Impact and mechanism of the COVID-19 on the spatio-temporal patterns of Zhangjiajie tourism network attention

Based on the spatial-temporal perspective of geography, this paper quantitatively measures the impact of COVID-19 on the spatial-temporal pattern of tourism network attention in Zhangjiajie, and finally summarizes the influencing factors and mechanisms. The results show as follows. (1) From the perspective of time, the online attention of tourism in Zhangjiajie shows a trend of "decline to rebound, and to stability", which reflects the temporal mobility of the effect of COVID-19 on the tourism. (2) From the spatial dimension, the scale-order of attention to the Zhangjiajie' s tourism network is relatively stable, and the effect of COVID-19 on the tourism shows a trend of "distance decay" on the whole. (3) The adjustment of tourists' perception of tourism risk, destination familiarity and location, tourists' risk tolerance and authority restriction are the influencing factors of tourism net-work attention. These factors interact with each other to drive the spatio-temporal change of tourism network attention.

Unraveling the Impact of COVID-19 Pandemic Dynamics on Commercial Water-Use Variation

Water use was impacted significantly by the COVID-19 pandemic. Although previous studies quantitatively investigated the effects of COVID-19 on water use, the relationship between water-use variation and COVID-19 dynamics (i.e., the spatial-temporal characteristics of COVID-19 cases) has received less attention. This study developed a two-step methodology to unravel the impact of COVID-19 pandemic dynamics on water-use variation. First, using a water-use prediction model, the water-use change percentage (WUCP) indicator, which was calculated as the relative difference between modeled and observed water use, i.e., water-use variation, was used to quantify the COVID-19 effects on water use. Second, two indicators, i.e., the number of existing confirmed cases (NECC) and the spatial risk index (SRI), were applied to characterize pandemic dynamics, and the quantitative relationship between WUCP and pandemic dynamics was examined by means of regression analysis. We collected and analyzed 6-year commercial water-use data from smart meters of Zhongshan District in Dalian City, Northeast China. The results indicate that commercial water use decreased significantly, with an average WUCP of 59.4%, 54.4%, and 45.7%during the three pandemic waves, respectively, in Dalian. Regression analysis showed that there was a positive linear relationship between water-use changes (i.e., WUCP) and pandemic dynamics (i.e., NECC and SRI). Both the number of COVID-19 cases and their spatial distribution impacted commercial water use, and the effects were weakened by restriction strategy improvement, and the accumulation of experience and knowledge about COVID-19. This study provides an in-depth understanding of the impact of COVID-19 dynamics on commercial water use. The results can be used to help predict water demand under during future pandemic periods or other types of natural and human-made disturbance.

Potential acceptance of COVID-19 vaccination among population residing in the urban and rural field practice areas of a tertiary care teaching hospital of West Bengal: a comparative study

Background: "Covishield" and "Covaxin" were the two vaccines which were approved for emergency use in India. As there is uncertainty regarding these vaccines, this study aimed to estimate the proportion of acceptance of vaccination against COVID-19 in West Bengal;to identify the possible barriers of vaccine acceptance prevalent among the study population and;to find out the association of acceptance of COVID-19 vaccination with the socio-demographic and other predictor variables. Methods: Oobservational study with cross sectional design was conductedon 294 participants in Muchisa, Budge Budge II and Ward No. 81 of Borough 10, Kolkata Municipal Corporation (KMC) selected by Stratified Random Sampling in 2021 using a predesigned, pretested and structured schedule. The statistical analysis was done using Microsoft Office Excel 2007 and Statistical Package for the Social Sciences Version 16.0. The data were explored using Pearson's chi-square test and logistic regression. A p-value of <0.05 was considered significant. Results: The potential acceptance of Covid-19 vaccination was 59.5% with higher adoption in urban population. The most potential barrier in acceptance of vaccine was "fear of side effects". Higher level of education and history of Covid-19 in the locality were associated with higher acceptance of the vaccination in both the urban and rural areas. Conclusion: A study emphasized on the need for mass campaigns and other behavior change communication activities for addressing the myths surrounding the disease and the newer vaccines and hence improving the currently low vaccine acceptance.

Genetic associations from the Long COVID-19 Host Genetics Initiative contribute to understanding of the genetics of prolonged symptoms in COVID-19

Background/Objectives: One of the most remarkable features of SARS-CoV-2 infection is that a large proportion of individuals are asymptomatic while others experience progressive, even lifethreatening acute respiratory distress syndrome, and some suffer from prolonged symptoms (long COVID). The contribution of host genetics to susceptibility and severity of infectious disease is well-documented, and include rare monogenic inborn errors of immunity as well as common genetic variation. Studies on genetic risk factors for long COVID have not yet been published. Method(s): We compared long COVID (1534) to COVID-19 patients (96,692) and population controls (800,353) using both questionnaire and EHR- based studies. First meta-analysis of 11 GWAS studies from 8 countries did not show genome-wide significant associations. Result(s): Testing 24 variants earlier associated to COVID-19 susceptibility or severity by COVID-19 Host Genetics Initiative showed genetic variation in rs505922, an intronic variant in ABO blood group gene, to be associated with long COVID compared to population controls (OR = 1.16, 95% CI: 1.07-1.27, p = 0.033). (Within-COVID analysis gave similar OR, but was not significant after conservative Bonferroni correction (OR = 1.17, 95% CI: 1.06-1.30, p = 092)). Conclusion(s): The first data freeze of the Long COVID Host Genetics Initiative suggests that the O blood group is associated with a 14% reduced risk for long COVID. The following data freezes with growing sample sizes will possibly elucidate long COVID pathophysiology and pave the way for possible treatments for long lasting COVID symptoms.

The Evolution of the Covid-19 Pandemic in Pediatric Patients with Sickle Cell Disease: From Alpha to Omicron

Background: Approximately two years ago, COVID-19 was declared a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and through genomic surveillance, we have seen the emergence of variants of SARS-CoV-2. In the United States, over 78 million cases and >900,000 deaths attributable to COVID-19 have been reported. SCD was identified as a risk factor for severe COVID-19 disease in adults and pediatric patients. The emergence of novel SARs- CoV-2 variants has led to challenges in diagnosis, treatment, and prediction of long-term sequelae in individuals with SCD and COVID-19. Aim(s): We compare the overall seasonal variation of COVID-19 variants and patterns of healthcare utilization and clinical presentation over time in pediatric patients with SCD and COVID-19 at Children's National Hospital (CNH). Method(s): Our single-center, observational cohort study included 193 pediatric patients with SCD (0-21 years) with PCR-confirmed SARSCoV- 2 infection between March 31, 2020, and January 31, 2022. Per the SECURE SCD Registry definitions, clinical severity was classified as asymptomatic, mild, moderate, and severe. Result(s): A total of 193 unique patients with SCD and positive SARS-CoV-2 PCRs between March 2020-January 2022 were included in our registry. Most patients were female (51.8%), and the mean age was 11.2 years (SD 6.5 years). Most of the cohort resides in Maryland (N=135), and HbSS was the dominant genotype (69.4%). During the alpha dominant variant of the COVID-19 pandemic (March 2020- June 2021) there were 70 cases, followed by 40 cases during the Delta variant (July 2021- December 19, 2021), and 83 cases during the Omicron variant dominance (from December 20, 2021-January 31,2022). There were 149 patients (77%) that presented to the emergency department (ED) or were hospitalized. There were a total of 80 hospitalizations (41.5%), and a relative comparison showed that the percentage of hospitalizations was highest during the delta wave (47.5%) and lowest during the omicron wave (36.1%) (p= 0.407). ED-only utilization was highest in the era of omicron (43.4%, N=36), followed by delta (32.5%, N=13), and then alpha (30%, N=21)(p=0.197). The most common SCD-related complication was vaso-occlusive (VOC) pain (33%, N=64) which accounted for half of all hospital admissions (51%, N=41 of 80). Acute chest syndrome (ACS) was reported in 40% (N=32) of admitted patients and was highest in the alpha era (54.8%, N=17). The use of blood transfusion therapy was highest in the alpha (N=17) and delta (N=14) variants, while Remdesivir use was highest in omicron (N=15). A total of 6 patients received monoclonal antibodies (Delta, N=4;omicron, N=2). Throughout all the variants, there was a significant difference in COVID-19 clinical severity (p>0.005). Of the patients classified as asymptomatic (13%, N=25), seventy-two percent (n=18) were diagnosed during the alpha variant. Mild severity was the most prevalent (69%, N=134), with the omicron variant having the highest cases (51.5%, N=69). Severe cases were observed in all variants (6.7%, N=13) but were most prevalent during the alpha variant (46.2%, N=6). Summary - Conclusion(s): Interestingly, while the relative percentage of hospitalizations was lowest during the omicron wave, it saw the highest percentages of ER utilization. Overall, COVID-19 remains mild in pediatric patients with SCD, and notably, there was higher health care utilization in the omicron era.

T-cell mediated response to emerging COVID19 strains in patients with cancer studied via deep learning

Cancer patients, particularly those receiving B cell-depleting therapy for lymphoid malignancies, are at risk of prolonged SARS-CoV-2 infection, poorer clinical outcomes, and delayed initiation or disruption of cancer-directed therapy (Lee at al., 2022, Clark et al., 2021). We first studied T-cell mediated response to the Wuhan strain of SARS-CoV-2 in a cohort of 69 patients with hematologic and solid cancers, including 18 patients who received prior B-cell depleting therapy. Patients with prolonged COVID-19 clearance, defined by a positive PCR test for longer than 30 days, had a broad but poorly converged CD8+ dominant response and a lacking CD4+ response. To conduct this analysis, we performed bulk T-cell receptor (TCR) sequencing of 121 blood samples and tracked over time TCR repertoire statistics such as clonality, convergence, breadth, and depth of COVID-19-associated TCRs during the active and convalescent periods of COVID-19 infection. These SARS-CoV-2-associated TCRs were identified leveraging immunoSEQ T-MAP database (Snyder et al., 2020), a set of TCR sequences derived from COVID-19 patients and experimentally identified as responsive to MHC Class I and II epitopes from the Wuhan SARS-CoV-2 strain using the multiplex identification of TCR antigen assay (Klinger et al., 2015). To extend our TCR repertoire analysis to other SARS-CoV-2 variants, including Omicron, we developed a deep learning (DL) method to predict TCR specificities for new SARS-CoV-2 epitopes. This DL approach also permits the identification of SARS-CoV2-responsive TCRs private to an individual. Combining this DL approach with our TCR statistics methodology, we studied the dynamics of T-cell response to COVID-19 vaccinations in a cohort of 50 patients with cancer and analyzed TCR repertoire characteristics associated with different degrees of COVID-19 severity in a cohort of 42 cancer patients who contracted the Omicron. Understanding cellular response to novel infections is critical for patient care in the context of cancer, and our novel DL-based approach can leverage existing datasets to analyze and track response to emerging viral strains.

Agility in supply chain planning and execution

For variability that can be described by a distribution function or estimated using frequency of occurrence, the combination of new digital sources of data and advanced analytics/optimization techniques is the subject of a lot of recent work in academia. However, not much has been written on the design and implementation of decision systems to detect and respond to "special," uncertain, and non-predictable causes of variation. The recent COVID-19 pandemic is an all too real example of how disruptive this variation can be to the unprepared. Clearly, structurally flexible and short lead time supply chains can help. Beyond this flexibility, we introduce the concept of agile supply chain planning, which deals with early detection and quick and effective decision-making. This chapter describes how agile supply chain planning systems can complement the advances made in dealing with normal variation by successfully dealing with "special" events and lead to significant impact both for drastic, generalized crises or less dramatic occasions. Keywords: agile;decision making;planning;execution;uncertainty, special causes of variation;digital fact based. © Springer Nature Switzerland AG 2023. All rights reserved.

Unbalanced emission reductions of different species and sectors in China during COVID-19 lockdown derived by multi-species surface observation assimilation

The unprecedented lockdown of human activities during the COVID-19 pandemic has significantly influenced social life in China. However, understanding the impact of this unique event on the emissions of different species is still insufficient, prohibiting the proper assessment of the environmental impacts of COVID-19 restrictions. Here we developed a multi-air-pollutant inversion system to simultaneously estimate the emissions of NOx, SO2, CO, PM2.5 and PM10 in China during COVID-19 restrictions with high temporal (daily) and horizontal (15 km) resolutions. Subsequently, contributions of emission changes versus meteorological variations during the COVID-19 lockdown were separated and quantified. The results demonstrated that the inversion system effectively reproduced the actual emission variations in multi-air pollutants in China during different periods of COVID-19 lockdown, which indicate that the lockdown is largely a nationwide road traffic control measure with NOx emissions decreasing substantially by ∼40 %. However, emissions of other air pollutants were found to only decrease by∼10% because power generation and heavy industrial processes were not halted during lockdown, and residential activities may actually have increased due to the stay-at-home orders. Consequently, although obvious reductions of PM2.5 concentrations occurred over the North China Plain (NCP) during the lockdown period, the emission change only accounted for 8.6 % of PM2.5 reductions and even led to substantial increases in O3. The meteorological variation instead dominated the changes in PM2.5 concentrations over the NCP, which contributed 90 % of the PM2.5 reductions over most parts of the NCP region. Meanwhile, our results suggest that the local stagnant meteorological conditions, together with inefficient reductions of PM2.5 emissions, were the main drivers of the unexpected PM2.5 pollution in Beijing during the lockdown period. These results highlighted that traffic control as a separate pollution control measure has limited effects on the coordinated control of O3 and PM2.5 concentrations under current complex air pollution conditions in China. More comprehensive and balanced regulations for multiple precursors from different sectors are required to address O3 and PM2.5 pollution in China.

Winter-autumn air pollution control plan in North China modified the PM2.5 compositions and sources in Central China

The additional impact of emission-reduction measures in North China (NC) during autumn and winter on the air quality of downwind regions is an interesting but less addressed topic. The mass concentrations of routine air pollutants, the chemical compositions, and sources of fine particles (PM2.5) for January 2018, 2019, and 2020 at a megacity of Central China were identified, and meteorology-isolated by a machine-learning technique. Their variations were classified according to air mass direction. An unexpectedly sharp increase in emission-related PM2.5 by 22.7% (18.0 mug m-3) and 25.7% (19.4 mug m-3) for air masses from local and NC in 2019 was observed compared to those of 2018. Organic materials exhibited the highest increase in PM2.5 compositions by 6.90 mug m-3 and 6.23 mug m-3 for the air masses from local and NC. PM2.5 source contributions related to emission showed an upsurge from 1.39 mug m-3 (biomass burning) to 24.9 mug m-3 (secondary inorganic aerosol) in 2019 except for industrial processes, while all reduced in 2020. From 2018 to 2020, the emission-related contribution of coal combustion to PM2.5 increased from 10.0% to 19.0% for air masses from the local area. To support the priority natural gas quotas in northern China, additional coal in cities of southern China was consumed, raising related emissions from transportation activities and road dust in urban regions, as well as additional biofuel consumption in suburban or rural regions. All these activities could explain the increased primary PM2.5 and related precursor NO2. This study gave substantial evidence of air pollution control measures impacting the downwind regions and promote the necessity of air pollution joint control across the administration.Copyright © 2023 Elsevier Ltd

Generalized Linear Regression Model to Determine the Threshold Effects of Climate Variables on Dengue Fever: A Case Study on Bangladesh

One of the leading causes of the increase in the intensity of dengue fever transmission is thought to be climate change. Examining panel data from January 2000 to December 2021, this study discovered the nonlinear relationship between climate variables and dengue fever cases in Bangladesh. To determine this relationship, in this study, the monthly total rainfall in different years has been divided into two thresholds: (90 to 360 mm) and (<90 or >360 mm), and the daily average temperature in different months of the different years has been divided into four thresholds: (16degreeC to <=20degreeC), (>20degreeC to <=25degreeC), (>25degreeC to <=28degreeC), and (>28degreeC to <=30degreeC). Then, quasi-Poisson and zero-inflated Poisson regression models were applied to assess the relationship. This study found a positive correlation between temperature and dengue incidence and furthermore discovered that, among those four average temperature thresholds, the total number of dengue cases is maximum if the average temperature falls into the threshold (>28degreeC to <=30degreeC) and minimum if the average temperature falls into the threshold (16degreeC to <=20degreeC). This study also discovered that between the two thresholds of monthly total rainfall, the risk of a dengue fever outbreak is approximately two times higher when the monthly total rainfall falls into the thresholds (90 mm to 360 mm) compared to the other threshold. This study concluded that dengue fever incidence rates would be significantly more affected by climate change in regions with warmer temperatures. The number of dengue cases rises rapidly when the temperature rises in the context of moderate to low rainfall. This study highlights the significance of establishing potential temperature and rainfall thresholds for using risk prediction and public health programs to prevent and control dengue fever.Copyright © 2023 Shamima Hossain.

Íkiz Açık Hipotezi Türkiye'de Geçerliliği: Zamanla Değişen Nedensellik Analizi

Finansal ve ekonomik istikrarın sürdürülebilirliği açısından iç ve dış dengenin birlikte sağlandığı maliye politikalarının uygulanması son derece önem arz etmektedir. Bu nedenle, cari işlemler ile bütçe açıkları arasındaki ilişkinin incelenmesi politika yapıcılar, araştırmacılar ve ekonomistler arasında tartışılan önemli konulardan biri haline gelmiştir. Bu kapsamda, çalışmada Türkiye için Ocak 1998 – Aralık 2021 döneminde ikiz açık hipotezi, Granger nedenselliğin geçici istikrarını yeniden değerlendirmek amacıyla kullanılan yinelemeli algoritmalar kullanılarak araştırılmaktadır. Zamanla değişen Granger nedensellik analizden elde edilen bulgular, Türkiye'de ele alınan dönemde cari işlemler dengesi ve bütçe açığı arasında çift yönlü bir nedensellik ilişkisi olduğunu ortaya koymaktadır. Bu sonuç, Türkiye'de ikiz açık hipotezinin geçerli olduğunu ifade etmektedir. Aynı zamanda, nedensellik ilişkisinde anlamlı geçici değişimlerin olduğu gözlenmektedir. Seçim ve kriz dönemlerinde ikiz açık hipotezinin varlığı mevcut iken, 2002-2008 ve 2015-2021 dönemlerinde cari işlemler hedeflemesi politikası uygulanmaktadır. COVID-19 pandemisi döneminde ise, cari işlemler dengesi ve bütçe açığı arasında çift yönlü asimetrik aktarım mekanizmasının işlediği ifade edilebilir.Alternate :Implementing fiscal policies to ensure domestic and foreign balances together is of vital importance to sustain financial and economic stability. Therefore, investigating the relationship between budget deficit and current account deficit is one of the issues debated by popolicymakersnd economists. In this context, the existence of the win deficit hypothesis in Turkey is researched through recursive algorithms used to revaluate the temporal stability of Granger causality for the periods of January 1998-December 2021. The findings from time-varying Granger causality analysis reveal the existence of a bidirectional causality relationship between budget deficit and current account deficit. This result means the validity of the twin deficit hypothesis in Turkey. Also, the temporal changes in the so-called casualty relationships are observed significantly. During the electoral and crisis periods there is the existence of the twin deficit hypothesis wthe hileaccountnt acount targeting policy is implemented during the periods of 2002-2008 and 2015-2021. In the COVID-19 period, a bidirectional asymmetric transmission mechanism between budget deficit andcurrentt account deficit is worked can be stated.

Getting it right first time: what have we learnt?

Getting It Right First Time (GIRFT) is a national programme of improvement to identify and reduce unwarranted variation and non-evidence-based practice in healthcare. It aims to improve patient care, increase productivity and reduce costs. Professor Tim Briggs, an orthopaedic surgeon, began the programme with a pilot review visiting every orthopaedic surgery department in England. He used publicly available data to illuminate variation, and worked with the clinicians and management to develop improvements. The impressive initial report in 2015 led to NHS Improvement investing £60m to expand the programme to 40 medical and surgical specialties. The follow-up Orthopaedic report detailed savings of £696m to the NHS. GIRFT is now sharing its data with the CQC and leading the charge with elective recovery following COVID-19. GIRFT differs from previous programmes of improvement through its peer led, supportive approach to promoting change with early engagement of both clinicians and management. Common themes run through the almost 40 specialty reports published to date: variation in procurement and litigation costs, huge variations in patient treatment options (often with a lack of evidence base) and poor data quality. Successfully applied in orthopaedic surgery, it has been taken on enthusiastically by other specialties. Whether it can deliver its objective of £1.4bn savings whilst improving patient outcomes is yet to be seen, but its approach is changing the culture of the NHS.

Tropical Conditions and Outbreak of COVID-19

Background: The new novel Coronavirus 2019 (nCOV-19 or COVID-19) has caused an unprecedented pandemic in humans. All nations have heightened their surveillances after the quick diagnosis of potential cases of the COVID-19. Objective(s): Recent statistics have mentioned that virus outbreak in tropical countries is relatively low compared to cold nations. To support this conclusion, we considered the six main tropical regions to investigate the pandemic distribution at the initial phase. Method(s): Chi-square test was applied to understand the correlation between outbreak and temperature changes. Significant probability P-value was set to P<0.01. P-values were calculated to both positive and death cases. Result(s): Out of 1211562 infected cases, 41776 cases (3.45%) were registered at hightemperature countries (P<0.0001) and 1161786 cases (96.55%) at other countries like European countries or the USA. Moreover, only 1433 mortality cases (2.2%) happened, and the remaining 97.8% of mortality happened among other nations. Conclusion(s): Similar to other respiratory viruses like flu and influenza, there is a low outbreak of COVID-19 in tropical nations compared to the other countries. Apart from weather conditions, it is also recommended to follow the serious preventive measures imposed by governments to survive this novel epidemic.Copyright © 2020

Impact of COVID-19 Pandemic on Antenatal Healthcare in Al-Ramadi city, Iraq

Background:Antenatal care (ANC) is a form of preventive medicine, pregnancy-specific, and general health advice effectively given in the antenatal period. ANC is conducted by healthcare personnel on pregnant women at regular intervals to protect and improve maternal and neonatal health. Objectives: The study aimed to know if the COVID-19 pandemic affects the coverage rate of ANC visits of pregnant women. Materials and methods: This is a retrospective cross-sectional study conducted at Al-Andalus Primary Healthcare Center which is located in the center of Al-Ramadi city, Iraq. We reviewed the records of all ANC first, second, and total visits of the months February, May, and October during the years 2019, 2020, and 2021. Results: The study showed the coverage rate of ANC visits was statistically significantly declined with the restrictions imposed in response to the COVID-19 pandemic. At the end of 2021, there are some elevation in the coverage rate of ANC visits.. Conclusion: The study concluded that the lockdown period in the pandemic state due to COVID-19 led to a significant decline in the coverage rate of ANC visits. © 2022, Al-Anbar Medical Journal.

Impact of coronavirus disease 2019 in seasonal variation of hemoglobin A1c in adults with type 1 diabetes and the effect of the mode of treatment: a single-center retrospective study for 2019 and 2021 and analysis by the mode of treatment.

The study was aimed to investigate the seasonal variation of hemoglobin A1c (HbA1c) in adults with type 1 diabetes (T1D) and the impact of coronavirus disease 2019 (COVID-19) by comparing 2019 and 2021 data and differences in treatment modes. This was a single-center retrospective observational study including 52 adult patients with T1D who regularly visited hospital in 2019 and 2021. Twenty-five patients used multiple daily injections (MDI)/self-measurement of blood glucose (SMBG), 16 used MDI/intermittently scanned continuous glucose monitoring (isCGM), 9 used sensor-augmented pump (SAP), and 2 used continuous subcutaneous insulin infusion (CSII)/isCGM. The mean HbA1c level was calculated for each month. The correlation between monthly means of temperature and HbA1c was investigated. Similar analyses were performed for the MDI/SMBG, MDI/isCGM, and SAP + CSII/isCGM groups. HbA1c levels in 2019 decreased in summer and increased in winter and showed a significant negative correlation with temperature (r = -0.652, p = 0.022). However, HbA1c in 2021 showed no seasonal variation and no correlation with temperature (r = -0.134, p = 0.678) and tended to decline after the three emergency declarations. HbA1c in the MDI/SMBG group showed the same trend as the whole group in 2019 and 2021. However, the effect of seasonal variation in HbA1c was lower in the MDI/isCGM group and the lowest in the SAP + CSII/isCGM group in 2019. The impact of emergency declaration on HbA1c level was small for the MDI/isCGM group and smaller for the SAP + CSII/isCGM group in 2021. The COVID-19 pandemic has affected the seasonal variation of HbA1c levels in T1D; the variation differed according to the treatment mode.

Genetic Determinants of the Acute Respiratory Distress Syndrome.

Acute respiratory distress syndrome (ARDS) is a life-threatening lung condition that arises from multiple causes, including sepsis, pneumonia, trauma, and severe coronavirus disease 2019 (COVID-19). Given the heterogeneity of causes and the lack of specific therapeutic options, it is crucial to understand the genetic and molecular mechanisms that underlie this condition. The identification of genetic risks and pharmacogenetic loci, which are involved in determining drug responses, could help enhance early patient diagnosis, assist in risk stratification of patients, and reveal novel targets for pharmacological interventions, including possibilities for drug repositioning. Here, we highlight the basis and importance of the most common genetic approaches to understanding the pathogenesis of ARDS and its critical triggers. We summarize the findings of screening common genetic variation via genome-wide association studies and analyses based on other approaches, such as polygenic risk scores, multi-trait analyses, or Mendelian randomization studies. We also provide an overview of results from rare genetic variation studies using Next-Generation Sequencing techniques and their links with inborn errors of immunity. Lastly, we discuss the genetic overlap between severe COVID-19 and ARDS by other causes.

Investigation of variants in genetics and virulence of Porcine Epidemic Diarrhea Virus after serial passage on Vero cells.

Porcine epidemic diarrhea virus (PEDV) is a highly contagious intestinal virus. However, the current PEDV vaccine, which is produced from classical strain G1, offers low protection against recently emerged strain G2. This study aims to develop a better vaccine strain by propagating the PS6 strain, a G2b subgroup originating from Vietnam, on Vero cells until the 100th passage. As the virus was propagated, its titer increased, and its harvest time decreased. Analysis of the nucleotide and amino acid variation of the PS6 strain showed that the P100PS6 had 11, 4, and 2 amino acid variations in the 0 domain, B domain, and ORF3 protein, respectively, compared to the P7PS6 strain. Notably, the ORF3 gene was truncated due to a 16-nucleotide deletion mutation, resulting in a stop codon. The PS6 strain's virulence was evaluated in 5-day-old piglets, with P7PS6 and P100PS6 chosen for comparison. The results showed that P100PS6-inoculated piglets exhibited mild clinical symptoms and histopathological lesions, with a 100% survival rate. In contrast, P7PS6-inoculated piglets showed rapid and typical clinical symptoms of PEDV infection, and the survival rate was 0%. Additionally, the antibodies (IgG and IgA) produced from inoculated piglets with P100PS6 bound to both the P7PS6 and P100PS6 antigens. This finding suggested that the P100PS6 strain was attenuated and could be used to develop a live-attenuated vaccine against highly pathogenic and prevalent G2b-PEDV strains.

Traceability of SARS-CoV-2 transmission through quasispecies analysis.

During COVID-19 pandemic, consensus genomic sequences were used for rapidly monitor the spread of the virus worldwide. However, less attention was paid to intrahost genetic diversity. In fact, in the infected host, SARS-CoV-2 consists in an ensemble of replicating and closely related viral variants so-called quasispecies. Here we show that intrahost single nucleotide variants (iSNVs) represent a target for contact tracing analysis. Our data indicate that in the acute phase of infection, in highly likely transmission links, the number of viral particles transmitted from one host to another (bottleneck size) is large enough to propagate iSNVs among individuals. Furthermore, we demonstrate that, during SARS-CoV-2 outbreaks when the consensus sequences are identical, it is possible to reconstruct the transmission chains by genomic investigations of iSNVs. Specifically, we found that it is possible to identify transmission chains by limiting the analysis of iSNVs to only three well-conserved genes, namely nsp2, ORF3, and ORF7.