bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison of a Commercially Available Prostate Segmentation Application to Traditional Prolate and Biproximate Ellipsoid Methods for Prostate Volume Measurement
Authors: Wasserman Neil F. , Spilseth Benjamin 
Published: October 2020

AbstractRationale and ObjectivesTo compare the a commercially available automatic and manually adjusted segmentation software program (DynaCAD®) to two ellipsoid volume methods using T2-weighted magnetic resonance imaging (MRI).Material and MethodsThis is a retrospective IRB-approved study of 146 patients randomly selected from 1600 consecutive men referred for T2-weighted MRI. All measurements were performed by a single expert senior radiologist. Total prostate volume was calculated using automatic DynaCAD®software (RCAD), manually adjusted DynaCAD®(ACAD), traditional ellipsoid method (TE) and a new alternative biproximate ellipsoid method (BE). Results were assessed with ANOVA and linear regression.ResultsMean volumes for RCAD, ACAD, BE and TE were 61.5, 58.4, 56, and 53.2 respectively. ANOVA showed no difference of the means (p> 0.05.) Linear regression showed a coefficient of determination (r2) between ACAD and TE of 0.92 and between ACAD and BE of 0.90. Using the planigraphic-based segmented ACAD as the “gold’ standard, RCAD overestimated volume by 5%. TE and BE underestimated prostatic volume by 4% and 9% respectively. ACAD processing time was 4.5 to 9.5 minutes (mean=6.6 min.) compared to 1.5 to 3.0 minutes (mean=2.3 min.) for prolate ellipsoid methods.ConclusionManually adjusted MRI T2-weighted segmentation is likely the most accurate measure of total prostate volume. DynaCAD appears to fulfill that function, but manual adjustment of automatic misregistration of boundaries is necessary. ACAD and RCAD are best applied to research use. Ellipsoid methods are faster, more convenient, nearly as accurate and more practical for clinical use.
Predictors and rates of PTSD, depression and anxiety in UK frontline health and social care workers during COVID-19
Authors: Greene Talya , Harju-Seppänen Jasmine , Adeniji Mariam , Steel Charlotte , Grey Nick , Brewin Chris R. , Bloomfield Michael A. , Billings Jo 
Published: October 2020

AbstractBackgroundStudies have shown that working in frontline healthcare roles during epidemics and pandemics was associated with PTSD, depression, anxiety, and other mental health disorders.ObjectivesThe objectives of this study were to identify demographic, work-related and other predictors for clinically significant PTSD, depression, and anxiety during the COVID-19 pandemic in UK frontline health and social care workers (HSCWs), and to compare rates of distress across different groups of HCSWs working in different roles and settings.MethodsA convenience sample (n=1194) of frontline UK HCSWs completed an online survey during the first wave of the pandemic (27 May – 23 July 2020). Participants worked in UK hospitals, nursing or care homes and other community settings. PTSD was assessed using the International Trauma Questionnaire (ITQ); Depression was assessed using the Patient Health Questionnaire-9 (PHQ-9); Anxiety was assessed using the Generalized Anxiety Disorder Scale (GAD-7).ResultsNearly 58% of respondents met the threshold for clinically significant PTSD, anxiety or depression, and symptom levels were high across occupational groups and settings. Logistic regression analyses found that participants who were concerned about infecting others, who felt they could not talk with their managers, who reported feeling stigmatised and who had not had reliable access to personal protective equipment (PPE) were more likely to meet criteria for a clinically significant mental disorder. Being redeployed during the pandemic, and having had COVID were associated with higher odds for PTSD. Higher household income was associated with reduced odds for a mental disorder.ConclusionsThis study identified predictors of clinically significant distress during COVID-19 and highlights the need for reliable access to PPE and further investigation of barriers to communication between managers and staff.
Activities of Daily Living with Bionic Arm Improved by Combination Training and Latching Filter in Prosthesis Control Comparison
Authors: Paskett Michael D. , Brinton Mark R. , Hansen Taylor C. , George Jacob A. , Davis Tyler S. , Duncan Christopher C. , Clark Gregory A. 
Published: October 2020

AbstractBackgroundAdvanced prostheses can restore function and improve quality of life for individuals with amputations. Unfortunately, most commercial control strategies do not utilize the rich control information from residual nerves and musculature. Continuous decoders can provide more intuitive prosthesis control from multi-channel neural or electromyographic recordings. Three components influence continuous decoder performance: the data used to train the algorithm, the algorithm, and smoothing filters on the algorithm’s output. As individual groups often focus on a single decoder, very few studies compare different decoders using otherwise similar experimental conditions.MethodsWe completed a two-phase head-to-head comparison of 12 continuous decoders using activities of daily living. In phase one, we compared two training types and a smoothing filter with three algorithms (modified Kalman filter, multi-layer perceptron, and convolutional neural network) in a clothespin relocation task. We compared training types that included data with only individual digit and wrist movements vs. combination movements (e.g., simultaneous grasp and wrist flexion). We also compared raw vs. nonlinearly smoothed algorithm outputs. In phase two, we compared the three algorithms in fragile egg, zipping, pouring, and folding tasks using the combination training and smoothing found beneficial in phase one. In both phases, we collected objective, performance-based (e.g., success rate) and subjective, user-focused (e.g., preference) measures.ResultsPhase one showed that combination training improved prosthesis control accuracy and speed, and that the nonlinear smoothing improved accuracy but generally reduced speed. Phase one importantly showed simultaneous movements were used in the task, and that the modified Kalman filter and multi-layer perceptron predicted more simultaneous movements than the convolutional neural network. In phase two, user-focused metrics favored the convolutional neural network and modified Kalman filter, whereas performance-based metrics were generally similar among all algorithms.ConclusionsThese results confirm that state-of-the-art algorithms, whether linear or nonlinear in nature, functionally benefit from training on more complex data and from output smoothing. These studies will be used to select a decoder for a long-term take-home trial with implanted neuromyoelectric devices. Overall, clinical considerations may favor the mKF as it is similar in performance, faster to train, and computationally less expensive than neural networks.
Validation of Visual and Auditory Digital Markers of Suicidality in Acutely Suicidal Psychiatric In-Patients
Authors: Galatzer-Levy Isaac R. , Abbas Anzar , Ries Anja , Homan Stephanie , Koesmahargyo Vidya , Yadav Vijay , Colla Michael , Scheerer Hanne , Vetter Stefan , Seifritz Erich , Scholz Urte , Kleim Birgit 
Published: October 2020

AbstractBackgroundMultiple symptoms of suicide risk are assessed based on visual and auditory information including flattened affect, reduced movement, and slowed speech. Objective quantification of such symptomatology from novel data sources can increase the sensitivity, scalability, and timeliness of suicide risk assessment.MethodsIn the current study we utilized video to quantify facial, vocal, and movement markers associated with mood, emotion, and motor functioning from a structured clinical conversation in 20 patients admitted to a psychiatric hospital following a suicide risk attempt. Measures were calculated using open source deep learning algorithms for processing facial expressivity, head movement, and vocal characteristics. Derived digital measures of flattened affect, reduced movement, and slowed speech were compared to suicide severity using the Beck Suicide Scale (BSS), controlling for age and gender using multiple linear regression.ResultsSuicide severity was associated with multiple visual and auditory markers including speech prevalence (β = −0.68; P =.017, r2 =.40, overall expressivity (β = −0.46; P = 0.10, r2 =.27), and head movement measured as head pitch variability (β = −1.24; P =.006, r2 =.48) and head yaw variability (β = −0.54; p =.055, r2 =.32).ConclusionsDigital measurements of facial affect, movement, and speech prevalence demonstrated strong effect sizes and significant linear associations with severity of suicidal ideation.
Rate of Antibiotic Use and Associated Risk Factors in COVID-19 Hospitalized Patients
Authors: Martin Alysa J. , Shulder Stephanie , Dobrzynski David , Quartuccio Katelyn , Pillinger Kelly E. 
Published: October 2020

AbstractBackgroundLiterature suggests that antibiotic prescribing in COVID-19 patients is high, despite low rates of confirmed bacterial infection. There are little data on what drives prescribing habits.ObjectiveThis study sought to determine antibiotic prescribing rates and risk factors for antibiotic prescribing in hospitalized patients. It was the first study to assess risk factors for receiving more than one course of antibiotics.MethodsThis was a retrospective, multi-center, observational study. Patients admitted from March 1, 2020 to May 31, 2020 and treated for PCR-confirmed COVID-19 were included. The primary endpoint was the rate of antibiotic use during hospitalization. Secondary endpoints included risk factors associated with antibiotic use, risk factors associated with receiving more than one antibiotic course, and rate of microbiologically confirmed infections.ResultsA total of 208 encounters (198 patients) were included in the final analysis. Eighty-three percent of patients received at least one course of antibiotics, despite low rates of microbiologically confirmed infection (12%). Almost one-third of patients (30%) received more than one course of antibiotics. Risk factors identified for both antibiotic prescribing and receiving more than one course of antibiotics were more serious illness, increased hospital length of stay, intensive care unit admission, mechanical ventilation, and acute respiratory distress syndrome.Conclusion and relevanceThere were high rates of antibiotic prescribing with low rates of bacterial co-infection. Many patients received more than one course of antibiotics during hospitalization. This study highlights the need for increased antibiotic stewardship practices in COVID-19 patients.
Cost and social distancing dynamics in a mathematical model of COVID-19 with application to Ontario, Canada
Authors: Moyles I. R. , Heffernan J. M. , Kong J. D. 
Published: October 2020

A mathematical model of COVID-19 is presented where the decision to increase or decrease social distancing is modelled dynamically as a function of the measured active and total cases as well as the perceived cost of isolating. Along with the cost of isolation, we define an overburden healthcare cost and a total cost. We explore these costs by adjusting parameters that could change with policy decisions. We observe that two disease prevention practices, namely increasing isolation activity and increasing incentive to isolate do not always lead to optimal health outcomes. We demonstrate that this is due to the fatigue and cost of isolation. We further demonstrate that an increase in the number of lock-downs, each of shorter duration can lead to minimal costs. Our results are compared to case data in Ontario, Canada from March to August 2020 and details of expanding the results to other regions are presented.Subject Areasmathematical modelling, epidemiology, infectious diseases
Mortality among Adults Ages 25-44 in the United States During the COVID-19 Pandemic
Authors: Faust Jeremy Samuel , Krumholz Harlan M. , Dickerson Katherine L. , Lin Zhenqiu , Gilman Cleavon , Walensky Rochelle P. 
Published: October 2020

AbstractIntroductionCoronavirus disease-19 (COVID-19) has caused a marked increase in all-cause deaths in the United States, mostly among adults aged 65 and older. Because younger adults have far lower infection fatality rates, less attention has been focused on the mortality burden of COVID-19 in this demographic.MethodsWe performed an observational cohort study using public data from the National Center for Health Statistics at the United States Centers for Disease Control and Prevention, and CDC Wonder. We analyzed all-cause mortality among adults ages 25-44 during the COVID-19 pandemic in the United States. Further, we compared COVID-19-related deaths in this age group during the pandemic period to all drug overdose deaths and opioid-specific overdose deaths in each of the ten Health and Human Services (HHS) regions during the corresponding period of 2018, the most recent year for which data are available.ResultsAs of September 6, 2020, 74,027 all-cause deaths occurred among persons ages 25-44 years during the period from March 1st to July 31st, 2020, 14,155 more than during the same period of 2019, a 23% relative increase (incident rate ratio 1.23; 95% CI 1.21–1.24), with a peak of 30% occurring in May (IRR 1.30; 95% CI 1.27-1.33). In HHS Region 2 (New York, New Jersey), HHS Region 6 (Arkansas, Louisiana, New Mexico, Oklahoma, Texas), and HHS Region 9 (Arizona, California, Hawaii, Nevada), COVID-19 deaths exceeded 2018 unintentional opioid overdose deaths during at least one month. Combined, 2,450 COVID-19 deaths were recorded in these three regions during the pandemic period, compared to 2,445 opioid deaths during the same period of 2018.MeaningWe find that COVID-19 has likely become the leading cause of death—surpassing unintentional overdoses—among young adults aged 25-44 in some areas of the United States during substantial COVID-19 outbreaks.NoteThe data presented here have since been updated. As a result, an additional 1,902 all-cause deaths occurring among US adults ages 25-44 during the period of interest are not accounted for in this manuscript.
Solar geoengineering could redistribute malaria risk in developing countries
Authors: Carlson Colin J. , Colwell Rita , Hossain Mohammad Sharif , Rahman Mohammed Mofizur , Robock Alan , Ryan Sadie J. , Alam Mohammad Shafiul , Trisos Christopher H. 
Published: October 2020

AbstractSolar geoengineering is often framed as a stopgap measure to decrease the magnitude, impacts, and injustice of climate change. However, the costs or benefits of geoengineering for human health are largely unknown. We project how geoengineering could impact malaria risk by comparing transmission suitability and populations-at-risk today against moderate and high emissions scenarios (RCP 4.5 and 8.5) with and without geoengineering over the next half-century. We show that if geoengineering deployment cools the tropics, it could help protect high elevation populations in eastern Africa from the encroachment of malaria, but could increase transmission in lowland sub-Saharan Africa and southern Asia. Compared to extreme warming, we also find that by 2070, geoengineering would nullify a projected reduction of nearly one billion people at risk of malaria. Our results indicate that geoengineering strategies designed to offset warming are not guaranteed to unilaterally improve health outcomes, and could produce regional trade-offs among Global South countries that are often excluded from geoengineering conversations.
Fetal and Neonatal Echocardiographic Analysis of Biomechanical Alterations for the Hypoplastic Left Heart
Authors: Meyers Brett A. , Bhattacharya Sayantan , Brindise Melissa C. , Loke Yue-Hin , Payne R. Mark , Vlachos Pavlos P. 
Published: October 2020

AbstractBackgroundHypoplastic left heart syndrome (HLHS) presents diagnostic and prognostic challenges while progressing toward heart failure (HF). Understanding the fetal and neonatal HLHS biomechanics, including novel hydrodynamic parameters, could help better planning of the long-term management of HLHS patients.ObjectivesCompare fetal and neonatal HLHS cardiac biomechanics against normal subjects using echocardiography.MethodsWe performed a retrospective study of 10 HLHS patients with echocardiograms at 33-weeks gestation and at the first week post-birth and 12 age-matched controls. We used in-house developed analysis algorithms to quantify ventricular biomechanics from four-chamber B-mode and color Doppler scans. Cardiac morphology, hemodynamics, tissue motion, deformation, and flow parameters were measured.ResultsTissue motion, deformation, and index measurements did not reliably capture biomechanical changes. Stroke volume and cardiac output were nearly twice as large for the HLHS right ventricle (RV) compared to the control RV and left ventricle (LV) due to RV enlargement. The enlarged RV exhibited disordered flow with higher energy loss (EL) compared to prenatal control LV and postnatal control RV and LV. Furthermore, the enlarged RV demonstrated elevated vortex strength (VS) compared to both the control RV and LV, prenatally and postnatally. The HLHS RV showed reduced relaxation with increased early filling velocity (E) compared prenatally to the LV and postnatally to the control RV and LV. Furthermore, increased recovery pressure (ΔP) was observed between the HLHS RV and control RV and LV, prenatally and postnatally.ConclusionsThe novel hydrodynamic parameters more reliably capture the HLHS alterations in contrast to traditional parameters.
Requirements for the containment of COVID-19 disease outbreaks through periodic testing, isolation, and quarantine
Authors: Mukhamadiarov Ruslan I. , Deng Shengfeng , Serrao Shannon R. , Priyanka  , Childs Lauren M. , Täuber Uwe C. 
Published: October 2020

AbstractWe employ individual-based Monte Carlo computer simulations of a stochastic SEIR model variant on a two-dimensional Newman–Watts small-world network to investigate the control of epidemic outbreaks through periodic testing and isolation of infectious individuals, and subsequent quarantine of their immediate contacts. Using disease parameters informed by the COVID-19 pandemic, we investigate the effects of various crucial mitigation features on the epidemic spreading: fraction of the infectious population that is identifiable through the tests; testing frequency; time delay between testing and isolation of positively tested individuals; and the further time delay until quarantining their contacts as well as the quarantine duration. We thus determine the required ranges for these intervention parameters to yield effective control of the disease through both considerable delaying the epidemic peak and massively reducing the total number of sustained infections.
Development of a predictive risk model for severe COVID-19 disease using population-based administrative data
Authors: Zhou Jiandong , Lee Sharen , Wang Xiansong , Li Yi , Wu William KK , Liu Tong , Cao Zhidong , Zeng Daniel Dajun , Kei Wong Ian Chi , Yung Cheung Bernard Man , Zhang Qingpeng , Tse Gary 
Published: October 2020

AbstractBackgroundRecent studies have reported numerous significant predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk score for prompt risk stratification. The objective is to develop a simple risk score for severe COVID-19 disease using territory-wide healthcare data based on simple clinical and laboratory variables.MethodsConsecutive patients admitted to Hong Kong’s public hospitals between 1st January and 22nd August 2020 diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8th September 2020.ResultsCOVID-19 testing was performed in 237493 patients and 4445 patients (median age 44.8 years old, 95% CI: [28.9, 60.8]); 50% male) were tested positive. Of these, 212 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, hypertension, stroke, diabetes mellitus, ischemic heart disease/heart failure, respiratory disease, renal disease, increases in neutrophil count, monocyte count, sodium, potassium, urea, alanine transaminase, alkaline phosphatase, high sensitive troponin-I, prothrombin time, activated partial thromboplastin time, D-dimer and C-reactive protein, as well as decreases in lymphocyte count, base excess and bicarbonate levels. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction.ConclusionsA simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results.
THE MATRIX IS EVERYWHERE: CACO3BIOMINERALIZATION BY THEBACILLUS LICHENIFORMISPLANKTONIC CELLS
Authors: Ivanova Lyubov A. , Golovkina Darya A. , Zhurishkina Elena V. , Garmay Yuri P. , Baranchikov Alexander Ye. , Tsvigun Natalia V. , Zabrodskaya Yana A. , Yapryntsev Alexey D. , Gorshkov Andrey N. , Lebedev Kirill I. , Shaldzhyan Aram A. , Kopitsa Gennady P. , Egorov Vladimir V. , Kulminskaya Anna A. 
Published: October 2020

ABSTRACTTo date, the mechanisms of CaCO3nucleus formation and crystal growth induced by bacterial cells still remain debatable. Here, an insight on the role of planktonic cells ofBacillus licheniformisDSMZ 8782 in the biomineralization is presented. We showed that during 14-days bacterial growth in a liquid urea/Ca2+-containing medium the transformation of CaCO3polymorphs followed the classical pathway “ACC-vaterite-calcite/aragonite”. By microscopic techniques, we detected the formation of extracellular matrix (ECM) around the cells at the stage of exponential growth and appearance of electron-dense inclusions at 24 h after the inoculation. The cells formed filaments and created a network, the nodes of which served as sites for further crystal growth. The ECM formation accompanied with the expression of proteins required for biofilm formation, the aldehyde/alcohol dehydrogenase, stress-associated Clp family proteins, and a porin family protein (ompA ortholog) associated with bacterial extracellular vesicles. We demonstrated that urea and CaCl2acted as denaturing agents causing matrix formation in addition to their traditional role as a source of carbonate and Ca2+ions. We showed that CaCO3nucleation occured insideB. licheniformiscells and further crystal growth and polymorphic transformations took place in the extracellular matrix without attaching to the cell surface. The spatial arrangement of the cells was important for the active crystal growth and dependent on environmental factors. The extracellular matrix played a double role being formed as a stress response and providing a favorable microenvironment for biomineralization (a high concentration of ions necessary for CaCO3crystal aggregation, fixation and stabilization).