In Vitro Diagnostic Assay to Detect SARS-CoV-2-Neutralizing Antibody in Patient Sera Using Engineered ACE-2 Mini-Protein.

The recent development and mass administration of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) vaccines allowed for disease control, reducing hospitalizations and mortality. Most of these vaccines target the SARS-CoV-2 Spike (S) protein antigens, culminating with the production of neutralizing antibodies (NAbs) that disrupt the attachment of the virus to ACE2 receptors on the host cells. However, several studies demonstrated that the NAbs typically rise within a few weeks after vaccination but quickly reduce months later. Thus, multiple booster administration is recommended, leading to vaccination hesitancy in many populations. Detecting serum anti-SARS-CoV-2 NAbs can instruct patients and healthcare providers on correct booster strategies. Several in vitro diagnostics kits are available; however, their high cost impairs the mass NAbs diagnostic testing. Recently, we engineered an ACE2 mimetic that interacts with the Receptor Binding Domain (RBD) of the SARS-2 S protein. Here we present the use of this engineered mini-protein (p-deface2 mut) to develop a detection assay to measure NAbs in patient sera using a competitive ELISA assay. Serum samples from twenty-one patients were tested. Nine samples (42.8%) tested positive, and twelve (57.1%) tested negative for neutralizing sera. The data correlated with the result from the standard commercial assay that uses human ACE2 protein. This confirmed that p-deface2 mut could replace human ACE2 in ELISA assays. Using bacterially expressed p-deface2 mut protein is cost-effective and may allow mass SARS-CoV-2 NAbs detection, especially in low-income countries where economical diagnostic testing is crucial. Such information will help providers decide when a booster is required, reducing risks of reinfection and preventing the administration before it is medically necessary.

Defining Key Performance Indicators for the California COVID-19 Exposure Notification System (CA Notify).

OBJECTIVES: Toward common methods for system monitoring and evaluation, we proposed a key performance indicator framework and discussed lessons learned while implementing a statewide exposure notification (EN) system in California during the COVID-19 epidemic. MATERIALS AND METHODS: California deployed the Google Apple Exposure Notification framework, branded CA Notify, on December 10, 2020, to supplement traditional COVID-19 contact tracing programs. For system evaluation, we defined 6 key performance indicators: adoption, retention, sharing of unique codes, identification of potential contacts, behavior change, and impact. We aggregated and analyzed data from December 10, 2020, to July 1, 2021, in compliance with the CA Notify privacy policy. RESULTS: We estimated CA Notify adoption at nearly 11 million smartphone activations during the study period. Among 1 654 201 CA Notify users who received a positive test result for SARS-CoV-2, 446 634 (27%) shared their unique code, leading to ENs for other CA Notify users who were in close proximity to the SARS-CoV-2-positive individual. We identified at least 122 970 CA Notify users as contacts through this process. Contact identification occurred a median of 4 days after symptom onset or specimen collection date of the user who received a positive test result for SARS-CoV-2. PRACTICE IMPLICATIONS: Smartphone-based EN systems are promising new tools to supplement traditional contact tracing and public health interventions, particularly when efficient scaling is not feasible for other approaches. Methods to collect and interpret appropriate measures of system performance must be refined while maintaining trust and privacy.

Designing a Framework for Remote Cancer Care Through Community Co-design: Participatory Development Study.

BACKGROUND: Recent shifts to telemedicine and remote patient monitoring demonstrate the potential for new technology to transform health systems; yet, methods to design for inclusion and resilience are lacking. OBJECTIVE: The aim of this study is to design and implement a participatory framework to produce effective health care solutions through co-design with diverse stakeholders. METHODS: We developed a design framework to cocreate solutions to locally prioritized health and communication problems focused on cancer care. The framework is premised on the framing and discovery of problems through community engagement and lead-user innovation with the hypothesis that diversity and inclusion in the co-design process generate more innovative and resilient solutions. Discovery, design, and development were implemented through structured phases with design studios at various locations in urban and rural Kentucky, including Appalachia, each building from prior work. In the final design studio, working prototypes were developed and tested. Outputs were assessed using the System Usability Scale as well as semistructured user feedback. RESULTS: We co-designed, developed, and tested a mobile app (myPath) and service model for distress surveillance and cancer care coordination following the LAUNCH (Linking and Amplifying User-Centered Networks through Connected Health) framework. The problem of awareness, navigation, and communication through cancer care was selected by the community after framing areas for opportunity based on significant geographic disparities in cancer and health burden resource and broadband access. The codeveloped digital myPath app showed the highest perceived combined usability (mean 81.9, SD 15.2) compared with the current gold standard of distress management for patients with cancer, the paper-based National Comprehensive Cancer Network Distress Thermometer (mean 74.2, SD 15.8). Testing of the System Usability Scale subscales showed that the myPath app had significantly better usability than the paper Distress Thermometer (t63=2.611; P=.01), whereas learnability did not differ between the instruments (t63=-0.311; P=.76). Notable differences by patient and provider scoring and feedback were found. CONCLUSIONS: Participatory problem definition and community-based co-design, design-with methods, may produce more acceptable and effective solutions than traditional design-for approaches.

COVID-19 vaccine delivery: an opportunity to set up systems for the future.

The race to develop safe and effective SARS-COV-2 vaccines has moved with unprecedented speed. There are now multiple promising candidates seeking emergency use authorization from the United States Food and Drug Administration and a host of candidates positioned for approval worldwide. Attention has now turned to allocation, distribution and verification of these vaccines, yet this focus exposes that the underlying infrastructure for global delivery and monitoring is threadbare and unevenly distributed. This presents both a barrier and an opportunity to deploy sustainable infrastructure. Major global stakeholders must convene quickly, collaborate, and collectively invest in global standards, legal models, common vocabularies and interoperable biometric-supported digital health technologies. As the COVID-19 vaccine effort scales, governments, private sector and NGOs have the chance to place lasting resources needed for equitable and effective delivery that can pay dividends into the future.

A Risk-Based Clinical Decision Support System for Patient-Specific Antimicrobial Therapy (iBiogram): Design and Retrospective Analysis.

BACKGROUND: There is a pressing need for digital tools that can leverage big data to help clinicians select effective antibiotic treatments in the absence of timely susceptibility data. Clinical presentation and local epidemiology can inform therapy selection to balance the risk of antimicrobial resistance and patient risk. However, data and clinical expertise must be appropriately integrated into clinical workflows. OBJECTIVE: The aim of this study is to leverage available data in electronic health records, to develop a data-driven, user-centered, clinical decision support system to navigate patient safety and population health. METHODS: We analyzed 5 years of susceptibility testing (1,078,510 isolates) and patient data (30,761 patients) across a large academic medical center. After curating the data according to the Clinical and Laboratory Standards Institute guidelines, we analyzed and visualized the impact of risk factors on clinical outcomes. On the basis of this data-driven understanding, we developed a probabilistic algorithm that maps these data to individual cases and implemented iBiogram, a prototype digital empiric antimicrobial clinical decision support system, which we evaluated against actual prescribing outcomes. RESULTS: We determined patient-specific factors across syndromes and contexts and identified relevant local patterns of antimicrobial resistance by clinical syndrome. Mortality and length of stay differed significantly depending on these factors and could be used to generate heuristic targets for an acceptable risk of underprescription. Combined with the developed remaining risk algorithm, these factors can be used to inform clinicians' reasoning. A retrospective comparison of the iBiogram-suggested therapies versus the actual prescription by physicians showed similar performance for low-risk diseases such as urinary tract infections, whereas iBiogram recognized risk and recommended more appropriate coverage in high mortality conditions such as sepsis. CONCLUSIONS: The application of such data-driven, patient-centered tools may guide empirical prescription for clinicians to balance morbidity and mortality with antimicrobial stewardship.

Dataset on optimization and development of a point-of-care glucometer-based SARS-CoV-2 detection assay using aptamers.

We present supplementary data for the published article, "Hitting the diagnostic sweet spot: Point-of-care SARS-CoV-2 salivary antigen testing with an off-the-shelf glucometer" [1]. The assay described is designed to be performed at home or in a clinic without expensive instrumentation or professional training. SARS-CoV-2 is detected by an aptamer-based assay that targets the Nucleocapsid (N) or Spike (S) antigens. Binding of the N or S protein to their respective aptamer results in the competitive release of a complementary antisense-invertase enzyme complex. The released enzyme then catalyzes the conversion of sucrose to glucose that is measured by an off-the-shelf glucometer. The data presented here describe the optimization of the assay parameters and their contribution to developing this aptamer-based assay to detect SARS-CoV-2. The assay performance was checked in a standard buffer, contrived samples, and patient samples validated with well-established scientific methods. The resulting dataset can be used to further develop glucometer-based assays for diagnosing other communicable and non-communicable diseases.

Hitting the diagnostic sweet spot: Point-of-care SARS-CoV-2 salivary antigen testing with an off-the-shelf glucometer.

Significant barriers to the diagnosis of latent and acute SARS-CoV-2 infection continue to hamper population-based screening efforts required to contain the COVID-19 pandemic in the absence of widely available antiviral therapeutics or vaccines. We report an aptamer-based SARS-CoV-2 salivary antigen assay employing only low-cost reagents ($3.20/test) and an off-the-shelf glucometer. The test was engineered around a glucometer as it is quantitative, easy to use, and the most prevalent piece of diagnostic equipment globally, making the test highly scalable with an infrastructure that is already in place. Furthermore, many glucometers connect to smartphones, providing an opportunity to integrate with contact tracing apps, medical providers, and electronic health records. In clinical testing, the developed assay detected SARS-CoV-2 infection in patient saliva across a range of viral loads - as benchmarked by RT-qPCR - within 1 h, with 100% sensitivity (positive percent agreement) and distinguished infected specimens from off-target antigens in uninfected controls with 100% specificity (negative percent agreement). We propose that this approach provides an inexpensive, rapid, and accurate diagnostic for distributed screening of SARS-CoV-2 infection at scale.

Computing SARS-CoV-2 Infection Risk From Symptoms, Imaging, and Test Data: Diagnostic Model Development.

BACKGROUND: Assigning meaningful probabilities of SARS-CoV-2 infection risk presents a diagnostic challenge across the continuum of care. OBJECTIVE: The aim of this study was to develop and clinically validate an adaptable, personalized diagnostic model to assist clinicians in ruling in and ruling out COVID-19 in potential patients. We compared the diagnostic performance of probabilistic, graphical, and machine learning models against a previously published benchmark model. METHODS: We integrated patient symptoms and test data using machine learning and Bayesian inference to quantify individual patient risk of SARS-CoV-2 infection. We trained models with 100,000 simulated patient profiles based on 13 symptoms and estimated local prevalence, imaging, and molecular diagnostic performance from published reports. We tested these models with consecutive patients who presented with a COVID-19-compatible illness at the University of California San Diego Medical Center over the course of 14 days starting in March 2020. RESULTS: We included 55 consecutive patients with fever (n=43, 78%) or cough (n=42, 77%) presenting for ambulatory (n=11, 20%) or hospital care (n=44, 80%). In total, 51% (n=28) were female and 49% (n=27) were aged <60 years. Common comorbidities included diabetes (n=12, 22%), hypertension (n=15, 27%), cancer (n=9, 16%), and cardiovascular disease (n=7, 13%). Of these, 69% (n=38) were confirmed via reverse transcription-polymerase chain reaction (RT-PCR) to be positive for SARS-CoV-2 infection, and 20% (n=11) had repeated negative nucleic acid testing and an alternate diagnosis. Bayesian inference network, distance metric learning, and ensemble models discriminated between patients with SARS-CoV-2 infection and alternate diagnoses with sensitivities of 81.6%-84.2%, specificities of 58.8%-70.6%, and accuracies of 61.4%-71.8%. After integrating imaging and laboratory test statistics with the predictions of the Bayesian inference network, changes in diagnostic uncertainty at each step in the simulated clinical evaluation process were highly sensitive to location, symptom, and diagnostic test choices. CONCLUSIONS: Decision support models that incorporate symptoms and available test results can help providers diagnose SARS-CoV-2 infection in real-world settings.

"Global Health": Time to Refocus while We still Have Time.

Two decades of growing resource availability from agencies and foundations in wealthy countries has transformed approaches to health in poorly resourced nations. This progress looks increasingly unstable as climate change, social unrest, and, now, disruptive pandemics present threats not only to health but also to the mechanisms that manage it, and to funding itself. The growth in "global health" schools, technology development laboratories, nongovernmental organizations and multilateral institutions in donor countries has delivered not only successes but also disappointment, and reflect a paradigm that is in many ways contrary to the principles of population-based ownership that they espouse. Although the COVID-19 crisis has underlined the importance of health access and health service capacity, we may have a limited window of opportunity in which to rethink the current model and improve both efficiency and effectiveness. With a dose of humility, we may all benefit from studying our own rhetoric on human-centered design and applying these principles across global health to ensure that our approach is effective, efficient, and defensible.