Data in Brief

Pollution of aquatic environments poses an increasingly severe threat to ecosystems worldwide, and understanding its molecular consequences for aquatic organisms requires extensive research and the development of advanced analytical tools. Phosphoproteomics can be particularly valuable for this purpose, as shifts in phosphorylation states can serve as early molecular indicators of toxic exposure. The cladoceran Daphnia is a keystone species in aquatic ecosystems, linking lower and higher trophic levels, and is therefore widely used as a model organism in ecotoxicology to study biological consequences of pollution. Here, we present a simple and effective strategy to analyse the phosphoproteome of Daphnia magna, a commonly used Daphnia species in ecotoxicology. Following TiO2-based phosphopeptide enrichment and LC-MS/MS analysis, we identified a comprehensive dataset of 3,532 phosphorylation sites across 1,329 phosphoproteins. These proteins were especially involved in signaling pathways and cellular structure and the vast majority have not yet been demonstrated in other Daphnia species. In conclusion, our results demonstrate that a straightforward phosphoproteomic LC-MS/MS workflow in D. magna can serve as a powerful tool for investigating adverse molecular effects caused by anthropogenic pollution, such as microplastics or pharmaceuticals. Statement of significanceThe dataset presented here demonstrates the feasibility of a simple yet effective strategy to perform phosphoprotemics in Daphnia magna, and it will be particularly valuable for future ecotoxicoproteomics research using this model organism.

Transcriptional bursts regulate gene expression by altering burst size or burst frequency. Here, we present a protocol that integrates fixed-cell smFISH and live-cell single-molecule imaging to analyze estrogen-responsive transcriptional bursting of the TFF1 gene in human breast cancer cell lines. This workflow enables measurement of burst size, burst initiation, and active allele frequency to determine how endocrine disruptor chemicals modulate transcriptional bursting dynamics. For complete details on the use and execution of this protocol, please refer to Day, Yasar et al.1

Introduction Air pollution is the largest environmental risk to human health. Children are disproportionately affected by air pollution and their exposure is amplified during physical activity. Observed concentrations of nitrogen dioxide in 1 in 4 London school playground exceeds the European limit, but the health impacts of air pollution exposure in London school playgrounds remain unexplored. Our study aims to assess and compare the acute changes in lung function and airway inflammation of primary school-aged children exercising in school playgrounds. Methods and analysis 330 children aged 8 to 11 years from ten London schools will be recruited to complete 90 minutes of physical activity and 90 minutes of rest in their school playground in a randomised crossover design. Pre-, post-, and 24-hour post-exposure oscillometry measurements will be performed with airway resistance at 5 Hz (R5) the primary physiological outcome. Nasal lavage samples will be collected pre-exposure and 24-hour post-exposure for analysis of inflammatory, oxidative, and vascular biomarkers, with IL-6 as the primary biological outcome. Mixed-effects regression models will examine associations between estimated pollutant exposures, exercise and physiological responses.

Background: Citrin deficiency, caused by biallelic pathogenic variants in SLC25A13, must be identified early to prevent serious complications such as hyperammonemia and liver failure. However, clinical diagnosis is often delayed due to its nonspecific presentation and limited sensitivity of amino acid-based newborn screening methods. Although genome-based evaluations are being investigated to address these issues, concerns about their cost, turnaround time, variant interpretation ability, and data handling highlight the need for a more practical yet reliable alternative. We investigated the feasibility of applying proteomic approach on dried blood spots (DBS), which are routinely used in newborn screening. Methods: We performed untargeted liquid chromatography-tandem mass spectrometry to analyze the proteome of DBS using a previously developed "non-targeted analysis of non-specifically DBS-absorbed proteins" (NANDA) workflow. SLC25A13 protein abundance was quantified in individuals with biallelic loss-of-function mutations, compound loss-of-function/missense mutations, and heterozygous carriers; this was also evaluated in healthy and diseased controls representing relevant differential diagnoses. To leverage proteomic information, we derived a multivariate proteomic signature using feature selection and evaluated its performance with leave-one-out cross-validation. Biological relevance was assessed by enrichment analysis, and complementary transcriptomics was performed using RNA sequencing. Results: A total of 7,474 proteins, including SLC25A13, were consistently detected in DBS. SLC25A13 was undetectable in individuals with biallelic loss-of-function mutations. However, individuals with compound loss-of-function/missense genotypes showed reduced but measurable SLC25A13 levels, comparable to those observed in heterozygous carriers. In contrast, a compact 15-protein signature accurately identified individuals with compound loss-of-function/missense genotypes (AUC, 0.99; sensitivity, 1.00; specificity, 0.95). The signature was enriched for Ca2+-response, and transcriptomics showed downregulation of genes related to multimodal ion channels in affected individuals compared to controls. Conclusions: DBS-based proteomic profiling may assist in the diagnosis of citrin deficiency through SLC25A13-quantification and a biologically plausible multivariate signature. More broadly, this strategy offers a promising new diagnostic layer for protein disorders, providing a proteomic readout in a clinically practical DBS format with potential utility for future diagnostic and screening applications.

Background Increasingly accessible satellite imagery provides scalable measures of the built and natural environment relevant to population health. However, whether such imagery can capture subnational variation in mortality and life expectancy remains unclear. We therefore assessed its predictive value for regional mortality and life expectancy across OECD regions. Methods We conducted an ecological, cross-sectional prediction study using 2023 data from OECD Territorial Level 3 (TL3) regions. Annual cloud-masked composites from the Harmonized Landsat and Sentinel-2 collection were processed in the Google Earth Engine, tiled at 224 x 224 pixels, and encoded with the pretrained Prithvi foundation model to derive region-level satellite embeddings. For each outcome, we trained LightGBM regressors for a country-only baseline, a satellite-only model, a combined model (country + satellite), and a final contextual model that additionally included prespecified socioeconomic and environmental covariates. Performance was evaluated using 10-fold outer cross-validation with held-out test folds; R2 was the primary metric. Results The analytic sample comprised 2,414 OECD TL3 regions across 38 countries, for which 939,959 satellite image tiles were processed. In paired bootstrap comparisons, adding satellite features to country indicators improved predictive performance for all outcomes, with incremental R2 ranging from 0.097 to 0.233. The final contextual model achieved R2 values of 0.78 (95% CI, 0.74-0.81) for crude mortality, 0.87 (0.84-0.89) for age-adjusted mortality, 0.86 (0.82-0.88) for infant mortality, and 0.76 (0.69-0.84) for life expectancy. In SHAP analyses, the aggregated satellite image effect consistently ranked among the top predictors across outcomes. Conclusion Satellite imagery captures subnational environmental heterogeneity relevant to regional mortality and life expectancy beyond country identity alone. Earth observation may therefore provide a scalable, complementary data source for characterizing geographic disparities in population health.

One of the prominent methods in neuroimaging data processing is SSM-PCA, which is based on principal component analysis and allows for the identification of diagnostically significant patterns in the form of statistical maps. We developed software, PIE Toolbox, employs SSM-PCA and classification based on the obtained diagnostic patterns revealed from functional and structural tomographic brain imaging. The program supports the entire analysis pipeline including preprocessing of brain images, diagnostic patterns extraction, building classification models, and prediction based on them. The resulting diagnostic patterns are weighted principal components obtained through SSM-PCA, or their linear combinations. PIE Toolbox allows selection of relevant structural and functional brain patterns, computation of their expression values in regions of interest, classification using support vector machines, and evaluation of model performance via cross-validation. This approach enables the use of patterns as features of intergroup differences for individual diagnosis. The software has been validated on both simulated and ADNI datasets.

Breast cancer (BC) remains a leading cause of morbidity and mortality worldwide. Early detection remains the most effective strategy for improving prognosis. We explored the urinary extracellular vesicle (uEV) proteome for changes linked to BC which could also be potential biomarkers. Urine samples were collected from 20 participants across four groups (n = 5 each): newly diagnosed BC patients, benign breast disease (BBD) patients, individuals with breast cancer symptoms (symptom control, SC), and age-matched healthy controls (HC). EVs were isolated using size exclusion chromatography and extracted proteins were analysed using a GeLC proteomic approach. Proteins were identified and quantified using Proteome Discoverer and further analysed using MetaboAnalystR, Funrich and Metascape. A total of 256 proteins were identified from the uEV preparations. BC comparisons with BBD, SC and HC identified 7 proteins differentially expressed proteins (DEP); SERPINB1 -- Serpin family B member 1, LCN1 -- Lipocalin 1, SIRPA -- Signal regulatory protein alpha, ACTB -- Actin, beta, YWHAZ --Tryptophan 5-monooxygenase activation protein zeta, Ig JCHAIN and APOA1 -- Apolipoprotein A1. Receiver Operator Characteristic (ROC) curve assessments suggested that each DEP protein had an area under the curve (AUC) of > 0.8. These findings highlight EV-derived proteins as promising non-invasive biomarkers for breast cancer detection, warranting further validation in larger cohorts.

Background Foodborne diseases cause substantial global morbidity and mortality, yet remain largely unattended. To support countries to address this public health concern, the World Health Assembly Resolution 73.5 called for strengthening global food safety efforts and led to the development of the WHO Global Strategy for Food Safety 2022-2030, adopted at the 75th WHA (2022). To this end, the World Health Organization (WHO) reconvened the Foodborne Disease Burden Epidemiology Reference Group (FERG) to advise and support the work to generate updated global, regional, and national estimates of the foodborne disease burden for the reference period 2000-2021. Methods We developed an incidence-based framework expanding coverage to 42 foodborne hazards. Standardized systematic reviews, Global Health Estimates and Global Burden of Disease envelopes, and United Nations population data informed the evidence base. Missing epidemiological data were imputed using Bayesian hierarchical meta-regression models. Disease models mapped acute and chronic health outcomes, applying updated disability weights, life tables, and probabilistic Monte Carlo calculations to estimate incidence, mortality, Years Lived with Disability, Years of Life Lost and Disability-Adjusted Life Years for all 194 WHO Member States. Transparency and analysis reproducibility were ensured through availed open-source R packages and standardized workflows. Results The computational framework provides annual, country-level estimates with improved internal consistency and an expanded hazard scope compared with the WHO 2015 edition. Advances include refined modelling, enhanced uncertainty propagation, and broader inclusion of microbial, parasitic, and chemical hazards. Persistent data gaps---especially in high-burden regions---were filled through extensive imputation. Conclusions The computational framework for the WHO 2026 edition delivers the most comprehensive and transparent assessment of the global burden of foodborne diseases to date. Despite remaining limitations, it enables routine monitoring, supports evaluation of global food safety efforts, and highlights priorities for strengthening national data systems.

Widespread exposure to multiple pesticides might potentially represent a genotoxic risk to humans. However the effects of these mixtures are largely unknown. Genotoxicity is a key characteristic of carcinogens, and its assessment represents an important component of the overall safety assessment of pesticides. In the present study, in vitro micronucleus test on intestinal Caco-2 human cells was performed according to OECD TG 487 in order to ascertain the genotoxicity of ten commonly used pesticides (dose range 0-100 mg L-1), tested as individual pesticides or mixtures. Significant dose-related increases in micronuclei were observed for exposures to lambda-cyhalothrin, tebuconazole, glyphosate, deltamethrin, fluopyram and the synergist piperonyl butoxide. Significant increases of micronuclei were also observed at different doses for cypermethrin, acetamiprid and cyprodinil, however these increases were not dose-dependent. Imazalil genotoxicity could not be analyzed due to confounding of high cytotoxicity even at low doses. Results show that the co-formulant piperonyl butoxide was genotoxic to human cell lines at all tested doses. Moreover, glyphosate, acetamiprid and fluopyram showed genotoxic effects at concentrations of 0.01-1.0 mg L-1. Although previously reported to be not genotoxic cyprodinil and deltamethrin were observed to be genotoxic to Caco-2 cells. A combination of 3 prioritzided pesticides (acetamiprid, glyphosate, tebuconazole) showed genotoxic effects even at the lowest dose. A combination of 8 prioritized pesticides showed genotoxicity at the highest dose. No synergistic interactions in micronuclei formation were evident in either the mixture of 3 or 8 prioritized pesticides. This study provides important information on the genotoxicity of different widely used pesticides and confirms the validity of a component-based approach in genotoxicity assessment of pesticide mixtures. This study was performed as part of the EU SPRINT (Sustainable Plant Protection Transition: A Global Health Approach) project.

Background: Traditional diagnostic models lack explainability, while multimodal language models prone to hallucination remain unsafe for medical education. An interactive, risk-free artificial intelligence framework is required to serve as a reliable clinical mentor for radiology trainees. Methods: We propose a multi-agent architecture decoupling deterministic image analysis from generative consultation. Specialized computer vision models perform anatomical localization and pathological segmentation. These quantitative outputs are synthesized into a structured payload, which grounds a locally hosted large language model (LLaVA 7B) using strict prompt guardrails and prerequisite protocols. Results: The system effectively eliminates visual hallucinations by intercepting unanchored queries. The artificial intelligence tutor successfully contextualizes spatial anomalies and baseline metrics, generating accurate conversational explanations and formally structured radiology reports while strictly enforcing medical safety disclaimers. Discussion and Conclusion: By anchoring language generation exclusively to verified algorithmic realities, this framework transforms opaque diagnostic models into safe, interactive educational simulators. This establishes a highly reliable paradigm for integrating explainable artificial intelligence into medical training.

The significant challenges associated with rare diseases in the medical and research domains include the scarcity of information, which is often confined to unstructured formats. Although existing approaches provide valuable insights, there is a need to develop effective methods to identify information pertinent to rare diseases for advancing rare disease research. We identified mentions of rare diseases in relevant texts and assessed their relevance using derived scores, the confidence score and semantic similarity from a fine-tuned BioMedBERT encoder. This encoder was fine-tuned using rare disease related text from Online Mendelian Inheritance in Man (OMIM), Orphanet, a manually validated dataset, and STS benchmark datasets. The process of identifying meaningful rare disease mentioned was presented through two case studies that retrieved relevant NIH-funded projects, utilizing a generated knowledge graph in Neo4j to host data on 2,067 GARD diseases with over 320,000 NIH funded projects. Through various case studies with NIH-funded projects related to rare diseases, we demonstrated the effectiveness of our approach in systematically providing rare disease related data to enhance our understanding of rare diseases for future investigations.

Since the release of the first ChatGPT model in 2022, large language models (LLMs) have evolved significantly, and an increasing number of users now turn to these generative information systems for inquiries as sensitive and consequential as those related to health. The primary objective is to identify the main strengths and weaknesses of generative AI systems when responding to information needs as critical as those arising in the health domain. The study was structured using a question-answer format, in which each question corresponded to a user query and each answer represented the output generated by a model in response. The study employed a human evaluation framework involving two distinct panels of clinical experts from different specialties. The evaluation criteria encompassed three dimensions: adherence to medical consensus; presence or absence of inappropriate or incorrect information; and the potential to cause harm to users. GPT-4o mini, Llama 3, and MedLlama 3 were selected as three representative systems for the experiments. This study presents a detailed analysis of the performance of widely used contemporary large language models in addressing common health-related queries posed by online users. The results reinforce the potential of LLMs as tools for online health information seeking among non-expert users. However, the performance limitations identified underscore the need for further studies to monitor the future development of these models. Among them, performance issues have been identified in areas where users may be more vulnerable, leading to the retrieval of clinically incorrect information, particularly in matters relating to rare diseases. Furthermore, it has been noted that these models can become trapped in obsolete medical knowledge due to continuous scientific progress.

INTRODUCTIONMounting evidence support exposome influences on brain function and health, complementing genome influences. Understanding the molecular imprint of exposome on brain metabolism and the biochemical communication between the body and brain can impact our fundamental understanding and treatment of neuropsychiatric diseases. METHODSLeveraging two complementary metabolomics platforms, we classified 1400 features in 514 brains from the ROSMAP collection. We evaluated the origin of these compounds using literature and databases. We correlated those metabolites with cognitive function using linear models. RESULTSWe identified over 230 non-endogenous compounds in the brain, including 103 drugs and metabolites, 120 dietary and microbial products and possibly 15 compounds from environmental exposures. Over 20 dietary and gut microbial compounds showed associations with cognition. DISCUSSIONComprehensive profiling of chemicals in the brain and the link to cognitive function provides foundational work to connect body and brain in the study of AD and related dementias.

Background and Objective: Access to real-world electronic health records (EHRs) remains limited by privacy, governance and annotation constraints, hindering the development of clinical natural language processing models. Realistic synthetic progress notes may provide EHR-like corpora that preserve clinically rigorous information on diagnoses, treatments, symptoms, imaging, laboratory findings and therapeutic trajectories without relying directly on sensitive patient records. This study evaluates whether large language models (LLMs) can generate realistic Spanish prostate cancer progress notes from published case reports, preserving clinical content, temporality and hospital-style conventions.

Bdelloid rotifers are known to survive desiccation and high doses of ionizing radiation. This extreme resistance is notably due to their capacity to cope with numerous DNA double-strand breaks (DSBs). Genes encoding key components of the non-homologous end joining (NHEJ) DNA repair pathway are strongly upregulated in the bdelloid rotifer Adineta vaga following exposure to ionizing radiation. Considering the notably high doses tolerated by these organisms, their capacity to efficiently restore genome integrity is particularly striking. Although NHEJ is generally regarded as less accurate than homologous recombination (HR), the absence of major genomic rearrangements in the descendants of irradiated rotifers suggests that DNA repair occurs with high fidelity. Terwagne et al. recently reported a delayed repair in germline nuclei, occurring during oocyte development when homologous chromosomes pair, thereby enabling template-based repair through HR. In this study, we established an in situ hybridization approach on A. vaga cryosections to investigate the spatial and temporal expression of key actors involved in NHEJ, HR, and Base excision repair (BER) pathways in somatic and germline tissues. We show that NHEJ (KU80) and BER-related genes (PARPs) as well as A. vaga Ligase E (putatively involved in DNA repair) are expressed early after radiation exposure in the somatic syncytium. In contrast, HR-related genes (Rad51: two paralogs, Rad54), as well as PCNA (involved in DNA replication, NER, BER, HR) are expressed later in maturing oocytes, indicating the activation of a delayed homologous recombination repair pathway in germline nuclei. Nurse cells, which express genes associated with both HR and NHEJ pathways, may rely on both mechanisms for their own DNA repair while also supplying mRNAs to the maturing oocyte. Our results provide new evidence for a differential regulation of DNA DSB repair pathways between soma and germline in bdelloids, with NHEJ predominating in somatic tissues and HR in the germline of A. vaga. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/722046v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@3b1f3borg.highwire.dtl.DTLVardef@17f5eb5org.highwire.dtl.DTLVardef@122ef14org.highwire.dtl.DTLVardef@7e4413_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOAbstract Figure:C_FLOATNO Summary of in situ hybridization results: genes coding for actors of NHEJ are expressed in the somatic nuclei and in the nurse nuclei of Adineta vaga individuals 2.5 hours post X-rays radiation, while genes coding for HR actors and PCNA (involved in multiple pathways including DNA replication and DNA repair: NER, BER, MR, HR) are expressed in the nurse nuclei 2.5 hours post radiation, and later in the maturing oocyte during oogenesis and in the laid eggs. Genes coding for actors highly expressed post-radiation, involved in the BER pathway appear to be only expressed in the somatic syncytium 2.5 hours post radiation, as well as the gene coding for the Ligase E, likely involved in DNA repair. C_FIG

The use of marine space by human activities is globally increasing, resulting in a competition with spatial management measures for marine conservation. Within the European Union (EU) these measures are currently implemented by the union member states to achieve the UN sustainable development goal (SDG) of protecting at least 10 % of the national marine waters. Further, the EU Marine Strategy Framework Directive (MSFD) and the Nature Restoration Regulation (NRL) are the two main legal means for the implementation of ambitious spatial conservation targets for benthic habitat types, which can range from 10 - 90 %. This study analysis how the targets of the MSFD and NRL are currently met in the German waters of the North Sea and which areas the full implementation of both legislations might require. A spatial optimisation tool ("prioritizr" in R) was used to identify optimised solutions for the conservation of up to 75 % of NRL benthic habitats. The current spatial conservation measures (which ban demersal trawling within certain zones of designated marine protected areas, MPA) are not sufficient to reach the targets of the MSFD and NRL. Extending the exclusion of demersal trawling to the entire area of the MPAs would achieve a sufficient coverage for all habitats except for offshore sand and mud habitats. These could be further protected, when including areas for offshore wind farms, where trawling is also banned. However, to date it is unclear, if and how these (or other human use) areas could be included into spatial conservation regimes, a debate that needs to be resolved to allow for the achievement of the ambitious MSFD and NRL targets.

Due to recent technological advances, in situ structural cell biology is becoming a high throughput microscopy technique as all the steps of the workflow, from sample preparation to data analysis, are executed faster, more reliable and more reproducible. Sample thinning by cryoFIB-SEM is an essential tool in preparing electron transparent lamellae of biological specimens suitable for further characterization by cryoET. Modern cryoFIB-SEM instruments can be operated remotely and are capable of automated and unsupervised lamellae preparation. To take full advantage of these developments they need a constant supply of LN2 to maintain cryogenic conditions inside the microscope chamber. Here, we introduce a custom automated LN2 refill system that is compatible with gas cooled cryostages, supports long-term cryoFIB-SEM operations and liberates the user from highly repetitive and manual work. We believe this solution can be utilized with other cryoSEM or cryoFIB-SEM devices requiring N2 gas-flow cooling.

Fresh-frozen tissues are considered the gold standard for proteomic analyses due to superior preservation of protein integrity; however, their use is limited by the logistical and financial requirements of long-term storage. Formaldehyde-fixed paraffin-embedded (FFPE) tissues provide a practical alternative owing to their stability and widespread availability in clinical settings. A critical step in FFPE proteomics is deparaffinization, which traditionally relies on organic solvents such as xylene, along with efficient reversal of formaldehyde-induced crosslinks. In this study, we evaluated multiple FFPE protein extraction and digestion workflows including chaotropic, surfactant-based, and detergent-free approaches in combination with xylene-free deparaffinization strategies, using label-free data-independent acquisition (DIA) LC-MS/MS. Among the tested methods, a chaotropic-, reductant-, and surfactant-free in-solution digestion workflow demonstrated robust protein and peptide recovery. A modified version of this protocol further improved peptide coverage while maintaining comparable protein depth. The applicability of the optimized workflow was assessed using FFPE needle biopsy samples from control, hepatic steatosis, and liver fibrosis groups. Distinct proteomic patterns were observed across conditions, with hepatic steatosis associated with early activation of stress-response pathways, while fibrosis showed evidence suggesting altered lipid metabolism. Overall, this study presents a simple, xylene-free, and MS-compatible workflow for FFPE proteomics that is suitable for low-input clinical samples and may support broader application of archival tissues in proteomic research.

Background: Climate mitigation policies can lower air pollutant concentrations and deliver substantial health co-benefits. The French Ecological Transition Agency (ADEME) proposed four contrasting Transitions 2050 net-zero scenarios. We quantified mortality, morbidity, and health-economic co-benefits from projected PM2.5 and NO2 reductions across all four scenarios in continental France. Methods: Emission projections were input to the CHIMERE chemistry-transport model to estimate PM2.5 and NO2 concentrations for 2030 and 2050. Health impacts were assessed using disease-specific cessation-lag assumptions relative to 2019, covering premature mortality, morbidity, DALYs, and economic benefits across nine outcomes (hypertension, lung cancer, ischaemic heart disease, stroke, COPD, type-2 diabetes, acute lower respiratory infections, and asthma in children and adults). Findings: Population exposure is projected to decline by about 40% for PM2.5 and 70% for NO2 by 2050, with health gains remaining substantial and broadly equivalent across all four scenarios and modest differences between sufficiency-oriented and technology-driven pathways. Under delayed-impact assumptions, avoided premature deaths ranged from 21,300 to 22,100 for PM2.5 and 24,500 to 26,200 for NO2. Morbidity and disability-adjusted life year (DALY) reductions, as well as economic savings, spanned similarly; total avoided morbidity cases were 84,000-88,000, direct medical cost reductions were e1.0-1.1 billion/year, and intangible cost savings of e41-43 billion and e36-39 billion, respectively. Interpretation: Health co-benefits are substantial, consistent across contrasting scenarios, and increase markedly from 2030 to 2050. Explicitly incorporating these co-benefits into climate policy appraisals may strengthen the case for ambitious mitigation and improve decision-maker acceptability.

Automated electroencephalography (EEG) seizure detection systems support clinical monitoring through alarm-driven workflows, in which the practical utility of a detector is determined by its event-level false-alarm rate. We examine the false-alarm structure produced by a strong window-level seizure detector on the Temple University Hospital Seizure Corpus (TUSZ) v2.0.6 and find that the false-alarm burden is unevenly distributed across subjects, with worst-decile subjects carrying substantially higher FA/24h than the cohort median. We propose ALARM-Net (Alarm-Level Adaptive Rejection Module), a detector-agnostic event-level alarm-suppression framework. ALARM-Net treats the window-level detector as a frozen black box, generates high-recall event proposals from its per-second probability timeline, and applies a regularized CatBoost classifier over 14 causal features summarizing each proposal's probability morphology, local pre-context, and alarm history. Operating-point selection is governed by predefined clinical constraints on the dev split (strict sensitivity loss [≤] 8 percentage points; FA/24h relative reduction [≥] 30%); the locked operating point is applied without modification to the held-out eval split. ALARM-Net reduces false alarms per 24 hours from 30.75 to 12.63 on dev (a 58.9% reduction) and from 19.43 to 4.60 on eval (a 76.3% reduction), with strict sensitivity loss of 7.5 and 7.8 percentage points respectively. The detector-only false-alarm burden is heavily concentrated on a small subset of subjects (worst-decile mean: 79.55 FA/24h on dev, 52.77 on eval), motivating the event-level suppression approach. Ablations across feature design, suppressor family, and rule-based baselines suggest that event-level reformulation and proposal morphology features, rather than the classifier family alone, drive the observed false-alarm reduction. ALARM-Net complements window-level seizure detectors and highlights the importance of event-level evaluation for clinically oriented seizure detection.