Sci Rep. 2025 Apr 23;15(1):14177. doi: 10.1038/s41598-025-94023-z.

ABSTRACT

Machine learning (ML) and deep neural networks (DNN) have emerged as powerful tools for enhancing intrusion detection systems (IDS) in cybersecurity. However, recent studies have revealed their vulnerability to adversarial attacks, where maliciously perturbed traffic samples can deceive trained DNN-based detectors, leading to incorrect classifications and compromised system integrity. While numerous defense mechanisms have been proposed to mitigate these adversarial threats, many fail to achieve a balance between robustness against adversarial attacks, maintaining high detection accuracy on clean data, and preserving the functional integrity of traffic flow features. To address these limitations, this research investigates and integrates a comprehensive ensemble of adversarial defense strategies, implemented in two key phases. During the training phase, adversarial training, label smoothing, and Gaussian augmentation are employed to enhance the model's resilience against adversarial perturbations. Additionally, a proactive preprocessing defense strategy is deployed during the testing phase, utilizing a denoising sparse autoencoder to cleanse adversarial input samples before they are fed into the IDS classifier. Comparative evaluations demonstrate that the proposed ensemble defense framework significantly improves the adversarial robustness and classification performance of DNN-based IDS classifiers. Experimental results, validated on the CICIDS2017 and CICIDS2018 datasets, show that the proposed approach achieves aggregated prediction accuracies of 87.34% and 98.78% under majority voting and weighted average schemes, respectively. These findings underscore the effectiveness of the proposed framework in combating adversarial threats while maintaining robust detection capabilities, thereby advancing the state-of-the-art in adversarial defense for intrusion detection systems.

PMID:40268978 | DOI:10.1038/s41598-025-94023-z

Sci Data. 2025 Apr 23;12(1):682. doi: 10.1038/s41597-025-04979-w.

ABSTRACT

Deep learning super-resolution microscopy has advanced rapidly in recent years. Super-resolution images acquired by single molecule localization microscopy (SMLM) are ideal sources for high-quality datasets. However, the scarcity of public datasets limits the development of deep learning methods. Here, we describe a biological image dataset, DL-SMLM, which provides paired low-resolution fluorescence images and super-resolution SMLM data for training super-resolution models. DL-SMLM consists of six different subcellular structures, including microtubules, lumen and membrane of endoplasmic reticulum (ER), Clathrin coated pits (CCPs), outer membrane of mitochondria (OMM) and inner membrane of mitochondria (IMM). There are 188 sets of raw SMLM data and 100 signal levels for each low-resolution image. This allows software developers to generate thousands of training pairs through data segmentation. The performance of the imaging system was further evaluated using DNA origami samples. Finally, we demonstrated examples of super-resolution models trained using data from DL-SMLM, highlighting the effectiveness of DL-SMLM for developing deep learning super-resolution microscopy.

PMID:40268962 | DOI:10.1038/s41597-025-04979-w

Interdiscip Sci. 2025 Apr 23. doi: 10.1007/s12539-025-00691-w. Online ahead of print.

ABSTRACT

Accurate tumor grading and regional identification of cervical tumors are important for diagnosis and prognosis. Traditional manual microscopy methods suffer from time-consuming, labor-intensive, and subjective bias problems, so tumor segmentation methods based on deep learning are gradually becoming a hotspot in current research. Cervical tumors have diverse morphologies, which leads to low similarity between the mask edge and ground-truth edge of existing semantic segmentation models. Moreover, the texture and geometric arrangement features of normal tissues and tumors are highly similar, which causes poor pixel connectivity in the mask of the segmentation model. To this end, we propose an end-to-end semantic consistency network with the edge learner and the connectivity enhancer, i.e., ERNet. First, the edge learner consists of a stacked shallow convolutional neural network, so it can effectively enhance the ability of ERNet to learn and represent polymorphic tumor edges. Second, the connectivity enhancer learns detailed information and contextual information of tumor images, so it can enhance the pixel connectivity of the masks. Finally, edge features and pixel-level features are adaptively coupled, and the segmentation results are additionally optimized by the tumor classification task as a whole. The results show that, compared with those of other state-of-the-art segmentation models, the structural similarity and the mean intersection over union of ERNet are improved to 88.17% and 83.22%, respectively, which reflects the excellent edge similarity and pixel connectivity of the proposed model. Finally, we conduct a generalization experiment on laryngeal tumor images. Therefore, the ERNet network has good clinical popularization and practical value.

PMID:40268829 | DOI:10.1007/s12539-025-00691-w

Neuroimage. 2025 Apr 21:121202. doi: 10.1016/j.neuroimage.2025.121202. Online ahead of print.

ABSTRACT

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) has emerged as a powerful tool to image endogenous or exogeneous macromolecules. CEST contrast highly depends on radiofrequency irradiation B1 level. Spatial inhomogeneity of B1 field would bias CEST measurement. Conventional interpolation-based B1 correction method required CEST dataset acquisition under multiple B1 levels, substantially prolonging scan time. The recently proposed supervised deep learning approach reconstructed B1 inhomogeneity corrected CEST effect at the identical B1 as of the training data, hindering its generalization to other B1 levels. In this study, we proposed a Conditional Variational Autoencoder (CVAE)-based generative model to generate B1 inhomogeneity corrected Z spectra from single CEST acquisition. The model was trained from pixel-wise source-target paired Z spectra under multiple B1 with target B1 as a conditional variable. Numerical simulation and healthy human brain imaging at 5 T were respectively performed to evaluate the performance of proposed model in B1 inhomogeneity corrected CEST MRI. Results showed that the generated B1-corrected Z spectra agreed well with the reference averaged from regions with subtle B1 inhomogeneity. Moreover, the performance of the proposed model in correcting B1 inhomogeneity in APT CEST effect, as measured by both MTRasym and [Formula: see text] at 3.5 ppm, were superior over conventional Z/contrast-B1-interplation and other deep learning methods, especially when target B1 were not included in sampling or training dataset. In summary, the proposed model allows generalized B1 inhomogeneity correction, benefiting quantitative CEST MRI in clinical routines.

PMID:40268259 | DOI:10.1016/j.neuroimage.2025.121202

Magn Reson Imaging. 2025 Apr 21:110396. doi: 10.1016/j.mri.2025.110396. Online ahead of print.

ABSTRACT

PURPOSE: To accelerate 3D whole-heart joint T1/T2 mapping for myocardial tissue characterization using an end-to-end deep learning algorithm for joint motion estimation and model-based motion-corrected reconstruction of multi-contrast undersampled data.

METHODS: A free-breathing high-resolution motion-compensated 3D joint T1/T2 water/fat sequence is employed. The sequence consists of the acquisition of four interleaved volumes with 2-echo encoding, resulting in eight volumes with different contrasts. An end-to-end non-rigid motion-corrected reconstruction network is used to estimate high quality motion-corrected reconstructions from the eight multi-contrast undersampled data for subsequent joint T1/T2 mapping. Reconstruction with the proposed approach was compared against state-of-the-art motion-corrected HD-PROST reconstruction.

RESULTS: The proposed approach yields images with good visual agreement compared to the reference reconstructions. The comparison of the quantitative values in the T1 and T2 maps showed the absence of systematic errors, and a small bias of -6.35 ms and -1.8 ms, respectively. The proposed reconstruction time was 24 seconds in comparison to 2.5 hours with motion-corrected HD-PROST, resulting in a reconstruction speed-up of over 370 times.

CONCLUSION: In conclusion, this study presents a promising method for efficient whole-heart myocardial tissue characterization. Specifically, the research highlights the potential of the multi-contrast end-to-end deep learning algorithm for joint motion estimation and model-based motion-corrected reconstruction of multi-contrast undersampled data. The findings underscore its ability to compute T1 and T2 values with good agreement when compared to the reference motion-corrected HD-PROST method, while substantially reducing reconstruction time.

PMID:40268172 | DOI:10.1016/j.mri.2025.110396

Methods. 2025 Apr 21:S1046-2023(25)00108-2. doi: 10.1016/j.ymeth.2025.04.011. Online ahead of print.

ABSTRACT

RNA modifications play a crucial role in enhancing the structural and functional diversity of RNA molecules and regulating various stages of the RNA life cycle. Among these modifications, N6-Methyladenosine (m6A) is the most common internal modification in eukaryotic mRNAs and has been extensively studied over the past decade. Accurate identification of m6A modification sites is essential for understanding their function and underlying mechanisms. Traditional methods predominantly rely on machine learning techniques to recognize m6A sites, which often fail to capture the contextual features of these sites comprehensively. In this study, we comprehensively summarize previously published methods based on machine learning and deep learning. We also validate multiple deep learning approaches on benchmark dataset, including previously underutilized methods in m6A site prediction, pre-trained models specifically designed for biological sequence and other basic deep learning methods. Additionally, we further analyze the dataset features and interpret the model's predictions to enhance understanding. Our experimental results clearly demonstrate the effectiveness of the deep learning models, elucidating their strong potential in accurately recognizing m6A modification sites.

PMID:40268153 | DOI:10.1016/j.ymeth.2025.04.011

Biochim Biophys Acta Mol Cell Res. 2025 Apr 21:119964. doi: 10.1016/j.bbamcr.2025.119964. Online ahead of print.

ABSTRACT

Although image analysis has emerged as a key technology in the study of organelle dynamics, the commonly used image-processing methods, such as threshold-based segmentation and manual setting of regions of interests (ROIs), are error-prone and laborious. Here, we present a highly accurate high-throughput image analysis pipeline called OrgaMeas for measuring the morphology and dynamics of organelles. This pipeline mainly consists of two deep learning-based tools: OrgaSegNet and DIC2Cells. OrgaSegNet quantifies many aspects of different organelles by precisely segmenting them. To further process the segmented data at a single-cell level, DIC2Cells automates ROI settings through accurate segmentation of individual cells in differential interference contrast (DIC) images. This pipeline was designed to be low cost and require less coding, to provide an easy-to-use platform. Thus, we believe that OrgaMeas has potential to be readily applied to basic biomedical research, and hopefully to other practical uses such as drug discovery.

PMID:40268058 | DOI:10.1016/j.bbamcr.2025.119964

Int J Biol Macromol. 2025 Apr 21:143308. doi: 10.1016/j.ijbiomac.2025.143308. Online ahead of print.

ABSTRACT

Biological interactions between RNA and small-molecule ligands play a crucial role in determining the specific functions of RNA, such as catalysis and folding, and are essential for guiding drug design in the medical field. Accurately predicting the binding sites of ligands within RNA structures is therefore of significant importance. To address this challenge, we introduced a computational approach named RLBSIF (RNA-Ligand Binding Surface Interaction Fingerprints) based on geometric deep learning. This model utilizes surface geometric features, including shape index and distance-dependent curvature, combined with chemical features represented by atomic charge, to comprehensively characterize RNA-ligand interactions through MaSIF-based surface interaction fingerprints. Additionally, we employ the ResNet18 network to analyze these fingerprints for identifying ligand binding pockets. Trained on 440 binding pockets, RLBSIF achieves an overall pocket-level classification accuracy of 90 %. Through a full-space enumeration method, it can predict binding sites at nucleotide resolution. In two independent tests, RLBSIF outperformed competing models, demonstrating its efficacy in accurately identifying binding sites within complex molecular structures. This method shows promise for drug design and biological product development, providing valuable insights into RNA-ligand interactions and facilitating the design of novel therapeutic interventions. For access to the related source code, please visit RLBSIF on GitHub (https://github.com/ZUSTSTTLAB/RLBSIF).

PMID:40268011 | DOI:10.1016/j.ijbiomac.2025.143308

Phys Med Biol. 2025 Apr 23. doi: 10.1088/1361-6560/adcfeb. Online ahead of print.

ABSTRACT

\textit{Objective.} This study investigates key factors influencing deep learning-based dose prediction models for head and neck cancer radiation therapy (RT). The goal is to evaluate model accuracy, robustness, and computational efficiency, and to identify key components necessary for optimal performance.
\\
\textit{Approach.} We systematically analyze the impact of input and dose grid resolution, input type, loss function, model architecture, and noise on model performance. Two datasets are used: a public dataset (OpenKBP) and an in-house clinical dataset (LUMC). Model performance is primarily evaluated using two metrics: dose score and dose-volume histogram (DVH) score.
\\
\textit{Main results.} 
High-resolution inputs improve prediction accuracy (dose score and DVH score) by 8.6--13.5\% compared to low resolution. Using a combination of CT, planning target volumes (PTVs), and organs-at-risk (OARs) as input significantly enhances accuracy, with improvements of 57.4--86.8\% over using CT alone. Integrating mean absolute error (MAE) loss with value-based and criteria-based DVH loss functions further boosts DVH score by 7.2--7.5\% compared to MAE loss alone. In the robustness analysis, most models show minimal degradation under Poisson noise (0--0.3 Gy) but are more susceptible to adversarial noise (0.2--7.8 Gy). Notably, certain models, such as SwinUNETR, demonstrate superior robustness against adversarial perturbations.
\\
\textit{Significance.}
These findings highlight the importance of optimizing deep learning models and provide valuable guidance for achieving more accurate and reliable radiotherapy dose prediction.

PMID:40267938 | DOI:10.1088/1361-6560/adcfeb

Nat Commun. 2025 Apr 23;16(1):3811. doi: 10.1038/s41467-025-58912-1.

ABSTRACT

Cells move directionally along gradients of substrate stiffness - a process called durotaxis. In the situations studied so far, durotaxis relies on cell-substrate focal adhesions to sense stiffness and transmit forces that drive directed motion. However, whether and how durotaxis can take place in the absence of focal adhesions remains unclear. Here, we show that confined cells can perform durotaxis despite lacking focal adhesions. This durotactic migration depends on an asymmetric myosin distribution and actomyosin retrograde flow. We propose that the mechanism of this focal adhesion-independent durotaxis is that stiffer substrates offer higher friction. We put forward a physical model that predicts that non-adherent cells polarise and migrate towards regions of higher friction - a process that we call frictiotaxis. We demonstrate frictiotaxis in experiments by showing that cells migrate up a friction gradient even when stiffness is uniform. Our results broaden the potential of durotaxis to guide any cell that contacts a substrate, and they reveal a mode of directed migration based on friction. These findings have implications for cell migration during development, immune response and cancer progression, which usually takes place in confined environments that favour adhesion-independent amoeboid migration.

PMID:40268931 | DOI:10.1038/s41467-025-58912-1

Nat Commun. 2025 Apr 24;16(1):3843. doi: 10.1038/s41467-025-59170-x.

ABSTRACT

The discovery of N6-methyladenine (6mA) in eukaryotic genomes, typically found in prokaryotic DNA, has revolutionized epigenetics. Here, we show that symmetric 6mA is essential in the early diverging fungus Rhizopus microsporus, as the absence of the MT-A70 complex (MTA1c) responsible for this modification results in a lethal phenotype. 6mA is present in 70% of the genes, correlating with the presence of H3K4me3 and H2A.Z in open euchromatic regions. This modification is found predominantly in nucleosome linker regions, influencing the nucleosome positioning around the transcription start sites of highly expressed genes. Controlled downregulation of MTA1c reduces symmetric 6mA sites affecting nucleosome positioning and histone modifications, leading to altered gene expression, which is likely the cause of the severe phenotypic changes observed. Our study highlights the indispensable role of the DNA 6mA in a multicellular organism and delineates the mechanisms through which this epigenetic mark regulates gene expression in a eukaryotic genome.

PMID:40268918 | DOI:10.1038/s41467-025-59170-x

Mol Plant Microbe Interact. 2025 Apr 23. doi: 10.1094/MPMI-04-25-0036-R. Online ahead of print.

ABSTRACT

Plants are sessile organisms and must accurately respond to a host variety of growth, developmental, and environmental signals throughout their life to maximize fitness. Plant cell surface receptor-like kinases are ideal for the perception of such signals and their transduction within the cell. The Strubbelig Receptor Family (SRF) is a group of leucine-rich repeat receptor-like kinases, several of which have unknown function. Here, we identify a role for SRF6 in the perception of cell wall damage and the activation of downstream immune responses. We show that SRF6 is necessary for proper immune responses following elicitation with a short-chain oligogalacturonic acid, including activation of defense genes and increased bacterial resistance. We also demonstrated the srf6 mutants are more sensitive to isoxaben treatment, suggesting enhanced cell wall integrity maintenance responses. These findings are compatible with the hypothesis that cell wall integrity maintenance responses are elevated when pattern-triggered immunity is compromised.

PMID:40268881 | DOI:10.1094/MPMI-04-25-0036-R

Anal Chem. 2025 Apr 23. doi: 10.1021/acs.analchem.5c00847. Online ahead of print.

ABSTRACT

Point-of-care testing (POCT) for urinary tract infection (UTI) holds significant importance in the field of disease prevention and control, as well as the advancement of personalized precision medicine. However, conventional methods for detecting UTIs continue to face challenges such as time-consuming and labor-intensive detection processes, and reliance on specialized equipment and personnel rendering them unsuitable for point-of-care applications, especially in resource-limited areas. Here, we propose a novel flexible programmable manually powered microfluidic (FPM) for rapid point-of-care diagnosis of UTIs. For the first time, the proposed FPMs was achieved through a combined strategy of laser printing, cutting, and laminating, with the entire process completed in under 15 min at a cost of less than $0.5, which effectively circumvent the traditionally time-consuming and labor-intensive soft lithography techniques. By incorporating a modular structure-based design concept, we successfully developed various types of portable FPMs with functionalities including parallel pumping, simultaneous releasing, quantitative dispensing, sequential releasing, cyclic motion of multiple liquids and concentration gradient generating. As a proof-of-concept demonstration, we initially employed a high-throughput parallel dispensing design to analyze six urinary biochemical markers within 1 min, presenting potential applicability for future at-home testing. We then integrated a manually powered concentration gradient generator with spatial confinement signal enhancement to enable rapid phenotypic antimicrobial susceptibility testing (AST) within three to 5 h, while achieving clinical diagnostic accuracy rates of up to 95.56%. Therefore, our proposed FPMs eliminate the need for external pumps or actuators and could serve as an affordable hand-held POCT tool for UTI diagnosis. Moreover, in resource-poor areas, they have potential utility as robust POCT devices addressing diverse rapid detection needs.

PMID:40268684 | DOI:10.1021/acs.analchem.5c00847

J Appl Microbiol. 2025 Apr 23:lxaf099. doi: 10.1093/jambio/lxaf099. Online ahead of print.

ABSTRACT

AIMS: This study aimed to determine the antibiofilm properties of Klebsiella pneumoniae phages previously isolated from Thai hospital sewage water. Furthermore, we aimed to develop a phage-embedded and coated alginate hydrogel, suitable as a wound dressing or surface coating to prevent K. pneumoniae proliferation and biofilm formation.

METHODS AND RESULTS: The biofilm forming capacity of six clinical K. pneumoniae isolates was determined by means of the crystal violet assay and four strains which exhibited strong adherence were selected for further characterisation. Two phages (vB_KpnA_GBH014 and vB_KpnM_GBH019) were found to both significantly prevent (P = <0.0005) and disrupt (P = <0.05) biofilms produced by their K. pneumoniae hosts as determined by optical density readings using the crystal violet assay. Furthermore, alginate layers embedded and coated with phages vB_KpnA_GBH014 and vB_KpnM_GBH019 produced antibiofilm surfaces. Viable counts of recovered biofilms showed that alginate hydrogels containing phage vB_KpnA_GBH014 or vB_KpnM_GBH019 were associated with significantly fewer K. pneumoniae versus no-phage controls (1.61×108 cfu ml-1 vs 1.67×104 cfu ml-1, P = <0.005 and 1.78×108 cfu ml-1 vs 6.11×102 cfu ml-1, P = <0.00005, respectively). Confocal microscopy further revealed a significant reduction in the biovolume of biofilms formed on phage embedded and coated alginate hydrogels compared to no-phage controls.

CONCLUSIONS: Phages vB_KpnA_GBH014 and vB_KpnM_GBH019 can both prevent and disrupt biofilms produced by clinical isolates of K. pneumoniae. Embedding and coating these phages into alginate produces an antibiofilm matrix which may have promise for coating medical devices or as a wound dressing.

PMID:40268347 | DOI:10.1093/jambio/lxaf099

Cell Syst. 2025 Apr 17:101264. doi: 10.1016/j.cels.2025.101264. Online ahead of print.

ABSTRACT

We present the "spatial transcriptomics imaging framework" (STIM), an imaging-based computational framework focused on visualizing and aligning high-throughput spatial sequencing datasets. STIM is built on the powerful, scalable ImgLib2 and BigDataViewer (BDV) image data frameworks and thus enables novel development or transfer of existing computer vision techniques to the sequencing domain characterized by datasets with irregular measurement-spacing and arbitrary spatial resolution, such as spatial transcriptomics data generated by multiplexed targeted hybridization or spatial sequencing technologies. We illustrate STIM's capabilities by representing, interactively visualizing, 3D rendering, automatically registering, and segmenting publicly available spatial sequencing data from 13 serial sections of mouse brain tissue and from 19 sections of a human metastatic lymph node. We demonstrate that the simplest alignment mode of STIM achieves human-level accuracy.

PMID:40267922 | DOI:10.1016/j.cels.2025.101264

BMJ Evid Based Med. 2025 Apr 23:bmjebm-2024-113497. doi: 10.1136/bmjebm-2024-113497. Online ahead of print.

ABSTRACT

OBJECTIVES: To investigate the patient-reported outcomes (PROs) and acupuncture-related adverse events (A-AEs) in acupuncture randomised controlled trials (RCTs).

DESIGN: Cross-sectional meta-epidemiological study.

DATA SOURCES: We comprehensively searched for eligible studies between 1 January 2014 and 1 May 2024, in MEDLINE, EMBASE, CENTRAL, CBM, CNKI, Wanfang Data and VIP Database.

ELIGIBILITY CRITERIA: RCTs that used acupuncture as the intervention group to obtain the efficacy and/or safety of acupuncture therapy. Acupuncture therapy should be based on Traditional Medicine theory.

MAIN OUTCOME MEASURES: We assessed (1) the general characteristics of acupuncture RCTs; (2) the general characteristics of PROs; (3) the reporting scores of PROs by the Extension of Consolidated Standards of Reporting Trials of Patient-Reported Outcomes (CONSORT PRO Extension); (4) the general characteristic of A-AEs; (5) the incidence of A-AEs.

RESULTS: We included 476 studies in this study. 296 (62.2%) used PROs as study outcomes, 272 (57.1%) reported safety outcomes. The Visual Analogue Scale (149, 23.7%) and the Pittsburgh Sleep Quality Index (42, 6.7%) were the most common PROs reported. The reporting of PROs was incomplete, with sufficiently reporting scores ranging from 2.7% to 97.6% across the CONSORT PRO Extension. 164 studies reported A-AEs, of which 141 reported specific details, and we found that the OR for the incidence of AEs in the acupuncture group compared to the control group was 1.434 (95% CI 1.148 to 1.793). We identified 1277 reports of A-AEs in eligible studies, predominantly tissue injury (eg, haematoma, bleeding), irritation (eg, pain, post-acupuncture discomfort), with no reports of serious A-AEs. The reporting of A-AEs lacked details on the acquisition methods (15.5%), occurrence time (5.5%), A-AEs' treatment (18.1%) and A-AEs' recovery (19.7%). Studies that reported funding, registry information, acupuncturist qualifications and non-significant primary outcomes were associated with the A-AEs' reporting, and the difference was statistically significant (p≤0.05).

CONCLUSION: This study found that the reporting of PROs and A-AEs was insufficient in acupuncture RCTs. Future studies should clarify the clinical significance of using PROs as outcomes and report AEs comprehensively to provide patients with sufficient information on the benefits and harms of acupuncture treatments.

PMID:40268340 | DOI:10.1136/bmjebm-2024-113497

Sci Transl Med. 2025 Apr 23;17(795):eadn5772. doi: 10.1126/scitranslmed.adn5772. Epub 2025 Apr 23.

ABSTRACT

Calciphylaxis is an orphan disease characterized by dermal microvessel thrombosis, inflicting painful cutaneous necrosis. It occurs predominantly in patients with end-stage kidney disease and has high mortality, elusive pathogenesis, and no approved therapies. We demonstrate that sera from patients with calciphylaxis induced de novo synthesis of interleukin-6 (IL-6) and soluble IL-6 receptor (IL-6R) and stimulated Janus kinase-2 (JAK) and signal transducer and activator of transcription (STAT)-3 phosphorylation in primary human dermal microvascular endothelial cells (ECs). Calciphylaxis skin demonstrated an altered microenvironment characterized by a gain of proximal and distal IL-6 ligand-receptor interactions. Microvessels are the predominant senders and recipients of IL-6 signaling, which, along with up-regulated A disintegrin and metalloproteinase 17 in dermal vasculature and interstitial IL-6R, supported trans-IL-6 signaling in calciphylaxis lesions. Calciphylaxis serum up-regulated thymidine phosphorylase (TYMP) in ECs. TYMP up-regulated IL-6, which activated tissue factor (TF), a primary trigger of the extrinsic coagulation cascade. IL-6-TF signaling in ECs was partially triggered by elevated IL-6 and kynurenine amounts in calciphylaxis serum and was inhibited by anti-IL-6 treatment. The TF-inducing ability of calciphylaxis serum is correlated with disease activity and response to IL-6 inhibitors in ECs. Calciphylaxis is therefore a combination of serum-inducing TYMP-IL-6-TF signaling in ECs and a heterogeneous permissive local dermal microenvironment. The latter is characterized by microvessels initiating IL-6 signaling and multiway cross-talk with adipocytes and eccrine glands, perpetuating the sinister thrombotic milieu. Our results support exploring the IL-6-TF-inducing ability of calciphylaxis serum as an activity marker and IL-6 as a therapeutic target for uremic calciphylaxis.

PMID:40267216 | DOI:10.1126/scitranslmed.adn5772

Cephalalgia. 2025 Apr;45(4):3331024251335927. doi: 10.1177/03331024251335927. Epub 2025 Apr 23.

ABSTRACT

BackgroundFocusing on calcitonin gene-related peptide (CGRP) as a specific target has changed and improved migraine management. After the positive results of monoclonal antibodies directed to the CGRP pathway (anti-CGRP mAbs), randomized controlled trials also demonstrated the efficacy of gepants in migraine prevention. The present study aimed to assess the effectiveness of atogepant in preventing migraine after a 12-week treatment in clinical practice.MethodsAdult patients with a clinical indication for atogepant 60 mg daily were screened for participation in this multicentric prospective observational cohort study. At baseline (T0) and after 12 weeks (T3) since the first atogepant administration, monthly migraine days (MMDs), monthly headache days (MHDs) and monthly acute medications (MAMs) were assessed. The co-primary endpoints were the changes in MMDs from T0 to T3 and the percentage of T3 Responders (those with a reduction of MMDs ≥50%, i.e. 50% response rate (RR)). At T0 and T3, we also collected the Headache Impact Test (HIT-6), the Migraine Disability Assessment (MIDAS) questionnaire, the Migraine Treatment Optimization Questionnaire-6 (mTOQ-6), the Migraine-Specific Quality-of-Life Questionnaire (MSQ), the 12-item Allodynia Symptom Checklist (ASC-12) and the Migraine Interictal Burden Scale (MIBS-4).ResultsOne hundred and six patients (56/106 (52.8%) with chronic migraine (CM), 93/106 (87.7%) female, aged 50.6 ± 13.2 years) from 10 Italian centers completed the 12-week observation since the first atogepant tablet intake. From baseline to T3, a reduction of 6.9 MMDs (SD 9.7; p < 0.001) was achieved in the whole group and, specifically, of -4.9 (SD 6.6; p < 0.001) in episodic migraine (EM) and of -8.6 (SD 11.7; p < 0.001) in CM patients. Overall, 60/106 (56.6%) of patients were Responders (60.0% in the EM and 46.4% in the CM group). Non-Responders previously experienced more ineffective treatments than Responders with anti-CGRP mAbs (65.2% vs. 43.3%, respectively, p = 0.031) and with onabotulinumtoxinA (56.5% vs. 28.3%, p = 0.005), and presented more medication overuse at baseline (55.7% vs. 44.3%, p = 0.003). However, no baseline characteristics were significantly associated with the Responder status in the multiple regression analysis. For T0 to T3, MAMs, MIDAS, ASC-12 and mTOQ-6 reduced (p ≤ 0.001 consistently), and MSQ role-function restriction increased (p = 0.026), whereas HIT-6 and MIBS-4 did not change. Only seven subjects (7/106, 6.6%) dropped out of atogepant treatment: four for lack of effectiveness and three for adverse events or poor tolerability.ConclusionsThe STAR study demonstrates the effectiveness and tolerability of atogepant 60 mg at 12 weeks in a real-world setting. Previous ineffective anti-CGRP mAbs were not a relevant prognostic factor.Trial RegistrationThe study was preregistered on clinicaltrial.gov, NCT06414044.

PMID:40267275 | DOI:10.1177/03331024251335927

N Engl J Med. 2025 Apr 24;392(16):1569-1581. doi: 10.1056/NEJMoa2411664.

ABSTRACT

BACKGROUND: In bronchiectasis, neutrophilic inflammation is associated with an increased risk of exacerbations and disease progression. Brensocatib, an oral, reversible inhibitor of dipeptidyl peptidase 1 (DPP-1), targets neutrophil serine proteases, key mediators of neutrophilic inflammation.

METHODS: In a phase 3, double-blind trial, we randomly assigned patients with bronchiectasis (in a 1:1:1 ratio for adults and a 2:2:1 ratio for adolescents) to receive brensocatib (10 mg or 25 mg once per day) or placebo. The primary end point was the annualized rate of adjudicated pulmonary exacerbations over a 52-week period. The secondary end points, listed in hierarchical testing order, were the time to the first exacerbation during the 52-week period; the percentage of patients remaining exacerbation-free at week 52; the change in forced expiratory volume in 1 second (FEV1); the annualized rate of severe exacerbations; and change in quality of life.

RESULTS: A total of 1721 patients (1680 adults and 41 adolescents) underwent randomization and received brensocatib or placebo. The annualized rate of pulmonary exacerbations was 1.02 in the 10-mg brensocatib group, 1.04 in the 25-mg brensocatib group, and 1.29 in the placebo group (rate ratio, brensocatib vs. placebo, 0.79 [95% confidence interval {CI}, 0.68 to 0.92; adjusted P = 0.004] with the 10-mg dose and 0.81 [95% CI, 0.69 to 0.94; adjusted P = 0.005] with the 25-mg dose). The hazard ratio for the time to the first exacerbation was 0.81 (95% CI, 0.70 to 0.95; adjusted P = 0.02) with the 10-mg dose and 0.83 (95% CI, 0.70 to 0.97; adjusted P = 0.04) with the 25-mg dose. In each brensocatib group, 48.5% of patients remained exacerbation-free at week 52, as compared with 40.3% in the placebo group (rate ratio, 1.20 [95% CI, 1.06 to 1.37; adjusted P = 0.02] with the 10-mg dose and 1.18 [95% CI, 1.04 to 1.34; adjusted P = 0.04] with the 25-mg dose). At week 52, FEV1 had declined by 50 ml with the 10-mg dose, 24 ml with the 25-mg dose, and 62 ml with placebo (least-squares mean difference vs. placebo, 11 ml [95% CI, -14 to 37; adjusted P = 0.38] with the 10-mg dose and 38 ml [95% CI, 11 to 65; adjusted P = 0.04] with the 25-mg dose). The incidence of adverse events was similar across groups, except for a higher incidence of hyperkeratosis with brensocatib.

CONCLUSIONS: Among patients with bronchiectasis, once-daily treatment with brensocatib (10 mg or 25 mg) led to a lower annualized rate of pulmonary exacerbations than placebo, and the decline in FEV1 was less with the 25-mg dose of brensocatib than with placebo. (Funded by Insmed; ASPEN ClinicalTrials.gov number, NCT04594369; EudraCT number, 2020-003688-25.).

PMID:40267423 | DOI:10.1056/NEJMoa2411664

Comput Biol Med. 2025 Apr 22;192(Pt A):110160. doi: 10.1016/j.compbiomed.2025.110160. Online ahead of print.

ABSTRACT

The generation of Synthetic Computed Tomography (sCT) images has become a pivotal methodology in modern clinical practice, particularly in the context of Radiotherapy (RT) treatment planning. The use of sCT enables the calculation of doses, pushing towards Magnetic Resonance Imaging (MRI) guided radiotherapy treatments. Moreover, with the introduction of MRI-Positron Emission Tomography (PET) hybrid scanners, the derivation of sCT from MRI can improve the attenuation correction of PET images. Deep learning methods for MRI-to-sCT have shown promising results, but their reliance on single-centre training dataset limits generalisation capabilities to diverse clinical settings. Moreover, creating centralised multi-centre datasets may pose privacy concerns. To address the aforementioned issues, we introduced FedSynthCT-Brain, an approach based on the Federated Learning (FL) paradigm for MRI-to-sCT in brain imaging. This is among the first applications of FL for MRI-to-sCT, employing a cross-silo horizontal FL approach that allows multiple centres to collaboratively train a U-Net-based deep learning model. We validated our method using real multicentre data from four European and American centres, simulating heterogeneous scanner types and acquisition modalities, and tested its performance on an independent dataset from a centre outside the federation. In the case of the unseen centre, the federated model achieved a median Mean Absolute Error (MAE) of 102.0 HU across 23 patients, with an interquartile range of 96.7-110.5 HU. The median (interquartile range) for the Structural Similarity Index (SSIM) and the Peak Signal to Noise Ratio (PNSR) were 0.89 (0.86-0.89) and 26.58 (25.52-27.42), respectively. The analysis of the results showed acceptable performances of the federated approach, thus highlighting the potential of FL to enhance MRI-to-sCT to improve generalisability and advancing safe and equitable clinical applications while fostering collaboration and preserving data privacy.

PMID:40267535 | DOI:10.1016/j.compbiomed.2025.110160