bluuebunny/crossref_metadata_2025_split huggingface dataset:
Normothermic Ex Vivo Liver Perfusion Prevents Intrahepatic Platelet Sequestration After Liver Transplantation
Authors: Kollmann Dagmar , Linares-Cervantes Ivan , Ganesh Sujani , Rosales Roizar , Hamar Matyas , Goto Toru , Urbanellis Peter , Tessandier Nicolas , Boilard Eric , Bruguera Claudia , Wiebe Aryn , Bartczak Agata , Yip Paul , Adeyi Oyedele , Selzner Markus , Selzner Nazia 
Published: February 2020

Background.
The detrimental role of platelets in sinusoidal endothelial cell (SEC) injury during liver transplantation (LT) has been previously addressed after static cold storage (SCS), however, it is currently unknown after normothermic ex vivo liver perfusion (NEVLP).

Methods.
Pig LT was performed with livers from heart-beating donors or donation after circulatory death (DCD) donors subjected to SCS or NEVLP (n = 5/group).

Results.
All pigs except for 1 (DCD-SCS-group) survived 4 days. The heart-beating donor- and DCD-NEVLP-groups showed significantly lower aspartate transaminase-levels compared with the SCS-groups 3 hours post-LT (P = 0.006), on postoperative day (POD) 2 (P = 0.005), POD3 (P = 0.007), and on POD4 (P = 0.012). Post-LT total platelet count recovered faster in the NEVLP than in the SCS-groups at 12 hours (P = 0.023) and 24 hours (P = 0.0038). Intrahepatic sequestration of platelets was significantly higher in the SCS-groups 3 hours postreperfusion and correlated with severity of SEC injury. In both SCS-groups, levels of tumor growth factor-β were higher 3 hours post-LT, on POD1 and on POD3. Moreover, platelet factor 4 levels and platelet-derived extracellular vesicles were increased in the SCS-groups. Hyaluronic acid levels were significantly higher in the SCS-groups, indicating a higher grade of endothelial cell dysfunction. Platelet inhibition achieved by pretreatment with clopidogrel (n = 3) partly reversed the detrimental effects on SEC injury and therefore provided further evidence of the important role of platelets in ischemia/reperfusion injury and SEC injury.

Conclusions.
Normothermic perfusion of liver grafts before transplantation effectively reduced platelet aggregation and SEC injury, which translated into an improved posttransplant organ function.
Exploring modularity in biological networks
Authors: Serban Maria 
Published: February 2020

Network theoretical approaches have shaped our understanding of many different kinds of biological modularity. This essay makes the case that to capture these contributions, it is useful to think about the role of network models in exploratory research. The overall point is that it is possible to provide a systematic analysis of the exploratory functions of network models in bioscientific research. Using two examples from molecular and developmental biology, I argue that often the same modelling approach can perform one or more exploratory functions, such as introducing new directions of research, offering a complementary set of concepts, methods and algorithms for individuating important features of natural phenomena, generating proofs of principle demonstrations and potential explanations for phenomena of interest and enlarging the scope of certain research agendas.
This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.
‘Hierarchy’ in the organization of brain networks
Authors: Hilgetag Claus C. , Goulas Alexandros 
Published: February 2020

Concepts shape the interpretation of facts. One of the most popular concepts in systems neuroscience is that of ‘hierarchy’. However, this concept has been interpreted in many different ways, which are not well aligned. This observation suggests that the concept is ill defined. Using the example of the organization of the primate visual cortical system, we explore several contexts in which ‘hierarchy’ is currently used in the description of brain networks. We distinguish at least four different uses, specifically, ‘hierarchy’ as a topological sequence of projections, as a gradient of features, as a progression of scales, or as a sorting of laminar projection patterns. We discuss the interpretation and functional implications of the different notions of ‘hierarchy’ in these contexts and suggest that more specific terms than ‘hierarchy’ should be used for a deeper understanding of the different dimensions of the organization of brain networks.
This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.
Hierarchy and levels: analysing networks to study mechanisms in molecular biology
Authors: Bechtel William 
Published: February 2020

Network representations are flat while mechanisms are organized into a hierarchy of levels, suggesting that the two are fundamentally opposed. I challenge this opposition by focusing on two aspects of the ways in which large-scale networks constructed from high-throughput data are analysed in systems biology: identifying clusters of nodes that operate as modules or mechanisms and using bio-ontologies such as gene ontology (GO) to annotate nodes with information about where entities appear in cells and the biological functions in which they participate. Of particular importance, GO organizes biological knowledge about cell components and functions hierarchically. I illustrate how this supports mechanistic interpretation of networks with two examples of network studies, one using epistatic interactions among genes to identify mechanisms and their parts and the other using deep learning to predict phenotypes. As illustrated in these examples, when network research draws upon hierarchical information such as provided by GO, the results not only can be interpreted mechanistically but provide new mechanistic knowledge.
This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.
Network architectures supporting learnability
Authors: Zurn Perry , Bassett Danielle S. 
Published: February 2020

Human learners acquire complex interconnected networks of relational knowledge. The capacity for such learning naturally depends on two factors: the architecture (or informational structure) of the knowledge network itself and the architecture of the computational unit—the brain—that encodes and processes the information. That is, learning is reliant on integrated network architectures at two levels: the epistemic and the computational, or the conceptual and the neural. Motivated by a wish to understand conventional human knowledge, here, we discuss emerging work assessing network constraints on the learnability of relational knowledge, and theories from statistical physics that instantiate the principles of thermodynamics and information theory to offer an explanatory model for such constraints. We then highlight similarities between those constraints on the learnability of relational networks, at one level, and the physical constraints on the development of interconnected patterns in neural systems, at another level, both leading to hierarchically modular networks. To support our discussion of these similarities, we employ an operational distinction between the modeller (e.g. the human brain), the model (e.g. a single human’s knowledge) and the modelled (e.g. the information present in our experiences). We then turn to a philosophical discussion of whether and how we can extend our observations to a claim regarding explanation and mechanism for knowledge acquisition. What relation between hierarchical networks, at the conceptual and neural levels, best facilitate learning? Are the architectures of optimally learnable networks a topological reflection of the architectures of comparably developed neural networks? Finally, we contribute to a unified approach to hierarchies and levels in biological networks by proposing several epistemological norms for analysing the computational brain and social epistemes, and for developing pedagogical principles conducive to curious thought.This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.
From inert matter to the global society life as multi-level networks of processes
Authors: Chavalarias David 
Published: February 2020

A few billion years have passed since the first life forms appeared. Since then, life has continued to forge complex associations between the different emergent levels of interconnection it forms. The advances of recent decades in molecular chemistry and theoretical biology, which have embraced complex systems approaches, now make it possible to conceptualize the questions of the origins of life and its increasing complexity from three complementary notions of closure: processes closure, autocatalytic closure and constraints closure. Developed in the wake of the second-order cybernetics, this triple closure approach, that relies on graph theory and complex networks science, sketch a paradigm where it is possible to go up the physical levels of organization of matter, from physics to biology and society, without resorting to strong reductionism. The phenomenon of life is conceived as the contingent complexification of the organization of matter, until the emergence of life forms, defined as a network of auto-catalytic process networks, organized in a multi-level manner. This approach of living systems, initiated by Maturana & Varela and Kauffman, inevitably leads to a reflection on the nature of cognition; and in the face of the deep changes that affected humanity as a complex systems, on the nature of cultural evolution. Faced with the major challenges that humanity will have to address in the decades to come, this new paradigm invites us to change our conception of causality by shifting our attention from state change to process change and to abandon a widespread notion of 'local' causality in favour of complex systems thinking. It also highlights the importance of a better understanding of the influence of social networks, recommendation systems and artificial intelligence on our future collective dynamics and social cognition processes.This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.
A descriptive study of the impact of diseases control and prevention on the epidemics dynamics and clinical features of SARS-CoV-2 outbreak in Shanghai, lessons learned for metropolis epidemics prevention
Authors: Lu Hongzhou , Ai Jingwen , Shen Yinzhong , Li Yang , Li Tao , Zhou Xian , Zhang Haocheng , Zhang Qiran , Ling Yun , Wang Sheng , Qu Hongping , Gao Yuan , Li Yingchuan , Yu Kanglong , Zhu Duming , Zhu Hecheng , Tian Rui , Zeng Mei , Li Qiang , Song Yuanlin , Li Xiangyang , Xu Jinfu , Xu Jie , Mao Enqiang , Hu Bijie , Li Xin , Zhu Lei , Zhang Wenhong 
Published: February 2020

AbstractObjectiveTo describe and evaluate the impact of diseases control and prevention on epidemics dynamics and clinical features of SARS-CoV-2 outbreak in Shanghai.DesignA retrospective descriptive studySettingChinaParticipantsEpidemiology information was collected from publicly accessible database. 265 patients admitted to Shanghai Public Health Center with confirmed COVID-19 were enrolled for clinical features analysis.Main outcome measurePrevention and control measures taken by Shanghai government, epidemiological, demographic, clinical, laboratory and radiology data were collected. Weibull distribution, Chi-square test, Fisher’s exact test, t test or Mann-Whitney U test were used in statistical analysis.ResultsCOVID-19 transmission rate within Shanghai had reduced over 99% than previous speculated, and the exponential growth has been stopped so far. Epidemic was characterized by the first stage mainly composed of imported cases and the second stage where >50% of cases were local. The incubation period was 6.4 (95% CI 5.3 to 7.6) days and the mean onset-admission interval was 5.5 days (95% CI, 5.1 to 5.9). Median time for COVID-19 progressed to severe diseases were 8.5 days (IQR: 4.8-11.0 days). By February 11th, proportion of patients being mild, moderate, severe and critically ill were 1.9%(5/265), 89.8%(238/265), 3.8%(10/265), 4.5%(12/265), respectively; 47 people in our cohort were discharged, and 1 patient died.ConclusionStrict controlling of the transmission rate at the early stage of an epidemic in metropolis can quickly prohibit the spread of the diseases. Controlling local clusters is the key to prevent outbreaks from imported cases. Most COVID-19 severe cases progressed within 14 days of disease onset. Multiple systemic laboratory abnormalities had been observed before significant respiratory dysfunction.
Novel Coronavirus 2019 (Covid-19) epidemic scale estimation: topological network-based infection dynamics model
Authors: Tang Keke , Huang Yining , Chen Meilian 
Published: February 2020

AbstractBackgroundsAn ongoing outbreak of novel coronavirus pneumonia (Covid-19) hit Wuhan and hundreds of cities, 29 territories in global. We present a method for scale estimation in dynamic while most of the researchers used static parameters.MethodsWe use historical data and SEIR model for important parameters assumption. And according to the time line, we use dynamic parameters for infection topology network building. Also, the migration data is used for Non-Wuhan area estimation which can be cross validated for Wuhan model. All data are from public.ResultsThe estimated number of infections is 61,596 (95%CI: 58,344.02-64,847.98) by 25 Jan in Wuhan. And the estimation number of the imported cases from Wuhan of Guangzhou was 170 (95%CI: 161.27-179.26), infections scale in Guangzhou is 315 (95%CI: 109.20-520.79), while the imported cases is 168 and the infections scale is 339 published by authority.ConclusionsUsing dynamic network model and dynamic parameters for different time periods is an effective way for infections scale modeling.
Emotional and behavioural pathways to adolescent substance use and antisocial behaviour: results from the UK Millennium Cohort Study_PREPRINT
Authors: Picoito João , Santos Constança , Nunes Carla 
Published: February 2020

AbstractThis study examines the emotional and behavioural pathways to adolescent substance use and antisocial behaviour. Using a sample of 17,223 participants from the UK Millennium Cohort Study, we applied parallel-process growth mixture modelling on emotional and behavioural symptoms in those aged 3 to 14 and employed latent class analysis to identify patterns of substance use and antisocial behaviours at age 14. We then performed a multinomial regression analysis to explore the association between emotional and behavioural trajectories and patterns of adolescent substance use and antisocial behaviours, controlling for sociodemographic, family and maternal factors. We found five trajectories of emotional and behavioural symptoms and four classes of adolescence substance use and antisocial behaviour. Children and adolescents in the ‘high externalising and internalising’and‘moderate externalising’trajectories were more likely to belong to any problematic behaviour class, especially the ‘poly-substance use and antisocial behaviours’class. Inclusion in the‘moderate externalising and internalising (childhood limited)’class was associated with higher odds of belonging to the‘alcohol and tobacco’class. These associations remained significant after controlling for important sociodemographic and contextual factors, such as maternal substance use, poverty, and parental status. Interventions on adolescent health promotion and risk behaviour prevention need to address the clustering of substance use and antisocial behaviour as well as the significant influence of early and chronic internalising and externalising symptoms on the aetiology of these behaviours.
COVID-19 in Wuhan: Immediate Psychological Impact on 5062 Health Workers
Authors: Zhu Zhou , Xu Shabei , Wang Hui , Liu Zheng , Wu Jianhong , Li Guo , Miao Jinfeng , Zhang Chenyan , Yang Yuan , Sun Wenzhe , Zhu Suiqiang , Fan Yebin , Hu Junbo , Liu Jihong , Wang Wei 
Published: February 2020

ABSTRACTBACKGROUNDThe outbreak of COVID-19 has laid unprecedented psychological stress on health workers (HWs). We aimed to assess the immediate psychological impact on HWs at Tongji Hospital in Wuhan, China.METHODSWe conducted a single-center, cross-sectional survey of HWs via online questionnaires between February 8th and 10th, 2020. We evaluated stress, depression and anxiety by Impact of Event Scale-Revised (IES-R), Patient Health Questionnaire-9 (PHQ-9), and Generalized Anxiety Disorder 7-item (GAD-7), respectively. We also designed a questionnaire to assess the effect of psychological protective measures taken by Tongji Hospital. Multivariate logistic regression was used to identify predictors of acute stress, depression, and anxiety.RESULTSWe received 5062 completed questionnaires (response rate, 77.1 percent). 1509 (29.8 percent), 681 (13.5 percent) and 1218 (24.1 percent) HWs reported stress, depression and anxiety symptoms. Women (hazard ratio[HR], 1.31; P=0.032), years of working> 10 years (HR, 2.02; P<0.001), concomitant chronic diseases (HR, 1.51; P<0.001), history of mental disorders (HR, 3.27; P<0.001), and family members or relatives confirmed or suspected (HR, 1.23; P=0.030) were risk factors for stress, whereas care provided by hospital and department administrators(odds ratio [OR], 0.76; P=0.024) and full coverage of all departments with protective measures (OR, 0.69; P=0.004) were protective factors.CONCLUSIONSWomen and those who have more than 10 years of working, concomitant chronic diseases, history of mental disorders, and family members or relatives confirmed or suspected are susceptible to stress, depression and anxiety among HWs during the COVID-19 pandemic. Psychological protective measures implemented by the hospital could be helpful.
Clinical Characteristics of 24 Asymptomatic Infections with COVID-19 Screened among Close Contacts in Nanjing, China
Authors: Hu Zhiliang , Song Ci , Xu Chuanjun , Jin Guangfu , Chen Yaling , Xu Xin , Ma Hongxia , Chen Wei , Lin Yuan , Zheng Yishan , Wang Jianming , Hu Zhibin , Yi Yongxiang , Shen Hongbing 
Published: February 2020

AbstractBackgroundPrevious studies have showed clinical characteristics of patients with the 2019 novel coronavirus disease (COVID-19) and the evidence of person-to-person transmission. Limited data are available for asymptomatic infections. This study aims to present the clinical characteristics of 24 cases with asymptomatic infection screened from close contacts and to show the transmission potential of asymptomatic COVID-19 virus carriers.MethodsEpidemiological investigations were conducted among all close contacts of COVID-19 patients (or suspected patients) in Nanjing, Jiangsu Province, China, from Jan 28 to Feb 9, 2020, both in clinic and in community. Asymptomatic carriers were laboratory-confirmed positive for the COVID-19 virus by testing the nucleic acid of the pharyngeal swab samples. Their clinical records, laboratory assessments, and chest CT scans were reviewed.FindingsNone of the 24 asymptomatic cases presented any obvious symptoms before nucleic acid screening. Five cases (20.8%) developed symptoms (fever, cough, fatigue, etc.) during hospitalization. Twelve (50.0%) cases showed typical CT images of ground-glass chest and 5 (20.8%) presented stripe shadowing in the lungs. The remaining 7 (29.2%) cases showed normal CT image and had no symptoms during hospitalization. These 7 cases were younger (median age: 14.0 years;P= 0.012) than the rest. None of the 24 cases developed severe COVID-19 pneumonia or died. The median communicable period, defined as the interval from the first day of positive nucleic acid tests to the first day of continuous negative tests, was 9.5 days (up to 21 days among the 24 asymptomatic cases). Through epidemiological investigation, we observed a typical asymptomatic transmission to the cohabiting family members, which even caused severe COVID-19 pneumonia.InterpretationThe asymptomatic carriers identified from close contacts were prone to be mildly ill during hospitalization. However, the communicable period could be up to three weeks and the communicated patients could develop severe illness. These results highlighted the importance of close contact tracing and longitudinally surveillance via virus nucleic acid tests. Further isolation recommendation and continuous nucleic acid tests may also be recommended to the patients discharged.
Evidence of unexplained discrepancies between planned and conducted statistical analyses: a review of randomized trials
Authors: Cro Suzie , Forbes Gordon , Johnson Nicholas A , Kahan Brennan C 
Published: February 2020

AbstractBackgroundChoosing or altering the planned statistical analysis approach after examination of trial data (often referred to as ‘p-hacking’) can bias results of randomized trials. However, the extent of this issue in practice is currently unclear. We conducted a review of published randomized trials to evaluate how often a pre-specified analysis approach is publicly available, and how often the planned analysis is changed.MethodsA review of randomised trials published between January and April 2018 in six leading general medical journals. For each trial we established whether a pre-specified analysis approach was publicly available in a protocol or statistical analysis plan, and compared this to the trial publication.ResultsOverall, 89 of 101 eligible trials (88%) had a publicly available pre-specified analysis approach. Only 22/89 trials (25%) had no unexplained discrepancies between the pre-specified and conducted analysis. Fifty-four trials (61%) had one or more unexplained discrepancies, and in 13 trials (15%) it was impossible to ascertain whether any unexplained discrepancies occurred due to incomplete reporting of the statistical methods. Unexplained discrepancies were most common for the analysis model (n=31, 35%) and analysis population (n=28, 31%), followed by the use of covariates (n=23, 26%) and the approach for handling missing data (n=16, 18%). Many protocols or statistical analysis plans were dated after the trial had begun, so earlier discrepancies may have been missed.ConclusionsUnexplained discrepancies in the statistical methods of randomized trials are common. Increased transparency is required for proper evaluation of results.
Electrocortical temporal complexity during wakefulness and sleep: an updated account
Authors: González Joaquín , Cavelli Matias , Mondino Alejandra , Pascovich Claudia , Castro-Zaballa Santiago , Rubido Nicolás , Torterolo Pablo 
Published: February 2020

AbstractThe states of sleep and wakefulness are critical physiological processes associated with different brain patterns of activity. The intracranial electroencephalogram allows us to measure these changes, thus, it is a critical tool for its study. Recently, we showed that the electrocortical temporal complexity decreased from wakefulness to sleep. Nevertheless, the origin of this complex activity remains a controversial topic due to the existence of possible artifacts contaminating the brain signals. In this work, we showed that complexity decreases during sleep, independently of the electrode configuration employed. This fact strongly suggests that the basis for the behavioral-state differences in complexity does not have an extracranial origin; i.e., generated from the brain.