Med. 2025 Apr 25:100676. doi: 10.1016/j.medj.2025.100676. Online ahead of print.

ABSTRACT

BACKGROUND: Anti-melanoma differentiation-associated protein 5 dermatomyositis (MDA5+DM) is a potentially fatal subtype of dermatomyositis. The most severe cases are characterized by rapidly progressive interstitial lung disease (RPILD), the leading cause of death in these patients. There is currently no curative treatment for these patients, and indeed, MDA5+DM-RPILD is considered one of the most challenging pathologies in medicine. Nevertheless, the recent introduction of CD19 chimeric antigen receptor (CAR)-T cell therapies appears to offer a serious opportunity to develop solutions for complex autoimmune diseases refractory to multiple immunosuppressant treatments, mainly rheumatic diseases such as rheumatoid arthritis, dermatomyositis, and systemic lupus erythematosus.

METHODS: In this report, we describe the first use of a second-generation CD19 CAR-T cell therapy (ARI-0001) in a pediatric patient with severe MDA5+DM-RPILD.

FINDINGS: Conventional treatments stabilized MDA5+DM-RPILD before CAR-T cell inoculation (-34 days). The presence of CD19+ B lymphocytes that might serve as target cells in deeper tissues was suspected due to CAR-T cell expansion in a context of B cell aplasia. No fever or cytokine release syndrome/cell-associated neurotoxicity syndrome was evident. In global terms, B cell reconstitution and cutaneous, motor, respiratory, and neurological improvements were observed gradually in the patient in an immunosuppressant-free context (-7 to +325 days).

CONCLUSIONS: A pediatric patient with aggressive MDA5+DM-RPILD achieved progressive long-term improvement and immunosuppressant-free remission over 11 months after compassionate use of a CD19 CAR-T cell therapy (ARI-0001).

FUNDING: This work was supported by the Programa Investigo (PI_SEPE_APM) and grants from the ISC-III (PI22/01226) from the Comunidad de Madrid (S2022/BMD-7225) and from the CRIS Cancer Foundation.

PMID:40306284 | DOI:10.1016/j.medj.2025.100676

Radiat Oncol. 2025 Apr 30;20(1):68. doi: 10.1186/s13014-025-02643-6.

ABSTRACT

BACKGROUND: To quantify morphological and dosimetric variations in nasopharyngeal carcinoma (NPC) radiotherapy via autosegmented fan-beam computed tomography (FBCT) and to inform decision-making regarding appropriate objectives and optimal timing for adaptive radiotherapy (ART).

METHODS: This retrospective study analyzed 23 NPC patients (681 FBCT scans) treated at Sun Yat-sen Cancer Center from August 2022 to May 2024. The inclusion criterion was as follows: ≥1 weekly FBCT via a CT-linac with ≤ 2 fractions between scans. Four deep learning-based autosegmentation models were developed to assess weekly volume, Dice similarity coefficient (DSC), and dose variations in organs at risk (OARs) and target volumes.

RESULTS: A systematic review of autosegmentation on FBCT scans demonstrated satisfactory accuracy overall, and missegmentation was manually modified. Linear decreases in volume and/or DSC were observed in the parotid glands, submandibular glands, thyroid, spinal cord, and target volumes (R² > 0.7). The linear dose variation included coverage of the low risk planning target volume (-3.01%), the mean dose to the parotid glands (+ 2.45 Gy) and thyroid (+ 1.18 Gy), the D1% of the brainstem (+ 0.56 Gy), and the maximum dose to the spinal cord (+ 1.12 Gy). The greatest reduction in target volume coverage was noted in PGTVns, reaching 7.15%. The most significant dose changes occurred during weeks 3-6.

CONCLUSIONS: During NPC radiotherapy, the progressive dose deviations may not be corrected through repositioning alone, necessitating ART intervention. As dose variations in OARs rarely exceed 3 Gy and target coverage fluctuations remain within 10%, ART does not need to be performed frequently, and weeks 3-6 represent the most appropriate window.

PMID:40307931 | DOI:10.1186/s13014-025-02643-6

BMC Res Notes. 2025 Apr 30;18(1):200. doi: 10.1186/s13104-025-07263-7.

ABSTRACT

OBJECTIVE: Detecting small, faraway objects in real-time surveillance is challenging due to limited pixel representation, affecting classifier performance. Deep Learning (DL) techniques generate feature maps to enhance detection, but conventional methods suffer from high computational costs. To address this, we propose Multi-Scale Region-wise Pixel Analysis with GAN for Tiny Object Detection (MSRP-TODNet). The model is trained and tested on VisDrone VID 2019 and MS-COCO datasets. First, images undergo two-fold pre-processing using Improved Wiener Filter (IWF) for artifact removal and Adjusted Contrast Enhancement Method (ACEM) for blurring correction. The Multi-Agent Reinforcement Learning (MARL) algorithm splits the pre-processed image into four regions, analyzing each pixel to generate feature maps. These are processed by the Enhanced Feature Pyramid Network (EFPN), which merges them into a single feature map. Finally, a Generative Adversarial Network (GAN) detects objects with bounding boxes.

RESULTS: Experimental results on the DOTA dataset demonstrate that MSRP-TODNet outperforms existing state-of-the-art methods. Specifically, it achieves an mAP @0.5 of 84.2%, mAP @0.5:0.95 of 54.1%, and an F1-Score of 84.0%, surpassing improved TPH-YOLOv5, YOLOv7-Tiny, and DRDet by margins of 1.7%-6.1% in detection performance. These results demonstrate the framework's effectiveness for accurate, real-time small object detection in UAV surveillance and aerial imagery.

PMID:40307915 | DOI:10.1186/s13104-025-07263-7

J Cheminform. 2025 Apr 30;17(1):66. doi: 10.1186/s13321-025-01015-2.

ABSTRACT

The cytochrome P450 (CYP) superfamily metabolises a wide range of compounds; however, drug-induced CYP inhibition can lead to adverse interactions. Identifying potential CYP inhibitors is crucial for safe drug administration. This study investigated the application of deep learning techniques to the prediction of CYP inhibition, focusing on the challenges posed by limited datasets for CYP2B6 and CYP2C8 isoforms. To tackle these limitations, we leveraged larger datasets for related CYP isoforms, compiling comprehensive data from public databases containing IC50 values for 12,369 compounds that target seven CYP isoforms. We constructed single-task, fine-tuning, multitask, and multitask models incorporating data imputation on the missing values. Notably, the multitask models with data imputation demonstrated significant improvement in CYP inhibition prediction over the single-task models. Using the most accurate prediction models, we evaluated the inhibitory activity of approved drugs against CYP2B6 and CYP2C8. Among the 1,808 approved drugs analysed, our multitask models with data imputation identified 161 and 154 potential inhibitors of CYP2B6 and CYP2C8, respectively. This study underscores the significant potential of multitask deep learning, particularly when utilising a graph convolutional network with data imputation, to enhance the accuracy of CYP inhibition predictions under the conditions of limited data availability.Scientific contributionThis study demonstrates that even with small datasets, accurate prediction models can be constructed by utilising related data effectively. Also, our imputation techniques on the missing values improved the prediction accuracy of CYP2B6 and CYP2C8 inhibition significantly.

PMID:40307863 | DOI:10.1186/s13321-025-01015-2

Vis Comput Ind Biomed Art. 2025 May 1;8(1):11. doi: 10.1186/s42492-025-00194-x.

ABSTRACT

Hypertensive retinopathy (HR) occurs when the choroidal vessels, which form the photosensitive layer at the back of the eye, are injured owing to high blood pressure. Artificial intelligence (AI) in retinal image analysis (RIA) for HR diagnosis involves the use of advanced computational algorithms and machine learning (ML) strategies to recognize and evaluate signs of HR in retinal images automatically. This review aims to advance the field of HR diagnosis by investigating the latest ML and deep learning techniques, and highlighting their efficacy and capability for early diagnosis and intervention. By analyzing recent advancements and emerging trends, this study seeks to inspire further innovation in automated RIA. In this context, AI shows significant potential for enhancing the accuracy, effectiveness, and consistency of HR diagnoses. This will eventually lead to better clinical results by enabling earlier intervention and precise management of the condition. Overall, the integration of AI into RIA represents a considerable step forward in the early identification and treatment of HR, offering substantial benefits to both healthcare providers and patients.

PMID:40307650 | DOI:10.1186/s42492-025-00194-x

J Imaging Inform Med. 2025 Apr 30. doi: 10.1007/s10278-025-01514-6. Online ahead of print.

ABSTRACT

This study aimed to evaluate the image quality of virtual monochromatic images (VMIs) reconstructed with deep learning image reconstruction (DLIR) using dual-energy CT (DECT) to diagnose pancreatic ductal adenocarcinoma (PDAC). Fifty patients with histologically confirmed PDAC who underwent multiphasic contrast-enhanced DECT between 2019 and 2022 were retrospectively analyzed. VMIs at 40-100 keV were reconstructed using hybrid iterative reconstruction (ASiR-V 30% and ASiR-V 50%) and DLIR (TFI-M) algorithms. Quantitative analyses included contrast-to-noise ratios (CNR) of the major abdominal vessels, liver, pancreas, and the PDAC. Qualitative image quality assessments included image noise, soft-tissue sharpness, vessel contrast, and PDAC conspicuity. Noise power spectrum (NPS) analysis was performed to examine the variance and spatial frequency characteristics of image noise using a phantom. TFI-M significantly improved image quality compared to ASiR-V 30% and ASiR-V 50%, especially at lower keV levels. VMIs with TFI-M showed reduced image noise and higher pancreas-to-tumor CNR at 40 keV. Qualitative evaluations confirmed DLIR's superiority in noise reduction, tissue sharpness, and vessel conspicuity, with substantial interobserver agreement (κ = 0.61-0.78). NPS analysis demonstrated effective noise reduction across spatial frequencies. DLIR significantly improved the image quality of VMIs on DECT by reducing image noise and increasing CNR, particularly at lower keV levels. These improvements may improve PDAC detection and assessment, making it a valuable tool for pancreatic cancer imaging.

PMID:40307592 | DOI:10.1007/s10278-025-01514-6

Sci Rep. 2025 Apr 30;15(1):15219. doi: 10.1038/s41598-025-99717-y.

ABSTRACT

This study aimed to develop a deep learning (DL)-based deliverable whole pelvic volumetric arc radiation therapy (VMAT) for patients with gynecologic cancer using a prototype DL-based automated planning support system, named RatoGuide, to evaluate its clinical validity. In our hospital, 110 patients with gynecologic cancer were registered. The prescribed dose was 50.4 Gy/28 fr. A DL-based three-dimensional dose prediction model was first trained by the dose distribution and structure data of whole pelvic VMAT (n = 100) created on the Monaco treatment planning system (TPS). The structure data of the test data (n = 10) were then input to RatoGuide, and RatoGuide predicted the dose distribution of the whole pelvic VMAT plan (PreDose). We established deliverable plans with Monaco and Eclipse TPS (DeliDose) based on PreDose and vendor-supplied optimization objectives. Medical physicists then manually developed plans (CliDose) for the test data. Finally, we evaluated and compared the dose distribution and dose constraints of PreDose, DeliDose, and CliDose. DeliDose, in both Eclipse and Monaco, was comparable to PreDose in most Dose constraints, planning target volume (PTV) coverage, and Dmax of the bladder, rectum, and bowel bag were better for DeliDose than for PreDose. Additionally, DeliDose demonstrated no significant difference from CliDose in most dose constraints. The blinded average scores of radiation oncologists for DeliDose and CliDose were 4.2 ± 0.4 and 4.3 ± 0.5, respectively, in Eclipse, and 4.0 ± 0.6 and 3.9 ± 0.5, respectively, in Monaco (5 is the max score and 3 is clinically acceptable). We indicated that RatoGuide can eliminate variations in plan quality between hospitals in whole pelvic VMAT irradiation and help develop VMAT plans in a short time.

PMID:40307456 | DOI:10.1038/s41598-025-99717-y

Sci Rep. 2025 Apr 30;15(1):15186. doi: 10.1038/s41598-025-99651-z.

ABSTRACT

Optimal selection of X-ray imaging parameters is crucial in coronary angiography and structural cardiac procedures to ensure optimal image quality and minimize radiation exposure. These anatomydependent parameters are organized into customizable organ programs, but manual selection of the programs increases workload and complexity. Our research introduces a deep learning algorithm that autonomously detects three target anatomies:the left coronary artery (LCA), right coronary artery (RCA), and left ventricle (LV),based on singleX-ray frames without vessel structure and enables adjustment of imaging parameters by choosing the appropriate organ program. We compared three deep-learning architectures: ResNet-50 for image data, a Multilayer Perceptron (MLP) for angulation data, and a multimodal approach combining both. The dataset for training and validation included 275 radiographic sequences from clinical examinations, incorporating coronary angiography, left ventriculography, and corresponding C-arm angulation, using only the first non-contrast frame of the sequence for the possibility of adapting the system before the actual contrast injection. The dataset was acquired from multiple sites, ensuring variation in acquisition and patient statistics. An independent test set of 146 sequences was used for evaluation. The multimodal model outperformed the others, achieving an average F1 score of 0.82 and an AUC of 0.87, matching expert evaluations. The model effectively classified cardiac anatomies based on pre-contrast angiographic frames without visible coronary or ventricular structures. The proposed deep learning model accurately predicts cardiac anatomy for cine acquisitions, enabling the potential for quick and automatic selection of imaging parameters to optimize image quality and reduce radiation exposure. This model has the potential to streamline clinical workflows, improve diagnostic accuracy, and enhance safety for both patients and operators.

PMID:40307429 | DOI:10.1038/s41598-025-99651-z

Sci Rep. 2025 Apr 30;15(1):15202. doi: 10.1038/s41598-025-99785-0.

ABSTRACT

Computational methods for generating molecules with specific physiochemical properties or biological activity can greatly assist drug discovery efforts. Deep learning generative models constitute a significant step towards that direction. We introduce a novel approach that utilizes a Reinforcement Learning paradigm, called proximal policy optimization, for optimizing molecules in the latent space of a pretrained generative model. Working in the latent space of a generative model lets us bypass the need for explicitly defining chemical rules when computationally designing molecules. The generation of molecules is achieved through navigating the latent space for identifying regions that correspond to molecules with desired properties. Proximal policy optimization is a state-of-the-art policy gradient algorithm capable of operating in continuous high-dimensional spaces in a sample-efficient manner. We have paired our optimization framework with the latent spaces of two different architectures of autoencoder models showing that the method is agnostic to the underlying architecture. We present results on commonly used benchmarks for molecule optimization that demonstrate that our method has comparable or even superior performance to state-of-the-art approaches. We additionally show how our method can generate molecules that contain a pre-specified substructure while simultaneously optimizing for molecular properties, a task highly relevant to real drug discovery scenarios.

PMID:40307420 | DOI:10.1038/s41598-025-99785-0

Sci Rep. 2025 Apr 30;15(1):15136. doi: 10.1038/s41598-025-99713-2.

ABSTRACT

Affordable and efficient agricultural methods enhance crop yield and water management by optimizing resources. Precise irrigation relies on accurate estimation of reference evapotranspiration (ETo). Numerous analytical and empirical methods exist to compute ETo but these methods are costlier, requires time and perform poorly under limited availability of meteorological data. This study first evaluated the performances of three deep learning sequential models-Long short-term memory (LSTM), Neural Basis Expansion Analysis for Time Series (N-BEATS) and, Temporal Convolutional Network model (TCN), for predicting daily ETo possessing temporal characteristics. In this TCN is considered as baseline model to be compared with other models. In the results, TCN performed better, so it is further utilized to evaluate two strategies of ETo prediction that makes the second objective of the paper. In the first approach, historic data is used to predict future ETo using TCN which is standard method. And, in recursive approach, TCN predicted climatological data and, ETo is computed. This is required for better irrigation planning in data-scarce situations. The results demonstrate that the TCN model provided satisfactory performance with the Nash-Sutcliffe Efficiency (NSE) = 0.99, Theil U2 = 0.005, RMSE = 0.092 and, MAE = 0.048. Also, with the recursive strategy, ETo values computed found more accurate than using standard approach. Thus, comparative study among sequential architecture revealed TCN outperformed LSTM and N-BEATS models and, is an efficient method for predicting ETo time-series and, could also assist in the precise management of water resources in data scarcity.

PMID:40307385 | DOI:10.1038/s41598-025-99713-2

Sci Rep. 2025 Apr 30;15(1):15211. doi: 10.1038/s41598-025-99795-y.

ABSTRACT

Despite being one of the most prevalent cancers, prostate cancer (PCa) shows a significantly high survival rate, provided there is timely detection and treatment. Currently, several screening and diagnostic tests are required to be carried out in order to detect PCa. These tests are often invasive, requiring either a biopsy (Gleason score and ISUP) or blood tests (PSA). Computational methods have been shown to help this process, using multiparametric MRI (mpMRI) data to detect PCa, effectively providing value during the diagnosis and monitoring stages. While delineating lesions requires a high degree of experience and expertise from the radiologists, being subject to a high degree of inter-observer variability, often leading to inconsistent readings, these computational models can leverage the information from mpMRI to locate the lesions with a high degree of certainty. By considering as positive samples only those that have an ISUP≥2 we can train aggressive index lesion detection models. The main advantage of this approach is that, by focusing only on aggressive disease, the output of such a model can also be seen as an indication for biopsy, effectively reducing unnecessary biopsy screenings. In this work, we utilize both the highly heterogeneous ProstateNet dataset, and the PI-CAI dataset, to develop accurate aggressive disease detection models.

PMID:40307379 | DOI:10.1038/s41598-025-99795-y

J Nat Med. 2025 Apr 30. doi: 10.1007/s11418-025-01896-5. Online ahead of print.

ABSTRACT

This study aimed to investigate the effects of astragaloside IV(AS-IV) and quercetin (QCT) on autophagic activity, pyroptosis, and epithelial-mesenchymal transdifferentiation (EMT) in the context of idiopathic pulmonary fibrosis (IPF), utilizing both in vivo and in vitro models. In the in vivo component of the research, C57BL/6 J mice were subjected to bleomycin (BLM) modeling, followed by AS-IV + QCT intervention at low, medium, and high doses for 14 and 28 days. Pathological changes in lung tissue were assessed through HE and Masson staining. Additionally, the expression levels of autophagy and pyroptosis-related proteins in serum and bronchoalveolar lavage fluid were examined via Western blot analysis. In the in vitro experiment, RAW264.7 macrophage cells were co-cultured with MLE-12 alveolar epithelial cells (3:1 ratio), implementing BLM and NLR family pyrin domain-containing protein (NLRP3) + BLM models to induce IPF. The effects of AS-IV and QCT on these cells were evaluated by electron microscopy to observe structural changes, while Western blot and ELISA were used to measure the expression of autophagy and pyroptosis-related proteins. Results showed that AS-IV and QCT significantly enhanced autophagic activity, evidenced by increased levels of LC3II and beclin-1 and decreased levels of P62. Additionally, both compounds reduced the expression of pyroptosis-related proteins (NLRP3, Caspase-1, IL-1β, and IL-18) and slowed the progression of EMT in alveolar epithelial cells. These findings propose that AS-IV and QCT inhibit the EMT process in IPF by activating autophagic mechanisms while suppressing pyroptosis, thereby underscoring their potential as innovative therapeutic strategies for IPF and highlighting the promising implications of herbal compounds in its prevention and treatment.

PMID:40307659 | DOI:10.1007/s11418-025-01896-5

Sci Rep. 2025 Apr 30;15(1):15255. doi: 10.1038/s41598-025-98569-w.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a chronic and fatal fibrosis disease. Due to the limited understanding of its pathogenesis and the fact that its detection largely depends on the operator's technical level and the accuracy of the equipment, the diagnosis and treatment of the disease have significant limitations. In this research, bleomycin was used to establish IPF models of C57/BL6N mice with different injury degrees, and proteomics technology extracted interstitial fluid of lung tissue to analyze the mechanism of fibrosis at different stages. Compared with the normal group, the alveolar area, collagen deposition, tidal volume, and respiratory rate of the experimental group decreased at all periods, and the difference was most significant on the 14th day of modeling. Proteomic techniques, including gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, showed that the progression of pulmonary fibrosis was related to different pathways: glucose metabolism, lipid transport, glycoprotein metabolism, synthesis of sulfur compounds, and other energy metabolism, calcium ion transport were dominant in the early stage of fibrosis and the acute inflammatory stage. The endoplasmic reticulum stress pathway was dominant in the extreme stage of fibrosis, and blood flow shear stress, Extracellular matrix (ECM) receptor activation, and other extracellular matrix-related pathways were dominant in the late stage of fibrosis. Moreover, western bolt validation experiments also confirmed that C/EBP-homologous protein (CHOP), Heat Shock Protein 60 (HSP60), and Alpha smooth muscle actin(α-SMA) proteins were increased in expression related to this pathway at the extreme stage of fibrosis, suggesting that the disruption of ion balance in the endoplasmic reticulum induced by endoplasmic reticulum stress or the disturbance of protein processing and transportation were involved in the occurrence and development of pulmonary fibrosis in mice. The above results are expected to provide ideas for clinical interpretation of the mechanism of pulmonary fibrosis and provide vital data support for its accurate diagnosis and effective treatment.

PMID:40307370 | DOI:10.1038/s41598-025-98569-w

Eur Respir Rev. 2025 Apr 30;34(176):240194. doi: 10.1183/16000617.0194-2024. Print 2025 Apr.

ABSTRACT

BACKGROUND: The unpredictable trajectory and heterogeneity of interstitial lung disease (ILDs) make prognostication challenging. Current prognostic indices and outcome measures have several limitations. Quantitative computed tomography (qCT) provides automated numerical assessment of CT imaging and has shown promise when applied to the prognostication and disease monitoring of ILD. This systematic review aims to highlight the current evidence underpinning the prognostic value of qCT in predicting outcomes in ILD.

METHODS: A comprehensive search of four databases (Medline, EMCare, Embase and CINAHL (Cumulative Index to Nursing and Allied Health Literature)) was conducted for studies published up to and including 22 November 2024. A modified CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) checklist was used for data extraction. The risk of bias was assessed using a Quality in Prognostic Studies template.

RESULTS: The search identified 1134 unique studies, of which 185 studies met inclusion and exclusion criteria. Commonly studied ILD subtypes included idiopathic pulmonary fibrosis (41%, n=75), mixed subtypes (26%, n=48) and systemic sclerosis ILD (16%, n=30). Numerous studies showed significant prognostic signals, even when adjusted for common covariates and/or significant correlation between serial qCT biomarkers and conventional outcome measures. Heterogenous and nonstandardised reporting methods meant that direct comparison or meta-analysis of studies was not possible. Studies were limited by the use of retrospective methodology without prospective validation and significant study attrition.

DISCUSSION: qCT has shown efficacy in the prognostication and disease monitoring of a range of ILDs. Hurdles exist to widespread adoption including governance concerns, appropriate algorithm anchoring and standardisation of image acquisition. International collaboration is underway to address these hurdles, paving the way for regulatory approval and ultimately patient benefit.

PMID:40306954 | DOI:10.1183/16000617.0194-2024

Thorax. 2025 Apr 29:thorax-2024-222321. doi: 10.1136/thorax-2024-222321. Online ahead of print.

ABSTRACT

Volatile organic compounds (VOCs) in exhaled breath have shown promise as biomarkers in idiopathic pulmonary fibrosis (IPF). We analysed breath from 57 people with IPF using thermal desorption-gas chromatography-mass spectrometry to identify VOCs related to lung function change over 12 months. A LASSO regression model selected 63 VOCs associated with relative change in forced vital capacity (8 with correlation coefficient (CC) ≥0.20 on Spearman's rank analysis), and 28 associated with relative change in diffusion capacity of the lung for carbon monoxide % predicted (12 with CC ≥0.20). Secondary analyses demonstrated a correlation between VOCs and baseline lung function parameters and association with survival. This study suggests that there may be a volatile signature of prognosis in IPF that merits further validation.

PMID:40306949 | DOI:10.1136/thorax-2024-222321

J Anim Sci Biotechnol. 2025 May 1;16(1):62. doi: 10.1186/s40104-025-01181-1.

ABSTRACT

BACKGROUND: Lactate is a classical byproduct of glucose metabolism, and the main lactate production pathway depends on glycolysis. Lactate stabilized HIF1α by inhibiting PHD activity, leading to hypoxic stress response and exacerbating glycolysis in multiple tissues. However, the redox induction mechanism of lactate in mammary gland has not been understood yet. Herein, we describe a lactate-responsive HIF1α/circadian control mechanism in oxidative stress in the mammary glands of dairy cows.

RESULTS: The in vivo study showed that dairy cows with high lactate concentrations are associated with reduced milk yield and more ROS accumulation in mammary gland. Western blot results in MAC-T cells showed positive correlation between lactate concentrations, expression of HIF1α and oxidative stress indicators, but not circadian core components. To test how lactate-mediated HIF1α dysfunction leads to cell protection process, we investigated altered expression of circadian core related genes following HIF1α stabilization. We found that stabilized HIF1α by lactate inhibited stimulated expression of circadian core components due to the similarity of HRE and E-box transcription elements. Furthermore, we found that lactate treatment strengthened the binding of HIF1α with BMAL1, HMOX1 and FOXO3 in MAC-T cells. Moreover, HIF1α knockdown altered expression of circadian rhythm related genes and reduced oxidative stress state.

CONCLUSION: In summary, our study highlights the central role of competitive transcriptional element occupancy in lactate-mediated oxidative stress of mammary gland, which is caused by HIF1α stabilization and circadian rhythm dysfunction. Our findings introduce a novel nutritional strategy with potential applications in dairy farming for optimizing milk production and maintaining mammary gland health.

PMID:40307878 | DOI:10.1186/s40104-025-01181-1

BMC Cancer. 2025 May 1;25(Suppl 1):733. doi: 10.1186/s12885-025-14089-w.

ABSTRACT

Accurately resolving the composition of tumor-infiltrating leukocytes is pivotal for advancing cancer immunotherapy strategies. Despite the success of some clinical trials, applying these strategies remains limited due to the challenges in deciphering the immune microenvironment. In this study, we developed a streamlined, two-step workflow to address the complexity of bioinformatics processes involved in analyzing immune cell composition from transcriptomics data. Our dockerized toolkit, DOCexpress_fastqc, integrates the hisat2-stringtie pipeline with customized scripts within Galaxy/Docker environments, facilitating RNA sequencing (RNA-seq) gene expression profiling. The output from DOCexpress_fastqc is seamlessly formatted with mySORT, a web application that employs a deconvolution algorithm to determine the immune content across 21 cell subclasses. We validated mySORT using synthetic pseudo-bulk data derived from single-cell RNA sequencing (scRNA-seq) datasets. Our predictions exhibit strong concordance with the ground-truth immune cell composition, achieving Pearson's correlation coefficients of 0.871 in melanoma patients and 0.775 in head and neck cancer patients. Additionally, mySORT outperforms existing methods like CIBERSORT in accuracy and provides a wide range of data visualization features, such as hierarchical clustering and cell complexity plots. The toolkit and web application are freely available for the research community, providing enhanced resolution for conventional bulk RNA sequencing data and facilitating the analysis of immune microenvironment responses in immunotherapy. The mySORT demo website and Docker image are free at https://mysort.iis.sinica.edu.tw and https://hub.docker.com/r/lsbnb/mysort_2022 .

PMID:40307726 | DOI:10.1186/s12885-025-14089-w

Nature. 2025 Apr 30. doi: 10.1038/s41586-025-08941-z. Online ahead of print.

ABSTRACT

Land plants thrive in soils showing vastly different properties and environmental stresses1. Root systems can adapt to contrasting soil conditions and stresses, yet how their responses are programmed at the individual cell scale remains unclear. Using single-cell RNA sequencing and spatial transcriptomic approaches, we showed major expression changes in outer root cell types when comparing the single-cell transcriptomes of rice roots grown in gel versus soil conditions. These tissue-specific transcriptional responses are related to nutrient homeostasis, cell wall integrity and defence in response to heterogeneous soil versus homogeneous gel growth conditions. We also demonstrate how the model soil stress, termed compaction, triggers expression changes in cell wall remodelling and barrier formation in outer and inner root tissues, regulated by abscisic acid released from phloem cells. Our study reveals how root tissues communicate and adapt to contrasting soil conditions at single-cell resolution.

PMID:40307555 | DOI:10.1038/s41586-025-08941-z

Nature. 2025 Apr 30. doi: 10.1038/s41586-025-08937-9. Online ahead of print.

ABSTRACT

A high-fat, low-fibre Western-style diet (WD) induces microbiome dysbiosis characterized by reduced taxonomic diversity and metabolic breadth1,2, which in turn increases risk for a wide array of metabolic3-5, immune6 and systemic pathologies. Recent work has established that WD can impair microbiome resilience to acute perturbations such as antibiotic treatment7,8, although little is known about the mechanism of impairment and the specific consequences for the host of prolonged post-antibiotic dysbiosis. Here we characterize the trajectory by which the gut microbiome recovers its taxonomic and functional profile after antibiotic treatment in mice on regular chow (RC) or WD, and find that only mice on RC undergo a rapid successional process of recovery. Metabolic modelling indicates that a RC diet promotes the development of syntrophic cross-feeding interactions, whereas in mice on WD, a dominant taxon monopolizes readily available resources without releasing syntrophic byproducts. Intervention experiments reveal that an appropriate dietary resource environment is both necessary and sufficient for rapid and robust microbiome recovery, whereas microbial transplant is neither. Furthermore, prolonged post-antibiotic dysbiosis in mice on WD renders them susceptible to infection by the intestinal pathogen Salmonella enterica serovar Typhimurium. Our data challenge widespread enthusiasm for faecal microbiota transplant (FMT) as a strategy to address dysbiosis, and demonstrate that specific dietary interventions are, at a minimum, an essential prerequisite for effective FMT, and may afford a safer, more natural and less invasive alternative.

PMID:40307551 | DOI:10.1038/s41586-025-08937-9

Sci Rep. 2025 Apr 30;15(1):15201. doi: 10.1038/s41598-025-97684-y.

ABSTRACT

Boolean networks (BNs) are vital modeling tools in systems biology for biomolecular regulatory networks. After a transient phase, BNs converge to attractors that represent distinct cell types or conditions. Therefore, methods to control the long-term behavior of BNs have important implications for biological and genetic applications. In this paper, we propose a method to enforce convergence of a BN to a desired attractor from any initial state through a simple intervention: fixing a specific subset of network variables at definite values. We refer to this method as the global stabilization of a BN to a target attractor. Utilizing the algebraic state space representation of BNs, we introduce novel matrix tools to formulate this intervention method, as well as develop a foundation for analyzing the stabilizability of BNs. We derive necessary and sufficient conditions for the global stabilizability of BNs and utilize these criteria to identify a minimal subset of network variables-termed the global stabilizing kernel-whose regulation ensures that the BN converges to the desired attractor. Finally, we apply our proposed method to determine the stabilizing kernels of several biomolecular regulatory network models and demonstrate how they can be steered to their target attractors, showcasing the applicability of our approach. We also apply our method to identify the stabilizing kernels of 480 randomly generated BNs. Our experiments suggest that, on average, only a relatively small portion (approximately 25%) of the network nodes need to be manipulated for the networks to converge to their primary attractors.

PMID:40307350 | DOI:10.1038/s41598-025-97684-y