Miracle-dz/UniFAR-FTUs huggingface dataset:
Links between air pollution and COVID-19 in England

In December 2019, a novel disease, coronavirus disease 19 (COVID-19), emerged in Wuhan, People’s Republic of China. COVID-19 is caused by a novel coronavirus (SARS-CoV-2) presumed to have jumped species from another mammal to humans. This virus has caused a rapidly spreading global pandemic. To date, over 300,000 cases of COVID-19 have been reported in England and over 40,000 patients have died. While progress has been achieved in managing this disease, the factors in addition to age that affect the severity and mortality of COVID-19 have not been clearly identified. Recent studies of COVID-19 in several countries identified links between air pollution and death rates. Here, we explored potential links between major fossil fuel-related air pollutants and SARS-CoV-2 mortality in England. We compared current SARS-CoV-2 cases and deaths from public databases to both regional and subregional air pollution data monitored at multiple sites across England. After controlling for population density, age and median income, we show positive relationships between air pollutant concentrations, particularly nitrogen oxides, and COVID-19 mortality and infectivity. Using detailed UK Biobank data, we further show that PM2.5 was a major contributor to COVID-19 cases in England, as an increase of 1 m3 in the long-term average of PM2.5 was associated with a 12% increase in COVID-19 cases. The relationship between air pollution and COVID-19 withstands variations in the temporal scale of assessments (single-year vs 5-year average) and remains significant after adjusting for socioeconomic, demographic and health-related variables. We conclude that a small increase in air pollution leads to a large increase in the COVID-19 infectivity and mortality rate in England. This study provides a framework to guide both health and emissions policies in countries affected by this pandemic.
Risks of use and non-use of antibiotics in primary care: qualitative study of prescribers’ views

Purpose The emergence of antimicrobial resistance has led to increasing efforts to reduce unnecessary use of antibiotics in primary care, but potential hazards from bacterial infection continue to cause concern. This study investigated how primary care prescribers perceive risk and safety concerns associated with reduced antibiotic prescribing. Methods Qualitative study using semistructured interviews conducted with primary care prescribers from 10 general practices in an urban area and a shire town in England. A thematic analysis was conducted. Results Thirty participants were recruited, including twenty-three general practitioners, five nurses and two pharmacists. Three main themes were identified: risk assessment, balancing treatment risks and negotiating decisions and risks. Respondents indicated that their decisions were grounded in clinical risk assessment, but this was informed by different approaches to antibiotic use, with most leaning towards reduced prescribing. Prescribers’ perceptions of risk included the consequences of both inappropriate prescribing and inappropriate withholding of antibiotics. Sepsis was viewed as the most concerning potential outcome of non-prescribing, leading to possible patient harm and potential litigation. Risks of antibiotic prescribing included antibiotic resistant and Clostridium difficile infections, as well as side effects, such as rashes, that might lead to possible mislabelling as antibiotic allergy. Prescribers elicited patient preferences for use or avoidance of antibiotics to inform management strategies, which included educational advice, advice on self-management including warning signs, use of delayed prescriptions and safety netting. Conclusions Attitudes towards antibiotic prescribing are evolving, with reduced antibiotic prescribing now being approached more systematically. The safety trade-offs associated with either use or non-use of antibiotics present difficulties especially when prescribing decisions are inconsistent with patients’ expectations.
Why lockdown? Simplified arithmetic tools for decision-makers, health professionals, journalists and the general public to explore containment options for the novel coronavirus

Half the world's population is already under lock-down and the remainder will have to follow if the ongoing novel coronavirus 2019 (COVID-19) virus pandemic is to be contained. Faced with such brutally difficult decisions, it is essential that as many people as possible understand (1) why lock-down interventions represent the only realistic way for individual countries to contain their national-level epidemics before they turn into public health catastrophes, (2) why these need to be implemented so early, so aggressively and for such extended periods, and (3) why international co-operation to conditionally re-open trade and travel between countries that have successfully eliminated local transmission represents the only way to contain the pandemic at global level. Here we present simplified arithmetic models of COVID-19 transmission, control and elimination in user-friendly Shiny and Excel formats that allow non-specialists to explore, query, critique and understand the containment decisions facing their country and the world at large. Based on parameter values representative of the United Republic of Tanzania, which is still early enough in its epidemic cycle and response to avert a national catastrophe, national containment and elimination with less than 10 deaths is predicted for highly rigorous lock down within 5 weeks of the first confirmed cases and maintained for 15 weeks. However, elimination may only be sustained if case importation from outside the country is comprehensively contained by isolating for three weeks all incoming travellers, except those from countries certified as COVID-free in the future. Any substantive relaxation of these assumptions, specifically shortening the lock-down period, less rigorous lock-down or imperfect importation containment, may facilitate epidemic re-initiation, resulting in over half a million deaths unless rigorously contained a second time. Removing contact tracing and isolation has minimal impact on successful containment trajectories because high incidence of similar mild symptoms caused by other common pathogens attenuates detection success of COVID-19 testing. Nevertheless, contact tracing is recommended as an invaluable epidemiological surveillance platform for monitoring and characterizing the epidemic, and for understanding the influence of interventions on transmission dynamics.
Computational Modeling of Attentional Impairments in Disruptive Mood Dysregulation and Attention Deficit/Hyperactivity Disorder

Objective: Computational models provide information about cognitive components underlying behavior. When applied to psychopathology-relevant processes, they offer additional insight to observed differences in behavioral performance. Drift diffusion models have been successfully applied to investigate processing efficiency during binary choice tasks. Using these, we examine the association between psychopathology (irritability and inattention) and processing efficiency under different attentional demands. Method: 187 youth with ADHD, DMDD, both disorders, or no major psychopathology (M age=13.09, SD=2.55; 34% female) completed an Eriksen Flanker task. Of these, 87 provided complete data on dimensional measures of the core symptom of DMDD, irritability, and those of ADHD, inattention and hyperactivity. Results: In a categorical analysis (n=187), we found a ADHD-by-DMDD-by task condition interaction on processing efficiency (b=-1.07, p=.01, 95%CI=[-1.89,-.24]), where increases in processing efficiency for non-conflict conditions were larger in youth without psychopathology relative to patients. Analysis of symptom severity (n=87) across diagnosis was consistent with the above analysis, revealing an interaction between symptom dimensions and task condition on processing efficiency (b=-0.10, p=.018, 95%CI[-.18, -.02]). Inattention, and its combined effect with irritability, predicted the magnitude of difference in processing efficiency between conflict and non-conflict conditions. Discussion: Reduced processing efficiency may represent a shared cognitive endophenotype between ADHD and DMDD. Youth high in irritability or inattention may have difficulty adjusting processing efficiency to changing task demands possibly reflecting impairments in cognitive flexibility.
Applying the SEIR Model in Forecasting The COVID-19 Trend in Malaysia: A Preliminary Study

On March 18, 2020 the Malaysian government implemented a 14-day Movement Control Order (MCO) as part of the mitigation plan in controlling the COVID-19 epidemic in the country. The MCO aims to limit the contact rates among the population and hence prevent the surge of infected individuals. However, the trend of the epidemic before and after the MCO was not apparent. By applying the Susceptible, Exposed, Infectious and Removed (SEIR) mathematical model, we aimed to forecast the trend of COVID-19 epidemic in Malaysia using data from March 17 to 27, 2020. Based on several predetermined assumptions, the results of the analyses showed that after the implementation of the 14-day MCO from March 18 to 31, 2020, it is forecasted that the epidemic in Malaysia will peak approximately in the end of April 2020 and will subside by about the first week of July 2020. The MCO will flatten the epidemic curve but will prolong the duration of the epidemic. Decision to extend the duration of the MCO should depend on the consideration of socioeconomic factors as well. Keywords: SEIR model, COVID-19, forecasting, trend, Malaysia
Influence of a Dietary Supplement on the Gut Microbiome of Overweight Young Women

Abstract Background: The importance of the gut microbiome cannot be overstated. Besides the help in digestion, it influences the immune system, produces immunologic and neurologic communication molecules, hormones and vitamins. The gut microbiome is also involved in controlling the BMI (Body Mass Index). Objectives: We attempted to influence the gut microbiome with a herbal yeast-based dietary supplement in 26 women (BASEC ID 2016-02162; ClinicalTrials.gov NCT03223987, BS2016.1) with a BMI between 30 and 35 and and aged 25 to 35 years within 3 weeks. The clinical trial was conducted in accordance with the Declaration of Helsinki. Our hypothesis was that the supplement modifies the microbiome individually by more than 20%. Methods: With shotgun sequencing in the 2 stool samples of each participant we found 68 bacterial species. Results: The microbiome was altered by more than 20%. Five bacterial species were identical to probiotic species known as good butyrate producers and described as beneficial for the gut. Seventy percent of our study cohort showed an increase in the majority of these beneficial microbes during the study. The ratio of Firmicutes to Bacteroidetes is an important parameter in analyzing overweight persons. Twelve of the participants initially showed a F/B ratio (Firmicutes to Bacteroidetes ratio) above 1.6. After three weeks, five of these women normalized. Conclusions: A baker s yeast-based food supplement is able to modulate the gut microbiome of overweight young women within 3 weeks.
Prioritisation of potential anti-SARS-CoV-2 drug repurposing opportunities based on ability to achieve adequate target site concentrations derived from their established human pharmacokinetics

Many papers are emerging that describe the in vitro antiviral activity of drugs that may be repurposed for therapy or chemoprophylaxis against SARS-CoV-2. However, no comprehensive evaluation of these molecules in the context of the achievable plasma pharmacokinetics after administration of approved doses and schedules to humans has been conducted. Moreover, most publications have focussed on 50% maximum effective concentrations (EC50), which may be an insufficiently robust indicator of antiviral activity because of marked differences in the slope of the concentration-response curve between drugs. Accordingly, in vitro anti-SARS-CoV-2 activity data was digitised from all available publications up to 13th April 2020 and used to recalculate an EC90 value for each drug. EC90 values were then expressed as a ratio to the achievable maximum plasma concentrations (Cmax) reported for each drug after administration of the approved dose to humans (Cmax/EC90 ratio). Only 14 of the analysed drugs achieved a Cmax/EC90 ratio above 1 meaning that plasma Cmax concentrations exceeded those necessary to inhibit 90% of SARS-CoV-2 replication. A more in-depth assessment of these drugs demonstrated that only nitazoxanide, nelfinavir, tipranavir (boosted with ritonavir) and sulfadoxine achieved plasma concentrations above their anti-SARS-CoV-2 activity across their entire approved dosing interval at their approved human dose. For all drugs reported, the unbound lung to plasma tissue partition coefficient (KpUlung) was also simulated and used along with reported Cmax and fraction unbound in plasma to derive a lung Cmax/EC50 as a better indicator of potential human efficacy (lung Cmax/EC90 ratio was also calculable for a limited number of drugs). Using this parameter hydroxychloroquine, chloroquine, mefloquine, atazanavir (boosted with ritonavir), tipranavir (boosted with ritonavir), ivermectin, azithromycin and lopinavir (boosted with ritonavir) were all predicted to achieve lung concentrations over 10-fold higher than their reported EC50. This analysis was not possible for nelfinavir because insufficient data were available to calculate KpUlung but nitozoxanide and sulfadoxine were also predicted to exceed their reported EC50 by 3.1- and 1.5-fold in lung, respectively. The antiviral activity data reported to date is of variable quality and conducted under different conditions by different investigators. However, this analysis has prioritised candidates with the best chance for success in therapy or chemoprevention of Covid-19 based upon the currently available in vitro activity and human plasma pharmacokinetic data. Future studies should focus on EC90 values and discuss findings in the context of achievable exposures in humans, especially within target compartments such as the lung, in order to maximise the potential for success of proposed human clinical trials.
VEGF mRNA levels are upregulated by lipopolyssacharide in the cervix of pregnant mice

Cervical remodeling (CR) is one of the few biological processes that entails physiological inflammation, and can be significantly enhanced by inflammation‐inducing agents, such as lipopolyssacharide (LPS), a component of bacteria cell wall. Infection is one of the leading causes of preterm labor, and CR and inflammation are both closely associated with microvascular alterations. Of note, we recently identified and characterized expression of a potent regulator of the vasculature, vascular endothelial growth factor (VEGF) and VEGF signaling molecules in the cervix of pregnant and parturient rats. Our long term goal is to investigate whether the mechanism(s) underlying LPS‐enhanced CR involve VEGF and alters microvasculature. Here, we sought to characterize the pattern of VEGF mRNA expression in LPS‐treated pregnant mice using PCR. LPS dissolved in normal saline was administered intra‐cervically in a dose‐dependent manner to three groups of mice at day 12 of pregnancy (n=3 per group), namely vehicle only, 250ng and 300ng. The cervix was harvested 6 hrs later and levels of VEGF mRNA were analyzed by PCR. Our preliminary data revealed that LPS up regulated levels of VEGF mRNA in a dose‐dependent manner. These studies show that LPS‐induced preterm labor involves enhanced VEGF synthesis, which may lead to abnormal microvascular remodeling and mobilization and infiltration of vascular‐derived CR mediators.
Sequential Data Assimilation of the Stochastic SEIR Epidemic Model for Regional COVID-19 Dynamics

Newly emerging pandemics like COVID-19 call for predictive models to implement precisely tuned responses to limit their deep impact on society. Standard epidemic models provide a theoretically well-founded dynamical description of disease incidence. For COVID-19 with infectiousness peaking before and at symptom onset, the SEIR model explains the hidden build-up of exposed individuals which creates challenges for containment strategies. However, spatial heterogeneity raises questions about the adequacy of modeling epidemic outbreaks on the level of a whole country. Here, we show that by applying sequential data assimilation to the stochastic SEIR epidemic model, we can capture the dynamic behavior of outbreaks on a regional level. Regional modeling, with relatively low numbers of infected and demographic noise, accounts for both spatial heterogeneity and stochasticity. Based on adapted models, short-term predictions can be achieved. Thus, with the help of these sequential data assimilation methods, more realistic epidemic models are within reach.
Estimating COVID-19 outbreak risk through air travel

Background: COVID-19 has spread rapidly across the globe during the first several months of 2020, creating a pandemic. Substantial, non-discriminatory limitations have been imposed on air travel to inhibit this spread. As the disease prevalence and incidence will decrease, more specific control measures will be sought so that commercial air travel can continue to operate yet not impose a high threat of COVID-19 resurgence. Methods: We use modelled global air travel data and population density estimates to analyse the risk posed by 1364 airports to initiate a COVID-19 outbreak. We calculate the risk using a probabilistic approach that considers the volume of air travelers between airports and the R0 of each location, scaled by population density. This exercise is performed globally as well as specifically for two potentially vulnerable locations: Africa and India. Results: We show that globally, many of the airports posing the highest risk are in China and India. An outbreak of COVID-19 in Africa is most likely to originate in a passenger travelling from Europe. On the other hand, the highest risk to India is from domestic travellers. Our results are robust to changes in the underlying epidemiological assumptions. Conclusions: Variation in flight volumes and destinations creates a non-uniform distribution of the risk different airports pose to resurgence of a COVID-19 outbreak. We suggest the method presented here as a tool for the estimation of this risk. Our method can be used to inform efficient allocation of resources, such as tests identifying infected passengers, so that they could be differentially deployed in various locations.
Patient-derived mutations impact pathogenicity of SARS-CoV-2

The sudden outbreak of the severe acute respiratory syndrome-coronavirus (SARS-CoV-2) has spread globally with more than 1,300,000 patients diagnosed and a death toll of 70,000. Current genomic survey data suggest that single nucleotide variants (SNVs) are abundant. However, no mutation has been directly linked with functional changes in viral pathogenicity. Here we report functional characterizations of 11 patient-derived viral isolates, all of which have at least one mutation. Importantly, these viral isolates show significant variation in cytopathic effects and viral load, up to 270-fold differences, when infecting Vero-E6 cells. We observed intrapersonal variation and 6 different mutations in the spike glycoprotein (S protein), including 2 different SNVs that led to the same missense mutation. Therefore, we provide direct evidence that the SARS-CoV-2 has acquired mutations capable of substantially changing its pathogenicity.
Trends and prediction in daily incidence and deaths of COVID-19 in the United States: a search-interest based model

Background and Objectives: The coronavirus disease 2019 (COVID-19) infected 586,000 patients in the U.S. However, the COVDI-19 daily incidence and deaths in the U.S. are poorly understood. Internet search interest was found highly correlated with COVID-19 daily incidence in China, but not yet applied to the U.S. Therefore, we examined the association of search interest with COVID-19 daily incidence and deaths in the U.S. Methods: We extracted the COVDI-19 daily incidence and death data in the U.S. from two population-based datasets. The search interest of COVID-19 related terms was obtained using Google Trends. Pearson correlation test and general linear model were used to examine correlations and predict future trends, respectively. Results: There were 555,245 new cases and 22,019 deaths of COVID-19 reported in the U.S. from March 1 to April 12, 2020. The search interest of COVID, COVID pneumonia, and COVID heart were correlated with COVDI-19 daily incidence with about 12-day of delay (Pearson r=0.978, 0.978 and 0.979, respectively) and deaths with 19-day of delay (Pearson r=0.963, 0.958 and 0.970, respectively). The COVID-19 daily incidence and deaths appeared to both peak on April 10. The 4-day follow-up with prospectively collected data showed moderate to good accuracies for predicting new cases (Pearson r=-0.641 to -0.833) and poor to good acuracies for daily new deaths (Pearson r=0.365 to 0.935). Conclusions: Search terms related to COVID-19 are highly correlated with the trends in COVID-19 daily incidence and deaths in the U.S. The prediction-models based on the search interest trend reached moderate to good accuracies.
No Evidence for Temperature-Dependence of the COVID-19 Epidemic

The pandemic of the COVID-19 extended from China across the north-temperate zone, and more recently to the tropics and southern hemisphere. The hypothesis that COVID-19 spread is temperature-dependent was tested based on data derived from nations across the world and provinces in China. No evidence of a pattern between spread rates and ambient temperature was found, suggesting that the SARS-CoV-2 is unlikely to behave as a seasonal respiratory virus.
Clinical characteristics of 101 COVID-19 nonsurvivors in Wuhan, China: a retrospective study

Background The outbreak of COVID-19 has aroused global concerns. We aimed to describe the clinical characteristics of COVID-19 nonsurvivors and analyze possible causes for the rapid disease progress to death. Methods Patients with confirmed COVID-19 died in Renmin Hospital of Wuhan University before February 15, 2020, were identified. We obtained epidemiological, demographic, and clinical data from electronic medical records. Results Among 101 nonsurvivors, the median age was 71.0 years (IQR, 59.0-80.0), 59.4% were male, 79.2% had one or more comorbidities including hypertension (58.4%), cardiovascular disease (22.8%), diabetes (20.8%) etc. The most common symptoms were fever (76.2%), cough (58.4%) and dyspnea (54.5%). Respiratory failure (99.0%), acute cardiac injury (52.5%), sepsis (40.6%) and acute kidney injury (23.8%) were most common complications. Compared with patients died after 3 days of admission, patients died within 3 days of admission had significantly higher white blood cell count (10.8 vs 6.7×10 9 /L, P =0.001) and neutrophil count (8.9 vs 5.5×10 9 /L, P =0.001), longer prothrombin time (13.2 vs 12.5 s, P =0.040), higher D-dimer concentration (7.64 vs 2.82, P =0.040), higher lactate level (2.9 vs 2.2 mmol/L, P =0.042), lower oxygen saturation (85.0% vs 91.0%, P =0.008), and were more likely to suffer sepsis (52.1% vs 30.2%, P =0.025). P <0.05 was considered statistically significant.