Eur Radiol. 2025 Aug 6. doi: 10.1007/s00330-025-11801-z. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aimed to evaluate the potential additional value of deep radiomics for assessing residual cancer burden (RCB) in locally advanced breast cancer, after neoadjuvant chemotherapy (NAC) but before surgery, compared to standard predictors: tumor volume and subtype.

MATERIALS AND METHODS: This retrospective study used a 105-patient single-institution training set and a 41-patient external test set from three institutions in the LIMA trial. DCE-MRI was performed before and after NAC, and RCB was determined post-surgery. Three networks (nnU-Net, Attention U-net and vector-quantized encoder-decoder) were trained for tumor segmentation. For each network, deep features were extracted from the bottleneck layer and used to train random forest regression models to predict RCB score. Models were compared to (1) a model trained on tumor volume and (2) a model combining tumor volume and subtype. The potential complementary performance of combining deep radiomics with a clinical-radiological model was assessed. From the predicted RCB score, three metrics were calculated: area under the curve (AUC) for categories RCB-0/RCB-I versus RCB-II/III, pathological complete response (pCR) versus non-pCR, and Spearman's correlation.

RESULTS: Deep radiomics models had an AUC between 0.68-0.74 for pCR and 0.68-0.79 for RCB, while the volume-only model had an AUC of 0.74 and 0.70 for pCR and RCB, respectively. Spearman's correlation varied from 0.45-0.51 (deep radiomics) to 0.53 (combined model). No statistical difference between models was observed.

CONCLUSIONS: Segmentation network-derived deep radiomics contain similar information to tumor volume and subtype for inferring pCR and RCB after NAC, but do not complement standard clinical predictors in the LIMA trial.

KEY POINTS: Question It is unknown if and which deep radiomics approach is most suitable to extract relevant features to assess neoadjuvant chemotherapy response on breast MRI. Findings Radiomic features extracted from deep-learning networks yield similar results in predicting neoadjuvant chemotherapy response as tumor volume and subtype in the LIMA study. However, they do not provide complementary information. Clinical relevance For predicting response to neoadjuvant chemotherapy in breast cancer patients, tumor volume on MRI and subtype remain important predictors of treatment outcome; deep radiomics might be an alternative when determining tumor volume and/or subtype is not feasible.

PMID:40770139 | DOI:10.1007/s00330-025-11801-z

Commun Biol. 2025 Aug 6;8(1):1159. doi: 10.1038/s42003-025-08590-y.

ABSTRACT

Deep learning for microbiome analysis has shown potential for understanding microbial communities and human phenotypes. Here, we propose an approach, Transformer-based Robust Principal Component Analysis(TRPCA), which leverages the strengths of transformer architectures and interpretability of Robust Principal Component Analysis. To investigate benefits of TRPCA over conventional machine learning models, we benchmarked performance on age prediction from three body sites(skin, oral, gut), with 16S rRNA gene amplicon(16S) and whole-genome sequencing(WGS) data. We demonstrated prediction of age from longitudinal samples and combined classification and regression tasks via multi-task learning(MTL). TRPCA improves age prediction accuracy from human microbiome samples, achieving the largest reduction in Mean Absolute Error for WGS skin (MAE: 8.03, 28% reduction) and 16S skin (MAE: 5.09, 14% reduction) samples, compared to conventional approaches. Additionally, TRPCA's MTL approach achieves an accuracy of 89% for birth country prediction across 5 countries, while improving age prediction from WGS stool samples. Notably, TRPCA uncovers a link between subject and error prediction through residual analysis for paired samples across sequencing method (16S/WGS) and body site(oral/gut). These findings highlight TRPCA's utility in improving age prediction while maintaining feature-level interpretability, and elucidating connections between individuals and microbiomes.

PMID:40770074 | DOI:10.1038/s42003-025-08590-y

Sci Rep. 2025 Aug 7;15(1):28847. doi: 10.1038/s41598-025-14576-x.

ABSTRACT

Data binary encoding has proven to be a versatile tool for optimizing data processing and memory efficiency in various machine learning applications. This includes deep barcoding, generating barcodes from deep learning feature extraction for image retrieval of similar cases among millions of indexed images. Despite the recent advancement in barcode generation methods, converting high-dimensional feature vectors (e.g., deep features) to compact and discriminative binary barcodes is still an urgent necessity and remains an unresolved problem. Difference-based binarization of features is one of the most efficient binarization methods, transforming continuous feature vectors into binary sequences and capturing trend information. However, the performance of this method is highly dependent on the ordering of the input features, leading to a significant combinatorial challenge. This research addresses this problem by optimizing feature sequences based on retrieval performance metrics. Our approach identifies optimal feature orderings, leading to substantial improvements in retrieval effectiveness compared to arbitrary or default orderings. We assess the performance of the proposed approach in various medical and non-medical image retrieval tasks. This evaluation includes medical images from The Cancer Genome Atlas (TCGA), a comprehensive publicly available dataset, as well as COVID-19 Chest X-rays dataset. In addition, we evaluate the proposed approach on non-medical benchmark image datasets, such as CIFAR-10, CIFAR-100, and Fashion-MNIST. Our findings demonstrate the importance of optimizing binary barcode representation to significantly enhance accuracy for fast image retrieval across a wide range of applications, highlighting the applicability and potential of barcodes in various domains.

PMID:40770058 | DOI:10.1038/s41598-025-14576-x

Sci Rep. 2025 Aug 6;15(1):28744. doi: 10.1038/s41598-025-13646-4.

ABSTRACT

Decoding of continuous speech from electroencephalography (EEG) presents a promising avenue for understanding neural mechanisms of auditory processing and developing applications in hearing diagnostics. Recent advances in deep learning have improved decoding accuracy. However, challenges remain due to the low signal-to-noise ratio of the recorded brain signals. This study explores the application of contrastive learning, a self-supervised learning technique, to learn robust latent representations of EEG signals. We introduce a novel model architecture that leverages contrastive learning and transformer networks to capture relationships between auditory stimuli and EEG responses. Our model is evaluated on two tasks from the ICASSP 2023 Auditory EEG Decoding Challenge: a binary stimulus classification task (match-mismatch) and stimulus envelope decoding. We achieve state-of-the-art performance on both tasks, significantly outperforming previous winners with 87% accuracy in match-mismatch classification and a 0.176 Pearson correlation in envelope regression. Furthermore, we investigate the impact of model architecture, training set size, and finetuning on decoding performance, providing insights into the factors influencing model generalizability and accuracy. Our findings underscore the potential of contrastive learning for advancing the field of auditory EEG decoding and its potential applications in clinical settings.

PMID:40770040 | DOI:10.1038/s41598-025-13646-4

Nat Commun. 2025 Aug 6;16(1):7234. doi: 10.1038/s41467-025-62431-4.

ABSTRACT

Aging increases the risk of developing fibrotic diseases by hampering tissue regeneration after injury. Using longitudinal single-cell RNA-seq and spatial transcriptomics, here we compare the transcriptome of bleomycin (BLM) -induced fibrotic lungs of young and aged male mice, at 3 time points corresponding to the peak of fibrosis, regeneration, and resolution. We find that lung injury shifts the transcriptomic profiles of three pulmonary capillary endothelial cells (PCEC) subpopulations. The associated signatures are linked to pro-angiogenic signaling with strong Lrg1 expression and do not progress similarly throughout the resolution process between young and old animals. Moreover, part of this set of resolution-associated markers is also detected in PCEC from samples of patients with idiopathic pulmonary fibrosis. Finally, we find that aging also alters the transcriptome of PCEC, which displays typical pro-fibrotic and pro-inflammatory features. We propose that age-associated alterations in specific PCEC subpopulations may interfere with the process of lung progenitor differentiation, thus contributing to the persistent fibrotic process typical of human pathology.

PMID:40769983 | DOI:10.1038/s41467-025-62431-4

Eur Respir Rev. 2025 Aug 6;34(177):250023. doi: 10.1183/16000617.0023-2025. Print 2025 Jun.

ABSTRACT

Several interstitial lung diseases (ILDs) with different aetiologies and pathogenic mechanisms may exhibit a progressive behaviour, similar to idiopathic pulmonary fibrosis, with comparable functional decline and early mortality. Progressive pulmonary fibrosis (PPF) is not a diagnosis but rather reflects a clinical phenotype. Identifying progression is challenging as variability exists, both between different ILDs as well as in the context of the same entity. The American Thoracic Society/European Respiratory Society guidelines provide a useful framework for recognising the progressive behaviour of individual ILDs. Nevertheless, sometimes the "one-size-fits-all" approach to PPF may not lead to the best management decisions for individual patients. Real-life clinical practice presents multiple hurdles for practising clinicians and it is of utmost importance to target early those individuals that will benefit from antifibrotic treatment. This review aims to highlight several clinical points and suggest that, in certain cases, the strict rule of initiating antifibrotic treatment only upon disease progression may warrant some flexibility, particularly in the context of everyday clinical practice. Emphasis is placed on critically examining the criteria used to define progression across different ILDs, commenting on clinical issues such as disease severity at baseline, prevention of acute exacerbations, the definition of "standard treatment", the need for early access to appropriate treatment, prediction of progression, personalised medicine and an aetiologic approach. Engaging technology and artificial intelligence will play a role in the future. Until then, the best possible management decisions will rely on the judgment of treating clinicians, guided by existing evidence and patient needs.

PMID:40769536 | DOI:10.1183/16000617.0023-2025

Autoimmun Rev. 2025 Aug 4:103900. doi: 10.1016/j.autrev.2025.103900. Online ahead of print.

ABSTRACT

Autoimmune connective tissue disease associated with interstitial lung disease (CTD-ILD) includes rheumatoid arthritis-associated ILD, systemic sclerosis-associated-ILD, and several other ILDs. Many patients with CTD-ILD-as well as individuals with other ILDs-develop a progressive pulmonary fibrosis (PPF) similar to idiopathic pulmonary fibrosis (IPF). PPF is characterized by worsening respiratory symptoms, declining lung function despite current pharmacotherapies, and ultimately early death. Current pharmacotherapies for CTD-ILD and PPF include glucocorticoids, immunosuppressants, and anti-fibrotic agents. Due to the scarcity of randomized clinical trials for CTD-ILD, many pharmacotherapies are generally administered off-label (although several are approved in Japan), with notable exceptions including nintedanib, an anti-fibrotic agent approved for SSc-ILD and chronic progressive fibrosing ILD in several countries. As the available agents only slow the decline of pulmonary function and are associated with treatment-limiting side effects, there is a need for more efficacious and tolerable pharmacotherapies for CTD-ILD and PPF. Promising compounds in clinical trials include nerandomilast (a preferential phosphodiesterase 4B inhibitor), admilparant (a lysophosphatidic acid receptor 1 antagonist), and inhaled treprostinil (a prostacyclin analogue). Nerandomilast may have both anti-fibrotic and immunomodulatory properties; in preclinical models of PPF, it reduced neutrophils and macrophages and down-regulated pro-fibrotic signaling pathways. Hopefully, therefore, this pipeline will produce new medications to ease the collectively large burden of CTD-ILD and PPF.

PMID:40769405 | DOI:10.1016/j.autrev.2025.103900

Eur J Med Chem. 2025 Jul 31;298:118013. doi: 10.1016/j.ejmech.2025.118013. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive multifactorial lung disorder characterized by excessive deposition of fibrotic connective tissue. The activation of farnesoid X receptor (FXR) and peroxisome proliferator-activated receptor δ (PPARδ) demonstrated therapeutic efficacy in reducing fibrotic pathology, respectively, suggesting that dual FXR/PPARδ up-regulators may provide a prospective approach to address the polypharmacy dilemma in fibrotic diseases. Herein, the identification campaign of 6-(piperazin-1-yl)imidazo[1,2-b]pyridazine derivatives as FXR/PPARδ dual agonists was described through hybridation of FXR agonist GW-4064 and PPARδ agonist GW-0742. The following exhaustive in vitro FXR and PPARδ activation studies culminated in the optimization of compound 10g, which displayed potent dual-target activities with an FXR agonistic EC50 of 12.28 nM and 69 % PPARδ activation at 100 nM. In a Bleomycin-induced murine in vivo pulmonary fibrosis model, 10g (40 mg/kg, QD) significantly attenuated collagen deposition and reduced the expression of α-SMA in lung tissue. Taken together, these results shed new light on the discovery of novel FXR/PPARδ agonists for the treatment of IPF.

PMID:40768861 | DOI:10.1016/j.ejmech.2025.118013

Environ Microbiol Rep. 2025 Aug;17(4):e70170. doi: 10.1111/1758-2229.70170.

ABSTRACT

Biogeochemical cycles in the ocean are strongly influenced by microbial activity, which affects nutrient and organic matter cycling. These processes, influenced by factors such as temperature, salinity, density and inorganic nutrients, drive the vertical stratification of microbial communities, which subsequently influence the chemistry at different depth layers. Sequencing technology has expanded our understanding of oceanic prokaryotic communities' taxonomic and functional potential. However, there is limited information on how these communities vary across gradients. In this study, we conducted metagenomic analyses on samples from the eastern North Pacific, collected across a longitudinal transect around 45°N and throughout the entire water column. We assessed taxonomic and functional classification, focusing on the roles of prokaryotic communities in biogeochemical cycling. Our results revealed that the surface community was dominated by the SAR11 clade, followed by Flavobacterales and Rhodobacterales. The deep layers harboured a more diverse community, where Thaumarchaeota accounted for the most significant proportion. This clear taxonomic stratification led to variations in the communities' functional capabilities across different depth layers. Photosynthesis and heterotrophy dominated the surface layers, whereas the deeper layers exhibited a mix of metabolic features, allowing organisms to potentially utilise both inorganic and organic carbon sources.

PMID:40769940 | DOI:10.1111/1758-2229.70170

Life Sci. 2025 Aug 5:123893. doi: 10.1016/j.lfs.2025.123893. Online ahead of print.

NO ABSTRACT

PMID:40769822 | DOI:10.1016/j.lfs.2025.123893

Genes Dev. 2025 Aug 6. doi: 10.1101/gad.352747.125. Online ahead of print.

ABSTRACT

Regulators of chromatin accessibility play key roles in cell fate transitions, triggering the onset of novel transcription programs as cells differentiate. In the Drosophila male germline stem cell lineage, tMAC, a master regulator of spermatocyte differentiation that binds thousands of loci, is required for local opening of chromatin, allowing activation of spermatocyte-specific promoters. Here we show that a cell type-specific surveillance system involving the multiple zinc finger protein Kmg and the pipsqueak domain protein Dany dampens transcriptional output from weak tMAC-dependent promoters and counteracts tMAC binding at thousands of additional cryptic promoters, thus preventing massive expression of aberrant protein-coding transcripts. ChIP-seq showed Kmg enriched at the tMAC-bound promoters that it repressed, consistent with direct action. In contrast, Kmg and Dany did not repress highly expressed tMAC-dependent genes, where they colocalized with their binding partner, the chromatin remodeler Mi-2 (NuRD), along the transcribed regions rather than at the promoter. We discuss a model where Kmg, together with Dany and Mi-2, dampens expression from weak or ectopic promoters while allowing robust transcription from highly expressed Aly-dependent genes.

PMID:40769719 | DOI:10.1101/gad.352747.125

J Theor Biol. 2025 Aug 4:112231. doi: 10.1016/j.jtbi.2025.112231. Online ahead of print.

ABSTRACT

Colorectal cancer (CRC) poses a major public health challenge due to its increasing prevalence, particularly among younger populations. Microsatellite instability-high (MSI-H) CRC and deficient mismatch repair (dMMR) CRC constitute 15% of all CRC and exhibit remarkable responsiveness to immunotherapy, especially with PD-1 inhibitors. Despite this, there is a significant need to optimise immunotherapeutic regimens to maximise clinical efficacy and patient quality of life. To address this, we employ a novel framework driven by delay integro-differential equations to model the interactions among cancer cells, immune cells, and immune checkpoints in locally advanced MSI-H/dMMR CRC (laMCRC). Several of these components are being modelled deterministically for the first time in cancer, paving the way for a deeper understanding of the complex underlying immune dynamics. We consider two compartments: the tumour site and the tumour-draining lymph node, incorporating phenomena such as dendritic cell (DC) migration, T cell proliferation, and CD8+ T cell exhaustion and reinvigoration. Parameter values and initial conditions are derived from experimental data, integrating various pharmacokinetic, bioanalytical, and radiographic studies, along with deconvolution of bulk RNA-sequencing data from the TCGA COADREAD and GSE26571 datasets. We finally optimised neoadjuvant treatment with pembrolizumab, a widely used PD-1 inhibitor, to balance efficacy, efficiency, and toxicity in laMCRC patients. We mechanistically analysed factors influencing treatment success and improved upon currently FDA-approved therapeutic regimens for metastatic MSI-H/dMMR CRC, demonstrating that a single medium-to-high dose of pembrolizumab may be sufficient for effective tumour eradication while being efficient, safe and practical.

PMID:40769472 | DOI:10.1016/j.jtbi.2025.112231

Nature. 2025 Aug 6. doi: 10.1038/s41586-025-09461-6. Online ahead of print.

ABSTRACT

Migration of transplanted allogeneic myeloid cells into the brain following systemic hematopoietic stem and progenitor cells transplantation (HCT) holds great promise as a therapeutic modality to correct genetic deficiencies in the brain such as lysosomal storage diseases.1-3 However, the toxic myeloablation required for allogeneic HCT can cause serious, life-threatening side effects limiting its applicability. Moreover, transplanted allogeneic myeloid cells are highly vulnerable to rejection even in an immune-privileged organ like the brain. Here we report a brain-restricted, high-efficiency microglia replacement approach without myeloablative preconditioning. Unlike previous assumptions, we found that hematopoietic stem cells are not required to repopulate the myeloid compartment of the brain environment. In contrast, Sca1- committed progenitor cells were highly efficient to replace microglia following intracerebral injection. This finding enabled the development of brain-restricted preconditioning and avoided long-term peripheral engraftment thus eliminating complications such as graft-vs-host disease. Evaluating its therapeutic potential, we found that our allogeneic microglia replacement method rescues the murine model of Sandhoff disease, a lysosomal storage disease caused by hexosaminidase B deficiency. In support of the translational relevance of this approach, we discovered that human induced pluripotent stem cell-derived myeloid progenitor cells display a similar engraftment potential following brain-restricted conditioning. Our results overcome current limitations of conventional HCT and may pave the way for the development of allogeneic microglial cell therapies for the brain.

PMID:40769206 | DOI:10.1038/s41586-025-09461-6

Sci Rep. 2025 Aug 7;15(1):28849. doi: 10.1038/s41598-025-13234-6.

ABSTRACT

Myocarditis and pericarditis are managed with various treatments, yet prior studies and case reports indicate that certain drug classes may elevate the risk for these inflammatory cardiac conditions. This research aimed to systematically identify the leading drugs most frequently associated with myocarditis and pericarditis cases. Analyses were carried out using the global database of individual case safety reports from 1968 to 2024. We identified the drugs most frequently reported in signal detection with myocarditis and pericarditis, selecting the top 10 drugs based on record count, excluding those used in the treatment of inflammatory cardiac conditions to avoid potential confounding. Two statistical indicators, the information component (IC) with IC025 and reporting odds ratio (ROR) with 95% confidence interval (CI) were used to conduct the disproportionality analysis in this study. The following five drugs were consistently observed with both myocarditis and pericarditis: clozapine, mesalazine, smallpox vaccine, influenza vaccine, and COVID-19 mRNA vaccine. The other leading drugs differed by condition, with nivolumab, pembrolizumab, ipilimumab, valproate, and metronidazole appearing more frequently for myocarditis, and ribavirin, sulfasalazine, methotrexate, omalizumab, and heparin for pericarditis. Each of these drugs showed a significant signal detection with myocarditis (ROR, 83.22 [95% CI, 81.17-85.33]; IC, 3.96 [IC025, 3.94]) and pericarditis (42.16 [41.19-43.16]; 3.66 [3.64]). Although our findings did not allow for causal inference, these findings highlight the importance of monitoring for possible adverse carditis cases when prescribing these drugs. Further studies are encouraged to investigate underlying mechanisms, assess individual patient risk factors, and explore the long-term impacts associated with myocarditis and pericarditis in relation to drug.

PMID:40770014 | DOI:10.1038/s41598-025-13234-6

J Am Dent Assoc. 2025 Aug;156(8):638-648.e6. doi: 10.1016/j.adaj.2025.05.016.

ABSTRACT

BACKGROUND: Given the high prevalence of prescription drug use in the US population and the possible drug-related oral and dental adverse events (AEs), it is important to characterize drug-related oral and dental AEs from available data sources. The objectives of this study were to identify and characterize various drug-related oral and dental AE pairs reported in the literature and drug labeling.

TYPES OF STUDIES REVIEWED: A systematic descriptive review of the literature was performed after applying the search criteria in PubMed, Embase, and Web of Science. A full-text review of drug labeling was conducted in FDALabel (US Food and Drug Administration) with natural language processing using terms from Medical Dictionary for Regulatory Activities, Version 26.0. Comparative data analysis was performed. Heat maps and tree maps were generated for drug-oral and dental AE pair characterization.

RESULTS: The authors reviewed 505 published articles and 2,663 drug labelings. They identified 180 major drug-oral and dental AE pairs reported in the literature but not found in drug labeling. The drug-oral and dental AE pairs most frequently reported in the literature and listed in drug labeling were oral and dental local hypersensitivity reactions (977), Stevens-Johnson syndrome and toxic epidermal necrolysis (419), mucositis and stomatitis (337), and orofacial clefts (151). With respect to oral and dental AEs in labeling, most warnings and precautions were for drug-induced systemic lupus erythematosus and most boxed warnings were for Stevens-Johnson syndrome and toxic epidermal necrolysis.

CONCLUSIONS AND PRACTICAL IMPLICATIONS: The results of this study advance understanding of major oral and dental AEs across various therapeutic areas captured in the literature and labeling. The authors characterized those drugs and drug classes associated with major labeled oral and dental AEs (ie, requiring labeling in warnings and precautions or boxed warnings). To the authors' knowledge, such comprehensive descriptive information was not available previously.

PMID:40769649 | DOI:10.1016/j.adaj.2025.05.016

Ann Oncol. 2025 Aug 4:S0923-7534(25)00912-3. doi: 10.1016/j.annonc.2025.07.015. Online ahead of print.

ABSTRACT

PURPOSE: Trastuzumab deruxtecan (T-DXd), an antibody-drug conjugate treatment for multiple solid tumors, carries risk of interstitial lung disease/pneumonitis (ILD). Management guidelines generally mandate interrupting T-DXd treatment for grade 1 ILD, with possible retreatment following resolution of imaging findings. This pooled analysis examined T-DXd retreatment duration and ILD recurrence following recovery from grade 1 ILD.

PATIENTS AND METHODS: Data were pooled from 9 clinical trials of patients with various HER2-positive, HER2-low, or HER2 (ERBB2)-mutant solid tumors treated with T-DXd (5.4-8.0 mg/kg). ILD events were reported and graded by investigators and confirmed as drug-related by an independent ILD adjudication committee. Patients who recovered from a first investigator-assessed grade 1 and adjudication committee-confirmed drug-related ILD event (ILD1) could receive T-DXd retreatment. Patients were evaluated until disease progression or data cutoff.

RESULTS: Among 2145 pooled patients, 9% (193/2145) had grade 1 ILD1, of which 23.3% (45/193) were retreated with T-DXd. Median retreatment duration was 85 days (range, 1-848); 17.8% (8/45) of patients received T-DXd retreatment for ≥1 year. ILD recurrence (ILD2) occurred in 33.3% (15/45) of retreated patients; median time to ILD2 from T-DXd retreatment was 64 days (range, 22-391) and were low-grade events (grade 1, n=6; grade 2, n=9; no grade ≥3 or fatal events). Reasons for T-DXd retreatment discontinuation were disease progression (33.3% [15/45]); ILD recurrence (20% [9/45]); non-ILD adverse events (17.8% [8/45]); physician's decision (4.4% [2/45]). At the analysis cutoff, 24.4% (11/45) of retreated patients were still receiving treatment, and most patients with ILD2 (60% [9/15]) had recovered with/without sequelae.

CONCLUSIONS: This first large-scale pooled analysis demonstrates the safety of T-DXd retreatment after recovery from grade 1 ILD. ILD recurred in one-third of patients; all recurrence events were grade 1/2 and manageable using existing treatment guidelines. T-DXd retreatment following resolution of grade 1 drug-related ILD has potential to maximize therapeutic benefit.

PMID:40769277 | DOI:10.1016/j.annonc.2025.07.015

Respir Med Res. 2025 Jun 16;88:101186. doi: 10.1016/j.resmer.2025.101186. Online ahead of print.

ABSTRACT

BACKGROUND: Cystic fibrosis (CF) patients often undergo treatment with CFTR modulators, which have demonstrated high efficacy but also potential involvement in drug interactions. Inquiries regarding the risks of drug interactions with complementary and alternative medicine products (CAMp) and self-medication drugs have become frequent among patients and prescribers at Toulouse University Hospital. Currently, there is lack of literature on these practices within CF patients, particularly in France, and more so since the advent of CFTR modulators.

METHODS: This observational monocentric study (MUCAUTOMED) aimed to characterize and quantify the prevalence of CAMp utilization among CF patients under our hospital's care. A secondary objective was to assess and describe the prevalence of self-medication practices. Surveys were administered to outpatients during visits from January 10 to June 6, 2022.

RESULTS: Out of 171 included patients, responses from 64 adults and 69 children were analyzed (response rate 133/171 = 77.8 %). CAMp usage was reported by 56.3 % of adults and 46.4 % of children. Most patients use CAMp for enhancing wellness, addressing digestive concerns, and managing respiratory issues. Remarkably, 71.4 % of participants were unaware of potential drug interactions with CAMp, and 48.9 % initiated such use without consulting healthcare professionals. Notably, a significant correlation between CAMp utilization and self-medication was identified within the pediatric population.

CONCLUSION: Our investigation underscores a notably high prevalence of CAMp use among the CF population. Given these findings, it is imperative to routinely discuss CAMp utilization and self-medication practices when initiating CFTR modulator therapy. A multidisciplinary approach is recommended to address potential interactions that may impact overdosing and underdosing, ensuring patients and families are informed of associated risks. Registration number 2021-A02593-38.

PMID:40768781 | DOI:10.1016/j.resmer.2025.101186

Proc Natl Acad Sci U S A. 2025 Aug 12;122(32):e2506821122. doi: 10.1073/pnas.2506821122. Epub 2025 Aug 6.

ABSTRACT

The emergence of immune-evasive SARS-CoV-2 Omicron subvariants highlights the need to develop a mucosal SARS-CoV-2 vaccine that can provide broad protection against virus infection and transmission. Here, we developed an intranasal monovalent SARS-CoV-2 vaccine expressing the six-proline-stabilized prefusion spike proteins (preS-6P) of Omicron XBB.1.5 based on the attenuated mumps virus (MuV) Jeryl Lynn (JL1) vaccine strain. We also developed an intranasal trivalent vaccine expressing the preS-6P of ancestral SARS-CoV-2 WA1 and two Omicron subvariants, BA.1 and XBB.1.5, using the attenuated measles virus (MeV) and MuV-JL1 and JL2 vaccine strains, respectively. Intranasal immunization of hamsters with the monovalent rMuV-JL1-XBB.1.5 or the trivalent vaccine induced high levels of neutralizing antibodies (NAbs) that efficiently neutralized Omicron subvariants XBB.1.5, EG.5, and JN.1, providing complete protection against these Omicron subvariants. Similar levels of Omicron XBB.1.5 NAbs were detected in monovalent rMuV-JL1-XBB.1.5 and trivalent vaccine groups even when hamsters had been preimmunized with the rMuV-JL2-WA1 vaccine, suggesting that both intranasal vaccines are effective in the presence of immune imprinting induced by the spike of SARS-CoV-2 WA1. Intranasal, but not subcutaneous, immunization generated high levels of S-specific mucosal IgA antibodies as well as lung-resident memory T cells in IFNAR1-/- mice. Finally, intranasal immunization with the trivalent vaccine efficiently blocked transmission of SARS-CoV-2 WA1 and Omicron XBB.1.5 among hamsters in a direct contact transmission setting. In summary, we have developed intranasal MeV and MuV-based trivalent vaccines that induce broad NAbs, robust mucosal immunity, and strong protection against both virus challenge and virus transmission.

PMID:40768351 | DOI:10.1073/pnas.2506821122

Phytomedicine. 2025 Aug 5;146:157127. doi: 10.1016/j.phymed.2025.157127. Online ahead of print.

ABSTRACT

BACKGROUND: Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKI) are widely regarded as the most promising strategy to treat non-small cell lung cancer (NSCLC). While the existing development model cannot meet the requirements.

METHODS: Here, we developed a drug prediction and screening platform based on state-of-the-art deep learning (DL) algorithms and cell membrane chromatography (CMC). Using this platform, we discovered several novel EGFR antagonists from massive molecules. The interactions between target compounds and EGFR were investigated using frontal analysis, Surface plasmon resonance analysis, cellular thermal shift assay, and molecule docking. Xenografts models were established to study the anti-tumor activity of most promising compound. Proteomics, immunofluorescence, and Western blots were conducted to explore its mechanism.

RESULTS: The deep neural network had good performance, with an area under the curve of 0.950 ± 0.012. Among the top 100 molecules predicted by the model, 25 compounds had retention on SNAP-tagged (ST)-EGFR/CMC, of which 12 molecules exhibited significant anti-tumor activity. Interaction analysis revealed that Geraniin, Brazilin, and 7-Epitaxol would directly bind to EGFR and inhibit its activation. 7-Epitaxol exhibited significant anti-tumor activity in vivo and in vitro. 7-Epitaxol combined with EGFR and inhibited its phosphorylation, blocked PI3K/AKT pathways, thereby exerting its anti-tumor activity by promoting autophagy in A549 cells.

CONCLUSION: The results provide a novel and powerful platform for drug discovery and development in NSCLC research. By using this platform, Geraniin, Brazilin, and 7-epitaxol were identified as novel EGFR antagonists. We also innovatively demonstrated that 7-epitaxol promotes autophagy in NSCLC.

PMID:40768807 | DOI:10.1016/j.phymed.2025.157127

Cogn Emot. 2025 Aug 6:1-20. doi: 10.1080/02699931.2025.2543569. Online ahead of print.

ABSTRACT

Deep neural networks (DNNs) provide a useful computational framework for constructing cognitive models of emotion understanding. This paper provides a focused discussion of the use of DNNs in this context. It begins by defining three key components of emotion understanding - perception, prediction, and regulation - and discussing how each can be modelling using different deep learning architectures. It continues by positioning what DNN models can contribute to affective science in relation to important existing theoretical perspectives, including both domain-general frameworks like Bayesian cognitive modelling, and domain-specific frameworks, such as the theory of constructed emotion. The paper highlights both the strengths and limitations of DNNs as cognitive models and provides guidance for how to capitalise on the former while mitigating the latter.

PMID:40768739 | DOI:10.1080/02699931.2025.2543569