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Acta Psychologica Apr 2024The COVID-19 pandemic led to temporary closures of schools in Germany that were unpredictable, either short-term or sustained over many weeks depending on local COVID-19...
CONTEXT AND PROBLEM
The COVID-19 pandemic led to temporary closures of schools in Germany that were unpredictable, either short-term or sustained over many weeks depending on local COVID-19 incidence rates which varied in different regions over time. The COVID-19 pandemic created new stressors in schools to deliver the curriculum with reduced lessons and online teaching. COVID-19 also had a marked effect on research in schools.
AIM
The INSIDE project is a nationwide German study that investigates the effects of inclusive schooling in secondary education. The data collection started in 2018 and is still ongoing. During the COVID-19 pandemic the drop-out of many schools lead to a sample attrition down to 39.3 % of the original sample. It is investigated whether imputation of missing values in the dependent variables school grades (N = 2999) produces different results than listwise deletion of cases (N = 383).
HYPOTHESIS
It is hypothesized that missing data in the longitudinal data design were not missing at random (NMAR). It is further hypothesized based on previous research that in the larger, imputed sample, Math grades will deteriorate more than German grades.
RESULTS
Two datasets with observed, respectively, imputed data showed no difference in parents' educational attainment and gender proportion. Larger integrative schools were less likely to drop out than smaller single type schools. Pupils of 'surviving' schools showed equivalent grades for German and Mathematics, while including predicted grades of pupils in 'stressed' drop-out schools showed a decline in Mathematics but not in German subject grades.
Topics: Humans; Pandemics; COVID-19; Schools; Educational Status; Mathematics
PubMed: 38387167
DOI: 10.1016/j.actpsy.2024.104174 -
PloS One 2023Executive function is a core deficit in children with attention deficit hyperactivity disorder (ADHD). This study systematically reviewed the evidence for the effects of... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Executive function is a core deficit in children with attention deficit hyperactivity disorder (ADHD). This study systematically reviewed the evidence for the effects of physical activity (PA) interventions on executive function in children and adolescents with ADHD and explored the moderating effects of key variables of PA on executive function.
METHODS
Relevant literature in four electronic databases, Pubmed, Web of Science, Cochrane Library, and Embase, were systematically searched. Revman 5.4 was used for data analysis, and combined effect sizes, heterogeneity tests, subgroup analyses, and sensitivity analyses were calculated. Egger's test in Stata 15.0 was used for publication bias testing.
RESULTS
A total of 24 articles with 914 participants were included. Meta-analysis showed that PA interventions improved inhibitory control (SMD = -0.50, 95%CI [-0.71, -0.29], P < 0.00001), working memory (SMD = -0.50, 95%CI [-0.83, -0.16], P = 0.004) and cognitive flexibility in children and adolescents with ADHD (SMD = -0.45, 95%CI [-0.81, -0.09], P = 0.01). Subgroup analysis revealed a moderating effect of intervention intensity, motor skill type, sessions of PA, and weekly exercise volume on executive function.
CONCLUSION
PA interventions had positive effects on improvements in core executive functions in children and adolescents with ADHD and were influenced by intervention intensity, type of motor skill, sessions of PA, and amount of exercise. This has practical implications for the formulation of PA interventions programs.
Topics: Adolescent; Child; Humans; Attention Deficit Disorder with Hyperactivity; Data Analysis; Executive Function; Exercise; Memory, Short-Term
PubMed: 37590250
DOI: 10.1371/journal.pone.0289732 -
JAMA Network Open Dec 2023There are persistent questions about suicide deaths among US veterans who served in the Vietnam War. It has been believed that Vietnam War veterans may be at an...
IMPORTANCE
There are persistent questions about suicide deaths among US veterans who served in the Vietnam War. It has been believed that Vietnam War veterans may be at an increased risk for suicide.
OBJECTIVE
To determine whether military service in the Vietnam War was associated with an increased risk of suicide, and to enumerate the number of suicides and analyze patterns in suicides among Vietnam War theater veterans compared with the US population.
DESIGN, SETTING, AND PARTICIPANTS
This cohort study compiled a roster of all Vietnam War-era veterans and Vietnam War theater veterans who served between February 28, 1961, and May 7, 1975. The 2 cohorts included theater veterans, defined as those who were deployed to the Vietnam War, and nontheater veterans, defined as those who served during the Vietnam War era but were not deployed to the Vietnam War. Mortality in these 2 cohorts was monitored from 1979 (beginning of follow-up) through 2019 (end of follow-up). Data analysis was performed between January 2022 and July 2023.
MAIN OUTCOMES AND MEASURES
The outcome of interest was death by suicide occurring between January 1, 1979, and December 31, 2019. Suicide mortality was ascertained from the National Death Index. Hazard ratios (HRs) that reflected adjusted associations between suicide risk and theater status were estimated with Cox proportional hazards regression models. Standardized mortality rates (SMRs) were calculated to compare the number of suicides among theater and nontheater veterans with the expected number of suicides among the US population.
RESULTS
This study identified 2 465 343 theater veterans (2 450 025 males [99.4%]; mean [SD] age at year of entry, 33.8 [6.7] years) and 7 122 976 nontheater veterans (6 874 606 males [96.5%]; mean [SD] age at year of entry, 33.3 [8.2] years). There were 22 736 suicides (24.1%) among theater veterans and 71 761 (75.9%) among nontheater veterans. After adjustments for covariates, Vietnam War deployment was not associated with an increased risk of suicide (HR, 0.94; 95% CI, 0.93-0.96). There was no increased risk of suicide among either theater (SMR, 0.97; 95% CI, 0.96-0.99) or nontheater (SMR, 0.97; 95% CI, 0.97-0.98) veterans compared with the US population.
CONCLUSIONS AND RELEVANCE
This cohort study found no association between Vietnam War-era military service and increased risk of suicide between 1979 and 2019. Nonetheless, the 94 497 suicides among all Vietnam War-era veterans during this period are noteworthy and merit the ongoing attention of health policymakers and mental health professionals.
Topics: Male; Humans; Cohort Studies; Suicide; Veterans; Vietnam; Data Analysis
PubMed: 38153739
DOI: 10.1001/jamanetworkopen.2023.47616 -
Gene Jun 2024The chaos theory, a field of study in mathematics and physics, offers a unique lens through which to understand the dynamics of the COVID-19 pandemic. This theory, which... (Review)
Review
The chaos theory, a field of study in mathematics and physics, offers a unique lens through which to understand the dynamics of the COVID-19 pandemic. This theory, which deals with complex systems whose behavior is highly sensitive to initial conditions, can provide insights into the unpredictable and seemingly random nature of the pandemic's spread. In this review, we will discuss some literature data with the aim of showing how chaos theory could provide valuable perspectives in understanding the complex and dynamic nature of the COVID-19 pandemic. In particular, we will emphasize how the chaos theory can help in dissecting the unpredictable, non- linear progression of the disease, the importance of initial conditions, and the complex interactions between various factors influencing its spread. These insights are crucial for developing effective strategies to manage and mitigate the impact of the pandemic.
Topics: Humans; Nonlinear Dynamics; COVID-19; Pandemics
PubMed: 38458366
DOI: 10.1016/j.gene.2024.148334 -
Bulletin of Mathematical Biology Oct 2023Computing has revolutionised the study of complex nonlinear systems, both by allowing us to solve previously intractable models and through the ability to visualise...
Computing has revolutionised the study of complex nonlinear systems, both by allowing us to solve previously intractable models and through the ability to visualise solutions in different ways. Using ubiquitous computing infrastructure, we provide a means to go one step further in using computers to understand complex models through instantaneous and interactive exploration. This ubiquitous infrastructure has enormous potential in education, outreach and research. Here, we present VisualPDE, an online, interactive solver for a broad class of 1D and 2D partial differential equation (PDE) systems. Abstract dynamical systems concepts such as symmetry-breaking instabilities, subcritical bifurcations and the role of initial data in multistable nonlinear models become much more intuitive when you can play with these models yourself, and immediately answer questions about how the system responds to changes in parameters, initial conditions, boundary conditions or even spatiotemporal forcing. Importantly, VisualPDE is freely available, open source and highly customisable. We give several examples in teaching, research and knowledge exchange, providing high-level discussions of how it may be employed in different settings. This includes designing web-based course materials structured around interactive simulations, or easily crafting specific simulations that can be shared with students or collaborators via a simple URL. We envisage VisualPDE becoming an invaluable resource for teaching and research in mathematical biology and beyond. We also hope that it inspires other efforts to make mathematics more interactive and accessible.
Topics: Humans; Models, Biological; Mathematical Concepts; Nonlinear Dynamics; Mathematics; Students
PubMed: 37823924
DOI: 10.1007/s11538-023-01218-4 -
Bioinformatics (Oxford, England) Jul 2023Heterogeneity in human diseases presents clinical challenges in accurate disease characterization and treatment. Recently available high throughput multi-omics data may...
MOTIVATION
Heterogeneity in human diseases presents clinical challenges in accurate disease characterization and treatment. Recently available high throughput multi-omics data may offer a great opportunity to explore the underlying mechanisms of diseases and improve disease heterogeneity assessment throughout the treatment course. In addition, increasingly accumulated data from existing literature may be informative about disease subtyping. However, the existing clustering procedures, such as Sparse Convex Clustering (SCC), cannot directly utilize the prior information even though SCC produces stable clusters.
RESULTS
We develop a clustering procedure, information-incorporated Sparse Convex Clustering, to respond to the need for disease subtyping in precision medicine. Utilizing the text mining approach, the proposed method leverages the existing information from previously published studies through a group lasso penalty to improve disease subtyping and biomarker identification. The proposed method allows taking heterogeneous information, such as multi-omics data. We conduct simulation studies under several scenarios with various accuracy of the prior information to evaluate the performance of our method. The proposed method outperforms other clustering methods, such as SCC, K-means, Sparse K-means, iCluster+, and Bayesian Consensus Clustering. In addition, the proposed method generates more accurate disease subtypes and identifies important biomarkers for future studies in real data analysis of breast and lung cancer-related omics data. In conclusion, we present an information-incorporated clustering procedure that allows coherent pattern discovery and feature selection.
AVAILABILITY AND IMPLEMENTATION
The code is available upon request.
Topics: Humans; Bayes Theorem; Cluster Analysis; Precision Medicine; Multiomics; Data Analysis; Neoplasms
PubMed: 37382570
DOI: 10.1093/bioinformatics/btad417 -
CPT: Pharmacometrics & Systems... Feb 2024Bayesian estimation is a powerful but underutilized tool for answering drug development questions. In this tutorial, the principles of Bayesian model development,...
Bayesian estimation is a powerful but underutilized tool for answering drug development questions. In this tutorial, the principles of Bayesian model development, assessment, and prior selection will be outlined. An example pharmacokinetic (PK) model will be used to demonstrate the implementation of Bayesian modeling using the nonlinear mixed-effects modeling software NONMEM.
Topics: Humans; Bayes Theorem; Software; Nonlinear Dynamics; Models, Biological
PubMed: 38017712
DOI: 10.1002/psp4.13088 -
Scientific Reports Sep 2023The purpose of this study is to understand psychosocial impacts on cancer survivors using the patient-reported outcomes measurement information system (PROMIS)...
The purpose of this study is to understand psychosocial impacts on cancer survivors using the patient-reported outcomes measurement information system (PROMIS) Psychosocial Illness Impact banks. Cancer survivors (n = 509; age: 59.5 ± 1.4; 51.5% men) completed the PROMIS positive and negative illness impact items consisting of four sub-domains: self-concept (SC), social impact (SI), stress response (SR), and spirituality (Sp). Illness impact was defined as changed scores from items measuring "current" experiences to recalled experiences prior to cancer diagnosis. Descriptive statistics, effect sizes (ES), and coefficient of variation (CV) were calculated at item and sub-domain levels. Analysis of variance was used to identify potentially influential factors on the impacts. Our study found survivors reported stronger positive than negative impacts (overall ES mean: 0.30 vs. 0.23) in general; and more moderate (ES ≧ 0.30) positive than negative impacts at the item level, 54.3% (25 of 46) and 40% (16 of 40) for positive and negative items, respectively. Participants reported more positive impacts on SI and Sp but more negative impacts on SR. The CV results showed more individual differences appeared on positive SC items. Younger survivors reported stronger positive and negative impacts. Women reported higher positive impacts. Survivors with higher education levels tended to have higher positive SI impacts, while those with a lower family income reported higher negative SI and negative SR impacts. We conclude positive and negative psychosocial impacts coexisted-the strength of impacts varied across sub-domains. Age, gender, education, and family income influenced the psychosocial impacts reported by survivors. These findings provide a foundation to develop interventions to strengthen positive and minimize negative impacts and improve cancer survivors' overall well-being.
Topics: Male; Humans; Female; Middle Aged; Cancer Survivors; Neoplasms; Survivors; Correlation of Data; Educational Status
PubMed: 37679401
DOI: 10.1038/s41598-023-41822-x -
Briefings in Bioinformatics Sep 2023Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security,... (Review)
Review
Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more. However, the mutational space involved is too vast to be handled through experimental means alone. Leveraging accumulative protein databases, machine learning (ML) models, particularly those based on natural language processing (NLP), have considerably expedited protein engineering. Moreover, advances in topological data analysis (TDA) and artificial intelligence-based protein structure prediction, such as AlphaFold2, have made more powerful structure-based ML-assisted protein engineering strategies possible. This review aims to offer a comprehensive, systematic, and indispensable set of methodological components, including TDA and NLP, for protein engineering and to facilitate their future development.
Topics: Artificial Intelligence; Protein Engineering; Natural Language Processing; Antibodies; Data Analysis
PubMed: 37580175
DOI: 10.1093/bib/bbad289 -
Journal of Medical Internet Research Jan 2024The systematic review of clinical research papers is a labor-intensive and time-consuming process that often involves the screening of thousands of titles and abstracts....
BACKGROUND
The systematic review of clinical research papers is a labor-intensive and time-consuming process that often involves the screening of thousands of titles and abstracts. The accuracy and efficiency of this process are critical for the quality of the review and subsequent health care decisions. Traditional methods rely heavily on human reviewers, often requiring a significant investment of time and resources.
OBJECTIVE
This study aims to assess the performance of the OpenAI generative pretrained transformer (GPT) and GPT-4 application programming interfaces (APIs) in accurately and efficiently identifying relevant titles and abstracts from real-world clinical review data sets and comparing their performance against ground truth labeling by 2 independent human reviewers.
METHODS
We introduce a novel workflow using the Chat GPT and GPT-4 APIs for screening titles and abstracts in clinical reviews. A Python script was created to make calls to the API with the screening criteria in natural language and a corpus of title and abstract data sets filtered by a minimum of 2 human reviewers. We compared the performance of our model against human-reviewed papers across 6 review papers, screening over 24,000 titles and abstracts.
RESULTS
Our results show an accuracy of 0.91, a macro F-score of 0.60, a sensitivity of excluded papers of 0.91, and a sensitivity of included papers of 0.76. The interrater variability between 2 independent human screeners was κ=0.46, and the prevalence and bias-adjusted κ between our proposed methods and the consensus-based human decisions was κ=0.96. On a randomly selected subset of papers, the GPT models demonstrated the ability to provide reasoning for their decisions and corrected their initial decisions upon being asked to explain their reasoning for incorrect classifications.
CONCLUSIONS
Large language models have the potential to streamline the clinical review process, save valuable time and effort for researchers, and contribute to the overall quality of clinical reviews. By prioritizing the workflow and acting as an aid rather than a replacement for researchers and reviewers, models such as GPT-4 can enhance efficiency and lead to more accurate and reliable conclusions in medical research.
Topics: Humans; Biomedical Research; Consensus; Data Analysis; Problem Solving; Systematic Reviews as Topic; Natural Language Processing; Artificial Intelligence; Workflow
PubMed: 38214966
DOI: 10.2196/48996