bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lockdown exit strategies and risk of a second epidemic peak: a stochastic agent-based model of SARS-CoV-2 epidemic in France
Authors: Hoertel Nicolas , Blachier Martin , Blanco Carlos , Olfson Mark , Massetti Marc , Rico Marina Sánchez , Limosin Frédéric , Leleu Henri 
Published: May 2020

AbstractMost European countries have responded to the COVID-19 threat by nationwide implementation of barrier measures and lockdown. However, assuming that population immunity will build up through the epidemic, it is likely to rebound once these measures are relaxed, possibly leading to a second or multiple repeated lockdowns. In this report, we present results of epidemiological modelling that has helped inform policy making in France. We used a stochastic agent-based microsimulation model of the COVID-19 epidemic in France, and examined the potential impact of post-quarantine measures, including social distancing, mask-wearing, and shielding of the population the most vulnerable to severe COVID-19 infection, on the disease’s cumulative incidence and mortality, and on ICU-bed occupancy. The model calibrated well and variation of model parameter values had little impact on outcome estimates. While quarantine is effective in containing the viral spread, it would be unlikely to prevent a rebound of the epidemic once lifted, regardless of its duration. Both social distancing and mask-wearing, although effective in slowing the epidemic and in reducing mortality, would also be ineffective in ultimately preventing the overwhelming of ICUs and a second lockdown. However, these measures coupled with shielding of vulnerable people would be associated with better outcomes, including lower cumulative incidence, mortality, and maintaining an adequate number of ICU beds to prevent a second lockdown. Benefits would nonetheless be markedly reduced if these measures were not applied by most people or not maintained for a sufficiently long period, as herd immunity progressively establishes in the less vulnerable population.
RSI model: COVID-19 in Germany Alternating quarantine episodes and normal episodes
Authors: Knoch Carolin , Hartmann Timo , Wildblood Alan 
Published: May 2020

AbstractThis paper was developed in the Civil Systems Engineering Department at the Technical University of Berlin. The background is the spread of COVID-19 pandemic in Germany. Our current situation is a threat from the COVID-19 virus without vaccine and medication. Germany has increased its intensive capacities, intensified research in its analyzes and research projects and is currently testing the influence of various measures on the rate of expansion.The goal now is to find a strategy that slows the spread so that medical capacities are not overloaded. The approach is to alternate between quarantine and normal episodes and the result is an oscillation in the number of cases between two limits.The mathematical model is an SRI model that can be used to calculate the development of the case numbers. All uncertain parameters are varied at significant intervals. Influence parameters and control parameters were defined. By adjusting the length of the episode to match the speed of spread of the virus, the gap can be bridged until a vaccine has been developed or sufficient immunity is available in the population. By observing the capacity limits in connection with the number of cases, the need for a new quarantine episode and the possibility of initiating a normal episode can be predetermined. This does not exceed the capacity limit.In quarantine, contagion within households is limited by the size of the household; only system-critical professions continue to work in public. All others must remain in the homeschooling / home office. In the normal episode, everyone can work, go to school, have social contacts. A solution could be found in all parameter combinations. The ratio of the normal episode duration / quarantine episode lies between 0.3 and 0.95 in the examination area.
Profiling COVID-19 pneumonia progressing into the cytokine storm syndrome: results from a single Italian Centre study on tocilizumab versus standard of care
Authors: Quartuccio Luca , Sonaglia Arianna , McGonagle Dennis , Fabris Martina , Peghin Maddalena , Pecori Davide , Monte Amato De , Bove Tiziana , Curcio Francesco , Bassi Flavio , De Vita Salvatore , Tascini Carlo 
Published: May 2020

AbstractObjectiveApproximately 5% of patients with coronavirus disease 2019 (COVID-19) develop a life-threatening pneumonia that often occurs in the setting of increased inflammation or “cytokine storm”. Anti-cytokine treatments are being evaluated but optimal patient selection remains unclear, and the aim of our study is to address this point.MethodsBetween February 29 to April 6, 2020, 111 consecutive hospitalized patients with COVID-19 pneumonia were evaluated in a single centre retrospective study. Patients were divided in two groups: 42 severe cases (TOCI) with adverse prognostic features including raised CRP and IL-6 levels, who underwent anti-cytokine treatments, mostly tocilizumab, and 69 standard of care patients (SOC).ResultsIn the TOCI group, all received anti-viral therapy and 40% also received glucocorticoids. In TOCI, 62% of cases were ventilated and there were 3 deaths (17.8±10.6 days, mean follow up) with 7/26 cases remaining on ventilators, without improvement, and 17/26 developed bacterial superinfection. One fatality occurred in the 15 TOCI cases treated on noninvasive ventilation and 1 serious bacterial superinfection. Of the 69 cases in SOC, there was no fatalities and no bacterial complications. The TOCI group had higher baseline CRP and IL-6 elevations (p<0.0001 for both) and higher neutrophils and lower lymphocyte levels (p= 0.04 and p=0.001, respectively) with the TOCI ventilated patients having higher markers than non-ventilated TOCI patients.ConclusionHigher inflammatory markers, more infections and worse outcomes characterized ventilated TOCI cases compared to ward based TOCI. Despite the confounding factors, this suggests that therapy time in anti-cytokine randomized trials will be key.FundingThis research received no external funding.Conflicts of Interest“The authors declare no conflict of interest.”HighlightsThere is an urgent need for markers of prognosis in COVID-19.Higher inflammatory markers best select tocilizumab treatment.The ward based tocilizumab group showed better responses and less infections than ICU tocilizumab group.The former group may be the best for evaluating the impact of anti-cytokine therapy in COVID-19.The known poor risk factors for COVID-19 infection were present in the TOCI treated rather than in the good prognosis standard of care group.
TRACKING AND PREDICTING COVID-19 RADIOLOGICAL TRAJECTORY USING DEEP LEARNING ON CHEST X-RAYS: INITIAL ACCURACY TESTING
Authors: Duchesne S. , Gourdeau D. , Archambault P. , Chartrand-Lefebvre C. , Dieumegarde L. , Forghani R. , Gagné C. , Hains A. , Hornstein D. , Le H. , Lemieux S. , Lévesque M.H. , Martin D. , Rosenbloom L. , Tang A. , Vecchio F. , Potvin O. , Duchesne N. 
Published: May 2020

ABSTRACTBackgroundDecision scores and ethically mindful algorithms are being established to adjudicate mechanical ventilation in the context of potential resources shortage due to the current onslaught of COVID-19 cases. There is a need for a reproducible and objective method to provide quantitative information for those scores.PurposeTowards this goal, we present a retrospective study testing the ability of a deep learning algorithm at extracting features from chest x-rays (CXR) to track and predict radiological evolution.Materials and MethodsWe trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from two open-source datasets (last accessed on April 9, 2020)(Italian Society for Medical and Interventional Radiology and MILA). Data collected form 60 pairs of sequential CXRs from 40 COVID patients (mean age ± standard deviation: 56 ± 13 years; 23 men, 10 women, seven not reported) and were categorized in three categories: “Worse”, “Stable”, or “Improved” on the basis of radiological evolution ascertained from images and reports. Receiver operating characteristic analyses, Mann-Whitney tests were performed.ResultsOn patients from the CheXnet dataset, the area under ROC curves ranged from 0.71 to 0.93 for seven imaging features and one diagnosis. Deep learning features between “Worse” and “Improved” outcome categories were significantly different for three radiological signs and one diagnostic (“Consolidation”, “Lung Lesion”, “Pleural effusion” and “Pneumonia”; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between “Worse” and “Improved” cases with 82.7% accuracy.ConclusionCXR deep learning features show promise for classifying the disease trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions.
Catalytic cleavage of HEAT and subsequent covalent binding of the tetralone moiety by the SARS-CoV-2 main protease
Authors: Günther Sebastian , Reinke Patrick Y. A. , Oberthuer Dominik , Yefanov Oleksandr , Ginn Helen , Meier Susanne , Lane Thomas J. , Lorenzen Kristina , Gelisio Luca , Brehm Wolfgang , Dunkel Illona , Domaracky Martin , Saouane Sofiane , Lieske Julia , Ehrt Christiane , Koua Faisal , Tolstikova Alexandra , White Thomas A. , Groessler Michael , Fleckenstein Holger , Trost Fabian , Galchenkova Marina , Gevorkov Yaroslav , Li Chufeng , Awel Salah , Peck Ariana , Xavier P. Lourdu , Barthelmess Miriam , Schlünzen Frank , Werner Nadine , Andaleeb Hina , Ullah Najeeb , Falke Sven , Franca Bruno Alves , Schwinzer Martin , Brognaro Hévila , Seychell Brandon , Gieseler Henry , Melo Diogo , Zaitseva-Kinneberg Joanna I. , Norton-Baker Brenna , Knoska Juraj , Esperanza Gisel , Mashhour Aida Rahmani , Guicking Filip , Hennicke Vincent , Fischer Pontus , Rogers Cromarte , Monteiro Diana C. F. , Hakanpää Johanna , Meyer Jan , Noei Heshmat , Gribbon Phil , Ellinger Bernhard , Kuzikov Maria , Wolf Markus , Zhang Linlin , Sun Xinyuanyuan , Pletzer-Zelgert Jonathan , Wollenhaupt Jan , Feiler Christian , Weiss Manfred , Schulz Eike-Christian , Mehrabi Pedram , Schmidt Christina , Schubert Robin , Han Huijong , Krichel Boris , Fernández-García Yaiza , Escudero-Pérez Beatriz , Günther Stephan , Turk Dusan , Uetrecht Charlotte , Beck Tobias , Tidow Henning , Chari Ashwin , Zaliani Andrea , Rarey Matthias , Cox Russell , Hilgenfeld Rolf , Chapman Henry N. , Pearson Arwen R. , Betzel Christian , Meents Alke 
Published: May 2020

AbstractHere we present the crystal structure of SARS-CoV-2 main protease (Mpro) covalently bound to 2-methyl-1-tetralone. This complex was obtained by co-crystallization of Mprowith HEAT (2-(((4-hydroxyphenethyl)amino)methyl)-3,4-dihydronaphthalen-1(2H)-one) in the framework of a large X-ray crystallographic screening project of Mproagainst a drug repurposing library, consisting of 5632 approved drugs or compounds in clinical phase trials. Further investigations showed that HEAT is cleaved by Mproin an E1cB-like reaction mechanism into 2-methylene-1-tetralone and tyramine. The catalytic Cys145 subsequently binds covalently in a Michael addition to the methylene carbon atom of 2-methylene-1-tetralone. According to this postulated model HEAT is acting in a pro-drug-like fashion. It is metabolized by Mpro, followed by covalent binding of one metabolite to the active site. The structure of the covalent adduct elucidated in this study opens up a new path for developing non-peptidic inhibitors.
Group Testing Performance Evaluation for SARS-COV-2 Massive Scale Screening and Testing
Authors: Nalbantoglu Ozkan Ufuk 
Published: May 2020

ABSTRACTThe capacity of current molecular testing convention does not allow high-throughput and community level scans of COVID-19 infections. The diameter in current paradigm of shallow tracing is unlikely to reach the silent clusters that might be as important as the symptomatic cases in the spread of the disease. Group testing is a feasible and promising approach when the resources are scarce and when a relatively low prevalence regime is observed on the population. We employed group testing with a sparse random pooling scheme and conventional group test decoding algorithms both for exact and inexact recovery. Our simulations showed that significant reduction in per case test numbers (or expansion in total test numbers preserving the number of actual tests conducted) for very sparse prevalence regimes is available. Currently proposed COVID-19 group testing schemes offer a gain up to 10X scale-up. There is a good probability that the required scale up to achieve massive scale testing might be greater in certain scenarios. We investigated if further improvement is available, especially in sparse prevalence occurrence where outbreaks are needed to be avoided by population scans. Our simulations show that sparse random pooling can provide improved efficiency gains compared to row-column group testing or Reed-Solomon error correcting codes. Therefore, we propose that special designs for different scenarios could be available and it is possible to scale up testing capabilities significantly.
The interplay between Bag-1, Hsp70, and Hsp90 reveals that inhibiting Hsp70 rebinding is essential for Glucocorticoid Receptor activity
Authors: Kirschke Elaine , Roe-Zurz Zygy , Noddings Chari , Agard David 
Published: May 2020

AbstractThe glucocorticoid receptor (GR), like many signaling proteins requires Hsp90 for sustained activity. Previous biochemical studies revealed that the requirement for Hsp90 is explained by its ability to reverse Hsp70-mediated inactivation of GR through a complex process requiring both cochaperones and Hsp90 ATP hydrolysis. How ATP hydrolysis on Hsp90 enables GR reactivation is unknown. The canonical mechanism of client release from Hsp70 requires ADP:ATP exchange, which is normally rate limiting. Here we show that independent of ATP hydrolysis, Hsp90 acts as an Hsp70 nucleotide exchange factor (NEF) to accelerate ADP dissociation, likely coordinating GR transfer from Hsp70 to Hsp90. As Bag-1 is a canonical Hsp70 NEF that can also reactivate Hsp70:GR, the impact of these two NEFs was compared. Simple acceleration of Hsp70:GR release was insufficient for GR reactivation as Hsp70 rapidly re-binds and re-inactivates GR. Instead, inhibition of GR re-inactivation by Hsp70 is critical. This can be accomplished by high non-physiological Bag-1 concentrations, which also inhibit Hsp70:ATP binding. In contrast, in an ATP-hydrolysis dependent process, Hsp90 plays a unique role by kinetically partitioning GR into a state that can bind ligand, but is protected from Hsp70 inactivation, thus allowing GR to be activated by its ligand but still able to re-enter the chaperone cycle. At physiologic concentrations, Bag-1 works synergistically with Hsp90 to accelerate the first rate-limiting step in GR reactivation. The net effect is that the chaperone machinery cyclically dictates the on and off rates for GR ligand, providing a timer controlling the persistence of activated GR.Significance StatementThe glucocorticoid receptor (GR) is an essential transcription regulatory factor. Like many signaling proteins, GR activity is regulated by two essential molecular chaperones: Hsp90 and Hsp70. Functioning like a toggle switch, Hsp70 first inactivates GR, and then Hsp90 reactivates GR in an Hsp90 ATP hydrolysis dependent manner. Here, an intricate set of biochemistry experiments uncover fundamental principles governing how these chaperone systems collaboratively regulate GR activity. While Hsp90 promotes GR release from Hsp70 by modulating Hsp70’s nucleotide state, this occurs independently of Hsp90 ATP hydrolysis. Instead, ATP hydrolysis on Hsp90 facilitates a second essential reactivation step resulting in an Hsp90-bound GR state that protects GR from Hsp70 re-inactivation. A kinetic partitioning model best describes chaperone modulation of GR’s activity.
Bacterial load slopes as biomarkers of tuberculosis therapy success, failure, and relapse
Authors: Magombedze Gesham , Pasipanodya Jotam G. , Gumbo Tawanda 
Published: May 2020

ABSTRACTBackgroundTuberculosis is expensive to treat, especially since therapy duration is at least six-months, and patients must be followed for up to two years in order to document relapse. There is an urgent need to discover biomarkers that are predictive of long-term treatment outcomes. Currently, tuberculosis programs use two-months sputum conversion for clinical decision making, while phase I clinical trials use extended [14 day] early bactericidal activity [EBA] to triage regimens. Our objective was to develop early treatment stage biomarkers that are predictive of long-term outcomes.Methods and FindingsData from 1,924 patients in the REMoxTB study was divided into [1] a derivation data-set of 318 patients on six-months standard therapy, [2] two sets of validation datasets comprised of 319 patients on six-months standard therapy, and 1,287 patients randomized to four-months experimental therapy. Sputum time-to-positivity [TTP] data was modeled using a system of ordinary differential equations that identified bacillary kill rates [termed γ-slopes], for fast-replicating bacteria [γf] and for semi-dormant/non-replicating persistent bacteria [γs], and to estimate time-to-extinction for all bacteria sub-populations in each patient. Time-to-extinction is used to predict the minimum therapy duration required to achieve cure. Using the derivation dataset, machine learning identified the γs slope, calculated using first 8 weeks of therapy TTP data, as the highest ranked predictor for treatment outcomes. We then computed γs slope thresholds that would reliably predict relapse-free cure for 2, 3, 4, and 6 months therapy duration regimens, and used these to create a diagnostic rule. In the first-validation dataset for six-months therapy duration, the γs-derived decision rule demonstrated a sensitivity of 92% and a specificity of 89%; among patients with positive biomarker the relative risk [RR] of failure was 20.40 [95% confidence interval (CI): 7.17-58.08]. In comparison, two-month sputum culture conversion had a sensitivity of 33% and specificity of 71% [RR=1.20 (95% CI: 0.60-2.34)], while for extended-EBA sensitivity was 14% and specificity was 92% [RR=1.71 [95% CI: 0.73-3.48]. In the second validation dataset for four-months therapy duration, the γs-derived diagnostic rule sensitivity was 81% while specificity was 87% for picking failure versus cure [RR=14.51 (95% CI: 8.33-25.41)]ConclusionsThe ability to predict treatment outcomes during the first eight-weeks of therapy could accelerate evaluation of novel regimens, development of new clinical trial designs, as well as allow personalization of therapy duration in routine treatment programs. Future research applying these diagnostic rules to different clinical trials data are required.
Mechanical dysfunction induced by a hypertrophic cardiomyopathy mutation is the primary driver of cellular adaptation
Authors: Clippinger Sarah R. , Cloonan Paige E. , Wang Wei , Greenberg Lina , Stump W. Tom , Angsutararux Paweorn , Nerbonne Jeanne M. , Greenberg Michael J. 
Published: May 2020

AbstractFamilial hypertrophic cardiomyopathy (HCM), a leading cause of sudden cardiac death, is primarily caused by mutations in sarcomeric proteins. The pathogenesis of HCM is complex, with functional changes that span scales from molecules to tissues. This makes it challenging to deconvolve the biophysical molecular defect that drives the disease pathogenesis from downstream changes in cellular function. Here, we examined a HCM mutation in troponin T, R92Q. We demonstrate that the primary molecular insult driving the disease pathogenesis is mutation-induced alterations in tropomyosin positioning, which causes increased molecular and cellular force generation during calcium-based activation. We demonstrate computationally that these increases in force are direct consequences of the initial molecular insult. This altered cellular contractility causes downstream alterations in gene expression, calcium handling, and electrophysiology. Taken together, our results demonstrate that molecularly driven changes in mechanical tension drive the early disease pathogenesis, leading to activation of adaptive mechanobiological signaling pathways.
A GRAB sensor reveals activity-dependent non-vesicular somatodendritic adenosine release
Authors: Wu Zhaofa , Cui Yuting , Wang Huan , Song Kun , Yuan Zhengwei , Dong Ao , Wu Hao , Wan Yi , Pan Sunlei , Peng Wanling , Jing Miao , Xu Min , Luo Minmin , Li Yulong 
Published: May 2020

AbstractThe purinergic signaling molecule adenosine (Ado) modulates many physiological and pathological brain functions,but its spatiotemporal release dynamics in the brain remains largely unknown. We developed a genetically encodedGPCR-Activation–BasedAdosensor (GRABAdo) in which Ado-induced changes in the human A2Areceptor are reflected by fluorescence increases. This GRABAdorevealed that neuronal activity-induced extracellular Ado elevation was due to direct Ado release from somatodendritic regions of the neuron, requiring calcium influx through L-type calcium channels, rather than the degradation of extracellular ATP. The Ado release was slow (∼30 s) and depended on equilibrative nucleoside transporters (ENTs) rather than conventional vesicular release mechanisms. Thus, GRABAdoreveals an activity-dependent slow Ado release from somatodendritic region of the neuron, potentially serving modulating functions as a retrograde signal.
Validity of natural isotope abundance correction for metabolic flux analysis
Authors: Nilsson Roland 
Published: May 2020

AbstractA pervasive issue in stable isotope tracing and metabolic flux analysis is the presence of naturally occurring isotopes such as13C. For mass isotopomer distributions (MIDs) measured by mass spectrometry, it is common practice to correct for natural isotopes within metabolites of interest using a particular linear transform based on binomial distributions. However, the origin and mathematical derivation of this transform is rather obscure, and it may be difficult for nonexperts to understand precisely how to interpret the resulting corrected MIDs. Moreover, corrected MIDs are often used to fit metabolic network models and infer metabolic fluxes, which implicitly assumes that corrected MIDs will yield the same flux solution as the actual observed MIDs. Yet, there seems to be no published proof of this important property. Here, we provide a detailed derivation of the MID-correcting linear transform, reflecting its historical development, and describe some interesting properties. We also provide a proof that for metabolic flux analysis on noise-free MID data at steady state, observed and corrected MIDs indeed yield the same flux solution. On the other hand, for noisy MID data, the flux solution will generally differ between the two representations.