bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rapid Serological Assays and SARS-CoV-2 Real-Time Polymerase Chain Reaction Assays for the Detection of SARS-CoV-2: Comparative Study
Authors: Paradiso Angelo Virgilio , De Summa Simona , Loconsole Daniela , Procacci Vito , Sallustio Anna , Centrone Francesca , Silvestris Nicola , Cafagna Vito , De Palma Giuseppe , Tufaro Antonio , Garrisi Vito Michele , Chironna Maria 
Published: September 2020

Background
Real-time polymerase chain reaction (RT-PCR) testing for the identification of viral nucleic acid is the current standard for the diagnosis of SARS-CoV-2 infection, but technical issues limit its utilization for large-scale screening. Serological immunoglobulin M (IgM)/IgG testing has been proposed as a useful tool for detecting SARS-CoV-2 exposure.

Objective
The objective of our study was to compare the results of the rapid serological VivaDiag test for SARS-CoV-2–related IgM/IgG detection with those of the standard RT-PCR laboratory test for identifying SARS-CoV-2 nucleic acid.

Methods
We simultaneously performed both serological and molecular tests with a consecutive series of 191 symptomatic patients. The results provided by a new rapid serological colorimetric test for analyzing IgM/IgG expression were compared with those of RT-PCR testing for SARS-CoV-2 detection.

Results
Of the 191 subjects, 70 (36.6%) tested positive for SARS-CoV-2 based on RT-PCR results, while 34 (17.3%) tested positive based on serological IgM/IgG expression. Additionally, 13 (6.8%) subjects tested positive based on serological test results, but also tested negative based on RT-PCR results. The rapid serological test had a sensitivity of 30% and a specificity of 89% compared to the standard RT-PCR assay. Interestingly, the performance of both assays improved 8 days after symptom appearance. After 10 days had passed since symptom appearance, the predictive value of the rapid serological test was higher than that of the standard molecular assay (proportion of positive results: 40% vs 20%). Multivariate analysis showed that age >58 years (P<.01) and period of >15 days after symptom onset (P<.02) were significant and independent factors associated with serological test positivity.

Conclusions
The rapid serological test analyzed in this study seems limited in terms of usefulness when diagnosing SARS-CoV-2 infection. However, it may be useful for providing relevant information on people’s immunoreaction to COVID-19 exposure.
Agreement Between Spatiotemporal Gait Parameters Measured by a Markerless Motion Capture System and Two Reference Systems—a Treadmill-Based Photoelectric Cell and High-Speed Video Analyses: Comparative Study
Authors: García-Pinillos Felipe , Jaén-Carrillo Diego , Soto Hermoso Victor , Latorre Román Pedro , Delgado Pedro , Martinez Cristian , Carton Antonio , Roche Seruendo Luis 
Published: September 2020

Background
Markerless systems to capture body motion require no markers to be attached to the body, thereby improving clinical feasibility and testing time. However, the lack of markers might affect the accuracy of measurements.

Objective
This study aimed to determine the absolute reliability and concurrent validity of the Kinect system with MotionMetrix software for spatiotemporal variables during running at a comfortable velocity, by comparing data between the combination system and two widely used systems—OptoGait and high-speed video analysis at 1000 Hz.

Methods
In total, 25 runners followed a running protocol on a treadmill at a speed of 12 km/h. The Kinect+MotionMetrix combination measured spatiotemporal parameters during running (ie, contact time, flight time, step frequency, and step length), which were compared to those obtained from two reference systems.

Results
Regardless of the system, flight time had the highest coefficients of variation (OptoGait: 16.4%; video analysis: 17.3%; Kinect+MotionMetrix: 23.2%). The rest of the coefficients of variation reported were lower than 8.1%. Correlation analysis showed very high correlations (r>0.8; P<.001) and almost perfect associations (intraclass correlation coefficient>0.81) between systems for all the spatiotemporal parameters except contact time, which had lower values. Bland-Altman plots revealed smaller systematic biases and random errors for step frequency and step length and larger systematic biases and random errors for temporal parameters with the Kinect+MotionMetrix system as compared to OptoGait (difference: contact time +3.0%, flight time −7.9%) and high-speed video analysis at 1000 Hz (difference: contact time +4.2%, flight time −11.3%). Accordingly, heteroscedasticity was found between systems for temporal parameters (r2>0.1).

Conclusions
The results indicate that the Kinect+MotionMetrix combination slightly overestimates contact time and strongly underestimates flight time as compared to the OptoGait system and high-speed video analysis at 1000 Hz. However, it is a valid tool for measuring step frequency and step length when compared to reference systems. Future studies should determine the reliability of this system for determining temporal parameters.
Using WeChat, a Chinese Social Media App, for Early Detection of the COVID-19 Outbreak in December 2019: Retrospective Study
Authors: Wang Wenjun , Wang Yikai , Zhang Xin , Jia Xiaoli , Li Yaping , Dang Shuangsuo 
Published: September 2020

Background
A novel coronavirus, SARS-CoV-2, was identified in December 2019, when the first cases were reported in Wuhan, China. The once-localized outbreak has since been declared a pandemic. As of April 24, 2020, there have been 2.7 million confirmed cases and nearly 200,000 deaths. Early warning systems using new technologies should be established to prevent or mitigate such events in the future.

Objective
This study aimed to explore the possibility of detecting the SARS-CoV-2 outbreak in 2019 using social media.

Methods
WeChat Index is a data service that shows how frequently a specific keyword appears in posts, subscriptions, and search over the last 90 days on WeChat, the most popular Chinese social media app. We plotted daily WeChat Index results for keywords related to SARS-CoV-2 from November 17, 2019, to February 14, 2020.

Results
WeChat Index hits for “Feidian” (which means severe acute respiratory syndrome in Chinese) stayed at low levels until 16 days ahead of the local authority’s outbreak announcement on December 31, 2019, when the index increased significantly. The WeChat Index values persisted at relatively high levels from December 15 to 29, 2019, and rose rapidly on December 30, 2019, the day before the announcement. The WeChat Index hits also spiked for the keywords “SARS,” “coronavirus,” “novel coronavirus,” “shortness of breath,” “dyspnea,” and “diarrhea,” but these terms were not as meaningful for the early detection of the outbreak as the term “Feidian”.

Conclusions
By using retrospective infoveillance data from the WeChat Index, the SARS-CoV-2 outbreak in December 2019 could have been detected about two weeks before the outbreak announcement. WeChat may offer a new approach for the early detection of disease outbreaks.
Mobile Health App for Prostate Cancer Patients on Androgen Deprivation Therapy: Qualitative Usability Study
Authors: Nabi Junaid , Cone Eugene B , Vasavada Anjali , Sun Maxine , Kilbridge Kerry L , Kibel Adam S , Berry Donna L , Trinh Quoc-Dien 
Published: September 2020

BackgroundAndrogen deprivation therapy (ADT) increases the risk of metabolic adverse effects among patients with prostate cancer. The transformative impact of mobile health (mHealth) apps may benefit men managing activity and nutrition at home.ObjectiveThis study aimed to evaluate the usability and patient experience of a newly developed mHealth app among prostate cancer patients on ADT and physicians’ beliefs about the potential benefits of using this app.MethodsThis study took place over 2 months, beginning in March 2019. A sample of 5 patients (age 45-75 years) initiating ADT participated in a semistructured focus group discussion with a facilitator. The study participants also included 5 specialist physicians who provided in-depth interviews. An institutional review board–approved script was used to guide both the focus group and physician interviews. Usability was tested through specific scenarios presented to the patients, including downloading the mHealth app, entering information on physical activity and meals, and navigating the app. The focus group and interviews were audio recorded and transcribed. Content analysis was used to analyze the transcripts iteratively and exhaustively. Thematic discrepancies between reviewers were resolved through consensus.ResultsThe mean age of the patients was 62 years. This group included 4 White and 1 Latin American patients. The physician specialists included 2 urologists, 2 medical oncologists, and 1 radiation oncologist. Analyses revealed that the patients appreciated the holistic care enabled by the app. Difficulties were observed with registration of the app among 60% (3/5) of the patients; however, all the patients were able to input information about their physical activity and navigate the options within the app. Most patients (4/5, 80%) were able to input data on their recent meal. Among the health care physicians, the dominant themes reflected in the interviews included undermining of patients ability to use technology, patients’ fear of technology, and concern for the ability of older patients to access technology.ConclusionsThe patients reported an overall positive experience of using an mHealth app to record and track diet and exercise. Usability was observed to be an important factor for adoption and was determined by ease of registration and use, intuitive appearance of the app, and focus on holistic cancer care. The physicians believed that the app was easy to use but raised concerns about usability among older men who may not typically use smartphone apps.
Building a Pharmacogenomics Knowledge Model Toward Precision Medicine: Case Study in Melanoma
Authors: Kang Hongyu , Li Jiao , Wu Meng , Shen Liu , Hou Li 
Published: September 2020

Background
Many drugs do not work the same way for everyone owing to distinctions in their genes. Pharmacogenomics (PGx) aims to understand how genetic variants influence drug efficacy and toxicity. It is often considered one of the most actionable areas of the personalized medicine paradigm. However, little prior work has included in-depth explorations and descriptions of drug usage, dosage adjustment, and so on.

Objective
We present a pharmacogenomics knowledge model to discover the hidden relationships between PGx entities such as drugs, genes, and diseases, especially details in precise medication.

Methods
PGx open data such as DrugBank and RxNorm were integrated in this study, as well as drug labels published by the US Food and Drug Administration. We annotated 190 drug labels manually for entities and relationships. Based on the annotation results, we trained 3 different natural language processing models to complete entity recognition. Finally, the pharmacogenomics knowledge model was described in detail.

Results
In entity recognition tasks, the Bidirectional Encoder Representations from Transformers–conditional random field model achieved better performance with micro-F1 score of 85.12%. The pharmacogenomics knowledge model in our study included 5 semantic types: drug, gene, disease, precise medication (population, daily dose, dose form, frequency, etc), and adverse reaction. Meanwhile, 26 semantic relationships were defined in detail. Taking melanoma caused by a BRAF gene mutation into consideration, the pharmacogenomics knowledge model covered 7 related drugs and 4846 triples were established in this case. All the corpora, relationship definitions, and triples were made publically available.

Conclusions
We highlighted the pharmacogenomics knowledge model as a scalable framework for clinicians and clinical pharmacists to adjust drug dosage according to patient-specific genetic variation, and for pharmaceutical researchers to develop new drugs. In the future, a series of other antitumor drugs and automatic relation extractions will be taken into consideration to further enhance our framework with more PGx linked data.
Identification of Risk Factors and Symptoms of COVID-19: Analysis of Biomedical Literature and Social Media Data
Authors: Jeon Jouhyun , Baruah Gaurav , Sarabadani Sarah , Palanica Adam 
Published: September 2020

Background
In December 2019, the COVID-19 outbreak started in China and rapidly spread around the world. Lack of a vaccine or optimized intervention raised the importance of characterizing risk factors and symptoms for the early identification and successful treatment of patients with COVID-19.

Objective
This study aims to investigate and analyze biomedical literature and public social media data to understand the association of risk factors and symptoms with the various outcomes observed in patients with COVID-19.

Methods
Through semantic analysis, we collected 45 retrospective cohort studies, which evaluated 303 clinical and demographic variables across 13 different outcomes of patients with COVID-19, and 84,140 Twitter posts from 1036 COVID-19–positive users. Machine learning tools to extract biomedical information were introduced to identify mentions of uncommon or novel symptoms in tweets. We then examined and compared two data sets to expand our landscape of risk factors and symptoms related to COVID-19.

Results
From the biomedical literature, approximately 90% of clinical and demographic variables showed inconsistent associations with COVID-19 outcomes. Consensus analysis identified 72 risk factors that were specifically associated with individual outcomes. From the social media data, 51 symptoms were characterized and analyzed. By comparing social media data with biomedical literature, we identified 25 novel symptoms that were specifically mentioned in tweets but have been not previously well characterized. Furthermore, there were certain combinations of symptoms that were frequently mentioned together in social media.

Conclusions
Identified outcome-specific risk factors, symptoms, and combinations of symptoms may serve as surrogate indicators to identify patients with COVID-19 and predict their clinical outcomes in order to provide appropriate treatments.
A Computer-Interpretable Guideline for COVID-19: Rapid Development and Dissemination
Authors: Nan Shan , Tang Tianhua , Feng Hongshuo , Wang Yijie , Li Mengyang , Lu Xudong , Duan Huilong 
Published: September 2020

Background
COVID-19 is a global pandemic that is affecting more than 200 countries worldwide. Efficient diagnosis and treatment are crucial to combat the disease. Computer-interpretable guidelines (CIGs) can aid the broad global adoption of evidence-based diagnosis and treatment knowledge. However, currently, no internationally shareable CIG exists.

Objective
The aim of this study was to establish a rapid CIG development and dissemination approach and apply it to develop a shareable CIG for COVID-19.

Methods
A 6-step rapid CIG development and dissemination approach was designed and applied. Processes, roles, and deliverable artifacts were specified in this approach to eliminate ambiguities during development of the CIG. The Guideline Definition Language (GDL) was used to capture the clinical rules. A CIG for COVID-19 was developed by translating, interpreting, annotating, extracting, and formalizing the Chinese COVID-19 diagnosis and treatment guideline. A prototype application was implemented to validate the CIG.

Results
We used 27 archetypes for the COVID-19 guideline. We developed 18 GDL rules to cover the diagnosis and treatment suggestion algorithms in the narrative guideline. The CIG was further translated to object data model and Drools rules to facilitate its use by people who do not employ the non-openEHR archetype. The prototype application validated the correctness of the CIG with a public data set. Both the GDL rules and Drools rules have been disseminated on GitHub.

Conclusions
Our rapid CIG development and dissemination approach accelerated the pace of COVID-19 CIG development. A validated COVID-19 CIG is now available to the public.
Increased Internet Searches for Insomnia as an Indicator of Global Mental Health During the COVID-19 Pandemic: Multinational Longitudinal Study
Authors: Lin Yu-Hsuan , Chiang Ting-Wei , Lin Yu-Lun 
Published: September 2020

Background
Real-time global mental health surveillance is urgently needed for tracking the long-term impact of the COVID-19 pandemic.

Objective
This study aimed to use Google Trends data to investigate the impact of the pandemic on global mental health by analyzing three keywords indicative of mental distress: “insomnia,” “depression,” and “suicide.”

Methods
We examined increases in search queries for 19 countries. Significant increases were defined as the actual daily search value (from March 20 to April 19, 2020) being higher than the 95% CIs of the forecast from the 3-month baseline via ARIMA (autoregressive integrated moving average) modeling. We examined the correlation between increases in COVID-19–related deaths and the number of days with significant increases in search volumes for insomnia, depression, and suicide across multiple nations.

Results
The countries with the greatest increases in searches for insomnia were Iran, Spain, the United States, and Italy; these countries exhibited a significant increase in insomnia searches on more than 10 of the 31 days observed. The number of COVID-19–related deaths was positively correlated to the number of days with an increase in searches for insomnia in the 19 countries (ρ=0.64, P=.003). By contrast, there was no significant correlation between the number of deaths and increases in searches for depression (ρ=–0.12, P=.63) or suicide (ρ=–0.07, P=.79).

Conclusions
Our analysis suggests that insomnia could be a part of routine mental health screening during the COVID-19 pandemic.
Impact of Nutrition Education on the Nutrition Capacity of Caregivers and Nutrition Outcomes of Indigenous Mbororo Children in the West Region of Cameroon: Protocol for a Cluster Randomized Controlled Trial
Authors: Manjong Florence Titu , Verla Vincent Siysi , Egbe Thomas Obinchemti , Nsagha Dickson Shey 
Published: September 2020

Background
Inadequate diets and life-threatening infections have profound adverse implications for child growth, development, and survival, particularly among indigenous peoples. Evidence of the effectiveness of community-based nutrition education interventions in improving child feeding and nutrition outcomes among indigenous Mbororo population in Cameroon is scarce.

Objective
This study aims to investigate the impact of culturally tailored community-based nutrition education intervention on caregivers’ knowledge, attitude, and practice regarding complementary feeding and on nutrition outcomes of indigenous Mbororo children (aged 3-59 months) in the Foumban and Galim health districts of the West Region of Cameroon.

Methods
A two-arm cluster randomized controlled trial will be conducted in the Foumban Health District and Galim Health District. The intervention and control arms will each comprise 5 clusters with 121 child–caregiver pairs. Participants in the intervention arm will be organized into 5 caregivers’ peer-support platforms. A total of 12 educational sessions will be assigned to the intervention group by trained female Mbororo nutrition volunteers (n=6) and community health workers (n=6). The control arm will receive routine facility-based nutrition education. Data will be collected at 3-month and 6-month follow-up. Both descriptive statistics and multivariate logistic models will be used to estimate the effect of culturally tailored community-based nutrition education intervention (independent variable) on outcome variables (caregivers’ knowledge, attitude, and practice), child growth (weight, height/length, weight for age), and morbidity status (diarrhea, cough, and fever) between both arms. Data assessors will be blinded to the group allocation. Ethical approval (reference no. 2019/1002-07/UB/SG/IRB/FHS) was obtained from the Faculty of Health Sciences Institutional Review Board at the University of Buea.

Results
Baseline data were collected in September 2019. In February 2020, 10 Mbororo communities (clusters) with 242 child–caregiver pairs were selected and allocated to the experimental and control arm in a 1:1 ratio. Community nutrition volunteers (n=6) and community health workers (n=6) were selected and trained. Data collection and analysis are ongoing, and results are not available for this manuscript.

Conclusions
The findings of this study will provide evidence on the impact of culturally tailored and health belief model–based nutrition education on behavior change as a complementary strategy for strengthening health facility–based approaches in the reduction of malnutrition burden among the study population

International Registered Report Identifier (IRRID)
DERR1-10.2196/23115
Consolidação de uma gramática nacional: a implementação do genitivo “dele” nas Minas setecentistas
Authors: Coelho Shirlene Ferreira , Da Silva José Edilmo Ferreira , Coelho Sueli Maria 
Published: September 2020

O Português do Brasil reúne vários estudos (SILVA, 1982, 1984, 1991; CUNHA, 2007) que investigam o uso dos possessivos seu e dele sob uma perspectiva variacionista. Tem-se atribuído esse fenômeno a uma mudança no sistema pronominal, que se desestabilizou com a entrada da forma você em lugar de tu (KATO, 1985; PERINI, 1985; CERQUEIRA, 1996a, 1996b), gerando ambiguidade de interpretação em alguns contextos. A forma genitiva dele surge, então, como variante preferida por ter função desambiguizadora. Nesse contexto, este estudo compara os usos da forma possessiva padrão com a inovadora, para verificar se os reflexos da aludida desestabilização do quadro pronominal já se faziam sentir nas Minas setecentistas. Adotando-se uma metodologia quantitativa e qualitativa de pesquisa, embasada teoricamente nos pressupostos sociolinguísticos labovianos, chega-se à conclusão de que, nas Minas setecentistas, o possessivo de terceira pessoa já era opaco, o que sugere a concorrência entre as formas tu e você nesta região no século XVIII.
As estratégias argumentativas do regime militar em Atos Institucionais
Authors: De Oliveira Ana Paula Santos 
Published: September 2020

Com o objetivo de desvelar as principais estratégias argumentativas utilizadas pela ditadura militar brasileira (1964-85) a partir de seus 17 Atos Institucionais (AIs), este estudo se ancora na Teoria da argumentação, desde a Nova retórica (PERELMAN; TYTECA, 2005), e na Análise de Discurso de filiação pecheutiana (PÊCHEUX, 1988; ORLANDI, 2012; COURTINE, 2019; INDURSKY, 2013). Nessa perspectiva, a análise do corpus se direciona para a dimensão argumentativa das memórias discursivas (VITALE, 2015), cujos mecanismos têm a função de provocar a adesão a determinados dizeres. Seus resultados indicam que o Estado militar recorreu à estratégia da definição ao nomear a destituição do presidente João Goulart de autêntica revolução, afastando, com isso, a ideia de ação ilegítima, além de ter utilizado a técnica de inclusão da parte no todo, por meio da qual apresenta a tomada do poder como um movimento civil-militar, e a de vínculo causal, quando justifica a ação como necessária para salvar o Brasil do comunismo. As análises apontam ainda para uma série de deslizamentos de sentidos, o que comprova que a língua funciona na tensão entre o mesmo e o diferente, sob a determinação da processualidade histórica, refletida na materialidade discursiva.