bluuebunny/crossref_metadata_2025_split huggingface dataset:
The rise and spread of the SARS-CoV-2 AY.122 lineage in Russia
Authors: Klink Galya V. , Safina Ksenia , Nabieva Elena , Shvyrev Nikita , Garushyants Sofya , Alekseeva Evgeniia , Komissarov Andrey B. , Danilenko Daria M. , Pochtovyi Andrei A. , Divisenko Elizaveta V. , Vasilchenko Lyudmila A. , Shidlovskaya Elena V. , Kuznetsova Nadezhda A. ,   The Coronavirus Russian Genetics Initiative (CoRGI) Consortium, Samoilov Andrei E. , Neverov Alexey D. , Popova Anfisa V. , Fedonin Gennady G. ,   The CRIE Consortium, Akimkin Vasiliy G. , Lioznov Dmitry , Gushchin Vladimir A. , Shchur Vladimir , Bazykin Georgii A. 
Published: December 2021

AbstractBackgroundDelta has outcompeted most preexisting variants of SARS-CoV-2, becoming the globally predominant lineage by mid-2021. Its subsequent evolution has led to emergence of multiple sublineages, many of which are well-mixed between countries.AimHere, we aim to study the emergence and spread of the Delta lineage in Russia.MethodsWe use a phylogeographic approach to infer imports of Delta sublineages into Russia, and phylodynamic models to assess the rate of their spread.ResultsWe show that nearly the entire Delta epidemic in Russia has probably descended from a single import event despite genetic evidence of multiple Delta imports. Indeed, over 90% of Delta samples in Russia are characterized by the nsp2:K81N+ORF7a:P45L pair of mutations which is rare outside Russia, putting them in the AY.122 sublineage. The AY.122 lineage was frequent in Russia among Delta samples from the start, and has not increased in frequency in other countries where it has been observed, suggesting that its high prevalence in Russia has probably resulted from a random founder effect.ConclusionThe apartness of the genetic composition of the Delta epidemic in Russia makes Russia somewhat unusual, although not exceptional, among other countries.
Early detection of pancreatic ductal adenocarcinomas with an ensemble learning model based on a panel of protein serum biomarkers
Authors: Nené Nuno R. , Ney Alexander , Nazarenko Tatiana , Blyuss Oleg , Johnston Harvey E. , Whitwell Harry J. , Sedlak Eva , Gentry-Maharaj Aleksandra , Costello Eithne , Greenhalf William , Jacobs Ian , Menon Usha , Hsuan Justin , Pereira Stephen P. , Zaikin Alexey , Timms John F. 
Published: December 2021

AbstractEarlier detection of pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcomes, as it is mostly detected at advanced stages which are associated with poor survival. Developing non-invasive blood tests for early detection would be an important breakthrough. The primary objective of the work presented here was to use a unique dataset, that is both large and prospectively collected, to quantify a set of 96 cancer-associated proteins and construct multi-marker models with the capacity to accurately predict PDAC years before diagnosis. The data is part of a nested case control study within UK Collaborative Trial of Ovarian Cancer Screening and is comprised of 219 samples, collected from a total of 143 post-menopausal women who were diagnosed with pancreatic cancer within 70 months after sample collection, and 248 matched non-cancer controls. We developed a stacked ensemble modelling technique to achieve robustness in predictions and, therefore, improve performance in newly collected datasets. With a pool of 10 base-learners and a Bayesian averaging meta-learner, we can predict PDAC status with an AUC of 0.91 (95% CI 0.75 - 1.0), sensitivity of 92% (95% CI 0.54 - 1.0) at 90% specificity, up to 1 year to diagnosis, and at an AUC of 0.85 (95% CI 0.74 - 0.93) up to 2 years to diagnosis (sensitivity of 61%, 95 % CI 0.17 - 0.83, at 90% specificity). These models also use clinical covariates such as hormone replacement therapy use (at randomization), oral contraceptive pill use (ever) and diabetes and outperform biomarker combinations cited in the literature.
EpiLPS: a fast and flexible Bayesian tool for near real-time estimation of the time-varying reproduction number
Authors: Gressani Oswaldo , Wallinga Jacco , Althaus Christian , Hens Niel , Faes Christel 
Published: December 2021

AbstractIn infectious disease epidemiology, the instantaneous reproduction numberR(t) is a timevarying metric defined as the average number of secondary infections generated by individuals who are infectious at timet. It is therefore a crucial epidemiological parameter that assists public health decision makers in the management of an epidemic. We present a new Bayesian tool for robust estimation of the time-varying reproduction number. The proposed methodology smooths the epidemic curve and allows to obtain (approximate) point estimates and credible envelopes ofR(t) by employing the renewal equation, using Bayesian P-splines coupled with Laplace approximations of the conditional posterior of the spline vector. Two alternative approaches for inference are presented: (1) an approach based on a maximum a posteriori argument for the model hyperparameters, delivering estimates ofR(t) in only a few seconds; and (2) an approach based on a MCMC scheme with underlying Langevin dynamics for efficient sampling of the posterior target distribution. Case counts per unit of time are assumed to follow a Negative Binomial distribution to account for potential excess variability in the data that would not be captured by a classic Poisson model. Furthermore, after smoothing the epidemic curve, a “plug-in” estimate of the reproduction number can be obtained from the renewal equation yielding a closed form expression ofR(t) as a function of the spline parameters. The approach is extremely fast and free of arbitrary smoothing assumptions. EpiLPS is applied on data of SARS-CoV-1 in Hong-Kong (2003), influenza A H1N1 (2009) in the USA and current SARS-CoV-2 pandemic (2020-2021) for Belgium, Portugal, Denmark and France.Author summaryThe instantaneous reproduction numberR(t) is a key metric that provides important insights into an epidemic outbreak. We present a flexible Bayesian approach called EpiLPS (Epidemiological modeling with Laplacian-P-splines) for smooth estimation of the epidemic curve andR(t). Computational speed and absence of arbitrary assumptions on smoothing makes EpiLPS an interesting tool for near real-time estimation of the reproduction number. An R software package is available (https://github.com/oswaldogressani).
Prevalence of SARS-CoV-2 infection among COVID-19 RT-PCR laboratory workers in Bangladesh
Authors: Khan Mohammad Jahidur Rahman , Shumu Samshad Jahan , Raihan Ruksana , Mannan Nusrat , Reza Md. Selim , Khan Nazia Hasan , Bhuiyan Amirul Huda , Deb Paroma , Mim Farzana , Akram Arifa 
Published: December 2021

AbstractBackgroundHealth care workers (HCWs) at the frontline are confronting a substantial risk of infection during the coronavirus disease 2019 (COVID-19) pandemic. This emerging virus created specific hazards to researchers and laboratory staff in a clinical setting, underlined by rapid and extensive worldwide transmission. This study aimed to investigate the prevalence of SARS-CoV-2 infection among COVID-19 RT-PCR laboratory health workers in Bangladesh.Materials & MethodsThis retrospective study was conducted between October 2 to December 2, 2020. A total of 508 participants, including doctors, scientific officers, medical technologists, and cleaners working in several COVID-19 RT-PCR laboratories, were included in this study. Data were collected from each participant using a semi-structured questionnaire prepared in the format of an anonymous Google form. All participants provided informed consent. The Ethical clearance was obtained from the Institutional Ethics Review Committee of Shaheed Suhrawardy Medical College, Dhaka, Bangladesh. All statistical analyses were performed using SPSS (Statistical Package for the Social Sciences) version 25.0 software (SPSS, Inc).ResultsOut of the 508 participants, 295 tested positive for SARS CoV-2 RT-PCR. Among the positive cases, 202 were men, 93 were women, with the median age of 30 years. The most positive cases were medical technologists (53.22%) followed by doctors (28.8%). Out of the 271 symptomatic positive cases, the most typical symptoms were fever (78.5%), fatigue (70%), loss of smell and taste (65%), cough (64%), and others. Hypertension, obesity, and diabetes were found in 8.8%, 8.8%, and 7.1% positive cases. A + blood group was present in 37% of the positive cases, followed by the B+ blood group (27%) and O+ blood group (25%). Inadequate supply of personal protection equipment (PPE), absence of negative pressure ventilation, laboratory contamination, and no training on molecular test methods were found in 13.8%, 67.8%, 44.7%, and 40.6% of positive cases, respectively.ConclusionEvaluating the infection status of laboratory health workers is crucial for drawing attention from the public, providing practical suggestions for government agencies, and increasing protective measures for laboratory health workers.
Facial basal cell carcinoma: a clinicopathological analysis of incomplete excision
Authors: Layton Thomas 
Published: December 2021

AbstractBasal cell carcinoma (BCC) is the most common malignancy in humans. Incomplete excision following conventional surgical excision requires careful consideration given the potential for disease recurrence. We performed a retrospective study analyzing facial BCC treated by conventional surgical excision to investigate factors influencing the likelihood of tumour clearance. In total, 456 cases of facial BCC were reviewed to collect a dataset of 50 tumours with involved margins and these compared to 50 completely excised tumours from the same cohort. Statistical comparison between incomplete and complete excision cases was performed using several metrics including tumour location, histology, grade, surgeon experience and method of wound reconstruction. Interestingly, our results demonstrated that only tumour location and histological type had a statistically significant impact on the completeness of resection. Infiltrative, morphoeic and mixed tumours had a higher chance of incomplete excision, as did tumours located on the inner canthus and ala nasi. In addition, the overall incomplete excision rate was 10.96% in line with previous studies. Our results help inform surgical practice and support consideration of extending the recommended macroscopic surgical margin for higher risk tumours. More research is needed to further categorize facial BCC to optimize surgical management.
Heterogeneity in statin responses explained by variation in the human gut microbiome
Authors: Wilmanski Tomasz , Kornilov Sergey A. , Diener Christian , Conomos Mathew , Lovejoy Jennifer C. , Sebastiani Paola , Orwoll Eric S. , Hood Leroy , Price Nathan D. , Rappaport Noa , Magis Andrew T. , Gibbons Sean M. 
Published: December 2021

AbstractStatins remain one of the most prescribed medications worldwide. While effective in decreasing atherosclerotic cardiovascular disease risk, statin use is associated with several side effects for a subset of patients, including disrupted metabolic control and increased risk of type II diabetes. We investigated the potential role of the gut microbiome in modifying patient response to statin therapy. In a cohort of >1840 individuals, we find that the hydrolyzed substrate for 3-hydroxy-3-methylglutarate-CoA (HMG-CoA) reductase, HMG, may serve as a reliable marker for statin on-target effects. Through exploring gut microbiome associations between blood-derived measures of statin effectiveness and metabolic health parameters among statin users and non-users, we find that heterogeneity in statin response is associated with variation in the gut microbiome. A Bacteroides rich, α-diversity depleted, microbiome composition corresponds to the strongest statin on-target response, but also greatest disruption to glucose homeostasis, indicating lower treatment doses and/or complementary therapies may be beneficial in those individuals. Our findings suggest a potential path towards personalizing statin treatment through gut microbiome monitoring.
Homologous and Heterologous Vaccine Boost Strategies for Humoral and Cellular Immunologic Coverage of the SARS-CoV-2 Omicron Variant
Authors: Tan C. Sabrina , Collier Ai-ris Y. , Liu Jinyan , Yu Jingyou , Chandrashekar Abishek , McMahan Katherine , Wan Huahua , He Xuan , Jacob-Dolan Catherine , Sellers Daniel , Ventura John D. , Bartsch Yannic , Hauser Blake M. , Munt Jennifer E. , Mattocks Melissa , Stephenson Kathryn E. , Vidal Samuel J. , Jaegle Kate , Rowe Marjorie , Hemond Rachel , Rivera Lorraine Bermudez , Anioke Tochi , Barrett Julia , Chung Benjamin , Gardner Sarah , Gebre Makda S. , Hachmann Nicole , Lifton Michelle , Miller Jessica , Nampanya Felix , Powers Olivia , Sciacca Michaela , Siamatu Mazuba , Surve Nehalee , Tostanoski Lisa H. , VanWyk Haley , Wu Cindy , Baric Ralph S. , Schmidt Aaron G. , Alter Galit , Barouch Dan H. 
Published: December 2021

AbstractThe rapid spread of the highly mutated SARS-CoV-2 Omicron variant has raised substantial concerns about the protective efficacy of currently available vaccines. We assessed Omicron-specific humoral and cellular immune responses in 65 individuals who were vaccinated with two immunizations of BNT162b2 and were boosted after at least 6 months with either Ad26.COV2.S (Johnson & Johnson; N=41) or BNT162b2 (Pfizer; N=24) (Table S1).
Time trends in social contacts of individuals according to comorbidity and vaccination status, before and during the COVID-19 pandemic
Authors: Godbout Aurélie , Drolet Mélanie , Mondor Myrto , Simard Marc , Sauvageau Chantal , De Serres Gaston , Brisson Marc 
Published: December 2021

ABSTRACTBackgroundAs we are confronted with more transmissible/severe variants with immune escape and the waning of vaccine efficacy, it is particularly relevant to understand how the social contacts of individuals at greater risk of COVID-19 complications evolved over time. We described time trends in social contacts of individuals according to comorbidity and vaccination status before and during the first three waves of the COVID-19 pandemic in Quebec, Canada.MethodsWe used data from CONNECT, a repeated cross-sectional population-based survey of social contacts conducted before (2018/2019) and during the pandemic (April 2020 to July 2021). We recruited non-institutionalized adults from Quebec, Canada, by random digit dialling. We used a self-administered web-based questionnaire to measure the number of social contacts of participants (two-way conversation at a distance ≤2 meters or a physical contact, irrespective of masking). We compared the mean number of contacts/day according to the comorbidity status of participants (pre-existing medical conditions with symptoms/medication in the past 12 months) and 1-dose vaccination status during the third wave. All analyses were performed using weighted generalized linear models with a Poisson distribution and robust variance.ResultsA total of 1441 and 5185 participants with and without comorbidities, respectively, were included in the analyses. Contacts significantly decreased from a mean of 6.1 (95%CI 4.9–7.3) before the pandemic to 3.2 (95%CI 2.5–3.9) during the first wave among individuals with comorbidities, and from 8.1 (95%CI 7.3–9.0) to 2.7 (95%CI 2.2–3.2) among individuals without comorbidities. Individuals with comorbidities maintained fewer contacts than those without comorbidities in the second wave, with a significant difference before the Christmas 2020/2021 holidays (2.9 (95%CI 2.5–3.2) v 3.9 (95%CI 3.5–4.3); P<0.001). During the third wave, contacts were similar for individuals with (4.1, 95%CI 3.4– 4.7) and without comorbidities (4.5, 95%CI 4.1–4.9; P=0.27). This could be partly explained by individuals with comorbidities vaccinated with their first dose who increased their contacts to the level of those without comorbidities.ConclusionsIt will be important to closely monitor COVID-19-related outcomes and social contacts by comorbidity and vaccination status to inform targeted or population-based interventions (e.g., booster doses of the vaccine).
Quantifying the impact of individual and collective compliance with infection control measures for ethical public health policy
Authors: Roberts Daniel , Jamrozik Euzebiusz , Heriot George S. , Slim Anja C. , Selgelid Michael J. , Miller Joel C. 
Published: December 2021

AbstractInfectious disease control measures often require collective compliance of large numbers of individuals to benefit public health. This raises ethical questions regarding the value of the public health benefit created by individual and collective compliance. Answering these requires estimating the extent to which individual actions prevent infection of others. We develop mathematical techniques enabling quantification of the impacts of individuals or groups complying with three public health measures: border quarantine, isolation of infected individuals, and prevention via vaccination/prophylaxis. The results suggest that (i) these interventions exhibit synergy: they become more effective on a per-individual basis as compliance increases and (ii) There is often significant “overdetermination” of transmission: if a susceptible person contacts multiple infectious individuals, an intervention preventing one transmission may not change the ultimate outcome (thus risk imposed by some individuals may erode the benefits of others’ compliance). These results have implications for public health policy during epidemics.
A systematic review of anaesthetic agents used in Drug Induced Sedation Endoscopy (DISE) and a description of a new DISE technique
Authors: Sanders Oliver , Kotecha Bhik , Veer Vik 
Published: December 2021

AbstractObjectivesDrug induced sleep endoscopy (DISE) is a standardly used investigation for surgical planning in obstructive sleep apnoea management once conservative treatments have proven inadequate. There are a variety of anaesthetic agents used to obtain sedation necessary for DISE. These agents may have different effect on the upper airway and other parameters important in the diagnosis of the site of collapse during sleep. We aimed to review the commonly agents and evaluate the significance of their impact on the the diagnosis.MethodsA search was conducted through PubMed looking for studies on commonly used anaesthetic agents and their effect on the upper airway and cardiopulmonary parameters. Results: Of the 109 studies yielded by the search, 19 were deemed relevant to the review and met all inclusion criteria. The agents reviewed were: propofol, dexmedetomidine, remifentanil, isoflurane, sevoflurane, midazolam and topical lidocaine. A meta-analysis was not conducted due to the limited number of relevant studies and the heterogeneity of outcomes measured. All agents examined gave some element of airway collapse and impact on cardiopulmonary measures. Most of these effects were shown to be dose-dependent. Of the agents considered dexmedetomidine and propofol gave the most consistently reliable and physiologically safe representations of upper airway collapse seen in OSA patients.ConclusionThere is limited information and no industry standard for the sedative regimen used for DISE. Of the agents reviewed those that caused least cardiopulmonary instability, respiratory depression and exaggerated upper airway collapse were deemed the most appropriate for DISE. The agent that best meet these criteria is dexmedetomidine followed by propofol.
Post-traumatic Stress Risk among COVID-19 Survivors in Colombia
Authors: Campo-Arias Adalberto , Pedrozo-Pupo John Carlos , Herazo Edwin 
Published: December 2021

AbstractThe study’s objective was to establish the prevalence and variables associated with post-traumatic stress disorder risk (PTSD-R) in a sample of COVID-19 survivors in Santa Marta, Colombia. A cross-sectional study was designed with a non-probabilistic sample of adult COVID-19 survivors. Participants were demographically characterized and completed scales for depression risk, insomnia risk, and PTSD-R. Three hundred and thirty COVID-19 survivors between 18 and 89 years participated; 61.52% were women. The frequency of depression risk was 49.70%; insomnia risk, 60.61%; and PTSD-R, 13.33%. Depression risk (OR = 41.43, 95% CI 5.54 – 311.63), insomnia risk (OR = 5.25, 95% CI 1.77 – 18.71), low income (OR = 3.46, 95% CI 1.38 – 8.67) and being married or free union (OR = 2.65, 95% CI 1.13 – 6.22) were associated with PTSD-R. In conclusion, two out of every fifteen COVID-19 survivors are in PTSD-R. Depression and insomnia risk are strongly associated with PTSD-R in COVID-19 survivors.
Fusion of Imaging and Non-Imaging Data for Disease Trajectory Prediction for COVID-19 Patients
Authors: Tariq Amara , Tang Siyi , Sakhi Hifza , Celi Leo Anthony , Newsome Janice M. , Rubin Daniel L. , Trivedi Hari , Gichoya Judy Wawira , Banerjee Imon 
Published: December 2021

ABSTRCATPurposeThis study investigates whether graph-based fusion of imaging data with non-imaging EHR data can improve the prediction of disease trajectory for COVID-19 patients, beyond the prediction performance of only imaging or non-imaging EHR data.Materials and MethodsWe present a novel graph-based framework for fine-grained clinical outcome prediction (discharge, ICU admission, or death) that fuses imaging and non-imaging information using a similarity-based graph structure. Node features are represented by image embedding and edges are encoded with clinical or demographic similarity.ResultsOur experiments on data collected from Emory Healthcare network indicate that our fusion modeling scheme performs consistently better than predictive models using only imaging or non-imaging features, with f1-scores of 0.73, 0.77, and 0.66 for discharge from hospital, mortality, and ICU admission, respectively. External validation was performed on data collected from Mayo Clinic. Our scheme highlights known biases in the model prediction such as bias against patients with alcohol abuse history and bias based on insurance status.ConclusionThe study signifies the importance of fusion of multiple data modalities for accurate prediction of clinical trajectory. Proposed graph structure can model relationships between patients based on non-imaging EHR data and graph convolutional networks can fuse this relationship information with imaging data to effectively predict future disease trajectory more effectively than models employing only imaging or non-imaging data. Forecasting clinical events can enable intelligent resource allocation in hospitals. Our graph-based fusion modeling frameworks can be easily extended to other prediction tasks to efficiently combine imaging data with non-imaging clinical data.