bluuebunny/crossref_metadata_2025_split huggingface dataset:
The OBS UK Dashboard: an Interactive Tool for Representative Trial Site Selection to Facilitate Equality and Diversity in Maternity Research
Authors: Elsmore Amy , Rai Tanvi , Pallmann Philip , Townson Julia , Kotecha Sarah , Black Mairead , Sanders Julia , Collis Rachel , Collins Peter , Karunakaran Bala , Wu Pensee , Bell Sarah , Parry-Smith William 
Published: August 2024

Abstract

Background
Obstetric Bleeding Study UK (award ID:152057) is a NIHR-funded stepped wedge cluster randomised controlled trial of a complex intervention for postpartum haemorrhage. This was developed in Wales and evaluated in a feasibility study, with improvements in maternal outcomes observed. Generalisability of the findings is limited by lack of control data and limited ethnic diversity in the Welsh obstetric patient population compared to the United Kingdom (UK): 94% of the Welsh population identifies as White, versus 82% in the UK from Census 2021. Non-white ethnicity and socioeconomic deprivation are linked to increased risk of adverse maternal outcomes. Traditionally, decisions regarding site selection are based on desire to complete trials on target in ‘tried and tested’ research active institutions. To ensure widespread applicability of the results and investigate the impact of ethnicity and social deprivation on trial outcomes, maternity units were recruited that represent the ethnic diversity and social deprivation profiles of the UK.
Method
Using routinely collected, publicly available data an interactive dashboard was developed that demonstrates the demographics of the population served by each maternity unit in the UK, to inform site recruitment. Data on births per year, ethnic and socioeconomic group of the population for each maternity unit, across the UK were integrated into the dashboard.
Results
The dashboard demonstrates that OBS UK trial sites reflect the ethnic and socioeconomic diversity of the UK (study vs UK population ethnicity: White 79.2% vs 81.7%, Asian 10.5% vs 9.3%., Black 4.7% vs 4.0%, Mixed 2.5% vs 2.9%, Other 3.0% vs 2.1%) with variation in site demography, size, and location. Missing data varied across sites and nations and is presented.
Conclusion
The NIHR equality, diversity, and inclusion strategy states studies must widen participation, facilitating individuals from all backgrounds to engage. The development of this novel interactive dashboard demonstrates an innovative way of achieving this. Future maternity researchers should consider using this tool to ensure inclusivity and establish results that are clinically relevant and applicable across the National Health Service.
Trial Registration
ISRCTN 17679951
Role of Inflammation in Depressive and Anxiety Disorders, Affect, and Cognition: Genetic and Non-Genetic Findings in the Lifelines Cohort Study.
Authors: Giollabhui Naoise Mac , Slaney Chloe , Hemani Gibran , Foley Eimear , Most Peter van der , Nolte Ilja , Snieder Harold , Smith George Davey , Khandaker Golam , Hartman Catharina 
Published: August 2024

Abstract

Inflammation is associated with a range of neuropsychiatric symptoms; however, the nature of the causal relationship is unclear. We used complementary non-genetic, genetic risk score (GRS), and Mendelian randomization (MR) analyses to examine whether inflammatory markers are associated with affect, depressive and anxiety disorders, and cognition. We tested in ≈ 55,098 (59% female) individuals from the Dutch Lifelines cohort the concurrent/prospective associations of C-reactive protein (CRP) with: depressive and anxiety disorders; positive/negative affect; and attention, psychomotor speed, episodic memory, and executive functioning. Additionally, we examined the association between inflammatory GRSs (CRP, interleukin-6 [IL-6], IL-6 receptor [IL-6R and soluble IL-6R (sIL-6R)], glycoprotein acetyls [GlycA]) on these same outcomes (Nmax=57,946), followed by MR analysis examining evidence of causality of CRP on outcomes (Nmax=23,268). In non-genetic analyses, higher CRP was associated with a depressive disorder, lower positive/higher negative affect, and worse executive function, attention, and psychomotor speed after adjusting for potential confounders. In genetic analyses, CRPGRS was associated with any anxiety disorder (β = 0.002, p = 0.037) whereas GlycAGRS was associated with major depressive disorder (β = 0.001, p = 0.036). Both CRPGRS (β = 0.006, p = 0.035) and GlycAGRS (β = 0.006, p = 0.049) were associated with greater negative affect. Inflammatory GRSs were not associated with cognition, except sIL-6RGRS which was associated with poorer memory (β=-0.009, p = 0.018). There was weak evidence for a CRP-anxiety association using MR (β = 0.12; p = 0.054). Genetic and non-genetic analyses provide consistent evidence for an association between CRP and negative affect. These results suggest that dysregulated immune physiology may impact a broad range of trans-diagnostic affective symptoms.
Developing a machine learning model for predicting 30-day major adverse cardiac and cerebrovascular events in patients undergoing noncardiac surgery
Authors: Suh Jung-Won , Kwun Ju-Seung , Ahn Houng-beom , Kang Si-Hyuck , Yoo Sooyoung , Kim Seok , Song Wongeun , Hyun Junho , Oh Ji Seon , Baek Gakyoung 
Published: August 2024

Abstract

To reduce unnecessary delays and manage medical costs efficiently for low-risk patients undergoing noncardiac surgery, we developed a predictive model for major adverse cardiac and cerebrovascular events (MACCE) using the OMOP Common Data Model (CDM) and machine learning algorithms. This retrospective study collected data from 46,225 patients at Seoul National University Bundang Hospital and 396,424 patients at Asan Medical Center. Patients aged 65 or older undergoing non-cardiac, non-emergency surgeries with at least 30 days of observation were included. Machine learning models were developed using the OHDSI open-source patient-level prediction package in R version 4.1.0. All models outperformed the Revised Cardiac Risk Index (RCRI), with the random forest model achieving an AUROC of 0.817 in external validation and demonstrating moderate calibration. Key predictors included previous diagnoses and laboratory measurements, highlighting their importance in perioperative risk prediction. Our model shows promise for improving clinical practice and reducing medical costs.
Comparative study of Cognitive-Behavioral Therapy with Pharmacotherapy for Persistent Postural-Perceptual Dizziness: A Pilot Study
Authors: Kang Jeongae , Tanaka Tsunehiko , Yagi Chihiro , Horii Arata 
Published: August 2024

Abstract

We conducted a non-randomized, open-label, parallel-group study on patients with persistent postural-perceptual dizziness (PPPD) to compare cognitive-behavioral therapy ([CBT] group, n = 20) received a 6-session CBT program with pharmacotherapy ([PT] group, n = 22) received serotonin-related antidepressants. Outcomes were assessed using the Dizziness Handicap Inventory (DHI), the Hospital Anxiety and Depression Scale (HADS), and the Niigata PPPD Questionnaire (NPQ). Assessments were performed before and 3 months post-treatment. There were no significant differences in patients background such as sex, age, precipitating conditions, and vestibular function tests, however, illness duration was significantly longer in the CBT group than the PT group. Both CBT and PT significantly lowered the levels of dizziness-related handicaps in daily life (DHI), depression (HADS-D), anxiety (HADS-A), and symptom exacerbations by triggers (NPQ), while two-way ANOVA revealed no treatment x time interaction. Nonetheless, CBT yielded a larger Hedges’ g effect size in improving DHI, HADS-A and NPQ than PT. While PT is reportedly sometimes discontinued due to adverse events such as vomiting, all patients in the CBT group could complete the program with no adverse effects. CBT is a potential therapy for PPPD which is safe and effective as equal to or greater than PT even for patients with prolonged illness. Clinical trial registration numbers and date of registration: UMIN-CTR: UMIN000054701, 18/06/2024
Identification of Plasma Protein Targets in Sepsis through Integrative Genomic and Proteomic Analysis
Authors: Chen Xu , Shi Kaijia , Zheng Yi , Tian Weikun , Qi Yuanjun , Zhang Na , Wang Xueyuan , Tian Xinghan 
Published: August 2024

Abstract

Objective: To identify plasma protein targets with potential therapeutic implications for sepsis using genetic and proteomic data integration.
Methods: We explored plasma proteomics data from deCODE Genetics, which measured 4,719 proteins in 35,559 Icelandic individuals, and genomics data on sepsis from 361,141 participants in the UK Biobank. Utilizing cis-pQTLs as instrumental variables, we conducted Mendelian Randomization to identify circulating plasma proteins causally linked to the risk of sepsis. After adjusting for false discovery rate (FDR), the associated proteins were further analyzed through Protein-Protein Interaction analysis and Bayesian colocalization. Ultimately, protein exhibiting the strongest colocalization evidence was subjected to molecular docking to identify targeted therapeutics for sepsis.
Results: From 229 initial proteins, 27 significant proteins pass FDR correction. Among these, 11 proteins showed positive associations and 16 demonstrated negative associations with sepsis risk. Protein-Protein Interactions analysis indicated strong interactions among 15 proteins related to immune and inflammatory responses. Bayesian colocalization analysis identified GFER protein as having the strongest evidence of colocalization. GFER protein demonstrated stable binding with nicotinamide, positioning them as high-potential drug targets.
Conclusion: Our results highlight the effectiveness of integrating genetic and proteomic data to identify new therapeutic targets for sepsis. GFER protein is particularly promising candidates for further therapeutic development.
Comprhensive Pan-cancer Analysis of MEX3A Gene and Its Related Mechanism
Authors: Wang Zhiwei , Feng Eryou , Lin Dasheng , Lin Wentao 
Published: August 2024

Abstract

Objective
Cancer remains a significant global health challenge with a high mortality rate. MEX3A is implicated in tumor development and progression by regulating cell proliferation and migration. This study aims to elucidate the impact of MEX3A expression on osteosarcoma and unravel the pathogenic mechanisms of MEX3A in pan-cancer.
Methods
A pan-cancer analysis of MEX3A was performed using data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The study examined MEX3A expression differences, the relationship between expression levels and patient survival, genetic alterations, immune infiltration, and associated cellular pathways to understand MEX3A's roles and underlying mechanisms in cancer pathogenesis and prognosis.
Results
The analysis results of different datasets showed that low MEX3A expression was associated with an inferior prognosis in leukemia (LAML), while high MEX3A expression was associated with poorer prognosis in adrenocortical cancer (ACC), kidney chromophobe (KICH), liver hepatocellular carcinoma (LIHC), mesothelioma (MESO), pheochromocytoma and paraganglioma (PCPG), sarcoma (SARC), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC). The expression of MEX3A was positively correlates with all six immune cell types infiltration in ACC, SARC, and THCA. MEX3A expression has a negative correlation with monocyte and M1 macrophage infiltration in SARC, whereas a positive correlation was observed with M0 macrophages, plasma B cells, and naive B cells. Spearman's rank correlation coefficient results indicated a positive correlation between MEX3A expression and TMB in thymoma (THYM), BRCA, KICH, LAML, LUAD, and PRAD, while a negative correlation was observed in UCS, COAD, and esophageal carcinoma (ESCA). The correlation between MEX3A expression and MSI revealed a positive correlation with UVM, BLCA, CESC, KICH, LIHC, and LUSC, while a negative correlation with MSI was observed in COAD, lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), BRCA, LUAD, and LAML. MEX3A expression is positively correlated with five DNA repair genes in non-Hodgkin's squamous cell carcinoma (NHSC), KIRP, LUAD, OV, PRAD, and UCEC, with a high level of significance. And in SARC, MEX3A expression showed a strong correlation with MSH2, MSH6, and PMS2 and a moderate correlation with EPCAM. Enrichment analysis showed that MEX3A was highly expressed in the SPLICEOSOME, AMINOACYL-tRNA BIOSYNTHESIS, ASTHMA, COMPLEMENT AND COAGULATION CASCADES, INTESTINAL IMMUNE NETWORK FOR IgA PRODUCTION, and AUTOIMMUNE THYROID DISEASE pathways.
Conclusion
Elevated MEX3A expression correlates with poor prognosis in osteosarcoma, breast invasive carcinoma (BRCA), and lung adenocarcinoma (LUAD), underscoring its potential as a prognostic marker in these cancers.
Evening Exercise Does Not Affect Sleep Health: A Systematic Review and Meta-Analysis
Authors: Siranart Noppachai , Tokavanich Nithi , Keawkanha Ponthakorn , Sowalertrat Walit , Pajareya Patavee , Joensahakij Setthawut , Prasitlumkum Narut , Navaravong Leenhapong , Cheungpasitporn Wisit , Chokesuwattanaskul Ronpichai 
Published: August 2024

Abstract

Background: The relationships between exercise timing and health outcomes including anthropometric indices, metabolic markers, and sleep health have not been clearly established. To gain better insights, we performed the meta-analysis to determine the effects of morning versus evening exercise on various health outcomes.
Methods: We collected studies from MEDLINE, EMBASE, and Cochrane databases from inception to March 2023 to perform a meta-analysis. Exercise is defined as performing activity at least moderate intensity. For the outcomes of interest, the mean differences (MD) were employed using a random-effects model, the generic inverse variance method of DerSimonian and Laird.
Results: A total of 716 patients were analyzed from 14 studies including randomized controlled trial and observational studies. We found no significant associations between exercise timing and several health-related parameters. Specifically, the data showed no notable differences in body weight (MD: -0.12 kg [95% CI −0.81 to 0.58]) and BMI (MD: -0.07 kg/m2 [95% CI −0.46 to 0.32]) when comparing morning and evening exercise sessions. Similarly, sleep latency was not significantly affected by exercise timing (MD: -2.24 minutes [95% CI −5.83 to 1.35]). Furthermore, changes in metabolic biomarkers, including LDL (MD: -6.64 mg/dL [95% CI −20.08 to 6.79]) and HDL (MD: 0.09 mg/dL [95% CI 0 to 0.17]), were not significantly associated with the timing of exercise.
Conclusion: This meta-analysis found no significant relationship between exercise timing and health outcomes including anthropometric indices, metabolic markers, and sleep health. While considered as hypothesis generating, timing of exercise should equally prune patients’ health outcomes.
Urban health state estimation based on multi-source heterogeneous data of traffic and air pollution
Authors: Sun Rongrong , Liu Lingfei , Zheng Chao , Tang Qiankun 
Published: August 2024

Abstract

In order to comprehend the impact of urban health on individuals' lives, behaviors, and choices in metropolitan areas, we suggest a framework known as the Health of the Public (HOTP) framework. This framework utilizes diverse sources of urban data to identify urban health. Firstly, we carry out pre-processing and feature processing of air quality data. Subsequently, we pre-process traffic congestion data and process its relevant features. Finally, we integrate the results from both data feature extraction processes to reason and evaluate the urban health status. The predicted results of our HOTP method corresponded with the actual urban operations. This demonstrates that our methodology proficiently integrates metropolitan commute information and atmospheric contamination data to accurately evaluate the overall health level of the city.
Predicting antipsychotic responsiveness using a machine learning classifier trained on plasma levels of inflammatory markers in schizophrenia
Authors: Lee Jimmy , Yee Jie Yin , Phua Ser-Xian , See Yuen Mei , Andiappan Anand , Goh Wilson 
Published: August 2024

Abstract

Background
In this research study, we apply machine learning techniques to navigate the multifaceted landscape of schizophrenia. Our method entails the development of predictive models, emphasizing peripheral inflammatory biomarkers, which are classified into treatment response subgroups: antipsychotic-responsive, clozapine-responsive, and clozapine-resistant.
Methods
The study comprises 146 schizophrenia patients (49 antipsychotics-responsive, 68 clozapine-responsive, 29 clozapine-resistant) and 49 healthy controls. Protein levels of immune biomarkers were quantified using the Olink Target 96 Inflammation Panel. To predict labels, a support vector machine classifier is trained on the Olink data matrix and evaluated via leave-one-out cross-validation. Associated protein biomarkers are identified via recursive feature elimination.
Findings
We constructed three separate predictive models for binary classification: one to discern healthy controls from individuals with schizophrenia (AUC = 0.74), another to differentiate individuals who were responsive to antipsychotics (AUC = 0.88), and a third to distinguish treatment-resistant individuals (AUC = 0.78). Employing machine learning techniques, we identified features capable of distinguishing between treatment response subgroups.
Interpretation
In this study, support vector machine demonstrates the power of machine learning to uncover subtle signals often overlooked by traditional statistics. Unlike t-tests, it handles multiple features simultaneously, capturing complex data relationships. Chosen for simplicity, robustness, and reliance on strong feature sets, its integration with artificial intelligence techniques like SHapely Additive exPlanations enhances model interpretability, especially for biomarker screening. This study highlights the potential of integrating machine learning techniques in clinical practice. Not only does it deepen our understanding of schizophrenia's heterogeneity, but it also holds promise for enhancing predictive accuracy, thereby facilitating more targeted and effective interventions in the treatment of this complex mental health disorder.
Increase in Religiosity Over Time is Associated with Reduced Cystatin C Levels Among U.S. Adults
Authors: Boateng Augustine Cassis Obeng , Britt Katherine Caroll , Sebu Joshua , Oh Hayoung , Lekwauwa Ruby , Doolittle Benjamin 
Published: August 2024

Abstract

Background: Religiosity is associated with better health, yet the underlying mechanism is unclear. Cystatin C is a marker of oxidative stress, vascular dysfunction, and inflammation. We assessed the influence of religiosity on cystatin C levels among participants in the Health and Retirement Study (HRS).
Method: This is a retrospective secondary analysis of the HRS data from 2006 (wave 1), 2010 (wave 2), and 2014 (wave 3), a nationally representative study from the United States. A total of 2,358 participants with complete cases were included in the panel data analysis examining change in religiosity on cystatin C levels. Religiosity and cystatin C were assessed using the Brief Multidimensional Measure of Religiousness/Spirituality scale and serum cystatin C levels, respectively, controlling for covariates.
Results: Approximately 59.1% of the participants were female, 87.4% were white, and 12.6% belonged to other races. The average years of education was 13.2 years. From 2006 to 2014, mean cystatin C levels increased from 1.016mg/L to 1.215mg/L, indicating worsening kidney function over time. In the final model, an increase in religiosity over time was negatively associated with cystatin C levels (B: -0.003, 95% CI: -0.005, -0.001). Higher education (B: -0.011, 95% CI: -0.018, -0.005) and being married (B: -0.039, 95% CI: -0.077, -0.001) were negatively associated with cystatin C levels. In contrast, diagnoses of hypertension (B: 0.094,95% CI: 0.064, 0.125), diabetes (B: 0.109, 95% CI: 0.067, 0.152), and overweight (B=0.072, 95% CI: 0.041, 0.102) were positively associated with cystatin C levels. No observed relationship between sex, race, and cystatin c levels.
Conclusion: Increased religiosity is associated with lower cystatin C levels and may play a role in modulating the course of illnesses to which oxidative stress, vascular dysfunction, or inflammation may contribute. This study will enhance our understanding of how religiosity might influence kidney health and quality of life.
The Model Predict Acceptance and Rejection of Babies Among Postpartum Mothers
Authors: Manurung Suryani , Marianthi Dewi , Hasnani Fenti , Rahyani Ni Komang Yuni 
Published: August 2024

Abstract

Background
Infant abandonment throughout the world, especially in Southeast Asia, is relatively high. The stress of life changes a mother's behaviour negatively, resulting in the baby being abandoned.
Objective
This research aims to develop a model of postnatal care to predict the rejection and acceptance of the baby.
Method
Cross-sectional design. The sample was 205 respondents. Likert scale instrument with favourable and unfavourable items. Inclusion criteria are mothers who have babies aged 0–40 days. Analysis used IBM Statistics 22, Lisrel 8.80 software. Statistical analysis: univariate, explanatory factor analysis (EFA), confirmatory factor analysis (CFA), validity, reliability, cut-of-point, and categorization.
Results
Results: The average age of women was 26.63 (SD = 3.9), with a range of 19–39 years. EFA results in a Bartlett's Sphericity value < .05 and the correlation between variables is quite large. The total variance explained is seven indicator variables out of 77.71 totalling 40 items. Analysis with CFA on 40 items and meeting the goodness of fit criteria resulted in 18 items or 4 indicator variables, consisting of Emotional Stability (4 items), Personality (Active & passive-aggressive) (4 items), and Responsiveness emotional (6 items), responsiveness/Neglect (4 items). Construct reliability of the four indicator variables is .9820 (> .70). Variance extracted .75 (> .5). 18 items were declared valid and reliable. The cut-of-point score with 3 categories, namely low score < 42 (baby acceptance), medium score 42 ≤ X < 66 (risk of baby rejection), and high score ≥ 66 (baby rejection).
Conclusion
Model of mother-infant parenting patterns during the postpartum period which describes the baby's acceptance and rejection was built with 18 items and 4 indicator variables. Strategies to strengthen the mother's ability to caring the baby from preconception until the postpartum period.