bluuebunny/crossref_metadata_2025_split huggingface dataset:
A blueprint for biobanking in everyday clinical practice in psychiatry: The Munich Mental Health Biobank
Authors: Kalman JL , Burkhardt G , Adorjan K , Barton BB , De Jonge S , Eser-Valeri D , Falter-Wagner CM , Heilbronner U , Jobst A , Keeser D , Koenig C , Koller G , Koutsouleris N , Kurz C , Landgraf D , Merz K , Musil R , Nelson AM , Padberg F , Papiol S , Pogarell O , Perneczky R , Raabe F , Reinhard MA , Richter A , Rüther T , Simon MS , Schmitt A , Slapakova L , Scheel N , Schüle C , Wagner E , Wichert SP , Zill P , Falkai P , Schulze TG , Schulte EC 
Published: May 2022

AbstractTranslational research on complex, multifactorial mental health disorders, such as bipolar disorder, major depressive disorder, schizophrenia, and substance use disorders requires databases with large-scale, harmonized, and integrated real-world and research data.The Munich Mental Health Biobank (MMHB) is a mental health-specific biobank that was established in 2019 to collect, store, connect, and supply such high-quality phenotypic data and biospecimens from patients and study participants, including healthy controls, recruited at the Department of Psychiatry and Psychotherapy and the Institute of Psychiatric Phenomics and Genomics, University Hospital of the Ludwig-Maximilians-University (LMU), Munich, Germany. Participants are asked to complete a questionnaire that assesses sociodemographic and cross-diagnostic clinical information, provide blood samples, and grant access to their existing medical records. The generated data and biosamples are available to both academic and industry researchers. In the current manuscript, we outline the workflow and infrastructure of the MMHB, describe the clinical characteristics and representativeness of the sample collected so far, and reveal future plans for expansion and application.As of October 31, 2021, the MMHB contains a continuously growing set of data from 578 patients and 104 healthy controls (46.37% female; median age, 38.31 years). The five most common mental health diagnoses in the MMHB are recurrent depressive disorder (38.78%; ICD-10: F33), alcohol-related disorders (19.88%; ICD-10: F10), schizophrenia (19.69%; ICD-10: F20), depressive episode (15.94%; ICD-10: F32), and personality disorders (13.78%; ICD-10: F60). Compared with the average patient treated at the recruiting hospitals, MMHB participants have significantly more mental health-related contacts, less severe symptoms, and a higher level of functioning. The distribution of diagnoses is also markedly different in MMHB participants compared with individuals who did not participate in the biobank.After establishing the necessary infrastructure and initiating recruitment, the major tasks for the next phase of the MMHB project are to improve the pace of participant enrollment, diversify the sociodemographic and diagnostic characteristics of the sample, and improve the utilization of real-world data generated in routine clinical practice.
Testing the Triple Network Model of Psychopathology in a Transdiagnostic Neurodevelopmental Cohort
Authors: Jones Jonathan S. ,   The CALM Team, Leyland-Craggs Amelia , Astle Duncan E. 
Published: May 2022

AbstractBackgroundThe triple network model of psychopathology posits that altered connectivity between the Salience (SN), Central Executive (CEN), and Default Mode Networks (DMN) may underlie neurodevelopmental conditions. However, this has yet to be tested in a transdiagnostic sample of youth.MethodsWe investigated triple network connectivity in a sample of 175 children (60 girls) that represent a heterogeneous population who are experiencing neurodevelopmental difficulties in cognition and behavior, and 60 comparison children (33 girls) without such difficulties. Hyperactivity/impulsivity and inattention were assessed by parent-report and resting-state functional Magnetic Resonance Imaging data were acquired. Functional connectivity was calculated between independent network components and regions of interest. We then examined whether connectivity between the SN, CEN and DMN was dimensionally related to hyperactivity/impulsivity and inattention, whilst controlling for age, gender, and motion.ResultsHyperactivity/impulsivity was associated with decreased segregation between the SN, CEN, and DMN in at-risk children, whereas it was associated with increased segregation of the CEN and DMN in comparison children. We replicated these effects in networks and regions derived from an adult parcellation of brain function and when using increasingly stringent exclusion criteria for in-scanner motion.ConclusionsTriple network connectivity characterizes transdiagnostic neurodevelopmental difficulties with hyperactivity/impulsivity. This may arise from delayed network segregation, difficulties sustaining CEN activity to regulate behavior, and/or a heightened developmental mismatch between neural systems implicated in cognitive control relative to those implicated in reward/affect processing.
Association of socioeconomic status with arterial stiffness in older African American and White Adults: The ARIC Study Cohort
Authors: Spikes T.A. , Alam A.B. , Lewis T.T. , Windham B.G. , Kucharska-Newton A. , Alonso A. 
Published: May 2022

AbstractBackgroundThe contributions of traditional CVD risk factors do not fully explain the racial and socioeconomic disparities in arterial stiffness. The extent to which the association of socioeconomic status (SES) with subclinical CVD is different for older African American and White adults is unclear.Methods and ResultsCross-section associations of individual SES [education and income] with carotid femoral pulse wave velocity (cfPWV), a subclinical marker of arterial stiffness, and modification of this relationship by race were examined among 3,342 participants of the Atherosclerosis Risk in Communities (ARIC) Study (age=75±4.9 years, 64% female, 23% African American, free of CVD in 2011-2013) using multivariable linear regression. Post-graduate education compared to less than high school, was associated with lower cfPWV (less stiffness) in African Americans (β = −1.28 m/s; 95% CI, −1.97, −0.59), but not in Whites (β = −0.69 m/s; 95% CI, −1.39, 0.01). Income ≥$50K as compared to <$25K, was associated with lower cfPWV both in African Americans (β = −0.82 m/s; 95% CI, −1.42, −0.22) and Whites (β = −0.76 m/s; 95% CI, −1.19, −0.32). However, interaction of race with individual measures of SES on cfPWV in African American and White adults were not statistically significant (p-value for interaction >0.05).ConclusionsHigher SES was cross-sectionally associated with lower arterial stiffness in this cohort; the data did not support differences by race. Prospective studies of SES and cfPWV among a racially socioeconomically diverse cohort merit further study.
Novel method for estimating right atrial pressure with point-of-care ultrasound (POCUS)
Authors: Istrail Larry , Kiernan Joseph , Stepanova Maria 
Published: May 2022

AbstractCurrent noninvasive estimation of right atrial pressure (RAP) by either bedside jugular venous pressure (JVP) exam or inferior vena cava (IVC) measurement during a formal echocardiogram offer imprecise estimates of actual RAP. We enrolled 41 patients in a prospective, blinded study to validate a novel point-of-care ultrasound method to estimate RAP. Two subjects were excluded and 39 were included in the final analysis. The ultrasound estimate of RAP (RAPU) was compared to the RAP measurement during right heart catheterization (RAPi) both as measured and corrected for the mid-AP diameter. The correlation coefficient between RAPi and corrected RAPU measurements was +0.72, regression R2 0.52, bias −0.60 mmHg (95% confidence interval [CI], −1.60 to +0.39 mmHg) with the limits of agreement −5.56 to +7.24 mmHg, and 3 mmHg accuracy of 26 (67%). Similarly, for the uncorrected RAPU measurement, the correlation coefficient was +0.75, regression R2 0.56, bias −0.49 mmHg (95% CI, −1.42 to +0.43 mmHg) with the limits of agreement −5.56 to +7.24 mmHg, and 3 mmHg accuracy of 29 (74%). This simple bedside evaluation of right atrial depth and the right jugular vein correlates with actual right atrial pressure better than traditional IVC parameters, and can accurately estimate RAP within 3mmHg in most patients.
Precision Digital Intervention for Depression Based on Social Rhythm Principles Adds Significantly to Outpatient Treatment
Authors: Frank Ellen , Wallace Meredith , Matthews Mark J. , Kendrick Jeremy , Leach Jeremy , Moore Tara , Aranovich Gabriel , Choudhury Tanzeem , Shah Nirav R. , Framroze Zeenia , Posey Greg , Burgess Samuel , Kupfer David J. 
Published: May 2022

AbstractWe conducted a 16-week randomized controlled trial in psychiatric outpatients with a lifetime diagnosis of a mood and/or anxiety disorder to measure the impact of a first of its kind precision digital intervention based on social rhythm regulation principles (Cue). The full intent to treat (ITT) sample consisted of 133 individuals, aged 18 to 65. The primary sample of interest were individuals with moderately severe to severe depression at study entry (baseline PHQ-8 score greater than or equal to 15; N=28).Cue is a novel digital intervention platform that capitalizes on the smartphone’s ability to continuously monitor depression relevant behavior patterns and use each patient’s behavioral data to provide timely, personalized micro interventions, making this the first example of a digital precision intervention that we are aware of. Participants were randomly allocated to receive Cue plus care as usual or digital monitoring only plus care as usual.Within the depressed and full ITT samples, we fit a mixed effects model to test for group differences in the slope of depressive symptoms over 16 weeks. To account for the nonlinear trajectory with more flexibility, we also fit a mixed effects model considering week as a categorical variable and used the resulting estimates to test the group difference in PHQ change from baseline to 16 weeks.In the depressed at entry sample, we found evidence for benefit of Cue. The large group difference in the slope of PHQ-8 (Cohen’s d= -0.72) indicated a meaningfully more rapid rate of improvement in the intervention group than in the control group. The Cue group also demonstrated significantly greater improvement in PHQ-8 from baseline to 16 weeks (p=0.009). In the full sample, the group difference in the slope of PHQ-8 was negligible (Cohen’s d=-0.10); however, the Cue group again demonstrated significantly greater improvement from baseline to 16 weeks (p=0.040).We are encouraged by the size of the intervention effect in those who were acutely ill at baseline and by the finding that across all participants, 80% of whom were receiving pharmacotherapy, we observed significant benefit of Cue at 16 weeks of treatment. These findings suggest that a social rhythm focused digital intervention platform may represent a useful and accessible adjunct to antidepressant treatment.(https://clinicaltrials.gov/ct2/show/NCT03152864?term=ellen+frank&draw=2&rank=3)
Antibody responses to known and unknown SARS-CoV-2 infections after mRNA vaccine booster
Authors: Demonbreun Alexis R. , Sancilio Amelia , Vaught Lauren A. , Reiser Nina L. , Pesce Lorenzo , Sode Eoin P. , Mustanski Brian , D’Aquila Richard , McNally Elizabeth M. , McDade Thomas W. 
Published: May 2022

AbstractWe followed a fully-vaccinated (two mRNA vaccine doses) community cohort (n=41) without prior COVID-19 diagnosis from September 2021 through March 2022 through the Omicron wave following a booster mRNA vaccination. 19.5% of participants reported a known SARS-CoV-2 infection during the Omicron wave, which was confirmed by anti-nucleocapsid IgG. An additional 36.5% also developed anti-nucleocapsid IgG after the boost, consistent with unknown, asymptomatic SARS-CoV-2 infection during this period. Infection defined by anti-nucleocapsid IgG, whether known to participant or not, increased anti-spike IgG levels, relative to those lacking anti-nucleocapsid IgG, at 120 days post-booster.
Functional genomics in primary T cells and monocytes identifies mechanisms by which genetic susceptibility loci influence systemic sclerosis risk
Authors: González-Serna David , Shi Chenfu , Kerick Martin , Hankinson Jenny , Ding James , McGovern Amanda , Tutino Mauro , Martin Gonzalo Villanueva , Ortego-Centeno Norberto , Luis Callejas José , Martin Javier , Orozco Gisela 
Published: May 2022

ABSTRACTObjectivesSystemic sclerosis (SSc) is a complex autoimmune disease with a strong genetic component. However, most of the genes associated to the disease are still unknown because associated variants affect mostly non-coding intergenic elements of the genome. The challenge now is to use functional genomics to translate the genetic findings into a better understanding of the disease.MethodsPromoter capture Hi-C and RNA sequencing experiments were performed in CD4+T cells and CD14+monocytes samples from 10 SSc patients and 5 healthy controls to link SSc-associated variants with their target genes, followed by differential expression and differential interaction analyses between cell types.ResultsWe linked SSc-associated loci to 39 new potential target genes and confirm 7 previously known genes. We highlight novel causal genes, such asCXCR5as the most probable candidate gene for theDDX6locus. Some previously known SSc associated genes such asIRF8, STAT4, orCD247interestingly showed cell type specific interactions. We also identified 15 potential drug targets already in use in other similar immune-mediated diseases that could be repurposed for SSc treatment. Furthermore, we observed that interactions are directly correlated with the expression of important genes implicated in cell type specific pathways and find evidence that chromatin conformation is associated with genotype.ConclusionsOur study reveals potential causal genes for SSc-associated loci, some of them acting in a cell type specific manner, suggesting novel biological mechanisms that might mediate SSc pathogenesis.
Interactions between Perceived Stress and Microbial-Host Immune Components in Pregnancy
Authors: Bernabé Beatriz Peñalver , Maki Pauline M. , Cunningham Janet L. , Eisenlohr-Moul Tory , Tussing-Humphreys Lisa , Carroll Ian M. , Meltzer-Brody Samantha , Gilbert Jack A. , Kimmel Mary 
Published: May 2022

ABSTRACTBackgroundHigher stress during pregnancy associates with negative outcomes and elevated inflammation. The gut microbiota reflects the environment, lifestyle, and the perinatal period. The gut microbiota, alongside host immune responses, has the potential to aid in identifying when stress is excessive.MethodsTwo U.S. cohorts, of 84 pregnant individuals, composed of urban women of color and suburban white women, completed the Perceived Stress Scale-10 (PSS-10) and provided fecal and blood samples at two time points. Confirmatory Factor Analysis assessed the robustness of a two-factor PSS-10 (Emotional Distress and Self-Efficacy) in the two cohorts. Gut microbiota composition was measured by 16S rRNA amplicon sequencing and the immune system activity was assessed with a panel of 21 T-cell related cytokines and chemokines.ResultsEmotional Distress levels were higher in the suburban compared to the urban cohort, but levels of Self-Efficacy were similar. Emotional Distress and Self-Efficacy levels were associated with distinct taxonomical signatures and the gut microbiota data improved the prediction of Self-Efficacy levels compared with models based on socio-demographic characteristics alone. Integration of self-reported symptoms, microbial and immune information revealed a possible mediation effect of Bacteroides uniformis between the immune system (through CXCL11) and Self-Efficacy.ConclusionsThe study identified links between distinct taxonomical and immunological signatures with perceived stress. The data is congruent with a model where gut microbiome and immune factors may modulate neural plasticity resulting in increased Self-Efficacy during pregnancy. The predictive value of these peripheral markers merit further study.
Behavioral Effects in Disorders of Consciousness Following Transcranial Direct Current Stimulation: A Systematic Review and Individual Patient Data Meta-analysis of Randomized Clinical Trials
Authors: Xu Zeyu , Zheng Ruizhe , Xia Tiantong , Qi Zengxin , Zang Di , Wang Zhe , Wu Xuehai 
Published: May 2022

AbstractBackgroundIn patients with Disorders of Consciousness (DoC), transcranial direct current stimulation (tDCS) was a promising intervention for it. However, uncertainties remain about the treatment effect and the optimal treatment strategy of the tDCS in the DoC.ObjectiveIn this meta-analysis of individual patient data (IPD), we assess whether utilizing tDCS as a treatment in DoC could improve patients’ behavioral performance and whether patient characteristics or tDCS protocol could modify the treatment effect.MethodsWe searched PubMed, Embase, and the Cochrane Central Register of Controlled Trials through April 7, 2022, using the terms “persistent vegetative state,” “minimally conscious state,” “disorder of consciousness,” or “unresponsive wakefulness syndrome,” and “transcranial direct current stimulation” to identify Randomized Controlled Trials (RCTs) in English-language publication. Studies were eligible for inclusion if they reported pre- and post-tDCS Coma Recovery Scale-Revised (CRS-R) scores. From the included studies, any patients who had incomplete data were excluded. We performed a meta-analysis to assess the treatment effect of the tDCS compared with sham control. Additionally, a subgroup analysis was performed to determine whether patients’ baseline characteristics could modify the treatment effect and the optimal tDCS protocol.ResultsWe identified 145 papers, eight trials (including 181 patients) were finally included in the analysis, and one individual data were excluded because of incompletion. Our meta-analysis demonstrated a mean difference change in the CRS-R score of 0.89 (95% CI, 0.17-1.61) between tDCS and sham-control, favoring tDCS. The subgroup analysis showed that patients who were male or in minimally conscious state (MCS) were associated with a greater improvement in CRS-R score and that adopting 5 or more sessions of tDCS protocol might have a better treatment effect than just one session.ConclusiontDCS can improve the behavioral performance of DoC patients. However, heterogeneity existed within the patients’ baseline condition and the stimulation protocol. There should be more exploration of the optimal tDCS protocol and the most beneficial patient group based on the mechanism of tDCS in the future.
Association of sleep abnormalities in older adults with risk of developing Parkinson’s disease
Authors: Otaiku Abidemi I. 
Published: May 2022

AbstractBackgroundParkinson’s disease (PD) is associated with abnormalities of sleep macro- and microstructure as measured using polysomnography (PSG). Whether sleep abnormalities precede the development of PD is unknown. This study investigated the association between PSG measured sleep abnormalities in older adults and the risk of developing PD.Methods2,770 men aged 67 years or older from the ancillary sleep study of the Osteoporotic Fractures in Men Study (MrOS), a population-based cohort from the USA, who were free from PD at baseline (December 2003 – March 2005) and in whom there was PSG data available, were included in this analysis. Incident PD was based on a clinical diagnosis from a medical professional. Multivariable logistic regression was used to estimate odds ratios (OR) for incident PD by quartiles of PSG measures.FindingsDuring a median follow-up of 10.1 years, 70 (2.5%) cases of incident PD were identified. Longer total sleep time, lower rapid eye movement sleep (REM) percentage, a lower α/θ ratio during non-REM sleep and higher minimum oxygen saturations during REM sleep, were independently associated with an increased risk of developing PD during follow-up. Conversely, a higher awakening index was independently associated with a decreased risk of developing PD. The OR for the highest risk quartiles compared to the lowest risk quartiles, ranged from 2.3 to 4.0 (P < 0.05). The associations remained significant when incident PD cases occurring within the first two years of follow-up were excluded from the analyses.ConclusionsMacro- and microstructural sleep abnormalities precede the development of PD by several years and can identify individuals at high risk of developing PD in the future. Future studies are needed to determine whether sleep abnormalities represent preclinical markers of PD or causal risk factors.
Implementing a data infrastructure for precision oncology projects leveraging REDCap
Authors: Vesteghem Charles , Dahl Simon Christian , Brøndum Rasmus Froberg , Sønderkær Mads , Bødker Julie Støve , Schmitz Alexander , Weischenfeldt Joachim , Pedersen Inge Søkilde , Sommer Mia , Rytter Anne Stoffersen , Nielsen Marlene Maria , Ladekarl Morten , Severinsen Marianne Tang , Dybkær Karen , Grønbæk Kirsten , El-Galaly Tarec , Roug Anne Stidsholt , Bøgsted Martin 
Published: May 2022

ABSTRACTObjectivesTo facilitate clinical implementation and research in precision oncology, notably the pairing of patients, variants and treatments to identify candidates for clinical trials, we have built a data infrastructure to 1) capture and store data, 2) reduce manual tasks for clinical and genomic data collection and management, 3) combine data for quality controls, reporting and findability.InfrastructureThe infrastructure uses REDCap repositories to capture and store data. The structure of these repositories is customized for each project. Additionally, a cross-project web platform was developed using software development best practices and state-of-the-art web technologies to circumvent REDCap’s limitations and integrate other third-party resources. Using REDCap’s application programming interfaces, this platform allowed validation of data across multiple repositories, easy import of data from external sources, generation of overviews of included patients and available data, combination of genomic and clinical data to generate tumour board reports and the findability of data. Its design was driven by data stewardship best practices.UsageAcross four precision medicine projects, the infrastructure has been used to collect data for 1921 patients, including 453 genomic data files. The custom-built web platform made it possible to import, validate, and present data in a comprehensive manner. This included building tumour board reports for clinicians, combining clinical and genomic data, and search functionalities for researchers.DiscussionREDCap allowed us to capitalize on the numerous data capture and management features developed in this solution. Designing a cross-project platform guarantees long-term relevance where developments can be mutualised across projects and allowed us to make the overall solution more compliant with the FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Further developments should be considered, notably automatic retrieval of data from electronic health records to limit the number of manual tasks.ConclusionThe proposed infrastructure allowed our precision oncology projects to gain efficiency in data collection and increase data quality by reducing manual work, and it gave a straightforward and customized access to data for researchers and clinicians.