Clinical Tumor Dormancy.

This chapter summarizes clinical evidence on tumor dormancy, with a special focus on our research supporting the role of dormancy both in local and distant recurrence of breast cancer following mastectomy. Starting from these premises, we propose a model of neoplastic development that allows us to elucidate several relevant clinical phenomena, including the mammographic paradox, the significance of ipsilateral breast tumor recurrence after conservative surgery, and the effect of surgeries performed after the removal of the primary. We will discuss the biological implications of the dormancy-based model, which are at odds with Somatic Mutation Theory. We will then review new models, alternatives to the Somatic Mutation Theory, for cancer development, with special emphasis on the Dynamic System Theory and the originality of its conceptual approach. Finally, we will put particular emphasis on the view of cancer development as a tissue-level process. We believe that this will help harmonize the molecular biology research with the new conceptual approach and bridge the knowledge gap on dormancy between bench and bedside.

Effects of different polysaccharide colloids on the structure and physicochemical properties of peanut protein and wheat gluten composite system under extrusion.

The structure and physicochemical properties of the complex system of peanut protein and gluten with different concentrations (0 %, 0.5 %, 1 %, and 2 %) of carboxymethyl cellulose (CMC) or sodium alginate (SA) under high-moisture extrusion were studied. The water absorption index and low-field nuclear magnetic resonance showed that adding 0.5 % SA could significantly improve the water uniformity of peanut protein extrudates, while the increase in water absorption was not significant. The texture properties showed that adding CMC or SA increased the hardness, vertical shearing force, and parallel shearing force of the system. Furthermore, adding 0.5 % SA increased approximately 33 % and 75.2 % of the tensile distance and strength of the system, respectively. The secondary structure showed that CMC or SA decreased the proportion of α-helix, ß-turn, and random coil, while increased ß-sheet proportion. The results of hydrophobicity, unextractable protein, and endogenous fluorescence revealed that CMC and SA reduced the surface hydrophobicity of the system and caused fluorescence quenching in the system. Additionally, it was found that CMC generally increased the free sulfhydryl group content, while SA exhibited the opposite effect.

Putting the pieces together: towards an integrative framework for healthcare performance.

PURPOSE: In the past few decades, performance measuring systems have become important managerial tools for healthcare organizations. Healthcare performance metrics are a useful tool in understanding how healthcare organizations achieve their goals while satisfying the needs of their patients and conforming to national and international standards. Various efforts have been made to assess healthcare performance. Most of these measures are focused on a single perspective or developed by a single source to meet management and strategic objectives on time. DESIGN/METHODOLOGY/APPROACH: We develop a review of the literature to shed light on the measures used to assess performance in the healthcare sector at various points in time, as well as to establish a thorough understanding of healthcare performance measurement. FINDINGS: Developing real-time digital traceability of metrics and an integrative perspective that increases the actionability of information acquired is an attractive potential made possible by the introduction of new technologies and the digitization of data. ORIGINALITY/VALUE: We conclude that a proper measurement system should be one to combine patient, physician, non-medical staff and system perspective, which will further facilitate the assessment of healthcare performance and the comparative function.

Network motifs in cellular neurophysiology.

Concepts from network science and graph theory, including the framework of network motifs, have been frequently applied in studying neuronal networks and other biological complex systems. Network-based approaches can also be used to study the functions of individual neurons, where cellular elements such as ion channels and membrane voltage are conceptualized as nodes within a network, and their interactions are denoted by edges. Network motifs in this context provide functional building blocks that help to illuminate the principles of cellular neurophysiology. In this review we build a case that network motifs operating within neurons provide tools for defining the functional architecture of single-neuron physiology and neuronal adaptations. We highlight the presence of such computational motifs in the cellular mechanisms underlying action potential generation, neuronal oscillations, dendritic integration, and neuronal plasticity. Future work applying the network motifs perspective may help to decipher the functional complexities of neurons and their adaptation during health and disease.

Adaptive robust structure exploration for complex systems based on model configuration and fusion.

Analyzing and obtaining useful information is challenging when facing a new complex system. Traditional methods often focus on specific structural aspects, such as communities, which may overlook the important features and result in biased conclusions. To address this, this article suggests an adaptive algorithm for exploring complex system structures using a generative model. This method calculates and optimizes node parameters, which can reflect the latent structural characteristics of the complex system. The effectiveness and stability of this method have been demonstrated in comparative experiments on 10 sets of benchmark networks using our model parameter configuration scheme. To enhance adaptability, algorithm fusion strategies were also proposed and tested on two real-world networks. The results indicate that the algorithm can uncover multiple structural features, including clustering, overlapping, and local chaining. This adaptive algorithm provides a promising approach for exploring complex system structures.

Major Role of Multiscale Entropy Evolution in Complex Systems and Data Science.

Complex systems are prevalent in various disciplines encompassing the natural and social sciences, such as physics, biology, economics, and sociology. Leveraging data science techniques, particularly those rooted in artificial intelligence and machine learning, offers a promising avenue for comprehending the intricacies of complex systems without necessitating detailed knowledge of underlying dynamics. In this paper, we demonstrate that multiscale entropy (MSE) is pivotal in describing the steady state of complex systems. Introducing the multiscale entropy dynamics (MED) methodology, we provide a framework for dissecting system dynamics and uncovering the driving forces behind their evolution. Our investigation reveals that the MED methodology facilitates the expression of complex system dynamics through a Generalized Nonlinear Schrödinger Equation (GNSE) that thus demonstrates its potential applicability across diverse complex systems. By elucidating the entropic underpinnings of complexity, our study paves the way for a deeper understanding of dynamic phenomena. It offers insights into the behavior of complex systems across various domains.

Advances in Adjoint Functions of Connection Number in Water Resources Complex Systems: A Systematic Review.

The adjoint function of connection number has unique advantages in solving uncertainty problems of water resource complex systems, and has become an important frontier and research hotspot in the uncertainty research of water resource complex problems. However, in the rapid evolution of the adjoint function, some problems greatly limit the application of the adjoint function in the research of water resources. Therefore, based on bibliometric analysis, development, practical application issues, and prospects of the hot directions are analyzed. It is found that the development of the connection number of water resource set pair analysis can be divided into three stages: (1) relatively sluggish development before 2005, (2) a period of rapid advancement in adjoint function research spanning from 2005 to 2017, and (3) a subsequent surge post-2018. The introduction of the adjoint function of connection number promotes the continuous development of set pair analysis of water resources. Set pair potential and partial connection number are the crucial research directions of the adjoint function. Subtractive set pair potential has rapidly developed into a relatively independent and important trajectory. The research on connection entropy is comparatively less, which needs to be further strengthened, while that on adjacent connection number is even less. The adjoint function of set pair potential can be divided into three major categories: division set pair potential, exponential set pair potential, and subtraction set pair potential. The subtraction set pair potential, which retains the original dimension and quantity variation range of the connection number, is widely used in water resources and other fields. Coupled with the partial connection number, a series of new connection number adjoint functions have been developed. The partial connection number can be mainly divided into two categories: total partial connection number, and semi-partial connection number. Among these, the calculation expression and connotation of total partial connection numbers have not yet reached a consensus, accompanied by the slow development of high-order partial connection numbers. Semi-partial connection number can describe the mutual migration movement between different components of the connection number, which develops rapidly. With the limitations and current situation described above, promoting the exploration and application of the adjoint function of connection number in the field of water resources and other fields of complex systems has become the focus of future research.

Can the heptapeptide ASSIVSF of the ß2-adrenoceptor recognize ephedrine and pseudoephedrine epimers in a complex system?

Epimer separation is crucial in the field of analytical chemistry, separation science, and the pharmaceutical industry. No reported methods could separate simultaneously epimers or even isomers and remove other unwanted, co-existing, interfering substances from complex systems like herbal extracts. Herein, we prepared a heptapeptide-modified stationary phase for the separation of 1R,2S-(-)-ephedrine [(-)-Ephe] and 1S,2S-(+)-pseudoephedrine [(+)-Pse] epimers from Ephedra sinica Stapf extract and blood samples. The heptapeptide stationary phase was comprehensively characterized by scanning electron microscopy, X-ray photoelectron spectroscopy, and Fourier transform infrared spectroscopy. The separation efficiency of the heptapeptide column was compared with an affinity column packed with full-length ß2-AR functionalized silica gel (ß2-AR column). The binding affinity of the heptapeptide with (+)-Pse was 3-fold greater than that with (-)-Ephe. Their binding mechanisms were extensively characterized by chromatographic analysis, ultraviolet spectra, circular dichroism analysis, isothermal titration calorimetry, and molecule docking. An enhanced hydrogen bonding was clearly observed in the heptapeptide-(+)-Pse complex. Such results demonstrated that the heptapeptide can recognize (+)-Pse and (-)-Ephe epimers in a complex system. This work, we believe, was the first report to simultaneously separate epimers and remove non-specific interfering substances from complex samples. The method was potentially applicable to more challenging sample separation, such as chiral separation from complex systems.

Evaluation of the coordinated development level in the coastal eco-environmental complex system: A case study of Jiaozhou Bay, China.

Given the exponential population growth and remarkable socio-economic advancements, coastal areas face increasingly complex challenges in eco-environmental management due to anthropogenic pressures. With the current emphasis on high-quality economic development, there is an urgent need to establish and evaluate a comprehensive indicator system to ensure the sustainable development of the coastal eco-environment and to meet evolving management demands. Research on the coordinated development level of coastal eco-environmental complex system, based on the concept of land-sea coordination, plays a pivotal role in promoting the resolution of eco-environmental issues in coastal areas, achieving sustainable socio-economic development in these regions. In this study, we construct an indicator system for the eco-environmental complex system in Jiaozhou Bay (JZB) coastal zone, China, comprising six sub-systems and thirty indicators. The comprehensive development level and coupling coordination degree model (CCDM) are employed in this study to analyze the indicator system in 1980-2020, aiming to elucidate the processes involved in the improvements in this complex system. The findings indicate: (i) the system's comprehensive development level evaluation and coupling coordination degree (CCD) exhibit a two-stage pattern: a declining trend in 1980-2005, followed by a rising trend in 2005-2020. (ii) despite improvements, the comprehensive development level and the CCD of the system in 2020 still hold potential for further enhancement compared to 1980; and (iii) policymaking and changes in anthropogenic pressures in coastal areas are the primary factors influencing the performance of the system. In the future, policymaking can reduce anthropogenic pressures on the coastal eco-environment, improve the comprehensive development level and CCD of the complex system, and encourage a commitment to sustainable development.

Predicting the long-term collective behaviour of fish pairs with deep learning.

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social interactions in the fish species Hemigrammus rhodostomus. We compare the results of our deep learning approach with experiments and with the results of a state-of-the-art analytical model. To that end, we propose a systematic methodology to assess the faithfulness of a collective motion model, exploiting a set of stringent individual and collective spatio-temporal observables. We demonstrate that machine learning (ML) models of social interactions can directly compete with their analytical counterparts in reproducing subtle experimental observables. Moreover, this work emphasizes the need for consistent validation across different timescales, and identifies key design aspects that enable our deep learning approach to capture both short- and long-term dynamics. We also show that our approach can be extended to larger groups without any retraining, and to other fish species, while retaining the same architecture of the deep learning network. Finally, we discuss the added value of ML in the context of the study of collective motion in animal groups and its potential as a complementary approach to analytical models.

Strategies to improve care for older adults who present to the emergency department: a systematic review.

BACKGROUND: The aim of this systematic review was to examine the relationship between strategies to improve care delivery for older adults in ED and evaluation measures of patient outcomes, patient experience, staff experience, and system performance. METHODS: A systematic review of English language studies published since inception to December 2022, available from CINAHL, Embase, Medline, and Scopus was conducted. Studies were reviewed by pairs of independent reviewers and included if they met the following criteria: participant mean age of ≥ 65 years; ED setting or directly influenced provision of care in the ED; reported on improvement interventions and strategies; reported patient outcomes, patient experience, staff experience, or system performance. The methodological quality of the studies was assessed by pairs of independent reviewers using The Joanna Briggs Institute critical appraisal tools. Data were synthesised using a hermeneutic approach. RESULTS: Seventy-six studies were included in the review, incorporating strategies for comprehensive assessment and multi-faceted care (n = 32), targeted care such as management of falls risk, functional decline, or pain management (n = 27), medication safety (n = 5), and trauma care (n = 12). We found a misalignment between comprehensive care delivered in ED for older adults and ED performance measures oriented to rapid assessment and referral. Eight (10.4%) studies reported patient experience and five (6.5%) reported staff experience. CONCLUSION: It is crucial that future strategies to improve care delivery in ED align the needs of older adults with the purpose of the ED system to ensure sustainable improvement effort and critical functioning of the ED as an interdependent component of the health system. Staff and patient input at the design stage may advance prioritisation of higher-impact interventions aligned with the pace of change and illuminate experience measures. More consistent reporting of interventions would inform important contextual factors and allow for replication.

An interpretative review of the wastewater-based surveillance of the SARS-CoV-2: where do we stand on its presence and concern?

Wastewater-based epidemiology (WBE) has been used for monitoring infectious diseases like polio, hepatitis, etc. since the 1940s. It is also being used for tracking the SARS-CoV-2 at the population level. This article aims to compile and assess the information for the qualitative and quantitative detection of the SARS-CoV-2 in wastewater. Based on the globally published studies, we highlight the importance of monitoring SARS-CoV-2 presence/detection in the wastewater and concurrently emphasize the development of early surveillance techniques. SARS-CoV-2 RNA sheds in the human feces, saliva, sputum and mucus that ultimately reaches to the wastewater and brings viral RNA into it. For the detection of the virus in the wastewater, different detection techniques have been optimized and are in use. These are based on serological, biosensor, targeted PCR, and next generation sequencing for whole genome sequencing or targeted amplicon sequencing. The presence of the SARS-CoV-2 RNA in wastewater could be used as a potential tool for early detection and devising the strategies for eradication of the virus before it is spread in the community. Additionally, with the right and timely understanding of viral behavior in the environment, an accurate and instructive model that leverages WBE-derived data may be created. This might help with the creation of technological tools and doable plans of action to lessen the negative effects of current viral epidemics or future potential outbreaks on public health and the economy. Further work toward whether presence of viral load correlates with its ability to induce infection, still needs evidence. The current increasing incidences of JN.1 variant is a case in point for continued early detection and surveillance, including wastewater.

Interfacial interactions of polyethylene terephthalate microplastics and malachite green, tetracycline in aqueous environments.

Polyethylene terephthalate microplastics (PET-MPs) are one of pivotal nondegradable emerging pollutant. Here the variation of the surface physicochemical characteristics of PET-MPs with UV irradiation aging and the adsorption behaviors of PET-MPs in malachite green (MG), tetracycline (TC) solution and the effect of coexisting Cu(II) were comparatively investigated. The yellowing, weakened hydrophobicity, and increased surface negative charge, crystallinity degree and oxygen-containing functional groups were manifested specifically by the aged PET-MPs. Different from the single system, the hydrophobic interaction and metal ion bridging complexation dominated the adsorption of MG and TC, respectively, in the binary solution. While in the ternary solution, cationic ion competition of Cu(II) with MG decreased its capture, and the formation of PET-Cu(II)-TC ternary complexes promoted TC adsorption. Moreover, PET-MPs could serve as an efficient vector for MG and TC in MG/TC/Cu(II) ternary system, indicating PET-MPs tend to carry more varieties in the complex environment, that may increase the environmental risk of PET-MPs.

The role of social attraction and social avoidance in shaping modular networks.

How interactions between individuals contribute to the emergence of complex societies is a major question in behavioural ecology. Nonetheless, little remains known about the type of immediate social structure (i.e. social network) that emerges from relationships that maximize beneficial interactions (e.g. social attraction towards informed individuals) and minimize costly relationships (e.g. social avoidance of infected group mates). We developed an agent-based model where individuals vary in the degree to which individuals signal benefits versus costs to others and, on this basis, choose with whom to interact depending on simple rules of social attraction (e.g. access to the highest benefits) and social avoidance (e.g. avoiding the highest costs). Our main findings demonstrate that the accumulation of individual decisions to avoid interactions with highly costly individuals, but that are to some extent homogeneously beneficial, leads to more modular networks. On the contrary, individuals favouring interactions with highly beneficial individuals, but that are to some extent homogeneously costly, lead to less modular networks. Interestingly, statistical models also indicate that when individuals have multiple potentially beneficial partners to interact with, and no interaction cost exists, this also leads to more modular networks. Yet, the degree of modularity is contingent upon the variability in benefit levels held by individuals. We discuss the emergence of modularity in the systems and their consequences for understanding social trade-offs.

Slack Is Needed to Solve the Shortage of Nurses.

Healthcare systems are facing a shortage of nurses. This article identifies some of the major causes of this and the issues that need to be solved. We take a perspective derived from queuing theory: the patient-nurse relationship is characterized by a scarcity of time and resources, requiring comprehensive coordination at all levels. For coordination, we take an information-theoretic perspective. Using both perspectives, we analyze the nature of healthcare services and show that ensuring slack, meaning a less than exhaustive use of human resources, is a sine qua non to having a good, functioning healthcare system. We analyze what coordination efforts are needed to manage relatively simple office hours, wards, and home care. Next, we address the level of care where providers cannot themselves prevent the complexity of organization that possibly damages care tasks and job quality. A lack of job quality may result in nurses leaving the profession. Job quality, in this context, depends on the ability of nurses to coordinate their activities. This requires slack resources. The availability of slack that is efficient depends on a stable inflow and retention rate of nurses. The healthcare system as a whole should ensure that the required nurse workforce will be able to coordinate and execute their tasks. Above that, workforce policies need more stability.

An Entropy Generation Rate Model for Tropospheric Behavior That Includes Cloud Evolution.

A postulate that relates global warming to higher entropy generation rate demand in the tropospheric is offered and tested. This article introduces a low-complexity model to calculate the entropy generation rate required in the troposphere. The entropy generation rate per unit volume is noted to be proportional to the square of the Earth's average surface temperature for a given positive rate of surface warming. The main postulate is that the troposphere responds with mechanisms to provide for the entropy generation rate that involves specific cloud morphologies and wind behavior. A diffuse-interface model is used to calculate the entropy generation rates of clouds. Clouds with limited vertical development, like the high-altitude cirrus or mid-altitude stratus clouds, are close-to-equilibrium clouds that do not generate much entropy but contribute to warming. Clouds like the cumulonimbus permit rapid vertical cloud development and can rapidly generate new entropy. Several extreme weather events that the Earth is experiencing are related to entropy-generating clouds that discharge a high rate of rain, hail, or transfer energy in the form of lightning. The water discharge from a cloud can cool the surface below the cloud but also add to the demand for a higher entropy generation rate in the cloud and troposphere. The model proposed predicts the atmospheric conditions required for bifurcations to severe-weather clouds. The calculated vertical velocity of thunderclouds associated with high entropy generation rates matches the recorded observations. The scale of instabilities for an evolving diffuse interface is related to the entropy generation rate per unit volume. Significant similarities exist between the morphologies and the entropy generation rate correlations in vertical cloud evolution and directionally solidified grainy microstructures. Such similarities are also explored to explore a generalized framework of pattern evolution and establish the relationships with the corresponding entropy generation rate. A complex system like the troposphere can invoke multiple phenomena that dominate at different spatial scales to meet the demand for an entropy generation rate. A few such possibilities are presented in the context of rapid and slow changes in weather patterns.

What does it mean to be an agent?

Artificial intelligence (AI) has posed numerous legal-ethical challenges. These challenges are particularly acute when dealing with AI demonstrating substantial computational prowess, which is then correlated with agency or autonomy. A common response to considering this issue is to inquire whether an AI system is "conscious" or not. If it is, then it could constitute an agent, actor, or person. This framing is, however, unhelpful since there are many unresolved questions about consciousness. Instead, a practical approach is proposed, which could be used to better regulate new AI technologies. The value of the practical approach in this study is that it (1) provides an empirically observable, testable framework that contains predictive value; (2) is derived from a data-science framework that uses semantic information as a marker; (3) relies on a self-referential logic which is fundamental to agency; (4) enables the "grading" or "ranking" of AI systems, which provides an alternative method (as opposed to current risk-tiering approaches) and measure to determine the suitability of an AI system within a specific domain (e.g., such as social domains or emotional domains); (5) presents consistent, coherent, and higher informational content as opposed to other approaches; (6) fits within the conception of what informational content "laws" are to contain and maintain; and (7) presents a viable methodology to obtain "agency", "agent", and "personhood", which is robust to current and future developments in AI technologies and society.

Do you see the problem? Visualising a generalised 'complex local system' of antibiotic prescribing across the United Kingdom using qualitative interview data.

Antimicrobial resistance (AMR) is often referred to as a complex problem embedded in a complex system. Despite this insight, interventions in AMR, and in particular in antibiotic prescribing, tend to be narrowly focused on the behaviour of individual prescribers using the tools of performance monitoring and management rather than attempting to bring about more systemic change. In this paper, we aim to elucidate the nature of the local antibiotic prescribing 'system' based on 71 semi-structured interviews undertaken in six local areas across the United Kingdom (UK). We applied complex systems theory and systems mapping methods to our qualitative data to deepen our understanding of the interactions among antibiotic prescribing interventions and the wider health system. We found that a complex and interacting set of proximal and distal factors can have unpredictable effects in different local systems in the UK. Ultimately, enacting performance management-based interventions in the absence of in-depth contextual understandings about other pressures prescribers face is a recipe for temporary solutions, waning intervention effectiveness, and unintended consequences. We hope our insights will enable policy makers and academics to devise and evaluate interventions in future in a manner that better reflects and responds to the dynamics of complex local prescribing systems.

Information integration during bioelectric regulation of morphogenesis of the embryonic frog brain.

Spatiotemporal patterns of cellular resting potential regulate several aspects of development. One key aspect of the bioelectric code is that transcriptional and morphogenetic states are determined not by local, single-cell, voltage levels but by specific distributions of voltage across cell sheets. We constructed and analyzed a minimal dynamical model of collective gene expression in cells based on inputs of multicellular voltage patterns. Causal integration analysis revealed a higher-order mechanism by which information about the voltage pattern was spatiotemporally integrated into gene activity, as well as a division of labor among and between the bioelectric and genetic components. We tested and confirmed predictions of this model in a system in which bioelectric control of morphogenesis regulates gene expression and organogenesis: the embryonic brain of the frog Xenopus laevis. This study demonstrates that machine learning and computational integration approaches can advance our understanding of the information-processing underlying morphogenetic decision-making, with a potential for other applications in developmental biology and regenerative medicine.

COVID-19-Related Age Profiles for SARS-CoV-2 Variants in England and Wales and States of the USA (2020 to 2022): Impact on All-Cause Mortality.

Since 2020, COVID-19 has caused serious mortality around the world. Given the ambiguity in establishing COVID-19 as the direct cause of death, we first investigate the effects of age and sex on all-cause mortality during 2020 and 2021 in England and Wales. Since infectious agents have their own unique age profile for death, we use a 9-year time series and several different methods to adjust single-year-of-age deaths in England and Wales during 2019 (the pre-COVID-19 base year) to a pathogen-neutral single-year-of-age baseline. This adjusted base year is then used to confirm the widely reported higher deaths in males for most ages above 43 in both 2020 and 2021. During 2020 (+COVID-19 but no vaccination), both male and female population-adjusted deaths significantly increased above age 35. A significant reduction in all-cause mortality among both males and females aged 75+ could be demonstrated in 2021 during the widespread COVID-19 vaccination period; however, deaths below age 75 progressively increased. This finding arises from a mix of vaccination coverage and year-of-age profiles of deaths for the different SARS-CoV-2 variants. In addition, specific effects of age around puberty were demonstrated, where females had higher deaths than males. There is evidence that year-of-birth cohorts may also be involved, indicating that immune priming to specific pathogen outbreaks in the past may have led to lower deaths for some birth cohorts. To specifically identify the age profile for the COVID-19 variants from 2020 to 2023, we employ the proportion of total deaths at each age that are potentially due to or 'with' COVID-19. The original Wuhan strain and the Alpha variant show somewhat limited divergence in the age profile, with the Alpha variant shifting to a moderately higher proportion of deaths below age 84. The Delta variant specifically targeted individuals below age 65. The Omicron variants showed a significantly lower proportion of overall mortality, with a markedly higher relative proportion of deaths above age 65, steeply increasing with age to a maximum around 100 years of age. A similar age profile for the variants can be seen in the age-banded deaths in US states, although they are slightly obscured by using age bands rather than single years of age. However, the US data shows that higher male deaths are greatly dependent on age and the COVID variant. Deaths assessed to be 'due to' COVID-19 (as opposed to 'involving' COVID-19) in England and Wales were especially overestimated in 2021 relative to the change in all-cause mortality. This arose as a by-product of an increase in COVID-19 testing capacity in late 2020. Potential structure-function mechanisms for the age-specificity of SARS-CoV-2 variants are discussed, along with potential roles for small noncoding RNAs (miRNAs). Using data from England, it is possible to show that the unvaccinated do indeed have a unique age profile for death from each variant and that vaccination alters the shape of the age profile in a manner dependent on age, sex, and the variant. The question is posed as to whether vaccines based on different variants carry a specific age profile.