Institute for Medical Informatics and Biostatistics

Microbial competition for trace metals shapes their communities and interactions with humans and plants. Many bacteria scavenge trace metals with metallophores, small molecules that chelate environmental metal ions. Metallophore production may be predicted by genome mining, where genomes are scanned for homologs of known biosynthetic gene clusters (BGCs). However, accurately detecting non-ribosomal peptide (NRP) metallophore biosynthesis requires expert manual inspection, stymieing large-scale investigations. Here, we introduce automated identification of NRP metallophore BGCs through a comprehensive algorithm, implemented in antiSMASH, that detects chelator biosynthesis genes with 97% precision and 78% recall against manual curation. We showcase the utility of the detection algorithm by experimentally characterizing metallophores from several taxa. High-throughput NRP metallophore BGC detection enabled metallophore detection across 69,929 genomes spanning the bacterial kingdom. We predict that 25% of all bacterial non-ribosomal peptide synthetases encode metallophore production and that significant chemical diversity remains undiscovered. A reconstructed evolutionary history of NRP metallophores supports that some chelating groups may predate the Great Oxygenation Event. The inclusion of NRP metallophore detection in antiSMASH will aid non-expert researchers and continue to facilitate large-scale investigations into metallophore biology.

Microbial competition for trace metals shapes their communities and interactions with humans and plants. Many bacteria scavenge trace metals with metallophores, small molecules that chelate environmental metal ions. Metallophore production may be predicted by genome mining, where genomes are scanned for homologs of known biosynthetic gene clusters (BGCs). However, accurately detecting non-ribosomal peptide (NRP) metallophore biosynthesis requires expert manual inspection, stymieing large-scale investigations. Here, we introduce automated identification of NRP metallophore BGCs through a comprehensive algorithm, implemented in antiSMASH, that detects chelator biosynthesis genes with 97% precision and 78% recall against manual curation. We showcase the utility of the detection algorithm by experimentally characterizing metallophores from several taxa. High-throughput NRP metallophore BGC detection enabled metallophore detection across 69,929 genomes spanning the bacterial kingdom. We predict that 25% of all bacterial non-ribosomal peptide synthetases encode metallophore production and that significant chemical diversity remains undiscovered. A reconstructed evolutionary history of NRP metallophores supports that some chelating groups may predate the Great Oxygenation Event. The inclusion of NRP metallophore detection in antiSMASH will aid non-expert researchers and continue to facilitate large-scale investigations into metallophore biology.

Early prediction of remnant liver growth after portal vein embolization (PVE) would enable earlier surgery in patients with resectable liver malignancy, decreasing the risk of tumor progression. This study aims to demonstrate the feasibility of 4D flow MRI to detect changes in portal blood flow before and after PVE and determine whether 4D flow MRI can predict hypertrophy of nonembolized liver lobes (future liver remnant) in a porcine model. Thirteen juvenile pigs were included in this prospective study. In 11 pigs, 2 of the 4 portal vein branches were selectively embolized using polyvinyl alcohol particles. Two pigs served as controls. All animals were imaged before, 1 hour, and 1 and 2 weeks after PVE using 4D flow and T1-weighted 3D spoiled gradient-echo MRI. Flow measurements were correlated with liver volumetry measurements. The predictive value of flow changes immediately after PVE for future liver remnant hypertrophy at 2 weeks was estimated using linear mixed-effect model analysis. Twelve pigs completed the study. One treatment pig died from complications of anesthesia. Immediately after PVE, relative flow in the 2 embolized portal vein branches decreased from 17.0±2.1% to 3.4±2.1% of total portal flow, respectively. Relative flow increased from 16.6±3.8% to 32.6±3.8% in the remaining 2 nonembolized portal vein branches supplying the future liver remnant. Each 1% increase in relative blood flow directly after the procedure predicted a 4.8±0.7cm 3 (estimate±SE) increase in volume of the future liver remnant at 2 weeks ( P <0.0001). Conversely, in embolized lobes, each 1% flow decrease predicted a volume reduction of 3.4±1.0cm 3 ( P =0.003). 4D flow MRI successfully quantified portal blood flow before and after portal vein embolization in a porcine model. Notably, immediate postembolization changes in portal flow were predictive of lobar volume changes observed at 2 weeks. This porcine model thus provides a valuable platform for investigating the relationship between portal hemodynamics and hypertrophy of the future liver remnant. Furthermore, it will enable systematic evaluation of various embolization agents and techniques, supporting the refinement of interventional strategies in hepatic regenerative research.

Findings from multiple studies are used to develop informed decisions for future research and clinical practices. Evidence synthesis methods, in general, are recommended for synthesizing findings from multiple studies. Among all evidence synthesis methods, a high-quality systematic review is required for answering a specific, focused research question from an abundance of literature. Although the choice of a systematic review among all other evidence syntheses depends on the formulated question related to estimation or hypothesis, and the availability of high-quality original studies, evidence syntheses not adhering to systematic review guidelines are also referred to as systematic reviews in the literature. Moreover, planning, designing, analyzing, and reporting a systematic review and meta-analysis requires multiple steps that are not included in the standard checklist documents, such as preferred reporting items for systematic review and meta-analysis (PRISMA) or meta-analysis of observational studies in epidemiology (MOOSE). It is also unclear to investigators how to properly select a type of evidence synthesis method and the steps involved in conducting a meta-analysis. Although multiple documents are available for selecting appropriate evidence synthesis methods, writing a protocol for a systematic review and meta-analysis, and conducting meta-analyses, there is no single report that consolidates all of these in a comprehensive framework. Given emerging challenges in the quality of published systematic reviews, this report aims to facilitate a framework for approaches and methods in evidence synthesis (FRAMES) to readers in (a) understanding types of evidence synthesis methods with specific focus to a systematic reviews and meta-analyses, (b) explaining the objectives of meta-analysis study, (c) describing the steps and resources in conducting a systematic review and meta-analysis, and (d) exemplifying the challenges and their potential solutions in a meta-analysis study. This report provides evidence-based biostatistics checklists for conducting a systematic review and meta-analysis, and writing steps for a systematic review study. I believe the adoption of this guidance document in research and training for the conduct and writing of an evidence synthesis study can have a far-reaching impact on producing high-quality evidence for research and clinical practices.

General-purpose large language models (LLMs) often struggle in specialized biomedical applications due to their limited access to up-to-date, structured knowledge and domainspecific tools. Although recent studies show proof of life for AI agents in increasingly complex scientific tasks, few studies offer insights at an atomic-level. We present the first large-scale evaluation of LLM tool-calling capabilities, focused on genomics annotation tasks such as variant-to-position and variant-to-gene mapping. Our study benchmarks over one hundred LLMs, via OpenRouter's metagateway, using a standardized tool-calling protocol to retrieve information directly from biomedical APIs (e.g., NCBI dbSNP, Entrez Gene). Our experimental results show that models equipped with structured tool access significantly outperform prompt-only baselines in accuracy, factual consistency, and verifiability. These findings demonstrate the necessity of tool augmentation for reliable biomedical reasoning and provide practical insights for building and testing LLM-based agents across diverse biomedical workflows.

Climate change and enhanced pollution levels are subjecting plants and crops to an increased number of different stressors, simultaneously or sequentially, generating conditions of multifactorial stress combination (MFSC). Although MFSC was shown to severely diminish plant growth, yield, and survival, how plants acclimate to increased levels of stress complexity is largely unknown. Here, we reveal that the Arabidopsis thaliana transcriptional regulator basic helix-loop-helix 35 (bHLH35) is required for plant acclimation to a specific set of MFSC conditions that includes a combination of salinity, excess light, and heat, occurring simultaneously (but not to each of these stresses applied individually or in any other combination). Under the three-stress combination, bHLH35 interacts with no apical meristem/transcription activator factor/cup-shaped cotyledon 69 (NAC069), binds the promoter of lateral organ boundaries domain 31 (LBD31), and regulates the expression of transcripts involved in flavonoid metabolism and ethylene signaling. Our findings uncover a high degree of specificity in plant responses to stress combination, suggesting that different conditions of MFSC could require the function of specific genetic programs for acclimation.

Abstract The increasing scale and complexity of proteomics data demand robust, scalable, and interpretable quality control (QC) frameworks to ensure data reliability and reproducibility. Here, we present pmultiqc, an open-source Python package that standardizes and generates web-based QC reports across multiple proteomics data analysis platforms. Built on top of the widely adopted MultiQC framework, pmultiqc offers specialized modules tailored to mass spectrometry workflows, with full initial support for quantms, DIA-NN, MaxQuant/MaxDIA, and mzIdentML/mzML-based pipelines. The package computes a wide range of QC metrics, including raw intensity distributions, identification rates, retention time consistency, and missing value patterns, and presents them in interactive, publication-ready reports. By leveraging sample metadata in the SDRF format, pmultiqc enables metadata-aware QC and introduces, for the first time in proteomics, QC reports and metrics guided by standardized sample metadata. Its modular architecture allows easy extension to new workflows and formats. Alongside comprehensive documentation and examples for running pmultiqc locally or integrated into existing workflows, we offer a cloud-based service that enables users to generate QC reports from their own data or public PRIDE datasets.

Although videolaryngoscopy is particularly helpful in patients with limited mouth opening, a severely restricted mouth opening is its hardest limitation. This analysis aimed to explore critical lower limits for interincisor distances that optimally predict (i) difficult Macintosh videolaryngoscopy or (ii) the risk of Macintosh videolaryngoscopy failure and may warrant consideration of alternative or awake techniques. This analysis from a prospective observational study includes 192 adults at increased risk for difficult Macintosh videolaryngoscopy undergoing head and neck surgery managed with Macintosh videolaryngoscopy (C-MAC) as the first-choice technique. Interincisor distances were measured before and after anesthesia induction. Five clinical indicators (outcome measures) for difficult Macintosh videolaryngoscopy (multiple attempts, conversion to hyperangulated videolaryngoscopy, documentation of a difficult videolaryngoscopic intubation, hyperangulated videolaryngoscopy recommended for future anesthesia, anesthesia alert card issued) and 2 key indicators for the risk of Macintosh videolaryngoscopy failure (vocal cords not visible, awake flexible endoscopy recommended for future anesthesia) were systematically assessed. Mouth opening was grouped in 2 mm intervals; the Youden index was calculated for each of these intervals and the maximum Youden index (Jmax) was noted for each of the 7 indicator. For each mouth opening interval, the deviation from this maximum Youden index (Jd-max) and the mean Jd-max across all indicators were calculated to identify optimal thresholds. The incidence of difficult videolaryngoscopic intubation was 56.8% (109/192). Optimal discrimination between easy and difficult Macintosh videolaryngoscopy (best mean Jd-max values) was derived from interincisor distances between 23 and 35 mm. The incidence of failed Macintosh videolaryngoscopy was 26.0% (50/192) in our study cohort and showed a clear inverse correlation with the interincisor distance. Discrimination between low and high risk of Macintosh videolaryngoscopy failure was optimal (best mean Jd-max values) between 21 and 23 mm. After anesthesia induction, interincisor distances increased in 31.8% (61/192), remained unchanged in 17.2% (33/192) but decreased in 51.0% (98/192) of the patients. The highest risk of reduction was found in individuals with degenerative cervical joint diseases (odds ratio [OR] = 6.43 [1.27-32.5]; P = .02), the lowest in those with facial fractures (OR = 0.27 [0.08-0.92]; P = .04) compared with a reference category. Our findings suggest that among individuals with expected difficult airway management, interincisor distances below 21 to 23 mm indicate increased risk of Macintosh videolaryngoscopy failure and may warrant alternative or awake techniques. Caution should be exercised as the mouth opening frequently decreases after anesthesia induction. Results should not be extrapolated to routine intubations in noncomplex patients.

IntroductionA key methodological challenge in epidemiological studies using a cluster analysis approach is the choice of an appropriate set of controls. This challenge becomes particularly complex when cases are geo-referenced and the outcome is rare. In fact, in such situations, controls need to be sampled from a comprehensive primary base, where population is defined both geographically and temporally. Furthermore, if cases are geo-referenced, the controls need to be geo-referenced too. However, selecting and geo-referencing such controls can be highly resource-intensive, both in terms of time and cost. ObjectiveThus, the main objective of our study is to use publicly available data and established geo-statistical techniques to simulate a geo-referenced population (GRP). This simulated geo-referenced population will be then used as the primary basis for the extraction of controls in a cluster analysis that will focus on childhood leukaemia incident cases in Germany. Methods For the period 2000-2020, we used population counts of persons aged 0 to 14 years from the WorldPop’s (WP) project at the University of Southampton and available in 100×100m grid cells [1]. The WP project employs a top-down modelling approach and uses different types of variables (rural settlements, industrial areas, schools, etc.) to estimate age-specific (0, 1–4, 5–9, 10-14 years) and sex-specific counts of persons in a grid [2,3,4]. The observed population figures (RP) at the municipality level were provided by the German Childhood Cancer Registry and were used as constraint values. To simulate a georeferenced population, the WP was used as a probability distribution function for sampling, with replacement, a number of cells equal to the RP. Afterwards, a uniform distribution was applied to randomly sample inside each picked cell a number of points (coordinates) equal to the times the cell was extracted. Here are shown results for three years: 2004, 2011 and 2019 and three simulated GRPs. Whereby the three simulations refer to the geographical level used to constrain the simulated population to the real population, i.e. the overall Childhood German population (S1), the childhood population at the state level (Bundesland) (S2), and the population at province level (Landkreis). For evaluation purposes, the percentage differences between the SP and the RP for all German municipalities were computed and summarized as the median and interquartile range (IQR). In addition, the root mean squared error (RMSE) was calculated. ResultsIn Germany, the number of children between 0 and 14 years old was 12,045,019 in 2004, 10,832,081 in 2011, and 11,396,196 in 2019. The WP estimations for the same years were 12,178,611, 10,886,770, and 10,457,921, respectively. When using the overall childhood population of Germany as the constrain for the simulation (S1), in 2004 we observe an overestimation of the population in the eastern Germany and an underestimation elsewhere (Figure 1; a) (median percentage difference =-7.1; IQR: -13.3 – 7.8); RMSE of 17.8. Percentage differences decreases in the third simulation (S3: median= -3.5; IQR: -12.2 – 5.1; a RMSE=6.5). Simulations for 2011 show, in general, better results with S3 as best performance (median=-1.2; IQR: -10.1 – 7.3; RMSE 4.4). Generally, results observed in 2019 are similar to those observed in 2011. ConclusionsDespite being computationally the most time-consuming, S3 shows the best performances in terms of narrower interquartile ranges and a more centered distribution. Thus, the simulated georeferenced population obtained using the RP at the province level as the constrain can be considered the optimal one. Further investigations are needed to shed light on the geographical differences observed in 2004. The inconsistencies between the WP and the RP must be considered as a limitation when interpreting our results. However, this is an innovative method which allows the future use of the overall georeferenced population or a selection of it for cluster analyses. The application of this method to each year of interest and the cluster analysis itself are pending.

The chemical composition of wood plays a pivotal role in the adaptability and structural integrity of trees. However, few studies have investigated the environmental factors that determine lignin composition and its biological significance in plants. Here, we examined the lignin syringyl-to-guaiacyl (S/G) ratio in members of a Populus trichocarpa population sourced from their native habitat and conducted a genome wide association study to identify genes linked to lignin formation. Our results revealed many significant associations, suggesting that lignin biosynthesis is a complex polygenic trait. Additionally, we found an increase in the S/G ratio from northern to southern geographic origin of the trees sampled, along with a corresponding metabolic and transcriptional reprogramming of xylem cell wall biosynthesis. Further molecular analysis identified a mutation in a cell wall laccase genetically associated with higher S/G ratios that predominate in trees from warmer lower latitudes. Collectively, our findings suggest that lignin heterogeneity arises from an evolutionary process enabling poplar adaptation to different climatic challenges.