bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel web-based 24-hour dietary recall tool in line with the Nova food processing classification: description and evaluation
Authors: Neri Daniela , Gabe Kamila Tiemann , Santos Costa Caroline Dos , Steele Euridice Martinez , Rauber Fernanda , Marchioni Dirce Maria , Louzada Maria Laura , Levy Renata Bertazzi , Monteiro Carlos Augusto 
Published: May 2023

ABSTRACTObjectiveThis paper describes the first web-based self-completed 24-hour recall designed to categorize food intake according to Nova groups - Nova24h – and its agreement with a reference tool in estimating the dietary relative contribution of the four Nova food groups (% of total energy intake).DesignComparisons of estimates of dietary relative contributions of Nova groups obtained by Nova24h and one standard interviewer-led 24-hour recall.SettingNationwide adult cohort study in Brazil.ParticipantsThe subjects were 186 participants of the NutriNet Brasil Cohort Study (n=186).ResultsNo statistically significant differences were observed between the Nova24h and the reference tool mean contributions of unprocessed or minimally processed foods (52.3% vs. 52.6%), processed culinary ingredients (11.6% vs. 11.9%), processed foods (17.1% vs. 14.7%) and ultra-processed foods (19.0% vs. 20.9%). Intraclass correlation coefficients between individual estimates obtained for each Nova group showed moderate to good agreement (0.54-0.78). Substantial or almost perfect agreement between the tools was seen regarding the ability to rank participants according to quintiles of contribution of each Nova group (PABAK 0.69-0.81).ConclusionsNova24h is a suitable tool for estimating the dietary relative energy contribution of Nova food groups in the NutriNet Brasil cohort. New studies are necessary to verify its adequacy in other populations.
Estimated Effects of Amyloid Reduction on Cognitive Change: A Bayesian Update across a Range of Priors
Authors: Ackley Sarah F. , Wang Jingxuan , Chen Ruijia , Power Melinda C. , Allen Isabel Elaine , Glymour M. Maria 
Published: May 2023

AbstractIntroductionResults of the CLARITY-AD and GRADUATE I and II trials rekindled discussion on the impact of amyloid-targeting drugs. We use a Bayesian approach to quantify how a rational observer would have updated their prior beliefs based on new trial results.MethodsWe used publicly available data from the CLARITY-AD and GRADUATE I & II trials to estimate the effect of reducing amyloid on CDR-SB score. A range of prior positions were then updated according to Bayes Theorem using these estimates.ResultsAfter updating with new trial data, a wide range of starting positions resulted in credible intervals that did not include no effect of amyloid reduction on CDR-SB.DiscussionFor a range of starting beliefs and assuming veracity of underlying data, rational observers would conclude there is a small benefit of amyloid-reductions on cognition. This benefit must be weighed against opportunity cost and side effect risk.
Genetic liability for cardiovascular disease, physical activity, and mortality – findings from The Finnish Twin Cohort
Authors: Joensuu Laura , Waller Katja , Kankaanpää Anna , Palviainen Teemu , Kaprio Jaakko , Sillanpää Elina 
Published: May 2023

AbstractBackgroundAn overall healthy lifestyle may mitigate the risk of all-cause and cardiovascular disease (CVD) mortality despite the inherited risk for CVD. We assessed if similar phenomenon is observed independently with physical activity (PA).MethodsIn a prospective older Finnish Twin Cohort, genetic liability for coronary heart disease, systolic and diastolic blood pressure was estimated with polygenic risk scores (PRSs) derived from the Pan UK Biobank (N≈400,000) and >1,000,000 genetic variants, and leisure-time PA longitudinally with validated and structured questionnaires three times during 1975–1990 among 4,897 participants aged 33–60-years (54.3% women). Interactions of PRSs and PA with mortality and the independent main effects of different PA metrics with mortality were evaluated with Cox proportional hazards models. A co-twin control design with 180 monozygotic twin pairs discordant for physical activity was used for causal inference.ResultsDuring the 17.4-y (mean) follow-up (85,136 person-years), 1,195 participants died with 389 CVD deaths. Although the favorable associations of an overall healthy lifestyle with mortality risk reduction replicated in the cohort, no interactions or significant independent main effects were observed with any of the assessed PA metrics. Adherence to World Health Organizations PA guidelines or vigorous PA during the 15-year observational period did not decrease mortality in more active twins compared to their less active identical twin brother or sister. The findings did not vary with genetic CVD risk.ConclusionsNo evidence was found that PA mitigates inherited CVD risk or is causally associated with mortality challenging some of our current understanding.
Excellent reliability of the six-minute walking distance measured by FeetMe®insoles at home
Authors: Mostovov Andrey , Jacobs Damien , Farid Leila , Dhellin Paul , Baille Guillaume 
Published: May 2023

AbstractBackgroundWearable technology provides an opportunity for new ways of monitoring patient gait remotely, through at-home self-administered six-minute walk tests (6MWTs). The purpose of this study was to evaluate the reliability and repeatability of FeetMe®insoles, a wearable gait assessment device, for measuring the six-minute walking distance (6MWD) during tests conducted independently at home by healthy volunteers.MethodsParticipants (n=21) performed two 6MWTs at home while wearing the FeetMe®insoles, and two 6MWTs at hospital while wearing FeetMe®insoles and being assessed by a rater. All assessments were performed with a one-week interval between tests, no assistance was provided to the participants at home.Results and conclusionThe agreement between the 6MWD measurements made at baseline and at Week 1 was good for all test configurations and was highest for the at-home FeetMe®measurements, with an intraclass correlation coefficient (ICC) of 0.95, standard error of the measurement (SEM) of 15.02 m and coefficient of variation (CV) of 3.33%, compared to ICCs of 0.79 and 0.78, SEMs of 25.65 and 26.65 and CVs of 6.24% and 6.10% for the rater and FeetMe®measurements at hospital, respectively. Our work demonstrates that the FeetMe®system could provide a reliable solution allowing individuals to self-administer 6MWTs independently at home.Author SummaryAt-home patients monitoring using wearable tools presents numerous advantages for regular care and clinical studies. Patients benefit from the convenience of not having to travel to a clinic for assessments, which is particularly helpful for those in remote areas or with mobility issues. Besides, at-home monitoring allows for more frequent assessments, leading to more accurate clinical decision-making and timely intervention, which ultimately results in enhanced patient care. For the same reasons, it can improve patients recruitment and retention in clinical studies.The six-minute walk test (6MWT) is a commonly used standardized assessment of functional capacity in patients with various diseases. We evaluated the reliability and repeatability of FeetMe®insoles, a wearable gait assessment device, for measuring the six-minute walking distance (6MWD) at home. Our analysis of the data from 21 healthy volunteers showed that the FeetMe®insoles were as reliable at home as they were in the standard clinical setting and as a conventional way of assessing the 6MWD. In addition, the insoles provided extensive gait analysis, which may allow for more precise conclusions regarding the patient’s state and its evolution than the 6MWD alone. We conclude that the FeetMe®device is an excellent tool for at-home patients monitoring.
Discovery of Novel Digital Biomarkers for Type 2 Diabetic Nephropathy Classification via Integration of Urinary Proteomics and Pathology
Authors: Lucarelli Nicholas , Yun Donghwan , Han Dohyun , Ginley Brandon , Moon Kyung Chul , Rosenberg Avi Z. , Tomaszewski John E. , Zee Jarcy , Jen Kuang-Yu , Han Seung Seok , Sarder Pinaki 
Published: May 2023

AbstractBackgroundThe heterogeneous phenotype of diabetic nephropathy (DN) from type 2 diabetes complicates appropriate treatment approaches and outcome prediction. Kidney histology helps diagnose DN and predict its outcomes, and an artificial intelligence (AI)- based approach will maximize clinical utility of histopathological evaluation. Herein, we addressed whether AI-based integration of urine proteomics and image features improves DN classification and its outcome prediction, altogether augmenting and advancing pathology practice.MethodsWe studied whole slide images (WSIs) of periodic acid-Schiff-stained kidney biopsies from 56 DN patients with associated urinary proteomics data. We identified urinary proteins differentially expressed in patients who developed end-stage kidney disease (ESKD) within two years of biopsy. Extending our previously published human-AI-loop pipeline, six renal sub-compartments were computationally segmented from each WSI. Hand-engineered image features for glomeruli and tubules, and urinary protein measurements, were used as inputs to deep-learning frameworks to predict ESKD outcome. Differential expression was correlated with digital image features using the Spearman rank sum coefficient.ResultsA total of 45 urinary proteins were differentially detected in progressors, which was most predictive of ESKD (AUC=0.95), while tubular and glomerular features were less predictive (AUC=0.71 andAUC=0.63, respectively). Accordingly, a correlation map between canonical cell-type proteins, such as epidermal growth factor and secreted phosphoprotein 1, and AI-based image features was obtained, which supports previous pathobiological results.Conclusions:Computational method-based integration of urinary and image biomarkers may improve the pathophysiological understanding of DN progression as well as carry clinical implications in histopathological evaluation.Significance StatementThe complex phenotype of diabetic nephropathy from type 2 diabetes complicates diagnosis and prognosis of patients. Kidney histology may help overcome this difficult situation, particularly if it further suggests molecular profiles. This study describes a method using panoptic segmentation and deep learning to interrogate both urinary proteomics and histomorphometric image features to predict whether patients progress to end-stage kidney disease since biopsy date. A subset of urinary proteomics had the most predictive power in identifying progressors, which could annotate significant tubular and glomerular features related to outcomes. This computational method, which aligns molecular profiles and histology, may improve our understanding of pathophysiological progression of diabetic nephropathy as well as carry clinical implications in histopathological evaluation.
Types of Traumatic Experiences in Drug Overdose-Related Deaths: An Exploratory Latent Class Analysis
Authors: Hong Judy H. , de Dios Constanza , Badawi Jessica C. , Tonkin Sarah S. , Schmitz Joy M. , Walss-Bass Consuelo , Meyer Thomas D. 
Published: May 2023

AimDrug overdose related-deaths in the US are increasing, with over 100,000 deaths occurring in 2020, an increase of 30% from the previous year and the highest number recorded in a single year. It is widely known that experiences of trauma and substance use very often co-occur, but little is known about the role of trauma in the context of drug overdose-related deaths. Latent class analysis (LCA) was used to classify drug overdose-related deaths based on type of traumatic experiences and individual, social, and substance use characteristics.MethodsPsychological autopsy data were obtained from the University of Texas Health Science Center at Houston (UTHealth) Brain Collection. A total of 31 drug overdose-related deaths collected from January 2016 through March 2022 were included in this study. LCA was used to identify latent factors via experience of four trauma categories (illness/accidents, sexual/interpersonal violence, death/trauma to another, other situations where life was in danger). Generalized linear modeling (GLM) was used to explore differences on demographic, social, substance use, and psychiatric variables between the latent classes in separate models.ResultsLCA identified 2 classes: C1 (n=12; 39%) was characterized by higher incidence of overall trauma exposure as well as variation in trauma type; C2 (n=19; 61%) had lower levels of overall trauma exposure with sexual/interpersonal violence as the most frequent. GLMs indicated that C1 membership was associated with higher incidence of polysubstance use, being married, and having suicidal ideation compared to C2 membership (ps<0.05).ConclusionAmong individuals who died by drug overdose, the exploratory LCA identified two distinct subgroups that differed in type of trauma experienced and substance use pattern, the first group having more “typical” characteristics of drug overdoses cases, the other group less typical. This suggests that those at risk of drug overdose may not always exhibit high-risk characteristics.
Vasectomy workforce and utilization in the United States, 2019
Authors: Bodas Mandar , Strasser Julia , Banawa Rachel , Luo Qian , Schenk Ellen , Chen Candice 
Published: May 2023

AbstractObjectiveTo examine the workforce that provides vasectomies in the United States using national-level medical claims data (IQVIA Dx, 2019).MethodsWe combined IQVIA Dx 2019 data on medical claims with information on clinician characteristics and clinician type from the National Plan and Provider Enumeration System (NPPES) and the American Medical Association (AMA) Masterfile. We assessed state-level trends in vasectomy provision. We used multivariate regressions to evaluate the association between clinician characteristics and two outcomes: providing at least one vasectomy and total volume of vasectomies in 2019.ResultsWe found total of 147,618 vasectomies performed by 7,592 clinicians in the IQVIA Dx 2019 data. About 76% clinicians were urologists, 16% family medicine specialists, and about 8% were general surgeons. Urologists performed about 91% of all vasectomies. Overall, about 92% of clinicians were located in urban areas. Multivariate regression analysis showed that clinicians that were female (Odds Ratio (OR) 0.12, 95% Confidence Interval (95% CI) (0.09 - 0.15)), graduated from osteopathic schools (OR 0.77, 95% CI (0.60 - 0.98)) had lower odds of providing vasectomies.ConclusionsClinicians from multiple specialties performed vasectomies in the U.S. Most often, this procedure was performed by male urologists practicing in urban areas. Wide state-level variations exist in vasectomy provision. Clinicians’ gender, location and type of medical degree received were associated with their vasectomy provision.
Radiogenomic profiling of prostate tumors prior to external beam radiotherapy converges on a transcriptomic signature of TGF-β activity driving tumor recurrence
Authors: Ku Anson T. , Shankavaram Uma , Trostel Shana Y. , Zhang Hong , Sater Houssein A. , Harmon Stephanie A. , Carrabba Nicole V. , Liu Yang , Wood Bradford J. , Pinto Peter A. , Choyke Peter L. , Stoyanova Radka , Davicioni Elai , Pollack Alan , Turkbey Baris , Sowalsky Adam G. , Citrin Deborah E. 
Published: May 2023

STRUCTURED ABSTRACTBackgroundPatients with localized prostate cancer have historically been assigned to clinical risk groups based on local disease extent, serum prostate specific antigen (PSA), and tumor grade. Clinical risk grouping is used to determine the intensity of treatment with external beam radiotherapy (EBRT) and androgen deprivation therapy (ADT), yet a substantial proportion of patients with intermediate and high risk localized prostate cancer will develop biochemical recurrence (BCR) and require salvage therapy. Prospective identification of patients destined to experience BCR would allow treatment intensification or selection of alternative therapeutic strategies.MethodsTwenty-nine individuals with intermediate or high risk prostate cancer were prospectively recruited to a clinical trial designed to profile the molecular and imaging features of prostate cancer in patients undergoing EBRT and ADT. Whole transcriptome cDNA microarray and whole exome sequencing were performed on pretreatment targeted biopsy of prostate tumors (n=60). All patients underwent pretreatment and 6-month post EBRT multiparametric MRI (mpMRI), and were followed with serial PSA to assess presence or absence of BCR. Genes differentially expressed in the tumor of patients with and without BCR were investigated using pathways analysis tools and were similarly explored in alternative datasets. Differential gene expression and predicted pathway activation were evaluated in relation to tumor response on mpMRI and tumor genomic profile. A novel TGF-β gene signature was developed in the discovery dataset and applied to a validation dataset.FindingsBaseline MRI lesion volume andPTEN/TP53status in prostate tumor biopsies correlated with the activation state of TGF-β signaling measured using pathway analysis. All three measures correlated with the risk of BCR after definitive RT. A prostate cancer-specific TGF-β signature discriminated between patients that experienced BCR vs. those that did not. The signature retained prognostic utility in an independent cohort.InterpretationTGF-β activity is a dominant feature of intermediate-to-unfavorable risk prostate tumors prone to biochemical failure after EBRT with ADT. TGF-β activity may serve as a prognostic biomarker independent of existing risk factors and clinical decision-making criteria.FundingThis research was supported by the Prostate Cancer Foundation, the Department of Defense Congressionally Directed Medical Research Program, National Cancer Institute, and the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research.
United States’ qualifying conditions compared to evidence of the 2017 National Academy of Sciences Report
Authors: Stains Elena L. , Kennalley Amy L. , Tian Maria , Boehnke Kevin F. , Kraus Chadd K. , Piper Brian J. 
Published: May 2023

AbstractObjectiveTo compare the 2017 National Academies of Sciences, Engineering, and Medicine (NAS) report to state medical cannabis (MC) laws defining approved qualifying conditions (QC) from 2017 to 2024 and to determine if there exist gaps in evidence-based decision making.MethodsThe 2017 NAS report assessed therapeutic evidence for over twenty medical conditions treated with MC. We identified the QCs of 38 states (including Washington, D.C.) where MC was legal in 2024. We also identified the QCs that these states used in 2017. QCs were then categorized based on NAS-established level of evidence: substantial/conclusive evidence of effectiveness, moderate evidence of effectiveness, limited evidence of effectiveness, limited evidence of ineffectiveness, and no/insufficient evidence to support or refute effectiveness. This study was completed between January 31, 2023 through May 20, 2024.ResultsMost states listed at least one QC with substantial evidence—80.0% of states in 2017 and 97.0% in 2024. However, in 2024 only 8.3% of the QCs on states’ QC lists met the standard of substantial evidence. Of the 20 most popular QCs in the country in 2017 and 2024, one only (chronic pain) was categorized by the NAS as having substantial evidence for effectiveness. However, seven (ALS, Alzheimer’s disease, epilepsy, glaucoma, Huntington’s disease, Parkinson’s disease, spastic spinal cord damage) were rated as either ineffective or insufficient evidence.ConclusionMost QCs lack evidence for use based on the 2017 NAS report. Many states recommend QCs with little evidence, such as amyotrophic lateral sclerosis (ALS), or even those for which MC is ineffective, like depression. There have been insufficient updates to QCs since the NAS report. These findings highlight a disparity between state-level MC recommendations and the evidence to support them.
Rare penetrant mutations confer severe risk of common diseases
Authors: Fiziev Petko , McRae Jeremy , Ulirsch Jacob C. , Dron Jacqueline S. , Hamp Tobias , Yang Yanshen , Wainschtein Pierrick , Ni Zijian , Schraiber Joshua G. , Gao Hong , Cable Dylan , Field Yair , Aguet Francois , Fasnacht Marc , Metwally Ahmed , Rogers Jeffrey , Marques-Bonet Tomas , Rehm Heidi L. , O’Donnell-Luria Anne , Khera Amit V. , Farh Kyle Kai-How 
Published: May 2023

AbstractWe examined 454,712 exomes for genes associated with a wide spectrum of complex traits and common diseases and observed that rare, penetrant mutations in genes implicated by genome-wide association studies confer ∼10-fold larger effects than common variants in the same genes. Consequently, an individual at the phenotypic extreme and at the greatest risk for severe, early-onset disease is better identified by a few rare penetrant variants than by the collective action of many common variants with weak effects. By combining rare variants across phenotype-associated genes into a unified genetic risk model, we demonstrate superior portability across diverse global populations compared to common variant polygenic risk scores, greatly improving the clinical utility of genetic-based risk prediction.One sentence summaryRare variant polygenic risk scores identify individuals with outlier phenotypes in common human diseases and complex traits.
Subclinical Vascular Composites Predict Clinical Cardiovascular Disease, Stroke, and Dementia: The Multi-Ethnic Study of Atherosclerosis (MESA)
Authors: Hughes Timothy M. , Tanley Jordan , Chen Haiying , Schaich Christopher L. , Yeboah Joseph , Espeland Mark A. , Lima Joao A. C. , Ambale-Venkatesh Bharath , Michos Erin D. , Ding Jingzhong , Hayden Kathleen , Casanova Ramon , Craft Suzanne , Rapp Stephen R. , Luchsinger José A. , Fitzpatrick Annette L. , Heckbert Susan R. , Post Wendy S. , Burke Gregory L. 
Published: May 2023

AbstractBackgroundSubclinical cardiovascular disease (CVD) measures may reflect biological pathways that contribute to increased risk for coronary heart disease (CHD) events, stroke, and dementia beyond conventional risk scores.MethodsThe Multi-Ethnic Study of Atherosclerosis (MESA) followed 6,814 participants (45-84 years of age) from baseline in 2000-2002 to 2018 over 6 clinical examinations and annual follow-up interviews. MESA baseline subclinical CVD procedures included: seated and supine blood pressure, coronary calcium scan, radial artery tonometry, and carotid ultrasound. Baseline subclinical CVD measures were transformed into z-scores before factor analysis to derive composite factor scores. Time to clinical event for all CVD, CHD, stroke and ICD code-based dementia events were modeled using Cox proportional hazards models reported as area under the curve (AUC) with 95% Confidence Intervals (95%CI) at 10 and 15 years of follow-up. All models included all factor scores together and adjustment for conventional risk scores for global CVD, stroke, and dementia.ResultsAfter factor selection, 24 subclinical measures aggregated into four distinct factors representing: blood pressure, arteriosclerosis, atherosclerosis, and cardiac factors. Each factor significantly predicted time to CVD events and dementia at 10 and 15 years independent of each other and conventional risk scores. Subclinical vascular composites of arteriosclerosis and atherosclerosis best predicted time to clinical events of CVD, CHD, stroke, and dementia. These results were consistent across sex and racial and ethnic groups.ConclusionsSubclinical vascular composites of arteriosclerosis and atherosclerosis may be useful biomarkers to inform the vascular pathways contributing to events of CVD, CHD, stroke, and dementia.