bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification on Admission of COVID-19 Patients at Risk of Subsequent Rapid Clinical Deterioration
Authors: Beals J. , Barnes J. , Durand D. , Rimar J. , Donohue T. , Hoq M. , Belk K. , Amin A. , Rothman M.J.
Published: August 2020

Introduction

Recent localized surges in COVID-19 cases have resulted in the hospitals serving those areas being overwhelmed. In such cases, the ability to rapidly and objectively determine a patient’s acuity and predict near-term care needs is a major challenge. At issue is the clinician’s ability to correctly identify patients at risk for subsequent rapid clinical deterioration. Data-driven tools that can support such determinations in real-time may be a valuable adjunct to clinician judgement during COVID-19 surges.

Objective

To assess the effectiveness of the Rothman Index (RI) predictive model in distinguishing the risk of subsequent deterioration or elevated care needs among hospitalized COVID-19 patients at the time of hospital admission.

Methods

We evaluated the initial RI score on admission to predict COVID-19 patient risk for 216 COVID-19 patients discharged from March 21stto June 7th, 2020 at Sinai LifeBridge Hospital and 1,453 COVID-19 patients discharged from any of Yale New Haven Health System’s Yale New Haven, Bridgeport, and Greenwich hospitals from April 1stto April 28th, 2020. In-hospital mortality as a function of age and RI on admission for COVID-19 and non-COVID-19 patients were compared. AUC values using each COVID-19 patient’s initial RI on admission to predict in-hospital mortality, mechanical ventilation, and ICU utilization were computed, as were precision and recall for mortality prediction at specific RI thresholds.

Results

The RI computed at the time of admission provides a high degree of objective discrimination to differentiate the COVID-19 population into high and low risk populations at the outset of hospitalization. The high risk segment based on initial RI constitutes 20-30% of the COVID-19 positive population with mortality rates from 40-50%. The low risk segment based on initial RI constitutes 40%-55% of the population with mortality rates ranging from 1%-8%. Of note is that COVID-19 patients who present with heightened but generally unremarkable acuity can be identified early as having considerably elevated risk for subsequent physiological deterioration.

Conclusion

COVID-19 patients exhibit elevated mortality rates compared to non-COVID-19 medical service patients and may be subject to rapid deterioration following hospital admission. A lack of predictive indicators for identifying patients at high risk of subsequent deterioration or death can pose a challenge to clinicians. The RI has excellent performance characteristics when stratifying risk among COVID-19 patients at the time of admission. The RI can assist clinicians in real-time with a high degree of objective discrimination by segmenting the COVID-19 population into high and low risk populations. This supports rapid and optimal patient bed assignment and resource allocation.
Comparison of RNA extraction methods for the detection of SARS-CoV-2 by RT-PCR
Authors: Eichhoff Ossia M. , Bellini Elisa , Lienhard Reto , Stark Wendelin J. , Bechtold Philippe , Grass Robert N. , Bosshard Philipp P. , Levesque Mitchell P.
Published: August 2020

Objectives

The SARS-CoV-2 pandemic outbreak has stressed health care systems as well as medical supply chains, but diagnostic testing is an essential public health measure to control viral spread. Here we test the suitability of different RNA extraction methods for integration into a diagnostic workflow for coronavirus testing.

Methods

We applied six RNA extraction methods on the same 24 SARS-CoV-2 positive patient samples and quantified their results by subsequent reverse-transcriptase PCR (RT-PCR) of three viral genes. These methods included a) column-based extraction, b) phenol-chloroform extraction, as well as c) extraction using magnetic beads (i.e., one commercial kit as well as three different magnetic beads in combination with home-brewed buffers and solutions).

Results

We achieved diagnostic-quality RT-PCR results with all methods, and there was no significant difference between the tested methods, except for one magnetic bead protocol with home-brewed buffers, in which the number of positive tested genes was significantly lower.

Conclusions

Five of the six RNA extraction methods are interchangeable in a diagnostic workflow. Since some methods are more scalable than others, and have comparable results on RT-PCR quantitation, they may be more amenable to high-throughput sample processing pipelines.

Graphical abstract
Quality of life and anxiety in patients with non-muscle invasive bladder cancer: a prospective study
Authors: Vaioulis Alexandros , Konstantinos Bonotis , Perivoliotis Konstantinos , Yiannis Kiouvrekis , Stavros Gravas , Vasilios Tzortzis , Anastasios Karatzas
Published: August 2020

Introduction

We evaluated anxiety and quality of life (QoL) in patients who were operated for non-muscle invasive bladder cancer (NMIBC)

Methods

The present study is a prospective analysis of patients with histopathologically confirmed NMIBCs after they were submitted to transurethral resection of the tumour (TURBT). Eligible were all adult patients with a single or multiple NMIBCs. All included patients followed therapy with either BCG or Epirubicin instillations. The SF-36 questionnaire Physical and Mental health aspects were used for QoL assessment. Similarly, the STAI-Y was introduced for the state (STAI-Y1) and trait anxiety (STAI-Y2) evaluation.

Results

In total, 117 eligible patients were included. Regarding SF-36 Physical a 6 months decrease was followed by an improvement at 12 months. Similarly, an increase of the SF-36 Mental health score was identified. In contrast to STAI-Y2, a long-term reduction of the state anxiety was identified. Preoperative SF-36 Physical was inversely correlated with age, while absence of alcohol was associated with lower mental health. Overall, patient characteristics, habits and the administered treatment did not affect the postoperative QoL and anxiety.

Conclusions

Patient QoL and anxiety improved during follow up. Although certain characteristics were related to QoL and anxiety, further larger scale studies are required.
Clinical characteristics of COVID-19 and the model for predicting the occurrence of critically ill patients: a retrospective cohort study
Authors: Ouyang Jing , Shan Xuefeng , Wang Xin , Zhang Xue , Chen Yaling , Qi Miaomiao , Xia Chao , Gu Dongqing , Chen Yaokai , Zhang Ben
Published: August 2020

Background

The present study aim to comprehensively report the epidemiological and clinical characteristics of the COVID-19 patients and to develop a multi-feature fusion model for predicting the critical ill probability.

Methods

It was a retrospective cohort study that incorporating the laboratory-confirmed COVID-19 patients in the Chongqing Public Health Medical Center. The prediction model was constructed with least absolute shrinkage and selection operator (LASSO) logistic regression method and the model was further tested in the validation cohort. The performance was evaluated by the receiver operating curve (ROC), calibration curve and decision curve analysis (DCA).

Results

A total of 217 patients were included in the study. During the treatment, 34 patients were admitted to intensive care unit (ICU) and no developed death. A model incorporating the demographic and clinical characteristics, imaging features and laboratory findings were constructed to predict the critical ill probability and it was proved to have good calibration, discrimination ability and clinic use.

Conclusions

The prevalence of critical ill was relatively high and the model may help the clinicians to identify the patients with high risk for developing the critical ill, thus to conduct timely and targeted treatment to reduce the mortality rate.
Deep Learning for Automated Recognition of Covid-19 from Chest X-ray Images
Authors: Duong Linh T. , Nguyen Phuong T. , Iovino Ludovico , Flammini Michele
Published: August 2020

Background

The pandemic caused by coronavirus in recent months is having a devastating global effect, which puts the world under the most ever unprecedented emergency. Currently, since there are not effective antiviral treatments for Covid-19 yet, it is crucial to early detect and monitor the progression of the disease, thus helping to reduce mortality. While a corresponding vaccine is being developed, and different measures are being used to combat the virus, medical imaging techniques have also been investigated to assist doctors in diagnosing this disease.

Objective

This paper presents a practical solution for the detection of Covid-19 from chest X-ray (CXR) images, exploiting cutting-edge Machine Learning techniques.

Methods

We employ EfficientNet and MixNet, two recently developed families of deep neural networks, as the main classification engine. Furthermore, we also apply different transfer learning strategies, aiming at making the training process more accurate and efficient. The proposed approach has been validated by means of two real datasets, the former consists of 13,511 training images and 1,489 testing images, the latter has 14,324 and 3,581 images for training and testing, respectively.

Results

The results are promising: by all the experimental configurations considered in the evaluation, our approach always yields an accuracy larger than 95.0%, with the maximum accuracy obtained being 96.64%.

Conclusions

As a comparison with various existing studies, we can thus conclude that our performance improvement is significant.
Mass screening of asymptomatic persons for SARS-CoV-2 using saliva
Authors: Yokota Isao , Shane Peter Y , Okada Kazufumi , Unoki Yoko , Yang Yichi , Inao Tasuku , Sakamaki Kentaro , Iwasaki Sumio , Hayasaka Kasumi , Sugita Junichi , Nishida Mutsumi , Fujisawa Shinichi , Teshima Takanori
Published: August 2020

Background

COVID-19 has rapidly evolved to become a global pandemic due largely to the transmission of its causative virus through asymptomatic carriers. Detection of SARS-CoV-2 in asymptomatic people is an urgent priority for the prevention and containment of disease outbreaks in communities. However, few data are available in asymptomatic persons regarding the accuracy of PCR testing. Additionally, although self-collected saliva has significant logistical advantages in mass screening, its utility as an alternative specimen in asymptomatic persons is yet to be determined.

Methods

We conducted a mass-screening study to compare the utility of nucleic acid amplification, such as reverse transcriptase polymerase chain reaction (RT-PCR) testing, using NPS and saliva samples from each individual in two cohorts of asymptomatic persons: the contact tracing cohort and the airport quarantine cohort.

Results

In this mass-screening study including 1,924 individuals, the sensitivity of nucleic acid amplification testing with nasopharyngeal and saliva specimens were 86% (90%CI:77-93%) and 92% (90%CI:83-97%), respectively, with specificities greater than 99.9%. The true concordance probability between the nasopharyngeal and saliva tests was estimated at 0.998 (90%CI:0.996-0.999) on the estimated airport prevalence, 0.3%. In positive individuals, viral load was highly correlated between NPS and saliva.

Conclusion

Both nasopharyngeal and saliva specimens had high sensitivity and specificity. Self-collected saliva is a valuable specimen to detect SARS-CoV-2 in mass screening of asymptomatic persons.
Health care seeking behaviors in Puerperal sepsis in rural Sindh, Pakistan: a qualitative study
Authors: Ariff Shabina , Khan Ubaidullah , Turab Ali , Kureishy Sumra , Raza Farrukh , Tabassum Farhana , Mir Fatima , LeFevre Amnesty , Bartlett Linda A. , Habib Atif , Soofi Sajid Bashir , Bhutta Zulfiqar A
Published: August 2020

Background

Puerperal sepsis (PS) is one of the major causes of maternal death, contributing to 26 000 deaths per year in developing countries. Early recognition and treatment are essential to managing PS, but numerous social, cultural and technical barriers prevent or delay access to care and necessary medical attention. Through this qualitative study, we identified barriers to care seeking for puerperal sepsis among recently delivered women in Matiari, Pakistan.

Methods

We conducted 20 in-depth interviews among recently delivered women with and without sepsis and their family members. Key informant interviews were conducted with 14 healthcare providers and traditional birth attendants. The themes used for content analysis were knowledge of danger signs, factors affecting care seeking and local treatment practices for postpartum sepsis.

Results

Recently delivered women, their family members and traditional birth attendants were unaware of the word PS or the local translated term for PS. However, they were familiar with most of the individual symptoms associated with PS. Healthcare providers were aware of the condition and the associated symptoms. The healthcare providers understanding of the seriousness of PS was directly proportional their age and clinical experience. The most common barriers to care seeking was the division of labor within the household, obtaining permission from the primary decision maker, access to transportation, lack of financial resources and support from family members.

Discussion

In rural Pakistan, limited knowledge of PS, division of labor within the household, obtaining permission from primary decision maker and access to transportation and financial resources were identified as barriers to seeking care among recently delivered women.

Conclusion

To improve maternal care seeking behaviors for PS, interventions focusing on increasing knowledge of PS, addressing gender inequality, implementing an affordable community transport service and enhancing TBA’s knowledge and skills to manage PS need to be implemented.
Evaluation of gait initiation parameters using inertial sensors in Huntington’s Disease: Insights into anticipatory postural adjustments and cognitive interference
Authors: Desai Radhika , Fritz Nora E. , Muratori Lisa , Hausdorff Jeffrey M. , Busse Monica , Quinn Lori
Published: August 2020

Background and Purpose

Understanding the contribution of anticipatory postural adjustments (APA) on walking ability in individuals with Huntington’s disease (HD) may provide insight into motor planning and the functional consequences of HD-specific cortical-basal ganglia pathway dysfunctions. The purpose of this study was to evaluate inertial measurement unit (IMU)-derived measures of APAs and first step parameters, and their contribution to gait speed, in individuals with and without manifest HD during a single-task and cognitive load condition.

Methods

33 individuals with manifest HD and 15 age-matched healthy controls wore three Opal APDM IMUs during a 14-meter walk during a single task and cognitive load condition. APA acceleration amplitudes, APA durations, first step range of motion (ROMs), and first step duration were compared, along with their relationship to gait speed.

Results

Individuals with HD had significantly greater APA acceleration amplitudes, smaller first step ROMs and longer first step durations compared to healthy controls. No difference in APA durations were present between groups across conditions. Linear model results and significant correlations between mediolateral APA acceleration amplitudes and APA durations were found.

Conclusions

Larger acceleration amplitudes, smaller first step ROMs of greater duration, accompanied by the preservation of APA durations reveal a discrepancy in movement scaling in HD. Additionally, the mediolateral component of the APA is likely a rate-limiting factor that drives a compensatory response in gait initiation. Additional research is needed to explore the neural correlates of HD-related movement scaling.
Topological data analysis model for the spread of the coronavirus
Authors: Chen Yiran , Volić Ismar
Published: August 2020

Abstract

We apply topological data analysis, specifically the Mapper algorithm, to the U.S. COVID-19 data. The resulting Mapper graphs provide visualizations of the pandemic that are more complete than those supplied by other, more standard methods. They encode a variety of geometric features of the data cloud created from geographic information, time progression, and the number of COVID-19 cases. They reflect the development of the pandemic across all of the U.S. and capture the growth rates as well as the regional prominence of hot-spots. The Mapper graphs allow for easy comparisons across time and space and have the potential of becoming a useful predictive tool for the spread of the coronavirus.
Causal associations between COVID-19 and Atrial Fibrillation: A bidirectional Mendelian randomization study
Authors: Zhang Xiaoyu , Wang Biyan , Liu Di , Zhang Jinxia , Tian Qiuyue , Meng Xiaoni , Zhang Jie , Hou Haifeng , Song Manshu , Wang Wei , Wang Youxin
Published: August 2020

Background

Observational studies showed that coronavirus disease 2019 (COVID-19) attacks universally and its most menacing progression uniquely endangers the elderly with cardiovascular disease (CVD). Whether COVID-19 is causally related to increasing susceptibility and severity of atrial fibrillation (AF), the main form of CVD, remains still unknown.

Methods

The study aims to investigate the bidirectional causal relations of COVID-19 with AF using two-sample Mendelian randomization (MR) analysis.

Results

MR evidence suggested genetically predicted severe COVID-19 was significantly associated with higher risk of AF (odds ratio [OR], 1.041; 95% confidence interval (CI), 1.007-1.076; P = 0.017), while genetically predicted AF was not causally associated with severe COVID-19 (OR, 0.831; 95% CI, 1.619-1.115; P=0.217). There was limited evidence to support association of genetically proxied COVID-19 with risk of AF (OR, 1.051; 95% CI, 0.991-1.114; P=0.097), and vice versa (OR, 0.163; 95% CI, 0.004-6.790; P=0.341). MR-Egger indicated no evidence of pleiotropic bias.

Conclusion

Overall, severe COVID-19 may causally affect AF through independent biological pathway. Survivors from severe COVID-19 might be of high risk of AF in the future.
Data Mining Approach to Analyze Covid19 Dataset of Brazilian Patients
Authors: Chire Josimar
Published: August 2020

The pandemic originated by coronavirus(covid19), name coined by World Health Organization during the first month in 2020. Actually, almost all the countries presented covid19 positive cases and governments are choosing different health policies to stop the infection and many research groups are working on patients data to understand the virus, at the same time scientists are looking for a vacuum to enhance imnulogy system to tack covid19 virus. One of top countries with more infections is Brazil, until August 11 had a total of 3,112,393 cases. Research Foundation of Sao Paulo State(Fapesp) released a dataset, it was an innovative in collaboration with hospitals(Einstein, Sirio-Libanes), laboratory(Fleury) and Sao Paulo University to foster reseach on this trend topic. The present paper presents an exploratory analysis of the datasets, using a Data Mining Approach, and some inconsistencies are found, i.e. NaN values, null references values for analytes, outliers on results of analytes, encoding issues. The results were cleaned datasets for future studies, but at least a 20% of data were discarded because of non numerical, null values and numbers out of reference range.
Mortality Associated With Intubation and Mechanical Ventilation in Patients with COVID-19
Authors: Zimmerman Patrick W. , Stroever Stephanie J. , Burton Timothy , Hester Karri E. , Kim Minha , Fahy Ryan T. , Corbitt Kimberly A. , Petrini Joann R. , Nicastro Jeffrey
Published: August 2020

Objective

The need for critical care, hemodynamic support, renal replacement therapy, and mechanical ventilation have emerged as key features of the SARS-nCoV-2 (COVID-19) pandemic. The primary aim of this study was to determine the in-hospital mortality rate of mechanically ventilated patients. We also sought to determine the risk of in-hospital mortality by age, gender, race, ethnicity, and body mass index.

Methods

We performed a retrospective cohort study to determine the mortality rate among inpatient adults with COVID-19 on mechanical ventilation in the Nuvance Health system between March 1, 2020 and July 17, 2020. Patients were included if they were 18 years or older, had a laboratory confirmed COVID-19 diagnosis, were admitted to hospitals within the Nuvance Health network (7 hospitals), and were on mechanical ventilation at any time during their inpatient stay.

Results

Overall mortality in our cohort of 304 patients was 53.3%. Multivariable logistic regression including age, gender, race, ethnicity, and BMI demonstrated patients over 71 years old had greater risk of mortality compared to patients ages 61-70, and females had half the risk compared to males. There was no significant difference in risk of mortality given race, ethnicity, or BMI.

Conclusions

In adult patients with confirmed COVID-19 infection requiring mechanical ventilation and intensive care, advanced age (>71 years old) and male gender are associated with increased risk of mortality. This information contributes to a collective body of evidence to support ongoing planning and decision-making among clinicians and for directed infection prevention programming.
The landscape of childhood vaccine exemptions in the United States
Authors: Zipfel Casey M , Garnier Romain , Kuney Madeline , Bansal Shweta
Published: August 2020

Once-eliminated vaccine-preventable childhood diseases, such as measles, are resurging across the United States. Understanding the spatio-temporal trends in vaccine exemptions is crucial to targeting public health intervention to increase vaccine uptake and anticipating vulnerable populations as cases surge. However, prior available data on childhood disease vaccination is either on too rough a spatial scale for this spatially-heterogeneous issue, or is only available for small geographic regions, making general conclusions infeasible. Here, we have collated school vaccine exemption data across the United States and provide it at the county-level for all years available. We demonstrate the fine-scale spatial heterogeneity in vaccine exemption levels, and show that many counties may fall below the herd immunity threshold. We also show that vaccine exemptions increase over time in most states, and non-medical exemptions are highly prevalent where allowed. Our dataset also highlights the need for greater data sharing and standardized reporting across the United States.
Designed Variants of ACE2-Fc that Decouple Anti-SARS-CoV-2 Activities from Unwanted Cardiovascular Effects
Authors: Liu Pan , Xie Xinfang , Gao Li , Jin Jing
Published: August 2020

Angiotensin-converting enzyme 2 (ACE2) is the entry receptor for SARS-CoV-2, and recombinant ACE2 decoys are being evaluated as new antiviral therapies. We designed and tested an antibody-like ACE2-Fc fusion protein, which has the benefit of long pharmacological half-life and the potential to facilitate immune clearance of the virus. Out of a concern that the intrinsic catalytic activity of ACE2 may unintentionally alter the balance of its hormonal substrates and cause adverse cardiovascular effects in treatment, we performed a mutagenesis screening for inactivating the enzyme. Three mutants, R273A, H378A and E402A, completely lost their enzymatic activity for either surrogate or physiological substrates. All of them remained capable of binding SARS-CoV-2 and could suppress the transduction of a pseudotyped virus in cell culture. This study established new ACE2-Fc candidates as antiviral treatment for SARS-CoV-2 without potentially harmful side effects from ACE2’s catalytic actions toward its vasoactive substrates.
A generative network model of neurodevelopment
Authors: Akarca Danyal , Vértes Petra E , Bullmore Edward T ,   the CALM team, Astle Duncan E
Published: August 2020

The emergence of large-scale brain networks, and their continual refinement, represent crucial developmental processes that can drive individual differences in cognition and which are associated with multiple neurodevelopmental conditions. But how does this organization arise, and what mechanisms govern the diversity of these developmental processes? There are many existing descriptive theories, but to date none are computationally formalized. We provide a mathematical framework that specifies the growth of a brain network over developmental time. Within this framework macroscopic brain organization, complete with spatial embedding of its organization, is an emergent property of a generative wiring equation that optimizes its connectivity by renegotiating its biological costs and topological values continuously over development. The rules that govern these iterative wiring properties are controlled by a set of tightly framed parameters, with subtle differences in these parameters steering network growth towards different neurodiverse outcomes. Regional expression of genes associated with the developmental simulations converge on biological processes and cellular components predominantly involved in synaptic signaling, neuronal projection, catabolic intracellular processes and protein transport. Together, this provides a unifying computational framework for conceptualizing the mechanisms and diversity of childhood brain development, capable of integrating different levels of analysis – from genes to cognition.
A comparative analysis of chromatin accessibility in cattle, pig, and mouse tissues
Authors: Halstead Michelle M , Kern Colin , Saelao Perot , Wang Ying , Chanthavixay Ganrea , Medrano Juan F , Van Eenennaam Alison L , Korf Ian , Tuggle Christopher K , Ernst Catherine W , Zhou Huaijun , Ross Pablo J
Published: August 2020

Background

Although considerable progress has been made towards annotating the noncoding portion of the human and mouse genomes, regulatory elements in other species, such as livestock, remain poorly characterized. This lack of functional annotation poses a substantial roadblock to agricultural research and diminishes the value of these species as model organisms. As active regulatory elements are typically characterized by chromatin accessibility, we implemented the Assay for Transposase Accessible Chromatin (ATAC-seq) to annotate and characterize regulatory elements in pigs and cattle, given a set of eight adult tissues.

Results

Overall, 306,304 and 273,594 active regulatory elements were identified in pig and cattle, respectively. 71,478 porcine and 47,454 bovine regulatory elements were highly tissue-specific and were correspondingly enriched for binding motifs of known tissue-specific transcription factors. However, in every tissue the most prevalent accessible motif corresponded to the insulator CTCF, suggesting pervasive involvement in 3-D chromatin organization. Taking advantage of a similar dataset in mouse, open chromatin in pig, cattle, and mice were compared, revealing that the conservation of regulatory elements, in terms of sequence identity and accessibility, was consistent with evolutionary distance; whereas pig and cattle shared about 20% of accessible sites, mice and ungulates only had about 10% of accessible sites in common. Furthermore, conservation of accessibility was more prevalent at promoters than at intergenic regions.

Conclusions

The lack of conserved accessibility at distal elements is consistent with rapid evolution of enhancers, and further emphasizes the need to annotate regulatory elements in individual species, rather than inferring elements based on homology. This atlas of chromatin accessibility in cattle and pig constitutes a substantial step towards annotating livestock genomes and dissecting the regulatory link between genome and phenome.
Pandemic danger to the deep: the risk of marine mammals contracting SARS-CoV-2 from wastewater
Authors: Mathavarajah Sabateeshan , Stoddart Amina K. , Gagnon Graham A. , Dellaire Graham
Published: August 2020

We are in unprecedented times with the ongoing COVID-19 pandemic. The pandemic has impacted public health, the economy and our society on a global scale. In addition, the impacts of COVID-19 permeate into our environment and wildlife as well. Here, we discuss the essential role of wastewater treatment and management during these times. A consequence of poor wastewater management is the discharge of untreated wastewater carrying infectious SARS-CoV-2 into natural water systems that are home to marine mammals. Here, we predict the susceptibility of marine mammal species using a modelling approach. We identified that many species of whale, dolphin and seal, as well as otters, are predicted to be highly susceptible to infection by the SARS-CoV-2 virus. In addition, geo-mapping highlights how current wastewater management in Alaska may lead to susceptible marine mammal populations being exposed to the virus. Localities such as Cold Bay, Naknek, Dillingham and Palmer may require additional treatment of their wastewater to prevent virus spillover through sewage. Since over half of these susceptibility species are already at risk worldwide, the release of the virus via untreated wastewater could have devastating consequences for their already declining populations. For these reasons, we discuss approaches that can be taken by the public, policymakers and wastewater treatment facilities to reduce the risk of virus spillover in our natural water systems. Thus, we indicate the potential for reverse zoonotic transmission of COVID-19 and its impact on marine wildlife; impacts that can be mitigated with appropriate action to prevent further damage to these vulnerable populations.
In SilicoDesign of siRNAs Targeting Existing and Future Respiratory Viruses with VirusSi
Authors: Zhang Dingyao , Lu Jun
Published: August 2020

The COVID-19 pandemic has exposed global inadequacies in therapeutic options against both the COVID-19-causing SARS-CoV-2 virus and other newly emerged respiratory viruses. In this study, we present the VirusSi computational pipeline, which facilitates the rational design of siRNAs to target existing and future respiratory viruses. Mode A of VirusSi designs siRNAs against an existing virus, incorporating considerations on siRNA properties, off-target effects, viral RNA structure and viral mutations. It designs multiple siRNAs out of which the top candidate targets >99% of SARS-CoV-2 strains, and the combination of the top four siRNAs is predicted to target all SARS-CoV-2 strains. Additionally, we develop Greedy Algorithm with Redundancy (GAR) and Similarity-weighted Greedy Algorithm with Redundancy (SGAR) to support the Mode B of VirusSi, which pre-designs siRNAs against future emerging viruses based on existing viral sequences. Time-simulations using known coronavirus genomes as early as 10 years prior to the COVID-19 outbreak show that at least three SARS-CoV-2-targeting siRNAs are among the top 30 pre-designed siRNAs. Before-the-outbreak pre-design is also possible against the MERS-CoV virus and the 2009-H1N1 swine flu virus. Our data support the feasibility of pre-designing anti-viral siRNA therapeutics prior to viral outbreaks. We propose the development of a collection of pre-designed, safety-tested, and off-the-shelf siRNAs that could accelerate responses toward future viral diseases.
msiPL: Non-linear Manifold and Peak Learning of Mass Spectrometry Imaging Data Using Artificial Neural Networks
Authors: Abdelmoula Walid M. , Lopez Begona Gimenez-Cassina , Randall Elizabeth C. , Kapur Tina , Sarkaria Jann N. , White Forest M. , Agar Jeffrey N. , Wells William M. , Agar Nathalie Y.R.
Published: August 2020

Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving clinical diagnosis, biomarker discovery, metabolomics research and pharmaceutical applications. The large data size and high dimensional nature of MSI pose computational and memory complexities that hinder accurate identification of biologically-relevant molecular patterns. We propose msiPL, a robust and generic probabilistic generative model based on a fully-connected variational autoencoder for unsupervised analysis and peak learning of MSI data. The method can efficiently learn and visualize the underlying non-linear spectral manifold, reveal biologically-relevant clusters of tumor heterogeneity and identify underlying informative m/z peaks. The method provides a probabilistic parametric mapping to allow a trained model to rapidly analyze a new unseen MSI dataset in a few seconds. The computational model features a memory-efficient implementation using a minibatch processing strategy to enable the analyses of big MSI data (encompassing more than 1 million high-dimensional datapoints) with significantly less memory. We demonstrate the robustness and generic applicability of the application on MSI data of large size from different biological systems and acquired using different mass spectrometers at different centers, namely: 2D Matrix-Assisted Laser Desorption Ionization (MALDI) Fourier Transform Ion Cyclotron Resonance (FT ICR) MSI data of human prostate cancer, 3D MALDI Time-of-Flight (TOF) MSI data of human oral squamous cell carcinoma, 3D Desorption Electrospray Ionization (DESI) Orbitrap MSI data of human colorectal adenocarcinoma, 3D MALDI TOF MSI data of mouse kidney, and 3D MALDI FT ICR MSI data of a patient-derived xenograft (PDX) mouse brain model of glioblastoma.

Significance

Mass spectrometry imaging (MSI) provides detailed molecular characterization of a tissue specimen while preserving spatial distributions. However, the complex nature of MSI data slows down the processing time and poses computational and memory challenges that hinder the analysis of multiple specimens required to extract biologically relevant patterns. Moreover, the subjectivity in the selection of parameters for conventional pre-processing approaches can lead to bias. Here, we present a generative probabilistic deep-learning model that can analyze and non-linearly visualize MSI data independent of the nature of the specimen and of the MSI platform. We demonstrate robustness of the method with application to different tissue types, and envision it as a new generation of rapid and robust analysis for mass spectrometry data.
The Automated Optimisation of a Coarse-Grained Force Field Using Free Energy Data
Authors: Caceres-Delpiano Javier , Wang Lee-Ping , Essex Jonathan W.
Published: August 2020

Atomistic models provide a detailed representation of molecular systems, but are sometimes inadequate for simulations of large systems over long timescales. Coarse-grained models enable accelerated simulations by reducing the number of degrees of freedom, at the cost of reduced accuracy. New optimisation processes to parameterise these models could improve their quality and range of applicability. We present an automated approach for the optimisation of coarse-grained force fields, by reproducing free energy data derived from atomistic molecular simulations. To illustrate the approach, we implemented hydration free energy gradients as a new target for force field optimisation in ForceBalance and applied it successfully to optimise the un-charged side-chains and the protein backbone in the SIRAH protein coarse-grain force field. The optimised parameters closely reproduced hydration free energies of atomistic models and gave improved agreement with experiment.
Standing on elevated platform changes postural responses during arm movement
Authors: Mochizuki Luis , Pennone Juliana , Bigongiari Aline , Cosme Renata Garrido , Cajueiro Monique Oliveira Baptista , Amadio Alberto Carlos
Published: August 2020

This study investigated the muscle activity during the preparatory (anticipatory postural adjustment, APA), execution (online postural adjustments, OPA), and compensatory (compensatory postural adjustment, CPA) phases during standing with eyes opened or closed on an elevated platform. Eight healthy young women stood in the upright position, with eyes opened or closed, and did as-fast-as-they-could shoulder flexions on the ground and on 1-m-height-portable-elevated-platform. The surface EMG of trunk (lumbar extensor, and rectus abdominis) and lower limb (rectus femoris, biceps femoris, tibialis anterior and gastrocnemius lateralis) muscles during this task were recorded (1 kHz sampling frequency) and compared during these three phases. Analysis of variance was applied to compare the effects of height (floor and elevated platform), vision (open and closed), and postural adjustment (APA, OPA and CPA) into the activity of each muscle. These muscles were more active during OPA (p<0.0001) and less active during APA. On the elevated platform, these postural muscles presented more activty during APA (p<0.001). During the most stable condition (on the ground with eyes opened), muscle activity during APA and OPA was negatively correlated, and not correlated between OPA and CPA. Our results suggest postural control adapts to sensory, motor, and cognitive conditions. Therefore, the increased demand for postural control, generated due to the height of the support base, provokes the need for greater flexibility of postural synergies and causes a change in muscle activity.

Summary Statement

We discuss how postural muscle activity behaves before and after a fast upper arms movement when someone stands on a elevated platform or on the ground.
Use of Therapeutic Drug Monitoring to Characterize Cefepime-Induced Neurotoxicity
Authors: Venugopalan Veena , Nys Cara , Hurst Natalie , Chen Yiqing , Bruzzone Maria , Cherabuddi Kartikeya , Iovine Nicole , Liu Jiajun , Al-Shaer Mohammad H. , Scheetz Marc H. , Rhodes Nathaniel , Peloquin Charles A. , Klinker Kenneth
Published: August 2020

Background

The incidence of cefepime-induced neurotoxicity (CIN) in hospitalized patients is highly variable. Although greater cefepime exposures incite neurotoxicity, data evaluating trough thresholds associated with CIN remains limited. The objectives of this study were to evaluate the incidence of CIN, assess the relationship between cefepime trough concentrations and CIN, investigate clinical factors associated with CIN, and describe electroencephalogram (EEG) abnormalities in CIN.

Methods

This was a retrospective study of adult patients who had received ≥ 5 days of cefepime with ≥ 1 trough concentration > 25 mg/L. Potential CIN cases were identified utilizing neurological symptoms, neurologist assessments, EEG findings and improvement of neurotoxicity after cefepime discontinuation.

Results

One-hundred and forty-two patients were included. The incidence of CIN was 13% (18/142). The mean cefepime trough concentration in CIN patients was significantly greater than the non-neurotoxicity group (74.2 mg/L ± 41.1 vs. 46.6 mg/L ± 23, p=0.015). Lower renal function (creatinine clearance < 30 ml/min), greater time to therapeutic drug monitoring (TDM) (≥72 hours), and each 1 mg/mL rise in cefepime trough were independently associated with increased risk of CIN. Moderate generalized slowing of the background rhythm was the most common EEG pattern associated with CIN.

Conclusion

Cefepime should be used cautiously in hospitalized patients due to the risk of neurotoxicity. Patients with greater renal function and those who had early cefepime TDM (≤ 72 hours) had lower risk of CIN.
Impact of contact tracing on COVID-19 mortality: An impact evaluation using surveillance data from Colombia
Authors: Vecino-Ortiz Andres I. , Villanueva Congote Juliana , Zapata Bedoya Silvana , Cucunuba Zulma M
Published: August 2020

Background

Contact tracing is a key part of the public health surveillance toolkit. However, it is labor intensive and costly to carry it out. Some countries have faced challenges implementing contact tracing, and no impact evaluations to our knowledge have assessed its impact on COVID-19 mortality. This study assesses the impact of contact tracing in a middle-income country and provides data to support the expansion of contact tracing strategies with the aim of improving infection control.

Methods

We obtained publicly available data on all confirmed COVID-19 cases in Colombia between March 2 and June 16, 2020. (N=54,931 cases over 135 days of observation). We proxied contact tracing performance as the proportion of cases identified through contact tracing out of all cases identified, as suggested by WHO guidelines. We calculated the daily proportion of cases identified through contact tracing across 37 geographical units (32 departments and five districts). Further, we used a sequential log-log fixed-effects model to estimate the 21-days, 28-days, 42-days and 56-days lagged impact of the proportion of cases identified through contact tracing on the daily number of COVID-19 deaths. Both the proportion of cases identified through contact tracing and the daily number of COVID-19 deaths are smoothed using 7-day moving averages. Models control for prevalence of active cases, second-degree polynomials, and mobility indices. Robustness checks to include supply-side variables were performed.

Results

We found that a 10 percent increase in the proportion of cases identified through contact tracing is related to COVID-19 mortality reductions between 0.8% and 3.4%. Our models explain between 47%-70% of the variance in mortality. Results are robust to changes of specification and inclusion of supply-side variables.

Conclusion

Contact tracing is instrumental to contain infectious diseases and its prioritization as a surveillance strategy will have a substantial impact on reducing deaths while minimizing the impact on the fragile economic systems of lower and middle-income countries. This study provides lessons for other LMIC.
Robust, reproducible clinical patterns in hospitalised patients with COVID-19
Authors: Millar Jonathan E , Neyton Lucile , Seth Sohan , Dunning Jake , Merson Laura , Murthy Srin , Russell Clark D , Keating Sean , Swets Maaike , Sudre Carole , Spector Timothy , Ourselin Sebastien , Steves Claire , Wolf Jonathan ,   ISARIC-4C Investigators, Docherty Annemarie , Harrison Ewen , Openshaw Peter , Semple Calum , Baillie J. Kenneth
Published: August 2020

Background

Severe COVID-19 is characterised by fever, cough, and dyspnoea. Symptoms affecting other organ systems have been reported. However, it is the clinical associations of different patterns of symptoms which influence diagnostic and therapeutic decision-making. In this study, we applied simple machine learning techniques to a large prospective cohort of hospitalised patients with COVID-19 identify clinically meaningful sub-groups.

Methods

We obtained structured clinical data on 59 011 patients in the UK (the ISARIC Coronavirus Clinical Characterisation Consortium, 4C) and used a principled, unsupervised clustering approach to partition the first 25 477 cases according to symptoms reported at recruitment. We validated our findings in a second group of 33 534 cases recruited to ISARIC-4C, and in 4 445 cases recruited to a separate study of community cases.

Findings

Unsupervised clustering identified distinct sub-groups. First, a core symptom set of fever, cough, and dyspnoea, which co-occurred with additional symptoms in three further patterns: fatigue and confusion, diarrhoea and vomiting, or productive cough. Presentations with a single reported symptom of dyspnoea or confusion were common, and a subgroup of patients reported few or no symptoms. Patients presenting with gastrointestinal symptoms were more commonly female, had a longer duration of symptoms before presentation, and had lower 30-day mortality. Patients presenting with confusion, with or without core symptoms, were older and had a higher unadjusted mortality. Symptom clusters were highly consistent in replication analysis using a further 35446 individuals subsequently recruited to ISARIC-4C. Similar patterns were externally verified in 4445 patients from a study of self-reported symptoms of mild disease.

Interpretation

The large scale of the ISARIC-4C study enabled robust, granular discovery and replication of patient clusters. Clinical interpretation is necessary to determine which of these observations have practical utility. We propose that four patterns are usefully distinct from the core symptom groups: gastro-intestinal disease, productive cough, confusion, and pauci-symptomatic presentations. Importantly, each is associated with an in-hospital mortality which differs from that of patients with core symptoms. These observations deepen our understanding of COVID-19 and will influence clinical diagnosis, risk prediction, and future mechanistic and clinical studies.

Funding

Medical Research Council; National Institute Health Research; Well-come Trust; Department for International Development; Bill and Melinda Gates Foundation; Liverpool Experimental Cancer Medicine Centre.
Analysis of cardiomyocyte nuclei in human cardiomyopathy reveals orientation dependent defects in shape
Authors: Brayson Daniel , Ehler Elisabeth , dos Remedios Cristobal G. , Shanahan Catherine M.
Published: August 2020

Cardiomyopathies are progressive diseases of heart muscle often caused by mutations in genes encoding sarcomeric, cytoskeletal and nucleoskeletal proteins though in many cases the cause of disease is not identified. Whilst nucleus hypertrophy has been described, it is not known whether nucleus shape changes are a general feature of cardiomyopathy. Due to the rod-shaped nature of cardiomyocytes and their elliptical nuclei we hypothesised that orientation of analysis would be an important determinant of any changes observed between patients exhibiting primarily unexplained cardiomyopathy and control samples from non-failing donors. To investigate this we performed image analysis of cardiomyocyte nuclei in myocardial cryosections from a cohort of cardiomyopathy patients. We discovered that circularity, solidity and aspect ratio were sensitive to orientation of the myocardium and that in the transverse plane only circularity was reduced in cardiomyocyte nuclei of cardiomyopathy patients. These findings show that orientation dependent changes in nucleus shape may be a property of cardiomyopathy and with appropriate follow up studies, may prove to have mechanistic and diagnostic value.
Proportion of idiopathic pulmonary fibrosis risk explained by known genetic loci
Authors: Leavy Olivia C , Ma Shwu-Fan , Molyneaux Philip L , Maher Toby M , Oldham Justin M , Flores Carlos , Noth Imre , Jenkins R Gisli , Dudbridge Frank , Wain Louise V , Allen Richard J
Published: August 2020

Genome-wide association studies have identified 14 genetic loci associated with susceptibility to idiopathic pulmonary fibrosis (IPF), a devastating lung disease with poor prognosis. Of these, the variant with the strongest association, rs35705950, is located in the promoter region of the MUC5B gene and has a risk allele (T) frequency of 30-35% in IPF cases. Here we present estimates of the proportion of disease liability explained by each of the 14 IPF risk variants as well as estimates of the proportion of cases that can be attributed to each variant. We estimate that rs35705950 explains 5.9-9.4% of disease liability, which is much lower than previously reported estimates. Of every 100,000 individuals with the rs35705950_GG genotype we estimate 30 will have IPF, whereas for every 100,000 individuals with the rs35705950_GT genotype 152 will have IPF. Quantifying the impact of genetic risk factors on disease liability improves our understanding of the underlying genetic architecture of IPF and provides insight into the impact of genetic factors in risk prediction modelling.