A survey on clinical presentation and nutritional status of infants with suspected cow' milk allergy

<p>Abstract</p> <p>Background</p> <p>Cow's milk is the most common food allergen in infants and the diagnosis of cow's milk allergy is difficult, even with the use of several diagnostic tests. Therefore, elimination diets and challenge tests are essential for the diagnosis and treatment of this disorder. The aim of this study is to report the clinical presentation and nutritional status of children evaluated by pediatric gastroenterologists for the assessment of symptoms suggestive of cow's milk allergy.</p> <p>Methods</p> <p>An observational cross-sectional study was performed among 9,478 patients evaluated by 30 pediatric gastroenterologists for 40 days in 5 different geographical regions in Brazil. Clinical data were collected from patients with symptoms suggestive of cow's milk allergy. The nutritional status of infants (age ≤ 24 months) seen for the first time was evaluated according to z-scores for weight-for-age, weight-for-height, and height-for-age. Epi-Info (CDC-NCHS, 2000) software was used to calculate z-scores.</p> <p>Results</p> <p>The prevalence of suspected cow's milk allergy in the study population was 5.4% (513/9,478), and the incidence was 2.2% (211/9,478). Among 159 infants seen at first evaluation, 15.1% presented with a low weight-for-age z score (< -2.0 standard deviation - SD), 8.7% with a low weight-for-height z score (< -2.0 SD), and 23.9% with a low height-for-age z score (< -2.0 SD).</p> <p>Conclusion</p> <p>The high prevalence of nutritional deficits among infants with symptoms suggestive of cow's milk allergy indicates that effective elimination diets should be prescribed to control allergy symptoms and to prevent or treat malnutrition.</p

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed

Comparisons of Allergenic and Metazoan Parasite Proteins:Allergy the Price of Immunity

Allergic reactions can be considered as maladaptive IgE immune responses towards environmental antigens. Intriguingly, these mechanisms are observed to be very similar to those implicated in the acquisition of an important degree of immunity against metazoan parasites (helminths and arthropods) in mammalian hosts. Based on the hypothesis that IgE-mediated immune responses evolved in mammals to provide extra protection against metazoan parasites rather than to cause allergy, we predict that the environmental allergens will share key properties with the metazoan parasite antigens that are specifically targeted by IgE in infected human populations. We seek to test this prediction by examining if significant similarity exists between molecular features of allergens and helminth proteins that induce an IgE response in the human host. By employing various computational approaches, 2712 unique protein molecules that are known IgE antigens were searched against a dataset of proteins from helminths and parasitic arthropods, resulting in a comprehensive list of 2445 parasite proteins that show significant similarity through sequence and structure with allergenic proteins. Nearly half of these parasite proteins from 31 species fall within the 10 most abundant allergenic protein domain families (EF-hand, Tropomyosin, CAP, Profilin, Lipocalin, Trypsin-like serine protease, Cupin, BetV1, Expansin and Prolamin). We identified epitopic-like regions in 206 parasite proteins and present the first example of a plant protein (BetV1) that is the commonest allergen in pollen in a worm, and confirming it as the target of IgE in schistosomiasis infected humans. The identification of significant similarity, inclusive of the epitopic regions, between allergens and helminth proteins against which IgE is an observed marker of protective immunity explains the 'off-target' effects of the IgE-mediated immune system in allergy. All these findings can impact the discovery and design of molecules used in immunotherapy of allergic conditions

A stoichiometric approach to estimate sources of mineral‐associated soil organic matter

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this recordData availability statement: The data that support the findings of this study are openly available in Supporting Information and Zenodo at https://doi.org/10.5281/zenodo.10147884Mineral-associated soil organic matter (MAOM) is the largest, slowest cycling pool of carbon (C) in the terrestrial biosphere. MAOM is primarily derived from plant and microbial sources, yet the relative contributions of these two sources to MAOM remain unresolved. Resolving this issue is essential for managing and modeling soil carbon responses to environmental change. Microbial biomarkers, particularly amino sugars, are the primary method used to estimate microbial versus plant contributions to MAOM, despite systematic biases associated with these estimates. There is a clear need for independent lines of evidence to help determine the relative importance of plant versus microbial contributions to MAOM. Here, we synthesized 288 datasets of C/N ratios for MAOM, particulate organic matter (POM), and microbial biomass across the soils of forests, grasslands, and croplands. Microbial biomass is the source of microbial residues that form MAOM, whereas the POM pool is the direct precursor of plant residues that form MAOM. We then used a stoichiometric approach—based on two-pool, isotope-mixing models—to estimate the proportional contribution of plant residue (POM) versus microbial sources to the MAOM pool. Depending on the assumptions underlying our approach, microbial inputs accounted for between 34% and 47% of the MAOM pool, whereas plant residues contributed 53%–66%. Our results therefore challenge the existing hypothesis that microbial contributions are the dominant constituents of MAOM. We conclude that biogeochemical theory and models should account for multiple pathways of MAOM formation, and that multiple independent lines of evidence are required to resolve where and when plant versus microbial contributions are dominant in MAOM formation.U.S. DOE OBERNatural Environment Research Council (NERC)National Key Research and Development Plan Project of ChinaNational Natural Science Foundation of ChinaRUDN University Strategic Academic Leadership Progra