bluuebunny/crossref_metadata_2025_split huggingface dataset:
Epidemic prevention and control measures in China significantly curbed the epidemic of COVID-19 and influenza
Authors: Kong Xiang-Sha , Liu Feng , Wang Hai-Bo , Yang Rui-Feng , Chen Dong-Bo , Wang Xiao-Xiao , Lu Feng-Min , Rao Hui-Ying , Chen Hong-Song 
Published: April 2020

AbstractAt the end of 2019, an outbreak of unknown pathogen pneumonia occurred in China, then it was named corona virus disease 2019 (COVID-19). With the rapid spread of COVID-19, a series of strict prevention and control measures were implemented to cut the spread of the epidemic. Influenza as a respiratory tract infection disease as COVID-19 might also be controlled. To assess the effects, we used the total passenger numbers sent in mainland China from 2018 to 2020 and the daily number of railway passenger (DNRP) flow in 2020 during Spring Festival travel rush to reflect the population movement and further to analyze newly and cumulative confirmed COVID-19 and influenza. We found that with implementing the series measures on COVID-19, not only COVID-19, but also influenza mitigated in China. The prevention and control measures for COVID-19 might be used in controlling respiratory tract diseases, and reducing the national health economic burden. When other countries issue measures on COVID-19 and influenza, they should consider adopting more aggressive epidemic prevention and control strategies.
COVID-19 pandemic in the African continent: Forecasts of cumulative cases, new infections, and mortality
Authors: Achoki T , Alam U , Were L , Gebremedhin T , Senkubuge F , Lesego A , Liu S , Wamai R , Kinfu Y 
Published: April 2020

BackgroundThe epidemiology of COVID-19 remains speculative in Africa. To the best of our knowledge, no study, using robust methodology provides its trajectory for the region or accounts for local context. This paper is the first systematic attempt to provide prevalence, incidence, and mortality estimates across Africa.MethodsCaseloads and incidence forecasts are from a co-variate-based instrumental variable regression model. Fatality rates from Italy and China were applied to generate mortality estimates after making relevant health system and population-level characteristics related adjustments between each of the African countries.ResultsBy June 30 2020, around 16.3 million people in Africa will contract COVID-19 (95% CI 718,403 to 98,358,799). Northern and Eastern Africa will be the most and least affected areas. Cumulative cases by June 30 are expected to reach around 2.9 million (95% CI 465,028 to 18,286,358) in Southern Africa, 2.8 million (95% CI 517,489 to 15,056,314) in Western Africa, and 1.2 million (95% CI 229,111 to 6,138,692) in Central Africa. Incidence for the month of April 2020 is expected to be highest in Djibouti, 32.8 per 1000 (95% CI 6.25 to 171.77), while Morocco will experience among the highest fatalities (1,045 deaths, 95% CI 167 to 6,547).ConclusionLess urbanized countries with low levels of socio-economic development (hence least connected to the world), are likely to register lower and slower transmissions at the early stages of an epidemic. However, the same enabling factors that worked for their benefit can hinder interventions that have lessened the impact of COVID-19 elsewhere.
Estimating Probabilities of Success of Clinical Trials for Vaccines and Other Anti-Infective Therapeutics
Authors: Wong Chi Heem , Siah Kien Wei , Lo Andrew W. 
Published: April 2020

AbstractA key driver in biopharmaceutical investment decisions is the probability of success of a drug development program. We estimate the probabilities of success (PoS) of clinical trials for vaccines and other anti-infective therapeutics using 43,414 unique triplets of clinical trial, drug, and disease between January 1, 2000, and January 7, 2020, yielding 2,544 vaccine programs and 6,829 non-vaccine programs targeting infectious diseases. The overall estimated PoS for an industry-sponsored vaccine program is 39.6%, and 16.3% for an industry-sponsored anti-infective therapeutic. Among industry-sponsored vaccines programs, only 12 out of 27 disease categories have seen at least one approval, with the most successful being against monkeypox (100%), rotavirus (78.7%), and Japanese encephalitis (67.6%). The three infectious diseases with the highest PoS for industry-sponsored non-vaccine therapeutics are smallpox (100%), CMV (31.8%), and onychomycosis (29.8%). Non-industry-sponsored vaccine and non-vaccine development programs have lower overall PoSs: 6.8% and 8.2%, respectively. Viruses involved in recent outbreaks—MERS, SARS, Ebola, Zika—have had a combined total of only 45 non-vaccine development programs initiated over the past two decades, and no approved therapy to date (Note: our data was obtained just before the COVID-19 outbreak and do not contain information about the programs targeting this disease.) These estimates offer guidance both to biopharma investors as well as to policymakers seeking to identify areas most likely to be undeserved by private-sector engagement and in need of public-sector support.
Protein Classifier for Thyroid Nodules Learned from Rapidly Acquired Proteotypes
Authors: Sun Yaoting , Selvarajan Sathiyamoorthy , Zang Zelin , Liu Wei , Zhu Yi , Zhang Hao , Chen Hao , Cai Xue , Gao Huanhuan , Wu Zhicheng , Chen Lirong , Teng Xiaodong , Zhao Yongfu , Mantoo Sangeeta , Lim Tony Kiat-Hon , Hariraman Bhuvaneswari , Yeow Serene , bin Syed Abdillah Syed Muhammad Fahmy , Lee Sze Sing , Ruan Guan , Zhang Qiushi , Zhu Tiansheng , Wang Weibin , Wang Guangzhi , Xiao Junhong , He Yi , Wang Zhihong , Sun Wei , Qin Yuan , Xiao Qi , Zheng Xu , Wang Linyan , Zheng Xi , Xu Kailun , Shao Yingkuan , Liu Kexin , Zheng Shu , Aebersold Ruedi , Li Stan Z. , Kon Oi Lian , Iyer N. Gopalakrishna , Guo Tiannan 
Published: April 2020

SUMMARYUp to 30% of thyroid nodules cannot be accurately classified as benign or malignant by cytopathology. Diagnostic accuracy can be improved by nucleic acid-based testing, yet a sizeable number of diagnostic thyroidectomies remains unavoidable. In order to develop a protein classifier for thyroid nodules, we analyzed the quantitative proteomes of 1,725 retrospective thyroid tissue samples from 578 patients using pressure-cycling technology and data-independent acquisition mass spectrometry. With artificial neural networks, a classifier of 14 proteins achieved over 93% accuracy in classifying malignant thyroid nodules. This classifier was validated in retrospective samples of 271 patients (91% accuracy), and prospective samples of 62 patients (88% accuracy) from four independent centers. These rapidly acquired proteotypes and artificial neural networks supported the establishment of an effective protein classifier for classifying thyroid nodules.
Phylogeography and Resistome Epidemiology of Gram-Negative Bacteria in Africa: A Systematic Review and Genomic Meta-Analysis from a One-Health Perspective
Authors: Sekyere John Osei , Reta Melese Abate 
Published: April 2020

AbstractObjectives/BackgroundsAntibiotic resistance (ABR) remains a major threat to public health and infectious disease management globally. However, ABR ramifications in developing countries is worsened by limited molecular diagnostics, expensive therapeutics, inadequate skilled clinicians and scientists, and unsanitary environments.MethodsData on specimens, species, clones, resistance genes, mobile genetic elements, and diagnostics were extracted and analysed from English articles published between 2015 and December 2019. The genomes and resistomes of the various species, obtained from PATRIC and NCBI, were analysed phylogenetically using RAxML and annotated with Figtree. The phylogeography of resistant clones/clades was mapped manually.Results & conclusionThirty species from 31 countries and 24 genera from 41 countries were respectively analysed from 146 articles and 3028 genomes. Genes mediating resistance to β-lactams (including blaTEM-1, blaCTX-M, blaNDM, blaIMP, blaVIM, blaOXA-48/181), fluoroquinolones (oqxAB, qnrA/B/D/S, gyrA/B and parCE mutations etc.), aminoglycosides (including armA, rmtC/F), sulphonamides (sul-1/2/3), trimethoprim (dfrA), tetracycline (tet(A/B/C/D/G/O/M/39)), colistin (mcr-1), phenicols (catA/B, cmlA), and fosfomycin (fosA) were mostly found in Enterobacter spp. and K. pneumoniae, and also in Serratia marcescens, Escherichia coli, Salmonella enterica, Pseudomonas, Acinetobacter baumannii, etc. on mostly IncF-type, IncX3/4, ColRNAI, and IncR plasmids, within IntI1 gene cassettes, insertion sequences and transposons. Clonal and multiclonal outbreaks and dissemination of resistance genes across species, countries and between humans, animals, plants and the environment were observed; E. coli ST103, K. pneumoniae ST101, S. enterica ST1/2 and V. cholerae ST69/515 were common strains. Most pathogens were of human origin and zoonotic transmissions were relatively limited. One Health studies in Africa are needed.Highlights/SignificanceAntibiotic resistance (AR) is one of the major public health threats and challenges to effective containment and treatment of infectious bacterial diseases worldwide. Herein, we used different methods to map out the geographical hotspots, sources and evolutionary epidemiology of AR. Escherichia coli, Klebsiella pneumoniae, Salmonella enterica, Acinetobacter baumanii, Pseudomonas aeruginosa, Enterobacter spp., Neisseria meningitis/gonnorhoeae, Vibrio cholerae, Campylobacter jejuni etc. were common pathogens shuttling AR genes. Transmission of same clones/strains across countries and between animals, humans, plants and the environment were observed. We recommend Enterobacter spp. or K. pneumoniae as better model/index organisms for AR surveillance.
Estimating cost-benefit of quarantine length for COVID-19 mitigation
Authors: Eilersen Andreas , Sneppen Kim 
Published: April 2020

ABSTRACTThe international community has been put in an unprecedented situation by the COVID-19 pandemic. Creating models to describe and quantify alternative mitigation strategies becomes increasingly urgent. In this study, we propose an agent-based model of disease transmission in a society divided into closely connected families, workplaces, and social groups. This allows us to discuss mitigation strategies, including targeted quarantine measures. We find that workplace and more diffuse social contacts are roughly equally important to disease spread, and that an effective lockdown must target both. We examine the cost-benefit of replacing a lockdown with tracing and quarantining contacts of the infected. Quarantine can contribute substantially to mitigation, even if it has short duration and is done within households. When reopening society, testing and quarantining is a strategy that is much cheaper in terms of lost workdays than a long lockdown of workplaces. A targeted quarantine strategy is quite efficient with only 5 days of quarantine, and its effect increases when testing is more widespread.
Recovery Ratios Reliably Anticipate COVID-19 Pandemic Progression
Authors: Nicolau Dan V. , Hasson Alexander , Bafadhel Mona 
Published: April 2020

AbstractThe COVID-19 pandemic is placing unprecedented demands on healthcare systems worldwide and exacting a massive humanitarian toll. This makes the development of accurate predictive models imperative, not just for understanding the course of the pandemic but more importantly for gaining insight into the efficacy of public health measures and planning accordingly. Epidemiological models are forced to make assumptions about many unknowns and therefore can be unreliable. Here, taking an empirical approach, we report a 20-30 day lag between the peak of confirmed to recovered cases and the peak of daily deaths in each country, independent of the epoch of that country in its pandemic cycle. This analysis is expected to be largely independent of the proportion of the population being tested and therefore should aid in planning around the timing and easing of public health measures. Our data also suggests broad predictions for the number of fatalities, generally somewhat lower than most other models. Finally, our model suggests that the world as a whole is shortly to enter a recovery phase, at least as far as the first pandemic wave is concerned.
Improvement in Sleep Duration was Associated with Higher Cognitive Function among Middle-Aged and Elderly Chinese Participants
Authors: Hua Jianian , Sun Hongpeng , She Yueping 
Published: April 2020

AbstractStudy objectivesRodent studies suggested that improvement in sleep duration might correlate with better cognitive function. We aimed to examine the associations between changes in sleep duration and cognitive function.Methods10325 individuals from the China Health and Retirement Longitudinal Study (CHARLS) were included. Self-reported nocturnal sleep duration and cognitive function were assessed in CHARLS 2011, 2013 and 2015 (Wave 1, Wave 2, Wave3). Cognitive function was assessed by a global cognition score, which included three domains: episodic memory, figure drawing and Telephone Interview of Cognitive Status (TICS). Generalized additive models (GAM) and Generalized estimation equations (GEE) were used to examine the associations between baseline sleep duration and longitudinal cognitive function. We used generalized linear models (GLM) to study the associations between changes in sleep duration and cognitive function in Wave 3.ResultsAfter adjusting for potential confounders, change from short sleep duration (SSD) to moderate sleep duration (MSD) was associated with better global cognition scores (β=0.54, P <0.01). Change from SSD to long sleep duration (LSD) (β=-0.94, P <0.001) or change from LSD to SSD (β=-1.38, P <0.01) was associated with lower global cognition. For individuals with MSD, ≥2 h increase (β=-0.89, P <0.001) or decrease (β=-0.70, P <0.001) in sleep duration was associated with lower global cognition.ConclusionsFor short sleepers, improvement in sleep duration correlated with better cognition. For long sleepers, there was no need to reduce sleep duration. Excessive changes or deviation from the moderate duration was associated with lower cognition.
Scirpy: A Scanpy extension for analyzing single-cell T-cell receptor sequencing data
Authors: Sturm Gregor , Szabo Tamas , Fotakis Georgios , Haider Marlene , Rieder Dietmar , Trajanoski Zlatko , Finotello Francesca 
Published: April 2020

AbstractSummaryAdvances in single-cell technologies have enabled the investigation of T cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses in cancer, but also in infectious diseases like COVID-19. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T cell receptors. Here we proposeScirpy, a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data.Availability and implementationScirpysource code and documentation are available athttps://github.com/icbi-lab/scirpy.
Analysing recovery from pandemics by Learning Theory: the case of CoVid-19
Authors: Duffey Romney B , Zio Enrico 
Published: April 2020

AbstractWe present a method for predicting the recovery time from infectious diseases outbreaks such as the recent CoVid-19 virus. The approach is based on the theory of learning from errors, specifically adapted to the control of the virus spread by reducing infection rates using countermeasures such as medical treatment, isolation, social distancing etc. When these are effective, the infection rate, after reaching a peak, declines following a given recovery rate curve. We use presently available data from China, South Korea and others to make actual predictions of the time needed for securing minimum infection rates in the future.
Twitter and Pain: An Observational Analysis of Pain-Related Tweets in Ireland
Authors: Mullins Cormac , O’Carroll Robert ffrench , Lane Justin , O’Connor Therese 
Published: April 2020

ABSTRACTIntroductionStudies involving Twitter and chronic pain can provide highly valuable patient-generated information. The aim of this paper was to examine pain-related tweets in Ireland over a two-week period 22nd June 2017-5th July 2017 using pain-related keywords. We wished to identify demographic details regarding the Twitter users; most common topics discussed; sentiment analysis; and reach of tweetsMethodsA third-party data analytics company used 24 pain-related keywords over a 14-day period between the dates 22nd June – 5th July 2017.ResultsThere were 941 tweets identified during the study from 715 contributors. These reached 2.62 million accounts and generated 2.88 million impressions. The most frequently occurring keywords were headache (n=321); migraine (n=147); back pain (n=123); cannabis (n=114); and chronic pain (n=85). There were 1.94 times as many tweets from females as males. The highest proportion of tweets from female users was in the fibromyalgia (83%) and migraine (60%) categories, and from males in the sciatica (35%), chronic pain (34%) and back pain (32%) categories. Cannabis-related tweets reflected mostly non-personal content (90%), with a highly positive sentiment, and the highest reach per tweet. The largest amount of advice was offered in the back pain category. Retweets were more likely to reflect a positive sentiment.ConclusionA substantial discussion of pain-related topics took place on Twitter during our study period. This provided real-time, dynamic information from individuals on discussion topics in pain medicine. This can be used to gain a greater understanding of the pain experience. As patients are increasingly acquiring healthcare information through online sources, high quality information from approved sources should be promoted on such platforms.
Global convergence of COVID-19 basic reproduction number and estimation from early-time SIR dynamics
Authors: Katul Gabriel G. , Mrad Assaad , Bonetti Sara , Manoli Gabriele , Parolari Anthony J. 
Published: April 2020

AbstractThe SIR (‘susceptible-infectious-recovered’) formulation is used to uncover the generic spread mechanisms observed by COVID-19 dynamics globally, especially in the early phases of infectious spread. During this early period, potential controls were not effectively put in place or enforced in many countries. Hence, the early phases of COVID-19 spread in countries where controls were weak offer a unique perspective on the ensemble-behavior of COVID-19 basic reproduction number Ro. The work here shows that there is global convergence (i.e. across many nations) to an uncontrolled Ro = 4.5 that describes the early time spread of COVID-19. This value is in agreement with independent estimates from other sources reviewed here and adds to the growing consensus that the early estimate of Ro = 2.2 adopted by the World Health Organization is low. A reconciliation between power-law and exponential growth predictions is also featured within the confines of the SIR formulation. Implications for evaluating potential control strategies from this uncontrolled Ro are briefly discussed in the context of the maximum possible infected fraction of the population (needed for assessing health care capacity) and mortality (especially in the USA given diverging projections). Model results indicate that if intervention measures still result in Ro> 2.7 within 49 days after first infection, intervention is unlikely to be effective in general for COVID-19. Current optimistic projections place mortality figures in the USA in the range of 100,000 fatalities. For fatalities to be confined to 100,000 requires a reduction in Ro from 4.5 to 2.7 within 17 days of first infection assuming a mortality rate of 3.4%.
Optimal Control applied to a SEIR model of 2019-nCoV with social distancing
Authors: Mallela Abhishek 
Published: April 2020

AbstractDoes the implementation of social distancing measures have merit in controlling the spread of the novel coronavirus? In this study, we develop a mathematical model to explore the effects of social distancing on new disease infections. Mathematical analyses of our model indicate that successful eradication of the disease is strongly dependent on the chosen preventive measure. Numerical computations of the model solution demonstrate that the ability to flatten the curve becomes easier as social distancing is strictly enforced. Based on our model, we also formulate an optimal control problem and solve it using Pontryagin’s Maximum Principle and an efficient numerical iterative method. Our numerical results of an optimal 2019-nCoV treatment protocol that yields a minimum disease burden from this disease indicates that social distancing is vitally important.
Fine-scale heterogeneity in Schistosoma mansoni force of infection measured through antibody response
Authors: Arnold Benjamin F. , Kanyi Henry , Njenga Sammy M. , Rawago Fredrick O. , Priest Jeffrey W. , Secor W. Evan , Lammie Patrick J. , Won Kimberly Y. , Odiere Maurice R. 
Published: April 2020

AbstractIdentifying populations with active transmission and monitoring changes in transmission is centrally important in guiding schistosomiasis control programs. Traditionally, human Schistosoma mansoni infections have been detected in stool using microscopy, which is logistically difficult at program scale and has low sensitivity when people have low infection burdens. We compared serological measures of transmission based on antibody response to schistosomiasis soluble egg antigen (SEA) with stool-based measures of infection among 3,663 preschool-age children in an area endemic for S. mansoni in western Kenya. Serological measures of transmission closely aligned with stool-based measures of infection, and serological measures provided better resolution for between-community differences at lower levels of infection. Serology enabled fine- scale measures of heterogeneity in force of infection both geographically and by age. Our results show that serologic surveillance platforms represent an important new opportunity to guide and monitor schistosomiasis control programs.
A separable neural code in monkey IT enables perfect CAPTCHA decoding
Authors: Katti Harish , Arun S. P. 
Published: April 2020

ABSTRACTReading distorted letters is easy for us but so challenging for machine vision that it is used on websites as CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart). How does our brain solve this problem? One solution is to have neurons invariant to letter distortions but selective for letter combinations. Another is for neurons to separately encode letter distortions and combinations. Here, we provide evidence for the latter using neural recordings in the monkey inferior temporal (IT) cortex. Neurons encoded letter distortions as a product of letter and distortion tuning, and letter combinations as a sum of letters. These rules were sufficient for perfect CAPTCHA decoding and were also present in neural networks trained for word recognition. Taken together, our findings suggest that a separable neural code enables efficient letter recognition.