bluuebunny/crossref_metadata_2025_split huggingface dataset:
Perceptions of Rural Access to Prenatal Care in the United States by Patients, Nurses, Midwives, and Physicians
Authors: Durfee Stephanie , Nazarenko Danielle N. , Agbemenu Kafuli 
Published: October 2024

Abstract

Background:
Despite substantial investments in maternity care, the United States continues to experience higher maternal mortality rates than most high-income countries. Rural regions lack adequate prenatal care services, a contributing factor to disparate maternal health outcomes.

Methods:
This integrative review on patient, nurse, midwife, physician, and community perspectives on accessing prenatal care in rural areas of the United States follows Whittemore and Knafl's integrative review framework, including a systematic literature search in various databases from January 1, 2010, to May 1, 2024. Eligible articles were published in English, peer-reviewed, and focused on subjective accounts of accessing prenatal care in rural areas of the United States. Seven articles met inclusion criteria and were assessed for quality using the Joanna Briggs Institute checklists.

Results:
Three major categories influencing access to prenatal care in rural areas were developed. Individual factors included transportation, family support, motivation, and work commitments. Socioeconomic factors encompass health insurance, rurality, income, and housing. System factors involved the qualities of nurses, midwives, physicians, the clinic environment, and collaboration within the health care system. The findings emphasized the complexity of rural prenatal care access.

Conclusions:
Understanding patient and care giver perspectives on accessing prenatal care in rural areas is crucial for addressing the factors influencing maternal outcomes. Rural pregnant women face unique challenges, contributing to an increased risk of maternal morbidity and mortality. Further research is needed to explore their experiences and develop interventions to improve access and outcomes in rural areas.
Beta-Lactam Antibiotic Exposure During Pediatric Extracorporeal Membrane Oxygenation: Retrospective Cohort Analysis of Drug Levels Using Standard Dosing, 2018–2020
Authors: Marsaux Alice , Léger Pierre-Louis , Rambaud Jérôme , Bille Emmanuelle , Renolleau Sylvain , Tréluyer Jean Marc , Gana Inès , Lorrot Matthie , Grimaud Marion , Toubiana Julie , Béranger Agathe , Benaboud Sihem , Oualha Mehdi 
Published: October 2024

OBJECTIVES:
Children on extracorporeal membrane oxygenation (ECMO) are at high risk of infection that may worsen prognosis. Even though treatment with beta-lactam antibiotics is frequent, dosing is not adapted to altered pharmacokinetic and pharmacodynamic characteristics of children on ECMO. There is, therefore, a risk of inadequate drug levels when using standard dosing. In this study, we aimed to describe beta-lactam exposures of children on ECMO using current dosing and to identify factors associated with inadequate exposure. The optimal pharmacokinetic/pharmacodynamic target was considered as a plasma concentration four times above the minimum inhibitory concentration throughout the dosing interval target.

DESIGN:
Two-center retrospective cohort study.

SETTING:
Two PICUs in Paris, France.

PATIENTS:
Children (from birth to 18 yr) undergoing venovenous or venoarterial ECMO, from 2018 to 2020.

INTERVENTIONS:
None.

MEASUREMENTS AND MAIN RESULTS:
There were 57 patients who received 11 different beta-lactams, with 226 plasma concentrations analyzed. A total of 32 infections were documented. Overall, 133 of 226 concentrations (58.8%) were insufficient, primarily in samples from children younger than 28 days (p = 0.035), with low body weight (p = 0.013), or in instances of hypoalbuminemia (p = 0.011) and increased renal clearance (p = 0.032). Supratherapeutic concentrations were observed in 25 of 226 samples (11.1%), associated with being taken from patients with renal impairment (p < 0.01).

CONCLUSIONS:
In this retrospective cohort of pediatric ECMO cases, there is an associated risk of underexposure when prescribing conventional dosing of beta-lactams, which are likely associated with renal impairment and fluid overload. Prospective testing of therapeutic drug monitoring combined with pharmacokinetic/pharmacodynamic models should be tested as a risk-reduction strategy in this vulnerable population.
Effects of Papaverine on Experimentally Induced Myringosclerosis
Authors: Durna Yusuf Muhammed , Edizer Deniz Tuna , Yigit Ozgur , Yasak Ahmet Gorkem , Kelten Talu Esra Canan 
Published: October 2024

Objective:
This study aims to investigate the effects of papaverine on experimentally induced myringosclerosis (MS). MS is characterized by hyaline degeneration and calcification of the lamina propria of the tympanic membrane and can adversely affect hearing function if left untreated. The study examined the potential effects of both systemic and topical applications of papaverine on this process.

Materials and Methods:
In the study, 20 Wistar albino rats were used. The rats were divided into 4 groups and different treatment methods were applied in each group. In group 1, myringotomy was performed, but no additional intervention was performed. In group 2, saline-impregnated gelfoam was applied after myringotomy. In groups 3 and 4, topical and systemic applications of papaverine hydrochloride were performed. Tympanic membranes were examined under a microscope after 21 days, and the severity of MS and histopathologic changes were evaluated.

Results:
The papaverine-treated groups observed a significant increase in otomicroscopic sclerosis, the degree of inflammation, and tympanic membrane thickness. The degree of fibrosis was higher in group 3 compared with the other groups. The results showed that papaverine administration increased the occurrence of MS.

Conclusion:
This study suggests that papaverine may increase MS through its vasodilating effect. Vasodilation is thought to cause an increase in oxygen-derived free radicals, which may contribute to the pathogenesis of MS. The results suggest that the effects of papaverine on MS should be carefully evaluated.
Diverse Participant Recruitment for Infant Sequencing in the BabySeq Project
Authors: del Rosario Maya C. , Walmsley Sheyenne A. , Harrison Barbara W. , Stephens Crystal T. , Zettler Bethany , Rivera-Cruz Greysha , Agrawal Priyal , Brower Amy , Chigbu Stephanie , Christensen Kurt D. , Genetti Casie A. , Givens Richetta , Gold Nina B. , Reeves Inez V. , Schichter Isabella , Shariat Habib , Simon Sandra , Smith Hadley Stevens , Uveges Melissa , Green Robert C. , Holm Ingrid A. , Pereira Stacey 
Published: October 2024

ABSTRACTPurposeIt is essential that studies of genomic sequencing (GS) in newborns and children include individuals from under-represented racial and ethnic groups (URG) to ensure future applications are equitably implemented. We conducted interviews with parents from URG to better understand their perspectives on GS research, develop strategies to reduce barriers to enrollment, and facilitate research participation.MethodsSemi-structured interviews with 50 parents from URG.ResultsNearly all parents (44) said they would be interested in participating in an infant GS study. Parents were interested in participating in GS research for reasons including clinical utility, personal utility, and/or family health benefits. Deterrents to enrollment cited by parents were discomfort with enrollment procedures (e.g., not wanting a heel stick), limited emotional bandwidth, unfavorable perceptions of the study, and concerns about potential results. Most parents (35 of 40) said they would want to receive all types of genetic results, including actionable and non-actionable, as well as childhood- and adult-onset.ConclusionOur findings demonstrate that parents from URG are interested in participating in GS research. Based upon these findings, we provide recommendations for designing GS studies that are responsive to their concerns.
Staff experiences and perspectives of delivering an integrated child health and social care service in community settings: A qualitative exploration using the SELFIE framework
Authors: Litchfield I , Harper L , Abbas S , Dutton F , Wolhuter C , Bird C 
Published: October 2024

IntroductionIt’s suggested that integrated care is well-placed to address the prevalence of chronic conditions, obesity, and mental ill health in children and young people living in minoritized and economically-marginalised communities in high-income countries. This work describes staff perspectives of delivering an integrated place-based service providing multidisciplinary clinical care and early intervention social support to children and young people in an ethnically diverse and economically disadvantaged community in the UK.Materials and MethodsWe conducted a qualitative exploration of the experiences of staff delivering the service and used a directed content analysis to populate and present the results within the Sustainable integrated chronic care model for multi-morbidity: delivery, financing, and performance (SELFIE) framework. The first part of the analysis presented here focusses on the domain of Service delivery, relating to the availability and access of care.ResultsA total of 14 staff were interviewed including clinicians from primary and secondary care, social care providers, local voluntary groups, and school-based family mentors. Staff described at a Micro-level how the service increased engagement of families and facilitated referral to social support and preventative care; at a Meso-level the benefits of collocation, collaborative working, and community outreach were described. Finally at the Macro level, improvements to the access and availability of appropriate care were observed despite limited engagement by the local care system.ConclusionsThe pilot appeared to deliver multiple benefits for both patients and staff and the broader health economy particularly through collocating health care and social support. However, to implement truly integrated care, greater institutional commitment and leadership are needed.
Analysis of R-loop forming regions identifiesRNU2-2PandRNU5B-1as neurodevelopmental disorder genes
Authors: Jackson Adam , Thaker Nishi , Blakes Alexander , Banka Siddharth 
Published: October 2024

AbstractR-loops are critical for gene regulation but their contribution to human Mendelian disorders has not been explored. We show that in rare disease cohortsde novovariants are highly prevalent in genomic regions which form R-loops. Using this insight, we demonstrate that variants in two novel disorder genes,RNU2-2PandRNU5B-1, together with the recently describedRNU4-2provide a genetic explanation for an exceptionally large proportion of individuals with suspected rare Mendelian disorders.
How to improve polygenic prediction from whole-genome sequencing data by leveraging predicted epigenomic features?
Authors: Zeng Wanwen , Guo Hanmin , Liu Qiao , Wong Wing Hung 
Published: October 2024

AbstractPolygenic risk scores (PRS) are crucial in genetics for predicting individual susceptibility to complex diseases by aggregating the effects of numerous genetic variants. Whole-genome sequencing (WGS) has revolutionized our ability to detect rare and evende novovariants, creating an exciting opportunity for developing new PRS methods that can effectively leverage rare variants and capture the complex relationships among different variants. Furthermore, regulatory mechanisms play a crucial role in gene expression and disease manifestation, offering avenues to further enhance the performance and interpretation of PRS predictions. Through simulation studies, we highlighted aspects where current PRS methods face challenges when applied to WGS data, aiming to shed light on potential opportunities for further improvement. To address these challenges, we developed Epi-PRS, an approach that leverages the power of genomic large language models (LLM) to impute epigenomic signals across diverse cellular contexts, for use as intermediate variables between genotype and phenotype. A pretrained LLM is employed to transform genotypes into epigenomic signals using personal diploid sequences as inputs, and the genetic risk is then estimated based on the imputed personal epigenomic signals. Epi-PRS enhances the assessment of personal variant impacts, enabling a comprehensive and holistic consideration of genotypic and regulatory information within large genomic regions. Our simulation results demonstrated that incorporating the nuanced effects of non-linear models, rare variants, and regulatory information can provide more precise PRS prediction and better understanding of genetic risk. Applying Epi-PRS to real data from the UK Biobank, our results further showed that Epi-PRS significantly outperforms existing PRS methods in two major diseases: breast cancer and diabetes. This study suggests that PRS methods can benefit from incorporating non-linear models, rare variants, and regulatory information, highlighting the potential for significant advancements in disease risk modeling and enhancing the understanding of precision medicine.Significance StatementEpi-PRS improves polygenic risk scoring by integrating genomic large language models (LLMs) to impute epigenomic signals as intermediaries between genotype and phenotype. This approach enables a more comprehensive assessment of personal variant impacts by incorporating non-linear models, rare variants, and regulatory mechanisms. By leveraging the power of genomic LLM trained on massive amount of reference epigenomics data, Epi-PRS has demonstrated superior performance over existing PRS methods in predicting genetic risk for breast cancer and diabetes in UK Biobank data. These results highlight the potential of Epi-PRS to improve disease risk modeling and advance the field of precision medicine.
Unravelling the joint genetic architecture between psychiatic and insulin-related traits in the general population
Authors: Sakic Barbara , Erdogan Izel , Fanelli Giuseppe , Arenella Martina , Mota Nina Roth , Bralten Janita 
Published: October 2024

AbstractAttention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) are heritable disorders that frequently co-occur with insulin resistance (IR)-related conditions. Traditional genetic case-control comparisons are challenged by the extent of heterogeneity and comorbidity within and across these conditions. In this study we step away from univariate analyses to let biology guide us to the potential genetic links between insulin and psychiatry-related traits.We used large-scale population-based genetic studies (N= 17,666-697,734) and applied genomic structural equation modeling to identify the factor structure best representing the joint genetic architecture of symptom scores of ADHD, ASD, and OCD, and five IR-related traits: body mass index (BMI), fasting plasma glucose (FPG), fasting plasma insulin, glycated haemoglobin (HbA1c), and homeostatic model assessment for IR. Subsequently we performed multivariate genome-wide association analyses on the psychiatry-IR related factors to explore genetic associations to unravel its biological basis. Factor analyses indicated that a three-factor model fitted the data best (x2(df=9)=18.79, AIC=56.8, CFI=0.99, SRMR=0.068). One factor included ADHD traits and three IR-related traits (BMI, FPG, HbA1c), while another encompassed OCD traits and HbA1c. The last factor included solely IR-related traits. Gene-wide analyses revealed 57 genes significantly associated with the ADHD-IR factor (p< 2.961e-06) and one gene,MTNR1B(p=3.44e-07), with the OCD/OCS-IR factor. Gene-set analyses found associations with neurodevelopmental pathways.Our findings suggest a shared genetic liability between psychiatric symptoms and IR-related traits in the general population, offering new perspectives on the molecular genetics underlying the overlap between psychiatric and IR-related somatic conditions as well as biologically informed clustering within psychiatry.
SNP rs7549881, near SGIP1 at 1p31.3, is significantly associated with digestive disorders and Parkinsonism in women
Authors: Lehrer Steven , Rheinstein Peter 
Published: October 2024

AbstractBackgroundA recent study identified a mutation in the SH3GL Interacting Endocytic Adaptor 1 (SGIP1) gene, located at 1p31.3, linked to early-onset Parkinsonism in 2 sisters in an Arab family with a history of young-onset Parkinson symptoms. SGIP1 had not been previously associated with Parkinsonism, a group of disorders including Parkinson’s disease (PD), characterized by motor dysfunction and cognitive decline. SGIP1 plays a role in endocytosis, particularly clathrin-mediated synaptic vesicle recycling, essential for synaptic proteostasis. In a fruit fly model, SGIP1 deletion resulted in synaptic dysfunction, impaired protein recycling, and brain cell degeneration, which mirrors Parkinsonism. We aimed to explore the genetic association of SGIP1 in Parkinson’s disease using data from the UK Biobank, specifically examining the nearby common intron variant rs7549881 at 1p31.3.MethodsThis study utilized data from the UK Biobank, a large biomedical database with genetic and health information from approximately 500,000 participants. We analyzed the association between rs7549881 and PD. The study included all participants with self-reported or ICD-diagnosed PD and restricted analysis to those with at least seven years of education. Genetic association analysis was performed using PLINK and SPSS, with logistic regression adjusting for covariates such as age, sex, and smoking. Additionally, a Phenome-Wide Association Study (PheWAS) was conducted to examine rs7549881 across a wide range of phenotypes.ResultsThe study analyzed data from 385,629 subjects, with PD patients having a mean age of 63 ± 5 years. Among females, 26% of those homozygous for the rs7549881 GG allele had PD, compared to 22.2% who did not have PD (p = 0.004). This effect was not significant in males. Logistic regression revealed that male sex (OR 1.828, p < 0.001), age (OR 1.141 per year, p < 0.001), and constipation (OR 4.726, p = 0.001) increased PD risk, while cigarette smoking decreased it (OR 0.748, p < 0.001). The GG genotype was associated with increased PD risk when compared to the AA phenotype (OR 1.208, p = 0.015). PheWAS identified significant associations of rs7549881 with digestive disorders, including functional digestive disorders and non-infectious gastroenteritis.ConclusionThe study demonstrated an association between the rs7549881 variant and PD in females, highlighting the potential role of SGIP1 in neurodegeneration. Additionally, the PheWAS findings link this variant to gastrointestinal disorders, supporting the emerging concept of a gut-brain axis in PD. The results suggest that SGIP1 may influence both neuronal and gastrointestinal functions, providing a new avenue for understanding the genetic mechanisms underlying Parkinson’s disease and its non-motor symptoms.
A Study on the Correlation between Adolescents’ Online Social Connections and Anxiety and Depression
Authors: Mengxuan Li , Jiajun Xu , Ying Liu , Jiaqi Xu , Hong Chen 
Published: October 2024

AbstractOBJECTIVETo investigate the current status of anxiety and depression among adolescents and to analyze the correlation between adolescents’ online social connections and their depression and anxiety.METHODSThe study adopted a cross-sectional survey research design, conducted in a middle school located in a city in Sichuan Province. Classes were selected through simple random sampling, with all students in the selected classes serving as study participants. Anxiety, depression, and online social connections were assessed using the Chinese version of the DSM-5 Anxiety Inventory for Children and Adolescents, the DSM-5 Depression Inventory for Children and Adolescents, and the Revised Social Connectedness Scale, respectively.RESULTSThe prevalence rates of anxiety and depression among the participants were found to be 49.1% and 47.1%, respectively. A significant positive correlation was identified between online social connections and both anxiety and depression.CONCLUSIONThe findings indicate a concerning prevalence of anxiety and depression among adolescents. It is crucial for healthcare professionals to recognize the multifaceted factors contributing to these mental health challenges and to remain vigilant regarding adolescent mental health. Leveraging the impact of online social connections, early psychological intervention strategies should be implemented to promote healthy online relationships and mitigate the risks of anxiety and depression among adolescents.
Advancing patient care: Machine learning models for predicting grade 3+ toxicities in gynecologic cancer patients treated with HDR brachytherapy
Authors: Portocarrero-Bonifaz Andres , Syed Salman , Kassel Maxwell , McKenzie Grant W. , Shah Vishwa M. , Forry Bryce M. , Gaskins Jeremy T. , Sowards Keith T. , Avula Thulasi Babitha , Masters Adrianna , Schneider Jose G. , Silva Scott R. 
Published: October 2024

AbstractBackgroundGynecological cancers are among the most prevalent cancers in women worldwide. Brachytherapy, often used as a boost to external beam radiotherapy, is integral to treatment. Advances in computation, algorithms, and data availability have popularized machine learning.ObjectiveTo develop and compare machine learning models for predicting grade 3 or higher toxicities in gynecological cancer patients treated with high dose rate (HDR) brachytherapy, aiming to contribute to personalized radiation treatments.MethodsA retrospective analysis on gynecological cancer patients who underwent HDR brachytherapy with Syed-Neblett or Tandem and Ovoid applicators from 2009 to 2023. After exclusions, 233 patients were included. Dosimetric variables for the high-risk clinical target volume (HR-CTV) and organs at risk, along with tumor, patient, and toxicity data, were collected and compared between groups with and without grade 3 or higher toxicities using statistical tests. Six supervised classification machine learning models (Logistic Regression, Random Forest, K-Nearest Neighbors, Support Vector Machines, Gaussian Naive Bayes, and Multi-Layer Perceptron Neural Networks) were constructed and evaluated. The construction process involved sequential feature selection (SFS) when appropriate, followed by hyperparameter tuning. Final model performance was characterized using a 25% withheld test dataset.ResultsThe top three ranking models were Support Vector Machines, Random Forest, and Logistic Regression, with F1 testing scores of 0.63, 0.57, and 0.52; normMCC testing scores of 0.75, 0.77, and 0.71; and accuracy testing scores of 0.80, 0.85, and 0.81, respectively. The SFS algorithm selected 10 features for the highest-ranking model. In traditional statistical analysis, HR-CTV volume, Charlson Comorbidity Index, Length of Follow-Up, and D2cc - Rectum differed significantly between groups with and without grade 3 or higher toxicities.ConclusionsMachine learning models were developed to predict grade 3 or higher toxicities, achieving satisfactory performance. Machine learning presents a novel solution to creating multivariable models for personalized radiation therapy care.