Physical activity and screen time: prospective associations with child emotional and behavioral well-being during the COVID pandemic
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Microglia dysfunction, neurovascular inflammation and focal neuropathologies are linked to IL-1- and IL-6-related systemic inflammation in COVID-19
COVID-19 is associated with diverse neurological abnormalities, but the underlying mechanisms are unclear. We hypothesized that microglia, the resident immune cells of the brain, are centrally involved in this process. To study this, we developed an autopsy platform allowing the integration of molecular anatomy, protein and mRNA datasets in postmortem mirror blocks of brain and peripheral organ samples from cases of COVID-19. We observed focal loss of microglial P2Y12R, CX3CR1–CX3CL1 axis deficits and metabolic failure at sites of virus-associated vascular inflammation in severely affected medullary autonomic nuclei and other brain areas. Microglial dysfunction is linked to mitochondrial injury at sites of excessive synapse and myelin phagocytosis and loss of glutamatergic terminals, in line with proteomic changes of synapse assembly, metabolism and neuronal injury. Furthermore, regionally heterogeneous microglial changes are associated with viral load and central and systemic inflammation related to interleukin (IL)-1 or IL-6 via virus-sensing pattern recognition receptors and inflammasomes. Thus, SARS-CoV-2-induced inflammation might lead to a primarily gliovascular failure in the brain, which could be a common contributor to diverse COVID-19-related neuropathologies.
Inequality in pandemic effects on school track placement and the role of social and academic embeddedness
Using register data and linked student-level sociometric survey data from the Netherlands, this study examines whether the impact of the COVID-19 pandemic on schooling outcomes (track recommendation and track enrollment in the seventh and ninth grades) is conditional on students’ academic and social embeddedness in the school setting. We estimated the counterfactual outcomes for the cohort that went through the school transition during the pandemic based on the outcomes of the pre-pandemic cohort, with similar earlier achievements, schools, and social backgrounds. Results show that the pandemic’s effect on tracking outcomes is weaker than its effect on student test scores elsewhere reported. Nevertheless, the pandemic has had stronger adverse impact on disadvantaged students. Moreover, student self-efficacy, academic motivation, and parental involvement are related to more negligible negative pandemic effects on schooling outcomes. We find no evidence for an association between student grit or parental network centrality and the magnitude of estimated pandemic effects.
Grit and academic resilience during the COVID-19 pandemic
Grit, a non-cognitive skill that indicates perseverance and passion for long-term goals, has been shown to predict academic achievement. This paper provides evidence that grit also predicts student outcomes during the challenging period of the Covid-19 pandemic. We use a unique dataset from a digital learning platform in the United Arab Emirates to construct a behavioral measure of grit. We find that controlling for baseline achievement, students who were grittier according to this measure before the pandemic, register lower declines in math and science scores during the coronavirus period. Using machine learning, behavioral data obtained from the platform prior to the pandemic can explain 77% of the variance in academic resilience. A survey measure of grit coming from the same students, on the other hand, does not have significant predictive power over performance changes. Our findings have implications for interventions on non-cognitive skills, as well as how data from digital learning platforms can be used to predict student behavior and outcomes, which we expect will be increasingly relevant as AI-based learning technologies become more common.
COVID-19 health data prediction: a critical evaluation of CNN-based approaches
The COVID-19 pandemic has significantly accelerated the demand for accurate and efficient prediction models to support effective disease management, containment strategies, and informed decision-making. Predictive models capable of analyzing complex health data are essential for monitoring disease trends, evaluating risk factors, and optimizing resource allocation during the pandemic. Among various machine learning approaches, convolutional neural networks (CNNs) have emerged as powerful tools due to their ability to process large volumes of high-dimensional health data, such as medical images, time-series data, and patient demographics, with impressive precision. This research seeks to systematically examine the challenges and limitations inherent in utilizing CNNs for COVID-19 health data prediction, offering a comprehensive perspective grounded in data science research. Key areas of investigation include issues related to data quality and availability, such as incomplete, noisy, and imbalanced datasets, which often hinder the training of robust models. Additionally, architectural constraints of CNNs, including their sensitivity to hyperparameter tuning and reliance on substantial computational resources, are explored as critical bottlenecks that impact scalability and efficiency. A significant focus is placed on generalization challenges, where models trained on specific datasets struggle to adapt to unseen data from diverse populations or clinical settings, limiting their applicability in real-world scenarios. The study further highlights a reported accuracy of 63%, underscoring the need for improved methodologies to enhance model performance and reliability. By addressing these challenges, this research aims to provide actionable insights and practical recommendations to optimize the use of CNNs for COVID-19 health data prediction. In particular, the study emphasizes the importance of incorporating advanced strategies such as transfer learning, data augmentation, and regularization techniques to overcome dataset limitations and enhance model robustness. The integration of multimodal approaches combining medical images with auxiliary data, such as patient demographics and laboratory results, is proposed to improve contextual understanding and diagnostic precision. Finally, the research underscores the necessity of interdisciplinary collaboration, leveraging domain expertise from data scientists, healthcare professionals, and epidemiologists to develop holistic solutions for tackling the complexities of COVID-19 prediction. By shedding light on the limitations and potential of CNNs in this domain, this study aims to guide researchers and practitioners in making informed decisions about model design, implementation, and optimization. Ultimately, it contributes to advancing AI-driven diagnostics and predictive modeling for COVID-19 and other public health crises, fostering the development of scalable and reliable tools for better healthcare outcomes.
Symptomatic associations and sexual differences in depression and communication
Previous studies have explored the associations between parental and offspring’s depression and parent-child communication. However, few studies have investigated their symptomatic associations and potential sex differences. Therefore, this study aims to examine their associations and sex differences in parents and offspring. Based on the China Family Panel Studies (CFPS)-2020 study, depressive symptoms and parent-child communication were measured by the 8-item Center for Epidemiologic Studies Depression Scale (CESD-8) and independent questions, respectively. Network analysis was used to investigate the associations and to compare the sex differences of parents and offspring. A total of 1710 adolescents were included after cleaning process (N = 28,530). There were significantly stronger associations in boys’ “anhedonia” and “arguments with parents”, and in girls’ “happiness” and parents’ “joyfulness”. Furthermore, there were same-sex depression associations between children and parents (e.g., boys’ “despair”–fathers’ “joyfulness”; girls’ “anhedonia”–mothers’ “loneliness”). These results would help us to better understand the in depression and communication nuanced associations and to develop effective strategies for improving parental and offspring’s mental health.
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