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Patients and clinicians want to know who is protected from SARS-CoV-2 infection. This update is part of a living systematic review to understand the potential value of antibody testing as a correlate of protection against infection. Because the previous review found that seroconversion was a near-universal consequence of recent infection in immunocompetent patients, this review examines the risk for reinfection in adults after SARS-CoV-2 infection, whether diagnosed by polymerase chain reaction test, antibody test, or a combination of both., Background: The strength and duration of immunity from infection with SARS-CoV-2 are important for public health planning and clinical practice. Purpose: To synthesize evidence on protection against reinfection after SARS-CoV-2 infection. Data Sources: MEDLINE (Ovid), the World Health Organization global literature database, ClinicalTrials.gov, COVID19reviews.org, and reference lists. Study Selection: Longitudinal studies that compared the risk for reinfection after SARS-CoV-2 infection versus infection risk in individuals with no prior infection. Data Extraction: Two investigators sequentially extracted study data and rated quality. Data Synthesis: Across 18 eligible studies, reinfection risk ranged from 0% to 2.2%. In persons with recent SARS-CoV-2 infection compared with unvaccinated, previously uninfected individuals, 80% to 98% of symptomatic infections with wild-type or Alpha variants were prevented (high strength of evidence). In the meta-analysis, previous infection reduced risk for reinfection by 87% (95% CI, 84% to 90%), equaling 4.3 fewer infections per 100 persons in both the general population (risk difference, −0.043 [CI, −0.071 to −0.015]) and health care workers (risk difference, −0.043 [CI, −0.069 to −0.016]), and 26.6 fewer infections per 100 persons in care facilities (risk difference, −0.266 [CI, −0.449 to −0.083]). Protection remained above 80% for at least 7 months, but no study followed patients after the emergence of the Delta or Omicron variant. Results for the elderly were conflicting. Limitation: Methods to ascertain and diagnose infections varied. Conclusion: Before the emergence of the Delta and Omicron variants, persons with recent infection had strong protection against symptomatic reinfections for 7 months compared with unvaccinated, previously uninfected individuals. Protection in immunocompromised persons, racial and ethnic subgroups, and asymptomatic index case patients is unclear. The durability of protection in the setting of the Delta and Omicron variants is unknown. Primary Funding Source: Agency for Healthcare Research and Quality. (PROSPERO: CRD42020207098)

BackgroundSustainability science is an emerging area within implementation science. There is limited evidence regarding strategies to best support the continued delivery and sustained impact of evidence-based interventions (EBIs). To build such evidence, clear definitions, and ways to operationalize strategies specific and/or relevant to sustainment are required. Taxonomies and compilations such as the Expert Recommendations for Implementing Change (ERIC) were developed to describe and organize implementation strategies. This study aimed to adapt, refine, and extend the ERIC compilation to incorporate an explicit focus on sustainment. We also sought to classify the specific phase(s) of implementation when the ERIC strategies could be considered and applied.MethodsWe used a two-phase iterative approach to adapt the ERIC. This involved: (1) adapting through consensus (ERIC strategies were mapped against barriers to sustainment as identified via the literature to identify if existing implementation strategies were sufficient to address sustainment, needed wording changes, or if new strategies were required) and; (2) preliminary application of this sustainment-explicit ERIC glossary (strategies described in published sustainment interventions were coded against the glossary to identify if any further amendments were needed). All team members independently reviewed changes and provided feedback for subsequent iterations until consensus was reached. Following this, and utilizing the same consensus process, the Exploration, Preparation, Implementation and Sustainment (EPIS) Framework was applied to identify when each strategy may be best employed across phases.ResultsSurface level changes were made to the definitions of 41 of the 73 ERIC strategies to explicitly address sustainment. Four additional strategies received deeper changes in their definitions. One new strategy was identified: Communicate with stakeholders the continued impact of the evidence-based practice. Application of the EPIS identified that at least three-quarters of strategies should be considered during preparation and implementation phases as they are likely to impact sustainment.ConclusionA sustainment-explicit ERIC glossary is provided to help researchers and practitioners develop, test, or apply strategies to improve the sustainment of EBIs in real-world settings. Whilst most ERIC strategies only needed minor changes, their impact on sustainment needs to be tested empirically which may require significant refinement or additions in the future.

We investigated the relationship between the presence of helminth parasites in European badgers, and their tuberculosis (TB) status, culled as part of the bovine TB eradication programme in Ireland. Data on the worm burden or faecal egg or larval count was available for all helminth taxa recorded. Lymph node tissue samples were taken from the badgers and tested for TB. We then explored the correlation, in full-grown badgers, between the likelihood of

Introduction: Nonrandomized studies of interventions (NRSIs) are observational or experimental studies of the effectiveness and/or harms of interventions, in which participants are not randomized to intervention groups. There is increasingly widespread recognition that advancements in the design and analysis of NRSIs allow NRSI evidence to have a much more prominent role in decision making, and not just as ancillary evidence to randomized controlled trials (RCTs). Objective: To guide decisions about inclusion of NRSIs for addressing the effects of interventions in systematic reviews (SRs), this chapter updates the 2010 guidance on inclusion of NRSIs in Agency for Healthcare Research and Quality (AHRQ) Evidence-based Practice Center (EPC) SRs. The chapter focuses on considerations for decisions to include or exclude NRSIs in SRs. Methods: In November 2020, AHRQ convened a 20-member workgroup that comprised 13 members representing 8 of 9 AHRQ-appointed EPCs, 3 AHRQ representatives, 1 independent consultant with expertise in SRs, and 3 representatives of the AHRQ-appointed Scientific Resource Center. The workgroup received input from the full EPC Program regarding the process and specific issues through discussions at a virtual meeting and two online surveys regarding challenges with NRSI inclusion in SRs. One survey focused on current practices by EPCs regarding NRSI inclusion in ongoing and recently completed SRs. The other survey focused on the appropriateness, completeness, and usefulness of existing EPC Program methods guidance. The workgroup considered the virtual meeting and survey input when identifying aspects of the guidance that needed updating. The workgroup used an informal method for generating consensus about guidance. Disagreements were resolved through discussion. Results: We outline considerations for the inclusion of NRSIs in SRs of intervention effectiveness. We describe the strengths and limitations of RCTs, study design features and types of NRSIs, and key considerations for making decisions about inclusion of NRSIs (during the stages of topic scoping and refinement, SR team formation, protocol development, SR conduct, and SR reporting). We discuss how NRSIs may be applicable for the decisional dilemma being addressed in the SR, threats to the internal validity of NRSIs, as well as various data sources and advanced analytic methods that may be used in NRSIs. Finally, we outline an approach to incorporating NRSIs within an SR and key considerations for reporting. Conclusion: The main change from the previous guidance is the overall approach to decisions about inclusion of NRSIs in EPC SRs. Instead of recommending NRSI inclusion only if RCTs are insufficient to address the Key Question, this updated guidance handles NRSI evidence as a valuable source of information and lays out important considerations for decisions about the inclusion of NRSIs in SRs of intervention effectiveness. Different topics may require different decisions regarding NRSI inclusion. This guidance is intended to improve the utility of the final product to end-users. Inclusion of NRSIs will increase the scope, time, and resources needed to complete SRs, and NRSIs pose potential threats to validity, such as selection bias, confounding, and misclassification of interventions. Careful consideration must be given to both concerns.

Nanoparticles are increasingly employed as a vehicle for the targeted delivery of therapeutics to specific cell types. However, much remains to be discovered about the fundamental biology that dictates the interactions between nanoparticles and cells. Accordingly, few nanoparticle-based targeted therapeutics have succeeded in clinical trials. One element that hinders our understanding of nanoparticle-cell interactions is the presence of heterogeneity in nanoparticle dosage data obtained from standard experiments. It is difficult to distinguish between heterogeneity that arises from stochasticity in nanoparticle-cell interactions, and that which arises from heterogeneity in the cell population. Mathematical investigations have revealed that both sources of heterogeneity contribute meaningfully to the heterogeneity in nanoparticle dosage. However, these investigations have relied on simplified models of nanoparticle internalisation. Here we present a stochastic mathematical model of nanoparticle internalisation that incorporates a suite of relevant biological phenomena such as multistage internalisation, cell division, asymmetric nanoparticle inheritance and nanoparticle saturation. Critically, our model provides information about nanoparticle dosage at an individual cell level. We perform model simulations to examine the influence of specific biological phenomena on the heterogeneity in nanoparticle dosage in the absence of heterogeneity in the cell population. Under certain modelling assumptions, we derive analytic approximations of the nanoparticle dosage distribution. We demonstrate that the analytic approximations are accurate, and show that nanoparticle dosage can be described by a Poisson mixture distribution with rate parameters that are a function of Beta-distributed random variables. We discuss the implications of the analytic results with respect to parameter estimation and model identifiability from standard experimental data. Finally, we highlight extensions and directions for future research.

SUMMARYSARS-CoV-2 infection causes a spectrum of clinical outcomes and diverse memory responses. Population studies indicate that viral neutralizing antibody responses are protective, but do not always develop post-infection. Other antiviral antibody effector functions, T-cell responses, or immunity to seasonal coronaviruses (OC43, 229E) have been implicated but not defined in all ages. Here, we identify that children and adult subjects generate polyfunctional antibodies to the spike protein after asymptomatic infection or mild disease, with some subjects developing cellular responses without seroconversion. Diversity in immunity was explained by two clusters distinguished by CD4+ T-cell cytokines, age, and antibodies to seasonal coronaviruses. Post-vaccination neutralizing responses were predicted by specific post-infection immune measures, including IL-2, spike-IgA, OC43-IgG1, 229E-IgM. We confirm a key role for CD4+ T cell cytokines in functionality of anti-spike antibodies, and show that antibody diversity is impacted by age, Th/Th2 cytokine biases, and antibody isotypes to SARS-CoV-2 and seasonal coronaviruses.