bluuebunny/crossref_metadata_2025_split huggingface dataset:
The kidney releases a non-polymerizing form of Uromodulin in the urine and circulation that retains the external hydrophobic patch domain
Authors: Micanovic Radmila , LaFavers Kaice A. , Patidar Kavish R. , Ghabril Marwan S. , Doud Emma H. , Mosley Amber L. , Sabo Angela R. , Khan Shehnaz , El-Achkar Tarek M. 
Published: March 2021

AbstractUromodulin (Tamm-Horsfall protein, THP) is a glycoprotein uniquely produced in the kidney. It is released by cells of the thick ascending limbs (TAL) apically in the urine, and basolaterally in the renal interstitium and systemic circulation. Processing of mature urinary THP, which polymerizes into supra-molecular filaments, requires cleavage of an external hydrophobic patch (EHP) at the C terminus. However, THP in the circulation is not polymerized, and it remains unclear if non-aggregated forms of THP exist natively in the urine. We propose that an alternative processing path, which retains the EHP domain, can lead to a non-polymerizing form of THP. We generated an antibody that specifically recognizes THP with retained EHP (THP+EHP) and established its presence in the urine in a non-polymerized native state. Proteomic characterization of urinary THP+EHP revealed its C-terminus to end at F617. In the human kidney, THP+EHP was not only detected in TAL cells, but also diffusely in the renal parenchyma. Using immunoprecipitation followed by proteomic sequencing and immunoblotting, we then demonstrated that serum THP has also retained EHP. In a small cohort of patients at risk for acute kidney injury (AKI), admission urinary THP+EHP was significantly lower in patients who subsequently developed AKI during hospitalization. Our findings uncover novel insights into uromodulin biology by establishing the presence of an alternative path for cellular processing, which could explain the release of non-polymerizing THP in the circulation. Larger studies are needed to establish the utility of urinary THP+EHP as a sensitive biomarker of kidney health and susceptibility to injury.
A Systematic Review and Meta-Analysis on Catastrophic Cost incurred by Tuberculosis Patients and their Households
Authors: Ghazy Ramy Mohamed , Saeh Haider M. El , Abdulaziz Shaimaa , Hammouda Esraa Abdellatif , Elzorkany Amira , Kheder Heba , Zarif Nardine , Elrewany Ehab , ElHafeez Samar Abd 
Published: March 2021

AbstractBackgroundAs one of the World Health Organization (WHO) End Tuberculosis (TB) Strategy is to reduce the proportion of TB affected families that face catastrophic costs to 0% by 2020. This systematic review and meta-analysis aimed to estimate the pooled proportion of TB affected households who face catastrophic cost.MethodA search of the online database through September 2020 was performed. A total of 5114 articles were found, of which 29 articles got included in quantitative synthesis. Catastrophic cost is defined if total cost related to TB exceeded 20% of annual pre-TB household income. R software was used to estimate the pooled proportion at 95% confidence intervals (CIs) using the fixed/random-effect models.ResultThe proportion of patients faced catastrophic cost was 43% (95% CI 34-52, I2= 99%); 32% (95% CI 29 – 35, I2= 70%) among drug sensitive, and 80% (95% CI 74-85, I2= 54%) among drug resistant, and 81% (95%CI 78-84%, I2= 0%) among HIV patients. Regarding active versus passive case finding the pooled proportion of catastrophic cost was 12% (95% CI 9-16, I2= 95%) versus 42% (95% CI 35-50, I2= 94%). The pooled proportion of direct cost to the total cost was 45% (95% CI 39-51, I2= 91%). The pooled proportion of patients facing catastrophic health expenditure (CHE) at cut of point of 10% of their yearly income was 45% (95% CI 35-56, I2= 93%) while at 40% of their capacity to pay was 63% (95% CI 40-80, I2= 96%).ConclusionDespite the ongoing efforts, there is a significant proportion of patients facing catastrophic cost, which represent a main obstacle against TB control.PROSPERO registrationCRD42020221283
Prediction of intensive care unit mortality based on missing events
Authors: Shoji Tatsuma , Yonekura Hiroshi , Yoshiharu Sato , Kawasaki Yohei 
Published: March 2021

AbstractBackgroundThe increasing availability of electronic health records has made it possible to construct and implement models for predicting intensive care unit (ICU) mortality using machine learning. However, the algorithms used are not clearly described, and the performance of the model remains low owing to several missing values, which is unavoidable in big databases.MethodsWe developed an algorithm for subgrouping patients based on missing event patterns using the Philips eICU Research Institute (eRI) database as an example. The eRI database contains data associated with 200,859 ICU admissions from many hospitals (>400) and is freely available. We then constructed a model for each subgroup using random forest classifiers and integrated the models. Finally, we compared the performance of the integrated model with the Acute Physiology and Chronic Health Evaluation (APACHE) scoring system, one of the best known predictors of patient mortality, and the imputation approach-based model.ResultsSubgrouping and patient mortality prediction were separately performed on two groups: the sepsis group (the ICU admission diagnosis of which is sepsis) and the non-sepsis group (a complementary subset of the sepsis group). The subgrouping algorithm identified a unique, clinically interpretable missing event patterns and divided the sepsis and non-sepsis groups into five and seven subgroups, respectively. The integrated model, which comprises five models for the sepsis group or seven models for the non-sepsis group, greatly outperformed the APACHE IV or IVa, with an area under the receiver operating characteristic (AUROC) of 0.91 (95% confidence interval 0.89–0.92) compared with 0.79 (0.76–0.81) for the APACHE system in the sepsis group and an AUROC of 0.90 (0.89–0.91) compared with 0.86 (0.85–0.87) in the non-sepsis group. Moreover, our model outperformed the imputation approach-based model, which had an AUROC of 0.85 (0.83–0.87) and 0.87 (0.86–0.88) in the sepsis and non-sepsis groups, respectively.ConclusionsWe developed a method to predict patient mortality based on missing event patterns. Our method more accurately predicts patient mortality than others. Our results indicate that subgrouping, based on missing event patterns, instead of imputation is essential and effective for machine learning against patient heterogeneity.Trial registrationNot applicable.
Novel clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health
Authors: Su Chang , Zhang Yongkang , Flory James H , Weiner Mark G. , Kaushal Rainu , Schenck Edward J. , Wang Fei 
Published: March 2021

AbstractThe coronavirus disease 2019 (COVID-19) is heterogeneous and our understanding of the biological mechanisms of host response to the novel viral infection remains limited. Identification of meaningful clinical subphenotypes may benefit pathophysiological study, clinical practice, and clinical trials. Here, our aim was to derive and validate COVID-19 subphenotypes using machine learning and routinely collected clinical data, assess temporal patterns of these subphenotypes during the pandemic course, and examine their interaction with social determinants of health (SDoH). We retrospectively analyzed 14418 COVID-19 patients in five major medical centers in New York City (NYC), between March 1 and June 12, 2020. Using clustering analysis, four biologically distinct subphenotypes were derived in the development cohort (N = 8199). Importantly, the identified subphenotypes were highly predictive of clinical outcomes (especially 60-day mortality). Sensitivity analyses in the development cohort, and re-derivation and prediction in the internal (N = 3519) and external (N = 3519) validation cohorts confirmed the reproducibility and usability of the subphenotypes. Further analyses showed varying subphenotype prevalence across the peak of the outbreak in NYC. We also found that SDoH specifically influenced mortality outcome in Subphenotype IV, which is associated with older age, worse clinical manifestation, and high comorbidity burden. Our findings may lead to a better understanding of how COVID-19 causes disease in different populations and potentially benefit clinical trial development. The temporal patterns and SDoH implications of the subphenotypes may add new insights to health policy to reduce social disparity in the pandemic.
Calling differential DNA methylation at cell-type resolution: avoiding misconceptions and promoting best practices
Authors: Jing Han , Zheng Shijie C. , Breeze Charles E. , Beck Stephan , Teschendorff Andrew E. 
Published: March 2021

AbstractThe accurate detection of cell-type specific DNA methylation alterations in the context of general epigenome studies is an important task to improve our understanding of epigenomics in disease development. Although a number of statistical algorithms designed to address this problem have emerged, the task remains challenging. Here we show that a recent commentary by Rahmani et al, that aims to address misconceptions and best practices in the field, continues to suffer from critical misconceptions in how statistical algorithms should be compared and evaluated. In addition, we report contradictory results on real EWAS datasets.
Sex-specific DNA methylation changes in Alzheimer’s disease pathology
Authors: Zhang Lanyu , Young Juan I. , Gomez Lissette , Silva Tiago C. , Schmidt Michael A. , Cai Jesse , Chen Xi , Martin Eden R. , Wang Lily 
Published: March 2021

AbstractSex is an important factor that contributes to the clinical and biological heterogeneities in Alzheimer’s disease (AD), but the regulatory mechanisms underlying sex disparity in AD are still not well understood. DNA methylation is an important epigenetic modification that regulates gene transcription and is known to be involved in AD. We performed the first large-scale sex-specific meta-analysis of DNA methylation changes in AD, by re-analyzing four recent epigenome-wide association studies totaling more than 1000 postmortem prefrontal cortex brain samples using a uniform analytical pipeline. For each cohort we employed two complementary analytical strategies, a sex-stratified analysis that examined methylation-Braak stage associations in male and female samples separately, and a sex-by-Braak stage interaction analysis that compared the magnitude of these associations between different sexes. Our analysis uncovered 14 novel CpGs, mapped to genes such asTMEM39AandTNXBthat are associated with AD in a sex-specific manner.TMEM39Ais known to be involved in inflammation, dysregulated type I interferon responses, and other immune processes.TNXBencodes tenascin proteins, which are extracellular matrix glycoproteins demonstrated to modulate synaptic plasticity in the brain. Moreover, for many previously implicated AD genes, such asMBPandAZU1, our analysis provided the new insights that they were predominately driven by effects in only one sex. These sex-specific DNA methylation changes were enriched in divergent biological processes such as integrin activation in females and complement activation in males. Importantly, a number of drugs commonly prescribed for AD patients also targeted these genes with sex-specific DNA methylation changes. Our study implicated multiple new loci and biological processes that affected AD in a sex-specific manner and highlighted the importance of sex-specific treatment regimens for AD patients.
The Socioeconomic Gradient in Epigenetic Aging Clocks: Evidence from the Multi-Ethnic Study of Atherosclerosis and the Health and Retirement Study
Authors: Schmitz Lauren L. , Zhao Wei , Ratliff Scott M. , Goodwin Julia , Miao Jiacheng , Lu Qiongshi , Guo Xiuqing , Taylor Kent D. , Ding Jingzhong , Liu Yongmei , Levine Morgan , Smith Jennifer A. 
Published: March 2021

AbstractEpigenetic clocks have been widely used to predict disease risk in multiple tissues or cells. Their success as a measure of biological aging has prompted research on the connection between epigenetic pathways of aging and the socioeconomic gradient in health and mortality. However, studies examining social correlates of epigenetic aging have yielded inconsistent results. We conducted a comprehensive, comparative analysis of associations between various dimensions of socioeconomic status (SES) (education, income, wealth, occupation, neighborhood environment, and childhood SES) and eight epigenetic clocks in two large U.S. aging studies: The Multi-Ethnic Study of Atherosclerosis (MESA) (n=1,211) and the Health and Retirement Study (HRS) (n=4,018). In both studies, we found robust associations between SES measures in adulthood and the GrimAge and DunedinPoAm clocks (Bonferroni corrected p-value<0.01). In the HRS, significant associations with the Levine and Yang clocks are also evident. These associations are only partially mediated by smoking, alcohol consumption, and obesity, which suggests that differences in health behaviors alone cannot explain the SES gradient in epigenetic aging. Further analyses revealed concurrent associations between polygenic risk for accelerated intrinsic epigenetic aging, SES, and the Levine clock, indicating that genetic predisposition and social disadvantage may contribute independently to faster epigenetic aging.
The Risks and Benefits of Providing HIV Services during the COVID-19 Pandemic
Authors: Stover John , Kelly Sherrie L. , Mudimu Edinah , Green Dylan , Smith Tyler , Taramusi Isaac , Bansi-Matharu Loveleen , Martin-Hughes Rowan , Phillips Andrew N. , Bershteyn Anna ,   for the HIV Modeling Consortium
Published: March 2021

AbstractIntroductionThe COVID-19 pandemic has caused widespread disruptions including to health services. In the early response to the pandemic many countries restricted population movements and some health services were suspended or limited. In late 2020 and early 2021 some countries re-imposed restrictions. Health authorities need to balance the potential harms of additional SARS-CoV-2 transmission due to contacts associated with health services against the benefits of those services, including fewer new HIV infections and deaths. This paper examines these trade-offs for select HIV services.MethodsWe used four HIV simulation models (Goals, HIV Synthesis, Optima HIV and EMOD) to estimate the benefits of continuing HIV services in terms of fewer new HIV infections and deaths. We used three COVID-19 transmission models (Covasim, Cooper/Smith and a simple contact model) to estimate the additional deaths due to SARS-CoV-2 transmission among health workers and clients. We examined four HIV services: voluntary medical male circumcision, HIV diagnostic testing, viral load testing and programs to prevent mother-to-child transmission. We compared COVID-19 deaths in 2020 and 2021 with HIV deaths occurring now and over the next 50 years discounted to present value. The models were applied to countries with a range of HIV and COVID-19 epidemics.ResultsMaintaining these HIV services could lead to additional COVID-19 deaths of 0.002 to 0.15 per 10,000 clients. HIV-related deaths averted are estimated to be much larger, 19 - 146 discounted deaths per 10,000 clients.DiscussionWhile there is some additional short-term risk of SARS-CoV-2 transmission associated with providing HIV services, the risk of additional COVID-19 deaths is at least 100 times less than the HIV deaths averted by those services. Ministries of Health need to take into account many factors in deciding when and how to offer essential health services during the COVID-19 pandemic. This work shows that the benefits of continuing key HIV services are far larger than the risks of additional SARS-CoV-2 transmission.
PuBliCiTy: Python Bioimage Computing Toolkit
Authors: Cicconet Marcelo 
Published: March 2021

AbstractThe Python Bioimage Computing Toolkit (PuBliCiTy) is an evolving set of functions, scripts, and classes, written primarily in Python, to facilitate the analysis of biological images, of two or more dimensions, from electron or light microscopes. While the early development was guided by the goal of replacing an existing internal code-base with Python code, the effort later came to include novel tools, specially in the areas of machine learning infrastructure and model development. The toolkit is built on top of the so-calledpython data science stack, which includesnumpy, scipy, scikit-image, scikit-learn, andpandas. It also contains some deep learning models, written in TensorFlow and PyTorch, and a web-app for image annotation, which uses Flask as the web framework. The main features of the toolkit are: (1) simplifying the interface of some routinely used functions from underlying libraries; (2) providing helpful tools for the analysis of large images; (3) providing a web interface for image annotation, which can be used remotely and on tablets with pencils; (4) providing machine learning model implementations that are easy to read, train, and deploy – written in a way that minimizes complexity for users without a computer science or software development background. The source code is released under an MIT-like license atgithub.com/hms-idac/PuBliCiTy. Details, tutorials, and up-to-date documentation can be found at the project’s page as well.Project pagegithub.com/hms-idac/PuBliCiTy
Periodic synchronization of dengue epidemics in Thailand: the roles played by temperature and immunity
Authors: García-Carreras Bernardo , Yang Bingyi , Grabowski Mary K. , Sheppard Lawrence W. , Huang Angkana T. , Salje Henrik , Clapham Hannah Eleanor , Iamsirithaworn Sopon , Doung-Ngern Pawinee , Lessler Justin , Cummings Derek A. T. 
Published: March 2021

AbstractThe spatial distribution of dengue and its vectors (spp. Aedes) may be the widest it has ever been, and projections suggest that climate change may allow the expansion to continue. However, the largest impacts of climate change on dengue might be in regions where the pathogen is already endemic. In these areas, the waxing and waning of immunity has a large impact on temporal dynamics of cases of dengue haemorrhagic fever. Here, we use 51 years of data across 72 provinces and characterise spatio-temporal patterns of dengue in Thailand, where dengue has caused almost 1.5 million cases over the last thirty years, and examine the roles played by temperature and dynamics of immunity in giving rise to those patterns. We find that timescales of multiannual oscillations in dengue vary in space and time and uncover an interesting spatial phenomenon: Thailand has experienced multiple, periodic synchronization events. We show that patterns in synchrony of dengue are consistent with those observed in temperature. Applying a temperature-driven dengue model, we explore how dynamics of immunity interact with temperature to produce the observed multiannual dynamics and patterns in synchrony. While multiannual oscillations are readily produced by immunity in absence of multiannual timescales in temperature, synchrony in temperature can synchronise dengue dynamics in different locations. However, at higher mean temperatures and lower seasonal variation, immune dynamics become more predominant, and dengue dynamics become more insensitive to multiannual fluctuations in temperature. These findings can help underpin predictions of disease patterns as global temperatures rise.Author summary
Effects of accumulated temperature on growth curve ofGynostemma pentaphyllumstem and leaf under the conditions of Simulated light under the forest
Authors: chunyao Dun , Songsheng Wan , Shuanglong Li , yong Zeng , Daikun Wu 
Published: March 2021

AbstractThe effects of accumulated temperature on the growth curve and leaf number growth curve ofGynostemma pentaphyllumwere studied. The growth curve of twigs and strands were studied by curve regression analysis. The results showed that the growth ofGynostemma pentaphyllumLeaf growth curve was optimized, and the growth curve of stem and leaf ofGynostemma pentaphyllumwas established, and the curve was fitted and analyzed. Three - dimensional model of the effect of accumulated temperature on the stem length and leaf number ofGynostemma pentaphyllum. The results show that the stem length L and the accumulated temperature t of the strands are in the logistic function, the mathematical model was L=-112.69/(1+(t/892.1)3.31)+130.54, the number of gibbere leaf number n is logistic function with the accumulated temperature t, The model was n=-27.86/(1+(t/1159.77)0.26)+30.37, and the growth model could reflect the dynamic growth ofGynostemma pentaphyllumgrowth. The experimental results provide a theoretical basis for choosing suitable habitat forGynostemma pentaphyllumunder the forest.
Plasma concentrations of progesterone in the preceding estrous cycle are associated with the intensity of estrus and fertility of Holstein cows
Authors: Madureira A.M.L. , Burnett T.A. , Borchardt S. , Heuwieser W. , Baes C.F. , Vasconcelos J.L.M. , Cerri R.L.A. 
Published: March 2021

AbstractThe aim of this study was to determine the association between concentrations of progesterone(P4)during previous the estrous cycle with the intensity of spontaneous or estrogen-induced estrous expression and pregnancy per artificial insemination(P/AI). A total of 1,953 AI events from lactating Holstein cows were used, consisting of 1,289 timed AI events from experiment 1(Exp. 1)and 664 AI events from experiment 2(Exp. 2). In Exp. 1, cows were bred after a timed AI protocol based on estradiol and P4. In Exp. 2 animals were bred upon spontaneous estrus detection. In both experiments cows were continuously monitored by an automated activity monitor(AAM). The intensity of estrous expression was determined for each event and classified as either high or low intensity using the median of each experiment. Blood samples were collected for P4 analysis in Exp. 1 at –4 d, 0 d, and 7 d relative to timed AI, and in Exp. 2 immediately following estrus (0 d), 7 d, 14 d, and 21 d post-AI. Concentration of P4 was classified as greater or lower according to the median value in each experiment. Cows with lower concentrations of P4 at AI had greater estrous expression in Exp. 1 (363.6 ± 5.2 vs. 275.9 ± 8.0 % relative increase) and Exp. 2 (76.7 ± 1.9 vs. 67.4 ± 4.7 index; and 12.5 ± 0.5 vs. 9.3 ± 1.8 h). Cows with a greater intensity of estrous expression at timed AI had greater concentrations of P4 at –4 d than cows with lower intensity estrus or no estrous expression (4.6 ± 0.2 vs. 3.6 ± 0.2 vs. 3.7 ± 0.2 ng/mL). Cows with greater concentrations of P4 at –4 d had greater P/AI (32.8 ± 4.4 vs. 22.4 ± 4.5 %), whereas cows with lesser concentrations of P4 at d0 for either timed AI (35.2 ± 3.4 vs. 19.6 ± 4.6 %) or spontaneous estrus (31.8 ± 2.8 vs. 23.4 ± 3.2 %) had greater P/AI. Cows with greater concentrations of P4 7 d post-timed AI had greater P/AI compared with cows that had lower concentration of P4 (39.1 ± 2.9 vs. 24.7 ± 2.6 %). Similarly, cows that had lower concentrations of P4 at 7 d, 14 d and 21 d post-spontaneous estrus tended to have lower P/AI when compared with cows with greater concentrations of P4. Overall, concentrations of P4 prior to and at AI were associated with greater estrous intensity and P/AI at both spontaneous and timed AI events.