Cell Host Microbe. 2025 Mar 25:S1931-3128(25)00091-5. doi: 10.1016/j.chom.2025.03.008. Online ahead of print.

ABSTRACT

Plasmodium parasites undergo development and replication within hepatocytes before infecting erythrocytes and initiating clinical malaria. Although type I interferons (IFNs) are known to hinder Plasmodium infection within the liver, the underlying mechanisms remain unclear. Here, we describe two IFN-I-driven hepatocyte antimicrobial programs controlling liver-stage malaria. First, oxidative defense by NADPH oxidases 2 and 4 triggers a pathway of lysosomal fusion with the parasitophorous vacuole (PV) to help clear Plasmodium. Second, guanylate-binding protein (GBP) 1-mediated disruption of the PV activates the caspase-1 inflammasome, inducing pyroptosis to remove infected host cells. Remarkably, both human and mouse hepatocytes enlist these cell-autonomous immune programs to eliminate Plasmodium, with their pharmacologic or genetic inhibition leading to profound malarial susceptibility in vivo. In addition to identifying IFN-I-mediated cell-autonomous immune circuits controlling Plasmodium infection in the hepatocytes, our study also extends the understanding of how non-immune cells are integral to protective immunity against malaria.

PMID:40168996 | DOI:10.1016/j.chom.2025.03.008

Eur J Cancer. 2025 Mar 30;220:115393. doi: 10.1016/j.ejca.2025.115393. Online ahead of print.

ABSTRACT

The annual Immuno-Oncology "Think Tank" held in October 2023 in Siena reviewed the rapidly evolving systems-biological approaches which are now providing a deeper understanding of tumor and tumor microenvironment heterogeneity. Based on this understanding opportunities for novel therapies may be identified to overcome resistance to immunotherapy. There is increasing evidence that malignant disease processes are not limited to purely intracellular or genetic events but constitute a dynamic interaction between the host and disease. Tumor responses are influenced by many host tissue determinants across different cellular compartments, which can now be investigated by high-throughput molecular profiling technologies, often labelled with a suffix "-omics". "Omics" together with ever increasing computational power, fast developments in machine learning, and high-resolution detection tools offer an unrivalled opportunity to connect high-dimensional data and create a holistic view of disease processes in cancer. This review describes advances in several state-of-the-art "-omics" approaches with perspectives on how these can be applied to the clinical development of new immunotherapeutic strategies and ultimately adopted in clinical practice.

PMID:40168935 | DOI:10.1016/j.ejca.2025.115393

Nat Commun. 2025 Apr 1;16(1):3137. doi: 10.1038/s41467-025-58321-4.

ABSTRACT

In sickle cell disease (SCD), the β6Glu→Val substitution in the β-globin leads to red blood cell sickling. The transplantation of autologous, genetically modified hematopoietic stem and progenitor cells (HSPCs) is a promising treatment option for patients with SCD. We completed a Phase I/II open-label clinical trial (NCT03964792) for patients with SCD using a lentiviral vector (DREPAGLOBE) expressing a potent anti-sickling β-globin. The primary endpoint was to evaluate the short-term safety and secondary endpoints included the efficacy and the long-term safety. We report on the results after 18 to 36 months of follow-up. No drug-related adverse events or signs of clonal hematopoiesis were observed. Despite similar vector copy numbers in the drug product, gene-marking in peripheral blood mononuclear cells and correction of the clinical phenotype varied from one patient to another. Single-cell transcriptome analyses show that in the patients with poor engraftment, the most immature HSCs display an exacerbated inflammatory signature (via IL-1 or TNF-α and interferon signaling pathways). This signature is accompanied by a lineage bias in the HSCs. Our clinical data indicates that the DREPAGLOBE-based gene therapy (GT) is safe. However, its efficacy is variable and probably depends on the number of infused HSCs and intrinsic, engraftment-impairing inflammatory alterations in HSCs. Trial: NCT03964792.

PMID:40169559 | DOI:10.1038/s41467-025-58321-4

Biomed Pharmacother. 2025 Mar 31;186:117957. doi: 10.1016/j.biopha.2025.117957. Online ahead of print.

ABSTRACT

BACKGROUND: Overactive neutrophilic inflammation causes damage to the airways and death in people with cystic fibrosis (CF), a genetic disorder resulting from mutations in the CFTR gene. Reducing the impact of inflammation is therefore a major concern in CF. Evidence indicates that dysfunctional NRF2 signaling in CF individuals may impair their ability to regulate their oxidative and inflammatory responses, although the role of NRF2 in neutrophil-dominated inflammation and tissue damage associated with CF has not been determined. Therefore, we examined whether curcumin, an activator of NRF2, might provide a beneficial effect in the context of CF.

METHODS: Combining Cftr-depleted zebrafish as an innovative biomedical model with CF patient-derived airway organoids (AOs), we aimed to understand how NRF2 dysfunction leads to abnormal inflammatory status and tissue remodeling and determine the effects of curcumin in reducing inflammation and tissue damage in CF.

RESULTS: We demonstrate that NFR2 is instrumental in regulating neutrophilic inflammation and repair processes in vivo, thereby preventing inflammatory damage. Importantly, curcumin treatment restores NRF2 activity in both CF zebrafish and AOs. Curcumin reduces neutrophilic inflammation in CF context, by rebalancing the production of epithelial ROS and pro-inflammatory cytokines. Furthermore, curcumin improves tissue repair by reducing CF-associated fibrosis. Our findings demonstrate that curcumin prevents CF-mediated inflammation via activating the NRF2 pathway.

CONCLUSIONS: This work highlights the protective role of NRF2 in limiting inflammation and injury and show that therapeutic strategies to normalize NRF2 activity, using curcumin or others NRF2 activators, might simultaneously reduce airway inflammation and damage in CF.

PMID:40168724 | DOI:10.1016/j.biopha.2025.117957

PLoS Pathog. 2025 Apr 1;21(4):e1013017. doi: 10.1371/journal.ppat.1013017. Online ahead of print.

ABSTRACT

Anthrax lethal toxin (LT) and edema toxin (ET) are two of the major virulence factors of Bacillus anthracis, the causative pathogen of anthrax disease. While the roles of LT in anthrax pathogenesis have been extensively studied, the pathogenic mechanism of ET remains poorly understood. ET is a calmodulin-dependent adenylate cyclase that elevates intracellular cAMP by converting ATP to cAMP. Thus, it was postulated that the ET-induced in vivo toxicity is mediated by certain cAMP-dependent events. However, mechanisms linking cAMP elevation and ET-induced damage have not been established. Cholera toxin is another bacterial toxin that increases cAMP. This toxin is known to cause severe intestinal fluid secretion and dehydration by cAMP-mediated activation of protein kinase A (PKA), which in turn activates cystic fibrosis transmembrane conductance regulator (CFTR). The cAMP-activated PKA phosphorylation of CFTR on the surface of intestinal epithelial cells leads to an efflux of chloride ions accompanied by secretion of H2O into the intestinal lumen, causing rapid fluid loss, severe diarrhea and dehydration. Due to similar in vivo effects, it was generally believed that ET and cholera toxin would exhibit a similar pathogenic mechanism. Surprisingly, in this work, we found that cAMP-mediated PKA/CFTR activation is not essential for ET to exert its in vivo toxicity. Instead, our data suggest that ET-induced ATP depletion may play an important role in the toxin's pathogenesis.

PMID:40168442 | DOI:10.1371/journal.ppat.1013017

J Clin Oncol. 2025 Apr 1:JCO2401501. doi: 10.1200/JCO-24-01501. Online ahead of print.

ABSTRACT

PURPOSE: Artificial intelligence (AI) holds significant promise for improving cancer diagnosis and treatment. Here, we present a foundation AI model for prognosis prediction on the basis of standard hematoxylin and eosin-stained histopathology slides.

METHODS: In this multinational cohort study, we developed AI models to predict prognosis from histopathology images of patients with GI cancers. First, we trained a foundation model using over 130 million patches from 104,876 whole-slide images on the basis of self-supervised learning. Second, we fine-tuned deep learning models for predicting survival outcomes and validated them across seven cohorts, including 1,619 patients with gastric and esophageal cancers and 2,594 patients with colorectal cancer. We further assessed the model for predicting survival benefit from adjuvant chemotherapy.

RESULTS: The AI models predicted disease-free survival and disease-specific survival with a concordance index of 0.726-0.797 for gastric cancer and 0.714-0.757 for colorectal cancer in the validation cohorts. The models stratified patients into high-risk and low-risk groups, with 5-year survival rates of 49%-52% versus 76%-92% in gastric cancer and 43%-72% versus 81%-98% in colorectal cancer. In multivariable analysis, the AI risk scores remained an independent prognostic factor after adjusting for clinicopathologic variables. Compared with stage alone, an integrated model consisting of stage and image information improved prognosis prediction across all validation cohorts. Finally, adjuvant chemotherapy was associated with improved survival in the high-risk group but not in the low-risk group (treatment-model interaction P = .01 and .006) for stage II/III gastric and colorectal cancer, respectively.

CONCLUSION: The pathology foundation model can accurately predict survival outcomes and complement clinicopathologic factors in GI cancers. Pending prospective validation, it may be used to improve risk stratification and inform personalized adjuvant therapy.

PMID:40168636 | DOI:10.1200/JCO-24-01501

PLoS One. 2025 Apr 1;20(4):e0321410. doi: 10.1371/journal.pone.0321410. eCollection 2025.

ABSTRACT

[This corrects the article DOI: 10.1371/journal.pone.0312177.].

PMID:40168295 | DOI:10.1371/journal.pone.0321410

IEEE Trans Cybern. 2025 Apr 1;PP. doi: 10.1109/TCYB.2025.3539990. Online ahead of print.

ABSTRACT

As a longstanding scientific challenge, accurate and timely ocean forecasting has always been a sought-after goal for ocean scientists. However, traditional theory-driven numerical ocean prediction (NOP) suffers from various challenges, such as the indistinct representation of physical processes, inadequate application of observation assimilation, and inaccurate parameterization of models, which lead to difficulties in obtaining effective knowledge from massive observations, and enormous computational challenges. With the successful evolution of data-driven deep learning in various domains, it has been demonstrated to mine patterns and deep insights from the ever-increasing stream of oceanographic spatiotemporal data, which provides novel possibilities for revolution in ocean forecasting. Deep-learning-based ocean forecasting (DLOF) is anticipated to be a powerful complement to NOP. Nowadays, researchers attempt to introduce deep learning into ocean forecasting and have achieved significant progress that provides novel motivations for ocean science. This article provides a comprehensive review of the state-of-the-art DLOF research regarding model architectures, spatiotemporal multiscales, and interpretability while specifically demonstrating the feasibility of developing hybrid architectures that incorporate theory-driven and data-driven models. Moreover, we comprehensively evaluate DLOF from datasets, benchmarks, and cloud computing. Finally, the limitations of current research and future trends of DLOF are also discussed and prospected.

PMID:40168238 | DOI:10.1109/TCYB.2025.3539990

IEEE Trans Cybern. 2025 Mar 31;PP. doi: 10.1109/TCYB.2025.3550203. Online ahead of print.

ABSTRACT

The acceptance of academic papers involves a complex peer-review process that requires substantial human and material resources and is susceptible to biases. With advancements in deep learning technologies, researchers have explored automated approaches for assessing paper acceptance. Existing automated academic paper rating methods primarily rely on the full content of papers to estimate acceptance probabilities. However, these methods are often inefficient and introduce redundant or irrelevant information. Additionally, while Bert can capture general semantic representations through pretraining on large-scale corpora, its performance on the automatic academic paper rating (AAPR) task remains suboptimal due to discrepancies between its pretraining corpus and academic texts. To address these issues, this study proposes LMCBert, a model that integrates large language models (LLMs) with momentum contrastive learning (MoCo). LMCBert utilizes LLMs to extract the core semantic content of papers, reducing redundancy and improving the understanding of academic texts. Furthermore, it incorporates MoCo to optimize Bert training, enhancing the differentiation of semantic representations and improving the accuracy of paper acceptance predictions. Empirical evaluations demonstrate that LMCBert achieves effective performance on the evaluation dataset, supporting the validity of the proposed approach. The code and data used in this article are publicly available at https://github.com/iioSnail/LMCBert.

PMID:40168236 | DOI:10.1109/TCYB.2025.3550203

IEEE Trans Med Imaging. 2025 Mar 31;PP. doi: 10.1109/TMI.2025.3556310. Online ahead of print.

ABSTRACT

The scarcity of annotations has become a significant obstacle in training powerful deep-learning models for medical image segmentation, limiting their clinical application. To overcome this, semi-supervised learning that leverages abundant unlabeled data is highly desirable to enhance model training. However, most existing works still focus on specific medical tasks and underestimate the potential of learning across diverse tasks and datasets. In this paper, we propose a Versatile Semi-supervised framework (VerSemi) to present a new perspective that integrates various SSL tasks into a unified model with an extensive label space, exploiting more unlabeled data for semi-supervised medical image segmentation. Specifically, we introduce a dynamic task-prompted design to segment various targets from different datasets. Next, this unified model is used to identify the foreground regions from all labeled data, capturing cross-dataset semantics. Particularly, we create a synthetic task with a CutMix strategy to augment foreground targets within the expanded label space. To effectively utilize unlabeled data, we introduce a consistency constraint that aligns aggregated predictions from various tasks with those from the synthetic task, further guiding the model to accurately segment foreground regions during training. We evaluated our VerSemi framework against seven established SSL methods on four public benchmarking datasets. Our results suggest that VerSemi consistently outperforms all competing methods, beating the second-best method with a 2.69% average Dice gain on four datasets and setting a new state of the art for semi-supervised medical image segmentation. Code is available at https://github.com/maxwell0027/VerSemi.

PMID:40168233 | DOI:10.1109/TMI.2025.3556310

IEEE Trans Med Imaging. 2025 Apr 1;PP. doi: 10.1109/TMI.2025.3556420. Online ahead of print.

ABSTRACT

Graph deep learning (GDL) has demonstrated impressive performance in predicting population-based brain disorders (BDs) through the integration of both imaging and non-imaging data. However, the effectiveness of GDL-based methods heavily depends on the quality of modeling multi-modal population graphs and tends to degrade as the graph scale increases. Moreover, these methods often limit interactions between imaging and non-imaging data to node-edge interactions within the graph, overlooking complex inter-modal correlations and resulting in suboptimal outcomes. To address these challenges, we propose MM-GTUNets, an end-to-end Graph Transformer-based multi-modal graph deep learning (MMGDL) framework designed for large-scale brain disorders prediction. To effectively utilize rich multi-modal disease-related information, we introduce Modality Reward Representation Learning (MRRL), which dynamically constructs population graphs using an Affinity Metric Reward System (AMRS). We also employ a variational autoencoder to reconstruct latent representations of non-imaging features aligned with imaging features. Based on this, we introduce Adaptive Cross-Modal Graph Learning (ACMGL), which captures critical modality-specific and modality-shared features through a unified GTUNet encoder, taking advantages of Graph UNet and Graph Transformer, along with a feature fusion module. We validated our method on two public multi-modal datasets ABIDE and ADHD-200, demonstrating its superior performance in diagnosing BDs. Our code is available at https://github.com/NZWANG/MM-GTUNets.

PMID:40168232 | DOI:10.1109/TMI.2025.3556420

IEEE J Biomed Health Inform. 2025 Apr 1;PP. doi: 10.1109/JBHI.2025.3556775. Online ahead of print.

ABSTRACT

Epilepsy affects millions worldwide, posing significant challenges due to the erratic and unexpected nature of seizures. Despite advancements, existing seizure prediction techniques remain limited in their ability to forecast seizures with high accuracy, impacting the quality of life for those with epilepsy. This research introduces the Coherence-based Seizure Prediction (CoSP) method, which integrates coherence analysis with deep learning to enhance seizure prediction efficacy. In CoSP, electroencephalography (EEG) recordings are divided into 10-second segments to extract channel pairwise coherence. This coherence data is then used to train a four-layer convolutional neural network to predict the probability of being in a preictal state. The predicted probabilities are then processed to issue seizure warnings. CoSP was evaluated in a pseudo-prospective setting using long-term iEEG data from ten patients in the NeuroVista seizure advisory system. CoSP demonstrated promising predictive performance across a range of preictal intervals (4 to 180 minutes). CoSP achieved a median Seizure Sensitivity (SS) of 0.79, a median false alarm rate of 0.15 per hour, and a median Time in Warning (TiW) of 27%, highlighting its potential for accurate and reliable seizure prediction. Statistical analysis confirmed that CoSP significantly outperformed chance (p = 0.001) and other baseline methods (p <0.05) under similar evaluation configurations.

PMID:40168220 | DOI:10.1109/JBHI.2025.3556775

IEEE J Biomed Health Inform. 2025 Apr 1;PP. doi: 10.1109/JBHI.2025.3556676. Online ahead of print.

ABSTRACT

Deformable image registration is crucial for medical image analysis, yet the complexity of deep learning networks often limits their deployment on resource-limited devices. Current distillation methods in registration tasks fail to effectively transfer complex deformation handling capabilities to non-iterative lightweight networks, leading to insignificant performance improvement. To address this, we propose the Framework for Iterative to Non-iterative Distillation (FIND), which efficiently transfers these capabilities to a Non-Iterative Lightweight (NIL) network. FIND employs a dual-step process: first, using recurrent distillation to derive a high-performance non-iterative teacher assistant from an iterative network; second, using advanced feature distillation from the assistant to the lightweight network. This enables NIL to perform rapid, effective registration on resource-limited devices. Experiments across four datasets show that NIL can achieve up to 60 times faster performance on CPU and 89 times on GPU than compared deep learning methods, with superior registration accuracy improvements of up to 3.5 points in Dice scores. Code is available at https://anonymous.4open.science/r/FIND-7A16.

PMID:40168217 | DOI:10.1109/JBHI.2025.3556676

IEEE J Biomed Health Inform. 2025 Apr 1;PP. doi: 10.1109/JBHI.2025.3556717. Online ahead of print.

ABSTRACT

Substantial progress has been made in developing deep-learning models for clinical diagnosis. While excelling in diagnostics, the broader clinical decision-making process also involves establishing optimal follow-up intervals (TCU), crucial for prognosis and timely treatment. To fully support clinical practice, it is imperative that deep learning models contribute to both initial diagnosis and TCU prediction. However, relying on separate monolithic models is computationally demanding and lacks interpretability, hindering clinician trust. Our proposed bilateral model, emphasizing ophthalmological cases, offers both initial diagnoses and follow-up predictions, enhancing interpretability and trust in clinical applications as clinicians are more likely to trust recommendations, knowing the diagnosis used is correct. Inspired by clinical practice, the model integrates hierarchical inference and self-supervised learning techniques to enhance predictive accuracy and interpretability. Consisting of a sparse autoencoder, diagnosis classifier, and TCU classifier, the model leverages insights from clinicians and observations of ophthalmological datasets to capture salient features and facilitate robust learning. By employing shared weights for encoding and diagnosing each organ, the model optimizes efficiency and doubles the effective dataset size. Experimental results on an ophthalmological dataset demonstrate superior performance compared to baseline models, with the hierarchical inference structure providing valuable insights into the model's decision-making process. The bilateral model not only enhances predictive modeling for conditions affecting bilaterally symmetrical organs but also empowers clinicians with interpretable outputs crucial for informed clinical decision-making, thereby advancing clinical practice and improving patient care.

PMID:40168215 | DOI:10.1109/JBHI.2025.3556717

IEEE Trans Neural Syst Rehabil Eng. 2025 Apr 1;PP. doi: 10.1109/TNSRE.2025.3555542. Online ahead of print.

ABSTRACT

Auditory attention decoding from electroencephalogram (EEG) could infer to which source the user is attending in noisy environments. Decoding algorithms and experimental paradigm designs are crucial for the development of technology in practical applications. To simulate real-world scenarios, this study proposed a cue-masked auditory attention paradigm to avoid information leakage before the experiment. To obtain high decoding accuracy with low latency, an end-to-end deep learning model, AADNet, was proposed to exploit the spatiotemporal information from the short time window of EEG signals. The results showed that with a 0.5-second EEG window, AADNet achieved an average accuracy of 93.46% and 91.09% in decoding auditory orientation attention (OA) and timbre attention (TA), respectively. It significantly outperformed five previous methods and did not need the knowledge of the original audio source. This work demonstrated that it was possible to detect the orientation and timbre of auditory attention from EEG signals fast and accurately. The results are promising for the real-time multi-property auditory attention decoding, facilitating the application of the neuro-steered hearing aids and other assistive listening devices.

PMID:40168202 | DOI:10.1109/TNSRE.2025.3555542

PLoS Biol. 2025 Apr 1;23(4):e3003062. doi: 10.1371/journal.pbio.3003062. eCollection 2025 Apr.

ABSTRACT

The human fungal pathogen Candida tropicalis is widely distributed in clinical and natural environments. It is known to be an obligate diploid organism with an incomplete and atypical sexual cycle. Azole-resistant C. tropicalis isolates have been observed with increasing prevalence in many countries in recent years. Here, we report that tebuconazole (TBZ), a triazole fungicide widely used in agriculture, can induce ploidy plasticity and the formation of haploid cells in C. tropicalis. The evolved C. tropicalis strains with ploidy variations exhibit a cross-resistance between TBZ and standard azoles used in clinical settings (such as fluconazole and voriconazole). Similar to its diploid cells, these newly discovered C. tropicalis haploid cells are capable of undergoing filamentation, white-opaque switching, and mating. However, compared to its diploid cells, these haploid C. tropicalis cells grow more slowly under in vitro culture conditions and are less virulent in a mouse model of systemic infection. Interestingly, flow cytometry analysis of a clinical strain with extremely low genome heterozygosity indicates the existence of natural C. tropicalis haploids. Discovery of this C. tropicalis haploid state sheds new light into the biology and genetic plasticity of C. tropicalis and could provide the framework for the development of new genetic tools in the field.

PMID:40168394 | DOI:10.1371/journal.pbio.3003062

Proc Natl Acad Sci U S A. 2025 Apr 8;122(14):e2412818122. doi: 10.1073/pnas.2412818122. Epub 2025 Apr 1.

ABSTRACT

The tumor microenvironment (TME) encompasses various cell types, blood and lymphatic vessels, and noncellular constituents like extracellular matrix (ECM) and cytokines. These intricate interactions between cellular and noncellular components contribute to the development of a malignant TME, such as immunosuppressive, desmoplastic, angiogenic conditions, and the formation of a niche for cancer stem cells, but there is limited understanding of the specific subtypes of stromal cells involved in this process. Here, we utilized p16-CreERT2-tdTomato mouse models to investigate the signaling networks established by senescent cancer stromal cells, contributing to the development of a malignant TME. In pancreatic ductal adenocarcinoma (PDAC) allograft models, these senescent cells were found to promote cancer fibrosis, enhance angiogenesis, and suppress cancer immune surveillance. Notably, the selective elimination of senescent cancer stromal cells improves the malignant TME, subsequently reducing tumor progression in PDAC. This highlights the antitumor efficacy of senolytic treatment alone and its synergistic effect when combined with conventional chemotherapy. Taken together, our findings suggest that the signaling crosstalk among senescent cancer stromal cells plays a key role in the progression of PDAC and may be a promising therapeutic target.

PMID:40168129 | DOI:10.1073/pnas.2412818122

Mol Ecol Resour. 2025 Apr 1:e14110. doi: 10.1111/1755-0998.14110. Online ahead of print.

ABSTRACT

The percomorph fish clade Gobioidei is a suborder that comprises over 2200 species distributed in nearly all aquatic habitats. To understand the genetics underlying their species diversification, we sequenced and annotated the genome of the loach goby, Rhyacichthys aspro, an early-diverging group, and compared it with nine additional Gobioidei species. Within Gobioidei, the loach goby possesses the smallest genome at 594 Mb, and a rise in species diversity from early-diverging to more recently diverged lineages is mirrored by enlarged genomes and a higher presence of transposable elements (TEs), particularly DNA transposons. These DNA transposons are enriched in genic and regulatory regions and their copy number increase is strongly correlated with substitution rate, suggesting that DNA repair after transposon excision/insertion leads to nearby mutations. Consequently, the proliferation of DNA transposons might be the crucial driver of Gobioidei diversification and adaptability. The loach goby genome also points to mechanisms of ecological adaptation. It contains relatively few genes for lateral line development but an overrepresentation of synaptic function genes, with genes putatively under selection linked to synapse organisation and calcium signalling, implicating a sensory system distinct from other Gobioidei species. We also see an overabundance of genes involved in neurocranium development and renal function, adaptations likely connected to its flat morphology suited for strong currents and an amphidromous life cycle. Comparative analyses with hill-stream loaches and the European eel reveal convergent adaptations in body shape and saltwater balance. These findings shed new light on the loach goby's survival mechanisms and the broader evolutionary trends within Gobioidei.

PMID:40168108 | DOI:10.1111/1755-0998.14110

Clin Exp Dermatol. 2025 Apr 1:llaf147. doi: 10.1093/ced/llaf147. Online ahead of print.

ABSTRACT

This is a secondary analysis of a multicentre randomised controlled trial of ciclosporin and methotrexate in children and young people (CYP) with severe atopic dermatitis (AD). Longitudinal trough ciclosporin and erythrocyte methotrexate polyglutamates (MTX-PG) concentrations were measured to evaluate their associations with treatment response and adverse events. Both ciclosporin (4 mg/kg/day) and methotrexate (0.4 mg/kg/week) led to a significant reduction in disease severity scores over the 36-week treatment period. Higher trough ciclosporin concentrations were associated with lower disease severity scores and may serve as a useful tool for therapeutic drug monitoring of ciclosporin in CYP with AD. However, in contrast to a previously published study, steady-state erythrocyte-MTX-PG concentrations showed no significant association with treatment response. Drug concentrations were comparable between patients with and without drug-related adverse events.

PMID:40168525 | DOI:10.1093/ced/llaf147

Methods Mol Biol. 2025;2914:323-332. doi: 10.1007/978-1-0716-4462-1_22.

ABSTRACT

Selecting a fluid near an affected organ can improve the likelihood of identifying a biomarker panel from pathological tissue. Cerebrospinal fluid (CSF), in close contact with the brain, is a valuable source of biomarkers for neurological disorders due to the inaccessibility of brain tissue. Moreover, the altered CSF proteome identified in neurological diseases can facilitate the repurposing of drugs already used for other therapeutic purposes. In this context, Connectivity Map (CMap) is a valuable tool as it provides information on compounds and gene modifications that can be utilized to reverse specific pathological signatures. Analyzing CSF differential proteomics through the CMap framework offers an efficient and cost-effective approach to identifying potential novel therapies for neurodegenerative diseases.

PMID:40167927 | DOI:10.1007/978-1-0716-4462-1_22