Commun Biol. 2025 May 30;8(1):836. doi: 10.1038/s42003-025-08184-8.

ABSTRACT

Multiplexed imaging techniques require identifying different cell types in the tissue. To utilize their potential for cellular and molecular analysis, high throughput and accurate analytical approaches are needed in parsing vast amounts of data, particularly in clinical settings. Nuclear segmentation errors propagate in all downstream steps of cell phenotyping and single-cell spatial analyses. Here, we benchmark and compare the nuclear segmentation tools commonly used in multiplexed immunofluorescence data by evaluating their performance across 7 tissue types encompassing ~20,000 labeled nuclei from human tissue samples. Pre-trained deep learning models outperform classical nuclear segmentation algorithms. Overall, Mesmer is recommended as it exhibits the highest nuclear segmentation accuracy with 0.67 F1-score at an IoU threshold of 0.5 on our composite dataset. Pre-trained StarDist model is recommended in case of limited computational resources, providing ~12x run time improvement with CPU compute and ~4x improvement with the GPU compute over Mesmer, but it struggles in dense nuclear regions.

PMID:40447729 | DOI:10.1038/s42003-025-08184-8

Spectrochim Acta A Mol Biomol Spectrosc. 2025 May 27;342:126487. doi: 10.1016/j.saa.2025.126487. Online ahead of print.

ABSTRACT

Drug repositioning and reuse is a cost-effective strategy for the development of new drugs, and drug co-crystal is a fast and effective technical means. Acetylsalicylic acid is a BCS II drug, which has the limitations of high permeability and low solubility, and the safety and efficacy of the drug have been greatly affected. Co-crystallization with other forming agents is considered to be a promising technical means, which can not only increase the solubility, but also improve the dissolution rate and stability. In this paper, the cocrystal of acetylsalicylic acid and arginine was prepared by grinding method. The physical and chemical characterization of the raw material, the mixture and the obtained cocrystal was carried out by XRD, terahertz spectroscopy (THz-TDS) and Raman spectroscopy (Raman). The obvious difference was observed on the characteristic peaks of the cocrystal, which proved the formation of the cocrystal. Understanding the basic properties of lattice vibration during the eutectic process is challenging, yet it can be accomplished through theoretical calculations. By employing density-functional theory (DFT) calculations, the molecular configurations and vibration spectra of the two drug cocrystals can be obtained, enabling a deeper understanding of the vibration modes of drug molecules in the low-frequency range. Moreover, this study demonstrates the sensitivity of terahertz time-domain spectroscopy (TDS) technology in detecting intermolecular hydrogen-bond interactions in drug cocrystals. When comparing cocrystal molecules with active pharmaceutical ingredient (API) molecules, it is found that cocrystals possess better binding energy, driven by intermolecular hydrogen bonds and dispersion forces.

PMID:40446719 | DOI:10.1016/j.saa.2025.126487

EBioMedicine. 2025 May 29;117:105779. doi: 10.1016/j.ebiom.2025.105779. Online ahead of print.

ABSTRACT

BACKGROUND: Motor neuron disease (MND) leads to progressive functional decline, making reliable measures of disease progression critical for patient care and clinical trials. Current clinical outcome measures lack the ability to continuously and objectively track functional decline in daily life of patients with MND. This study assessed and validated wrist-worn accelerometry outcome measures for continuous monitoring in MND, with the potential to refine clinical trial outcomes.

METHODS: This longitudinal study included 95 patients with MND who wore an ActiGraph GT9X Link device on their non-dominant wrist for 8 days, with follow-up every 3-4 months. Accelerometer data were processed using ActiLife and GGIR. Joint models were used to simultaneously investigate the longitudinal change in ALS Functional Rating Scale-Revised (ALSFRS-R) scores and accelerometer-derived outcomes alongside their relationship with overall survival. Sample size estimates for clinical trials were generated using both accelerometer- and ALSFRS-R-based outcomes, and principal component analysis (PCA) explored outcome relationships.

FINDINGS: Accelerometer outcomes showed a slower rate of decline (-0.03 to -0.07 SD/month) compared to ALSFRS-R (-0.10 SD/month) and had stronger correlations with ALSFRS-R motor subdomains (partial r: 0.60-0.73). PCA revealed that longitudinal measures of accelerometry were distinct from the ALSFRS-R, highlighting the complementary nature of these measures. Peak 6-min activity predicted smaller clinical trial sample sizes for studies over 12 months. Accelerometer-derived outcomes were not significantly associated with survival.

INTERPRETATION: Wrist-worn accelerometry offers a practical solution for continuous monitoring in MND, complementing ALSFRS-R. Measures of peak performance, and specifically peak 6-min activity shows promise, potentially reducing sample sizes and improving disease tracking over longer duration studies. Further refinement and validation are needed to adopt actigraphy measures as clinical assessment outcomes.

FUNDING: This study was supported by Wesley Medical Research (2016-32), the Honda Foundation, Motor Neurone Disease Research Australia, and FightMND. CJH received a Higher Degree Research Scholarship from UQ. STN received support from the Scott Sullivan Fellowship (MND and Me Foundation/RBWH Foundation), a FightMND Mid-Career Fellowship, and the AIBN.

PMID:40446399 | DOI:10.1016/j.ebiom.2025.105779

Anim Microbiome. 2025 May 30;7(1):53. doi: 10.1186/s42523-025-00405-z.

ABSTRACT

Several studies have indicated that the dysregulation of microbial metabolites and the inflammatory environment resulting from microbial dysbiosis may contribute to the occurrence and progression of cardiovascular diseases. Therefore, restoring the disordered gut microbiota in patients with colorectal cancer by fecal microbiota transplantation (FMT) has the potential to reduce the incidence of cardiac disease. In this study, we identified cardiac dysfunction in azomethane and dextran sodium sulfate-induced colorectal cancer mice. Intestinal microbes from healthy mice were transferred to colorectal cancer mice, which vastly reversed the disorder of the gut microbiota and effectively alleviated cardiac dysfunction. Moreover, FMT regulated the expression of serum metabolites such as uridine triphosphate (UTP), tiamulin, andrographolide, and N-Acetyl-D-glucosamine, as well as cytokines like TGF-β, IRF5, and β-MHC in the heart. These findings uncover that the disturbed gut microbiota causes cardiac dysfunction in colorectal cancer mice by modulating the expression of serum metabolites and cytokines, which could be alleviated by treatment with FMT.

PMID:40448218 | DOI:10.1186/s42523-025-00405-z

NPJ Antimicrob Resist. 2025 May 30;3(1):46. doi: 10.1038/s44259-025-00115-1.

ABSTRACT

The lack of novel antimicrobial compounds in the development pipeline cries for innovative approaches regarding their discovery. In this Perspective, we discuss how microbial interactions play a significant role in shifting a pathogen's response to antibacterial treatment and negatively impact patient outcomes. Furthermore, we argue that interspecies interactions are often overlooked in treatment selection and current drug screening approaches, and modeling disease-relevant polymicrobial communities could help in unraveling novel strategies to eradicate pathogens.

PMID:40447766 | DOI:10.1038/s44259-025-00115-1

Thorax. 2025 May 30:thorax-2024-222242. doi: 10.1136/thorax-2024-222242. Online ahead of print.

ABSTRACT

BACKGROUND: Despite significant clinical improvements, there is evidence of persisting airway inflammation in people with cystic fibrosis (CF) established on elexacaftor/tezacaftor/ivacaftor (ETI) therapy. As CF is a multi-system disease, systemic immune profiles can reflect local inflammation within the lungs and other organs. Understanding systemic inflammation after ETI therapy may reveal important translational insights. This study aims to profile systemic inflammatory changes and relate these to the well-documented improvements observed with ETI therapy.

METHODS: We conducted a single-centre longitudinal study with 57 CF subjects initiating ETI therapy. All participants were Phe508del homozygous or Phe508del/minimal function. Blood samples were collected pre-ETI and 3-12 months post-therapy initiation. Analyses included mass spectrometry-based proteomics, a multiplex immunoassay, and flow cytometry for peripheral immune cell counts and phenotype. Controls samples were provided by 29 age-matched healthy controls.

RESULTS: Systemic inflammation reduced with ETI therapy; however, the immune profile remained distinct from healthy controls. ETI reduced neutrophil counts and was associated with a more mature, less inflammatory phenotype, as well as a shift towards an immune resolving state associated with increased CD206 expression. Cytokines known to influence neutrophil levels reduced with therapy. Despite ETI therapy, neutrophil and monocyte counts remained elevated compared with healthy controls. There was no obvious association between the ETI-related improvements in systemic inflammation and lung function.

CONCLUSIONS: Patients with CF showed evidence of persisting systemic inflammation despite ETI therapy, which may have long-term potentially adverse effects on respiratory and other organ systems.

PMID:40447326 | DOI:10.1136/thorax-2024-222242

Int J Biol Macromol. 2025 May 28:144741. doi: 10.1016/j.ijbiomac.2025.144741. Online ahead of print.

ABSTRACT

Polymeric hydrogels are paramount to outstanding applications in biology, medicine, pharmacy. Their similarity to living tissues is leveraged in clinical branches (oncology, cardiology, immunology, neurology, wound healing) for delivering a large range of drugs (encompassing DNA, RNA, protein molecules) and realizing in-vivo models of stimuli-responsive or controlled drug release. Rubber elasticity theory and the swollen network hypothesis are key for properly designing the geometric and mechanical features of hydrogels and polymer networks. The assumption of a Gaussian distribution of end-to-end lengths in a polymer molecule, however, can break down in several cases. Here, strongly supported by Low field NMR and rheology experiments, we propound the generalized Weibull law of extreme value statistics (EVS) to have universal validity in hydrogel materials. Mesh size values that account for an intrinsic statistical dependence between monomeric positions (or stiffness) show much better agreement with measurements conducted on physically crosslinked samples (agar, alginate and scleroglucan), including sputum specimens (rich in mucins) from patients affected by chronic respiratory conditions (cystic fibrosis) and on chemically crosslinked samples (poly-vinylpyrrolidone, PVP; poly-(ethylene-glycol/propylene-glycol), PEG/PPG). Across all ten gels, the Gaussian distribution yields the smallest average mesh size, ranging roughly from 7 nm for the densest alginate 2 % (9 gl-1) hydrogel to about 80 nm for one of the sputum. Working with the pierced Gaussian inflates the mesh size to ≈1.5 × the Gaussian value, with increases from a modest +4 % in alginate 1 % up to nearly +100 % in the open PVP network (48 → 98 nm). The generalized Weibull distribution usually falls between the two Gaussians, yet in agar 1 % and scleroglucan 2 % it overtakes the pierced Gaussian (e.g. 20.2 > 15.8 nm for agar 1 %), reflecting a strong heavy-tailed distribution. The predicted mesh order therefore is Gaussian < generalized Weibull ≈ pierced Gaussian, with the precise ranking ruled by the width and skewness of each network statistics. Overall, our findings - being straightforward to apply - will profoundly impact on the description, conception and control of polymer networks, which often demand advanced instrumental techniques for compensating the lack of adequate predictive models. Among other relevant implications, aside from drug delivery, we highlight the characterization of the photothermal (or thermoplasmonic) response of hydrogel matrices hosting metal nanoparticles (e.g. with applications in hyperthermia cancer treatment and enhanced chemical processes). On the theoretical side, we emphasize the study of transport and thermomechanical properties of polymeric networks.

PMID:40446991 | DOI:10.1016/j.ijbiomac.2025.144741

BMC Med Inform Decis Mak. 2025 May 30;25(1):200. doi: 10.1186/s12911-025-03029-0.

ABSTRACT

PURPOSE: This study aims to develop a deep learning framework that leverages modality imputation and subregion segmentation to improve grading accuracy in high-grade gliomas.

MATERIALS AND METHODS: A retrospective analysis was conducted using data from 1,251 patients in the BraTS2021 dataset as the main cohort and 181 clinical cases collected from a medical center between April 2013 and June 2018 (51 years ± 17; 104 males) as the external test set. We propose a PatchGAN-based modality imputation network with an Aggregated Residual Transformer (ART) module combining Transformer self-attention and CNN feature extraction via residual links, paired with a U-Net variant for segmentation. Generative accuracy used PSNR and SSIM for modality conversions, while segmentation performance was measured with DSC and HD95 across necrotic core (NCR), edema (ED), and enhancing tumor (ET) regions. Senior radiologists conducted a comprehensive Likert-based assessment, with diagnostic accuracy evaluated by AUC. Statistical analysis was performed using the Wilcoxon signed-rank test and the DeLong test.

RESULTS: The best source-target modality pairs for imputation were T1 to T1ce and T1ce to T2 (p < 0.001). In subregion segmentation, the overall DSC was 0.878 and HD95 was 19.491, with the ET region showing the highest segmentation accuracy (DSC: 0.877, HD95: 12.149). Clinical validation revealed an improvement in grading accuracy by the senior radiologist, with the AUC increasing from 0.718 to 0.913 (P < 0.001) when using the combined imputation and segmentation models.

CONCLUSION: The proposed deep learning framework improves high-grade glioma grading by modality imputation and segmentation, aiding the senior radiologist and offering potential to advance clinical decision-making.

PMID:40448035 | DOI:10.1186/s12911-025-03029-0

BMC Med Imaging. 2025 May 30;25(1):197. doi: 10.1186/s12880-025-01737-7.

ABSTRACT

OBJECTIVE: This study aimed to construct a novel model, Multi-Spatial Attention U-Net (MSAU-Net) by incorporating our proposed Multi-Spatial Attention (MSA) block into the U-Net for the automated segmentation of the gallbladder on CT images.

METHODS: The gallbladder dataset consists of CT images of retrospectively-collected 152 liver cancer patients and corresponding ground truth delineated by experienced physicians. Our proposed MSAU-Net model was transformed into two versions V1(with one Multi-Scale Feature Extraction and Fusion (MSFEF) module in each MSA block) and V2 (with two parallel MSEFE modules in each MSA blcok). The performances of V1 and V2 were evaluated and compared with four other derivatives of U-Net or state-of-the-art models quantitatively using seven commonly-used metrics, and qualitatively by comparison against experienced physicians' assessment.

RESULTS: MSAU-Net V1 and V2 models both outperformed the comparative models across most quantitative metrics with better segmentation accuracy and boundary delineation. The optimal number of MSA was three for V1 and two for V2. Qualitative evaluations confirmed that they produced results closer to physicians' annotations. External validation revealed that MSAU-Net V2 exhibited better generalization capability.

CONCLUSION: The MSAU-Net V1 and V2 both exhibited outstanding performance in gallbladder segmentation, demonstrating strong potential for clinical application. The MSA block enhances spatial information capture, improving the model's ability to segment small and complex structures with greater precision. These advantages position the MSAU-Net V1 and V2 as valuable tools for broader clinical adoption.

PMID:40448013 | DOI:10.1186/s12880-025-01737-7

Discov Oncol. 2025 May 30;16(1):966. doi: 10.1007/s12672-025-02771-1.

ABSTRACT

Cancer has increasingly been recognized as a genetic disease, influenced by lifestyle changes, dietary patterns, and environmental pollutants. Lung cancer remains one of the most lethal malignancies worldwide, necessitating precise diagnostic and therapeutic approaches. Among these types, lung cancer is the third most common cancer, which affects all over the population. Lung cancer is a cancer that forms in tissues of the lung, usually in the cells that line the air passages. There are two main types of lung cancer: small cell and non-small cell lung cancer. These two types grow differently and are treated differently. This review explores the application of advanced deep learning (DL) techniques in enhancing biosignature immunotherapy tools for the prediction of immune phenotypes in lung cancer patients. The study systematically analyses recent research integrating multi-modal biomedical data, such as radiomics, genomics, transcriptomics, and histopathological images, to develop robust DL-based predictive models. A well-defined literature search strategy, inclusion/exclusion criteria, and a PRISMA-guided screening process ensure transparency and reproducibility. Emphasis is placed on identifying key predictive biomarkers, including Programmed Death-Ligand 1 (PD-L1) expression, Tumor Mutational Burden (TMB), Microsatellite Instability (MSI), and APOBEC mutational signatures, which are vital for personalizing immunotherapy. The review also incorporates a quality assessment framework to evaluate the methodological rigor of the included studies. Enhanced technical details, such as model architecture, validation strategies, hyperparameter tuning, and standardized performance metrics like AUC-ROC and Harrell's C-index, are presented to facilitate cross-study comparisons. This review underscores the transformative role of DL in precision oncology and highlights the potential for integrating biosignatures into clinical workflows to improve immunotherapy outcomes in lung cancer.

PMID:40447924 | DOI:10.1007/s12672-025-02771-1

Sci Rep. 2025 May 30;15(1):19008. doi: 10.1038/s41598-025-02924-w.

ABSTRACT

Cardiovascular disease (CVD) is one of the severe disorders that requires effectual solutions. CVD mainly affects heart functionality in the human body. The impacts of heart disorders are hazardous, which primarily spread from arrhythmia and higher hypertension to heart attack or stroke and also death. Employing newly established data analysis techniques and inspecting a patient's health record might help recognize CVD promptly. In general, pervasive healthcare (PH) services have the potential to enhance healthcare and the excellence of the lifespan of chronic disease patients over constant monitoring. However, the conventional risk evaluation techniques are neither dynamic nor accurate because they stick to the arithmetical data and ignore the significant time-based effects of the crucial signs. So, recent work has utilized machine learning and deep learning methodologies for predicting CVD on clinical datasets. These methods can decrease death rates by predicting CVD depending on the medical data and the patient's severity level. This manuscript presents a deep convolutional fuzzy neural networks with stork optimization on cardiovascular disease classification (DCFNN-SOCVDC) technique for PH services. The main goal of the DCFNN-SOCVDC method is to detect and classify CVD in the healthcare environment. At first, the presented DCFNN-SOCVDC model performs data preprocessing by utilizing Z-score normalization to preprocess the medical data. For the feature selection process, the presented DCFNN-SOCVDC technique utilizes an arithmetic optimization algorithm model. Besides, the deep convolutional fuzzy neural network (DCFNN) method is employed to identify and classify CVD. Eventually, the presented DCFNN-SOCVDC approach employs a stork optimization algorithm method for the hyperparameter tuning method involved in the DCFNN model. The performance of the DCFNN-SOCVDC approach is evaluated using a CVD dataset, and the results are assessed based on various metrics. The performance validation of the DCFNN-SOCVDC approach portrayed a superior accuracy value of 99.05% over recent models.

PMID:40447750 | DOI:10.1038/s41598-025-02924-w

Sci Rep. 2025 May 30;15(1):19034. doi: 10.1038/s41598-025-04136-8.

ABSTRACT

Breast cancer diagnosis remains a crucial challenge in medical research, necessitating accurate and automated detection methods. This study introduces an advanced deep learning framework for histopathological image classification, integrating AlexNet and Gated Recurrent Unit (GRU) networks, optimized using the Hippopotamus Optimization Algorithm (HOA). Initially, DenseNet-41 extracts intricate spatial features from histopathological images. These features are then processed by the hybrid AlexNet-GRU model, leveraging AlexNet's robust feature extraction and GRU's sequential learning capabilities. HOA is employed to fine-tune hyperparameters, ensuring optimal model performance. The proposed approach is evaluated on benchmark datasets (BreakHis and BACH), achieving a classification accuracy of 99.60%, surpassing existing state-of-the-art models. The results demonstrate the efficacy of integrating deep learning with bio-inspired optimization techniques in breast cancer detection. This research offers a robust and computationally efficient framework for improving early diagnosis and clinical decision-making, potentially enhancing patient outcomes.

PMID:40447726 | DOI:10.1038/s41598-025-04136-8

Sci Rep. 2025 May 30;15(1):19071. doi: 10.1038/s41598-025-02194-6.

ABSTRACT

With advancements in drone control technology, low-altitude remote sensing image processing holds significant potential for intelligent, real-time urban management. However, achieving high accuracy with deep learning algorithms remains challenging due to the stringent requirements for low computational cost, minimal parameters, and real-time performance. This study introduces the Global Object-Oriented Dynamic Network (GOOD-Net) algorithm, comprising three fundamental components: an object-oriented, dynamically adaptive backbone network; a neck network designed to optimize the utilization of global information; and a task-specific processing head augmented for detailed feature refinement. Novel module components, such as the ReSSD Block, GPSA, and DECBS, are integrated to enable fine-grained feature extraction while maintaining computational and parameter efficiency. The efficacy of individual components in the GOOD-Net algorithm, as well as their synergistic interaction, is assessed through ablation experiments. Evaluation conducted on the VisDrone dataset demonstrates substantial enhancements. Furthermore, experiments assessing robustness and deployment on edge devices validate the algorithm's scalability and practical applicability. Visualization methods further highlight the algorithm's performance advantages. This research presents a scalable object detection framework adaptable to various application scenarios and contributes a novel design paradigm for efficient deep learning-based object detection.

PMID:40447715 | DOI:10.1038/s41598-025-02194-6

Sci Rep. 2025 May 30;15(1):19081. doi: 10.1038/s41598-025-02728-y.

ABSTRACT

To address inter-frame motion artifacts in ultrasound quantitative high-definition microvasculature imaging (qHDMI), we introduced a novel deep learning-based motion correction technique. This approach enables the derivation of more accurate quantitative biomarkers from motion-corrected HDMI images, improving the classification of thyroid nodules. Inter-frame motion, often caused by carotid artery pulsation near the thyroid, can degrade image quality and compromise biomarker reliability, potentially leading to misdiagnosis. Our proposed technique compensates for these motion-induced artifacts, preserving the fine vascular structures critical for accurate biomarker extraction. In this study, we utilized the motion-corrected images obtained through this framework to derive the quantitative biomarkers and evaluated their effectiveness in thyroid nodule classification. We segregated the dataset according to the amount of motion into low and high motion containing cases based on the inter-frame correlation values and performed the thyroid nodule classification for the high motion containing cases and the full dataset. A comprehensive analysis of the biomarker distributions obtained after using the corresponding motion-corrected images demonstrates the significant differences between benign and malignant nodule biomarker characteristics compared to the original motion-containing images. Specifically, the bifurcation angle values derived from the quantitative high-definition microvasculature imaging (qHDMI) become more consistent with the usual trend after motion correction. The classification results demonstrated that sensitivity remained unchanged for groups with less motion, while improved by 9.2% for groups with high motion. These findings highlight that motion correction helps in deriving more accurate biomarkers, which improves the overall classification performance.

PMID:40447670 | DOI:10.1038/s41598-025-02728-y

Nat Commun. 2025 May 30;16(1):5037. doi: 10.1038/s41467-025-60434-9.

ABSTRACT

Visualizing high-dimensional data is essential for understanding biomedical data and deep learning models. Neighbor embedding methods, such as t-SNE and UMAP, are widely used but can introduce misleading visual artifacts. We find that the manifold learning interpretations from many prior works are inaccurate and that the misuse stems from a lack of data-independent notions of embedding maps, which project high-dimensional data into a lower-dimensional space. Leveraging the leave-one-out principle, we introduce LOO-map, a framework that extends embedding maps beyond discrete points to the entire input space. We identify two forms of map discontinuity that distort visualizations: one exaggerates cluster separation and the other creates spurious local structures. As a remedy, we develop two types of point-wise diagnostic scores to detect unreliable embedding points and improve hyperparameter selection, which are validated on datasets from computer vision and single-cell omics.

PMID:40447630 | DOI:10.1038/s41467-025-60434-9

Sci Rep. 2025 May 30;15(1):19036. doi: 10.1038/s41598-025-00236-7.

ABSTRACT

Optimal surgical methods require accurate prediction of extraction difficulty and complications. Although various automated methods related to third molar (M3) extraction have been proposed, none fully predict both extraction difficulty and post-extraction complications. This study proposes an automatic diagnosis method based on state-of-the-art semantic segmentation and classification models to predict the extraction difficulty of maxillary and mandibular M3s and possible complications (sinus perforation and inferior alveolar nerve (IAN) injury). A dataset of 4,903 orthopantomographys (OPGs), annotated by experts, was used. The proposed diagnosis method segments M3s (#18, #28, #38, #48), second molars (#17, #27, #37, #47), maxillary sinuses, and inferior alveolar canal (IAC) in OPGs using a segmentation model and extracts the region of interest (RoI). Using the RoI as input, the classification model predicts extraction difficulty and complication possibilities. The model achieved 87.97% and 88.85% accuracy in predicting maxillary and mandibular M3 extraction difficulty, with area under the receiver operating characteristic curve (AUROC) of 96.25% and 97.3%, respectively. It also predicted the possibility of sinus perforation and IAN injury with 91.45% and 88.47% accuracy, and AUROC of 91.78% and 94.13%, respectively. Our results show that the proposed method effectively predicts the extraction difficulty and complications of maxillary and mandibular M3s using OPG, and could serve as a decision support system for clinicians before surgery.

PMID:40447616 | DOI:10.1038/s41598-025-00236-7

Nat Commun. 2025 May 30;16(1):5027. doi: 10.1038/s41467-025-60093-w.

ABSTRACT

The objective of optical super-resolution imaging is to acquire reliable sub-diffraction information on bioprocesses to facilitate scientific discovery. Structured illumination microscopy (SIM) is acknowledged as the optimal modality for live-cell super-resolution imaging. Although recent deep learning techniques have substantially advanced SIM, their transparency and reliability remain uncertain and under-explored, often resulting in unreliable results and biological misinterpretation. Here, we develop Bayesian deep learning (BayesDL) for SIM, which enhances the reconstruction of densely labeled structures while enabling the quantification of super-resolution uncertainty. With the uncertainty, BayesDL-SIM achieves high-fidelity distribution-informed SIM imaging, allowing for the communication of credibility estimates to users regarding the model outcomes. We also demonstrate that BayesDL-SIM boosts SIM reliability by identifying and preventing erroneous generalizations in various model misuse scenarios. Moreover, the BayesDL uncertainty shows versatile utilities for daily super-resolution imaging, such as error estimation, data acquisition evaluation, etc. Furthermore, we demonstrate the effectiveness and superiority of BayesDL-SIM in live-cell imaging, which reliably reveals F-actin dynamics and the reorganization of the cell cytoskeleton. This work lays the foundation for the reliable implementation of deep learning-based SIM methods in practical applications.

PMID:40447610 | DOI:10.1038/s41467-025-60093-w

Arch Bronconeumol. 2025 May 15:S0300-2896(25)00175-9. doi: 10.1016/j.arbres.2025.05.001. Online ahead of print.

NO ABSTRACT

PMID:40447522 | DOI:10.1016/j.arbres.2025.05.001

BMC Genomics. 2025 May 30;26(1):550. doi: 10.1186/s12864-025-11734-3.

ABSTRACT

BACKGROUND: Crossover and recombination create genetic diversity that reflects differences in the DNA sequences of different organisms. We previously reported that trinucleotide 2-repeat units (T2Us) are sites of crossover and consequent colonization, which are massively spread and shared across the genomes of human and several other primates. These sites underscore the preference for AT- over CG-rich sequences as recombination sites.

METHODS: We extended our study to simpler repeat cores, consisting of AT/TA and CG/GC dinucleotides. An algorithm was designed to extract the genomic regions with a higher probability of recombination. To this end, we hypothesized that dinucleotide 3-repeat units (D3Us) are, at least in part, the basic overlapping units resulting from unequal crossover between dinucleotide 2-repeat units (D2Us). We mapped TATATA, ATATAT, CGCGCG, and GCGCGC across the human genome and analyzed their colonization (the distance between consecutive D3Us < 500 bp). We also studied several randomly selected colonies of diverse sizes in up to 100 vertebrate species using the UCSC and Ensembl Genome Browsers.

RESULTS: We found approximately four million AT/TA D3Us and one hundred thousand CG/GC D3Us across the human genome. The majority of these D3Us resided in colonies and spread ubiquitously along all chromosomes. AT/TA colonies were significantly larger and more intricate than CG/GC colonies. D2Us and D3Us were the primary sites of unequal crossover in these colonies, resulting in the emergence of primary recombinants (overlapping recombinants of D2Us/D3Us) and a vast repertoire of secondary recombinants (non-overlapping recombinants of D2Us/D3Us) and eventually, colonies of enormous intricacy and significance based on Poisson distribution. Intricacy was consistently detected across diverse colony sizes, from the smallest to the largest. The randomly selected colonies that were studied in other species were specific to or of their largest size in human.

CONCLUSION: We report ubiquitous and intricate colonies, in which D2Us and D3Us were the primary sites of crossover and recombination. It is plausible that minimal repeats such as D2Us, D3Us, and T2Us mark recombination as a ubiquitous rule across the human genome. This phenomenon is likely to transform our understanding of the magnitude, biological, and evolutionary outcomes of crossover and recombination.

PMID:40447998 | DOI:10.1186/s12864-025-11734-3

Geroscience. 2025 May 31. doi: 10.1007/s11357-025-01716-4. Online ahead of print.

ABSTRACT

Certain epigenetic modifications, such as the methylation of CpG sites, can serve as biomarkers for chronological age. Previously, we introduced the BayesAge frameworks for accurate age prediction through the use of locally weighted scatterplot smoothing (LOWESS) to capture the nonlinear relationship between methylation or gene expression and age, and maximum likelihood estimation (MLE) for bulk bisulfite and RNA sequencing data. Here, we introduce MicroBayesAge, a maximum likelihood framework for age prediction using DNA microarray data that provides less biased age predictions compared to commonly used linear methods. Furthermore, MicroBayesAge enhances prediction accuracy relative to previous versions of BayesAge by subdividing input data into age-specific cohorts and employing a new two-stage process for training and testing. Additionally, we explored the performance of our model for sex-specific age prediction which revealed slight improvements in accuracy for male patients, while no changes were observed for female patients.

PMID:40447915 | DOI:10.1007/s11357-025-01716-4