Large-Scale Hollow-Core 3D Printing: Variable Cross-Section and Printing Features for Lightweight Plastic Elements.

Hollow-core 3D printing (HC3DP) proposes a new method for the production of lightweight, material-efficient thermoplastic 3D printed elements. This new fabrication approach promises material savings of 50-80%, while increasing the extrusion rate significantly (factor 10). This development pushes HC3DP to a similar fabrication speed as high-resolution concrete 3D printing. However, fundamental research on printing features enabled by this novel 3D printing approach is missing. Therefore, this article investigates printing with user-controlled bead dimensions (same nozzle, different size). It is showcased that the size of the extruded cross-section is determined by the positive air pressure used to inflate the beads. Multiple samples are printed, changing the layer height and width significantly without making changes to the hardware setup. Sections of 3DP samples are analyzed and the parameters of 3DP beads are determined. Furthermore, a set of bespoke 3D printed nozzles is introduced to subdivide the HC3DP beads into distinct areas. So far only regular beads, such as hollow tubes, have been used for 3D printing. Samples of those bespoke sections are analyzed to investigate their behavior when used for 3D printing. Finally, large-scale 3D printing experiments are conducted to investigate how printing features like bridging, cantilevering, or nonplanar 3D printing can be manufactured with hollow extrusion beads. In summary, this article provides insights into the fundamental 3D printing behaviors of HC3DP, showcases new design possibilities with bespoke and variable cross-sections, and finally proposes new research trajectories based on the findings presented.

On the Unequal Burden of Obesity: Obesity's Adverse Consequences Are Contingent on Regional Obesity Prevalence.

Obesity has adverse consequences for those affected. We tested whether the association between obesity and its adverse consequences is reduced in regions in which obesity is prevalent and whether lower weight bias in high-obese regions can account for this reduction. Studies 1 and 2 used data from the United States (N = 2,846,132 adults across 2,546 counties) and United Kingdom (N = 180,615 adults across 380 districts) that assessed obesity's adverse consequences in diverse domains: close relationships, economic outcomes, and health. Both studies revealed that the association between obesity and its adverse consequences is reduced (or absent) in high-obese regions. Study 3 used another large-scale data set (N = 409,837 across 2,928 U.S. counties) and revealed that lower weight bias in high-obese regions seems to account for (i.e., mediate) the reduction in obesity's adverse consequences. Overall, our findings suggest that obesity's adverse consequences are partly social and, thus, not inevitable.

Temporal and spatial characteristics of forest pests in China and their association with large-scale circulation indices.

The increase in extreme climate events in recent years has been considered as an important factor affecting forest pests. Understanding the responses of forest pests to climate is helpful for revealing the trends in forest pest dynamics and proposing effective control measures. In this study, the relationship between the dynamics of all forest pests, independent forest diseases, and forest insect pests with the climate was evaluated in China, and the corresponding differences among forest pests, diseases and insect pests were assessed. Based on cross-wavelet transform and wavelet coherence analysis, the influences of teleconnection factors on the relationship between climate and forest pests were quantitatively analyzed to determine the roles of these factors. The results indicate that (i) three types of disasters in most parts of China have decreased from 1979 to 2019, while forest pests and forest insect pests in the southwestern region have increased; (ii) the relationship among Forest Pest Occurrence Area Rate and climate factors such as the Multivariate ENSO index, Southern Oscillation index, Arctic Oscillation (AO), Atlantic Multidecadal Oscillation (AMO), and Sunspot is more significant; (iii) the cycle is short in most regions, with oscillations in 2-4 years bands being the main variation periods of disasters in East, Central, and South China; (iv) There is a significant correlation between climate and disasters in the periods of 2-4 or 8-10 years. The AO, AMO, and Sunspot were important driving factors affecting the relationship between climate and disasters. Specifically, the Sunspot had the greatest impact among these factors.

Factor affecting severe atherothrombotic cerebral infarction in patients with type 2 diabetes mellitus: Large-scale claim database analysis of Japan.

AIMS: This study aimed to investigate the factors associated with the exacerbation of the severity of atherothrombotic brain infarction at discharge in patients with type 2 diabetes using a large-scale claims database. MATERIALS AND METHODS: This retrospective cross-sectional study utilized the Medical Data Vision administrative claims database, a nationwide database in Japan using acute care hospital data, and the Diagnosis Procedure Combination system. Diagnosis Procedure Combination data collected between April 1, 2008, and December 31, 2022, were extracted. Patients with type 2 diabetes were included. Severe atherothrombotic brain infarction was defined as a modified Rankin scale score of ≥3. RESULTS: Severe atherothrombotic brain infarction occurred in 43,916/99,864 (44.0%) patients with type 2 diabetes. The odds ratio for severe atherothrombotic brain infarction increased significantly per 10 year increments in age (odds ratio: 1.69, 95% confidence interval: 1.66-1.71). A body mass index of <25 kg/m2, with a body mass index of ≥25 kg/m2 as reference, also increased the risk for severe atherothrombotic brain infarction (odds ratio: 1.11, 95% confidence interval: 1.08-1.15). The odds ratios in insulin and dipeptidyl peptidase 4 inhibitor use were significantly higher than 1. In particular, statin use (odds ratio: 0.85, 95% confidence interval: 0.83-0.88), fibrate use (odds ratio: 0.68, 95% confidence interval: 0.59-0.78), aspirin use (odds ratio: 0.78, 95% confidence interval: 0.75-0.80), and P2Y12 inhibitor use (odds ratio: 0.88, 95% confidence interval: 0.85-0.91) were associated with a lower odds ratio for severe atherothrombotic brain infarction. CONCLUSIONS: The active management of lipid levels using statins and fibrates may be beneficial in preventing the exacerbation of atherothrombotic brain infarction in type 2 diabetes patients.

Low-pass whole genome sequencing of circulating tumor cells to evaluate chromosomal instability in triple-negative breast cancer.

Chromosomal Instability (CIN) is a common and evolving feature in breast cancer. Large-scale Transitions (LSTs), defined as chromosomal breakages leading to gains or losses of at least 10 Mb, have recently emerged as a metric of CIN due to their standardized definition across platforms. Herein, we report the feasibility of using low-pass Whole Genome Sequencing to assess LSTs, copy number alterations (CNAs) and their relationship in individual circulating tumor cells (CTCs) of triple-negative breast cancer (TNBC) patients. Initial assessment of LSTs in breast cancer cell lines consistently showed wide-ranging values (median 22, range 4-33, mean 21), indicating heterogeneous CIN. Subsequent analysis of CTCs revealed LST values (median 3, range 0-18, mean 5), particularly low during treatment, suggesting temporal changes in CIN levels. CNAs averaged 30 (range 5-49), with loss being predominant. As expected, CTCs with higher LSTs values exhibited increased CNAs. A CNA-based classifier of individual patient-derived CTCs, developed using machine learning, identified genes associated with both DNA proliferation and repair, such as RB1, MYC, and EXO1, as significant predictors of CIN. The model demonstrated a high predictive accuracy with an Area Under the Curve (AUC) of 0.89. Overall, these findings suggest that sequencing CTCs holds the potential to facilitate CIN evaluation and provide insights into its dynamic nature over time, with potential implications for monitoring TNBC progression through iterative assessments.

Father's involvement associated with rural children's depression and anxiety: A large-scale analysis based on data from seven provinces in China.

To investigate the relationship between father involvement in parenting and mental health problems among children and adolescents in rural China. The Rural Children's Mental Health dataset includes mental health information from 2,489 children and adolescents aged 5-16 in seven provinces in China. The relationship between father involvement in children and adolescents depression risk and anxiety was analyzed by Spearman's correlation analysis, logistic regression analysis, and restricted cubic spline. Father involvement was significantly and negatively associated with depression scores (r = -0.38, P < 0.001) and anxiety scores (r = -0.18, P < 0.001) in rural Chinese children and adolescents. Both multivariate models indicate that the highest level of father involvement has a protective effect on the risk of depression among children and adolescents (OR = 0.268 and 0.303, 95% CI: 0.149~0.483 and 0.144~0.636), while the association with anxiety risk is only significant in the multivariate model 1 (OR = 0.570, 95% CI: 0.363~0.896). Father involvement is a protective factor for the risk of depression among children and adolescents in rural China. The level of father involvement should be increased, and active participation should be encouraged to reduce the risk of depression in their children and to further promote the mental health of children and adolescents in China.

Optimal Sensor Placement for Enhanced Efficiency in Structural Health Monitoring of Medium-Rise Buildings.

Output-only modal analysis using ambient vibration testing is ubiquitous for the monitoring of structural systems, especially for civil engineering structures such as buildings and bridges. Nonetheless, the instrumented nodes for large-scale structural systems need to cover a significant portion of the spatial volume of the test structure to obtain accurate global modal information. This requires considerable time and resources, which can be challenging in large-scale projects, such as the seismic vulnerability assessment over a large number of facilities. In many instances, a simple center-line (stairwell case) topology is generally used due to time, logistical, and economic constraints. The latter, though a fast technique, cannot provide complete modal information, especially for torsional modes. In this research, corner-line instrumented nodes layouts using only a reference and a roving sensor are proposed, which overcome this issue and can provide maximum modal information similar to that from 3D topologies for medium-rise buildings. Parametric studies are performed to identify the most appropriate locations for sensor placement at each floor of a medium-rise building. The results indicate that corner locations at each floor are optimal. The proposed procedure is validated through field experiments on two medium-rise buildings.

Long-term mesoscale imaging of 3D intercellular dynamics across a mammalian organ.

A comprehensive understanding of physio-pathological processes necessitates non-invasive intravital three-dimensional (3D) imaging over varying spatial and temporal scales. However, huge data throughput, optical heterogeneity, surface irregularity, and phototoxicity pose great challenges, leading to an inevitable trade-off between volume size, resolution, speed, sample health, and system complexity. Here, we introduce a compact real-time, ultra-large-scale, high-resolution 3D mesoscope (RUSH3D), achieving uniform resolutions of 2.6 × 2.6 × 6 µm3 across a volume of 8,000 × 6,000 × 400 µm3 at 20 Hz with low phototoxicity. Through the integration of multiple computational imaging techniques, RUSH3D facilitates a 13-fold improvement in data throughput and an orders-of-magnitude reduction in system size and cost. With these advantages, we observed premovement neural activity and cross-day visual representational drift across the mouse cortex, the formation and progression of multiple germinal centers in mouse inguinal lymph nodes, and heterogeneous immune responses following traumatic brain injury-all at single-cell resolution, opening up a horizon for intravital mesoscale study of large-scale intercellular interactions at the organ level.

Large-Scale Atomic Strain Defects on Palladium Surfaces for Enhanced Oxygen Reduction and Zinc-Air Batteries.

Designing nano-electrocatalysts rich in surface defects is critical to improve their catalytic performance. However, prevailing synthesis techniques rely heavily on complex procedures that compromise defect extensiveness and uniformity, casting a high demand for methods capable of synthesizing large-scale crystalline defects. An innovative design strategy is herein proposed that induces ample strain/dislocation defects during the growth of palladium (Pd), which is well-known as a good oxygen reduction reaction (ORR) catalyst. The controlled defect engineering on Pd core is achieved by the tensile stress exerted from an intentionally applied Fe3O4 skin layer during synthesis, which changes the surface free energy of Pd to stabilize the defect presence. With such large-scale crystalline defects, this Pd catalyst exhibits significantly higher ORR activity than commercial Pt/C, enabling its promising future in zinc-air battery catalysis. Additionally, the protective Fe3O4 skin covering the catalyst also enhances its catalytic stability. Theoretical calculations show that the superior catalytic property of such defect-engineered Pd is associated with the correspondingly modified adsorption energy of *O intermediates onto its surface, which further improves the reaction rate and thus boosts ORR kinetics. Findings here are expected to provide a paradigm for designing efficient and stable metal catalysts with plentiful large-scale strain defects.

Clearing the plate: a strategic approach to mitigate well-to-well contamination in large-scale microbiome studies.

Large-scale studies are essential to answer questions about complex microbial communities that can be extremely dynamic across hosts, environments, and time points. However, managing acquisition, processing, and analysis of large numbers of samples poses many challenges, with cross-contamination being the biggest obstacle. Contamination complicates analysis and results in sample loss, leading to higher costs and constraints on mixed sample type study designs. While many researchers opt for 96-well plates for their workflows, these plates present a significant issue: the shared seal and weak separation between wells leads to well-to-well contamination. To address this concern, we propose an innovative high-throughput approach, termed as the Matrix method, which employs barcoded Matrix Tubes for sample acquisition. This method is complemented by a paired nucleic acid and metabolite extraction, utilizing 95% (vol/vol) ethanol to stabilize microbial communities and as a solvent for extracting metabolites. Comparative analysis between conventional 96-well plate extractions and the Matrix method, measuring 16S rRNA gene levels via quantitative polymerase chain reaction, demonstrates a notable decrease in well-to-well contamination with the Matrix method. Metagenomics, 16S rRNA gene amplicon sequencing (16S), and untargeted metabolomics analysis via liquid chromatography-tandem mass spectrometry (LC-MS/MS) confirmed that the Matrix method recovers reproducible microbial and metabolite compositions that can distinguish between subjects. This advancement is critical for large-scale study design as it minimizes well-to-well contamination and technical variation, shortens processing times, and integrates with automated infrastructure for enhancing sample randomization and metadata generation. IMPORTANCE: Understanding dynamic microbial communities typically requires large-scale studies. However, handling large numbers of samples introduces many challenges, with cross-contamination being a major issue. It not only complicates analysis but also leads to sample loss and increased costs and restricts diverse study designs. The prevalent use of 96-well plates for nucleic acid and metabolite extractions exacerbates this problem due to their wells having little separation and being connected by a single plate seal. To address this, we propose a new strategy using barcoded Matrix Tubes, showing a significant reduction in cross-contamination compared to conventional plate-based approaches. Additionally, this method facilitates the extraction of both nucleic acids and metabolites from a single tubed sample, eliminating the need to collect separate aliquots for each extraction. This innovation improves large-scale study design by shortening processing times, simplifying analysis, facilitating metadata curation, and producing more reliable results.

Effect of swimming initiation period and continuation frequency on motor competence development in children aged up to 3 years: the Japan environment and children's study.

BACKGROUND: Although involvement of toddlers in swimming activities has increased recently, information regarding the impact of swimming during toddlerhood on subsequent child motor competence development is scarce. This study aimed to determine how swimming experience, particularly the timing of initiation and the continuity of swimming activities up to the age of 3 years, affects motor competence development. METHODS: This prospective cohort study included data on children aged 1.5 and 3 years (100,286 mother-child pairs) from the Japan Environment and Children's Study. The outcomes measured were gross and fine motor function, using the Japanese version of the Ages and Stages Questionnaire (Third edition). We assessed how these functions correlated with the continuous pattern of swimming pool use frequency from age 1 up to 3 years. RESULTS: The group that used a swimming pool once a month or more from age 1-1.5 years but stopped from age 2-3 years showed consistently significant negative associations with gross motor development delay (minimum adjusted odds ratio [aOR]: 0.66, 95% confidence interval [CI]: 0.60-0.73) and fine motor development delay (minimum aOR: 0.66, 95% CI: 0.58-0.76). The group that continued swimming once a month or more from age 1-3 years showed consistently significant negative associations with gross motor development delay (minimum aOR: 0.64, 95% CI: 0.54-0.75) and fine motor development delay (minimum aOR: 0.42, 95% CI: 0.31-0.55). CONCLUSIONS: These results suggest that swimming experience starting around age 1 year is positively associated with gross and fine motor function development. The beneficial impact on gross motor function persisted from age 1-3 years. In contrast, the effects on fine motor function were not evident until age ≥ 2.5 years after starting swimming at approximately age 1 year. These findings underscore the potential benefits of early swimming experiences in enhancing overall motor skills development during early childhood.

DENOISING: Dynamic enhancement and noise overcoming in multimodal neural observations via high-density CMOS-based biosensors.

Large-scale multimodal neural recordings on high-density biosensing microelectrode arrays (HD-MEAs) offer unprecedented insights into the dynamic interactions and connectivity across various brain networks. However, the fidelity of these recordings is frequently compromised by pervasive noise, which obscures meaningful neural information and complicates data analysis. To address this challenge, we introduce DENOISING, a versatile data-derived computational engine engineered to adjust thresholds adaptively based on large-scale extracellular signal characteristics and noise levels. This facilitates the separation of signal and noise components without reliance on specific data transformations. Uniquely capable of handling a diverse array of noise types (electrical, mechanical, and environmental) and multidimensional neural signals, including stationary and non-stationary oscillatory local field potential (LFP) and spiking activity, DENOISING presents an adaptable solution applicable across different recording modalities and brain networks. Applying DENOISING to large-scale neural recordings from mice hippocampal and olfactory bulb networks yielded enhanced signal-to-noise ratio (SNR) of LFP and spike firing patterns compared to those computed from raw data. Comparative analysis with existing state-of-the-art denoising methods, employing SNR and root mean square noise (RMS), underscores DENOISING's performance in improving data quality and reliability. Through experimental and computational approaches, we validate that DENOISING improves signal clarity and data interpretation by effectively mitigating independent noise in spatiotemporally structured multimodal datasets, thus unlocking new dimensions in understanding neural connectivity and functional dynamics.

Resting-state EEG microstates predict mentalizing ability as assessed by the Reading the Mind in the Eyes test.

Microstates analysis of electroencephalography (EEG) has gained increasing attention among researchers and clinicians as a valid tool for investigating temporal dynamics of large-scale brain networks with a millisecond time resolution. Although microstates analysis has been widely applied to elucidate the neurophysiological basis of various cognitive functions in both clinical and non-clinical samples, its application in relation to socio-affective processing has been relatively under-researched. Therefore, the main aim of the current study was to investigate the relationship between EEG microstates and mentalizing (i.e., the ability to understand the mental states of others). Eighty-two participants (thirty-six men; mean age: 24.28 ± 7.35 years; mean years of education: 15.82 ± 1.77) underwent a resting-state EEG recording and performed the Reading the Mind in the Eyes Test (RMET). The parameters of the microstates were then calculated using Cartool v. 4.09 software. Our results showed that the occurrence of microstate map C was independently and positively associated with the RMET total score and contributed to the prediction of mentalizing performance, even when controlling for potential confounding variables (i.e., age, sex, education level, tobacco and alcohol use). Since microstate C is involved in self-related processes, our findings may reflect the link between self-awareness of one's own thoughts/feelings and the enhanced ability to recognize the mental states of others at the neurophysiological level. This finding extends the functions traditionally attributed to microstate C, i.e. mind-wandering, self-related thoughts, prosociality, and emotional and interoceptive processing, to include mentalizing ability.

Promoting Collaborative Scholarship During the COVID-19 Pandemic Through an Innovative COVID-19 Data Explorer and Repository at Yale School of Medicine: Development and Usability Study.

BACKGROUND: The COVID-19 pandemic sparked a surge of research publications spanning epidemiology, basic science, and clinical science. Thanks to the digital revolution, large data sets are now accessible, which also enables real-time epidemic tracking. However, despite this, academic faculty and their trainees have been struggling to access comprehensive clinical data. To tackle this issue, we have devised a clinical data repository that streamlines research processes and promotes interdisciplinary collaboration. OBJECTIVE: This study aimed to present an easily accessible up-to-date database that promotes access to local COVID-19 clinical data, thereby increasing efficiency, streamlining, and democratizing the research enterprise. By providing a robust database, a broad range of researchers (faculty and trainees) and clinicians from different areas of medicine are encouraged to explore and collaborate on novel clinically relevant research questions. METHODS: A research platform, called the Yale Department of Medicine COVID-19 Explorer and Repository (DOM-CovX), was constructed to house cleaned, highly granular, deidentified, and continually updated data from over 18,000 patients hospitalized with COVID-19 from January 2020 to January 2023, across the Yale New Haven Health System. Data across several key domains were extracted including demographics, past medical history, laboratory values during hospitalization, vital signs, medications, imaging, procedures, and outcomes. Given the time-varying nature of several data domains, summary statistics were constructed to limit the computational size of the database and provide a reasonable data file that the broader research community could use for basic statistical analyses. The initiative also included a front-end user interface, the DOM-CovX Explorer, for simple data visualization of aggregate data. The detailed clinical data sets were made available for researchers after a review board process. RESULTS: As of January 2023, the DOM-CovX Explorer has received 38 requests from different groups of scientists at Yale and the repository has expanded research capability to a diverse group of stakeholders including clinical and research-based faculty and trainees within 15 different surgical and nonsurgical specialties. A dedicated DOM-CovX team guides access and use of the database, which has enhanced interdepartmental collaborations, resulting in the publication of 16 peer-reviewed papers, 2 projects available in preprint servers, and 8 presentations in scientific conferences. Currently, the DOM-CovX Explorer continues to expand and improve its interface. The repository includes up to 3997 variables across 7 different clinical domains, with continued growth in response to researchers' requests and data availability. CONCLUSIONS: The DOM-CovX Data Explorer and Repository is a user-friendly tool for analyzing data and accessing a consistently updated, standardized, and large-scale database. Its innovative approach fosters collaboration, diversity of scholarly pursuits, and expands medical education. In addition, it can be applied to other diseases beyond COVID-19.

Pilot-scale and large-scale Fenton-like applications with nano-metal catalysts: From catalytic modules to scale-up applications.

Recently, great efforts have been made to advance the pilot-scale and engineering-scale applications of Fenton-like processes using various nano-metal catalysts (including nanosized metal-based catalysts, smaller nanocluster catalysts, and single-atom catalysts, etc.). This step is essential to facilitate the practical applications of advanced oxidation processes (AOPs) for these highly active nano-metal catalysts. Before large-scale implementation, these nano-metal catalysts must be converted into the effective catalyst modules (such as catalytic membranes, fluidized beds, or polypropylene sphere suspension systems), as it is not feasible to use suspended powder catalysts for large-scale treatment. Therefore, the pilot-scale and engineering applications of nano-metal catalysts in Fenton-like systems in recent years is exciting. In addition, the combination of life cycle assessment (LCA) and techno-economic analysis (TEA) can provide a useful support tool for engineering scale Fenton-like applications. This paper summarizes the designs and fabrications of various advanced modules based on nano-metal catalysts, analyzes the advantages and disadvantages of these catalytic modules, and further discusses their Fenton-like pilot scale or engineering applications. Concepts of future Fenton-like engineering applications of nano-metal catalysts were also discussed. In addition, current challenges and future expectations in pilot-scale or engineering applications are assessed in conjunction with LCA and TEA. These challenges require further technological advances to enable larger scale engineering applications in the future. The aim of these efforts is to increase the potential of nanoscale AOPs for practical wastewater treatment.

A Micromanipulation-Actuated Large-Scale Screening to Identify Optimized Microphysiological Model Parameters in Skeletal Muscle Regeneration.

Hydrogel-based 3D cell cultures are extensively utilized to create biomimetic cellular microstructures. However, there is still lack of effective method for both evaluation of the complex interaction of cells with hydrogel and the functionality of the resulting micro-structures. This limitation impedes the further application of these microstructures as microphysiological models (microPMs) for the screening of potential culture condition combinations to enhance the skeletal muscle regeneration. This paper introduces a two-probe micromanipulation method for the large-scale assessment of viscoelasticity and contractile force (CF) of skeletal muscle microPMs, which are produced in high-throughput via microfluidic spinning and 96-well culture. The collected data demonstrate that viscoelasticity parameters (E* and tanδ) and CF both measured in a solution environment are indicative of the formation of cellular structures without hydrogel residue and the subsequent generation of myotubes, respectively. This study have developed screening criterias that integrate E*, tanδ, and CF to examine the effects of multifactorial interactions on muscle fiber repair under hypoxic conditions and within bioprinted bipennate muscle structures. This approach has improved the quality of hypoxic threshold evaluation and aligned cell growth in 3D. The proposed method is useful in exploring the role of different factors in muscle tissue regeneration with limited resources.

Expandable hESC-derived cardiovascular progenitor cells generate functional cardiac lineage cells for microtissue construction.

BACKGROUND: Cardiovascular progenitor cells (CPCs) derived from human embryonic stem cells (hESCs) are considered valuable cell sources for investigating cardiovascular physiology in vitro. Meeting the diverse needs of this application requires the large-scale production of CPCs in an in vitro environment. This study aimed to use an effective culture system utilizing signaling factors for the large-scale expansion of hESC-derived CPCs with the potential to differentiate into functional cardiac lineage cells. METHODS AND RESULTS: Initially, CPCs were generated from hESCs using a 4-day differentiation protocol with a combination of four small molecules (CHIR99021, IWP2, SB-431542, and purmorphamine). These CPCs were then expanded and maintained in a medium containing three factors (bFGF, CHIR, and A83-01), resulting in a > 6,000-fold increase after 8 passages. These CPCs were successfully cryopreserved for an extended period in late passages. The expanded CPCs maintained their gene and protein expression signatures as well as their differentiation capacity through eight passages. Additionally, these CPCs could differentiate into four types of cardiac lineage cells: cardiomyocytes, endothelial cells, smooth muscle cells, and fibroblasts, demonstrating appropriate functionality. Furthermore, the coculture of these CPC-derived cardiovascular lineage cells in rat tail collagen resulted in cardiac microtissue formation, highlighting the potential of this 3D platform for studying cardiovascular physiology in vitro. CONCLUSION: In conclusion, expandable hESC-derived CPCs demonstrated the ability to self-renewal and differentiation into functional cardiovascular lineage cells consistently across passages, which may apply as potential cell sources for in vitro cardiovascular studies.

Systematic review of protective factors related to academic resilience in children and adolescents: unpacking the interplay of operationalization, data, and research method.

Identifying protective factors that promote academic resilience is vital. Nevertheless, due to the variations in the operationalizations of academic resilience, timeframes, data sources, and employed research methods, it remains unclear whether the impact of protective factors identified across studies can be attributed to the factors themselves or to these variations. By addressing these uncertainties, this study aims to provide an overview of the protective factors that have been extensively investigated in academic resilience and their degree of influence. A literature search found 119 empirical studies on protective factors in education settings for children and adolescents. The review analyzed five protective factors groups (individual, family, school, peer, community), three operationalizations of academic resilience (simultaneous, progressive, instrumental), two timeframes (longitudinal, non-longitudinal), three data sources (self-collected, national/local assessments, international large-scale assessments), and commonly employed research methods. The studies analyzed in this review yielded mixed results regarding the impact of the examined protective factors, with measurement instruments and statistical power playing a significant role in explaining the variations. Individual and school-level characteristics emerged as the most well-studied protective factors; individual characteristics were often investigated through "instrumental" operationalization and structural equational models, whereas school-level characteristics were typically explored through "simultaneous" or "progressive" operationalizations and multilevel modeling. Approximately 31 and 16% of the studies utilized national assessments and international large-scale assessment data, respectively. Both data sources promoted the exploration of school-level factors, with the former facilitating the exploration of protective factors across time and the latter contributing to the investigation of teaching-related factors.

Dataset factors associated with age-related changes in brain structure and function in neurodevelopmental conditions.

With brain structure and function undergoing complex changes throughout childhood and adolescence, age is a critical consideration in neuroimaging studies, particularly for those of individuals with neurodevelopmental conditions. However, despite the increasing use of large, consortium-based datasets to examine brain structure and function in neurotypical and neurodivergent populations, it is unclear whether age-related changes are consistent between datasets and whether inconsistencies related to differences in sample characteristics, such as demographics and phenotypic features, exist. To address this, we built models of age-related changes of brain structure (regional cortical thickness and regional surface area; N = 1218) and function (resting-state functional connectivity strength; N = 1254) in two neurodiverse datasets: the Province of Ontario Neurodevelopmental Network and the Healthy Brain Network. We examined whether deviations from these models differed between the datasets, and explored whether these deviations were associated with demographic and clinical variables. We found significant differences between the two datasets for measures of cortical surface area and functional connectivity strength throughout the brain. For regional measures of cortical surface area, the patterns of differences were associated with race/ethnicity, while for functional connectivity strength, positive associations were observed with head motion. Our findings highlight that patterns of age-related changes in the brain may be influenced by demographic and phenotypic characteristics, and thus future studies should consider these when examining or controlling for age effects in analyses.