bluuebunny/crossref_metadata_2025_split huggingface dataset:
Early-onset schizophrenia is associated with immune-related rare variants in a Chinese sample
Authors: Zhong Yuanxin , Tubbs Justin D. , Leung Perry B.M. , Zhan Na , Hui Tomy C.K. , Ho Karen K. Y. , Hung Karen S. Y. , Cheung Eric F. C. , So Hon-Cheong , Lui Simon S.Y. , Sham Pak C. 
Published: November 2023

AbstractBackgroundRare variants are likely to contribute to schizophrenia (SCZ), given the large discrepancy between the heritability estimated from twin and GWAS studies. Furthermore, the nature of the rare-variant contribution to SCZ may vary with the “age-at-onset” (AAO), since early-onset has been suggested as being indicative of neurodevelopment deviance.ObjectiveTo examine the association of rare deleterious coding variants in early- and adult-onset SCZ in a Chinese sample.MethodExome sequencing was performed on DNA from 197 patients with SCZ spectrum disorder and 82 healthy controls (HC) of Chinese ancestry recruited in Hong Kong. We also gathered AAO information in the majority of SCZ samples. Patients were classified into early-onset (EOS, AAO<18) and adult-onset (AOS, AAO>18). We collapsed the rare variants to improve statistical power and examined the overall association of rare variants in SCZ versus HC, EOS versus HC, and AOS versus HC at the gene and gene-set levels by Sequence Kernel Association Test. The quantitative rare-variant association test of AAO was also conducted. We focused on variants which were predicted to have a medium or high impact on the protein-encoding process as defined by Ensembl. We applied a 100000-time permutation test to obtain empirical p-values, with significance threshold set at p < 1e-3to control family-wise error rates. Moreover, we compared the burden of targeted rare variants in significant risk genes and gene sets in cases and controls.ResultsBased on several binary-trait association tests (i.e., SCZ vs HC, EOS vs HC and AOS vs HC), we identified 7 candidate risk genes and 20 gene ontology biological processes (GOBP) terms, which exhibited higher burdens in SCZ than in controls. Based on quantitative rare-variant association tests, we found that alterations in 5 candidate risk genes and 7 GOBP pathways were significantly correlated with AAO. Based on biological and functional profiles of the candidate risk genes and gene sets, our findings suggested that, in addition to the involvement of perturbations in neural systems in SCZ in general, altered immune responses may be specifically implicated in EOS.ConclusionDisrupted immune responses may exacerbate abnormal perturbations during neurodevelopment and trigger the early onset of SCZ. We provided evidence of rare variants increasing SCZ risk in the Chinese population.
Public Health and Social Measures (PHSM) interventions to control COVID-19 An Overview of Systematic Reviews
Authors: Fadlallah Racha , El-Jardali Fadi , Kalach Nour , Karroum Lama Bou , Hoteit Reem , Aoun Andrew , Al-Hakim Lara , Verdugo-Paiva Francisca , Rada Gabriel , Fretheim Atle , Lewin Simon , Ludolph Ramona , Akl Elie A. 
Published: November 2023

AbstractResponse to the COVID-19 pandemic included a wide range of Public Health and Social Measures (PHSM). PHSM refer to a broad array of nonpharmaceutical interventions implemented by individuals, communities and governments to reduce the risk and scale of transmission of epidemic- and pandemic-prone infectious diseases. In order to inform decisions by the public, health workforce and policy-makers, there is a need to synthesize the large volume of published work on COVID-19. This study protocol describes the methodology for an overview of reviews focusing on the effectiveness and/or unintended health and socio-economic consequences of PHSM implemented during the COVID-19 pandemic. Findings can shape policy and research related to PHSM moving forward.
Exploring the possibility of collaboration for biomedical professionals and traditional healers: A Systematic Review
Authors: Ilozumba Onaedo , Koirala Suyasha , Meka Anthony , Ossai Edmund , Choudhury Sopna Mannan , Wagner Ryan , Lilford Richard 
Published: November 2023

ABSTRACTIntroductionTraditional healers play a crucial role in healthcare provision, particularly in low- and middle-income countries. Thus, there is a global interest in understanding the possibilities for collaboration between traditional healers and biomedical professionals. We believe there is the need for a comprehensive review on collaboration between traditional healers and biomedical professionals. Therefore, the aim of this review is to synthesise the literature on collaboration between traditional healer’s and biomedical professional including relevant interventions.MethodsA systematic review was conducted, utilizing a search strategy in PubMed, Web of Science, SCOPUS, and Google Scholar. Articles addressing collaboration between traditional healers and biomedical professionals were included, with a focus on attitudes, perceptions, interventions, and collaborative models. Data extraction followed a predefined template and the D’Amour et al. framework was employed for analysis.ResultsThe review identified 29 relevant articles, predominantly conducted in Africa. The majority of studies (n=22) explored attitudes and perceptions, revealing a willingness among traditional healers to collaborate, while biomedical professionals exhibited mixed feelings. Seven studies focused on interventions aimed at fostering collaboration primarily focused on improving referral systems and educational initiatives. These studies found positive outcomes. Examining collaboration through the lens of D’Amour et al.’s framework revealed that trust was a significant barrier to collaboration.ConclusionThis review highlights a willingness to collaborate amongst of traditional healers and biomedical professionals and provides some successful examples of working across systems. It also reveals areas for attention in developing collaborative models of working.
Consistency, completeness and external validity of ethnicity recording in NHS primary care records: a cohort study in 25 million patients’ records at source using OpenSAFELY
Authors:   The OpenSAFELY Collaborative, Andrews Colm D , Mathur Rohini , Massey Jon , Park Robin , Hopcroft Lisa , Curtis Helen J , Mehrkar Amir , Bacon Seb , Hickman George , Smith Rebecca , Evans David , Ward Tom , Davy Simon , Inglesby Peter , Dillingham Iain , Maude Steven , O’Dwyer Thomas , Butler-Cole Ben , Bridges Lucy , Bates Chris , Parry John , Hester Frank , Harper Sam , Cockburn Jonathan , Goldacre Ben , MacKenna Brian , Tomlinson Laurie , Walker Alex J , Hulme William J 
Published: November 2023

AbstractBackgroundEthnicity is known to be an important correlate of health outcomes, particularly during the COVID-19 pandemic, where some ethnic groups were shown to be at higher risk of infection and adverse outcomes. The recording of patients’ ethnic groups in primary care can support research and efforts to achieve equity in service provision and outcomes; however the coding of ethnicity is known to present complex challenges. We therefore set out to describe ethnicity coding in detail with a view to supporting the use of this data in a wide range of settings, as part of wider efforts to robustly describe and define methods of using administrative data.MethodsWe describe the completeness and consistency of primary care ethnicity recording in the OpenSAFELY-TPP database, containing linked primary care and hospital records in >25 million patients in England. We also compared the ethnic breakdown in OpenSAFELY-TPP with that of the 2021 UK census.Results78.2% of patients registered in OpenSAFELY-TPP on 1 January 2022 had their ethnicity recorded in primary care records, rising to 92.5% when supplemented with hospital data. The completeness of ethnicity recording was higher for women than for men. The rate of primary care ethnicity recording ranged from 77% in the South East of England to 82.2% in the West Midlands. Ethnicity recording rates were higher in patients with chronic or other serious health conditions. For each of the five broad ethnicity groups, primary care recorded ethnicity was within 2.9 percentage points of the population rate as recorded in the 2021 Census for England as a whole. For patients with multiple ethnicity records, 98.7% of the latest recorded ethnicities matched the most frequently coded ethnicity. Patients whose latest recorded ethnicity was categorised as Other were most likely to have a discordant ethnicity recording (32.2%).ConclusionsPrimary care ethnicity data in OpenSAFELY is present for over three quarters of all patients, and combined with data from other sources can achieve a high level of completeness. The overall distribution of ethnicities across all English OpenSAFELY-TPP practices was similar to the 2021 Census, with some regional variation. This report identifies the best available codelist for use in OpenSAFELY and similar electronic health record data.
Evaluating and mitigating unfairness in multimodal remote mental health assessments
Authors: Jiang Zifan , Seyedi Salman , Griner Emily , Abbasi Ahmed , Rad Ali Bahrami , Kwon Hyeokhyen , Cotes Robert O. , Clifford Gari D. 
Published: November 2023

AbstractResearch on automated mental health assessment tools has been growing in recent years, often aiming to address the subjectivity and bias that existed in the current clinical practice of the psychiatric evaluation process. Despite the substantial health and economic ramifications, the potential unfairness of those automated tools was understudied and required more attention.In this work, we systematically evaluated the fairness level in a multimodal remote mental health dataset and an assessment system, where we compared the fairness level in race, gender, education level, and age.Demographic parity ratio (DPR)andequalized odds ratio (EOR)of classifiers using different modalities were compared, along with the F1 scores in different demographic groups. Post-training classifier threshold optimization was employed to mitigate the unfairness.No statistically significant unfairness was found in the composition of the dataset. Varying degrees of unfairness were identified among modalities, with no single modality consistently demonstrating better fairness across all demographic variables. Post-training mitigation effectively improved both DPR and EOR metrics at the expense of a decrease in F1 scores.Addressing and mitigating unfairness in these automated tools are essential steps in fostering trust among clinicians, gaining deeper insights into their use cases, and facilitating their appropriate utilization.Author summaryIn this work, we systematically explored and discussed the unfairness reporting and mitigation of automated mental health assessment tools. These tools are becoming increasingly important in mental health practice, especially with the rise of telehealth services and large language model applications. However, they often carry inherent biases. Without proper assessment and mitigation, they potentially lead to unfair treatment of certain demographic groups and significant harm. Proper unfairness reporting and mitigation of these tools is the first step to building trust among clinicians and patients and ensuring appropriate application.Using our previously developed multimodal mental health assessment system, we evaluated the unfairness level of using various types of features of the subjects for mental health assessment, including facial expressions, acoustic features of the voice, emotions expressed through language, general language representations generated by large language models, and cardiovascular patterns detected from the face. We analyzed the system’s fairness across different demographics: race, gender, education level, and age. We found no single modality consistently fair across all demographics. While unfairness mitigation methods improved the fairness level, we found a trade-off between the performance and the fairness level, calling for broader moral discussion and investigation on the topic.
Generating synthetic data in digital pathology through diffusion models: a multifaceted approach to evaluation
Authors: Pozzi Matteo , Noei Shahryar , Robbi Erich , Cima Luca , Moroni Monica , Munari Enrico , Torresani Evelin , Jurman Giuseppe 
Published: November 2023

AbstractSynthetic data has recently risen as a new precious item in the computational pathologist’s toolbox, supporting several tasks such as helping with data scarcity or augmenting training set in deep learning. Nonetheless, the use of such novel resources requires a carefully planned construction and evaluation, to avoid pitfalls such as the generation of clinically meaningless artifacts.As the major outcome described in the current manuscript, a novel full stack pipeline is introduced for the generation and evaluation of synthetic pathology data powered by a diffusion model. The workflow features, as characterizing elements, a new multifaceted evaluation strategy with an embedded explainability procedure effectively tackling two critical aspects of the use of synthetic data in health-related domains.An ensemble-like strategy is adopted for the evaluation of the produced data, with the threefold aim of assessing the similarity of real and synthetic data through a set of well-established metrics, evaluating the practical usability of the generated images in deep learning models complemented by explainable AI methods, and validating their histopathological realism through a dedicated questionnaire answered by three professional pathologists.The pipeline is demonstrated on the public GTEx dataset of 650 WSIs, including five different tissues, conditioning the training step of the underlying diffusion model. An equal number of tiles from each of these five tissues are then generated. Finally, the reliability of the generated data is assessed using the proposed evaluation pipeline, with encouraging results. We show that each of these evaluation steps are necessary as they provide complementary information on the generated data’s quality.Overall, all the aforementioned features characterize the proposed workflow as a fully-fledged solution for generative AI in digital pathology representing a potentially useful tool for the digital pathology community in their transition towards digitalization and data-driven modeling.
Three components of glucose dynamics – value, variability, and autocorrelation – are independently associated with coronary plaque vulnerability
Authors: Sugimoto Hikaru , Hironaka Ken-ichi , Yamada Tomoko , Otowa-Suematsu Natsu , Hirota Yushi , Otake Hiromasa , Hirata Ken-Ichi , Sakaguchi Kazuhiko , Ogawa Wataru , Kuroda Shinya 
Published: November 2023

ABSTRACTImpaired glucose homeostasis leads to numerous complications, with coronary artery disease (CAD) being a major contributor to healthcare costs worldwide. Given the limited efficacy of current CAD screening methods, we investigated the association between glucose dynamics and a predictor of coronary events measured by virtual histology-intravascular ultrasound (%NC), with the aim of predicting CAD using easy-to-measure indices. We found that continuous glucose monitoring (CGM)-derived indices, particularly average daily risk ratio (ADRR) and AC_Var, exhibited stronger predictive capabilities for %NC compared to commonly used indices such as fasting blood glucose (FBG), hemoglobin A1C (HbA1c), and plasma glucose level at 120 min during oral glucose tolerance tests (PG120). Factor analysis identified three distinct components underlying glucose dynamics – value, variability, and autocorrelation – each independently associated with %NC. ADRR was influenced by the first two components and AC_Var by the third. FBG, HbA1c, and PG120 were influenced only by the value component, making them insufficient for %NC prediction. Our results were validated using data sets from Japan (n=64), America (n=53), and China (n=100). CGM-derived indices reflecting the three components of glucose dynamics can serve as more effective screening tools for CAD risk assessment, complementing or possibly replacing traditional diabetes diagnostic methods.
Using the Public Health and Social Measures Severity Index for analysing government responses to COVID-19 in the WHO European Region
Authors: Wippel Christoph , Owen Lisa , Cocciolone Dominic , Sane Jussi , Addo Jennifer , Gapp Christian , Latta Aimee , Lindmark Sandra , Schmidt Tanja 
Published: November 2023

AbstractPublic health and social measures (PHSM), such as adaptions to the operation of schools, businesses and workplaces, international travel measures and restrictions on gatherings and people’s movements, as well as individual preventive measures such as physical distancing, have been a vital set of tools utilized by many countries to mitigate the spread of SARS-CoV-2 virus throughout the COVID-19 pandemic. In January 2020, the WHO Regional Office for Europe started to systematically monitor, collect and categorize data on response measures taken by its Member States. In order to visualize and analyse the collected data, WHO developed a methodology for quantifying the response measures into an index, capturing the severity of these policy measures in terms of six key indicators. The aim of this article is to describe the methodology underlying the index, including data collection, categorization and calculation, in order to provide researchers and policy-makers with a better understanding of its application. Furthermore, it provides an overview and examples of possible applications of the index, as well as serve as a reference for subsequent research on the effectiveness of PHSM, including empirical studies and models that can help to guide health policies, their timing and severity.
Visceral Adiposity and Subclinical Left Ventricular Remodeling
Authors: Luu Judy , Gebhard Catherine , Friedrich Matthias G. , Desai Dipika , Schulze Karleen M , de Souza Russell , Al-Khazraji Baraa K. , Awadalla Philipp , Lettre Guillaume , Ho Vikki , Dummer Trevor , Hicks Jason , Piche Marie-Eve , Poirier Paul , Teo Koon K. , Yusuf Salim , Tardif Jean-Claude , Vena Jennifer , Lee Douglas S. , Marcotte Francois , Larose Eric , Smith Eric E. , Anand Sonia S. 
Published: November 2023

AbstractIntroductionVisceral adiposity is emerging as a key driver of cardio-metabolic risk factors and cardiovascular disease (CVD), but its relationship with cardiac structure and function is not well characterized across sexes. Using the Canadian Alliance for Healthy Heart and Minds (CAHHM), a large population-based cohort study, we sought to determine the association of visceral adipose tissue (VAT) on subclinical left ventricular (LV) remodeling in males and females.MethodsAs part of the CAHHM study, 6522 participants free of clinical CVD (mean age: 57.4 [8.8 SD] years; 3,671 females, 56%) underwent magnetic resonance imaging (MRI) in which LV parameters and VAT volume were measured. Information about demographic factors, CV risk factors, and anthropometric measurements were obtained. Subclinical cardiac remodelling was defined as altered LV concentricity, represented by increased LV mass-to-volume ratio (LVMV).ResultsMales had a higher VAT volume (80.8 mL; 95% CI: 74.6 t 86.9) compared to females (64.7 mL; 95% CI: 58.5 to 70.8), adjusted for age and height. Among both males and females, VAT was significantly associated with subclinical cardiac remodeling (increased LVMV), independent of other CV risk factors. In multiple regression models adjusted for cardiovascular risk factors, age, and height, every 1 sex-specific standard deviation increase in VAT corresponded to an increase of 0.037 g/mL in LVMV (95% CI: 0.032 to 0.041; p<0.001), which was consistent across both sexes. Notably, a 1 standard deviation increase in VAT is associated with a LVMV that is 20 times higher than what is observed with natural aging alone (0.0020 g/mL rise in LVMV (95% CI 0.0016 to 0.0025), and 1.5 times higher than the impact of an integrated measure of CV risk factors (0.024 g/mL; 95% CI: 0.020 to 0.028).ConclusionVAT significantly influences subclinical cardiac remodeling in both males and females, independent of other cardiovascular risk factors and age. Further research to understand the pathways by which VAT contributes to accelerated cardiac aging is needed.
Exposure to heavy metalsin uteroand autism spectrum disorder at age 3: A meta-analysis of two longitudinal cohorts of siblings of children with autism
Authors: Dou John F. , Schmidt Rebecca J. , Volk Heather E. , Nitta Manon M. , Feinberg Jason I. , Newschaffer Craig J. , Croen Lisa A. , Hertz-Picciotto Irva , Fallin M. Daniele , Bakulski Kelly M. 
Published: November 2023

AbstractBackgroundAutism spectrum disorder (ASD) is a prevalent and heterogeneous neurodevelopmental disorder. Risk is attributed to genetic and prenatal environmental factors, though the environmental agents are incompletely characterized.MethodsIn Early Autism Risk Longitudinal Investigation (EARLI) and Markers of Autism Risk in Babies Learning Early Signs (MARBLES), two pregnancy cohorts of siblings of children with ASD, maternal urinary metals concentrations at two time points during pregnancy were measured using inductively coupled plasma mass spectrometry. At age three, clinicians assessed ASD with DSM-5 criteria. Using multivariable log binomial regression, we examined each metal for association with ASD status, adjusting for gestational age at urine sampling, child sex, maternal age, and maternal education, and meta-analyzed across the two cohorts.ResultsIn EARLI (n=170) 17.6% of children were diagnosed with ASD, and an additional 43.5% were classified as having other non-neurotypical development (Non-TD). In MARBLES (n=156), 22.7% were diagnosed with ASD, while an additional 11.5% had Non-TD. In earlier pregnancy metals measures, having cadmium concentration over the level of detection was associated with 1.78 (1.19, 2.67) times higher risk of ASD, and 1.43 (1.06, 1.92) times higher risk of Non-TD. A doubling of early pregnancy cesium concentration was marginally associated with 1.81 (0.95, 3.42) times higher risk of ASD, and 1.58 (0.95, 2.63) times higher risk of Non-TD.ConclusionExposurein uteroto elevated levels of cadmium and cesium, as measured in maternal urine collected during pregnancy, was associated with increased risk of developing ASD.