Project description:To analyse gene expression pattern in different disease state of COVID-19 patients. Experimental workflow: 1) rRNA was removed by using RNase H method, 2) QAIseq FastSelect RNA Removal Kit was used to remove the Globin RNA, 3) The purified fragmented cDNA was combined with End Repair Mix, then add A-Tailing Mix, mix well by pipetting, incubation, 4) PCR amplification, 5) Library quality control and pooling cyclization, 6) The RNA library was sequenced by MGI2000 PE100 platform with 100bp paired-end reads. Analysis steps: 1) RNA-seq raw sequencing reads were filtered by SOAPnuke (Li et al., 2008) to remove reads with sequencing adapter, with low-quality base ratio (base quality < 5) > 20%, and with unknown base (’N’ base) ratio > 5%. 2) Reads aligned to rRNA by Bowtie2 (v2.2.5) (Langmead and Salzberg, 2012) were removed. 3) The clean reads were mapped to the reference genome using HISAT2 (Kim et al., 2015). Bowtie2 (v2.2.5) was applied to align the clean reads to the transcriptome. 4)Then the gene expression level (FPKM) was determined by RSEM (Li and Dewey, 2011). Genes with FPKM > 0.1 in at least one sample were retained.
Project description:To analyse gene expression pattern in different disease state of COVID-19 patients. Experimental workflow: 1) Small RNA enrichment and purification, 2) Adaptor ligation and Unique molecular identifiers (UMI) labeled Primer addition, 3) RT-PCR, Library quantitation and pooling cyclization, 4) Library quality control, 5) Small RNAs were sequenced by BGI500 platform with 50bp single-end reads resulting in at least 20M reads for each sample. Analysis steps: 1) Small RNA raw sequencing reads with low quality tags (which have more than four bases whose quality is less than ten, or have more than six bases with a quality less than thirteen.), the reads with poly A tags, and the tags without 3’ primer or tags shorter than 18nt were removed. 2) After data filtering, the clean reads were mapped to the reference genome and other sRNA database including miRbase, siRNA, piRNA and snoRNA using Bowtie2 (Langmead and Salzberg, 2012). Particularly, cmsearch (Nawrocki and Eddy, 2013) was performed for Rfam mapping. 3) The small RNA expression level was calculated by counting absolute numbers of molecules using unique molecular identifiers (UMI, 8-10nt). MiRNA with UMI count lager than 1 in at least one sample were considered as expressed.
Project description:Throughout 2020, national and subnational governments worldwide implemented nonpharmaceutical interventions (NPIs) to contain the spread of COVID-19. These included community quarantines, also known as lockdowns, of varying length, scope, and stringency that restricted mobility. To assess the effect of community quarantines on urban mobility in the Philippines, we analyze a new source of data: cellphone-based origin-destination flows made available by a major telecommunication company. First, we demonstrate that mobility dropped to 26% of the pre-lockdown level in the first month of lockdown and recovered and stabilized at 70% in August and September of 2020. Then we quantify the heterogeneous effects of lockdowns by city's employment composition. A city with 10 percentage points more employment share in work-from-home friendly sectors is found to have experienced an additional 2.8% decrease in mobility under the most stringent lockdown policy. Similarly, an increase of 10 percentage points in employment share in large and medium-sized firms was associated with a1.9% decrease in mobility on top of the benchmark reduction. We compare our findings with cross-country evidence on lockdowns and mobility, discuss the economic implications for containment policies in the Philippines, and suggest additional research that can be based on this novel dataset.
Project description:Objective: To evaluate the feasibility of a smartphone remote patient monitoring approach in a real-life Parkinson's disease (PD) cohort during the Italian COVID-19 lockdown. Methods: Fifty-four non-demented PD patients who were supposed to attend the outpatient March clinic were recruited for a prospective study. All patients had a known UPDRS-III and a modified Hoehn and Yahr (H&Y) score and were provided with a smartphone application capable of providing indicators of gait, tapping, tremor, memory and executive functions. Different questionnaires exploring non-motor symptoms and quality of life were administered through phone-calls. Patients were asked to run the app at least twice per week (i.e., full compliance). Subjects were phone-checked weekly throughout a 3-week period for compliance and final satisfaction questionnaires. Results: Forty-five patients (83.3%) ran the app at least once; Twenty-nine (53.7%) subjects were half-compliant, while 16 (29.6%) were fully compliant. Adherence was hindered by technical issues or digital illiteracy (38.7%), demotivation (24%) and health-related issues (7.4%). Ten patients (18.5%) underwent PD therapy changes. The main factors related to lack of compliance included loss of interest, sadness, anxiety, the absence of a caregiver, the presence of falls and higher H&Y. Gait, tapping, tremor and cognitive application outcomes were correlated to disease duration, UPDRS-III and H&Y. Discussion: The majority of patients were compliant and satisfied by the provided monitoring program. Some of the application outcomes were statistically correlated to clinical parameters, but further validation is required. Our pilot study suggested that the available technologies could be readily implemented even with the current population's technical and intellectual resources.
Project description:Globally, ambient air pollution claims ~9 million lives yearly, prompting researchers to investigate changes in air quality. Of special interest is the impact of COVID-19 lockdown. Many studies reported substantial improvements in air quality during lockdowns compared with pre-lockdown or as compared with baseline values. Since the lockdown period coincided with the onset of the rainy season in some tropical countries such as Nigeria, it is unclear if such improvements can be fully attributed to the lockdown. We investigate whether significant changes in air quality in Nigeria occurred primarily due to statewide COVID-19 lockdown. We applied a neural network approach to derive monthly average ground-level fine aerosol optical depth (AODf) across Nigeria from year 2001-2020, using the Multi-angle Implementation of Atmospheric Correction (MAIAC) AODs from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) satellites, AERONET aerosol optical properties, meteorological and spatial parameters. During the year 2020, we found a 21% or 26% decline in average AODf level across Nigeria during lockdown (April) as compared to pre-lockdown (March), or during the easing phase-1 (May) as compared to lockdown, respectively. Throughout the 20-year period, AODf levels were highest in January and lowest in May or June, but not April. Comparison of AODf levels between 2020 and 2019 shows a small decline (1%) in pollution level in April of 2020 compare to 2019. Using a linear time-lag model to compare changes in AODf levels for similar months from 2002 to 2020, we found no significant difference (Levene's test and ANCOVA; α = 0.05) in the pollution levels by year, which indicates that the lockdown did not significantly improve air quality in Nigeria. Impact analysis using multiple linear regression revealed that favorable meteorological conditions due to seasonal change in temperature, relative humidity, planetary boundary layer height, wind speed and rainfall improved air quality during the lockdown.
Project description:The causative organism, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), exhibits a wide spectrum of clinical manifestations in disease-ridden patients. Differences in the severity of COVID-19 ranges from asymptomatic infections and mild cases to the severe form, leading to acute respiratory distress syndrome (ARDS) and multiorgan failure with poor survival. MiRNAs can regulate various cellular processes, including proliferation, apoptosis, and differentiation, by binding to the 3′UTR of target mRNAs inducing their degradation, thus serving a fundamental role in post-transcriptional repression. Alterations of miRNA levels in the blood have been described in multiple inflammatory and infectious diseases, including SARS-related coronaviruses. We used microarrays to delineate the miRNAs and snoRNAs signature in the peripheral blood of severe COVID-19 cases (n=9), as compared to mild (n=10) and asymptomatic (n=10) patients, and identified differentially expressed transcripts in severe versus asymptomatic, and others in severe versus mild COVID-19 cases. A cohort of 29 male age-matched patients were selected. All patients were previously diagnosed with COVID-19 using TaqPath COVID-19 Combo Kit (Thermo Fisher Scientific, Waltham, Massachusetts), or Cobas SARS-CoV-2 Test (Roche Diagnostics, Rotkreuz, Switzerland), with a CT value < 30. Additional criterion for selection was age between 35 and 75 years. Participants were grouped into severe, mild and asymptomatic. Classifying severe cases was based on requirement of high-flow oxygen support and ICU admission (n=9). Whereas mild patients were identified based on symptoms and positive radiographic findings with pulmonary involvement (n=10). Patients with no clinical presentation were labelled as asymptomatic cases (n=10).
Project description:In the wake of the COVID-19 pandemic many countries implemented containment measures to reduce disease transmission. Studies using digital data sources show that the mobility of individuals was effectively reduced in multiple countries. However, it remains unclear whether these reductions caused deeper structural changes in mobility networks and how such changes may affect dynamic processes on the network. Here we use movement data of mobile phone users to show that mobility in Germany has not only been reduced considerably: Lockdown measures caused substantial and long-lasting structural changes in the mobility network. We find that long-distance travel was reduced disproportionately strongly. The trimming of long-range network connectivity leads to a more local, clustered network and a moderation of the "small-world" effect. We demonstrate that these structural changes have a considerable effect on epidemic spreading processes by "flattening" the epidemic curve and delaying the spread to geographically distant regions.
Project description:ObjectivesThis cross-sectional study aims to investigate health-related behaviors including tobacco consumption among patients with cardiovascular diseases (CVD), during the first COVID-19-related lockdown.MethodsAfter 5 weeks of COVID-19 lockdown, 220 patients with chronic coronary syndromes (CCS) and 124 with congestive heart failure (CHF) answered a phone questionnaire.ResultsAmong these 344 patients, 43 (12.5%) were current smokers, and none had quit during the lockdown. When compared with non-smokers, smokers were 15 years younger, more often diabetic, more likely to live in an urban than a rural lockdown location, and more often in the CCS cohort (p = 0.011). Smokers described greater psychological impairment, but their rates of decrease in physical activity and of increase in screen time were similar to non-smokers. More than one-third (13/43) increased their tobacco consumption, which was mainly related to stress or boredom, but not driven by media messages on a protective effect of nicotine.ConclusionsDuring the first COVID-19 lockdown, we found a decrease in favorable lifestyle behaviors among patients with CVD. Strikingly, one-third of smokers with CCS or CHF increased their tobacco consumption. Given the major impact of persistent smoking in patients with CVD, this highlights the need for targeted prevention strategies, in particular during such periods.
Project description:ObjectivesMeasures to control the on-going COVID-19 pandemic such as quarantine and social distancing, together with information overload about the sporadic spread of the disease have negatively impacted many individuals' mental and psychosocial health. This study aimed to investigate the prevalence of self-reported mental health parameters and the coping mechanisms of employees and students in a Saudi State University.MethodsAn online survey in both Arabic and English was launched targeting students, staff and faculty of King Saud University from May 11 to June 6, 2020, the peak of Saudi Arabia's nationwide lockdown. A total of 1542 respondents (726 males and 816 females) aged 20-65 years old participated.ResultsMajority of the respondents claimed to have suffered from anxiety (58.1%), depression (50.2%) and insomnia (32.2%) during the lockdown. On average, 65.3% respondents agreed that family bond strengthened during lockdown. Those in the highest quartile of family bonding score (Q4) were 41% [odds ratio (OR) and 95% confidence interval (CI) of 0.59 (0.39-0.87), p < 0.001] and 59% [OR 0.41 (CI 0.27-0.64), p < 0.001] were less likely to be anxious and depressed, respectively, even after adjusting for covariates. This independent and significant inverse association was more apparent in females than males.ConclusionSelf-reported acute mental health disorders were common within the academic community during the COVID-19 lockdown. Strength of family bonding as a coping mechanism was instrumental in preserving mental well-being, especially in females.