Infrared spectroscopy of liquid biopsies is a time‐ and cost‐effective approach that may advance biomedical diagnostics. However, the molecular nature of disease‐related changes of infrared molecular fingerprints (IMFs) remains poorly understood, impeding the method's applicability. Here we probe 148 human blood sera and reveal the origin of the variations in their IMFs. To that end, we supplemented infrared spectroscopy with biochemical fractionation and proteomic profiling, providing molecular information about serum composition. Using lung cancer as an example of a medical condition, we demonstrate that the disease‐related differences in IMFs are dominated by contributions from twelve highly abundant proteins—that, if used as a pattern, may be instrumental for detecting malignancy. Tying proteomic to spectral information and machine learning advances our understanding of the infrared spectra of liquid biopsies, a framework that could be applied to probing of any disease. Human biofluids provide systemic molecular information. By combining vibrational spectroscopy, biochemical fractionation, and mass spectroscopic proteomic profiling of blood serum, we extend the molecular understanding of the infrared spectral fingerprints of human blood. Using lung cancer as an example of a medical condition, we provide a general framework informative for the future of disease probing. Infrared spectroscopy is a well‐established method of studying chemical substances via analyzing the vibrational transitions that are characteristic of their molecular structure.Thorough exploration of the molecular origin of IMFs would be instrumental for successful application and verification of molecular fingerprinting in clinical settings.It had been suggested that large variations in blood‐based IR spectra may be caused by a varying albumin‐to‐globulin ratio.In order to explore the dependence of the IMF of human blood serum on its molecular composition, spectroscopic molecular fingerprinting should be ultimately combined with a technique that is able to provide molecular‐specific information over a high dynamic range.−1 was put forward as the most informative for disease detection, and it was suggested that changes in the protein secondary structure might be correlated with lung cancer.With the gained understanding of the molecular composition underlying the IMFs of human blood serum, we compare the samples of lung cancer patients (tumor node metastasis (TNM) clinical stages II and III) with reference individuals matched in age, gender and smoking status. We focused on lung cancer as a prototypical disease for which non‐invasive early detection from blood profiling would be highly beneficial.[In this study, we obtain reproducible, cost‐ and time‐efficient IMFs of human sera and use proteomic measurements to facilitate their understanding at a molecular‐level. In particular, we reveal a pattern of changes of human blood serum composition, which correlates with the presence of lung cancer and results in an observable difference between IMFs of blood sera of lung cancer patients compared to the reference group. Both spectral and molecular information was used to build explainable classification models for lung cancer detection.−1  is characteristic of the secondary structure of the proteins.−1) is often referred to as “carbohydrate region”, because of the typical absorption patterns that glycans exhibit here.−1, 2852 cm−1 and 2926 cm−1.We recorded infrared absorption spectra of liquid human blood sera in the range from 1000 to 3000 cm1 Figure . The speAttributing the distinct features of the mid‐infrared absorption spectrum of human blood serum to a specific molecular class is somewhat oversimplified, since absorption spectra of various biological molecules often overlap. In order to gain deeper insight into the origins of different spectral features, we built a comprehensive model of the human blood serum absorption. To this end, we used a set of 148 blood serum samples Figure .−1 . Next, we biochemically fractionated each sample into three fractions ‐depleted proteins and HSA‐enriched proteins) and recorded their IMFs . Figure In order to estimate the contribution of each protein to the IMF of blood serum, we modeled the absorption spectra of every individual's serum as a sum of IR absorption spectra of proteins multiplied by their respective concentrations, measured by proteomics Eq. :(1)IMFν˜ν˜represents wavenumber, Ci—concentration of the protein i in mg mL−1, Siν˜—absorption spectrum of the protein i for 1 mg mL−1.where i=1) and building complexity by adding proteins one by one, in the order as listed in Table S1. Figure We started by taking into account the spectral contribution of HSA only (−1) and C−H stretches (at 2852 cm−1 and 2926 cm−1) known to be unique for lipids.In Figure Having obtained a simple model of the IR absorption of human blood serum, we can address the question how this absorption changes as a consequence of a disease. In this study we focused on lung cancer, as the most common cause of cancer‐related deaths worldwide.−6 (Table S3), strongly suggesting that the differences between the IMFs of two cohorts are statistically significant. To further quantify these differences, we applied support vector machine (SVM) algorithm to classify the samples into two classes—cancer cases and reference individuals. To that end, the data were split into train and test sets, employing 10‐times repeated 10‐fold cross‐validation. The area under the curve (AUC) of the receiver operating characteristics (ROC) curve was used as a measure of classification efficiency. For the classification of lung cancer patients versus references, the model reveals an AUC of 0.85±0.1, implying that the SVM model can, in principle, be trained to distinguish between the two cohorts.We find that infrared molecular fingerprints of lung cancer patients clearly differ from that of reference individuals. The black line in Figure −1, as well as in the Amide I and Amide II regions. Such shape could result from changes in the proteins secondary structure, as previously suggestedWe find that the differential fingerprint of lung cancer has a specific shape, with prominent features around 1000–1200 cm−6 (Table S3), while the metabolite fraction is not significantly different in the samples from reference individuals versus these of the lung cancer patients. This finding is confirmed by the AUC values: for the metabolite fraction the AUC is 0.62±0.2, while for the HSA‐enriched fraction it is 0.82±0.1, and for the HSA‐depleted fraction 0.75±0.1. Thus, we turned to the proteomic measurements of the same sample set—aiming for the identification of individual proteins responsible for the observed changes in the IMFs.The HSA‐enriched and HSA‐depleted fractions reflect the largest differences between lung cancer and reference samples with p‐values below 10In line with previous research,[The first question we have addressed is: which proteins do we have to consider in order to model the differences in the IMFs between the lung cancer patients and reference individuals. The differential fingerprint is affected by the disease‐related absolute change in the protein concentration due to the linear character of the absorption measurement. Therefore, we ranked all detected proteins according to the absolute difference in average concentration between lung cancer and reference samples, as measured by MS (Table S5). Out of ten proteins that are most extensively changing, eight are also among the ten most abundant proteins in the blood sera.We further identify other proteins reflecting the differences between the two sample sets, such as alpha‐1‐acid glycoprotein‐1 and alpha‐1‐antichymotrypsin: although their concentrations in non‐symptomatic subjects are below the ten most abundant proteins, they are changing significantly in lung cancer patients and thus have to be taken into account to accurately model the disease differential fingerprint. In total, we considered twelve proteins for the model of lung cancer differential fingerprint, as shown in Figure p=0.1), being not sufficiently large (−9 %) as compared to between‐person variability (reference range ±45 %−1) due to its initially high abundance leading to an observable change in the infrared absorption. It is therefore important to take the albumin level into account when modelling the impact of a disease on an IMF. Moreover, previous studies have demonstrated lower level of HSA in lung cancer patients than in general population.[The change in the concentrations of some proteins, for example, HSA, does not reach statistical significance . In line with only a minor contribution of low‐abundant proteins and metabolites to the IR spectra of HSA‐enriched fraction, we find that the model very well reproduces the experimental differential fingerprint Figure .In summary, we observe statistically significant differences between the IMFs of blood serum of lung cancer pateints when compared to the IMFs of reference individuals. Biochemical fractionation and proteomic profiling of the very same sample set facilitated identification of the compounds responsible for these differences and revealed previously unappreciated pattern of changes in the concentrations of well known proteins that we find to be characteristic of lung cancer.Although FTIR has been used over decades and blood‐based studies suggested the applicability of this approach to disease diagnostics, the molecular nature of blood‐based infrared molecular fingerprints (IMFs) and changes therein has not been well understood. Being cost‐ and time‐efficient, suitable for high‐throughput approaches, IMFs could greatly contribute to clinical diagnostics if their robust correlation with any given condition is reproducibly demonstrated. Molecular understanding of the IMFs along with computational models may open up a path towards informed choice of biofluid (e.g. serum vs. plasma), improved sample preparation and possibly even initial steps of the biomarker identification. Here we examined the samples with two independent techniques—IR spectroscopy and mass spectrometry (MS)‐based proteomics—with the goal to elucidate the molecular entities dominating human blood‐based IMFs.As a first step to decompose chemical complexity of IMFs, we established a protocol for highly reproducible fractionation of crude human blood sera into three fractions: human serum albumin (HSA)‐enriched proteins, HSA‐depleted proteins, and metabolites. The strongest IR absorption signal in human blood serum arises from proteins. We therefore measured their relative concentrations in the samples using MS‐based proteomic profiling and used the concentrations of ten most abundant proteins to reconstruct individual spectra of the human blood serum. This concept is shown in the bottom part of Figure Infrared molecular fingerprints acquired by field‐resolved spectroscopyIn this study we use lung cancer as a case scenario of a medical condition, the outcome of which could significantly benefit from early detection. We find that IMFs of sera samples of lung cancer patients differ significantly from that of reference individuals. Using MS‐based proteomics, we identify a pattern of known highly‐abundant proteins that determine the observed change in the IMFs of blood sera. Some of them have been previously linked to cancer: unexplained hypoalbuminaenia has been associated with increased cancer risk,Importantly, although these proteins are not specifically challenging to detect and measure, they have previously not been used in a combined fashion to help detect or diagnose lung cancer. It is meanwhile widely accepted that using multiple biomarking molecules together, as a pattern, is more effective and robust for detecting a particular health condition than using a single biomarker.[Lung cancer induces a number of changes in the levels of blood serum proteins that have been previously linked to acute‐phase response, and it is well‐known that cancer is often associated with inflammatory states.[This study featured case‐control design, which is limited in that between‐person variability potentially masks the disease‐related signals. It has been recently demonstrated that IMFs of healthy individuals remain stable over clinically relevant periods of time,As the focus of future healthcare is shifting from treatment to early detection and prevention, such rapid, cost‐effective and holistic approaches as infrared molecular fingerprinting of body liquids will become ever more relevant. So far, infrared spectral changes in complex bioliquids were linked to multiple diseases but have remained uninterpretable with regard to which specific molecule accounts for a spectral change. In this study we looked systematically into the contributions of different constituents of blood serum to the overall IMF. In particular, we showed that the IMFs of blood serum can be to a high extent modelled using the concentrations of the ten most abundant proteins. With non‐metastatic lung cancer as an example of a medical condition, we showed that a number of highly abundant acute‐phase proteins are up‐ and down‐regulated in cancer patients compared to the reference group, leading to an observable change in the IMFs of blood serum. Accompanied by a meaningful molecular annotation, this change is more likely to find its use in everyday clinical practice.The paradigm presented here could in principle be used for any pathophysiological condition. After having recorded the IMFs of patients and compared them to matched reference individuals, one could use biochemical fractionation to determine which molecular class is responsible for the disease‐related differences and perform in‐depth omics profiling of the identified fraction Figure . This woThe authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.


Treron phoenicoptera) feeding in the wild. We aimed to test whether different types of food objects requiring different searching strategies elicit different eye/hemisphere biases. When feeding on relatively large, uniformly colored food objects (mahua flowers) which can be present or absent in the viewed patch, the majority of pigeons relied mostly on the left eye–right hemisphere. In contrast, when feeding on smaller and more abundant food objects, with color cues signaling its ripeness (sacred figs), right‐eye (left‐hemisphere) preference prevailed. Our results demonstrate that oppositely directed visual biases previously found in different experimental tasks occur in natural feeding situations in the form of lateralized viewing strategies specific for different types of food. The results suggest that pigeons rely on the hemisphere providing more advantages for the consumption of the particular type of food objects, implying the relevance of brain lateralization as a plastic adaptation to ecological demands. We assessed the success of food discrimination and consumption to examine the link between lateralization and cognitive performance. The use of the preferred eye resulted in better discrimination of food items. Discrimination accuracy and feeding efficiency were significantly higher in lateralized individuals. The results showed that visual lateralization impacted pigeons’ feeding success, implicating important fitness benefits associated with lateralization.Current research suggests that hemispheric lateralization has significant fitness consequences. Foraging, as a basic survival function, is a perfect research model to test the fitness impact of lateralization. However, our understanding of lateralized feeding behavior is based predominantly on laboratory studies, while the evidence from wild animals in natural settings is limited. Here we studied visual lateralization in yellow‐footed green pigeons ( The direction of visual lateralization is dependent on the type of food in wild pigeons. The use of the preferred eye resulted in better discrimination of food items. Lateralized individuals showed higher feeding success than non‐lateralized. T. phoenicoptera is a common resident species at both study sites  compared the data between study trees and (b) compared the data on birds from the same flock  and from a synthetic flock . The data collected in Kanha and Pench were evidently from different individuals because of the distance between the two study sites.2.2Madhuca longifolia, and sacred fig, Ficus religiosa. The distance between six study trees at one study site (three trees of each species) ranged between 320 and 1307 m.At each study site, pigeons were observed feeding on two species of trees: mahua tree, Mahua is a medium to large deciduous tree found in many parts of India and belonging to the family Sapotaceae. Mahua tree flowers are characterized by thick fleshy corollas which is the adaptation for pollination by fruit bats  were scored by both raters (KK and AG) independently at the beginning of the study. The degree of agreement between the two raters was quantified by kappa. In determining the eye used for the monocular inspection, the inter‐rater agreement was 90%  for the mahua flowers and 88%  for the sacred fig fruits. Since this agreement between the two raters corresponds to the “almost perfect agreement” level  used for monocular inspection of the patch prior to peck was recorded. If no monocular inspection was evident before the peck, it was recorded as non‐lateral. Whether or not the peck was successful was assessed based on the presence of swallowing head movement after the peck. From the voice recording, we subsequently scored the time from landing to the first peck of a flower/fruit and the total time of the feeding visit. The time when the pigeon “froze” for more than 5 s without displaying food searching behavior was excluded from the total feeding time. In the rare cases when the feeding was interrupted by social interactions with another individual, the observation was ceased and this individual was further excluded from the analysis to minimize the potential effect of social factors on lateralization and feeding success scores.2.4z test to classify each individual as having left/right eye preference or being non‐lateralized. To test population bias, lateralization index (LI) was calculated for each individual using the formula LI = (L− R)/(L + R), where L and R are the number of the left‐ or right‐eye inspections. LI scores range on a continuum from −1.0 to 1.0, with positive values indicating the left‐side bias and negative values indicating the right‐side bias. The absolute values of the LI (ABS‐LI) were used to assess the strength of preference. Population‐level lateralization based on LI scores was tested with a one‐sample Wilcoxon signed rank test, while the impact of the type of food objects on the strength of preference (ABS‐LI) was explored using a Mann‐Whitney test.Only the pigeons with at least 15 monocular inspections were included in the analysis. A number of left‐ and right‐eye inspections were compared with a binomial We analyzed the lateralization impact on feeding accuracy and efficiency based on the frequency of pecking errors (pecks not resulted in food consumption) and ingestion rate (food items consumed per minute), respectively. The frequency of pecking errors was compared between lateralized and non‐lateralized pigeons (Mann–Whitney test). Further, in lateralized individuals, we assessed the influence of the eye used for inspection of the patch on the accuracy of the peck by comparing the proportion of the errors made under left‐ and right‐eye viewing conditions (Wilcoxon matched‐pairs signed rank test). For this analysis, we used a subsample of lateralized pigeons with six pecking errors per individual. The birds which made fewer errors were excluded. In birds with more than six errors, only the first six were used for a balanced design Tables . For theTo investigate the influence of potential sampling of the same individual within different flocks, we applied a Monte Carlo simulation approach and generate synthetic flock datasets Table . A real z test) as a dependent variable and study sites as factors as well as with Mann‐Whitney test on LI scores. For both mahua flowers and fig fruits, we used LI scores to test relationships between lateralization in pigeons and (a) a particular tree on which feeding birds were observed, and (b) the flock, the pigeon belonged to using linear regression analyses (LI vs. particular tree/flock) and Kruskal–Wallis tests (based on LIs).The data from two study sites were compared to test the consistency of lateralization for each type of food with a multinomial regression with individual preferences  to total lateral peck was compared between lateralized and non‐lateralized individuals (Mann–Whitney test).All analyses were two‐tailed with α set at 0.05.33.1In total, the consumption of 2480 mahua flowers by yellow‐footed green pigeons was analyzed . For 1565 flowers monocular inspection prior to pecking was recorded . We recorded 15 or more monocular inspections of mahua tree flowers for 74 pigeons (44 at the “Pench” study site and 30 at the “Kanha” study site).For the sacred fig, we recorded consumption of 1778 fruits , and for 930 fruits monocular inspection prior to pecking was observed . For a total of 43 pigeons 15 or more monocular inspections was recorded (25 at the “Pench” site and 18 at the “Kanha” site).U = 688, p < .001; Tables Time from landing on a tree to the first peck (feeding latency) was higher during feeding on sacred fig fruits (mean 15.0 ± 1 s) than during feeding on mahua tree flowers  and fig fruits . In contrast, when feeding on fig fruits, the majority of lateralized pigeons had a right‐eye preference  and the right‐eye preference for fig fruits . While the direction of lateralization was different for mahua flowers and fig fruits, the strength of lateralization (based on Abs‐LI) did not differ between two types of food objects .The significant majority of pigeons displayed individual preferences for one eye when inspecting both mahua flowers  as compared to lateralized individuals , and vice versa; the left‐lateralized pigeons were more likely to make an error after the right‐eye inspection .In lateralized individuals, we further compared the error rate was under left‐ and right‐eye viewing conditions during feeding on mahua flowers. The proportion of pecks not resulted in food consumption was higher when the pigeons used their non‐preferred eye. The individuals with the right‐eye preference were more likely to make pecking errors when they used their left eye for food inspection prior to the peck . When feeding on fig fruits, individuals with the right‐eye preference were significantly more efficient than individuals with the left‐eye preference . At the same time, no difference in feeding efficiency was found between left‐ and right‐lateralized pigeons during feeding on mahua flowers . In addition, we found that time between landing on a tree to the first peck was shorter in lateralized pigeons than in non‐lateralized for both types of food objects .To assess the effect of lateralization on individuals’ feeding efficiency, we compared the ingestion rate between lateralized and non‐lateralized pigeons and between right‐lateralized and left‐lateralized pigeons. The efficiency of feeding was significantly lower in non‐lateralized individuals for both types of food  or fig fruits . A multinomial regression analysis on the distribution of left, right, and non‐lateralized pigeons  also failed to reveal the influence of the study site  did not differ between two study sites either for mahua flowers  and a particular tree on which feeding birds were observed for both mahua flowers and fig fruits  was significantly higher in the case of sacred fig than in the case of mahua tree. That is, pigeons needed more time to look for the ripe fruits and choose the most promising patch to start feeding. In addition, when pigeons were feeding on fig fruits, almost every peck was followed by swallowing, implying a very low error rate in choosing a food item suitable for consumption. Feeding on mahua flowers, in contrast, was associated with a noticeable portion of unsuccessful pecks (not followed by the food item consumption), indicating a higher error rate and, potentially, more difficulties in making the correct choice of the food item. This result implies that the two types of food objects studied here require different types of analysis to be involved in feeding.Considering feeding on two types of food as two distinctive cognitive tasks, we can further try to explain the directions of the revealed preferences. A large amount of evidence on vertebrates indicates that the brain hemispheres use distinctive cognitive strategies, with the right hemisphere relying mostly on more systematic and spatially focused analysis, and the left hemisphere takes responsibility for categorical distinctions and the selection of targeted stimulus from among alternatives  when feeding on mahua flowers in lateralized and non‐lateralized pigeons and found the impact of lateralization on food discrimination accuracy. Pigeons with significant visual preferences were more successful in pecking food items suitable for consumption. This result suggests that in pigeons, being lateralized improves food discrimination accuracy and, consequently, enhances feeding success and confers fitness benefits. Furthermore, we found that feeding accuracy was higher when the individuals with the right‐eye preference used their right eye  to view the food prior to pecking. Thus, regardless of the direction of preference, the use of the preferred eye provides better discrimination of food items, demonstrating a background of the prevalence of individual‐level lateralization in the population.Turdus migratorius, egg‐rejecter hosts, which were more lateralized in the inspection of clutch showed higher rates of model eggs rejection  between lateralized and non‐lateralized pigeons. Faster consumption can be especially advantageous in the case of foraging on a fruiting (flowering) tree when the food is available only for a limited time, and there are numerous hetero‐ and conspecific competitors around  was associated with greater feeding success. From an evolutionary standpoint, this result may illustrate the simple background for the unequal numbers of left‐ and right‐lateralized individuals in the population. As the individuals with the right‐eye preference enjoy fitness benefits from higher feeding efficiency, they have a greater chance of survival and, consequently, prevail in the population.For mahua flowers, no significant difference in feeding efficiency was found between the individuals lateralized in the opposite directions. This result indicates that while being lateralized increases consumption rate, the particular direction of the lateralization does not further improve it. Why then do the pigeons with the left‐eye preference for feeding on mahua flowers prevail in the population? It is conceivable, that while the greater involvement of the right hemisphere doesn't result in faster food consumption, it confers other important advantages to the individuals. Some specific functions of the right hemisphere, for example, the superiority in the decisions based on a memory‐based exemplar strategy ; Data curation ; Formal analysis ; Funding acquisition ; Investigation ; Methodology ; Project administration ; Resources ; Supervision ; Validation ; Visualization ; Writing – original draft ; Writing – review & editing . Andrey Giljov: Conceptualization ; Data curation ; Formal analysis ; Funding acquisition ; Investigation ; Methodology ; Project administration ; Resources ; Supervision ; Validation ; Visualization ; Writing – original draft ; Writing – review & editing .https://doi.org/10.6084/m9.figshare.16574840.This article has earned an Open Data Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at Tables S1–S7Click here for additional data file.


Free‐roaming animal populations are hard to count, and professional experts are a limited resource. There is vast untapped potential in the data collected by nonprofessional scientists who volunteer their time to population monitoring, but citizen science (CS) raises concerns around data quality and biases. A particular concern in abundance modeling is the presence of false positives that can occur due to misidentification of nontarget species. Here, we introduce Integrated Abundance Models (IAMs) that integrate citizen and expert data to allow robust inference of population abundance meanwhile accounting for biases caused by misidentification. We used simulation experiments to confirm that IAMs successfully remove the inflation of abundance estimates caused by false‐positive detections and can provide accurate estimates of both bias and abundance. We illustrate the approach with a case study on unowned domestic cats, which are commonly confused with owned, and infer their abundance by analyzing a combination of CS data and expert data. Our case study finds that relying on CS data alone, either through simple summation or via traditional modeling approaches, can vastly inflate abundance estimates. IAMs provide an adaptable framework, increasing the opportunity for further development of the approach, tailoring to specific systems and robust use of CS data. Integrated Abundance Models (IAMs) address the key challenge in abundance modelling of false positives that can occur due to misidentification of nontarget species. Integrating data sources of differing quality improves precision of abundance estimates by accounting for misidentification biases. The framework described is adaptable, and we hope it provides a useful introduction to the concept to allow further optimization of the approach and tailoring to specific systems Indeed, it is advocated and used to model species distributions . We test model performance under different scenarios, including the degree of variation in the high‐quality data, the prevalence of high‐quality data included, bias in detection data and ecological differences.To demonstrate the approach further, we describe a case study of our own application of an IAM to estimate the number of unowned cats within an urban area. Determining the number of unowned cats in urban areas is difficult in part because of problems accurately distinguishing owned from unowned cats. Indeed, many research studies in urban areas focus on free‐ranging cat populations due to an inability to differentiate between cat subgroups  with spatial variation at each site described by a Poisson distribution with a mean (Eλ).Ecological. The species has a local abundance in the N‐mixture models are described briefly here for context but are explained extensively elsewhere  are described by a binomial distribution with a sample size iN and detection probability p.Observation. The observed counts at each site and during each replicate survey , whereby expert consensus is available on the abundance of individuals in some, but not necessarily all sites; (b) an observation process that can account for both false‐positive and false‐negative errors in the observed replicate counts for each site . IAMs assume that replicate counts are conducted over a period of population closure.Existing Bayesian approaches to N‐mixture models provide the basic framework to model abundance from replicate counts. Here, we propose an integrated modeling approach to analyze multiple data sets simultaneously. Specifically, an IAM differs from an N‐mixture model in two key ways: (a) The addition of a independently collected high‐quality data set (2.4w) at sites i are linked to true site‐specific population sizes (iN) via a Poisson distribution, which is suited due to its natural constraints to yield integer values of zero and above. Such assumptions are commonly seen in integrated population models, specifically the modeling of population count data, whereby counts through time are assumed to not be subject to systematic biases, but rather observation error . True site‐specific population sizes (iN) are therefore a function of whether a site is occupied or not and a site‐specific mean iλ. When expert data on occurrence can be inferred from expert consensus, this can be included in iz.whereby k) can be included to mimic overdispersion or underdispersion of the observation error variance.A feature of the Poisson distribution is that its variance is equal to its mean. Although this assumption is commonplace in many demographic studies  at sites i are linked to true site‐specific population sizes via a mean of iN and a variance scaled to iN. A k less than one would imply reduced variance relative to a Poisson distribution and k greater than one implies increased variance relative to Poisson. The above enables variance to be zero  if a site is unoccupied. An additional option would be to obtain estimates of experts’ precision, through the collection of supplementary data, and use these estimates as prior information in the IAM.Here, observed expert counts  at each site i and during each replicate survey j are linked to true site‐specific population sizes (iN) via a detection probability (p) and the expected number of misidentifications (m). We apply a Poisson distribution to account for additional stochasticity in spatial replicates not accounted for in the systematic biases (m and p).The CS data consists of spatial and temporal replicates. Instead of applying an N‐mixture model, an IAM accounts for both detection probability and misidentification of target species in CS counts.2.6i is a common parameter to both the CS data and expert data. Combining their likelihoods borrows strength from the few well‐studied sites to infer detection biases across locations and to inform abundance estimates. A benefit of this particular integrated modeling approach is that it allows estimation of misidentification, which is otherwise unidentifiable from CS data alone.Inference is based on the joint likelihood that is a product of the likelihoods from the single data sets. Expert data are not required for all sites, but there must be some overlap of expert data and CS data for a subset of sites. A key element is that N2.7To test the performance of the IAM, we simulated pseudo‐data using known parameter values to create 100 independent data sets for 30 different scenarios across five broad simulation experiments , identical to Poisson distribution (k = 1) and then variance increases up to double that assumed by a Poisson observation error (k = 2). We also tested how the IAM performs with variation in the prevalence of expert data included, in abundance, and in levels of site occupancy.We first explored the effect of the quality of expert count data by simulating variance in observations. Given m, ranging from no misidentification to 25 false‐positive identifications per site.Finally, we tested the performance of IAMs in response to manipulation of the misidentification parameter p) was held at 0.8 for all scenarios. For each of the above scenarios, all other parameters remained constant to test the parameter of interest , and when expert data are only available for a subset of sites it is removed randomly using a random number generator  that we refer to as N, alongside the detection and misidentification parameters.For each scenario, we explore performance in terms of accuracy , precision , and bias . The model provides per‐site abundance estimates Appendix ; however33.1k > 1), resulted in reduced accuracy and precision in estimates of abundance and detection (Appendix k = 1.5) and only 64% of simulations were accurate when variance was double that expected from a Poisson distribution (k = 2). However, misidentification estimates were unchanged by variance in expert counts  for all scenarios and parameters, likely due to reductions in precision in situations where there is low expert coverage Figure . Althoug3.3The IAM performed without bias under simulated scenarios with different levels of misidentification Figure . All par3.3.1Abundance estimates had high accuracy and no bias regardless of the size of the underlying sample population. Precision in estimates reduced with population size, as would be expected with variability increasing with abundance Figure . In cont3.3.2The IAM had high accuracy, low bias, and a constant degree of precision in estimates of abundance, regardless of the occupancy of the population Figure . However3.4To illustrate the biases risked by ignoring misidentification and to show that IAMs remove these biases, we analyze data from a study of unowned cats in an urban area. Data were collected as part of a wider community outreach program in Bulwell, Nottingham, UK called “Bulwell Cat Watch”. The project itself combines community engagement with neutering and rehoming operations with the aim to bring about human behavior change and control cat numbers . At that stage, residents were asked how many cats they know of locally and how many they think were owned in the form of a multiple‐choice question with the following options: none, 1–2, 3–4, 5–9, 10, or more, from which the number of unowned cats was derived. When a range was selected, the central value was taken; for ten or more, we used 15 (the average from reports when 10 or more was specified). Location data were available for 695 survey responses, within which there were estimates of 1,318 unowned cats.Survey data: The first consisted of an initial cross‐sectional random‐sample door‐to‐door survey carried out with approximately 10% of households  recorded when and where an unowned cat was found or where unowned cats were not present. These data are considered of higher quality, due to the ability of the CT to correctly identify an unowned cat and with no risk of double counting the same individual. Unowned cats can be either stray or feral. Protocols to accurately identify a stray cat included: scanning for a microchip, attaching a paper collar to notify potential owners, advertising online, door‐to‐door notifications, local posters and contacting other animal welfare organizations, including veterinary practices. If no owner was found during this process, it was identified as unowned.Feral cats were more likely to be identified via behavioral means, as they have not been socialized to humans, they will be more fearful and will not approach humans  enabling geo‐referencing of unowned cat location data.3.4.4Estimates of unowned cats obtained from the public are prone to biases due to misidentification with the owned cat population and duplicate sightings from closely situated residents.To account for duplicate sightings, the CS data required clustering to account for neighbors in close‐proximity reporting the same cats and for a certain degree of cat movement. There is limited understanding of urban unowned cats in the UK; however, studies of urban unowned cats in other areas indicate home range sizes between 3.7 and 10.4 ha for urban areas  integrating survey data with expert data and (b) integrating report data with expert data. Expert data were not available for all sites, 75% expert coverage for the survey data (21 out of 28 sites) and 91% expert coverage for the report data (20 out of 22 sites).3.4.5Assessing model fit of Bayesian hierarchical models applied to field data is complex, with each proposed solution associated with its own strengths and weaknesses . Third, our case study includes two forms of CS data. If biases are accurately accounted for and true abundance is identifiable, we would expect there to be overlap in the abundance estimate when these two models are run separately . Fourth, we ran further cross‐validation on our IAM by removing one expert data point at a time, conducting the analysis, and checking how model predictions match up. The rationale being if there were heterogeneity unaccounted for and/or specific sites that may be outliers worthy of investigation, then the removal of those data points would result in outcomes inconsistent with the overall model . Finally, we simulated data sets structured according to the raw data and parameterized using the model estimates to check model performance under similar scenarios  with high‐quality expert counts from a subset of locations. IAMs perform well in terms of precision, accuracy, and lack of bias across a wide range of simulation experiments. Our case study illustrated the applicability of IAMs to real‐world data, offering a solution to the problem of misidentification bias. We discuss the benefits and limitations of this framework, alongside possible avenues of development of this new toolkit.IAMs provide an ideal opportunity to integrate data sets accounting for different biases between citizen scientists and experienced surveyors to help provide unbiased estimates of the abundance of important animal populations. This model does not require error‐free expertise, increasing the applicability of this approach. Data derived from expertise in our model framework are still subject to an observation error similar to that commonly used in state‐space models of population counts where it is applied to estimate temporal measures of abundance ; data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); project administration (lead); resources (lead); software (lead); writing–original draft (lead); writing–review and editing . Dave Hodgson: Methodology (supporting); writing–original draft (supporting); writing–review and editing .For the simulation study, data were completely simulated which did not require approval from an ethics committee. The case study example used was based on a subset of data collected as part of a study that had been approved by University of Bristol Faculty of Health Science Research Ethics Committee approval number 38661.Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.Appendix S3Click here for additional data file.


Investments in the beginning was described as creating contact, showing the agenda and being adaptive, while explore the patient perspective included showing awareness, being explorative and creating a participating climate. Show empathy consisted of showing openness, being confirmative and creating acceptance, while showing a structured follow‐up plan, being open minded and invitational and creating motivation to build on were descriptions of invest in the end. Awareness of potential facilitators and barriers for patient‐centred communication during the beginning, middle and end of a continuous positive airway pressure treatment consultation can be used to improve contextual conditions and personal communication competences among practitioners working with continuous positive airway pressure treatment initiation.Adherence to continuous positive airway pressure treatment for obstructive sleep apnea tends to be poor. Communication influences adherence but has not previously been investigated from a practitioner perspective, although shared decision‐making is known to be of great importance. The aim was to describe how practitioners experience communication with patients with obstructive sleep apnea during the initial visit at a continuous positive airway pressure treatment clinic, with focus on facilitators and barriers related to the 4 Habits Model, a communication model comprised of four types of interrelated skills to make encounters more patient‐centred: investing in the beginning; exploring the patient perspective; showing empathy; and investing in the end. A descriptive design with qualitative content analysis was used. A deductive analysis was carried out based on interviews with 24 strategically selected practitioners from seven continuous positive airway pressure treatment clinics. The 4 Habits Model was used as a framework for identifying facilitators and barriers to communication.  The pilot tested interview guide was based on five questions, as follows. (a) Can you describe situations that affect communication between you and the patient during the initial visit? (b) Can you describe situations at the beginning of the visit that affect communication? (c) Can you describe situations that affect how you explore the patients’ situation? (d) Can you describe situations that affect how you create contact and show empathy during the conversation? (e) Can you describe situations that affect the ending of the conversation? Probing questions, such as: “Can you describe this in more detail?”, “How did it affect the conversation?”, “What did you say next?” and “What were you thinking?” were used. The duration of the audio‐recorded interviews ranged from 15 to 75 min.2.3Verbatim transcripts of all interviews were produced, resulting in 295 A4 pages of single‐spaced text in 12‐point Times font. The deductive analysis was performed by a multi‐professional team of clinicians and researchers . Credibility was based on the connection between the aim and the chosen method, i.e. qualitative content analysis (Graneheim & Lundman, 5Creating contact, showing the agenda and being adaptive were used in the beginning of conversation, while showing awareness, being explorative and creating a participating climate were used to explore the patient perspective. Showing empathy consisted of showing openness, being confirmative and creating acceptance, while showing a structured follow‐up plan, being open minded and invitational and creating motivation to build on were descriptions of investing in the end.None.


Integr Environ Assess Manag 2021;17:552–561. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology   Chemistry (SETAC)The guidance document on risk assessment for birds and mammals (GD) provides generic residue values of pesticides on potential diet items for use in wildlife risk assessments. For most diet items, these values are based on a large number of residue studies. However, the default residues per unit dose  values for fruits were taken from a few literature trials of unclear relevance for regulatory purposes. These trials were conducted according to neither current European Union (EU) agricultural practice nor to recommendations given in the GD about how to conduct wildlife‐relevant residue studies. Therefore, field study data on fruit residue levels from applications of pesticides in fruiting crops were compiled and evaluated. Corresponding studies had been conducted during the last 26 y in the EU. In the final data set, 291 studies provided 1002 residue values in different fruits, including grapes, berries , fruits from orchards , gourds , and strawberries. This data set provides a basis for revising registration‐relevant RUD values for fruits as potential diet items for birds and mammals in environmental wildlife risk assessments. The objective of this study was to estimate the resulting residue levels in different fruits determined under field conditions following the application of pesticides across their growing areas within the EU in diverse climatic areas that can be used directly in wildlife risk assessments. The large data set of usually about more than 100 residue values per “fruit group,” all evaluated at EU member state level, revealed significantly lower RUDs compared to the current default RUDs presented in the GD. These new RUD values for fruits should be considered for use as default values in future bird and mammal risk assessments and in respective guidance documents.  Extensive data sets on pesticide residues in fruit are analyzed, which considerably increases the knowledge base in this area.The extent of the data sets used to calculate new fruit residues per unit dose (RUD) values is a major strength.This work provides valuable information for regulatory risk assessments and tests some of the underlying assumptions that play a role in the risk assessment of pesticides to birds and mammals.It adds to the ongoing revision of the European Food Safety Authority's guidance document on risk assessment for birds and mammals. Potential negative effects of pesticides on nontarget wildlife need to be assessed according to the European Union (EU) Regulation 1107/2009 (EC 2009). For this purpose, the current European Food Safety Authority (EFSA) guidance document (GD) on risk assessment for birds and mammals EFSA .The current scheme of the European environmental risk assessment follows a tiered approach, starting with worst‐case assumptions. For a risk assessment on birds and mammals exposed to pesticides, a toxicological endpoint, determined in toxicological studies, is compared to exposure estimates for representative wild bird and mammal species . Moreover, for some crops, such as strawberries, no crop‐specific residue data are available in Baril et al.  residue values were then used as the basis for statistical analysis to derive representative RUD values in fruits as potential diet items for frugivorous birds and mammals.In addition, we investigated in detail the relationship between the application rates used in these studies and the resulting residue levels. The RUD concept in the EFSA GD is based on a linear extrapolation of expected environmental concentrations across different application rates. Therefore, we tested whether this basic assumption is also fulfilled in the fruit residue data set here. Furthermore, we examined for each fruit category (according to the GD) whether the fruit species within each group show significant differences from each other.Finally, we provide new default RUD values for environmental risk assessments for frugivorous scenarios of birds and mammals, which are considered more appropriate and reliable than the current default values (see Table n = 1–8) per study after pesticide application (insecticides and fungicides)  and evaluated at EU member state level as acceptable.Percentage recovery of spiked samples during analysis was not below 80%, ensuring that the residues found do not significantly underestimate the actual value.For “grapes” and “large fruits from orchards,” only trials with 1 application were considered due to the large number of available trials covering these crop groups.For “berries,” “gourds,” “small fruits from orchards,” and “strawberries,” trials with up to 8 applications were used. As a conservative approach, the first residue measurement directly after the last application was considered.In total, 291 field studies  conducted between 1991 and 2017 were available, measuring residue levels on fruits in different numbers of separate field trials (Relevant information regarding the pesticide used (active substance [a.s.]), application method, application rate, concentration of a.s., fruit type, BBCH growth stage , country, time of sampling, and the residue concentrations was extracted from each study report. Residue levels after last treatment  were taken for further analysis.The final data set consisted of 1002 initial or maximum residue values  from the following fruit types: grapes, currants, raspberries, gooseberries, apples, peaches, pears, lemons, mandarins, oranges, apricots, cherries, plums, pumpkins, cucumbers, squash, melons, and strawberries. The active substances used in 34% of the trials were insecticides and in 66% were fungicides.The GD EFSA  defines Data analysis was performed using Microsoft Excel  without overlooking potentially relevant differences between subsets of the data. Therefore, the data were examined with regard to identifying differences in residue levels attributable to recognizable fruit characteristics or groups of fruit within the groups of crops treated together in risk assessments under the current GD EFSA . HoweverWe calculated the RUD for each residue value by dividing the highest measured value by the applied rate of pesticide (or the rate at last treatment in cases of more than 1 application) to be most conservative. Successive applications are usually undertaken to maintain an efficiency level. In any case, higher residues and therefore RUDs will result from multiple applications irrespective of application intervals and rates applied before. Hence, using the last application rate before first samplings will lead to a conservative estimation of the RUD.P < 0.05 were considered to indicate statistically significant differences. If tests did not indicate significance, the data were not separated but were further analyzed together in order not to split the data set into unnecessarily small groups.Then we tested whether the data distributions of the resulting RUD values followed Gaussian distributions using the Kolmogorov and Smirnov method  per diet fraction or fruit group, even if from a statistical point of view the median would be more representative for such data distributions, that is, if the data did not follow a normal distribution.We computed medians and quantiles as representative parameters per subset due to data distributions. However, means and standard deviation were also calculated and are given in table format as is done for the current default residue data in the GD EFSA . It shouThe data set was analyzed in terms of identifiable groups (subsets) within the regulatory‐relevant fruit groups to ascertain possible different residue loads due to the geographical area from which the data originate.With regard to the EU pesticide risk assessment scheme in ecotoxicology, the EU is divided into 3 regulatory zones, North, Central, and South EC . BesidesWithin the regulatory‐relevant groups of berries, gourds, and fruits from orchards, we examined whether the appearance and surface texture of fruits potentially had any impact on residue levels.Because for berries, gourds, small fruits from orchards, and strawberries, trials with up to 8 applications were used, testing was done to check whether the number of applications has a significant effect on the level of residues, which would mean that pooling the data is not appropriate.When calculating the exposure, the RUD values are multiplied by the respective application rate according to the application pattern. Therefore, all subsets or fruit groups, respectively, were examined to determine whether the basic assumption of a linear relationship between application rate and resulting residue levels was confirmed. This was done because a linear relationship makes it possible to use the residue data derived from any study at any application rate. Hence, a linear function should at least approximately describe the relationship with sufficient reliability. To test for linearity, we performed a linear regression and then tested whether the data differ significantly from the determined linear equation with the runs test (RT)  overestimated or underestimated the measured values. Therefore, the calculated residues were compared with the measured residues at the same application rate. To be conservative in terms of risk assessment, it was therefore requested that a higher proportion (at least >2/3) needed to exhibit the higher values than those measured by calculating the residues according to the RUD method.The purpose of the present analysis was to investigate the relationship between pesticide application rates and the residue levels in fruits that are treated together in risk assessments as determined in crop groups. Therefore, the results are presented with regard to the crop groups as given in the GD EFSA .P = 0.17). In addition, no significant deviations from linearity were found for both S‐EU  and C‐EU . This confirms that both data sets meet the requirements for RUD calculation according to the GD . Therefore, we followed the classification in the GD . Therefore, these potential subsets were grouped. The runs test indicated no significant deviation from linearity , confirming that the residue data in large orchard fruit meet the requirements for the RUD calculation according to the GD (EFSA P = 0.62). It was therefore considered acceptable to combine the zonal data to derive an overall RUD value for large fruits from orchards , thus only slightly exceeding the BBCH range given for the corresponding frugivorous scenario in orchards according to the GD EFSA , which i GD EFSA . The medn = 192). The developmental stages of the fruits analyzed range from BBCH 77 to 88, thereby slightly exceeding the fruit growth stages in the GD crop group “orchards” . Measured residue levels of studies with 1 or 2 applications, based on sampling taken on the day of application, did not differ significantly from each other . The runs test again indicated no significant deviation from linearity , confirming that the residue data in small orchard fruit meet the requirements for the RUD calculation (see EFSA In the GD EFSA  residue see EFSA .P < 0.02), with a median of 2.0 mg/kg for S‐EU (n = 77) and a median of 1.4 mg/kg for C‐EU (n = 115). In the S‐EU, apricots accounted for about 67% of the residues, whereas in the C‐EU, cherries and plums accounted for 84% of the residues. Therefore, the data for further analysis were kept separately by location.The S‐EU data were available from southern France, Italy, Spain, and Greece and C‐EU data from Germany, northern France, Austria, the Netherlands, Belgium, Hungary, Poland, the Czech Republic, and the United Kingdom for small fruits from orchards. Here, the residue levels of the 2 geographical subsets based on the different regulatory zones differed significantly from each other . Plums exhibited significantly lower residue levels  than either cherries  or apricots  .Comparing differences between RUDs of individual fruit samples , variation among different fruits was apparent . The number of applications (up to 8 treatments in some studies) also had no significant influence on the residue level measured on fruit samples after the last application in each case . Therefore, the runs test and the other analyses were done with the full data set, irrespective of fruit shape or number of applications.Fruits such as pumpkins, cucumbers, squashes, and melons are combined in the EFSA GD as gourds. The  application took place during fruit development and maturity of fruits and seeds (BBCH 71–89), thus matching the requirements for fruiting vegetables scenarios as given in the GD EFSA . With 26P = 0.46). This confirms that residues in gourds meet the requirements for the RUD calculation. The comparison of the 2 geographical subsets revealed significant differences in residue data between studies conducted in S‐EU  with a median of 0.7 mg/kg (n = 209) and C‐EU  with a median of 0.4 mg/kg . In the interests of a conservative approach and for pragmatic reasoning, the data set leading to the higher of the 2 values is selected to provide a single value for risk assessment for gourds , indicating that size and skin structure had no significant influence on the residue content. For gooseberries only a limited number of residue measurements were available (n = 3), which excludes any meaningful statistical comparison.In the EFSA  GD categP = 0.098). No deviations from a linear model were indicated for S‐EU  and C‐EU . This confirms that both data sets meet the requirements for the RUD calculation. The median RUDs of S‐EU  and C‐EU  did not differ significantly from each other  and an overall RUD value was thus derived (see Table The number of applications (up to 5) did not significantly influence the final RUD values  differ significantly from those in other berries  . This indicates that strawberries should also be treated as a separate crop in the future.The GD EFSA  defines P = 0.154) or between S‐EU and C‐EU . Therefore, a single RUD value for strawberries can be recommended (see Table P = 0.0001).A risk assessment for frugivorous birds is required in strawberries from flowering through development of fruit to maturity of fruits (BBCH 61–89) in the GD EFSA . In the To check whether the assumption of a linear relationship when using this value in risk assessments would over‐ or underestimate the risk due to exposure, the calculated residues (RUD concept) were compared with the measured residues. An underestimation of the actual residue level should be avoided. An evaluation of the cases in which the predicted values of the linear model are compared with the measured residues showed that in 81% of cases . Baril et al.  for all required fruiting crop groups are provided (including strawberries). These RUD values are mostly significantly lower compared to the default RUDs EFSA . HoweverResults section, regarding fruit residues, a subdivision into different zones seems not to be justified or required. In general, there were only small or negligible differences in residue levels between trials conducted in S‐EU and C‐EU. If a statistically significant difference was found , the absolute difference between mean RUD values was still small. Nevertheless, in these cases, the zonal data set was evaluated separately and the highest value of an identifiable group of fruits is proposed to be taken as default to follow a conservative risk assessment approach (such as Tier 1). Hence, a joint consideration of the data appears more reasonable than to make further distinctions, and we propose to use a single RUD value per fruit group for S‐EU and C‐EU zones in general. This pragmatic approach is in line with the current use of the GD  or within the group of berries .n = 44), covering all other small orchard fruits for first tier evaluations (n = 192).In the category of “small fruits from orchards,” different fruits have been combined  as per EFSA Results section, the number of applications had no significant influence on the residue level in all tested crop groups. This indicates that the selected application intervals of modern plant protection products generally do not lead to a build‐up of residues in fruits but rather maintain an even level to be efficient against the targets.For “grapes” and “large fruits from orchards,” it was possible to consider only trials with 1 single application due to the large data set available. For the categories of “other berries,” “gourds,” “small fruits from orchards,” and “strawberries,” studies with more than 1 application were also used to obtain a larger data set. In these cases, values from the day of the last application were taken, representing a conservative estimate of RUD values because RUDs should be highest after the last application. However, as shown in the The GD EFSA  requiresThe results of the runs test indicate that the residue data in strawberries deviates from a linear relationship between residue and application rate. Previously published results also indicate that the relationship between application rate and residue concentration might not actually be linear in general (Bennett et al. As shown by Baril et al. , the meaThe study of the fruit residues indicates that fruit residues are based on a complex interaction between fruit characteristics, abiotic factors during application, and chemical properties of the substance. However, the analysis of the linearity in the relationship between application rate and residues confirms that the current RUD concept can calculate expected residues following different application rates with reliable accuracy. Even in cases where the linearity is not given, as in the “strawberry” group, the method will rather overestimate the actual mean residue level. The RUD concept, therefore, can be considered valid and conservative in regard to the reported fruit residues proposed to be used as defaults in first tier risk assessments.For small orchard and berry fruits, our RUD data sets are of a similar order of magnitude to the data sets of Baril et al. , whereasIn summary, the present work contributes significantly to bringing more reliability into EU risk assessment procedures for pesticides by assessing residue levels in fruits determined under agricultural field conditions in nearly all fruit‐growing areas of Europe. Based on a large experimental data set of near to or exceeding 100 residue trials per fruit group from studies evaluated in EU member states, considerably lower RUD values compared to the current default EFSA  RUD valuThese new RUD values are considered relevant and appropriate for use in wildlife risk assessments of pesticides in Europe. The proposed default values follow the current EFSA  GD crop List of fruit residue values used for analysis:GrapesOther berriesGourdsLarge fruits from orchardsSmall fruits from orchardsStrawberriesThis article contains online‐only Supplemental Data.Supporting information.Click here for additional data file.


Symptom scores were strongly positively correlated with functional difficulty scores  and moderately negatively correlated with overall health . This is the first study reporting on severity phenotypes in a largely nonhospitalized PCS cohort. Severity phenotypes might help stratify patients for targeted interventions and planning of care pathways.There is currently limited information on clinical severity phenotypes of symptoms and functional disability in post‐coronavirus disease 2019 (COVID) Syndrome (PCS). A purposive sample of 370 PCS patients from a dedicated community COVID‐19 rehabilitation service was assessed using the COVID‐19 Yorkshire Rehabilitation Scale where each symptom or functional difficulty was scored on a 0–10 Likert scale and also compared with before infection. Phenotypes based on symptom severity were extracted to identify any noticeable patterns. The correlation between symptom severity, functional disability, and overall health was explored. The mean age was 47 years, with 237 (64%) females. The median duration of symptoms was 211 days (interquartile range 143–353). Symptoms and functional difficulties increased substantially when compared to before infection. Three distinct severity phenotypes of mild ( The Real‐Time Assessment of Community Transmission (REACT) study analyzed more than half a million community cases and identified two symptom clusters – a large cohort with fatigue symptoms and a smaller cohort with respiratory symptoms.With the growing number of cases of PCS, there is a need to understand symptom clusters, symptom severity, and interference with daily functioning. By stratifying patients based on severity, appropriate interventions and treatment plans can be prescribed, and the trajectory of the condition mapped. The aim of this study was to explore the presence of symptoms severity phenotypes in a community PCS cohort, including a large proportion of nonhospitalized participants, and understand the relationship between severity of symptoms, functional disability, and overall health in PCS.22.1This service evaluation study was conducted within a COVID Rehabilitation service in the North of England. This is a multidisciplinary rehabilitation service, which offers specialist assessment and treatment for PCS, delivered virtually and/or in person, delivered by a specialist multidisciplinary team. Patients are referred via General Practitioners (GPs) when symptoms have persisted beyond 12 weeks and cannot be explained through alternative medical assessment. Patients were not required to have had a positive polymerase chain reaction (PCR) test or antibody test as these were not widely available to the general population of the UK at the start of the pandemic.2.2Consecutive patients referred to the COVID Rehabilitation service between February 2 and May 3, 2021 were considered for this service evaluation study regardless of whether they had been previously hospitalized with COVID‐19. All patients had agreed for their data to be used anonymously for research purposes or service evaluation. The eligible patients were prompted to complete the latest self‐report version of C19‐YRS by one of the research team members. In total, responses were received from 370 patients.2.3The information gathered on C19‐YRS includes demographic information, medical history, and 16 key symptoms of PCS  and their impact on five daily functions .2.4Overall severity of the 12 most reported symptoms was defined as the mean symptom score, with a mean score of 6 or more considered “severe,” 3 to 5.9 “moderate”, and less than 3 “mild.” Demographic details and brief clinical history were summarized overall and by symptom severity. Functional abilities were reported on the same 0–10 scale.All pairwise Spearman's correlations across symptoms and functional abilities were calculated and presented graphically as a heat map. Symptoms were then categorized as severe (6 or more) or not, and the frequency of each combination of multiple severe symptoms was presented as an UpSet diagram.Cluster analysis was used to identify any groupings or co‐occurrence of symptoms that could indicate potential different phenotypes amongst the participants. Two approaches were used: k‐means partition cluster analysis and a hierarchical agglomerative cluster analysis using average‐linkage between clusters and the Euclidian dissimilarity measure. Robustness of results was assessed using 2, 3, and 4 clusters and with different starting values  and using different weights and dissimilarity measures . All statistical analyses were carried out using Stata version 16.1 and R version 4.0.5.2.5Data were collected in the service as part of routine clinical evaluation and ethical approval was obtained for the secondary analysis of anonymized data collected for the primary clinical purpose which has been completed. A favorable ethical opinion was received from the University of Leeds School of Medicine Research Ethics Committee (Ref MREC 20‐041).3n = 304). The demographic measures and the medical histories for all 370 patients are presented in Table SD = 14), with a mean bodyweight of 82 kg (SD = 22) and a mean body mass index (BMI) of 29 kg/m2 (SD = 8).The majority of participants had not needed any hospitalization during the infection (https://observatory.leeds.gov.uk/population/). Patients from black and particularly Asian ethnic groups reported experiencing more severe symptoms than patients from white ethnic groups.Patients were predominantly of white ethnicity (84%) with only 57 patients (16%) from black, Asian, other, or mixed ethnic groups, which is slightly lower than the proportion in the general population of the region served  of patients were still employed or studying on the same hours as before infection with COVID‐19, and 41 (11%) were in the same role as homemakers, still on maternity leave, retired or unemployed. However, 144 (40%) had reduced their work hours, were still on sick leave, or had stopped work altogether because of ill health.Table Radar plot mapping of the average individual symptom score for each of the three categories Figure  showed tFatigue was the most common symptom experienced, with 353 (95%) reporting this to some extent, followed by 334 (90%) reporting anxiety, 329 (89%) some pain or discomfort, 316 (85%) some breathlessness, and 315 (85%) some cognitive difficulties. Overall symptom scores were negatively correlated with perceived overall health  and positively correlated with overall functional difficulty scores . Figure The UpSet diagram in Figure Cluster analysis did not identify any consistent symptom groupings or phenotypes. Dendrograms from average‐linkage hierarchical cluster analysis with Euclidean dissimilarity measures found no distinct separation of subtypes Figure , as indi4This is the first study in the current literature from a community specialist COVID‐19 rehabilitation service reporting on symptom severity phenotypes in a cohort of largely nonhospitalized individuals. We found the severity of a range of individual symptoms was related to the underlying severity of the condition, regardless of hospitalization status during the acute phase of illness. The symptom severity can be clearly categorized into mild, moderate, and severe severity phenotypes. The severity of individual symptoms within each category was similar with a strong correlation between symptoms severity and functional difficulty and moderate correlation between symptom severity and overall health status. Using a range of methods, we did not find evidence for phenotypes based on the type of symptoms.The severity of individual symptoms within each category being similar might indicate a common underlying pathophysiological mechanism for symptoms in PCS. This also explains the fluctuant nature of the condition where the individual experiences flare‐up of all symptoms (bad days) and symptom‐free days (good days). We did not find any one symptom determining this association or driving other symptoms which are again supportive of underlying common mechanisms in the condition. We have some pointers in the literature towards these common mechanisms in PCS such as vascular damage (hypercoagulability),There is a concern in PCS that many symptoms such as fatigue or anxiety or mood disorders may already be present in much of the general population before infection. We were however able to show in this study that, for a large number of symptoms, these were not pre‐existing before infection, albeit scored retrospectively. This supports the de novo (new‐onset) nature of the symptoms attributable to the condition of PCS. The data collected in this study also suggests that the C19‐YRS scale can be used to capture pre‐illness symptoms even though there is likely to be a certain degree of recall bias.Given the growing number of people with PCS , the findings of severity phenotypes in this study could have widespread implications for the provision and resourcing of services to support people living with the condition. The stratification based on the severity of cases could help national and local providers to plan services and interventions that might be directed towards these categories. Mild cases can be investigated in Tier 3 primary care services  and offered resources such as Your Covid recovery websiteOne weakness of many previous studies has been the reliance on cohorts entirely comprised of individuals previously hospitalized with COVID‐19. Our inclusion of a large proportion of nonhospitalized patients, and the consistency of their symptoms with those of hospitalized patients, implies that our reported symptoms are unlikely to be a result of the hospital or intensive care experience but are due to the unique underlying pathophysiological mechanism of PCS. This is in keeping with other studies which have shown a similar burden of symptoms in nonhospitalized patients.It is important to remember that PCS is a fluctuating or episodic condition and that symptom severity can vary over time within the same individual.There are some limitations to this study. First, a relatively small sample size precludes determining distinct symptom‐based phenotype patterns to be estimated with sufficient precision to be identified. It is possible that rarer phenotypes exist that did not present with sufficient numbers in our study sample. Instead, all groupings of individual symptoms based on correlations between them were dominated by the overall severity across all symptoms. Second, it is worth noting that symptoms and their severity were self‐reported by patients, so there could be a degree of subjectivity in their recording, that they tend to grade severity similar across the symptoms. It is possible that milder cases not presenting to PCS centers for management might present with more distinct symptom clusters that would indicate different underlying pathophysiological mechanisms. Finally, there is an element of recall bias in reporting pre‐illness scores, but this had no bearing on the findings of this study.5This is the first study in the current literature reporting on severity phenotypes in a largely nonhospitalized PCS cohort. Severity phenotypes might help stratify patients for targeted interventions and planning of care pathways. Further research is needed to understand the common mechanisms and pathophysiological basis of PCS. Specific symptom‐based phenotype could not be identified in this cohort but needs to be explored in larger population studies.The authors declare that there are no conflict of interests.Manoj Sivan conceptualized the study, led the development of the C19‐Yorkshire Rehabilitation Scale, and led the writing of the manuscript. Amy Parkin, Sophie Makower, and Manoj Sivan were responsible for the data collection and organization and contributed to the writing of the final drafts of the manuscript. Darren Greenwood is the senior biostatistician in the team and performed all the statistical analyses presented in the draft and contributed to the writing of the manuscript.


Metal–organic frameworks (MOFs) and covalent organic frameworks (COFs) have been thoroughly investigated with regards to applications in gas separation membranes in the past years. More recently, new preparation methods for MOFs and COFs as particles and thin‐film membranes, as well as for mixed‐matrix membranes (MMMs) have been developed. We will highlight novel processes and highly functional materials: Zeolitic imidazolate frameworks (ZIFs) can be transformed into glasses and we will give an insight into their use for membranes. In addition, liquids with permanent porosity offer solution processability for the manufacture of extremely potent MMMs. Also, MOF materials influenced by external stimuli give new directions for the enhancement of performance by in situ techniques. Presently, COFs with their large pores are useful in quantum sieving applications, and by exploiting the stacking behavior also molecular sieving COF membranes are possible. Similarly, porous polymers can be constructed using MOF templates, which then find use in gas separation membranes. Metal–organic frameworks, covalent organic frameworks, polymers, and composites are disruptive key technologies for gas separation membranes. This Minireview highlights the state of the art in materials and material development and their utilization in membrane applications in highly relevant processes such as carbon capture, energy‐ and resource‐efficient separations of basic chemicals, and quantum sieving. They evaluated the different morphologies by the MMM performance in precombustion hydrogen separation and find that 1) downsizing to nanocrystals increases the performance drastically, whereas 2) the use of nanosheets increases the performance further and leads to benchmark performances.gZIF‐62.As an important consequence of their liquid‐state processability,gZIF‐62 film on a porous α‐Al2O3 support , 34.5 (CO2/N2), and 36.6 (CO2/CH4).When it comes to membrane applications, MOF glasses have two conceptual advantages: 1) The glasses can be easily processed and deposited in their liquid state and 2) there are no grains or grain boundaries in the isotropic glass. Grain boundaries are unavoidable in polycrystalline MOF membranes and represent a fundamental problem, since these boundaries represent defects or microscopic cracks that can significantly compromise the selectivity of the membrane. Wang and Jin et al. reported the first ZIF glass membrane made of an agZIF‐62 imbedded in a polyimide matrix.2/N2 selectivity by ≈27 % upon thermal transformation of the crystalline ZIF‐62 to agZIF‐62 at 440 °C.Another recent proof‐of‐concept study reported an MMM consisting of a42) than polymeric films, neat MOF membranes are hard to apply in industrial settings.2 has been made available by liquid‐phase epitaxyNeat MOF membranes are usually grown on ceramic supports by solvothermal methods. Since ceramic membranes are 1000 times more expensive . The symmetry is reduced to the monoclinic space group Cm and the symmetry switches further to R3m at higher fields. As consequence of the E‐field‐driven transformation, a change in rotational energy barriers and a higher framework stiffness arises, which increases molecular sieving.Cm space group during the growth process. This leads to a very good molecular‐sieving ZIF‐8(Cm) membrane that outperforms the usual ZIF‐8(I4‾3m) membrane.Whereas the response of MOFs to applied pressure and temperature is a well‐known concept,cis–trans isomerization, which effects adsorption of CO2 by hindering its diffusion via interaction with its quadrupolar moment.The described approaches intend to either enhance the properties of the MOF or COF membranes by the combination of polymers with enhanced processability and stability, or enhance the performance of polymeric materials by the advantages of MOFs such as their high degree of order across multiple length scales, making it possible to implement high‐throughput computational screening approaches.[72 capture and the sustainable production of CH4. To make use of the potentially best materials for these processes, a targeted material development, rather than synthesis of more and more novel materials, is crucial. We have described the non‐ideal polymer–filler effects in MMMs, which are already known but too often neglected.There is a critical need for disruptive technologies such as membranes to lower the energy use of the chemical industry and reduce greenhouse gas emission worldwide. MOFs and COFs are materials with extraordinary properties to help separations in the petrochemical sector, such as propylene/propane, as well as in direct COThe authors declare no conflict of interest.Bahram Hosseini Monjezi studied Chemistry at the Isfahan University of Technology (Iran) and received his master's degree with Prof. Mehran Ghiaci in 2012. After several years at the Research Institute of Petroleum Industry (Iran), he returned to academia and is currently conducting PhD research in the group of Alexander Knebel at the Karlsruhe Institute of Technology (KIT) at the Institute of Functional Interfaces (IFG) (Germany). His work focuses on the synthesis and properties of porous‐organic/metal–organic framework membranes and thin films.Ksenia Kutonova studied Chemistry and Chemical Engineering at the Tomsk Polytechnic University (Russia) and received her PhD in Organic Chemistry there in 2016. Since 2017 she has conducting postdoctoral research in Prof. Stefan Bräse's group at Karlsruhe Institute of Technology. Her research interests are organic building blocks and synthetic transformations for the preparation of application‐driven materials.Manuel Tsotsalas studied Chemistry and obtained his PhD in 2010 at the University of Münster (Germany) with research stays at the University of Rennes (France) and Cornell University (USA). After a postdoctoral stay with Susumu Kitagawa and Shuhei Furukawa at Kyoto University (Japan), he moved to the Karlsruhe Institute of Technology, where he obtained his Habilitation in 2019. He currently leads a Helmholtz Young Investigator Group there. His research interests focus porous polymers and their application as novel nanomembranes and bioactive surface coatings.Sebastian Henke studied Chemistry at the Ruhr‐University in Bochum (Germany), where he completed his PhD in Inorganic Chemistry with Roland A. Fischer in 2011. He then joined the lab of Sir Anthony K. Cheetham at the Department of Materials Science & Metallurgy of the University of Cambridge (UK). After a short stint in industry, he joined the Department of Chemistry and Chemical Biology at Technische Universität Dortmund as Junior Professor for Materials Chemistry in 2016. His research is focused on stimuli‐responsive MOFs and MOF glasses.Alexander Knebel studied Chemistry at the Leibniz University Hanover (Germany) and received his PhD in Physical Chemistry with Jürgen Caro in 2018. He conducted research with Jorge Gascon at the KAUST Catalysis Centre of the King Abdullah University of Science Technology (Saudi Arabia). Afterwards he was a postdoctoral fellow at the Institute of Functional Interfaces (IFG) with Christof Wöll at the Karlsruhe Institute of Technology (KIT) (Germany), where he recently became Junior Research Group Leader. In his research, he combines MOFs, COFs, porous liquids, and polymer mixed‐matrix systems in thin‐film gas‐separation membranes.


Killian–Jamieson diverticulum (KJD) is a rare type of esophageal diverticulum less commonly encountered compared with Zenker's diverticulum (ZD). Endoscopic approach for these diverticula has been rapidly evolving. Currently, a flexible endoscopic septum division is considered the first‐line treatment for symptomatic ZD patients, however reported recurrence rates are over 10% according to recent literature. With the advent of submucosal tunneling technique established by per‐oral endoscopic myotomy for achalasia, it has been applied to treat ZD named as Zenker's diverticulum per‐oral endoscopic myotomy (Z‐POEM) as a minimally invasive treatment. Although there are very few reports utilizing submucosal tunneling approach to KJD, we have opted to perform Z‐POEM in order to safely perform complete dissection of the muscle septum while maintaining mucosal integration. Due to the difficulty of anatomical location of KJD, we created mucosal incision and subsequent submucosal tunnel directly at the level of the septum as opposed to creating a submucosal tunnel few centimeters proximal to the septum as being previously proposed. We report a case in which this technique was successfully performed with complete resolution of dysphagia without any adverse event. This technique permits to perform complete myotomies without the fear of causing perforation. Although larger cohorts are required to assess its safety and efficacy, Z‐POEM to treat KJD seems to be promising. Killian–Jamieson diverticulum (KJD) is a rare type of esophageal diverticulum less commonly encountered compared with Zenker's diverticulum (ZD).This is a case of a 40‐year‐old Japanese female with no significant past medical history who presented with a few‐year history of solid food dysphagia. Symptoms gradually progressed and started to affect her daily activities. No previous consultation and treatment were noted. The patient underwent a thorough evaluation that includes upper endoscopy, barium esophagogram, and computed tomography (CT) scan of the thorax and upper abdomen.Upper endoscopy showed diverticulum located at the anterolateral (11 o'clock position) of the upper esophagus Figure . Barium Per‐oral endoscopic septotomy was performed under general anesthesia, with the use of a single‐channel therapeutic endoscope . The super‐soft hood  made of silicon was used as a distal attachment. Food residual was removed, and the diverticulum was confirmed on the left side of the true lumen of the esophagus. Saline with indigo carmine was injected into the submucosa to create a bleb at the top of the septum Figure . Mucosa KJD is a rare type of lateral pharyngo‐esophageal diverticulum distinct entity different from ZD.In summary, Z‐POEM technique seems to be a promising minimally invasive approach theoretically enabling to perform complete septotomy even in tight working space such as in case with KJD. Larger cohorts are required for better understanding to see which patients benefit from this endoscopic approach.Haruhiro Inoue is an advisor of Olympus Corporation and Top Corporation. He has also received educational grants from Olympus Corp. and Takeda Pharmaceutical Co. All authors have no conflicts of interests to declare pertaining to this report.None.


Despite its reported benefits, breastfeeding rates are low globally, and support systems such as the Baby Friendly Initiative (BFI) have been established to support healthy infant feeding practices and infant bonding. Increasingly reviews are being undertaken to assess the overall impact of BFI accreditation. A systematic synthesis of current reviews has therefore been carried out to examine the state of literature on the effects of BFI accreditation. A systematic search of CINAHL, MEDLINE, Maternal and Infant Health, Scopus, the Cochrane Library and PROSPERO was undertaken. Study selection, data extraction and critical appraisal of included reviews using the AMSTAR‐2 tool were undertaken by two authors, with disagreements resolved through discussion with the third author. Due to heterogeneity, a narrative synthesis of findings was applied. Fourteen reviews met the inclusion criteria. Overall confidence in the results of the review was rated as high for three reviews, low for two reviews and critically low for nine reviews. Most evidence suggests some increase in breastfeeding initiation, exclusivity and duration of breastfeeding, and one main trial suggests decreased gastrointestinal infection and allergic dermatitis in infants. However, overall certainty in the evidence was rated as very low across all outcomes due to concerns over risk of bias within and heterogeneity between the original studies. More contemporary, good‐quality randomised controlled trials or well‐controlled prospective comparative cohorts are required to better evaluate the impact of full BFI accreditation, with particular attention paid to the context of the research and to long‐term maternal and infant health outcomes. Current evidence suggests there may be some improvement in initiation and breastfeeding duration with Baby Friendly Initiative accreditation, especially in low‐income countries; however, confidence in these findings was very low. There is minimal evidence of the impact of BFI accreditation on maternal and infant health outcomes.The majority of current evidence assessing BFI accreditation was of poor methodology or at high risk of bias.More contemporary, good‐quality randomised controlled trials or well‐controlled prospective comparative cohorts are required to better evaluate the impact of BFI accreditation.Particular attention is needed to the context of the research, both background socio‐economic and breastfeeding practices, and to explore longer term outcomes to see if benefits in breastfeeding duration are sustained.1The World Health Organization (WHO) recommends that breastfeeding is initiated within 1 h of birth . A systematic search of CINAHL, MEDLINE, Maternal and Infant Health, Scopus, the Cochrane Library and PROSPERO registry of systematic reviews was undertaken. The WHO website was also searched for relevant publications. The search strategy included terms around ‘baby friendly initiative’ and ‘systematic review’. An example full search strategy within one database can be found in Appendix 2.2Retrieved citations were screened by two independent researchers against the eligibility criteria Table  by title2.3Data extraction was undertaken by two researchers using a pre‐defined data extraction table. Authors of the systematic reviews were contacted where required for additional information.2.4Two reviewers independently assessed included systematic reviews for risk of bias using the Assessment of Multiple Systematic Reviews v2 (AMSTAR‐2) checklist  and according to stage of Baby Friendly Initiative accreditation.3Of the 316 citations identified after removing duplicates, 54 articles were screened at full text. Of these, 16 articles (covering 14 separate systematic reviews) were included (see flow diagram of study selection in Appendix E). Appendix 3.1Characteristics of the included systematic reviews can be found in Table 3.2Table Only one review  conducted in Belarus, appearing in 11 of the 14 reviews  was necessary for improvements in both exclusive or any breastfeeding , economic backgrounds, cultures, rates of breastfeeding initiation or rates of exclusive or any breastfeeding at various time points postpartum may respond differently to BFI accreditation (Fallon et al., 4.2While evaluation of individual BFI steps was not the focus of this overview, it is important to discuss the impact of individual steps, as the causal mechanism for how BFI practices may improve breastfeeding rates have not been fully identified (Munn et al., 4.3The cost of delivering BFI was not an outcome assessed within any of the included systematic reviews. A systematic review that did not meet the criteria for inclusion within this overview (Carroll et al., BFI has been important in raising the profile of breastfeeding globally. However, the evidence currently is not sufficient to proclaim BFI as a superior intervention to all other breastfeeding interventions. The effectiveness of BFI is especially questioned in areas where evidence‐based practices to support breastfeeding already exist (Brodribb et al., 4.4The main limitation of this overview is the limited quality of the included reviews and studies. The majority of current evidence is based on before‐after studies or cohort studies which did not control for other confounding factors.It is recognised that not all of the studies incorporated into the included systematic reviews were full BFI accreditation studies with some including studies of individual steps or various combinations of BFI steps. One review also incorporated other ‘structured support’ interventions alongside BFI interventions.When judging the quality of included reviews, the authors were only able to assess the information contained within the published articles, alongside some basic searches for review protocols. However, due to word limitations, it is recognised that the published information may not fully reflect all of the processes undertaken within a review.4.5Currently, there is a lack of clear evidence around long‐term improved duration of breastfeeding and health benefits of BFI, particularly within high‐income countries. Caution is required when determining the potential impact of BFI accreditation in different global contexts using currently available evidence.Qualitative evidence around BFI has found women to describe that their reality of breastfeeding differed from their expectations and that they can feel pressurised to breastfeed or guilt when unable to succeed in hospitals with BFI accreditation (Fallon et al., 5This overview suggests that there may be some improvement in initiation and breastfeeding duration from Baby Friendly Initiative accreditation especially in low‐income countries, although the duration of any improvements in breastfeeding is uncertain and confidence in these findings was very low due to the poor methodological quality of existing evidence. Evidence around the impact of BFI accreditations on long‐term health of mothers and babies is currently minimal. Well‐designed controlled trials are required to better evaluate the short‐ and long‐term impact of BFI accreditation.The authors have no competing interests to declare.FF, AM and HS developed the protocol; FF and AM screened titles, abstracts and full texts against inclusion criteria; FF and AM assessed risk of bias; HS resolved any disagreement over inclusion and risk of bias assessments; FF and AM extracted data; FF, AM and HS performed the analysis; FF drafted the manuscript; AM and HS revised the manuscript and FF, AM and HS agreed the final manuscript.