bluuebunny/crossref_metadata_2025_split huggingface dataset:
Organoid models: the future companions of personalized drug development
Authors: Piraino Francesco , Costa Mariana , Meyer Marine , Cornish Georgina , Ceroni Camilla , Garnier Virginie , Hoehnel-Ka Sylke , Brandenberg Nathalie 
Published: April 2024

Abstract
High failure rates of the current drug development process are driving exemplary changes toward methodologies centered on human disease in-vitro modeling. Organoids are self-organized tissue sub-units resembling their organ of origin and are widely acknowledged for their unique potential in recapitulating human physio-pathological mechanisms. They are transformative for human health by becoming the platform of choice to probe disease mechanisms and advance new therapies. Furthermore, the compounds’ validation as therapeutics represents another point of the drug development pipeline where organoids may provide key understandings and help pharma organizations replace or reduce animal research. In this review, we focus on gastrointestinal organoid models, which are currently the most advanced organoid models in drug development. We focus on experimental validations of their value, and we propose avenues to enhance their use in drug discovery and development, as well as precision medicine and diagnostics.
Current carrying tribological properties of multi arc ion plated titanium nitride doped silver coating
Authors: Hao Wu , Xia Yanqiu , Yi Zhang 
Published: April 2024

Abstract
Sliding electrical contact materials play a crucial role in the transmission and conversion of electrical energy, but due to various factors such as force, electricity, and heat, the interface exhibits complex wear behavior. A single solid or liquid lubrication system can no longer meet the growing performance requirements of current carrying tribology. In this study, a TiN-Ag coating was prepared using multi arc ion plating technology, and a solid–liquid composite lubrication system was formed with ionic liquid and polyurea grease, respectively. Through current carrying friction and wear tests, their tribological properties, electrical contact resistance(ECR) values, and stability were tested, and compared with the results obtained during dry friction. The coating and worn surfaces were analyzed using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). The results indicated that compared with dry friction, TiN-Ag coatings lubricated with ionic liquids and polyurea grease showed higher friction reduction, wear resistance, and conductivity, especially the synergistic effect between ionic liquids and coatings is prominent. The behavior of ionic liquids under voltage was analyzed, and it was found that ionic liquids formed a physical adsorption film composed of a mixture of anions and cations on the worn surface. The ordered layered structure improved the tribological performance of the system.
Reductions in World Health Organization risk drinking level are associated with improvements in sleep problems among individuals with alcohol use disorder
Authors: Garcia Christian C , Richards Dylan K , Tuchman Felicia R , Hallgren Kevin A , Kranzler Henry R , Aubin Henri-Jean , O’Malley Stephanie S , Mann Karl , Aldridge Arnie , Hoffman Michaela , Anton Raymond F , Witkiewitz Katie 
Published: April 2024

Abstract

Aims
Among individuals with alcohol use disorder (AUD), sleep disturbances are pervasive and contribute to the etiology and maintenance of AUD. However, despite increased attention toward the relationship between alcohol use and sleep, limited empirical research has systematically examined whether reductions in drinking during treatment for AUD are associated with improvements in sleep problems.

Methods
We used data from a multisite, randomized, controlled trial that compared 6 months of treatment with gabapentin enacarbil extended-release with placebo for adults with moderate-to-severe AUD (N = 346). The Timeline Follow-back was used to assess WHO risk drinking level reductions and the Pittsburgh Sleep Quality Index was used to assess sleep quality over the prior month at baseline and the end of treatment.

Results
Sleep problem scores in the active medication and placebo groups improved equally. Fewer sleep problems were noted among individuals who achieved at least a 1-level reduction (B = −0.99, 95% confidence interval (CI) [−1.77, −0.20], P = .014) or at least a 2-level reduction (B = −0.80, 95% CI [−1.47, −0.14], P = .018) in WHO risk drinking levels at the end of treatment. Reductions in drinking, with abstainers excluded from the analysis, also predicted fewer sleep problems at the end of treatment (1-level: B = −1.01, 95% CI [−1.83, −0.20], P = .015; 2-level: B = −0.90, 95% CI [−1.59, −0.22], P = .010).

Conclusions
Drinking reductions, including those short of abstinence, are associated with improvements in sleep problems during treatment for AUD. Additional assessment of the causal relationships between harm-reduction approaches to AUD and improvements in sleep is warranted.
SEGCECO: Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication
Authors: Vasighizaker Akram , Hora Sheena , Zeng Raymond , Rueda Luis 
Published: April 2024

Abstract
Recent advances in single-cell RNA sequencing technology have eased analyses of signaling networks of cells. Recently, cell–cell interaction has been studied based on various link prediction approaches on graph-structured data. These approaches have assumptions about the likelihood of node interaction, thus showing high performance for only some specific networks. Subgraph-based methods have solved this problem and outperformed other approaches by extracting local subgraphs from a given network. In this work, we present a novel method, called Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication (SEGCECO), which uses an attributed graph convolutional neural network to predict cell–cell communication from single-cell RNA-seq data. SEGCECO captures the latent and explicit attributes of undirected, attributed graphs constructed from the gene expression profile of individual cells. High-dimensional and sparse single-cell RNA-seq data make converting the data into a graphical format a daunting task. We successfully overcome this limitation by applying SoptSC, a similarity-based optimization method in which the cell–cell communication network is built using a cell–cell similarity matrix which is learned from gene expression data. We performed experiments on six datasets extracted from the human and mouse pancreas tissue. Our comparative analysis shows that SEGCECO outperforms latent feature-based approaches, and the state-of-the-art method for link prediction, WLNM, with 0.99 ROC and 99% prediction accuracy. The datasets can be found at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84133 and the code is publicly available at Github https://github.com/sheenahora/SEGCECO and Code Ocean https://codeocean.com/capsule/8244724/tree.
KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition
Authors: Wang Xinyan , Yang Kuo , Jia Ting , Gu Fanghui , Wang Chongyu , Xu Kuan , Shu Zixin , Xia Jianan , Zhu Qiang , Zhou Xuezhong 
Published: April 2024

Abstract
The accurate identification of disease-associated genes is crucial for understanding the molecular mechanisms underlying various diseases. Most current methods focus on constructing biological networks and utilizing machine learning, particularly deep learning, to identify disease genes. However, these methods overlook complex relations among entities in biological knowledge graphs. Such information has been successfully applied in other areas of life science research, demonstrating their effectiveness. Knowledge graph embedding methods can learn the semantic information of different relations within the knowledge graphs. Nonetheless, the performance of existing representation learning techniques, when applied to domain-specific biological data, remains suboptimal. To solve these problems, we construct a biological knowledge graph centered on diseases and genes, and develop an end-to-end knowledge graph completion framework for disease gene prediction using interactional tensor decomposition named KDGene. KDGene incorporates an interaction module that bridges entity and relation embeddings within tensor decomposition, aiming to improve the representation of semantically similar concepts in specific domains and enhance the ability to accurately predict disease genes. Experimental results show that KDGene significantly outperforms state-of-the-art algorithms, whether existing disease gene prediction methods or knowledge graph embedding methods for general domains. Moreover, the comprehensive biological analysis of the predicted results further validates KDGene’s capability to accurately identify new candidate genes. This work proposes a scalable knowledge graph completion framework to identify disease candidate genes, from which the results are promising to provide valuable references for further wet experiments. Data and source codes are available at https://github.com/2020MEAI/KDGene.
Rationale and design of the Innsbruck Diabetic Kidney Disease Cohort (IDKDC)—a prospective study investigating etiology and progression of early-stage chronic kidney disease in type 2 diabetes
Authors: Plattner Clemens , Sallaberger Sebastian , Bohn Jan-Paul , Zavadil Claudia , Keller Felix , Soleiman Afschin , Tiefenthaler Martin , Mayer Gert , Pirklbauer Markus 
Published: April 2024

ABSTRACT

Background
The development of chronic kidney disease (CKD) in about 20%–40% of patients with type 2 diabetes (T2D) aggravates cardiovascular morbidity and mortality. Pathophysiology is of increasing relevance for individual management and prognosis, though it is largely unknown among T2D patients with CKD as histologic work-up is not routinely performed upon typical clinical presentation. However, as clinical parameters do not appropriately reflect underlying kidney pathology, reluctance regarding timely histologic assessment in T2D patients with CKD should be critically questioned. As the etiology of CKD in T2D is heterogeneous, we aim to assess the prevalence and clinical disease course of typical diabetic vs atypical/non-specific vs non-diabetic vs coexisting kidney pathologies among T2D patients with mild-to-moderate kidney impairment [KDIGO stage G3a/A1–3 or G2/A2–3; i.e. estimated glomerular filtration rate (eGFR) 59–45 mL/min irrespective of albuminuria or eGFR 89–60 mL/min and albuminuria &gt;30 mg/g creatinine].

Methods
The Innsbruck Diabetic Kidney Disease Cohort (IDKDC) study aims to enroll at least 65 T2D patients with mild-to-moderate kidney impairment to undergo a diagnostic kidney biopsy. Six-monthly clinical follow-ups for up to 5 years will provide clinical and laboratory data to assess cardio-renal outcomes. Blood, urine and kidney tissue specimen will be biobanked to identify diagnostic and prognostic biomarkers.

Conclusions
While current risk assessment is primarily based on clinical parameters, our study will provide the scientific background for a potential change of the diagnostic standard towards routine kidney biopsy and clarify its role for individual risk prediction regarding cardio-renal outcome in T2D patients with mild-to-moderate kidney impairment.
Racial and Ethnic Disparities in Health-Related Outcomes in Crohn’s Disease: Results From the National Health and Wellness Survey
Authors: Burbage Sabree C , Krupsky Kathryn L , Cambron-Mellott M Janelle , Way Nate , Patel Aarti A , Liu Julia J 
Published: April 2024

Abstract

Background
Crohn’s disease (CD) is a chronic inflammatory condition affecting the entire gastrointestinal tract that is associated with significant humanistic, clinical, and economic burdens. Few studies have assessed the association between CD severity and patient-reported outcomes (PROs), healthcare resource utilization (HCRU), and medical costs; even fewer have examined differences in disease outcomes among patients of various racial/ethnic groups.

Methods
In this cross-sectional study, sociodemographic data, PROs, and economic outcomes for participants with self-reported CD were collected from the National Health and Wellness Survey (2018–2020). Multivariable analyses were used to assess the association of CD severity and race/ethnicity with health-related quality of life (HRQoL), work productivity and activity impairment (WPAI), HCRU, and medical costs.

Results
Analyses included 1077 participants with CD (818 non-Hispanic White, 109 non-Hispanic Black, and 150 Hispanic). Participants with self-reported moderate/severe CD reported significantly worse HRQoL and WPAI, greater HCRU, and higher medical costs than those with self-reported mild CD. Non-Hispanic Black participants reported better HRQoL and fewer healthcare provider visits than non-Hispanic White participants. There were no significant differences in PROs between non-Hispanic White and Hispanic groups. Interactions between race/ethnicity and CD severity emerged for some, but not all groups: Specifically, non-Hispanic Black participants with moderate/severe CD reported greater absenteeism and more gastroenterologist visits than non-Hispanic Black participants with mild CD.

Conclusions
Participants with moderate/severe CD reported worse PROs, greater HCRU, and higher medical costs than those with mild CD. Additionally, racial/ethnic differences were found across several HCRU and economic outcomes. Further research is needed to better understand factors contributing to burden among patients with varying CD severity across racial/ethnic groups.
COVID-19 acts like a stress test, uncovering the vulnerable part of the human body: a retrospective study of 1640 cases in China
Authors: He Tian-Yi , Zhou Hong-Yu , Zhu Ming-Hui , Zhang Ji-Li 
Published: April 2024

Abstract

Background
Since the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection exhibits multi-organ damage with diverse complications, the correlation between age, gender, medical history and clinical manifestations of novel coronavirus disease 2019 (COVID-19) patients was investigated.

Methods
1640 patients who were infected with SARS-CoV-2 and hospitalized at the First Affiliated Hospital of Ningbo University from 22 December 2022 to 1 March 2023 were categorized and analysed. Normal distribution test and variance homogeneity test were performed. Based on the test results, one-way analysis of variance, Pearson's chi-squared test and logistic regression analysis were conducted in the study.

Results
According to the ANOVA, there was a significant difference in the age distribution (P = .001) between different clinical presentations, while gender did not (P = .06). And regression analysis showed that age, hypertension, atherosclerosis and cancer were significant hazard factors for the development of predominant clinical manifestations in patients hospitalized with novel COVID-19. Additionally, infection with SARS-CoV-2 has the potential to exacerbate the burden on specific diseased or related organs.

Conclusion
The elderly who are infected with SARS-CoV-2 ought to be treated with emphasis not only on antiviral therapy but also on individualized treatment that takes their medical history and comorbidities into account.
The Longitudinal Dyadic Associations Between Social Participation and Cognitive Function in Older Chinese Couples
Authors: Hou Jianhua , Chen Tianyong , Yu Nancy Xiaonan 
Published: April 2024

Abstract

Objectives
Based on the “linked lives” tenant of the life course perspective, this longitudinal study aims to examine the actor and partner effects of social participation on cognitive function in older Chinese couples.

Methods
A total of 1,706 couples aged 60 and older were included in the final analyses. Social participation was measured using 2 questions regarding types of activities and frequency. Cognitive function was measured using a combination of memory, orientation, visuoconstruction, attention, and calculation. The lagged-dependent APIM was used to model the dyadic associations between social participation and cognitive function.

Results
The time-averaged actor effects of both husbands’ and wives’ social participation on their own cognitive function were significant (p &lt; .001 for both). The time-averaged partner effect of husbands’ social participation on wives’ cognitive function was significant (p &lt; .001) but the reverse—the effect of wives’ social participation on husbands’ cognitive function—was not (p = .381). The time-specific actor and partner effects were not significant (p &gt; .05 for all).

Discussion
Our findings indicate an asymmetrical pattern of actor–partner interdependence, where husbands’ social participation may affect their wives’ cognitive function on average, but wives’ social participation does not affect their husbands’ cognitive function. Clinical practitioners should invite both partners, especially husbands, to participate in social participation interventions to facilitate crossover benefits for wives. Moreover, policymakers should build more facilities to encourage older couples to engage in social activities to prevent cognitive decline.
Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma
Authors: Zhu Jingjing , Wu Ke , Liu Shuai , Masca Alexandra , Zhong Hua , Yang Tai , Ghoneim Dalia H , Surendran Praveen , Liu Tanxin , Yao Qizhi , Liu Tao , Fahle Sarah , Butterworth Adam , Alam Md Ashad , Vadgama Jaydutt V , Deng Youping , Deng Hong-Wen , Wu Chong , Wu Yong , Wu Lang 
Published: April 2024

Abstract

Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy, largely due to the paucity of reliable biomarkers for early detection and therapeutic targeting. Existing blood protein biomarkers for PDAC often suffer from replicability issues, arising from inherent limitations such as unmeasured confounding factors in conventional epidemiologic study designs. To circumvent these limitations, we use genetic instruments to identify proteins with genetically predicted levels to be associated with PDAC risk. Leveraging genome and plasma proteome data from the INTERVAL study, we established and validated models to predict protein levels using genetic variants. By examining 8,275 PDAC cases and 6,723 controls, we identified 40 associated proteins, of which 16 are novel. Functionally validating these candidates by focusing on 2 selected novel protein-encoding genes, GOLM1 and B4GALT1, we demonstrated their pivotal roles in driving PDAC cell proliferation, migration, and invasion. Furthermore, we also identified potential drug repurposing opportunities for treating PDAC.

Significance
PDAC is a notoriously difficult-to-treat malignancy, and our limited understanding of causal protein markers hampers progress in developing effective early detection strategies and treatments. Our study identifies novel causal proteins using genetic instruments and subsequently functionally validates selected novel proteins. This dual approach enhances our understanding of PDAC etiology and potentially opens new avenues for therapeutic interventions.
Urban subsurface exploration improved by denoising of virtual shot gathers from distributed acoustic sensing ambient noise
Authors: Ehsaninezhad Leila , Wollin Christopher , Rodríguez Tribaldos Verónica , Schwarz Benjamin , Krawczyk Charlotte M 
Published: April 2024

SUMMARY
Ambient noise tomography on the basis of distributed acoustic sensing (DAS) deployed on existing telecommunication networks provides an opportunity to image the urban subsurface at regional scales and high-resolution. This capability has important implications in the assessment of the urban subsurface’s potential for sustainable and safe utilization, such as geothermal development. However, extracting coherent seismic signals from the DAS ambient wavefield in urban environments at low cost remains a challenge. One obstacle is the presence of complex sources of noise in urban environments, which may not be homogeneously distributed. Consequently, long recordings are required for the calculation of high-quality virtual shot gathers, which necessitates significant time and computational cost. In this paper, we present the analysis of 15 d of DAS data recorded on a pre-existing fibre optic cable (dark fibres), running along an 11-km-long major road in urban Berlin (Germany), hosting heavy traffic including vehicles and trains. To retrieve virtual shot gathers, we apply interferometric analysis based on the cross-correlation approach where we exclude low-quality virtual shot gathers to increase the signal-to-noise ratio of the stacked gathers. Moreover, we modify the conventional ambient noise interferometry workflow by incorporating a coherence-based enhancement approach designed for wavefield data recorded with large-N arrays. We then conduct multichannel analysis of surface waves to retrieve 1-D velocity models for two exemplary fibre subsegments, and compare the results of the conventional and modified workflows. The resulting 1-D velocity models correspond well with available lithology information. The modified workflow yields improved dispersion spectra, particularly in the low-frequency band (&lt;1 Hz) of the signal. This leads to an increased investigation depth along with lower uncertainties in the inversion result. Additionally, these improved results were achieved using significantly less data than required using conventional approaches, thus opening the opportunity for shortening required acquisition times and accordingly lowering costs.
Social determinants of health Z-code documentation practices in mental health settings: a scoping review
Authors: Hendricks-Sturrup Rachele M , Yankah Sandra E , Lu Christine Y 
Published: April 2024

Abstract
Mental health remains an urgent global priority, alongside efforts to address underlying social determinants of health (SDoH) that contribute to the onset or exacerbate mental illness. SDoH factors can be captured in the form of International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM), SDoH Z codes. In this scoping review, we describe current SDoH Z-code documentation practices, with a focus on mental health care contexts. Among 2 743 061 374 health care encounters noted across 12 studies in the United States, SDoH Z-code documentation rates ranged from 0.5% to 2.4%. Documentation often involved patients under 64 years of age who are publicly insured and experience comorbidities, including depression, bipolar disorder and schizophrenia, chronic pulmonary disease, and substance abuse disorders. Documentation varied across hospital types, number of beds per facility, patient race/ethnicity, and geographic region. Variation was observed regarding patient sex/gender, although SDoH Z codes were more frequently documented for males. Documentation was most observed in government, nonfederal, and private not-for-profit hospitals. From these insights, we offer policy and practice recommendations, as well as considerations for patient data privacy, security, and confidentiality, to incentivize more routine documentation of Z codes to better assist patients with complex mental health care needs.