The EU has many plans to foster equity and spatial justice. However, each has separate reference points, and it is difficult to find an overall vision. To demonstrate, we analyse two sectoral strategies to identify their implications for spatial justice strategies. Education focuses on early investment and public service reform. Health prioritises intersectoral action to address the ‘social determinants’ beyond the control of health services. Both warn against equating territorial cohesion or spatial justice with equal access to public services. These findings could inform European Commission strategy, but it tends to respond with renewed rhetoric rather than reconsidering its approach. The IMAJINE  project seeks to understand and support this pursuit of spatial justice in the EU. The aim of this paper is to establish how intersectoral equity initiatives (and multi‐level policy‐making) contribute to policies designed to reduce territorial inequalities.The European Union (EU) is committed to achieving greater equity within and across member states. This broad concept describes reducing unfair inequalities, and connects to similar values such as justice. As such, the EU seeks to foster equity in multiple connected ways, including in multiple levels of government . Fourth, separate equity initiatives exist in multiple sectors even when their stated aims are intersectoral .However, this aim is easier said than done, since there is no single coherent overarching approach to equity or justice in theory or practice. Rather, the meaning of key concepts is subject to continuous contestation and change, in four main ways. First, while ‘spatial justice’ draws attention to the geographic aspects of socio‐economic inequalities, it remains ‘ill‐defined’ in relation to other key aims  and means   and who should take responsibility for making sense of them (the EU or regions?).To do so, we build on two key insights from current spatial justice research. First, regional variations in access to public services (2020: 187). Similarly, Madanipour et al. (multi‐level governance (MLG), devolving the responsibility to invest within regions, and making sure that ‘sectoral policies which have a spatial impact’ and ‘regional policy’ are ‘more coherent’. Further, Jones et al.  is beyond the scope of this paper, since almost zero articles (included in our reviews) try to measure the discrete impact of policy instruments.We used the qualitative systematic review method to synthesise empirical data from equity policy research. We published two large  separate reviews of global equity policy research in health and education , Health is a human right to be enjoyed by everyone and supported by governments and international organisations  or decentralise The (limited) impact of subnational government autonomy on health spending and outcomes  is a global strategy promoted by the World Health Organisation (WHO) and receiving high rhetorical support from most country governments and the EU. In policy documents and research, there is a clear HiAP narrative with the following elements  and vertical equity .The difference between ‘merit’, which contributes to ‘severe inequalities’ in outcomes, a threshold of attainment, which maintains ‘relative advantages’, and justice, to redistribute resources to reduce geographical inequalities or pursue equality of outcomes such as attainment.Equity based on The extent to which the state should take responsibility for education inequalities Gilead, : 439.These debates inform competing international agendas  narrative supported by organisations such as the World Bank, identifying the role of education to boost human capital and economic competitiveness in a global knowledge economy, promoting a combination of non‐state solutions to equity  and ‘new public management’ measures to encourage improvements to education system performance and school performance  support the earliest possible investment in high‐quality education, particularly for ‘children from low socio‐economic background’, and (2) criticise the inequity of higher education investment, since participation is limited and the tax system does not offset their higher earnings .Hippe et al.’s (2016: 5) update to Commission work pays more attention to gender, immigration and regional disparities. However, they largely maintain the original focus on school quality and performance as the main vehicle for equity, recommending that school systems foster ‘autonomy coupled with accountability’ and ‘competition sponsored by public funding’, while teachers receive ‘rewards for their performance’. One rationale for this Commission focus is the political context in which redistributional policies would address the relationship between income and education attainment, but remain ‘controversial’ and less amenable to public policy, while policies to foster equal ‘opportunities’ to access high‐quality education would be more popular  and some redistribution of resources across schools or regions. In contrast, researchers supportive of social justice approaches identify the inevitably unequal  outcomes from neoliberal systems. Measures sold in the name of education equity exacerbate inequalities via school competition and incentive schemes that produce greater segregation across schools (via selection) and within them (via ‘tracking’ measures), while half‐hearted measures to address multiculturalism and migration do not mitigate against unequal outcomes in relation to race and ethnicity  into technical discussions regarding proper processes . A shift of focus from unequal levels of regional GDP offers a broader and more nuanced perspective on territorial inequalities, but may also represent a way to reduce political attention to those inequalities.Overall, these comparisons suggest that a rhetorical focus on collaborative policy‐making and equal access to public services is not a panacea for spatial justice and territorial cohesion policies:


The preferences of autism stakeholders regarding the top priorities for future autism research are largely unknown.This study had two objectives: First, to examine what autism stakeholders think new research investments should be and the attributes of investment that they consider important, and second, to explore the feasibility, acceptability and outcomes of two prioritization exercises among autism stakeholders regarding their priorities for future research in autism.This was  a prospective stakeholder‐engaged iterative study consisting of best–worst scaling (BWS) and direct prioritization exercise.A national snowball sample of 219 stakeholders was included: adults with autism, caregivers, service providers and researchers.The main outcomes measures were attributes that participants value in future research investments, and priority research investments for future research.Two hundred and nineteen participants completed the exercises, of whom 11% were adults with autism, 58% were parents/family members, 37% were service providers and 21% were researchers. Among stakeholders, the BWS exercises were easier to understand than the direct prioritization, less frequently skipped and yielded more consistent results. The proportion of children with autism affected by the research was the most important attribute for all types of stakeholders. The top three priorities among future research investments were (1) evidence on which child, family and intervention characteristics lead to the best/worst outcomes; (2) evidence on how changes in one area of a child's life are related to changes in other areas; and (3) evidence on dietary interventions. Priorities were similar for all stakeholder types.The values and priorities examined here provide a road map for investigators and funders to pursue autism research that matters to stakeholders.Stakeholders completed a BWS and direct prioritization exercise to inform us about their priorities for future autism research. Three separate pairs of focus groups comprised of adults with autism (n = 6), parents (n = 5) and professionals (n = 6) were conducted to develop the survey. Recruitment was accomplished through clinical contacts, and participants were diverse with respect to age and race. We met with each stakeholder group separately so that participants would feel free to express opinions that might not be shared by other stakeholder types, such as the need for pharmaceutical treatment. We met with each group twice. In the first meeting, we discussed values that underlie treatment choice. In the second meeting, we reviewed the list of values and discussed future research investments. The product of these focus groups included lists of values and investments that stakeholders agreed were complete and meaningful. The survey consisted of two parts: A BWS experiment to examine how much autism stakeholders valued each of nine attributes derived from stakeholder value statements of future research investments and a direct prioritization exercise. The direct prioritization exercise had two subsections: First, stakeholders ranked each of the nine attributes from the BWS experiment, and second, stakeholders ranked 17 future research directions from highest to lowest priority.The design was a prospective stakeholder‐engaged iterative study. A survey instrument, developed with stakeholder input in an iterative process from formulation to finalization of the document  and a research assistant. We invited participation in the web‐based survey through national advocacy groups, such as Autism Speaks, The Autism Society of America and Mental Health America; internet blogs by adults with autism; professional groups such as the Association of University Centers on Disabilities; state Developmental Disabilities Councils; the Study to Explore Early Development in autism; and numerous individual and tweeted invitations to complete and share the survey. Participants in the in‐person setting received $100 in thanks for their efforts, while participants in the web survey did not receive payment. Data collection occurred between May and December 2014.2.3In the BWS exercise, 9 two‐level attributes described what people value in future research investments, based on qualitative work conducted before the survey. Sample attributes include ‘the treatment BUILDS ON STRENGTHS of the child with autism’ versus ‘the treatment FOCUSES ON REDUCING PROBLEMS with autism rather than building on strengths’ and ‘the treatment addresses MULTIPLE ASPECTS OF AUTISM in an integrated manner’ versus ‘the treatment focuses on a SINGLE ASPECT OF AUTISM’ Appendix . A fractIn the direct prioritization exercise, participants were shown the nine attributes from the BWS exercise and 17 future research investments to prioritize.In the in‐person settings, we described the goals of the study and our interest in learning which type of prioritization exercise worked best. At the close of the survey exercise, we discussed reactions. Discussions were audio‐recorded and transcribed. We used open coding to assess question‐asking about our methodology and reactions.2.4To assess the feasibility and acceptability of the exercises for respondents, we recorded questions during implementation and feedback from discussion. We also calculated missingness in each type of prioritization exercise and rates of completion. For the direct prioritization exercise, rankings of attributes and future research investments were calculated, both overall and by subgroup, from the mean number of stars allocated to each attribute or investment.β0 + β1bw_indic + β2..9attributes + β10..26attribute‐levels. Choice indicates whether the given attribute level was chosen. Bw_indic indicates whether the outcome is a choice of ‘best’ (rather than ‘worst’). β2..9 are the attribute coefficients (eight coefficients are estimated – nine attributes minus the attribute assigned as the reference attribute), and β10..26 are the attribute‐level coefficients for attribute‐level combinations (17 coefficients estimated – 9 attributes of two levels each (18), minus one attribute level assigned as reference attribute level). In results, reference categories of attributes and attribute levels are presented as 0. We explored differences in preferences between stakeholder types  by running the same multinomial logit model within each subgroup. Parameter estimates describe participants' preferences for one attribute or attribute level compared with a reference attribute and attribute level. A statistically significant coefficient indicates the importance of an attribute level in determining overall utility and in influencing preferences. Attribute importance  is calculated from the range of β across levels (βmax − βmin for each attribute). The priority, or utility, of a given future research investment is the sum of the predicted log odds of choosing its attributes and attribute levelsβ for each attribute level by the actual attribute level present in that research investment, and then summing all the products.To model participants' choices from the BWS exercise, we estimated a multinomial logit (MNL) model in NLOGIT 5.0. Each attribute level was coded as 1 when it was chosen as most favourite (‘best’), −1 when it was chosen as least favourite (‘worst’) and 0 when the attribute level was not chosen as either most or the least favourite. Therefore, in the MNL model, each person contributes observations for ‘best’ (1 or 0), ‘worst’ (−1 or 0) for each attribute in each scenario, yielding up to a total of 180 observations per respondent—18 scenarios of five attributes * 2 choices (best or worst).We compared rankings of future research investments, as well as individual attributes, between the direct prioritization and BWS using Spearman correlations. The rankings for the BWS were based on attribute importance and priority/utility calculations described above.3n = 101), and 118 completed the web survey, for a total of 219 participants. The demographics of the participants are shown in Table Stakeholders in North Carolina, Georgia, California and Colorado completed the survey in person n = 01, and 1Postsurvey discussions after in‐person implementation gave people a chance to convey what they felt was important; to consider differences of opinion, experience, past need and current need; and to affirm the value of each other's opinions. These discussions typically lasted a full hour after completion of the paper survey. The group format encouraged people to complete the survey and rewarded them with an opportunity for expression that the web survey lacked. Stakeholders reported that they found the discussion of research attributes engaging. Participants expressed enthusiasm for the research topic and eagerness to discuss what they valued and what they felt were important knowledge gaps and important future research investments. Participants stated that the BWS exercises were easier to understand than the direct prioritization. They asked questions to understand directions more frequently in direct prioritization. In‐person notes and recordings did not allow for distinction of stakeholder type in these discussions.Compared with the direct prioritization questions, the BWS scenarios also were less frequently skipped (10% vs. 26% missing items on average) and yielded more consistent results between stakeholder types. However, stakeholders expressed dislike for the forced‐choice aspect of BWS. Participants were more likely to complete the survey in the in‐person setting than on the web. Missing responses and completion rates did not vary by stakeholder type.Table 3.1Figure r = .70, p = .03). In the direct prioritization exercise, as in the BWS model, stakeholders selected the proportion of children with autism affected by the research as the most valued attribute. Among adults with autism, the direct prioritization identified treatment that develops life skills and treatment that builds on strengths as neither highest nor lowest valued, differing somewhat from the BWS, but otherwise consistent with the BWS.The direct prioritization method yielded overall attribute rankings (not shown) that were consistent with the rankings from the full‐sample BWS model . The second and third priorities under the direct prioritization approach were (2) evidence on integrated treatment approaches, in which multiple treatments are combined in a comprehensive manner, and (3) evidence on behavioural interventions.The direct prioritization exercise identified the same top stakeholder priority overall for future autism research, but yielded results that generally differed from those of the BWS exercise for some of the other priorities . BWS worked better than direct prioritization for these autism stakeholders. Findings provided a ranking of research attributes and investment priorities that can help provide a road map for researchers and funders to engage stakeholders in identifying specific research questions and pursue research that matters to stakeholders.Drs. Howard, Ellis, Jonas, Carey, Morrissey and Thomas participated in the study design. Drs. Howard and Thomas participated in the acquisition of data. Drs. Davis, Howard and Thomas participated in the drafting of the final manuscript. All authors participated in the critical review of the manuscript. All authors have given final approval to the manuscript.The authors declare no conflicts of interest.Supporting information.Click here for additional data file.


Surprisingly, the A‐cation cross‐exchange proceeds as fast as the halide  exchange with the help of free A‐oleate complexes present in the freshly prepared colloidal perovskite NC solutions. This enabled the preparation of double ‐ and triple (MACsFA)‐cation perovskite NCs with an optical band gap that is finely tunable by their A‐site composition. The optical spectroscopy together with structural analysis using XRD and atomically resolved high‐angle annular dark‐field scanning transmission electron microscopy (HAADF‐STEM) and integrated differential phase contrast (iDPC) STEM indicates the homogeneous distribution of different cations in the mixed perovskite NC lattice. Unlike halide ions, the A‐cations do not phase‐segregate under light illumination.We report here fast A‐site cation cross‐exchange between APbX The A‐cation cross‐exchange between perovskite nanocrystals (NCs) is found to be as fast as the halide exchange in the presence of excess A‐oleate complexes in the colloidal solution. The synthesis of a wide range of mixed (double and triple)‐cation and mixed halide perovskite NCs is demonstrated. The mixed A‐cations were found to be uniformly distributed across the crystal lattice and they do not segregate under intense light illumination. Although the NCs were well purified, the colloidal suspension still contained at least the minimum amount of ligands to stabilize the NCs and these ligands can assist the cation cross‐exchange. The reaction time can vary based on the amount of ligands left in the solution after washing. So, the amount of MeOAc used for washing the NCs and the number of washing cycles are very important.4NX further increased the kinetic barrier and the reaction was not complete even after 24 hrs  cross‐exchange in halide perovskite NCs at room temperature, and it is as fast as typically found in halide exchange. The controlled experiments revealed that the residual A‐oleate complexes present in the freshly purified pristine NCs facilitate the fast cross‐exchange, in a similar way as the alkylammonium halides do for the halide exchange. We thus prepared double and triple cation perovskite NCs by mixing the respective mono‐cation perovskites. This is the first demonstration of the preparation of triple cation perovskite NCs by cation exchange. The optical properties along with high‐resolution HAADF‐STEM and iDPC‐STEM imaging suggested the homogeneous distribution of the different cations in the mixed perovskite crystal lattice. Unlike halide ions in mixed halide LHP systems, the A‐cations do not segregate under intense light illumination. The band gap of mixed‐cation perovskite NCs is further tunable across the visible range by varying the halide composition. We anticipate that these mixed‐cation perovskite NC systems are expected to be promising for the development of efficient and stable optoelectronic devices, similar to thin film‐based mixed‐cation perovskite systems, which have already shown great promise.The authors declare no conflict of interest.1As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.Supporting InformationClick here for additional data file.


Tissue damage caused by an infection oran autoimmune disease triggers degradation of collagen in the extracellular matrix (ECM), which further enhances inflammation. Therefore, improving ECM in aninflamed tissue can be exploited as a potential therapeutic target. A recentstudy emphasised an innovative approach against COVID‐19 using polymerised type I collagen (PTIC) that improves disease severity through a hitherto unknownmechanism. In this paper, we provide an overview of potential mechanism thatmay explain the anti‐inflammatory effect of collagen peptides. In addition,the paper includes a brief summary of possible side effect of collagendeposition in inflammatory diseases. Altogether, current knowledge suggeststhat collagen may potentially reduce the residual risk in inflammatorydiseases; however, the detailed mechanism remains elusive. It is a highly dynamic yet strictly regulated tissue component and the balance between its synthesis and degradation is essential for tissue architecture, physiological homeostasis and repair.2A recent randomised, placebo‐controlled clinical trial demonstrated an innovative approach of treating symptomatic outpatient COVID‐19 infection by intramuscular injection of polymerised type I collagen (PTIC).Leishmania.+ and CD8+ T cells and impairing antigen‐lymphocyte interactions.It is known that there is a close interplay between the ECM and both adaptive and innate immunity, as collagen deposition and degradation are closely related to the activity of immune cells. On the one hand, MMPs produced by activated immune cells such as monocytes/macrophages promote the migration of immune cells, thereby enhancing MMP production and inflammation. On the other hand, degraded collagen itself acts as a strong chemoattractant for immune cells.Furthermore, collagen type I deposition and the fibrosis score were significantly more prominent in the lungs of COVID‐19 patients. This patient group also presented more pronounced infiltration of the lungs by different immune cell types, such as macrophages and neutrophils.3regs) and IL‐10‐expressing peripheral cells was increased after intervention.+/IL17A+ T‐cell number and an increase in Tregs and CD4+/IFN‐γ+ T cells.Intra‐articular administration of PTIC in patients with symptomatic knee osteoarthritis (OA) not only led to a significant clinical improvement but also to a significant reduction in the erythrocyte sedimentation rate (ESR) as well as to a significant reduction in interleukin (IL)‐1β and tumour necrosis factor (TNF)‐α‐expressing peripheral cells compared to placebo. Furthermore, the number of regulatory T cells  and collagen fragments as well as the number of infiltrating immune cells in the circulation or bronchial fluid could be biomarkers to assess the progression of the disease and may provide evidence for the mechanism of PTIC action against COVID‐19. Detailed information on the mechanism will help prevent potential side effects after systemic injection of collagen compounds.Altogether, the current knowledge sheds light on the role of collagen in inflammatory disease development and opens up new perspectives by using collagen as an innovative anti‐inflammatory therapeutic target, yet the mechanism and potential side effects should be carefully investigated.The authors declare no conflict of interest.


Light‐driven homogeneous and heterogeneous catalysis require a complex interplay between light absorption, charge separation, charge transfer, and catalytic turnover. Optical and irradiation parameters as well as reaction engineering aspects play major roles in controlling catalytic performance. This multitude of factors makes it difficult to objectively compare light‐driven catalysts and provide an unbiased performance assessment. This Scientific Perspective highlights the importance of collecting and reporting experimental data in homogeneous and heterogeneous light‐driven catalysis. A critical analysis of the benefits and limitations of the commonly used experimental indicators is provided. Data collection and reporting according to FAIR principles is discussed in the context of future automated data analysis. The authors propose a minimum dataset as a basis for unified collecting and reporting of experimental data in homogeneous and heterogeneous light‐driven catalysis. The community is encouraged to support the future development of this parameter list through an open online repository. This Scientific Perspective describes current challenges in determining, reporting, and comparing performance indicators in homogeneous and heterogeneous light‐driven catalysis. The need for comparative data analysis in the field is highlighted, and its role in future data reporting and big‐data analysis is discussed. A minimum set of experimental parameters to be reported for light‐driven catalysis is proposed. Central pillars are photo(electro)chemical water splitting,1, 2 tHowever, to further develop light‐driven catalytic systems, it is imperative to experimentally identify, report, and compare reactivity, stability, and performance. Thus, well‐defined protocols, standardized experimental setups, and experimentally accessible performance indicators are urgently required to provide quantitative comparability22, 23In contrast, light‐driven catalysis comprises an enormous range of systems where mechanistic steps and effects span across many orders of magnitude in time and space Figure .[29, 1.2−) or the electron hole (h+)) is transferred to the respective reaction site, which drives the catalytic turnover. Often, sacrificial electron donors or acceptors are used to transfer redox equivalents.[Light‐driven catalysis typically relies on the interplay between multiple components and processes Figure . The iniivalents.33, 34As catalytic processes typically show maximum performance in a narrow window of operation, defining a standard set of reaction conditions would be inherently biased and is not used in the field. Instead, a set of experimentally accessible, bias‐free performance indicators is required, which can be widely used to evaluate and compare light‐driven catalytic performance. While this Scientific Perspective outlines the steps required for objective comparison in light‐driven catalysis, researchers have to evaluate whether comparison between two systems is meaningful. The authors believe that comparison is most valuable for systems performing related catalytic reactions, while comparison between two unrelated systems is not trivial and often also not necessary.22.1Light‐driven catalysis is controlled by the species that directly participate in the reaction .Concentration and ratio of reagents significantly affect photochemical reactivity and can lead to changes from virtually non‐reactive to highly reactive.[The electron donor/acceptor affects the observed reactivity, as redox potentials Solvent type and solution pH influence solubilities, component stability, and component interactions. The solvent itself can (and regularly does) participate in reactions, either by providing a dielectric, polarizable medium for the reaction,Temperature and pressure of the reaction system need to be monitored and documented, in particular for biphasic gas–liquid reactions, as changes in temperature2.2Light‐driven reactions in solution are highly dependent on the kinetics of many elementary processes, which need to occur in a specific order Figure . Key facIntermolecular and supramolecular interactions, including electrostatic, van der Waals, π‐π stacking or hydrogen‐bonding interactions, as well as host–guest complex formation can control the aggregation behavior of molecular components in solution.[Kinetic rate matching. The elementary steps in light‐driven catalysis occur on vastly different time scales, ranging from femto‐ to milliseconds, and beyond.2.3Heterogeneous light‐driven catalysis uses solid‐state compounds as a light absorber and catalyst, or as support for immobilizing molecular components.Optical effects, such as scattering and reflection at the interface between the solvent and the solid‐state compounds, affect the photon flux at catalyst particle, and thus the overall reaction rate.[Mass transport affects catalytic reactivity by controlling convective and diffusive transport of reactants to and products away from the catalyst or light absorber,[33.1The optical characteristics of the light source  are key parameters for light‐driven catalysis.17, 54Spectrally resolved incident photon flux describes the photon flux that reaches the inside of the reactor. It is an ideal basis for quantitative interpretation of light‐driven catalytic reactivity data, as incident photons can be considered additional reagents required for the light‐driven process under study.[As a practical alternative to improve evaluation and reproducibility, we recommend the following minimum information to be reported: the geometric arrangement and relative positioning of light source and photoreactor, as well as the emission characteristics of the light source, and the overall reactor design and the reactor materials.81, 82The absorbed photon flux forms a basis for photonic efficiency determinations, and therefore requires quantitative determination (see below). However, determining photon fluxes is error prone and requires careful experimentation.[The photoreactor design affects the photon flux as well as the local reaction environment. Hydrodynamic parameters such as (non‐)ideal mixing, dead zones, and residence time distributions have significant impacts on light‐driven reactivity.3.2Interfacing molecular catalysis with reaction engineering holds vast possibilities to increase photochemical performance.Temporal and spatial changes of reaction conditions. Light‐intensity drops exponentially along the ray trajectory (Beer–Lambert law). For strongly absorbing systems, or systems using high‐intensity irradiation, this results in pronounced reaction rate gradients along the light‐beam trajectories. For example, a highly absorbing reaction system with an overall absorbance A=2 will show a 50 % decrease in light intensity within only 15 % of the optical beam path in the reactor. Large reactor volumes are therefore not irradiated and cannot participate in light‐driven reactions. The problem is compounded if mixing within the reactor is not ideal, which can result in detrimental side reactions; e.g., increased degradation of light absorber or catalyst.Reagent consumption and intermediate/product formation further affect light‐driven catalysis; their detailed impact on reactivity is often not well understood. Possible effects range from time‐dependent changes of the optical density of the reaction solution to aggregation‐induced reactivity,96, 97Impact of reactor design and operation. Continuously and discontinuously operated reactors feature strikingly different properties. Ideal, discontinuous batch reactors  have a spatially homogeneous concentration field, which changes with the reaction progress. In contrast, ideal, continuous plug‐flow reactors  show a temporally constant but spatially changing concentration field along the direction of flow. The extent that reactants are mixed with products differs, leading to a different availability of reactants and products near the catalytic sites during the reaction progress and thus to a different reaction performance. Differences in heat and mass transport depend on the reactor type, and all these factors directly affect the (light‐driven) reactivity. Various laboratory scale reactors have recently been reported to address the challenges of comparability.[Flow photoreactors. Over the last decade, continuously operated photomicroreactors have received widespread attention in the field of flow photochemistry.[44.1Accurate determining and reporting of experimental parameters for light‐driven systems is the basis for performance comparison and provides critical information for identifying future directions in the field Figure . HoweverNote, however, that comparison between reaction systems is not always meaningful, even if all relevant parameters have been reported. It also requires in‐depth understanding of the reaction systems. For example, using photon flux as a basis for comparison is only meaningful if the systems to be compared are all limited by photon flux. This is often not the case; e.g., in photo‐initiated radical reactions or systems where light‐harvesting units are coupled with additional catalysts. Thus, meaningful comparison requires both comprehensive parameter reporting and understanding of the underlying reaction processes.4.2The absolute molar amount of a target product formed is the basis of any catalyst evaluation and should be reported to facilitate data evaluation and comparison; however, absolute molar amounts are typically normalized with respect to other parameters, such as the amount of light absorber or catalyst, or the reaction time.TON) is one classical example for this approach, where the amount of product formed  is normalized to the amount of a catalytically active component, resulting in the following definition The turnover number (n(product), which is often equal to n(product formed), can be affected by delayed product release; e.g., from heterogeneous porous catalystsNote that this seemingly simple equation has sparked major debates in the context of comparing catalytic performances, partly based on the following considerations:n can be challenging if the number of sites that are accessible and active are not known .n(light‐absorbing species) is sometimes used instead of n; e.g., when photosensitizer degradation limits the catalytic performance.The determination of m of an active material can be used as alternative for normalization, giving mTON:If the molar amount of an active species is unknown, the total mass mTON as a performance indicator in light‐driven reactions.While rather common in thermal catalysis, this approach is not meaningful for light‐driven reactions since the correlation between activity and mass of catalytically active species is not always linear.4.3r, which can be calculated for a given reaction period, or averaged over the full reaction time:Normalization of converted or produced molar amounts with respect to the reaction time gives a reaction rate Note that average reaction rates have only limited significance, as they can vary significantly as the reaction progresses. Reporting the reaction rates as a function of the reaction time is recommended to provide an in‐depth understanding of their changes over time Figure .TOF) is a commonly used parameter that reflects changes of TON with reaction time:The turnover frequency :Simultaneous normalization of the converted or formed amount to the reaction time and reaction volume results in the space–time yield .72, 73ξext) is a variation of PE and can be used to evaluate the efficiency of the experimental setup rather than the efficiency of the catalystThe external photonic efficiency ξext is a varSCE) can be calculated using the Gibbs free energy change ΔG:When sunlight‐ or solar‐simulator‐driven chemical reactions are studied, the solar‐to‐chemical conversion efficiency (SCE is based on well‐defined irradiation conditions (intensity and spectrum); however, it can only be used for systems that operate with primarily visible light. In contrast to PE, SCE provides no information on the spectral response of the system.55.1Performance indicators are prone to systematic errors caused by the experimental setup. Given the broad range of parameters that affect light‐driven catalytic activity, the authors see an urgent need for a detailed and consistent recording, documenting, and reporting of experimental data to enable independent evaluation and comparability, as well as reproduction of catalytic performance.24, 255.2To date, there are no standardized reporting strategies that describe a minimum experimental dataset required for publication of catalytic research results.120 The Thus, a future database for light‐driven catalysis requires a consensus on the specifications for reported data and information. This is a crucial task for the communities involved and will have major long‐term impacts on the development of light‐driven chemistry.122 Pion5.3The complexity of light‐driven catalysis will still require experimental studies to assess and confirm predictions from simulations and provide experimental evidence. Here, high‐throughput experimentation124 can 6Performance indicators are only meaningful when reported together with a comprehensive set of experimental details. The authors envision that the research community will commit to the advantages of standardized reporting and Big Data and will move to publishing strategies that give access to fundamental experimental data, such as amounts and component concentrations as a function of time. This would allow independent and retrospective calculation of performance indicators.Table https://github.com/photonZfeed/photoComparison).https://doi.org/10.5281/zenodo.5911870).To achieve this goal, this table is provided as an open document in a persistent repository molecular devices based on metal complexes for light‐driven hydrogen evolution, water oxidation, and other energy‐relevant chemical conversions.Benjamin Dietzek‐Ivanšić is Professor of Physical Chemistry at Friedrich Schiller University and heads the research department Functional Interfaces at the Leibniz Institute of Photonic Technology. He is interested in studying photoactive molecules and materials; e.g., in photocatalysis.Carsten Streb is currently Professor of Inorganic Chemistry at Johannes Gutenberg University Mainz. His current research is focused on designing polyoxometalate‐based functional materials and composites to address global chemical challenges with a focus on energy conversion and energy storage.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.Supporting InformationClick here for additional data file.


Climate change is expected to increase the frequency and severity of droughts. These events, which can cause significant perturbations of terrestrial ecosystems and potentially long‐term impacts on ecosystem structure and functioning after the drought has subsided are often called ‘drought legacies’. While the immediate effects of drought on ecosystems have been comparatively well characterized, our broader understanding of drought legacies is just emerging. Drought legacies can relate to all aspects of ecosystem structure and functioning, involving changes at the species and the community scale as well as alterations of soil properties. This has consequences for ecosystem responses to subsequent drought. Here, we synthesize current knowledge on drought legacies and the underlying mechanisms. We highlight the relevance of legacy duration to different ecosystem processes using examples of carbon cycling and community composition. We present hypotheses characterizing how intrinsic (i.e. biotic and abiotic properties and processes) and extrinsic  factors could alter resilience trajectories under scenarios of recurrent drought events. We propose ways for improving our understanding of drought legacies and their implications for subsequent drought events, needed to assess the longer‐term consequences of droughts on ecosystem structure and functioning. Climate change increases the frequency and severity of drought events, which can have lasting impacts on ecosystem structure and functioning after the drought has subsided. In this review, we synthesize the current knowledge on drought legacies and the underlying mechanisms, highlight the relevance of legacy duration for different ecosystem processes on the examples of carbon cycling and community composition, and develop hypotheses on how intrinsic (i.e. biotic and abiotic properties and processes) and extrinsic  factors could alter resilience trajectories under scenarios of recurrent drought events. In addition to these intrinsic factors a range of extrinsic factors, including drought Although the relevance of drought legacies for a longer‐term perspective on ecosystem resilience  synthesize our current understanding of drought legacies and the underlying mechanisms from species and communities to ecosystem (biotic and abiotic) scale and (ii) summarize the legacy duration of previously documented drought legacy responses. Furthermore, we (iii) develop hypotheses as to how drought legacies of intrinsic factors could influence the resilience trajectories of ecosystem responses to subsequent drought events relating to extrinsic factors.2Drought legacies are commonly defined as any alterations of an ecosystem state or processes that occur after a drought has subsided , that is the interactions between plants, soil organisms, and abiotic soil factors, which lead to altered plant composition and performance and have cascading effects on ecosystem properties (Buchenau et al., A major driver of drought legacies in grasslands is related to drought‐induced changes in canopy structure (Beloiu et al., microclimate, with consequences for the composition and biodiversity of the understory as well as nutrient and C cycling (Kane et al., soil properties, by altering the chemical and physical soil structure. Drought has been shown to increase the soil water repellency (Goebel et al., Drought legacies have been shown to lead to reduced leaf area index in grasslands and forest (Jiao et al., 4To date few studies have explicitly looked into drought legacy duration, which has been best documented for C cycle processes. Here, we synthesize drought legacy duration post‐drought for a range of C cycle parameters and for community properties, which both strongly depend on the plant functional types and the specific response parameter studied Figure .In grasslands, most C‐cycle related legacies return to pre‐disturbance level roughly within the first year after the drought and can last several years for forests Figure . This isOverall, flux parameters return to pre‐disturbance levels within the first year Figure , while bFurthermore, we observed that legacies in community properties, such as species abundance, composition, and richness tend to last longer in woody species and understory compared to grasslands (see Figure Overall, the temporal aspect of drought legacies and their dependencies are still poorly understood across response parameters and plant functional types. This is especially relevant for long‐term legacies that are related to community properties (Hillebrand & Kunze, 5sensu Ingrisch & Bahn, While legacies after a drought event have been increasingly studied in recent years, we still lack a profound understanding of how these drought legacies alter the resilience (i.e. resistance and recovery [post‐recovery state following the antecedent drought event. Relations can be manifold and depend on the particular IF, hence for simplicity we only present one option here, showing the highest resilience when the IF reveals no legacy from the previous drought (Figure First, we hypothesize that the resilience of an IF to a subsequent drought depends on its t Figure .adaptation and degradation of all further IFs of the ecosystem (Figures Second, we suggest that the resilience of an IF to a subsequent drought depends on the  Figures  and 4. W Figures . Importa Figures .adaptations imply that species become more adjusted to drought, which can moderate the impact of a subsequent drought event. For example, a reduced xylem conduit size in trees can reduce the risk of hydraulic failure and thereby increase resistance to a subsequent drought (Gessler et al., On the species scale, Long‐term adaptations on the community scale have been documented for all plant functional types. Such adaptations frequently involve increased dominance of drought adapted species (Hoover et al., On the ecosystem scale, increased N availability upon rewetting can favor resistance to and recovery from subsequent drought (Legay et al., degradations of intrinsic factors can have an important influence on ecosystem responses to subsequent droughts. In fact, it has been suggested that an increasing amount of land area globally may be degraded by aridity in the long‐term due to shifts in precipitation regimes (Berdugo et al., In addition to drought‐induced adaptations, Finally, we hypothesize that the resilience trajectories of an IF to a subsequent drought event are strongly influenced by extrinsic factors, including drought timing, frequency, and severity Figure . Next to62, N deposition and land‐use changes, as well as interactions with other climate extremes, such as heatwaves and heavy precipitation events. Accounting for these potential interactions and the implications of drought legacies for subsequent drought events is essential for understanding and projecting the long‐term consequences of a changing climate for ecosystems.In times of increasing severity and frequency of drought events in many parts of the world, it is essential to not only assess the concurrent effects of droughts, but to understand the lasting consequences such extreme events may have on ecosystems. In our review, we have provided a broad overview of drought legacies and the underpinning mechanisms from species to community and ecosystem scale. To date, quantitative analyses of drought legacy responses have mainly focused on aboveground growth‐related parameters and some community attributes, suggesting that the legacy duration can differ vastly for different parameters and different plant functional types. For a more in‐depth understanding of drought legacies on ecosystems, it will be important for future studies to extend the observational timescale and explicitly consider a range of interrelated biotic and abiotic factors, including above‐belowground interactions. To advance the field, it will be essential to illuminate the particular role of adaptation and degradation of properties and processes across scales in determining ecosystem resilience to subsequent drought events. Furthermore, future studies should consider potential interactions of drought legacies with other global change factors such as warming, elevated COThe authors declare no conflict of interest.Appendix S1.Click here for additional data file.


Systemic chemotherapy combining biological targeted therapies is the standard therapy for patients with metastatic colorectal cancer (mCRC), but effective markers are needed to identify clinical responders. Circulating tumour cells (CTCs) have been associated with prognosis in patients with mCRC. This study aimed to explore the relationship between CTC number and the clinical response of patients with advanced CRC.− fluorescence in situ hybridization immunofluorescence were used to detect peripheral blood CTCs in 79 patients with advanced CRC. Fisher's exact test and Spearman's rank correlation coefficient were used to analyse the correlation between CTC number and efficacy of chemotherapy. Kaplan–Meier and Cox regression analyses were used to evaluate progression‐free survival (PFS).Epithelial cell adhesion molecule‐independent enrichment and CD45r =4.891, p = 0.031). High CTC numbers were associated with a poor treatment response . Dynamic decrease in CTC number was associated with clinical response (p = 0.046). High baseline CTC number and carcinoembryonic antigen levels were prognostic factors for unfavourable PFS in multivariable analysis . Compared with the CTC‐positive group, the CTC‐negative group showed superior PFS  among CRC patients receiving first‐line treatment.Among the evaluable patients, CTCs were significantly correlated with clinical response (CTC number is a feasible biomarker for predicting outcomes in mCRC patients receiving systemic chemotherapy. Circulating tumour cells have been associated with prognosis in patients with metastatic colorectal cancer (mCRC). CTC positivity at baseline, as well as without a reduction in the number of CTCs during chemotherapy, in mCRC patients is positively correlated with disease progression. CTC number may be used as a new biomarker for predicting disease progression and survival.Colorectal cancer (CRC) is the third most common cancer and second most common cause of cancer‐related death worldwide. More than 1.9 million new cases of CRC and 935,000 deaths occurred worldwide in 2020 , high CA199 levels , high CEA levels  and high CTC levels  were associated with a poorer PFS. Multivariate analysis revealed that high CEA levels and high CTC levels were poor prognostic factors for PFS  = 2.01, In this single‐institution retrospective study, we enrolled 78 evaluable patients with mCRC who received first‐line treatment. High baseline CTC levels were associated with a poor treatment response. The dynamic decrease in CTC number was associated with a clinical response. High baseline CTC number and CEA levels were poor prognostic factors for PFS. Compared with the CTC‐positive group, the CTC‐negative group showed a superior PFS in CRC patients receiving first‐line treatment.CTCs are rare malignant cells found in the bloodstream that originate from the primary tumour or metastatic sites. CTCs may include not only epithelial tumour cells but also tumour cells undergoing epithelial–mesenchymal transition and tumour stem cells [Numerous technologies that rely on the physical and biological properties of CTCs have been developed to enrich CTCs from normal haematopoietic cells. These enrichment techniques can broadly be divided into immunocapture methods that differentiate cells based on epithelial cell surface marker expression, notably EpCAM, and those that differentiate on the basis of distinct biophysical properties [− FISH that combined double probes of CEP8, ‐7 and an anti‐CD45 antibody for detection to improve the positivity rate. In their study, 84% of patients had a CTC number ≥2 before treatment, and the results showed that the sensitivity of CTCs for diagnosis was 84% and the specificity was 97.6% with dual probes [However, EpCAM expression on the surface of epithelial tumour cells is highly heterogeneous, and some types of cancer cells do not express EpCAM. The clinical application of such techniques to isolate CTCs shed from different solid tumours is limited [In our study, CTC number was significantly correlated with the clinical response in patients with mCRC who received first‐line treatment. Patients in a CTC‐negative group and with CTC reduction during chemotherapy revealed a good prognosis, consistent with previous studies [High baseline CEA levels were prognostic factors for PFS in the univariate and multivariate analyses. However, no associations were found between the baseline tumour markers and clinical response in Fisher's exact test, which may be related to the length of follow‐up. Some enrolled patients accepted the first‐line therapy and did not reach the PFS time. Additional samples and a longer follow‐up time are required to validate the efficacy of this tumour marker in the clinic.Our study had some limitations. The small sample size included from a single medical centre might have limited the statistical significance of the impact of the clinical variables on the survival outcome. Additionally, the different first‐line chemotherapy regimens in the enrolled patients might have been confounded by CTCs relevant to PFS, the tumour response and prognostic analysis. Furthermore, the short follow‐up time might have limited the conclusions that could be drawn. The present results require more samples for further validation.Positivity for CTCs at baseline without a reduction in CTC number during chemotherapy in mCRC patients was positively correlated with PD. Higher CTC numbers are related to a poor prognosis. Because blood collection is simple, convenient and minimally invasive, CTC number may be used as a new biomarker for predicting disease progression and survival. The relationship between CTC number and efficacy of chemotherapy in patients with mCRC should be investigated further.The study was approved by the Ethics Board of Shanghai General Hospital.Informed consent to participate in the study was obtained from all the patients.No material has been reproduced from other sources.The authors declare that they have no conflicts of interest.Li Qi conceived of the presented idea. All authors collected the data. Shen Feifei and Zhu Yiwen analysed, interpreted the data and wrote the manuscript. Li Qi and Liu Chuan supervised and supported the study and critically revised the manuscript. All authors approved the final version of the manuscript.Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.


Peripheral blood film showed a spectrum of atypical lymphocytes characterized by cleaved, lobulated, and clover leaf‐like nuclei, and basophilic cytoplasm with approximately twice the size of an erythrocyte, namely clover‐leaf cells  studies were assigned by the hematologist for disease staging. Routine complete blood count with differential revealed leukocytosis  by the chairman.No patient identifiable images or data have been included in the article. Verbal informed consent was obtained from the patient's next‐of‐kin.


Clustering is a promising technique for optimizing energy consumption in sensor-enabled Internet of Things (IoT) networks. Uneven distribution of cluster heads (CHs) across the network, repeatedly choosing the same IoT nodes as CHs and identifying cluster heads in the communication range of other CHs are the major problems leading to higher energy consumption in IoT networks. In this paper, using fuzzy logic, bio-inspired chicken swarm optimization (CSO) and a genetic algorithm, an optimal cluster formation is presented as a Hybrid Intelligent Optimization Algorithm (HIOA) to minimize overall energy consumption in an IoT network. In HIOA, the key idea for formation of IoT nodes as clusters depends on finding chromosomes having a minimum value fitness function with relevant network parameters. The fitness function includes minimization of inter- and intra-cluster distance to reduce the interface and minimum energy consumption over communication per round. The hierarchical order classification of CSO utilizes the crossover and mutation operation of the genetic approach to increase the population diversity that ultimately solves the uneven distribution of CHs and turnout to be balanced network load. The proposed HIOA algorithm is simulated over MATLAB2019A and its performance over CSO parameters is analyzed, and it is found that the best fitness value of the proposed algorithm HIOA is obtained though setting up the parameters Over the years, revolutionary development in IoT devices has opened the paradigm for dynamic sensing technology that provides seamless communication over the Internet . WirelesClustering is a robust and scalable approach to lower energy consumption with better network throughput ,5. It isThe system model includes cluster-based IoT architecture with aid the feature of cloud network, where energy consumption of the cluster network is evaluated.An energy optimization problem is formulated in terms of a fitness function that minimizes the intra- and inter-cluster distance in the IoT network.We present HIOA to generate an optimal set of CHs to minimize the overall energy consumption. This employs FL for the creation of the initial population. Further, CSGO divided the IoT node into a hierarchal structure to increase the population diversity that optimizes the formulated fitness function using crossover and mutation.Finally, extensive simulation over different CSGO parameters and comparison with the state-of-the-art algorithms has been performed for critical performance evaluation.A critical investigation of CSO techniques concluded that it has better ability for IoT network-centric feature selection with faster convergence rate over fuzzy logic and genetic algorithm due to the effective balance between network uncertainty and finding the parameter optima . In fuzzThe rest of the paper is divided into the following sections. As mentioned above, recent studies ,22,23,24In paper , a FuzzyIn , authorsFrom the above comparative study of In the energy-constraint IoT network, all the smart IoT nodes are deployed in the environment randomly. These sensor nodes form clusters as a Fuzzy logic-based chicken swarm genetic optimization (HIOA) algorithm. Only CH is able to send aggregated data to nearby edge nodes. Finally, edge node transferred data to the cloud network for data storage that can be used for data analytics with the following assumptions about IoT nodes below. All the smart IoT sensor nodes are homogenous in nature in terms of energy and computation capabilities . All these wireless IoT nodes periodically observe the environment and send data to their nearby CH. Initially, all the IoT nodes start with an equal quantity of energy stored in the battery. Using the received signal strength indicator (RSSI), IoT nodes calculate their distance from edge nodes by finding their co-ordinate  between parent and child node in one round as follows:We consider Overall, each cluster exhausts the total energy is the summation of energy consumed by parent CH and its child IoT nodes during one round as follows:The primary objective of the proposed HIOA approach is to generate an optimal set of CHs to minimize the overall energy consumption and prolong IoT network lifetime, as shown in In the first optimization phase, a tentative number of CHs is selected based on three parameters in an IoT network: residual energy  Fuzzification—this step creates MFs of the crisp input variables according to intersection point. (ii) Fuzzy rule-base—all θ) swarm updating frequency as time steps. The position of the rooster is updated using mth rooster in the D-dimension space, rth rooster and mth hens’ is updated as follows:mth chicks follows their mother position (FL) as follows:mth chick’s mother, such that Algorithm 1: HIOABeginRead network configurationFirst phase:FLIS optimizationInput: Execute the FLIS engine based on rules define in Table 2Return the chance of an IoT node selected as CHOutput: Tentative set Second phase:CSGO algorithmInitialization:a.assign b.Initialize the population matrixc.Include the output of FLIS (set of k number of CHs) as one feasible solution.Selection:Reconstruction of infeasible solution takes places those have CHs less thanCalculate the fitness value of each row of Optimum set . Else if m== hen then   update its position using Equation (10). Else if m==chick then   update its position using Equation (12). end if  Convert Y into binary form using Equation (7) // Single-point Crossoverfordo  generate two child offspring  set end for   // end of for loop in line no. 22  // Mutationfordo   ifthen  integer   ifthen      set else     set end if   end for  // end of for loop in line no. 27     Reconstruction of infeasible solution takes places those have CHs less than  Updating the fitness value of each   If fitness value of new solution is better than previous one, then update optimum CH set with new solution.End for // end of for loop in line no. 17. End for // end of for loop in line no. 14Output: Return the optimum set In the second optimization phase, the set of tentative CHs resulting from FLIS optimization serves as the initial population of the CSGO to generate a better set of CH compared to the first FLIS optimization phase. In the proposed CSGO algorithm, binary representation is used to represent the IoT nodes as CH (1) or a normal IoT node (0). The binary index  CSGO parameter analysis and (ii) network parameter analysis. In addition, 200 IoT nodes are randomly distributed over the IoT network size 150 × 150 mIn this subsection, the consequences of four different parameters on the fitness performance of the presented algorithm HIOA are analyzed. These parameters are (i) population size  to divide the population and their fitness values are evaluated in order to find the best of nodes as CHs using crossover and mutation operation of GA. Simulations conclude that our proposed algorithm HIOA is robust to different CSO parameters and finds a near-optimal solution with low time complexity and higher convergence speed over state-of-the-art algorithms. The particle implementation of the simulate model can be used for load balancing or self-organizing structure in IoT nodes (electric vehicles) for the energy buying market.The presented HIOA model effectively captures the rapid and frequently changing traffic patterns due to the inherent feature of GA and the dynamic classification property of CSO with a lower rate of latency for convergence. As the proposed model has no prediction ability for a sustainable network where electric vehicles are used as nodes, to add these features in the future, we included a neural network or deep learning approach for the formation of clusters based on past experience . The nea


Models that project the impact and cost‐effectiveness of HIV pre‐exposure prophylaxis (PrEP) must specify how PrEP use aligns with HIV exposure. We hypothesized that varying PrEP use according to individual‐level partnership dynamics rather than prioritization to population subgroups based on average risk will result in larger incidence reductions and greater efficiency.We used an individual‐based network transmission model calibrated to HIV dynamics in Eswatini to simulate PrEP use among individuals ages 15–34 between 2022 and 2031 under two paradigms of PrEP delivery: “Risk Group” and “Partnership.” In the “Risk Group” paradigm, we varied PrEP coverage by risk groups  defined by average partnership frequency and concurrency. In the “Partnership” paradigm, all individuals are potentially eligible for PrEP, but we assumed use occurs only during partnerships and varied prioritization by partner HIV status (no prioritization to high prioritization with HIV‐positive partners). We calculated person‐time on PrEP and incidence relative to a no PrEP scenario and estimated efficiency as the person‐years of PrEP needed to avert one additional infection (NNT).In the Risk Group paradigm, restricting PrEP to the high‐risk group was the most efficient (NNT = 17), but the number of infections averted was limited by the small size of the high‐risk group. Expanding PrEP use to all risk groups averted up to three times more infections but with lower efficiency (NNT = 202). PrEP use under the Partnership paradigm was 2–6 times more efficient (NNT = 33–102) than the Risk Group paradigm with all groups eligible for PrEP. A 33% reduction in incidence among 15‐ to 34‐year‐olds was achieved at 46% (95% CI: 39–52%) PrEP coverage in the Risk Group paradigm and 6% (95% CI: 5–7%) to 17% (95% CI: 14–20%) in the Partnership paradigm.Modelling PrEP use based on risk groups resulted in a sharp trade‐off between PrEP efficiency and impact, whereas PrEP use predicated on partnerships resulted in much higher efficiency for widespread PrEP availability. Model estimates of PrEP impact and cost‐effectiveness in generalized epidemics are strongly influenced by assumptions about how PrEP use aligns with individual‐level HIV exposure heterogeneity. Daily oTo estimate the cost‐effectiveness of PrEP use among subgroups with different levels of risk, mathematical models must specify heterogeneity in HIV exposure. A common approach is to stratify the simulated population into a few broadly defined “risk group” categories with different average levels of partnership turnover, mixing patterns, sexual frequency and condom use , 9. As aDespite its convenience, the risk group approach imposes restrictions for modelling PrEP. HIV exposure is highly dynamic depending on an individual's current number and types of partnerships, partner HIV status or use of other preventive interventions. In the “prevention‐effective” paradigm, these time‐varying behaviours also influence individual decisions to start and stop PrEP . IndividHere, we use an individual‐based model previously calibrated to the HIV epidemic in Eswatini to evaluate the effect of aligning PrEP use with HIV exposure on estimates of the impact and efficiency of PrEP scale‐up in young men and women. We compared models in which PrEP coverage varies by risk group to models in which PrEP use occurs only while individuals are in partnerships and varies by partner HIV status. We estimated HIV incidence and person‐years on PrEP for each PrEP implementation paradigm at increasing levels of PrEP coverage and compared the projected relationship between additional PrEP use and impact across model assumptions.22.1We used Epidemiological MODelling Software (EMOD), an open‐source, stochastic individual‐based model that simulates demography, disease progression, partnership networks and intervention use among a collection of individuals . For thi2.2We focused this analysis on individuals ages 15–34 and restricted our analyses to a 10‐year period from 2022 through 2031. We simulated PrEP use among 15‐ to 34‐year‐olds according to two paradigms Figure . In sens2.3https://github.com/dallenroberts/prep‐risk).We directly compared estimates of PrEP impact and efficiency between the Partnership and the Risk Group approaches as follows. For each PrEP simulation, we calculated total person‐time on PrEP, PrEP coverage among 15‐ to 34‐year‐olds, the percentage of infections averted relative to a scenario without PrEP and the relative risk (RR) of infection among 15‐ to 34‐year‐olds compared to a scenario without PrEP Table . As a pr33.1The relationship between additional person‐time on PrEP and infections averted depended strongly on the PrEP scenario assumed Figure . In the The incidence reduction among 15‐ to 34‐year‐olds at given PrEP coverage levels also depended strongly on the assumed PrEP scenario Figure . In the The distribution of averted infections by risk group also depended on the assumed PrEP scenario . These average incidence rates determine the efficiency of PrEP use in the Risk Group paradigm, whereas the Partnership paradigm prioritizes PrEP use during times of higher HIV risk by further considering whether the individual has a partner and the partner's HIV status. This model specification allows individuals to cycle on and off PrEP in accordance with their time‐varying HIV risk.Estimates of the cost‐effectiveness of PrEP use in sub‐Saharan Africa will depend on the assumed alignment of PrEP use and HIV risk. While most prior models of PrEP use in sub‐Saharan Africa have used risk groups to reflect HIV exposure heterogeneity, a recent analysis from South Africa used an individual‐based model to estimate the cost‐effectiveness of PrEP use concentrated during periods of condomless sex . Under tEvidence from PrEP implementation studies suggests that the Partnership paradigm may better reflect patterns of PrEP use compared to the Risk Group paradigm. PrEP uptake, continuation and adherence are higher among clients who report a partner with known HIV‐positive status or of unknown status , 26, 27 To achieve the impact estimated in the Partnership scenarios, individuals must be able to start and stop PrEP in accordance with HIV risk. Therefore, PrEP must be widely available and easily accessible. Increased distance to PrEP services is a barrier to use , 43, andOur results have several limitations. First, we chose a model previously calibrated to Eswatini that allowed comparative modelling of PrEP use based on risk groups versus individual partnerships across a wide range of scenarios in a high HIV incidence setting. As such, our results do not correspond to a specific PrEP delivery programme in Eswatini. Updated calibrations with more recent data  would be needed to make specific projections. In addition, programme costs will depend on the platform used to provide PrEP services ; for the5Model assumptions that govern the alignment of PrEP use with HIV exposure strongly influence projections of PrEP impact. Prioritizing PrEP use based on risk groups results in a substantial trade‐off between infections averted and efficiency and may underestimate cost‐effectiveness. Assuming instead that PrEP use is prioritized based on partnership characteristics results in much higher efficiency for use in the general population. Individual‐based models may allow a better representation of the dynamics of PrEP use in relation to time‐varying individual‐level heterogeneity in HIV exposure to improve estimates of the cost‐effectiveness of PrEP programmes.RVB reports grants from the Bill & Melinda Gates Foundation (BMGF) and the National Institutes of Health (NIH); and Regeneron Pharmaceuticals for conference abstract and manuscript writing support, outside the submitted work. JEH reports grants from the National Institutes of Health (NIH); and consultative fees from Merck outside the submitted work. No other authors report competing interests.DAR, AA and RVB designed the research study. DAR and DB performed the modelling analyses. DAR, AA, JEH and RVB provided critical interpretation of results. DAR wrote the first draft of the paper. All authors have read and approved the final manuscript.DAR received support from the US National Institute of Health (NIH) [F30MH122300]. JEH received support from the US National Institute of Health [K24MH114732]. This work is based on research funded in part by the Bill & Melinda Gates Foundation, including models and data analysis performed by the Institute for Disease Modeling at the Bill & Melinda Gates Foundation.The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Mental Health or the National Institutes of Health.Supplementary File 1: Additional details on risk group sizes, incidence rates and partnership dynamics.Click here for additional data file.Supplementary File 2: Model parameters and calibration details.Click here for additional data file.


Public resources to answer pertinent research questions about the impact of illness and treatment on people with mental health problems are limited. To target funds effectively and efficiently and maximize the health benefits to populations, prioritizing research areas is needed. Research agendas are generally driven by researcher and funder priorities, however, there is growing recognition of the need to include user‐defined research priorities to make research more relevant, needs‐based and efficient.To gain consensus on top priorities for research into early intervention in psychosis through a robust, democratic process for prioritization enlisting the views of key stakeholders including users, carers and healthcare professionals. We also sought to determine which user‐prioritized questions were supported by scientific evidence.We used a modified nominal group technique to gain consensus on unanswered questions that were obtained by survey and ranked at successive stages by a steering group comprising users, carer representatives and clinicians from relevant disciplines and stakeholder bodies. We checked each question posed in the survey was unanswered in research by reviewing evidence in five databases .Two hundred and eighty‐three questions were submitted by 207 people. After checking for relevance, reframing and examining for duplicates, 258 questions remained. We gained consensus on 10 priority questions; these largely represented themes around access and engagement, information needs before and after treatment acceptance, and the influence of service‐user (SU) priorities and beliefs on treatment choices and effectiveness. A recovery SUtheme identified specific self‐management questions and more globally, a need to fully identify factors that impact recovery.Published research findings indicated that the priorities of service users, carers and healthcare professionals were aligned with researchers' and funders' priorities in some areas and misaligned in others providing vital opportunities to develop research agendas that more closely reflect users' needs.Initial results were presented at stakeholder workshops which included service‐users, carers, health professionals and researchers during a consensus workshop to prioritize research questions and allow the opportunity for feedback. Patient and public representatives formed part of the steering group and were consulted regularly during the research process. Research agendas are typically agreed upon at strategic levels by government officials and experts. Few strategic research plans exemplify community participation and the criteria and mechanisms that guide how decisions are made lack transparency.Uncertainty can influence patient outcomes across all areas of decision‐making in mental health including assessing intake acceptance, assigning diagnoses, attributing causal explanations and treatment and care planning.Researchers are beginning to acknowledge that scientifically important questions may not be directly relevant to patientsThe aim of this project was to generate a set of user‐identified research priorities for individuals in the early phase of psychosis. This is a fertile area of research. The development of specialist research units, regional, pan‐continental, and international networks, field‐specific conferences, journals and citation indices evidence this. Some researchers argue that bringing research interest and priorities closer to the need and demand for services is essential to identify and prioritize gaps in knowledge,2Using mixed methods, we systematically identified and prioritized unanswered questions in early psychosis research. We implemented a staged process of surveying to gather priorities, systematic literature searches to determine unanswered questions and a modified nominal group workshop to determine priorities. Our methods were informed by the recommendations of Chalmers et al.2.1http://www.vip.bmh.manchester.ac.uk/).The steering group (VIP‐SG) was established to shape the survey question and design the survey, determine the scope of the project and comment on the processes and procedures for identifying, ranking, and prioritizing uncertainties, agreeing on methods and providing expertise in gaining final consensus. The VIP‐SG comprised service user representatives, clinical representatives, information specialists, leadership and administrative support. Representatives were selected for their involvement with relevant professional groups to represent multiple professional disciplines, user–researcher involvement, and user and carer representation.We identified key themes among relevant policies, guidelines and performance targets as a starting point for discussions around the scope of the project, initially asking the group to consider the appropriateness of the themes and generate further discussion. These included the Mental Health Programme Implementation Guide 2001,2.2Responses to questions were gathered during two phases of the survey and these were delivered online on both occasions. We piloted the survey in the first phase to identify any implementation issues and ensure high‐quality data. The Phase 1 survey was developed by the VIP‐SG adopting a comprehensive approach to generating uncertainties reflecting the wide‐ranging research areas within EI. These included all aspects of illness, recovery, and treatment given that psychotic illnesses are complex, and outcomes of psychosis and subthreshold illnesses framed in a transdiagnostic framework are heterogeneous. Questions were developed in six themes covering community engagement, recovery, treatment refractoriness, effective interventions, EI and physical health improvement . The first author reviewed submissions twice, initially developing indicative questions and on the second review developing and assigning each question to a theme and subtheme which were used to organize the data and identify duplicates. Indicative questions were developed where there were multiple questions with similar meanings and these were assigned red, amber and green classifications based on the degree of confidence held by the researcher regarding how accurately the question retained its intended meaning based on the participants' submission. A consensus meeting between L. R. and R. L. M. was held to determine iterative questions and questions, not in remit which was removed, for example, questions out of scope, philosophical questions not answerable by research and basic research. Iterative questions are determined following an iterative process of refining the questions and the purpose was twofold; we aimed to refine questions to ensure they were researchable and answerable and to ensure that we retained the meaning of each question posed, whether framed in commentary or question, to the greatest extent possible. When respondents asked about NHS service access we considered an effectiveness question to precede this, that is, if a question asked about cognitive behavioural therapy (CBT) access we included a question about whether CBT was effective. Other examples include home treatment, routine physical health screening, public and targeted education, self‐management and supported employment. The iterative process of refining and reformulating questions was based on the agreement between two researchers (L. R. and R. L. M.). See Table 2.4The search strategy was developed by an information specialist (O. S.) consulting with the wider specialist library team at Central Manchester Foundation NHS Trust and the first author (L. R.). For each question a systematic search of the literature was conducted in Medline, EMBASE, Cinahl, PsycINFO, Cochrane Database of Systematic Reviews and the Cochrane Controlled Register of Trials (CENTRAL) where applicable. Specific search strategies were developed for each database and for each question, using a combination of keywords and MeSH/Map terms to include the following: ‘psychosis’, ‘first‐episode psychosis’, ‘early intervention’ and question‐specific keywords. No limits were set on the publication date and level 1 evidence was retrieved comprising systematic reviews, meta‐analyses or qualitative metasyntheses . We included reviews that focused on psychosis and schizophrenia spectrum diagnoses to extract information about first‐episode illnesses if available. Searches were conducted by a team of five information specialists at Manchester Royal Infirmary Library at Central Manchester Foundation Trust and Wythenshawe Academy Library at University Hospital of South Manchester NHS Foundation Trust between February and May 2018. In addition, representatives from VIP‐SG were asked to contribute papers they believed were relevant to answering the questions we obtained. L. R. checked the results and supplemented them with known systematic reviews that were not obtained during searches where available. The search results were analysed and evidence summaries were developed for each question where available and a question was considered to have met the certainty criterion if a systematic review concluded that there was sufficient evidence to answer the question.2.5All questions taken forward to prioritization were ranked in order of the 30 most important questions via an online survey by each VIP‐SG member and these were collated to develop a shortlist of priorities among treatment and illness uncertainties. VIP‐SG members, patients and carers were then recruited to participate in the final prioritization workshop using a modified nominal group technique. Attendees ordered the organization of questions where necessary, voted on their priorities among similarly themed questions and gained consensus on final priorities through small‐group discussions. The final 10 were agreed upon by the entire group during the consensus workshop which comprised service‐users, carers, healthcare professionals, advocates and researchers. Each member was allocated equal voting weight.2.6We adhered to ethical principles contained in organizational policies, but no identifying personal data were obtained and approval by University Research Ethics Committee was not required.33.1Two hundred and seven people answered all or part of the free‐text surveys; there were 80 responses to the Phase 1 survey and 137 responses to the Phase 2 survey. See Table 3.2It took 248 h of librarian time to conduct searches of the databases against the iterative questions comprising over 1000 searches. Relevant systematic reviews were returned in searches for 52 (18.4%) of the iterative questions and the number of available reviews for each question that had a review ranged from 1 to 10 reviews with varying degrees of evidence provided in each review. There were several areas where the evidence was extensive and robust which were also equivocal given the nature of the question posed, for example, no outcome identified, too broad to consider the findings to answer the question wholly or not first‐episode specific. These areas were the effect of lifestyle and diet interventions, the effect of public education campaigns on enhancing awareness, increasing help‐seeking and reducing the duration of untreated psychosis, the effect of family interventions, the effect of interventions to promote occupation, labour force participation, recreation and social network enhancement, prevention of psychosis, tailored interventions to manage weight gain, the effect of psychological interventions and cognitive remediation, influence of cannabis use and causal mechanisms in psychosis. There were reviews that gathered data on schizophrenia, but conclusions could not be drawn about the first episode or the early phase of illness, for example, managing sexual risk and safety, the effect of home‐based treatment and the long‐term effect of novel pharmacological treatments at this stage of illness. There were several qualitative metasyntheses exploring views about medication effectiveness, choice and lifestyle interventions, positive aspects of psychotic experiences, and the role of social support and networks during early psychosis. None of the systematic reviews reported a conclusive answer and we brought forward all questions for prioritization.n = 4), caregivers/family members (n = 4), clinicians/clinical leads (n = 8), policy administrators (n = 1) and educators (n = 2). See Table From 258 priority questions, there were 237 that were endorsed at least once by members of the steering group. Members of the group ranked their top 30 priority questions and once collated, we took forward 42 questions for final prioritization; included questions were endorsed four or more times. The final consensus workshop was attended by service‐users (tors n =  and educ4A final list of 10 priorities was obtained by consensus agreement by stakeholders comprising users of EI services and clinicians and can be considered an important record of user‐defined uncertainties providing intelligence concerning unanswered questions fundamental to the EI paradigm. Knowledge of uncertainties and unanswered questions is a necessary foundation for identifying future directions in research and can support international research efforts towards advancing early psychosis care and treatment. A key uncertainty involved preventing delays and promoting access to treatment and few reviews focused on this despite its centrality to the EI paradigm. Remarkably few studies examined the needs of users for information before gaining access to treatment or school‐based programmes to detect emerging psychosis. Meta‐analytic and narrative reviews emerged evaluating complex, multifocal interventions, discrete professional and patient levels initiatives and different service configurations for shortening delays in accessing care.Prioritizing reducing delays aligns strongly with national research strategy and frameworks recommending a focus on prevention, earlier detection and treatment of mental illnessThe top 10 emphasized an absence of evidence for interventions to enhance community participation and increase users' ability to self‐manage. Reviews obtained in searches largely dealt with recovery using researcher‐defined outcomes.Indeed, a prominent question considered by the consensus group asked specifically about how research funding decisions were made, this question was not retained in the final 10 but captures the essence of this project in that ultimately these priorities will be used by funders and researchers to identify future research priorities and carve out strategic pathways to conduct research of relevance to service users, carers and healthcare professionals. Prioritization is only of value if research follows5This project was a substantial group effort from the VIP‐SG to engage relevant stakeholders in identifying priorities. Targeted activities such as stakeholder review with collaborative networks for applied research, engagement and presentation to user involvement groups, service‐user research groups and charities, and a strong online and social media presence have been a successful strategy eliciting pertinent questions from interested groups. Many questions posed were specific to the field and while relevant, iterative questions tended towards questioning the effectiveness, value and availability of general mental healthcare and treatment.To promote nontokenistic involvement, efforts need to be taken to curb power imbalance and ensure active involvement from service users in mixed stakeholder groups.Lastly, methodologies used to obtain evidence to answer user‐defined questions were predominantly articulated through an experience or values‐based lens before identifying iterative questions. A qualitative inquiry may be best suited to answering these questions but is misaligned with the level 1 evidence that is sought to determine whether there is sufficient evidence in support of particular questions. Among the available level 1 evidence, methods for determining whether a question is answered or not can be subjective and available methodologies require greater scientific consideration as well as an understanding of the necessity for this in user‐defined priorities. In terms of including qualitative evidence, including metasyntheses may mitigate against the mismatch between the available evidence and user‐identified priorities somewhat as there is an increasing acceptance of these synthesis methods in healthcare decision‐making.6Service‐user, carer and healthcare professional defined priorities focused on enhancing evidence for increasing community participation, ability to self‐manage and promoting recovery in daily life. Published research findings indicated that the priorities of service users, carers and healthcare professionals were aligned with researchers and funders in some areas and misaligned in others providing vital opportunities to develop research agendas that more closely reflect users' needs.Laoise Renwick drafted the manuscript. Laoise Renwick was responsible for the organization and preparation of the manuscript. Laoise Renwick was responsible for the concept of the priority‐setting exercise in collaboration with Rebecca Lauren Morris. Olivia Schaff devised the search strategy, and Olivia Schaff and Laoise Renwick at the Trust conducted the searches. Caitlin McWilliams and Susan Ramsdale assisted with project management. Laoise Renwick and Rebecca Lauren Morris were responsible to question development. Laoise Renwick had oversight of the steering committee, ranking and consensus workshop.The authors have no conflicts of interest to declare.Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.