Wastewater monitoring for detection of public health markers during the COVID-19 pandemic: Near-source monitoring of schools in England over an academic year.

BACKGROUND: Schools are high-risk settings for infectious disease transmission. Wastewater monitoring for infectious diseases has been used to identify and mitigate outbreaks in many near-source settings during the COVID-19 pandemic, including universities and hospitals but less is known about the technology when applied for school health protection. This study aimed to implement a wastewater surveillance system to detect SARS-CoV-2 and other public health markers from wastewater in schools in England. METHODS: A total of 855 wastewater samples were collected from 16 schools (10 primary, 5 secondary and 1 post-16 and further education) over 10 months of school term time. Wastewater was analysed for SARS-CoV-2 genomic copies of N1 and E genes by RT-qPCR. A subset of wastewater samples was sent for genomic sequencing, enabling determination of the presence of SARS-CoV-2 and emergence of variant(s) contributing to COVID-19 infections within schools. In total, >280 microbial pathogens and >1200 AMR genes were screened using RT-qPCR and metagenomics to consider the utility of these additional targets to further inform on health threats within the schools. RESULTS: We report on wastewater-based surveillance for COVID-19 within English primary, secondary and further education schools over a full academic year (October 2020 to July 2021). The highest positivity rate (80.4%) was observed in the week commencing 30th November 2020 during the emergence of the Alpha variant, indicating most schools contained people who were shedding the virus. There was high SARS-CoV-2 amplicon concentration (up to 9.2x106 GC/L) detected over the summer term (8th June - 6th July 2021) during Delta variant prevalence. The summer increase of SARS-CoV-2 in school wastewater was reflected in age-specific clinical COVID-19 cases. Alpha variant and Delta variant were identified in the wastewater by sequencing of samples collected from December to March and June to July, respectively. Lead/lag analysis between SARS-CoV-2 concentrations in school and WWTP data sets show a maximum correlation between the two-time series when school data are lagged by two weeks. Furthermore, wastewater sample enrichment coupled with metagenomic sequencing and rapid informatics enabled the detection of other clinically relevant viral and bacterial pathogens and AMR. CONCLUSIONS: Passive wastewater monitoring surveillance in schools can identify cases of COVID-19. Samples can be sequenced to monitor for emerging and current variants of concern at the resolution of school catchments. Wastewater based monitoring for SARS-CoV-2 is a useful tool for SARS-CoV-2 passive surveillance and could be applied for case identification and containment, and mitigation in schools and other congregate settings with high risks of transmission. Wastewater monitoring enables public health authorities to develop targeted prevention and education programmes for hygiene measures within undertested communities across a broad range of use cases.

Organic Electrochemical Transistors (OECTs): Advancements and Exciting Prospects for Future Biosensing Applications

Over the past few decades, the field of organic electronics has depicted proliferated growth, due to the advantageous characteristics of organic semiconductors, such as tunability through synthetic chemistry, simplicity in processing, cost-effectiveness, and low-voltage operation, to cite a few. Organic electrochemical transistors (OECTs) have recently emerged as a highly promising technology in the area of biosensing and flexible electronics. OECT-based biosensors are capable of sensing brain activities, tissues, monitoring cells, hormones, DNAs, and glucose. Sensitivity, selectivity, and detection limit are the key parameters adopted for measuring the performance of OECT-based biosensors. This article highlights the advancements and exciting prospects of OECTs for future biosensing applications, such as cell-based biosensing, chemical sensing, DNA/ribonucleic acid (RNA) sensing, glucose sensing, immune sensing, ion sensing, and pH sensing. OECT-based biosensors outperform other conventional biosensors because of their excellent biocompatibility, high transconductance, and mixed electronic-ionic conductivity. At present, OECTs are fabricated and characterized in millimeter and micrometer dimensions, and miniaturizing their dimensions to nanoscale is the key challenge for utilizing them in the field of nanobioelectronics, nanomedicine, and nanobiosensing.

eCOVID-19: Development of ontology-based clinical decision support system for COVID-19

Introduction: Humankind is passing through a period of significant instability and a worldwide health catastrophe that has never been seen before. COVID-19 spread over the world at an unprecedented rate. In this context, we undertook a rapid research project in the Sultanate of Oman. We developed ecovid19 application, an ontology-based clinical decision support system (CDSS) with teleconference capability for easy, fast diagnosis and treatment for primary health centers/Satellite Clinics of the Royal Oman Police (ROP) of Sultanate of Oman. Material and Methods: The domain knowledge and clinical guidelines are represented using ontology. Ontology is one of the most powerful methods for formally encoding medical knowledge. The primary data was from the ROP hospital's medical team, while the secondary data came from articles published in reputable journals. The application includes a COVID-19 Symptom checker for the public users with a text interface and an AI-based voice interface and is available in English and Arabic. Based on the given information, the symptom checker provides recommendations to the user. The suspected cases will be directed to the nearby clinic if the risk of infection is high. Based on the patient's current medical condition in the clinic, the CDSS will make suitable suggestions to triage staff, doctors, radiologists, and lab technicians on procedures and medicines. We used Teachable Machine to create a TensorFlow model for the analysis of X-rays. Our CDSS also has a WebRTC (Web Real-Time Communication system) based teleconferencing option for communicating with expert clinicians if the patient develops difficulties or if expert opinion is requested. Results: The ROP hospital's specialized doctors tested our CDSS, and the user interfaces were changed based on their suggestions and recommendations. The team put numerous types of test cases to assess the clinical efficacy. Precision, sensitivity (recall), specificity, and accuracy were adequate in predicting the various categories of patient instances. Conclusion: The proposed CDSS has the potential to significantly improve the quality of care provided to Oman's citizens. It can also be tailored to fit other terrifying pandemics. © 2022, Published by Frontiers in Health Informatics.

Evaluation of variant calling algorithms for wastewater-based epidemiology using mixed populations of SARS-CoV-2 variants in synthetic and wastewater samples.

Wastewater-based epidemiology has been used extensively throughout the COVID-19 (coronavirus disease 19) pandemic to detect and monitor the spread and prevalence of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) and its variants. It has proven an excellent, complementary tool to clinical sequencing, supporting the insights gained and helping to make informed public-health decisions. Consequently, many groups globally have developed bioinformatics pipelines to analyse sequencing data from wastewater. Accurate calling of mutations is critical in this process and in the assignment of circulating variants; yet, to date, the performance of variant-calling algorithms in wastewater samples has not been investigated. To address this, we compared the performance of six variant callers (VarScan, iVar, GATK, FreeBayes, LoFreq and BCFtools), used widely in bioinformatics pipelines, on 19 synthetic samples with known ratios of three different SARS-CoV-2 variants of concern (VOCs) (Alpha, Beta and Delta), as well as 13 wastewater samples collected in London between the 15th and 18th December 2021. We used the fundamental parameters of recall (sensitivity) and precision (specificity) to confirm the presence of mutational profiles defining specific variants across the six variant callers. Our results show that BCFtools, FreeBayes and VarScan found the expected variants with higher precision and recall than GATK or iVar, although the latter identified more expected defining mutations than other callers. LoFreq gave the least reliable results due to the high number of false-positive mutations detected, resulting in lower precision. Similar results were obtained for both the synthetic and wastewater samples.

Impact of the COVID-19 pandemic on blood donation and transfusions in the United States in 2020.

INTRODUCTION: Reports have suggested the COVID-19 pandemic resulted in blood donation shortages and adverse impacts on the blood supply. Using data from the National Blood Collection and Utilization Survey (NBCUS), we quantified the pandemic's impact on red blood cell (RBC) and apheresis platelet collections and transfusions in the United States during year 2020. METHODS: The 2021 NBCUS survey instrument was modified to include certain blood collection and utilization variables for 2020. The survey was distributed to all US blood collection centers, all US hospitals performing ≥1000 surgeries annually, and a 40% random sample of hospitals performing 100-999 surgeries annually. Weighting and imputation were used to generate national estimates for whole blood and apheresis platelet donation; RBC and platelet transfusion; and convalescent plasma distribution. RESULTS: Whole blood collections were stable from 2019 (9,790,000 units; 95% CI: 9,320,000-10,261,000) to 2020 (9,738,000 units; 95% CI: 9,365,000-10,110,000). RBC transfusions decreased by 6.0%, from 10,852,000 units (95% CI: 10,444,000-11,259,000) in 2019 to 10,202,000 units (95% CI: 9,811,000-10,593,000) in 2020. Declines were steepest during March-April 2020, with transfusions subsequently rebounding. Apheresis platelet collections increased from 2,359,000 units (95% CI: 2,240,000-2,477,000) in 2019 to 2,408,000 units (95% CI: 2,288,000-2,528,000) in 2020. Apheresis platelet transfusions increased from 1,996,000 units (95% CI: 1,846,000-2,147,000) in 2019 to 2,057,000 units (95% CI: 1,902,000-2,211,000) in 2020. CONCLUSION: The COVID-19 pandemic resulted in reduced blood donations and transfusions in some months during 2020 but only a minimal annualized decline compared with 2019.

Evaluating the Performance of a Commercially Available AI Algorithm for Automated Detection of Pulmonary Embolism on CECT and CTPA of COVID-19 Patients

Objective To investigate the performance of a commercially available artificial intelligence (AI) algorithm for detection of pulmonary embolism (PE) on contrast-enhanced CTs in patients hospitalized for COVID-19. Patients & Methods Retrospective analysis was performed of all contrast-enhanced chest CTs on patients admitted for COVID-19 between March 2020 and December 2021. Based on the original radiology reports, all PE-positive exams were included (n=527). Using a reversed flow single gate diagnostic accuracy case-control model, a randomly selected cohort of PE-negative exams (n=977) was included. Pulmonary parenchymal disease severity was assessed for all included studies using a semi-quantitative system, the Total Severity Score (TSS). All included CTs were sent for interpretation by the commercially available AI algorithm, Aidoc. Discrepancies between AI and original radiology reports were resolved by three blinded radiologists, who rendered a final determination of indeterminate, positive, or negative. Results A total of 78 studies were found to be discrepant, of which 13 (16.6%) were deemed indeterminate by readers and excluded. The sensitivity and specificity of AI was 93.2%;(95% confidence interval [CI] 90.6-95.2%), and 99.6%;(95% CI 98.9-99.9%), respectively. AI's accuracy for all TSS groups (mild, moderate, severe) was high (98.4%, 96.7%, and 97.2%, respectively). AI was more accurate in PE detection on CTPAs vs CECTs (P < .001), with optimal HU of 362 (P=.048). Conclusion The AI algorithm demonstrated high sensitivity, specificity, and accuracy for PE on contrast enhanced CTs in COVID-19 patients regardless of parenchymal disease. Accuracy was significantly affected by the mean attenuation of the pulmonary vasculature. How this affects the legitimacy of the binary outcomes reported by AI is not yet known.

Evaluating the Performance of a Commercially Available Artificial Intelligence Algorithm for Automated Detection of Pulmonary Embolism on Contrast-Enhanced Computed Tomography and Computed Tomography Pulmonary Angiography in Patients With Coronavirus Disease 2019.

Objective: To investigate the performance of a commercially available artificial intelligence (AI) algorithm for the detection of pulmonary embolism (PE) on contrast-enhanced computed tomography (CT) scans in patients hospitalized for coronavirus disease 2019 (COVID-19). Patients and Methods: Retrospective analysis was performed of all contrast-enhanced chest CT scans of patients admitted for COVID-19 between March 1, 2020 and December 31, 2021. Based on the original radiology reports, all PE-positive examinations were included (n=527). Using a reversed-flow single-gate diagnostic accuracy case-control model, a randomly selected cohort of PE-negative examinations (n=977) was included. Pulmonary parenchymal disease severity was assessed for all the included studies using a semiquantitative system, the total severity score. All included CT scans were sent for interpretation by the commercially available AI algorithm, Aidoc. Discrepancies between AI and original radiology reports were resolved by 3 blinded radiologists, who rendered a final determination of indeterminate, positive, or negative. Results: A total of 78 studies were found to be discrepant, of which 13 (16.6%) were deemed indeterminate by readers and were excluded. The sensitivity and specificity of AI were 93.2% (95% CI, 90.6%-95.2%) and 99.6% (95% CI, 98.9%-99.9%), respectively. The accuracy of AI for all total severity score groups (mild, moderate, and severe) was high (98.4%, 96.7%, and 97.2%, respectively). Artificial intelligence was more accurate in PE detection on CT pulmonary angiography scans than on contrast-enhanced CT scans (P<.001), with an optimal Hounsfield unit of 362 (P=.048). Conclusion: The AI algorithm demonstrated high sensitivity, specificity, and accuracy for PE on contrast-enhanced CT scans in patients with COVID-19 regardless of parenchymal disease. Accuracy was significantly affected by the mean attenuation of the pulmonary vasculature. How this affects the legitimacy of the binary outcomes reported by AI is not yet known.

Cytokine Storm, Immunomodulators and Mucormycosis in COVID-19: Bench To Bed Side

The world is undergoing its biggest health crisis named coronavirus disease, which is associated with increased proinflammatory cytokine storm, which ultimately leads to various medical complications including acute respiratory distress syndrome. The treatment protocol was always controversial due to the excessive use of corticosteroids in aggressive pneumonia and associated hyperinflammatory conditions.The excessive use, misuse, and rampant use of steroids may lead to various coinfection like mucormycosis which is referred to as black fungus that manifests within the skin and also affects the lungs and brain which may be more fatal. It is necessary to have early diagnosis and management to tackle the severity of post covid coinfection. Copyright © RJPT All right reserved.

Rapid Assessment of SARS-CoV-2 Variant-Associated Mutations in Wastewater Using Real-Time RT-PCR.

Within months of the COVID-19 pandemic being declared on March 20, 2020, novel, more infectious variants of SARS-CoV-2 began to be detected in geospatially distinct regions of the world. With international travel being a lead cause of spread of the disease, the importance of rapidly identifying variants entering a country is critical. In this study, we utilized wastewater-based epidemiology (WBE) to monitor the presence of variants in wastewater generated in managed COVID-19 quarantine facilities for international air passengers entering the United Kingdom. Specifically, we developed multiplex reverse transcription quantitative PCR (RT-qPCR) assays for the identification of defining mutations associated with Beta (K417N), Gamma (K417T), Delta (156/157DEL), and Kappa (E154K) variants which were globally prevalent at the time of sampling (April to July 2021). The assays sporadically detected mutations associated with the Beta, Gamma, and Kappa variants in 0.7%, 2.3%, and 0.4% of all samples, respectively. The Delta variant was identified in 13.3% of samples, with peak detection rates and concentrations observed in May 2021 (24%), concurrent with its emergence in the United Kingdom. The RT-qPCR results correlated well with those from sequencing, suggesting that PCR-based detection is a good predictor for variant presence; although, inadequate probe binding may lead to false positive or negative results. Our findings suggest that WBE coupled with RT-qPCR may be used as a rapid, initial assessment to identify emerging variants at international borders and mass quarantining facilities. IMPORTANCE With the global spread of COVID-19, it is essential to identify emerging variants which may be more harmful or able to escape vaccines rapidly. To date, the gold standard to assess variants circulating in communities has been the sequencing of the S gene or the whole genome of SARS-CoV-2; however, that approach is time-consuming and expensive. In this study, we developed two duplex RT-qPCR assays to detect and quantify defining mutations associated with the Beta, Gamma, Delta, and Kappa variants. The assays were validated using RNA extracts derived from wastewater samples taken at quarantine facilities. The results showed good correlation with the results of sequencing and demonstrated the emergence of the Delta variant in the United Kingdom in May 2021. The assays developed here enable the assessment of variant-specific mutations within 2 h after the RNA extract was generated which is essential for outbreak rapid response.

Retrospective Review of SARS-CoV-2 Infection in Immunocompromised Children at a Single Pediatric Institution

Background. Immunocompromised individuals are considered higher risk for severe COVID-19 disease. Much of this data was collected from adults with limited pediatric reporting, although a recent study showed no worse outcome in immunocompromised children. In this study, we characterize our experience of immunocompromised children who were hospitalized with acute SARS-CoV-2 infection and aimed to identify a sub-group of patients who had worse outcomes. Methods. We reviewed charts of all immunocompromised children hospitalized with SARS-CoV-2 RT-PCR positive results at our hospital from March 2020-April 2022. Disease severity was characterized according to the NIH COVID-19 guidelines. Clinical characteristics and outcomes were assessed. Individuals were grouped into solid organ transplant recipients, solid tumors, liquid tumors, and stem cell transplant recipients. Proportions were calculated per group in addition to the exact binomial confidence intervals (95%). Results. We identified 39 pediatric aged patients ranging from 7 months to 20 years of age with a median age of 10. 35.9% (14/39) of individuals were solid organ transplant recipients, 25.6% (10/39) had solid tumors, 33% (13/39) had liquid tumors, and 5% (2/39) were stem cell transplant recipients. 17.9% were asymptomatic, 66.7% had mild disease, and 15.4% had severe/critical COVID-19 disease. The median time of positive SARS CoV-2 RT-PCR was 32 days. Six children had prolonged SARS-CoV-2 RT-PCR positive testing between 29-97 days with a mean of 49 days. Viral strand specific RNA testing for replicating virus was also persistently positive. Six experienced severe/critical disease resulting in death of three. Most of the patients were treated with predominantly T cell depleting agents (~74%). However, four patients who had prolonged active critical infection were treated with B cell depleting agents or had known B cell dysfunction. Conclusion. The majority of the immunocompromised children in our cohort had mild COVID-19 disease. A subset of individuals experienced ongoing prolonged RT-PCR positivity and testing indicating persistent viral replication, with three demonstrating an inability to control the virus and resulting in death. Worse outcomes may be related to their immune dysfunction and use of B cell depleting agents.

Comparison of multiple whole-genome and Spike-only sequencing protocols for estimating variant frequencies via wastewater-based epidemiology (preprint)

Sequencing of SARS-CoV-2 in wastewater provides a key opportunity to monitor the prevalence of variants spatiotemporally, potentially facilitating their detection simultaneously with, or even prior to, observation through clinical testing. However, there are multiple sequencing methodologies available. This study aimed to evaluate the performance of alternative protocols for detecting SARS-CoV-2 variants. We tested the detection of two synthetic RNA SARS-CoV-2 genomes in a wide range of ratios and at two concentrations representative of those found in wastewater using whole-genome and Spike-gene-only protocols utilising Illumina and Oxford Nanopore platforms. We developed a Bayesian hierarchical model to determine the predicted frequencies of variants and the error surrounding our predictions. We found that most of the sequencing protocols detected polymorphic nucleotide frequencies at a level that would allow accurate determination of the variants present at higher concentrations. Most methodologies, including the Spike-only approach, could also predict variant frequencies with a degree of accuracy in low-concentration samples but, as expected, with higher error around the estimates. All methods were additionally confirmed to detect the same prevalent variants in a set of wastewater samples. Our results provide the first quantitative statistical comparison of a range of alternative methods that can be used successfully in the surveillance of SARS-CoV-2 variant frequencies from wastewater.

Exploring digital exclusion for patients using remote consultations at Moorfields Eye Hospital

Purpose : The use of video consultations was scaled urgently at Moorfields Eye Hospital due the COVID-19 pandemic, and has been sustained within the Trust. This provision was much needed and initiated without the usual stakeholder engagement. Digital exclusion will drive health inequalities in our patients, unless we fully understand it and create solutions to make our services accessible for all. The aim of the project is to understand the reasons why patients failed to utilise digital services during the pandemic. Methods : A retrospective analysis of all patient-initiated video consultation cancellations from December 2020 to November 2021 was undertaken. All rebooked appointments were excluded from analysis. Reasons for cancellation were extracted from the Patient Appointment System (PAS) to identify those who were digitally excluded. Patients who had opted out of data sharing or cancelled their video consultation but had attended another subsequent appointment were excluded from the analysis for digital exclusion. Results : Over a 1-year period, 10,457 video consultations were undertaken at Moorfields Eye Hospital. 5% (535) of appointments were cancelled by patients. Of these, 14% (73 patients) were digitally excluded. Digital exclusion was due to 3 main factors;lack of resources (53%), lack of skills (19%), lack of trust in the video consultation model (19%), or a combination of these factors (9%). The age range of digitally excluded patients was 9 to 89 years old. Those most digitally excluded were the 70-79 year olds (26%, 19 patients). The least digitally excluded age group were the 20-29 years olds (1%, 1 patient). In terms of sub-speciality, 52% (38 patients) were from the adnexal service, 27% (20 patients) from general ophthalmology, 12% (9 patients) from paediatric ophthalmology, and the remainder from ocular oncology (4%), strabismus (3%) and medical retina (1%). Conclusions : The reasons for digital exclusion are complex, but need to be understood and addressed, if we are to continue to scale digital services in the health sector and without widening health inequalities. Our work identified 3 main factors, with lack of resources being the overarching reason. Further implementation research in the fields of digital resource provision coupled with education may enable greater inclusion of this group of patients and enhance digital healthcare provision equality.

The Association Between State-Issued Mask Mandates and County COVID-19 Hospitalization Rates.

CONTEXT: Mask mandates are one form of nonpharmaceutical intervention that has been utilized to combat the spread of SARS-CoV2, the virus that causes COVID-19. OBJECTIVE: This study examines the association between state-issued mask mandates and changes in county-level and hospital referral region (HRR)-level COVID-19 hospitalizations across the United States. DESIGN: Difference-in-difference and event study models were estimated to examine the association between state-issued mask mandates and COVID-19 hospitalization outcomes. PARTICIPANTS: All analyses were conducted with US county-level data. INTERVENTIONS: State-issued mask mandates. County-level data on the mandates were collected from executive orders identified on state government Web sites from April 1, 2020, to December 31, 2020. MAIN OUTCOME MEASURES: Daily county-level (and HRR-level) estimates of inpatient beds occupied by patients with confirmed or suspected COVID-19 were collected by the US Department of Health and Human Services. RESULTS: The state issuing of mask mandates was associated with an average of 3.6 fewer daily COVID-19 hospitalizations per 100 000 people (P < .05) and a 1.2-percentage-point decrease in the percentage of county beds occupied with COVID-19 patients (P < .05) within 70 days of taking effect. Event study results suggest that this association increased the longer mask mandates were in effect. In addition, the results were robust to analyses conducted at the HRR level. CONCLUSIONS: This study demonstrated that state-issued mask mandates were associated with reduction in COVID-19 hospitalizations across the United States during the earlier portion of the pandemic. As new variants of the virus cause spikes in COVID-19 cases, reimposing mask mandates in indoor and congested public areas, as part of a layered approach to community mitigation, may reduce the spread of COVID-19 and lessen the burden on our health care system.

Brain tumor patients and COVID-19 vaccines: results of an international survey.

Background: As the COVID-19 pandemic continues to unfold, the advent of multiple approved vaccines has led to a milestone in the fight against the virus. While vaccination rates and side effects are well established in the general population, these are largely unknown in patients with brain tumors. The purpose of this study was to determine if brain tumor patients and their caregivers have received a COVID-19 vaccine, and explore their thoughts and opinions on these vaccines. Methods: An anonymous 31-question online survey available in 8 languages was conducted from June 30, 2021 to August 31, 2021. The survey was open to adult brain tumor patients over the age of 18 and included both categorical and open-ended questions. Descriptive statistics and modified thematic analyses were performed for all questions as appropriate. Results: A total of 965 unique surveys were completed from 42 countries. The vast majority of both brain tumor patients and their caregivers have been vaccinated against COVID-19 (84.5% and 89.9%, respectively). No patient reported serious adverse events from any vaccine. Less than 10% of patients decided against receiving a vaccination against COVID-19, with the most common reason being concerns over the safety of the vaccine. Patients wanted more specific information on how COVID-19 vaccines might impact their future brain tumor treatment. Conclusions: In conclusion, the majority of brain tumor patients and their caregivers have received COVID-19 vaccines with no major side effects. Patients want more information on how COVID-19 vaccines might directly impact their brain tumor and future management.

Predictors of Severe COVID-19 Outcome: A Meta-Analysis of Common Laboratory Bio Markers

COVID-19 is a multisystem sickness caused by the complex interaction of inflammatory, immunological, and coagulative cascade. Risk assessment, choice of appropriate treatments, monitoring, and timely discharge are important aspects of COVID patient management. Along with the proper clinical evaluation, laboratory markers can offer extra information that can have a substantial influence on these aspects of patient management. To identify potential markers of COVID-19-related mortality, a meta-analysis of papers concerning C-Reactive Protein (CRP), D-Dimer, Ferritin, Interleukin-6 (IL-6), Neutrophil-to-Lymphocyte ratio (N/L ratio), Procalcitonin and mortality was conducted. D-Dimer and N/L ratios were found to be significantly different between the non-survivor and survivor groups. Overall meta-risk of death associatedwith the laboratory values was as follows;CRP HR 2.39 (1.05- 3.74), D-Dimer HR 1.25 (1.03-2.48), Ferritin HR 3.37 (0.86-5.88),IL-6 HR 3.07 (-1.35-7.49), N/L ratio HR 1.20 (1.14-1.63),Procalcitonin OR 3.60 (1.60-8.10). More study is needed to determine whether these laboratory biomarkers can be employed inthe development of a clinical scoring system to help in patient triage. © The Electrochemical Society

Trends in secondary inorganic aerosol pollution in China and its responses to emission controls of precursors in wintertime

The Chinese government recently proposed ammonia (NH3) emission reductions (but without a specific national target) as a strategic option to mitigate fine particulate matter (PM2.5) pollution. We combined a meta-analysis of nationwide measurements and air quality modeling to identify efficiency gains by striking a balance between controlling NH3 and acid gas (SO2 and NOx) emissions. We found that PM2.5 concentrations decreased from 2000 to 2019, but annual mean PM2.5 concentrations still exceeded 35 µg m-3 at 74 % of 1498 monitoring sites during 2015–2019. The concentration of PM2.5 and its components were significantly higher (16 %–195 %) on hazy days than on non-hazy days. Compared with mean values of other components, this difference was more significant for the secondary inorganic ions SO42-, NO3-, and NH4+ (average increase 98 %). While sulfate concentrations significantly decreased over this period, no significant change was observed for nitrate and ammonium concentrations. Model simulations indicate that the effectiveness of a 50 % NH3 emission reduction for controlling secondary inorganic aerosol (SIA) concentrations decreased from 2010 to 2017 in four megacity clusters of eastern China, simulated for the month of January under fixed meteorological conditions (2010). Although the effectiveness further declined in 2020 for simulations including the natural experiment of substantial reductions in acid gas emissions during the COVID-19 pandemic, the resulting reductions in SIA concentrations were on average 20.8 % lower than those in 2017. In addition, the reduction in SIA concentrations in 2017 was greater for 50 % acid gas reductions than for the 50 % NH3 emission reductions. Our findings indicate that persistent secondary inorganic aerosol pollution in China is limited by emissions of acid gases, while an additional control of NH3 emissions would become more important as reductions of SO2 and NOx emissions progress.

Necrotizing pancreatitis secondary to COVID-19

Case Report A 29-year-old male with a past medical history significant for severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection presented with epigastric pain, vomiting, fever, and inability to tolerate oral intake for 1 day. The patient was diagnosed with COVID-19 six weeks prior to presentation and four weeks later was diagnosed with idiopathic acute pancreatitis. He reported initial resolution of pain, however symptoms recurred for one day prior to this admission. The patient denied a history of alcohol use disorder. He is a lifetime nonsmoker. He does not take any medications. Vital signs were stable and he was afebrile. Labs on presentation were remarkable for elevated lipase of 1,527 and leukocytosis (23,000). The patient was still positive for COVID-19. However, he maintained oxygen saturation >95% on room air with no apparent distress. On physical examination, he had severe tenderness to palpation at the epigastrium and left upper and lower quadrants. Abdominal ultrasound had no evidence of gallstones. Triglyceride levels were within normal limits. CT abdomen showed necrotizing pancreatitis. MRCP showed evidence of acute pancreatitis with peripancreatic acute necrotic collection in the pancreatic head measuring up to 8.8 cm. Intrinsic T1 signal within the peripancreatic collection compatible with hemorrhagic pancreatitis. There was about 30% pancreatic parenchymal necrosis in the pancreatic head. A nonocclusive thrombus involving the main portal vein was also seen. Autoimmune workup was negative. In the setting of hemorrhagic pancreatitis, treatment with anticoagulation was deferred. The patient was treated with supportive measures, including intravenous fluids and adequate pain control with eventual advancement of his diet. He was started on empiric antibiotics and discharged for outpatient follow-up. SARS-CoV2 is known to cause many extrapulmonary effects, including transaminitis, myocarditis and pericarditis. There have been rare cases of SARS-CoV2-induced acute pancreatitis reported in the literature. The exact mechanism behind pancreatic injury in the setting of SARS-CoV2 infection remains unclear, but it is hypothesized that it may occur secondary to the presence of SARS-CoV2 receptors on the pancreas. The main receptor used by SARS-CoV2 is angiotensin- converting enzyme 2, which is also expressed in the GI tract. The most common causes of acute pancreatitis, including alcohol abuse, gallstones, medications and autoimmune causes were ruled out in our patient. In a patient with no particular risk factors, it is likely that SARS-CoV2 precipitated his first episode of acute pancreatitis. Moreover, it is well known that acute pancreatitis can commonly lead to necrotizing pancreatitis as the initial injury and inflammation from the first attack can cause the pancreatic tissue to necrotize and later become infected. This case highlights that SARS-CoV2 can be a possible etiology of acute pancreatitis and its local complications.

City-wide wastewater genomic surveillance through the successive emergence of SARS-CoV-2 Alpha and Delta variants (preprint)

Genomic surveillance of SARS-CoV-2 has been essential to provide an evidence base for public health decisions throughout the SARS-CoV-2 pandemic. Sequencing data from clinical cases has provided data crucial to understanding disease transmission and the detection, surveillance, and containment of outbreaks of novel variants, which continue to pose fresh challenges. However, genomic wastewater surveillance can provide important complementary information by providing estimates of variant frequencies which do not suffer from sampling bias, and capturing all variants circulating in a population. Here we show that genomic SARS-CoV-2 wastewater surveillance can detect fine-scale differences within urban centres, specifically within the city of Liverpool, UK, during the emergence of Alpha and Delta variants between November 2020 and June 2021. Overall, the correspondence between wastewater and clinical variant frequencies demonstrates the reliability of wastewater surveillance. Yet, discrepancies between the two approaches in when the Alpha variant was first detected emphasises that wastewater monitoring can also capture missing information resulting from asymptomatic cases or communities less engaged with testing programmes, as found by a simultaneous surge testing effort across the city.

Neutrophil Count is Predicitive of Mortality in COVID(Long): An Easy-to-Use Biomarker Based Modelto Predict Mortality at Admission

Introduction: The second wave of COVID has been devastating inIndia and many developing countries. The mortality has been reported40% higher than in the first wave overwhelming the nation's healthinfrastructure. Despite better understanding of the disease andestablished treatment protocols including steroids and heparin;thesecond wave was disastrous. Subsequent waves have the potential tofurther cripple health care deliveries affecting non COVID care alsoacross many developing economies. It is then important to identifyand triage high risk patients to best use the limited resources.Aims &Objectives: The objective of this study was to identifypotential predictors of mortality in the second wave who accessedhealth care at our academic setup.Materials &Methods: All patients admitted at our centre from 01February through June 15 2021 were included in the analysis. Areduced set of potential predictor variables was selected a priori,which included routine investigations sent on patient admission at ourcenter. These were bundled as groups namely;coagulation markers(INR, APTT, Fibrinogen, d-Dimer), Inflammatory markers (ESR,CRP, Ferritin), Hemogram, Liver function tests, Renal function tests,Arterial blood gas analytics and Glucose levels at admission (measured using the arterial blood gas analyzer).We used a two stage model building process.Result: We collected data from 790 patients. The overall mortalityrate was 10% (79 patients). The median age of patients in the cohortwas 57 years (range 1-99). Patients travelled a distance of 25 km (1-262 km) to seek care. We identified 78 candidate predictor variablesmeasured at hospital admission.n entering variables into a logisticregression model [least absolute shrinkage and selection operator] 4variables were retained within the final model. We identified 4important (Table 1) predictors of mortality by using this modelling:LDH, Oxygen Saturation in Abg (SO2), Neutrophil count and Glucose level at admission >LDH 675 U/L, Oxygen SO2 C 94%Neutrophil count C 7000/mm3 and Glucose value > 132 mg/dL].Using a ROC a 'c' measure of 0.834 corresponded to the modeldiscriminating the response.Conclusions: In our analysis, 4 variables which include LDH, Oxygen saturation, Neutrophil count and Glucose measurements atadmission are important predictors of mortality. Their role need moreresearch;possibly reflective of roles of NETs in the inflammatorycascade of severe covid.

Antibody-response to mRNA SARS-CoV2 vaccination in patients after allogeneic stem cell transplantation

Introduction: Patients after allogeneic stem cell transplantation are at high risk for infection-related complications and vaccination efficacy might be impaired depending on the immune reconstitution. In this study we evaluate the response of 182 patients to mRNA vaccines against SARS-CoV2. Methods: During routine follow up visits, patients were asked about their vaccination status and if they had a previous infection with SARS-CoV2. In fully vaccinated patients, the antibody titer was measured using the Roche Elecsys Anti-SARS-CoV2 S test. A titer of <1 U/l was considered as negative, titers of >250 U/ml as a high antibody titer and a titer of 50-249 U/ml as a low antibody titer. Patient characteristics were evaluated by chart review to identify risk factors for poor vaccination response. Results: The majority of patients developed a high antibody titer (138 out 182 patients, 75.8%). Risk factors for a low antibody titer were im-munosuppressive therapy, a lymphocyte count <0.9 G/l, ongoing treatment for the underlying malignancy and active GvHD. The vaccine (Moderna vs Pfizer), donor type, underlying disease, a previous SARS-CoV2 infection and sex did not significantly influence the response to the vaccination. Discussion: While patients undergoing allogeneic stem cell transplantation have been excluded from the initial registration trials, our large patient cohort confirms the data of previous smaller studies, showing that most patients do have a good response to mRNA vaccines against SARS-CoV2. Nevertheless, a significant proportion of patients shows an inadequate vaccination response and thus qualifies for a third vaccination.