms of how reliable and unreliable environmental cues shape developmental trajectories.