Gene-environment pathways to cognitive intelligence and psychotic-like experiences in children.

In children, psychotic-like experiences (PLEs) are related to risk of psychosis, schizophrenia, and other mental disorders. Maladaptive cognitive functioning, influenced by genetic and environmental factors, is hypothesized to mediate the relationship between these factors and childhood PLEs. Using large-scale longitudinal data, we tested the relationships of genetic and environmental factors (such as familial and neighborhood environment) with cognitive intelligence and their relationships with current and future PLEs in children. We leveraged large-scale multimodal data of 6,602 children from the Adolescent Brain and Cognitive Development Study. Linear mixed model and a novel structural equation modeling (SEM) method that allows estimation of both components and factors were used to estimate the joint effects of cognitive phenotypes polygenic scores (PGSs), familial and neighborhood socioeconomic status (SES), and supportive environment on NIH Toolbox cognitive intelligence and PLEs. We adjusted for ethnicity (genetically defined), schizophrenia PGS, and additionally unobserved confounders (using computational confound modeling). Our findings indicate that lower cognitive intelligence and higher PLEs are significantly associated with lower PGSs for cognitive phenotypes, lower familial SES, lower neighborhood SES, and less supportive environments. Specifically, cognitive intelligence mediates the effects of these factors on PLEs, with supportive parenting and positive school environments showing the strongest impact on reducing PLEs. This study underscores the influence of genetic and environmental factors on PLEs through their effects on cognitive intelligence. Our findings have policy implications in that improving school and family environments and promoting local economic development may enhance cognitive and mental health in children.

Childhood is a critical period for brain development. Difficult experiences during this developmental phase may contribute to reduced intelligence and poorer mental health later in life. Genetics and environmental factors also play roles. For example, having family support or a higher family income has been linked to better brain health outcomes for children. Delusions or hallucinations, or other psychotic-like experiences during childhood, are linked with poor mental health later in life. Children who experience psychotic-like episodes between the ages of nine and eleven have a higher risk of developing schizophrenia or related conditions. Environmental circumstances during childhood also appear to play a crucial role in shaping the risk of schizophrenia or related conditions. Park, Lee et al. show that positive parenting and supportive school and neighborhood environments boost child intelligence and mental health. In the experiments, Park, Lee et al. analyzed data on 6,602 children to determine how genetics and environmental factors shaped their intelligence and mental health. The models show that children with higher intelligence have a lower risk of psychosis. Both genetics and supportive environments contribute to higher intelligence. Complex interactions between biology and social factors shape children's intelligence and mental health. Beneficial genetics and coming from a family with more financial resources are helpful. Yet, social environments, such as having parents who use positive child-rearing practices, or having supportive schools or neighborhoods, have protective effects that can offset other disadvantages. Policies that help parents, encourage supportive school environments, and strengthen neighborhoods may boost children's intelligence and mental health later in life.

A human-machine collaborative approach measures economic development using satellite imagery.

Machine learning approaches using satellite imagery are providing accessible ways to infer socioeconomic measures without visiting a region. However, many algorithms require integration of ground-truth data, while regional data are scarce or even absent in many countries. Here we present our human-machine collaborative model which predicts grid-level economic development using publicly available satellite imagery and lightweight subjective ranking annotation without any ground data. We applied the model to North Korea and produced fine-grained predictions of economic development for the nation where data is not readily available. Our model suggests substantial development in the country's capital and areas with state-led development projects in recent years. We showed the broad applicability of our model by examining five of the least developed countries in Asia, covering 400,000 grids. Our method can both yield highly granular economic information on hard-to-visit and low-resource regions and can potentially guide sustainable development programs.

Radiographic assessment of splenic size and correlation with splenic measurements estimated by use of computed tomography in healthy cats.

OBJECTIVE: To investigate radiographic variables for correlation with splenic size as estimated with CT in cats. ANIMALS: 38 healthy adult cats. PROCEDURES: The width and height of the splenic head and total length, segmental length, and width of the spleen were measured on radiographic and CT images obtained from 10 cats in prospective, exploratory experiments. Distance between the splenic head and left kidney, anatomic locations of the head and tail of the spleen, and CT-derived splenic volume were also assessed. Correlation and agreement between radiographic and CT measurements and interobserver agreement for measurements with each method were determined. A retrospective evaluation of radiographs obtained without sedation or anesthesia for 28 cats was performed to establish preliminary guidelines for the measurement deemed the most reliable estimator of splenic size. RESULTS: Radiographic measurements of total and segmental splenic length were significantly correlated with the respective CT measurements and with splenic volume. Agreement between radiographic and CT measurements of segmental length was good; interobserver agreement was excellent for all variables. In retrospective evaluations, median segmental length of the spleen was 57.87 mm (range, 34.72 to 105.44 mm) on radiographs; the caudal border of the splenic head on lateral views was located from the cranial part of L1 to the caudal part of L2, and the caudal border of the splenic tail on ventrodorsal views was located from the caudal part of L2 to the caudal part of L5. CONCLUSIONS AND CLINICAL RELEVANCE: Results indicated that segmental length of the spleen on radiographs is a reliable estimator of splenic size in healthy cats.

Effects of Community-Based Primary Care Management on Patients With Hypertension and Diabetes.

As the prevalence of chronic diseases is continuously increasing, the socioeconomic cost of those conditions in Korea is also rising. In order to effectively manage chronic diseases, the "Community-Based Primary Care Project" was implemented from 2014 to 2016 and focused on primary medical care and physician-led chronic disease management. The purpose of this study is evaluating the effects of the project through the DID (difference in difference) model. The project's database and the National Health Insurance claims database were both used to compare the project and control groups (n = 6092 vs 24 368). Results of the analysis show that medication adherence was increased more in the project group compared with the control group. Hospitalization days, outpatient days, and number of primary medical clinic visits increased more in the participant group than the control group. As the project showed an improvement in treatment persistence, it will be necessary to monitor for a longer period of time.

Review of mobile terminal-based tools for diabetes diet management.

Changing dietary habits is one of the most challenging tasks of diabetes self-management. Mobile terminals are increasingly used as platforms for tools to support diet management and health promotion. We present literature describing mobile terminal-based support tools for management of diabetes focused on diet. We also propose a summary of key success factors for designing such tools and discuss recommendations for future research.