bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep learning predicts post-surgical recurrence of hepatocellular carcinoma from digital whole-slide images
Authors: Yamashita Rikiya , Long Jin , Saleem Atif , Rubin Daniel L. , Shen Jeanne 
Published: August 2020

AbstractRecurrence risk stratification of patients undergoing primary surgical resection for hepatocellular carcinoma (HCC) is an area of active investigation, and several staging systems have been proposed to optimize treatment strategies. However, as many as 70% of patients still have tumor recurrence at 5 years post-surgery. Routine hematoxylin and eosin (H&E)-stained histopathology slides may contain morphologic features associated with tumor recurrence. In this study, we developed and independently validated a deep learning-based system (HCC-SurvNet) that provides risk scores for disease recurrence after primary surgical resection, directly from H&E-stained digital whole-slide images of formalin-fixed, paraffin embedded liver resections. Our model achieved a concordance index of 0.724 on a held-out internal test set of 53 patients, and 0.683 on an external test set of 198 patients, exceeding the performance of standard staging using the American Joint Committee on Cancer (AJCC)/International Union against Cancer (UICC) Tumor-Node-Metastasis (TNM) classification system, on both the internal and external test cohorts (p = 0.018 and 0.025, respectively). We observed statistically significant differences in the survival distributions between low- and high-risk subgroups, as stratified by the risk scores predicted by HCC-SurvNet on both the internal and external test sets (log-rank p-value: 0.0013 and < 0.0001, respectively). On multivariable Cox proportional hazards analysis, the risk score was an independent risk factor for post-surgical recurrence, on both the internal (hazard ratio (HR) = 7.44 (95% CI: 1.60, 34.6), p = 0.0105) and external (HR = 2.37 (95% CI: 1.27, 4.43), p = 0.0069) test sets. Our results suggest that deep learning-based models can provide recurrence risk scores which may augment current patient stratification methods, and help refine the clinical management of patients undergoing primary surgical resection for HCC.
The Association between Sleep Quality and Type Two Diabetes at 20 Year Follow-up in the Southall And Brent REvisited (SABRE) Cohort: a Tri-ethnic Analysis
Authors: Ong Zhen L. , Chaturvedi Nishi , Tillin Therese , Dale Caroline , Garfield Victoria 
Published: August 2020

AbstractObjectiveThe risk of developing type 2 diabetes associated with poor sleep quality is comparable to that of traditional risk factors (e.g. overweight, physical inactivity). In the United Kingdom, these traditional risk factors could not explain the two to three-fold excess risks in South Asian and African Caribbean men compared to Europeans. This study investigates the (i)the association between mid-life sleep quality and later-life type 2 diabetes risk and (ii)a potential modifying effect of ethnicity.Research Design and MethodsThe Southall and Brent REvisited (SABRE) cohort comprised Europeans, South Asians, and African Caribbeans (median follow-up = 19 years). Complete case analysis was performed on 2190 participants without diabetes at baseline (age= 51.7± 7SD). Competing risks regressions were used to estimate the hazard ratios (HRs) of developing type 2 diabetes associated with four self-reported baseline sleep exposures (difficulty falling asleep, early morning waking, waking up tired and snoring) while adjusting for confounders. Modifying effects of ethnicity were analysed by (i) testing for interactions and (ii) performing ethnicity-stratified analysis.ResultsSnoring was strongly associated with increased type 2 diabetes risk but only among South Asians in a fully-adjusted model (HR 1.42, 95%CI=1.08-1.85, P=0.011). Our results revealed no elevated risk for any of the sleep exposures across all three ethnic groups.ConclusionsThe association between snoring and type 2 diabetes appeared to be modified by ethnicity, with South Asians at greatest risk.
Phylogenetic analysis of SARS-CoV-2 in the Boston area highlights the role of recurrent importation and superspreading events
Authors: Lemieux Jacob E. , Siddle Katherine J. , Shaw Bennett M. , Loreth Christine , Schaffner Stephen F. , Gladden-Young Adrianne , Adams Gordon , Fink Timelia , Tomkins-Tinch Christopher H. , Krasilnikova Lydia A. , DeRuff Katherine C. , Rudy Melissa , Bauer Matthew R. , Lagerborg Kim A. , Normandin Erica , Chapman Sinead B. , Reilly Steven K. , Anahtar Melis N. , Lin Aaron E. , Carter Amber , Myhrvold Cameron , Kemball Molly E. , Chaluvadi Sushma , Cusick Caroline , Flowers Katelyn , Neumann Anna , Cerrato Felecia , Farhat Maha , Slater Damien , Harris Jason B. , Branda John , Hooper David , Gaeta Jessie M. , Baggett Travis P. , O’Connell James , Gnirke Andreas , Lieberman Tami D. , Philippakis Anthony , Burns Meagan , Brown Catherine M. , Luban Jeremy , Ryan Edward T. , Turbett Sarah E. , LaRocque Regina C. , Hanage William P. , Gallagher Glen R. , Madoff Lawrence C. , Smole Sandra , Pierce Virginia M. , Rosenberg Eric , Sabeti Pardis C. , Park Daniel J. , Maclnnis Bronwyn L. 
Published: August 2020

AbstractSARS-CoV-2 has caused a severe, ongoing outbreak of COVID-19 in Massachusetts with 111,070 confirmed cases and 8,433 deaths as of August 1, 2020. To investigate the introduction, spread, and epidemiology of COVID-19 in the Boston area, we sequenced and analyzed 772 complete SARS-CoV-2 genomes from the region, including nearly all confirmed cases within the first week of the epidemic and hundreds of cases from major outbreaks at a conference, a nursing facility, and among homeless shelter guests and staff. The data reveal over 80 introductions into the Boston area, predominantly from elsewhere in the United States and Europe. We studied two superspreading events covered by the data, events that led to very different outcomes because of the timing and populations involved. One produced rapid spread in a vulnerable population but little onward transmission, while the other was a major contributor to sustained community transmission, including outbreaks in homeless populations, and was exported to several other domestic and international sites. The same two events differed significantly in the number of new mutations seen, raising the possibility that SARS-CoV-2 superspreading might encompass disparate transmission dynamics. Our results highlight the failure of measures to prevent importation into MA early in the outbreak, underscore the role of superspreading in amplifying an outbreak in a major urban area, and lay a foundation for contact tracing informed by genetic data.
Population pharmacokinetic modeling of pyrotinib in patients with HER2-positive advanced or metastatic breast cancer
Authors: Wen Hai-ni , Liu Yi-xi , Xu Da , Zhao Kai-jing , Jiao Zheng 
Published: August 2020

AbstractObjectivePyrotinib, a novel oral irreversible dual pan-ErbB tyrosine kinase inhibitor (TKI), has been approved in China for the treatment of HER2-positive advanced or metastatic breast cancer. This study aimed to conduct a population pharmacokinetics (PK) analysis of pyrotinib and to evaluate the impact of certain HER2-positive breast cancer patient characteristics on pyrotinib’s PK.MethodA total of 1152 samples, provided by 59 adult female patients from two phase I clinical trials, were analyzed by nonlinear mixed-effects modeling. Monte Carlo simulation was conducted to assess the impact of covariates following exposure to pyrotinib.ResultsThe PK of pyrotinib was adequately described by a one-compartment model with first-order absorption and elimination. Patient’s age and total protein levels can affect pyrotinib’s apparent volume of distribution, and concomitant use of montmorillonite powder had significant effects on the bioavailability of pyrotinib. No PK interactions were observed between capecitabine and pyrotinib.ConclusionIn this study, a population PK model of pyrotinib was developed to determine the influence of patient characteristics on the PK of pyrotinib. While patient age and total protein levels can significantly affect the apparent distribution volume of pyrotinib, the magnitude of the impact was limited, thus no dosage adjustment was recommended. Furthermore, concomitant use of montmorillonite powder for diarrhea can decrease the bioavailability of pyrotinib by 50.3%.
Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning
Authors: Navlakha Saket , Morjaria Sejal , Perez-Johnston Rocio , Zhang Allen , Taur Ying 
Published: August 2020

AbstractBackgroundAccurately predicting outcomes for cancer patients with COVID-19 has been clinically challenging. Numerous clinical variables have been retrospectively associated with disease severity, but the predictive value of these variables, and how multiple variables interact to increase risk, remains unclear.MethodsWe used machine learning algorithms to predict COVID-19 severity in 354 cancer patients at Memorial Sloan Kettering Cancer Center in New York City. Using clinical variables only collected on or before a patient’s COVID-19 positive date (time zero), we sought to classify patients into one of three possible future outcomes: Severe-early (the patient required high levels of oxygen support within 3 days of being tested positive for COVID-19), Severe-late (the patient required high levels of oxygen after 3 days), and Non-severe (the patient never required oxygen support).ResultsOur algorithm classified patients into these classes with an AUROC ranging from 70-85%, significantly outperforming prior methods and univariate analyses. Critically, classification accuracy is highest when using a potpourri of clinical variables — including patient demographics, pre-existing diagnoses, laboratory and radiological work, and underlying cancer type — suggesting that COVID-19 in cancer patients comes with numerous, combinatorial risk factors.ConclusionsOverall, we provide a computational tool that can identify high-risk patients early in their disease progression, which could aid in clinical decision-making and selecting treatment options.
The Futures of the Pandemic in the USA : A Timed Intervention Model
Authors: Hachtel Gary D. 
Published: August 2020

AbstractWe propose a novel Timed Intervention exended SEIR model for predicting the evolution of the Covid 19 Pandemic in the USA. The model can, by sparameter, assignment, be reduced to the model of Peng et al, which appeared in February, 2020 (Reference [2]). Novel aspects of the proposed model includeFormulation of a “Protected” population P, which can be viewed as a “Sheltered in Place”, unexposed population which, starting at time t = τP, builds up and stores a reservoir of unexposed Population;This “Protection Intervention” provides the basis for a second Timed Release Intervention: on receiving a “reopening signal” at time t = τR, this second intervention initiates a release of this stored population back into the general population S.;Selection of model parameters to optimize the approximation of the model up to the present, and then projecting simulation of the model based on the value thus obtained 100,200, 365, and 730 days into the future.This model shows excellent qualitative results and quantitative results that are good, considering the chosen nationwide scope. These results compare favorably to University of Washington IHME [5] projections, as published Daily on the http://Worldometers.info website. The qualitative and quantitative behavior of all 7 state variables S, P, E, I, Q, R, D will be illustrated and discussed, in this and future further Intervention cycles1
Wastewater sample site selection to estimate geographically-resolved community prevalence of COVID-19: A research protocol
Authors: Yeager Ray A. , Holm Rochelle H. , Saurabh Kumar , Fuqua Joshua L , Talley Daymond , Bhatnagar Aruni , Smith Ted 
Published: August 2020

AbstractBackgroundWastewater monitoring for virus infections within communities can complement conventional clinical surveillance. Currently, most SARS-CoV-2 testing is performed during clinical encounters with symptomatic individuals, and therefore likely underrepresents actual population prevalence. Randomized testing on a regular basis to estimate population-level infection rates is prohibitively costly and is hampered by a range of barriers associated with participation in clinical research. In comparison, community-level fecal monitoring can be performed through wastewater surveillance and can effectively surveil communities with less temporal lag than other surveillance methods. However, epidemiologically-defined protocols for wastewater sample site selection are lacking.MethodsHerein we describe methods for developing a geographically-resolved population-level wastewater sampling approach in Jefferson County, Kentucky which may have general applicability for cities throughout the United States. This approach was developed by the selection of sampling locations along sewer lines transporting raw wastewater from geographically and demographically distinct areas that correspond with locations where random testing of residents occurs.ConclusionsDevelopment of this protocol for population-level sampling for SARS-CoV-2 prevalence in wastewater can be utilized to inform consistent wastewater monitoring among cities for up-to-date and geographically-resolved information on COVID-19 prevalence within communities. This information could substantially supplement public health surveillance of COVID-19 and thus serve to better guide targeted mitigation strategies throughout the United States.
Hesitant or not? A global survey of potential acceptance of a COVID-19 vaccine
Authors: Lazarus Jeffrey V , Ratzan Scott , Palayew Adam , Gostin Lawrence O , Larson Heidi J , Rabin Kenneth , Kimball Spencer , El-Mohandes Ayman 
Published: August 2020

AbstractA number of COVID-19 vaccines are under development, with one or more possibly becoming available in 2021. We conducted a global survey in June 2020 of 13,426 people in 19 countries to determine potential acceptance rates of a COVID-19 vaccine and factors influencing acceptance. We ran univariate logistic regressions to examine the associations with demographic variables. 71.5% reported they would be very or somewhat likely to take a COVID-19 vaccine; 61.4% reported they would accept their employer’s recommendation to take a COVID-19 vaccine. Differences in acceptance across countries ranged from almost 9 in 10 (China) to fewer than 6 in 10 (Russia). Respondents reporting higher levels of trust in information from government sources were more likely to accept a vaccine, and take their employer’s advice to do so. Targeted interventions addressing age, sex, income, and education level are required to increase and sustain public acceptance of a COVID-19 vaccine.
Predicting deseasonalised serum 25 hydroxy vitamin D concentrations in the D-Health Trial: an analysis using boosted regression trees
Authors: Waterhouse Mary , Baxter Catherine , Romero Briony Duarte , McLeod Donald S. A. , English Dallas R. , Armstrong Bruce K. , Clarke Michael W. , Ebeling Peter R. , Hartel Gunter , Kimlin Michael G. , O’Connell Rachel L. , Pham Hai , Harris Rachael M. Rodney , van der Pols Jolieke C. , Venn Alison J. , Webb Penelope M. , Whiteman David C. , Neale Rachel E. 
Published: August 2020

ABSTRACTBackgroundThe D-Health Trial aims to determine whether monthly high-dose vitamin D supplementation can reduce the mortality rate and prevent cancer. We did not have adequate statistical power for subgroup analyses, so could not justify the high cost of collecting blood samples at baseline. To enable future exploratory analyses stratified by baseline vitamin D status, we developed a model to predict baseline serum 25 hydroxy vitamin D [25(OH)D] concentration.MethodsWe used data and serum 25(OH)D concentrations from participants who gave a blood sample during the trial for compliance monitoring and were randomised to placebo. Data were partitioned into training (80%) and validation (20%) datasets. Deseasonalised serum 25(OH)D concentrations were dichotomised using cut-points of 50 nmol/L, 60 nmol/L and 75 nmol/L. We fitted boosted regression tree models, based on 13 predictors, and evaluated model performance using the validation data.ResultsThe training and validation datasets had 1788 (10.5% <50 nmol/L, 23.1% <60 nmol, 48.8 <75 nmol/L) and 447 (11.9% <50 nmol/L, 25.7% <60 nmol/L, and 49.2% <75 nmol/L) samples, respectively. Ambient UV radiation and total intake of vitamin D were the strongest predictors of ‘low’ serum 25(OH)D concentration. The area under the receiver operating characteristic curves were 0.71, 0.70, and 0.66 for cut-points of <50 nmol/L, <60 nmol/L and <75 nmol/L respectively.ConclusionsWe exploited compliance monitoring data to develop models to predict serum 25(OH)D concentration for D-Health participants at baseline. This approach may prove useful in other trial settings where there is an obstacle to exhaustive data collection.
An examination of COVID-19 mitigation efficiency among 23 countries
Authors: Chen Yi-Tui , Yu Shih-Heng , Su Emily Chia-Yu 
Published: August 2020

AbstractThe purpose of this paper is to compare the relative mitigation efficiency of COVID-19 transmission among 23 selected countries, including 19 countries in the G20, two heavily infected countries (Iran and Spain), and two highly populous countries (Pakistan and Nigeria). This paper evaluated the mitigation efficiency for each country at each stage by using data envelopment analysis (DEA) tools and analyzed changes in mitigation efficiency across stages. Pearson correlation tests were conducted between each change to examine the impact of efficiency ranks in the previous stage on subsequent stages. An indicator was developed to judge epidemic stability and was applied to practical cases involving lifting travel restrictions and restarting the economy in some countries.The results showed that Korea and Australia performed with the highest efficiency in preventing the diffusion of COVID-19 for the whole period covering 120 days since the first confirmed case, while the USA ranked at the bottom. China, Japan, Korea and Australia were judged to have recovered from the attack of COVID-19 due to higher epidemic stability.
Precision Prostate Cancer Screening with a Polygenic Risk Score
Authors: Tasa Tõnis , Puustusmaa Mikk , Tõnisson Neeme , Kolk Berit , Padrik Peeter 
Published: August 2020

AbstractProstate cancer (PC) is the second-most common type of cancer and the fifth-leading cause of cancer-related death in men worldwide. Genome-wide association studies have identified numerous genetic variants (SNPs) independently associated with PC. The effects of such SNPs can be combined into a single polygenic risk score (PRS). Stratification of men according to PRS could be applied in secondary prevention. We aimed to construct a PRS model and to develop a pipeline for personalized prostate cancer screening.Previously published PRS models for predicting the risk of prostate cancer were collected from the literature. These were validated on the Estonian Biobank (EGC) consisting of a total of 16,390 quality-controlled genotypes with 262 prevalent and 428 incident PC cases and on 209 634 samples in the UK Biobank with 3254 prevalent cases and 6959 incident cases. The best performing model was selected based on the AUC in prevalent data and independently validated in both incident datasets. Using Estonian PC background information, we performed absolute risk simulations and developed individual risk-based clinical follow-up recommendations.The best-performing PRS included 121 SNPs. The C-index of the Cox regression model associating PC status with PRS was 0.641 (SE = 0.015) with a hazard ratio of 1.65 (95% confidence interval 1.51 – 1.81) on the incident EGC dataset. The model is able to identify individuals with more than a 3-fold risk increase. The risk of an average 45-year old could be attained by individuals between the ages of 41 and 52. A 41-year old male on the 95th percentile has the same risk as an average 45-year old but by age 55, he has attained the same genetic risk as an average 68-year-old.PRS is a powerful predictor of prostate cancer risk that can be combined with current non-invasive practices of PC screening. We have developed PRS-based recommendations for personalized PSA testing. Our approach is easily adaptable to other nationalities by using population-specific background data of other genetically similar populations.
Meta-analysis shows that context-dependent mating behaviour is inconsistent or weak across animals
Authors: Dougherty Liam R. 
Published: August 2020

AbstractAnimals often need to invest significantly in mating behaviour in order to successfully mate. However, the expression of mating behaviour can be costly, especially in unfavourable environments, so animals are expected to adjust their behaviour in a context-dependent way to mitigate these costs. I systematically searched the literature for studies measuring animal mating behaviour (sexual signalling, response to sexual signals, or the strength of mate choice) in more than one environment, and used a phylogenetically-controlled meta-analysis to identify environmental factors influencing these behaviours. Across 222 studies, the strength of mate choice was significantly context-dependent, and most influenced by population density, population sex ratio, and predation risk. However, the average effect sizes were typically small. The amount of sexual signalling and the strength of response to sexual signals were not significantly related to the environment. Overall, this suggests that the evidence for context-dependent mating behaviour across animals is surprisingly weak.
Deep learning-based molecular morphometrics for kidney biopsies
Authors: Zimmermann Marina , Klaus Martin , Wong Milagros N. , Thebille Ann-Katrin , Gernhold Lukas , Kuppe Christoph , Halder Maurice , Kranz Jennifer , Wanner Nicola , Braun Fabian , Wulf Sonia , Wiech Thorsten , Panzer Ulf , Krebs Christian F. , Hoxha Elion , Kramann Rafael , Huber Tobias B. , Bonn Stefan , Puelles Victor G. 
Published: August 2020

AbstractMorphologic examination of tissue biopsies is essential for histopathological diagnosis. However, accurate and scalable cellular quantification in human samples remains challenging. Here, we present a deep learning-based approach for antigen-specific cellular morphometrics in human kidney biopsies, which combines indirect immunofluorescence imaging with U-Net-based architectures for image-to-image translation and dual segmentation tasks, achieving human-level accuracy. In the kidney, podocyte loss represents a hallmark of glomerular injury and can be estimated in diagnostic biopsies. Thus, we profiled over 27,000 podocytes from 110 human samples, including patients with anti-neutrophil cytoplasmic antibody-associated glomerulonephritis (ANCA-GN), an immune-mediated disease with aggressive glomerular damage and irreversible loss of kidney function. Previously unknown morphometric signatures of podocyte depletion were identified in patients with ANCA-GN, which allowed patient classification and showed potential for risk stratification in combination with routine clinical tools. Together, our approach enables robust and scalable molecular morphometric analysis of human tissues, yielding deeper biological insights into the human kidney pathophysiology.SummaryDeep learning enables robust and scalable molecular morphometric analysis of human tissues, yielding deeper biological insights into the human kidney pathophysiology.