bluuebunny/crossref_metadata_2025_split huggingface dataset:
Self-modulation of motor cortex activity after stroke alters behavior and corticospinal tract structure: a randomized controlled trial
Authors: Sanders Zeena-Britt , Fleming Melanie K. , Smejka Tom , Marzolla Marilien C. , Zich Catharina , Rieger Sebastian W. , Lührs Michael , Goebel Rainer , Sampaio-Baptista Cassandra , Johansen-Berg Heidi 
Published: September 2021

AbstractReal-time functional magnetic resonance imaging (fMRI) neurofeedback allows individuals to self-modulate their ongoing brain activity. This may be a useful tool in clinical disorders which are associated with altered brain activity patterns. Motor impairment after stroke has previously been associated with decreased laterality of motor cortex activity. Here we examined whether chronic stroke survivors were able to use real-time fMRI neurofeedback to increase laterality of motor cortex activity and assessed effects on motor performance and on brain structure and function. We carried out a randomized, double-blind, sham-controlled trial in which 24 chronic stroke survivors with mild to moderate upper limb impairment experienced three training days of either Real (n=12) or Sham (n=12) neurofeedback. Stroke survivors were able to use Real neurofeedback to increase laterality of motor cortex activity within, but not across, training days. Improvement in gross hand motor performance assessed by the Jebsen Taylor Test (JTT) was observed in the Real neurofeedback group compared to Sham. However, there were no improvements on the Action Research Arm Test (ARAT) or the Upper Extremity Fugl-Meyer (UE-FM) score. Additionally, decreased white-matter asymmetry of the corticospinal tracts was detected 1-week after neurofeedback training, indicating that the tracts become more similar with Real neurofeedback. Changes in the affected corticospinal tract was positively correlated with neurofeedback performance. Therefore, here we demonstrate that chronic stroke survivors are able to use fMRI neurofeedback to self-modulate motor cortex activity, and that training is associated with improvements in hand motor performance and with white matter structural changes.
Status Quo and Influencing Factors of Family Doctors Performance in Dongguan City
Authors: Wang Jiao , Xiao Junhui , Li Xiali , Zhang Lin , Wang Na 
Published: September 2021

AbstractBackgroundThe low-performance rate of family doctors is the most important problem in the implementation of the contract service system of family doctors in China, and improving the performance rate of family doctors has become a topic of common concern. We investigated the performance behavior of family doctors in Dongguan, Guangdong Province, to understand the current situation of family doctors performance in Dongguan, and to explore the influencing factors of their performance.ObjectivesTo understand the status quo of family doctors performance in Dongguan City, Guangdong Province, and to explore the influencing factors of family doctors agreement fulfillment rate.MethodsThe multi-stage sampling method was used to randomly select family doctors in Dongguan City, Guangdong Province, to investigate the status quo of family doctors performance in community health service institutions in Dongguan City. Logistic regression analysis was used to investigate the influencing factors of family doctors performance, and decision trees and associations were used to further identify the influencing factors of family doctors performance.Results100 family doctors participated in the survey, and 75% of them were generally able to perform their duties. Logistic regression analysis shows that whether a family doctor signs a contract with a resident is the influencing factor of family doctor performance behavior, and the higher the family doctor signing rate, the higher the family doctor performance rate. The results of decision tree analysis showed that the biggest influence factor of family doctors performance behavior is whether the family doctors are contracted residents or not. ROC curve was 0.860, and pAUC was 0.073 respectively. The most influential factor in decreasing the average Gini index of random forest is whether the family doctor signed a contract or not. The results of association rules showed that there was a correlation between whether the family doctor signed a contract or not and the performance of the family doctor.ConclusionsThe performance rate of family doctors in Dongguan City, Guangdong Province is relatively high. Whether family doctors sign contracts with residents is the influencing factor of family doctors performance. Improving the contract rate of family doctors is the key to improve the performance rate of family doctors.
The COMBAT-TB Workbench: Making powerful TB bioinformatics accessible
Authors: van Heusden Peter , Mashologu Ziphozakhe , Lose Thoba , Warren Robin , Christoffels Alan 
Published: September 2021

AbstractWhole Genome Sequencing (WGS) is a powerful method for detecting drug resistance, genetic diversity and transmission dynamics of Mycobacterium tuberculosis. Implementation of WGS in public health microbiology laboratories is impeded by a lack of user-friendly, automated and semi-automated pipelines. We present the COMBAT-TB workbench, a modular, easy to install application that provides a web based environment for Mycobacterium tuberculosis bioinformatics. The COMBAT-TB Workbench is built using two main software components: the IRIDA Platform for its web-based user interface and data management capabilities and the Galaxy bioinformatics workflow platform for workflow execution. These components are combined into a single easy to install application using Docker container technology. We implemented two workflows, for M. tuberculosis sample analysis and phylogeny, in Galaxy. Building our workflows involved updating some Galaxy tools (Trimmomatic, snippy and snp-sites) and writing new Galaxy tools (snp-dists, TB-Profiler, tb_variant_filter and TB Variant Report). The irida-wf-ga2xml tool was updated to be able to work with recent versions of Galaxy and was further developed into IRIDA plugins for both workflows. In the case of the M. tuberculosis sample analysis an interface was added to update the metadata stored for each sequence sample with results gleaned from the Galaxy workflow output. Data can be loaded into the COMBAT-TB Workbench via the web interface or via the command line IRIDA uploader tool. The COMBAT-TB Workbench application deploys IRIDA, the COMBAT-TB IRIDA plugins, the MariaDB database and Galaxy using Docker containers (https://github.com/COMBAT-TB/irida-galaxy-deploy).ImportanceWhile the reduction in cost of WGS is making sequencing more affordable in Lower and Middle Income Countries, public health laboratories in these countries seldom have access to bioinformaticians and system support engineers adept at using the Linux command line and complex bioinformatics software. The COMBAT-TB Workbench provides an open source, modular, easy to deploy and use environment for managing and analysing M. tuberculosis WGS data and thereby makes WGS usable in practice in the LMIC context.
Structural and diffusion weighted brain imaging biomarkers for ADHD and its symptomology in very young (4–7-year-old) children
Authors: Öztekin Ilke , Garic Dea , Finlayson Mark A. , Graziano Paulo A. , Dick Anthony Steven 
Published: September 2021

ABSTRACTThe current study aimed to identify the key neurobiology of Attention-Deficit/Hyperactivity Disorder (ADHD), as it relates to ADHD diagnostic category and symptoms of hyperactive/impulsive behavior and inattention. To do so, we adapted a predictive modeling approach to identify the key structural and diffusion weighted brain imaging measures, and their relative standing with respect to teacher ratings of executive function – EF (measured by the Metacognition Index of the Behavior Rating Inventory of Executive Function– BRIEF), negativity and emotion regulation – ER (measured by the Emotion Regulation Checklist, ERC), in a critical young age range (ages 4 to 7, mean age 5.52 years, 82.2% Hispanic/Latino), where initial contact with educators and clinicians typically take place. Teacher ratings of EF and ER were predictive of both ADHD diagnostic category and symptoms of hyperactive/impulsive behavior and inattention. Among the neural measures evaluated, the current study identified the critical importance of the largely understudied diffusion weighted imaging measures for the underlying neurobiology of ADHD and its associated symptomology. Specifically, our analyses implicated the inferior frontal gyrus, the pericallosal sulcus, and the caudate as critical predictors of ADHD diagnostic category and its associated symptomology, above and beyond teacher ratings of EF and ER. Collectively, the current set of findings have implications for theories of ADHD, the relative utility of neurobiological measures with respect to teacher ratings of EF and ER, and the developmental trajectory of its underlying neurobiology.
Prediction of radiation-induced hypothyroidism using radiomic data analysis does not show superiority over standard normal tissue complication models
Authors: Smyczynska Urszula , Grabia Szymon , Nowicka Zuzanna , Papis-Ubych Anna , Bibik Robert , Latusek Tomasz , Rutkowski Tomasz , Fijuth Jacek , Fendler Wojciech , Tomasik Bartłomiej 
Published: September 2021

AbstractState-of-art normal tissue complication probability (NTCP) models do not take into account more complex individual anatomical variations, which can be objectively quantitated and compared in radiomic analysis. The goal of this project was development of radiomic NTCP model for radiation-induced hypothyroidism (RIHT) using imaging biomarkers (radiomics). We gathered CT images and clinical data from 98 patients, who underwent intensity-modulated radiation therapy (IMRT) for head and neck cancers with a planned total dose of 70.0 Gy (33–35 fractions). During the 28-month (median) follow-up 27 patients (28%) developed RIHT. For each patient, we extracted 1316 radiomic features from original and transformed images using manually contoured thyroid masks. Creating models based on clinical, radiomic features or a combination thereof, we considered 3 variants of data preprocessing. Based on their performance metrics (sensitivity, specificity), we picked best models for each variant ((0.8, 0.96), (0.9, 0.93), (0.9, 0.89) variant-wise) and compared them with external NTCP models ((0.82, 0.88), (0.82, 0.88), (0.76, 0.91)). Our models reach accuracy comparable with or better than previously presented non-radiomic NTCP models. The benefit of our approach is obtaining the RIHT predictions before treatment planning to adjust IMRT plan to avoid the thyroid region in most susceptible patients.
Cross-sectional assessment of predictors for COVID-19 vaccine uptake: an online survey in Greece
Authors: Galanis Petros , Vraka Irene , Siskou Olga , Konstantakopoulou Olympia , Katsiroumpa Aglaia , Moisoglou Ioannis , Kaitelidou Daphne 
Published: September 2021

AbstractBackgroundA high level of COVID-19 vaccine uptake in the general population is essential to control the pandemic.ObjectiveTo estimate the percentage of the general population vaccinated against the COVID-19 and to investigate the factors associated with COVID-19 vaccine uptake.MethodsWe conducted an online cross-sectional study in Greece during August 2021. We included individuals over 18 years of age. Independent variables included socio-demographic data of the participants and attitudes towards COVID-19 vaccination and pandemic. Our outcome variable was COVID-19 vaccination status, measured through “yes/no” answers.ResultsMost participants had been vaccinated against the COVID-19 (87.8%), while about half had been vaccinated against the influenza (52.5%). Multivariate analysis identified that increased age and higher level of education were associated with an increased likelihood of COVID-19 vaccination. Also, participants working in health services, participants without a previous COVID-19 diagnosis and those with previous seasonal influenza vaccination history had a greater probability to take a COVID-19 vaccine. Additionally, increased self-perceived severity of COVID-19, knowledge regarding COVID-19, and trust in COVID-19 vaccines and scientists were associated with COVID-19 vaccine uptake. On the other hand, the likelihood of vaccination was lower for participants who were more concerned about the side effects of COVID-19 vaccination.ConclusionsUnderstanding the factors affecting individuals’ decision to take a COVID-19 vaccine is essential to improve the COVID-19 vaccination coverage rate. Policymakers and scientists should scale up their efforts to increase the COVID-19 vaccination rate among specific population groups such as young people, people with low level of education, people with negative attitudes towards vaccination, etc.
Neutralizing efficacy of vaccines against the SARS-CoV-2 Mu variant
Authors: Miyakawa Kei , Jeremiah Sundararaj Stanleyraj , Kato Hideaki , Ryo Akihide 
Published: September 2021

AbstractThe rise of mutant strains of SARS-CoV-2 poses an additional problem to the existing pandemic of COVID-19. There are rising concerns about the Mu variant which can escape humoral immunity acquired from infections from previous strains or vaccines. We examined the neutralizing efficacy of the BNT162b2 mRNA vaccine against the Mu variant and report that the vaccine has 76% neutralizing effectiveness against the Mu compared to 96% with the original strain. We also show that Mu, similar to the Delta variant, causes cell-to-cell fusion which can be an additional factor for the variant to escape vaccine-mediated humoral immunity. Despite the rise in vaccine escape strains, the vaccine still possesses adequate ability to neutralize majority of the mutants.
Assessing the impact of temperature and humidity exposures during early infection stages on case-fatality of COVID-19: a modelling study in Europe
Authors: Liang Jingbo , Yuan Hsiang-Yu 
Published: September 2021

AbstractBackgroundAlthough associations between key weather indicators (i.e. temperature and humidity) and COVID-19 mortality has been reported, the relationship between these exposures among different timing in early infection stages (from virus exposure up to a few days after symptom onset) and the probability of death after infection (also called case fatality rate, CFR) has yet to be determined.MethodsWe estimated the instantaneous CFR of eight European countries using Bayesian inference in conjunction with stochastic transmission models, taking account of delays in reporting the number of newly confirmed cases and deaths. The exposure-lag–response associations between fatality rate and weather conditions to which patients were exposed at different timing were obtained using distributed lag nonlinear models coupled with mixed-effect models.ResultsOur results showed that the Odds Ratio (OR) of death is negatively associated with the temperature, with two maxima (OR=1.29 (95% CI: 1.23, 1.35) at -0.1°C; OR=1.12 (95% CI: 1.08, 1.16) at 0.1°C) occurred at the time of virus exposure and after symptom onset. Two minima (OR=0.81 (95% CI: 0.71, 0.92) at 23.2°C; OR=0.71 (95% CI: 0.63, 0.80) at 21.7°C) also occurred at these two distinct periods correspondingly. Low humidity (below 50%) during the early stages and high humidity (approximately 89%) after symptom onset were related to the lower fatality.ConclusionEnvironmental conditions may affect not only the initial viral load when exposure to viruses but also individuals’ immunity response around symptom onset. Warmer temperatures and higher humidity after symptom onset were related to the lower fatality.HighlightsTemperature and humidity conditions that patients were exposed to during their early infection stages were associated with COVID-19 case fatality rate.Warmer temperatures (> 20°C) at infection time or after symptom onset, but not during the incubation period, were associated with lower death risk. Low relative humidity (< 50%) during the early stages and high relative humidity (> 85%) after symptom onset were related to higher death risk.Creating optimal indoor conditions for cases who are under quarantine/isolation may reduce their risk of death.
Development and validation of an electronic health records-based opioid use disorder algorithm by expert clinical adjudication
Authors: Ranapurwala Shabbar I. , Alam Ishrat Z. , Pence Brian W. , Carey Timothy S. , Christensen Sean , Clark Marshall , Chelminski Paul R. , Wu Li-Tzy , Greenblatt Lawrence H. , Korte Jeffrey E. , Wolfson Mark , Douglas Heather E. , Bowlby Lynn A. , Capata Michael , Marshall Stephen W. 
Published: September 2021

AbstractBackgroundIn the US, over 200 lives are lost from opioid overdoses each day. Accurate and prompt diagnosis of opioid use disorders (OUD) may contribute substantially to prevention of overdose deaths. However, OUD research is limited, the specificity and sensitivity of OUD ICD codes are unknown, and the ICD codes are known to underestimate OUD prevalence. We developed and validated algorithms to identify OUD from EHR data and examine validity of ICD-based definitions for OUD.MethodsThrough multiple iterations, we developed EHR-based algorithms to identify OUD. These algorithms and ICD-based OUD definition were validated against a total of 169 independent gold standard EHR chart reviews conducted by an expert adjudication panel of eight pain and addiction medicine clinical experts across four large healthcare systems. The experts relied on clinical judgement and current Diagnostic and Statistical Manual of Mental Disorders-5 criteria for making OUD diagnoses.ResultsOf the 169 EHR charts, 81 (48%) were reviewed by more than one expert and exhibited 85% agreement between the reviewing experts. The OUD ICD codes alone had 10% sensitivity and 99% specificity, underscoring the strong potential for OUD underestimation in studies depending on ICD codes alone. In comparison, after four iterations, the algorithms identified OUD with a 23% sensitivity and 98% specificity.Conclusions and RelevanceThis is the first study to evaluate the validity of OUD ICD codes and develop validated EHR-based algorithms to address OUD underestimation. This work has the potential to inform future research on early intervention and prevention of OUD.
The unmutated common ancestor of Gp41 conformational epitope targeting variable heavy chain 1-02 utilizing antibodies maintains antibody dependent cell cytotoxicity
Authors: Wrotniak Brian H. , Garrett Meghan , Baron Sarah , Sojar Hakimuddin , Shon Alyssa , Asiago-Reddy Elizabeth , Yager Jessica , Kalams Spyros , Croix Michael , Hicar Mark D 
Published: September 2021

AbstractIn studies on monoclonal Abs (mAbs) from long-term non-progressors (LTNPs), our laboratory has previously described highly mutated Abs against a complex conformational epitope with contributions from both gp41 heptad repeat regions. Despite using the VH1-02 gene segment, known to contribute to some of the broadest neutralizing Abs against HIV, members of these Abs, termed group 76C Abs, did not exhibit broad neutralization.Because of the excessive mutations and use of VH1-02, our goal was to characterize the non-neutralizing functions of Abs of group 76C, to assess targeting of the epitope in various clinical presentations, and to assess the development of these Abs by comparison to their predicted common ancestor. Serum competition assays showed group 76C Abs were enriched in LTNPs, in comparison to VRC-01. Specific group 76C clones 6F5 and 6F11, expressed as recombinant Abs, both have robust ADCC activity, despite their sequence disparity. Sequence analysis predicted the common ancestor of this clonal group would utilize the germline non-mutated variable gene. We produced a recombinant ancestor Ab (76Canc) with a heavy chain utilizing the germline variable gene sequence paired to the 6F5 light chain. Competition with group 76C recombinant Ab 6F5 confirms 76Canc binds HIV envelope constructs near the original group C epitope. 76Canc demonstrates comparable ADCC to 6F5 and 6F11 when targeting both clade B and C HIV constructs. The functional capability of Abs utilizing germline VH1-02 has implications for disease control and vaccine development.
Modeling the transmission of the SARS-CoV-2 delta variant in a partially vaccinated population
Authors: de León Ugo Avila-Ponce , Avila-Vales Eric , Huang Kuan-lin 
Published: September 2021

AbstractIn a population with ongoing vaccination, the trajectory of a pandemic is determined by how the virus spreads in unvaccinated and vaccinated individuals that exhibit distinct transmission dynamics based on different levels of natural and vaccine-induced immunity. We developed a mathematical model that considers both subpopulations and immunity parameters including vaccination rates, vaccine effectiveness, and a gradual loss of protection. The model forecasted the spread of the SARS-CoV-2 delta variant in the US under varied transmission and vaccination rates. We further obtained the control reproduction number and conducted sensitivity analyses to determine how each parameter may affect virus transmission. Our results show that a combination of strengthening vaccine-induced immunity and preventative behavioral measures will likely be required to deaccelerate the rise of infectious SARS-CoV-2 variants.One-Sentence SummaryMathematical models considering vaccinated and unvaccinated individuals help forecast and manage the spread of new SARS-CoV-2 variants.
Sleep in the Intensive Care Unit through the Lens of Breathing and Heart Rate Variability: A Cross-Sectional Study
Authors: Ganglberger Wolfgang , Krishnamurthy Parimala Velpula , Quadri Syed A. , Tesh Ryan A. , Bucklin Abigail A. , Adra Noor , Da Silva Cardoso Madalena , Leone Michael J. , Hemmige Aashritha , Rajan Subapriya , Panneerselvam Ezhil , Paixao Luis , Higgins Jasmine , Ayub Muhammad Abubakar , Shao Yu-Ping , Coughlin Brian , Sun Haoqi , Ye Elissa M. , Cash Sydney S. , Thompson B. Taylor , Akeju Oluwaseun , Kuller David , Thomas Robert J. , Westover M. Brandon 
Published: September 2021

AbstractBackgroundFull polysomnography, the gold standard of sleep measurement, is impractical for widespread use in the intensive care unit (ICU). Wrist-worn actigraphy and subjective sleep assessments do not measure sleep physiology adequately. Here, we explore the feasibility of estimating conventional sleep indices in the ICU with heart rate variability (HRV) and respiration signals using artificial intelligence methods.MethodsWe used deep learning models to stage sleep with HRV (through electrocardiogram) and respiratory effort (through a wearable belt) signals in critically ill adult patients admitted to surgical and medical ICUs, and in covariate-matched sleep laboratory patients. We analyzed the agreement of the determined sleep stages between the HRV- and breathing-based models, computed sleep indices, and quantified breathing variables during sleep.ResultsWe studied 102 adult patients in the ICU across multiple days and nights, and 220 patients in a clinical sleep laboratory. We found that sleep stages predicted by HRV- and breathing-based models showed agreement in 60% of the ICU data and in 81% of the sleep laboratory data. In the ICU, deep NREM (N2 + N3) proportion of total sleep duration was reduced (ICU 39%, sleep laboratory 57%, p<0.01), REM proportion showed heavy-tailed distribution, and the number of wake transitions per hour of sleep (median = 3.6) was comparable to sleep laboratory patients with sleep-disordered breathing (median = 3.9). Sleep in the ICU was also fragmented, with 38% of sleep occurring during daytime hours. Finally, patients in the ICU showed faster and less variable breathing patterns compared to sleep laboratory patients.ConclusionsCardiovascular and respiratory signals encode sleep state information, which can be utilized to measure sleep state in the ICU. Using these easily measurable variables can provide automated information about sleep in the ICU.