Helmholtz Centre for Environmental Research

Introduction The emergence of large language models heralds a new chapter in natural language processing, with immense potential for improving medical care and especially medical oncology. One recent and publicly available example is Generative Pretraining Transformer 4 (GPT‐4). Our objective was to evaluate its ability to rephrase original surgical reports into simplified versions that are more comprehensible to patients. Specifically, we aimed to investigate and discuss the potential, limitations, and associated risks of using these simplified reports for patient education and information in gynecologic oncology. Material and Methods We tasked GPT‐4 with generating simplified versions from n = 20 original gynecologic surgical reports. Patients were provided with both their original report and the corresponding simplified version generated by GPT‐4. Alongside these reports, patients received questionnaires designed to facilitate a comparative assessment between the original and simplified surgical reports. Furthermore, clinical experts evaluated the artificial intelligence (AI)‐generated reports with regard to their accuracy and clinical quality. Results The simplified surgical reports generated by GPT‐4 significantly improved our patients' understanding, particularly with regard to the surgical procedure, its outcome, and potential risks. However, despite the reports being more accessible and relevant, clinical experts highlighted concerns about their lack of medical precision. Conclusions Advanced language models like GPT‐4 can transform unedited surgical reports to improve clarity about the procedure and its outcomes. It offers considerable promise for enhancing patient education. However, concerns about medical precision underscore the need for rigorous oversight to safely integrate AI into patient education. Over the medium term, AI‐generated, simplified versions of these reports—and other medical records—could be effortlessly integrated into standard automated postoperative care and digital discharge systems.

The bidirectional relationship between plant species richness and community biomass is often variable and poorly resolved in natural grassland ecosystems, impeding progress in predicting impacts of environmental changes. Most biological communities have long-tailed species abundance distributions (for example, biomass, cover, number of individuals), a general property that may provide predictive power for species richness and community biomass. Here we show mathematical relationships between community characteristics and the abundance of dominant species arising from long-tailed distributions and test these predictions using observational and experimental data from 76 grassland sites across 6 continents. We find that community biomass provides little predictive ability for community richness, consistent with previous findings. By contrast, the relative abundance of dominant species quantitatively predicts species richness, whereas their absolute abundance quantitatively predicts community biomass under both ambient and altered environmental conditions, as expected mathematically. These results are robust to the type of abundance measure used. Three types of simulated data further show the generality of these results. Our integrative framework, arising from a few dominant species and mathematical properties of species abundance distributions, fills a persistent gap in our ability to predict community richness and biomass under ambient and anthropogenically altered conditions.

Immunotherapy has become standard of care in the treatment of diffuse large B-cell lymphoma (DLBCL). Changes in immunophenotypes observed at first diagnosis predict therapy outcome but little is known about the resolution of these alterations in remission. Comprehensive characterization of immune changes from fresh, peripheral whole blood revealed a functionally relevant increase of myeloid-derived suppressor cells, reduced naïve T-cells, and an increase of activated and terminally differentiated T-cells before treatment which aggravated after therapy. Suggesting causal relation, injection of lymphoma in mice induced similar changes in the murine T cells. Distinct immune imprints were found in breast cancer and AML survivors. Identified alterations persisted beyond five years of ongoing complete remission and in DLBCL correlated with increased pro-inflammatory markers such as IL-6, B2M, or sCD14. The chronic inflammation was associated with functionally blunted T-cell immunity against SARS-CoV-2-specific peptides and reduced responses correlated with reduced Tn-cells. Persisting inflammation was confirmed by deep sequencing and by cytokine profiles, together pointing towards a compensatory activation of innate immunity. The persisting, lymphoma-induced immune alterations in remission may explain long-term complications, have implications for vaccine strategies, and are likely relevant for immunotherapies.

Rationale Liquid chromatography‐isotope ratio mass spectrometry (LC‐IRMS) is used to analyze stable carbon isotope ratios of polar nonvolatile compounds. However, challenges with the persulfate‐based oxidation interface have been reported, particularly for molecules with recalcitrant structures like those found in neonicotinoids. This study systematically investigates the oxidation efficiency of neonicotinoid‐related structures in a commercial LC‐IRMS. Methods Neonicotinoid proxies of varying molecular complexity were evaluated for carbon recovery and stable carbon isotope ratio accuracy. LC‐IRMS parameters such as oxidant concentration, reaction time, temperature, acid concentration, and the presence of AgNO 3 catalyst were varied. Carbon recoveries and δ ¹³ C biases were determined by injecting an oxidation‐independent inorganic carbon standard under identical conditions. Elemental analyzer isotope ratio mass spectrometry (EA‐IRMS) was used to normalize δ ¹³ C values. Results Several neonicotinoid derivatives exhibited low carbon recovery and significant δ ¹³ C bias. Increasing oxidant concentration, reactor temperature, and reaction time improved recoveries but did not fully mitigate isotopic biases. The addition of AgNO 3 improved carbon recoveries for most derivatives but introduced variability in δ ¹³ C values, likely due to shifts in reaction mechanisms. A workflow to identify oxidation problems during method development was proposed. Conclusions Optimization of LC‐IRMS oxidation parameters is critical for urea, guanidine, and nitroguanidine derivatives and similar compounds. A systematic evaluation of oxidation efficiencies under different conditions is needed for optimal mineralization and thus more accurate δ ¹³ C ratios.

Plastics have become an integral part of modern life, and linked to that fact, the demand for and global production of plastics are still increasing. However, the environmental pollution caused by plastics has reached unprecedented levels. The accumulation of small plastic fragments—microplastics and nanoplastics—potentially threatens organisms, ecosystems, and human health. Researchers commonly employ non-destructive analytical methods to assess the presence and characteristics of microplastic particles in environmental samples. However, these techniques require extensive sample preparation, which represents a significant limitation and hinders a direct on-site analysis. In this context, previous investigations showed the potential of fluorescence lifetime imaging microscopy (FLIM) for fast and reliable identification of microplastics in an environmental matrix. However, since microplastics receive an environmental coating after entering nature, a challenge arises from organic contamination on the surface of microplastic particles. How this influences the fluorescence signal and the possibility of microplastic detection are unknown. To address this research gap, we exposed acrylonitrile butadiene styrene (ABS) and polyethylene terephthalate (PET) plastic samples to peptides, proteins, bacteria, and a filamentous fungus to induce organic contamination and mimic environmental conditions. We analyzed the fluorescence spectra and lifetimes of the samples using fluorescence spectroscopy and frequency-domain fluorescence lifetime imaging microscopy (FD-FLIM), respectively. Our results demonstrate that reliably identifying and differentiating ABS and PET was possible via FD-FLIM, even in the presence of these biological contaminations. These findings highlight the potential of this technique as a valuable tool for environmental monitoring and plastic characterization, offering a rapid and efficient alternative to currently used analytical methods. Graphical Abstract

Background Few studies showed associations of childhood allergic diseases with epigenetic aging using traditional clocks trained mainly on adults. Tracking DNA methylation variation early in life has suggested poor performance of these clocks in children. Therefore, we aim to elucidate the association between allergic diseases and epigenetic age using a pediatric clock. Methods We used data from the German LISA birth cohort study at six (N = 234) and ten (N = 227) years. DNA methylation was measured in blood using the Infinium Methylation EPIC BeadChip. We calculated epigenetic age using the pediatric clock developed by Wu et al. (Aging 2019) (median absolute error = 0.04 years, Spearman correlation with chronological age r = 0.75). Linear mixed models were used to examine longitudinal associations of epigenetic age acceleration with doctor‐diagnosed asthma, rhinitis, and eczema, or a combination thereof (“any allergy”) as well as aeroallergen sensitization. Replication was performed in BAMSE at the 8‐year follow‐up (N = 625) using linear models. Additionally, epigenetic age in adults from KORA F4 was estimated using Horvath's clocks and associations with allergic diseases were tested applying linear models. Results Having any allergy was significantly associated with a mean epigenetic age acceleration of 0.34 years (95% CI = [0.06; 0.63]) using Wu's clock in LISA. Associations with consistent effect directions were found for allergic rhinitis, asthma, and eczema. No associations with aeroallergen sensitization were observed. In BAMSE, an inverse association of epigenetic age acceleration with eczema was found (−0.52 years, 95% CI = [−0.97; −0.07]). In KORA, hay fever was significantly associated with accelerated epigenetic age when using the Horvath pan‐tissue clock (1.05 years, 95% CI = [0.21; 1.89]). Conclusions We found an increase in epigenetic age in children with allergic diseases from LISA. Our results suggest that epigenetic age acceleration seems to be related to the persistent burden of allergic diseases, but not to non‐symptomatic aeroallergen sensitization.

Background Prenatal exposure to maternal asthma may influence DNA methylation patterns in offspring, potentially affecting their susceptibility to later diseases including asthma. Objective To investigate the relationship between parental asthma and newborn blood DNA methylation. Methods Epigenome-wide association analyses were conducted in 13 cohorts on 7433 newborns with blood methylation data from the Illumina450K or EPIC array. We used fixed effects meta-analyses to identify differentially methylated CpGs (DMCs) and comb-p to identify differentially methylated regions (DMRs) associated with maternal asthma during pregnancy and maternal asthma ever. Paternal asthma was analyzed for comparison. Models were adjusted for covariates and cell-type composition. We examined whether implicated sites related to gene expression analyses in publicly available data for childhood blood and adult lung. Results We identified 27 CpGs associated with maternal asthma during pregnancy at False Discovery Rate < 0.05 but none for maternal asthma ever. Two distinct CpGs were associated with paternal asthma. We observed 5 DMRs associated with maternal asthma during pregnancy 3 associated with maternal asthma ever and 13 DMRs associated with paternal asthma. Gene expression analysis using data in blood from 832 children and lung from 424 adults showed associations between identified DMCs using maternal asthma and expression of several genes, including HLA genes and HOXA5, previously implicated in asthma or lung function. Conclusion Parental asthma, especially maternal asthma during pregnancy, may be associated with alterations in newborn DNA methylation. These findings might shed light on underlying mechanisms for asthma susceptibility.

Global warming is altering soil moisture (SM) droughts in Europe with a strong drying trend projected in the Mediterranean and wetting trends projected in Scandinavia. Central Europe, including Germany, lies in a transitional zone showing weaker and diverging change signals exposing the region to uncertainties. The recent extreme drought years in Germany, which resulted in multi‐sectoral impacts accounting to combined drought and heat damages of 35 billion Euros and large scale forest losses, underline the relevance of studying future changes in SM droughts. To analyze the projected SM drought changes and associated uncertainties in Germany, we utilize a large ensemble of 57 bias‐adjusted and spatially disaggregated regional climate model simulations to run the hydrologic model mHM at a high spatial resolution of approximately 1.2 km. We show that projections of future changes in soil moisture droughts over Germany depend on the emission scenario, the soil depth and the timing during the vegetation growing period. Most robust and widespread increases in soil moisture drought intensities are projected for upper soil layers in the late growing season (July–September) under the high emission scenario. There are greater uncertainties in the changes in soil moisture droughts in the early vegetation growing period (April–June). We find stronger imprints of changes in meteorological drivers controlling the spatial disparities of SM droughts than regional diversity in physio‐geographic landscape properties. Our study provides nuanced insights into SM drought changes for an important climatic transition zone and is therefore relevant for regions with similar transitions.

Objectives We described waning in anti-SARS-CoV-2 IgG in adult general populations infected during the first wave of the COVID-19 pandemic in 2020 across three European countries. Methods Coordinated analyses were conducted separately in three population-based cohorts with complementary follow-up schedules: the KoCo19 (Germany), EpiCov (France), and CON-VINCE (Luxembourg) cohorts. Serological follow-up was based on the anti-SARS-CoV-2 ELISA-S IgG (Euroimmun) assay. We selected all adults aged 18–79 who had a positive serology (IgG optical density (OD) ratio ≥1.1) between February and July 2020, and at least one subsequent IgG measurement within the following 12 months, while still non-vaccinated. Results The proportion of seroreversion was 0% within the four first months, based on Koco19 data (n = 65 participants). In the longer term, 31.3% of participants had seroreverted at 6 months (95%CI: 24.4–39.1) (based on EpiCov data, n = 599), 31.3% (95%CI: 11.0–58.7) at 12 months (based on CON-VINCE data, n = 16). From EpiCov data, both baseline low IgG levels and seroneutralization negativity remained predictive of seroreversion in multivariable analysis. Conclusion From population-based cohorts, anti-S IgG levels remained stable during the first 4 months following SARS-CoV-2 infection. Most of the decay occurred afterward; nearly one-third of people seroreverted 6 and 12 months later. Low IgG levels and seroneutralization negativity were independent predictors of seroreversion.

High-content screening (HCS) for bioimaging is a powerful approach to studying biological processes, enabling the acquisition of large amounts of images from biological samples. However, it generates massive amounts of metadata, making HCS experiments a unique data management challenge. This data includes images, reagents, protocols, analytic outputs, and phenotypes, all of which must be stored, linked, and made accessible to users, scientists, collaborators, and the broader community to ensure sharable results. This study showcases different approaches using Workflow Management Systems (WMS) to create reusable semi-automatic workflows for HCS bioimaging data management, leveraging the image data management platform OMERO. The three developed workflows demonstrate the transition from a local file-based storage system to an automated and agile image data management framework. These workflows facilitate the management of large amounts of data, reduce the risk of human error, and improve the efficiency and effectiveness of image data management. We illustrate how applying WMS to HCS data management enables us to consistently transfer images across different locations in a structured and reproducible manner, reducing the risk of errors and increasing data consistency and reproducibility. Furthermore, we suggest future research direction, including developing new workflows and integrating machine learning algorithms for automated image analysis. This study provides a blueprint for developing efficient and effective image data management systems for HCS experiments and demonstrates how different WMS approaches can be applied to create reusable, semi-automated workflows for HCS bioimaging data management using OMERO.

Background The prevalence of metabolic syndrome (MetS) has increased rapidly, with considerable variation between European countries. The study examined the relationship between air pollutants, greenspace, and MetS and its components in the Czech and Swiss populations. Methods Cross-sectional data from the Czech Health, Alcohol and Psychosocial Factors in Eastern Europe (HAPIEE) (n = 4,931) and the Swiss cohort Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA) (n = 4,422) cohorts included participants aged 44–73 years. MetS was defined as abdominal obesity plus two additional components (hypertension, diabetes, low high-density lipoprotein cholesterol, and elevated triglycerides). Annual mean concentrations of PM 10 , PM 2.5 , NO 2 , and greenspace (defined as the annual mean of normalized difference vegetation index within 500 m) were assigned to the individual residential level. We estimated odds ratios (OR) using multivariable logistic regressions with cluster-robust standard error, controlling for multiple confounders. Results The prevalence of MetS was significantly higher in the Czech (51.1%) compared with Swiss (35.8%) population as were the concentration means of PM 10 and PM 2.5 . In HAPIEE, a 5 μg/m ³ increase in PM 2.5 was associated with 14% higher odds of MetS (OR = 1.14; 95% confidence interval [CI] = 1.01, 1.28). In SAPALDIA, no evidence was found for the associations between air pollutants and MetS (e.g. OR = 1.01; 95% CI = 0.90, 1.13 for PM 2.5 ). No protective effects of normalized difference vegetation index on MetS were observed. Upon inspection of MetS components, PM 2.5 and PM 10 exposures were associated with higher odds of hypertension and elevated triglycerides in HAPIEE only, while PM 2.5 , PM 10, and NO 2 were associated with higher odds of diabetes in SAPALDIA only. Conclusion Individuals with higher exposures to PM 2.5 may be at higher risk of MetS. The differential associations with MetS components between the cohorts deserve further investigation.

Objective Concentrations of soluble alpha klotho (sαKL) are higher in active acromegaly compared to healthy controls. However, reference intervals based on large population-based samples are lacking, and the impact of many biological variables is unclear. Design Cross-sectional study Methods We measured sαKL concentrations in samples from an adult population (20-89 years, 435 males, 455 females). Associations with sex, age, body mass index, waist-hip-ratio, estimated glomerular filtration rate (eGFR), IGF-I and IGFBP 3, glucose-, lipid-, calcium- and liver-metabolism, fasting, and estrogen status were analyzed. Reference intervals were calculated using LMS quantile regression with a Box-Cox transformation to normality. We also analyzed sαKL in patients with non-functioning pituitary adenoma (NFPA, n=18) and prolactinoma (n=65). Results Across all ages, sαKL concentrations (pg/mL, median (IQR)) were slightly, but significantly higher in females compared to males (678 (537-859) vs. 651 (537-812), p=0.01), suggesting an impact of estrogens. SαKL exhibited a weak negative correlation with age, and positive correlations with eGFR and IGF-I (p<0.001 for both). Correlations to other biological factors including glucose, liver and calcium metabolism and duration of fasting were negligible (p>0.05 for all). Compared to sαKL, IGF-I more often was correlated significantly to other biological variables. SαKL was not different in patients with NFPA, but slightly higher in patients with prolactinoma (p<0.05). Conclusion Our findings suggest sαKL is a stable GH-sensitive biomarker, that may be less impacted by biological variables compared to IGF-I and IGFBP 3. Our reference intervals will facilitate the potential use of sαKL in GH-related diseases.

Alpine treeline ecotones, when viewed up close, display considerable variation in spatial patterns, which have been associated with different responses to climate change. Two important dimensions of treeline‐ecotone spatial patterns are the abruptness of the change in tree height (“abrupt” vs. “gradual”) and the change in canopy cover (“discrete” vs. “diffuse”) when moving from closed forest to treeless alpine vegetation. These dimensions are suited to classify treeline ecotones into different types of patterns, but this is typically done intuitively without explicitly stated criteria, and patterns are not quantified. Consistent, robust metrics allowing comparisons between sites are lacking. We suggest several metrics to quantify abruptness and discreteness of treeline ecotones and describe how to derive these metrics from point‐pattern data of tree positions and sizes, and from high‐resolution treecover data. We developed these based on field data from the Spanish Pyrenees and an extensive dataset of treeline patterns created by the individual‐based Spatial Treeline‐Ecotone Model (STEM). We quantified the abruptness of a treeline by the largest change in canopy height, determined in 5‐m bands, between the top of the ecotone (i.e., alpine vegetation) and the first band where canopy height exceeds 3 m. We quantified the discreteness by the steepness of a logistic function fitted to tree cover. Band widths and cut‐off values were optimised for our data. Although they can be flexibly adjusted to specific case studies, standard settings are recommended to assure comparability. Our results indicate that the “discreteness” metric provides a satisfactory quantification of this pattern dimension within the dataset used here, whereas the “abruptness” pattern dimension turned out to be more difficult to capture. The metrics developed here may provide field researchers with a tool to compare their field sites in a standardised way, and potentially promote synthesis on treeline data and dynamics on a global scale.

Motorized traffic often causes road noise directly in front of our homes and windows. Yet long-term exposure to noise impact life's quality and can potentially cause negative effects on human health. Furthermore, social and behavioral effects have been measured. To protect people's health and well-being from such noise, the European Noise Directive (END, 2002/49/EC) obliges countries to produce strategic noise maps every five years for large agglomerations and along major roads, which are then used for noise action planning. Besides that, the official noise maps are a valuable data source for environmental exposure analyses. However, the END has some limitations. The definition of urban agglomerations is vague, different input parameterizations lead to data inconsistencies across administrative units, undefined post processing methods introduce geometric artifacts, and topological errors incompliant to the common Simple Features Implementation Specification hinder working with the published geodata. The aim of this article is to provide practical insights for end-users and stipulate for concise regulations. Moreover, we highlight that these variations limit the comparability of maps in environmental impact assessments. We compile 84 separate noise assessments in Germany reported according to the END to review shape and structure of the geographic data. Graphical representations are used to show in particular how vertices are connected to polygons in noise contour maps and that these geometric alterations effect the eventual statistics on exposed population shares. We aggregate spatial metrics to assess the reported data's spatial properties in an automatic manner, e.g. when receiving data in future mapping rounds. Along with our quality assessment, a nationwide dataset on road traffic noise was produced. Depicting the yearly averaged noise level indicator L den , which integrates exposure at day, evening and night, for 2017, it serves as common ground for environmental health analyses. The examination of different raster to polygon conversion implementations is fundamental to other geodata managers outside the domain of noise mapping, as well.

If health impairments due to coronavirus disease 2019 (COVID-19) persist for 12 weeks or longer, patients are diagnosed with Post-COVID Syndrome (PCS), or Long-COVID. Although the COVID-19 pandemic has largely subsided in 2024, PCS is still a major health burden worldwide, and identifying potential genetic modifiers of PCS remains of great clinical and scientific interest. We therefore performed a case-control type genome-wide association study (GWAS) of three recently developed PCS (severity) scores in 2,247 participants of COVIDOM, a prospective, multi-centre, population-based cohort study of SARS-CoV-2-infected individuals in Germany. Each PCS score originally represented the weighted sum of the binary indicators of all, or a subset, of 12 PCS symptom complexes, assessed six months or later after the PCR test-confirmed SARS-CoV-2 infection of a participant. For various methodical reasons, however, the PCS scores were dichotomized along their respective median values in the present study, prior to the GWAS. Of the 6,383,167 single nucleotide polymorphisms included, various variants were found to be associated with at least one of the PCS scores, although not at the stringent genome-wide statistical significance level of 5 × 10− 8. With p = 6.6 × 10− 8, however, the genotype-phenotype association of SNP rs9792535 at position chr9:127,166,653 narrowly missed this threshold. The SNP is located in a region including the NEK6, PSMB7 and ADGRD2 genes which, however, does not immediately suggest an etiological connection to PCS. As regards functional plausibility, variants of a possible effect mapped to the olfactory receptor gene region (lead SNP rs10893121 at position chr11:123,854,744; p = 2.5 × 10− 6). Impairment of smell and taste is a pathognomonic feature of both, acute COVID-19 and PCS, and our results suggest that this connection may have a genetic basis. Three other genotype-phenotype associations pointed towards a possible etiological role in PCS of cellular virus repression (CHD6 gene region), activation of macrophages (SLC7A2) and the release of virus particles from infected cells (ARHGAP44). All other gene regions highlighted by our GWAS did not relate to pathophysiological processes currently discussed for PCS. Therefore, and because the genotype-phenotype associations observed in our GWAS were generally not very strong, the complexity of the genetic background of PCS appears to be as high as that of most other multifactorial traits in humans.

Metaproteomics is an emerging approach for studying microbiomes, offering the ability to characterize proteins that underpin microbial functionality within diverse ecosystems. As the primary catalytic and structural components of microbiomes, proteins provide unique insights into the active processes and ecological roles of microbial communities. By integrating metaproteomics with other omics disciplines, researchers can gain a comprehensive understanding of microbial ecology, interactions, and functional dynamics. This review, developed by the Metaproteomics Initiative (www.metaproteomics.org), serves as a practical guide for both microbiome and proteomics researchers, presenting key principles, state‐of‐the‐art methodologies, and analytical workflows essential to metaproteomics. Topics covered include experimental design, sample preparation, mass spectrometry techniques, data analysis strategies, and statistical approaches.

Acupuncture is used worldwide to treat migraine, but its scientific mechanism remains unclear. Here, we report a ¹ H NMR metabolomics study involving 40 migraine patients and 10 healthy individuals randomly receiving acupuncture or sham acupuncture, followed by machine learning techniques and functional analysis. We found that acupuncture at acupoints particularly enhanced anaerobic glycolysis and modified mitochondrial function by adjusting the levels of plasma pyruvic acid ( p = 0.012), lactic acid ( p = 0.031) and citrate ( p = 0.00079) at a Bonferroni-corrected level of significance compared to the pre-treatment level of these three metabolites in migraine patients. Therefore, acupuncture supplies energy to migraine patients and relieves migraine attacks. In contrast, we observed that sham acupuncture may partially supply energy to migraine patients through lipid metabolism by changing the levels of plasma lipid ( p = 0.0012), glycerine ( p = 0.021), and pyruvic acid ( p = 0.047) at a Bonferroni-corrected level of significance. The functional network analysis further indicates this different way of supplying energy contributes to the different effects of acupuncture and sham acupuncture. Our findings reveal novel metabolic evidence for the specific effect of acupuncture in relation to sham acupuncture. This metabolic evidence could enlighten a brand new direction into acupuncture analgesia mechanism, which in turn would pose fresh challenges for future acupuncture research.