bluuebunny/crossref_metadata_2025_split huggingface dataset:
Inhalation: A means to explore and optimize nintedanib’s pharmacokinetic/pharmacodynamic relationship
Authors: Epstein-Shochet Gali , Pham Stephen , Beck Steven , Naiel Safaa , Mekhael Olivia , Revill Spencer , Hayat Aaron , Vierhout Megan , Bardestein-Wald Becky , Shitrit David , Ask Kjetil , Montgomery A. Bruce , Kolb Martin R. , Surber Mark W. 
Published: March 2020

ABSTRACTOral nintedanib is marketed for the treatment of idiopathic pulmonary fibrosis (IPF). While effective slowing fibrosis progression, as an oral medicine nintedanib is limited. To reduce side effects and maximize efficacy, nintedanib was reformulated as a solution for nebulization and inhaled administration. To predict effectiveness treating IPF, the nintedanib pharmacokinetic/pharmacodynamic relationship was dissected. Pharmacokinetic analysis indicated oral-delivered nintedanib plasma exposure and lung tissue partitioning were not dose-proportional and resulting lung levels were substantially higher than blood. Although initial-oral absorbed nintedanib efficiently partitioned into the lung, only a quickly eliminated fraction appeared available to epithelial lining fluid (ELF). Because IPF disease appears to initiate and progress near the epithelial surface, this observation suggests short duration nintedanib exposure (oral portion efficiently partitioned to ELF) is sufficient for IPF efficacy. To test this hypothesis, exposure duration required for nintedanib activity was explored.In vitro, IPF-cellular matrix (IPF-CM) increased primary normal human fibroblast (nHLF) aggregate size and reduced nHLF cell count. IPF-CM also increased nHLF ACTA2 and COL1A expression. Whether short duration (inhalation mimic) or continuous exposure (oral mimic), nintedanib (1-100 nM) reversed these effects.In vivo, intubated silica produced a strong pulmonary fibrotic response. Once-daily (QD) 0.021, 0.21 and 2.1 mg/kg intranasal (IN; short duration inhaled exposure) and twice daily (BID) 30 mg/kg oral (PO; long duration oral exposure) showed that at equivalent-delivered lung concentrations, QD short duration inhaled nintedanib exposure (0.21 mg/kg IN vs. 30 mg/kg PO) exhibited equivalent-to-superior activity as BID oral (reduced silica-induced elastance, alpha-smooth muscle actin, interleukin-1 beta (IL-1β) and soluble collagen, and lung macrophage and neutrophils). An increased lung dose (2.1 mg/kg IN vs. 30 mg/kg PO) exhibited increased effect by further reducing silica-induced elastance, IL-1β and soluble collagen. Neither oral nor inhaled nintedanib reduced silica-induced parenchymal collagen. Both QD inhaled and BID oral nintedanib reduced silica-induced inflammatory index with oral achieving significance. In summary, nintedanib pulmonary anti-fibrotic activity can be achieved using small, infrequent inhaled doses to deliver oral equivalent-to-superior therapeutic effect.
A two-stage ECG signal denoising method based on deep convolutional network
Authors: Qiu Lishen , Cai Wenqiang , Yu Jie , Zhong Jun , Wang Yan , Li Wanyue , Chen Ying , Wang Lirong 
Published: March 2020

AbstractElectrocardiogram (ECG) is an effective and non-invasive indicator for the detection and prevention of arrhythmia. ECG signals are susceptible to noise contamination, which can lead to errors in ECG interpretation. Therefore, ECG pretreatment is important for accurate analysis. In this paper, a method of noise reduction based on deep learning is proposed. The method is divided into two stages, and two corresponding models are formed. In the first stage, a one-dimensional U-net model is designed for ECG signal denoising to eliminate noise as much as possible. The one-dimensional DR-net model in the second stage is used to reconstruct the ECG signal and to correct the waveform distortion caused by noise removal in the first stage. In this paper, the U-net and the DR-net are constructed by the convolution method to achieve end-to-end mapping from noisy ECG signals to clean ECG signals. The ECG data used in this paper are from CPSC2018, and the noise signal is from MIT-BIH Noise Stress Test Database (NSTDB). In the experiment, the improvement in the signal-to-noise ratioSNRimp, the root mean square error decreaseRMSEde, and the correlation coefficientP, are used to evaluate the performance of the network. This two-stage method is compared with FCN and U-net alone. The experimental results show that the two-stage noise reduction method can eliminate complex noise in the ECG signal while retaining the characteristic shape of the ECG signal. According to the results, we believe that the proposed method has a good application prospect in clinical practice.
Correlation between hypophosphatemia and the severity of Corona Virus Disease 2019 patients
Authors: Xue Xiaobo , Ma Jing , Zhao Yuxia , Zhao Aibin , Liu Xiaohong , Guo Wei , Yan Fang , Wang Zhixin , Guo Yanqing , Fan Mengbai 
Published: March 2020

AbstractObjectiveRetrospectively analyze the clinical data of Corona Virus Disease 2019 (COVID-19) patients and explore the value of serum phosphorus level in evaluating the severity and prognosis of the disease.MethodsCOVID-19 patients transferred from the first emergency ward of Taiyuan fourth people’s Hospital from February 8 to March 3, 2020 were enrolled. The information of general conditions, clinical manifestations, laboratory indexes, nucleic acid detection and treatment were collected. The changes of blood phosphorus level and absolute value of lymphocytes in ordinary and severe/critical patients were recorded and compared.ResultsA total of 32 patients with COVID-19 were collected, including 12 cases of common type and 20 cases of severe/critical type. Before treatment, the serum phosphorus levels of the two groups were significantly lower than the normal level, and the serum phosphorus levels of the severe/critical patients were lower than those of the common type patients (t = 2.767, P < 0.010). After treatment, the serum phosphorus levels of the two groups reached normal, and there was no significant difference between the two groups (t = 0.231, P >0.819). The level of lymphocytes in severe/critical patients was lower than that in normal patients (t = 4.636, P < 0.001) before treatment. After treatment, the absolute value of lymphocytes in the two groups reached normal, and there was no significant difference between the two groups (t=1.208,P=0.237). There was a positive correlation between lymphocytes and serum phosphorus, and the correlation coefficient was 0.479.Conclusionhypophosphatemia is related to the severity of COVID-19, and strengthening the monitoring of serum phosphorus level of COVID-19’s severe/critical patients and correcting hypophosphatemia in time are of significance to improve the prognosis.
Work Intensity and Labour Supply
Authors: Donsimoni Jean Roch 
Published: March 2020

AbstractWe develop a model where individuals accumulate fatigue from work intensity when choosing hours worked. Fatigue captures intertemporal costs of labour supply and leads to a utility loss. As fatigue increases, individuals optimally choose to work fewer hours. The model also predicts that if individuals cannot easily shift consumption over time, they will work fewer hours but accumulate more fatigue when work intensity increases. Calibration to 19 European countries provides evidence for the claim that a higher share of the service sector is linked to increasing work fatigue and that public provisions of healthcare improves recovery and mental health.JEL codesE71, I12, J22
Automated classification of neurodegenerative parkinsonian syndromes using multimodal magnetic resonance imaging in a clinical setting
Authors: Chougar Lydia , Faouzi Johann , Pyatigorskaya Nadya , Gaurav Rahul , Biondetti Emma , Villotte Marie , Valabrègue Romain , Corvol Jean-Christophe , Brice Alexis , Mariani Louise-Laure , Cormier Florence , Vidailhet Marie , Dupont Gwendoline , Piot Ines , Grabli David , Payan Christine , Colliot Olivier , Degos Bertrand , Lehéricy Stéphane 
Published: March 2020

ABSTRACTBackgroundSeveral studies have shown that machine learning algorithms using MRI data can accurately discriminate parkinsonian syndromes. Validation under clinical conditions is missing.ObjectivesTo evaluate the accuracy for the categorization of parkinsonian syndromes of a machine learning algorithm trained with a research cohort and tested on an independent clinical replication cohort.Methods361 subjects, including 94 healthy controls, 139 patients with PD, 60 with PSP with Richardson’s syndrome, 41 with MSA of the parkinsonian variant (MSA-P) and 27 with MSA of the cerebellar variant (MSA-P), were recruited. They were divided into a training cohort (n=179) scanned in a research environment, and a replication cohort (n=182), scanned in clinical conditions on different MRI systems. Volumes and DTI metrics in 13 brain regions were used as input for a supervised machine learning algorithm.ResultHigh accuracy was achieved using volumetry in the classification of PD versus PSP, PD versus MSA-P, PD versus MSA-C, PD versus atypical parkinsonian syndromes and PSP versus MSA-C in both cohorts, although slightly lower in the replication cohort (balanced accuracy: 0.800 to 0.915 in the training cohort; 0.741 to 0.928 in the replication cohort). Performance was lower in the classification of PSP versus MSA-P and MSA-P versus MSA-C. When adding DTI metrics, the performance tended to increase in the training cohort, but not in the replication cohort.ConclusionsA machine learning approach based on volumetric and DTI data can accurately classify subjects with early-stage parkinsonism, scanned on different MRI systems, in the setting of their clinical workup.
Clinical characteristics associated with COVID-19 severity in California
Authors: Rubin Samuel J. S. , Falkson Samuel R. , Degner Nicholas , Blish Catherine 
Published: March 2020

AbstractGiven the rapidly progressing COVID-19 pandemic, this report on a US cohort of 54 COVID-19 patients from Stanford Hospital and data regarding risk factors for severe disease obtained at initial clinical presentation is of high importance and is immediately clinically relevant. We identified low presenting oxygen saturation as predictive of severe disease outcomes, such as diagnosis of pneumonia, acute respiratory distress syndrome (ARDS), and admission to the ICU, and also replicated data from China suggesting a link between hypertension and disease severity. Clinicians will benefit by tools to rapidly risk stratify patients at presentation by likelihood of progression to severe disease.
Evaluation of Group Testing for SARS-CoV-2 RNA
Authors: Sinnott-Armstrong Nasa , Klein Daniel L. , Hickey Brendan 
Published: March 2020

AbstractDuring the current COVID-19 pandemic, testing kit and RNA extraction kit availability has become a major limiting factor in the ability to determine patient disease status and accurately quantify prevalence. Current testing strategies rely on individual tests of cases matching restrictive diagnostic criteria to detect SARS-CoV-2 RNA, limiting testing of asymptomatic and mild cases. Testing these individuals is one effective way to understand and reduce the spread of COVID-19.Here, we develop a pooled testing strategy to identify these low-risk individuals. Drawing on the well-studied group testing literature, modeling suggests practical changes to testing protocols which can reduce test costs and stretch a limited test kit supply. When most tests are negative, pooling reduces the total number of tests up to four-fold at 2% prevalence and eight-fold at 0.5% prevalence. At current SARS-CoV-2 prevalence, randomized group testing optimized per country could double the number of tested individuals from 1.85M to 3.7M using only 671k more tests.This strategy is well-suited to supplement testing for asymptomatic and mild cases who would otherwise go untested, and enable them to adopt behavioral changes to slow the spread of COVID-19.
In search for the hotspots of Disease X: A biogeographic approach to mapping the predictive risk of WHO’s Blueprint Priority Diseases
Authors: Jagadesh Soushieta , Combe Marine , Nacher Mathieu , Gozlan Rodolphe Elie 
Published: March 2020

AbstractAnthropization of natural habitats including climate change along with overpopulation and global travel have been contributing to emerging infectious diseases outbreaks. The recent COVID-19 outbreak in Wuhan, highlights such threats to human health, social stability and global trade and economy. We used species distribution modelling and environmental data from satellite imagery to model Blueprint Priority Diseases occurrences. We constructed classical regression and Support Vector Machine models based on environmental predictor variables such as landscape, tree cover loss, climatic covariates. Models were evaluated and a weighed mean was used to map the predictive risk of disease emergence. We mapped the predictive risk for filovirus, Nipah, Rift Valley Fever and coronavirus diseases. Elevation, tree cover loss and climatic covariates were found to significant factors influencing disease emergence. We also showed the relevance of disease biogeography and in the identification potential hotspots for Disease X in regions in Uganda and China.Article Summary LineIn our study with the use of a biogeographic approach, we were able to identify Wuhan as a potential hotspot of disease emergence in the absence of COVID-19 data and we confirm that distribution of disease emergence in humans is spatially dependent on environmental factors.
A modified SEIR model to predict the COVID-19 outbreak in Spain and Italy: simulating control scenarios and multi-scale epidemics
Authors: López Leonardo , Rodó Xavier 
Published: March 2020

AbstractAfter the spread of SARS-CoV-2 epidemic out of China, evolution in the pandemic worldwide shows dramatic differences among countries. In Europe, the situation of Italy first and later Spain has generated great concern, and despite other countries show better prospects, large uncertainties yet remain on the future evolution and the efficacy of containment, mitigation or attack strategies. Here we applied a modifiedSEIRcompartmental model accounting for the spread of infection during the latent period, in which we also incorporate effects of varying proportions of containment. We fit data to quarantined populations in order to account for the uncertainties in case reporting and study the scenario projections for the 17 individual regions (CCAA). Results indicate that with data for March 23, the epidemics follows an evolution similar to the isolation of 1, 5 percent of the population and if there were no effects of intervention actions it might reach a maximum over 1.4Minfected aroundApril27. The effect on the epidemics of the ongoing partial confinement measures is yet unknown (an update of results with data until March 31st is included), but increasing the isolation around ten times more could drastically reduce the peak to over 100kcases by early April, while each day of delay in taking this hard containment scenario represents an 90 percent increase of the infected population at the peak. Dynamics at the sub aggregated levels of CCAA show epidemics at the different levels of progression with the most worrying situation in Madrid an Catalonia. Increasing alpha values up to 10 times, in addition to a drastic reduction in clinical cases, would also more than halve the number of deaths. Updates for March 31st simulations indicate a substantial reduction in burden is underway. A similar approach conducted for Italy pre- and post-interventions also begins to suggest substantial reduction in both infected and deaths has been achieved, showing the efficacy of drastic social distancing interventions.
STeCC: Smart Testing with Contact Counting Enhances Covid-19 Mitigation by Bluetooth App Based Contact Tracing
Authors: Gorji Hossein , Arnoldini Markus , Jenny David F. , Duc Alexandre , Hardt Wolf-Dietrich , Jenny Patrick 
Published: March 2020

AbstractCovid-19 mitigation commonly involves social distancing. Due to its high economic toll and its impact on personal freedom, we need to ease social distancing and deploy alternative measures, while preventing a second wave of infections. Bluetooth app-based contact tracing has been proposed, focusing on symptomatic cases and isolating their contacts. However, this approach would miss many transmissions by asymptomatic cases. To improve effectiveness of app-based mitigation, we propose to complement contact tracing with Smart Testing relying on Contact Counting (STeCC). STeCC focuses virus RNA testing to people with exceptionally high numbers of contacts. These people are at particularly high risk to become infected (with or without symptoms) and transmit the virus. Mathematical modeling shows that a mitigation strategy combining STeCC and contact tracing in one app will be more efficient than contact tracing and works when ≈50% (instead of ≥60%) of the total population participate. Similarly, it requires 50-100 fold less tests than randomized virus testing alone. These gains in efficiency may be critical for success. STeCC could be integrated in the current Bluetooth tracing apps. Thus, STeCC is technically feasible and can reduce the pandemic’s reproduction number by 2.4-fold (e.g. from R0=2.4 to Reff=1) with realistic test numbers (≈166 per 100’000 people per day), when a realistic fraction of the population would use the app (i.e. ≈50% in total population). Thereby, STeCC efficiently complements the portfolio of mitigation strategies, which allow easing social distancing without compromising public health.
Temperature, humidity, and wind speed are associated with lower Covid-19 incidence
Authors: Islam Nazrul , Shabnam Sharmin , Erzurumluoglu A Mesut 
Published: March 2020

AbstractIn absence of empirical research data, there has been considerable speculative hypothesis on the relationship between climatic factors (such as temperature and humidity) and the incidence of Covid-19. This study analyzed the data from 310 regions across 116 countries that reported confirmed cases of Covid-19 by March 12, 2020, and found that temperature, humidity, and wind speed were inversely associated with the incidence rate of Covid-19 after adjusting for the regional and temporal trend in the incidence of Covid-19, columnar density of ozone, precipitation probability, sea-level air-pressure, and length of daytime.
Modeling the Epidemic Dynamics of COVID-19 Outbreak in Iran
Authors: Einian Majid , Tabarraei Hamid Reza 
Published: March 2020

AbstractThe COVID-19 impact on global health and economic system has been profound and unseen since the Spanish flu of 1918-19. Iran is one of the countries that has been severely affected partly because of slow responses to the crisis, ill-preparedness of the health system, and fragile health infrastructure and shortage of protective equipment due to economic sanctions. Due to shortcomings in the reported data, this note tries to estimate a model-based number of infected cases and examines the effectiveness of different policy responses to contain this crisis. Our results show that in an optimistic estimation, the number of unidentified cases can be 3 to 6 times more than the reported numbers. Social distancing alone cannot be an effective policy at this stage of pandemic unless at least 80 percent of the population confine themselves for an extended period of time. An alternative policy is to increase testing extensively and isolate identified cases actively combined with effective social distancing. Otherwise, many lives will be lost and the health system will collapse, adding to the ongoing economic crisis as a result of sanctions for many years to come.
What and When Local Predictors Drive Tadpole Diversity in Subtropical Temporary Ponds?
Authors: Dalmolin Diego Anderson , dos Santos Tiago Gomes , Tozetti Alexandro Marques , Ramos Pereira Maria João 
Published: March 2020

ABSTRACTWe evaluated seasonal variation in taxonomic, functional and phylogenetic diversity and redundancy of tadpoles in 401 points of 10 ponds in southern Brazil. We predicted i) congruent patterns between all components of diversity and environmental descriptors; ii) stronger effects of environment in the diversity components in seasons when the water level in ponds is low; iii) diversity components to be influenced by distinct sets of environmental factors in different periods. Predictions were tested using Linear Mixed Models. We observed positive influence of water depth on taxonomic, functional and phylogenetic diversity, as well as on functional redundancy during periods when the water level in ponds is high. Phylogenetic redundancy was not explained by any of the selected environmental variables. When the water level in ponds is low none of the environmental descriptors affects any of the diversity components. Environmental filtering seems to strongly influence tadpole community structure in temporary ponds, at least in periods when water depth gradients create a variety of micro-habitats allowing diverse sets of species to settle and co-occur. These species sets are then filtered according to their swimming and foraging abilities along the depth gradient, where intermediate depths should contain the greatest tadpole diversity.
Anthrax Toxin as a Molecular Platform to Target Nociceptive Neurons and Modulate Pain
Authors: Yang Nicole J. , Isensee Jörg , Neel Dylan , Liu Sai Man , Zhang Han Xiong Bear , Belu Andreea , Palan Shilpa , Kennedy-Curran Angela , Röderer Pascal , Nitzsche Anja , Lu Mike , Pentelute Bradley L. , Brüstle Oliver , Tripathi Vineeta , Park Jin Mo , Collier R. John , Foster Keith A. , Bean Bruce P. , Leppla Stephen H. , Hucho Tim , Chiu Isaac M. 
Published: March 2020

ABSTRACTBacterial toxins are able to act on neurons to modulate signaling and function. Here, we find that nociceptive sensory neurons that mediate pain are enriched in the receptor for anthrax toxins, ANTXR2. Anthrax Edema Toxin (ET) induced cAMP and PKA signaling in Nav1.8+nociceptive neurons and modulated painin vivo. Peripherally administered ET mediated mechanical allodynia in naïve mice and duringB. anthracisinfection. Intrathecally administered ET produced analgesic effects, potently blocking pain-like behaviors in multiple mouse models of inflammatory and chronic neuropathic pain. Nociceptor-specific ablation of ANTXR2 attenuated ET-induced signaling and analgesia. Modified anthrax toxin successfully delivered exogenous protein cargo into nociceptive neurons, illustrating utility of the anthrax toxin system as a molecular platform to target pain. ET further induced signaling in human iPSC-derived sensory neurons. Our findings highlight novel interactions between a bacterial toxin and nociceptors that may be utilized for developing new pain therapeutics.SUMMARYANTXR2 expression on nociceptive neurons allows selective targeting and modulation of pain by native and engineered anthrax toxins.
The Probability of Fusions Joining Sex Chromosomes and Autosomes
Authors: Anderson Nathan W. , Blackmon Heath 
Published: March 2020

AbstractChromosome fusion and fission are primary mechanisms of karyotype evolution. In particular, the fusion of a sex chromosome and an autosome has been proposed as a mechanism to resolve intralocus sexual antagonism. If sexual antagonism is common throughout the genome, we should expect to see an excess of fusions that join sex chromosomes and autosomes. Here, we present a null model that provides the probability of a sex chromosome autosome fusion, assuming all chromosomes have an equal probability of being involved in a fusion. This closed-form expression is applicable to both male and female heterogametic sex chromosome systems and can accommodate unequal proportions of fusions originating in males and females.