bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association of Toll-like receptor 7 variants with life-threatening COVID-19 disease in males
Authors: Fallerini Chiara , Daga Sergio , Mantovani Stefania , Benetti Elisa , Pujol Aurora , Picchiotti Nicola , Schluter Agatha , Planas-Serra Laura , Troya Jesús , Baldassarri Margherita , Fava Francesca , Ludovisi Serena , Castelli Francesco , Quiros-Roldan Maria Eugenia , Vaghi Massimo , Rusconi Stefano , Siano Matteo , Bandini Maria , Furini Simone , Mari Francesca ,   GEN-COVID Multicenter Study, Renieri Alessandra , Mondelli Mario U. , Frullanti Elisa 
Published: November 2020

ABSTRACTBackgroundCOVID-19 clinical presentation ranges from asymptomatic to fatal outcome. This variability is due in part to host genome specific mutations. Recently, two families in which COVID-19 segregates like an X-linked recessive monogenic disorder environmentally conditioned by SARS-CoV-2 have been reported leading to identification of loss-of-function variants in TLR7.ObjectiveWe sought to determine whether the two families represent the tip of the iceberg of a subset of COVID-19 male patients.MethodsWe compared male subjects with extreme phenotype selected from the Italian GEN-COVID cohort of 1178 SARS-CoV-2-infected subjects (<60y, 79 severe cases versus 77 control cases). We applied the LASSO Logistic Regression analysis, considering only rare variants on the young male subset, picking up TLR7 as the most important susceptibility gene.ResultsRare TLR7 missense variants were predicted to impact on protein function in severely affected males and in none of the asymptomatic subjects. We then investigated a similar white European cohort in Spain, confirming the impact of TRL7 variants. A gene expression profile analysis in peripheral blood mononuclear cells after stimulation with TLR7 agonist demonstrated a reduction of mRNA level of TLR7, IRF7, ISG15, IFN-□ and IFN-γ in COVID-19 patients compared with unaffected controls demonstrating an impairment in type I and II INF responses.ConclusionYoung males with TLR7 loss-of-function mutations and severe COVID-19 in the two reported families represent only a fraction of a broader and complex host genome situation. Specifically, missense mutations in the X-linked recessive TLR7 disorder may significantly contribute to disease susceptibility in up to 4% of severe COVID-19.Clinical ImplicationIn this new yet complex scenario, our observations provide the basis for a personalized interferon-based therapy in patients with rare TLR7 variants.CAPSULE SUMMARYOur results in large cohorts from Italy and Spain showed that X-linked recessive TLR7 disorder may represent the cause of disease susceptibility to COVID-19 in up to 4% of severely affected young male cases.
Validation and implementation of a direct RT-qPCR method for rapid screening of SARS-CoV-2 infection by using non-invasive saliva samples
Authors: Brotons Pedro , Perez-Argüello Amaresh , Launes Cristian , Torrents Francesc , Saucedo Jesica , Claverol Joana , Garcia-Garcia Juan Jose , Rodas Gil , Fumado Vicky , Jordan Iolanda , Gratacos Eduard , Bassat Quique , Muñoz-Almagro Carmen 
Published: November 2020

AbstractBackgroundThere is an urgent need to curb COVID-19 pandemic through early identification of asymptomatic but infectious cases. We aimed to validate and implement an optimised screening method for detection of SARS-CoV-2 RNA combining use of self-collected raw saliva samples, single-step heat-treated virus inactivation and RNA extraction, and direct RT-qPCR.Methods and findingsThe study was conducted in Sant Joan de Deu University Hospital (Barcelona, Spain), including: i) analytical validation against standard RT-qPCR in saliva samples; ii) diagnostic validation against standard RT-qPCR using paired saliva-nasopharyngeal samples obtained from asymptomatic teenagers and young and older adults in a youth sports academy; and iii) high throughput pilot screening of asymptomatic health workers and other staff in the study site.The proposed method had comparable analytical performance to standard RT-qPCR in saliva. Diagnostic validation included saliva samples self-collected with supervision by 173 participants during 9-12 weeks and nasopharyngeal samples collected from them. At baseline, all participants (100.0%) were negative for SARS-CoV-2 in both paired saliva-nasopharyngeal samples. In the following weeks, standard RT-qPCR yielded 23 positive results in nasopharyngeal samples whereas paired saliva specimens yielded 22 (95.7%) positive and one inconclusive result.A total of 2,709 participants engaged in the pilot screening, with high rate of participation (83.4% among health workers). Only 17 (0.6%) of saliva samples self-collected by participants in an unsupervised manner were invalid. Saliva was positive in 24 (0.9%) out of 2,692 valid specimens and inconclusive in 27 (1.0%). All 24 saliva-positive participants and 4 with saliva inconclusive results were positive by standard RT-qPCR in nasopharyngeal samples. The pilot showed potential for rapid analytical workflow (up to 384 batched samples can be processed in <2 hours).ConclusionDirect RT-qPCR on self-collected raw saliva is a simple, rapid, and accurate method with potential to be scaled up for enhanced SARS-CoV-2 community-wide screening.
Tripterygium glycosides as a potential treatment for CAR-T induced cytokine release syndrome: implication of monocyte depletion
Authors: Xu Zuqiong , Tian Fang , Chen Biqing , Kong Xiangtu , Dai Xingbin , Cao Jiang , Jiang Pengjun , Tan Jianxin , Lu Lu , Wang Xiachang , Lv Qi , Kang Di , Xu Miao , Hu Yingying , Yang Aiping , Wang Qian , Yang Zhong-Fa , Sun Xuemei , Ma Leyuan , Hu Lihong , Zhu Xuejun 
Published: November 2020

SummaryBackgroundCytokine release syndrome (CRS) is a potentially life-threatening complication of chimeric antigen receptor T (CAR-T) cell therapy. Recent studies indicated critical roles of macrophages and monocytes in CAR-T induced CRS. Here, we report rapid dissipation of CAR-T induced CRS in two patients after receiving Tripterygium glycosides (TG). Effects of triptolide, the major active component of TG, on macrophages and monocytes were examined in animal models.MethodsTwo patients with CRS after CAR-T cell therapy (for hematological malignancy) received TG (50 mg, p.o.). Flow cytometry analysis and single cell RNA sequencing (scRNAseq) were conducted to examine the effects of TG on immune cells. Potential effects of triptolide were also examined ex vivo using patient-derived monocytes, as well as in mice.FindingsRapid alleviation of fever and cytokine storm was observed within 72 hours after TG treatment. Blood concentration of triptolide ranged from 21 to 154 ng/mL during treatment. Flow cytometry and scRNAseq showed selective depletion of monocytes with minimal impact on CAR-T cells in both patients. In ex vivo experiments with patient-derived monocytes, triptolide dramatically inhibited the synthesis of pro-inflammatory cytokines (e.g., IL-6, IL-10, and IP-10). Triptolide also rapidly and selectively depleted peritoneal concanavalin A activated macrophages and monocytes in mice.InterpretationTG could be a promising treatment for CAR-T induced CRS, as well as other diseases with similar mechanisms, e.g., hemophagocytic lymphohistiocytosis and COVID-19. Our preliminary findings require further verification with properly designed clinical trials.
Meningioma epigenetic grouping reveals biologic drivers and therapeutic vulnerabilities
Authors: Choudhury Abrar , Magill Stephen T. , Eaton Charlotte D. , Prager Briana C. , Chen William C. , Seo Kyounghee , Lucas Calixto-Hope G. , Villanueva-Meyer Javier E. , Lam Tai-Chung , Pu Jenny Kan-Suen , Li Lai-Fung , Leung Gilberto Ka-Kit , Vasudevan Harish N. , Liu S. John , Chan Jason W. , Qiu Zhixin , Zhang Michael Y. , Martin Michael V. , Susko Matthew S. , Braunstein Steve E. , Oberheim Bush Nancy Ann , Schulte Jessica , Butowski Nicholas , Sneed Penny K. , Berger Mitchel S. , Perry Arie , Phillips Joanna J. , Solomon David A. , Costello Joseph F. , McDermott Michael W. , Rich Jeremy N. , Raleigh David R. 
Published: November 2020

SUMMARYMeningiomas arising from the meningothelial central nervous system lining are the most common primary intracranial tumors, and a significant cause of neurologic morbidity and mortality1. There are no effective medical therapies for meningioma patients2,3, and new treatments have been encumbered by limited understanding of meningioma biology. DNA methylation profiling provides robust classification of central nervous system tumors4, and can elucidate targets for molecular therapy5. Here we use DNA methylation profiling on 565 meningiomas integrated with genetic, transcriptomic, biochemical, and single-cell approaches to show meningiomas are comprised of 3 epigenetic groups with distinct clinical outcomes and biological features informing new treatments for meningioma patients. Merlin-intact meningiomas (group A, 34%) have the best outcomes and are distinguished by a novel apoptotic tumor suppressor function ofNF2/Merlin. Immune-enriched meningiomas (group B, 38%) have intermediate outcomes and are distinguished by immune cell infiltration,HLAexpression, and lymphatic vessels. Hypermitotic meningiomas (group C, 28%) have the worst outcomes and are distinguished by convergent genetic mechanisms misactivating the cell cycle. Consistently, we find cell cycle inhibitors block meningioma growth in cell culture, organoids, xenografts, and patients. Our results establish a framework for understanding meningioma biology, and provide preclinical rationale for new therapies to treat meningioma patients.
Peptide microarray based analysis of antibody responses to SARS-CoV-2 identifies unique epitopes with potential for diagnostic test development
Authors: Holenya Pavlo , Lange Paul Joris , Reimer Ulf , Woltersdorf Wolfram , Panterodt Thomas , Glas Michael , Wasner Mark , Eckey Maren , Drosch Michael , Hollidt Jörg-Michael , Naumann Michael , Kern Florian , Wenschuh Holger , Lange Robert , Schnatbaum Karsten , Bier Frank F. 
Published: November 2020

SummaryHumoral immunity to the Severe Adult Respiratory Syndrome (SARS) Coronavirus (CoV)-2 is not fully understood yet but may be a crucial factor of immune protection. The possibility of antibody cross-reactivity between SARS-CoV-2 and other human coronaviruses (HCoVs) would have important implications for immune protection but also for the development of specific diagnostic ELISA tests. Using peptide microarrays, n=24 patient samples and n=12 control samples were screened for antibodies against the entire SARS-CoV-2 proteome as well as the Spike (S), Nucleocapsid (N), VME1 (V), R1ab, and Protein 3a (AP3A) of the HCoV strains SARS, MERS, OC43 and 229E. While widespread cross-reactivity was revealed across several immunodominant regions of S and N, IgG binding to several SARS-CoV-2-derived peptides provided statistically significant discrimination between COVID-19 patients and controls. Selected target peptides may serve as capture antigens for future, highly COVID-19-specific diagnostic antibody tests.
Evaluation of SARS-CoV2 antibody Rapid Diagnostic Test kits (RDTs) and Real Time-Polymerase Chain Reaction (Rt-PCR) for COVID-19 Diagnosis in Kaduna, Nigeria
Authors: Ige Oluwafemi , Buru Ayuba Sunday , Lamido Tanko Zainab , Mohammed Tahir , Dogara Livingstone , Patience Ijei Ifeoma , Halima Bello-Manga , Reward Audu , Samuel Ige , Grace Nmadu 
Published: November 2020

AbstractThe emergence of the RNA virus SARS-CoV2, the causative agent of COVID-19 and its declaration by the World Health Organization (WHO) as a pandemic has disrupted the delicate balance in health indices globally. Its attendant immune dysregulation and pathobiology is still evolving. Currently, real time PCR is the gold standard diagnostic test, however there are several invalidated antibody-based tests available for possible community screening. With ongoing community transmission in Nigeria, neither the true burden of COVID-19 nor the performance of these kits is presently known. This study therefore, compared the performance of the SARS CoV2 antibody test and the real time Polymerase Chain Reaction (Rt-PCR) in the diagnosis of COVID-19. For the purpose of this evaluation, we used the diagnostic test kit by Innovita® Biological Technology CO., LTD China, a total of 521 venous blood samples were collected from consenting patients for the SARS COVID-19 rapid diagnostic kit and Oral and Nasopharyngeal swabs were collected and analyzed using the real time Polymerase chain reaction technique for nucleic acid detection and quantification.
Cytokine ranking via mutual information algorithm correlates cytokine profiles with presenting disease severity in patients with COVID-19
Authors: Huntington Kelsey E. , Louie Anna D. , Lee Chun Geun , Elias Jack A. , Ross Eric A. , El-Deiry Wafik S. 
Published: November 2020

ABSTRACTAlthough the range of immune responses to COVID-19 infection is variable, cytokine storm is observed in many affected individuals. To further understand the disease pathogenesis and, consequently, to develop an additional tool for clinicians to evaluate patients for presumptive intervention we sought to compare plasma cytokine levels between a range of donor and patient samples grouped by a COVID-19 Severity Score (CSS) based on need for hospitalization and oxygen requirement. Here we utilize a mutual information algorithm that classifies the information gain for CSS prediction provided by cytokine expression levels and clinical variables. Using this methodology, we found that a small number of clinical and cytokine expression variables are predictive of presenting COVID-19 disease severity, raising questions about the mechanism by which COVID-19 creates severe illness. The variables that were the most predictive of CSS included clinical variables such as age and abnormal chest x-ray as well as cytokines such as macrophage colony-stimulating factor (M-CSF), interferon-inducible protein 10 (IP-10) and Interleukin-1 Receptor Antagonist (IL-1RA). Our results suggest that SARS-CoV-2 infection causes a plethora of changes in cytokine profiles and that particularly in severely ill patients, these changes are consistent with the presence of Macrophage Activation Syndrome and could furthermore be used as a biomarker to predict disease severity.
Toward Understanding COVID-19 Pneumonia: A Deep-learning-based Approach for Severity Analysis and Monitoring the Disease
Authors: Zandehshahvar Mohammadreza , van Assen Marly , Maleki Hossein , Kiarashi Yashar , De Cecco Carlo N. , Adibi Ali 
Published: November 2020

ABSTRACTWe report a new approach using artificial intelligence to study and classify the severity of COVID-19 using 1208 chest X-rays (CXRs) of 396 COVID-19 patients obtained through the course of disease at Emory Healthcare affiliated hospitals (Atlanta, GA, USA). Using a two-stage transfer learning technique to train a convolutional neural network (CNN), we show that the algorithm is able to classify four classes of disease severity (normal, mild, moderate, and severe) with average area under curve (AUC) of 0.93. In addition, we show that the outputs of different layers of the CNN under dominant filters provide valuable insight about the subtle patterns in the CXRs, which can improve the accuracy in the reading of CXRs by a radiologist. Finally, we show that our approach can be used for studying the disease progression in single patients and its influencing factors. The results suggest that our technique can form the foundation of a more concrete clinical model to predict the evolution of COVID-19 severity and the efficacy of different treatments for each patient through using CXRs and clinical data in early stages. This will be essential in dealing with the upcoming waves of COVID-19 and optimizing resource allocation and treatment.
The impact of patient body mass index on surgeon posture during simulated laparoscopy
Authors: Sers Ryan , Forrester Steph , Zecca Massimiliano , Ward Stephen , Moss Esther 
Published: November 2020

AbstractLaparoscopy is a cornerstone of modern surgical care. Despite clear advantages for the patients, it has been associated with inducing upper body musculoskeletal disorders amongst surgeons due to the propensity of non-neutral postures. Furthermore, there is a perception that patients with obesity exacerbate these factors. Therefore, novice, intermediate and expert surgeon upper body posture was objectively quantified using inertial measurement units and the LUBA ergonomic framework was used to assess the subsequent postural data during laparoscopic training on patient models that simulated BMI’s of 20, 30, 40 and 50 kg/m2. In all experience groups, the posture of the upper body significantly worsened during simulated surgery on the BMI 50 kg/m2 model as compared to on the baseline BMI model of 20 kg/m2. These findings suggest that performing laparoscopic surgery on patients with severe obesity increases the prevalence of non-neutral upper body posture and may further increase the risk of musculoskeletal disorders in surgeons.
Disentangling the roles of human mobility and deprivation on the transmission dynamics of COVID-19 using a spatially explicit simulation model
Authors: Banks C.J. , Colman E. , Doherty T. , Tearne O. , Arnold M. , Atkins K. , Balaz D. , Beaunée G. , Bessell P. , Enright J. , Kleczkowski A. , Rossi G. , Ruget A.-S. , Kao R.R. 
Published: November 2020

SummaryRestrictions on mobility are a key component of infectious disease controls, preventing the spread of infections to as yet unexposed areas, or to regions which have previously eliminated outbreaks. However, even under the most severe restrictions, some travel must inevitably continue, at the very minimum to retain essential services. For COVID-19, most countries imposed severe restrictions on travel at least as soon as it was clear that containment of local outbreaks would not be possible. Such restrictions are known to have had a substantial impact on the economy and other aspects of human health, and so quantifying the impact of such restrictions is an essential part of evaluating the necessity for future implementation of similar measures.In this analysis, we built a simulation model using National statistical data to record patterns of movements to work, and implement levels of mobility recorded in real time via mobile phone apps. This model was fitted to the pattern of deaths due to COVID-19 using approximate Bayesian inference. Our model is able to recapitulate mortality considering the number of deaths and datazones (DZs, which are areas containing approximately 500-1000 residents) with deaths, as measured across 32 individual council areas (CAs) in Scotland. Our model recreates a trajectory consistent with the observed data until 1st of July. According to the model, most transmission was occurring “locally” (i.e. in the model, 80% of transmission events occurred within spatially defined “communities” of approximately 100 individuals). We show that the net effect of the various restrictions put into place in March can be captured by a reduction in transmission down to 12% of its pre-lockdown rate effective 28th March. By comparing different approaches to reducing transmission, we show that, while the timing of COVID-19 restrictions influences the role of the transmission rate on the number of COVID-related deaths, early reduction in long distance movements does not reduce death rates significantly. As this movement of individuals from more infected areas to less infected areas has a minimal impact on transmission, this suggests that the fraction of population already immune in infected communities was not a significant factor in these early stages of the national epidemic even when local clustering of infection is taken into account.The best fit model also shows a considerable influence of the health index of deprivation (part of the “index of multiple deprivations”) on mortality. The most likely value has the CA with the highest level of health-related deprivation to have on average, a 2.45 times greater mortality rate due to COVID-19 compared to the CA with the lowest, showing the impact of health-related deprivation even in the early stages of the COVID-19 national epidemic.
Acronym Disambiguation in Clinical Notes from Electronic Health Records
Authors: Link Nicholas B. , Huang Selena , Cai Tianrun , He Zeling , Sun Jiehuan , Dahal Kumar , Costa Lauren , Cho Kelly , Liao Katherine , Cai Tianxi , Hong Chuan ,   in collaboration with the Million Veteran Program
Published: November 2020

ABSTRACTObjectiveThe use of electronic health records (EHR) systems has grown over the past decade, and with it, the need to extract information from unstructured clinical narratives. Clinical notes, however, frequently contain acronyms with several potential senses (meanings) and traditional natural language processing (NLP) techniques cannot differentiate between these senses. In this study we introduce an unsupervised method for acronym disambiguation, the task of classifying the correct sense of acronyms in the clinical EHR notes.MethodsWe developed an unsupervised ensemble machine learning (CASEml) algorithm to automatically classify acronyms by leveraging semantic embeddings, visit-level text and billing information. The algorithm was validated using note data from the Veterans Affairs hospital system to classify the meaning of three acronyms: RA, MS, and MI. We compared the performance of CASEml against another standard unsupervised method and a baseline metric selecting the most frequent acronym sense. We additionally evaluated the effects of RA disambiguation on NLP-driven phenotyping of rheumatoid arthritis.ResultsCASEml achieved accuracies of 0.947, 0.911, and 0.706 for RA, MS, and MI, respectively, higher than a standard baseline metric and (on average) higher than a state-of-the-art unsupervised method. As well, we demonstrated that applying CASEml to medical notes improves the AUC of a phenotype algorithm for rheumatoid arthritis.ConclusionCASEml is a novel method that accurately disambiguates acronyms in clinical notes and has advantages over commonly used supervised and unsupervised machine learning approaches. In addition, CASEml improves the performance of NLP tasks that rely on ambiguous acronyms, such as phenotyping.