scholarly journals Identifying and prioritizing potential human-infecting viruses from their genome sequences

PLoS Biology ◽  
2021 ◽  
Vol 19 (9) ◽  
pp. e3001390
Author(s):  
Nardus Mollentze ◽  
Simon A. Babayan ◽  
Daniel G. Streicker
Keyword(s):  
High Risk ◽  
Severe Acute Respiratory Syndrome ◽  
Low Cost ◽  
Characteristic Curve ◽  
Early Investigation ◽  
Avian Host ◽  
Viral Genomes ◽  
Virus Surveillance ◽  
Zoonotic Risk

Determining which animal viruses may be capable of infecting humans is currently intractable at the time of their discovery, precluding prioritization of high-risk viruses for early investigation and outbreak preparedness. Given the increasing use of genomics in virus discovery and the otherwise sparse knowledge of the biology of newly discovered viruses, we developed machine learning models that identify candidate zoonoses solely using signatures of host range encoded in viral genomes. Within a dataset of 861 viral species with known zoonotic status, our approach outperformed models based on the phylogenetic relatedness of viruses to known human-infecting viruses (area under the receiver operating characteristic curve [AUC] = 0.773), distinguishing high-risk viruses within families that contain a minority of human-infecting species and identifying putatively undetected or so far unrealized zoonoses. Analyses of the underpinnings of model predictions suggested the existence of generalizable features of viral genomes that are independent of virus taxonomic relationships and that may preadapt viruses to infect humans. Our model reduced a second set of 645 animal-associated viruses that were excluded from training to 272 high and 41 very high-risk candidate zoonoses and showed significantly elevated predicted zoonotic risk in viruses from nonhuman primates, but not other mammalian or avian host groups. A second application showed that our models could have identified Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) as a relatively high-risk coronavirus strain and that this prediction required no prior knowledge of zoonotic Severe Acute Respiratory Syndrome (SARS)-related coronaviruses. Genome-based zoonotic risk assessment provides a rapid, low-cost approach to enable evidence-driven virus surveillance and increases the feasibility of downstream biological and ecological characterization of viruses.

scholarly journals Identifying and prioritizing potential human-infecting viruses from their genome sequences

2020 ◽  
Author(s):  
Nardus Mollentze ◽  
Simon A. Babayan ◽  
Daniel G. Streicker
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
High Risk ◽  
Rapid Assessment ◽  
Machine Learning Algorithms ◽  
Pandemic Preparedness ◽  
Genome Sequences ◽  
Viral Genomes ◽  
Virus Surveillance ◽  
Model Training

AbstractRapid assessment of which animal viruses may be capable of infecting humans is currently intractable, but would allow their prioritization for further investigation and pandemic preparedness. We developed machine learning algorithms that identify candidate zoonoses using evolutionary signals of host range encoded in viral genomes. This reduces lists of hundreds of viruses with uncertain human infectivity to tractable numbers for prioritized research, generalizes to virus families excluded from model training, can distinguish high risk viruses within families that contain a minority of zoonotic species, and could have identified the exceptional risk of SARS-CoV-2 prior to its emergence. Genome-based risk assessment allows identification of high-risk viruses immediately upon discovery, increasing both the feasibility and likelihood of downstream virological and ecological characterization and allowing for evidence-driven virus surveillance.

scholarly journals Microstructure and Thermoelectric Properties of (Bi0.48Sb1.52)Te3 Thick Films Prepared with Tape Casting Method

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 140
Author(s):  
Lichen Liu ◽  
Ziping Cao ◽  
Min Chen ◽  
Jun Jiang
Keyword(s):  
Thermoelectric Properties ◽  
Low Cost ◽  
Thick Films ◽  
Tape Casting ◽  
Casting Process ◽  
Casting Method ◽  
Glass Substrates ◽  
Conductive Films ◽  
Casting Technology

This paper reports the fabrication and characterization of (Bi0.48Sb1.52)Te3 thick films using a tape casting process on glass substrates. A slurry of thermoelectric (Bi0.48Sb1.52)Te3 was developed and cured thick films were annealed in a vacuum chamber at 500–600 °C. The microstructure of these films was analyzed, and the Seebeck coefficient and electric conductivity were tested. It was found that the subsequent annealing process must be carefully designed to achieve good thermoelectric properties of these samples. Conductive films were obtained after annealing and led to acceptable thermoelectric performance. While the properties of these initial materials are not at the level of bulk materials, this work demonstrates that the low-cost tape casting technology is promising for fabricating thermoelectric modules for energy conversion.

scholarly journals Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedded clustering

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
José Castela Forte ◽  
Galiya Yeshmagambetova ◽  
Maureen L. van der Grinten ◽  
Bart Hiemstra ◽  
Thomas Kaufmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Mortality Risk ◽  
Clustering Algorithms ◽  
Kidney Injury ◽  
Risk Groups ◽  
Admission Diagnosis ◽  
Dutch Hospital ◽  
Cluster Membership

AbstractCritically ill patients constitute a highly heterogeneous population, with seemingly distinct patients having similar outcomes, and patients with the same admission diagnosis having opposite clinical trajectories. We aimed to develop a machine learning methodology that identifies and provides better characterization of patient clusters at high risk of mortality and kidney injury. We analysed prospectively collected data including co-morbidities, clinical examination, and laboratory parameters from a minimally-selected population of 743 patients admitted to the ICU of a Dutch hospital between 2015 and 2017. We compared four clustering methodologies and trained a classifier to predict and validate cluster membership. The contribution of different variables to the predicted cluster membership was assessed using SHapley Additive exPlanations values. We found that deep embedded clustering yielded better results compared to the traditional clustering algorithms. The best cluster configuration was achieved for 6 clusters. All clusters were clinically recognizable, and differed in in-ICU, 30-day, and 90-day mortality, as well as incidence of acute kidney injury. We identified two high mortality risk clusters with at least 60%, 40%, and 30% increased. ICU, 30-day and 90-day mortality, and a low risk cluster with 25–56% lower mortality risk. This machine learning methodology combining deep embedded clustering and variable importance analysis, which we made publicly available, is a possible solution to challenges previously encountered by clustering analyses in heterogeneous patient populations and may help improve the characterization of risk groups in critical care.

scholarly journals The Role of [18F]Fluciclovine PET/CT in the Characterization of High-Risk Primary Prostate Cancer: Comparison with [11C]Choline PET/CT and Histopathological Analysis

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1575
Author(s):  
Lucia Zanoni ◽  
Riccardo Mei ◽  
Lorenzo Bianchi ◽  
Francesca Giunchi ◽  
Lorenzo Maltoni ◽  
...  
Keyword(s):  
Bone Marrow ◽  
High Risk ◽  
Malignant Lesion ◽  
Lesion Detection ◽  
Histopathological Analysis ◽  
Choline Pet ◽  
Primary Prostate Cancer ◽  
Pet Ct

The primary aim of the study was to evaluate the role of [18F]Fluciclovine PET/CT in the characterization of intra-prostatic lesions in high-risk primary PCa patients eligible for radical prostatectomy, in comparison with conventional [11C]Choline PET/CT and validated by prostatectomy pathologic examination. Secondary aims were to determine the performance of PET semi-quantitative parameters (SUVmax; target-to-background ratios [TBRs], using abdominal aorta, bone marrow and liver as backgrounds) for malignant lesion detection (and best cut-off values) and to search predictive factors of malignancy. A six sextants prostate template was created and used by PET readers and pathologists for data comparison and validation. PET visual and semi-quantitative analyses were performed: for instance, patient-based, blinded to histopathology; subsequently lesion-based, un-blinded, according to the pathology reference template. Among 19 patients included (mean age 63 years, 89% high and 11% very-high-risk, mean PSA 9.15 ng/mL), 45 malignant and 31 benign lesions were found and 19 healthy areas were selected (n = 95). For both tracers, the location of the “blinded” prostate SUVmax matched with the lobe of the lesion with the highest pGS in 17/19 cases (89%). There was direct correlation between [18F]Fluciclovine uptake values and pISUP. Overall, lesion-based (n = 95), the performance of PET semiquantitative parameters, with either [18F]Fluciclovine or [11C]Choline, in detecting either malignant/ISUP2-5/ISUP4-5 PCa lesions, was moderate and similar (AUCs ≥ 0.70) but still inadequate (AUCs ≤ 0.81) as a standalone staging procedure. A [18F]Fluciclovine TBR-L3 ≥ 1.5 would depict a clinical significant lesion with a sensitivity and specificity of 85% and 68% respectively; whereas a SUVmax cut-off value of 4 would be able to identify a ISUP 4-5 lesion in all cases (sensitivity 100%), although with low specificity (52%). TBRs (especially with threshold significantly higher than aorta and slightly higher than bone marrow), may be complementary to implement malignancy targeting.

scholarly journals Investigation of Four Clusters of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Rwanda, 2020

2021 ◽  
Vol 18 (13) ◽  
pp. 7018
Author(s):  
Olivier Nsekuye ◽  
Edson Rwagasore ◽  
Marie Aime Muhimpundu ◽  
Ziad El-Khatib ◽  
Daniel Ntabanganyimana ◽  
...  
Keyword(s):  
High Risk ◽  
Severe Acute Respiratory Syndrome ◽  
Symptom Onset ◽  
Attack Rate ◽  
Positive Sample ◽  
Contact Tracing ◽  
Face To Face ◽  
Laboratory Confirmation ◽  
Closed Environment ◽  
Index Cases

We reported the findings of the first Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) four clusters identified in Rwanda. Case-investigations included contact elicitation, testing, and isolation/quarantine of confirmed cases. Socio-demographic and clinical data on cases and contacts were collected. A confirmed case was a person with laboratory confirmation of SARS-CoV-2 infection (PCR) while a contact was any person who had contact with a SARS-CoV-2 confirmed case within 72 h prior, to 14 days after symptom onset; or 14 days before collection of the laboratory-positive sample for asymptomatic cases. High risk contacts were those who had come into unprotected face-to-face contact or had been in a closed environment with a SARS-CoV-2 case for >15 min. Forty cases were reported from four clusters by 22 April 2020, accounting for 61% of locally transmitted cases within six weeks. Clusters A, B, C and D were associated with two nightclubs, one house party, and different families or households living in the same compound (multi-family dwelling). Thirty-six of the 1035 contacts tested were positive (secondary attack rate: 3.5%). Positivity rates were highest among the high-risk contacts compared to low-risk contacts (10% vs. 2.2%). Index cases in three of the clusters were imported through international travelling. Fifteen of the 40 cases (38%) were asymptomatic while 13/25 (52%) and 8/25 (32%) of symptomatic cases had a cough and fever respectively. Gatherings in closed spaces were the main early drivers of transmission. Systematic case-investigations contact tracing and testing likely contributed to the early containment of SARS-CoV-2 in Rwanda.

scholarly journals CHARGE-AF in a national routine primary care electronic health records database in the Netherlands: validation for 5-year risk of atrial fibrillation and implications for patient selection in atrial fibrillation screening

Open Heart ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. e001459
Author(s):  
Jelle C L Himmelreich ◽  
Wim A M Lucassen ◽  
Ralf E Harskamp ◽  
Claire Aussems ◽  
Henk C P M van Weert ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Primary Care ◽  
High Risk ◽  
The Netherlands ◽  
Patient Selection ◽  
Low Cost ◽  
Calibration Plot ◽  
Primary Care Data ◽  
C Statistic ◽  
Kaplan Meier

AimsTo validate a multivariable risk prediction model (Cohorts for Heart and Aging Research in Genomic Epidemiology model for atrial fibrillation (CHARGE-AF)) for 5-year risk of atrial fibrillation (AF) in routinely collected primary care data and to assess CHARGE-AF’s potential for automated, low-cost selection of patients at high risk for AF based on routine primary care data.MethodsWe included patients aged ≥40 years, free of AF and with complete CHARGE-AF variables at baseline, 1 January 2014, in a representative, nationwide routine primary care database in the Netherlands (Nivel-PCD). We validated CHARGE-AF for 5-year observed AF incidence using the C-statistic for discrimination, and calibration plot and stratified Kaplan-Meier plot for calibration. We compared CHARGE-AF with other predictors and assessed implications of using different CHARGE-AF cut-offs to select high-risk patients.ResultsAmong 111 475 patients free of AF and with complete CHARGE-AF variables at baseline (17.2% of all patients aged ≥40 years and free of AF), mean age was 65.5 years, and 53% were female. Complete CHARGE-AF cases were older and had higher AF incidence and cardiovascular comorbidity rate than incomplete cases. There were 5264 (4.7%) new AF cases during 5-year follow-up among complete cases. CHARGE-AF’s C-statistic for new AF was 0.74 (95% CI 0.73 to 0.74). The calibration plot showed slight risk underestimation in low-risk deciles and overestimation of absolute AF risk in those with highest predicted risk. The Kaplan-Meier plot with categories <2.5%, 2.5%–5% and >5% predicted 5-year risk was highly accurate. CHARGE-AF outperformed CHA2DS2-VASc (Cardiac failure or dysfunction, Hypertension, Age >=75 [Doubled], Diabetes, Stroke [Doubled]-Vascular disease, Age 65-74, and Sex category [Female]) and age alone as predictors for AF. Dichotomisation at cut-offs of 2.5%, 5% and 10% baseline CHARGE-AF risk all showed merits for patient selection in AF screening efforts.ConclusionIn patients with complete baseline CHARGE-AF data through routine Dutch primary care, CHARGE-AF accurately assessed AF risk among older primary care patients, outperformed both CHA2DS2-VASc and age alone as predictors for AF and showed potential for automated, low-cost patient selection in AF screening.

scholarly journals Isolation and characterization of the new SARS-CoV-2 variant in travellers from the United Kingdom to India: VUI-202012/01 of the B.1.1.7 lineage

2021 ◽  
Vol 28 (2) ◽  
Author(s):  
Pragya D Yadav ◽  
Dimpal A Nyayanit ◽  
Rima R Sahay ◽  
Prasad Sarkale ◽  
Jayshri Pethani ◽  
...  
Keyword(s):  
United Kingdom ◽  
Severe Acute Respiratory Syndrome ◽  
Surveillance System ◽  
The United Kingdom ◽  
Isolation And Characterization ◽  
New Variant ◽  
The Uk ◽  
Local Transmission

We have isolated the new severe acute respiratory syndrome coronavirus-2 variant of concern 202 012/01 from the positive coronavirus disease 2019 cases that travelled from the UK to India in the month of December 2020. This emphasizes the need for the strengthened surveillance system to limit the local transmission of this new variant.

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