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The HAVCR1-Centric Host Factor Network Drives Zika Virus Vertical Transmission
No full textZika virus (ZIKV) vertical transmission results in devastating congenital malformations and pregnancy complications; however, the specific receptor and host factors facilitating ZIKV maternal-fetal transmission remain elusive. Here, we employ a genome-wide CRISPR screening and identify multiple placenta-intrinsic factors modulating ZIKV infection. Our study unveils that hepatitis A virus cellular receptor 1 (HAVCR1) serves as a primary receptor governing ZIKV entry in placental trophoblasts. The GATA3-HAVCR1 axis regulates heterogeneous cell tropism in the placenta. Notably, placenta-specific Havcr1 deletion in mice significantly impairs ZIKV transplacental transmission and associated adverse pregnancy outcomes. Mechanistically, the immunoglobulin variable-like domain of HAVCR1 binds to ZIKV via domain III of envelope protein and virion-associated phosphatidylserine. Proteomic profiling and function analyses reveal that AP2S1 cooperates with HAVCR1 for ZIKV internalization through clathrin-mediated endocytosis. Overall, our work underscores the pivotal role of HAVCR1 in mediating ZIKV vertical transmission and highlights a therapeutic target for alleviating congenital Zika syndrome
Quantitative Profiling pH Heterogeneity of Acidic Endolysosomal Compartments Using Fluorescence Lifetime Imaging Microscopy
No full textThe endolysosomal system plays a crucial role in maintaining cellular homeostasis and promoting organism fitness. The pH of its acidic compartments is a crucial parameter for proper function, and it is dynamically influenced by both intracellular and environmental factors. Here, we present a method based on fluorescence lifetime imaging microscopy (FLIM) for quantitatively analyzing the pH profiles of acidic endolysosomal compartments in diverse types of primary mammalian cells and in live organism Caenorhabditis elegans. This FLIM-based method exhibits high sensitivity in resolving subtle pH differences, thereby revealing heterogeneity within a cell and across cell types. This method enables rapid measurement of pH changes in the acidic endolysosomal system in response to various environmental stimuli. Furthermore, the fast FLIM measurement of pH-sensitive dyes circumvents the need for transgenic reporters and mitigates potential confounding factors associated with varying dye concentrations or excitation light intensity. This FLIM approach offers absolute pH quantification and highlights the significance of pH heterogeneity and dynamics, offering a valuable tool for investigating lysosomal functions and their regulation in various physiological and pathological contexts
Investigative Genetic Genealogy Practices Warranting Policy Attention: Results of a Modified Policy Delphi
No full textA technique known as investigative genetic genealogy (IGG) was first introduced to criminal investigations in 2018, and it has since been used by U.S. law enforcement to help identify hundreds of criminal perpetrators and unidentified human remains. As expertise in IGG grows, policymakers have shown interest in regulating it. To help inform these efforts and to promote coherence in IGG governance as it expands, we recruited experts representing a spectrum of IGG-relevant professions and perspectives to identify and prioritize IGG practices for policy attention and to develop policy options for addressing them. In two rounds of a modified policy Delphi, 31 participants prioritized nine IGG practices for policy attention. These top priority practices relate to: consent and notification; case eligibility and criteria; data management, privacy, and security; and governance and accountability. Participants expressed a range of opinions, some strongly held, and did not reach complete consensus with respect to any of the practices. However, convergence was strongest with respect to law enforcement participation in direct-to-consumer genetic genealogy databases against terms of service, which a large majority opposed and almost half evaluated as top priority for policy attention. Participants also voiced strong and consistent concern about management of data and samples collected and generated during IGG and the governance of private laboratories involved in IGG. Our study demonstrates the feasibility and value of engaging with diverse experts over an extended period on a pressing matter of public policy and provides a needed empirical foundation for IGG policymaking
Longitudinal Phage-Bacteria Dynamics in the Early Life Gut Microbiome
No full textMicrobial colonization of the human gut occurs soon after birth, proceeds through well-studied phases and is affected by lifestyle and other factors. Less is known about phage community dynamics during infant gut colonization due to small study sizes, an inability to leverage large databases and a lack of appropriate bioinformatics tools. Here we reanalysed whole microbial community shotgun sequencing data of 12,262 longitudinal samples from 887 children from four countries across four years of life as part of the The Environmental Determinants of Diabetes in the Young (TEDDY) study. We developed an extensive metagenome-assembled genome catalogue using the Marker-MAGu pipeline, which comprised 49,111 phage taxa from existing human microbiome datasets. This was used to identify phage marker genes and their integration into the MetaPhlAn 4 bacterial marker gene database enabled simultaneous assessment of phage and bacterial dynamics. We found that individual children are colonized by hundreds of different phages, which are more transitory than bacteria, accumulating a more diverse phage community over time. Type 1 diabetes correlated with a decreased rate of change in bacterial and viral communities in children aged one and two. The addition of phage data improved the ability of machine learning models to discriminate samples by country. Finally, although phage populations were specific to individuals, we observed trends of phage ecological succession that correlated well with putative host bacteria. This resource improves our understanding of phage-bacteria interactions in the developing early life microbiome
Designing a Narrative-Based Video Game for Adolescents Coping with a Parent\u27s Cancer
Get PDFWe developed the narrative video game Sophia that immerses users in the story of a 13-year-old girl during her father\u27s 18-month battle with glioblastoma and her journey toward resilience during her high school years following his passing. The game is designed to engage adolescents coping with parental cancer in multiple gameplay modes and levels, the character, and the outcome of the story as a way to build connections and explore the themes of illness, dying, bereavement, and resilience. The scenes at each level correspond to the stages of glioblastoma illness as well as the teenage milestones she passes through without her father (e.g. seeing her friends\u27 fathers at school events, trying out for sports teams, learning to drive, and applying for college). Our next step is to utilize a stakeholder advisory board to review content and make suggestions for adaptations. Finally, we will evaluate the usability, feasibility, acceptability, perception, and impact of the game on adolescent participants experiencing parental cancer and/or the loss of a parent from cancer
A Scoping Review of Omop Cdm Adoption for Cancer Research Using Real World Data
Get PDFThe Observational Medical Outcomes Partnership (OMOP) common data model (CDM) supports large-scale research by enabling distributed network analyses. However, the breadth of its adoption in cancer research is not well understood. We conducted a scoping review to describe the adoption of the OMOP CDM in cancer research. A total of 49 unique articles were included in the review, with 30 on the data analysis theme, and 20 on the infrastructure theme. This review highlighted that while the OMOP CDM ecosystem has enabled successful data support for cancer research, particularly for collaborative studies, ongoing model development and iterative improvement remain needed to fulfill additional research data needs. Expanding disease sites, specifically for rare cancers, integrating more diverse types of data sources, improving data quality, adopting advanced analytics methodology, and increasing multisite evaluations serve as important opportunities to facilitate secondary usage of observational data in future cancer research
Baseline Predictors of Responders to Auricular Point Acupressure in Chronic Low Back Pain
No full textBackground: Chronic low back pain (cLBP) is a major cause of disability, with varied patient responses to treatments. Auricular point acupressure (APA) has shown potential as a non-pharmacological intervention, but individual responses may differ significantly.
Objective: This study aimed to determine the predictability of baseline characteristics, including functional disability, symptom severity, and treatment expectancy, on clinically significant responses to APA in reducing pain and improving function.
Methods: A secondary analysis was performed using data from a randomized controlled trial with 263 cLBP patients. Participants were randomly assigned to targeted APA (T-APA), non-targeted APA (NT-APA), or to a control group. APA responders were defined as those with at least a 1.5-point reduction in pain intensity or a 2.5-point improvement in the Roland-Morris Disability Questionnaire (RMDQ). Predictors of response were assessed using logistic regression and machine learning models, including the Random Forest and Support Vector Machine (SVM).
Results: Baseline pain, physical function, sleep disturbance, and treatment expectancy were key predictors. The Random Forest model had the highest accuracy for T-APA; however, logistic regression performed best in NT-APA. SVM was most accurate in the control group, with predictive accuracy varying by group (AUC 60.9%-80%). The Least Absolute Shrinkage and Selection Operator (LASSO) method was found to be overly aggressive, often eliminating important variables.
Conclusion: This study highlights the variability in APA treatment responses for cLBP. While predictive models provide useful insights, further research with larger datasets is needed to improve prediction accuracy and generalizability, enhancing personalized treatment approaches for cLBP
Development and Validation of a Dynamic Real-Time Risk Prediction Model for Intensive Care Units Patients Based on Longitudinal Irregular Data: Multicenter Retrospective Study
No full textBackground: Timely and accurate prediction of short-term mortality is critical in intensive care units (ICUs), where patients\u27 conditions change rapidly. Traditional scoring systems, such as the Simplified Acute Physiology Score and Acute Physiology and Chronic Health Evaluation, rely on static variables collected within the first 24 hours of admission and do not account for continuously evolving clinical states. These systems lack real-time adaptability, interpretability, and generalizability. With the increasing availability of high-frequency electronic medical record (EMR) data, machine learning (ML) approaches have emerged as powerful tools to model complex temporal patterns and support dynamic clinical decision-making. However, existing models are often limited by their inability to handle irregular sampling and missing values, and many lack rigorous external validation across institutions.
Objective: We aimed to develop a real-time, interpretable risk prediction model that continuously assesses ICU patient mortality using irregular, longitudinal EMR data, with improved performance and generalizability over traditional static scoring systems.
Methods: A time-aware bidirectional attention-based long short-term memory (TBAL) model was developed using EMR data from the MIMIC-IV (Medical Information Mart for Intensive Care) and eICU Collaborative Research Database (eICU-CRD) databases, comprising 176,344 ICU stays. The model incorporated dynamic variables, including vital signs, laboratory results, and medication data, updated hourly, to perform static and continuous mortality risk assessments. External cross-validation and subgroup sensitivity analyses were conducted to evaluate robustness and fairness. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), accuracy, and F1-score. Interpretability was enhanced using integrated gradients to identify key predictors.
Results: For the static 12-hour to 1-day mortality prediction task, the TBAL model achieved AUROCs of 95.9 (95% CI 94.2-97.5) and 93.3 (95% CI 91.5-95.3) and AUPRCs of 48.5 and 21.6 in MIMIC-IV and eICU-CRD, respectively. Accuracy and F1-scores reached 94.1 and 46.7 in MIMIC-IV and 92.2 and 28.1 in eICU-CRD. In dynamic prediction tasks, AUROCs reached 93.6 (95% CI 93.2-93.9) and 91.9 (95% CI 91.6-92.1), with AUPRCs of 41.3 and 50, respectively. The model maintained high recall for positive cases (82.6% and 79.1% in MIMIC-IV and eICU-CRD). Cross-database validation yielded AUROCs of 81.3 and 76.1, confirming generalizability. Subgroup analysis showed stable performance across age, sex, and severity strata, with top predictors including lactate, vasopressor use, and Glasgow Coma Scale score.
Conclusions: The TBAL model offers a robust, interpretable, and generalizable solution for dynamic real-time mortality risk prediction in ICU patients. Its ability to adapt to irregular temporal patterns and to provide hourly updated predictions positions it as a promising decision-support tool. Future work should validate its utility in prospective clinical trials and investigate its integration into real-world ICU workflows to enhance patient outcomes
Examining Demographic, Geographic, and Temporal Patterns of Melanoma Incidence in Texas From 2000 to 2018: Retrospective Study
No full textBackground: Melanoma currently ranks as the fifth leading cancer diagnosis and is projected to become the second most common cancer in the United States by 2040. Melanoma detected at earlier stages may be treated with less-risky and less-costly therapeutic options.
Objective: This study aims to analyze temporal and spatial trends in melanoma incidence by stage at diagnosis (overall, early, and late) in Texas from 2000 to 2018, focusing on demographic and geographic variations to identify high-risk populations and regions for targeted prevention efforts.
Methods: We used melanoma incidence data from all 254 Texas counties from the Texas Cancer Registry (TCR) from 2000 to 2018, aggregated by county and year. Among these, 250 counties reported melanoma cases during the period. Counties with no cases reported in a certain year were treated as having no cases. Melanoma cases were classified by SEER Summary Stage and stratified by the following four key covariates: age, sex, race and ethnicity, and stage at diagnosis. Incidence rates (IRs) were calculated per 100,000 population, and temporal trends were analyzed using joinpoint regression to determine average annual percentage changes (AAPCs) with 95% CIs for the whole time period (2000-2018), the most recent 10-year period (2009-2018), and the most recent 5-year period (2014-2018). Heat map visualizations were developed to assess temporal trends by patient age, year of diagnosis, stage at diagnosis, sex, and race and ethnicity. Spatial cluster analysis was conducted using Getis-Ord Gi* statistics to identify county-level geographic clusters of high and low melanoma incidence by stage at diagnosis.
Results: A total of 82,462 melanoma cases were recorded, of which 74.7% (n=61,588) were early stage, 11.3% (n=9,352) were late stage, and 14% (n=11,522) were of unknown stage. Most cases were identified as males and non-Hispanic White individuals. Melanoma IRs increased from 2000 to 2018, particularly among older adults (60+ years; AAPC range 1.20%-1.84%; all P values were \u3c .001), males (AAPC 1.59%; P\u3c .001), and non-Hispanic White individuals (AAPC of 3.24% for early stage and 2.38% for late stage; P\u3c .001 for early stage and P = .03 for late state). Early-stage diagnoses increased while the rates of late-stage diagnoses remained stable for the overall population. The spatial analysis showed that urban areas had higher early-stage incidence rates (P=.06), whereas rural areas showed higher late-stage incidence rates (P=.05), indicating possible geographic-based differences in access to dermatologic care.
Conclusions: Melanoma incidence in Texas increased over the study time period, with the most-at-risk populations being non-Hispanic White individuals, males, and individuals aged 50 years and older. The stable rates of late-stage melanoma among racial and ethnic minority populations and rural populations highlight potential differences in access to diagnostic care. Future prevention efforts may benefit from increasing access to dermatologic care in areas with higher rates of late-stage melanoma at diagnosis
A Mapping Review and Critique of the Literature on Translation, Dissemination, and Implementation Capacity Building Initiatives for Different Audiences
No full textBackground: Capacity building is critical for research and practice as the fields of dissemination, implementation and translation science continue to grow. Some scholars state that capacity building should be grounded in competencies. However, the fields are unclear in determining which competencies are relevant for whom, including the content and appropriate level of information and skills for different roles. The goal of this study was to catalogue competencies across current D&I capacity building initiatives.
Methods: We conducted a mapping review to examine to what extent are theories or frameworks used to guide capacity building, who is being trained, to what extent do capacity building initiatives include a health equity focus, which competencies are being outlined or suggested, how are they being defined, and whether the competencies can be organized along different roles of participants. As a mapping review, we broadly searched for papers using the keywords training D&I OR training implementation OR training translation OR training dissemination and included debate and empirical papers about capacity building initiatives in the sample.
Results: A total of 42 articles (from 2011 to 2024) were reviewed, including training development and/or evaluation (n = 25) and conceptual (n = 17) articles. Of the training articles, 13 (52%) specified a framework that guided training. Participants in training included graduate students, researchers, practitioners, and mixed audiences. Fourteen (56%) of the trainings were conducted in the USA, seven (28%) in Canada and other countries. The length of training ranged from two days to two years. Four trainings had an explicit focus on equity. A total of 307 unique competencies were identified and divided into themes: Knowledge, Skills, Engagement with Other Disciplines, Equity, Attitude and Relational Aspects, Capacity Building, Quality Improvement, and Mentorship.
Conclusions: While there are many D&I capacity building initiatives, we found little consistency in competencies that guided training activities for diverse audiences. Few training activities explicitly identified guiding theories or frameworks or tailored competencies toward different levels of interest in D&I research. Even fewer had an explicit focus on health equity. As the fields continue to foster capacity building programs, it will be important to think critically about the types of competencies we are developing for whom, how, and why