bluuebunny/crossref_metadata_2025 huggingface dataset:
Global surveillance of potential antiviral drug resistance in SARS-CoV-2: proof of concept focussing on the RNA-dependent RNA polymerase
Authors: Mari Alfredo , Roloff Tim , Stange Madlen , Søgaard Kirstine K. , Asllanaj Erblin , Tauriello Gerardo , Alexander Leila Tamara , Schweitzer Michael , Leuzinger Karoline , Gensch Alexander , Martinez Aurélien , Bielicki Julia , Pargger Hans , Siegemund Martin , Nickel Christian H. , Bingisser Roland , Osthoff Michael , Bassetti Stefano , Sendi Parham , Battegay Manuel , Marzolini Catia , Seth-Smith Helena M.B. , Schwede Torsten , Hirsch Hans H. , Egli Adrian
Published: January 2021

Antiviral treatments for COVID-19 have involved many repurposed drugs. Currently, SARS-CoV-2 RNA-dependent RNA polymerase (RdRp, encoded bynsp12-nsp7-nsp8) has been targeted by numerous inhibitors with debated clinical impact. Among these, remdesivir has been conditionally approved for the treatment of COVID-19 patients. Although the emergence of antiviral resistance, an indirect proxy for antiviral efficacy, poses a considerable healthcare threat, an evolutionary perspective on emerging resistant mutants is still lacking.

Here we show that SARS-CoV-2 RdRp is under purifying selection, that potential escape mutations are rare, and unlikely to lead to viral fitness loss.

In more than 56,000 viral genomes from 105 countries dating from December 2019 to July 2020 we found negative selective pressure affectingnsp12(Tajima’s D = −2.62), with potential antiviral escape mutations in only 0.3% of sequenced genomes. Those affected known key residues, such as Nsp12:Val473 and Nsp12:Arg555. Of the potential escape mutations found globally,in silicostructural models show that this rarely implies loss of stability in RdRp. No potential escape mutation were found in our local cohort of remdesivir treated patients from the first wave (n=8). Our results indicate that RdRp is a suitable drug target, and that remdesivir does not seem to exert high selective pressure. Our study could be the starting point of a larger monitoring effort of drug resistance throughout the COVID-19 pandemic. We recommend the application of repetitive genome sequencing of SARS-CoV-2 from patients treated with antivirals to provide early insights into the evolution or antiviral resistance.
Speech pause distribution as an early marker for Alzheimer’s disease
Authors: Pastoriza-Domínguez Patricia , Torre Iván G. , Diéguez-Vide Faustino , Gómez-Ruiz Isabel , Geladó Sandra , Bello-López Joan , Ávila-Rivera Asunción , Matías-Guiu Jordi , Pytel Vanesa , Hernández-Fernández Antoni
Published: January 2021

Background

Pause duration analysis is a common feature in the study of discourse in Alzheimer’s disease (AD) and may also be helpful for its early detection. However, studies involving patients with amnestic mild cognitive impairment (aMCI) have yielded varying results.

Objectives

To characterize the probability density distribution of speech pause durations in AD, two multi-domain amnestic MCI patients (with memory encoding deficits, a-mdMCI-E, and with retrieval impairment only, a-mdMCI-R) and healthy controls (HC) in order check whether there are significant differences between them.

Method

112 picture-based oral narratives were manually transcribed and annotated for the automatic extraction and analysis of pause durations. Different probability distributions were tested for the fitting of pause durations while truncating shorter ranges. Recent findings in the field of Statistics were considered in order to avoid the inherent methodological uncertainty that this type of analysis entails.

Results

A lognormal distribution (LND) explained the distribution of pause duration for all groups. Its fitted parameters (µ,σ) followed a gradation from the group with shorter durations and a higher tendency to produce short pauses (HC) to the group with longer pause durations and a considerably higher tendency to produce long pauses with greater variance (AD). Importantly, a-mdMCI-E produced significantly longer pauses and with greater variability than their a-mdMCI-R counterparts (α= 0.05).

Conclusion

We report significant differences at the group level in pause distribution across all groups of study that could be used in future diagnostic tools and discuss the clinical implications of these findings, particularly regarding the characterization of aMCI.
COVID-19 vaccine hesitancy worldwide: a systematic review of vaccine acceptance rates
Authors: Sallam Malik
Published: January 2021

Introduction

Utility of vaccine campaigns to control coronavirus disease 2019 (COVID-19) is not merely dependent on vaccine efficacy and safety. Vaccine acceptance among the general public and the healthcare workers, appears to have a decisive role for successful control of the pandemic.

Aim

To provide an up-to-date assessment of COVID-19 vaccination acceptance rates worldwide.

Methods

A systematic search of the peer-reviewed English survey literature indexed in PubMed was done on December 25, 2020. Results from 30 studies, met the inclusion criteria and formed the basis for final COVID-19 vaccine acceptance estimates. Results of an additional recent survey from Jordan and Kuwait was considered in this review as well.

Results

Survey studies on COVID-19 vaccine acceptance rates were found from 33 different countries. Among adults representing the general public, the highest COVID-19 vaccine acceptance rates were found in Ecuador (97.0%), Malaysia (94.3%), Indonesia (93.3%) and China (91.3%). On the other hand, the lowest COVID-19 vaccine acceptance rates were found in Kuwait (23.6%), Jordan (28.4%), Italy (53.7), Russia (54.9%), Poland (56.3%), US (56.9%), and France (58.9%). Only eight surveys among healthcare workers (doctors, nurses) were found, with vaccine acceptance rates ranging from 27.7% in the Democratic Republic of the Congo to 78.1% in Israel. In a majority of survey studies among the general public (62%), the acceptance of COVID-19 vaccination showed a level of ≥ 70%.

Conclusions

Low rates of COVID-19 vaccine acceptance were reported in the Middle East, Russia, Africa and several European countries. This could represent a major problem in the global efforts that aim to control the current COVID-19 pandemic. More studies are recommended to address the scope of COVID-19 vaccine hesitancy. Such studies are particularly needed in the Middle East Africa, Eastern Europe, Central Asia, Middle and Latin America.

Graphical Abstract

COVID-19 vaccine acceptance rates worldwide

For countries with more than one survey study, the vaccine acceptance rate of the latest survey was used in this graph. The estimates were also based on studies from the general population, except in the following cases were no studies from the general public were found (Australia: parents/guardians; DRC: healthcare workers; Hong Kong: healthcare workers; Malta: healthcare workers).
PREDICTORS OF UPTAKE OF A POTENTIAL COVID-19 VACCINE AMONG NIGERIAN ADULTS
Authors: Olomofe Charles Oluwatemitope , Soyemi Victor Kehinde , Udomah Bolaji Felicia , Owolabi Adeyinka Olabisi , Ajumuka Emmanuel Eziashi , Igbokwe Chukwudum Martin , Ashaolu Uriel Oludare , Adeyemi Ayodele Olusola , Aremu-Kasumu Yetunde Bolatito , Dada Olufunke Folasade , Ochieze John Chikezie , Fayemi Olaniyi Bamidele , Ologunde Kehinde Williams , Popoola Gbenga Omotade , Ariyo Olumuyiwa Elijah
Published: January 2021

Background

The Coronavirus diseases (COVID-19) pandemic is not abating and there is no approved treatment yet. The development of vaccines is hoped to help in addressing this disease outbreak. However, in the face of anti-vaccines uprise, it is important to understand the factors that may influence the uptake of COVID-19 vaccines as this will influence how successful the fight against COVID-19 will be in the long term.

Methods

A cross-sectional study among 776 adult Nigerians (age ≥18 years) was conducted in the 36 States of Nigeria and the Capital City with online questionnaire. The questionnaire consisted of 5 sections: socio-demographic characteristics of respondents, respondent’s knowledge of COVID-19, respondents risk perception of COVID-19, vaccination history of respondents, and willingness to receive COVID-19 vaccine. Descriptive analysis of variables was done and multivariate analysis using logistic regression was carried out to determine the predictors of uptake of a potential COVID-19 vaccine. The level of significance was predetermined at a p-value < 0.05. Data analysis was done with SPSS version 21.

Results

Most of the respondents were male (58.1%). Most participants were willing to take a potential COVID-19 vaccine (58.2%), while 19.2% would not take it with 22.6% indecisive. 53.5% would prefer a single dose COVID-19 vaccine. For vaccine uptake, being male (p= 0.002) and the perception that “vaccines are good” (p< 0.001) were the positive predictor of uptake of a potential COVID-19 vaccine.

Conclusion

Most Nigerians were willing to take a potential COVID-19 vaccine with the male gender and perception that “vaccines are good” being positive predictors. There is a need for public enlightenment aim at encouraging those that are indecisive or averse to receiving COVID-19 vaccines.
Characterizing Two Outbreak Waves of COVID-19 in Spain Using Phenomenological Epidemic Modelling
Authors: López Miguel , Peinado Alberto , Ortiz Andrés
Published: January 2021

Since the first case reported of SARS-CoV-2 the end of December 2019 in China, the number of cases quickly climbed following an exponential growth trend, demonstrating that aglobal pandemicis possible. As of December 3, 2020, the total number of cases reported are around 65,527,000 contagions worldwide, and 1,524,000 deaths affecting 218 countries and territories. In this scenario, Spain is one of the countries that has suffered in a hard way, the ongoing epidemic caused by the novel coronavirus SARS-CoV-2, namely COVID-19 disease. In this paper, we present the utilization of phenomenological epidemic models to characterize the two first outbreak waves of COVID-19 in Spain. The study is driven using a two-step phenomenological epidemic approach. First, we use a simple generalized growth model to fit the main parameters at the early epidemic phase; later, we apply our previous finding over a logistic growth model to that characterize both waves completely. The results show that even in the absence of accurate data series, it is possible to characterize the curves of case incidence, and even construct short-term forecast in the near time horizon.
Cyproterone Acetate and risk of Meningioma
Authors: Mikkelsen Anders P. , Greiber Iben K. , Scheller Nikolai M. , Hilden Malene , Lidegaard Øjvind
Published: January 2021

Cyproterone acetate (CPA) is a synthetic steroid hormone. We assessed the association between the use of CPA and the risk of developing meningioma.

In a historical prospective cohort study, using Danish national healthcare registers we included a cohort of 5,730,654 individuals, among whom 1,982 were exposed to CPA. During follow-up, we identified 8,957 cases of meningioma, of which 16 were exposed to CPA. From 2013 to 2019 the number of new users increased from 18.1 to 62.3 new users per million, while the proportion of new users who were transgender increased from 18.4 to 68.3%. Analyses showed a significantly increased risk of meningioma according to cumulative dose of CPA; 0.1-10 grams of CPA, incidence rate 78.8 (95% CI 15.7-141.9) per 100.000 person years and adjusted hazard ratio 7.0 (3.1-15.6); >10 grams of CPA, incidence 187.5 (71.3-303.7) and adjusted hazard ratio 19.2 (10.3-35.8), as compared to the background population.

In conclusion, the cumulative dose of CPA was associated with an increased incidence and hazard ratio of meningioma, showing a dose-response relationship. The number of new CPA users increased more than 3-fold from 2013 to 2019, primarily driven by new transgender users.
Efficiency of Artificial Intelligence in Detecting COVID-19 Pneumonia and Other Pneumonia Causes by Quantum Fourier Transform Method
Authors: Acar Erdi , Öztoprak Bilge , Reşorlu Mustafa , Daş Murat , Yılmaz İhsan , Öztoprak İbrahim
Published: January 2021

The new coronavirus (COVID-19) appeared in Wuhan in December 2019 and has been announced as a pandemic by the World Health Organization (WHO). Currently, this deadly pandemic has caused more than 1 million deaths worldwide. Therefore, it is essential to detect positive cases as early as possible to prevent the further spread of this outbreak. Currently, the most widely used COVID-19 detection technique is a real-time reverse transcription-polymerase chain reaction (RT-PCR). However, RT-PCR is time-consuming to confirm infection in the patient. Because RT-PCR is less sensitive, it provides high false-negative results. Computed tomography (CT) is recommended as a solution to this problem by healthcare professionals because of its higher sensitivity for early and rapid diagnosis. In addition, radiation used in CT poses a serious threat to patients. In this study, we propose a CNN-based method to distinguish COVID-19 pneumonia from other types of viral and bacterial pneumonia using low-dose CT images to reduce the radiation dose used in CT. In our study, we used a data set consisting of 7717 CT images of 350 patients that we collected from Çanakkale Onsekiz Mart University Research Hospital. We used a CNN-based network that suppresses noise to remove interference from low-dose CT images. In the image preprocessing phase, we provided lung segmentation from CT images and applied quantum Fourier transform. By evaluating all possible variations of local knowledge at the same time with quantum Fourier transformation, the most informative spatial information was extracted. In CNN-based architecture, we used pre-trained ResNet50v2 as a feature extractor and fine-tune by training with our dataset. We visualized the efficiency of the ResNet50v2 network using the t-SNE method. We performed the classification process with a fully connected layer. We created a heat map using the GradCam technique to see where the model focuses on the images while classifying. In this experimental study, the results of 99.5%, 99.2%, 99.0%, 99.7%, and 99.1%, were obtained in the context of performance criteria such as accuracy, precision, sensitivity, specificity, and f1 score, respectively. This study revealed the artificial intelligence-based computer-aided diagnosis (CAD)system as an effective and fast method to accurately diagnose COVID-19 pneumonia.
COVID-19: Can early home treatment with Azithromycin alone or with Zinc help prevent hospitalisation, death, and long-COVID-19? A review
Authors: Lepere Philippe , Escarguel Bruno , Yolartiran Selda , Escarguel Claude
Published: January 2021

Introduction

The effects of the SARS-CoV-2 pandemic continues to disrupt health systems worldwide, leading to population lockdowns in many countries. Preventing hospitalisation, death and long-COVID-19 with repurposed drugs remains a priority. Hydroxychloroquine (HCQ) and azithromycin (AZM) are the most commonly used in ambulatory care, with divergent results. With the aim of decentralizing early treatment to family practitioners, we addressed the question: Can early home treatment with AZM alone or with zinc help prevent hospitalisation, death, and long-COVID-19?

Methodology

We conducted a scoping review of articles published from 31st December 2019 to 5th November 2020 in Pubmed, Google Scholar, MedRxiv, and BioRxiv databases, and a review of undergoing clinical trials published in the Clinicaltrial.gov database.

Results

Many studies report on outpatient treatment with a combination of AZM + HCQ versus AZM alone, and few studies propose the addition of Zinc (Zn) to AZM. In addition, we identified 5 clinical trials currently recruiting individuals for early outpatient treatment with AZM. However, we failed in identifying any study or clinical trial conducted with family practitioners responding to our question.

Discussion

The antiviral, anti-inflammatory, immunomodulatory benefits of AZM + Zn make this drugs combination a good candidate therapy to treat flu-like-COVID-19 and atypical pneumoniae. The antibacterial action of AZM can also help disrupting the bacteria/virus cooperation that is poorly documented. Considering pros and cons of macrolide use (including antimicrobial resistance), we call for early use of this therapy by family practitioners for home treatment of individuals presenting mild or moderate symptoms under rigorous scientific guidance to prevent hospitalisation, death and long-COVID.
Estimating event ban effects on COVID-19 outbreak in Japan
Authors: Kurita Junko , Sugawara Tamie , Ohkusa Yasushi
Published: January 2021

Background

Before Olympic and Paralympic Games in Tokyo, whether an audience shall be allowed or not has been a subject of concern in Japan as of early June, 2021. Object: We evaluated effects of professional baseball games with audiences as an example of the large sports events, on COVID-19 infectiousness.

Method

We regressed the effective reproduction number R(t) on a dummy variable for professional baseball games with audiences as along with temperature, humidity, mobility, and countermeasures. We examined two study periods: those including and excluding before initiation of the games in 2020.

Results

Estimation results indicate that the period with audiences exhibited significantly lower infectiousness than when audiences were excluded before initiation of the games with audience attendance. However, audiences were found to have a negative but insignificant effect when compared to the period before initiation of the attended games.

Discussion and Conclusion

This study found no clear evidence indicating that big sports events with audiences raise the COVID-19 infectiousness.
Early measurement of blood sST2 is a good predictor of death and poor outcomes in patients admitted for COVID-19 infection
Authors: Sánchez-Marteles M , Rubio-Gracia J , Peña-Fresneda N. , Garcés-Horna V , Gracia-Tello B , Martínez-Lostao L , Crespo-Aznárez S , Pérez-Calvo JI , Giménez-López I
Published: January 2021

Importance

Although several biomarkers have shown correlation to prognosis in COVID-19 patients, their clinical value is limited because of lack of specificity, suboptimal sensibility, or poor dynamic behavior.

Objective

In search of better prognostic markers in COVID-19, we hypothesized that circulating soluble ST2 (sST2) could be associated to a worse outcome, prompted by our previous knowledge of sST2 involvement in heart failure-associated lung deterioration, and by mounting evidence favoring a role of IL-33/ST2 axis in the disease.

Design, Setting and participants

One hundred and fifty-two patients admitted for confirmed COVID-19 infection were included in a prospective non-interventional, observational study carried out in a tertiary teaching center. Blood samples were drawn at admission, 48-72 hours later and at discharge. sST2 concentrations, and routine blood laboratory were analyzed.

Main outcomes

Primary end-points were admission at intensive care unit (ICU) and, mortality. Other outcomes were a need for high oxygen flow therapy (HOF) or increasing treatment at 48/72 hours.

Results

Median age was 57.5 years (SD: 12.8), 60.4% males. Ten per cent of patients (n=15) were derived to ICU and/or died during admission. The rest stayed hospitalized 8(IQR:6) days on average. About 34% (n=47), 38% (n=53) and 48.5% (n=66) needed HOF, up-titrate therapy or both, respectively.

Median (IQR) sST2 serum concentration (ng/mL) rose to 53.1(30.9) at admission, peaked at 48-72h (79.5[64]) and returned to admission levels at discharge (44.9[36.7]), remaining significantly elevated above healthy donor values (18.6[15.1]).

A concentration of sST2 above 58.9 ng/mL identified patients progressing to ICU admission or death. These results remained significant after multivariable analysis. The area under the receiver operating characteristics curve (AUC) of sST2 for the occurrence of end-points was 0.776 (p=0.001). Admission sST2 was higher in patients who needed up-tritate therapy.

Conclusions and relevance

In patients admitted for COVID-19 infection, measurement of sST2 measurement early within 24h after at admission was able to identify patients at risk of severe complications or death.
Sociodemographic Characteristics of Missing Data in Digital Phenotyping
Authors: Kiang Mathew V , Chen Jarvis T , Krieger Nancy , Buckee Caroline O , Alexander Monica J , Baker Justin T , Buckner Randy L , Coombs Garth , Rich-Edwards Janet W , Carlson Kenzie W , Onnela Jukka-Pekka
Published: January 2021

The ubiquity of smartphones, with their increasingly sophisticated array of sensors, presents an unprecedented opportunity for researchers to collect diverse, temporally-dense data about human behavior while minimizing participant burden. Researchers increasingly make use of smartphone applications for “digital phenotyping,” the collection of phone sensor and log data to study the lived experiences of subjects in their natural environments. While digital phenotyping has shown promise in fields such as psychiatry and neuroscience, there are fundamental gaps in our knowledge about data collection and non-collection (i.e., missing data) in smartphone-based digital phenotyping. Here, we show that digital phenotyping presents a viable method of data collection, over long time periods, across diverse study participants with a range of sociodemographic characteristics. We examined accelerometer and GPS sensor data of 211 participants, amounting to 29,500 person-days of observation, using Bayesian hierarchical negative binomial regression. We found that iOS users had higher rates of accelerometer non-collection but lower GPS non-collection than Android users. For GPS data, rates of non-collection did not differ by race/ethnicity, education, age, or gender. For accelerometer data, Black participants had higher rates of non-collection while Asian participants had slightly lower non-collection. For both sensors, non-collection increased by 0.5% to 0.9% per week. These results demonstrate the feasibility of using smartphone-based digital phenotyping across diverse populations, for extended periods of time, and within diverse cohorts. As smartphones become increasingly embedded in everyday life, the insights of this study will help guide the design, planning, and analysis of digital phenotyping studies.
PD-L1+and Hyal2+myeloid cells in renal cell carcinoma: a case report
Authors: Kwenda Elizabeth , Dominguez-Gutierrez Paul R. , O’Malley Padraic , Crispen Paul L. , Kusmartsev Sergei
Published: January 2021

RCC patients frequently have increased numbers of immunosuppressive myeloid cells in circulation. High numbers of myeloid derived suppressor cells (MDSCs) in the blood are associated with immune suppression as well as with cancer-related inflammation which drives the mobilization of myeloid cells to tumor tissue. Here we show that peripheral blood from a previously untreated renal cell carcinoma patient has increased numbers of monocytic CD33+CD11b+MDSCs, which also co-expressed PD-L1 and membrane-bound enzyme hyaluronidase 2 (Hyal2). PD-L1 expression is associated with immune suppression, whereas expression of Hyal2 is associated with inflammation, because Hyal2+myeloid cells can degrade the extracellular hyaluronan (HA), leading to the accumulation of pro-inflammatory HA fragments with low molecular weight. These findings implicate the potential involvement of monocytic MDSCs in both tumor-associated immune suppression and cancer-related inflammation. Analysis of organoid-like tumor-tissue slice cultures prepared from cancer tissue of the same patient revealed the significant presence of PD-L1+HLA-DR+macrophage-like or dendritic cell-like antigen-presenting cells in tumor stroma. Interestingly that stroma-associated PD-L1+cells frequently have intracellular hyaluronan. Collectively, data presented in this study suggest that the interplay between tumor-recruited myeloid cells and stromal hyaluronan may contribute to the inflammation and immune tolerance in cancer.
The Joint Impact of COVID-19 Vaccination and Non-Pharmaceutical Interventions on Infections, Hospitalizations, and Mortality: An Agent-Based Simulation
Authors: Patel Mehul D. , Rosenstrom Erik , Ivy Julie S. , Mayorga Maria E. , Keskinocak Pinar , Boyce Ross M. , Lich Kristen Hassmiller , Smith Raymond L. , Johnson Karl T. , Swann Julie L.
Published: January 2021

Background

Vaccination against SARS-CoV-2 has the potential to significantly reduce transmission and morbidity and mortality due to COVID-19. This modeling study simulated the comparative and joint impact of COVID-19 vaccine efficacy and coverage with and without non-pharmaceutical interventions (NPIs) on total infections, hospitalizations, and deaths.

Methods

An agent-based simulation model was employed to estimate incident SARS-CoV-2 infections and COVID-19-associated hospitalizations and deaths over 18 months for the State of North Carolina, a population of roughly 10.5 million. Vaccine efficacy of 50% and 90% and vaccine coverage of 25%, 50%, and 75% (at the end of a 6-month distribution period) were evaluated. Six vaccination scenarios were simulated with NPIs (i.e., reduced mobility, school closings, face mask usage) maintained and removed during the period of vaccine distribution.

Results

In the worst-case vaccination scenario (50% efficacy and 25% coverage), 2,231,134 new SARS-CoV-2 infections occurred with NPIs removed and 799,949 infections with NPIs maintained. In contrast, in the best-case scenario (90% efficacy and 75% coverage), there were 450,575 new infections with NPIs maintained and 527,409 with NPIs removed. When NPIs were removed, lower efficacy (50%) and higher coverage (75%) reduced infection risk by a greater magnitude than higher efficacy (90%) and lower coverage (25%) compared to the worst-case scenario (absolute risk reduction 13% and 8%, respectively).

Conclusion

Simulation results suggest that premature lifting of NPIs while vaccines are distributed may result in substantial increases in infections, hospitalizations, and deaths. Furthermore, as NPIs are removed, higher vaccination coverage with less efficacious vaccines can contribute to a larger reduction in risk of SARS-CoV-2 infection compared to more efficacious vaccines at lower coverage. Our findings highlight the need for well-resourced and coordinated efforts to achieve high vaccine coverage and continued adherence to NPIs before many pre-pandemic activities can be resumed.
An analysis of patients’ perspectives on qualitative olfactory dysfunction using social media
Authors: Parker Jane K. , Kelly Christine E. , Smith Barry , Hopkins Claire , Gane Simon B.
Published: January 2021

Background

The impact of qualitative olfactory disorders is underestimated. Parosmia is the triggered perception of distorted odours whereas phantosmia is the perception of odours in the absence of a trigger. Both can arise from post-infectious anosmia and have increased substantially since the outbreak of COVID-19.

Methodology/Principal

Thematic analysis of a social media support group for parosmia and phantosmia was used to articulate the perspectives and concerns of those affected by these debilitating olfactory disorders.

Results

A novel symptom (olfactory perseveration) was identified where a triggered, identifiable, and usually unpleasant olfactory percept persisted in the absence of an ongoing stimulus. Fluctuations in intensity and duration of perseveration, parosmia and phantosmia were observed. Coffee, meat, onion, and toothpaste were identified as common triggers of these disorders, but people struggled to describe the distortions, using words associated with disgust and revulsion. Common strategies to avoid triggers may result in a diet lacking in both nutrition and reward. The emotional aspect of living with qualitative olfactory dysfunction was evident and highlighted the detrimental impact on mental health.

Conclusions

The data acquired can inform rehabilitation strategies and drive our ongoing research into understanding the molecular triggers associated with parosmia, and research into patient benefit.
Patient-Reported Outcomes of Ugandans Living with Autoimmune Rheumatic Diseases
Authors: Bongomin Felix , Sekimpi Maria , Natukunda Barbra , Makhoba Anthony , Kaddumukasa Mark
Published: January 2021

Purpose

This study aimed to assess the patient–reported outcomes (PROs) in rheumatic patients attending two tertiary rheumatology clinics in Uganda.

Methods

A cross-sectional, clinical audit of patients aged 16 years or older with a confirmed diagnosis of rheumatic disease and receiving disease modifying anti-rheumatic drugs (DMARDs) was conducted between September and December 2020. Health index and overall self-rated health status were assessed using the ED-5D-5L tool. Comparisons for variables was performed using Student’s t-test or Mann-Whitney U for continuous numerical data while categorical data was compared using either Χ2 tests or Fisher’s exact tests as appropriate.

Results

We enrolled 74 eligible patients: 48 (64.9%) had rheumatoid arthritis (RA), 14 (18.9%) had systemic lupus erythematosus (SLE), and 12 (16.2%) had other autoimmune rheumatic disorders. Majority (n=69, 93.2%) were female with a mean ±SD age of 45 ± 17 years. Fourteen (18.9%) patients were on concomitant herbal medication while using DMARDs and 26 (35.1%) self-reported at least 1 adverse drug reactions to the DMARDS. Any level of problem was reported by 54 (72.5%) participants for mobility, 47 (63.5%) for self-care, 56 (75.6%) for usual activity, 66 (89.1%) for pain and discomfort, and 56 (75.6%) for anxiety/depression. Patients with SLE had higher median health index compared to those other autoimmune rheumatic disorders (p<0.0001). Overall self-rated health status was comparable across clinical diagnoses (p=0.2), but better for patients who received care from private (Nsambya Hospital) compared to public hospital (Mulago Hospital) (65 vs. 50, p=0.009).

Conclusion

There is a substantial negative impact of autoimmune rheumatic diseases on quality of life of patients, especially those receiving care from a public facility in Uganda.

Clinical Significance

Adverse drug reactions to DMARDs was reported by more than one-third of the patients

SLE patients have better quality of life compared to patients with other autoimmune rheumatic disease.

Concomitant use of herbal medication is common and associated with lower health index and lower overall self-rated health status.

Autoimmune rheumatic diseases impose a heavy financial burden on affected patients, over 70% of the study patients required financial support for management of their disease and a high proportion of these patients were not on their DMARD therapy the week prior to their scheduled clinic appoints.
Simple quantitative assessment of the outdoor versus indoor airborne transmission of viruses and covid-19
Authors: Rowe B.R. , Canosa A. , Drouffe J.M. , Mitchell J.B.A.
Published: January 2021

In this paper we develop a simple model of the inhaled flow rate of aerosol particles of respiratory origin i.e. that have been exhaled by other humans. A connection is made between the exposure dose and the probability of developing an airborne disease. This allows a simple assessment of the outdoor versus indoor risk of contamination to be made in a variety of meteorological situations. It is shown quantitatively that for most cases, the outdoor risk is orders of magnitude less than the indoor risk and that it can become comparable only for extremely specific meteorological and geographical situations. It sheds a light on various observations of Covid-19 spreading in mountain valleys with temperature inversions while at the same time other areas are much less impacted.

Highlights

Risk of covid-19 airborne transmission.

Quantitative assessment of outdoor versus indoor airborne risk of transmission.

Meteorological and geographical influence on covid-19 airborne transmission.
Need of care in interpreting Google Trends-based COVID-19 infodemiological study results: potential risk of false-positivity
Authors: Sato Kenichiro , Mano Tatsuo , Iwata Atsushi , Toda Tatsushi
Published: January 2021

Objective

Google Trends (GT) is being used as an epidemiological tool to study coronavirus disease (COVID-19) by identifying keywords in search trends that are predictive for the COVID-19 epidemiological burden. However, many of the earlier GT-based studies include potential statistical fallacies by measuring the correlation between non-stationary time sequences without adjusting for multiple comparisons or the confounding of media coverage, leading to concerns about the increased risk of obtaining false-positive results. In this study, we aimed to apply statistically more favorable methods to validate the earlier GT-based COVID-19 study results.

Methods

We extracted the relative GT search volume for keywords associated with COVID-19 symptoms, and evaluated their Granger-causality to weekly COVID-19 positivity in eight English-speaking countries and Japan. In addition, the impact of media coverage on keywords with significant Granger-causality was further evaluated using Japanese regional data.

Results

Our Granger causality-based approach largely decreased (by up to approximately one-third) the number of keywords identified as having a significant temporal relationship with the COVID-19 trend when compared to those identified by the Pearson correlation-based approach. “Sense of smell” and “loss of smell” were the most reliable GT keywords across all the evaluated countries; however, when adjusted with their media coverage, these keyword trends did not Granger-cause the COVID-19 positivity trends (in Japan).

Conclusion

Our results suggest that some of the search keywords reported as candidate predictive measures in earlier GT-based COVID-19 studies may potentially be unreliable; therefore, caution is necessary when interpreting published GT-based study results.
Sub-5-minute Detection of SARS-CoV-2 RNA using a Reverse Transcriptase-Free Exponential Amplification Reaction, RTF-EXPAR
Authors: Carter Jake G. , Iturbe Lorea Orueta , Duprey Jean-Louis H. A. , Carter Ian R. , Southern Craig D. , Rana Marium , Bosworth Andrew , Beggs Andrew D. , Hicks Matthew R. , Tucker James H. R. , Dafforn Timothy R.
Published: January 2021

We report a rapid isothermal method for detecting SARS-CoV-2, the virus responsible for COVID-19. The procedure uses a novel reverse transcriptase-free (RTF) approach for converting RNA into DNA, which triggers a rapid amplification using the Exponential Amplification Reaction (EXPAR). Deploying the RNA-to-DNA conversion and amplification stages of the RTF-EXPAR assay in a single step results in the detection of a sample of patient SARS-CoV-2 RNA in under 5 minutes.
Divergences on expected pneumonia cases during the COVID-19 epidemic in Catalonia: A time-series analysis of primary care electronic health records covering about 6 million people
Authors: Coma Ermengol , Méndez-Boo Leonardo , Mora Núria , Guiriguet Carolina , Benítez Mència , Fina Francesc , Fàbregas Mireia , Balló Elisabet , Ramos Francisa , Medina Manuel , Argimon Josep M.
Published: January 2021

Background

Pneumonia is one of the complications of COVID-19. Primary care electronic health records (EHR) have shown the utility as a surveillance system.

Aim

To analyze the trends of pneumonia during two waves of COVID-19 pandemic in order to use it as a clinical surveillance system and an early indicator of severity.

Methods

Time series analysis of pneumonia cases, January 2014-December 2020. We collected pneumonia diagnoses from primary care EHR, covering >6 million people in Catalonia (Spain). We compared the trend of pneumonia in the season 2019-2020 with that in the previous years. We estimated the expected pneumonia cases with data from 2014 to 2018 using a time series regression adjusted by seasonality and influenza epidemics.

Results

Between 4 March and 5 May 2020, 11,704 excess pneumonia cases (95% CI: 9,909 to 13,498) were identified. We observed a second excess pneumonia period from 22 october to 15 november of 1,377 excess cases (95% CI: 665 to 2,089). In contrast, we observed two great periods with reductions of pneumonia cases in children, accounting for 131 days and 3,534 less pneumonia cases (95% CI: 1,005 to 6,064) from March to July; and 54 days and 1,960 less pneumonia cases (95% CI 917 to 3,002) from October to December.

Conclusions

Diagnoses of pneumonia from the EHR could be used as an early and low cost surveillance system to monitor the spread of COVID-19.
Bayesian forecasting for intravenous tobramycin dosing in adults with Cystic Fibrosis using one versus two serum concentrations in a dosing interval
Authors: Drennan Philip G. , Thoma Yann , Barry Lucinda , Matthey Johan , Sivam Sheila , van Hal Sebastiaan J.
Published: January 2021

Background

Intravenous tobramycin requires therapeutic drug monitoring (TDM) to ensure safety and efficacy when used for prolonged treatment, as in infective exacerbations of Cystic Fibrosis (CF). The 24 hour area under the concentration time curve (AUC24) is widely used to guide dosing, however there remains variability in practice around methods for its estimation.

Objectives

To determine the potential for a sparse sampling strategy using a single post-infusion tobramycin concentration and Bayesian forecasting, to assess the AUC24 in routine practice.

Methods

Adults with CF receiving once daily tobramycin had paired concentrations measured 2 hours (c1) and 6 hours (c2) following end of infusion as routine monitoring. We estimated AUC24 exposures using Tucuxi, a Bayesian forecasting application incorporating a validated population pharmacokinetic model. We performed simulations to estimate AUC24 using the full dataset using c1 and c2, compared to estimates using depleted datasets (c1 or c2 only), with and without concentration data from earlier in the course. We assessed agreement between each simulation condition and the reference graphically, and numerically using median difference (Δ) AUC24, and (relative) root mean square error (rRMSE) as measures of bias and accuracy respectively.

Results

55 patients contributed 512 concentrations from 95 tobramycin courses and 256 TDM episodes. Single concentration methods performed well, with median ΔAUC24 <2 mg.h.l-1 and rRMSE of <15% for sequential c1 and c2 conditions.

Conclusions

Bayesian forecasting, using single post-infusion concentrations taken 2-6 hours following tobramycin administration can adequately estimate true exposure in this patient group and are suitable for routine TDM practice.

Key Points

-

In stable adult patients with Cystic fibrosis without significant renal impairment, Bayesian forecasting allows accurate estimation of tobramycin AUC24 using a single blood sample taken 2-6 hours post-infusion with acceptable accuracy, especially when including prior measured concentrations.

-

A single sample approach with Bayesian forecasting is logistically less complicated than a two-sample approach, and could facilitate best-practice TDM in the outpatient setting.

-

A more intensive sampling strategy with Bayesian forecasting using two tobramycin concentrations in a dosing interval should be considered in unstable patients, or where observed concentrations deviate significantly from model predictions.
LAVA: An integrated framework for local genetic correlation analysis
Authors: Werme J. , van der Sluis S. , Posthuma D. , de Leeuw C. A.
Published: January 2021

Genetic correlation (rg) analysis is commonly used to identify traits that may have a shared genetic basis. Traditionally,rgis studied on a global scale, considering only the average of the shared signal across the genome; though this approach may fail to detect scenarios where thergis confined to particular genomic regions, or show opposing directions at different loci. Tools dedicated to localrganalysis have started to emerge, but are currently restricted to analysis of two phenotypes. For this reason, we have developed LAVA, an integrated framework for localrganalysis which, in addition to testing the standard bivariate localrg’s between two traits, can evaluate the local heritability for all traits of interest, and analyse conditional genetic relations between several traits using partial correlation or multiple regression. Applied to 20 behavioural and health phenotypes, we show considerable heterogeneity in the bivariate localrg’s across the genome, which is often masked by the globalrgpatterns, and demonstrate how our conditional approaches can elucidate more complex, multivariate genetic relations between traits.
Disrupted dynamic functional network connectivity among cognitive control networks in the progression of Alzheimer’s disease
Authors: Sendi Mohammad S. E. , Zendehrouh Elaheh , Fu Zening , Liu Jingyu , Du Yuhui , Mormino Elizabeth , Salat David H. , Calhoun Vince D. , Miller Robyn. L.
Published: January 2021

Background

Alzheimer’s disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from rs-fMRI. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations.

Methods

Group independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states and then estimated a hidden Markov model and the occupancy rate for each subject. Finally, we investigated the link between CCN dFC connectivity features with AD progression.

Results

Progression of AD symptoms were associated with increases in connectivity within the middle frontal gyrus. Also, the AD with mild and severer symptoms showed less connectivity within the inferior parietal lobule and between this region with the rest of CCN. Finally, comparing with mild dementia, we found that the normal brain spends significantly more time in a state with lower within middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease.

Conclusion

Our results suggest that AD progress not only alters the CCN connectivity strength but also changes the temporal properties in this brain network. This suggests the temporal and spatial pattern of CCN as a biomarker that differentiates different stages of AD.

Impact Statement

By assuming that functional connectivity is static over time, many of previous studies have ignored the brain dynamic in Alzheimer’s disease progression. Here, a longitudinal resting-state functional magnetic resonance imaging data are used to explore the temporal changes of functional connectivity in the cognitive control network in Alzheimer’s disease progression. The result of this study would increase our understanding about the underlying mechanisms of Alzheimer’s Disease and help in finding future treatment of this neurological disorder.
scJoint: transfer learning for data integration of atlas-scale single-cell RNA-seq and ATAC-seq
Authors: Lin Yingxin , Wu Tung-Yu , Wan Sheng , Yang Jean Y.H. , Wong Wing H. , Wang Y. X. Rachel
Published: January 2021

Single-cell multi-omics data continues to grow at an unprecedented pace, and effectively integrating different modalities holds the promise for better characterization of cell identities. Although a number of methods have demonstrated promising results in integrating multiple modalities from the same tissue, the complexity and scale of data compositions typically present in cell atlases still pose a significant challenge for existing methods. Here we present scJoint, a transfer learning method to integrate atlas-scale, heterogeneous collections of scRNA-seq and scATAC-seq data. scJoint leverages information from annotated scRNA-seq data in a semi-supervised framework and uses a neural network to simultaneously train labeled and unlabeled data, enabling label transfer and joint visualization in an integrative framework. Using multiple atlas data and a biologically varying multi-modal data, we demonstrate scJoint is computationally efficient and consistently achieves significantly higher cell type label accuracy than existing methods while providing meaningful joint visualizations. This suggests scJoint is effective in overcoming the heterogeneity in different modalities towards a more comprehensive understanding of cellular phenotypes.
Learning attention-controllable border-ownership for objectness inference and binding
Authors: Dedieu Antoine , Rikhye Rajeev V. , Lázaro-Gredilla Miguel , George Dileep
Published: January 2021

Human visual systems can parse a scene composed of novel objects and infer their surfaces and occlusion relationships without relying on object-specific shapes or textures. Perceptual grouping can bind together spatially disjoint entities to unite them as one object even when the object is entirely novel, and bind other perceptual properties like color and texture to that object using object-based attention. Border-ownership assignment, the assignment of perceived occlusion boundaries to specific perceived surfaces, is an intermediate representation in the mammalian visual system that facilitates this perceptual grouping. Since objects in a scene can be entirely novel, inferring border ownership requires integrating global figural information, while dynamically postulating what the figure is, a chicken-and egg process that is complicated further by missing or conflicting local evidence regarding the presence of boundaries. Based on neuroscience observations, we introduce a model – the cloned Markov random field (CMRF)– that can learn attention-controllable representations for border-ownership. Higher-order contour representations that distinguish border-ownerships emerge as part of learning in this model. When tested with a cluttered scene of novel 2D objects with noisy contour-only evidence, the CMRF model is able to perceptually group them, despite clutter and missing edges. Moreover, the CMRF is able to use occlusion cues to bind disconnected surface elements of novel objects into coherent objects, and able to use top-down attention to assign border ownership to overlapping objects. Our work is a step towards dynamic binding of surface elements into objects, a capability that is crucial for intelligent agents to interact with the world and to form entity-based abstractions.