bluuebunny/crossref_metadata_2025_split huggingface dataset:
Histology-informed microstructural diffusion simulations for MRI cancer characterisation — the Histo-μSim framework
Authors: Grigoriou Athanasios , Macarro Carlos , Palombo Marco , Navarro-Garcia Daniel , Voronova Anna , Bernatowicz Kinga , Barba Ignasi , Escriche Alba , Greco Emanuela , Abad María , Simonetti Sara , Serna Garazi , Mast Richard , Merino Xavier , Roson Núria , Escobar Manuel , Vieito Maria , Nuciforo Paolo , Toledo Rodrigo , Garralda Elena , Sala-Llonch Roser , Fieremans Els , Novikov Dmitry S. , Perez-Lopez Raquel , Grussu Francesco 
Published: July 2024

AbstractDiffusion Magnetic Resonance Imaging (dMRI) simulations in geometries mimicking the complexity of human tissues at the microscopic scale enable the development of innovative biomarkers with unprecedented fidelity to histology. To date, approaches of this kind have focussed heavily on brain imaging. Nevertheless, simulation-informed dMRI has huge potential also in other applications, as for example in body cancer imaging, where new non-invasive biomarkers are still sought. This article fills this gap by introducing a Monte Carlo diffusion simulation framework informed by histology, for enhanced body dMR microstructural imaging — theHisto-μSimapproach. We generate dictionaries of synthetic dMRI signals with coupled tissue properties from virtual cancer environments, reconstructed from hematoxylin-eosin stains of human liver biopsies. These enable the data-driven estimation of innovative microstructural tissue properties, such as the intrinsic extra-cellular diffusivity, or cell size (CS) distribution moments. We compareHisto-μSimto metrics from well-established analytical multi-compartment modelsin silico, on fixed mouse tissues scannedex vivo(kidneys, spleens, and breast tumours) and in cancer patientsin vivo. Results suggest thatHisto-μSimis feasible in clinical settings, and that it delivers metrics that more accurately reflect the underlying histology as compared to analytical models. In conclusion,Histo-μSimoffers histologically-meaningful tissue descriptors that may increase the specificity of dMRI towards cancer, and thus play a crucial role in precision oncology.
Multi-omics characterization of type 2 diabetes associated genetic variation
Authors: Mandla Ravi , Lorenz Kim , Yin Xianyong , Bocher Ozvan , Huerta-Chagoya Alicia , Arruda Ana Luiza , Piron Anthony , Horn Susanne , Suzuki Ken , Hatzikotoulas Konstantinos , Southam Lorraine , Taylor Henry , Yang Kaiyuan , Hrovatin Karin , Tong Yue , Lytrivi Maria , Rayner Nigel W. , Meigs James B. , McCarthy Mark I. , Mahajan Anubha , Udler Miriam S. , Spracklen Cassandra N. , Boehnke Michael , Vujkovic Marijana , Rotter Jerome I. , Eizirik Decio L. , Cnop Miriam , Lickert Heiko , Morris Andrew P. , Zeggini Eleftheria , Voight Benjamin F. , Mercader Josep M. 
Published: July 2024

AbstractDiscerning the mechanisms driving type 2 diabetes (T2D) pathophysiology from genome-wide association studies (GWAS) remains a challenge. To this end, we integrated omics information from 16 multi-tissue and multi-ancestry expression, protein, and metabolite quantitative trait loci (QTL) studies and 46 multi-ancestry GWAS for T2D-related traits with the largest, most ancestrally diverse T2D GWAS to date.Of the 1,289 T2D GWAS index variants, 716 (56%) demonstrated strong evidence of colocalization with a molecular or T2D-related trait, implicating 657cis-effector genes, 1,691 distal-effector genes, 731 metabolites, and 43 T2D-related traits. We identified 773 of thesecis-and distal-effector genes using either expression QTL data from understudied ancestry groups or inclusion of T2D index variants enriched in underrepresented populations, emphasizing the value of increasing population diversity in functional mapping. Linking these variants, genes, metabolites, and traits into a network, we elucidated mechanisms through which T2D-associated variation may impact disease risk. Finally, we showed that drugs targeting effector proteins were enriched in those approved to treat T2D, highlighting the potential of these results to prioritize drug targets for T2D.These results represent a leap in the molecular characterization of T2D-associated genetic variation and will aid in translating genetic findings into novel therapeutic strategies.
Early combination of sotrovimab with nirmatrelvir/ritonavir or remdesivir is associated with low rate of persisting SARS CoV-2 infection in immunocompromised outpatients with mild-to-moderate COVID-19: a prospective single center study
Authors: Gentile I. , Viceconte G. , Cuccurullo F. , Pietroluongo D. , D’Agostino A. , Silvitelli M. , Mercinelli S. , Scotto R. , Grimaldi F. , Palmieri S. , Gravetti A. , Trastulli F. , Moccia M. , Buonomo A.R. 
Published: July 2024

AbstractBackgroundImmunocompromised patients are at high risk of developing persisting/prolonged COVID-19 Data concerning early combined use of antivirals and monoclonal antibodies in this population are scarce.Research design and methodsWe performed an observational, prospective study, enrolling immunocompromised outpatient adults with mild-to-moderate COVID-19 treated with a combination of sotrovimab plus one antiviral (remdesivir or nirmatrelvir/ritonavir) within 7 days from symptoms’ onset.ResultsWe enrolled 52 patients. No patient was hospitalized within 30 days from the disease onset, needed oxygen administration or died within 60 days, or experienced a reinfection or a clinical relapse within 90 days.Clearance rates were 67% and 97% at 14th day after the end of therapy and at the end of follow-up, respectively.Factors associated with longer infection were initiation of therapy after 3 days from symptoms onset, and enrollment more than 180 days from the beginning of the study. However, only the latter factor was independently associated with longer SARS-CoV-2 infection, suggesting a loss of efficacy of this strategy with the evolution of SARS-CoV-2 variantsConclusionsEarly administration of combination therapy with a direct antiviral and sotrovimab seems to be effective in preventing hospitalization, progression to severe COVID-19 and the development of prolonged/persisting SARS-CoV-2 infection in immunocompromised patients.
Insights to obstructive jaundice: comprehensive analysis and machine learning-based diagnostics in over 5000 individuals
Authors: Wen Ningyuan , Wang Yaoqun , Xiong Xianze , Xu Jianrong , Wang Shaofeng , Tian Yuan , Zeng Di , Pu Xingyu , Liu Geng , Li Bei , Lu Jiong , Cheng Nansheng 
Published: July 2024

AbstractBackgroundObstructive jaundice is a common problem associated with diverse etiologies which has not been thoroughly investigated in large-scale cohorts. Our study involved the largest retrospective cohort of obstructive jaundice to date, exploring the spectrum of diseases while establishing a diagnostic system with machine learning (ML) methods based on routine laboratory tests.MethodsThis study involves two retrospective observational cohorts from China. The biliary surgery cohort (BS cohort, n=349) served for initial data exploration and external validation of ML models, while the large general cohort (LG cohort, n=5726) enabled comprehensive data analysis and ML model construction. Interpretable ML techniques were employed to derive insights from the models.ResultsThe LG cohort exhibited a more diverse disease spectrum compared to the BS cohort, with pancreatic adenocarcinoma, common bile duct stones, distal cholangiocarcinoma, perihilar cholangiocarcinoma, and acute pancreatitis (non-calculous) identified as the top five causes of obstructive jaundice. Traditional serum markers such as CA 19-9 and CEA did not emerge as standalone diagnostic markers for obstructive jaundice. Leveraging ML techniques, we developed two models collectively named as the MOLT model: one effectively distinguishes between benign and malignant causes (AUROC=0.862), while the other provides nuanced insights by further categorizing malignancies into three tiers and benign diseases into two (ACC=0.777). Interpretable ML tools revealed key features contributing to the decision-making process of each model.ConclusionsThrough our study, we uncovered the diagnostic potential of routine laboratory tests in obstructive jaundice, enabling the development of a practical diagnostic tool based on interpretable ML models. These findings may pave the way for personalized and user-friendly diagnosis of obstructive jaundice, thereby aiding clinical decision-making.
Prevalence and associated factors of suicidal behavior among adolescents in rural Bangladesh
Authors: Mumu Rifa Tamanna , Shaikh Md Parvez , Mitra Dipak Kumar 
Published: July 2024

AbstractBackgroundAmong the four leading causes of worldwide death, suicide is one, which is prevailing especially in lower-middle-income countries. However, the number of studies is minimal based on adolescent suicidal behavior in rural Bangladesh. This study aims to identify the prevalence and associated factors of suicidal behavior in adolescents in a remote subdistrict in Bangladesh.MethodA cross-sectional study was performed in Lohagara, a rural subdistrict in southern Bangladesh from July, 15 to August 14, 2024. 350 subjects were recruited for the study, all of whom were school-going adolescents aged 11 to 17. The Bengali-translated versions of the Suicidal Behavior-Revised Questionnaire (SBQ-R) and the Depression, Anxiety, and Stress Scale - 21 Items (DASS-21), as well as another structured questionnaire, were used to obtain data from participants. Data were analyzed by STATA version 17.ResultThe prevalence of suicidal behavior among adolescents is 12.3% (95% CI: 9.2% to 16.2%) (overall SBQ-R score of 7 or more). Having step-parents, peer conflict, stress, anxiety, and a family history of suicide are found to be significantly associated with the development of suicidal behavior in adolescents. Unmarried and single relationship status and the overprotective attitude of parents play a protective role in growing young adults’ suicidal tendencies.ConclusionThe notable prevalence of suicidal behavior in adolescents underscores the need for screening and intervention at an early age. Different health promotional and educational programs can be organized in schools and communities. The ultimate goal is to protect adolescents with adequate counseling, care, and support.
Using a hybrid of artificial intelligence and template-based method in automatic item generation to create multiple-choice questions in medical education: Hybrid AIG
Authors: Kıyak Yavuz Selim , Kononowicz Andrzej A. 
Published: July 2024

AbstractObjectivesTemplate-based automatic item generation (AIG) is more efficient than traditional item writing but it still heavily relies on expert effort in model development. While non-template-based AIG, leveraging artificial intelligence (AI), offers efficiency, it faces accuracy challenges. We aimed to integrate these approaches for leading to a significant rise in efficiency for AIG without sacrificing accuracy.Material and MethodsWe proposed the Hybrid AIG method that utilizes AI to generate item models (templates) and cognitive models to combine the advantages of the two AIG approaches. The Hybrid AIG consists of seven steps. The first five steps are carried out by an expert in a customized AI environment. Following a final expert review (Step 6), the content in the template can be used for item generation through a traditional (non-AI) software (Step 7). We used two multiple-choice questions for demonstration.ResultsWe demonstrated that AI is capable of generating item models and cognitive models for AIG under the guidance of a human expert. Leveraging AI in template development has substantially reduced the time investment from five hours to less than 10 minutes, and made it significantly less challenging.ConclusionsThe Hybrid AIG method transcends the traditional template-based approach by marrying the “art” that comes from AI as a “black box” with the “science” of algorithmic generation under the oversight of expert as a “marriage registrar”. It does not only capitalize on the strengths of both approaches but also mitigates their weaknesses, offering a human-AI collaboration to increase efficiency in medical education.
Height-age as an alternative to height-for-age z-scores to assess the effect of interventions on child linear growth in low- and middle-income countries
Authors: Watson Kelly M , Dasiewicz Alison SB , Bassani Diego G , Chen Chun-Yuan , Qamar Huma , O’Callaghan Karen M , Roth Daniel E 
Published: July 2024

AbstractBackgroundAssessments of the efficacy of interventions to improve child growth are often based on differences in mean height-for-age z-scores (HAZ) and stunting (HAZ<-2) in randomized controlled trials (RCTs). However, this approach does not account for children’s starting skeletal age and does not enable assessment of the extent to which interventions optimized linear growth.ObjectiveTo develop and apply a new method using height-age to express linear growth effects in RCTs.MethodsLongitudinal individual participant data (IPD) from a Bangladeshi trial cohort were used to compare height-age estimates derived from individual-level heights, mean raw height, or mean HAZ. Then, using average height-age as a proxy for skeletal age, we developed the ‘proportion of maximal benefit’ (PMB) metric to quantify intervention effects relative to optimal growth for children’s starting skeletal age. Optimal growth occurs when height-age increases in parallel with chronological age (i.e., PMB=100%) whereas no effect (versus control) corresponds to a PMB of 0%. Linear growth outcomes in 4 published RCTs of nutrition-specific interventions were re-expressed as mean height-age and PMB, and compared to effects conventionally expressed as intervention-versus-control mean differences (MD) in HAZ.ResultsMean height-age could be derived from any published estimate of mean raw height or mean HAZ; however, to calculate the PMB, height or HAZ data were required at both the beginning and end of the observation period. Interpretations of intervention effects were consistent when expressed as either the height-age MD or HAZ MD. In contrast, the PMB does not have a corresponding metric on the HAZ scale, and therefore provided a new way to quantify intervention efficacy.ConclusionHeight-age can be used as an alternative to HAZ to express intervention effects. The PMB has the advantage of conveying the extent to which an intervention improved average linear growth in relation to a biologically-defined benchmark.
Trans-eQTL mapping prioritisesUSP18as a negative regulator of interferon response at a lupus risk locus
Authors: Freimann Krista , Brümmer Anneke , Warmerdam Robert , Rupall Tarran S. , Hernández-Ledesma Ana Laura , Chiou Joshua , Holzinger Emily R. , Maranville Joseph C. , Nakic Nikolina , Ongen Halit , Stefanucci Luca , Turchin Michael C. ,   eQTLGen Consortium, Franke Lude , Võsa Urmo , Jones Carla P. , Medina-Rivera Alejandra , Trynka Gosia , Kisand Kai , Bergmann Sven , Alasoo Kaur 
Published: July 2024

AbstractAlthough genome-wide association studies have provided valuable insights into the genetic basis of complex traits and diseases, translating these findings to causal genes and their downstream mechanisms remains challenging. We performedtransexpression quantitative trait locus (trans-eQTL) meta-analysis in 3,734 lymphoblastoid cell line samples, identifying four robust loci that replicated in an independent multi-ethnic dataset of 682 individuals. We prioritised a missense variant in the ubiquitin specific peptidase 18 (USP18)gene that is a known negative regulator of interferon signalling and has previously been associated with increased risk of systemic lupus erythematosus (SLE). The SLE risk allele increased the expression of 50 interferon-inducible genes, suggesting that the risk allele impairs USP18’s ability to effectively limit the interferon response. Intriguingly, theUSP18 trans-eQTL signal would not have been discovered in a meta-analysis of up to 43,301 whole blood samples, reaffirming the importance of capturing context-specific genetic effects for GWAS interpretation.
An evaluation of reproducibility and errors in published sample size calculations performed using G*Power
Authors: Thibault Robert T. , Zavalis Emmanuel A. , Malički Mario , Pedder Hugo 
Published: July 2024

AbstractBackgroundPublished studies in the life and health sciences often employ sample sizes that are too small to detect realistic effect sizes. This shortcoming increases the rate of false positives and false negatives, giving rise to a potentially misleading scientific record. To address this shortcoming, many researchers now use point-and-click software to run sample size calculations.ObjectiveWe aimed to (1) estimate how many published articles report using the G*Power sample size calculation software; (2) assess whether these calculations are reproducible and (3) error-free; and (4) assess how often these calculations use G*Power’s default option for mixed-design ANOVAs—which can be misleading and output sample sizes that are too small for a researcher’s intended purpose.MethodWe randomly sampled open access articles from PubMed Central published between 2017 and 2022 and used a coding form to manually assess 95 sample size calculations for reproducibility and errors.ResultsWe estimate that more than 48,000 articles published between 2017 and 2022 and indexed in PubMed Central or PubMed report using G*Power (i.e., 0.65% [95% CI: 0.62% - 0.67%] of articles). We could reproduce 2% (2/95) of the sample size calculations without making any assumptions, and likely reproduce another 28% (27/95) after making assumptions. Many calculations were not reported transparently enough to assess whether an error was present (75%; 71/95) or whether the sample size calculation was for a statistical test that appeared in the results section of the publication (48%; 46/95). Few articles that performed a calculation for a mixed-design ANOVA unambiguously selected the non-default option (8%; 3/36).ConclusionPublished sample size calculations that use G*Power are not transparently reported and may not be well-informed. Given the popularity of software packages like G*Power, they present an intervention point to increase the prevalence of informative sample size calculations.
Trends, Gender, and Racial Disparities in Patients with Mortality Due to Paroxysmal Tachycardia: A Nationwide Analysis from 1999-2020
Authors: Goyal Aman , Saeed Humza , Yamin Saif , Abdullah  , Sultan Wania , Arshad Muhammad Khubaib , Sulaiman Samia Aziz , Mahalwar Gauranga 
Published: July 2024

AbstractBackgroundParoxysmal tachycardia encompasses various heart rhythm disorders that cause rapid heart rates. Its episodic occurrence makes it difficult to identify and measure its prevalence and trends in the population. Additionally, there is limited data on disparities and trends in mortality due to paroxysmal tachycardia, which is essential for assessing current medical approaches and identifying at-risk populations.MethodsOur study examined death certificates from 1999 to 2020 using the CDC WONDER Database to identify deaths caused by paroxysmal tachycardia in individuals aged 25 and older, using the ICD-10 code I47. Age-adjusted mortality rates (AAMRs) and annual percent changes (APC) were calculated by year, gender, age group, race/ethnicity, geographic location, and urbanization status.ResultsBetween 1999 and 2020, 155,320 deaths were reported in patients with paroxysmal tachycardia. Overall, AAMR decreased from 4.8 to 3.7 per 100,000 population between 1999 and 2020, despite showing a significant increase from 2014 to 2020 (APC: 4.33; 95% CI: 3.53 to 5.56). Men had consistently higher AAMRs than women (4.7 vs. 2.2). Furthermore, we found that AAMRs were highest among Non-Hispanic (NH) Black or African Americans and lowest in NH Asian or Pacific Islanders (4 vs. 1.9). Nonmetropolitan areas had higher AAMRs than metropolitan areas (3.6 vs. 3.2).ConclusionsOur analysis showed a significant decrease in mortality from paroxysmal tachycardia since 1999, although there has been a slight increase in recent years. However, disparities remain, with higher AAMRs among men, NH Black or African Americans, and residents of non-metropolitan areas. These findings call for immediate public health actions to curb the rising trends and reduce potential disparities.Clinical Perspectives What is New?In this analysis of population-level US mortality data from 1999 to 2020, we observed an overall decrease in mortality due to Paroxysmal Tachycardia, despite a significant upward trend from 2014 to 2020.Older adults had higher age-adjusted mortality rates than young and middle-aged adults, and men had higher mortality rates than women.Non-Hispanic Black or African Americans had the highest mortality rates among racial groups, and those in non-metropolitan areas had higher rates than those in metropolitan areas.What Are the Clinical Implications?By addressing the effects of the pandemic and racial disparities, healthcare providers and policymakers can develop effective strategies to address this concerning trend.Education should not only identify risk factors but also work to change the socio-cultural conditions that cause these risks.
Pharmacological Antagonism of Ccr2+ Cell Recruitment to Facilitate Regenerative Tendon Healing
Authors: Smolyak Gilbert , Rodenhouse Andrew , Nichols Anne E.C. , Ketonis Constantinos , Loiselle Alayna E. 
Published: July 2024

ABSTRACTSuccessful tendon healing requires sufficient deposition and remodeling of new extracellular matrix at the site of injury, with this process mediating in part through fibroblast activation via communication with macrophages. Moreover, resolution of healing requires clearance or reversion of activated cells, with chronic interactions with persistent macrophages impairing resolution and facilitating the conversion the conversion to fibrotic healing. As such, modulation of the macrophage environment represents an important translational target to improve the tendon healing process. Circulating monocytes are recruited to sites of tissue injury, including the tendon, via upregulation of cytokines including Ccl2, which facilitates recruitment of Ccr2+ macrophages to the healing tendon. Our prior work has demonstrated that Ccr2-/-can modulate fibroblast activation and myofibroblast differentiation. However, this approach lacked temporal control and resulted in healing impairments. Thus, in the current study we have leveraged a Ccr2 antagonist to blunt macrophage recruitment to the healing tendon in a time-dependent manner. We first tested the effects of Ccr2 antagonism during the acute inflammatory phase and found that this had no effect on the healing process. In contrast, Ccr2 antagonism during the late inflammatory/ early proliferative period resulted in significant improvements in mechanical properties of the healing tendon. Collectively, these data demonstrate the temporally distinct impacts of modulating Ccr2+ cell recruitment and Ccr2 antagonism during tendon healing and highlight the translational potential of transient Ccr2 antagonism to improve the tendon healing process.