Pan paniscus), and compared it with data derived from over 200 vegetation plots across their 50 km2+ home ranges. We demonstrate that food species abundance estimates derived from the CCM are comparable to those derived from traditional vegetation plot sampling in less than 2 years of data collection, and agreement improved when accounting for aspects of consumer selectivity in objective vegetation plot sampling . Density correlated between CCM and plot‐derived estimates and was relatively insensitive to home range inclusion and other species characteristics, however, it was sensitive to sampling frequency. Agreement between the methods in relative distribution of resources performed better across species than expected by chance, although measures of dispersion correlated poorly. Once tested in other systems, the CCM may provide a robust measure of food availability for use in relative food availability indices and can be incorporated into existing observational data collection. The CCM has an advantage over traditional sampling methods as it incorporates sampling biases relevant to the consumer, thereby serving as a promising method for animal behavioral research.Characteristics of food availability and distribution are key components of a species' ecology. Objective ecological surveying used in animal behavior research does not consider aspects of selection by the consumer and therefore may produce imprecise measures of availability. We propose a method to integrate ecological sampling of an animal's environment into existing behavioral data collection systems by using the consumer as the surveyor. Here, we evaluate the consumer‐centric method (CCM) of assessing resource availability for its ability to measure food resource abundance, distribution, and dispersion. This method catalogs feeding locations observed during behavioral observation and uses aggregated data to characterize these ecological metrics. We evaluated the CCM relative to traditional vegetation plot surveying using accumulated feeding locations across 3 years visited by a tropical frugivore, the bonobo ( Pan paniscus) as an exemplar, we demonstrate that food species abundance estimates derived from our method are comparable to those derived from traditional vegetation plot sampling in less than 2 years of data collection, and agreement improved when accounting for aspects of consumer selectivity in objective vegetation plot sampling . Once tested in other systems, our method may provide a robust measure of food availability for use in relative food availability indices, and has an advantage over traditional sampling methods as it incorporates sampling biases relevant to the consumer, thereby serving as a promising method for animal behavioral research.We propose a method to integrate ecological sampling of an animal's environment into existing behavioral data collection systems by using the consumer as the surveyor. Using a population of bonobos ( Plot sampling is the most common sampling method in studies of frugivorous or folivorous animals, and involves the placement of randomly or systematically placed plots of fixed size across the area of interest, and all individuals contained within those plots are inventoried and frequently measured for size , direct measurement of exploitable resources offers more accurate insights into the resources available to a consumer . Distribution provides information about where and how many individuals are located within a space rather than simply about the number of individuals available globally within that system. Measures of dispersion , such as Morisita's index . In other words, aggregations of data on species' presence can be used to generate broader‐scale predictions of relative abundance , and consequently cannot be used universally in all animal consumer applications. First, as the CCM relies on the aggregation of data over time, data collected on resource locations must be representative of the species' same distribution over time, and therefore the method must only be applied to estimate resources that are immobile, discrete, and spatially explicit entities. Second, to allow for data accumulation, the CCM can only be applied to consumers who reuse the same space over time, such as a home range, and assumes that they have equal access to all the areas of this space , as a case study. Unlike existing metrics of food abundance derived from behavioral observation , which can only serve as proxies of difficult‐to‐measure food abundance, in the case of the CCM, we can evaluate the estimates provided by the CCM against availability estimates provided by more traditional plot sampling. Specifically, we investigated whether behavioral data on feeding locations (trees and lianas) provide a reliable dataset allowing inference about food species' (1) densities, (2) distribution, and (3) dispersion. We additionally assess (4) the minimum sampling effort required and (5) for what characteristics of a food species this method can be considered most reliable.To answer this question, we evaluate the CCM relative to traditional plot‐based ecological data collection using accumulated feeding locations from two social groups of a tropical frugivore, the bonobo  and consequently an inability to distinguish individual trees on a small scale, we summarized feeding tree locations of each group into the presence or absence of each species in 50 × 50 m “observational cells” Figure , in orde2.22 cells to distribute sampling plots evenly throughout the home ranges of the groups, and to permit the compilation of different sampling schemes between data collectors  and was determined to be of sufficient sampling depth . (3) We calculated the number of 50 × 50 m vegetation plots in which each species was present per total number of vegetation plots sampled for a more direct comparison with the CCM (hereafter “Plot Presence”).To compare estimated species densities derived from each dataset (CCM or vegetation plots), we derived three different indices . (1) We r) as a measure of the strength of agreement between methods. We conducted the pair‐wise comparisons while assessing the influence of sampling effort on method agreement by varying levels of home range usage (kernel % range from 20% to 95% in increments of 1%) and dietary inclusion (top 10 most consumed species until full diet) for each group. We only considered comparisons with at least 10 species in at least 10 vegetation plots as a means of imposing minimum thresholds necessary to avoid distortion of comparison metrics due to small sample sizes. We chose 10 species as a minimum threshold as this is a commonly recommended minimum sample size for basic regression analyses , we do not draw inference based on p‐values but instead focus only on correlation coefficients.Finally, to identify potential dataset minima required for reliable and stable density indices derived from the CCM , we evaluated the pattern of correlation strength between indices from each method as the dataset grew over time , and set the minimum as the point from which the correlation coefficient remains relatively stable. We describe 2.3.22+ home range  across species in all grid cell sizes and groups for predicting abundance per cell based on CCM. Average r did not vary substantially with cell size or between groups , range: 0.32–0.81; Table 4Here, we demonstrate the applicability of the consumer‐centric method (CCM) for measuring resource density and distribution in an animal's landscape. We demonstrate that food species availability estimates derived from the CCM method are modestly comparable to estimates derived from traditional vegetation plot sampling following a relatively short data collection timeframe, including before data have reached saturation. The method also shows promise for characterizing the distribution of food patches within a landscape, but current analytical power was likely insufficient to adequately evaluate this relationship. Furthermore, we argue that the CCM has an advantage over traditional sampling methods for some research questions as it incorporates sampling bias important to the consumer into the quantification of the ecological landscape. We discuss the consequences of this advantage regarding the utility of the CCM in studies of animal ecology.4.1The CCM estimates of density showed moderate similarity to estimates from traditional ecological sampling. Behavioral ecologists have previously used consumption rates to infer about the abundance of food resources Hutto, . These mAlthough we found a significant but minor periphery effect on agreement between methods in the presence/absence of species, the correlation of density estimates between methods was unaltered by the percentage of home range inclusion. The lack of a spatial effect on agreement between the methods is in some part likely to be a result of home range selection on the part of the consumer  appears to offer a compromise between maintenance of dietary relevance while maximizing fidelity with density estimates as assessed by objective plot measurements. This minimum translated to species consumed in approximately at least 60 locations over our 3‐year dataset. A general consequence of sampling frequency by a consumer is that estimates improve in precision as data accumulate over time. While species in our dataset were variable in “saturation level,” rates of new locations sampled by the bonobos slowed over the course of data collection and inter‐method correlation of species densities stabilized after fewer than 2 years of data collection (approximately 600 days).Tree stands in the Kokolopori forest remained relatively stable, with the loss of monitored individuals averaging about 2.0% per year . For locations like Kokolopori where tree stand is relatively stable from year to year, the CCM is likely to be able to provide estimates of species abundances within a reasonable margin of error. Intervals between vegetation plot surveys in animal studies are frequently longer than the CCM's 600‐day minimum, therefore the CCM may be better suited to adapt to environmental changes than plot sampling because users can restrict their data aggregation to a specified time window (as long as this window exceeds the minimum), thereby creating a dynamic measurement of availability that is continuously updated as new data accumulate. Such an approach would allow users to evade the duplication of surveying efforts required to capture tree stand changes using traditional plot sampling. In contrast, the CCM may not be suitable for research locations where tree stand is frequently disturbed , as the minimum necessary monitoring interval in CCM may be too long to account for abrupt or short‐term changes to the environment. In these environments, traditional plot sampling methods may be better suited as long as they are performed at commensurate intervals relative to individual resource turnover. As our results indicate that sampling rate affects the stability of estimates , we anticipate that this general minimum will be longer for species with slower sampling frequency, that is, for less frequently consumed, masting, or aseasonally consumed species. A myriad of other factors are likely to contribute to the speed at which data stabilize in methods like the CCM. We therefore recommend that researchers studying other systems evaluate the applicability of a method such as the CCM against resource stability and the traits of the consumer, relative to the suitability of more traditional methods  correlated weakly between the methods across species regardless of the scale of comparison . A greater proportion of species reached significant agreement between methods in smaller rather than larger cell sizes, potentially as a function of proximity between bonobo foraging behavior and sampled plots . Nevertheless, our finding that correlations of distribution within species were significant across a greater proportion of food species than expected by chance  and that the rates at which bonobos missed the presence of a species in a cell are likewise better than common rates of species misses between multiple observers sampling the same plot . Reducing our plot dataset to a single selection criterion (tree/liana sizes selected by the consumer) increased the correlations between CCM and vegetation plot measures by an average of 4%–7% as a simple demonstration of the inadequacies of consumer‐objective plots in mirroring consumer behavior. Second, that bonobos missed or ignored certain food resources in cells identified to contain them underlines further how researchers are likely unaware of relevant selection criteria that impact the measurement of true resource availability. Because apes possess mental maps of their environments and are known to adjust travel to target preferred food sources . Nevertheless, if rapid density assessment is preferable for a project, traditional ecological sampling may remain a preferred method due to a 600 person‐day burn‐in time required (this study) by the CCM before estimates become reliably stable per social group relative to 150 person‐days of plots for both groups. However, these 150 person‐days are supplementary to observational data, insomuch as person‐days necessary to collect both sets of data must be considered additive to observational data collection. Yet, if databases of feeding locations are already available, adapting these data to CCM estimation of resource density or distribution saves researchers from needing to collect additional data to quantify resource abundance.While this method is best applied to estimate the availability of discrete, immobile, and spatially explicit resources, these advantages transcend application beyond bonobos and allow researchers to evaluate the strengths of the method for their investigations across all potential consumers who meet these criteria ; formal analysis (supporting); investigation (supporting); methodology (supporting); writing – review and editing (supporting). Roger Mundry: Formal analysis (supporting); investigation (supporting); methodology (supporting); writing – review and editing (supporting). Miguel Adan Pascual: Data curation (supporting); investigation (supporting); writing – review and editing (supporting). Stefano Lucchesi: Data curation (supporting); investigation (supporting); writing – review and editing (supporting). Bienfait Kambale: Validation (supporting); writing – review and editing (supporting). Martin Surbeck: Conceptualization (supporting); funding acquisition (lead); investigation (supporting); methodology (supporting); resources ; writing – review and editing (supporting). Erin G. Wessling: Conceptualization (lead); data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); writing – original draft (lead); writing – review and editing (lead).The authors have no conflict of interest to declare.Appendix S1.Click here for additional data file.


Despite our growing understanding of the global carbon cycle, scientific consensus on the drivers and mechanisms that control dissolved organic carbon (DOC) turnover in aquatic systems is lacking, hampered by the mismatch between research that approaches DOC reactivity from either intrinsic  or extrinsic  perspectives. Here we propose a conceptual view of DOC reactivity in which the combination of intrinsic and extrinsic factors controls turnover rates and determines which reactions will occur. We review three major types of reactions  from an intrinsic chemical perspective and further define the environmental features that modulate the expression of chemically inherent reactivity potential. Finally, we propose hypotheses of how extrinsic and intrinsic factors together shape patterns in DOC turnover across the land‐to‐ocean continuum, underscoring that there is no intrinsic DOC reactivity without environmental context. By acknowledging the intrinsic–extrinsic control duality, our framework intends to foster improved modeling of DOC reactivity and its impact on ecosystem services. Dissolved organic carbon (DOC) undergoes numerous transformations as it flows from land to ocean, with both positive and negative effects on ecosystem services. Mineralization of DOC into greenhouse gases contributes to climate warming . Intrinsically, aromatic compounds are expected to be highly reactive due to their efficient absorption of natural UV light  are responsible for DOC removal.For any given inherent reactivity, DOC turnover rates in aquatic environments are strongly influenced by physical, biological and chemical drivers Figure . As in sEa) in the Arrhenius equation or by the Q10 factor, which describes the increase in degradation rate per 10°C increment. The temperature dependence of bioreactions is modulated by molecular features  and may reach near‐optimum capacity for bulk DOC turnover within days (Judd et al., Extrinsic controls on bioreactivity include factors in the environment that influence DOC losses by stressing bacteria. However, there is no consensus on the influence of viral activity (Bonilla‐Findji et al., In summary, extrinsic DOC bioreactivity is generally dominated by positive effects from temperature and nutrients, but lack of oxygen may have large negative impacts Figure .Rates of direct photochemical reactions scale in proportion to incoming solar UV light, which is affected by extrinsic factors like sun angle, cloud cover, ozone layer thickness, and shading effects of particles and colored substances (Koehler et al., Furthermore, water pH is a strong photoreactivity regulator Figure . As pH dFlocculation in the environment may be triggered by a specific compound, colloid, or particle surface, a so‐called coagulant or flocculant. Naturally occurring coagulants include metal cations, mineral particles, and positively charged polysaccharides or proteins. Long experience from treating drinking water (Matilainen et al., On mineral surfaces, the potential for DOC sorption is strongly linked to particle size. Indeed, clay has orders of magnitude more surface area per volume available for DOC interactions compared to larger particles Mayer,  and is aFlocculation interacts strongly with photoreactivity since partial photooxidation of DOC is a major source of anionic organic acids that may in turn flocculate rapidly in reactions with positively charged surfaces (von Wachenfeldt et al., Our synthesis thus far illustrates that DOC reactivity is complex and regulated by different combinations of intrinsic and extrinsic factors that interact directly and via reaction feedback loops Figure . ResearcTo our knowledge, only one modeling study has attempted to predict DOC turnover in the aquatic continuum with explicit consideration of bioreactivity, photoreactivity, and flocculation while addressing some inherent organic matter properties by modeling aromatic and nonaromatic DOC separately (Anderson et al., The most common framework to model DOC turnover under the joint influence of intrinsic and extrinsic controls is to use water residence time as the basis for predictions (e.g., Catalan et al. Mechanistic models could supplement current efforts to understand the turnover of DOC in aquatic environments and predict future changes. In aquatic ecology, trait‐based mechanistic approaches are a rapidly developing field (Kiorboe et al., Based on our framework, a suite of hypotheses has been formulated Figure . Intrins10 of biological degradation. Thus, the contributions of different processes to the bulk DOC turnover in the future will depend on multiple global changes that affect both the inherent DOC properties and the environment where this DOC is processed.Current anthropogenic changes are systematically impacting DOC characteristics in freshwaters (Xenopoulos et al., Now more than ever, an ability to accurately model the turnover and fate of DOC is critical to developing sound management strategies capable of addressing pressing environmental challenges. Simultaneous consideration of intrinsic and extrinsic controls on DOC reactivity opens up new avenues for research into DOC turnover in response to such changes and into effects on ecosystem services. Moreover, the distribution of intrinsic and extrinsic reactivity potentials in the aquatic continuum can provide new understanding of the relative importance of biological, photochemical, and flocculation reactions during transit from land to sea. We anticipate that models that simultaneously address different types of DOC reactions while considering both intrinsic and extrinsic controls of reactivity will be needed to take the research field to the next level.The authors declare no conflict of interest.


A 44‐year‐old female patient was referred for patch testing because of two episodes of severe face eczema. The patient reported that the dermatitis developed after the use of the anti‐aging cream Bionike Defence My Age Day Cream®  and was successfully treated with an oral corticosteroid.Patch testing was performed with the SIDAPA  baseline series and the integrative eyelids series . Patch‐test chambers  were applied on the upper part of the patientʼs back. The readings on day (D) 2 and D3, according to the Italian guidelines,The label on the cream reported pentylene glycol (PTG) among the top ingredients. As we were not able to contact the manufacturer, we performed patch tests only with PTG 5% and 10% aq.; both the concentrations gave a positive reaction (+) at D3. PTG was obtained from Symrise  and tested at 5% and 10% concentrations according to previous studies.We also tested propylene glycol (PG) 5% pet. and 30% aq., with no reaction.Patch tests with PTG and PG at the same concentrations were performed in 15 healthy subjects with no reactions.Pentylene glycol  is a preservative, solvent, and humectant that might be used increasingly in cosmetic products. It is considered to be both a weak irritant and a weak allergen.Few cases of sensitization to PTG have been described in the literatureDue to the similar chemical structure between PTG and PG, differing only in a longer carbon chain and the position of alcohol groups in PTG, a cross‐reaction could be expected. In our patient no cross‐reactions were observed, confirming what has been observed in the literature.Further studies are strongly needed define the real allergenic potential of this molecule, which is used frequently in cosmetics that are formulated for sensitive and atopic skin. It is also desirable to determine the correct concentration and the vehicle for PTG to be used in patch tests. Finally, the occurrence of cross‐reactivity between different glycols should be deeply investigated, perhaps using higher patch‐test concentrations and later readings.The authors declare no funding and no conflicts of interest related directly to the work being submitted.Monica Corazza: Conceptualization (lead); data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); writing – review and editing (lead). Cecilia Schenetti: Conceptualization ; data curation ; formal analysis ; investigation ; writing – original draft (lead); writing – review and editing (supporting). Natale Schettini: Data curation (supporting); investigation (supporting). Pierantonia Zedde: Data curation (supporting); investigation (supporting). Alessandro Borghi: Formal analysis ; investigation ; supervision (lead); validation ; writing – review and editing (supporting).


Plasmodium falciparum infections are a relatively rare but potentially deadly disease found in returning travellers. We compare the national treatment guidelines of non‐endemic countries with the WHO guidelines for the treatment of Plasmodium falciparum infections.Review. We identified non‐endemic countries with an incidence rate of imported malaria of at least one per 100,000 population and at least 50 cases annually. Using PubMed and Google Search, we reviewed national guidelines published before 1 March 2021.Thirteen guidelines were identified. For uncomplicated falciparum malaria, 11 of 13 countries (85%) recommend an artemisinin‐based combination therapy as first‐line regimen in adults, of which artemether–lumefantrine was the most common. For severe malaria, all guidelines recommend the use of intravenous artesunate. Only three countries adjust treatment recommendations based on expected artemisinin resistance.Treatment guidelines for uncomplicated falciparum malaria in non‐endemic countries generally adhere to WHO recommendations but often fail to mention the risk of drug resistance in returning travellers. Artemisinin‐based Combination Therapies (ACTs) should be the first choice for all uncomplicated malaria cases. Furthermore, the choice between ACTs should be based on regional resistance patterns. Plasmodium species. These ACTs are artemether–lumefantrine, artesunate–amodiaquine, artesunate–mefloquine, artesunate–sulphadoxine–pyrimethamine, dihydroartemisinin–piperaquine and artesunate–pyronaridine [Malaria remains an important cause of morbidity and mortality in large parts of the world. WHO currently recommends the use of artemisinin‐based combination therapies (ACTs) for all naridine . These Anaridine . In addiPlasmodium falciparum infections are a relatively rare but potentially fatal cause of disease found in returning travellers. Even though Europe is a non‐endemic region, each year around 7300 travellers return with malaria [ malaria . In the  malaria , 5.Plasmodium falciparum is already widely established in most parts of the world [Antimalarial resistance should be taken into account when choosing an antimalarial treatment. For instance, chloroquine‐resistant he world . In addihe world , 8, 9. Che world . In the Plasmodium falciparum is associated with the highest morbidity and mortality. The aim of this review is therefore to report how closely national treatment guidelines of non‐endemic countries follow WHO recommendations for Plasmodium falciparum malaria treatment, taking into consideration disease severity and artemisinin resistance ’ and the name of the country of interest. We included the latest guidelines published before 1 March 2021. Where needed, Google Translate was used.Plasmodium falciparum malaria in adults, children and pregnant women. We also assessed whether the prevalence of antimalarial resistance in the country of transmission was considered when choosing an antimalarial. Definitions of (un)complicated malaria and artemisinin drug resistance can be found in the WHO guidelines on malaria treatment [Guidelines on prophylaxis were excluded, as were sub‐national guidelines. We reviewed the guidelines while focussing on first‐ and second‐line treatments for uncomplicated and severe or complicated reatment . AdditioBased on the incidence of imported malaria, as specified in the Methods section, we selected 17 countries, of which 13 were found to have a national guideline: Australia , BelgiumPlasmodium falciparum malaria [Table Plasmodium falciparum. An ACT is the first choice for chloroquine‐resistant Plasmodium falciparum. Not a single country advises more than two ACTs as potential first‐line treatments.For uncomplicated ia Table  in adultEleven countries specify treatment recommendations for children. In seven of those guidelines, artemether–lumefantrine is the first choice, followed by dihydroartemisinin–piperaquine (three countries) and atovaquone–proguanil (two countries).Treatment protocols for pregnant women are specified in 12 guidelines. Only The Netherlands recommend the use of artemether–lumefantrine as first‐line treatment in the first trimester. Quinine is advised six times as first‐line treatment in the first trimester, (hydro)chloroquine and atovaquone–proguanil both once. In the second or third trimester, 10 countries recommend the use of an ACT as first‐line treatment, most often artemether–lumefantrine.Nine of the 12 countries that recommend artemether–lumefantrine as an option, recommended to take it with food or a fatty drink.Plasmodium falciparum malaria [Table Regarding severe ia Table , all couPlasmodium falciparum malaria in travellers from the GMS. The Swiss guideline suggests taking atovaquone–proguanil over artemether–lumefantrine or dihydroartemisinin–piperaquine for uncomplicated, or as follow‐on medication for severe Plasmodium falciparum malaria due to resistance‐related treatment failures in the GMS. The other countries do not adjust their recommendations for travellers from a region with artemisinin resistance.Only three guidelines  incorporate artemisinin resistance patterns in the GMS into their recommendations on treating patients from this region. The French guideline advises the combination of intravenous quinine and artesunate instead of artesunate monotherapy for severe malaria in this group. The Australian guideline also recommends to take the origin of the infection into account when treating a case of severe malaria and recommended seeking expert advice. Additionally, the Australian guideline offers the options of prolonging treatment with artemether–lumefantrine, switching to atovaquone‐proguanil or switching to quinine combined with doxycycline or clindamycin for uncomplicated Malaria remains a potentially deadly disease in returning travellers. Drug resistance patterns and treatment options are rapidly evolving, stressing the need for up‐to‐date guidelines on malaria treatment. This study provides an overview of the state of malaria treatment guidelines across a multitude of non‐endemic counties. Comparing national treatment guidelines with WHO recommendations showed many countries still recommending suboptimal treatment regimens. Improvements can be made by prescribing ACTs as first‐line treatment and by taking the origin of infection into account.Plasmodium falciparum malaria. They are more effective and have fewer adverse effects than other antimalarials such as atovaquone–proguanil, quinine or mefloquine [ACTs are already generally accepted as the best treatment regimens for uncomplicated floquine , 31, 32.Plasmodium falciparum, as chloroquine‐sensitive Plasmodium falciparum is still endemic in parts of Latin America. Their guideline suggests different treatments for those groups, preferring (hydroxy)chloroquine as first‐line treatment when possible. However, WHO recommendations make no distinction between chloroquine‐resistant strains of Plasmodium falciparum and recommend using an ACT for all forms of Plasmodium falciparum malaria [Canada still recommends the use of quinine in combination with doxycycline or clindamycin as first‐line treatment, due to unavailability of ACTs. This combination of drugs is accountable for more adverse effects than ACTs . Atovaqu malaria .Guidelines specifying treatment protocols for children did not differ much from adult treatment protocols. WHO recommends the use of ACTs here as well but with different dosages depending on body weight . In 10 cThe use of ACTs in the first trimester of pregnancy is still debated. Current WHO recommendations still advise the use of quinine with clindamycin in the first trimester due to limited clinical safety data for ACTs . An ACT In general, artemether–lumefantrine was the most used ACT. Higher levels of lumefantrine are associated with lower rates of treatment failure . For thiRegarding severe malaria, all countries included in this review recommend intravenous artesunate over intravenous quinine. Parenteral artesunate is superior to quinine in both adults and children , 5. GuidSevere malaria should be treated with oral follow‐on medication to eradicate the remaining parasitaemia. WHO recommends a full dose of effective ACTs orally after the initial parenteral treatment. Combining the data from uncomplicated malaria treatment protocols, we found 10 of 13 countries including an ACT as first‐line follow‐up treatment. Three countries explicitly advise a non‐ACT as follow‐on medication: atovaquone–proguanil (Germany), atovaquone–proguanil or quinine with doxycycline/clindamycin (Canada), and artesunate or quinine with doxycycline (Ireland). Still, as described above, an ACT is superior to non‐ACTs such as atovaquone–proguanil or quinine as follow‐on medication.Plasmodium falciparum malaria strain. In a recent prospective study, conventional treatment with dihydroartemisinin–piperaquine showed therapy failure rates up to 93% in the GMS [With artemisinin and partner drug resistance spreading in the GMS, travellers returning from this region could be infected by an artemisinin and partner drug‐resistant  the GMS . Only th the GMS . HoweverSo far, artemisinin resistance seems to have been contained to the GMS. And although some partner drugs have been used in monotherapy at large scale in the last decades, this has not led to widescale ACT failure outside of the GMS . HoweverPlasmodium falciparum infection returning from Southeast Asia should not be treated with dihydroartemisinin–piperaquine. Instead, patients returning from this region could be treated with artesunate–mefloquine or artemether–lumefantrine.Again, we believe taking a travellers' origin into consideration is necessary as some ACTs show treatment failure in the GMS. For example, a patient with a Plasmodium falciparum malaria. A similar review on the treatment of other malaria species will be conducted in the near future.A limitation of this study is the small number of countries included. However, most of the countries included face at least 50 cases of imported malaria annually. Because there is, to our knowledge, no overview on malaria prevalence outside endemic regions, other non‐endemic countries with substantial numbers of imported malaria may have been missed in this review. Furthermore, we have no insight into which extent clinicians follow the national guidelines or the WHO guidelines. This review assessed guidelines solely focussing on the treatment of In conclusion, many national malaria treatment guidelines are not in line with WHO recommendations. Too often, non‐ACTs such as atovaquone–proguanil and quinine are recommended as first‐line treatment. When an ACT is advised as the first choice, few guidelines follow‐up with an alternative ACT as the second choice. An ACT should not only be the first but also the second choice of treatment, in children, adults and second and third trimester pregnant women. Furthermore, most guidelines do not mention artemisinin and partner drug resistance, especially in the GMS. Failing to distinguish between travellers from the GMS and other parts of the world could lead to therapy failure in the first group. We recommend keeping a travellers' origin in mind, especially in the case of treatment failure and choosing a suitable ACT based on regional resistance patterns.


HCES food intakes (g/week) were estimated using adult male equivalents, adjusted for breastfeeding. Paired HCES‐ and 24HR‐defined LP inputs and outputs were compared using percentage agreement. Mean overall percentage agreements were 42%, 63%, and 80%, for food, food subgroup, and food‐group model parameters, respectively. HCES food lists were on average 1.3 times longer than 24HR. Similar nutrient gaps (77–100% agreement), food sources of nutrients (71–100% agreement), and FBRs (80–100% agreement) were identified. The results suggest that HCES data can be used in Optifood analyses for 12‐ to 23‐month‐old children, despite recognized challenges of using it to estimate dietary intakes of young children compared with older age groups. Further analyses, however, are required for different age groups and locations to confirm expectations that it would perform equally well.A barrier to using Optifood linear programming (LP), which identifies nutrient gaps and supports population‐specific food‐based recommendation (FBR) development, is the requirement for dietary intake data. We investigated whether Household Consumption and Expenditure Surveys (HCESs) could be used instead of individual‐level 24‐h recalls (24HRs). The 24HR data from 12‐ to 23‐month‐old breastfeeding children in rural Kenya, Uganda, Guatemala, and Bangladesh were paired with HCES food consumption data from similar areas  could be used instead of individual‐level 24‐h recalls (24HRs). The 24HR data from 12–23‐month‐old breastfeeding children in rural Kenya, Uganda, Guatemala, and Bangladesh were paired with HCES food consumption data from similar areas and time periods.  Linear programming (LP) has been used in tools based on mathematical modeling, such as the Optifood software, to identify “problem nutrients”  and the best food sources of nutrients in the local food supply to provide them and to develop evidence‐based, population‐specific food‐based recommendations (FBRs).Optifood contains four analytical modules. Module 1 tests model parameters to ensure realistic diets will be generated. Module 2 identifies the nutritionally best diets within and outside of the population's average food consumption patterns to identify problem nutrients and best local food sources of nutrients that are used to decide on FBRs to test. Module 3 then tests and compares alternative sets of FBRs in terms of the level of nutrient adequacy that can be met if they are put into practice within the context of current diets. The optional fourth module optimizes diets based on cost.A key factor limiting the wider application of Optifood LP analysis to inform nutrition programs has been the paucity of quantitative dietary intake data to define the model parameters. Modeling realistic diets using LP analysis in Optifood requires data on the current food consumption patterns of a specific population , including food types, quantities consumed, and frequency of consumption.One potential source of routinely collected food consumption data for LP analyses is nationally representative and publicly accessible Household Consumption and Expenditure Surveys (HCESs). These surveys include Household Budget Surveys, Living Standards Measurement Surveys, Household Income and Expenditure Surveys, and Integrated Household Surveys.Many HCESs include food consumption modules that use closed food questionnaires to ask respondents which foods were consumed by household members over the past 7 or 14 days and their quantities. Increasingly, while not designed for this purpose, household‐level consumption data are being repurposed to estimate individual‐level food consumption.Validation studies comparing AME‐redistributed HCES food consumption data with those derived from 24HRs show that estimates of population‐level food and nutrient intakes, nutrient intake gaps, and/or prevalence of inadequacies in Bangladesh, Uganda, Ethiopia, Cameroon, and Mongolia are similar.Given the potential of using HCES data as an LP input when quantitative dietary data are not available, in this study, we aimed to determine the percentage agreement between LP input parameters generated using AME‐redistributed HCES consumption data and individual 24‐HR data. We used the LP input parameters from the HCES data and the 24‐HR data separately in the Optifood LP software to identify “problem nutrients” and formulate FBRs for 12‐ to 23‐month‐old children. We then compared the problem nutrients and FBRs generated using the two sets of data. We conducted this analysis in LMICs located in eight diverse geographical regions. The results of this study will indicate whether HCES data can be used to develop model parameters and generate FBRs using LP analyses in Optifood. These FBRs could then be field tested and used to inform programmatic decisions for infant and young child nutrition.Pairs of individual‐ and household‐level food consumption data were identified. The inclusion criteria for these datasets were that they were (1) collected from 12‐ to 23‐month‐old children living in an LMIC, (2) the sample size of each 24HR dataset was ≥50 children, (3) paired HCES‐derived household‐consumption data and individual‐level dietary data were available for the same geographic area, (4) the paired HCES and 24HR datasets were collected within approximately 3 years of each other, and (5) paired HCES data were collected using questionnaires that included ≥100 foods.The input model parameters, which were generated from the individual‐ and household‐level consumption data, included a list of available foods, a median daily portion size for each food (g/day), an upper and lower limit on the number of servings per week for each food, food subgroup, and food group, and a median number of servings per week for each food group . These parameters were generated, for each dataset, using a Microsoft® Access 2010 program developed to process dietary consumption data for analysis with Optifood.The food lists consisted of foods and beverages consumed by ≥5% of the households with a child aged 12–23 months (HCES data) or by the child (24HR data). Food or beverage items were excluded if they had no nutritional value  or, for the HCES data, if they would not typically be consumed by young children . Breastmilk was included in all food lists.The portion sizes (g/day), for each food in the food list from 24HR data, were median daily intakes for the consumers of each food. For breastmilk, the daily portion size was estimated by multiplying the recommended daily energy intake by 0.39 (recommended proportion of energy intake from breastmilk for 12‐ to 23‐month‐old children) and converting it to a gram weight assuming an energy content of 0.66 kcal/g.The lower and upper limits on the number of servings per week for each food subgroup and food group were defined as the 10th and 90th percentiles of consumption, and the food group goals were the 50th percentile for all children per dataset. For the 24HR datasets, the lower and upper limits on the number of servings per week for each food were based on the percentage of children in each dataset who had consumed it, as described elsewhere.All LP analyses were done using modules 1–3 in the Optifood software as described elsewhere.“Problem nutrients” were defined as those that did not achieve 100% of their RNIs in the module 3–maximized diet (run without testing FBRs). The module 3–minimized diets simulate the lowest values in each nutrient's intake distribution. For these analyses, we defined a “minimized” model diet's nutrient content that met or exceeded 65% of the nutrient's RNITo standardize the module 3 analyses and enhance objectivity when making HCES and 24HR result comparisons, we (1) defined FBRs as the number of servings per week from individual food subgroups; (2) selected up to eight food subgroups to test as FBRs in module 3, which were those food subgroups that provided at least 5% of total nutrient content for the highest number of nutrients in the module 2 diet; (3) tested all possible combinations of these eight FBRs ; (4) modeled the maximum number of servings per week allowed for each FBR (food subgroup) tested; and (5) selected the final set of FBRs for each data source and geographic area based on the combination with the highest number of minimized modeled diet nutrient contents that were ≥65% RNI using the lowest number of individual FBRs.The LP inputs for each paired dataset were compared using the percentage interpair agreement or the ratio of HCES value divided by its paired individual 24HR value. Two methods were used to calculate the percentage interpair agreement, which were distinguished as the overall percentage agreement and the eligible percentage agreement. The overall percentage agreement was calculated as the number of matched items  divided by the total number of these items across the paired HCES and 24HR datasets  multiplied by 100. The overall percentage agreement was used to estimate interpair agreement for input model parameters , assuming they should be the same in paired contexts, and for the classification of nutrients as “problem nutrients” or “adequate nutrients” (model outputs), as the nutrients modeled were identical. The eligible percentage agreement was calculated as the number of matched items divided by the number of items that occurred in both the paired HCES and 24HR analyses multiplied by 100. The eligible percentage agreement was used to estimate interpair agreement for model outputs  because results were dependent on the food lists modeled .Eligible data pairs for 12‐ to 23‐month‐old children were identified for eight different areas across four countries from Latin America, South Asia, and East Africa , 63% (range: 48–73%), and 80.4% (range: 57–90%), respectively (Table The number of problem nutrients identified using the HCES‐generated model parameters was lower (three dataset pairs), the same as (four dataset pairs), or higher (one dataset pair) than those from the 24HR‐generated model parameters, although the mean overall percent agreement for problem nutrients was high at 95% . The mean overall percent agreement for identifying food subgroup sources of nutrients was 81%  was 3.5  contributes to this low overall percent agreement at the food level, which may have been the result of differences in survey designs and the methods of assessment used in the paired data sources. HCESs were conducted over a 1‐year period compared with 2‐ to 6‐month periods for the 24HR surveys, which would capture to a greater extent than the 24HRs the seasonal variation in food availability and consumption. The higher percentage of seasonal fruits and vegetables in the HCES compared with 24HR food lists suggests that it was a contributing factor. The longer recall period of the HCES  compared with the 24HR recall  might also contribute to paired data‐source differences in food lists because it would capture less frequently consumed foods that may have been missed by a 24HR. Even though the closed, predefined food questionnaire used in an HCES might have missed some foods captured by the open, questioning approach of the 24HR, it also may have increased the number of foods recalled by reducing memory errors or introducing a social desirability bias, where participants report eating foods that were not consumed. The higher number of foods from the MFE food group recorded in the HCES than 24HR food lists suggests this might have occurred. Unfortunately, we cannot distinguish between modifiable factors  and unmodifiable factors  that might have contributed to the low overall percent agreement at the model food list level. The length and specificity of food questionnaires in HCESs vary between countries and survey years,Other factors might have contributed to the low overall percent agreement at the food level, such as the age of the population modeled. Previous studies have shown that redistributed household‐level consumption data do not accurately estimate the food consumption of children under 2 years of age.Foods consumed outside the home are often underrepresented or overlooked when asking about household‐level consumption, compared with individual 24HRs.Despite this low overall percent agreement for HCES‐ and 24HR‐generated food lists, the overall percent agreement for food groups and food subgroups was moderate to high. More importantly, when examined by individual food groups and food subgroups, the overall percent agreement was only low for food groups of little nutritional importance , and it was high for nutritionally important food subgroups, such as small fish with bones, vitamin A–source dark green leafy vegetables, vitamin C–rich vegetables, milk, whole grain cereal, cooked beans, peas and lentils, and other fruit. This general pattern of agreement  might account for the relatively high percent agreement of model outputs, including the identification of problem nutrients and good food subgroup sources of nutrients, the selection of FBRs, and predicting the nutritional benefits of adopting a set of FBRs. The sets of FBRs selected for young children across diverse geographical areas were similar, and most paired sets included the food subgroups of milk, whole grain cereals, cooked beans and lentils, and vitamin A–source dark green leafy vegetables. These results suggest that an important criterion when deciding on whether to use HCES data for an Optifood analyses is the extent to which food items from nutritious food subgroups are captured in its predefined food list. The critical nutritious food subgroups might also vary by age, sex, or physiological group because of differences in their nutrient requirements per unit energy intake. Thus, further analysis is recommended before extrapolating these promising results to other age or sex groups.ProPAN and Trials of Improved Practices, where caregivers are asked to trial and provide feedback on the recommendations.The approach we used to select and test FBRs in this study was more rigid than what would typically be followed in an Optifood analysis in order to reduce subjectivity when making paired data‐source (HCES versus 24HR) comparisons of generated results. This means that the final sets of FBRs presented here differ from those previously published using the same 24HR data.Nutrients we identified across all or almost all regions as problem nutrients  were consistent with published Optifood analyses that have been done for the same age group. Zinc, iron, and calcium were identified as problem nutrients in almost all (six out of seven) studies and niacin in half of the eligible studies.The strengths of this study were the analyses of paired dataset from diverse geographical regions, including South Asia, Latin America, and Eastern Africa, and the inclusion of paired datasets from regions of Kenya experiencing marked differences in levels of food security. Also, we developed a method for processing HCES data that adjusts for breastmilk consumption, which addresses concerns raised about using HCES data to estimate the food consumption of children under 2 years of age.A limitation of this study is that there is no gold standard against which HCES‐derived LP inputs can be compared. The 24HR is an imperfect method of dietary intake assessment that is prone to recall bias, inaccurate or imprecise quantity estimates, and short recall periods.The LP‐based Optifood tool can objectively and rapidly inform both nutrition‐specific and ‐sensitive programming. It has been successfully applied to identify problem nutrients in local food systems, develop population‐specific FBRs, test the nutrition potential of existing or proposed dietary recommendations, and inform the design of other intervention strategies, such as the use of special fortified foods, multiple micronutrient powders, or lipid‐based nutrient supplements.F.K., E.F., M.W., K.S., and G.B. conceptualized the analyses. F.K. and E.F. performed the analyses, interpreted the results, and take responsibility for the integrity of the data analyzed. F.K. and E.F. wrote the manuscript. All other authors read and approved the final manuscript.The authors declare no competing interests.https://publons.com/publon/10.1111/nyas.14709.The peer review history for this article is available at Figure S1. Percent of foods within each food group that was only reported in the HCES‐derived food list, the 24‐h‐recall–derived food list, and in both datasets across all eight geographical areas.Table S1. Maximum amount each food was modeled (g/week) and the ratio of these amounts by dataset type, region, and food group.Table S2. Number of matched maximum food portions (g/week), median ratio of maximum food portions, the percentage of ratios that were within given limits by selected food groups, and the percentage of HCES maximum food portions that were higher than 24‐h maximum food portions.Table S3. Agreement in the food groups and food subgroups modeled between paired 24HR‐ and HCES‐derived model parameters, by geographical area.Table S4. Summary of agreement between paired 24HR‐ and HCES‐derived model parameters at the food group level.Table S5. Number of modeled nutrients for which each food subgroup was identified as a good nutrient source and the percentage eligible and overall agreement between 24HR and HCES food list pairs, by geographical area.Table S6. Draft individual food‐based recommendations (expressed as the number of average portions per food subgroup per week) tested , by geographic area and data source.Table S7. Final food‐based recommendations (FBRs) selected (expressed as grams per week), by geographical areaTable S8. Nutrients that remained below 65% of the recommended levels when the final sets of food‐based recommendations were tested  and data pair percent agreements.Click here for additional data file.


The morbidity and mortality from COVID‐19 infection are higher in pregnant women compared to their nonpregnant counterparts. As real‐world evidence accumulates demonstrating there is no increased risk of adverse maternal and neonatal outcomes associated with COVID‐19 vaccination during pregnancy, guidelines have evolved from a case‐by‐case benefit‐risk decision through to clear recommendation in April 2021 for COVID‐19 vaccination in pregnancy. However, vaccine hesitancy is a barrier to uptake, especially among the younger population and individuals of ethnic minority backgrounds; pregnant women have additional concerns. Trust in the importance and effectiveness of the vaccine, trust in public health agencies and science, together with good communication methods regarding the safety of COVID‐19 vaccines are strong factors for vaccination acceptance in pregnancy. Lack of trust in the health system was worsened by initial knowledge gaps in the information provided about COVID‐19 infection and the safety and immunogenicity of COVID‐19 vaccines. This was exacerbated by access to incorrect information and misinformation to fill in those knowledge gaps, especially with the increased use of social media. To provide advice and reassurance on COVID‐19 vaccine safety to pregnant women, healthcare professionals involved in their care should have the knowledge and skills to provide risk‐benefit communication and would benefit from access to training in science communication. Clinical pharmacologists have the expertise to appraise and synthesize emerging pharmacovigilance data, which can inform and support risk‐benefit communication by other clinicians. Information should be strategically directed at individual audiences, taking their perspectives and foundational belief systems into consideration. The COVID‐19 pandemic has cost the lives of over six million people worldwide,The administration of vaccines to pregnant women has its own complexities. Of note, the majority of pregnant women take medicines that are not licensed for use in pregnancy due to their exclusion from clinical trials; evidence of the safety of medications in pregnancy is primarily derived from postmarket surveillance data.Against this backdrop, efforts to achieve vaccination have been hampered by scepticism towards the COVID‐19 disease itself as well as towards vaccines. This has been fuelled by conspiracy theories on social media and other media platforms which became a major means of connecting people when restrictions for physical interaction with others were in place. The problem of “vaccine hesitancy” was highlighted as one of the major threats to global health by the World Health Organization even before the current pandemic and has been defined as “the delay in acceptance or refusal of vaccines despite availability of vaccine services”.n = 1738) of all women who delivered (n = 4064) had received any COVID‐19 vaccine, with only 32.3% having received two doses. This is compared to 84.7% (n = 803 241) of women aged 18‐44 years in the general population (n = 947 984) having received any vaccination with 77.4% having received two doses by October 2021.Historically, pregnant women have been concerned about taking vaccinations, not only for their health, but more importantly for the safety of their babies,A more positive trend in COVID‐19 vaccine uptake in pregnancy has been noted from statistics between August and November 2021. In October 2021, 41.3% of women giving birth in England had received at least one dose of vaccine compared to 22.5% in August 2021. These improvements were noted across demographics: 13.3% of pregnant black women had a first dose of a COVID‐19 vaccine by delivery, up from 5.5% in November 2021. However, vaccine coverage is still low in pregnant women of black ethnicity and in pregnant women living in deprived areas of England compared to the general cohort of pregnant women in the United KingdomInformation provided by health professionals and scientists is generally regarded by the public to be credible.Ensuring transparency in vaccine communication, including the anticipated and emerging adverse event profiles, is important so the public can be assured they are making an evidence‐based risk‐benefit decision when receiving vaccines. Clinical pharmacologists are in a unique position to appraise and assimilate emerging evidence on the safety of vaccines, with emphasis on the role of pharmacovigilance and postmarket surveillance. Their in‐depth understanding and involvement in all aspects of drug development and regulation as well as the fact that patient safety is at the cornerstone of all clinical pharmacology efforts puts risk‐benefit communication within the purview of clinical pharmacologists. This synthesis of evidence can inform concise summaries and support communications for healthcare professionals with direct patient‐facing roles who may have less familiarity with pharmacovigilance methodology. Communicating the risks and benefits of vaccines is especially pertinent for healthcare providers from ethnic minority backgrounds. It has been shown that provision of information about the vaccine from someone patients can identify with as a trusted source, such as healthcare workers from different ethnic backgrounds, promotes vaccine acceptance among ethnic minorities.To provide adequate advice and reassurance on COVID‐19 vaccine safety in pregnancy, healthcare professionals involved in their care should have the required knowledge and expertise to provide effective risk‐benefit communication. The importance of communication skills training for all healthcare workers engaged in providing services to pregnant people cannot be overemphasized. Communications training of healthcare personnel who are trusted sources of healthcare information for minority ethnic groups should include strategies to initiate discussions about vaccinations and tailoring conversations to address vaccine beliefs has been recommended.There are no competing interests to declare.


Aging and cancer are clearly associated processes, at both the epidemiological and molecular level. Epigenetic mechanisms are good candidates to explain the molecular links between the two phenomena, but recent reports have also revealed considerable differences, particularly regarding the loss of DNA methylation in the two processes. The large‐scale generation and availability of genome‐wide epigenetic data now permits systematic studies to be undertaken which may help clarify the similarities and differences between aging and cancer epigenetic alterations. In addition, the development of epigenetic clocks provides a new dimension in which to investigate diseases at the molecular level. Here, we examine current and future questions about the roles of DNA methylation mechanisms as causal factors in the processes of aging and cancer so that we may better understand if and how aging‐associated epigenetic alterations lead to tumorigenesis. It seems certain that comprehending the molecular mechanisms underlying epigenetic clocks, especially with regard to somatic stem cell aging, combined with applying single‐cell epigenetic‐age profiling technologies to aging and cancer cohorts, and the integration of existing and upcoming epigenetic evidence within the genetic damage models of aging will prove to be crucial to improving understanding of these two interrelated phenomena. Aging and cancer are interrelated processes which share common epigenetic alterations. Nonetheless, there are differences in some of the DNA methylation changes which occur in both phenomena. The recent development of epigenetic clocks will help dissect the common and specific epigenetic characteristics of aging and cancer, although the mechanisms underlying epigenetic clocks are yet to be clarified, particularly in relation to somatic stem cell epigenetic aging. Raúl Fernández Pérez designed all aspects of the research, collected the data, performed analyses, and wrote the manuscript. All authors revised, read, and approved the final manuscript.The Spanish Association Against Cancer (grant number PROYE18061FERN to MFF), the Asturias Government (PCTI) co‐funding 2018‐2022/FEDER (grant number IDI/2018/146 to MFF), Fundación General CSIC (grant number 0348_CIE_6_E to MFF), the Health Institute Carlos III  co‐funding FEDER (grant number PI21/01067 to MFF and AFF) and the Spanish Ministry of Science and Innovation (grant number SGL2021‐03‐039/40 to M.F.F.) co‐funding NextGenerationEU. JRT is supported by a Juan de la Cierva fellowship from the Spanish Ministry of Science and Innovation (grant number IJC2018‐36825‐I). RFP is supported by the Severo Ochoa program (grant number BP17‐114). We also acknowledge support from IUOPA‐ISPA‐FINBA  and Consorcio Centro de Investigación Biomédica en Red (CIBERER‐ISCIII).None declared.


Bacillus subtilis, and possibly other bacteria. In a recent study, monomeric forms of ParA/Soj have been implicated in regulating aspects of chromosome dynamics during B. subtilis sporulation. In this commentary, I will discuss the known roles of ParABS systems, explore why sporulation is a valuable model for studying these proteins, and the new insights into the role of monomeric ParA/Soj. Finally, I will touch upon some of the future work that remains.ParABS (Soj‐Spo0J) systems were initially implicated in plasmid and chromosome segregation in bacteria. However, it is now increasingly understood that they play multiple roles in cell cycle events in  Bacillus subtilis, as well as discuss a recent study that implicates a monomeric variant of ParA/Soj as a novel regulator of SMC release and redistribution after loading onto the DNA, a process that is critical for chromosome reorganization during axial filament formation in early sporulation.ParABS (Soj‐Spo0J) systems and SMC complexes are critical mediators of chromosome dynamics in a range of bacteria. In this commentary, I review the roles of these systems in  Binding of ATP to empty (or Apo)‐ParA monomers causes a conformational change that drives dimerization and nonspecific DNA binding  system drives chromosome segregation has been subject to intensive research in a range of bacterial species .Several critical features of the DNA relay system enable efficient chromosome segregation. The first is that upon DNA replication the majority of ParA is dimeric and bound nonspecifically across the chromosome, such that bound dimers can interact with a ParB‐2B. subtilis, dimer and monomer forms of Soj were shown to activate or inhibit DNA replication, respectively   and Spo0J (ParB) have been implicated in controlling other key aspects of the bacterial cell cycle. In These studies have highlighted the diverse roles that ParA/Soj and ParB/Spo0J can play in bacteria and demonstrated that these proteins have critical roles that lie beyond DNA relay‐based chromosome segregation.3B. subtilis cells can initiate sporulation  and mother cell (σE), which drive differential gene expression in the two compartments  , and the segregating chromosome in each sporulating cell carrying the spoIIIE36 mutation will get stuck in the translocase at the same point, effectively synchronizing the population of cells in the assay to provide homogenous readouts and a more user‐friendly timescale for experiments. This approach has therefore been widely used to identify a range of genes involved in chromosome movement and capture, including soj and spo0J  Figure  (Wu & Er) Figure . spoIIIEvely Wu, . As a revely Wu,   monomeric variants of Soj have active roles in chromosome segregation; (2) one of these monomers, Apo‐Soj, is a likely regulator of SMC complex release from parS sites; (3) a major redistribution of SMC complexes along the chromosome drives axial filament formation (or Stage I) during the initial stages of B. subtilis sporulation.In our recent manuscript , suggesting that Soj was operating in the same capture pathway as these factors  and sojK16A (Apo‐Soj hereafter) that act, respectively, as ATP‐bound or empty monomers in vitro  that abolishes the interaction with Soj and therefore generates an asporogenous phenotype, but retains the ability to support SMC  mutant, the negative outcome of Apo‐Soj in capturing the origin in the prespore was significantly rescued. These observations pointed to the effects of Apo‐Soj on SMC being mediated through an interaction with the Spo0J N‐terminus   may help elucidate this further  mediate their effect on SMC release from Caulobacter crescentus was dominant lethal, and when an Apo‐ParA‐mCherry fusion (which could grow) was expressed alongside wild‐type ParA, cells displayed a minicell phenotype  is in regulating aspects of the cell cycle in other bacterial species. A recent preprint has suggested that Apo‐ParA in B. subtilis is its role at the cell pole. It could be that polar Soj is simply a manifestation of the need to anchor origins at the pole during sporulation. However, since it also localizes to the pole during vegetative growth, it remains possible that there is another role for this population of Soj—for example, to provide a dimerization hub for ATP‐Soj away from the origin. It is known that recruitment of Soj to the cell pole is dependent on MinD  . In the model Gram‐positive bacterium David M. Roberts: Conceptualization ; writing – original draft ; Writing – review & editing .None declared.None required.


High school canteens are an ideal setting for public health nutrition intervention, and choice architecture strategies that facilitate the purchase of healthier foods and beverages from school canteens are recommended by the World Health Organization. The rapid uptake of online lunch ordering within school canteens provides a unique opportunity to implement choice architecture strategies that support healthier food choices with high fidelity. Despite this, no trial has tested the efficacy of choice architecture strategies within an online lunch ordering system on improving the nutritional quality of high school student lunch purchases. The objective of this study was to assess the impact of embedding choice architecture strategies into an online lunch ordering system on the nutritional quality of the school canteen lunch purchases of high school students (aged 12–19 years).A cluster randomized controlled trial was conducted with nine high schools in one Australian state. Schools were randomized to receive either a 2-month choice architecture intervention , or usual online ordering. Nutrient quality of online canteen lunch purchases was assessed using routine data collected by the online ordering system. Primary outcomes were the proportion of ‘Everyday’, ‘Occasional’, and ‘Should not be sold’ items purchased, categorized using the state healthy canteen policy. Secondary outcomes were the mean energy, saturated fat, sugar, and sodium content of purchases and the mean weekly revenue from online lunch orders. Linear mixed models were analyzed to assess outcomes.: 5 schools, 675 students) showed significant between group differences over time for the intervention group for the mean percentage of online lunch items per student that were ‘Everyday’  and ‘Should not be sold’ . There were no between group differences over time in the mean percentage of online lunch items that were ‘Occasional’; the average energy, saturated fat, sugar, or sodium content of lunch orders. There was also no difference in mean weekly revenue from high school student online lunch orders (P = 0.23).Analysis of the student cohort (Intervention: 4 schools, 656 students; ControlThese findings suggest that a low intensity, choice architecture intervention embedded within an online ordering system can increase the purchase of healthier food items for high school students in one Australian state without any adverse impact on canteen revenue.This trial was prospectively registered on Open Science Framework on 23rd October 2020 as osf.io/h8zfr. Unhealthy dietary patterns, including excess intake of energy, saturated fat, sugar and salt, are a leading cause of disease burden . Whilst Schools are a recommended setting for the delivery of public health nutrition programs , as theyTo improve adolescent nutrition, Australian governments like those globally have introduced policies which aim to influence the relative availability of healthier foods from school canteens . For exan = 6) of included high school studies were conducted in the United States and were non-randomized trials (n = 4). While such findings suggest that choice architecture strategies may encourage healthier purchasing among high school students, conducting randomized trials to examine the impact of such strategies in high schools that have different food service systems to the US  are warranted to address the existing evidence gaps.In order to maximize the impact of canteen-based interventions on adolescent diet, other environmental strategies that encourage healthier canteen purchasing are warranted. Choice architecture strategies that nudge people towards healthier food choices through small modifications to the food environment are recommended by the World Health Organization and have shown promise in school food settings for children and adolescents –18. For P < 0.001) and decreasing the purchase of less healthy foods by up to 8% (P < 0.001) [Online school canteens allow users to access a menu, order and pay for menu items via the web or a mobile app, and are common in Australia and New Zealand . They pr< 0.001) , 20. As < 0.001) , the effClick & Crunch High Schools’) into an online canteen ordering system on the proportion of healthy (‘Everyday’) and less healthy  online lunch items purchased by high school students. The secondary aims were to assess the efficacy of Click & Crunch High Schools on the average energy, saturated fat, sugar and sodium content of high school student online lunch orders and the weekly revenue from online lunch orders.The primary aims of the study were to assess the efficacy of embedding a choice architecture intervention  were randomized in a 1:1 ratio to receive either a 2-month multi-strategy behavioral intervention embedded in their online canteen or a usual practice control (the standard online canteen).The study was approved by the University of Newcastle Human Research Ethics Committee (H-2017–0402), New South Wales Department of Education and School Communities (SERAP 2018065) and relevant Catholic School Dioceses. The trial was prospectively registered on Open Science Framework on 23rd October 2020  and is rThe study took place in the state of New South Wales (NSW), Australia, a geographically and socio-economically diverse region with 827 schools enrolling high school students  and apprn = 134). Schools that had already participated in an ‘online canteen’ research program conducted by the team (n = 6) and catholic schools where ethical approval was not obtained by the diocese in time were also ineligible (n = 18). A post hoc exclusion criteria was added for schools that already had > 50% of their online menu labelled according to the NSW Healthy School Canteen Strategy due to the limited scope of applying labelling strategies as part of the intervention (n = 1 school).All government and non-government (independent or catholic) schools in NSW that: catered for high school students (including combined schools that catered for students from grades K-12); and used an online canteen provided by Flexischools, were eligible to participate  was eligible for inclusion in the trial. Students who were in Grade 12 during the baseline data collection period were excluded as they would have left the school prior to the intervention period . Furthermore, students with recurring lunch orders that were placed prior to the intervention period were excluded due to these participants not being exposed to the intervention strategies.n = 10). A recruitment manager oversaw recruitment of schools into the trial and monitored consent rates. Approximately 1 to 2 weeks after study invitations were posted to schools, a member of the research team telephoned the canteen manager to provide an overview of the study, confirm interest and answer any enquiries. Consent for study participation and access to canteen purchasing data was sought from the school Principal.All 134 schools in the sampling frame were approached from October 2020 to January 2021 (approximately 12 ‘school’ weeks) via mail and telephone with recruitment occurring until the required sample of schools consented to participate , and assuming a standard deviation of 31% on the mean percentage of ‘Everyday’ items purchased by students , and an Following school recruitment, schools were block randomized by an independent statistician using a random number function in Microsoft Excel®. Block randomization occurred at the unit of the school in randomly sequenced blocks of two and four. Given evidence that there are differences in healthy food availability between government and non-government schools (Catholic and Independent) , the ranClick & Crunch High Schools’ intervention sought to encourage the purchase of healthier (i.e. ‘Everyday’) items from the school’s online canteen menu. The intervention was based on the principles of choice architecture which aim to structure environments in ways that cue desirable and automatic health related behaviors [The ‘ehaviors  such as ehaviors . The selehaviors , 20, 29;ehaviors  and; iiiAll users of the online canteen at intervention schools had access to the ‘Click & Crunch High Schools’ intervention which was integrated into the school’s existing online canteen for 2 months (February to April 2021). Specifically, the ‘Click & Crunch High Schools’ intervention involved the following strategies Menu labelling: Each item on the online menu was labelled with a colored symbol. The labelling system was based on the NSW Healthy School Canteen Strategy [Strategy  and on pStrategy , 32. Smaii)Positioning: Menu items were ordered to give healthier items positions of greatest prominence. This occurred at the food category level (e.g. drinks) and at the item level (e.g. plain milk). The healthiest food categories  and items within categories (e.g. those that were labelled ‘Everyday’) were positioned first. Where there were multiple flavors of an ‘Occasional’ or ‘Should not be sold’ food (e.g. multiple flavors of chips), users were required to first ‘click’ on the item before the full list of flavors were displayed.iii)Feedback: Prior to each lunch order being confirmed within the online ordering system, the user received feedback summarizing the nutritional content of the order. This was displayed using: i) a pie chart showing the proportion of ‘Everyday’, ‘Occasional’ and ‘Should not be sold’ items within the order; and ii) a tailored message based on the proportion of ‘Everyday’ items in the order as follows:“Try adding some ‘Everyday’ items for a more balanced meal”.< 99% ‘Everyday’ items: “Excellent choice! 100% ‘Everyday’ items”.100% ‘Everyday’ items: iv)Prompts: When a user selected an ‘Occasional’ or ‘Should not be sold’ hot food item, the user received a prompt to include a fruit or vegetable snack and water. Due to programming limitations, this strategy was applied to all hot food items typically sold as a single item  rather than items that were typically purchased in multiples . Additionally, healthy food categories  also had a positive purchase prompt (e.g. “This is a good choice”) and an attractive image representing the food category applied immediately adjacent to the category name.The labels and prompts were manually added to the online menus in intervention schools by the online canteen provider (based on a template provided by the research team). Once the online menus had been labelled, the positioning and feedback strategies were automatically applied to the menus. For example, when an ‘Everyday’ label was manually applied to a menu item, it would automatically position the item to appear first within the category list.To facilitate canteen managers’ understanding of the labelling system and the labels that were applied to their online menu, each canteen manager in the intervention group received one ‘menu feedback report’. This feedback report provided an outline of how each menu item was labelled according to the NSW Healthy School Canteen Strategy . The feedback report also included graphical feedback comparing the online canteen menu to the recommendations of the NSW Healthy School Canteen Strategy  and provided suggestions on how to improve the relative availability of ‘Everyday’ items.Immediately after the intervention was applied  and mid-way through the intervention period  a member of the research team monitored each school’s online canteen menu via the provider’s website to ensure that all items (including any new items) were correctly classified, and the intervention strategies were applied accordingly. If the research team identified an unlabeled or incorrectly labeled item during these checks, the online canteen provider was notified and instructed to apply the intervention strategies as outlined in Fig. Control schools received no change to their online canteen menu, nor did they receive a ‘menu feedback report’ from the research team.Student purchasing data that was automatically collected by Flexischools was used to evaluate the primary and secondary trial outcomes. Trial outcomes used data from all online lunch purchases made by eligible students during the 2-month baseline and 2-month follow up periods. The baseline period was from week 3–10 of Term 4, 2020 (October to December), and the follow up period was immediately following intervention commencement from week 3–10 of Term 1, 2021 (February to April).School characteristics, such as the number of enrolments, year range, sector (government vs non-government), school type (combined schools that enroll both primary and high school students vs high school only) and postcode were obtained from the government ‘MySchool’ website . For comTo describe the proportion of high school students that use online canteens, the number of online canteen users per school  was obtained from the purchase data supplied by Flexischools and divided by the number of high school enrolments collected via the ‘MySchool’ website for that school. These proportions were then averaged across all nine schools participating in the trial.The primary outcomes were the mean percentage of all online lunch items purchased per student over the 2-month data collection periods that were classified as i) ‘Everyday’, ii) ‘Occasional’ and iii) ‘Should not be sold’ according to the NSW Healthy School Canteen Strategy . After rAs per previously established procedures , 34 the To assess any potentially adverse outcome of the intervention (e.g. reductions in revenue from applying the intervention), purchase data that was automatically collected by Flexischools was used to calculate the mean weekly revenue from all high school student online lunch orders for the weeks that the canteen was operational at baseline and follow-up.To describe any changes in the availability of online menu items that resulted from the intervention, the proportion of items that were: ‘Everyday’, ‘Occasional’ and ‘Should not be sold’ on the online menu were assessed at baseline and follow-up based on the dietitian menu assessment procedures described above.The analysis was conducted using an intention-to-treat approach whereby all student orders and schools were analyzed based on the groups to which they were originally allocated. Primary outcomes and secondary nutrition outcomes and subgroup analyses included data from all students that had baseline purchasing data. The school revenue outcome included all available data including from students without baseline purchasing data.For each student, the primary trial outcomes were calculated by tallying the ‘total number of individual lunch items’ purchased over each of the two 2-month data collection periods (October – December 2020 and February – April 2021), and assigning scores of 0–100 corresponding to the percentage of those items that were classified as ‘Everyday’, ‘Occasional’ and ‘Should not be sold’. For example, if a student purchased 20 items over a data collection period and 10 were ‘Everyday’, a score of 50 would be assigned to represent 50% of items purchased that were classified as ‘Everyday’. These scores were then averaged across all students within the intervention and control groups. Three separate linear mixed models were analyzed to assess the primary trial outcomes. Each model compared intervention and control groups over time by including a group by time interaction fixed effect. All models included a random intercept for school , a nested random intercept and random time effect for students (to account for repeated measurements between time points), and fixed effects for Socio-Economic Indexes for Australia (SEIFA)  and schoAll secondary trial outcomes, including differences in the i) nutritional content  of all online lunch orders placed by students and ii) average weekly revenue that the canteen was operational were assessed using linear mixed models with a group by time interaction fixed effect. All models included a random intercept for school  and fixed effects for sector and SEIFA. For secondary nutrition outcomes, a nested random intercept and random time effect for students (to account for repeated measurements within and between time points) were also included.Subgroup analyses were conducted on the primary outcome in relation to ‘Everyday’ items, i.e. students’ mean percentage of ‘Everyday’ items and; i) student grade (grade 7 to 9 vs grade 10 to 11); ii) frequency of online canteen ordering (‘low’ < 1 order/week on average vs ‘high’ ≥ 1 order/week on average) and iii) school sector (government vs non-government). This was assessed as per the primary outcomes with the additional inclusion of a 3-way interaction term (group by time by subgroup) into the models.A mixed logistic regression model, including a school random effect, was used to investigate whether being lost to follow-up was significantly associated with group (intervention vs control). Separate mixed logistic regression models were then used to assess whether that association differed on baseline characteristics  or nutrition outcomes, by adding an interaction fixed effect for each model.Descriptive statistics were used to describe school characteristics, menu item availability and usage rates. All statistical analyses were performed in SAS version 9.3.P = 0.025), however there were no significant differences between the two groups being lost to follow-up based on student grade; frequency of online canteen use; baseline nutrients  or; ‘Everyday’ or ‘Should not be sold’ item classification. There were however higher odds of being lost to follow-up for participants in the intervention group compared to the control group as the proportion of ‘Occasional’ items purchased increased . The baseline characteristics between groups are described in the tables below  was 29.1% .The proportion of unlabeled or incorrectly labelled items across the 4 intervention schools was 0%  and 0.20% , indicating a high level of intervention fidelity.P < 0.001) and ‘Should not be sold’  (Table P = 0.47).The results from the mixed model analysis indicate that there were significant between group differences over time favoring the intervention group for the mean percentage of all online lunch items per student that were Everyday  Table . There wP = 0.63), saturated fat , sugar  and sodium  content of student online lunch orders.There were no between group differences over time in the average energy .There were no significant between group differences in the average weekly revenue from online lunch purchases over time , ‘Occasional’ (14.9 and 20.0%) and ‘Should not be sold’ (15.7 and 12.0%) items were similar by intervention and control menus respectively. At follow up, the mean availability of ‘Everyday’ (72.0 and 67.0%); ‘Occasional’ (14.8 and 21.6%); and ‘Should not be sold’ items (13.2 and 11.5%) were similar by intervention and control menus respectively (no significance testing conducted).P < 0.001 respectively). Encouragingly, the intervention did not have any adverse impact on canteen revenue, a frequently reported barrier by canteen managers to the uptake and adherence of public health nutrition interventions in canteens. These findings suggest that a choice architecture intervention embedded within an online ordering system can improve the selection of healthier food options for high school students in one Australian state.To our knowledge, this is the first randomized controlled trial to assess the efficacy of a multi-strategy choice architecture intervention for improving the nutritional quality of high school students’ purchases from online canteens. The study found that over time, high school student lunch purchases in the intervention group contained a significantly higher average proportion of ‘Everyday’ items and lower proportion of ‘Should not be sold’ items than those in the control group  and those classified as ‘Should Not Be Sold’ are ‘discretionary’ foods , 37. TogP = .012) and 36 to 44% (P < .001) respectively after a 2 month intervention [To our knowledge, no studies have tested the efficacy of delivering choice architecture strategies via web or digital modalities to improve the selection or purchase of healthier foods from high schools. A before and after study conducted in two New York high school lunch rooms using strategies directly applied in the physical food service environment including placement, prompts, and improved access to healthier foods, found that students selection of fruit and vegetables increased from 47 to 54%  rather than by their individual nutrients (e.g. kilojoule labelling). Therefore, students would be made aware of a menu item’s overall nutritional quality, but not the specific contribution of the underlying nutrients such as the energy, sugar, sodium, or saturated fat. It is therefore possible for students to switch from categories of food  without observing differences in energy, sugar, sodium or saturated fat. For example, a ‘Should not be sold’ item such as confectionary in a serve size of 12.5 g may contain approximately 200 kJ, 12 g of sugar (94 g per 100 g), 0 g of saturated fat, 80 mg of sodium, and offers little nutritional value . In contFinally, the study found no differences in intervention effectiveness over time by student grade (grade 7–9 vs 10–11), frequency of online canteen use (≥1 order/week vs < 1 order/week) or school sector (government vs non-government). This indicates that the intervention has a broad public benefit across varying social strata and subgroups, however it should be noted that the analysis was exploratory, potentially underpowered and the findings in relation to student age was borderline significant and may warrant further investigation.The strengths of this study include the use of a randomized controlled trial design, the application of a state government labelling system, high intervention fidelity, use of objective and routinely collected purchasing data, and the inclusion of both government and non-government schools. The findings however should also be considered in the context of the study’s limitations. First, whilst NSW survey data suggests that at least 60% of high school students purchase their lunch at least weekly from a school canteen, our study found relatively low use of online canteens by high school students, with up to 32% ordering their lunch at least weekly from these systems. This may suggest that high school students use the school canteen for ‘spontaneous’ or over the counter purchases rather than for ordering their lunch. Therefore, the impact of this efficacious intervention may be enhanced by combining other strategies targeting the physical food environment of high school canteens. Second, the small number of schools (clusters) may have affected randomization and overall generalizability of findings. Encouragingly though, the study included data from 1331 students and the school and user characteristics were relatively similar between intervention and control groups. Third, the study was undertaken in one Australian jurisdiction and the generalizability of the findings may be limited to jurisdictions with similar school food service models (online lunch ordering) and/or menu labelling systems (traffic light labels). In addition to Australia, online lunch ordering is becoming increasingly common across schools in the USA  and UnitThis study provides evidence supporting the efficacy of a behavioral intervention embedded in online canteens to improve the healthiness of student purchases from high school canteens in one Australian state. Given the evidence of its efficacy and ability to be implemented at high fidelity, and evidence of no adverse impact on canteen revenue, this behavioral intervention may represent an appealing option in isolation or in combination with broader public health nutrition strategies in the Australian high school setting. However, in order to explore the long-term effectiveness of the intervention and its effect at scale further research is warranted over a longer period of time and involving more high schools across multiple jurisdictions where online lunch ordering is utilized.


Pelvic fragility fractures, such as pubic ramus fractures, are a common and painful condition in the elderly population. Despite this, there are few regional anaesthesia options available to effectively relieve pain in these fracture types and avoid potential side effects from opioid administration. This case report describes an elderly patient with a superior ramus fracture, who received effective pain relief with motor sparing, using a standard‐volume point‐of‐care ultrasound‐guided pericapsular nerve group (PENG) block performed in the emergency department. The standard‐volume PENG block performed by an emergency clinician appears to be a safe, effective and feasible regional anaesthesia technique for superior pubic ramus fracture, with the additional benefit of motor sparing that may potentially facilitate earlier mobilisation and discharge. Pelvic fragility fractures in the elderly population, such as pubic ramus fractures, are a common presentation to the emergency department (ED).The point of care ultrasound (POCUS) guided pericapsular nerve group (PENG) block is an emerging regional anaesthesia technique that may be safely used to provide effective analgesia for these patients in the ED.We report the first case of the standard‐volume POCUS‐guided PENG block for acute pain in superior pubic ramus fracture with motor sparing. This may provide additional benefits such as avoidance of opioid‐related side effects and early mobilisation to facilitate discharge. Consent to publish the case report was obtained from the patient and approved by the North Coast New South Wales Human Research and Ethics Committee (EC00415).An 82‐year‐old woman was brought to the ED with right hip pain following an unwitnessed fall. A PENG block was performed due to the suspicion of neck‐of‐femur fracture on the X‐ray. Subsequent CT imaging confirmed an isolated right superior pubic ramus fracture.The PENG block was performed using the POCUS guidance by an emergency clinician under aseptic conditions. With the patient in a supine position, a curvilinear probe  was placed in the inguinal crease, with 45° medial rotation. The probe was moved cephalad to visualise the anterior–inferior iliac spine (AIIS), the iliopubic eminence (IPE) and the psoas tendon Figure . A smallImmediately prior to block, the patient's visual analogue scale pain score was 7, reducing to a score of 1 at 13 min post‐block. The patient performed a straight leg raise at this time. The patient only received immediate‐release 5 mg oxycodone orally 3 h prior to the block. The patient subsequently had no requirements for opioid analgesia for 24 h and consistently scored a pain score of 0–1. The patient mobilised to baseline and was discharged the following morning.Regional anaesthesia options for pelvic fractures, such as pubic ramus fractures, are neuraxial and lumbar plexus blocks. These complex techniques are not feasible or safe to routinely perform in the ED. The PENG block is emerging as a suitable alternative in the ED for this common yet neglected fracture.The specific sensory innervation of the superior and inferior pubic rami is not known,The ideal injectate volume for the PENG block has not yet been elucidated.In this case report, effective analgesia was achieved with motor‐sparing effects using the standard‐volume PENG block in the ED for an elderly patient with a superior pubic ramus fracture. Performing a PENG block in this patient group may particularly have a role in cases where oral analgesia is inadequate and allow early mobilisation. Prospective studies are required to determine the safety, feasibility and effectiveness of the PENG block, including the ideal injectate volume, for elderly patients with pubic ramus fractures in the ED.No funding information is provided.No conflict of interest is declared.


Pulmonary amyloidosis should be included in the differential diagnosis of calcified lung nodules, and more careful preparation for bleeding should be taken when performing bronchoscopy. While management does not require aggressive treatment, follow‐up is necessary to monitor for multiple myeloma and malignant lymphoma Calcified nodules can present as a diagnostic challenge. Pulmonary amyloidosis should be included in the differential diagnosis of calcified lung nodules, and more careful preparation for bleeding should be taken when performing bronchoscopy. Management of nodular pulmonary amyloidosis does not require aggressive treatment, but follow‐up is necessary for complications such as multiple myeloma and malignant lymphoma. A 76‐year‐old man presented with a chest abnormal shadow during an annual check‐up. His past medical history and family history were unremarkable. He was a current smoker, with 100 pack years. A spindle‐shaped calcification was observed in the left middle lung field Figure . The cheCalcification in nodular pulmonary amyloidosis is frequently observed.Hiroyuki Miura and Shinichi Goto were responsible for the conception of the study. Jun Miura was responsible for writing and revising the work. Yamamoto diagnosed this cancer pathologically. All authors contributed to the final version of this manuscript and approved it for publishing.None declared.The authors declare that appropriate written informed consent was obtained for the publication of this manuscript and accompanying images.