bluuebunny/crossref_metadata_2025_split huggingface dataset:
Novel visualization of the spatiotemporal relationship between ictal spiking and LFP supports the involvement of mid-range excitatory circuits during human focal seizures
Authors: Lee Somin , Deshpande Sarita S. , Merricks Edward M. , Schlafly Emily , Goodman Robert , McKhann Guy M. , Eskandar Emad N. , Madsen Joseph R. , Cash Sydney S. , van Putten Michel J.A.M. , Schevon Catherine A. , van Drongelen Wim 
Published: May 2022

AbstractThe relationship between action potentials and the associated local field potential (LFP) in neural recordings is typically studied only in the temporal domain using the spike-triggered average (STA). In this study, we present a novel approach, termed the spatiotemporal spike-centered average (st-SCA), that allows for visualization of the spike-LFP relationship in both the temporal and spatial domains. In this method, a 3D spatiotemporal topography of spike-associated LFP is calculated from a 2D spatial average of the LFP centered around the time and location of individual spikes. We applied this method to 25 microelectrode array (MEA) recordings obtained from seven patients with pharmacoresistant focal epilepsy during ictal and interictal states. Five patients in this dataset had MEA implants in recruited cortex, and two had implants in unrecruited cortex. Of the five patients with arrays implanted in recruited territory, three showed STAs that resembled sine cardinal (sinc) functions, and two showed non-sinc functions. Using the st-SCA, we found that the patients who showed a sinc-function pattern in the temporal domain showed a donut-shaped ring of LFP activity in the spatial domain. This observation was corroborated by a theoretical model describing an ictal spike as measured by a macroelectrode. The model also revealed a special symmetry wherein temporal component of the st-SCA predicts the spatial component when they both approximate sinc-functions. Supporting this theoretical derivation, a radial cut of the donut-shaped st-SCA showed a spatial pattern consistent with a sinc-function. This spatial sinc-function had peaks separated by ∼2.5mm—a measurement that supports the role of mid-range excitatory connections during ictal activity. In sum these findings suggest that patients whose seizures engage mid-range connections may be identifiable by the spatiotemporal features of ictal spike-associated LFP activity.
Covid-19 vaccine effectiveness against general SARS-CoV-2 infection from the omicron variant: A retrospective cohort study
Authors: Rennert Lior , Ma Zichen , McMahan Christopher S. , Dean Delphine 
Published: May 2022

ABSTRACTObjectiveTo estimate the effectiveness of 2-dose and 3-dose mRNA vaccination (BNT162b2 and mRNA-1273) against general SARS-CoV-2 infection (asymptomatic or symptomatic) caused by the omicron variant.DesignPropensity-score matched retrospective Cohort Study.SettingLarge public university undergoing weekly Covid-19 testing in South Carolina, USA.ParticipantsPopulation consists of 24,145 university students and employees undergoing weekly Covid-19 testing between January 3rd and January 31st, 2022. The analytic sample was constructed via propensity score matching on vaccination status: Unvaccinated, completion of 2-dose mRNA series within previous 5 months, and receipt of mRNA booster dose within previous 5 months. The resulting analytic sample consists of 1,944 university students and 658 university employees.InterventionVaccination with a two dose or 3 dose regimen of the BNT162b2 or mRNA-1273 vaccine.ResultsBooster protection against any SARS-CoV-2 infection was 66.4% among employees (95% CI: 46.1-79.0%; P<.001) and 45.4% among students (95% CI: 30.0-57.4%; P<.001). Compared to the 2-dose mRNA series, estimated increase in protection from the booster dose was 40.8% among employees (P=.024) and 37.7% among students (P=.001). We did not have enough evidence to conclude a statistically significant protective effect of the 2-dose mRNA vaccination series, nor did we have enough evidence to conclude that protection waned in the 5-month period after receipt of the 2nd or 3rd mRNA dose. Furthermore, we did not find evidence that protection varied by manufacturer.ConclusionsCovid-19 mRNA booster doses offer moderate protection against any SARS-CoV-2 infection caused by the omicron variant and provide a substantial increase in protection relative to the 2-dose mRNA vaccination series.
Clinical and Virological Features of patients hospitalized with different types of COVID-19 vaccination in Mexico City
Authors: Hernández-Terán Alejandra , GarciaDiego Pamela , Villanueva-Reza Marco , Boukadida Celia , Taboada Blanca , Porras Eduardo , Ahumada-Topete Victor , Diaz Kathia Tapia , Matias-Florentino Margarita , Pérez-García Marissa , Ávila-Rios Santiago , Mejía-Nepomuceno Fidencio , Serna-Muñoz Ricardo , Juárez-Hernández Fortunato , Jiménez-Corona Maria Eugenia , Becerril-Vargas Eduardo , Barreto Omar , Jose Arturo Martínez-Orozco , Pérez-Padilla Rogelio , Arias Carlos F. , Vázquez-Pérez Joel Armando 
Published: May 2022

ABSTRACTCoronavirus disease 2019 (COVID-19) vaccines are very effective at protecting against severe disease and death. However, the impact of the vaccine used, viral variants, and host factors on disease severity remain poorly understood. Here we compared COVID-19 clinical presentations and outcomes in vaccinated and unvaccinated patients in Mexico City. From March to September 2021, clinical and demographic characteristics were obtained from 1,014 individuals with a documented SARS-CoV-2 infection, and viral variants were identified in a subset of 386 patients. We compared unvaccinated, partially vaccinated, and fully vaccinated patients, stratifying by age groups. We fitted multivariate statistical models to evaluate the impact of vaccination status, SARS-CoV-2 lineages, vaccine types, and clinical parameters. Most hospitalized patients were unvaccinated. In patients over 61 years old, mortality was significantly higher in unvaccinated compared to fully vaccinated individuals. In patients aged 31 to 60 years, vaccinated patients were more likely to be outpatients (46%) than unvaccinated individuals (6.1%). We found immune disease and age above 61 years old as risk factors. While fully vaccination was found as the most protective factor against in-hospital death. This study suggests that vaccination is essential to reduce mortality in a comorbid population such as that of Mexico.
Estimating Counterfactual Placebo HIV Incidence in HIV Prevention Trials Without Placebo Arms Based on Markers of HIV Exposure
Authors: Zhu Yifan , Gao Fei , Glidden David , Donnell Deborah , Janes Holly 
Published: May 2022

SummaryGiven recent advances in HIV prevention, future trials of many experimental interventions are likely to be “active-controlled” designs, whereby HIV negative individuals are randomized to the experimental intervention or an active control known to be effective based on a historical trial. The efficacy of the experimental intervention to prevent HIV infection relative to placebo cannot be evaluated directly based on the trial data alone. One approach that has been proposed is to leverage an HIV exposure marker, such as incident rectal gonorrhea which is highly correlated with HIV infection in populations of men who have sex with men (MSM). Assuming we can fit a model associating HIV incidence and incidence of the exposure marker, based on data from multiple historical studies, incidence of the marker in the active-controlled trial population can be used to infer the HIV incidence that would have been observed had a placebo arm been included, i.e. a “counterfactual placebo”, and to evaluate efficacy of the experimental intervention relative to this counterfactual placebo. We formalize this approach and articulate the underlying assumptions, develop an estimation approach and evaluate its performance in finite samples, and discuss the implications of our findings for future development and application of the approach in HIV prevention. Improved HIV exposure markers and careful assessment of assumptions and study of their violation are needed before the approach is applied in practice.
ArcticAI: A Deep Learning Platform for Rapid and Accurate Histological Assessment of Intraoperative Tumor Margins
Authors: Levy Joshua , Davis Matthew , Chacko Rachael , Davis Michael , Fu Lucy , Goel Tarushii , Pamal Akash , Nafi Irfan , Angirekula Abhinav , Christensen Brock , Hayden Matthew , Vaickus Louis , LeBoeuf Matthew 
Published: May 2022

AbstractSuccessful treatment of solid cancers relies on complete surgical excision of the tumor either for definitive treatment or before adjuvant therapy. Radial sectioning of the resected tumor and surrounding tissue is the most common form of intra-operative and post-operative margin assessment. However, this technique samples only a tiny fraction of the available tissue and therefore may result in incomplete excision of the tumor, increasing the risk of recurrence and distant metastasis and decreasing survival. Repeat procedures, chemotherapy, and other resulting treatments pose significant morbidity, mortality, and fiscal costs for our healthcare system. Mohs Micrographic Surgery (MMS) is used for the removal of basal cell and squamous cell carcinoma utilizing frozen sections for real-time margin assessment while assessing 100% of the peripheral and deep margins, resulting in a recurrence rate of less than one percent. Real-time assessment in many tumor types is constrained by tissue size and complexity and the time to process tissue and evaluate slides while a patient is under general anesthesia. In this study, we developed an artificial intelligence (AI) platform, ArcticAI, which augments the surgical workflow to improve efficiency by reducing rate-limiting steps in tissue preprocessing and histological assessment through automated mapping and orientation of tumor to the surgical specimen. Using basal cell carcinoma (BCC) as a model system, the results demonstrate that ArcticAI can provide effective grossing recommendations, accurately identify tumor on histological sections, map tumor back onto the surgical resection map, and automate pathology report generation resulting in seamless communication between the surgical pathology laboratory and surgeon. AI-augmented-surgical excision workflows may make real-time margin assessment for the excision of more complex and challenging tumor types more accessible, leading to more streamlined and accurate tumor removal while increasing healthcare delivery efficiency.
Informative Missingness: What can we learn from patterns in missing laboratory data in the electronic health record?
Authors: Tan Amelia L.M. , Getzen Emily J. , Hutch Meghan R. , Strasser Zachary H. , Gutiérrez-Sacristán Alba , Le Trang T. , Dagliati Arianna , Morris Michele , Hanauer David A. , Moal Bertrand , Bonzel Clara-Lea , Yuan William , Chiudinelli Lorenzo , Das Priam , Zhang Harrison G. , Aronow Bruce J , Avilllach Paul , Brat Gabriel. A. , Cai Tianxi , Hong Chuan , Cava William G. La , Will Loh He Hooi , Luo Yuan , Murphy Shawn N. , Hgiam Kee Yuan , Omenn Gilbert S. , Patel Lav P. , Samayamuthu Malarkodi Jebathilagam , Shriver Emily R. , Hossein Abad Zahra Shakeri , Tan Byorn W.L. , Visweswaran Shyam , Wang Xuan , Weber Griffin M , Xia Zongqi , Verdy Bertrand ,   The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) (Collaborative Group/Consortium), Long Qi , Mowery Danielle L , Holmes John H. 
Published: May 2022

AbstractBackgroundIn electronic health records, patterns of missing laboratory test results could capture patients’ course of disease as well as reflect clinician’s concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to characterize the patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients.MethodsWe collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern.ResultsWith these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors.ConclusionThis work elucidates how missing data patterns in EHRs can be leveraged to identify quality control issues and relationships between laboratory measurements. Missing data patterns will allow sites to attain better quality data for subsequent analyses and help researchers identify which sites are better poised to study particular questions. Our results could also provide insight into some of the biological relationships between labs in EHR data for COVID-19 patients.
Cluster Analysis of Allergic Poly-Sensitizations in Urban Adults with Asthma
Authors: Patchett Brian J. , Nriagu Bede N. , Mavraj Granit , Patel Ruchi R. , MacLellan Christopher , Thakur Tushar , Schulman Edward S. 
Published: May 2022

AbstractIntroductionWhile reliable, quantitativein vitrotesting for sensitivity to aeroallergens has been available for decades, if and how asthma severity markers might be predictably expressed in clusters matched for comparable multiple sensitizations is unknown.ObjectiveOur aim is to use machine learning techniques to explore how allergic poly-sensitization (APS) clusters may serve as precision markers in adult urban patients with moderate to severe asthma.MethodsWe constructed a database of sensitizations to the 25 aeroallergens in the Zone 1 Northeastern US ImmunoCAP® assay. We used the Scikit-Learn® machine learning library to perform model-based clustering to identify APS clusters. Clusters were compared for differences in common clinical markers of asthma.ResultsThe database consisted of 509 patients. Unbiased mixture modeling identified ten clusters of increasing APS of varying size (n = 1 to 339) characterized by significant increases in mean serum immunoglobulin E (p<.001), peripheral blood eosinophil count (p<.001), and DLCO(p=.02). There was a significant decline in mean age at presentation (p<.001), FEV1/FVC (p=.01), and FEF25-75(p=.002), but not FEV1(p=.29), nor RV/TLC (p=.14) with increasing APS by simple linear regression. Finally, we identified two divergent paths for the poly-atopic march, one driven by perennial and the other by seasonal allergens.ConclusionWe conducted a pilot study for a novel machine learning understanding and approach to the classification of APS and potential influences if included in asthma cluster analyses. The methods used here can be easily applied to other geographic regions with different allergens
Mining for Equitable Health: Assessing the Impact of Missing Data in Electronic Health Records
Authors: Getzen Emily , Ungar Lyle , Mowery Danielle , Jiang Xiaoqian , Long Qi 
Published: May 2022

AbstractElectronic health records (EHRs) are collected as a routine part of healthcare delivery, and have great potential to be utilized to improve patient health outcomes. They contain multiple years of health information to be leveraged for risk prediction, disease detection, and treatment evaluation. However, they do not have standardized formatting, and can present significant analytical challenges– they contain multi-scale data from heterogeneous domains and include both structured and unstructured data. Data for individual patients are collected at irregular time intervals and with varying frequencies. In addition to the analytical challenges, EHRs can reflect inequity– patients belonging to different groups will have differing amounts of data in their health records. Many of these issues can contribute to biased data collection. The consequence is that the data for marginalized groups may be less informative due to more fragmented care, which can be viewed as a type of missing data problem. For EHRs data in this complex form, there is currently no framework for introducing missing values. There has also been little to no work in assessing the impact of missing data in EHRs. In this work, we simulate realistic missing data scenarios in EHRs to adequately assess their impact on predictive modeling. We incorporate the use of a medical knowledge graph to capture dependencies between medical events to create a more realistic missing data framework. In an intensive care unit setting, we found that missing data have greater negative impact on the performance of disease prediction models in groups that tend to have less access to healthcare, or seek less healthcare. We also found that the impact of missing data on disease prediction models is stronger when using the knowledge graph framework to introduce realistic missing values as opposed to random event removal.
Manual sub-segmentation of the cererbellum
Authors: Faber Jennifer , Heinz Lea-Sophie , Timmann Dagmar , Ernst Thomas M. , Deike-Hofmann Katerina , Klockgether Thomas 
Published: May 2022

1Background1.1AbstractThe protocol was developed in the context of generating manual sub-segmentations of the cerebellum to use as ground truth for training, validation and testing of a neural network. In this manuscript we provide methodological considerations and results of our manual sub-segmentation as well as a detailed description for manual sub-segmentation of the cerebellum into 10 hemispheric lobules and 5 vermal sub-segments as well as two cerebellar white matter labels based on a T1 weighted (T1w) MRI to foster reproducibility.
Comparative study between first and second wave of COVID-19 deaths in India - a single center study
Authors: Tendulkar Prakash , Pandey Pragya , Panda Prasan Kumar , Bhadoria Ajeet Singh , Kulshreshtha Poorvi , Mishra Mayank , Saxena Gaurika 
Published: May 2022

AbstractBackgroundThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is continuously evolving and many mutant variants of the virus are circulating in the world. Recurrent waves of COVID-19 have caused enormous mortality all over the world. It is of utmost importance for a health expert to understand the demographic and clinical attributes between the first and second waves of COVID-19 induced deaths.MethodThis was a hospital record based comparative study of baseline demographic, clinical and laboratory parameters of the first and second wave of COVID-19 in a tertiary care hospital in Uttarakhand, India. The study included all deceased patients admitted to the hospital during the first and second wave of COVID-19, i.e., between March 2020 to January 2021 and between March 2021 to June 2021, respectively.ResultThe study showed that there were more casualties in the second wave compared to the first, 475 (19.8%) and 424 (24.1%) respectively. There was no significant difference in terms of age. A male preponderance of mortality was evident in both the waves. The median duration of hospital stay was 5 (3-10) days in the second wave, which is significantly different from the corresponding duration in first wave (p<o.ooo). The most common clinical manifestation among the deceased were dyspnoea in both the waves, followed by fever and cough, the difference was statistically significant for cough (p< 0.000) The most prevalent comorbidity was diabetes mellitus (DM), followed by hypertension (HTN), with significant difference for HTN (p<0.003). The most frequently deranged lab parameter was lymphopenia with a significant difference across both the waves (p<0.000).ConclusionIn both the first and second COVID-19 waves, older males (>45 years) with comorbidities like HTN and DM were most susceptible for COVID-19 related mortality. The study also demonstrated that most of the baseline demographic and clinical characteristics which are attributed to the mortality were more common during the second wave of COVID-19.
Comparison of pandemic excess mortality in 2020-2021 across different empirical calculations
Authors: Levitt Michael , Zonta Francesco , Ioannidis John P.A. 
Published: May 2022

ABSTRACTDifferent modeling approaches can be used to calculate excess deaths for the COVID-19 pandemic period. We compared 6 calculations of excess deaths (4 previously published and two new ones that we performed with and without age-adjustment) for 2020-2021. With each approach, we calculated excess deaths metrics and the ratio R of excess deaths over recorded COVID-19 deaths. The main analysis focused on 33 high-income countries with weekly deaths in the Human Mortality Database (HMD at mortality.org) and reliable death registration. Secondary analyses compared calculations for other countries, whenever available. Across the 33 high-income countries, excess deaths were 2.0-2.8 million without age-adjustment, and 1.6-2.1 million with age-adjustment with large differences across countries. In our analyses after age-adjustment, 8 of 33 countries had no overall excess deaths; there was a death deficit in children; and 0.478 million (29.7%) of the excess deaths were in people <65 years old. In countries like France, Germany, Italy, and Spain excess death estimates differed 2 to 4-fold between highest and lowest figures. The R values’ range exceeded 0.3 in all 33 countries. In 16 of 33 countries, the range of R exceeded 1. In 25 of 33 countries some calculations suggest R>1 (excess deaths exceeding COVID-19 deaths) while others suggest R<1 (excess deaths smaller than COVID-19 deaths). Inferred data from 4 evaluations for 42 countries and from 3 evaluations for another 98 countries are very tenuous Estimates of excess deaths are analysis-dependent and age-adjustment is important to consider. Excess deaths may be lower than previously calculated.SIGNIFICANCE STATEMENTExcess deaths are a key metric for assessing the impact of a pandemic. They reflect the composite impact of deaths from infection, from indirect pandemic effects, and from the measures taken. Different modeling approaches can be used to calculate excess deaths for the COVID-19 pandemic. Here, we compare four previous calculations of excess deaths and two new ones that we performed with and without adjusting for changing age structure in the estimation. Proper age-adjustment results in substantial reduction in estimates of excess deaths for 2020-2021. While results from different calculation methods are correlated, the absolute differences in estimated excess deaths are very high in most countries. Extrapolations to countries without reliable death registration is extremely tenuous.