bluuebunny/crossref_metadata_2025_split huggingface dataset:
Discriminatory cytokine profiles predict muscle function, fatigue and cognitive function in patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)
Authors: Gusnanto A , Earl KE , Sakellariou GK , Owens DJ , Lightfoot A , Fawcett S , Owen E , Staunton CA , Shu T , Croden FC , Fenech M , Sinclair M , Ratcliffe L , Whysall KA , Haynes R , Wells NM , Jackson MJ , Close GL , Lawton C , Beadsworth MBJ , Dye L , McArdle A 
Published: August 2020

AbstractMyalgic Encephalomyelitis (ME) /Chronic Fatigue Syndrome (CFS) is a severely debilitating and complex illness of uncertain aetiology, affecting the lives of millions and characterised by prolonged fatigue. The initiating factors and mechanisms leading to chronic debilitating muscle fatigue in ME/CFS are unknown and are complicated by the time required for diagnosis. Both mitochondrial dysfunction and inflammation have been proposed to be central to the pathogenesis of ME/CFS. This original and extensive study demonstrated that although there was little dysfunction evident in the muscle mitochondria of patients with ME/CFS, particular blood plasma and skeletal muscle cytokines, when adjusted for age, gender and cytokine interactions could predict both diagnosis and a number of measures common to patients with ME/CFS. These included MVC and perceived fatigue as well as cognitive indices such as pattern and verbal reaction times. We employed advanced multivariate analyses to cytokine profiles that leverages covariation and intrinsic redundancy to identify patterns of immune signaling that can be evaluated for their predictions of disease phenotype. The current study identified discriminatory cytokine profiles that can be sufficiently used to distinguish HCs from patients with ME/CFS and provides compelling evidence that a limited number of cytokines are associated with diagnosis and fatigue. Moreover, this study demonstrates significant potential of using multiplex cytokine profiles and bioinformatics as diagnostic tools for ME/CFS, potentiating the possibility of not only diagnosis, but also being able to individually personalise therapies.SignificanceMyalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex, chronic, debilitating and potentially life-changing medical condition affecting children and adults of all ages, races and socio-economic groupings. Clinical presentation includes fluctuating fatigue of varying severity, with other symptoms, including myalgia, arthralgia, post-exertional fatigue, unrefreshed sleep, headache, upper respiratory tract symptoms, and cognitive impairment. With no biomarkers, or diagnostic tests, aetiology, epidemiology and pathophysiology remain unclear. This extensive study employed advanced multivariate analyses that leveraged covariation and intrinsic redundancy and identified discriminatory cytokine profiles that can be used to distinguish Healthy Controls (HCs) from patients with ME/CFS and a limited number of cytokines were associated with physical and cognitive fatigue. These findings are relevant to the potential of increasing numbers of patients developing chronic fatigue following Coronavirus disease 2019.
Blood donor exposome and impact of common drugs on red blood cell metabolism
Authors: Nemkov Travis , Stefanoni Davide , Bordbar Aarash , Issaian Aaron , Palsson Bernhard O. , Dumont Larry J , Hay Ariel , Song Anren , Xia Yang , Redzic Jasmina S. , Eisenmesser Elan Z. , Zimring James C , Kleinman Steve , Hansen Kirk C. , Busch Michael P. , D’Alessandro Angelo ,   for the Recipient Epidemiology and Donor Evaluation Study-III (REDS-III) RBC-Omics Study
Published: August 2020

AbstractComputational models based on recent maps of the red blood cell proteome suggest that mature erythrocytes may harbor targets for common drugs. This prediction is relevant to red blood cell storage in the blood bank, in which the impact of small molecule drugs or other xenometabolites deriving from dietary, iatrogenic or environmental exposures (“exposome”) may alter erythrocyte energy and redox metabolism and, in so doing, affect red cell storage quality and post-transfusion efficacy. To test this prediction, here we provide a comprehensive characterization of the blood donor exposome, including the detection of common prescription and off-the-counter drugs in 250 units donated by healthy volunteers from the REDS-III RBC Omics study. Based on high-throughput drug screenings of 1,366 FDA-approved drugs, we report a significant impact of ∼65% of the tested drugs on erythrocyte metabolism. Machine learning models built using metabolites as predictors were able to accurately predict drugs for several drug classes/targets (bisphosphonates, anticholinergics, calcium channel blockers, adrenergics, proton-pump inhibitors, antimetabolites, selective serotonin reuptake inhibitors, and mTOR) suggesting that these drugs have a direct, conserved, and significant impact on erythrocyte metabolism. We then focused on ranitidine – a common antiacid – as a representative drug with the potential to improve human erythrocyte storage quality and post-transfusion performances in mice. By combining tracing experiments with 1,2,3-13C3-glucose, proteome integral solubility alteration assays, genetic ablation of S1P synthesis capacity, in silico docking and 1D NMR, we show that ranitidine triggers metabolic mechanisms involving sphingosine 1-phosphate (S1P)-dependent modulation of erythrocyte glycolysis and/or direct binding to hemoglobin.Graphical AbstractRBC exposome from the REDS III study revealed that blood from a subset of donors contains traces of the most common drugs in the United States. RBCs can uptake these drugs, in some cases can metabolize them to their bioactive metabolites and in others the drug can directly impact RBC metabolism during storage.Key pointsBlood donor exposomes include metabolites of environmental exposure, traces of common prescription or off-the-counter drugs;65% of 1366 FDA- approved drug significantly affect RBC metabolism. Ranitidine significantly impacts glycolysis and S1P metabolism.
Look before diving into pooling of SARS-CoV-2 samples on high throughput analyzers
Authors: LeBlanc Jason J. , Patriquin Glenn , Pettipas Janice , Warhuus Michelle , Sarty Darren , Jackson Colleen , Heinstein Charles , MacDonald James , Haldane David , Hatchette Todd F 
Published: August 2020

AbstractGiven the unprecedented demand for SARS-CoV-2 testing during the COVID-19 pandemic, the benefits of specimen pooling have recently been explored. As previous studies were limited to mathematical modeling or testing on low throughput PCR instruments, this study aimed to assess pooling on high throughput analyzers. To assess the impact of pooling, SARS-CoV-2 dilutions were performed at varying pool depths (i.e. 1:2, 1:4, and 1:8) into test-negative nasopharyngeal or oropharynx/anterior nares swabs matrix. Testing was evaluated on the automated Roche Cobas 6800 system, or the Roche MagNApure LC 2.0 or MagNAPure 96 instruments paired with a laboratory-developed test using a 96-well PCR format. The frequency of detection in specimens with low viral loads was evaluated using archived specimens collected throughout the first pandemic wave. The proportion of detectable results per pool depths was used to estimate the potential impact. In addition, workflow at the analytical stage, and pre-and post-stages of testing were also considered. The current study estimated that pool depths of 1:2, 1:4, and 1:8 would have allowed the detection of 98.3%, 96.0%, and 92.6% of positive SARS-CoV-2 results identified in the first wave of the pandemic in Nova Scotia. Overall, this study demonstrated that pooling on high throughput instrumentation can dramatically increase the overall testing capacity to meet increased demands, with little compromising to sensitivity at low pool depths. However, the human resources required at the pre-analytical stage of testing is a particular challenging to achieve.
Gmo labeling in food products in montevideo, uruguay
Authors: Arleo M. , Benavente P. , Cardozo V. , Katz A. , Vázquez S. , Da Silva Tavares A. , Legnani M. , Martínez Debat C. 
Published: August 2020

AbstractMontevideo establishes the mandatory labeling of foods containing genetically modified material through the Departmental Decree No. 36.554, positioning Uruguay within the 65 countries that have incorporated this type of regulation.The Food Regulation Service, in its role of sanitary police, and through its Laboratory of Bromatology, in agreement with the Food Molecular Traceability Laboratory (Faculty of Sciences, University of the Republic), carried out the analysis of 206 products made with ingredients derived from corn and/or soybean, during the 2015-2017 period, within the framework of compliance with the aforementioned Decree.The strategy used consisted of the application of molecular techniques (Real Time PCR), for the detection of common sequences present in the transgenic events of soybean and corn, and the subsequent quantification of the content of GM material, in relation to an established labeling threshold of 1%.As a result of this study, it was found that 36.9% of the analyzed foods presented sequences derived from genetically modified plant organisms (GMOs); and in 95% of the cases, its content exceeded the threshold established for its labeling.This study, constitutes the first approach to the knowledge of different transgenic elements distribution in food commercialized in Montevideo.These results provide valuable information to both the consumer, for decision making about the food to be consumed, and also for the official control organizations, which must enforce the regulations.This type of work has demonstrated, once again, the importance of the interrelation between academy and state agencies, in the generation of knowledge and in the implementation of new analytical methodologies, as well as in the training of qualified human resources and in the compliance with current regulations.
Exhaled SARS-CoV-2 quantified by face-mask sampling in hospitalised patients with covid-19
Authors: Williams Caroline M. , Pan Daniel , Decker Jonathan , Fletcher Eve , Wisniewska Anika , Sze Shirley , Assadi Sara , Haigh Richard , Abdulwhhab Mohamad , Bird Paul , Holmes Christopher W , Al-Taie Alaa , Saleem Baber , Pan Jingzhe , Garton Natalie J , Pareek Manish , Barer Michael R 
Published: August 2020

ABSTRACTBackgroundHuman to human transmission of SARS-CoV-2 is driven by the respiratory route but little is known about the pattern and quantity of virus output from exhaled breath. We have previously shown that face-mask sampling (FMS) can detect exhaled tubercle bacilli and have adapted its use to quantify exhaled SARS-CoV-2 RNA in patients admitted to hospital with covid-19.MethodsBetween May and December 2020, we took two concomitant FMS and nasopharyngeal samples (NPS) over two days, starting within 24 hours of a routine virus positive NPS in patients hospitalised with covid-19, at University Hospitals of Leicester NHS Trust, UK. Participants were asked to wear a modified duckbilled facemask for 30 minutes, followed by a nasopharyngeal swab. Demographic, clinical, and radiological data, as well as International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) mortality and deterioration scores were obtained.Exposed masks were processed by removal, dissolution and analysis of sampling matrix strips fixed within the mask by RT-qPCR. Viral genome copy numbers were determined and results classified as Negative; Low: ≤999 copies; Medium: 1,000-99,999 copies and High ≥ 100,000 copies per strip for FMS or per 100µl for NPS.Results102 FMS and NPS were collected from 66 routinely positive patients; median age: 61 (IQR 49 - 77), of which FMS was positive in 37% of individuals and concomitant NPS was positive in 50%. Positive FMS viral loads varied over five orders of magnitude (<10-3.3 × 106 genome copies/strip); 21 (32%) patients were asymptomatic at the time of sampling. High FMS viral load was associated with respiratory symptoms at time of sampling and shorter interval between sampling and symptom onset (FMS High: median (IQR) 2 days (2-3) vs FMS Negative: 7 days (7-10), p=0.002). On multivariable linear regression analysis, higher FMS viral loads were associated with higher ISARIC mortality (Medium FMS vs Negative FMS gave an adjusted coefficient of 15.7, 95% CI 3.7-27.7, p=0.01) and deterioration scores (High FMS vs Negative FMS gave an adjusted coefficient of 37.6, 95% CI 14.0 to 61.3, p=0.002), while NPS viral loads showed no significant association.ConclusionWe demonstrate a simple and effective method for detecting and quantifying exhaled SARS-CoV-2 in hospitalised patients with covid-19. Higher FMS viral loads were more likely to be associated with developing severe disease compared to NPS viral loads. Similar to NPS, FMS viral load was highest in early disease and in those with active respiratory symptoms, highlighting the potential role of FMS in understanding infectivity.
Breastmilk; a source of SARS-CoV-2 specific IgA antibodies
Authors: van Keulen Britt J. , Romijn Michelle , Bondt Albert , Dingess Kelly A. , Kontopodi Eva , van der Straten Karlijn , den Boer Maurits A. , Bosch Berend J. , Brouwer Philip J.M. , de Groot Christianne J.M. , Hoek Max , Li Wentao , Pajkrt Dasja , Sanders Rogier W. , Schoonderwoerd Anne , Tamara Sem , Timmermans Rian A.H. , Vidarsson Gestur , Stittelaar Koert J. , Rispens Theo T. , Hettinga Kasper A. , van Gils Marit J. , Heck Albert J.R. , van Goudoever Johannes B. 
Published: August 2020

AbstractBackgroundSince the outbreak of COVID-19, many put their hopes in the rapid development of effective immunizations. For now patient isolation, physical distancing and good hygiene are the sole measures for prevention. Processed breast milk with antibodies against SaRS-CoV-2 may serve as additional protection. We aimed to determine the presence and neutralization capacity of antibodies against SaRS-CoV-2 in breastmilk of mothers who have recovered from COVID-19.MethodsThis prospective case control study included lactating mothers, recovered from (suspected) COVID-19 and healthy controls. Serum and breastmilk was collected. To assess the presence of antibodies in breastmilk and serum, we used multiple complementary assays, namely ELISA with the SARS-CoV-2 spike protein, SARS-CoV-2 receptor binding domain (RBD) and with the SARS-CoV-2 nucleocapsid (N) protein for IgG and bridging ELISA with the SARS-CoV-2 RBD and N protein for total Ig. To assess the effect of pasteurization breastmilk was exposed to Holder Pasteurization and High Pressure Pasteurization.ResultsBreastmilk contained antibodies against SARS-CoV-2 using any of the assays in 24 out of 29 (83%) proven cases, in six out of nine (67%) suspected cases and in none of the 13 controls.In vitroneutralization of SARS-CoV-2 clinical isolate virus strain was successful in a subset of serum (13%) and milk samples (26%). Although after pasteurization of the milk SARS-CoV-2 antibodies were detected with both methods of pasteurization, virus neutralizing capacity of those antibodies was only retained with the HPP approach.ConclusionBreastmilk of mothers who recovered from COVID-19 contains significant amounts of IgA against SARS-CoV-2, both before and after pasteurization.Key PointsQuestionDoes breastmilk of mothers who have recovered from coronavirus disease 2019 (COVID-19) contain antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)?FindingsWe provide multiple lines of evidence on the presence of a variety of antibodies against SARS-CoV-2, with no such antibodies present in the controls. These antibodies are capable of neutralizing a clinical isolate of SARS-CoV-2in vitro. We furthermore show that high pressure pasteurization hardly affects antibody levels and efficacy.MeaningBreastmilk, obtained from mothers who have recovered from COVID-19, may serve as a safe and widely applicable preventive strategy for vulnerable high risk populations
Accurately Differentiating COVID-19, Other Viral Infection, and Healthy Individuals Using Multimodal Features via Late Fusion Learning
Authors: Xu Ming , Ouyang Liu , Gao Yan , Chen Yuanfang , Yu Tingting , Li Qian , Sun Kai , Bao Forrest Sheng , Safarnejad Lida , Wen Jing , Jiang Chao , Chen Tianyang , Han Lei , Zhang Hengdong , Gao Yue , Yu Zhengmin , Liu Xiaowen , Yan Tianyu , Li Hebi , Robinson Patrick , Zhu Baoli , Liu Jie , Liu Yang , Zhang Zengli , Ge Yaorong , Chen Shi 
Published: August 2020

AbstractEffectively identifying COVID-19 patients using non-PCR clinical data is critical for the optimal clinical outcomes. Currently, there is a lack of comprehensive understanding of various biomedical features and appropriate technical approaches to accurately detecting COVID-19 patients. In this study, we recruited 214 confirmed COVID-19 patients in non-severe (NS) and 148 in severe (S) clinical type, 198 non-infected healthy (H) participants and 129 non-COVID viral pneumonia (V) patients. The participants’ clinical information (23 features), lab testing results (10 features), and thoracic CT scans upon admission were acquired as three input feature modalities. To enable late fusion of multimodality data, we developed a deep learning model to extract a 10-feature high-level representation of the CT scans. Exploratory analyses showed substantial differences of all features among the four classes. Three machine learning models (k-nearest neighbor kNN, random forest RF, and support vector machine SVM) were developed based on the 43 features combined from all three modalities to differentiate four classes (NS, S, V, and H) at once. All three models had high accuracy to differentiate the overall four classes (95.4%-97.7%) and each individual class (90.6%-99.9%). Multimodal features provided substantial performance gain from using any single feature modality. Compared to existing binary classification benchmarks often focusing on single feature modality, this study provided a novel and effective breakthrough for clinical applications. Findings and the analytical workflow can be used as clinical decision support for current COVID-19 and other clinical applications with high-dimensional multimodal biomedical features.One sentence summaryWe trained and validated late fusion deep learning-machine learning models to predict non-severe COVID-19, severe COVID-19, non-COVID viral infection, and healthy classes from clinical, lab testing, and CT scan features extracted from convolutional neural network and achieved predictive accuracy of > 96% to differentiate all four classes at once based on a large dataset of 689 participants.
Intertumoral Genetic Heterogeneity Generates Distinct Tumor Microenvironments in a Novel Murine Synchronous Melanoma Model
Authors: Qin Shuyang S. , Han Booyeon J. , Williams Alyssa , Jackson Katherine M. , Jewell Rachel , Chacon Alexander C. , Lord Edith M. , Linehan David C. , Kim Minsoo , Reuben Alexandre , Gerber Scott A. , Prieto Peter A. 
Published: August 2020

AbstractSynchronous metastatic melanoma, clinically defined as multiple lesions diagnosed within 6 months, has a poor prognosis. Despite recent advances in systemic immunotherapy, a majority of patients fail to respond or exhibit lesion-specific responses. While intertumoral heterogeneity has been clinically associated with lesion-specific therapeutic responses, no clear mechanism has been identified, largely due to the scarcity of preclinical models. We developed a novel murine synchronous melanoma model that recapitulates clinical intertumoral heterogeneity. We show that genetic differences between tumors generate distinct tumor immune microenvironments (TIME). These TIMEs can independently upregulate PD-1/PD-L1 expression in response to ongoing anti-tumor immunity and the presence of interferon-gamma. The simultaneous presence of multiple tumors can additionally alter the TIME of each tumor. As such, our model provides a unique approach to investigate the effects of intertumoral heterogeneity on mechanisms of immunotherapy resistance.
Long-range sequential dependencies precede complex syntactic production in language acquisition
Authors: Sainburg Tim , Mai Anna , Gentner Timothy Q 
Published: August 2020

AbstractTo convey meaning, human language relies on hierarchically organized, long-range relationships spanning words, phrases, sentences, and discourse. The strength of the relationships between sequentially ordered elements of language (e.g., phonemes, characters, words) decays following a power law as a function of sequential distance. To understand the origins of these relationships, we examined long-range statistical structure in the speech of human children at multiple developmental time points, along with non-linguistic behaviors in humans and phylogenetically distant species. Here we show that adult-like power-law statistical dependencies precede the production of hierarchically-organized linguistic structures, and thus cannot be driven solely by these structures. Moreover, we show that similar long-range relationships occur in diverse non-linguistic behaviors across species. We propose that the hierarchical organization of human language evolved to exploit pre-existing long-range structure present in much larger classes of non-linguistic behavior, and that the cognitive capacity to model long-range hierarchical relationships preceded language evolution. We call this the Statistical Scaffolding Hypothesis for language evolution.1Significance StatementHuman language is uniquely characterized by semantically meaningful hierarchical organization, conveying information over long timescales. At the same time, many non-linguistic human and animal behaviors are also often characterized by richly hierarchical organization. Here, we compare the long-timescale statistical dependencies present in language to those present in non-linguistic human and animal behaviors as well as language production throughout childhood. We find adult-like, long-timescale relationships early in language development, before syntax or complex semantics emerge, and we find similar relationships in non-linguistic behaviors like cooking and even housefly movement. These parallels demonstrate that long-range statistical dependencies are not unique to language and suggest a possible evolutionary substrate for the long-range hierarchical structure present in human language.
Susceptibility of raccoon dogs for experimental SARS-CoV-2 infection
Authors: Freuling Conrad M. , Breithaupt Angele , Müller Thomas , Sehl Julia , Balkema-Buschmann Anne , Rissmann Melanie , Klein Antonia , Wylezich Claudia , Höper Dirk , Wernike Kerstin , Aebischer Andrea , Hoffmann Donata , Friedrichs Virginia , Dorhoi Anca , Groschup Martin H. , Beer Martin , Mettenleiter Thomas C. 
Published: August 2020

AbstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in China at the end of 2019, and became pandemic. The zoonotic virus most likely originated from bats, but definite intermediate hosts have not yet been identified. Raccoon dogs (Nyctereutes procyonoides) are kept for fur production, in particular in China, and were suspected as potential intermediate host for both SARS-CoV6 and SARS-CoV2. Here we demonstrate susceptibility of raccoon dogs for SARS-CoV-2 infection after intranasal inoculation and transmission to direct contact animals. Rapid, high level virus shedding, in combination with minor clinical signs and pathohistological changes, seroconversion and absence of viral adaptation highlight the role of raccoon dogs as a potential intermediate host. The results are highly relevant for control strategies and emphasize the risk that raccoon dogs may represent a potential SARS-CoV-2 reservoir. Our results support the establishment of adequate surveillance and risk mitigation strategies for kept and wild raccoon dogs.Article Summary LineRaccoon dogs are susceptible to and efficiently transmit SARS-CoV2 and may serve as intermediate host