bluuebunny/crossref_metadata_2025 huggingface dataset:
Genomic rearrangements uncovered by genome-wide co-evolution analysis of a major nosocomial pathogen, Enterococcus faecium
Authors: Top Janetta , Arredondo-Alonso Sergio , Schürch Anita C. , Puranen Santeri , Pesonen Maiju , Pensar Johan , Willems Rob J. L. , Corander Jukka
Published: November 2020

Enterococcus faecium

is a gut commensal of the gastro-digestive tract, but also known as nosocomial pathogen among hospitalized patients. Population genetics based on whole-genome sequencing has revealed that

E. faecium

strains from hospitalized patients form a distinct clade, designated clade A1, and that plasmids are major contributors to the emergence of nosocomial

E. faecium

. Here we further explored the adaptive evolution of

E. faecium

using a genome-wide co-evolution study (GWES) to identify co-evolving single-nucleotide polymorphisms (SNPs). We identified three genomic regions harbouring large numbers of SNPs in tight linkage that are not proximal to each other based on the completely assembled chromosome of the clade A1 reference hospital isolate AUS0004. Close examination of these regions revealed that they are located at the borders of four different types of large-scale genomic rearrangements, insertion sites of two different genomic islands and an IS30-like transposon. In non-clade A1 isolates, these regions are adjacent to each other and they lack the insertions of the genomic islands and IS30-like transposon. Additionally, among the clade A1 isolates there is one group of pet isolates lacking the genomic rearrangement and insertion of the genomic islands, suggesting a distinct evolutionary trajectory. In silico analysis of the biological functions of the genes encoded in three regions revealed a common link to a stress response. This suggests that these rearrangements may reflect adaptation to the stringent conditions in the hospital environment, such as antibiotics and detergents, to which bacteria are exposed. In conclusion, to our knowledge, this is the first study using GWES to identify genomic rearrangements, suggesting that there is considerable untapped potential to unravel hidden evolutionary signals from population genomic data.
Development of a bedside score to predict dengue severity
Authors: Marois Ingrid , Forfait Carole , Inizan Catherine , Klement-Frutos Elise , Valiame Anabelle , Aubert Daina , Gourinat Ann-Claire , Laumond Sylvie , Barsac Emilie , Grangeon Jean-Paul , Cazorla Cécile , Merlet Audrey , Tarantola Arnaud , Dupont-Rouzeyrol Myrielle , Descloux Elodie
Published: November 2020

Background

In 2017, New Caledonia experienced an outbreak of severe dengue causing high hospital burden (4,379 cases, 416 hospital admissions, 15 deaths). We decided to build a local operational model predictive of dengue severity, which was needed to ease the healthcare circuit.

Methods

We retrospectively analyzed clinical and biological parameters associated with severe dengue in the cohort of patients hospitalized at the Territorial Hospital between January and July 2017 with confirmed dengue, in order to elaborate a comprehensive patient’s score. Patients were compared in univariate and multivariate analyses. Predictive models for severity were built using a descending step-wise method.

Results

Out of 383 included patients, 130 (34%) developed severe dengue and 13 (3.4%) died. Major risk factors identified in univariate analysis were: age, comorbidities, presence of at least one alert sign, platelets count <30×109/L, prothrombin time <60%, AST and/or ALT >10N, and previous dengue infection. Severity was not influenced by the infecting dengue serotype nor by previous Zika infection. Two models to predict dengue severity were built according to sex. Best models for females and males had respectively a median Area Under the Curve = 0.80 and 0.88, a sensitivity = 84.5% and 84.5%, a specificity = 78.6% and 95.5%, a positive predictive value = 63.3% and 92.9%, a negative predictive value = 92.8% and 91.3%. Models were secondarily validated on 130 patients hospitalized for dengue in 2018.

Conclusion

We built robust and efficient models to calculate a bedside score able to predict dengue severity in our setting. We propose the spreadsheet for dengue severity score calculations to health practitioners facing dengue outbreaks of enhanced severity in order to improve patients’ medical management and hospitalization flow.
COVID-19 Workplace Outbreaks by Industry Sector and their Associated Household Transmission, Ontario, Canada, January – June, 2020
Authors: Murti Michelle , Achonu Camille , Smith Brendan T. , Brown Kevin A. , Kim Jin Hee , Johnson James , Ravindran Saranyah , Buchan Sarah A.
Published: November 2020

Introduction

Workplaces requiring in-person attendance of employees for ongoing operations may be susceptible to SARS-CoV-2 outbreaks that impact workers as well as their close contacts. To understand industry sectors impacted by workplace outbreaks in the first wave of the pandemic, and the additional burden of illness through household transmission, we analyzed public health declared workplace outbreaks between January 21 to June 30, 2020, and their associated cases from January 21 to July 28.

Methods

Number, size and duration of outbreaks were described by sector, and outbreak cases were compared to sporadic cases in the same time frame. Address matching identified household cases with onset ≥2 days before, ≥2 days after, or within 1 day of the workplace outbreak case.

Results

There were 199 outbreaks with 1245 cases, and 68% of outbreaks and 80% of cases belonged to i) Manufacturing, ii) Agriculture, Forestry, Fishing, Hunting, iii) Transportation and Warehousing. Median size of outbreaks was 3 cases (range: 1-140), and lasted median 7days (range: 0-119). Outbreak cases were significantly more likely to be male, younger, healthier, and have better outcomes. There were 608 household cases associated with 339 (31%) outbreak cases with valid addresses, increasing the burden of illness by 56%. The majority of household cases (368, 60%) occurred after the outbreak case.

Conclusions

Workplace outbreaks primarily occurred in three sectors. COVID-19 prevention measures should target industry sectors at risk by preventing introduction from exposed employees, spread in the workplace, and spread outside of the workplace.

What is already known about this topic?

COVID-19 outbreaks occur within workplaces and can spread to the community

What is added by this report?

From January 21 – June 30, 2020, there were 199 workplace outbreaks in Ontario, Canada; 68% of outbreaks and 80% of outbreak-associated COVID-19 case were in three industry sectors: Manufacturing, Agriculture/Forestry/Fishing/Hunting, and Transportation/Warehousing. Household transmission occurred among 31% of outbreak cases, resulting in a 56% increase in workplace outbreak-associated cases when burden of household transmission is considered.

What are the implications for public health practice?

Workplace outbreak prevention measures should be targeted to industry sectors at risk by preventing introduction from exposed employees, spread in the workplace, and transmission to the greater community.
Design of COVID-19 Staged Alert Systems to Ensure Healthcare Capacity with Minimal Closures
Authors: Yang Haoxiang , Sürer Özge , Duque Daniel , Morton David P. , Singh Bismark , Fox Spencer J. , Pasco Remy , Pierce Kelly , Rathouz Paul , Du Zhanwei , Pignone Michael , Escott Mark E. , Adler Stephen I. , Johnston S. Claiborne , Meyers Lauren Ancel
Published: November 2020

Community mitigation strategies to combat COVID-19, ranging from healthy hygiene to shelter-in-place orders, exact substantial socioeconomic costs. Judicious implementation and relaxation of restrictions amplify their public health benefits while reducing costs. We derive optimal strategies for toggling between mitigation stages using daily COVID-19 hospital admissions. With public compliance, the policy triggers ensure adequate intensive care unit capacity with high probability while minimizing the duration of strict mitigation measures. In comparison, we show that other sensible COVID-19 staging policies, including France’s ICU-based thresholds and a widely adopted indicator for reopening schools and businesses, require overly restrictive measures or trigger strict stages too late to avert catastrophic surges. As cities worldwide face future pandemic waves, our findings provide a robust strategy for tracking COVID-19 hospital admissions as an early indicator of hospital surges and enacting staged measures to ensure integrity of the health system, safety of the health workforce, and public confidence.
The pathogenic p.R391G ABCC6 displays incomplete penetrance implying the necessity of an interacting partner for the development of pseudoxanthoma elasticum
Authors: Szeri Flora , Miko Agnes , Navasiolava Nastassia , Kaposi Ambrus , Verschuere Shana , Li Qiaoli , Terry Sharon F. , Boraldi Federica , Uitto Jouni , van de Wetering Koen , Martin Ludovic , Quaglino Daniela , Vanakker Olivier M. , Tory Kalman , Aranyi Tamas
Published: November 2020

ABCC6 promotes the efflux of ATP from hepatocytes to the bloodstream. ATP is then cleaved to AMP and pyrophosphate, a major inhibitor of ectopic calcification. Pathogenic variants of ABCC6 cause pseudoxanthoma elasticum, a recessive ectopic calcification disease of highly variable severity. One of the mechanisms influencing the heterogeneity of a disorder is the penetrance of pathogenic variants. Penetrance shows the proportion of carriers developing the phenotype; hence incomplete penetrance indicates that the disease does not necessarily develop in the presence of specific variants. Here, we investigated whether incomplete penetrance contributes to the heterogeneity of pseudoxanthoma elasticum. By integrating the clinical and genetic data of 589 patients, we created the largest European cohort. Based on allele frequencies compared to a reference cohort, we identified two incomplete penetrant variants, p.V787I and p.R391G, 6.5% and 2% penetrance, respectively. The characterization of the p.R391G variant suggested unaltered severity of the clinical phenotype. Based on our biochemical and localization studies, we hypothesize that the variant becomes deleterious only if an interacting partner is mutated simultaneously. Our data reveal the potential existence of the first interacting partner of ABCC6. Our data are also important for genetic counseling, as they suggest lower disease heritability of some variants.
Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2
Authors: Huisman Jana S. , Scire Jérémie , Angst Daniel C. , Li Jinzhou , Neher Richard A. , Maathuis Marloes H. , Bonhoeffer Sebastian , Stadler Tanja
Published: November 2020

The effective reproductive numberReis a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation ofRe, applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimatedRedropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimatedRe. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequentReestimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source ofReestimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data.
An integrative multi-omics analysis of 16 autoimmune diseases and cancer outcomes highlights immune-cell regulatory mechanisms and shared genetic architecture
Authors: Prince C , Mitchell R. E , Richardson T. G.
Published: November 2020

Background

Developing functional understanding into the causal molecular drivers of immunological disease is a critical challenge in genomic medicine. Here we systematically apply Mendelian randomization (MR), genetic colocalization, immune cell-type enrichment and phenome-wide association methods to investigate the effect of genetically predicted gene expression on 12 autoimmune and 4 cancer outcomes.

Results

Using whole blood derived estimates for regulatory variants from the eQTLGen consortium (n=31,684) we constructed genetic risk scores (r2<0.1) for 10,104 genes. Applying the inverse-variance weighted Mendelian randomization method transcriptome-wide whilst accounting for linkage disequilibrium structure identified 773 unique genes with evidence of a genetically predicted effect on at least one disease outcome (P<4.81 × 10−5). We next undertook genetic colocalization to investigate whether these effects may be confined to specific cell-types using gene expression data derived from 18 types of immune cells. This highlighted many cell-type dependent effects, such asPRKCQexpression and asthma risk (posterior probability of association (PPA)=0.998), which was T-cell specific, as well asTPM3expression and prostate cancer risk (PPA=0.821), which was restricted to monocytes. Phenome-wide analyses on 320 complex traits allowed us to explore the shared genetic architecture and prioritize key drivers of disease risk, such asCASP10which provided evidence of an effect on 7 cancer-related outcomes. Similarly, these evaluations of pervasive pleiotropy may be valuable for evaluations of therapeutic targets to help identify potential adverse effects.

Conclusions

Our atlas of results can be used to characterize known and novel loci in autoimmune disease and cancer susceptibility, both in terms of developing insight into cell-type dependent effects as well as dissecting shared genetic architecture and disease pathways. As exemplar, we have highlighted several key findings in this study, although similar evaluations can be conducted interactively athttp://mrcieu.mrsoftware.org/immuno_MR/.
Proteo-genomic analysis of SARS-CoV-2: A clinical landscape of SNPs, COVID-19 proteome and host responses
Authors: Tushir Sheetal , Kamanna Sathisha , Nath Sujith S , Bhat Aishwarya , Rose Steffimol , Aithal Advait R , Tatu Utpal
Published: November 2020

A novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of COVID-19 and continues to be a global health challenge. To understand viral disease biology, we have carried out proteo-genomic analysis using next generation sequencing (NGS) and mass-spectrometry on nasopharyngeal swabs of COVID-19 patients to examine clinical genome and proteome. Our study confirms the hyper mutability of SARS-CoV-2 showing multiple SNPs. NGS analysis detected 27 mutations of which 14 are synonymous, 11 are missense and 2 are extragenic in nature. Phylogenetic analysis of SARS-CoV-2 isolates indicated their close relation to Bangladesh isolate and multiple origins of isolates within a country. Our proteomic analysis, for the first time identified 13 different SARS-CoV-2 proteins from the clinical swabs. Of the total 41 peptides captured by HRMS, 8 matched to nucleocapsid protein, 2 to ORF9b, 1 to spike glycoprotein and ORF3a, with remaining mapping to ORF1ab polyprotein. Additionally, host proteome analysis revealed several key host proteins to be uniquely expressed in COVID-19 patients. Pathway analysis of these proteins points towards modulation in immune response, especially involving neutrophil and IL-12 mediated signaling. Besides revealing the aspects of host-virus pathogenesis, our study opens new avenues to develop better diagnostic markers and therapeutics.
Dietary Practice and Associated Factors among Pregnant Women in Nono Woreda west shoa, Oromia, Ethiopia
Authors: Demissie Dereje Bayissa , Erena Tesfaye , Kolola Tufa
Published: November 2020

Background

Pregnancy is a unique and critical stage of life during which extensive anatomical, physiological, biochemical and several other related changes take place. The everyday vitality prerequisites for health women of typical weight who have a modestly dynamic way of life, increment during pregnancy and depend on the trimester of the baby. Maternal undernutrition is a serious developmental challenge contributing a large share to the global disease burden. It is a major reason for the increased risk of adverse pregnancy outcomes, poor infant survival, and elevated risks of chronic diseases at later stages of life. Ethiopia has an unacceptably high burden of malnutrition and its consequences, and yet little is known about the determinants and responses to undernutrition during pregnancy

Objectives

To assess the dietary practice and associated factors among pregnant women in Nono Woreda, West Shoa Zone, Ethiopia.

Methods

A community-based cross-sectional study design with both quantitative and qualitative data collection was conducted. Simple random sampling was used to select 378 pregnant women. The data were collected using interviewer-administered questionnaire of Afan Oromo version. Data were entered using Epi info version 7 and analyzed by using Statistical Package for the Social Sciences (SPSS) software for Windows version 21. Multiple logistic regressions were run to assess factors that were associated with the dependent variable at P<0.05 and to control the confounders.

Result

Finally, the result of the study has shown that 31% of the study participants had good dietary practices while the rest 69% of pregnant women had poor dietary practices. Concerning dietary knowledge about a balanced diet, 63.5% of the study participants had good dietary knowledge while 36.5% had poor dietary knowledge about a balanced diet. Marital status, breastfeeding, health-seeking behavior, food avoiding, and dietary knowledge were shown to have a significant association (P < 0.05) with dietary practices of pregnant women. The quantitative study revealed that marital status (AOR =95%CI, 7.983(1.387, 45.947, P<0.02).

Conclusion

A dietary practice of pregnant women in the study area was poor. Marital statuses, breastfeeding, health-seeking behavior, food avoiding and dietary knowledge of balance diet were independent predictors of women dietary practices. Therefore, Health professional, Zonal health office, regional Bureau and health planners would be better to increasing awareness to have good dietary practices those women married and supported by household head.
Time of quickening is associated with the placental site and BMI in nulliparous women
Authors: Jaafar Negin , Pedersen Lars Henning , Petersen Olav Bjørn , Hvidman Lone
Published: November 2020

Introduction

Quickening, the first sensation of fetal movements, is an important milestone for pregnant women. Information on the expected gestational age at quickening may reduce anxiety and prevent delayed detection of intrauterine demise but the available data are from the 1980s before the emergence of modern ultrasound techniques.

Materials and methods

Prospective observational study on nulliparous women blinded for placental location in two hospitals in Denmark. The pregnant women were enrolled at the time of nuchal translucency scan, placental location was determined at time of second trimester scanning.. The women were blinded to placenta location before time of quickening. Time of quickening were reported by 122 women, 65 with an anterior and 57 with a posterior placenta. Thirteen women had a BMI >30 (10.7%).

Results

The mean gestational age for quickening was 19 + 0 weeks for nulliparous women. The timing depended on placental site; women with an anterior placenta experienced quickening 6.4 days later than the women with a posterior placenta. BMI > 30 was associated with a later time of quickening.

Conclusions

Anterior placental location is associated with delay in experience of fetal movements of 6.4 days and this may further be delayed in women with a BMI>30.
Accelerated Predictive Healthcare Analytics with Pumas, A High Performance Pharmaceutical Modeling and Simulation Platform
Authors: Rackauckas Chris , Ma Yingbo , Noack Andreas , Dixit Vaibhav , Mogensen Patrick Kofod , Elrod Chris , Tarek Mohammad , Byrne Simon , Maddhashiya Shubham , Calderón José Bayoán Santiago , Hatherly Michael , Nyberg Joakim , Gobburu Jogarao V.S. , Ivaturi Vijay
Published: November 2020

Pharmacometric modeling establishes causal quantitative relationships between administered dose, tissue exposures, desired and undesired effects and patient’s risk factors. These models are employed to de-risk drug development and guide precision medicine decisions. However, pharmacometric tools have not been designed to handle today’s heterogeneous big data and complex models. We set out to design a platform that facilitates domain-specific modeling and its integration with modern analytics to foster innovation and readiness in healthcare.

Pumas demonstrates estimation methodologies with dramatic performance advances. New ODE solver algorithms, such as coeficient-optimized higher order integrators and new automatic stiffness detecting algorithms which are robust to frequent discontinuities, give rise to a median 4x performance improvement across a wide range of stiff and non-stiff systems seen in pharmacometric applications. These methods combine with JIT compiler techniques, such as statically-sized optimizations and discrete sensitivity analysis via forward-mode automatic differentiation, to further enhance the accuracy and performance of the solving and parameter estimation process. We demonstrate that when all of these techniques are combined with a validated clinical trial dosing mechanism and non-compartmental analysis (NCA) suite, real applications like NLME fitting see a median 81x acceleration while retaining the same accuracy. Meanwhile in areas with less prior software optimization, like optimal experimental design, we see orders of magnitude performance enhancements over competitors. Further, Pumas combines these technical advances with several workflows that are automated and designed to boost productivity of the day-to-day user activity. Together we show a fast pharmacometric modeling framework for next-generation precision analytics.
Change of dominant strain during dual SARS-CoV-2 infection
Authors: Samoilov AE , Kaptelova VV , Bukharina AY , Shipulina OY , Korneenko EV , Lukyanov AV , Grishaeva AA , Ploskireva AA , Speranskaya AS , Akimkin VG
Published: November 2020

Background

The effect of SARS-CoV-2 mutations and viral load on the severity of COVID-19 is not well understood. The possibility of reinfection with SARS-CoV-2 has already been reported, but dual infection with SARS-CoV-2 is poorly described and is currently under discussion. We describe a study of two strains of SARS-CoV-2 detected in the same patient during the same disease presentation.

Methods

Two nasopharyngeal swabs were obtained eight days apart from the patient in their 90s, diagnosed with lobar pneumonia (J18.1). Both tests were positive for SARS-CoV-2 with high viral load (Ct = 13). We have performed high-throughput sequencing of SARS-CoV-2 genomes from both swabs.

Findings

Genomic analysis of SARS-CoV-2 revealed the presence of two genetically distant strains in both swabs. Detected strains belong to different phylogenetic clades (GH and GR) and differ in the seven nucleotide positions. The relative abundance of strains was 70% (GH) and 30% (GR) in the first swab, and 3% (GH) and 97% (GR).

Interpretation

Our findings suggest that the patient was infected by two genetically distinct SARS-CoV-2 strains at the same time. One of the possible explanations is that the second infection occurred in the hospital. Change of the dominant strain ratio during disease manifestation could be explained by the advantage or higher virulence of the strain belonging to the clade GR.
Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms
Authors: Jin Weiwei , Chowienczyk Philip , Alastruey Jordi
Published: November 2020

One of the European gold standard measurement of vascular ageing, a risk factor for cardiovascular disease, is the carotid-femoral pulse wave velocity (cfPWV), which requires an experienced operator to measure pulse waves at two sites. In this work, two machine learning pipelines were proposed to estimate cfPWV from the peripheral pulse wave measured at a single site, the radial pressure wave measured by applanation tonometry. The study populations were the Twins UK cohort containing 3,082 subjects aged from 18 to 110 years, and a database containing 4,374 virtual subjects aged from 25 to 75 years. The first pipeline uses Gaussian process regression to estimate cfPWV from features extracted from the radial pressure wave using pulse wave analysis. The mean difference and upper and lower limits of agreement (LOA) of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.2 m/s, and 3.75 m/s & −3.34 m/s, respectively. The second pipeline uses a recurrent neural network (RNN) to estimate cfPWV from the entire radial pressure wave. The mean difference and upper and lower LOA of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.05 m/s, and 3.21 m/s & −3.11m/s, respectively. The percentage error of the RNN estimates on the virtual subjects increased by less than 2% when adding 20% of random noise to the pressure waveform. These results show the possibility of assessing the vascular ageing using a single peripheral pulse wave (e.g. the radial pressure wave), instead of cfPWV. The proposed code for the machine learning pipelines is available from the following online depository (https://github.com/WeiweiJin/Estimate-Cardiovascular-Risk-from-Pulse-Wave-Signal).
Evaluation of the PREDIGT Score in Discriminating Parkinson Disease from Neurological Health
Authors: Li Juan , Mestre Tiago A. , Mollenhauer Brit , Frasier Mark , Tomlinson Julianna J. , Trenkwalder Claudia , Ramsay Tim , Manuel Douglas , Schlossmacher Michael G.
Published: November 2020

Background

We previously created the PREDIGT Score as an algorithm to predict the incidence of Parkinson disease. The model rests on a hypothesis-driven formula, [PR=(E+D+I)xGxT], that uses numerical values for five categories known to modulate Parkinson’s risk (PR): environmental exposure (E); DNA variants (D); evidence of gene-environment interactions (I); gender (G); and time (T). Notably, the formula does not rely on motor examination results.

Methods

To evaluate the PREDIGT Score, we tested it in two established case-control cohorts: ‘De Novo Parkinson Study’ (DeNoPa) and ‘Parkinson’s Progression Marker Initiative’ (PPMI). Using baseline data from 589 patients and 309 controls enrolled in the DeNoPa and PPMI cohorts, we evaluated the PREDIGT Score’s discriminative performance in distinguishing Parkinson’s patients from healthy controls by area-under-the-curve (AUC) analyses.

Findings

When examining cohorts separately and using all available variables in each cohort to calculate the PREDIGT Score, AUCs were 0.83 (95% CI 0.77-0.89) for DeNoPa and 0.87 (95% CI 0.84-0.9) for PPMI, respectively, in distinguishing Parkinson disease patients from healthy individuals. When combining DeNoPa and PPMI data sets by using eleven variables that had been collected in both cohorts, the PREDIGT Score discriminated patients from controls with an AUC of 0.84 (95% CI 0.81-0.87). The mean score of Parkinson disease patients was significantly higher than that of control individuals at 108.48 (+52.08) and 47.33 (+34.1), respectively (p < 0.0001).

Interpretation

Our results demonstrate a robust performance of the original PREDIGT Score in distinguishing patients diagnosed with Parkinson disease from neurologically healthy subjects without reliance on motor examination data. In future efforts, the predictive performance of the algorithm will be studied in longitudinal cohorts of at-risk persons.

Funding

Parkinson Canada, Michael J. Fox Foundation, Department of Medicine (The Ottawa Hospital), Uttra & Subash Bhargava Family, Paracelsus-Elena-Klinik Kassel, Parkinson Fond Deutschland, and Deutsche Parkinson Vereinigung.
Low blood pressure phenotype underpins the tendency to reflex syncope
Authors: Brignole Michele , Rivasi Giulia , Sutton Richard , Kenny Rose Anne , Morillo Carlos A , Sheldon Robert , Raj Satish R , Ungar Andrea , Furlan Raffaello , van Dijk Gert , Hamdan Mohamed , Hamrefors Viktor , Engström Gunnar , Park Chloe , Soranna Davide , Zambon Antonella , Parati Gianfranco , Fedorowski Artur
Published: November 2020

BACKGROUND

We hypothesized that cardiovascular physiology differs in reflex syncope patients compared with general population, predisposing such subjects to vasovagal reflex.

METHODS

In this multicohort cross-sectional study, we compared resting systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP) and heart rate (HR), collected from 6 community-based cohort studies (64,968 observations) with those from 6 databases of reflex syncope patients (6516 observations), subdivided by age decades and sex.

RESULTS

Overall, in males with reflex syncope, SBP (−3.4 mmHg) and PP (−9.2 mmHg) were lower and DBP (+2.8 mmHg) and HR (+5.1 bpm) were higher than in the general population; the difference in SBP was higher at ages >60 years. In females, PP (−6.0 mmHg) was lower and DBP (+4.7 mmHg) and HR (+4.5 bpm) were higher than in the general population; differences in SBP were less pronounced, becoming evident only above 60 years. Compared with males, SBP in females exhibited slower increase until age 40, and then demonstrated steeper increase that continued throughout life.

CONCLUSION

The patients prone to reflex syncope demonstrate a different resting cardiovascular hemodynamic profile characterized by reduced venous return and stroke volume, evidenced by lower SBP and PP, and compensatory increase in HR and vascular resistance, the latter expressed by elevated DBP. The data presented here contribute to our understanding why some subjects with similar demographic characteristics develop reflex syncope and others not.
Predicting Trends of Coronavirus Disease (COVID-19) Using SIRD and Gaussian-SIRD Models
Authors: Sedaghat Ahmad , Mosavi Amir
Published: November 2020

Eruption of COVID-19 patients in 215 countries worldwide have urged for robust predictive methods that can detect as early as possible size and duration of the contagious disease and also providing precision predictions. In many recent literatures reported on COVID-19, one or more essential parts of such investigation were missed. One of crucial elements for any predictive method is that such methods should fit simultaneously as many data as possible; these data could be total infected cases, daily hospitalized cases, cumulative recovered cases and deceased cases and so on. Other crucial elements include sensitivity and precision of such predictive methods on amount of data as the contagious disease evolved day by day. To show importance of these aspects, we have evaluated the standard SIRD model and a newly introduced Gaussian-SIRD model on development of COVID-19 in Kuwait. It is observed that SIRD model quickly pick up main trends of COVID-19 development; but Gaussian-SIRD model provides precise prediction at longer period of time.
Performance comparison: exome-sequencing as a single test replacing Sanger-sequencing
Authors: Fridman Hila , Bormans Concetta , Einhorn Moshe , Au Daniel , Bormans Arjan , Porat Yuval , Sanchez Luisa Fernanda , Manning Brent , Levy-Lahad Ephrat , Behar Doron M.
Published: November 2020

Systematic performance comparing the results of exome-sequencing as a single test replacing Sanger-sequencing of targeted gene(s) is still lacking. In this study we compared Sanger-sequencing results of 258 genes to those obtained from next generation sequencing (NGS) using two exome-sequencing enrichment kits: Agilent-SureSelectQXT and Illumina-Nextera. Sequencing was performed on leukocytes and buccal-derived DNA from a single individual, and all 258 genes were sequenced a total of 11 times (using different sequencing methods and DNA sources). Sanger-sequencing was completed for all exons, including flanking ±8bp regions. For the 258 genes, NGS mean coverage was >20x for >98% and >91% of the regions targeted by SureSelect and Nextera, respectively. Overall, 449 variants were identified in at least one experiment, and 407/449 (90.6%) were detected by all. Of the 42 discordant variants, 23 were determined as true calls, summing-up to a truth set of 430 variants. Sensitivity of true-variant detection was 99% for Sanger-sequencing and 97%-100% for the NGS experiments. Mean false-positive rates were 3.7E-6 for Sanger-sequencing, 2.5E-6 for SureSelect-NGS and 5.2E-6 for Nextera-NGS. Our findings suggest a high overall concordance between Sanger-sequencing and NGS. Both methods demonstrated false positive and false negative calls and similar performances. Consequently, high clinical suspicion for a specific diagnosis should override negative results of either Sanger-sequencing or NGS.
An Evolving-Dynamic Network Activity Approach to Epileptic Seizure Prediction using Machine Learning
Authors: Liu Christine Joy , Sorokin Jordan , Ganguli Surya , Huguenard John
Published: November 2020

Absence epilepsy is a neurological condition characterized by abnormally synchronous electrical activity within two mutually connected brain regions, the thalamus and cortex, that results in seizures and affects more than 6.5 million people. Epilepsy is commonly studied through the use of the electroencephalogram (EEG), a device that monitors brain waves over time. In this study, we introduced machine learning models to predict epileptic seizures in two ways, one to train logistic regression models to provide an accurate decision boundary to predict based off frequency features, and second to train convolutional neural networks to predict based off spectral power images from EEG. This pipeline employed a two model approach, using logistic regression and convolutional neural networks to predict seizures. The evaluation, performed on data from 9 mice, achieved prediction accuracies of 98%. The proposed methodology introduces a novel aspect of looking at predicting absence seizures, which are known to be short events, in addition to the comparison between a time-dependent and time-agnostic seizure prediction classifier. The overall goal of these experiments were to build a model that can accurately predict whether or not a seizure will occur.
Microfluidic Device Facilitates NovelIn VitroModeling of Human Neonatal Necrotizing Enterocolitis-on-a-Chip
Authors: Lanik Wyatt E. , Luke Cliff J. , Nolan Lila S. , Gong Qingqing , Rimer Jamie M. , Gale Sarah E. , Luc Raymond , Bidani Shay S. , Sibbald Carrie A. , Lewis Angela N. , Mihi Belgacem , Agrawal Pranjal , Goree Martin , Maestas Marlie , Hu Elise , Peters David G. , Good Misty
Published: November 2020

Necrotizing enterocolitis (NEC) is a deadly gastrointestinal disease of premature infants characterized by an exaggerated inflammatory response, dysbiosis of the gut microbiome, decreased epithelial cell proliferation, and gut barrier disruption. Here, we describe a novelin vitromodel of human neonatal small intestinal epithelium (Neonatal-Intestine-on-a-Chip) that mimics key features of intestinal physiology by utilizing a combination of premature infant intestinal enteroids co-cultured with human intestinal microvascular endothelial cells within a microfluidic device. We used our Neonatal-Intestine-on-a-Chip to recapitulate NEC pathophysiology in anin vitromodel system of the premature gut inoculated with infant-derived microbiota. This model, also known as NEC-on-a-Chip, emulates the prominent features of NEC, demonstrating significant upregulation of pro-inflammatory cytokines, decreased intestinal epithelial cell markers, reduced epithelial proliferation and disrupted epithelial barrier integrity. NEC-on-a-Chip provides a novel preclinical model of NEC, which may be used as a personalized medicine approach to test new therapeutics for this devastating disease.
Mapping the Cellular Origin and Early Evolution of Leukemia in Down Syndrome
Authors: Wagenblast Elvin , Araújo Joana , Gan Olga I. , Cutting Sarah K. , Murison Alex , Krivdova Gabriela , Azkanaz Maria , McLeod Jessica L. , Smith Sabrina A. , Marhon Sajid A , Gabra Martino , Chan-Seng-Yue Michelle , Garcia-Prat Laura , Salmena Leonardo , De Carvalho Daniel D , Chong Karen , Roifman Maian , Shannon Patrick , Wang Jean C Y , Hitzler Johann K. , Chitayat David , Dick John E. , Lechman Eric R.
Published: November 2020

Children with Down syndrome have a 150-fold increased risk of developing myeloid leukemia, but the mechanism of predisposition is unclear. As Down syndrome leukemogenesis initiates during fetal development, we characterized the cellular context of preleukemic initiation and leukemic progression using gene editing in human disomic and trisomic fetal liver hematopoietic cells and xenotransplantation.GATA1mutations caused transient preleukemia only when introduced into trisomy 21 long-term hematopoietic stem cells, where a subset of chromosome 21 miRNAs triggers predisposition to preleukemia. By contrast, progression to leukemia was independent of trisomy 21 and originated in various stem and progenitor cells through additional mutations in cohesin genes. CD117+/KIT cells mediated the propagation of preleukemia and leukemia, and functional KIT inhibition targeted preleukemic stem cells, blocking progression to leukemia.
Quantitatively Monitoringin situMitochondrial Thermal Dynamics by Upconversion Nanoparticles
Authors: Di Xiangjun , Wang Dejiang , Zhou Jiajia , Zhang Lin , Stenzel Martina , Su Qian Peter , Jin Dayong
Published: November 2020

Temperature dynamics reflect the physiological conditions of cells and organisms. Mitochondria regulates temperature dynamics in living cells, as they oxidize the respiratory substrates and synthesize ATP, with heat being released as a by-product of active metabolism. Here, we report an upconversion nanoparticles based thermometer that allowsin situthermal dynamics monitoring of mitochondria in living cells. We demonstrate that the upconversion nanothermometers can efficiently target mitochondria and the temperature responsive feature is independent of probe concentration and medium conditions. The relative sensing sensitivity of 3.2% K−1in HeLa cells allows us to measure the mitochondrial temperature difference through the stimulations of high glucose, lipid, Ca2+shock and the inhibitor of oxidative phosphorylation. Moreover, cells display distinct response time and thermal dynamic profiles under different stimulations, which highlights the potential applications of this thermometer to studyin situvital processes related to mitochondrial metabolism pathways and interactions between organelles.
Group-Specific Carbon Fixation Activity Measurements Reveal Distinct Responses to Oxygen Among Hydrothermal ventCampylobacteria
Authors: McNichol Jesse , Dyksma Stefan , Mußmann Marc , Seewald Jeffrey S. , Sylva Sean P. , Sievert Stefan M.
Published: November 2020

Molecular surveys of low temperature deep-sea hydrothermal vent fluids have shown thatCampylobacteria(prev.Epsilonproteobacteria) often dominate the microbial community and that three subgroups -Arcobacter,SulfurimonasandSulfurovum -frequently coexist. In this study, we used replicated radiocarbon incubations of deep-sea hydrothermal fluids to investigate the activities of each group under three distinct incubation conditions. In order to quantify group-specific radiocarbon incorporation, we used newly designed oligonucleotide probes forArcobacter, Sulfurimonas,andSulfurovumto quantify their activity using catalyzed-reporter deposition fluorescence in-situ hybridization (CARD-FISH) combined with fluorescence-activated cell sorting. All three groups actively fixed CO2in short-term (~ 20 h) incubations with either nitrate, oxygen, or no additions (control) at similar per-cell carbon fixation rates. Oxygen additions had the largest effect on community composition and overall cell numbers, and caused a pronounced shift in community composition at the amplicon sequence variant (ASV) level after only 20 h of incubation for all three groups. Interestingly, the effect of oxygen on carbon fixation rates appeared to depend on the initial starting community. Higher carbon fixation rates in oxygen-amended treatments were noted for all three taxa after an unintended disturbance to the sample site that may have selected for more oxygen-tolerant phylotypes. When viewed from a coarse taxonomic level, our data support assertions that these chemoautotrophic groups are functionally redundant in terms of their core metabolic capabilities since they were simultaneously active under all incubation conditions. In contrast, the higher resolution of amplicon sequencing allowed us to reveal finer-scale differences in growth that likely reflect adaptation of physiologically-distinct subtypes to varying oxygen concentrationsin situ. Despite this progress, we still know remarkably little about the factors that maintain genomic diversity and allow for stable co-existence among these three campylobacterial groups. Moving forward, we suggest that more subtle biological factors such as enzyme substrate specificity, motility, cell morphology, and tolerance to environmental stress should be more thoroughly investigated to better understand ecological niche differentiation at deep-sea hydrothermal vents.
Cwl0971, a novel peptidoglycan hydrolase, plays pleiotropic roles inClostridioides difficileR20291
Authors: Zhu Duolong , Patabendige Hiran Malinda Lamabadu Warnakulasur , Tomlinson Brooke Rene , Wang Shaohui , Hussain Syed , Flores Domenica , He Yongqun , Shaw Lindsey N , Sun Xingmin
Published: November 2020

Clostridioides difficileis a Gram-positive, spore-forming, toxin-producing anaerobe that can cause nosocomial antibiotic-associated intestinal disease. Although the production of toxin A (TcdA) and toxin B (TcdB) contribute to the main pathogenesis ofC. difficile, the mechanism of TcdA and TcdB release from cell remains unclear. In this study, we identified and characterized a new cell wall hydrolase Cwl0971 (CDR20291_0971) fromC. difficileR20291, which is involved in bacterial autolysis. The gene 0971 deletion mutant (R20291Δ0971) generated with CRISPR-AsCpfI exhibited significantly delayed cell autolysis and increased cell viability compared to R20291, and the purified Cwl0971 exhibited hydrolase activity forBacillus subtiliscell wall. Meanwhile, 0971 gene deletion impaired TcdA and TcdB release due to the decreased cell autolysis in the stationary / late phase of cell growth. Moreover, sporulation of the mutant strain decreased significantly compared to the wild type strain.In vivo, the defect of Cwl0971 decreased fitness over the parent strain in a mouse infection model. Collectively, Cwl0971 is involved in cell wall lysis and cell viability, which affects toxin release, sporulation, germination, and pathogenicity of R20291, indicating that Cwl0971 could be an attractive target forC. difficileinfection therapeutics and prophylactics.
The structure-fitness landscape of pairwise relations in generative sequence models
Authors: Marshall Dylan , Wang Haobo , Stiffler Michael , Dauparas Justas , Koo Peter , Ovchinnikov Sergey
Published: November 2020

If disentangled properly, patterns distilled from evolutionarily related sequences of a given protein family can inform their traits - such as their structure and function. Recent years have seen an increase in the complexity of generative models towards capturing these patterns; from sitewise to pairwise to deep and variational. In this study we evaluate the degree of structure and fitness patterns learned by a suite of progressively complex models. We introduce pairwise saliency, a novel method for evaluating the degree of captured structural information. We also quantify the fitness information learned by these models by using them to predict the fitness of mutant sequences and then correlate these predictions against their measured fitness values. We observe that models that inform structure do not necessarily inform fitness and vice versa, contrasting recent claims in this field. Our work highlights a dearth of consistency across fitness assays as well as divergently provides a general approach for understanding the pairwise decomposable relations learned by a given generative sequence model.
What is the uncertainty in efficacy of COVID-19 vaccines? A Bayesian analysis
Authors: Wibbens Phebo D.
Published: November 2020

This short paper reports a Bayesian analysis of the publicly available COVID-19 trial results. The analysis casts some doubts on whether the half+full dose regime of the AstraZeneca COVID-19 vaccine is truly more effective than the 2x full dose regime. The 95% posterior interval for the efficacy of the half+full dose regime is 66.6-96.3%, while for the 2x full dose regime it is 39.0-74.8%. Hence, it is possible that in both dosage regimes the vaccine has similar efficacy, around 70%. The estimated efficacy for the Pfizer vaccine is 89.9-97.4% and for Moderna 86.3-97.5%. These results should be interpreted with care though, since this analysis does not account for differences in for instance trial population, COVID-19 testing, and storage requirements for the various vaccines.
Increasing efficacy of contact-tracing applications by user referrals and stricter quarantining
Authors: Goldberg Leslie Ann , Jorritsma Joost , Komjáthy Júlia , Lapinskas John
Published: November 2020

We study the effects of two mechanisms which increase the efficacy of contact-tracing applications (CTAs) such as the mobile phone contact-tracing applications that have been used during the COVID-19 epidemic. The first mechanism is the introduction of user referrals. We compare four scenarios for the uptake of CTAs — (1) thep% of individuals that use the CTA are chosen randomly, (2) a smaller initial set of randomly-chosen users each refer a contact to use the CTA, achievingp% in total, (3) a small initial set of randomly-chosen users each refer around half of their contacts to use the CTA, achievingp% in total, and (4) for comparison, an idealised scenario in which thep% of the population that uses the CTA is thep% with the most contacts. Using agent-based epidemiological models incorporating a geometric space, we find that, even when the uptake percentagep% is small, CTAs are an effective tool for mitigating the spread of the epidemic in all scenarios. Moreover, user referrals significantly improve efficacy. In addition, it turns out that user referrals reduce the quarantine load. The second mechanism for increasing the efficacy of CTAs is tuning the severity of quarantine measures. Our modelling shows that using CTAs with mild quarantine measures is effective in reducing the maximum hospital load and the number of people who become ill, but leads to a relatively high quarantine load, which may cause economic disruption. Fortunately, under stricter quarantine measures, the advantages are maintained but the quarantine load is reduced. Our models incorporate geometric inhomogeneous random graphs to study the effects of the presence of super-spreaders and of the absence of long-distant contacts (e.g., through travel restrictions) on our conclusions.