bluuebunny/crossref_metadata_2025 huggingface dataset:
Preventing Pressure Injuries in Nursing Home Residents Using a Low-Profile Alternating Pressure Overlay: A Point-of-Care Trial
Authors: Stone Arthur
Published: September 2020

BACKGROUND

Pressure injuries (PIs) are a significant concern for patients with very limited mobility in skilled nursing facilities. Conflicting clinical guidelines and a lack of effectiveness data for the various support surfaces reduces the efficacy of PI prevention programs.

OBJECTIVE

To assess the preventive effectiveness (incidence of facility-acquired PIs) of a low-profile alternating pressure (AP) support surface plus facility-specific PI prevention programs in patients at high risk for PI.

DESIGN AND SETTING

Prospective, multicenter, point-of-care observational study in two for-profit nursing homes in the northeastern US.

PATIENTS AND INTERVENTION

A retrospective review of 101 residents was performed to determine baseline PI incidence. Then, a consecutive sample of 25 participants was selected based on the following eligibility criteria: high risk for PI, bedbound (20 hours or more per day), and stay in a mechanical ventilation unit for more than 5 days. The participants were placed on an AP overlay positioned above a facility-provided nonpowered reactive support surface.

MAIN OUTCOME MEASURE

The development of any new PI (stage 1-4), deep-tissue injury, or unstageable PI in participants using the AP overlay. The PI incidence for the AP group was compared with the retrospective baseline PI incidence from the same units in the two nursing homes.

MAIN RESULTS

The group using the AP overlay had a significantly lower PI incidence (0/25, 0%) compared with baseline (22/101, 21.8%; P < .001). Almost 80% of the study participants in the AP group were completely immobile, 100% of the participants were bowel- and bladder-incontinent, their average time on the AP overlay was 140.9 ± 94.1 days, and average length of stay in the facility was 633.9 ± 1,129.1 days.

CONCLUSIONS

The low-profile AP overlay was significantly more effective than facility-specific prevention programs alone in preventing PIs in a high-risk nursing home population over an extended period.
Prenatal exposure to particulate air pollution and gestational age at delivery in Massachusetts neonates 2001–2015
Authors: Qiu Xinye , Fong Kelvin C. , Shi Liuhua , Papatheodorou Stefania , Di Qian , Just Allan , Kosheleva Anna , Messerlian Carmen , Schwartz Joel D.
Published: September 2020

Background:

There is a lack of evidence on causal effects of air pollution on gestational age (GA) at delivery.

Methods:

Inverse probability weighting (IPW) quantile regression was applied to derive causal marginal population-level GA reduction for GA percentiles associated with increased ambient particulate matter with diameter <2.5 μm (PM2.5) levels at maternal residential address for each trimester and the month preceding delivery using Massachusetts birth registry 2001 to 2015. Stratified analyses were conducted for neonatal sex, maternal age/race/education, and extreme ambient temperature conditions.

Results:

For neonates at 2.5th, 10th, 25th, 50th, 75th, and 97.5th percentiles of GA at delivery, we estimated an adjusted GA reduction of 4.2 days (95% confidence interval [CI] = 3.4, 5.0), 1.9 days (1.6, 2.1), 1.2 days (1.0, 1.4), 0.82 days (0.72, 0.92), 0.74 days (0.54, 0.94), and 0.54 days (0.15, 0.93) for each 5 μg/m3 increment in third trimester average PM2.5levels. Final gestational month average exposure yielded a similar effect with greater magnitude. Male neonates and neonates of younger (younger than 35 years) and African American mothers as well as with high/low extreme temperature exposure in third trimester were more affected. Estimates were consistently higher at lower GA percentiles, indicating preterm/early-term births being more affected. Low-exposure analyses yielded similar results, restricting to areas with PM2.5levels under US ambient annual standard of 12 μg/m3.

Conclusions:

Prenatal exposure to PM2.5in late pregnancy reduced GA at delivery among Massachusetts neonates, especially among preterm/early-term births, male neonates, and neonates of younger and African American mothers. Exposure to extremely high/low temperature amplifies the effect of PM2.5on GA.
Xanthomonas euroxanthea sp. nov., a new xanthomonad species including pathogenic and non-pathogenic strains of walnut
Authors: Martins Leonor , Fernandes Camila , Blom Jochen , Dia Nay C. , Pothier Joël F. , Tavares Fernando
Published: September 2020

We describe a novel species isolated from walnut (Juglans regia) which comprises non-pathogenic and pathogenic strains on walnut. The isolates, obtained from a single ornamental walnut tree showing disease symptoms, grew on yeast extract–dextrose–carbonate agar as mucoid yellow colonies characteristic of

Xanthomonas

species. Pathogenicity assays showed that while strain CPBF 424T causes disease in walnut, strain CPBF 367 was non-pathogenic on walnut leaves. Biolog GEN III metabolic profiles disclosed some differences between strains CPBF 367 and CPBF 424T and other xanthomonads. Multilocus sequence analysis with seven housekeeping genes (fyuA, gyrB, rpoD, atpD, dnaK, efp, glnA) grouped these strains in a distinct cluster from

Xanthomonas arboricola

pv. juglandis and closer to

Xanthomonas prunicola

and

Xanthomonas arboricola

pv. populi. Average nucleotide identity (ANI) analysis results displayed similarity values below 93 % to

X. arboricola

strains. Meanwhile ANI and digital DNA–DNA hybridization similarity values were below 89 and 50 % to non-arboricola Xanthomonas strains, respectively, revealing that they do not belong to any previously described

Xanthomonas

species. Furthermore, the two strains show over 98 % similarity to each other. Genomic analysis shows that strain CPBF 424T harbours a complete type III secretion system and several type III effector proteins, in contrast with strain CPBF 367, shown to be non-pathogenic in plant bioassays. Taking these data altogether, we propose that strains CPBF 367 and CPBF 424T belong to a new species herein named Xanthomonas euroxanthea sp. nov., with CPBF 424T (=LMG 31037T=CCOS 1891T=NCPPB 4675T) as the type strain.
Roseomonas algicola sp. nov., isolated from a green alga, Pediastrum duplex
Authors: Kim Hyung Min , Khan Shehzad Abid , Han Dong Min , Chun Byung Hee , Jeon Che Ok
Published: September 2020

A Gram-stain-negative, strictly aerobic bacterium, designated strain PeD5T, was isolated from a green alga Pediastrum duplex from the Nakdong river of the Republic of Korea. Cells were non-motile cocci, catalase-negative and oxidase-positive. Growth of PeD5T was observed at 25–40 °C (optimum, 35 °C) and pH 5.0–10.0 (optimum, pH 7–8), and in the presence of 0–0.25% (w/v) NaCl (optimum, 0%). PeD5T contained C16:0, C18:1ω7c 11-methyl, summed feature 3 (comprising C16:1ω7c and/or C16:1ω6c) and summed feature 8 (comprising C18:1ω7c and/or C18:1ω6c) as major cellular fatty acids (>5%) and ubiquinone-10 as the sole isoprenoid quinone. Phosphatidylethanolamine, phosphatidylglycerol, diphosphatidylglycerol, an unidentified phospholipid and an unidentified aminolipid were detected as major polar lipids. The genomic DNA G+C content of PeD5T was 71.0 mol%. PeD5T was most closely related to

Roseomonas stagni

HS-69T with a 97.6% 16S rRNA sequence similarity and shared less than 96.3% 16S rRNA sequence similarities with type strains of other species. Phylogenetic analysis based on 16S rRNA gene sequences indicated that PeD5T formed a phyletic lineage with

Roseomonas stagni

HS-69T within the genus

Roseomonas

. On the basis of results of phenotypic, chemotaxonomic and molecular analysis, strain PeD5T clearly represents a novel species of the genus

Roseomonas

, for which the name Roseomonas algicola sp. nov. is proposed. The type strain is PeD5T (=KACC 19925T=JCM 33309T).
The Immune-Buffer COVID-19 Exit Strategy that Protects the Elderly
Authors: Rom-Kedar Vered , Yaniv Omer , Malka Roy , Shapiro Ehud
Published: September 2020

September 12, 2020

COVID-19 is a viral respiratory illness, caused by the SARS-CoV-2 virus with frequent symptoms of fever and shortness of breath [1]. COVID-19 has a high mortality rate among elders. The virus has spread world-wide, leading to shut-down of many countries around the globe with the aim of stopping the spread of the disease. To date, there are uncertainties regarding the main factors in the disease spread, so sever social distancing measures and broad testing are required in order to protect the population at risk. With the increasing spread of the virus, there is growing fraction of the general population that may be immune to COVID-19, following infection. This immunised cohort can be uncovered via large-scale screening for the SARS-CoV-2 (Corona) virus and/or its antibodies. We propose that this immune cohort be deployed as a buffer between the general population and the population most at risk from the disease. Here we show that under a broad range of realistic scenarios deploying such an immunized buffer between the general population and the population at risk may lead to a dramatic reduction in the number of deaths from the disease. This provides an impetus for: screening for the SARS-CoV-2 virus and/or its antibodies on the largest scale possible, and organizing at the family, community, national and international levels to protect vulnerable populations by deploying immunized buffers between them and the general population wherever possible.

Declarations of interest

none
Revealing the relevant spatiotemporal scale underlying whole-brain dynamics
Authors: Kobeleva Xenia , López-González Ane , Kringelbach Morten L. , Deco Gustavo
Published: September 2020

The brain rapidly processes and adapts to new information by dynamically switching between activity in whole-brain functional networks. In this whole-brain modelling study we investigate the relevance of spatiotemporal scale in whole-brain functional networks. This is achieved through estimating brain parcellations at different spatial scales (100-900 regions) and time series at different temporal scales (from milliseconds to seconds) generated by a whole-brain model fitted to fMRI data. We quantify a fingerprint of healthy dynamics quantifying the richness of the dynamical repertoire at each spatiotemporal scale by computing the entropy of switching activity between whole-brain functional networks. The results show that the optimal relevant spatial scale is around 300 regions and a temporal scale of around 150 milliseconds. Overall, this study provides much needed evidence for the relevant spatiotemporal scales needed to make sense of neuroimaging data.
Discovery of a Novel Inhibitor of Coronavirus 3CL Protease for the Potential Treatment of COVID-19
Authors: Boras Britton , Jones Rhys M. , Anson Brandon J. , Arenson Dan , Aschenbrenner Lisa , Bakowski Malina A. , Beutler Nathan , Binder Joseph , Chen Emily , Eng Heather , Hammond Holly , Hammond Jennifer , Haupt Robert E. , Hoffman Robert , Kadar Eugene P. , Kania Rob , Kimoto Emi , Kirkpatrick Melanie G. , Lanyon Lorraine , Lendy Emma K. , Lillis Jonathan R. , Logue James , Luthra Suman A. , Ma Chunlong , Mason Stephen W. , McGrath Marisa E. , Noell Stephen , Obach R. Scott , O’Brien Matthew N. , O’Connor Rebecca , Ogilvie Kevin , Owen Dafydd , Pettersson Martin , Reese Matthew R , Rogers Thomas F. , Rossulek Michelle I. , Sathish Jean G. , Shirai Norimitsu , Steppan Claire , Ticehurst Martyn , Updyke Lawrence W. , Weston Stuart , Zhu Yuao , Wang Jun , Chatterjee Arnab K. , Mesecar Andrew D. , Frieman Matthew B. , Anderson Annaliesa S. , Allerton Charlotte
Published: September 2020

COVID-19 caused by the SARS-CoV-2 virus has become a global pandemic. 3CL protease is a virally encoded protein that is essential across a broad spectrum of coronaviruses with no close human analogs. The designed phosphate prodrug PF-07304814 is metabolized to PF-00835321 which is a potent inhibitorin vitroof the coronavirus family 3CL pro, with selectivity over human host protease targets. Furthermore, PF-00835231 exhibits potentin vitroantiviral activity against SARS-CoV-2 as a single agent and it is additive/synergistic in combination with remdesivir. We present the ADME, safety,in vitro, andin vivoantiviral activity data that supports the clinical evaluation of this compound as a potential COVID-19 treatment.
SARS-CoV-2 infection severity is linked to superior humoral immunity against the spike
Authors: Guthmiller Jenna J. , Stovicek Olivia , Wang Jiaolong , Changrob Siriruk , Li Lei , Halfmann Peter , Zheng Nai-Ying , Utset Henry , Stamper Christopher T. , Dugan Haley L. , Miller William D. , Huang Min , Dai Ya-Nan , Nelson Christopher A. , Hall Paige D. , Jansen Maud , Shanmugarajah Kumaran , Donington Jessica S. , Krammer Florian , Fremont Daved H. , Joachimiak Andrzej , Kawaoka Yoshihiro , Tesic Vera , Madariaga Maria Lucia , Wilson Patrick C.
Published: September 2020

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently causing a global pandemic. The antigen specificity and kinetics of the antibody response mounted against this novel virus are not understood in detail. Here, we report that subjects with a more severe SARS-CoV-2 infection exhibit a larger antibody response against the spike and nucleocapsid protein and epitope spreading to subdominant viral antigens, such as open reading frame 8 and non-structural proteins. Subjects with a greater antibody response mounted a larger memory B cell response against the spike, but not the nucleocapsid protein. Additionally, we revealed that antibodies against the spike are still capable of binding the D614G spike mutant and cross-react with the SARS-CoV-1 receptor binding domain. Together, this study reveals that subjects with a more severe SARS-CoV-2 infection exhibit a greater overall antibody response to the spike and nucleocapsid protein and a larger memory B cell response against the spike.
High performance machine learning models can fully automate labeling of camera trap images for ecological analyses
Authors: Whytock Robin , Świeżewski Jędrzej , Zwerts Joeri A. , Bara-Słupski Tadeusz , Pambo Aurélie Flore Koumba , Rogala Marek , Bahaa-el-din Laila , Boekee Kelly , Brittain Stephanie , Cardoso Anabelle W. , Henschel Philipp , Lehmann David , Momboua Brice , Opepa Cisquet Kiebou , Orbell Christopher , Pitman Ross T. , Robinson Hugh S. , Abernethy Katharine A.
Published: September 2020

Ecological data are increasingly collected over vast geographic areas using arrays of digital sensors. Camera trap arrays have become the ‘gold standard’ method for surveying many terrestrial mammals and birds, but these arrays often generate millions of images that are challenging to process. This causes significant latency between data collection and subsequent inference, which can impede conservation at a time of ecological crisis. Machine learning algorithms have been developed to improve camera trap data processing speeds, but these models are not considered accurate enough for fully automated labeling of images.

Here, we present a new approach to building and testing a high performance machine learning model for fully automated labeling of camera trap images. As a case-study, the model classifies 26 Central African forest mammal and bird species (or groups). The model was trained on a relatively small dataset (c.300,000 images) but generalizes to fully independent data and outperforms humans in several respects (e.g. detecting ‘invisible’ animals). We show how the model’s precision and accuracy can be evaluated in an ecological modeling context by comparing species richness, activity patterns (n= 4 species tested) and occupancy (n =4 species tested) derived from machine learning labels with the same estimates derived from expert labels.

Results show that fully automated labels can be equivalent to expert labels when calculating species richness, activity patterns (n= 4 species tested) and estimating occupancy (n= 3 of 4 species tested) in completely out-of-sample test data (n= 227 camera stations,n= 23868 images). Simple thresholding (discarding uncertain labels) improved the model’s performance when calculating activity patterns and estimating occupancy, but did not improve estimates of species richness.

We provide the user-community with a multi-platform, multi-language user interface for running the model offline, and conclude that high performance machine learning models can fully automate labeling of camera trap data.
SpineS: An interactive time-series analysis software for dendritic spines
Authors: Argunşah Ali Özgür , Erdil Ertunç , Ghani Muhammad Usman , Cortés Yazmín Ramiro , Hobbiss Anna Felicity , Karayannis Theofanis , Çetin Müjdat , Israely Inbal , Ünay Devrim
Published: September 2020

Live fluorescence imaging has shown the dynamic nature of dendritic spines, with changes in shape occurring both during development and in response to activity. The structure of a dendritic spine positively correlates with its functional efficacy. Learning and memory studies have shown that great deal of the information stored by a neuron is contained in the synapses. High precision tracking of synaptic structures can give hints about the dynamic nature of memory and help us to understand how memories evolve both in biological and artificial neural networks. Experiments that aim to investigate the dynamics behind the structural changes of dendritic spines require the collection and analysis of large time-series datasets. In this paper, we present an open-source software called SpineS for the automatic longitudinal structural analysis of dendritic spines with additional features for manual intervention to ensure optimal analysis. Our extensive experimental analyses on multiple datasets demonstrate that SpineS can achieve a high-level performance on samples collected both by two-photon and confocal imaging systems.
Causality Mapping Using Resting-State fMRI reveals Suppressed Functional Connectivity in Schizophrenia Patients
Authors: Ahmed Fayyaz , Saghir Zunira , Aamir Namra , Abualait Turki , Chaudhary Safee Ullah , Bashir Shahid
Published: September 2020

Schizophrenia is a psychotic brain disorder in which patients exhibit aberrant connectivity between different regions of the brain. Neuroimaging is a state-of-the-art technique that is now increasingly been employed in clinical investigation of Schizophrenia. In the present study, we have used resting-state functional magnetic resonance neuroimaging (rsfMRI) to elucidate the cause-and-effect relationships among four regions of the brain including occipital, temporal, and frontal lobes and hippocampus in Schizophrenia. For that, we have employed independent component analysis, a seed-based temporal correlation analysis, and Granger causality analysis for measuring causal relationships amongst four regions of the brain in schizophrenia patients. Eighteen subjects with nine patients and nine controls were evaluated in the study. Our results show that Schizophrenia patients exhibit significantly different activation patterns across the selected regions of the brain in comparison with the control. In addition to that, we also observed an aberrant causal relationship between these four regions of the brain. In particular, the temporal and frontal lobes of patients with schizophrenia had a significantly lowered causal relationship with the other areas of the brain. Taken together, the study elucidates the dysregulated brain activity in Schizophrenia patients, decodes its causal mapping and provides novel insights towards employment in clinical evaluation of Schizophrenia.
Role of Weather Factors in COVID-19 Deaths in Tropical Climate: A Data-Driven Study Focused on Brazil Manuscript
Authors: Moozhipurath Rahul Kalippurayil
Published: September 2020

Background

Brazil reported 123,780 deaths across 27 administrative regions, making it the second-worst affected country after the US in terms of COVID-19 deaths as of 3 September 2020. Understanding the role of weather factors in COVID-19 in Brazil is helpful in the longterm mitigation strategy of COVID-19 in other tropical countries because Brazil experienced early large-scale outbreak among tropical countries. Recent COVID-19 studies indicate that relevant weather factors such as temperature, humidity, UV Index (UVI), precipitation, ozone, pollution and cloud cover may influence the spread of COVID-19. Yet, the magnitude and direction of those associations remain inconclusive. Furthermore, there is only limited research exploring the impact of these weather factors in a tropical country like Brazil. In this observational study, we outline the roles of 7 relevant weather factors including temperature, humidity, UVI, precipitation, ozone, pollution (visibility) and cloud cover in COVID-19 deaths in Brazil.

Methods

We use a log-linear fixed-effects model to a panel dataset of 27 administrative regions in Brazil across 182 days (n=3882) and analyze the role of relevant weather factors by using daily cumulative COVID-19 deaths in Brazil as the dependent variable. We carry out robustness checks using case-fatality-rate (CFR) as the dependent variable.

Findings

We control for all time-fixed and various time-varying region-specific factors confounding factors. We observe a significant negative association of COVID-19 daily deaths growth rate in Brazil with weather factors – UVI, temperature, ozone and cloud cover. Specifically, a unit increase in UVI, maximum temperature, and ozone independently associate with 6.0 percentage points [p<0.001], 1.8 percentage points [p<0.01] and 0.3 percentage points [p<0. 1] decline in COVID-19 deaths growth rate. Further, a unit percentage increase in cloud cover associates with a decline of 0.148 percentage points [p<0.05] in COVID-19 deaths growth rate. Surprisingly, contrary to other studies, we do not find evidence of any association between COVID-19 daily deaths growth rate and humidity, visibility and precipitation. We find our results to be consistent even when we use the CFR as the dependent variable.

Interpretation

We find independent protective roles of UVI, temperature, ozone and cloud cover in mitigating COVID-19 deaths, even in a tropical country like Brazil. We observe these results to be consistent across various model specifications, especially for UVI and cloud cover, even after incorporating additional time-varying weather parameters such as dewpoint, pressure, wind speed and wind gust. These results could guide health-related policy decision making in Brazil as well as similar tropical countries.
Rapid, accurate, nucleobase detection using FnCas9
Authors: Azhar Mohd. , Phutela Rhythm , Kumar Manoj , Ansari Asgar Hussain , Rauthan Riya , Gulati Sneha , Sharma Namrata , Sinha Dipanjali , Sharma Saumya , Singh Sunaina , Acharya Sundaram , Paul Deepanjan , Kathpalia Poorti , Aich Meghali , Sehgal Paras , Ranjan Gyan , Bhoyar Rahul C. ,   Indian CoV2 Genomics & Genetic Epidemiology (IndiCovGEN) Consortium, Singhal Khushboo , Lad Harsha , Patra Pradeep Kumar , Makharia Govind , Chandak Giriraj Ratan , Pesala Bala , Chakraborty Debojyoti , Maiti Souvik
Published: September 2020

Rapid detection of pathogenic sequences or variants in DNA and RNA through a point-of-care diagnostic approach is valuable for accelerated clinical prognosis as has been witnessed during the recent COVID-19 outbreak. Traditional methods relying on qPCR or sequencing are difficult to implement in settings with limited resources necessitating the development of accurate alternative testing strategies that perform robustly. Here, we present FnCas9 Editor Linked Uniform Detection Assay (FELUDA) that employs a direct Cas9 based enzymatic readout for detecting nucleotide sequences and identifying nucleobase identity without the requirement of trans-cleavage activity of reporter molecules. We demonstrate that FELUDA is 100% accurate in detecting single nucleotide variants (SNVs) including heterozygous carriers of a mutation and present a simple design strategy in the form of a web-tool, JATAYU, for its implementation. FELUDA is semi quantitative, can be adapted to multiple signal detection platforms and can be quickly designed and deployed for versatile applications such as infectious disease outbreaks like COVID-19. Using a lateral flow readout within 1h, FELUDA shows 100% sensitivity and 97% specificity across all range of viral loads in clinical samples. In combination with RT-RPA and a smartphone application True Outcome Predicted via Strip Evaluation (TOPSE), we present a prototype for FELUDA for CoV-2 detection at home.

Single sentence summary

A method to identify nucleotide sequence or nucleobase identity using FnCas9 and its implementation in the rapid and accurate diagnosis of SARS-CoV-2
Cross-Population Genetic Variation of Loci Identified by Genome-Wide Association Studies conducted in British participants of European-descent from the UK Biobank
Authors: De Lillo Antonella , D’Antona Salvatore , Fuciarelli Maria , Polimanti Renato
Published: September 2020

To provide novel insight regarding the inter-population diversity of loci associated with complex traits, we integrated genome-wide data from UK Biobank (UKB) and 1,000 Genomes Project (1KG) data representative of the genetic diversity among worldwide populations. We investigated genome-wide data of 4,359 traits from 361,194 UKB participants of European descent. Using 1KG data, we explored the allele frequency differences and linkage disequilibrium (LD) structure of UKB genome-wide significant (GWS) loci across worldwide populations. Functional annotation data were used to identify regulatory elements and evaluate the tagging properties of GWS variants. No significant difference was observed in allele frequency between UKB and 1KG GBR (British in England and Scotland). Considering other population groups, we identified genome-wide significant alleles with frequencies different from what expected by chance: UKB vs. 1KG Europeans without GBR (rs74945666; allele=T [0.908 vs. 0.03], standing height pGWAS=1.48×10-17), UKB vs. 1KG African (rs556562; allele=A [0.942 vs. 0.083], platelet count pGWAS=4.84×10-15), UKB vs. 1KG Admixed Americans (rs1812378; allele=T [0.931 vs. 0.089], standing height pGWAS=4.23×10-12), UKB vs. 1KG East Asian (rs55881864; allele=T [0.911 vs. 0.001], monocyte count pGWAS=7.29×10-13), and UKB vs. South Asian (rs74945666; allele=T [0.908 vs. 0.061], standing height pGWAS=1.48×10-17). LD-structure analysis and computational prediction showed differences in how these alleles tag functional elements across human populations. In conclusion, the human diversity of certain GWS loci appear to be affected by local adaptation while in other cases the associations may be biased by residual population stratification.
Thrombolysis in Acute Pulmonary Embolism: are we over – doing it?
Authors: Showkathali Refai , Yalamanchi Radhapriya , Ramakrishnan Balasubramaniam , Oomman Abraham , Sivaprakash Aruna , Kumar Pramod
Published: September 2020

Aim and Method

We aimed to study the clinical data and outcome of patients admitted in our centre with acute pulmonary embolism (PE) over a five year period from May 2013 to April 2018. The main outcome data included were- in-hospital bleeding, in hospital RV function improvement, PAH improvement, duration of hospital stay, 30 and 90 day mortality.

Results

A total of 114 (69 m, 55 f) patients with the mean age of 55+/− 15 yrs were included. Patients who had involvement of central pulmonary trunk- called as “Central PE” group (n = 82) and others as “Peripheral PE” group (n = 32). There were more women in the peripheral PE group (53.1% vs 34.1%, p 0.05), while RBBB (22% vs 3.1%, p 0.02) and RV dysfunction (59.8% vs 25%, p 0.002) were noted more in the central PE group. Systemic thrombolysis was done in 53 patients (49 central, 4 peripheral), of which only 3 had hypotension and 28 patients were in the Intermediate-high risk group. The overall in-hospital, 30 day and 90 day mortality were 3.6, 13.2 and 22.8% respectively. Bleeding was significantly higher in thrombolysed group compared to the non- thrombolysed group (18.9% vs 0, p 0.0003). However, improvement in pulmonary hypertension was noted more in thrombolysis group compared to non-thrombolytic group. (49% vs 21.2%, p 0.01).

Conclusion

This retrospective data from a tertiary centre in South India showed that short and mid-term mortality of patients with PE still remains high. The high non-guideline use of thrombolysis has reflected in the increased bleeding noted in our study.
Sleep-related traits and attention-deficit/hyperactivity disorder comorbidity: shared genetic risk factors, molecular mechanisms, and causal effects
Authors: Carpena Marina Xavier , Bonilla Carolina , Matijasevich Alicia , Martins-Silva Thais , Genro Julia P. , Hutz Mara Helena , Rohde Luis Augusto , Tovo-Rodrigues Luciana
Published: September 2020

Study Objectives

To evaluate the level of shared genetic components between attention-deficit/hyperactivity disorder (ADHD) and sleep phenotype, common pathways between them and a possible causal relationship between traits.

Methods

We used summary statistics of the largest genome-wide association studies available for ADHD and sleep-related phenotypes including insomnia, napping, daytime dozing, snoring, ease getting up, daytime sleepiness, sleep duration and chronotype. We estimated the genomic correlation between ADHD and sleep-related traits using cross-trait LD-score regression and investigated potential common mechanisms using gene-based cross-trait metanalyses and functional enrichment analyses. The causal effect between the sleep related traits and ADHD was estimated with two sample Mendelian randomization (TSMR), using the Inverse Variance Weighted method as the main estimator.

Results

Positive genomic correlation between insomnia, daytime napping, daytime dozing, snoring, daytime sleepiness, short and long sleep duration, and ADHD were observed. Insomnia, sleep duration, daytime sleepiness, and snoring shared genes with ADHD, which were involved in neurobiological functions and regulatory signaling pathways. The TSMR approach supported a causal effect of insomnia, daytime napping, and short sleep duration on ADHD, and of ADHD on long sleep duration and chronotype.

Conclusion

Our findings suggest that the comorbidity between sleep phenotypes and ADHD may be mediated by common genetic factors with an important role on neuronal signaling pathways. In addition, it may also exist a causal effect of sleep disturbances and short sleep duration on ADHD, reinforcing the role of these sleep phenotypes as predictors or early markers of ADHD.

Statement of Significance

Our findings suggest that the comorbidity between sleep phenotypes and ADHD may be linked to common genetic factors with an important role on neuronal signaling pathways. They also show that a causal effect of sleep disturbances and short sleep duration on ADHD may exist, reinforcing the role of these sleep phenotypes as ADHD early markers, being able to predict the disorder. This study adds important information about the relationship between sleep, circadian rhythm, and ADHD since it raises new questions about the complexity of the phenomenon involving them and may also provide new treatment insights in this research field.
Subtype Heterogeneity and Epigenetic Convergence in Neuroendocrine Prostate Cancer
Authors: Cejas Paloma , Xie Yingtian , Font-Tello Alba , Lim Klothilda , Syamala Sudeepa , Qiu Xintao , Tewari Alok K. , Shah Neel , Nguyen Holly M , Patel Radhika A. , Brown Lisha , Coleman Ilsa , Hackeng Wenzel M. , Brosens Lodewijk , Dreijerink Koen M.A. , Ellis Leigh , Abou Alaiwi Sarah , Seo Ji-Heui , Pomerantz Mark , Dall’Agnese Alessandra , Crowdis Jett , Van Allen Eliezer M. , Bellmunt Joaquim , Morrisey Colm , Nelson Peter S. , DeCaprio James , Farago Anna , Dyson Nicholas , Drapkin Benjamin , Liu X. Shirley , Freedman Matthew , Haffner Michael C. , Corey Eva , Brown Myles , Long Henry W.
Published: September 2020

Neuroendocrine carcinomas (NEC) are tumors expressing markers of neuronal differentiation that can arise at different anatomic sites but have strong histological and clinical similarities. Here we report the chromatin landscapes of a range of human NECs and show convergence to the activation of a common epigenetic program. With a particular focus on treatment emergent neuroendocrine prostate cancer (NEPC), we analyzed cell lines, patient-derived xenograft (PDX) models and human clinical samples to show the existence of two distinct NEPC subtypes based on the expression of the neuronal transcription factors ASCL1 and NEUROD1. While in cell lines and PDX models these subtypes are mutually exclusive, single cell analysis of human clinical samples exhibit a more complex tumor structure with subtypes coexisting as separate sub-populations within the same tumor. These tumor sub-populations differ genetically and epigenetically contributing to intra- and inter-tumoral heterogeneity in human metastases. Overall our results provide a deeper understanding of the shared clinicopathological characteristics shown by NECs. Furthermore, the intratumoral heterogeneity of human NEPCs suggests the requirement of simultaneous targeting of coexisting tumor populations as a therapeutic strategy.
Glutamine deprivation triggers NAGK-dependent hexosamine salvage
Authors: Campbell Sydney L. , Mesaros Clementina , Izzo Luke , Affronti Hayley , Noji Michael , Schaffer Bethany E. , Tsang Tiffany , Sun Kathryn , Trefely Sophie , Kruijning Salisa , Blenis John , Blair Ian A. , Wellen Kathryn E.
Published: September 2020

Tumors frequently exhibit aberrant glycosylation, which can impact cancer progression and therapeutic responses. The hexosamine biosynthesis pathway (HBP) produces uridine diphosphate N-acetylglucosamine (UDP-GlcNAc), a major substrate for glycosylation in the cell. Prior studies have identified the HBP as a promising therapeutic target in pancreatic ductal adenocarcinoma (PDA). The HBP requires both glucose and glutamine for its initiation. The PDA tumor microenvironment is nutrient poor, however, prompting us to investigate how nutrient limitation impacts hexosamine synthesis. Here, we identify that glutamine limitation in PDA cells suppresses de novo hexosamine synthesis but results in increased free GlcNAc abundance. GlcNAc salvage via N-acetylglucosamine kinase (NAGK) is engaged to feed UDP-GlcNAc pools.NAGKexpression is elevated in human PDA, andNAGKdeletion from PDA cells impairs tumor growth in mice. Together, these data identify an important role for NAGK-dependent hexosamine salvage in supporting PDA tumor growth.
pDeep3: Towards More Accurate Spectrum Prediction with Fast Few-Shot Learning
Authors: Tarn Ching , Zeng Wen-Feng
Published: September 2020

Spectrum prediction using deep learning has attracted a lot of attention in recent years. Although existing deep learning methods have dramatically increased the pre-diction accuracy, there is still considerable space for improvement, which is presently limited by the difference of fragmentation types or instrument settings. In this work, we use the few-shot learning method to fit the data online to make up for the shortcoming. The method is evaluated using ten datasets, where the instruments includes Velos, QE, Lumos, and Sciex, with collision energies being differently set. Experimental results show that few-shot learning can achieve higher prediction accuracy with almost negligible computing resources. For example, on the dataset from a untrained instrument Sciex-6600, within about 10 seconds, the prediction accuracy is increased from 69.7% to 86.4%; on the CID (collision-induced dissociation) dataset, the prediction accuracy of the model trained by HCD (higher energy collision dissociation) spectra is increased from 48.0% to 83.9%. It is also shown that, the method is not critical to data quality and is sufficiently efficient to fill the accuracy gap. The source code of pDeep3 is available athttp://pfind.ict.ac.cn/software/pdeep3.
Sense of self impacts spatial navigation and hexadirectional coding in human entorhinal cortex
Authors: Moon Hyuk-June , Gauthier Baptiste , Park Hyeong-Dong , Faivre Nathan , Blanke Olaf
Published: September 2020

Grid cells in entorhinal cortex (EC) encode an individual’s location in space and rely on environmental cues and self-motion cues derived from the individual’s body. Body-derived signals are also primary signals for the sense of self and self-identification with a body. However, it is currently unknown whether self-identification impacts grid cells and spatial navigation. Integrating the online manipulation of bodily signals, to modulate self-identification, into a spatial navigation task and with an fMRI measure to detect grid cell-like representation (GCLR) in humans, we report improved spatial navigation performance and decreased GCLR in EC when participants navigated with enhanced self-identification. These changes were further associated with increased activity in posterior parietal and retrosplenial cortex. Modulating self-identification by controlling online body-derived signals during spatial navigation, these data link the sense of self to spatial navigation and to entorhinal grid cell-like activity within a distributed cortical spatial navigation system.
PPARG in osteocytes is essential for sclerostin expression, bone mass, marrow adiposity and TZD-induced bone loss
Authors: Baroi Sudipta , Czernik Piotr J. , Chougule Amit , Griffin Patrick R. , Lecka-Czernik Beata
Published: September 2020

PPARG role in regulation of osteocyte function is largely unknown. We report that PPARG is essential for sclerostin production, a recently approved target to treat osteoporosis. There is an excellent correlation in osteocytes betweenSost/sclerostin and PPARG at the transcript and protein levels, and increased bone mass in mice with osteocyte-specific deletion of PPARG (γOTKO) correlated with increased WNT signaling and bone forming activity of endosteal osteoblasts and decreased marrow fat. The 8 kb sequence upstream ofSostgene transcription start site possesses multiple PPARG binding elements (PPREs) with at least two of them binding PPARG with dynamics reflecting its activation and the levels ofSosttranscript and sclerostin protein expression. Older γOTKOfemale mice are largely protected from TZD-induced bone loss providing proof of concept that PPARG in osteocytes can be pharmacologically targeted. Our study opens the possibility to consider repurposing PPARG as a target for treatment of osteoporosis.
Circadian rhythms of early afterdepolarizations and ventricular arrhythmias in a cardiomyocyte model
Authors: Diekman Casey O. , Wei Ning
Published: September 2020

Abstract

Sudden cardiac arrest is a malfunction of the heart’s electrical system, typically caused by ventricular arrhythmias, that can lead to sudden cardiac death (SCD) within minutes. Epidemiological studies have shown that SCD and ventricular arrhythmias are more likely to occur in the morning than in the evening, and laboratory studies indicate that these daily rhythms in adverse cardiovascular events are at least partially under the control of the endogenous circadian timekeeping system. However, the biophysical mechanisms linking molecular circadian clocks to cardiac arrhythmogenesis are not fully understood. Recent experiments have shown that L-type calcium channels exhibit circadian rhythms in both expression and function in guinea pig ventricular cardiomyocytes. We developed an electrophysiological model of these cells to simulate the effect of circadian variation in L-type calcium conductance. We found that there is a circadian pattern in the occurrence of early afterdepolarizations (EADs), which are abnormal depolarizations during the repolarization phase of a cardiac action potential that can trigger fatal ventricular arrhythmias. Specifically, the model produces EADs in the morning but not at other times of day. We show that the model exhibits a codimension-2 Takens-Bogdanov bifurcation that serves as an organizing center for different types of EAD dynamics. We also simulated a 2-D spatial version of this model across a circadian cycle. We found that there is a circadian pattern in the breakup of spiral waves, which represents ventricular fibrillation in cardiac tissue. Specifically, the model produces spiral wave breakup in the morning but not in the evening. Our study is the first to propose a link between circadian rhythms and EAD formation and suggests that the efficacy of drugs targeting EAD-mediated arrhythmias may depend on the time of day that they are administered.

Significance Statement

Why are life-threatening cardiac arrhythmias more likely to occur in the morning than in the evening? The electrical properties of the heart exhibit daily rhythms due to molecular circadian clocks within cardiomyocytes. Our computational model of ventricular myocytes shows that clock-controlled expression of a voltage-gated calcium ion channel leads to early afterdepolarizations (EADs) at certain times of the day. EADs, in which the membrane potential of a cardiomyocyte depolarizes a second time before fully repolarizing, can trigger arrhythmias. To our knowledge, this is the first study linking the circadian clock to EAD formation. Our results suggest that the efficacy of anti-arrhythmic medications targeting this calcium ion channel may depend on the time of day the drug is taken.
Changing reward expectation transforms spatial encoding and retrieval in the hippocampus
Authors: Krishnan Seetha , Cherian Chery , Sheffield Mark. E. J.
Published: September 2020

Internal states of reward expectation play a central role in influencing the strength of spatial memories. At the cellular level, spatial memories are represented through the firing dynamics of hippocampal place cells. However, it remains unclear how internal states of reward expectation influence place cell dynamics and exert their effects on spatial memories. Here we show that when reward expectation is altered, the same environment becomes encoded by a distinct ensemble of place cells at all locations. Further, when reward expectation is high versus low, place cells demonstrate enhanced reliability during navigation and greater stability across days at all locations within the environment. These findings reveal that when rewards are expected, neuromodulatory circuits that represent internal reward expectation support and strengthen the encoding and retrieval of spatial information by place cells at all locations that lead to reward. This enhanced spatial memory can be used to guide future decisions about which locations are most likely to lead to rewards that are crucial for survival.
A combined BSA-Seq and linkage mapping approach identifies genomic regions associated with Phytophthora root and crown rot resistance in squash
Authors: Vogel Gregory , LaPlant Kyle E. , Mazourek Michael , Gore Michael A. , Smart Christine D.
Published: September 2020

Phytophthora root and crown rot, caused by the soilborne oomycete pathogenPhytophthora capsici, leads to severe yield losses in squash (Cucurbita pepo). To identify quantitative trait loci (QTL) involved in resistance to this disease, we crossed a partially resistant squash breeding line with a susceptible zucchini cultivar and evaluated over 13,000 F2seedlings in a greenhouse screen. Bulked segregant analysis with whole genome resequencing (BSA-Seq) resulted in the identification of five genomic regions – on chromosomes 4, 5, 8, 12, and 16 – featuring significant allele frequency differentiation between susceptible and resistant bulks in each of two independent replicates. In addition, we conducted linkage mapping using a population of 176 F3families derived from individually genotyped F2individuals. Variation in disease severity among these families was best explained by a four-QTL model, comprising the same loci identified via BSA-Seq on chromosomes 4, 5, and 8 as well as an additional locus on chromosome 19, for a combined total of six QTL identified between both methods. Loci, whether those identified by BSA-Seq or linkage mapping, were of small to moderate effect, collectively accounting for 28-35% and individually for 2-10% of the phenotypic variance explained. However, a multiple linear regression model using one marker in each BSA-Seq QTL could predict F2:3disease severity with only a slight drop in cross-validation accuracy compared to genomic prediction models using genome-wide markers. These results suggest that marker-assisted selection could be a suitable approach for improving Phytophthora crown and root rot resistance in squash.
Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data
Authors: Deng Fei , Huang Jibing , Yuan Xiaoling , Cheng Chao , Zhang Lanjing
Published: September 2020

Most of the biomedical datasets, including those of ‘omics, population studies and surveys, are rectangular in shape and have few missing data. Recently, their sample sizes have grown significantly. Rigorous analyses on these large datasets demand considerably more efficient and more accurate algorithms. Machine learning (ML) algorithms have been used to classify outcomes in biomedical datasets, including random forests (RF), decision tree (DT), artificial neural networks (ANN) and support vector machine (SVM). However, their performance and efficiency in classifying multi-category outcomes in rectangular data are poorly understood. Therefore, we aimed to compare these metrics among the 4 ML algorithms. As an example, we created a large rectangular dataset using the female breast cancers in the Surveillance, Epidemiology, and End Results-18 (SEER-18) database which were diagnosed in 2004 and followed up until December 2016. The outcome was the 6-category cause of death, namely alive, non-breast cancer, breast cancer, cardiovascular disease, infection and other cause. We included 58 dichotomized features from ~53,000 patients. All analyses were performed using MatLab (version 2018a) and the 10-fold cross validation approach. The accuracy in classifying 6-category cause of death with DT, RF, ANN and SVM was 72.68%, 72.66%, 70.01% and 71.85%, respectively. Based on the information entropy and information gain of feature values, we optimized dimension reduction (i.e. reduce the number of features in models). We found 22 or more features were required to maintain the similar accuracy, while the running time decreased from 440s for 58 features to 90s for 22 features in RF, from 70s to 40s in ANN and from 440s to 80s in SVM. In summary, we here show that RF, DT, ANN and SVM had similar accuracy for classifying multi-category outcomes in this large rectangular dataset. Dimension reduction based on information gain will significantly increase model’s efficiency while maintaining classification accuracy.
Reducing antimicrobial usage in small-scale chicken farms in Vietnam: A three-year intervention study
Authors: Phu Doan Hoang , Van Cuong Nguyen , Truong Dinh Bao , Kiet Bach Tuan , Hien Vo Be , Viet Thu Ho Thi , Yen Lam Kim , Tuyet Minh Nguyen Thi , Padungtod Pawin , Setyawan Erry , Thwaites Guy , Rushton Jonathan , Carrique-Mas Juan
Published: September 2020

Indiscriminate antimicrobial use (AMU) in animal production is a driver of antimicrobial resistance globally, with a need to define sustainable AMU-reducing interventions in small-scale farms typical of low- and middle-income countries. We conducted a before-and-after intervention study on a random sample of small-scale chicken farms in the Vietnamese Mekong Delta from 2016 to 2019. A baseline was established before providing farms (n=102) with veterinary advice on chicken health and husbandry, and antimicrobial replacement products. Thirty-five (34.2%) farms entered the intervention phase; the remainder no longer continued raising chickens. The intervention reduced AMU (−66%) (hazard ratio [HR]=0.34; p=0.002) (baseline 343.4 Animal Daily Doses per 1,000 chicken-days) and mortality (−40%) (HR=0.60; p=0.005) (weekly baseline 1.60 per 100). Chicken bodyweight increased by 100g (p=0.002) in intervention flocks. Our findings demonstrate that in the Vietnamese context, AMU can be substantially reduced in small-scale chicken farms without compromising flock health by providing veterinary advice.