Ann Med. 2025 Dec;57(1):2514787. doi: 10.1080/07853890.2025.2514787. Epub 2025 Jun 5.

ABSTRACT

INTRODUCTION: As the life expectancy of people with cystic fibrosis (CF) increases, complications related to CF, such as CF-related diabetes (CFRD), are of great concern. Oral glucose tolerance test (OGTT) is the current gold standard test to screen for CFRD, which is associated with reduced lung function and body mass index (BMI). However, this is a cumbersome test with poor adherence, and emerging evidence suggests that HbA1c or serum fructosamine might be viable alternative screening tools.

RESEARCH DESIGN AND METHODS: A multi-center study across four Canadian adult CF centers was conducted to determine the ability of HbA1c and serum fructosamine levels to predict screening OGTT results. Cross-sectional outcome data, including ppFEV1 and BMI within two months of testing, were collected.

RESULTS: A total of 183 CFRD screening encounters over five years were included. HbA1c and the fructosamine-to-albumin ratio had similar predictive performances for CFRD as determined by OGTT-defined cutoffs (AUC both 0.68) and for impaired glucose tolerance (AUC 0.69 and 0.64, respectively). However, the specificity of FAR is lower, meaning fewer OGTTs can be avoided if FAR is used as a first-step screening test when screening for either CFRD and/or IGT compared to HbA1. The optimal HbA1c cut-off for CFRD screening was ≥5.5% (sensitivity, 95%; specificity, 32%). Regression analyses demonstrated a strong inverse correlation between HbA1c and ppFEV1 (p < 0.0001), while the OGTT was inversely correlated with ppFEV1 (p < 0.05), and the fructosamine-to-albumin ratio was inversely correlated with BMI (-0.9; 95% CI -1.5, -0.4; p = 0.002), but not with ppFEV1 within 2 months of testing.

CONCLUSION: HbA1c is validated as a first step in screening for CFRD, allowing one-third of the patients to avoid the OGTT. As HbA1c demonstrated a stronger correlation with ppFEV1 than the OGTT, consideration could be made to redefine CFRD based on HbA1c.

PMID:40471094 | DOI:10.1080/07853890.2025.2514787

JAMA Ophthalmol. 2025 Jun 5. doi: 10.1001/jamaophthalmol.2025.1513. Online ahead of print.

ABSTRACT

IMPORTANCE: As on-axis metrics, spherical equivalent refraction (SER) and axial length (AL) are limited in capturing individual-level differences in posterior segment anatomy.

OBJECTIVE: To propose a fundus-level metric-fundus refraction offset (FRO)-and investigate its association with ocular parameters derived from optical coherence tomography (OCT).

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional, population-based study used data from 45 180 healthy eyes in the UK Biobank (2009-2010). Fundus photographs from a random subset (70%) were used to train a deep learning model to predict SER, with the goal of developing a model that learned to capture the nonpathological variations in fundus appearance from -15.50 D to 9.25 D. The trained model was applied to the remaining subset (internal unseen set) to derive FRO for each eye. FRO was also computed for an external dataset (the Caledonian cohort, 2023-2024) with enhanced depth imaging OCT and AL data for 152 right eyes. Data were analyzed from July to November 2024.

EXPOSURE: FRO, defined as the error in fundus-predicted SER. A more negative FRO indicated a more myopic-looking fundus than typical for an eye with the same SER.

MAIN OUTCOMES AND MEASURES: The association between FRO and macular thickness (MT) was tested using linear mixed-effects regression in the internal unseen set, controlling for SER, age, sex, and race. In the external dataset, the associations of FRO with choroidal area, choroidal vascularity index (CVI), and MT were examined using linear fixed-effects regression, controlling for SER (and subsequently AL) and other aforementioned covariates.

RESULTS: High-quality OCT data were available from 9524 eyes in the internal unseen set and 152 eyes in the external dataset among individuals with a mean (SD) age of 54.5 (8.2) years and 19.3 (3.8) years, respectively. In the internal unseen set, a more negative FRO was independently associated with lower MT (β, 0.64; 95% CI, 0.37-0.90; P < .001). A similar association was observed in the external dataset-whether adjusted for SER (β, 2.45; 95% CI, 0.64-4.26; P = .008) or AL (β, 2.09; 95% CI, 0.28-3.91; P = .02). Additionally, CVI decreased as FRO became more negative-both in the SER-adjusted (β, 0.01; 95% CI, 0.01-0.02; P < .001) and AL-adjusted (β, 0.01, 95% CI, 0.004-0.02; P = .001) analyses.

CONCLUSION AND RELEVANCE: In this study, FRO reflected the individual-level mismatch between SER (or AL) and the anatomical severity of ametropia. This may have prognostic relevance for personalized risk prediction of myopia and its complications.

PMID:40471629 | DOI:10.1001/jamaophthalmol.2025.1513

Interdiscip Sci. 2025 Jun 5. doi: 10.1007/s12539-025-00718-2. Online ahead of print.

ABSTRACT

The medical field uses Magnetic Resonance Imaging (MRI) as an essential diagnostic tool which provides doctors non-invasive images of brain structures and pathological conditions. Brain tumor detection stands as a vital application that needs specific and effective approaches for both medical diagnosis and treatment procedures. The challenges from manual examination of MRI scans stem from inconsistent tumor features including heterogeneity and irregular dimensions which results in inaccurate assessments of tumor size. To address these challenges, this paper proposes an Automated Classification and Grading Diagnosis Model (ACGDM) using MRI images. Unlike conventional methods, ACGDM introduces a Multi-Scale Graph Neural Network (MSGNN), which dynamically captures hierarchical and multi-scale dependencies in MRI data, enabling more accurate feature representation and contextual analysis. Additionally, the Spatio-Temporal Transformer Attention Mechanism (STTAM) effectively models both spatial MRI patterns and temporal evolution by incorporating cross-frame dependencies, enhancing the model's sensitivity to subtle disease progression. By analyzing multi-modal MRI sequences, ACGDM dynamically adjusts its focus across spatial and temporal dimensions, enabling precise identification of salient features. Simulations are conducted using Python and standard libraries to evaluate the model on the BRATS 2018, 2019, 2020 datasets and the Br235H dataset, encompassing diverse MRI scans with expert annotations. Extensive experimentation demonstrates 99.8% accuracy in detecting various tumor types, showcasing its potential to revolutionize diagnostic practices and improve patient outcomes.

PMID:40471519 | DOI:10.1007/s12539-025-00718-2

Med Biol Eng Comput. 2025 Jun 5. doi: 10.1007/s11517-025-03386-y. Online ahead of print.

ABSTRACT

The integration of brain-computer interface (BCI) and virtual reality (VR) systems offers transformative potential for spatial cognition training and assessment. By leveraging artificial intelligence (AI) to analyze electroencephalogram (EEG) data, brain activity patterns during spatial tasks can be decoded with high precision. In this context, a hybrid neural network named MSFHNet is proposed, optimized for extracting spatiotemporal features from spatial cognitive EEG signals. The model employs a hierarchical architecture where its temporal module uses multi-scale dilated convolutions to capture dynamic EEG variations, while its spatial module integrates channel-spatial attention mechanisms to model inter-channel dependencies and spatial distributions. Cross-stacked modules further refine discriminative features through deep-level fusion. Evaluations demonstrate the superiority of MSFHNet in the beta2 frequency band, achieving 98.58% classification accuracy and outperforming existing models. This innovation enhances EEG signal representation, advancing AI-powered BCI-VR systems for robust spatial cognitive training.

PMID:40471491 | DOI:10.1007/s11517-025-03386-y

Clin Rheumatol. 2025 Jun 5. doi: 10.1007/s10067-025-07518-5. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aimed to develop a deep learning-based model to predict the risk of high-risk extra-glandular organ involvement (HR-OI) in patients with Sjogren's syndrome (SS) using whole-slide images (WSI) from labial gland biopsies.

METHODS: We collected WSI data from 221 SS patients. Pre-trained models, including ResNet50, InceptionV3, and EfficientNet-B5, were employed to extract image features. A classification model was constructed using multi-instance learning and ensemble learning techniques.

RESULTS: The ensemble model achieved high area under the receiver operating characteristic (ROC) curve values on both internal and external validation sets, indicating strong predictive performance. Moreover, the model was able to identify key pathological features associated with the risk of HR-OI.

CONCLUSIONS: This study demonstrates that a deep learning-based model can effectively predict the risk of HR-OI in SS patients, providing a novel basis for clinical decision-making. Key Points 1. What is already known on this topic? • Sjogren's syndrome (SS) is a chronic autoimmune disease affecting the salivary and lacrimal glands. • Accurate prediction of high-risk extra-glandular organ involvement (HR-OI) is crucial for timely intervention and improved patient outcomes in SS. • Traditional methods for HR-OI prediction rely on clinical data and lack objectivity. 2. What this study adds? • This study proposes a novel deep learning-based model using whole-slide images (WSI) from labial gland biopsies for predicting HR-OI in SS patients. • Our model utilizes pre-trained convolutional neural networks (CNNs) and a Vision Transformer (ViT) module to extract informative features from WSI data. • The ensemble model achieves high accuracy in predicting HR-OI, outperforming traditional methods. • The model can identify key pathological features in WSI data associated with HR-OI risk. 3. How this study might affect research, practice or policy? • This study provides a novel and objective approach for predicting HR-OI in SS patients, potentially leading to improved clinical decision-making and personalized treatment strategies. • Our findings encourage further investigation into the role of deep learning and WSI analysis in SS diagnosis and risk stratification. • The development of a non-invasive and objective diagnostic tool based on WSI analysis could benefit clinical practice and inform policy decisions regarding patient care for SS.The development of a non-invasive and objective diagnostic tool based on WSI analysis could benefit clinical practice and inform policy decisions regarding patient care for SS.

PMID:40471393 | DOI:10.1007/s10067-025-07518-5

Eur Spine J. 2025 Jun 5. doi: 10.1007/s00586-025-08946-2. Online ahead of print.

ABSTRACT

OBJECTIVES: To develop a deep learning model to automatically measure the curvature-related sagittal parameters on cervical spinal X-ray images.

METHODS: This retrospective study collected a total of 700 lateral cervical spine X-ray images from three hospitals, consisting of 500 training sets, 100 internal test sets, and 100 external test sets. 6 measured parameters and 34 landmarks were measured and labeled by two doctors and averaged as the gold standard. A Convolutional neural network (CNN) model was built by training on 500 images and testing on 200 images. Statistical analysis is used to evaluate labeling differences and model performance.

RESULTS: The percentages of the difference in distance between landmarks within 4 mm were 96.90% (Dr. A vs. Dr. B), 98.47% (Dr. A vs. model), and 97.31% (Dr. B vs. model); within 3 mm were 94.88% (Dr. A vs. Dr. B), 96.43% (Dr. A vs. model), and 94.16% (Dr. B vs. model). The mean difference of the algorithmic model in labeling landmarks was 1.17 ± 1.14 mm. The mean absolute error (MAE) of the algorithmic model for the Borden method, Cervical curvature index (CCI), Vertebral centroid measurement cervical lordosis (CCL), C0-C7 Cobb, C1-C7 Cobb, C2-C7 Cobb in the test sets are 1.67 mm, 2.01%, 3.22°, 2.37°, 2.49°, 2.81°, respectively; symmetric mean absolute percentage error (SMAPE) was 20.06%, 21.68%, 20.02%, 6.68%, 5.28%, 20.46%, respectively. Also, the algorithmic model of the six cervical sagittal parameters is in good agreement with the gold standard (intraclass correlation efficiency was 0.983; p < 0.001).

CONCLUSION: Our deep learning algorithmic model had high accuracy in recognizing the landmarks of the cervical spine and automatically measuring cervical spine-related parameters, which can help radiologists improve their diagnostic efficiency.

PMID:40471336 | DOI:10.1007/s00586-025-08946-2

Eur J Nucl Med Mol Imaging. 2025 Jun 5. doi: 10.1007/s00259-025-07370-4. Online ahead of print.

ABSTRACT

PURPOSE: To investigate the effects of the deep learning model RaDynPET on fourfold reduced-count whole-body PET examinations.

METHODS: A total of 120 patients (84 internal cohorts and 36 external cohorts) undergoing 18F-FDG PET/CT examinations were enrolled. PET images were reconstructed using OSEM algorithm with 120-s (G120) and 30-s (G30) list-mode data. RaDynPET was developed to generate enhanced images (R30) from G30. Two experienced nuclear medicine physicians independently evaluated subjective image quality using a 5-point Likert scale. Standardized uptake values (SUV), standard deviations, liver signal-to-noise ratio (SNR), lesion tumor-to-background ratio (TBR), and contrast-to-noise ratio (CNR) were compared. Subgroup analyses evaluated performance across demographics, and lesion detectability were evaluated using external datasets. RaDynPET was also compared to other deep learning methods.

RESULTS: In internal cohorts, R30 demonstrated significantly higher image quality scores than G30 and G120. R30 showed excellent agreement with G120 for liver and lesion SUV values and surpassed G120 in liver SNR and CNR. Liver SNR and CNR of R30 were comparable to G120 in thin group, and the CNR of R30 was comparable to G120 in young age group. In external cohorts, R30 maintained strong SUV agreement with G120, with lesion-level sensitivity and specificity of 95.45% and 98.41%, respectively. There was no statistical difference in lesion detection between R30 and G120. RaDynPET achieved the highest PSNR and SSIM among deep learning methods.

CONCLUSION: The RaDynPET model effectively restored high image quality while maintaining SUV agreement for 18F-FDG PET scans acquired in 25% of the standard acquisition time.

PMID:40471320 | DOI:10.1007/s00259-025-07370-4

Angew Chem Int Ed Engl. 2025 Jun 5:e202507102. doi: 10.1002/anie.202507102. Online ahead of print.

ABSTRACT

Organic cocrystals, particularly the evolution from binary to higher-order structures, have garnered considerable attention due to their tunable intermolecular interactions and unique material properties. Binary cocrystals, formed through π-π stacking, charge transfer, and hydrogen/halogen bonding, allow for precise control over molecular packing and enhanced optoelectronic properties. In contrast, higher-order cocrystals, incorporating three or more components, enable greater complexity and functional diversity. Strategies such as homologation via isostructural substitution, hierarchical intermolecular interactions and Long-range Synthon Aufbau Modules facilitate the synthesis of these advanced materials. The shift toward higher-order cocrystals paves the way for novel applications in fields such as deep learning for cocrystal prediction, drug design, organic solar cells, and NIR-II photothermal conversion. However, challenges related to molecular screening, ratio optimization, scalable synthesis, and long-term stability remain critical hurdles for the broader implementation of these materials in practical applications.

PMID:40471124 | DOI:10.1002/anie.202507102

Philos Trans A Math Phys Eng Sci. 2025 Jun 5;383(2298):20240236. doi: 10.1098/rsta.2024.0236. Epub 2025 Jun 5.

ABSTRACT

Solving computer vision problems through machine learning, one often encounters lack of sufficient training data. To mitigate this, we propose the use of ensembles of weak learners based on spectral total-variation (STV) features (Gilboa G. 2014 A total variation spectral framework for scale and texture analysis. SIAM J. Imaging Sci. 7, 1937-1961. (doi:10.1137/130930704)). The features are related to nonlinear eigenfunctions of the total-variation subgradient and can characterize well textures at various scales. It was shown (Burger M, Gilboa G, Moeller M, Eckardt L, Cremers D. 2016 Spectral decompositions using one-homogeneous functionals. SIAM J. Imaging Sci. 9, 1374-1408. (doi:10.1137/15m1054687)) that, in the one-dimensional case, orthogonal features are generated, whereas in two dimensions the features are empirically lowly correlated. Ensemble learning theory advocates the use of lowly correlated weak learners. We thus propose here to design ensembles using learners based on STV features. To show the effectiveness of this paradigm, we examine a hard real-world medical imaging problem: the predictive value of computed tomography (CT) data for high uptake in positron emission tomography (PET) for patients suspected of skeletal metastases. The database consists of 457 scans with 1524 unique pairs of registered CT and PET slices. Our approach is compared with deep-learning methods and to radiomics features, showing STV learners perform best (AUC=[Formula: see text]), compared with neural nets (AUC=[Formula: see text]) and radiomics (AUC=[Formula: see text]). We observe that fine STV scales in CT images are especially indicative of the presence of high uptake in PET.This article is part of the theme issue 'Partial differential equations in data science'.

PMID:40471027 | DOI:10.1098/rsta.2024.0236

Rev Sci Instrum. 2025 Jun 1;96(6):065202. doi: 10.1063/5.0256098.

ABSTRACT

The wide range of optical planes in underwater laser imaging results in the presence of a large number of noisy light bars in the background region. Since the shape and intensity of these noisy light bars are very similar to the target information, it is difficult to detect and eliminate them accurately. In this paper, a deep learning algorithm named YOLOv8-FWR is proposed, which can effectively improve the efficiency and quality of underwater laser imaging by combining with laser scanning imaging equipment. First, we introduce a novel pooling module called Focal_SPPF to mitigate the impact of background noise. Second, we propose a weighted feature Concat module to enhance the detection of small target light bars located at the object's edges. Finally, to enhance the model's adaptability for underwater deployment, we optimized the C2f module through structural reparameterization techniques. This approach effectively reduced the model's parameter count while enhancing its accuracy. We constructed a dataset containing a large amount of background noise by simulating the process of underwater laser scanning imaging and evaluated the effectiveness of the augmented model through ablation and comparison experiments. The experimental results indicate that our model outperforms the YOLOv8n by obtaining an 8.6% improvement on mAP50-95 and reducing the parameter count by 37%. A favorable balance between detection accuracy and number of parameters is achieved. Meanwhile, experiments on VOC2012 and the Underwater Detection Dataset (UDD) verify its good generalizability. Finally, we built a rotating line laser scanning imaging system and validated its effectiveness through underwater laser scanning experiments.

PMID:40471019 | DOI:10.1063/5.0256098

Adv Sci (Weinh). 2025 Jun 5:e17408. doi: 10.1002/advs.202417408. Online ahead of print.

ABSTRACT

This study presents BrainFusion, a unified software framework designed to improve reproducibility and support translational applications in multimodal brain-computer interface (BCI) and brain-body interaction research. While ​electroencephalography (EEG)​​-based BCIs have advanced considerably, integrating multimodal physiological signals remains hindered by analytical complexity, limited standardization, and challenges in real-world deployment. BrainFusion addresses these gaps through standardized data structures, automated preprocessing pipelines, cross-modal feature engineering, and integrated machine learning modules. Its application generator further enables streamlined deployment of workflows as standalone executables. Demonstrated in two case studies, BrainFusion achieves 95.5% accuracy in within-subject EEG-functional near-infrared spectroscopy (fNIRS)​​ motor imagery classification using ensemble modeling and 80.2% accuracy in EEG-electrocardiography (ECG)​​ sleep staging using deep learning, with the latter successfully deployed as an executable tool. Supporting EEG, fNIRS, electromyography (EMG)​, and ECG, BrainFusion provides a low-code, visually guided environment, facilitating accessibility and bridging the gap between multimodal research and application in real world.

PMID:40470749 | DOI:10.1002/advs.202417408

Naunyn Schmiedebergs Arch Pharmacol. 2025 Jun 5. doi: 10.1007/s00210-025-04305-6. Online ahead of print.

ABSTRACT

Chronic respiratory diseases represent a growing global health challenge, marked by rising prevalence and mortality rates. Inflammatory lung conditions including asthma, chronic obstructive pulmonary disease (COPD), acute respiratory distress syndrome, and idiopathic pulmonary fibrosis are major contributors to this burden. These disorders are typically associated with persistent inflammation in the airways or lung tissue, resulting in obstructive or restrictive patterns of respiratory dysfunction. Many of these diseases involve both acute exacerbations and chronic progression, making diagnosis and management particularly complex. Conventional treatments often rely on systemic drug administration, which can be limited by suboptimal therapeutic efficacy and undesirable effects on non-target organs. In contrast, inhalation-based drug delivery offers a more direct and efficient route to the lungs, enabling localized drug action, reduced systemic toxicity, and faster therapeutic onset. Among the various inhalation approaches, dry powder inhalers (DPIs) have gained increasing attention for their user-friendly design, dose consistency, and breath-actuated delivery mechanisms. This review explores recent advances in pulmonary drug delivery, with a particular focus on the design, development, and clinical potential of DPIs. By enhancing site-specific drug delivery and supporting the move toward personalized respiratory therapies, DPIs are poised to play a pivotal role in the future of respiratory disease management.

PMID:40471243 | DOI:10.1007/s00210-025-04305-6

J Am Chem Soc. 2025 Jun 5. doi: 10.1021/jacs.5c03996. Online ahead of print.

ABSTRACT

Ultranarrow crystalline one-dimensional nanostructures formed from soft materials facilitate precise structural control in nanomaterial design, which is essential for biomedicine and nanotechnology applications. Systematic control of their hierarchical structure is challenging due to the complexities of simultaneously manipulating multiple noncovalent interactions at such small scales. We employed a polypeptoid crystal motif as a supramolecular synthon to engineer ultranarrow crystalline nanofibers constrained to a single lattice axis by incorporating a single ionizable side chain into the hydrophobic core of a nanosheet-forming peptoid. Cryogenic transmission electron microscopy of the nanofibers revealed detailed molecular arrangements of a unit-cell cross-section and the presence of distinct pH-dependent lattice isoforms that resulted in morphological transformations. Molecular dynamics simulations demonstrated that the ionizable side chain plays a critical role in changing the local conformation of the unit cell, which further impacts the dimensionality of hierarchical structures. Moreover, these fibers were readily functionalized with biological ligands to afford one-dimensional (1D) protein arrays. This approach for the high-precision bottom-up assembly of ultranarrow 1D nanostructures offers significant potential for developing novel biomimetic nanostructures.

PMID:40471545 | DOI:10.1021/jacs.5c03996

Planta. 2025 Jun 5;262(1):17. doi: 10.1007/s00425-025-04735-9.

ABSTRACT

This review highlights the diverse modeling approaches essential for understanding the dynamics of plant circadian clock genes, which are key to optimizing plant growth, development, and resilience to environmental stress. The circadian clock in plants is a complex system governed by intricate transcriptional regulatory networks that orchestrate gene expression in response to environmental cues. These networks are crucial for understanding plant adaptation to daily changes and optimizing growth. This review provides a comprehensive account of various modeling approaches used to study plants' transcriptional regulatory network of circadian clock genes. Here, we review different computational methodologies like ordinary differential equation-based approaches, stochastic models, and spatial techniques that can be evaluated on their ability to capture the dynamics, variability, and interactions inherent to the circadian clock system. Moreover, the circadian clock's responsiveness to environmental cues, such as light, temperature, and other stressors plays a pivotal role in ensuring plant development. The modeling approaches must consider environmental factors influencing the transcriptional regulatory networks, which potentially alter the clock's phase, amplitude, and photoperiod. These adaptations are critical for plant survival, as they align physiological processes with specific hours of the day, enhancing resource use efficiency, and stress resilience. We highlight the respective strengths and limitations of different models emphasizing the importance of an integrative approach that combines multiple techniques which capture the essence of interactions of circadian clock components and their implications for plant growth, development and survival.

PMID:40471439 | DOI:10.1007/s00425-025-04735-9

J Am Chem Soc. 2025 Jun 5. doi: 10.1021/jacs.5c04942. Online ahead of print.

ABSTRACT

The ortho-nitrobenzyl (ONB) group is one of the most widely utilized photocages for spatiotemporal control of biological processes via the light-triggered activation of small molecules and macromolecules. However, a significant limitation is that ONB photocages typically absorb in the UV/blue light region, which is phototoxic to living systems and exhibits limited tissue penetration. In this study, we present a novel approach for near-infrared (NIR) light-triggered photodecaging of the ONB core using silicon rhodamine (SiR) as a photoredox catalyst. The reaction efficiently uncages ONB substrates under 660 nm light irradiation, achieving high yields across a diverse range of substrates, including amino acids, nucleotides, prodrugs, bioactive small molecules, caged fluorescent dyes, and proteins. Mechanistic studies demonstrate that the uncaging reaction proceeds through nitroreduction via a single electron transfer mechanism, followed by an electron cascade-triggered self-immolation process. The reaction has been successfully applied in both mammalian cells and bacteria. Furthermore, we developed a NIR light-activated prodrug release protocol for antibody-drug conjugates (ADCs) targeting noninternalizable cancer cell surface markers and demonstrated the utility of this approach in a tumor-bearing mouse model.

PMID:40471121 | DOI:10.1021/jacs.5c04942

J Virol. 2025 Jun 5:e0078925. doi: 10.1128/jvi.00789-25. Online ahead of print.

ABSTRACT

Herpesviruses are large DNA viruses that encode homologs of cellular enzymes. The viral ribonucleotide reductase, which consists of large R1 and small R2 subunits, is required for deoxyribonucleotide synthesis. However, herpesviruses have repurposed the R1 subunit for additional non-canonical functions in virus-host interaction and immune evasion. Here, we investigated the R1 proteins of Kaposi's sarcoma-associated herpesvirus (KSHV) and murine gammaherpesvirus 68 (MHV-68), two γ-herpesviruses of the genus Rhadinovirus. We show that the ORF61-encoded viral R1 proteins form elongated cytoplasmic condensates in infected cells, which structurally differ from the previously described R1 condensates of other herpesviruses. Fluorescently labeled ORF61 condensates exhibited the properties of solid aggregates, as determined by fluorescence recovery after photobleaching (FRAP). Correlative light and electron microscopy (CLEM) showed that ORF61 aggregates consist of filamentous bundles. The KSHV ORF61 protein interacted with the cellular cytosine deaminase APOBEC3B in infected cells and translocated it from the nucleus, the site of viral DNA replication, to the cytoplasmic aggregates. Aggregate formation and relocalization of APOBEC3B depended on a conserved Induced Protein Aggregation Motif (IPAM) in the C-terminal part of ORF61. A KSHV ORF61 IPAM mutant was vulnerable to APOBEC3B-mediated deamination and replicated to reduced titers. In contrast, MHV-68 ORF61 did not relocalize human or murine APOBEC3 proteins, suggesting that it engages different target proteins. The results show that rhadinovirus ORF61 proteins form elongated filamentous aggregates in infected cells to sequester and inactivate target proteins, such as APOBEC3B.IMPORTANCEHerpesviruses are large DNA viruses that encode enzymes similar to those in host cells. The R1 subunit of their ribonucleotide reductase is important for DNA synthesis and plays additional roles in immune evasion and virus-host interactions. This study focused on the R1 protein ORF61 of two γ-herpesviruses of the genus Rhadinovirus: KSHV and MHV-68. Unlike their homologs in other herpesviruses, KSHV and MHV-68 R1 proteins form cytoplasmic aggregates consisting of filamentous bundles in infected cells. KSHV ORF61 depletes the mutagenic cellular enzyme APOBEC3B from the nucleus, the site of viral DNA replication, and sequesters it in cytoplasmic aggregates, thereby protecting the viral genome from APOBEC3B-mediated mutations. This process relies on a specific conserved motif in ORF61. However, the MHV-68 ORF61 protein does not redistribute APOBEC3 proteins, suggesting that it binds different targets. These findings reveal how rhadinoviruses use filamentous ORF61 aggregates to manipulate host antiviral defenses.

PMID:40470958 | DOI:10.1128/jvi.00789-25

Elife. 2025 Jun 5;12:RP89170. doi: 10.7554/eLife.89170.

ABSTRACT

The successful integration of engineered gene circuits into host cells remains a significant challenge in synthetic biology due to circuit-host interactions, such as growth feedback, where the circuit influences cell growth and vice versa. Understanding the dynamics of circuit failures and identifying topologies resilient to growth feedback are crucial for both fundamental and applied research. Utilizing transcriptional regulation circuits with adaptation as a paradigm, we systematically study more than 400 topological structures and uncover various categories of failures. Three dynamical mechanisms of circuit failures are identified: continuous deformation of the response curve, strengthened or induced oscillations, and sudden switching to coexisting attractors. Our extensive computations also uncover a scaling law between a circuit robustness measure and the strength of growth feedback. Despite the negative effects of growth feedback on the majority of circuit topologies, we identify several circuits that maintain optimal performance as designed, a feature important for applications.

PMID:40470804 | DOI:10.7554/eLife.89170

Adv Sci (Weinh). 2025 Jun 5:e05075. doi: 10.1002/advs.202505075. Online ahead of print.

ABSTRACT

The synthesis of cellulose pellicles by bacteria offers an enticing strategy for the biofabrication of sustainable materials and biomedical devices. To leverage this potential, bacterial strains that overproduce cellulose are identified through directed evolution technology. While cellulose overproduction is linked with a specific genetic mutation, the effect of such mutation on the intracellular protein landscape and on the structure and mechanical properties of the cellulose pellicles is not yet understood. Here, the proteome of bacteria evolved to overproduce cellulose is studied and its effect on the structure and mechanics of the resulting cellulose pellicles is investigated. Proteomic analysis reveals that the protein landscape of the evolved bacteria shows pronounced differences from that of native microorganisms. Thanks to concerted changes in the proteome, the evolved bacteria can generate cellulose pellicles with exquisite structure and improved mechanical properties for applications in textiles, packaging, and medical implants.

PMID:40470676 | DOI:10.1002/advs.202505075

Insect Mol Biol. 2025 Jun 5. doi: 10.1111/imb.13006. Online ahead of print.

ABSTRACT

Cyantraniliprole (Cya), a diamide insecticide, is widely utilised for the management of Lepidoptera pests owing to its potent insecticidal efficacy and broad spectrum of activity. The extensive use and prolonged environmental persistence of this insecticide pose a significant threat to the sustainable development of sericulture. This study firstly assessed the lethal toxicity of cyantraniliprole to the 5th instar larvae of Bombyx mori. Exposure to cyantraniliprole (LC5, LC10 and LC20) resulted in a concentration-dependent reduction in larval weight, pupal weight and survival rate and a prolongation of larval development time. Moreover, cyantraniliprole LC10 resulted in substantial structural damage to the epithelial cells, suppressed the mRNA levels of oxidative phosphorylation genes, perturbed ATP synthesis and led to an imbalance of intracellular reactive oxygen species. Meanwhile, the starvation treatment suggested that the impacts of cyantraniliprole on silkworms cannot be solely ascribed to nutritional deficiencies. Additionally, the results revealed that cytochrome P450s might serve as a pivotal factor in the detoxification metabolism of cyantraniliprole in the midgut of silkworms. The findings of this study offer evidence for the ecological risk posed by environmental residues of cyantraniliprole to non-target organisms and are also of great significance for sericulture production.

PMID:40470600 | DOI:10.1111/imb.13006

Ital J Dermatol Venerol. 2025 Jun 5. doi: 10.23736/S2784-8671.25.08147-2. Online ahead of print.

ABSTRACT

Gender medicine has been achieving increasing importance. Gender differences in disease depend on hormonal status and may involve functions of the skin, immune responses and metabolic pathways, and have to do also with indications and response to treatments. Psoriasis is a common chronic inflammatory immune-mediated disease. The prevalence of psoriasis in the population is balanced between males and females, but early onset psoriasis is slightly more prevalent in males, with the latter suffering from a more severe disease. In general, male and female patients receive identical drugs at equivalent dosages. However, females receive systemic treatments less frequently compared to males. Males are more satisfied with their psoriasis treatment and respond better to biologics. Females have a significant higher rate of adverse events and drug-related discontinuation rate compared to males. About conventional systemic treatments for psoriasis during pregnancy, only cyclosporine is suggested when the benefits exceed the potential side effects, whereas methotrexate is contraindicated during pregnancy and lactation and in the three months before fatherhood and motherhood. Among the biologics, only certolizumab pegol is considered safe in pregnant patients, as it does not cross the maternal-placental barrier. Therefore, it is important to consider a gender perspective in the treatment of psoriasis, including her willingness to procreate. This is a narrative review highlighting the challenges that the healthcare dermatologists may face regarding management of psoriasis in female patients.

PMID:40470625 | DOI:10.23736/S2784-8671.25.08147-2