bluuebunny/crossref_metadata_2025_split huggingface dataset:
Restoration of Neck Extension after Severe Oncological Surgery of the Posterior Cervical Trunk
Authors: Lasso Jose M. 
Published: May 2021

Summary
Reconstruction of posterior cervical trunk defects secondary to tumor resection carries significant morbidity when vertebral hardware or the spinal cord is exposed, and neck extension is interrupted. Complete reconstruction includes the coverage and obliteration of dead spaces, but functional outcomes are necessary to prevent the head dropping. This report documents the first known technique, using a free latissimus dorsi neurovascular flap to provide neck extension after ablative oncological surgery affecting trapezium and paraspinal muscles of the neck. The author explains the method by using a branch of the accessory nerve as the donor nerve. While keeping the head in a neutral position, the tendinous part of the neurovascular flap was firmly attached to the occipital periosteum and to the cephalad remnants of the trapezius, splenius capitis, and semispinalis muscles. At the caudal portion of the defect, cardinal notches were used to set the muscle at rest, avoiding excessive fiber tension. The maximal length of the muscle at rest was measured before flap elevation, calculated via the anatomical 3D print model. The thoracodorsal nerve was stimulated until the muscle shortened its length to 50%. Head extension was tested several times via neurostimulation and electromyographic control. The described procedure may provide neck extension and circumvent the problem with donor nerve providing synergy to the desired function.
Survival and Outcomes After Cardiac Arrest With VA-ECMO Rescue Therapy
Authors: Schurr James W. , Noubani Mohammad , Santore Lee Ann , Rabenstein Andrew P. , Dhundale Kathleen , Fitzgerald Jillian , Cahill Jan , Bilfinger Thomas V. , Seifert Frank C. , McLarty Allison J. 
Published: May 2021

ABSTRACT

Background:
Extracorporeal membrane oxygenation (ECMO) use in patients with cardiac arrest is increasing. Utilization remains variable between centers using ECMO as a rescue therapy or early protocolized extracorporeal cardiopulmonary resuscitation.

Methods:
Single-center, retrospective evaluation of cardiac arrest with cardiopulmonary resuscitation and rescue ECMO support from 2011 through 2019. Study objectives included survival, non-neurologic, and neurologic outcomes; validation of the SAVE and modified SAVE (mSAVE) scores for survival and favorable neurologic outcome; and predictive factor identification in cardiac arrest with ECMO rescue therapy.

Results:
Eighty-nine patients were included. In-hospital survival was 38.2% and median CPC score was 2. Survivors had lower BMI (27.9 ± 4.2 kg/m2 vs. 32.3 ± 7.5 kg/m2, P = 0.003), less obesity (BMI ≥ 30 kg/m2) (26.5% vs. 49.1%, P = 0.035), shorter CPR duration (35.5 ± 31.7 m vs. 58.0 ± 49.5 m, P = 0.019), more tracheostomy (38.2% vs. 7.3%, P < 0.001), and less renal replacement therapy (RRT) (17.6% vs. 38.2%, P = 0.031). Patients with a favorable neurologic outcome had lower body weight (86.2 ± 17.9 kg vs. 98.1 ± 19.4 kg, P = 0.010), lower BMI (28.1 ± 4.5 kg/m2 vs. 33.9 ± 7.9 kg/m2, P < 0.001), and less obesity (29.7% vs. 56.3%, P = 0.026). mSAVE score predicted in-hospital survival (OR 1.11; 95%CI 1.03–1.19; P = 0.004) and favorable neurologic outcome (OR 1.11; 1.03–1.20; P = 0.009). Multivariate analysis for in-hospital survival included mSAVE, BMI, CPR-time, tracheostomy, and RRT (c-statistic: 0.864). Favorable neurologic outcome included mSAVE and BMI (c-statistic: 0.805).

Conclusions:
mSAVE, BMI, RRT, and tracheostomy are predictors of in-hospital survival and mSAVE and BMI are predictors of favorable neurologic outcome in cardiac arrest with ECMO rescue therapy.
Clinical coding of long COVID in English primary care: a federated analysis of 58 million patient records in situ using OpenSAFELY
Authors:   The OpenSAFELY Collaborative, Walker Alex J , MacKenna Brian , Inglesby Peter , Rentsch Christopher T , Curtis Helen J , Morton Caroline E , Morley Jessica , Mehrkar Amir , Bacon Seb , Hickman George , Bates Chris , Croker Richard , Evans David , Ward Tom , Cockburn Jonathan , Davy Simon , Bhaskaran Krishnan , Schultze Anna , Williamson Elizabeth J , Hulme William J , McDonald Helen I , Tomlinson Laurie , Mathur Rohini , Eggo Rosalind M , Wing Kevin , Wong Angel YS , Forbes Harriet , Tazare John , Parry John , Hester Frank , Harper Sam , O’Hanlon Shaun , Eavis Alex , Jarvis Richard , Avramov Dima , Griffiths Paul , Fowles Aaron , Parkes Nasreen , Douglas Ian J , Evans Stephen JW , Smeeth Liam , Goldacre Ben 
Published: May 2021

AbstractBackgroundLong COVID is a term to describe new or persistent symptoms at least four weeks after onset of acute COVID-19. Clinical codes to describe this phenomenon were released in November 2020 in the UK, but it is not known how these codes have been used in practice.MethodsWorking on behalf of NHS England, we used OpenSAFELY data encompassing 96% of the English population. We measured the proportion of people with a recorded code for long COVID, overall and by demographic factors, electronic health record software system, and week. We also measured variation in recording amongst practices.ResultsLong COVID was recorded for 23,273 people. Coding was unevenly distributed amongst practices, with 26.7% of practices having not used the codes at all. Regional variation was high, ranging between 20.3 per 100,000 people for East of England (95% confidence interval 19.3-21.4) and 55.6 in London (95% CI 54.1-57.1). The rate was higher amongst women (52.1, 95% CI 51.3-52.9) compared to men (28.1, 95% CI 27.5-28.7), and higher amongst practices using EMIS software (53.7, 95% CI 52.9-54.4) compared to TPP software (20.9, 95% CI 20.3-21.4).ConclusionsLong COVID coding in primary care is low compared with early reports of long COVID prevalence. This may reflect under-coding, sub-optimal communication of clinical terms, under-diagnosis, a true low prevalence of long COVID diagnosed by clinicians, or a combination of factors. We recommend increased awareness of diagnostic codes, to facilitate research and planning of services; and surveys of clinicians’ experiences, to complement ongoing patient surveys.
SARS-CoV-2 RNA and antibody detection in human milk from a prospective multicenter study in Spain
Authors: Bäuerl Christine , Randazzo Walter , Sánchez Gloria , Selma-Royo Marta , Garcia-Verdevio Elia , Martínez-Rodríguez Laura , Parra-Llorca Anna , Lerin Carles , Fumadó Victoria , Crovetto Francesca , Crispi Fátima , Pérez-Cano Francisco J , Rodríguez Gerardo , Ruíz-Redondo Gema , Campoy Cristina , Martínez-Costa Cecilia , Collado Maria Carmen 
Published: May 2021

AbstractBackgroundDuring the COVID-19 pandemic in 2020, breastfeeding in women positive for SARS-CoV-2 was compromised due to contradictory data regarding potential viral transmission. However, growing evidence confirms the relevant role of breast milk in providing passive immunity by generating and transmitting specific antibodies against the virus. Thus, our study aimed to develop and validate a specific protocol to detect SARS-CoV-2 in breast milk matrix as well as to determine the impact of maternal SARS-CoV-2 infection on presence, concentration, and persistence of specific SARS-CoV-2 antibodies.Study design/MethodsA prospective multicenter longitudinal study in Spain was carried out from April to December 2020. A total of 60 mothers with SARS-CoV-2 infection and/or recovered from COVID-19 were included (n=52 PCR-diagnosed and n=8 seropositive). Data from maternal-infant clinical records and symptomatology were collected. A specific protocol was validated to detect SARS-CoV-2 RNA in breast milk, targeting the N1 region of the nucleocapsid gene and the envelope (E) gene. Presence and levels of SARS-CoV-2 specific immunoglobulins (Igs) -IgA, IgG, and IgM-in breast milk samples from COVID-19 patients and from 13 women before the pandemic were also evaluated.ResultsAll breast milk samples showed negative results for SARS-CoV-2 RNA presence. We observed high intra- and inter-individual variability in the antibody response to the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein for each of the three isotypes IgA, IgM and IgG. Protease domain (MPro) antibodies were also detected in milk. In general, 82.9 % of the milk samples were positive for at least one of the three antibody isotypes, being 52.86 % of those positive for all three Igs. Positivity rate for IgA was relatively stable over time (65.2 – 87.5 %), whereas it raised continuously for IgG (47.8 % the first ten days to 87.5 % from day 41 up to day 206 post-PCR confirmation).ConclusionsConsidering the lack of evidence for SARS-CoV-2 transmission through breast milk, our study confirms the safety of breastfeeding practices and highlights the relevance of virus-specific SARS-CoV-2 antibody transfer, that would provide passive immunity to breastfed infants and protect them against COVID-19 disease. This study provides crucial data to support official breastfeeding recommendations based on scientific evidence.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence: Navigating the absence of a gold standard
Authors: Saeed Sahar , O’Brien Sheila F. , Abe Kento , Yi QiLong , Rathod Bhavisha , Wang Jenny , Fazel-Zarandi Mahya , Tuite Ashleigh , Fisman David , Wood Heidi , Colwill Karen , Gingras Anne-Claude , Drews Steven 
Published: May 2021

ABSTRACTBackgroundMultiple anti-SARS-CoV-2 immunoassays are available, but no gold standard exists. We assessed four assays using various methodological approaches to estimate SARS-COV-2 seroprevalence during the first COVID-19 wave in Canada.MethodsThis serial cross-sectional study was conducted using plasma samples from healthy blood donors between April-September 2020. Qualitative assessment of SARS-CoV-2 IgG antibodies was based on four assays: Abbott Architect SARS-Cov-2 IgG assay (target nucleocapsid) (Abbott-NP) and three in-house IgG ELISA assays (target spike glycoprotein (Spike), spike receptor binding domain (RBD), and nucleocapsid (NP)). Seroprevalence was estimated using multiple composite reference standards (CRS) and by a series of Bayesian Latent Class Models (BLCM) (using uninformative, weakly, and informative priors).Results8999 blood samples were tested. The Abbott-NP assay consistently estimated seroprevalence to be lower than the ELISA-based assays. Discordance between assays was common, 13 unique diagnostic phenotypes were observed. Only 32 samples (0.4%) were positive by all four assays. BLCM using uninformative priors predicted seroprevalence increased from 0.7% (95% credible interval (CrI); 0.4, 1.0%) in April/May to 0.8% (95% CrI 0.5, 1.2%) in June/July to 1.1% (95% CrI 0.7, 1.6) in August/September. Results from CRS were very similar to the BLCM. Assay characteristics varied considerably over time. Overall spike had the highest sensitivity (89.1% (95% CrI 79.2, 96.9%), while the sensitivity of the Abbott-NP assay waned from 65.3% (95% CrI 43.6, 85.0%) in April/May to 45.9% (95% CrI 27.8, 65.6) by August/September.DiscussionWe found low SARS-CoV-2 seroprevalence rates at the end of the first wave and estimates derived from single assays may be biased.SummaryMultiple anti-SARS-CoV-2 immunoassays are available, but no gold standard exists. We used four unique assays to estimate very low SARS-COV-2 seroprevalence during the first COVID-19 wave in Canada. Caution should be exercised when interpretating seroprevalence estimates from single assays.
Predicting pulmonary function from the analysis of voice: a machine learning approach
Authors: Alam Md. Zahangir , Simonetti Albino , Billantino Rafaelle , Tayler Nick , Grainge Chris , Siribaddana Pandula , Nouraei S. A. Reza , Batchelor James , Rahman M. Sohel , Mancuzo Eliane V. , Holloway John W , Holloway Judith A. , Rezwan Faisal I 
Published: May 2021

AbstractProviding proper timely treatment of asthma, self-monitoring can play a vital role in disease control. Existing methods (such as peak flow meter, smart spirometer) requires special equipment and are not always used by the patient. Using voice recording as surrogate measures of lung function can be used to assess asthma, which has good potential to self-monitor asthma and could be integrated into telehealth platforms. This study aims to apply machine learning approach to predict lung functions from recorded voice for asthma patients.A threshold-based mechanism was designed to separate speech and breathing from recordings (323 recordings from 26 participants) and features extracted from these were combined with biological attributes and lung function (percentage predicted forced expiratory volume in 1 second, FEV1%). Three predictive models were developed: (a) regression models to predict lung function, (b) multi-class classification models to predict the severity, and (c) binary classification models to predict abnormality. Random Forest (RF), Support Vector Machine (SVM), and Linear Regression (LR) algorithms were implemented to develop these predictive models. Training and test samples were separated (70%:30% using balanced portioning). Features were normalised and 10-fold cross-validation used to measure the model’s training performances on the training samples. Models were then run on the test samples to measure the final performances.The RF based regression model performed better with lowest root mean square error = 10.86, and mean absolute score = 11.47, as compared to other models. In predicting the severity of lung function, the SVM based model performed better with 73.20% accuracy. The RF based model performed better in binary classification models for predicting abnormality of lung function (accuracy = 0.85, F1-score = 0.84, and area under the receiver operating characteristic curve = 0.88).The proposed machine learning approach can predict lung function (in terms of FEV1%), from the recorded voice files, better than other published approaches. These models can be extended to predict both the severity and abnormality of lung function with reasonable accuracies. This technique could be used to develop future telehealth solutions including smartphone-based applications which have potential to aid decision making and self-monitoring in asthma.
Analysis of whole exome sequencing in severe mental illness hints at selection of brain development and immune related genes
Authors: Mahadevan Jayant , Pathak Ajai Kumar , Vemula Alekhya , Nadella Ravi Kumar , Viswanath Biju , Purushottam Meera , Jain Sanjeev , Mondal Mayukh 
Published: May 2021

AbstractEvolutionary trends may underlie some aspects of the risk for common, non-communicable disorders, including psychiatric disease. We analyzed whole exome sequencing data from 80 unique individuals from India coming from families with two or more individuals with severe mental illness. We used Population Branch Statistics (PBS) to identify variants and genes under positive selection and identified 75 genes as candidates for positive selection. Of these, 20 were previously associated with Schizophrenia, Alzheimer’s disease and cognitive abilities in genome wide association studies. We then checked whether any of these 75 genes were involved in common biological pathways or related to specific cellular or molecular functions. We found that immune related pathways and functions related to innate immunity such as antigen binding were over-represented. We also evaluated for the presence of Neanderthal introgressed segments in these genes and found Neanderthal introgression in a single gene out of the 75 candidate genes. However, the introgression pattern indicates the region is unlikely to be the source for selection. Our findings hint at how selection pressures in individuals from families with a history of severe mental illness may diverge from the general population. Further, it also provides insights into the genetic architecture of severe mental illness, such as schizophrenia and its link to immune factors.
Establishment of an evaluation panel for the decentralized technical evaluation of the sensitivity of 31 rapid detection tests for SARS-CoV-2 diagnostics
Authors: Puyskens Andreas , Krause Eva , Michel Janine , Nübling Micha , Scheiblauer Heinrich , Bourquain Daniel , Grossegesse Marica , Valusenko Roman , Corman Viktor , Drosten Christian , Zwirglmaier Katrin , Wölfel Roman , Lange Constanze , Kramer Jan , Friesen Johannes , Ignatius Ralf , Müller Michael , Schmidt-Chanasit Jonas , Emmerich Petra , Schaade Lars , Nitsche Andreas 
Published: May 2021

AbstractBackgroundThe detection of SARS-CoV-2 with rapid diagnostic tests has become an important tool to identify infected people and break infection chains. These rapid diagnostic tests are usually based on antigen detection in a lateral flow approach.Aims & MethodsWhile for PCR diagnostics the validation of a PCR assay is well established, for antigen tests e.g. rapid diagnostic tests there is no common validation strategy. Here we present the establishment of a panel of 50 pooled clinical specimens that cover a SARS-CoV-2 concentration range from approximately 1.1 × 109to 420 genome copies per mL of specimen. The panel was used to evaluate 31 rapid diagnostic tests in up to 6 laboratories.ResultsOur results show that there is significant variation in the detection limits and the clinical sensitivity of different rapid diagnostic tests. We conclude that the best rapid diagnostic tests can be applied to reliably identify infectious individuals who are presenting with SARS-CoV-2 loads correlated to 106genome copies per mL of specimen. Infected individuals displaying SARS-CoV-2 genome loads corresponding to less than 106genome copies per mL will be identified by only some rapid diagnostics tests, while many tests miss these viral loads to a large extent.ConclusionsSensitive RDTs can be applied to identify infectious individuals with high viral loads, but not to identify infected individuals.
Generalizable predictive modeling of semantic processing ability from functional brain connectivity
Authors: Meng Danting , Wang Suiping , Wong Patrick C.M. , Feng Gangyi 
Published: May 2021

AbstractSemantic processing (SP) is one of the critical abilities of humans for representing and manipulating meaningful and conceptual information. Neuroimaging studies of SP typically collapse data from many subjects, but both its neural organization and behavioral performance vary between individuals. It is not yet understood whether and how the individual variabilities in neural organizations contribute to the individual differences in SP behaviors. Here we aim to identify the neural signatures underlying SP variabilities by analyzing individual functional connectivity (FC) patterns based on a large-sample Human Connectome Project (HCP) dataset and rigorous predictive modeling. We used a two-stage predictive modeling approach to build an internally cross-validated model and to test the model’s generalizability with unseen data from different HCP sub-populations and task states as well as other out-of-sample datasets that are independent of the HCP. FC patterns within a putative semantic brain network were significantly predictive of individual SP scores summarized from five semantic tasks. This cross-validated predictive model can be used to predict unseen HCP data. The model generalizability was enhanced with FCs in language tasks than resting state and other task states and was better for females than males. The model constructed from the HCP dataset can be generalized to two independent cohorts that participated in different semantic tasks. FCs connecting to the Perisylvian language network show the most reliable contributions to predictive modeling and the out-of-sample generalization. These findings contribute to our understanding of the neural sources of individual differences in SP, which potentially lay the foundation for personalized education and improve intervention practice for patients with SP and language deficits.
Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise
Authors: Vo Huy D. , Forero Linda , Aguilera Luis , Munsky Brian 
Published: May 2021

AbstractDespite continued technological improvements, measurement errors will always reduce or distort the information that any real experiment can provide to quantify cellular dynamics. This problem becomes even more serious in the context of cell signaling studies that are specifically designed to quantify heterogeneity in single-cell gene regulation, where important RNA and protein copy numbers are themselves subject to the inherently random fluctuations of biochemical reactions. It is not clear how measurement noise should be managed in addition to other experiment design variables (e.g., sampling size, measurement times, or perturbation levels) to ensure that collected data will provide useful insights on signaling or gene expression mechanisms of interest. To address these fundamental single-cell analysis and experiment design challenges, we propose a computational framework that takes explicit consideration of measurement errors to analyze single-cell observations and Fisher Information Matrix-based criteria to decide between experiments. Using simulations and single-cell experiments for a reporter gene controlled by an HIV promoter construct, we demonstrate how our approach can analyze and redesign experiments to optimally harvest fluctuation information while mitigating the effects of image distortion.
De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet
Authors: Winkler Sebastian , Winkler Ivana , Figaschewski Mirjam , Tiede Thorsten , Nordheim Alfred , Kohlbacher Oliver 
Published: May 2021

AbstractBackgroundWith a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem.ResultsWe present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet is freely available as open-source software.ConclusionThe proposed algorithmic framework and its available implementation can serve as a valuable heuristic hypothesis generation tool contextualizing omics data within biomolecular networks.