bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification of the molecular regulation of differences in lipid deposition in dedifferentiated preadipocytes from different chicken tissues
Authors: Ma Zheng , Luo Na , Liu Lu , Cui Huanxian , Li Jing , Xiang Hai , Kang Huimin , Li Hua , Zhao Guiping 
Published: April 2021

Abstract
Background
A body distribution with high intramuscular fat and low abdominal fat is the ideal goal for broiler breeding. Preadipocytes with different origins have differences in terms of metabolism and gene expression. The transcriptome analysis performed in this study of intramuscular preadipocytes (DIMFPs) and adipose tissue-derived preadipocytes (DAFPs) aimed to explore the characteristics of lipid deposition in different chicken preadipocytes by dedifferentiation in vitro.

Results
Compared with DAFPs, the total lipid content in DIMFPs was reduced (P < 0.05). Moreover, 72 DEGs related to lipid metabolism were screened, which were involved in adipocyte differentiation, fatty acid transport and fatty acid synthesis, lipid stabilization, and lipolysis. Among the 72 DEGs, 19 DEGs were enriched in the PPAR signaling pathway, indicating its main contribution to the regulation of the difference in lipid deposition between DAFPs and DIMFPs. Among these 19 genes, the representative APOA1, ADIPOQ, FABP3, FABP4, FABP7, HMGCS2, LPL and RXRG genes were downregulated, but the ACSL1, FABP5, PCK2, PDPK1, PPARG, SCD, SCD5, and SLC27A6 genes were upregulated (P < 0.05 or P < 0.01) in the DIMFPs. In addition, the well-known pathways affecting lipid metabolism (MAPK, TGF-beta and calcium) and the pathways related to cell communication were enriched, which may also contribute to the regulation of lipid deposition. Finally, the regulatory network for the difference in lipid deposition between chicken DAFPs and DIMFPs was proposed based on the above information.

Conclusions
Our data suggested a difference in lipid deposition between DIMFPs and DAFPs of chickens in vitro and proposed a molecular regulatory network for the difference in lipid deposition between chicken DAFPs and DIMFPs. The lipid content was significantly increased in DAFPs by the direct mediation of PPAR signaling pathways. These findings provide new insights into the regulation of tissue-specific fat deposition and the optimization of body fat distribution in broilers.
Type I error control for cluster randomized trials under varying small sample structures
Authors: Nugent Joshua R. , Kleinman Ken P. 
Published: April 2021

Abstract
Background
Linear mixed models (LMM) are a common approach to analyzing data from cluster randomized trials (CRTs). Inference on parameters can be performed via Wald tests or likelihood ratio tests (LRT), but both approaches may give incorrect Type I error rates in common finite sample settings. The impact of different combinations of cluster size, number of clusters, intraclass correlation coefficient (ICC), and analysis approach on Type I error rates has not been well studied. Reviews of published CRTs find that small sample sizes are not uncommon, so the performance of different inferential approaches in these settings can guide data analysts to the best choices.

Methods
Using a random-intercept LMM stucture, we use simulations to study Type I error rates with the LRT and Wald test with different degrees of freedom (DF) choices across different combinations of cluster size, number of clusters, and ICC.

Results
Our simulations show that the LRT can be anti-conservative when the ICC is large and the number of clusters is small, with the effect most pronouced when the cluster size is relatively large. Wald tests with the between-within DF method or the Satterthwaite DF approximation maintain Type I error control at the stated level, though they are conservative when the number of clusters, the cluster size, and the ICC are small.

Conclusions
Depending on the structure of the CRT, analysts should choose a hypothesis testing approach that will maintain the appropriate Type I error rate for their data. Wald tests with the Satterthwaite DF approximation work well in many circumstances, but in other cases the LRT may have Type I error rates closer to the nominal level.
Deep generative models in DataSHIELD
Authors: Lenz Stefan , Hess Moritz , Binder Harald 
Published: April 2021

AbstractBackgroundThe best way to calculate statistics from medical data is to use the data of individual patients. In some settings, this data is difficult to obtain due to privacy restrictions. In Germany, for example, it is not possible to pool routine data from different hospitals for research purposes without the consent of the patients.MethodsThe DataSHIELD software provides an infrastructure and a set of statistical methods for joint, privacy-preserving analyses of distributed data. The contained algorithms are reformulated to work with aggregated data from the participating sites instead of the individual data. If a desired algorithm is not implemented in DataSHIELD or cannot be reformulated in such a way, using artificial data is an alternative. Generating artificial data is possible using so-called generative models, which are able to capture the distribution of given data. Here, we employ deep Boltzmann machines (DBMs) as generative models. For the implementation, we use the package “BoltzmannMachines” from the Julia programming language and wrap it for use with DataSHIELD, which is based on R.ResultsWe present a methodology together with a software implementation that builds on DataSHIELD to create artificial data that preserve complex patterns from distributed individual patient data. Such data sets of artificial patients, which are not linked to real patients, can then be used for joint analyses. As an exemplary application, we conduct a distributed analysis with DBMs on a synthetic data set, which simulates genetic variant data. Patterns from the original data can be recovered in the artificial data using hierarchical clustering of the virtual patients, demonstrating the feasibility of the approach. Additionally, we compare DBMs, variational autoencoders, generative adversarial networks, and multivariate imputation as generative approaches by assessing the utility and disclosure of synthetic data generated from real genetic variant data in a distributed setting with data of a small sample size.ConclusionsOur implementation adds to DataSHIELD the ability to generate artificial data that can be used for various analyses, e.g., for pattern recognition with deep learning. This also demonstrates more generally how DataSHIELD can be flexibly extended with advanced algorithms from languages other than R.
Predictors of the one-year-change in depressiveness in informal caregivers of community-dwelling people with dementia
Authors: Kürten Lara , Dietzel Nikolas , Kolominsky-Rabas Peter L. , Graessel Elmar 
Published: April 2021

AbstractBackgroundThe care of people with dementia is usually carried out by their family members, which can cause objective und subjective burden and raise their risk of depressiveness. Thus, the aim of this study is to identify predictors of the change in depressiveness of informal caregivers over 1 year in order to be able to derive hypotheses for interventions that promise success.MethodsThe Bavarian Dementia Survey (BayDem) is a multi-center, longitudinal study conducted at three different sites in Bavaria, Germany. Participants were people with dementia and their informal caregivers. Data was collected at baseline and after 12 months by standardized face-to-face interviews in cooperation with local players. The informal caregivers’ depressiveness was assessed with the WHO-5. Data was also collected on the people with dementia’s cognition (MMSE), behavioral symptoms (NPI) and comorbidities (Charlson Comorbidity Index) as well as caregivers’ social inclusion (LSNS), time spent on care and care contribution (RUD). For statistical analysis, a multiple regression model was used.ResultsThe data of 166 people with dementia and their informal caregivers was analyzed. Of the latter, 46% were categorized as “likely depressed”. The change in depressiveness over a year was significantly predicted by baseline depressiveness as well as an increase in the time informal caregivers spent supervising the person with dementia.ConclusionsInformal caregivers of people with dementia are at high risk of depression. The time spent supervising the person with dementia has a significant impact on increasing depressiveness. This highlights the importance of support services to provide the informal caregiver with relief and possibly reduce depressiveness.
‘Too old to test?’: A life course approach to HIV-related risk and self-testing among midlife-older adults in Malawi
Authors: Johnson Cheryl , Kumwenda Moses , Meghji Jamilah , Choko Augustine T. , Phiri Mackwellings , Hatzold Karin , Baggaley Rachel , Taegtmeyer Miriam , Terris-Prestholt Fern , Desmond Nicola , Corbett Elizabeth L. 
Published: April 2021

Abstract
Background
Despite the aging HIV epidemic, increasing age can be associated with hesitancy to test. Addressing this gap is a critical policy concern and highlights the urgent need to identify the underlying factors, to improve knowledge of HIV-related risks as well as uptake of HIV testing and prevention services, in midlife-older adults.

Methods
We conducted five focus group discussions and 12 in-depth interviews between April 2013 and November 2016 among rural and urban Malawian midlife-older (≥30 years) men and women. Using a life-course theoretical framework we explored how age is enacted socially and its implications on HIV testing and sexual risk behaviours. We also explore the potential for HIV self-testing (HIVST) to be part of a broader strategy for engaging midlife-older adults in HIV testing, prevention and care. Thematic analysis was used to identify recurrent themes and variations.

Results
Midlife-older adults (30–74 years of age) associated their age with respectability and identified HIV as “a disease of youth” that would not affect them, with age protecting them against infidelity and sexual risk-taking. HIV testing was felt to be stigmatizing, challenging age norms, threatening social status, and implying “lack of wisdom”. These norms drove self-testing preferences at home or other locations deemed age and gender appropriate. Awareness of the potential for long-standing undiagnosed HIV to be carried forward from past relationships was minimal, as was understanding of treatment-as-prevention. These norms led to HIV testing being perceived as a threat to status by older adults, contributing to low levels of recent HIV testing compared to younger adults.

Conclusions
Characteristics associated with age-gender norms and social position encourage self-testing but drive poor HIV-risk perception and unacceptability of conventional HIV testing in midlife-older adults. There is an urgent need to provide targeted messages and services more appropriate to midlife-older adults in sub-Saharan Africa. HIVST which has often been highlighted as a tool for reaching young people, may be a valuable tool for engaging midlife-older age groups who may not otherwise test.
Changes in the characteristics of dental emergencies under the influence of SARS-CoV-2 pandemic: a retrospective study
Authors: Wu Kan , Li Chunjie , Yang Zheng , Yang Shangchun , Yang Wenbing , Hua Chengge 
Published: April 2021

Abstract
Background
Further understanding of the distribution and changing characteristics of dental diseases is of great significance for all dental emergency centers for strengthening the medical staff’s treatment knowledge abilities and effective use of emergency resources in the face of public health emergencies involving highly infectious respiratory diseases.

Methods
The medical records of 4158 dental emergency patients in 2019 and 2020 were retrospectively analyzed and divided into pre-SARS-COV-2 group and SARS-COV-2 group according to time. The demographic data, date and time, diagnosis, and treatment methods of the two groups were statistically described, and the chi-squared test was used to analyze the differences. The medical records of 4158 dental emergency patients during the same period of two years in 2019 and 2020 were retrospectively analyzed and divided into SARS-COV-2 pre-group and SARS-COV-2 group according to time. The demographic data, date and time, diagnosis and treatment methods of the two groups were statistically described, and the chi-square test was used to determine the differences.

Results
During the SARS-COV-2 pandemic, the number of dental emergency visits increased by 29.7%. During the pandemic, males (n = 286, 58.1%) were more likely to visit dental emergency centers for trauma than females (n = 206, 41.9%) (P < 0.05); females (n = 242, 60.8%) were more likely to visit dental emergency centers for acute gingivitis and acute pericoronitis than males (n = 156, 39.2%) (P < 0.05). A major change in diagnosis was related to acute pulpitis (K04.0) and acute apical periodontitis (K04.4), which increased by 9.2%; acute gingivitis (K05.0) and acute pericoronitis (K05.2) increased by 3.5%; open wound of the lip and oral cavity (S01.5) decreased by 17.9%; other conditions (non-emergency diseases) increased by 6.8%, compared with the pre-SARS-COV-2 period. Among the treatment modalities, during the pre-SARS-COV-2 period, 304 patients (17.7%) received a prescription for antibiotics and analgesics, and 1485 (86.5%) received a prescription for local treatment. During the SARS-COV-2 period, 958 (39.2%) received a prescription for antibiotics and analgesics, and 1636 (67.0%) received a prescription for local treatment.

Conclusion
SARS-COV-2 pandemic led to changes in the characteristics of dental emergency patients. Trauma, acute pulpitis, and acute periodontitis are the leading reasons patients refer to dental emergency centers. Dental emergency centers should optimize treatment procedures, optimize the staff, and reasonably allocate materials according to the changes to improve the on-site treatment capacity and provide adequate dental emergency care.
Development and operation of a digital platform for sharing pathology image data
Authors: Kang Yunsook , Kim Yoo Jung , Park Seongkeun , Ro Gun , Hong Choyeon , Jang Hyungjoon , Cho Sungduk , Hong Won Jae , Kang Dong Un , Chun Jonghoon , Lee Kyoungbun , Kang Gyeong Hoon , Moon Kyoung Chul , Choe Gheeyoung , Lee Kyu Sang , Park Jeong Hwan , Jeong Won-Ki , Chun Se Young , Park Peom , Choi Jinwook 
Published: April 2021

Abstract
Background
Artificial intelligence (AI) research is highly dependent on the nature of the data available. With the steady increase of AI applications in the medical field, the demand for quality medical data is increasing significantly. We here describe the development of a platform for providing and sharing digital pathology data to AI researchers, and highlight challenges to overcome in operating a sustainable platform in conjunction with pathologists.

Methods
Over 3000 pathological slides from five organs (liver, colon, prostate, pancreas and biliary tract, and kidney) in histologically confirmed tumor cases by pathology departments at three hospitals were selected for the dataset. After digitalizing the slides, tumor areas were annotated and overlaid onto the images by pathologists as the ground truth for AI training. To reduce the pathologists’ workload, AI-assisted annotation was established in collaboration with university AI teams.

Results
A web-based data sharing platform was developed to share massive pathological image data in 2019. This platform includes 3100 images, and 5 pre-processing algorithms for AI researchers to easily load images into their learning models.

Discussion
Due to different regulations among countries for privacy protection, when releasing internationally shared learning platforms, it is considered to be most prudent to obtain consent from patients during data acquisition.

Conclusions
Despite limitations encountered during platform development and model training, the present medical image sharing platform can steadily fulfill the high demand of AI developers for quality data. This study is expected to help other researchers intending to generate similar platforms that are more effective and accessible in the future.
Development, validation, and proof-of-concept implementation of a two-year risk prediction model for undiagnosed atrial fibrillation using common electronic health data (UNAFIED)
Authors: Grout Randall W. , Hui Siu L. , Imler Timothy D. , El-Azab Sarah , Baker Jarod , Sands George H. , Ateya Mohammad , Pike Francis 
Published: April 2021

Abstract
Background
Many patients with atrial fibrillation (AF) remain undiagnosed despite availability of interventions to reduce stroke risk. Predictive models to date are limited by data requirements and theoretical usage. We aimed to develop a model for predicting the 2-year probability of AF diagnosis and implement it as proof-of-concept (POC) in a production electronic health record (EHR).

Methods
We used a nested case–control design using data from the Indiana Network for Patient Care. The development cohort came from 2016 to 2017 (outcome period) and 2014 to 2015 (baseline). A separate validation cohort used outcome and baseline periods shifted 2 years before respective development cohort times. Machine learning approaches were used to build predictive model. Patients ≥ 18 years, later restricted to age ≥ 40 years, with at least two encounters and no AF during baseline, were included. In the 6-week EHR prospective pilot, the model was silently implemented in the production system at a large safety-net urban hospital. Three new and two previous logistic regression models were evaluated using receiver-operating characteristics. Number, characteristics, and CHA2DS2-VASc scores of patients identified by the model in the pilot are presented.

Results
After restricting age to ≥ 40 years, 31,474 AF cases (mean age, 71.5 years; female 49%) and 22,078 controls (mean age, 59.5 years; female 61%) comprised the development cohort. A 10-variable model using age, acute heart disease, albumin, body mass index, chronic obstructive pulmonary disease, gender, heart failure, insurance, kidney disease, and shock yielded the best performance (C-statistic, 0.80 [95% CI 0.79–0.80]). The model performed well in the validation cohort (C-statistic, 0.81 [95% CI 0.8–0.81]). In the EHR pilot, 7916/22,272 (35.5%; mean age, 66 years; female 50%) were identified as higher risk for AF; 5582 (70%) had CHA2DS2-VASc score ≥ 2.

Conclusions
Using variables commonly available in the EHR, we created a predictive model to identify 2-year risk of developing AF in those previously without diagnosed AF. Successful POC implementation of the model in an EHR provided a practical strategy to identify patients who may benefit from interventions to reduce their stroke risk.
PAR-4/Ca2+-calpain pathway activation stimulates platelet-derived microparticles in hyperglycemic type 2 diabetes
Authors: Giannella Alessandra , Ceolotto Giulio , Radu Claudia Maria , Cattelan Arianna , Iori Elisabetta , Benetti Andrea , Fabris Fabrizio , Simioni Paolo , Avogaro Angelo , Vigili de Kreutzenberg Saula 
Published: April 2021

Abstract
Background
Patients with type 2 diabetes (T2DM) have a prothrombotic state that needs to be fully clarified; microparticles (MPs) have emerged as mediators and markers of this condition. Thus, we investigate, in vivo, in T2DM either with good (HbA1c ≤ 7.0%; GGC) or poor (HbA1c > 7.0%; PGC) glycemic control, the circulating levels of MPs, and in vitro, the molecular pathways involved in the release of MPs from platelets (PMP) and tested their pro-inflammatory effects on THP-1 transformed macrophages.

Methods
In 59 T2DM, and 23 control subjects with normal glucose tolerance (NGT), circulating levels of CD62E+, CD62P+, CD142+, CD45+ MPs were determined by flow cytometry, while plasma levels of ICAM-1, VCAM-1, IL-6 by ELISA. In vitro, PMP release and activation of isolated platelets from GGC and PGC were investigated, along with their effect on IL-6 secretion in THP-1 transformed macrophages.

Results
We found that MPs CD62P+ (PMP) and CD142+ (tissue factor-bearing MP) were significantly higher in PGC T2DM than GGC T2DM and NGT. Among MPs, PMP were also correlated with HbA1c and IL-6. In vitro, we showed that acute thrombin exposure stimulated a significantly higher PMP release in PGC T2DM than GGC T2DM through a more robust activation of PAR-4 receptor than PAR-1 receptor. Treatment with PAR-4 agonist induced an increased release of PMP in PGC with a Ca2+-calpain dependent mechanism since this effect was blunted by calpain inhibitor. Finally, the uptake of PMP derived from PAR-4 treated PGC platelets into THP-1 transformed macrophages promoted a marked increase of IL-6 release compared to PMP derived from GGC through the activation of the NF-kB pathway.

Conclusions
These results identify PAR-4 as a mediator of platelet activation, microparticle release, and inflammation, in poorly controlled T2DM.
Triglyceride–glucose index and the incidence of atherosclerotic cardiovascular diseases: a meta-analysis of cohort studies
Authors: Ding Xiaobo , Wang Xiaozhen , Wu Jing , Zhang Manli , Cui Meizi 
Published: April 2021

Abstract
Background
Insulin resistance has been demonstrated to be involved in the pathogenesis of atherosclerotic cardiovascular diseases (ASCVDs). This study evaluated the association between the triglyceride–glucose (TyG) index, a novel surrogate indicator of insulin resistance, and the incidence of ASCVDs in people without ASCVDs at baseline by performing a meta-analysis.

Methods
Cohort studies reporting the multivariate-adjusted association between the TyG index and the incidence of ASCVDs were obtained by searching the PubMed and Embase databases. A random-effects model incorporating intra-study heterogeneity was applied to combine the results.

Results
Eight cohort studies comprising 5,731,294 participants were included in this meta-analysis. The results showed that compared to those with the lowest TyG index category, participants with the highest TyG index category were independently associated with a higher risk of ASCVDs [hazard ratio (HR): 1.61, 95% confidence interval (CI) 1.29–2.01, I2 = 80%, P < 0.001]. This finding was consistent with the meta-analysis results with the TyG index analyzed as a continuous variable (HR per 1-unit increment of the TyG index: 1.39, 95% CI 1.18–1.64, I2 = 89%, P < 0.001). Subgroup analyses suggested that the age, sex, and diabetic status did not significantly affect the association (for subgroup analyses, all P > 0.05). Moreover, participants with the highest TyG index category were independently associated with a higher risk of coronary artery disease [(CAD), HR: 1.95, 95% CI 1.47–2.58, I2 = 92%, P < 0.001] and stroke (HR: 1.26, 95% CI 1.23–1.29, I2 = 0%, P < 0.001).

Conclusions
A higher TyG index may be independently associated with a higher incidence of ASCVDs, CAD, and stroke in people without ASCVDs at baseline.