Association of d-dimer levels with in-hospital outcomes among COVID-19 positive patients: a developing country multicenter retrospective cohort.

D-dimer levels, which originate from the lysis of cross-linked fibrin, are serially measured during coronavirus disease 2019 illness to rule out hypercoagulability as well as a septic marker. Methods: This multicenter retrospective study was carried out in two tertiary care hospitals in Karachi, Pakistan. The study included adult patients admitted with a laboratory-confirmed coronavirus disease 2019 infection, with at least one measured d-dimer within 24 h following admission. Discharged patients were compared with the mortality group for survival analysis. Results: The study population of 813 patients had 68.5% males, with a median age of 57.0 years and 14.0 days of illness. The largest d-dimer elevation was between 0.51-2.00 mcg/ml (tertile 2) observed in 332 patients (40.8%), followed by 236 patients (29.2%) having values greater than 5.00 mcg/ml (tertile 4). Within 45 days of hospital stay, 230 patients (28.3%) died, with the majority in the ICU (53.9%). On multivariable logistic regression between d-dimer and mortality, the unadjusted (Model 1) had a higher d-dimer category (tertile 3 and tertile 4) associated with a higher risk of death (OR: 2.15; 95% CI: 1.02-4.54, P=0.044) and (OR: 4.74; 95% CI: 2.38-9.46, P<0.001). Adjustment for age, sex, and BMI (Model 2) yields only tertile 4 being significant (OR: 4.27; 95% CI: 2.06-8.86, P<0.001). Conclusion: Higher d-dimer levels were independently associated with a high risk of mortality. The added value of d-dimer in risk stratifying patients for mortality was not affected by invasive ventilation, ICU stays, length of hospital stays, or comorbidities.

Antibody-receptor bioengineering and its implications in designing bioelectronic devices.

Antibodies play a crucial role in the defense mechanism countering pathogens or foreign antigens in eukaryotes. Its potential as an analytical and diagnostic tool has been exploited for over a century. It forms immunocomplexes with a specific antigen, which is the basis of immunoassays and aids in developing potent biosensors. Antibody-based sensors allow for the quick and accurate detection of various analytes. Though classical antibodies have prolonged been used as bioreceptors in biosensors fabrication due to their increased fragility, they have been engineered into more stable fragments with increased exposure of their antigen-binding sites in the recent era. In biosensing, the formats constructed by antibody engineering can enhance the signal since the resistance offered by a conventional antibody is much more than these fragments. Hence, signal amplification can be observed when antibody fragments are utilized as bioreceptors instead of full-length antibodies. We present the first systematic review on engineered antibodies as bioreceptors with the description of their engineering methods. The detection of various target analytes, including small molecules, macromolecules, and cells using antibody-based biosensors, has been discussed. A comparison of the classical polyclonal, monoclonal, and engineered antibodies as bioreceptors to construct highly accurate, sensitive, and specific sensors is also discussed.

Potential long-term effects of SARS-CoV-2 infection on the pulmonary vasculature: Multilayered cross-talks in the setting of coinfections and comorbidities.

The Coronavirus Disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and its sublineages pose a new challenge to healthcare systems worldwide due to its ability to efficiently spread in immunized populations and its resistance to currently available therapies. COVID-19, although targeting primarily the respiratory system, is also now well established that later affects every organ in the body. Most importantly, despite the available therapy and vaccine-elicited protection, the long-term consequences of viral infection in breakthrough and asymptomatic individuals are areas of concern. In the past two years, investigators accumulated evidence on how the virus triggers our immune system and the molecular signals involved in the cross-talk between immune cells and structural cells in the pulmonary vasculature to drive pathological lung complications such as endothelial dysfunction and thrombosis. In the review, we emphasize recent updates on the pathophysiological inflammatory and immune responses associated with SARS-CoV-2 infection and their potential long-term consequences that may consequently lead to the development of pulmonary vascular diseases.

Clinical presentation and outcomes of chronic kidney disease patients with COVID-19 admitted to the intensive care unit of a teaching hospital of Northern India during the third wave of the pandemic: A retrospective study.

Introduction: Chronic kidney disease (CKD) patients have impaired immune status; that's why these patients are prone to develop infection-related complications. The current study compares non-haemodialysis chronic kidney disease and end-stage renal disease (NO-HD-CKD and ESRD, respectively) patient outcomes, the data of which is sparse. Methods: Patients diagnosed with COVID-19 infection through reverse transcriptase polymerase chain reaction (RT-PCR) were retrospectively studied using electronic health records. Patients were divided into three categories: non-chronic kidney disease (NO-CKD), NO-HD-CKD, and ESRD, and the outcome was assessed. Results: Out of 745 patients, 92 (12.34%) had NO-HD-CKD and 31 (4.16%) had ESRD. CKD patients who were not on haemodialysis had higher rates of comorbidities and D-dimer and C-reactive protein (CRP) values compared to ESRD patients. The overall unadjusted mortality rate was found to be 17.44%, and it was 10.45% in case of NO-CKD patients, 58.69% for NO-HD-CKD patients, and 48.39% for ESRD patients. It was observed that patients having NO-HD-CKD had greater odds ratio of overall expiry in comparison to those without CKD in univariate analysis (OR: 1.58; 95% CI: 1.31-1.91). It was not significant in fully adjusted models (OR: 1.11; 95% CI: 0.88-1.40). Conclusions: During the third wave of COVID-19, we found higher mortality rates for cases with NO-HD-CKD and, to a lower extent, ESRD. However, patients with ESRD were observed to have good outcomes in comparison to those with NO-HD-CKD. Primary care physicians are the first point of contact for patients. Hence, it is critical for them to manage and to do proper referral of comorbid patients to higher centres.

Leveraging an established neighbourhood-level, open access wastewater monitoring network to address public health priorities: a population-based study.

BACKGROUND: Before the COVID-19 pandemic, the US opioid epidemic triggered a collaborative municipal and academic effort in Tempe, Arizona, which resulted in the world's first open access dashboard featuring neighbourhood-level trends informed by wastewater-based epidemiology (WBE). This study aimed to showcase how wastewater monitoring, once established and accepted by a community, could readily be adapted to respond to newly emerging public health priorities. METHODS: In this population-based study in Greater Tempe, Arizona, an existing opioid monitoring WBE network was modified to track SARS-CoV-2 transmission through the analysis of 11 contiguous wastewater catchments. Flow-weighted and time-weighted 24 h composite samples of untreated wastewater were collected at each sampling location within the wastewater collection system for 3 days each week (Tuesday, Thursday, and Saturday) from April 1, 2020, to March 31, 2021 (Area 7 and Tempe St Luke's Hospital were added in July, 2020). Reverse transcription quantitative PCR targeting the E gene of SARS-CoV-2 isolated from the wastewater samples was used to determine the number of genome copies in each catchment. Newly detected clinical cases of COVID-19 by zip code within the City of Tempe, Arizona were reported daily by the Arizona Department of Health Services from May 23, 2020. Maricopa County-level new positive cases, COVID-19-related hospitalisations, deaths, and long-term care facility deaths per day are publicly available and were collected from the Maricopa County Epidemic Curve Dashboard. Viral loads of SARS-CoV-2 (genome copies per day) measured in wastewater from each catchment were aggregated at the zip code level and city level and compared with the clinically reported data using root mean square error to investigate early warning capability of WBE. FINDINGS: Between April 1, 2020, and March 31, 2021, 1556 wastewater samples were analysed. Most locations showed two waves in viral levels peaking in June, 2020, and December, 2020-January, 2021. An additional wave of viral load was seen in catchments close to Arizona State University (Areas 6 and 7) at the beginning of the fall (autumn) semester in late August, 2020. Additionally, an early infection hotspot was detected in the Town of Guadalupe, Arizona, starting the week of May 4, 2020, that was successfully mitigated through targeted interventions. A shift in early warning potential of WBE was seen, from a leading (mean of 8·5 days [SD 2·1], June, 2020) to a lagging (-2·0 days [1·4], January, 2021) indicator compared with newly reported clinical cases. INTERPRETATION: Lessons learned from leveraging an existing neighbourhood-level WBE reporting dashboard include: (1) community buy-in is key, (2) public data sharing is effective, and (3) sub-ZIP-code (postal code) data can help to pinpoint populations at risk, track intervention success in real time, and reveal the effect of local clinical testing capacity on WBE's early warning capability. This successful demonstration of transitioning WBE efforts from opioids to COVID-19 encourages an expansion of WBE to tackle newly emerging and re-emerging threats (eg, mpox and polio). FUNDING: National Institutes of Health's RADx-rad initiative, National Science Foundation, Virginia G Piper Charitable Trust, J M Kaplan Fund, and The Flinn Foundation.

Temporal and spatial impact of lockdown during COVID-19 on air quality index in Haryana, India.

This paper presents the evaluation of air quality in different districts of Haryana. Geo-spatial techniques were used to estimate gaseous and particulate pollutant's spatial and temporal variation during complete nationwide lockdown period and same month of previous year 2019 (March to May). Data of six fixed pollutants were collected from Central Pollution Control Board (CPCB). In this context, the data of air pollutants (PM10, PM2.5, O3, NOx, SO2, and CO) were analyzed for 2019 and 2020. The Spatio-temporal distribution of the Air Quality Index (AQI) clearly depicts difference in lockdown and unlock period. The result was showed that the air quality was very poor to satisfactory in 2019 and an improvement was observed from satisfactory to good in 2020 due to COVID-19 lockdown. On the basis of result, it will be concluded that automobile and industry are the major contributor in increase the pollutant concentration.

Restrictions on air travel to India and the global airline industry - An event study and cross-sectional approach

Using the closing prices of 88 listed airline companies worldwide from November 16, 2020 to May 10, 2021, we employ the event study methodology to examine how air travel restrictions to India by several nations during April–May 2021 impacted the global airline industry. It is interesting to find out how the shocks that arise in India impact the global airline industry. We find that even after a prolonged pandemic, developed and emerging markets are sensitive to new information. The news content of the B.1.617 variant, named “variant of interest” by the WHO and air travel restrictions have significantly impacted the global airline industry. However, emerging markets are sensitive to travel restrictions. Furthermore, the impacts were less severe than those of the first wave in early 2020. Using cross-sectional regression, we also provide insights into which firm-specific characteristics drive firm's value during the pandemic. We find that while debt, past returns, and volatility positively impact the cumulative abnormal returns (CARs), the liquidity and total assets negatively impact the CARs in the shortest event window. To examine this research question, we conducted the first study during the second wave of the pandemic. These findings have important implications for the stakeholders.

Circulatory cytokine levels as a predictor of disease severity in COVID-19: a study from Western India

Background: Inflammatory response in COVID-19 responsible for acute respiratory distress syndrome (ARDS) and multiorgan failure and play a major role in morbidity and mortality of patients. The present study was undertaken to assess serum level of cytokines and its association with other inflammatory markers and disease severity in COVID-19 and hence their prognostic significance.

Occurrence of Z-drugs, benzodiazepines, and ketamine in wastewater in the United States and Mexico during the Covid-19 pandemic.

Z-drugs, benzodiazepines and ketamine are classes of psychotropic drugs prescribed for treating anxiety, sleep disorders and depression with known side effects including an elevated risk of addiction and substance misuse. These drugs have a strong potential for misuse, which has escalated over the years and was hypothesized here to have been exacerbated during the COVID-19 pandemic. Wastewater-based epidemiology (WBE) constitutes a fast, easy, and relatively inexpensive approach to epidemiological surveys for understanding the incidence and frequency of uses of these drugs. In this study, we analyzed wastewater (n = 376) from 50 cities across the United States and Mexico from July to October 2020 to estimate drug use rates during a pandemic event. Both time and flow proportional composite and grab samples of untreated municipal wastewater were analyzed using solid-phase extraction followed by liquid chromatography-tandem mass spectrometry to determine loadings of alprazolam, clonazepam, diazepam, ketamine, lorazepam, nordiazepam, temazepam, zolpidem, and zaleplon in raw wastewater. Simultaneously, prescription data of the aforementioned drugs were extracted from the Medicaid database from 2019 to 2021. Results showed high detection frequencies of ketamine (90 %), lorazepam (87 %), clonazepam (76 %) and temazepam (73 %) across both Mexico and United States and comparatively lower detection frequencies for zaleplon (22 %), zolpidem (9 %), nordiazepam (<1 %), diazepam (<1 %), and alprazolam (<1 %) during the pandemic. Average mass consumption rates, estimated using WBE and reported in units of mg/day/1000 persons, ranged between 62 (temazepam) and 1100 (clonazepam) in the United States. Results obtained from the Medicaid database also showed a significant change (p < 0.05) in the prescription volume between the first quarter of 2019 (before the pandemic) and the first quarter of 2021 (pandemic event) for alprazolam, clonazepam and lorazepam. Study results include the first detections of zaleplon and zolpidem in wastewater from North America.

DJ-1-Nrf2 axis is activated upon murine ß-coronavirus infection in the CNS.

Coronaviruses have emerged as alarming pathogens owing to their inherent ability of genetic variation and cross-species transmission. Coronavirus infection burdens the endoplasmic reticulum (ER.), causes reactive oxygen species production and induces host stress responses, including unfolded protein response (UPR) and antioxidant system. In this study, we have employed a neurotropic murine ß-coronavirus (M-CoV) infection in the Central Nervous System (CNS) of experimental mice model to study the role of host stress responses mediated by interplay of DJ-1 and XBP1. DJ-1 is an antioxidant molecule with established functions in neurodegeneration. However, its regulation in virus-induced cellular stress response is less explored. Our study showed that M-CoV infection activated the glial cells and induced antioxidant and UPR genes during the acute stage when the viral titer peaks. As the virus particles decreased and acute neuroinflammation diminished at day ten p.i., a significant up-regulation in UPR responsive XBP1, antioxidant DJ-1, and downstream signaling molecules, including Nrf2, was recorded in the brain tissues. Additionally, preliminary in silico analysis of the binding between the DJ-1 promoter and a positively charged groove of XBP1 is also investigated, thus hinting at a mechanism behind the upregulation of DJ-1 during MHV-infection. The current study thus attempts to elucidate a novel interplay between the antioxidant system and UPR in the outcome of coronavirus infection.

Mass trends of parabens, triclocarban and triclosan in Arizona wastewater collected after the 2017 FDA ban on antimicrobials and during the COVID-19 pandemic.

Antimicrobials like parabens, triclosan (TCS), and triclocarban (TCC) are of public health concern worldwide due to their endocrine-disrupting properties and ability to promote antimicrobial drug resistance in human pathogens. The overall use of antimicrobials presumably has increased during the COVID-19 pandemic, whereas TCS and TCC may have experienced reductions in use due to their recent ban from thousands of over-the-counter (OTC) personal care products by the U.S. Food and Drug Administration (FDA). No quantitative data are available on the use of parabens or the impact the FDA ban had on TCC and TCS. Here, we use wastewater samples (n = 1514) from 10 different communities in Arizona to measure the presence of the six different antimicrobial products (TCS, TCC, and four alkylated parabens [methylparaben (MePb), ethylparaben (EtPb), propylparaben (PrPb), butylparaben (BuPb)]) collected before and during the COVID-19 pandemic using a combination of solid-phase extraction, liquid chromatography/tandem mass spectrometry (LC-MS/MS), and isotope dilution for absolute quantitation. The average mass loadings of all antimicrobials combined (1,431 ± 22 mg/day per 1,000 people) after the onset of the local epidemic (March 2020 - October 2020) were significantly higher (945 ± 62 mg/day per 1,000 people; p < 0.05) than before the pandemic (January 2019 - February 2020). Overall, parabens (∑Pbs = 999 ± 16 mg/day per 1,000 people) were the most used antimicrobials, followed by TCS (117 ± 14 mg/day per 1,000 people) and TCC (117 ± 14 mg/day per 1,000 people). After the 2017 U.S. FDA ban, we found a statistically significant (p < 0.05) reduction in the mass loadings of TCS (-89%) and TCC (-80%) but a rise in paraben use (+72%). Mass flows of 3 of a total of 4 parabens (MePb, EtPb, and PrPb) in wastewater were significantly higher upon the onset of the epidemic locally (p < 0.05). This is the first longitudinal study investigating the use of antimicrobials during the COVID-19 pandemic by employing wastewater-based epidemiology. Whereas an overall increase in the use of antimicrobials was evident from analyzing Arizona wastewater, a notable reduction in the use of TCS and TCC was evident during the pandemic, triggered by the U.S. FDA ban.

Classification of COVID-19 from chest x-ray images using deep features and correlation coefficient.

COVID-19 is a viral disease that in the form of a pandemic has spread in the entire world, causing a severe impact on people's well being. In fighting against this deadly disease, a pivotal step can prove to be an effective screening and diagnosing step to treat infected patients. This can be made possible through the use of chest X-ray images. Early detection using the chest X-ray images can prove to be a key solution in fighting COVID-19. Many computer-aided diagnostic (CAD) techniques have sprung up to aid radiologists and provide them a secondary suggestion for the same. In this study, we have proposed the notion of Pearson Correlation Coefficient (PCC) along with variance thresholding to optimally reduce the feature space of extracted features from the conventional deep learning architectures, ResNet152 and GoogLeNet. Further, these features are classified using machine learning (ML) predictive classifiers for multi-class classification among COVID-19, Pneumonia and Normal. The proposed model is validated and tested on publicly available COVID-19 and Pneumonia and Normal dataset containing an extensive set of 768 images of COVID-19 with 5216 training images of Pneumonia and Normal patients. Experimental results reveal that the proposed model outperforms other previous related works. While the achieved results are encouraging, further analysis on the COVID-19 images can prove to be more reliable for effective classification.

Extensive Wastewater-Based Epidemiology as a Resourceful Tool for SARS-CoV-2 Surveillance in a Low-to-Middle-Income Country through a Successful Collaborative Quest: WBE, Mobility, and Clinical Tests

The COVID-19 pandemic has challenged healthcare systems worldwide. Efforts in low-to-middle-income countries (LMICs) cannot keep stride with infection rates, especially during peaks. A strong international collaboration between Arizona State University (ASU), Tec de Monterrey (TEC), and Servicios de Agua y Drenaje de Monterrey (Local Water Utilities) is acting to integrate wastewater-based epidemiology (WBE) of SARS-CoV-2 in the region as a complementary approach to aid the healthcare system. Wastewater was collected from four sewer catchments in the Monterrey Metropolitan area in Mexico (pop. 4,643,232) from mid-April 2020 to February 2021 (44 weeks, n = 644). Raw wastewater was filtered and filter-concentrated, the RNA was extracted using columns, and the Charité/Berlin protocol was used for the RT-qPCR. The viral loads obtained between the first (June 2020) and second waves (February 2021) of the pandemic were similar;in contrast, the clinical cases were fewer during the first wave, indicating poor coverage. During the second wave of the pandemic, the SARS-CoV-2 quantification in wastewater increased 14 days earlier than the COVID-19 clinical cases reported. This is the first long-term WBE study in Mexico and demonstrates its value in pandemic management.

Liver injury after SARS-CoV-2 vaccination: Features of immune-mediated hepatitis, role of corticosteroid therapy and outcome.

BACKGROUND AND AIMS: A few case reports of autoimmune hepatitis-like liver injury have been reported after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination. We evaluated clinical features, treatment response and outcomes of liver injury following SARS-CoV-2 vaccination in a large case series. APPROACH AND RESULTS: We collected data from cases in 18 countries. The type of liver injury was assessed with the R-value. The study population was categorized according to features of immune-mediated hepatitis (positive autoantibodies and elevated immunoglobulin G levels) and corticosteroid therapy for the liver injury. We identified 87 patients (63%, female), median age 48 (range: 18-79) years at presentation. Liver injury was diagnosed a median 15 (range: 3-65) days after vaccination. Fifty-one cases (59%) were attributed to the Pfizer-BioNTech (BNT162b2) vaccine, 20 (23%) cases to the Oxford-AstraZeneca (ChAdOX1 nCoV-19) vaccine and 16 (18%) cases to the Moderna (mRNA-1273) vaccine. The liver injury was predominantly hepatocellular (84%) and 57% of patients showed features of immune-mediated hepatitis. Corticosteroids were given to 46 (53%) patients, more often for grade 3-4 liver injury than for grade 1-2 liver injury (88.9% vs. 43.5%, p = 0.001) and more often for patients with than without immune-mediated hepatitis (71.1% vs. 38.2%, p = 0.003). All patients showed resolution of liver injury except for one man (1.1%) who developed liver failure and underwent liver transplantation. Steroid therapy was withdrawn during the observation period in 12 (26%) patients after complete biochemical resolution. None had a relapse during follow-up. CONCLUSIONS: SARS-CoV-2 vaccination can be associated with liver injury. Corticosteroid therapy may be beneficial in those with immune-mediated features or severe hepatitis. Outcome was generally favorable, but vaccine-associated liver injury led to fulminant liver failure in one patient.

Mining voices from self-expressed messages on social-media: Diagnostics of mental distress during COVID-19.

The COVID-19 pandemic has had a severe impact on mankind, causing physical suffering and deaths across the globe. Even those who have not contracted the virus have experienced its far-reaching impacts, particularly on their mental health. The increased incidences of psychological problems, anxiety associated with the infection, social restrictions, economic downturn, etc., are likely to aggravate with the virus spread and leave a longer impact on humankind. These reasons in aggregation have raised concerns on mental health and created a need to identify novel precursors of depression and suicidal tendencies during COVID-19. Identifying factors affecting mental health and causing suicidal ideation is of paramount importance for timely intervention and suicide prevention. This study, thus, bridges this gap by utilizing computational intelligence and Natural Language Processing (NLP) to unveil the factors underlying mental health issues. We observed that the pandemic and subsequent lockdown anxiety emerged as significant factors leading to poor mental health outcomes after the onset of COVID-19. Consistent with previous works, we found that psychological disorders have remained pre-eminent. Interestingly, financial burden was found to cause suicidal ideation before the pandemic, while it led to higher odds of depressive (non-suicidal) thoughts for individuals who lost their jobs. This study offers significant implications for health policy makers, governments, psychiatric practitioners, and psychologists.

COVID-19 cases and their outcome among patients with uncommon co-existing illnesses: A lesson from Northern India

Introduction Newer coexisting conditions should be identified in order to modify newer risk factors. Aim was to identify patients with non-classical or less common coexisting conditions in patients infected of COVID 19. Method Single centred study from June 2020 to May 2021 at a tertiary centre in North India. A preformed questionnaire was used to record clinical and laboratory parameters and to identify cases which are in addition to CDC list and Indian data. Results 0.67% (46) cases out of 6832 patients were identified to have non-classical coexisting illness. It was divided into 2 groups-infections A (60.1%) and non-infections B (39.9%). Group A included-tuberculosis- pulmonary (14.3%) & extra pulmonary (32.9%), bacterial (25.0%) viral infections [dengue, hepatitis B & C] (14.3%), HIV disease (10.7%) and malaria (3.6%). Group B included- organ transplant (27.8%), autoimmune [myasthenia gravis, polymyositis, psoriasis] (22.6%), haematologic [Haemophilia, ITP, Aplastic anaemia, APML, CML] (27.8%), uncommon malignancies [disseminated sacral chordoma and GTN] (11.1%) and snakebite (11.1%). Serum Procalcitonin was not helpful for diagnosis of bacterial infection in COVID-19 disease. Group A had significantly longer duration of illness, hepatitis and elevated CRP. The mortality in group A & B were 32.1% and 43.8% respectively. Death in non-severe COVID cases was in tetanus and snakebite. 30.7% death among tuberculosis patients. More than 70% of deaths were attributable to COVID 19 in both the groups. Conclusion In Indian settings, comorbidities like tuberculosis and bacterial infections can precipitate severe COVID 19 unlike other parts of the world where tuberculosis is relatively uncommon.

Ultrasensitive Aptasensors for the Detection of Viruses Based on Opto-Electrochemical Readout Systems.

Viral infections are becoming the foremost driver of morbidity, mortality and economic loss all around the world. Treatment for diseases associated to some deadly viruses are challenging tasks, due to lack of infrastructure, finance and availability of rapid, accurate and easy-to-use detection methods or devices. The emergence of biosensors has proven to be a success in the field of diagnosis to overcome the challenges associated with traditional methods. Furthermore, the incorporation of aptamers as bio-recognition elements in the design of biosensors has paved a way towards rapid, cost-effective, and specific detection devices which are insensitive to changes in the environment. In the last decade, aptamers have emerged to be suitable and efficient biorecognition elements for the detection of different kinds of analytes, such as metal ions, small and macro molecules, and even cells. The signal generation in the detection process depends on different parameters; one such parameter is whether the labelled molecule is incorporated or not for monitoring the sensing process. Based on the labelling, biosensors are classified as label or label-free; both have their significant advantages and disadvantages. Here, we have primarily reviewed the advantages for using aptamers in the transduction system of sensing devices. Furthermore, the labelled and label-free opto-electrochemical aptasensors for the detection of various kinds of viruses have been discussed. Moreover, numerous globally developed aptasensors for the sensing of different types of viruses have been illustrated and explained in tabulated form.

Impact of epidemics and pandemics on consumption pattern: evidence from Covid-19 pandemic in rural-urban India

PurposeThe present world is crippled with the pandemic coronavirus (Covid-19). The pandemic that originated in Wuhan city of China has sent every country in the world in an unprecedented situation that has social and economic impacts. This paper aims to explore whether epidemics and pandemics have any impact on consumption patterns among rural and urban consumers in India. Taking pandemic Covid-19 as a case study, it was explored how this pandemic impacted the consumption pattern of consumers in India;what are the similarities and/or differences between rural and urban consumers that are found in their consumption habits in the wake of Covid-19 pandemic.Design/methodology/approachThe required data was collected through questionnaires sent to respondents. Approximately 500 respondents were contacted through the mail to fill in the survey questionnaire. Despite the sincere efforts, a total of 175 complete survey questionnaires were filled in by respondents. The study used SPSS Statistics version 25 software for the analysis of data.FindingsIt was found that epidemics and pandemics have a profound impact on the pattern of consumption in India. The study reveals that consumers resort to panic buying in the initial stages of epidemics and pandemics. It was found that consumption habits of consumers went a sea change and they were spending largely on essentials only. The study also reveals that the majority of consumers would like to continue in the same consumption habits as that of during COVID-19. The consumption pattern of urban consumers witnessed more change than the consumption pattern of rural consumers. It is due to the closure of eateries and restaurants, shopping malls, movie theatres, etc., in urban areas that forced the change in the consumption pattern of urban consumers.Research limitations/implicationsThe research has a limitation of using a less sample size. For the generalizations, more robust studies can be conducted with more data.Practical implicationsThe findings of the study will give marketers an insight for framing their policies in the wake of epidemics and pandemics.Originality/valueThe research adds to the existing body of knowledge. There are plenty of studies on the behaviour of consumers. However, there are no major studies that focus on the behaviour of consumers during the outbreak of a pandemic. So, this study fills this gap in the existing body of knowledge.

Computational investigation reveals that the mutant strains of SARS-CoV2 have differential structural and binding properties.

BACKGROUND AND OBJECTIVES: Remarkable infectivity of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV2) is due to the rapid emergence of various strains which enable the virus to ruling the world. Over the course of SARS-CoV2 pandemic, the scientific communities worldwide are responding to newly emerging genetic variants. However, mechanism behind the persistent infection of these variants is still not known due to the paucity of study of these variants at molecular level. In this scenario, computational methods have immense utility in understanding the molecular and functional properties of different variants. METHODS: The various mutants (MTs) of SpikeS1 receptor binding domain (RBD) of highly infectious SARS-CoV2 strains were manifested and elucidated the protein structure and binding strength using molecular dynamics (MD) simulation and protein-protein docking approaches. RESULTS: MD simulation study showed that all MTs exhibited stable structures with altered functional properties. Furthermore, the binding strength of different MTs along with WT (wildtype) was revealed that MTs showed differential binding affinities to host protein with high binding strength exhibited by V367F and V483A MTs. CONCLUSION: Hence, this study shed light on the molecular basis of infection caused by different variants of SARS-CoV2, which might play an important role in to cease the transmission and pathogenesis of virus and also implicate in rational designing of a specific drug.

High-throughput sequencing of SARS-CoV-2 in wastewater provides insights into circulating variants.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) likely emerged from a zoonotic spill-over event and has led to a global pandemic. The public health response has been predominantly informed by surveillance of symptomatic individuals and contact tracing, with quarantine, and other preventive measures have then been applied to mitigate further spread. Non-traditional methods of surveillance such as genomic epidemiology and wastewater-based epidemiology (WBE) have also been leveraged during this pandemic. Genomic epidemiology uses high-throughput sequencing of SARS-CoV-2 genomes to inform local and international transmission events, as well as the diversity of circulating variants. WBE uses wastewater to analyse community spread, as it is known that SARS-CoV-2 is shed through bodily excretions. Since both symptomatic and asymptomatic individuals contribute to wastewater inputs, we hypothesized that the resultant pooled sample of population-wide excreta can provide a more comprehensive picture of SARS-CoV-2 genomic diversity circulating in a community than clinical testing and sequencing alone. In this study, we analysed 91 wastewater samples from 11 states in the USA, where the majority of samples represent Maricopa County, Arizona (USA). With the objective of assessing the viral diversity at a population scale, we undertook a single-nucleotide variant (SNV) analysis on data from 52 samples with >90% SARS-CoV-2 genome coverage of sequence reads, and compared these SNVs with those detected in genomes sequenced from clinical patients. We identified 7973 SNVs, of which 548 were "novel" SNVs that had not yet been identified in the global clinical-derived data as of 17th June 2020 (the day after our last wastewater sampling date). However, between 17th of June 2020 and 20th November 2020, almost half of the novel SNVs have since been detected in clinical-derived data. Using the combination of SNVs present in each sample, we identified the more probable lineages present in that sample and compared them to lineages observed in North America prior to our sampling dates. The wastewater-derived SARS-CoV-2 sequence data indicates there were more lineages circulating across the sampled communities than represented in the clinical-derived data. Principal coordinate analyses identified patterns in population structure based on genetic variation within the sequenced samples, with clear trends associated with increased diversity likely due to a higher number of infected individuals relative to the sampling dates. We demonstrate that genetic correlation analysis combined with SNVs analysis using wastewater sampling can provide a comprehensive snapshot of the SARS-CoV-2 genetic population structure circulating within a community, which might not be observed if relying solely on clinical cases.