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Therapeutic drug monitoring in children and adolescents treated with quetiapine for different psychiatric disorders: results of the TDM-VIGIL pharmacovigilance study
Item type: Journal Article
Kaiser A.; Egberts K.M.; Clement H.W.; et al. (2026)
Although quetiapine is increasingly used off-label in children and adolescents with various psychiatric disorders, there is little evidence on the relationship between quetiapine dose and serum concentration, and therapeutic reference ranges (TRR) have not yet been established for this age group. The aim of this study was to evaluate whether the TRR described for adults is applicable to minors. In addition, the study aimed to investigate the relationship between the daily dose, serum concentration and clinical outcome. The clinical course of 77 patients (females = 78%) aged between 12 and 17 years treated with quetiapine in the multicentre pharmacovigilance study TDM-VIGIL was systematically followed, and serum concentrations were measured. A positive mean correlation was observed between the weight-adjusted daily dose and serum concentration (rs = 0.580, p < 0.001). Most patients (84.7%) experienced no ADRs or ADRs not significantly interfering with the patients’ functioning. A positive linear relationship between the therapeutic response and serum concentration was found (rs = 0.238, p = 0.006). Only 38.5% of all quetiapine serum concentrations were within the recommended TRR described for adults. Using two different calculation methods, mean ± SD and IQR, our data hinted at considerably lower TRR for minors of about 14–291 ng/ml and 53–234 ng/ml respectively, compared to the suggested TRR for adults. This study has the potential to contribute to the definition of a TRR for quetiapine in minors. However, further controlled studies with larger sample sizes are needed to verify the preliminary results.
Highly boosted dielectron identification in proton-proton collisions at √s = 13 TeV
Item type: Journal Article
Hayrapetyan A.; Makarenko V.; Tumasyan A.; et al. (2026)
A new technique is developed to identify dielectrons (e+e−) with Lorentz boost γL?> 20 that produce one single merged cluster in the electromagnetic calorimeter of the CMS detector. The identification uses two multivariate models: one for the case where both electron tracks are reconstructed, and another where only one of the tracks is reconstructed. The efficiency is determined using proton-proton collision data collected at a center-of-mass energy of 13 TeV. Boosted J=ψ mesons decaying into e+e− pairs are used to estimate the efficiency of the model with two tracks, yielding an overall efficiency of 80%. The Z → μþμ−γ events, where the photon converts into a collimated dielectron, are used for the model with a single track, yielding an efficiency of about 60%. A dedicated energy correction for dielectron candidates is also developed using B± → J=ψK± → e+e−K± data.
Data-driven reduced modeling of delayed dynamical systems via spectral submanifolds
Item type: Journal Article
Abbasciano G.; Buza G.; Haller G. (2026)
We show how the recent extension of spectral submanifold (SSM) theory to delay differential equations (DDEs) enables data-driven model reduction of nonlinear delay systems. First, using a scalar DDE with a single discrete delay, we compare equation-based and data-driven SSM reductions, to illustrate the need for the latter. We then use the same algorithm to obtain purely data-driven, SSM-reduced, delay-free ordinary differential equation models for several nonlinear delayed systems. Our approach requires no information about the form of the underlying DDE, or about the number and magnitude of the delays it contains. Our SSM-reduced, low-dimensional models remain predictive even for chaotic dynamics. We also illustrate the use of parametric SSM reduction to capture bifurcations in systems with both distributed and discrete delays. Finally, we extend the theoretical underpinning of delayed SSM reductions to non-autonomous systems with periodic delays and apply these results to experimental data from a control system with feedback delay and quantization.
A multi-omics analysis of advanced triple-negative breast cancer identifies predictors to chemo-immunotherapy and on-treatment dynamics
Item type: Journal Article
Van Renne N.; Agostinetto E.; Joaquín García A.; et al. (2026)
No validated biomarkers beyond PD-L1 currently guide immune checkpoint inhibition (ICI) in advanced triple-negative breast cancer (TNBC), and how tumors remodel their microenvironment in response to ICI remains incompletely understood. We perform a temporal analysis of patients with advanced TNBC treated with chemo-immunotherapy in the SYNERGY trial (NCT03616886; n = 84) and the EORTC-1553-SPECTA initiative (NCT02834884; n = 38) using RNA sequencing, whole-exome sequencing, multiplex immunofluorescence, and imaging mass cytometry. A transcriptomic progression-free survival (PFS)-based risk signature stratifies outcomes and is validated across ICI-treated cohorts, while showing context-dependent behavior in non-ICI settings. Favorable outcomes are associated with immune-enriched tumors with organized lymphoid architecture, while poor outcomes are linked to fibrotic, angiogenic microenvironments with frequent PI3K alterations. Upon treatment, early progression was characterized by fibrovascular remodeling, macrophage expansion, and upregulation of alternative immunosuppressive mediators. These findings establish fibrovascular and myeloid reprogramming as a coordinated resistance program, identify actionable therapeutic targets, and support microenvironment stratification in future ICI-based trials.
A simple and interpretable method to predict head and neck offline adaptation needs based on anatomical changes
Item type: Journal Article
Flühmann T.; Guyer G.; Vallet V.; et al. (2026)
Background: Modern radiotherapy techniques do not automatically account for anatomical changes during treatment, which can lead to reduced target coverage, hotspots, or increased dose to healthy tissues. Adaptive Radiotherapy (ART) addresses this by updating the treatment plan based on the patient's current anatomy. In routine clinical practice, the decision to replan is often triggered by a suboptimal fit of the immobilization mask, identified by the radiation therapy technologist. Because this process is operator dependent and lacks methodological rigor, a quantitative decision-support method could substantially improve the replanning decision. Daily image-guided radiotherapy (IGRT) images provide valuable clinical information but often lack sufficient quality for accurate target contouring or reliable dose calculation to support straightforward clinical decision-making. This limitation motivates the need for a simpler, more practical, and scalable alternative. Purpose: This study develops an automated workflow to predict the need for replanning at every treatment fraction in head and neck (H&N) cancer patients using robust tools, thus requiring minimal manual validation while aiding the radio-oncologists in their decision-making. Methods: A retrospective dataset of 21 H&N cancer patients treated on a Tomotherapy Radixact system (Accuray, USA) with kVCT daily imaging was analyzed, of whom 14 underwent replanning during treatment. For each patient, the planning CT (pCT) and daily images were processed in RayStation treatment planning system (RaySearch Laboratories AB, Sweden) via scripts. Rigid registrations were used to align daily images to the pCT, and a patient-specific region of interest (ROI) was defined to consistently capture the same anatomical region across all treatment fractions. ROI volumes were intersected with the external contour and converted to relative volumetric changes with respect to the pCT. By grouping patients according to whether a replanning occurred during treatment, differences in mean volumetric change between replanned and non-replanned patients were assessed at each treatment fraction. To automatically detect clinically relevant anatomical changes, a simple, interpretable model based on three volumetric features (current volume loss, short-term trend, and long-term trend) was evaluated using cross-validation. Thresholds defining the trigger condition were optimized using a tailored loss function that prioritizes early detection and penalizes false negatives more strongly than false positives. Model variants using different feature subsets and hyperparameters were compared, and the final configuration was selected prioritizing recall, followed by specificity and early flagging. Results: Significant differences in volumetric change between replanned and non-replanned patients emerged at fraction 6 and remained significant for most subsequent fractions. The smallest adjusted (Formula presented.) -values occurred around fractions 22–25. Using leave-one-out cross-validation, the best-performing model flagged patients to be replanned with 93% recall, doing so on average 8.4 fractions before clinical intervention. Conclusions: Consistent differences in local tissue volume loss between replanned and non-replanned patients enabled the development of a simple automated workflow that triggers replanning for offline ART when learned volumetric thresholds are exceeded. This approach provides an objective complement to physician judgement and integrates seamlessly into existing IGRT workflows.