Nat Prod Rep. 2025 Jun 4. doi: 10.1039/d5np00002e. Online ahead of print.

ABSTRACT

Covering up to 2025.Plant-derived polyphenols of various chemical classes are widely distributed in dietary substances, e.g. fruits, nuts, vegetables and teas. Such phenolic derivatives are natural antioxidants and have been linked with numerous health benefits, notably anti-cancer and anti-inflammatory properties. Additionally, they may behave as mild estrogens, as in the case of genistein. However, there has often been no clear correlation between in vitro properties, as measured in cell lines for instance, and in vivo performance. Moreover, it is not always clear what the true active species might be, as most phenols are readily subject to phase II metabolism, generating predominantly glucuronides and sulfates. In this highlight, we seek to address the question of whether dietary substance metabolites, especially glucuronides, which have been more widely studied, do indeed possess distinct activities in their own right compared to their parent substances. In most cases this will refer to enzyme inhibition and/or interaction with cell lines. General observations concerning glucuronidation are provided, accompanied by practical comments concerning the synthesis of glucuronides, which are not always available or marketed in useful quantities. The main structural classes of natural polyphenols are introduced, with comments including synthetic details and biological properties for important members of each class.

PMID:40464213 | DOI:10.1039/d5np00002e

Pediatr Allergy Immunol. 2025 Jun;36(6):e70113. doi: 10.1111/pai.70113.

ABSTRACT

BACKGROUND: Long-acting β2-agonists (LABA) are commonly used to treat asthma. Some children do not respond well to LABA, which may be due to +46G>A-/rs1042713 (Arg16 amino acid) in the ADRB2 gene encoding the β2 receptor. Arg16Gly ADRB2 genotyping to guide treatment step-up decisions in children with uncontrolled asthma despite inhaled corticosteroids (ICS) has been shown to reduce asthma exacerbations. We investigated whether ADRB2 genotype-guided treatment is cost-saving.

METHODS: Total semi-annual healthcare and indirect costs for children with and without exacerbations were calculated using PUFFIN trial data. One hundred and two Dutch and Swiss children were randomised to a genotype-guided treatment arm (adding LABA [Gly16Gly] or double dose ICS [Arg16Arg/Arg16Gly]) or a control arm, where children were again randomised to LABA or double dose ICS. We used exacerbation rates of the PUFFIN and the PACT trials to calculate asthma-related healthcare costs per treatment arm, as PACT closely matches the PUFFIN design. The PACT trial randomised 91 children from England and Scotland with uncontrolled asthma to the genotype-guided treatment arm (LABA [Gly16Gly] or montelukast [Arg16Arg/Arg16Gly]) or the control arm (routine care as per British Thoracic Society guidelines).

RESULTS: Overall mean semi-annual costs per child were €56.24 lower in the genotype-guided treatment arm compared to the control arm (€771.07 [range €616.86-€925.28, 23 of 90 children experienced exacerbations] and €827.31 [range €661.85-€992.77, 40 of 103 experienced exacerbations], respectively).

CONCLUSION: A treatment strategy that includes ADRB2 genotype-guided treatment is potentially cost-saving compared to usual care. The decreased healthcare costs associated with a reduction in asthma exacerbations more than offset the incurred genotyping costs.

PMID:40464075 | DOI:10.1111/pai.70113

Front Genet. 2025 May 20;16:1490863. doi: 10.3389/fgene.2025.1490863. eCollection 2025.

ABSTRACT

INTRODUCTION: Hypertension (HTN) is a leading risk factor for several cardiovascular diseases. While some previous studies reported that CYP3A5 variants were associated with decreased blood pressure and risk of HTN, others reported no associations. Therefore, we aimed to analyze these associations in the UK Biobank, a population large enough to have sufficient power to detect meaningful associations.

METHODS: The association of CYP3A5 variants (*3, *6, *7) and CYP3A5 activity with systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and HTN diagnosis was analyzed in the UK Biobank (N = 487,171). Linear and logistic regression models were used, adjusting for age, sex, race, antihypertensives use, smoking status, and salt intake. Moreover, subgroup analyses were performed in Black participants, White participants, participants of East Asian and South Asian descent separately, using the same models.

RESULTS: Neither the CYP3A5 variants, nor the CYP3A5 activity showed significant associations with SBP, DBP, MAP, or HTN. In a sensitivity analysis based on different racial subgroups, only White participants showed significant associations between the CYP3A5*3 variant and slightly higher DBP (β = 0.10 mmHg, 95% CI: 0.02 to 0.18, P = 0.01), as well as between genotype-predicted CYP3A5 activity score and slightly lower DBP (β = -0.10 mmHg, 95% CI: -0.18 to -0.02, P = 0.01).

DISCUSSION: While some associations were statistically significant, the small effect sizes and lack of associations observed in the whole UK Biobank population suggest that CYP3A5 variation likely has no impact on blood pressure related phenotypes in a general population.

PMID:40463714 | PMC:PMC12129758 | DOI:10.3389/fgene.2025.1490863

Med Chem. 2025 Jun 3. doi: 10.2174/0115734064370188250527043536. Online ahead of print.

ABSTRACT

BACKGROUND: The advent of Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the etiological agent of the Coronavirus Disease 2019 (COVID-19) pandemic, has impacted physical and mental health worldwide. The lack of effective antiviral drugs necessitates a robust therapeutic approach to develop anti-SARS-CoV-2 drugs. Various investigations have recognized ACE2 as the primary receptor of SARS-CoV-2, and this amalgamation of ACE2 with the spike protein of the coronavirus is paramount for viral entry into the host cells and inducing infection. Consequently, restricting the virus's accessibility to ACE2 offers an alternative therapeutic approach to averting this illness.

OBJECTIVE: The study aimed to identify potent inhibitors with enhanced affinity for the ACE2 protein and validate their stability and efficacy against established inhibitors via molecular docking, machine learning, and MD simulations.

METHODOLOGY: 202 ACE2 inhibitors (PDB ID and 6LZG), comprising repurposed antiviral compounds and specific ACE2 inhibitors, were selected for molecular docking. The two most effective compounds obtained from docking were further analyzed using machine learning to identify potential compounds with enhanced ACE2-binding affinity. To refine the dataset, molecular decoys were generated through the Database of Useful Decoys: Enhanced (DUD-E) server, and Singular Value Decomposition (SVD) was applied for data preprocessing. The Tree-based Pipeline Optimization Tool (TPOT) was then utilized to optimize the machine learning pipeline. The most promising ML-predicted compounds were re-evaluated through docking and subjected to Molecular Dynamics (MD) simulations to evaluate their structural stability and interactions with ACE2. Finally, these compounds were evaluated against the top two pre-established inhibitors using various computational tools.

RESULTS: The two best pre-established inhibitors were identified as Birinapant and Elbasvir, while the best machine-learning-predicted compounds were PubChem ID: 23658468 and PubChem ID: 117637105. Pharmacophore studies were conducted on the most effective machine-learning-predicted compounds, followed by a comparative ADME/T analysis between the best ML-screened and pre-established inhibitors. The results indicated that the top ML compound (PubChem ID: 23658468) demonstrated favorable BBB permeability and a high HIA index, highlighting its potential for therapeutic applications. The ML-screened ligand demonstrated structural stability with an RMSD (0.24 nm) and greater global stability (Rg: 2.08 nm) than Birinapant. Hydrogen bonding interactions further validated their strong binding affinity. MM/PBSA analysis confirmed the ML-screened compound's stronger binding affinity, with a binding free energy of - 132.90 kcal/mol, indicating enhanced stability in complex formation.

CONCLUSION: The results emphasize the efficacy of integrating molecular docking, machine learning, and molecular dynamics simulations in facilitating the rapid identification of novel inhibitors. PubChem ID: 23658468 demonstrates robust binding affinity to ACE2 and favorable pharmacokinetic properties, establishing it as a promising candidate for further investigation.

PMID:40464176 | DOI:10.2174/0115734064370188250527043536

Exp Physiol. 2025 Jun 4. doi: 10.1113/EP092313. Online ahead of print.

ABSTRACT

Proprioception is essential for perception and action. Like any other sense, proprioception is also subject to illusions. In this study, we model classic proprioceptive illusions in which tendon vibrations lead to biases in estimating the state of the body. We investigate these illusions with task-driven models that have been trained to infer the state of the body from distributed sensory muscle spindle inputs (primary and secondary afferents). Recent work has shown that such models exhibit representations similar to the neural code along the ascending proprioceptive pathway. Importantly, we did not train the models on illusion experiments and simulated muscle-tendon vibrations by considering their effect on primary afferents. Our results demonstrate that task-driven models are indeed susceptible to proprioceptive illusions, with the magnitude of the illusion depending on the vibration frequency. This work illustrates that primary afferents alone are sufficient to account for these classic illusions and provides a foundation for future theory-driven experiments.

PMID:40464159 | DOI:10.1113/EP092313

Front Plant Sci. 2025 May 20;16:1524630. doi: 10.3389/fpls.2025.1524630. eCollection 2025.

ABSTRACT

The agricultural sector faces persistent threats from plant diseases and pests, with Tuta absoluta posing a severe risk to tomato farming by causing up to 100% crop loss. Timely pest detection is essential for effective intervention, yet traditional methods remain labor-intensive and inefficient. Recent advancements in deep learning offer promising solutions, with YOLOv8 emerging as a leading real-time detection model due to its speed and accuracy, outperforming previous models in on-field deployment. This study focuses on the early detection of Tuta absoluta-induced tomato leaf diseases in Sub-Saharan Africa. The first major contribution is the annotation of a dataset (TomatoEbola), which consists of 326 images and 784 annotations collected from three different farms and is now publicly available. The second key contribution is the proposal of a transfer learning-based approach to evaluate YOLOv8's performance in detecting Tuta absoluta. Experimental results highlight the model's effectiveness, with a mean average precision of up to 0.737, outperforming other state-of-the-art methods that achieve less than 0.69, demonstrating its capability for real-world deployment. These findings suggest that AI-driven solutions like YOLOv8 could play a pivotal role in reducing agricultural losses and enhancing food security.

PMID:40464016 | PMC:PMC12130032 | DOI:10.3389/fpls.2025.1524630

Front Plant Sci. 2025 May 20;16:1586268. doi: 10.3389/fpls.2025.1586268. eCollection 2025.

ABSTRACT

INTRODUCTION: Salvia miltiorrhiza Bunge (Danshen) is a traditional medicinal plant widely used in the treatment of cardiovascular and inflammatory diseases. Although various omics resources have been published, there remains a lack of an integrated platform to unify genomic, transcriptomic, proteomic, and metabolomic data.

METHODS: To address this gap, we constructed the S. miltiorrhiza Multi-omics Database (SmilODB, http://www.isage.top:56789/), which systematically integrates publicly available genome assemblies, transcriptome datasets, metabolic pathway annotations, and protein structural predictions. Protein structures were predicted using the RoseTTAFold algorithm, and all data were visualized using interactive heat maps, line charts, and histograms.

RESULTS: SmilODB includes: (i) two genome assemblies of S. miltiorrhiza, (ii) 48 tissue-specific transcriptome datasets from root, leaf, and other vegetative tissues, (iii) annotated biosynthetic pathways for bioactive compounds such as tanshinones and salvianolic acids, and (iv) 2,967 high-confidence protein models. The database also integrates bioinformatics tools such as genome browsers, BLAST, and gene heatmap generators.

DISCUSSION: SmilODB provides an accessible and comprehensive platform to explore multi-omics data related to S. miltiorrhiza. It serves as a valuable resource for both basic and applied research, facilitating advances in the understanding of this medicinal plant's molecular mechanisms and therapeutic potential.

PMID:40464010 | PMC:PMC12129996 | DOI:10.3389/fpls.2025.1586268

Front Physiol. 2025 May 20;16:1549380. doi: 10.3389/fphys.2025.1549380. eCollection 2025.

ABSTRACT

INTRODUCTION: Premature Ventricular Contractions (PVCs) can be warning signs for serious cardiac conditions, and early detection is essential for preventing complications. The use of deep learning models in electrocardiogram (ECG) analysis has aided more accurate and efficient PVC identification. These models automatically extract and analyze complex signal features, providing valuable clinical decision-making support. Here, we conducted a study focused on the practical applications of is technology.

METHODS: We first used the MIT-BIH arrhythmia database and a sparse low-rank algorithm to denoise ECG signals. We then transformed the one-dimensional time-series signals into two-dimensional images using Markov Transition Fields (MTFs), considering state transition probabilities and spatial location information to comprehensively capture signal features. Finally, we used the BiFormer classification model, which employs a Bi-level Routing Attention (BRA) mechanism to construct region-level affinity graphs, to retain only the regions highly relevant to our query. This approach filtered out redundant information, and optimized both computational efficiency and memory usage.

RESULTS: Our algorithm achieved a detection accuracy of 99.45%, outperforming other commonly-used PVC detection algorithms.

DISCUSSION: By integrating MTF and BiFormer, we effectively detected PVCs, facilitating an increased convergence between medicine and deep learning technology. We hope our model can help contribute to more accurate computational support for PVC diagnosis and treatment.

PMID:40463999 | PMC:PMC12129755 | DOI:10.3389/fphys.2025.1549380

Food Sci Nutr. 2025 Jun 2;13(6):e70377. doi: 10.1002/fsn3.70377. eCollection 2025 Jun.

ABSTRACT

The vine plant holds significant importance beyond grape farming due to its diverse products. Various grape-derived products, such as wine and molasses, highlight the vine plant's role as a valuable agricultural resource. Additionally, traditional cuisines around the world widely utilize grape leaves, contributing to their substantial economic value. However, diseases affecting grape leaves not only harm the plant and its yield but also render the leaves unsuitable for culinary use, leading to considerable economic losses for producers. Detecting diseases on grape leaves is a challenging and time-consuming task when performed manually. Thus, developing a deep learning-based model to automate the classification of grape leaf diseases is of critical importance. This study aims to classify the most common grape leaf diseases grape-scab (grape leaf blister mite) and downy mildew (grapevine downy mildew) alongside healthy leaves using deep learning techniques. Initially, we conducted a basic classification using pre-trained deep learning models. Subsequently, the Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation Networks (SE) were integrated into the most successful pre-trained classification model to enhance classification performance. As a result, the classification accuracy improved from 92.73% to 96.36%.

PMID:40463992 | PMC:PMC12129821 | DOI:10.1002/fsn3.70377

Front Oncol. 2025 May 20;15:1585891. doi: 10.3389/fonc.2025.1585891. eCollection 2025.

ABSTRACT

BACKGROUND AND OBJECTIVE: Accurate diagnosis of brain tumors significantly impacts patient prognosis and treatment planning. Traditional diagnostic methods primarily rely on clinicians' subjective interpretation of medical images, which is heavily dependent on physician experience and limited by time consumption, fatigue, and inconsistent diagnoses. Recently, deep learning technologies, particularly Convolutional Neural Networks (CNN), have achieved breakthrough advances in medical image analysis, offering a new paradigm for automated precise diagnosis. However, existing research largely focuses on single-task modeling, lacking comprehensive solutions that integrate tumor segmentation with classification diagnosis. This study aims to develop a multi-task deep learning model for precise brain tumor segmentation and type classification.

METHODS: The study included 485 pathologically confirmed cases, comprising T1-enhanced MRI sequence images of high-grade gliomas, metastatic tumors, and meningiomas. The dataset was proportionally divided into training (378 cases), testing (109 cases), and external validation (51 cases) sets. We designed and implemented BrainTumNet, a deep learning-based multi-task framework featuring an improved encoder-decoder architecture, adaptive masked Transformer, and multi-scale feature fusion strategy to simultaneously perform tumor region segmentation and pathological type classification. Five-fold cross-validation was employed for result verification.

RESULTS: In the test set evaluation, BrainTumNet achieved an Intersection over Union (IoU) of 0.921, Hausdorff Distance (HD) of 12.13, and Dice Similarity Coefficient (DSC) of 0.91 for tumor segmentation. For tumor classification, it attained a classification accuracy of 93.4% with an Area Under the ROC Curve (AUC) of 0.96. Performance remained stable on the external validation set, confirming the model's generalization capability.

CONCLUSION: The proposed BrainTumNet model achieves high-precision diagnosis of brain tumor segmentation and classification through a multi-task learning strategy. Experimental results demonstrate the model's strong potential for clinical application, providing objective and reliable auxiliary information for preoperative assessment and treatment decision-making in brain tumor cases.

PMID:40463867 | PMC:PMC12129765 | DOI:10.3389/fonc.2025.1585891

Front Dent Med. 2025 May 20;6:1534406. doi: 10.3389/fdmed.2025.1534406. eCollection 2025.

ABSTRACT

BACKGROUND: Third molar extraction, a common dental procedure, often involves complications, such as alveolar nerve injury. Accurate preoperative assessment of the extraction difficulty and nerve injury risk is crucial for better surgical planning and patient outcomes. Recent advancements in deep learning (DL) have shown the potential to enhance the predictive accuracy using panoramic radiographic (PR) images. This systematic review evaluated the accuracy and reliability of DL models for predicting third molar extraction difficulty and inferior alveolar nerve (IAN) injury risk.

METHODS: A systematic search was conducted across PubMed, Scopus, Web of Science, and Embase until September 2024, focusing on studies assessing DL models for predicting extraction complexity and IAN injury using PR images. The inclusion criteria required studies to report predictive performance metrics. Study selection, data extraction, and quality assessment were independently performed by two authors using the PRISMA and QUADAS-2 guidelines.

RESULTS: Six studies involving 12,419 PR images met the inclusion criteria. DL models demonstrated high accuracy in predicting extraction difficulty (up to 96%) and IAN injury (up to 92.9%), with notable sensitivity (up to 97.5%) for specific classifications, such as horizontal impactions. Geographically, three studies originated in South Korea and one each from Turkey and Thailand, limiting generalizability. Despite high accuracy, demographic data were sparsely reported, with only two studies providing patient sex distribution.

CONCLUSION: DL models show promise in improving the preoperative assessment of third molar extraction. However, further validation in diverse populations and integration with clinical workflows are necessary to establish its real-world utility, as limitations such as limited generalizability, potential selection bias and lack of long-term follow up remain challenges.

PMID:40463825 | PMC:PMC12129997 | DOI:10.3389/fdmed.2025.1534406

Front Artif Intell. 2025 May 20;8:1517986. doi: 10.3389/frai.2025.1517986. eCollection 2025.

ABSTRACT

Artificial Intelligence (AI) is revolutionizing climate modeling by enhancing predictive accuracy, computational efficiency, and multi-source data integration, playing a crucial role in sustainable urban planning. This Mini Review examines recent advancements in machine learning (ML) and deep learning (DL) techniques that improve climate risk assessment, resource optimization, and infrastructure resilience. Despite these innovations, significant challenges persist, including data quality inconsistencies, model interpretability limitations, ethical concerns, and the scalability of AI models across diverse urban contexts. To bridge these gaps, this review highlights key research directions, emphasizing the development of interpretable AI models, robust data governance frameworks, and scalable AI-driven solutions that help climate adaptation. By addressing these challenges, AI-based climate modeling can provide actionable insights for policymakers, urban planners, and researchers fostering climate-resilient and sustainable urban environments.

PMID:40463823 | PMC:PMC12129934 | DOI:10.3389/frai.2025.1517986

Proc SPIE Int Soc Opt Eng. 2025 Feb;13406:134060B. doi: 10.1117/12.3047410. Epub 2025 Apr 11.

ABSTRACT

Placenta volume measured from 3D ultrasound (3DUS) images is an important tool for tracking the growth trajectory and is associated with pregnancy outcomes. Manual segmentation is the gold standard, but it is time-consuming and subjective. Although fully automated deep learning algorithms perform well, they do not always yield high-quality results for each case. Interactive segmentation models could address this issue. However, there is limited work on interactive segmentation models for the placenta. Despite their segmentation accuracy, these methods may not be feasible for clinical use as they require relatively large computational power which may be especially prohibitive in low-resource environments, or on mobile devices. In this paper, we propose a lightweight interactive segmentation model aiming for clinical use to interactively segment the placenta from 3DUS images in real-time. The proposed model adopts the segmentation from our fully automated model for initialization and is designed in a human-in-the-loop manner to achieve iterative improvements. The Dice score and normalized surface Dice are used as evaluation metrics. The results show that our model can achieve superior performance in segmentation compared to state-of-the-art models while using significantly fewer parameters. Additionally, the proposed model is much faster for inference and robust to poor initial masks. The code is available at https://github.com/MedICL-VU/PRISM-placenta.

PMID:40463735 | PMC:PMC12128914 | DOI:10.1117/12.3047410

Pulm Circ. 2025 Jun 3;15(2):e70102. doi: 10.1002/pul2.70102. eCollection 2025 Apr.

ABSTRACT

Lung vasculature arises from both pulmonary and systemic (bronchial) circulations. Remodeling and structural changes in lung vasculature have been recognized in end-stage fibrotic lung diseases such as idiopathic pulmonary fibrosis (IPF) but have not been well characterized. The vasculature that expands and supplies lung cancers is better described, with the recent recognition that systemic bronchial circulation expands to be the main blood supply to primary lung tumors. Here, we use publicly available single-cell RNA-sequencing (scRNA-seq) data to compare vascular endothelial cell (EC) populations in multiple progressive interstitial lung diseases (ILD) and non-small cell lung cancer (NSCLC) to identify common and distinct features. Lung tissue specimens were collected from healthy lung tissue (n = 59), ILD (n = 97), chronic obstructive pulmonary disease (n = 22), and NSCLC (n = 8). We identify two subtypes of expanded EC populations in both ILD and NSCLC, "Bronch-1" and "Bronch-2", expressing transcripts associated with venules and angiogenic tip/stalk cells, respectively. Relative to pulmonary capillary and arterial ECs, bronchial ECs show low expression of transcripts associated with vascular barrier integrity. The pan-bronchial EC marker COL15A1 showed positive staining in lung parenchyma from patients with IPF, SSc-ILD, and NSCLC, whereas positive staining was limited to subpleural and peri-bronchial regions in non-fibrotic controls. In conclusion, expansion of a subset of ECs expressing markers of the bronchial circulation is one of the most pronounced changes in vascular cell composition across multiple ILDs and NSCLC. These data support additional studies to determine the role of the bronchial vasculature in ILD progression.

PMID:40463493 | PMC:PMC12130637 | DOI:10.1002/pul2.70102

bioRxiv [Preprint]. 2025 May 14:2025.05.08.652970. doi: 10.1101/2025.05.08.652970.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a debilitating lung disease with limited therapeutic options. The development of effective therapies has been hindered by the lack of models that recapitulate key features of human disease. Here we report a bleomycin-induced ferret PF model characterized by an irreversible decrease in pulmonary compliance and an increase of opacification, accompanied by "honeycomb cyst-like" structures and "proximalization" of distal lung epithelium. Cellular and molecular analysis by single-nucleus RNA sequencing revealed a significant shift in distal lung epithelium towards proximal epithelial phenotype. Importantly, a histopathological pattern of bronchiolization encompassing divergent atypical epithelial cells and KRT17 + /TP63 + /KRT5 low "basaloid-like" cells was present in the distal fibrotic lung lesions. Trajectory analysis revealed AT2 cells transition through multiple cell-states in bleomycin injured ferret lungs, particularly AT2 to KRT8 high /KRT7 low /SOX4 + and eventually to KRT8 high /KRT7 high /SFN + /TP63 + /KRT5 low "basaloid-like" cells. Further, immunofluorescence analyses demonstrated KRT7 and KRT8 populations reside overlaying the ACTA2-positive myofibroblasts in fibrotic foci, implying their pro-fibrogenic activity similar to human IPF lungs. Collectively, our results provide evidence that bleomycin-induced lung injury in ferrets recapitulates pathophysiological, cellular, and molecular features of human IPF, suggesting that they may be a reliable model for understanding mechanisms of IPF pathogenesis and for testing therapeutic strategies for treatment of IPF.

TAKE HOME MESSAGE: Bleomycin-induced acute lung injury in the ferret recapitulates pathophysiological, cellular, and molecular features of human IPF; thus the ferret may be a reliable species for studying mechanisms of IPF pathogenesis and testing therapeutic strategies.

PMID:40462941 | PMC:PMC12132224 | DOI:10.1101/2025.05.08.652970

Phys Chem Chem Phys. 2025 Jun 4. doi: 10.1039/d5cp01297j. Online ahead of print.

ABSTRACT

Structural biology is witnessing a transformative era with gas-phase techniques such as native mass spectrometry (MS), ion mobility, and single-particle imaging (SPI) emerging as critical tools for studying biomolecular assemblies like protein capsids in their native states. SPI with X-ray free-electron lasers has the potential to allow for capturing atomic-resolution structures of proteins without crystallization. However, determining particle orientation during exposure remains a major challenge, compounded by the heterogeneity of the protein complexes. Gas-phase Förster resonance energy transfer (FRET) offers a promising solution to assess alignment-induced structural perturbations, providing insights into the stability of the tertiary structure under various activation methods. This study employs molecular dynamics (MD) simulations to explore chromophore integration's effect on ubiquitin's structure and alignment properties in vacuum. Ubiquitin serves as an ideal model due to its small size, well-characterized properties, and computational simplicity. By investigating chromophore placement, we identified optimal sites for monitoring gas-phase denaturation and unfolding processes, advancing SPI applications and a broader understanding of protein stability in the gas phase.

PMID:40464121 | DOI:10.1039/d5cp01297j

Front Genet. 2025 May 20;16:1618183. doi: 10.3389/fgene.2025.1618183. eCollection 2025.

NO ABSTRACT

PMID:40463713 | PMC:PMC12129931 | DOI:10.3389/fgene.2025.1618183

Front Immunol. 2025 May 20;16:1588419. doi: 10.3389/fimmu.2025.1588419. eCollection 2025.

ABSTRACT

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease characterized by joint inflammation and damage, leading to disability and pain. The etiology of RA is undefined but considered multifactorial, as interactions between genetics and environmental factors lead to the generation of autoantibodies that target synovial joints. Smoking is a well-established and widely studied risk factor for RA development and is associated with a reduced response to treatments and poor clinical outcomes. Murine models of inflammatory arthritis have provided many insights into the pathogenesis of RA and have recently been used to explore the relationship between cigarette smoking and RA. In this review, we comprehensively appraise the current literature investigating cigarette smoke exposure in murine models of inflammatory arthritis, focused on RA. The current literature indicates that the influence of smoke exposure on molecular and disease outcomes depends on the timepoint of exposure and genetic background of the mice. Further, dose-dependent increases in disease manifestations reproduce human clinical data that the intensity of smoking is linked to disease but demosntrate that there may be a plateau effect. Finally, we consolidate mechanistic findings to describe a potential mechanism through which cigarette smoke exacerbates murine arthritis. Understanding how these factors, genetics, timing, and intensity of exposure modulate response to CS in inflammatory arthritis models may lead to better drug development and personalized treatment strategies, ultimately improving outcomes for RA patients with a smoking history.

PMID:40463383 | PMC:PMC12129895 | DOI:10.3389/fimmu.2025.1588419

Front Bioeng Biotechnol. 2025 May 20;13:1610782. doi: 10.3389/fbioe.2025.1610782. eCollection 2025.

ABSTRACT

[This corrects the article DOI: 10.3389/fbioe.2024.1500756.].

PMID:40462842 | PMC:PMC12132079 | DOI:10.3389/fbioe.2025.1610782

Development. 2025 Jun 2:dev.204312. doi: 10.1242/dev.204312. Online ahead of print.

ABSTRACT

Long-range morphogen gradients have been proposed to form by morphogen diffusion from a localized source to distributed sinks in the target tissue. The role of the complex tissue geometry in this process is, however, less well understood and has not been explicitly resolved in existing models. Here, we numerically reconstruct pore-scale 3D geometries of zebrafish epiboly from light-sheet microscopy volumes. In these high-resolution 3D geometries, we simulate Fgf8a gradient formation in the tortuous extracellular space. Our simulations show that when realistic embryo geometries are considered, a source-diffusion-degradation mechanism with additional binding to extracellular matrix polymers is sufficient to explain self-organized emergence and robust maintenance of Fgf8a gradients. The predicted normalized gradient is robust against changes in source and sink rates but sensitive to changes in the pore connectivity of the extracellular space, with lower connectivity leading to steeper and shorter gradients. This demonstrates the importance of considering realistic geometries when studying morphogen gradients.

PMID:40462756 | DOI:10.1242/dev.204312