bluuebunny/crossref_metadata_2025_split huggingface dataset:
New-onset Obesity After Lung Transplantation: Incidence, Risk Factors, and Clinical Outcomes
Authors: Jomphe Valérie , Bélanger Noémie , Beauchamp-Parent Caroline , Poirier Charles , Nasir Basil S. , Ferraro Pasquale , Lands Larry C. , Mailhot Geneviève 
Published: June 2022

Background.
Lung transplant (LTx) recipients who gain weight after transplantation may experience an upward shift in body mass index (BMI) that places them in the obese category. The incidence, risk factors, and impact on metabolic health and mortality of new-onset obesity have not been documented in the LTx setting.

Methods.
This single-center retrospective study included 564 LTx recipients. Individuals were stratified according to their BMI trajectories from pretransplant evaluation up to 10 y posttransplant. New-onset obesity was defined as a pretransplant BMI <30 kg/m2 and posttransplant BMI >30 kg/m2. The incidence, risk factors, and posttransplant diabetes mellitus, metabolic syndrome, and mortality of recipients with new-onset obesity were compared with those of nonobese (BMI <30 kg/m2, pre/post-LTx), consistently obese (BMI >30 kg/m2, pre/post-LTx), and obese recipients with weight loss (BMI >30 kg/m2 pre-LTx, BMI <30 kg/m2 post-LTx).

Results.
We found that 14% of recipients developed obesity after transplantation. Overweight individuals (odds ratio [OR]: 9.01; 95% confidence interval [CI] [4.86-16.69]; P < 0.001) and candidates with chronic obstructive pulmonary disease (OR: 6.93; 95% CI [2.30-20.85]; P = 0.001) and other diagnoses (OR: 4.28; 95% CI [1.22-14.98]; P = 0.023) were at greater risk. Multivariable regression analysis showed that new-onset obesity was associated with a greater risk of metabolic syndrome (hazard ratio: 1.70; 95% CI [1.17-2.46]; P = 0.005), but not of posttransplant diabetes mellitus, than nonobesity. Recipients with new-onset obesity had a survival comparable to that of consistently obese individuals.

Conclusions.
A greater understanding of the multifaceted nature of post-LTx obesity may lead to interventions that are better tailored to the characteristics of these individuals.
Genomic and transcriptomic analysis of sacred fig (Ficus religiosa)
Authors: Ashalatha K. L. , Arunkumar Kallare P , Gowda Malali 
Published: June 2022

AbstractBackgroundPeepal/Bodhi tree (Ficus religiosaL.) is an important, long-lived keystone ecological species. Communities on the Indian subcontinent have extensively employed the plant in Ayurveda, traditional medicine, and spiritual practices. The Peepal tree is often thought to produce oxygen both during the day and at night by Indian folks. The goal of our research was to produce molecular resources using whole-genome and transcriptome sequencing techniques.ResultsThe complete genome of the Peepal tree was sequenced using two next-generation sequencers Illumina HiSeq1000 and MGISEQ-2000. We assembled the draft genome of 406 Mb, using a hybrid assembly workflow. The genome annotation resulted in 35,093 protein-coding genes; 53% of its genome consists of repetitive sequences. To understand the physiological pathways in leaf tissues, we analyzed photosynthetically distinct conditions: bright sunny days and nights. The RNA-seq analysis supported the expression of 26,479 unigenes. The leaf transcriptomic analysis of the diurnal and nocturnal periods revealed the expression of the significant number of genes involved in the carbon-fixation pathway.ConclusionsThis study presents a draft hybrid genome assembly forF. religiosaand its functional annotated genes. The genomic and transcriptomic data-derived pathways have been analyzed for future studies on the Peepal tree.
An international observational study to assess the impact of the Omicron variant emergence on the clinical epidemiology of COVID-19 in hospitalised patients
Authors: Gonçalves Bronner P. , Hall Matthew , Jassat Waasila , Balan Valeria , Murthy Srinivas , Kartsonaki Christiana , Semple Malcolm G. , Rojek Amanda , Baruch Joaquín , Reyes Luis Felipe , Dasgupta Abhishek , Dunning Jake , Citarella Barbara Wanjiru , Pritchard Mark , Martín-Quiros Alejandro , Sili Uluhan , Baillie J. Kenneth , Aryal Diptesh , Arabi Yaseen , Rashan Aasiyah , Angheben Andrea , Caoili Janice , Carrier François Martin , Harrison Ewen M. , Gómez-Junyent Joan , Figueiredo-Mello Claudia , Douglas James Joshua , Nor Mohd Basri Mat , Chow Yock Ping , Wong Xin Ci , Bertagnolio Silvia , Thwin Soe Soe , Streinu-Cercel Anca , Salazar Leonardo , Rishu Asgar , Rangappa Rajavardhan , Ong David S.Y. , Hashmi Madiha , Carson Gail , Diaz Janet , Fowler Rob , Kraemer Moritz U.G. , Wils Evert-Jan , Horby Peter , Merson Laura , Olliaro Piero L. ,   ISARIC Clinical Characterisation Group
Published: June 2022

AbstractBackgroundWhilst timely clinical characterisation of infections caused by novel SARS-CoV-2 variants is necessary for evidence-based policy response, individual-level data on infecting variants are typically only available for a minority of patients and settings.MethodsHere, we propose an innovative approach to study changes in COVID-19 hospital presentation and outcomes after the Omicron variant emergence using publicly available population-level data on variant relative frequency to infer SARS-CoV-2 variants likely responsible for clinical cases. We apply this method to data collected by a large international clinical consortium before and after the emergence of the Omicron variant in different countries.ResultsOur analysis, that includes more than 100,000 patients from 28 countries, suggests that in many settings patients hospitalised with Omicron variant infection less often presented with commonly reported symptoms compared to patients infected with pre-Omicron variants. Patients with COVID-19 admitted to hospital after Omicron variant emergence had lower mortality compared to patients admitted during the period when Omicron variant was responsible for only a minority of infections (odds ratio in a mixed-effects logistic regression adjusted for likely confounders, 0.67 [95% confidence interval 0.61 – 0.75]). Qualitatively similar findings were observed in sensitivity analyses with different assumptions on population-level Omicron variant relative frequencies, and in analyses using available individual-level data on infecting variant for a subset of the study population.ConclusionsAlthough clinical studies with matching viral genomic information should remain a priority, our approach combining publicly available data on variant frequency and a multi-country clinical characterisation dataset with more than 100,000 records allowed analysis of data from a wide range of settings and novel insights on real-world heterogeneity of COVID-19 presentation and clinical outcome.
Machine learning approaches for classification of Plasmodium falciparum life cycle stages using single-cell transcriptomes
Authors: Shukla Swarnim , Choudhuri Soham , Iragavarapu Gayathri Priya , Ghosh Bhaswar 
Published: June 2022

AbstractMalaria, spread by the femaleAnophelesmosquito, is a highly fatal disease widespread in many parts of the world, causing 0.4 million deaths globally. Vital gene expressions form the basis in the detection of malaria infection levels. Quantification of malaria parasite infected RBCs and classification of its life cycle stages are done at macroscopic level by experts, for making informed decisions. Off late multiple computational approaches have been proposed to circumvent the problem of dimensionality leading to accurate predicted results. In this work a dimensionality reduction technique based on Genetic Algorithm (GA) is applied onP. falciparumsingle-cell transcriptomics to arrive at an optimized subset of features from the larger dataset. Features are chosen based on their class variants considering increased efficiency and accuracy, to separately transform the selected elements into a lower dimension. For the classification of the life cycle of malaria parasite based on single cell transcriptome data, a three-pronged approach employing the multiclass Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF) techniques is used. Distribution of cells was visualised and mapped using the R-based Seurat package. Further, we constructed protein interaction networks of the genes identified by the feature selection method and elucidated the role of the proteins in progression of the parasite through it’s life cycle. Our approach presents a novel protocol to implement ML techniques on scRNA seq datasets and subsequently harnessing the extracted information for biomarker/drug target detection.
An accurate and rapid Real-time PCR approach for human Monkeypox virus diagnosis
Authors: Wawina-Bokalanga Tony , Sklenovska Nikola , Vanmechelen Bert , Bloemen Mandy , Vergote Valentijn , Laenen Lies , André Emmanuel , Van Ranst Marc , Muyembe-Tamfum Jean-Jacques , Maes Piet 
Published: June 2022

ABSTRACTBackgroundHuman monkeypox (MPX) is an emerging zoonotic disease caused by monkeypox virus (MPXV), which is endemic to Western and Central Africa. Despite being identified in captive monkeys in 1958, and in humans from the Democratic Republic of Congo (DRC) in 1970, a MPXV diagnostic test is not routinely implemented in diagnostic laboratories. Currently, a multi-country outbreak of MPXV has been ongoing in several European and American countries since May 2022.MethodsTo develop this real-time PCR assay, five MPXV positive DNA samples were tested using MPXV-specific primers. The qPCR amplification was conducted on two different qPCR devices. We also tested samples, collected in May 2022, from pox-like lesions of MPXV-suspected patients in Belgium.ResultsThe performance of our real-time PCR was proved with five MPXV positive DNA from DRC. In addition, five out of ten clinical samples, from the current MPXV-outbreak, tested positive with Ct values ranging from 20.6 to 34.9.ConclusionsIn this study, we present an accurate and rapid real-time PCR approach, based on conserved regions of both MPXV clades, that can be used to test clinical samples from MPXV-suspected persons. Our real-time qPCR assay can be used for the 2022 multi-country outbreak of human MPXV.SummaryAn accurate and fast real-time PCR assay is indispensable to diagnose cases of monkeypox during the 2022 multi-country outbreak.
OpenSAFELY: Representativeness of Electronic Health Record platform OpenSAFELY-TPP data compared to the population of England
Authors: Andrews Colm D , Schultze Anna , Curtis Helen J , Hulme William J , Tazare John , Evans Stephen JW , Mehrkhar Amir , Bacon Seb , Hickman George , Bates Chris , Parry John , Hester Frank , Harper Sam , Cockburn Jonathan , Evans David , Ward Tom , Davey Simon , Inglesby Peter , Goldacre Ben , MacKenna Brian , Tomlinson Laurie , Walker Alex J 
Published: June 2022

AbstractBackgroundSince its inception in March 2020, data from the OpenSAFELY-TPP electronic health record platform has been used for more than 50 studies relating to the global COVID-19 emergency. OpenSAFELY-TPP data is derived from practices in England using SystmOne software, and has been used for the majority of these studies. We set out to investigate the representativeness of OpenSAFELY-TPP data by comparing it to national population estimates.MethodsWith the approval of NHS England, we describe the age, sex, Index of Multiple Deprivation and ethnicity of the OpenSAFELY-TPP population compared to national estimates from the Office for National Statistics. The five leading causes of death occurring between the 1st January 2020 and the 31st December 2020 were also compared to deaths registered in England during the same period.ResultsDespite regional variations, TPP is largely representative of the general population of England in terms of IMD (all within 1.1 percentage points), age, sex (within 0.1 percentage points), ethnicity and causes of death. The proportion of the five leading causes of death is broadly similar to those reported by ONS (all within 1 percentage point).ConclusionsData made available via OpenSAFELY-TPP is broadly representative of the English population.SummaryUsers of OpenSAFELY must consider the issues of representativeness, generalisability and external validity associated with using TPP data for health research. Although the coverage of TPP practices varies regionally across England, TPP registered patients are generally representative of the English population as a whole in terms of key demographic characteristics.Key messagesThere is regional variability across England in terms of key population characteristicsUsers of OpenSAFELY should carefully consider the issues of representativeness, generalisability and external validity associated with using TPP data for health research.TPP registered patients are a representative sub-sample of the English population as a whole in terms of age, sex, IMD and ethnicity.The proportions of the five leading causes of death in TPP in 2020 are broadly similar to those reported by ONS.
On the impacts of the COVID-19 pandemic on mortality: Lost years or lost days?
Authors: Rousson Valentin , Locatelli Isabella 
Published: June 2022

AbstractObjectiveTo quantify the (direct and indirect) impacts of the COVID-19 pandemic on mortality for actual populations of persons living in 12 European countries in 2020.MethodBased on demographic and mortality data, as well as remaining life expectancies found in the Human Mortality Database, we calculated a “population life lost” in 2020 for men and women living in Belgium, Croatia, Denmark, Finland, Hungary, Lithuania, Luxembourg, Norway, Portugal, Spain, Sweden and Switzerland. This quantity was obtained by dividing the total number of years lost in 2020 (estimated from all-cause mortality data and attributed directly or indirectly to COVID-19) by the size of the population.ResultsA significant population life loss was found in 8 countries in 2020, with men losing an average of 8.7, 5.0, 4.4, 4.0, 3.7, 3.4, 3.1 and 2.7 days in Lithuania, Spain, Belgium, Hungary, Croatia, Portugal, Switzerland and Sweden, respectively. For women, this loss was 5.5, 4.3, 3.7, 3.7, 3.1, 2.4, 1.6 and 1.4 days, respectively. No significant losses were found in Finland, Luxembourg, Denmark and Norway. Life loss was highly dependent on age, reaching 40 days at the age of 90 in some countries, while only a few significant losses occurred under the age of 60. Even in countries with a significant population life loss in 2020, it was on average about 30 times lower than in 1918, at the time of the Spanish flu.ConclusionsOur results based on the concept of population life loss were consistent with those based on the classical concept of life expectancy, confirming the significant impact of COVID-19 on mortality in 8 European countries in 2020. However, while life expectancy losses were typically counted in months or years, population life losses could be counted in days, a potentially useful piece of information from a public health perspective.
Auto-detection of motion artifacts on CT pulmonary angiograms with a physician-trained AI algorithm
Authors: Dasegowda Giridhar , Bizzo Bernardo C , Kaviani Parisa , Karout Lina , Ebrahimian Shadi , Digumarthy Subba R , Neumark Nir , Hillis James , Kalra Mannudeep K , Dreyer Keith J 
Published: June 2022

AbstractPurposeMotion-impaired CT images can result in limited or suboptimal diagnostic interpretation (with missed or miscalled lesions) and patient recall. We trained and tested an artificial intelligence (AI) model for identifying substantial motion artifacts on CT pulmonary angiography (CTPA) that have a negative impact on diagnostic interpretation.MethodsWith IRB approval and HIPAA compliance, we queried our multicenter radiology report database (mPower, Nuance) for CTPA reports between July 2015 - March 2022 for the following terms: “motion artifacts,” “respiratory motion,” “technically inadequate,” and “suboptimal” or “limited exam.” All CTPA reports belonged to two quaternary (Site A, n= 335; B, n= 259) and a community (C, n= 199) healthcare sites. A thoracic radiologist reviewed CT images of all positive hits for motion artifacts (present or absent) and their severity (no diagnostic effect or major diagnostic impairment). Coronal multiplanar images belonging to 793 CTPA exams were de-identified and exported offline into an AI model building prototype (Cognex Vision Pro, Cognex Corporation) to train an AI model to perform two-class classification (“motion” or “no motion”) with data from the three sites (70% training dataset, n= 554; 30% validation dataset, n= 239). Separately, data from Site A and Site C were used for training and validating; testing was performed on the Site B CTPA exams. A 5-fold repeated cross-validation was performed to evaluate the model performance with accuracy and receiver operating characteristics analysis (ROC).ResultsAmong the CTPA images from 793 patients (mean age 63 ± 17 years; 391 males, 402 females), 372 had no motion artifacts, and 421 had substantial motion artifacts. The statistics for the average performance of the AI model after 5-fold repeated cross-validation for the two-class classification included 94% sensitivity, 91% specificity, 93% accuracy, and 0.93 area under the ROC curve (AUC: 95% CI 0.89-0.97).ConclusionThe AI model used in this study can successfully identify CTPA exams with diagnostic interpretation limiting motion artifacts in multicenter training and test datasets.Clinical relevanceThe AI model used in the study can help alert the technologists about the presence of substantial motion artifacts on CTPA where a repeat image acquisition can help salvage diagnostic information.
Ribosomal RNA operons define a central functional compartment in the Streptomyces chromosome
Authors: Lorenzi Jean-Noël , Thibessard Annabelle , Lioy Virginia S. , Boccard Frédéric , Leblond Pierre , Pernodet Jean-Luc , Bury-Moné Stéphanie 
Published: June 2022

AbstractStreptomyces are prolific producers of specialized metabolites with applications in medicine and agriculture. These bacteria possess a large linear chromosome genetically compartmentalized: core genes are grouped in the central part, while terminal regions are populated by poorly conserved genes and define the chromosomal arms. In exponentially growing cells, chromosome conformation capture unveiled sharp boundaries formed by ribosomal RNA (rrn) operons that segment the chromosome into multiple domains. The first and last rrn operons delimit the highly expressed central compartment and the rather transcriptionally silent terminal compartments. Here we further explore the link between the genetic distribution of rrn operons and Streptomyces genomic compartmentalization. A large panel of genomes of species representative of the genus diversity revealed that rrn operons and core genes form a central skeleton, the former being identifiable from their core gene environment. We implemented a new nomenclature for Streptomyces genomes and trace their rrn-based evolutionary history. Remarkably, rrn operons are close to pericentric inversions. Moreover, the central compartment delimited by rrn operons has a very dense, nearly invariant core gene content. Finally, this compartment harbors genes with the highest expression levels, regardless of gene persistence and distance to the origin of replication. Our results highlight that rrn operons define the structural boundaries of a central functional compartment prone to transcription in Streptomyces.
Monitoring SARS-CoV-2 infection using a double reporter-expressing virus
Authors: Chiem Kevin , Park Jun-Gyu , Vasquez Desarey Morales , Plemper Richard K. , Torrelles Jordi B. , Kobie James J. , Walter Mark R. , Ye Chengjin , Martinez-Sobrido Luis 
Published: June 2022

ABSTRACTSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the highly contagious agent responsible for the coronavirus disease 2019 (COVID-19) pandemic. An essential requirement for understanding SARS-CoV-2 fundamental biology and the impact of anti-viral therapeutics are robust methods to detect for the presence of the virus in infected cells or animal models. Despite the development and successful generation of recombinant (r)SARS-CoV-2 expressing fluorescent or luciferase reporter genes, knowledge acquired from their use in in vitro assays and/or in live animals are limited to the properties of the fluorescent or luciferase reporter genes. Herein, for the first time, we engineered a replication-competent rSARS-CoV-2 that expresses both fluorescent (mCherry) and luciferase (Nluc) reporter genes (rSARS-CoV-2/mCherry-Nluc) to overcome limitations associated with the use of a single reporter gene. In cultured cells, rSARS-CoV-2/mCherry-Nluc displayed similar viral fitness as rSARS-CoV-2 expressing single reporter fluorescent and luciferase genes (rSARS-CoV-2/mCherry and rSARS-CoV-2/Nluc, respectively), or wild-type (WT) rSARS-CoV-2, while maintaining comparable expression levels of both reporter genes. In vivo, rSARS-CoV-2/mCherry-Nluc has similar pathogenicity in K18 human angiotensin converting enzyme 2 (hACE2) transgenic mice than rSARS-CoV-2 expressing individual reporter genes, or WT rSARS-CoV-2. Importantly, rSARS-CoV-2/mCherry-Nluc facilitates the assessment of viral infection and transmission in golden Syrian hamsters using in vivo imaging systems (IVIS). Altogether, this study demonstrates the feasibility of using this novel bireporter-expressing rSARS-CoV-2 for the study SARS-CoV-2 in vitro and in vivo.IMPORTANCEDespite the availability of vaccines and antivirals, the coronavirus disease 2019 (COVID-19) pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to ravage health care institutions worldwide. Previously, we have generated replication-competent recombinant (r)SARS-CoV-2 expressing fluorescent or luciferase reporter proteins to track viral infection in vitro and/or in vivo. However, these rSARS-CoV-2 are restricted to express only a single fluorescent or a luciferase reporter gene, limiting or preventing their use to specific in vitro assays and/or in vivo studies. To overcome this limitation, we have engineered a rSARS-CoV-2 expressing both fluorescent (mCherry) and luciferase (Nluc) genes and demonstrated its feasibility to study the biology of SARS-CoV-2 in vitro and/or in vivo, including the identification and characterization of neutralizing antibodies and/or antivirals. Using rodent models, we visualize SARS-CoV-2 infection and transmission through in vivo imaging systems (IVIS).