The World Health Organization (WHO) has developed a global roadmap to defeat meningitis by 2030. To advocate for and track progress of the roadmap, the burden of meningitis as a syndrome and by pathogen must be accurately defined. Three major global health models estimating meningitis mortality as a syndrome and/or by causative pathogen were identified and compared for the baseline year 2015. Two models, (1) the WHO and the Johns Hopkins Bloomberg School of Public Health’s Maternal and Child Epidemiology Estimation (MCEE) group’s Child Mortality Estimation (WHO-MCEE) and (2) the Institute for Health Metrics and Evaluation (IHME) Global Burden of Disease Study (GBD 2017), identified meningitis, encephalitis and neonatal sepsis, collectively, to be the second and third largest infectious killers of children under five years, respectively. Global meningitis/encephalitis and neonatal sepsis mortality estimates differed more substantially between models than mortality estimates for selected infectious causes of death and all causes of death combined. Estimates at national level and by pathogen also differed markedly between models. Aligning modelled estimates with additional data sources, such as national or sentinel surveillance, could more accurately define the global burden of meningitis and help track progress against the WHO roadmap.  The world saw great progress in reducing child mortality over the lifetime of the United Nations (UN) Millennium Development Goals (MDGs) with an estimated 54% decline in children under five years of age from 93 deaths per 1000 live births in 1990 to 43 per 1000 live births in 2015 [Haemophilus influenzae type b (Hib) and pneumococcal vaccines as well as the roll out of the meningococcal A vaccine, MenAfriVac, across some of the highest incidence areas of sub-Saharan Africa. Despite this, recent estimates have identified that the global burden of meningitis in all age groups remains high and progress lags substantially behind that of other vaccine preventable diseases [Among these preventable diseases, meningitis has one of the highest fatality rates and the potential to cause devastating epidemics. Since the turn of the century, we have seen advances as a result of widespread global introduction of diseases . Whilst diseases . Despitediseases ,6,7,8,9.Neisseria meningitidis (meningococcus), Streptococcus pneumoniae (pneumococcus), Haemophilus influenzae (Hi), and Streptococcus agalactiae (group B streptococcus (GBS)). In response to calls from governments, global health organisations, civil society, public health bodies, academia and the private sector, a World Health Organization (WHO)-led collaboration is developing a Defeating Meningitis by 2030 Global Roadmap . The RoaTo advocate for a global roadmap to defeat meningitis, the global burden of meningitis as a syndrome in relation to other infectious causes of death needs to be accurately described, and countries with the highest burden identified, so that efforts and resources can be targeted effectively. Estimates of pathogen-specific meningitis incidence and mortality at the global level can identify the need for new vaccines or support wider access to existing ones. Tracking trends in pathogen-specific meningitis and syndromic disease over time at the national and international level is vital to assess the impact of interventions such as vaccines implemented as part of the global roadmap to defeat meningitis. Vital registration systems and disease surveillance platforms are limited across many geographies and regions, so there is a reliance on modelled estimates to get a complete global picture of disease across all settings but cause of death estimates have been found to differ across these different modelling efforts . ModelleIn this paper we aim to compare the available modelled estimates for cases and deaths from meningitis as a syndrome, by causative pathogen and the methods used, in order to assess whether these models can be used with confidence by decision makers to prioritise recommendations from a plan to defeat meningitis, and by those needing to track progress on the WHO Defeating Meningitis by 2030 Global Roadmap’.Through attending key stakeholder meetings, we identified three modelling efforts that estimate the global burden of meningitis and neonatal sepsis: (1) WHO and the Johns Hopkins Bloomberg School of Public Health’s Maternal and Child Epidemiology Estimation (MCEE) group’s Child Mortality Estimation (WHO-MCEE), which estimates15 causes of death for children under five years of age ; (2) TheTwo additional models were also identified that estimated disease burden caused by pathogens of particular relevance to the WHO Defeating Meningitis by 2030 Global Roadmap: (1) the WHO-MCEE group’s estimates of the burden of pneumococcal and Hib disease in children , and (2)Not all of these efforts were directly comparable because they did not provide the same level of data or use the same indicators of burden .As WHO GHE estimates were an amalgamation of historical models (WHO-MCEE’s 2000–2016 and IHME’s GBD 2016) we did not consider them further in our analysis. We did not include GBS estimates from LSHTM in our analysis because the age categorisation (0–89 days) did not correspond with the disaggregated age categories of the other models and so did not allow for meaningful comparison.The scale of the global burden of meningitis deaths relative to all causes and leading infectious causes of death was assessed by comparing, death and mortality estimates from GBD 2017 and the WHO-MCEE’s 2000–2017 model according to the following syndromic cause of death categories “All causes”, “Infectious disease”, “meningitis/encephalitis” and “neonatal sepsis”.We considered the burden of meningitis and neonatal sepsis together for the purposes of comparison with other leading infectious causes of death because distinguishing between these syndromes is almost impossible based on clinical signs alone in the neonate ,17. LumbThe WHO-MCEE have historically estimated sepsis and meningitis in the neonatal period within the same cause category because of difficulties in distinguishing between these clinical syndromes in this age group. These causes were estimated separately for the first time in their latest modelling round by using the ratio of neonatal meningitis and neonatal sepsis deaths derived from IHME estimates. Because WHO-MCEE estimate meningitis/encephalitis as one cause category, GBD 2017 meningitis and encephalitis deaths were amalgamated for the purpose of comparison.Denominators used to report mortality rates were standardised across the models and, where necessary, recalculated to be expressed as deaths per 1000 live births in the neonatal period and deaths per 100,000 population in the post neonatal period. GBD 2017 mortality rates in the neonatal period were calculated from IHME live birth estimates for the year 2015. WHO-MCEE postneonatal mortality rates were calculated using UN population estimates for the year 2015 [Priority geographical areas for targeting a plan to defeat meningitis were identified from country-specific GBD 2017 and WHO-MCEE meningitis/encephalitis mortality estimates for the year 2015 in children under five years.Meningitis mortality and incidence estimates according to pathogen over time (2000–2015) were analysed using estimates produced by GBD 2017 and the WHO-MCEE pathogen model. Meningococcal meningitis is commonly associated with epidemics. As WHO-MCEE meningococcal meningitis estimates did not account for deaths and cases resulting from epidemics, estimates for ‘Hib meningitis’ and ‘pneumococcal meningitis’ mortality and incidence in the post neonatal period (28 days–<5 years) were the categories and age group used for comparison.An analysis of the estimation methodology for each model was also undertaken in an attempt to explain any inconsistencies between models.Overall, the WHO-MCEE estimated there to be approximately 100,000 fewer deaths in the under-five age group than the GBD 2017, with proportionally more under-five deaths occurring in the neonatal period (46% compared to GBD 2017’s 43%).The GBD 2017 estimated 34% more deaths from meningitis/encephalitis than the WHO-MCEE in the year 2015  . MeningiHowever, the WHO-MCEE estimated >100,000 more deaths than the GBD 2017 when neonatal sepsis deaths were combined with meningitis/encephalitis, due to the WHO-MCEE’s much higher estimate of neonatal sepsis deaths. Uncertainty intervals do not overlap between modelled estimates of deaths from neonatal sepsis in any of the age categories. This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.The WHO-MCEE model estimated meningitis/encephalitis and neonatal sepsis as the second largest infectious cause of death, co-ranked with diarrhoeal diseases, in children aged under five years in 2015, after acute respiratory infections . In contAt the country level, there was considerable variability in estimates of burden per population. For meningitis/encephalitis mortality rates, the WHO-MCEE model ranked Somalia highest for the year 2015 , whilst the GBD 2017 ranked Somalia 11th highest for the same year .For numbers of deaths, both models attribute approximately 70% of all meningitis/encephalitis deaths in children under five years to just 12 countries including India, Nigeria, Pakistan, Democratic Republic of Congo (DRC), Ethiopia, Niger, Afghanistan, Mali, Uganda and China. However, whilst Somalia and Chad feature in the top 12 (ranked 7th and 8th respectively) in the WHO-MCEE estimates, they did not feature in the GBD 2017 top 12, where Indonesia and Burkina Faso featured instead (ranked 8th and 9th highest respectively) .The GBD 2017 and WHO-MCEE’s pathogen models both estimated pneumococcal and Hib meningitis mortality and incidence in children aged 1 to 59 months at the national and global levels.A comparison of the global estimates for the year 2015  showed tDespite major differences in the relative proportions of meningitis deaths attributed to Hib and pneumococcal bacteria between models, both models agreed that Hib and pneumococcal meningitis combined were the underlying cause of approximately 40% of all meningitis/encephalitis deaths globally.When comparisons were made between the modelled estimates for Hib and pneumococcal meningitis incidence and mortality over time , both moOf note is that case fatality rates (CFRs) differed dramatically between the two sets of estimates. CFRs derived from WHO-MCEE global cases, and deaths estimates for Hib and pneumococcal meningitis in 2015, were 23% and 45%, respectively. However, CFRs calculated from GBD 2017 estimates were 8% for pneumococcal meningitis, 19% for Hib meningitis and 8% for meningococcal meningitis. Evidence from the literature closely agrees with the WHO-MCEE CFRs, consistently reporting higher CFRs from pneumococcal meningitis compared to Hib meningitis and meningococcal meningitis ,25,26.Whilst there was little difference between estimated mortality from all causes and infectious diseases in children under five years , between models there was a marked difference between meningitis/encephalitis and neonatal sepsis mortality estimates in this age group  .Further investigation into the modelling methods and underlying data showed that countries with the highest meningitis burden have the lowest quality death registration data. Whilst this is also the case for all causes of death, a higher proportion of meningitis/encephalitis death estimates were based on extrapolating from low-quality underlying data compared to all-cause death estimates. For example, 77% of meningitis/encephalitis deaths came from countries with no or very low-quality death registration data  compared to 60% of deaths due to all causes in the GBD 2017 model. Likewise, in the WHO-MCEE model, 95% of meningitis/encephalitis deaths were estimated using modelling underpinned by verbal autopsy (VA) studies compared to 90% of all cause deaths due to these countries having poor quality death registration data see . As woulTo estimate meningitis mortality by aetiology, both models applied a proportional split by pathogen to the country-specific meningitis death envelope and adjusted for vaccine coverage. Whilst GBD 2017 pathogen specific mortality proportions were informed by vital registration (VR) data from data rich locations, the WHO-MCEE model based mortality proportions on studies reporting the distribution of pathogen-specific meningitis cases adjusted by pathogen-specific CFRs to derive proportions of deaths. This approach was used due to a lack of literature reporting meningitis mortality fractions by pathogen. To adjust pathogen-specific estimates according to vaccine coverage, IHME ran a metaregression model (DisMod-MR 2.1) with pneumococcal and Hib vaccine coverage as covariates driving down the proportions of disease attributed to those pathogens. The WHO-MCEE model used a deterministic approach to account for vaccine use by calculating the percentage reduction in disease as a result of vaccine efficacy, coverage and, in the case of PCV, the vaccine product and proportion of disease caused by vaccine-specific serotypes.The models used very different approaches for estimating incidence by aetiology. The GBD 2017 calculated meningitis incidence independently from meningitis mortality using incidence data gathered from hospital records, claims data and a systematic review of the literature capturing incidence studies. The WHO-MCEE incidence estimates were derived by dividing pathogen-specific death estimates by literature-derived CFRs. The WHO-MCEE also published an update to a previous incidence-based model for Hib and pneumococcal meningitis , which pDespite major differences in the number of deaths attributed to meningitis, both models agree that there is a substantial burden of disease, with meningitis as either the 2nd or 3rd most important infectious syndrome. By far the biggest burden of meningitis is estimated to occur in countries with low quality or no death registration data where these models rely heavily on extrapolating from VA studies. Accurately attributing meningitis as a cause of death using VA is extremely challenging ,30,31 anIf these syndromic models systematically underestimate deaths from meningitis, this would result in an underestimate of incidence by pathogen in the WHO-MCEE model because incidence is derived by dividing estimated deaths by CFR based on location and pathogen. The GBD 2017 estimated pathogen-specific incidence separately to pathogen-specific deaths and produced higher estimates than the WHO-MCEE model, but the incidence estimates were out of line with deaths when literature-derived CFRs were applied. Following a meeting where results from this analysis were presented to all modelling groups, the IHME amended their methodology for calculating pathogen-specific incidence. In the recently published GBD 2019 model , publishUsing global health estimates to derive baseline numbers and targets against which progress can be measured is challenging. Estimates for the entire time series are updated with successive model iterations as new input data are considered and amendments are made to statistical modelling processes. This means that baseline estimates for a given year fluctuate with successive model iterations.It is vital that the methods used to derive estimates are clearly communicated. Across models it was unclear from published methods exactly how neonatal meningitis as a cause was disaggregated from neonatal sepsis, and other infectious conditions of the newborn, when we know that the majority of the underlying input data does not distinguish between these two causes of death. Unless methods are made transparent, it is difficult for policy makers to understand, and therefore trust, model outputs .Experts responsible for monitoring progress also need to know exactly how estimates were derived in order to assess whether they are capable of measuring progress against certain indicators. Whilst both models accounted for PCV and Hib vaccine impact, they did so using substantially different methods. The IHME’s GBD 2017 study accounted for vaccine impact by finding existing relationships between vaccine coverage and the proportion of pathogen-specific meningitis targeted by the vaccine (from countries where data is available) and using these existing relationships to make predictions where data is unavailable. Whilst this approach has an advantage of using as much raw data as possible, it does not distinguish between differences in vaccine products and the varying efficacy associated with different dosing schedules between countries. Although incidence proportion models included data from some countries in sub-Saharan Africa and Asia, the use of VR data alone to determine proportional cause of death means that vaccine effects on pathogen-specific mortality in high mortality countries with no vital registration data are heavily reliant on effects demonstrated in data-rich low-mortality countries. The WHO-MCEE, on the other hand, make predictions where data is sparse/unavailable by simulating the effect of a given vaccine over time on a country specific basis. Assumptions about vaccine impact are transparent and take into account differences in vaccine formulations and dosing schedules, but they may be applied to a pathogen specific meningitis death estimate which is highly uncertain.It is also important for decision makers to be aware that even in data-rich locations, global health estimates for the most recent year can be based on predictions rather than real underlying data. These estimates may, therefore, be unsuitable for tracking change as a result of a recent intervention, especially if the intervention has not been accounted for as a covariate in the model.The IHME’s GBD model is currently the only available complete source of information about the global and national burden of meningitis amongst all age groups and for most of the pathogens of interest to the global roadmap to defeat meningitis. The IHME have also improved some of their methods for the latest round of estimates by including more surveillance data from high mortality settings in the GBD 2019 than was included in the GBD 2017. Additionally, there are plans for future versions of the IHME’s model to include estimates on the incidence and mortality from GBS meningitis, one of the major causes of meningitis in neonates worldwide. However, tracking outputs from multiple models in parallel has advantages in identifying areas of higher uncertainty, generating opportunities for modellers to improve methods and prioritising further primary data collection/strengthening surveillance. An interactive visualisation has been created to track progress using estimates from all of the major global health estimation models .None of the models we assessed were able to accurately account for the fluctuating scales of periodic, large epidemics of meningitis, which are irregular and unpredictable in nature. Whilst GBD 2017 attempted to account for epidemic meningococcal meningitis deaths by adding these to the meningitis death envelope, they did not use equivalent methodology to account for epidemic meningococcal meningitis cases. The WHO-MCEE syndromic model attempted to account for epidemic disease by estimating the average increase in deaths in epidemic years relative to nonepidemic years and adding these to estimates in years with epidemics identified by WHO surveillance reports and published literature. This increases estimates during an epidemic year, but the underlying data from the country are not always reliable, and it does not accurately reflect the variation in the size of the epidemic for a given year. The WHO-MCEE pathogenic model only estimated pathogen-specific deaths for endemic disease, removing the simulated effects of epidemics from the syndromic model before applying proportional splits to the remaining meningitis envelope. Therefore, neither model estimating pathogen specific causes of meningitis was able to account for epidemic pneumococcal meningitis, yet this is an important consideration because it has been demonstrated as having a significant mortality burden .Considering the current limitations of modelled meningitis estimates, it is desirable to track progress alongside additional data where possible. Countries across the African meningitis belt experience the highest burden of meningitis globally because they are susceptible to large and devastating outbreaks of meningococcal disease linked to climatic factors such as dry winds, low humidity and high levels of dust in the air . Whilst Triangulating modelled estimates against surveillance data provides the opportunity to reality-check modelled outputs. Utilising surveillance data in combination with evidence of age and regionally specific CFRs has already successfully been used by experts wishing to monitor global progress towards the 2005 measles mortality reduction goal because measles mortality estimates calculated from vital registration data were considered an unreliable way to track progress . SurveilMore work is required to provide credible meningitis burden estimates for measuring progress. Currently meningitis mortality estimates are highly uncertain because the models rely heavily on death registration data, which is largely missing or incomplete in countries with the highest meningitis burden. Additionally, since postmortem examination is rarely performed in countries without vital registration systems, and the symptoms of meningitis can easily be mistaken for other diseases, there is a risk that the mortality burden of meningitis could be underestimated. Encouragingly, better data on cause of death are becoming available in regions where child mortality rates are the highest through the use of minimally invasive tissue sampling ,48 and iGlobal meningitis estimates should be interpreted with caution. Tracking progress towards controlling this disease should also include analysis of real surveillance data where available. The WHO Defeating Meningitis by 2030 Global Roadmap will improve awareness, diagnosis and surveillance of meningitis. As the roadmap drives more comprehensive data on meningitis, a convergence in modelled estimates and a more reliable picture of reductions in the burden of meningitis are anticipated.


There is increasing interest in understanding the potential for epigenetic factors to contribute to phenotypic diversity in evolutionary biology. One well studied epigenetic mechanism is DNA methylation, the addition of a methyl group to cytosines, which have the potential to alter gene expression depending on the genomic region in which it takes place. Obtaining information about DNA methylation at genome-wide scale has become straightforward with the use of bisulfite treatment in combination with reduced representation or whole-genome sequencing. While it is well recognized that methylation is tissue specific, a frequent limitation for many studies is that sampling-specific tissues may require sacrificing individuals, something which is generally undesirable and sometimes impossible. Instead, information about DNA methylation patterns in the blood is frequently used as a proxy tissue. This can obviously be problematic if methylation patterns in the blood do not reflect that in the relevant tissue. Understanding how, or if, DNA methylation in blood reflect DNA methylation patterns in other tissues is therefore of utmost importance if we are to make inferences about how observed differences in methylation or temporal changes in methylation can contribute to phenotypic variation. The aim of this review is to examine what we know about the potential for using blood samples in ecological epigenetic studies. I briefly outline some methods by which we can measure DNA methylation before I examine studies that have compared DNA methylation patterns across different tissues and, finally, examine how useful blood samples may be for ecological studies of DNA methylation. Ecological epigenetic studies are in their infancy, but it is paramount for the field to move forward to have detailed information about tissue and time dependence relationships in methylation to gain insights into if blood DNA methylation patterns can be a reliable bioindicator for changes in methylation that generate phenotypic variation in ecologically important traits. Over the past few years, there has been a tremendous increase in the number of studies that examine epigenetic mechanisms in natural populations e.g., . In thisEcological epigenetic studies are of rising interest, no doubt facilitated by decline in sequencing costs and the development of new methods to measure epigenetic mechanisms such as chromatin modification , non-coding RNAs and DNA methylation (bisulfite sequencing). While epigenetic modifications such as DNA methylation, histone modification and small RNA are interesting to study in their own right, of particular interest to evolutionary ecologists is the potential for epigenetic variation to contribute to phenotypic diversity in ecologically important traits within and between populations (Linaria vulgaris) two different flower phenotypes exists where the mutant has radial rather than bilateral flower arrangement and this variation is due to DNA methylation at the cycloidea gene (Astyanax mexicanus), the blind cave morph is a result of DNA methylation at several known eye development genes and that the observed differences in DNA methylation are causally involved and have been confirmed with functional studies on zebrafish (Phodopus sungorus), changes in hypothalamic DNA methylation at the dio3 gene are involved the seasonal response to daylength . The type of DNA methylation depends both on tissue as well as organism, for example, in plants CHH methylation is common, but in mammals and birds, DNA methylation occurs mainly in a CpG context  in non-model organisms whereby two restriction enzymes are used  to simultaneously screen a large number of loci -specific DNA methylation patterns are thus ubiquitous (Dicentrarchus labrax) where individuals were experimentally exposed to high temperatures during early development which resulted in an increase in DNA methylation at the cyp19a promoter region  and, in particular, if DNA methylation patterns measured in blood reflect that in other tissues since ecological epigenetic studies are often only able to sample peripheral tissues such as blood in birds and mammals.If blood could be used as a reliable tissue type for DNA methylation patterns in inaccessible but more relevant tissues, such as areas of the brain, this would obviously significantly ease sampling. A few studies have examined this and have found mixed results. Parus major). Examining 10.2 million CpG sites in the two tissue types they observe reduced CpG methylation within CpG islands and around the transcription start site (TSS) in both tissues and, consistent with these findings, there was reduced gene expression in the brain (no expression data were available for blood) within gene bodies and around the TSS. In a follow-up study on the same individual, It would be very useful to have information on sites or regions where DNA methylation is consistent between tissues within individuals but differ between individuals and thus might, therefore, contribute to observe between individual phenotypic differences. To my knowledge, this has not yet been done in ecological studies, but there is one such recent study on humans. This study highlights the potential for using non-invasive tissue samples, such as blood, to provide insights into the epigenetic regulation in other tissues. Similar studies on ecological model organisms would thus be highly useful. Moreover, if such regions are conserved across species , I am aware of only one ecological study on DNA methylation that has attempted to control for cell-type heterogeneity. In a study on the effect of resource availability on DNA methylation in yellow baboons . Thus, changes in DNA methylation over time in liver was strongly reflected by changes in DNA methylation in red blood cells, also when restricting this analysis to sites just located within the promoter region. This analysis provides important information that, at least for these two tissues in this species and time span, it is possible to detect changes in DNA methylation in one tissue (liver) from DNA methylation patterns in red blood cells. It is possible that, similar to the cross-tissue correlations in methylation levels observed in different species, there is also potential for tissue-specific effects where temporally or environmentally induced changes in DNA methylation can be detected in blood samples. If this is a general finding this would open the possibility to probe tissue specific changes in methylation from taking repeated blood samples from individuals. Although this would be very exciting, much more work is needed to confirm these findings in other tissues and species and, not least, link changes in methylation to changes in expression. For example, Lindner et al. (in review) did not find a correlation between the observed change in DNA methylation in liver between timepoints and a corresponding change in gene expression within the same tissue, although power was low as there were few CpG sites within the promoter region of each gene. Moreover, Lindner et al. (in review) unfortunately did not have methylation data from gonads and hypothalamus so could not examine if changes in DNA methylation were related to changes in expression in these tissues.Epigenetic mechanisms are thought to play an important role in environmentally induced changes in phenotypes   are small fragments of DNA present in the blood, urine, and other body fluids released by the host cells or microbial cells (


Aboriginal and Torres Strait Islander children experience some of the highest rates of otitis media in the world. Key risk factors for otitis media in Aboriginal children in Australia are largely social and environmental factors such as overcrowded housing, poverty and limited access to services. Despite this, little is known about how to address these risk factors. A scoping content review was performed to determine the relationship between social determinants of health and otitis media in Aboriginal and Torres Strait Islander children as described by peer-reviewed and grey literature.Search terms were established for location, population and health condition. The search terms were used to conduct a literature search using six health research databases. Following the exclusion process, articles were scoped, analysed and categorised using scoping parameters and a social determinants of health framework.Housing-related issues were the most frequently reported determinants for otitis media (56%). Two articles (4%) directly investigated the impact of social determinants of health on rates of otitis media within Aboriginal and Torres Strait Islander children. The majority of the literature (68%) highlights social determinants as playing a key role in the high rates of otitis media seen in Aboriginal populations in Australia. There were no intervention studies targeting social determinants as a means to reduce otitis media rates among Aboriginal and Torres Strait Islander children.This review identifies a disconnect between otitis media drivers and the focus of public health interventions within Aboriginal and Torres Strait Islander populations. Despite consensus that social determinants play a key role in the high rates of otitis media in Aboriginal and Torres Strait Islander children, the majority of intervention studies within the literature are focussed on biomedical approaches such as research on vaccines and antibiotics. This review highlights the need for otitis media intervention studies to shift away from a purely biomedical model and toward investigating the underlying social determinants of health. By shifting interventions upstream, otitis media rates may decrease within Aboriginal and Torres Strait Islander children, as focus is shifted away from a treatment-focussed model and toward a more preventative model. Otitis media (OM) is one of the leading causes of disease among Aboriginal and Torres Strait Islander  children , 2. OM rKey risk factors for OM in Aboriginal children include overcrowded housing, poor housing conditions, exposure to tobacco smoke, malnutrition, socioeconomic disadvantage and limited access to services –8. AboriSignificant health gaps have persisted in Aboriginal populations since the British invaded Australia in 1788 , 10. TheThis review aims to identify how social determinants are addressed in grey and peer-reviewed literature, regarding drivers of OM and proposed interventions aimed at minimising the health burden of OM among Aboriginal children. This review aims to identify gaps in the literature and guide further research, policy development and service provision.Given the broad nature of the research objective, a scoping content review was conducted to explore available research, to evaluate the need for further investigation, to describe key themes and to identify gaps in the literature. The framework proposed by Arksey and O’Malley  for condInitially, the research objective was established through preliminary review of the literature and discussion between the research team. Following the establishment of the research objective, the search strategy was developed by implementing inclusion and exclusion criteria, and keywords . Google Scholar results were limited to the first two pages, given the large number of results yielded and time constraints for conducting the literature search. An Excel spreadsheet was created to categorise the literature based on the following parameters: author, title, year, within Australia, ‘Aboriginal-related term’, ‘OM-related term’ and full text available. The literature was then systematically evaluated based on these criteria and included or excluded accordingly. Where there was any uncertainty regarding the suitability of an article, consensus on whether to include the article(s) was reached by the research team.InfoNet) [Following selection of the included literature, two separate Excel spreadsheets were created to analyse and report the results. One spreadsheet contained the peer-reviewed literature and the other contained the grey literature. The articles were systematically analysed based on the following parameters: ‘are social determinants mentioned?’, ‘what section are social determinants mentioned?’, ‘what social determinants are mentioned?’, ‘are social determinants mentioned as drivers of OM?’, ‘are social determinants discussed in regards to interventions for OM?’, ‘what is discussed in regards to future directions?’, ‘are interventions related to social determinants or mentioned but not fundamental to the discussion or conclusion?’, and ‘are social determinants related to one of three key areas of the Aboriginal and Torres Strait Islander social determinants of health framework?’- adapted from Department of Health and Ageing  , as showThe literature search was conducted using six specified databases and the exclusion process is detailed in Fig. Following the exclusion process, the included literature was evaluated by how OM related social determinants were addressed. 34 (68%) peer-reviewed and grey articles were identified as discussing social determinants, with 17 (34%) discussing social determinants as a significant factor for driving the high rates of OM and 17 (34%) articles identifying the need to address social determinants to reduce the high rates of OM in Aboriginal children. Of the 17 articles that discuss social determinants as important for OM management, 11 articles did not discuss this in detail - these articles did not provide specific recommendation or evidence for further research and policy development. For example, Sparrow et al.  (p14) stFurther evaluation of the literature revealed that 16 (32%) articles did not mention social determinants at all, with four articles (8%) providing analysis of social determinants of OM. These four articles presented social determinants as key priority areas for future intervention and provided supported recommendations to help address social determinants linked to OM. For example, Jacoby et al. [n = 22) did not address any of the three key areas of the social determinants of health framework.In addition to the social determinants-related scoping criteria, the literature was comprehensively assessed using the ‘Social determinants framework for Aboriginal and Torres Strait Islander health’.  The socin = 4). Sun et al. [Housing-related social determinants were reported most frequently within the literature, with 28 (56%) reports of housing related risk factors for OM . The next most frequently discussed social determinant was exposure to tobacco smoke, with 11 articles (22%) discussing this as a significant determinant for OM. Low socioeconomic status, low income and poverty (20%), access to services (18%), hygiene (16%), and education of the primary caregiver (14%) were among the most frequently mentioned determinants. Other reported determinants for OM were employment status and employment opportunities (12%), nutrition (12%), community involvement in service provision and planning (6%), and cultural and language differences  expA detailed analysis was performed on what recommendations were made in the literature . 31 (62%) of the peer-reviewed and grey articles did not discuss social determinants in future directions at all. 23 articles (46%) primarily recommended further research into antibiotic treatment and vaccine development, and the need for greater understanding of OM associated bacterial carriage. Five (10%) articles presented detailed recommendations for future research and policy development intended to address social determinants to reduce the high rates of OM in Aboriginal children.OM is one of the leading causes of preventable disease amongst Aboriginal children, and has been determined by The World Health Organisation to be a serious public health issue requiring urgent attention –3, 11. OThis study identifies how social determinants are addressed within grey and peer-reviewed literature, and summarises the primary determinants reported to be associated with OM and management recommendations within the literature. This study highlights gaps between factors reported to be associated with OM and recommended interventions within the literature. Given the significance of this gap, further research aimed at understanding social determinants associated with OM and identifying more effective management of the social determinants of OM within Aboriginal children is warranted. Furthermore, the inter-related nature of the social determinants of health is emphasised throughout this paper and helps to underline the challenge that an exclusively biomedical model poses in addressing specific aetiology .(p73–74)Notably, a shift in approaches to manage OM is desperately needed, in conjunction with further research to better understand the relationship between the social determinants of health and risk of OM in Aboriginal populations. This review demonstrates that there is an imbalanced research focus towards biomedical approaches in contrast to improving our understanding about how to address key social determinants contributing to high rates of OM in Aboriginal children. Using the social determinants of health framework, this review has identified significant shortcomings within the literature and the current public health management of OM in Aboriginal children. The social determinants of health framework used within this study identifies three key areas of Aboriginal health that are largely neglected by the available grey and peer-reviewed literature in relation to OM management. Although the literature mentions various social determinants that are consistent with the framework , none of the included articles evaluated these key areas of Aboriginal health with the objective to establish effective social, environmental, political or cultural-focussed interventions for OM. Further, the key social determinants of OM can be argued to stem from the persistent social, economic and cultural discrimination experienced by Aboriginal populations. Through evaluation using the social determinants of health framework, this review highlights the need to preserve Aboriginal culture, strengthen Aboriginal self-determination, respect and support Aboriginal connection to land, empower Aboriginal communities, improve education and employment opportunities for Aboriginal people, and address poor housing conditions and overcrowding within Aboriginal communities. Importantly, one of the most significant and achievable goals should be to ensure the adoption of co-creation and a decolonised approach to ear health research, and health research more broadly, in Aboriginal populations. Aboriginal self-determination and services that are embedded within community are key to improving the management of OM within Aboriginal populations . Such anThe most evident theme arising from this review was the importance of the home environment, with housing-related determinants reported almost three times more than the next most frequently reported risk factor. Despite acknowledgement of the association between housing and the prevalence of OM in Aboriginal children, there were no intervention studies within the reviewed literature that investigated how to effectively address the issue of housing in Aboriginal populations. Exposure to cigarette smoke and poor hygiene were not directly acknowledged as relating to housing within this review. However, these risk factors are likely to be influenced to some degree by the home environment, given the relatively high rates of smoking within the home in Aboriginal populations , 16. It Exposure to tobacco smoke is consistently reported as a key contributing factor for Aboriginal children developing OM. Aboriginal children who are exposed to tobacco smoke in the home and who do not attend day-care have been suggested to be at greatest risk of developing OM . This isEducation and employment of the primary caregiver is cited frequently as an important determinant for Aboriginal children developing OM. However, no paper within the reviewed literature discussed this any further than listing it as a significant contributing factor. It is important to highlight that low-level education and lack of employment opportunities consign many Aboriginal people to levels of poverty . (p108) Aboriginal community involvement is an area that requires greater emphasis and encouragement from public health promoters, policy makers and service providers. Programmes such as the ‘Healthy Ears, Happy Kids’,  ‘AborigiDespite the lack of research about how to effectively target the social determinants of OM, there is a growing body of research regarding diversifying health approaches to better address social determinants of health more broadly. The term ‘Integrated models of care’ has emerged within the literature, which describes the integration of biomedical services with non-medical community services  to provide a more comprehensive approach to target underlying risk factors for ill-health . Using aWhile this review presented a comprehensive analysis of both peer-reviewed and grey literature, this study excluded unpublished masters and doctoral theses. Despite this, findings by Vickers and Smith  followinThere is overwhelming consensus within the reviewed literature that Aboriginal children experience disproportionately high rates of OM when compared to non-Aboriginal children. The high rates of OM are linked to poor housing conditions, overcrowded housing, exposure to tobacco smoke, education, and overall social and economic disadvantage. Furthermore, there is disparity between reported risk factors of OM and current interventions aimed at reducing the burden of OM in Aboriginal populations. Current interventions are primary focussed on biomedical approaches such as investigating vaccines and antibiotics. Although vaccines and antibiotics are essential to the provision of high-quality clinical care for OM, a broader public health lens is required to address the underlying social factors reported to be driving the gap in OM rates between Aboriginal and non-Aboriginal children. It is important to mention that the Aboriginal understanding of health includes “body, mind, spirit, land, environment, custom, socioeconomic status, family and community” . (p417)


Larval source management was historically one of the most effective malaria control methods but is now widely deprioritized in Africa, where insecticide-treated nets (ITNs) and indoor residual spraying (IRS) are preferred. However, in Tanzania, following initial successes in urban Dar-es-Salaam starting early-2000s, the government now encourages larviciding in both rural and urban councils nationwide to complement other efforts; and a biolarvicide production-plant has been established outside the commercial capital. This study investigated key obstacles and opportunities relevant to effective rollout of larviciding for malaria control, with a focus on the meso-endemic region of Morogoro, southern Tanzania.Key-informants were interviewed to assess awareness and perceptions regarding larviciding among designated health officials  in nine administrative councils (n = 27). Interviewer-administered questionnaires were used to assess awareness and perceptions of community members in selected areas regarding larviciding (n = 490). Thematic content analysis was done and descriptive statistics used to summarize the findings.A majority of malaria control officials had participated in larviciding at least once over the previous three years. A majority of community members had neutral perceptions towards positive aspects of larviciding, but overall support for larviciding was high, although several challenges were expressed, notably: (i) insufficient knowledge for identifying relevant aquatic habitats of malaria vectors and applying larvicides, (ii) inadequate monitoring of programme effectiveness, (iii) limited financing, and (iv) lack of personal protective equipment. Although the key-informants reported sensitizing local communities, most community members were still unaware of larviciding and its potential.The larviciding programme was widely supported by both communities and malaria control officials, but there were gaps in technical knowledge, implementation and public engagement. To improve overall impact, it is important to: (i) intensify training efforts, particularly for identifying habitats of important vectors, (ii) adopt standard technical principles for applying larvicides or larval source management, (iii) improve financing for local implementation and (iv) improve public engagement to boost community awareness and participation. These lessons could also be valuable for other malaria endemic areas wishing to deploy larviciding for malaria control or elimination. The world has witnessed a significant reduction in malaria burden since 2000 , most ofEnvironmental management to eliminate mosquito breeding habitats was among the first malaria control strategies attempted in Tanzania. Efforts included improving drainage systems and the elimination of the permanent bodies of stagnant water near large human settlements , 23. In The Tanzania National Malaria Strategic Plan, 2014–2020 recommended implementation of larviciding in selected urban settings , in lineBacillus thuringiensis var. israelensis (Bti) and Bacillus sphaericus (Bs)  seminar. We were given a formula on how to calculate the amount of larvicides per liter, and they promised to share with us the template with the specific formula for the amount of diluted larvicides to apply in a breeding habitat. It was a PowerPoint presentation; it was all theoretical.” . Malaria focal persons reported that they had participated in at least one seminar on how to apply the larvicides, in 2018 and or 2019. Some of the MFPs were not holding their current positions in 2018 and had therefore only received one training session. The training, provided jointly by the Muheza College of Health and Allied Sciences  at Muhez“I called the volunteers to my office and explained how to dilute the larvicides and how to apply them to the breeding habitats. I did the training in my office. Then I provided them with the larvicides as well as masks to protect themselves.” .Unlike the MFPs, the VSOs and ward health officers reported not to have participated in the training programmes, but had instead received information on dilution and application methods from the MFPs. Ward health officers then passed on the information to the CHWs and the community volunteers who were responsible for the hands-on implementation of the larviciding. There was no formal mechanism of monitoring effectiveness of the larviciding. Some ward health officers stated that they kept track of the number of malaria cases at the health centers, and assumed that reduced cases meant that the larviciding was working. Other ward health officers reported that they asked community members if they had experienced a reduction in mosquito annoyance. Others relied on their own experience living in the communities to detect a reduction in mosquito abundance. All respondents reported that they believed that larvicides were effective based on these factors. Key challenges that district and ward health control officers faced during implementation of larviciding are summarized on Table Larviciding is considered as complementary option to be used alongside current major malaria control approaches, notably ITNs, IRS and case management . To acceThe key-informant interviews revealed significant knowledge inadequacies among MFPs, VSOs and ward health officers towards implementation of the larviciding. For instance, all participants knew that mosquitoes have an aquatic habitat stage; but a majority could not easily differentiate the aquatic habitats typical of malaria vector species. Moreover, these health officials reported that malaria vectors do prefer “fresher” water compared to other mosquitoes, but what majority meant by fresh water was any water that looked clean such as water in clay pots or buckets. Ward health officers, who are closely anchored in the community and provide guidance to the community health workers and volunteers during the larviciding, could not differentiate between malaria and non-malaria vectors’ aquatic habitats and reported to use different methods to apply and monitor effectiveness of the larvicides. This lack of adequate knowledge and uniformity might be attributable to the lack of training on how, where and when to apply the larvicides as accorded by WHO guidelines . Some ofInsufficient funding to assist with implementation of larviciding was one of the practical obstacles reported by the MFPs, VSOs and ward health officers. Funding was needed to offer incentives, cover transportation and larvicides costs, and provide personal protective gears to the CHWs and volunteers who did the actual job of applying the larvicides. A successful large-scale larviciding trials conducted in Dar-es-Salaam , 43 in eAnopheles funestus accounts for over 80% of the ongoing malaria transmission [An. funestus aquatic habitats alone could potentially reduce malaria transmission by 80% in Kilombero valley. In this valley, An. funestus aquatic habitats adhere to WHO criteria  for larviciding implementation [In Kilombero valley, smission . Thus, eentation . The appentation , 46.This present study also revealed the need to strengthen engagement of key stakeholders including the community. Despite efforts by district-level malaria control officials to inform and sensitize the residents, a majority of the community members surveyed were not aware of larviciding, did not know its function within malaria control efforts, and were not aware whether or not it had been implemented in their settings. This finding was in agreement with a previous study by Mboera et al.. in Mvomero district within Morogoro region, where only 17% of the survey respondents were aware of larviciding as a malaria control intervention . Both fiInsufficient support from local stakeholders within Morogoro region might have been among the obstacles towards effective implementation of larviciding. Engagement of other stakeholders particularly non-government organizations (NGOs) have shown to yield fruitful impact in the malaria control. For instance, collaboration between Urban Malaria Control Programme (UMCP) and Ifakara Health institute (IHI) in Dar-es-Salaam during early 2000s towards malaria control through larval source management led to a significant impact . Thus, eThe present study also revealed insufficient “early-on” involvement of VSOs and ward health officers during the budgeting and implementation planning. MFPs attend all council’s meeting that involve malaria control initiatives through district technical committee , and oftThe study results should be interpreted in the light of several limitations. A response bias may have resulted partially inaccurate responses on the survey. Social desirability bias may have resulted in respondents saying ‘I don’t know’ to most of the statements that assessed their perceptions of larviciding as a majority had early-on indicated that they were not aware of this intervention. Demand characteristics may have also resulted from both the key informants who may have reported insufficient knowledge or lack of resources hoping that these would be provided to them. In addition, the present study did not include district medical officers (DMO) who also plays a crucial role in planning, coordinating and implementing the delivery of health services at the district level .Both communities and district-level malaria control officials widely supported the larviciding programme, however, there were gaps in technical knowledge, implementation and stakeholder engagement. To maximize the overall impact of the programme, training efforts should be intensified, particularly for identification of aquatic habitats for important vectors and formal training should be given to the actual implementers (i.e. CHWs and volunteers) not just MFPs, VSOs and ward health officers. Standard technical principles for application of larvicides should strictly be adopted and improvement on financing at a district-level implementation. Furthermore, engagement of community members and other stakeholders such as NGOs should be improved to maximize awareness, participation and sustainability of the programme. These lessons learnt from Morogoro region shed the light for other malaria endemic areas on the possibility of deploying larviciding for malaria control or elimination.


More than 30% of the world population suffers from allergy. Allergic individuals are characterized by the production of immunoglobulin E (IgE) antibodies against innocuous environmental allergens. Upon allergen recognition IgE mediates allergen-specific immediate and late-phase allergic inflammation in different organs. The identification of the disease-causing allergens by demonstrating the presence of allergen-specific IgE is the key to precision medicine in allergy because it allows tailoring different forms of prevention and treatment according to the sensitization profiles of individual allergic patients. More than 30 years ago molecular cloning started to accelerate the identification of the disease-causing allergen molecules and enabled their production as recombinant molecules. Based on recombinant allergen molecules, molecular allergy diagnosis was introduced into clinical practice and allowed dissecting the molecular sensitization profiles of allergic patients. In 2002 it was demonstrated that microarray technology allows assembling large numbers of allergen molecules on chips for the rapid serological testing of IgE sensitizations with small volumes of serum. Since then microarrayed allergens have revolutionized research and diagnosis in allergy, but several unmet needs remain. Here we show that detection of IgE- and IgG-reactivity to a panel of respiratory allergens microarrayed onto silicon elements is more sensitive than glass-based chips. We discuss the advantages of silicon-based allergen microarrays and how this technology will allow addressing hitherto unmet needs in microarray-based allergy diagnosis. Importantly, it described how the assembly of silicon microarray elements may create different microarray formats for suiting different diagnostic applications such as quick testing of single patients, medium scale testing and fully automated large scale testing. Information on sensitizations not linked to symptoms require appropriate and time-consuming information of patients, and certain allergen molecules for obtaining complete results may still need to be discovered and included in the micro-arrays. Finally, it will be necessary to reduce the costs of micro-arrays, which are often driven not only by costs of goods for manufacturing but also by costs due to quality control, validation, and registration. However, once the latter issues are resolved one may expect that microarray-based allergy diagnosis will become a highly cost-effective diagnostic tool. One must consider that microarray-based allergy diagnostic approaches can provide more than hundred individual test results from one sample and present the comprehensive picture of allergen sensitization, which can be used for precision medicine treatment. In fact, unrecognized and untreated allergy is a major cost factor for the management of allergic diseases. With the adherence to proper treatment and the precise diagnosis and proper management of allergic diseases, it has been estimated that high costs can be saved    ; howeverponents) . The meatization . Likewistization –43.Microarrayed allergens have been used in research to address several questions. For example, it has been shown that adult allergic patients do not change their IgE sensitization profiles for a decade demonstrating that there is no acquisition or loss of IgE sensitizations in adult allergic patients . Furthere.g., allergen avoidance, early allergen-specific immunotherapy)  later in life.However, without any doubt, the most exciting results were obtained when microarrayed allergen molecules were used to study the development of the allergic sensitization profiles in longitudinal birth cohorts allowing to analyze the evolution of IgE sensitization profiles from birth to early adolescence , 48. Imptherapy) –55 to prIn allergy, like in other important diseases, it has become clear that it is necessary to transform healthcare towards the principles of “P4 Medicine” for predictive, preventive, personalized (precision), and participatory medicine by developing new diagnostic and predictive tests as well as therapies and preventive strategies which affect the course of disease or prevent the development of disease . AllergyThe diagnosis of food allergy is often challenging because the frequency of IgE-associated food allergy is often considered higher than it is in reality. For example, adverse reactions to cow’s milk due to lactose intolerance are much more common than IgE-mediated allergy . AlthougIn this context a study demonstrating different IgE sensitization profiles in children suffering from severe peanut allergy and in peanut-sensitized but asymptomatic children should be mentioned . ScreeniMicroarrayed allergen molecules are not only useful for the diagnosis of IgE-mediated food allergies but are also considered for the diagnosis of asthma triggered by allergens in sensitized allergic patients . The twoFor the diagnosis of RV-induced asthma, a chip containing peptides derived from the N-terminus of VP1 proteins from a representative number of RV strains covering RV-A, RV-B and RV-C species has been produced . This chAIT is an allergen-specific form of therapeutic vaccination which is based on the administration of the disease-causing allergens or modifications thereof with the goal to induce allergen-specific protective IgG antibodies and alterations of the cellular immune response to reduce symptoms of allergy upon allergen contact. Accordingly, the accurate prescription of AIT requires that the culprit allergens are identified. Since allergen sources often contain cross-reactive allergens the identification of the culprit allergen source can be challenging. It has therefore been suggested to use marker allergen molecules which are specific for given allergen sources as diagnostic marker allergens for improving the prescription of AIT . The marWhen patients develop such blocking IgG antibodies in the course of AIT, these IgG antibodies will then compete with IgE antibodies which then causes a reduction of IgE binding similar as it is observed by skin testing when allergen-specific IgG antibodies block IgE-mediated mast cell activation thus leading to a reduction of skin responses. The reduction of allergen-specific IgE binding by blocking IgG antibodies can only be measured with IgE binding assays containing small amounts of immobilized allergens such as the ImmunoCAP ISAC chip but not by the traditional ImmunoCAP test which contains larger amounts of immobilized allergens –85. No oSeveral studies support the concept of using molecular testing for the refined prescription of AIT , and theSince the first description of the use of microarrayed allergens in 2002 for allergy diagnosis the technology has become available world-wide and has been used extensively in research. However, several unmet needs remain which are summarized in The allergen molecules and allergen derived peptides and their quality can be considered the heart of any allergen microarray because it determines what application can be addressed with the allergen chip. For example, it is important to cover the most common respiratory, food, venom, and other allergen sources by a representative collection of allergen molecules. In this context it should be noted that there are different opinions regarding the inclusion of certain allergen molecules in screening assays. For example, some argue that the inclusion of venom allergens may create ethical and legal issues because in case of a positive or negative test result one cannot predict or exclude a sting reaction. However, this is in principle applicable for every IgE test result which needs to be considered in the context of clinical information and/or results from provocation testing. Therefore others think that the inclusion of venom allergens in screening tests is not a problem provided the patients are adequately informed about the relevance of the IgE test results.The addition of allergen-derived peptides may be interesting for example in the diagnosis of food allergy where sequential IgE epitopes play a role and for the monitoring of AIT-induced IgG responses. Since only small amounts of allergen are immobilized on microarrays it is important to use highly pure allergens of high quality to detect also low specific IgE responses. Natural allergen preparation containing impurities in terms of unrelated allergens or cross-reactive carbohydrates may give rise to unclear, false-positive test results. Although carbohydrates are highly cross-reactive, it seems that patients mount quite specific and selective IgE to certain carbohydrates which cannot be completely inhibited by pre-incubation of sera with a single carbohydrate  so that This allergen library should be as complete as possible to pick up every relevant IgE sensitization in a given population. For example it has been shown, that the MeDALL allergen chip, a customized allergen array based on the ImmunoCAP ISAC platform containing more than 170 allergen molecules , was more.g., MADx Allergen Explorer ALEX; containing 282 allergens: 156 extracts and 126 components)  . Both syponents) .In fact, the high sensitivity of protein assays on microarray silicon slides has then been demonstrated , 98. TheBelow, we have evaluated allergen microarrays based on silicon oxide surfaces and compared them with glass slides currently used in the ImmunoCAP ISAC platform confirming the higher sensitivity of the silicon technology. We are then also discussing advantages of this technology for the versatile production of different formats of allergen microarrays.Currently available microarray-based allergy tests are relatively expensive although one has to consider that a single microarray test result provides more than hundred individual test results. For example costs for one array may range between 60 and 100 Euro depending on prices requested by different manufacturers in different countries, and additional costs for the processing of one sample may vary considerably 50–300 Euro depending on costs of laboratory facilities and personnel. Therefore, it will be necessary to decrease the costs of production for the micro-arrays and the costs for the test procedure which is currently performed by hand pipetting.As mentioned above currently available microarrays are made for manual operation and thus individual testing requires wet laboratory facilities and relatively expensive scanning equipment for the analysis of results. The available formats thus can be used for manual analysis of relatively limited numbers of samples and require trained personnel. Unfortunately, no automatization for the processing of the available allergen arrays is available which would allow large scale and fully automated analysis of large numbers of patients. Therefore, there is still an unmet need for different formats of allergen arrays allowing different applications such as the occasional analysis of few or single serum samples yielding fast results with a minimum of equipment, the medium scale analysis of several serum samples and the fully automated analysis of large numbers of sera . In the first experiment we determined the sensitivity of IgE reactivity to Bet v 1 immobilized to glass and silicon chips using a human monoclonal chimeric IgE antibody (IgEmoAb) (In order to compare the glass surface which currently is used for ImmunoCAP ISAC with silicon slides –100 a paIgEmoAb)   allergen compared to the glass surface. Next, we tested sera from HDM and mite-  for automated processing of samples which can be incubated with samples, washed, developed, and read in machines without requiring hand pipetting. Thus microarrays printed on silicon elements allow assembling of different devices for testing based on one standardized element. Furthermore, silicon surfaces give 5–10-fold higher sensitivity as compared to glass which should allow detecting also low allergen-specific IgE levels with high precision, and the measurement can be done with very simple and inexpensive detection devices.Disadvantages of chips containing more than one array are that the spotting of the microarrays is performed directly on chips which are relatively large in comparison to a single silicon element. However, the single silicon elements can be arranged in much shorter distance close to each other for the spotting than preformed chips. Accordingly the spotting machine (microarray printer) makes much shorter movements when spotting closely assembled silicon elements as compared to premade chips which should speed up the production by shortening the production time. Another disadvantage of chips containing several microarrays is that one array of bad quality will lead to the discarding of a complete chip although the other arrays may meet the quality criteria. By contrast, when single silicon elements are used only the few poor quality elements will be discarded keeping the loss of material low. However, the most important advantage of microarrays based on single silicon elements is that after spotting, the single elements can be assembled in different formats for different uses. This allows producing chips containing only one microarray for fast testing of single serum samples. Furthermore, chips containing several silicon elements for testing of several sera can be assembled. Importantly, silicon elements can be also assembled in plates , by the Russian Academic Excellence Project 5-100, by a Megagrant of the Government of the Russian Federation, grant No 14.W03.31.0024, and by grant from HVD Life Science, Vienna, Austria.RV has received research grants from HVD Life Science, Vienna, Austria, and Viravaxx, Vienna, Austria, and serves as a consultant for Viravaxx.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Microbial activity mediates the fluxes of greenhouse gases. However, in the global models of the marine and terrestrial biospheres used for climate change projections, typically only photosynthetic microbial activity is resolved mechanistically. To move forward, we argue that global biogeochemical models need a theoretically grounded framework with which to constrain parameterizations of diverse microbial metabolisms. Here, we explain how the key redox chemistry underlying metabolisms provides a path towards this goal. Using this first-principles approach, the presence or absence of metabolic functional types emerges dynamically from ecological interactions, expanding model applicability to unobserved environments.“Nothing is less real than realism. It is only by selection, by elimination, by emphasis, that we get at the real meaning of things.” –Georgia O’Keefe Marine microbial activities fuel biogeochemical cycles that impact the climate, but global models do not account for the myriad physiological processes that microbes perform. Here the authors argue for a model framework that reinterprets the ocean as physics coupled to biologically-driven redox chemistry. Since these transformations respond to, and feedback on, changes in climate  reactions that supply energy for metabolisms can provide an additional organizing principle for explicit descriptions of microbial populations in ecosystem models. This redox basis can be exploited to quantitatively resolve chemical transformations in terms of assimilatory and respiratory fluxes. While not yet incorporated into earth system models, this view has been advocated for such applications20, and has been embraced and employed in the field of environmental biotechnology, such as in the interpretation and modeling of wastewater bioreactors21. Just as models of ocean and atmospheric circulation are constrained by conservation of energy and potential vorticity, complementing mass balance with powerful redox and energetic constraints enables self-consistent descriptions of diverse microbial metabolisms.As we expand models to include the full metabolic potential of microorganisms, how can we organize and reduce the complexity of the descriptions of metabolic diversity? Non-photosynthetic organisms oxidize chemical species for energy, and thus their respiration is biogeochemically significant9. In analogy to the use of redox chemistry, trait-based functional type models of phytoplankton have used cell size as an organizing principle for understanding phytoplankton biogeography, biodiversity, and impact on biogeochemistry23. These types of theoretical constraints allow for the inclusion of more functional types without introducing as many degrees of freedom as would be necessary if each were empirically described. The guiding perspective is that organizing complex biological behavior by its underlying chemical and physical constraints gives more universally applicable descriptions of large-scale biogeochemistry.This approach aims to advance ecological modeling beyond species-specific descriptions to those that matter for biogeochemical function, in line with trait-based modeling approachesWhen incorporating a redox-balanced approach into ecosystem models, microbial function emerges from underlying chemistry as a consequence of interactions between populations modeled as metabolic functional types and their environment. Resulting theoretically grounded ecosystem models independently simulate microbial growth, respiration, and abundances in ways that we can compare with observations such as sequencing datasets. Thus, sequencing datasets are used as critical tests for the models, as external constraints rather than as input to the models, allowing for an iterative relationship between theory, observations, and models.In contrast with empirically informed models, this approach involves constructing a model of microbial activity theoretically, and then comparing the results with the observations in order to gain an understanding of the system. The goal is to understand why biology functions as it does, in addition to anticipating global impacts. From a first-principles biogeochemical perspective with respect to physical and chemical forcing, genes are an intermediate step between forcing and function, with the detailed complexity of biological reality following the underlying chemical and physical constraints . This does not equate to thinking that biology (or genetic information) does not matter or can be replaced entirely in models by physics and chemistry. Rather, we want to fundamentally understand biological activity as an integrated part of an ecosystem, and physics and chemistry become tools for doing so.Here, we outline the basis for using redox chemistry as an organizing principle and its translation into quantitative descriptions of microbial activity that are simple enough for global earth system models. We then discuss the benefits of this approach in the context of their implications for improved understanding and projections of global change impacts. Finally, we discuss limitations and possible future developments.28. The information from sequencing in particular has allowed for a huge expansion of insight into the detailed in situ activity of uncultivated species. When investigating global-scale impacts, how do we decide which of these details may be bypassed for simplicity? Or, if this simplification is impossible, must we incrementally construct a microbial ecosystem model that incorporates all known micro-scale detail?From one perspective, microbial communities are characterized by interactions at the micro-scale: gene expression, enzymatic capabilities, metabolites, species-specific interdependencies, etc., as well as the physical and chemical environment surrounding small cells30. For example, it is well known that microbial communities in sediments and anoxic zones organize according to the redox tower – the ranking of half-reactions by electrochemical potential32. Furthermore, respiration by living organisms increases the entropy of the environment by dissipating concentrated sources of chemical energy in accordance with the Second Law of Thermodynamics34.Another way forward arises from a macro-scale perspective, which examines how ecosystem function relates to the chemical potential utilized by organisms for energy35 demonstrated that external conditions cause the seemingly random fluctuations observed in microbial growth, rather than stochastic variation in gene expression. This provides direct evidence of deterministic behavior, and so the authors conclude that microbial systems can in principle be determined by macroscopic laws.This perspective suggests that chemical potential can be used to predict the activity of microbial communities and their biogeochemical impact. However, given the notorious complexity of microbial cells and systems, which is many steps away from governing chemical or physical equations, how can we be sure that this activity is indeed predictable? Frentz et al.37, as captured in the phrase “everything is everywhere, the environment selects”38. Furthermore, recent evidence also shows that gene acquisitions and deletions happen quickly enough to allow for horizontal gene transfer to dominate bacterial adaptation43, implying that evolution can occur within few generations and thus on timescales similar to ecological interactions. Perhaps consequentially, similar geochemical environments have been demonstrated to have high-microbial functional redundancy despite different taxonomic compositions44. This may be interpreted with the hypothesis that physics and chemistry selects for metabolic traits, and that these traits can be housed in different organisms with taxonomic composition shaped by micro-scale or biotic interactions45.How is this determinism manifested? If microbial communities can respond relatively quickly to changes to their local environment, they may predictably optimize the exploitation of locally available resources. In the ocean, dispersal in microbes is thought to be a highly efficient process such that microbial communities can in effect draw from an extensive seed bank52, and is conceptually similar to other redox-balanced approaches to understanding microbial activity in sediments, soils, subsurfaces, and aquatic systems58. Illustrating the power of these approaches, anticipating metabolism from chemical potential resulted in a prediction that anaerobic ammonia oxidation (anammox) should exist decades before it was observed60. Quantitatively understanding microbially mediated rates of conversion of substrates has practical implications for wastewater treatment, and thus the field of biotechnology has established methodologies for an approach in textbook form21. Flux balance analysis (FBA) models can be considered as much more highly detailed analogs of this approach that resolve the mass and electron balances among a multitude of chemical reactions within a cell62.The prediction of microbial activity from environmental chemical potential has a long history in microbiologyWe can resolve microbial activity in global ecosystem models using the underlying redox chemistry of diverse metabolisms as a constraint. One specific way forward is to model distinct metabolisms as populations of metabolic functional types. This systematically quantifies relative rates of substrate consumption, biomass synthesis, and excretions of transformed products associated with each metabolism. Coupled with estimates of substrate uptake, this replaces implicit parameterizations of processes such as organic matter consumption, oxygen depletion, and denitrification with electron-balanced respiratory fluxes of dynamic microbial populations. Box 48. The ratio of anabolism and catabolism can then be represented by the fraction f of electrons fueling cell synthesis vs. respiration for energy, following ref. 21. This provides a yield y (moles biomass synthesized per mole substrate utilized) of each required substrate that reflects two inputs: electron fraction f and the coefficients of the half-reactions , or the oxidation of ammonia or nitrite using oxygen (chemoautotrophic nitrification) as exemplified in Table 2 produced by heterotrophic metabolisms as well as the CO2 fixed by chemoautotrophic metabolisms  redox reaction Fig. . Such agBox 1 Incorporating metabolic functional types into ecosystem modelsμ is described asVi is the specific uptake rate of substrate i, and yield yi is the biomass yield with respect to that substrate. Yields for the different substrates and elements are interlinked in the metabolic budget derived from the underlying redox chemistry. Yields reflect Gibbs free energies of reaction among other factors. In the simplest model, non-limiting substrates are consumed in proportion to the limiting resource according to the metabolic budget, although in reality they may accumulate in the form of storage molecules.A metabolic functional type can be represented as a population with a growth rate that is limited or co-limited by multiple required substrates. If Liebig’s Law of the Minimum is employed, the limiting growth rate Each metabolic functional type population can be incorporated into a multi-dimensional environmental model  with physical transport asB, loss rate L, velocity u, and diffusion coefficient κ. The loss rate function varies with biomass and represents a combination of processes, including predation, viral lysis, maintenance, and senescence. These processes remain largely unconstrained, although efforts have been made to relate losses to ecological dynamics108.for biomass concentration Viyi, versus that exiting the cell in modified form as a waste product, Vi(1 − yi)  photoprotective cellular machinery.Descriptions of microbial growth that reflect underlying chemical potential can enable predictions of many other metabolic transitions, such as nitrogen fixation, nitrification, and the transition to sulfur oxidation and reductionnts Fig. , as wellReplacing implicit descriptions of organic matter remineralization: The fate of organic matter dictates the amount of carbon sequestered in the marine and terrestrial biospheres. Microbial consumption mediates the carbon stored in soils, the carbon stored in the ocean as dissolved organic matter (DOM), and the sinking flux of organic carbon that constitutes the marine biological carbon pump4, without which atmospheric CO2 would be 100–200 ppm higher than current levels. We want to understand how these carbon reservoirs respond to changes in climate, such as increased temperatures and changes in precipitation patterns. However, in biogeochemical models, simple rate constants often dictate the remineralization of elements from organic back into inorganic constituents.Replacing simplistic parameterizations with dynamic metabolic functional types means that electron-balanced descriptions of growth and respiration instead drive the fate of organic matter in earth system models Fig. . In addi2 excreted and the absolute amount of biomass sustained on a given substrate, independent of uptake kinetics . Untangling the relationship between abundances and biogeochemical function is also necessary for interpretation of genetic evidence that provide insight into this complex ecosystem structure.71.Redox-balanced metabolic functional type modeling links rates of biomass synthesis associated with a particular metabolism to its rate of respiration as well as the standing stock of limiting nutrients. As functional type modeling is coupled with estimates of population loss rates due to grazing, viral lysis, or other mortality, simulations also resolve the standing stocks of functional biomass. This quantifies the relationship between biomass concentrations and volumetric rates of chemical transformations, emphasizing how relatively low biomass may be associated with relatively high bulk rates18  and nitrite (NO2−) concentrations is consistent with observed differences75, and it reflects that the oxidation of one mole of NH4+ generates three times more electrons than the oxidation of one mole of NO2−, with differences in cell size further contributing to differences in abundances  themselves are not necessarily the most concise or useful currency given functional redundancies, unattributed function, and variation in gene dosage from horizontal gene transfer as well as growth rate40. Recent gene-centric models aim to resolve the abundances of key genes as proxies for a predetermined set of metabolic pathways17. However, the parameters used to describe metabolic pathways in these models are estimated similarly to the redox-balanced yields and efficiencies described here.17 resolves biomass and nutrient concentrations prognostically, and then uses a three-part formula—representing constitutive, regulated, and steady state transcription—to diagnostically calculate transcription rates from modeled biomass and growth rates17. Thus, the two types of modeling are complimentary, with redox chemistry providing estimates of metabolic activity from fundamental principles, and the careful calibrations between activity and sequencing providing a comparative metric.The innovation of gene-centric models is the sophisticated conversion of estimates of biogeochemical activity and biomass to genes. For example, the model of Coles et al.Nitrospina-like 16S rRNA genes with two conversion factors: the cell elemental quota (fmol N cell−1) and the number of cellular gene copies. Conversion error arises since cell mass and size vary with growth rate82. Maintaining transparency of the conversion from predicted microbial activity to genes and transcripts allows interdisciplinary audiences to understand and critique the models.The examples here externalize the conversion between modeled activity and sequencing information as a transparent process. In Fig. Using chemical potential as a theoretically grounding organizing principle for the resolution of diverse metabolisms can greatly improve microbial descriptions in global biogeochemical models. However, the approach does have its limitations, which generally increase in significance with increased temporal or spatial resolution.83. On one hand, this is beneficial for resolution of microbial processes in fine-grained ocean circulation models where flow can vary on the order of days. However, incorporating another constraint, such as proteome allocation69, is necessary to inform these choices. For example, considering enzymatic allocation in combination with energetics allowed for the prediction of both the division of nitrification into a two-step process in mixed environments and the combined, complete pathway in one organism (comammox) in biofilms, which preceded observations of the latter86.Modeling metabolic diversity with functional type populations requires choosing how metabolisms are distributed among the populations. This has consequences when interpreting time-varying states: model solutions become dependent on the partitioning of metabolism among the functional types as the timescales of physical change approach the timescales of microbial growth . Such versatility characterizes key players in large-scale biogeochemistry, such as nitrite-oxidizing bacteria and photoheterotrophs92. Some seemingly inactive populations may slowly metabolize over long timescales, requiring longer model integration times and careful attention to their loss rates for resolution, while some populations are periodically active as revealed by high-resolution observations in time37.Also, the metabolic functional type approach resolves only active functional biomass, while evidence suggests that less than 10% to more than 75% of the microbial community may be inactive66 have been empirically supported93. For organic matter, future work is needed to develop suitable descriptions of consumption rates, whether empirical or theoretical. For example, descriptions require attention to the hydrolysis of organic compounds by extracellular enzymes and the ecology of sinking marine particles—the diffusion of monomer away from the particle, within-particle transport, and dynamic ecological interactions on particle surfaces, among other processes95.The proposed modeling approach relies on estimates of the limiting uptake rates of required substrates. In lieu of suitable theoretical descriptions, the use of empirically derived uptake kinetic parameters still employs the benefits of the redox-informed yields (Supplementary Note 97. This could be used to improve the phenomenological description of organic matter in models as labile vs. non-labile, for example, with a more mechanistic underpinning.In Fig. 98. Incorporating this excretion into an energetic framework would enhance studies of phytoplankton ecology, such as studies of photoautotrophic-heterotrophic interactions in the ocean surface or photoautotrophic-chemoautotrophic interactions at the base of the euphotic zone where some phytoplankton excrete nitrite due to incomplete reduction of nitrate99.Descriptions of phytoplankton are currently much more sophisticated than of bacteria and archaea in models, reflecting a longer history of comprehensive sets of observations. However, further work could develop simple descriptions of photoautotrophic metabolisms from underlying energetics by connecting the supply of photons to available energy for biosynthesis within the cell. Many biogeochemical models account for an inefficiency of phytoplankton metabolism with a parameter that dictates their excretions of dissolved organic matter100? This may improve resolution of time-varying metabolic versatility. However, if both steps of nitrification were a part of such a consortium, would the characteristic accumulation of nitrite be predicted (Supplementary Fig. As a more radical extension, can we progress past population modeling and model microbial consortia as one aggregate community biomassWe have described a useful approach for understanding and anticipating microbial control of biogeochemical cycling that is suitable for global applications. The approach aims to represent microbial growth and respiration explicitly and consistently from knowledge of chemical gradients in the environment, towards a goal of building an independently constructed theoretical ecosystem model that can then be compared to observations. Describing microbial communities with underlying energetic constraints connects metabolisms dynamically with global geochemical distributions, such as those of carbon dioxide, oxygen, and biologically available nitrogen. This deepens our understanding of microbial ecosystems and enables the incorporation of the feedbacks of microbial activity to changes in global biogeochemistry and the climate system.Supplementary InformationPeer Review File