Establishment of an infrastructure for rapid pandemic response at the Frederick National Laboratory

The sudden onset of the 2019 SARS-CoV-2 pandemic required agile development of standards and efficient validation of assays to assess prevalence of infection as well as immune responses to infection and vaccination. Leveraging their experience in HPV serology and standards, the Vaccine, Immunity and Cancer Directorate (VICD) at the Frederick National Laboratory for Cancer Research (FNCLR) pivoted to address this unmet need in SARS-Co-V2 serology clinical testing and research. This standardization effort required the collection and processing of large volumes of blood from SARS-Co-V2 infected and uninfected individuals into serum and peripheral blood mononuclear cells (PBMCs). Collaborations with specimen collection sites across the United States were established. Following qualification for anti-SARS-CoV-2 IgG and IgM levels in independent laboratories, VICD assembled reference evaluation panels, which were used to assist the FDA's performance evaluation of commercial assays submitted for EUA approval. To date, 185 different shipments of the standard or validation panel have been sent to both domestic and international labs. These materials are also available to the SARS-CoV-2 serology community for assay calibration and performance evaluation which greatly facilitates assay data harmonization. In addition, the NCI Serological Sciences Network (SeroNet) was born from this initiative and expertise, resulting in the establishment of Capacity Building Centers (CBCs) for sample collection from different healthy, cancer and immunocompromised cohorts at Mount Sinai, Arizona State University, the University of Minnesota, and Northwell Feinstein. The NCI and FNLCR simultaneously collaborated to develop a network of investigators focused on advancing research on the immune response to SARS-CoV-2 infection and vaccination among diverse and vulnerable populations, including cancer patients. Their research has resulted in over 326 peer-reviewed publications. The CBC's have enrolled patients in longitudinal studies, resulting in a centralized collection of annotated, well characterized serum, PBMCs and clinical data. Numerous cancer cohorts, but predominantly Multiple Myeloma, are included. Furthermore, technology development was supported at the CBC's. Based upon this success, the VICD in collaboration with NCI is pursuing an even more innovative effort in pandemic preparedness to establish a Center for Serology and Data Emergency Preparedness (CESDEP);a global network able to activate and pivot to address pandemic-level threats, while continuing to expand the development of immunological assays that can inform clinical decisions for cancer and other immunocompromised patients.

A Freehand 3D Ultrasound Carotid Scanning System for Point-of-Care Ultrasonography

Stroke is a leading cause of morbidity and mortality throughout the world. Three-dimensional ultrasound (3DUS) imaging was shown to be more sensitive to treatment effect and more accurate in stratifying stroke risk than two-dimensional ultrasound (2DUS) imaging. Point-of-care ultrasound screening (POCUS) is important for patients with limited mobility and at times when the patients have limited access to the ultrasound scanning room, such as in the COVID-19 era. We used an optical tracking system to track the 3D position and orientation of the 2DUS frames acquired by a commercial wireless ultrasound system and subsequently reconstructed a 3DUS image from these frames. The tracking requires spatial and temporal calibrations. Spatial calibration is required to determine the spatial relationship between the 2DUS machine and the tracking system. Spatial calibration was achieved by localizing the landmarks with known coordinates in a custom-designed Z-fiducial phantom in an 2DUS image. Temporal calibration is needed to synchronize the clock of the wireless ultrasound system and the optical tracking system so that position and orientation detected by the optical tracking system can be registered to the corresponding 2DUS frame. Temporal calibration was achieved by initiating the scanning by an abrupt motion that can be readily detected in both systems. This abrupt motion establishes a common reference time point, thereby synchronizing the clock in both systems. We demonstrated that the system can be used to visualize the three-dimensional structure of a carotid phantom. The error rate of the measurements is 2.3%. Upon in-vivo validation, this system will allow POCUS carotid scanning in clinical research and practices. © 2023 SPIE.

Performance and Early Results from the Earth Surface Mineral Dust Source Investigation (EMIT) Imaging Spectroscopy Mission

The Earth Surface Mineral Dust Source Investigation (EMIT) acquires new observations of the Earth from a state-of-the-art, optically fast F/1.8 visible to short wavelength infrared imaging spectrometer with high signal-to-noise ratio and excellent spectroscopic uniformity. EMIT was launched to the International Space Station from Cape Canaveral, Florida, on July 14, 2022 local time. The EMIT instrument is the latest in a series of more than 30 imaging spectrometers and testbeds developed at the Jet Propulsion Laboratory, beginning with the Airborne Imaging Spectrometer that first flew in 1982. EMIT's science objectives use the spectral signatures of minerals observed across the Earth's arid and semi-arid lands containing dust sources to update the soil composition of advanced Earth System Models (ESMs) to better understand and reduce uncertainties in mineral dust aerosol radiative forcing at the local, regional, and global scale, now and in the future. EMIT has begun to collect and deliver high-quality mineral composition determinations for the arid land regions of our planet. Over 1 billion high-quality mineral determinations are expected over the course of the one-year nominal science mission. Currently, detailed knowledge of the composition of the Earth's mineral dust source regions is uncertain and traced to less than 5,000 surface sample mineralogical analyses. The development of the EMIT imaging spectrometer instrumentation was completed successfully, despite the severe impacts of the COVID-19 pandemic. The EMIT Science Data System is complete and running with the full set of algorithms required. These tested algorithms are open source and will be made available to the broader community. These include calibration to measured radiance, atmospheric correction to surface reflectance, mineral composition determination, aggregation to ESM resolution, and ESM runs to address the science objectives. In this paper, the instrument characteristics, ground calibration, in-orbit performance, and early science results are reported. © 2023 IEEE.

Characterization and Optimization of Trajectory Errors on Remote-Controlled Differential Drive Robot through Speed Calibration

An increase in interest in research projects which involves the design of robotic systems that minimizes interactions between humans has been caused by the COVID-19 outbreak, as such technology can greatly benefit healthcare industries in preventing the spread of highly infectious diseases. The utilization of remote-controlled robots in many different fields, especially in the medical field is becoming more and more necessary. However, mobile robots are susceptible to both systematic and nonsystematic errors that cause deviations in its trajectory. In view thereof, the researchers explored the possibility of minimizing the trajectory errors through speed calibration. The differential drive robot was navigated to finish a five-meter linear path of forward and backward motion. The test was conducted with a default linear speed of 0.5 m/s in which a high trajectory error was observed. Upon changing the speed of the robot, the same trajectory test was conducted at four additional different speeds, namely;0.25 m/s, 0.35 m/s, 0.65m/s and 0.75 m/s. With the gathered data, the researchers conducted a linear least-squares regression model using MATLAB wherein there is only one predictor variable (speed of the robot) and one response variable (deviation). Based on the results, the researchers concluded that the speed of 0.35 m/s is the optimal speed in which the trajectory error of the robot is minimal. The researchers recommend improving the design of the caster wheels to minimize the effects of nonsystematic errors. © 2022 IEEE.

Charge Detection Mass Spectrometry: What's the "Big" Deal?

In 2002, the first fully humanized mAb was approved by the U.S. Food and Drug Administration (FDA) (3). [...]the biopharmaceutical industry is still in its infancy and new, more complex products are in development and will ikely dominant the market in the future. [...]the most common forms of LC-MS have limitations when characterizing large macromolecules (4). [...]in this column, we discuss the potential for charge detection MS (CDMS) as an analytical tool for characterizing large, complex, and heterogenous biopharmaceuticals. [...]in October 2021 at the American Association for Mass Spectrometry (ASMS) annua meeting in Philadelphia, TrueMass presented the first commercial CDMS (6,7). The cylinder is often inside an electrostatic linear ion trap (ELIT) instrument, where ions oscillate back and forth. [...]the oscillation frequency gives the m/z and the charge is determined by the magnitude.

Oxygen flow: How much do we know?

Introduction: Since the advent of Covid, oxygen has been the centre of discussion despite being the most important entity for the survival of human life. As various modes of its delivery has been in practice for a long time, using the non-rebreather mask has been a part of most guidelines for emergencies and hypoxia. This requires a higher flowrate of up to 15 l/min. Most flowmeters can deliver upto 75 l/min over the maximum calibrated mark.1 What may appear as a small rise from the maximum labelled mark can deliver more oxygen than required and result in wastage of oxygen or hide the severity of the patient's condition. Using this audit/ QIP we are trying to determine if the flowrates delivered are as per the prescription/protocol. Aim(s): To find out if the oxygen administration when it is prescribed at 15L/min is at the prescribed value and therefore quantify the amount of oxygen that is being wasted. Method(s): Data was collected randomly, observing the flow rates that the patients were receiving as prescribed or over the prescribed rate for those on non rebreather mask. Result(s): 54 observations were recorded from ED, ITU, AMU and theatre recovery across two hospitals. It was found that 57.4% of the patients were on flowrates more than the prescribed value. Conclusion & Discussion: More than half the patients requiring high flow rate of oxygen were on rates more than prescribed. This is potentially due to the lack of understanding of the calibration of the flowmeter. This results in the wastage of oxygen causing significant financial loss and an increase in the carbon dioxide emission impacting the environmental pollution. We plan to run an educational intervention for staff in these departments to emphasise the importance of administering drugs (including oxygen) as they are prescribed, and how easy it is to over administer oxygen.

External validation of the parental attitude about childhood vaccination scale.

Introduction: Internal validation techniques alone do not guarantee the value of a model. This study aims to investigate the external validity of the Parental Attitude toward Childhood Vaccination (PACV) scale for assessing parents' attitude toward seasonal influenza vaccination. Methods: Using a snowball sampling approach, an anonymous online questionnaire was distributed in two languages (English and Arabic) across seven countries. To assess the internal validity of the model, the machine learning technique of "resampling methods" was used to repeatedly select various samples collected from Egypt and refit the model for each sample. The binary logistic regression model was used to identify the main determinants of parental intention to vaccinate their children against seasonal influenza. We adopted the original model developed and used its predictors to determine parents' intention to vaccinate their children in Libya, Lebanon, Syria, Iraq, Palestine, and Sudan. The area under the curve (AUC) indicated the model's ability to distinguish events from non-events. We visually compared the observed and predicted probabilities of parents' intention to vaccinate their children using a calibration plot. Results: A total of 430 parents were recruited from Egypt to internally validate the model, and responses from 2095 parents in the other six countries were used to externally validate the model. Multivariate regression analysis showed that the PACV score, child age (adolescence), and Coronavirus disease 2019 (COVID-19) vaccination in children were significantly associated with the intention to receive the vaccination. The AUC of the developed model was 0.845. Most of the predicted points were close to the diagonal line, demonstrating better calibration (the prediction error was 16.82%). The sensitivity and specificity of the externally validated model were 89.64 and 37.89%, respectively (AUC = 0.769). Conclusion: The PACV showed similar calibration and discrimination across the six countries. It is transportable and can be used to assess attitudes towards influenza vaccination among parents in different countries using either the Arabic or English version of the scale.

L-Moments and calibration-based variance estimators under double stratified random sampling scheme: Application of Covid-19 pandemic

Extreme events gives rise to outrageous results in terms of population-related parameters and their estimates are usually done using traditional moments. Traditional moments are usually affected by extreme observations. This study aims to propose some new calibration estimators considering the L-Moments scheme for variance, which is one of the most important population parameters. a number of suitable calibration constraints under double stratified random sampling were defined for these estimators. The proposed estimators, which were based on L-Moments, were relatively more robust despite extreme values. The empirical efficiency of the proposed estimators was also assessed through simulation. Covid-19 pandemic data from January 22, 2020 to August 23, 2020 was taken into account in the simulation study. (c) 2023 Sharif University of Technology. All rights reserved.

Understanding occupancy pattern of university libraries in the post-pandemic era

Understanding indoor occupancy patterns is crucial for energy model calibration, efficient operations of fresh air systems, and COVID-19 exposure risk assessment. University libraries, as one of centers of campus life, due to the high mobility and "foot-voting” nature of them, i.e., occupants pick seats in the micro-environments they prefer, provide a non-intrusive opportunity to carry out post-occupancy evaluations. We conducted a long-term online monitoring of occupancy in libraries of a university in China by web-crawling the online seat reservation system, based on which, we constructed two sets of databases consisting of around 70 million records of nearly 3, 000 seats in 4 library sections, with seat-level resolution and sampling frequency up to every 10 seconds. The informative data set depicts not only the overall spatio-temporal occupancy patterns, but also nuances hidden within seats and visits. The daily flow of the main libraries exceeded two visits per seat. Half of the visitors stayed at the libraries for 3-6 hours during a single occupancy. Semester schedules and campus accessibility together influence students' decisions on when and which library to go, while even within the same zone, some seats were always more popular than their neighbours. "Semi-isolation” is one of the candidate attractive features proposed to understand the underlying patterns. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Measurement Uncertainty of D-Dimer Measured by the Particle Enhanced Immunoturbidimetric Assay

Objectives: D-dimer is a fibrin degradation product resulting from the degradation of fibrin polymers by plasmin. Although its main use is the evaluation of thromboembolic events, it is also used as an indicator of inflammation. In the recent COVID-19 pandemic, there has been a significant increase in its use as a biomarker. In this study, it was aimed to calculate the measurement uncertainty of D-dimer measured by particle enhanced immunoturbidimetric assay (PETIA). Materials-Methods: D-dimer measurements were made with the PETIA method (Improgen, Turkey) on the CoagXL (Diagon, Hungary) device installed in the Kulu State Hospital Laboratory. Uncertainty due to calibration and calibrator bias was calculated by running the Procal-D-dimer-DDI0511 (Improgen, Turkey) calibrator 10 times. Serocon external quality control data of the last 3 months were used for uncertainty arising from external quality control. Uncertainty due to repeatability was calculated using internal quality control data from the last 60 days studied with Serocon DDIM. Result(s): For D-dimer test, uncertainty due to repeatability was calculated as 0.0168, squared of uncertainty due to external quality control as 0.0123, uncertainty due to calibration 0.0250, and uncertainty due to calibrator deviation 0.0195. The standard uncertainty (uc) was 0.116 and the expanded uncertainty (%U) was calculated as 23%. Conclusion(s): In this study, the measurement uncertainty of the D-Dimer test measured by the particle-enhanced immunoturbidimetric assay was calculated as 23%. Interpretation by taking this measurement uncertainty into account, especially at medical decision levels, will contribute to the use of the measurement result for the benefit of the patient.

Development of Aptasensor for the Diagnosis of Covid-19

Objectives: The objective is to develop a low-cost, practical, portable aptasensor platform for the diagnosis of COVID-19. Materials -Methods: Amino-terminated aptamers to be used for the design of an aptasensor were synthesized by SELEX method, and interaction of aptamers with SARS-CoV-2 S1 protein was investigated by isothermal titration calorimetry (ITC). Gold electrodes were used to design the biosensor platform. After the electrode surface was functionalized with cysteamine, the amino-terminated aptamer was conjugated to the surface via glutaraldehyde crosslinker. Then, the surface characterization and analytical parameters of the designed sensing platform were determined by adding commercial S1 proteins on the surface using differential pulse voltammetry (DPV), cyclic voltammetry (CV) and impedance spectroscopy (EIS). To evaluate the working performance of the system, S1 proteins were added to the synthetic serum samples using the standard addition method and the measurements were repeated. Result(s): Surface characterization of the platform designed with EIS and CV measurements was performed and it was found that the modification was successfully performed. In addition, DPV results and analytical parameters of the platform (calibration plot, limit of detection(LOD) , repeatability, coefficient of variation) were determined and the working performance of system was evaluated. Moreover, working performance of the biosensor in real samples and its specificity for COVID -19 were determined by experiments with synthetic serum and influenza A and B proteins. Conclusion(s): According the results, the system has potential to be used for the detection of COVID -19, and also it can be rapidly adapted in different pandemic situations that may occur in the future.

Quantifying Population Immunity to Covid-19 in Washington State and Oregon

Background: Since early 2020, the novel SARS-CoV-2 virus has spread rapidly throughout the globe. Subsequently many individuals have developed some form of immunity due to either a prior infection, one or more vaccinations, or a combination of the two. Using local epidemic data and mathematical modeling, we enumerate the various immune populations in Washington State and Oregon and quantify the level of protection against infection and hospitalization. Method(s): We developed a compartmental model of ordinary differential equations, which stratifies the population by age (0-17 years, 18-49 years, 50-64 years, and 65+ years), region, type of immunity (naive, infectionderived, vaccine-derived, booster-derived, hybrid immunity, etc), and recency of immune conferring event (recent and waned). To track the number of individuals in each category we combine 1) literature-based estimates of susceptibility to infection and severe disease by age, immune status, and variant, 2) calibration to the number of severe infections (hospitalizations and deaths) and number of vaccinations and 3) validation with serological surveys of the population. Result(s): We estimate that by mid-April 2022 more than 95% of the populations of both Washington and Oregon had some immunity against COVID-19 infection and hospitalization. Younger age groups tended to have much higher rates of natural or hybrid immunity with 96% of 0-17-year-olds and 83% of 18-49-year-olds protected due to past infections. Overall, the population-level immunity against the Omicron variant reduced risk of infection by 59% (95% Credible Interval 54% - 62%) and risk of hospitalization by 79% (95% CI 77% - 81%) in Washington and 62% (95% CI 57% -66%) and 83% (95% CI 82% - 85%), respectively, in Oregon. There was similar population-level protection against Delta at the start of the Omicron wave in early December 2021, which reduced risk of infection by 60% (95% CI 56% - 63%) and risk of hospitalization 79% (95% CI 78% - 80%) in Washington and 66% (95%CI 63% - 70%) and 82% (81% - 83%), respectively, in Oregon. Conclusion(s): Very large waves of new infections throughout 2021 and early 2022, in addition to high levels of vaccination and boosting among the older age groups in Washington and Oregon have greatly reduced population susceptibility to currently circulating strains. However even very high population immunity has allowed for emergence of novel variants that escape existing immunity, highlighting the need for continued develop of new variantspecific boosters.

Clinical profile and outcomes of patients with COVID-19 respiratory failure requiring renal replacement therapy: a machine learning analysis of 13,576 patients

Introduction: Acute kidney injury (AKI) is a frequent and severe complication of COVID-19 infection in ICU patients. We propose a structured data-driven methodology and develop a model to predict the use of renal replacement therapy for patients on respiratory support with Covid-19 in 126 ICUs from 42 Brazilian hospitals. Method(s): Adult ICU patients (March 2020-December 2021) with confirmed SARS-CoV-2 infection and need of ventilatory support at D1 admission in the ICU. Main outcome was the need of RRT. We estimated 3 prediction models: Logistic Regression (LR), Random Forest (RF) and XGB Boosting. Models were derived in the training set and evaluated in the test set following an 80/20 split ratio, and models' parameters were selected using fivefold cross-validation. We evaluated and selected the best model in terms of discrimination (AUC) and calibration (Brier's Score). Variable importance was estimated for each predictor variable. Result(s): 13,575 ICU patients with need of respiratory support, of which 1828 (14%) needed RRT. ICU and hospital mortality were respectively 15.7%, 20.3% (non-RRT) and 54.3%, 69% (RRT). Mean age was 63.9 and 55.3 years (RRT vs non-RRT). Mean ICU LOS was 27.8 vs. 12 days, in RRT vs non-RRT. RF and XGB models both showed higher discrimination performance compared to LR (95% confidence interval [95% CI]: 0.84 [0.81-0.85] and 0.83 [0.80-0.85] vs 0.78 [0.75-0.80]). RF and XGB models presented similar calibration (Brier's Score: ([95% CI]: 0.09 [0.09- 0.10] and 0.09 [0.09-0.10]), also better than in LR (0.11 [0.10-0.12]). The final model (RF) showed no sign of under or overestimation of predicted probabilities in calibration plots. Conclusion(s): The need of RRT among patients on respiratory support diagnosed with Covid-19 was accurately predicted through machine learning methods. RF and XGB based models using data from general intensive care databases provides an accurate and practical approach for the early prediction of use of RRT in COVID-19 patients.

Improvement of Trajectory Errors on Remote- Controlled Differential Drive Robot via Mobilebased GUI through Bluetooth Connection

There has been an increase of interest and demand in the usage of logistic indoor service robots that are designed to minimize interactions between humans due to the occurrence of the COVID-19 outbreak. The application of the rising technology in the medical sector has great benefits in the industry, such as the prevention of the spread of highly infectious diseases using distance as a factor. Rooting from the purpose of the said robot, the main focus should be the ease of navigation through achieving the desired trajectory, in order to maximize the functionality, prevent collision, reduce user maneuvering difficulties, and such. Hence, this paper is focused on improving the trajectory errors on the robot navigation performance based on different control system designs specifically, a physical joystick controller and a mobile-based Bluetooth application controller. The design of the joystick is based on a pivot as its base which is directed to all angles and the design of the Bluetooth app is based on fourdirectional buttons that will operate upon clicking, and switching to other buttons to change commands. With this, the researchers conducted linear path and rotational tests using both remote control modes that are based on five varying speed values of 0.75 m/s, 0.5m/s, 0.35m/s, 0.25m/s, and 0.15 m/s. Based on the data analysis, the yielded results showed that using the Bluetooth app lowers the robot's trajectory error by 50% to 60% compared to using ajoystick to navigate to the desired point. Thus, the researchers concluded that the design of a control system greatly affects the robot navigation in achieving the desired trajectory. Considering the nonsystematic errors, a calibration based on the hardware structure design specifically on the caster wheel is recommended. © 2023 IEEE.

Estimating Moisture Content of Sausages with Different Types of Casings via Hyperspectral Imaging in Tandem with Multivariate

The moisture levels in sausages that were stored for 16 days and added with different concentrations of orange extracts to a modification solution were assessed using response surface methodology (RSM). Among the 32 treatment matrixes, treatment 10 presented a higher moisture content than that of treatment 19. Spectral pre-treatments were employed to enhance the model's robustness. The raw and pre-processed spectral data, as well as moisture content, were fitted to a regression model. The RSM outcomes showed that the interactive effects of [soy lecithin concentration] × [soy oil concentration] and [soy oil concentration] × [orange extract addition] on moisture were significant (p < 0.05), resulting in an R2 value of 78.28% derived from a second-order polynomial model. Hesperidin was identified as the primary component of the orange extracts using high-performance liquid chromatography (HPLC). The PLSR model developed from reflectance data after normalization and 1st derivation pre-treatment showed a higher coefficient of determination in the calibration set (0.7157) than the untreated data (0.2602). Furthermore, the selection of nine key wavelengths (405, 445, 425, 455, 585, 630, 1000, 1075, and 1095 nm) could render the model simpler and allow for easy industrial applications.

METFORMIN REDUCED SARS-CoV-2 VIRAL LOAD IN A PHASE 3 RANDOMIZED CLINICAL TRIAL

Background: Metformin has in vitro activity against SARS-CoV-2. In a published phase 3, quadruple-blinded, placebo-controlled randomized trial of outpatient COVID-19 therapy, metformin resulted in a 42% reduction in ER visits/hospitalizations/deaths by day 14, 58% reduction in hospitalizations/ death by day 28, and 42% reduction in Long Covid through 10 months. This analysis presents the results of viral load sampling performed during that clinical trial. Method(s): Covid-Out trial (NCT04510194) enrolled adults aged 30 to 85 within 3 days of a documented SARS-CoV-2 infection and < 7 days after symptom onset. The trial randomized 1323 participants to metformin (1000mg/day days 2-5;1500mg/day days 6 to 14), ivermectin, fluvoxamine, and/or exact-matching placebo in a 2x3 factorial trial design. Nasal swabs for viral load were an optional component, self-collected from the anterior nares on day 1, 5, and 10. Viral loads were measured via RT-qPCR using N1 and N2 targets in the SARSCoV- 2 nucleocapsid protein, with relative Ct values converted to absolute copy number via calibration to droplet digital PCR. A linear Tobit regression model was used to assess change over time while accounting for left censoring due to the viral load limit of detection. Results were adjusted for other treatment allocations within the factorial design, vaccination status, and baseline viral load. Repeated measures were accounted for using clustered standard errors within participants. Result(s): Samples were available from n = 945, 871, and 775 participants on days 1, 5, and 10, respectively. The mean change from baseline to followup was -0.64 log10 copies/mL (95%CI, -1.16 to -0.13) for metformin versus placebo, which equates to a 4.4-fold greater decrease. The mean change in SARS-CoV-2 from baseline to day 5 was -0.48 log10 copies/mL, and was -0.81 log10 copies/mL from baseline to day 10. The anti-viral effect increased with increased metformin dosing days 6-14. The antiviral effect was larger in those unvaccinated (mean -0.95 log copies/mL) than vaccinated (mean -0.39 log copies/mL). There was no change in viral load vs. placebo for ivermectin or fluvoxamine. Conclusion(s): Metformin lowered SARS-CoV-2 viral load in this quadrupleblinded, randomized clinical trial. The temporal relationship to dose titration suggests a dose-dependent effect. The magnitude of antiviral effect was similar to nirmatrelvir at day 5, greater than nirmatrelvir at day 10. Metformin is safe, widely available, and has few contraindications.

Benchmarking staffing in paediatric respiratory laboratories in Australia

Introduction/Aim: TSANZ Accreditation recommends sufficient staff to adequately meet service needs. There are no specific benchmarking FTE guidelines for both Scientific and Administrative staff. The aim of this project was to benchmark scientific and administrative staff in paediatric laboratories in Australia and NZ. Method(s): Eight paediatric laboratories from tertiary hospitals in Australia (7) and New Zealand (1) were invited to participate by providing the number and range of tests performed for years 2019, 2020, 2021. An activity study previously performed by 2 hospitals with input from a third was used to apply time weighting to each test. The hours taken for each test and Scientific FTE were analysed and results were evaluated via the program Tableau. Result(s): Two laboratories were unable to provide testing data and one laboratory had incomplete data sets making it difficult to determine FTE. Data was collated from five laboratories. Due to COVID-19 lockdowns in 2020-2021, only data from 2019 was used for benchmarking purposes. Total hours of testing per year were calculated and divided to give weekly amounts. This was then compared to the total amount of available weekly FTE hours. In 2019, scientific staff spent 26% (range 17%-36%) of their week in patient testing. During COVID-19 (2020 and 2021) the average testing time was 20% (range 14%-26% and 11%-28%). Laboratories with administrative staff spent more of their week in patient testing than those without (average: 28% vs. 23%). Conclusion(s): The raw number of tests a laboratory performs does not accurately define staffing FTE in paediatric laboratories in tertiary hospitals. Time weighting for specific tests should be considered when assessing staffing requirements. Laboratories with the most dedicated administrative FTE scientific staff were able to spend a greater portion of their week performing tests. Scientific staffs are required to undertake mandatory duties such as equipment calibration, meetings, professional development and research. Limitations to this study include differences in record keeping between laboratories. A more thorough time in motion study to include complex testing could be performed.

Global Veterinary Diagnostic Laboratory Equipment Management and Sustainability and Implications for Pandemic Preparedness Priorities1.

Substantial investments into laboratories, notably sophisticated equipment, have been made over time to detect emerging diseases close to their source. Diagnostic capacity has expanded as a result, but challenges have emerged. The Equipment Management and Sustainability Survey was sent to the Veterinary Services of 182 countries in mid-2019. We measured the status of forty types of laboratory equipment used in veterinary diagnostic laboratories. Of the 68,455 items reported from 227 laboratories in 136 countries, 22% (14,894/68,455) were improperly maintained, and 46% (29,957/65,490) were improperly calibrated. Notable differences were observed across World Bank income levels and regions, raising concerns about equipment reliability and the results they produce. Our results will advise partners and donors on how best to support low-resource veterinary laboratories to improve sustainability and fulfill their mandate toward pandemic prevention and preparedness, as well as encourage equipment manufacturers to spur innovation and develop more sustainable products that meet end-users' needs.

Model Diagnostics and Forecast Evaluation for Quantiles

Model diagnostics and forecast evaluation are closely related tasks, with the former concerning in-sample goodness (or lack) of fit and the latter addressing predictive performance out-of-sample. We review the ubiquitous setting in which forecasts are cast in the form of quantiles or quantile-bounded prediction intervals. We distinguish unconditional calibration, which corresponds to classical coverage criteria, from the stronger notion of conditional calibration, as can be visualized in quantile reliability diagrams. Consistent scoring functions-including, but not limited to, the widely used asymmetric piecewise linear score or pinball loss-provide for comparative assessment and ranking, and link to the coefficient of determination and skill scores.We illustrate the use of these tools on Engel's food expenditure data, the Global Energy Forecasting Competition 2014, and the US COVID-19 Forecast Hub.

Near Real-Time Social Distance Estimation In London

To mitigate the current COVID-19 pandemic, policy makers at the Greater London Authority, the regional governance body of London, UK, are reliant upon prompt, accurate and actionable estimations of lockdown and social distancing policy adherence. Transport for London, the local transportation department, reports they implemented over 700 interventions such as greater signage and expansion of pedestrian zoning at the height of the pandemic's first wave with our platform providing key data for those decisions. Large well-defined heterogeneous compositions of pedestrian footfall and physical proximity are difficult to acquire, yet necessary to monitor city-wide activity (busyness) and consequently discern actionable policy decisions. To meet this challenge, we leverage our existing large-scale data processing urban air quality machine learning infrastructure to process over 900 camera feeds in near real-time to generate new estimates of social distancing adherence, group detection and camera stability. In this work, we describe our development and deployment of a computer vision and machine learning pipeline. It provides near immediate sampling and contextualization of activity and physical distancing on the streets of London via live traffic camera feeds. We introduce a platform for inspecting, calibrating and improving upon existing methods, describe the active deployment on real-time feeds and provide analysis over an 18 month period.