bluuebunny/crossref_metadata_2025_split huggingface dataset:
AI-powered Coronary Artery Calcium Scans (AI-CAC) Enables Prediction of Heart Failure and Outperforms NT-proBNP: The Multi-Ethnic Study of Atherosclerosis
Authors: Naghavi Morteza , Reeves Anthony , Budoff Matthew , Li Dong , Atlas Kyle , Zhang Chenyu , Atlas Thomas , Henschke Claudia , Defilippi Christopher , Levy Daniel , Yankelevitz David 
Published: January 2024

AbstractIntroductionCoronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC for prediction of incident heart failure (HF) and compared it with 10 known clinical risk factors, NT-proBNP, and the Agatston CAC score.MethodsWe applied AI-CAC to 5750 CAC scans of asymptomatic individuals (ages 45-84, 52% women, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We then used the 15-year outcomes data and compared the C-statistic of AI-CAC with NT-proBNP and the Agatston score for predicting incident HF versus 10 known clinical risk factors (age, gender, body surface area, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL, total cholesterol, and hs-CRP).ResultsOver 15 years of follow-up, 256 HF events accrued. The ROC area under the curve for predicting HF with AI-CAC (0.826) was significantly higher than NT-proBNP (0.742) and Agatston score (0.712) (p<.0001), and comparable to clinical risk factors (0.818, p=0.4141). AI-CAC category-free NRI significantly improved on clinical risk factors (0.43), NT-proBNP (0.68), and Agatston score (0.71) for HF prediction at 15 years (p<0.0001).ConclusionAI-CAC significantly outperformed NT-proBNP and the Agatston CAC score and significantly improved category-free NRI of clinical risk factors for incident HF prediction.
Multi-omic network analysis identifies dysregulated neurobiological pathways in opioid addiction
Authors: Sullivan Kyle A. , Kainer David , Lane Matthew , Cashman Mikaela , Miller J. Izaak , Garvin Michael R. , Townsend Alice , Quach Bryan C. , Willis Caryn , Kruse Peter , Gaddis Nathan C. , Mathur Ravi , Corradin Olivia , Maher Brion S. , Scacheri Peter C. , Sanchez-Roige Sandra , Palmer Abraham A. , Troiani Vanessa , Chesler Elissa J. , Kember Rachel L. , Kranzler Henry R. , Justice Amy C. , Xu Ke , Aouizerat Bradley E. ,   VA Million Veteran Program, Hancock Dana B. , Johnson Eric O. , Jacobson Daniel A. 
Published: January 2024

AbstractOpioid addiction constitutes a public health crisis in the United States and opioids cause the most drug overdose deaths in Americans. Yet, opioid addiction treatments have limited efficacy. To help address this problem, we used network-based machine learning techniques to integrate results from genome-wide association studies (GWAS) of opioid use disorder and problematic prescription opioid misuse with transcriptomic, proteomic, and epigenetic data from the dorsolateral prefrontal cortex (dlPFC) in opioid overdose victims. We identified 211 highly interrelated genes identified by GWAS or dysregulation in the dlPFC of individuals with opioid overdose victims that implicated the Akt, BDNF, and ERK pathways, identifying 414 drugs targeting 48 of these opioid addiction-associated genes. This included drugs used to treat other substance use disorders and antidepressant drugs. Our synthesis of multi-omics using a systems biology approach revealed key gene targets that could contribute to drug repurposing, genetics-informed addiction treatment, and future discovery.
Cognitive impairment is a risk factor for long-term outcomes in septic patients: A retrospective observational study
Authors: Koami Hiroyuki , Sakamoto Yuichiro , Furukawa Yutaro , Mouri Kosuke , Matsuoka Ayaka , Shinada Kota , Nakayama Kento , Iwanaga Sachiko , Narumi Shogo , Koba Mayuko 
Published: January 2024

AbstractThe importance of long-term outcomes after sepsis has been increasingly emphasized in recent years; however, little is known about the relationship between pre-sepsis patient characteristics and long-term outcomes. This study examined the impact of patient characteristics pre-admission, specifically physical, cognitive, and emotional disorders, on long-term clinical outcomes of sepsis. From August 2014 to July 2016, this study included adult patients admitted to our hospital with a diagnosis of sepsis who were followed up for one year. These 76 patients were classified as Survivors (n=31) or Deceased (n=45), based on their one-year outcomes. In this single-center, retrospective study, 59% of sepsis patients died within one year. Multiple logistic regression models employing significant parameters from univariate analysis revealed that cognitive impairment (CI) is an independent risk factor for 1-year outcome [OR 0.184, 95% CI 0.047-0.722, P=0.0152]. Furthermore, through another univariate analysis and a log-rank test between the CI group and the Control group, created by reclassifying the same 76 patients, CI was significantly associated with a lower 1- year survival rate (25.0% vs 50.0%, P=0.03, log-rank P=0.0332). The composite outcome of either death or being bedridden at the point of discharge was also significantly higher in the CI group than the Control group (100.0% vs 75.0%, P=0.0028). These results indicate that pre-septic CI is a reliable, independent predictor of poor long-term clinical outcomes for sepsis patients.
UNDERSTANDING THE DETERMINANTS OF FLOURISHING AMONG ADOLESCENTS OF MAYUGE AND MPIGI DISTRICTS IN UGANDA: LINKING PHYSICAL, MENTAL, CULTURAL, ECONOMIC, AND SOCIAL DIMENSIONS THROUGH A GENDER-TRANSFORMATIVE LENS
Authors: Muthama Benson Mutuku , Henry Carol , White Judy , Lieffers Jessica , Fowler-Kerry Susan , Kikulwe Daniel , Tiessen Rebecca , Kibiri Lydia , Lough Benjamin , Musoke David , Tolossa Asrat Dibaba 
Published: January 2024

AbstractThe study sought to engage adolescents in Uganda to learn about their perspectives on the factors that enable or hinder their flourishing. This cross-sectional study was conducted in Mpigi and Mayuge districts in Uganda, using a survey with adolescent boys and girls aged 14-19 years. The survey examined seven aspects of human flourishing including: knowledge/education, emotional health, physical health, physical safety, financial security, social connectedness, and spiritual practices. The interpretation of scores and correlations was based on established classifications. Findings indicated that education, family, and community support were highly valued among participants. Among the respondents, eighty-five percent reported awareness of their right to education, and 79% expressed satisfaction with their access to primary or secondary education. Most respondents felt good about their emotional health (84%). Participants felt confidence in their decision-making abilities. Though the majority of respondents recognized the importance of a balanced diet to their own physical health, less than half reported being able to make decisions about what they eat. Most respondents reported feeling safe at home, with no significant differences by gender or region. Spiritual connectedness was also strong. Many reported that spirituality brought a sense of purpose, gratitude to their lives, and inner peace and harmony. Strong spiritual values within the communities were identified as important to their wellbeing; approximately seventy-seven percent expressed comfort in speaking to their faith leaders about their problems. Financial insecurity emerged as a significant obstacle (60%), however, eighty-four (84%) expressed confidence in finding work they liked, and most (88%) believed their future work would support them financially. Findings represent a first step in gaining a cross-cultural perspective of adolescent views on human flourishing. Findings may have implications for policymakers and decision-makers seeking to understand human flourishing among adolescents, and strategies to enable their flourishing.
Antiphospholipid antibodies exacerbate damage following oxygen deprivation-reperfusion injury in anin vivomodel for stroke and inex vivoblood derived endothelial cells
Authors: Pericleous Charis , Stuckey Daniel J. , Maughan Robert T. , Paschalaki Koralia , Kabir Lida , Bourke Lauren T. , Willis Rohan , Rahman Anisur , Randi Anna M. , Arachchillage Deepa J. , Lythgoe Mark , Giles Ian P. , Mason Justin C. , Ioannou Yiannis 
Published: January 2024

AbstractBackgroundProthrombotic antiphospholipid antibodies (aPL) found in patients with antiphospholipid syndrome (APS) are a recognised risk factor for ischemic stroke. However, it is unclear if aPL cause injury post thrombolysis leading to worse outcomes. We investigated whether aPL exacerbate reperfusion injury and sought to translate our findings in endothelial colony forming cells (ECFC) isolated from patients with APS.MethodsTransient ischemic stroke was induced in adult rats injected with serum-derived IgG from patients with APS (APS-IgG, containing aPL) or healthy controls (HC-IgG). Infarct size and intracellular signalling processes involved in ischemia-reperfusion injury were determined post reperfusion.In vitro, human umbilical vein endothelial cells (HUVEC) treated with IgG, as well as APS and HC ECFC, were exposed to hypoxia (0.1% O2). Cell death and relevant signalling mechanisms were assessed following reperfusion and compared to matched normoxic cultures.ResultsIn vivo, APS-IgG induced >2-fold larger infarcts and lower levels of active phosphorylated Akt, a key pro-survival kinase, compared to HC-IgG.In vitro, aPL-mediated cell death and suppression of Akt phosphorylation was confirmed in HUVEC exposed to IgG and hypoxia-reperfusion. Consistent with these findings, higher rates of cell death and reduced Akt phosphorylation following reperfusion were observed inex vivoAPS ECFC compared to HC ECFC. Treatment with the immunomodulating agent hydroxychloroquine ameliorated ECFC death and this effect was more pronounced in APS-derived cells.ConclusionPatient-derived IgG aPL exacerbate cell death following reperfusion in a novelin vivostroke model for APS, as well asin vitroHUVEC cultures. These observations are mimicked inex vivoAPS ECFC. Our findings describe a novel pathogenic role for aPL in mediating tissue injury in addition to their known thrombogenic properties and indicate potential for pharmacological intervention.
In major depression, complex intersections between adverse childhood experiences and negative life events impact neuroticism, brooding, suicidal tendencies, insomnia, and cognitive interference
Authors: Vasupanrajit Asara , Maes Michael , Jirakran Ketsupar , Tunvirachaisakul Chavit 
Published: January 2024

AbstractBackgroundThere is evidence that adverse childhood experiences (ACEs) and negative life events (NLEs) are associated with major depression (MDD).PurposeTo determine whether ACEs affect all features of mild MDD, including suicidal tendencies, brooding, neuroticism, insomnia, cognitive deficits, severity of depression and anxiety, and cognitive deficits, and whether NLEs mediate these effects.Patients and methodsThis study examines a cohort of 118 academic students, namely 74 students who satisfied the DSM-5-TR criteria for MDD and 44 normal control students. We assessed brooding, neuroticism, suicidal ideation and attempts, and the severity of depression, anxiety, insomnia, and the Stroop tests.ResultsOne validated factor could be extracted from brooding, neuroticism, current suicidal behaviors, and the severity of depression, anxiety, and insomnia, labeled the phenome of depression. A large part of the variance in the phenome of depression (55.0%) was explained by the combined effects of self-, relationships, and academic-related NLEs in conjunction with ACEs, including family dysfunction and abuse and neglect (both physical and emotional). The latter ACEs significantly interacted (moderating effect) with NLEs to impact the depression phenome. Although sexual abuse did not have direct effects on the phenome, its effects were mediated by NLEs. We discovered that increased sexual abuse, physical and emotional abuse and neglect, and ACEs related to family dysfunction predicted 22.5% of the variance in NLEs. Up to 18.5% of the variance in the Stroop test scores was explained by sexual abuse and the phenome of depression. The latter mediated the effects of NLEs and abuse, neglect, and family dysfunction on the Stroop test scores.ConclusionComplex intersections between ACEs and NLEs impact the phenome of depression, which comprises neuroticism, brooding, suicidal tendencies, and the severity of insomnia, anxiety, and depression, while sexual abuse together with other ACEs and NLEs may impact cognitive interference inhibition.
Understanding the Preferences of Young Women in Self-Sampling Interventions for STI Diagnosis: A Discrete Choice Experiment Protocol
Authors: Jaya Ziningi N. , Mapanga Witness , Mashamba-Thompson Tivani P 
Published: January 2024

AbstractIntroductionSexually transmitted infections (STIs) are a significant public health concern globally, particularly affecting young women. Early diagnosis and treatment are essential to reducing or stopping the continuous spread of infections and the development of the associated complications. Syndromic management, which is commonly used for STIs, presents several barriers, particularly for young women. This protocol is for a study that aims to understand young women’s preferences for a self-sampling intervention for STI diagnosis by using a Discrete Choice Experiment (DCE).Methods and analysisThe following attributes of a self-sampling intervention were identified through a Nominal Group Technique: accessibility, education, confidentiality, self-sampling method, youth-friendliness, and cost. A pilot study involving 20 participants was conducted to refine the DCE questionnaire. A total of 196 young women from underserved communities will be recruited. The participants will be sampled from communities, stratified by settlement type and socioeconomic status. Data will be analysed using the multinomial logit model and mixed logit model to assess preferences and heterogeneity.Ethics and disseminationThe study findings have the potential to inform policies for STI treatment and management to align healthcare services with user preferences. This can improve STI healthcare access for young women in underserved communities. Ethical approval was obtained, and results will be disseminated through peer-reviewed journals and health conferences.Strengths and limitations of this studyDCEs provide a platform for users or consumers to express their preference for particular goods or services based on their attribute selection.Previously STI healthcare service provision has not been aligned with the preferences of young women. Therefore, this will reveal their preferences for a self-sampling intervention for STI healthcare and management.In instances where user preferences do not align with current practices for STI healthcare, this will provide an opportunity for policies to be reviewed and amended accordingly.This type of impact on STI healthcare aligns with goal 3.1 of South Africa’s National Strategic Plan which seeks to improve access to healthcare services for STIs and other diseases (1). It also aligns with goal three of the United Nations which seeks to improve access to healthcare for all and thus achieve universal healthcare coverage (2, 3).Since our study will be conducted on young women residing in underserved urban populations, our findings may not be a true reflection of young women from diverse communities.
Survival Prediction Landscape: An In-Depth Systematic Literature Review on Activities, Methods, Tools, Diseases, and Databases
Authors: Abbasi Ahtisham Fazeel , Asim Muhammad Nabeel , Ahmed Sheraz , Vollmer Sebastian , Dengel Andreas 
Published: January 2024

ABSTRACTSurvival prediction integrates patient-specific molecular information and clinical signatures to forecast the anticipated time of an event, such as recurrence, death, or disease progression. Survival prediction proves valuable in guiding treatment decisions, optimizing resource allocation, and interventions of precision medicine. The wide range of diseases, the existence of various variants within the same disease, and the reliance on available data necessitate disease-specific computational survival predictors. The widespread adoption of artificial intelligence (AI) methods in crafting survival predictors has undoubtedly revolutionized this field. However, the ever-increasing demand for more sophisticated and effective prediction models necessitates the continued creation of innovative advancements. To catalyze these advancements, the need of the hour is to bring existing survival predictors knowledge and insights into a centralized platform. The paper in hand thoroughly examines 22 existing review studies and provides a concise overview of their scope and limitations. Focusing on a comprehensive set of 74 most recent survival predictors across 44 diverse diseases, it delves into insights of diverse types of methods that are used in the development of disease-specific predictors. This exhaustive analysis encompasses the utilized data modalities along with a detailed analysis of subsets of clinical features, feature engineering methods, and the specific statistical, machine or deep learning approaches that have been employed. It also provides insights about survival prediction data sources, open-source predictors, and survival prediction frameworks.
Symptom propagation in respiratory pathogens of public health concern: a review of the evidence
Authors: Asplin Phoebe , Mancy Rebecca , Finnie Thomas , Cumming Fergus , Keeling Matt J. , Hill Edward M. 
Published: January 2024

AbstractSymptom propagation occurs when the symptom set an individual experiences is correlated with the symptom set of the individual who infected them. Symptom propagation may dramatically affect epidemiological outcomes, potentially causing clusters of severe disease. Conversely, it could result in chains of mild infection, generating widespread immunity with minimal cost to public health.Despite accumulating evidence that symptom propagation occurs for many respiratory pathogens, the underlying mechanisms are not well understood. Here we conducted a scoping literature review for 14 respiratory pathogens to ascertain the extent of evidence for symptom propagation by two mechanisms: dose-severity relationships and route-severity relationships.We identify considerable heterogeneity between pathogens in the relative importance of the two mechanisms, highlighting the importance of pathogen-specific investigations. For almost all pathogens, including influenza and SARS-CoV-2, we found support for at least one of the two mechanisms. For some pathogens, including influenza, we found convincing evidence that both mechanisms contribute to symptom propagation.Furthermore, infectious disease models traditionally do not include symptom propagation. We summarise the present state of modelling advancements to address the methodological gap. We then investigate a simplified disease outbreak scenario, finding that under strong symptom propagation, quarantining mildly infected individuals can have negative epidemiological implications.
The Asia-Pacific body mass index classification and new-onset chronic kidney disease in non-diabetic Japanese adults: A community-based longitudinal study from 1998 to 2023
Authors: Okawa Yukari , Mitsuhashi Toshiharu , Tsuda Toshihide 
Published: January 2024

AbstractBackground/ObjectivesObesity is a risk factor for chronic kidney disease (CKD) in Asians. The Asia-Pacific body mass index (BMI) classification sets lower obesity cutoffs than the conventional BMI classification for all races, generally reflecting the lower BMIs in Asians. This longitudinal study evaluated the association between BMI, as classified by the Asia-Pacific BMI system, and CKD development in non-diabetic Asian adults.MethodsA population-based longitudinal study (1998–2023) was conducted in non-diabetic Japanese adults (hemoglobin A1c < 6.5%) in Zentsuji City (Kagawa Prefecture, Japan). The generalized gamma model was used to assess the relationship between time-varying BMI categories and CKD development, stratified by sex. CKD was defined as an estimated glomerular filtration rate of <60 mL/min/1.73 m2. BMI was calculated as weight (kg) divided by the square of height (m2) and categorized per the Asia-Pacific classification as overweight (23.0–24.9 kg/m2), obesity class I (25.0–29.9 kg/m2), and obesity class II (≥30.0 kg/m2).ResultsCKD developed in 34.2% of 3,098 men and 34.8% of 4,391 women. The mean follow-up times were 7.41 years for men and 8.25 years for women. During follow-up, the BMI distributions for men were 5.0% underweight, 43.3% normal weight, 25.6% overweight, 24.1% obesity class I, and 2.0% obesity class II; those for women were 7.7%, 50.5%, 20.5%, 18.3%, and 2.9%, respectively. Compared with normal weight, obesity class I was associated with a 6% (95% confidence interval [CI]: 2%–10%) shorter time to CKD onset in men and 5% (95% CI: 2%–7%) in women. In both sexes, obesity class II showed shorter survival times than normal weight by point estimates, although all 95% CIs crossed the null value.ConclusionsObesity, as classified by the Asia-Pacific BMI system, shortened the time to CKD onset in non-diabetic Asians. The conventional BMI cutoff for obesity (≥30.0 kg/m2) may be too high to identify CKD risk in this population. The findings of this study may be useful for public health professionals in designing interventions to prevent CKD.
Machine learning across multiple imaging and biomarker modalities in the UK Biobank improves genetic discovery for liver fat accumulation
Authors: Somineni Hari , Mukherjee Sumit , Amar David , Pei Jingwen , Guo Karl , Light David , Flynn Kaitlin ,   insitro Research Team, Probert Chris , Soare Thomas , Satapati Santhosh , Koller Daphne , Lloyd David J. , O’Dushlaine Colm 
Published: January 2024

AbstractMetabolic dysfunction-associated steatotic liver disease (MASLD), liver with more than 5.5% fat content, is a leading risk factor for chronic liver disease with an estimated worldwide prevalence of 30%. Though MASLD is widely recognized to be polygenic, genetic discovery has been lacking primarily due to the need for accurate and scalable phenotyping, which proves to be costly, time-intensive and variable in quality. Here, we used machine learning (ML) to predict liver fat content using three different data modalities available in the UK Biobank: dual-energy X-ray absorptiometry (DXA; n = 46,461 participants), plasma metabolites (n = 82,138), and anthropometric and blood-based biochemical measures (biomarkers; n = 262,927). Based on our estimates, up to 29% of participants in UKB met the criteria for MASLD. Genome-wide association studies (GWASs) of these estimates identified 15, 55, and 314 loci associated with liver fat predicted from DXA, metabolites and biomarkers, respectively, totalling 321 unique independent loci. In addition to replicating 9 of the 14 known loci at genome-wide significance, our GWASs identified 312 novel loci, significantly expanding our understanding of the genetic contributions to liver fat accumulation. Genetic correlation analysis indicated a strong correlation between ML-derived liver fat across modalities (rgranging from 0.85 to 0.96) and with clinically diagnosed MASLD (rgranging from 0.74 to 0.88), suggesting that a majority of the newly identified loci are likely to be relevant for clinical MASLD. DXA exhibited the highest precision, while biomarkers demonstrated the highest recall, respectively. Overall, these findings demonstrate the value of leveraging ML-based trait predictions across orthogonal data sources to improve our understanding of the genetic architecture of complex diseases.