bluuebunny/crossref_metadata_2025_split huggingface dataset:
Virtual Exam for Parkinson’s Disease Enables Frequent and Reliable Remote Measurements of Motor Function
Authors: Burq Maximilien , Rainaldi Erin , Ho King Chung , Chen Chen , Bloem Bastiaan R. , Evers Luc J.W. , Helmich Rick C. , Myers Lance , Marks William , Kapur Ritu 
Published: December 2021

AbstractSensor-based remote monitoring could help us better track Parkinson’s disease (PD) progression, and measure patients’ response to putative disease-modifying therapeutic interventions. To be useful, the remotely-collected measurements should be valid, reliable and sensitive to change, and people with PD must engage with the technology.We developed a smartwatch-based active assessment that enables unsupervised measurement of motor signs of PD. 388 study participants with early-stage PD (Personalized Parkinson Project, 64% men, average age 63 years) wore a smartwatch for a median of 390 days, allowing for continuous passive monitoring. Participants performed unsupervised motor tasks both in the clinic (once) and remotely (twice weekly for one year). Dropout rate was 2% at the end of follow-up. Median wear-time was 21.1 hours/day, and 59% of per-protocol remote assessments were completed.In-clinic performance of the virtual exam verified that most participants correctly followed watch-based instructions. Analytical validation was established for in-clinic measurements, which showed moderate-to-strong correlations with consensus MDS-UPDRS Part III ratings for rest tremor (ρ=0.70), bradykinesia (ρ=-0.62), and gait (ρ=-0.46). Test-retest reliability of remote measurements, aggregated monthly, was good-to-excellent (ICC: 0.75 - 0.96). Remote measurements were sensitive to the known effects of dopaminergic medication (on vs off Cohen’s d: 0.19 - 0.54). Of note, in-clinic assessments often did not reflect the patients’ typical status at home.This demonstrates the feasibility of using smartwatch-based unsupervised active tests, and establishes the analytical validity of associated digital measurements. Weekly measurements can create a more complete picture of patient functioning by providing a real-life distribution of disease severity, as it fluctuates over time. Sensitivity to medication-induced change, together with the improvement in test-retest reliability from temporal aggregation implies that these methods could help reduce sample sizes needed to demonstrate a response to therapeutic intervention or disease progression.
Infrared spectroscopy enables rapid, robust, portable COVID-19 saliva screening based on pathophysiological response to SARS-CoV-2
Authors: Kazmer Seth T. , Hartel Gunter , Robinson Harley , Richards Renee S. , Yan Kexin , Van Hal Sebastiaan J. , Chan Raymond , Hind Andrew , Bradley David , Zieschang Fabian , Rawle Daniel J. , Le Thuy T. , Reid David W. , Suhrbier Andreas , Hill Michelle M 
Published: December 2021

AbstractFourier-transform infrared (FTIR) spectroscopy provides a (bio)chemical snapshot of the sample, and was recently proposed for COVID-19 saliva screening in proof-of-concept cohort studies. As a step towards translation of this technology, we conducted controlled validation experiments in multiple biological systems. SARS-CoV-2 or UV-inactivated SARS-CoV-2 were used to infect Vero E6 cells in vitro, and K18-hACE2 mice in vivo. Potentially infectious culture supernatant or mouse oral lavage samples were treated with ethanol or Trizol to 75% (v/v) for attenuated total reflectance (ATR)-FTIR spectroscopy, or RT-PCR, respectively. The control condition, UV-inactivated SARS-CoV-2 elicited strong biochemical changes in culture supernatant/oral lavage despite lack of replication determined by RT-PCR or cell culture infectious dose 50%. Crucially, we show that active SARS-CoV-2 infection induced additional FTIR signals over the UV-inactivated SARS-CoV-2 infection, which correspond to innate immune response, aggregated proteins, and RNA. For human patient cohort prediction, we achieved high sensitivity of 93.48% on leave-on-out cross validation (n=104 participants) for predicting COVID-19 positivity using a partial least squares discriminant analysis model, in agreement with recent studies. However, COVID-19 patients negative on follow-up (RT-PCR on day of saliva sampling) were poorly predicted in this model. Importantly, COVID-19 vaccination did not lead to mis-classification of COVID-19 negatives. Meta-analysis revealed SARS-CoV-2 induced increase in Amide II band in all arms of this study and recent studies, indicative of altered β-sheet structures in secreted proteins. In conclusion, ATR-FTIR is a robust, simple, portable method for COVID-19 saliva screening based on detection of pathophysiological responses to SARS-CoV-2.
Diversity in antibody titer at an average of 87 days after the second dose of BNT162b2 mRNA coronavirus disease 2019 vaccine: data from a predominantly elderly population in a practice in Tokyo
Authors: Fuse-Nagase Yasuko , Nagase Mitsuo 
Published: December 2021

AbstractIt is very important for the elderly, who tend to have serious COVID-19 infection and high mortality rates, to maintain sufficient immunity. We reviewed the medical charts of predominantly elderly population to obtain the data on serum anti-SARS-CoV-2S antibody titer after complete vaccination with the BNT162b2 mRNA vaccine (two doses) and evaluated the background factors associated with the titer. We enrolled 230 participants (101 men and 129 women). Their average age was 71.9 (SD 12.5) years, and median was 72 years. The anti-SARS-CoV-2S antibody titer varied from 0.55 U/mL to 4920 U/mL. We found that the value of the titer varied widely. The value of the titer was negatively associated with age, alcohol consumption, time elapsed from second vaccine dose, and use of immunosuppressive medication. The result that the titer was negatively associated with aging suggests that the timing of additional shot should be carefully determined especially among elderly population.
Rapid and Durable Protection Against Marburg Virus with a Single-Shot ChAd3-MARV GP Vaccine
Authors: Hunegnaw Ruth , Honko Anna , Wang Lingshu , Carr Derick , Murray Tamar , Shi Wei , Dulan Caitlyn N. M. , Foulds Kathryn E. , Agans Krystle N. , Cross Robert W. , Geisbert Joan B. , Geisbert Thomas W. , Cheng Cheng , Ploquin Aurélie , Stanley Daphne A. , Nabel Gary J. , Sullivan Nancy J. 
Published: December 2021

AbstractMarburg virus (MARV) causes a severe hemorrhagic fever disease in primates with mortality rates in humans up to 90%. Since 2018, MARV has been identified as a priority pathogen by the WHO, needing urgent research and development of countermeasures due to the high public health risk it poses. Recently, the first case of MARV in West Africa underscored the significant outbreak potential of this virus. The potential for cross border spread as had occurred during the Ebola 2014-2016 outbreak illustrates the critical need for Marburg vaccines. To support regulatory approval of the ChAd3-Marburg vaccine that has completed Phase I trials, we show that a non-replicating chimpanzee-derived adenovirus vector with a demonstrated safety profile in humans (ChAd3) protected against a uniformly lethal challenge with Marburg-Angola. Protective immunity was achieved within 7 days of vaccination and was maintained through one year post vaccination, antigen-specific antibodies were a significant immune correlate of protection in the acute challenge model (p=0.0003), and predictive for protection with an AUC = 0.88. These results demonstrate that a single-shot ChAd3 MARV vaccine generated a protective immune response that was both rapid and durable with a significant immune correlate of protection that will support advanced clinical development.One Sentence SummaryA single-shot of non-replicating ChAd3-MARV vaccine demonstrated both rapid (within 1 week) and durable (12 months) protection against lethal Marburg virus infection in macaques.
A Pattern Recognition Diagnostic Model to Restore and Emulate Knee Mobility
Authors: Hollinger David S. , Gulfam Muhammad , Packer Sadaira , Sukmanto Bayu 
Published: December 2021

AbstractElectromyography (EMG) is an electrical voltage potential linked to muscle contraction, resulting in human joint motion, such as knee flexion. Knee injuries, such as knee osteoarthritis (KOA), disrupt functional mobility of the knee joint and subsequently atrophy the muscles controlling knee movement during activities of daily living (ADL). Consequently, weakened muscles exhibiting deteriorated EMG signal fidelity are hypothesized to have discernible signal patterns from a healthy individual’s EMG signals. Pattern recognition algorithms are useful for mapping a set of complex inputs (EMG signals and knee angles) to classify knee health status (injured vs. healthy). A secondary outcome is to predict future knee angles from previous input signals to inform a robotic knee exoskeleton to apply real-time torque assistance to a patient during ADL. A Decision Tree Classifier, Random Forest, Naive Bayes, and a Feed Forward Neural Network (Fully Connected) were used for binary classification (healthy vs. injured). Partial Least Squares Regression, Decision Tree Regressor, and XGBoost were used to predict future joint angles for the regression task (knee angle prediction). Overall, the Random Forest Classifier had the best overall classification performance. XGBoost and Decision Tree Regression performed the best among regression algorithms for predicting real-time angles during walking while Partial Least Squares Regression performed the best during the standing tasks. In summary, our Machine Learning methods are useful for assisting clinicians and patients during physical rehabilitation by providing quantitative insight into the patient’s neuromuscular control of the knee.
Effectiveness of BNT162b2 and mRNA-1273 Second Doses and Boosters for SARS-CoV-2 infection and SARS-CoV-2 Related Hospitalizations: A Statewide Report from the Minnesota Electronic Health Record Consortium
Authors: Drawz Paul E , DeSilva Malini , Bodurtha Peter , Benitez Gabriela Vazquez , Murray Anne , Chamberlain Alanna M , Dudley R Adams , Waring Stephen , Kharbanda Anupam B , Murphy Daniel , Muscoplat Miriam Halstead , Melendez Victor , Margolis Karen L , McFarling Lynn , Lupu Roxana , Winkelman Tyler N.A. , Johnson Steve 
Published: December 2021

AbstractUsing vaccine data combined with electronic health records, we report that mRNA boosters provide greater protection than a two-dose regimen against SARS-CoV-2 infection and related hospitalizations. The benefit of a booster was more evident in the elderly and those with comorbidities. These results support the case for COVID-19 boosters.
Echinacea as a Potential Force against Coronavirus Infections? A Mini-Review of Randomized Controlled Trials in Adults and Children
Authors: Simon Nicolussi , Karin Ardjomand-Woelkart , Rainer Stange , Giuseppe Gancitano , Peter Klein , Mercedes Ogal 
Published: December 2021

AbstractEchinacea purpurea was shown to broadly inhibit coronaviruses and SARS-CoV-2 in vitro. This review discusses the available clinical evidence from randomized, blinded and controlled human studies. Two RCTs with results on enveloped viruses, respectively coronavirus infections during prevention treatment were detected. Incidence and/or viral loads were measured by RT-PCR and symptom severity was recorded. Jawad et al. (2012) collected nasopharyngeal swabs from adults (N=755) over 4 months of continuous prevention. Overall, 24 and 47 enveloped virus infections occurred, including 21 and 33 coronavirus detections [229E; HKU1; OC43] with Echinaforce® extract [2’400mg daily] and placebo, respectively (p=0.0114). Ogal et al. (2021) administered the same extract [1’200mg] or control for 4 months to children (4 – 12 years) (N=203). Echinacea reduced the incidence of enveloped virus infections from 47 to 29 (p=0.0038) whereas 11 and 13 coronavirus detections [229E, OC43, NL63] were counted (p>0.05). Respiratory symptoms during coronavirus infections were significantly lower with area-under-curve AUC=75.8 (+/-50.24) versus 27.1 (+/-21.27) score points (p=0.0036). Importantly, viral loads in nasal secretions were significantly reduced by 98.5%, with Ct-values 31.1 [95% CI 26.3; 35.9] versus 25.0 [95% CI 20.5; 29.5] (p = 0.0479). Results from clinical studies confirm the antiviral activity found for Echinacea in vitro, embracing enveloped respiratory pathogens and therefore coronaviruses as well. Substantiating results from a new completed study seems to extrapolate these effects to the prevention of SARS-CoV-2 infection. As hypothesized, the testified broad antiviral activity of Echinacea extract appears to be inclusive for SARS-CoV-2.
An aging focused unobtrusive and Privacy-Preserving Digital Behaviorome
Authors: Schütz Narayan , Knobel Samuel E.J. , Botros Angela , Single Michael , Pais Bruno , Santschi Valérie , Gatica-Perez Daniel , Buluschek Philipp , Urwyler Prabitha , Gerber Stephan M. , Müri Réne M. , Mosimann Urs , Saner Hugo , Nef Tobias 
Published: December 2021

AbstractDigital measures are increasingly used as objective health measures in remote-monitoring settings. In addition to their use in purely clinical research, such as in clinical trials, one promising application area for sensor-derived digital measures is in technology-assisted ageing and ageing-related research. In this context, digital measures may be used to measure the risk of certain adverse events such as falls, and also to provide novel research insights into ageing and ageing-related conditions, like cognitive impairment. While major emphasis has been placed on deriving one or more digital measures from wearable devices, a more holistic approach inspired by systems biology that leverages large, non-exhaustive sets of digital measures may prove highly beneficial. Such an approach would be useful if combined with modern big data approaches like machine learning. As such, extensive sets of digital measures, which may be referred to as digital behavioromes, could help characterise new phenotypes in deep phenotyping efforts. These measures could also assist in the discovery of novel digital biomarkers or in the creation of digital clinical outcome assessments. While clinical research into digital measures focuses primarily on measures derived from wearable devices, proven technology used for long-term remote monitoring of older adults is generally contactless, unobtrusive, and privacy-preserving. In this context, we introduce and describe a digital behaviorome: a large, non-exhaustive set of digital measures based entirely on contactless, unobtrusive, and privacy-preserving sensor technologies. We also demonstrate how such a behaviorome can be used to build digital clinical outcome assessments that are relevant to ageing and derived from machine learning. These outcomes included fall risk, frailty, mild cognitive impairment, and late-life depression. With the exception of late-life depression, all digital outcome assessments demonstrated a promising ability (ROC AUC≥0.7) to discriminate between positive and negative health outcomes, often in the range of comparable work with wearable devices. Finally, we highlight the possibility of using these digital behaviorome-based outcome assessments to discover novel potential digital biomarkers for each outcome. Here, we found reasonable contributors but also some potentially interesting new candidates regarding fall risk and mild cognitive impairment.
Population immunity to pre-Omicron and Omicron SARS-CoV-2 variants in US states and counties through December 1, 2021
Authors: Klaassen Fayette , Chitwood Melanie H. , Cohen Ted , Pitzer Virginia E. , Russi Marcus , Swartwood Nicole A. , Salomon Joshua A. , Menzies Nicolas A. 
Published: December 2021

AbstractPrior infection and vaccination both contribute to population-level SARS-CoV-2 immunity. We used a Bayesian model to synthesize evidence and estimate population immunity to prevalent SARS-CoV-2 variants in the United States over the course of the epidemic until December 1, 2021, and how this changed with the introduction of the Omicron variant. We used daily SARS-CoV-2 infection estimates and vaccination coverage data for each US state and county. We estimated relative rates of vaccination conditional on previous infection status using the Census Bureau’s Household Pulse Survey. We used published evidence on natural and vaccine-induced immunity, including waning and immune escape. The estimated percentage of the US population with a history of SARS-CoV-2 infection or vaccination as of December 1, 2021, was 88.2% (95%CrI: 83.6%-93.5%), compared to 24.9% (95%CrI: 18.5%-34.1%) on January 1, 2021. State-level estimates for December 1, 2021, ranged between 76.9% (95%CrI: 67.6%-87.6%, West Virginia) and 94.4% (95%CrI: 91.2%-97.3%, New Mexico). Accounting for waning and immune escape, the effective protection against the Omicron variant on December 1, 2021, was 21.8% (95%CrI: 20.7%-23.4%) nationally and ranged between 14.4% (95%CrI: 13.2%-15.8%, West Virginia), to 26.4% (95%CrI: 25.3%-27.8%, Colorado). Effective protection against severe disease from Omicron was 61.2% (95%CrI: 59.1%-64.0%) nationally and ranged between 53.0% (95%CrI: 47.3%-60.0%, Vermont) and 65.8% (95%CrI: 64.9%-66.7%, Colorado). While over three-quarters of the US population had prior immunological exposure to SARS-CoV-2 via vaccination or infection on December 1, 2021, only a fifth of the population was estimated to have effective protection to infection with the immune-evading Omicron variant.SignificanceBoth SARS-CoV-2 infection and COVID-19 vaccination contribute to population-level immunity against SARS-CoV-2. This study estimates the immunity and effective protection against future SARS-CoV-2 infection in each US state and county over 2020-2021. The estimated percentage of the US population with a history of SARS-CoV-2 infection or vaccination as of December 1, 2021, was 88.2% (95%CrI: 83.6%-93.5%). Accounting for waning and immune escape, protection against the Omicron variant was 21.8% (95%CrI: 20.7%-23.4%). Protection against infection with the Omicron variant ranged between 14.4% (95%CrI: 13.2%-15.8%%, West Virginia) and 26.4% (95%CrI: 25.3%-27.8%, Colorado) across US states. The introduction of the immune-evading Omicron variant resulted in an effective absolute increase of approximately 30 percentage points in the fraction of the population susceptible to infection.
Risk of myocarditis following sequential COVID-19 vaccinations by age and sex
Authors: Patone Martina , Mei Xue W , Handunnetthi Lahiru , Dixon Sharon , Zaccardi Francesco , Shankar-Hari Manu , Watkinson Peter , Khunti Kamlesh , Harnden Anthony , Coupland Carol AC , Channon Keith M. , Mills Nicholas L , Sheikh Aziz , Hippisley-Cox Julia 
Published: December 2021

ABSTRACTIn an updated self-controlled case series analysis of 42,200,614 people aged 13 years or more, we evaluate the association between COVID-19 vaccination and myocarditis, stratified by age and sex, including 10,978,507 people receiving a third vaccine dose. Myocarditis risk was increased during 1-28 days following a third dose of BNT162b2 (IRR 2.02, 95%CI 1.40, 2.91). Associations were strongest in males younger than 40 years for all vaccine types with an additional 3 (95%CI 1, 5) and 12 (95% CI 1,17) events per million estimated in the 1-28 days following a first dose of BNT162b2 and mRNA-1273, respectively; 14 (95%CI 8, 17), 12 (95%CI 1, 7) and 101 (95%CI 95, 104) additional events following a second dose of ChAdOx1, BNT162b2 and mRNA-1273, respectively; and 13 (95%CI 7, 15) additional events following a third dose of BNT162b2, compared with 7 (95%CI 2, 11) additional events following COVID-19 infection. An association between COVID-19 infection and myocarditis was observed in all ages for both sexes but was substantially higher in those older than 40 years. These findings have important implications for public health and vaccination policy.FundingHealth Data Research UK.
Sharp Increases in Drug Overdose Deaths Among High-School-Age Adolescents During the US COVID-19 Epidemic and Illicit Fentanyl Crisis
Authors: Friedman Joseph , Godvin Morgan , Shover Chelsea , Gone Joseph P. , Hansen Helena , Schriger David 
Published: December 2021

AbstractAlthough overdose deaths in the US have increased exponentially for the past four decades, these shifts have historically affected adults, while pediatric overdose rates remained stable. However, this may be changing, given that the illicit drug supply has become increasingly hazardous in recent years, as illicitly-manufactured-fentanyls (IMFs) and other synthetic opioid and benzodiazepine analogues are increasingly sold as heroin and counterfeit prescription pills. We calculated drug overdose deaths per 100,000 population by 5-year age groups for the 2010-2021 period. For high-school-aged adolescents (age 14-18), we stratified rates by race/ethnicity, census region, associated substance, and ICD-10 cause-of-death intent categories. Adolescent overdose mortality saw a sharp increase between 2019 and 2020, from 2.35 per 100,000 to 4.58 per 100,000, representing a 94.3% increase, the largest percent increase of any 5-year age group. American Indian or Alaska Native (AIAN) adolescents, Latinx adolescents, and adolescents in the West census region were disproportionately affected, overdose death rates 2.15, 1.31, and 1.68 times the national average in 2021, respectively. Trends were driven by fatalities involving IMFs, which nearly tripled from 2019 to 2020, and represented 76.6% of adolescent overdose deaths in 2021. Sharp increases in adolescent drug overdose deaths, despite flat or declining drug use rates, and no increase in deaths from alcohol or most drugs, reinforce that rising fatalities are likely driven by an increasingly toxic, IMF-contaminated drug supply. Rising racial disparities in overdose require a prevention approach that ameliorates deep-seated social and economic inequalities as well as poor access to mental and physical healthcare and social services for AIAN and Latinx adolescents. Our results should also be understood in the context of rising rates of adolescent mental illness during the COVID-19 pandemic. These findings highlight the urgent need for accurate, harm-reduction-oriented education for early adolescents about the risks of an evolving drug supply, as well as greater access to naloxone and services that check drugs for the presence of IMFs.
Overlooked Trustworthiness of Explainability in Medical AI
Authors: Zhang Jiajin , Chao Hanqing , Kalra Mannudeep K. , Wang Ge , Yan Pingkun 
Published: December 2021

AbstractWhile various methods have been proposed to explain AI models, the trustworthiness of the generated explanation received little examination. This paper reveals that such explanations could be vulnerable to subtle perturbations on the input and generate misleading results. On the public CheXpert dataset, we demonstrate that specially designed adversarial perturbations can easily tamper saliency maps towards the desired explanations while preserving the original model predictions. AI researchers, practitioners, and authoritative agencies in the medical domain should use caution when explaining AI models because such an explanation could be irrelevant, misleading, and even adversarially manipulated without changing the model output. AI researchers, practitioners, and authoritative agencies in the medical domain should use caution when explaining AI models because such an explanation could be irrelevant, misleading, and even adversarially manipulated without changing the model output.