Mol Cell Biochem. 2025 Mar 4. doi: 10.1007/s11010-025-05243-w. Online ahead of print.

ABSTRACT

Emerging evidence indicates that chloride channels (ClCs) significantly affect the pathogenesis of inflammatory bowel disease (IBD) through their regulatory roles in mast cell function and epithelial integrity. IBD, encompassing conditions such as Crohn's disease and ulcerative colitis, involves chronic inflammation of the gastrointestinal tract, where channels influence immune responses, fluid balance, and cellular signalling pathways essential for maintaining mucosal homeostasis. This review examines the specific roles of ClC in mast cells, focussing on the regulation of mast cell activation, degranulation, cytokine release, and immune cell recruitment in inflamed tissues. Key channels, including Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) and ClC-2, are discussed in detail because of their involvement in maintaining intestinal epithelial barrier function, a critical factor disrupted in IBD. For example, CFTR facilitates chloride ion transport across epithelial cells, which is essential for mucosal hydration and maintenance of the intestinal barrier. Reduced CFTR function can compromise this barrier, permitting microbial antigens to penetrate the underlying tissues and triggering excessive immune responses. ClC-2, another chloride channel expressed in mast cells and epithelial cells, supports tight junction integrity, contributes to barrier function, and reduces intestinal permeability. Dysregulation of these channels is linked to altered mast cell activity and excessive release of pro-inflammatory mediators, exacerbating IBD symptoms, such as diarrhoea, abdominal pain, and tissue damage. Here, we review recent pharmacological strategies targeting ClC, including CFTR potentiators and ClC-2 activators, which show the potential to mitigate inflammatory responses. Additionally, experimental approaches for selective modulation of chloride channels in mast cells have been explored. Although targeting ClC offers promising therapeutic avenues, challenges remain in achieving specificity and minimizing side effects. This review highlights the therapeutic potential of Cl channel modulation in mast cells as a novel approach for IBD treatment, aiming to reduce inflammation and restore intestinal homeostasis in affected patients.

PMID:40038149 | DOI:10.1007/s11010-025-05243-w

Eur J Prev Cardiol. 2025 Mar 4:zwaf122. doi: 10.1093/eurjpc/zwaf122. Online ahead of print.

ABSTRACT

AIM: To assess the impact of antimicrobial resistance (AMR) on major adverse cardiovascular event (MACE) risk in patients with bronchiectasis.

METHODS: This retrospective study utilized data from the TriNetX research network, analysing patients with bronchiectasis categorized by the presence or absence of AMR. Primary outcomes included the risk of MACE (myocardial infarction, stroke and systemic thromboembolism, and cardiac arrest) and all-cause death. Cox regression analysis with 1:1 propensity score matching (PSM) was applied to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the primary outcomes. Subgroup analyses were conducted to validate results in clinically relevant subgroups.

RESULTS: Prior to PSM, patients with AMR (n=6,543, 61.0±22.0 years, 55.8% female) were younger, more often male, and presented a higher prevalence of cardiovascular risk factors than those without AMR (n=154,685, 67.3±16.0 years, 59.4% female). After PSM, no significant differences were found between groups. However, AMR patients showed a higher risk of MACE (HR 1.29, 95% CI 1.17-1.41) and all-cause death (HR 1.49, 95% CI 1.38-1.61) compared to non-AMR patients. The MACE risk was notably elevated among AMR patients without prior cardiovascular events (HR 1.56, 95% CI 1.34-1.81). Similar MACE risks were observed in cystic fibrosis (HR 1.24, 95% CI 0.86-1.78) and non-cystic fibrosis subgroups (HR 1.28, 95% CI 1.16-1.41), with consistent findings across different AMR types.

CONCLUSIONS: In patients with bronchiectasis, AMR is associated with an increased risk of MACE and all-cause death, suggesting that controlling AMR spread may confer broader health benefits, particularly in reducing cardiovascular risk.

PMID:40037796 | DOI:10.1093/eurjpc/zwaf122

J Orthop Surg Res. 2025 Mar 4;20(1):236. doi: 10.1186/s13018-025-05620-7.

ABSTRACT

BACKGROUND: Accurate measurement of the spinal alignment parameters is crucial for diagnosing and evaluating adolescent idiopathic scoliosis (AIS). Manual measurement is subjective and time-consuming. The recently developed artificial intelligence models mainly focused on measuring the coronal Cobb angle (CA) and ignored the evaluation of the sagittal plane. We developed a deep-learning model that could automatically measure spinal alignment parameters in biplanar radiographs.

METHODS: In this study, our model adopted ResNet34 as the backbone network, mainly consisting of keypoint detection and CA measurement. A total of 600 biplane radiographs were collected from our hospital and randomly divided into train and test sets in a 3:1 ratio. Two senior spinal surgeons independently manually measured and analyzed spinal alignment and recorded the time taken. The reliabilities of automatic measurement were evaluated by comparing them with the gold standard, using mean absolute difference (MAD), intraclass correlation coefficient (ICC), simple linear regression, and Bland-Altman plots. The diagnosis performance of the model was evaluated through the receiver operating characteristic (ROC) curve and area under the curve (AUC). Severity classification and sagittal abnormalities classification were visualized using a confusion matrix.

RESULTS: Our AI model achieved the MAD of coronal and sagittal angle errors was 2.15° and 2.72°, and ICC was 0.985, 0.927. The simple linear regression showed a strong correction between all parameters and the gold standard (p < 0.001, r2 ≥ 0.686), the Bland-Altman plots showed that the mean difference of the model was within 2° and the automatic measurement time was 9.1 s. Our model demonstrated excellent diagnostic performance, with an accuracy of 97.2%, a sensitivity of 96.8%, a specificity of 97.6%, and an AUC of 0.972 (0.940-1.000).For severity classification, the overall accuracy was 94.5%. All accuracy of sagittal abnormalities classification was greater than 91.8%.

CONCLUSIONS: This deep learning model can accurately and automatically measure spinal alignment parameters with reliable results, significantly reducing diagnostic time, and might provide the potential to assist clinicians.

PMID:40038733 | DOI:10.1186/s13018-025-05620-7

J Transl Med. 2025 Mar 4;23(1):267. doi: 10.1186/s12967-025-06234-7.

ABSTRACT

BACKGROUND: Homologous recombination deficiency (HRD) refers to the dysfunction of homologous recombination repair (HRR) at the cellular level. The assessment of HRD status has the important significance for the formulation of treatment plans, efficacy evaluation, and prognosis prediction of patients with ovarian cancer.

OBJECTIVES: This study aimed to construct a deep learning-based classifier for identifying tumor regions from whole slide images (WSIs) and stratify the HRD status of patients with ovarian cancer (OC).

METHODS: The deep learning models were trained on 205 H&E-stained sections which contained 205 ovarian cancer patients, 64 were found to have HRD status while 141 had homologous recombination proficiency (HRP) status from two institutions Memorial Sloan Kettering Cancer Center (MSKCC) and Zhongda Hospital, Southeast University. The framework includes tumor regions identification by UNet + + and subtypes of ovarian cancer classifier construction. Referring to the EasyEnsemble, we classified the HRP patients into three distributed subsets. These three subsets of HRP patients were combined with the HRD patients to establish three new training groups for subsequent model construction. The three models were integrated into a single model named Ensemble Model.

RESULTS: The UNet + + algorithm segmented tumor regions with 81.8% accuracy, 85.9% recall, 83.8% dice score and 68.3% IoU. The AUC of the Ensemble Model was 0.769 (Precision = 0.800, Recall = 0.727, F1-score = 0.762) in the study. The most discriminative features between HRD and HRP comprised S_mean_dln_obtuse_ratio, S_mean_dln_acute_ratio and mean_Graph_T-S_Betweenness_normed.

CONCLUSIONS: The models we constructed enables accurate discrimination between tumor and non-tumor tissues in ovarian cancer as well as the prediction of HRD status for patients with ovarian cancer.

PMID:40038690 | DOI:10.1186/s12967-025-06234-7

BMC Med Imaging. 2025 Mar 4;25(1):71. doi: 10.1186/s12880-025-01604-5.

ABSTRACT

BACKGROUND: Pulmonary diseases such as COVID-19 and pneumonia, are life-threatening conditions, that require prompt and accurate diagnosis for effective treatment. Chest X-ray (CXR) has become the most common alternative method for detecting pulmonary diseases such as COVID-19, pneumonia, and lung opacity due to their availability, cost-effectiveness, and ability to facilitate comparative analysis. However, the interpretation of CXRs is a challenging task.

METHODS: This study presents an automated deep learning (DL) model that outperforms multiple state-of-the-art methods in diagnosing COVID-19, Lung Opacity, and Viral Pneumonia. Using a dataset of 21,165 CXRs, the proposed framework introduces a seamless combination of the Vision Transformer (ViT) for capturing long-range dependencies, DenseNet201 for powerful feature extraction, and global average pooling (GAP) for retaining critical spatial details. This combination results in a robust classification system, achieving remarkable accuracy.

RESULTS: The proposed methodology delivers outstanding results across all categories: achieving 99.4% accuracy and an F1-score of 98.43% for COVID-19, 96.45% accuracy and an F1-score of 93.64% for Lung Opacity, 99.63% accuracy and an F1-score of 97.05% for Viral Pneumonia, and 95.97% accuracy with an F1-score of 95.87% for Normal subjects.

CONCLUSION: The proposed framework achieves a remarkable overall accuracy of 97.87%, surpassing several state-of-the-art methods with reproducible and objective outcomes. To ensure robustness and minimize variability in train-test splits, our study employs five-fold cross-validation, providing reliable and consistent performance evaluation. For transparency and to facilitate future comparisons, the specific training and testing splits have been made publicly accessible. Furthermore, Grad-CAM-based visualizations are integrated to enhance the interpretability of the model, offering valuable insights into its decision-making process. This innovative framework not only boosts classification accuracy but also sets a new benchmark in CXR-based disease diagnosis.

PMID:40038588 | DOI:10.1186/s12880-025-01604-5

Nat Struct Mol Biol. 2025 Mar 4. doi: 10.1038/s41594-025-01512-w. Online ahead of print.

ABSTRACT

Differential high-order chromatin interactions between homologous chromosomes affect many biological processes. Traditional chromatin conformation capture genome analysis methods mainly identify two-way interactions and cannot provide comprehensive haplotype information, especially for low-heterozygosity organisms such as human. Here, we present a pipeline of methods to delineate diploid high-order chromatin interactions from noisy Pore-C outputs. We trained a previously published single-nucleotide variant (SNV)-calling deep learning model, Clair3, on Pore-C data to achieve superior SNV calling, applied a filtering strategy to tag reads for haplotypes and established a haplotype imputation strategy for high-order concatemers. Learning the haplotype characteristics of high-order concatemers from high-heterozygosity mouse allowed us to devise a progressive haplotype imputation strategy, which improved the haplotype-informative Pore-C contact rate 14.1-fold to 76% in the HG001 cell line. Overall, the diploid three-dimensional (3D) genome interactions we derived using Dip3D surpassed conventional methods in noise reduction and contact distribution uniformity, with better haplotype-informative contact density and genomic coverage rates. Dip3D identified previously unresolved haplotype high-order interactions, in addition to an understanding of their relationship with allele-specific expression, such as in X-chromosome inactivation. These results lead us to conclude that Dip3D is a robust pipeline for the high-quality reconstruction of diploid high-order 3D genome interactions.

PMID:40038455 | DOI:10.1038/s41594-025-01512-w

Sci Rep. 2025 Mar 4;15(1):7604. doi: 10.1038/s41598-025-92096-4.

ABSTRACT

Burns represents a serious clinical problem because the diagnosis and assessment are very complex. This paper proposes a methodology that combines the use of advanced medical imaging with predictive modeling for the improvement of burn injury assessment. The proposed framework makes use of the Adaptive Complex Independent Components Analysis (ACICA) and Reference Region (TBSA) methods in conjunction with deep learning techniques for the precise estimation of burn depth and Total Body Surface Area analysis. It also allows for the estimation of the depth of burns with high accuracy, calculation of TBSA, and non-invasive analysis with 96.7% accuracy using an RNN model. Extensive experimentation on DCE-LUV samples validates enhanced diagnostic precision and detailed texture analysis. These technologies provide nuanced insights into burn severity, improving diagnostic accuracy and treatment planning. Our results demonstrate the potential of these methods to revolutionize burn care and optimize patient outcomes.

PMID:40038450 | DOI:10.1038/s41598-025-92096-4

Sci Rep. 2025 Mar 4;15(1):7540. doi: 10.1038/s41598-025-91622-8.

ABSTRACT

In the rapidly evolving field of healthcare, Artificial Intelligence (AI) is increasingly driving the promotion of the transformation of traditional healthcare and improving medical diagnostic decisions. The overall goal is to uncover emerging trends and potential future paths of AI in healthcare by applying text mining to collect scientific papers and patent information. This study, using advanced text mining and multiple deep learning algorithms, utilized the Web of Science for scientific papers (1587) and the Derwent innovations index for patents (1314) from 2018 to 2022 to study future trends of emerging AI in healthcare. A novel self-supervised text mining approach, leveraging bidirectional encoder representations from transformers (BERT), is introduced to explore AI trends in healthcare. The findings point out the market trends of the Internet of Things, data security and image processing. This study not only reveals current research hotspots and technological trends in AI for healthcare but also proposes an advanced research method. Moreover, by analysing patent data, this study provides an empirical basis for exploring the commercialisation of AI technology, indicating the potential transformation directions for future healthcare services. Early technology trend analysis relied heavily on expert judgment. This study is the first to introduce a deep learning self-supervised model to the field of AI in healthcare, effectively improving the accuracy and efficiency of the analysis. These findings provide valuable guidance for researchers, policymakers and industry professionals, enabling more informed decisions.

PMID:40038367 | DOI:10.1038/s41598-025-91622-8

Sci Rep. 2025 Mar 4;15(1):7644. doi: 10.1038/s41598-025-90511-4.

ABSTRACT

Loop closure detection is a key module in visual SLAM. During the robot's movement, the cumulative error of the robot is reduced by the loop closure detection method, which can provide constraints for the back-end pose optimization, and the SLAM system can build an accurate map. Traditional loop closure detection algorithms rely on the bag-of-words model, which involves a complex process, has slow loading speeds, and is sensitive to changes in illumination or viewing angles. Therefore, aiming at the problems of traditional methods, this paper proposes an algorithm based on the Siamese capsule neural network by using the deep learning method. We have designed a new feature extractor for capsule networks, and in order to further reduce the parameter count, we have performed pruning based on the characteristics of the capsule layer. The algorithm was tested on the CityCentre dataset and the New College dataset. Our experimental results show that the proposed algorithm in this paper has higher accuracy and robustness compared to traditional methods and other deep learning methods. Our algorithm demonstrates good robustness under changes in illumination and viewing angles. Finally, we evaluated the performance of the complete SLAM system on the KITTI dataset.

PMID:40038350 | DOI:10.1038/s41598-025-90511-4

Sci Rep. 2025 Mar 4;15(1):7552. doi: 10.1038/s41598-025-90616-w.

ABSTRACT

The process of image formulation uses semantic analysis to extract influential vectors from image components. The proposed approach integrates DenseNet with ResNet-50, VGG-19, and GoogLeNet using an innovative bonding process that establishes algorithmic channeling between these models. The goal targets compact efficient image feature vectors that process data in parallel regardless of input color or grayscale consistency and work across different datasets and semantic categories. Image patching techniques with corner straddling and isolated responses help detect peaks and junctions while addressing anisotropic noise through curvature-based computations and auto-correlation calculations. An integrated channeled algorithm processes the refined features by uniting local-global features with primitive-parameterized features and regioned feature vectors. Using K-nearest neighbor indexing methods analyze and retrieve images from the harmonized signature collection effectively. Extensive experimentation is performed on the state-of-the-art datasets including Caltech-101, Cifar-10, Caltech-256, Cifar-100, Corel-10000, 17-Flowers, COIL-100, FTVL Tropical Fruits, Corel-1000, and Zubud. This contribution finally endorses its standing at the peak of deep and complex image sensing analysis. A state-of-the-art deep image sensing analysis method delivers optimal channeling accuracy together with robust dataset harmonization performance.

PMID:40038324 | DOI:10.1038/s41598-025-90616-w

Sci Rep. 2025 Mar 4;15(1):7558. doi: 10.1038/s41598-025-88184-0.

ABSTRACT

Traffic signs are pivotal components of traffic management, ensuring the regulation and safety of road traffic. However, existing detection methods often suffer from low accuracy and poor real-time performance in dynamic road environments. This paper reviews traditional traffic sign detection methods and introduces an enhanced detection algorithm (YOLO-BS) based on YOLOv8 (You Only Look Once version 8). This algorithm addresses the challenges of complex backgrounds and small-sized detection targets in traffic sign images. A small object detection layer was incorporated into the YOLOv8 framework to enrich feature extraction. Additionally, a bidirectional feature pyramid network (BiFPN) was integrated into the detection framework to enhance the handling of multi-scale objects and improve the performance in detecting small objects. Experiments were conducted on the TT100K dataset to evaluate key metrics such as model size, recall, mean average precision (mAP), and frames per second (FPS), demonstrating that YOLO-BS surpasses current mainstream models with mAP50 of 90.1% and FPS of 78. Future work will refine YOLO-BS to explore broader applications within intelligent transportation systems.

PMID:40038318 | DOI:10.1038/s41598-025-88184-0

Jpn J Radiol. 2025 Mar 5. doi: 10.1007/s11604-025-01756-y. Online ahead of print.

ABSTRACT

PURPOSE: To assess the utility of dual-type deep learning (DL)-based image reconstruction with DL-based image denoising and super-resolution processing by comparing images reconstructed with the conventional method in head and neck fat-suppressed (Fs) T2-weighted imaging (T2WI).

MATERIALS AND METHODS: We retrospectively analyzed the cases of 43 patients who underwent head/neck Fs-T2WI for the assessment of their head and neck lesions. All patients underwent two sets of Fs-T2WI scans with conventional- and DL-based reconstruction. The Fs-T2WI with DL-based reconstruction was acquired based on a 30% reduction of its spatial resolution in both the x- and y-axes with a shortened scan time. Qualitative and quantitative assessments were performed with both the conventional method- and DL-based reconstructions. For the qualitative assessment, we visually evaluated the overall image quality, visibility of anatomical structures, degree of artifact(s), lesion conspicuity, and lesion edge sharpness based on five-point grading. In the quantitative assessment, we measured the signal-to-noise ratio (SNR) of the lesion and the contrast-to-noise ratio (CNR) between the lesion and the adjacent or nearest muscle.

RESULTS: In the qualitative analysis, significant differences were observed between the Fs-T2WI with the conventional- and DL-based reconstruction in all of the evaluation items except the degree of the artifact(s) (p < 0.001). In the quantitative analysis, significant differences were observed in the SNR between the Fs-T2WI with conventional- (21.4 ± 14.7) and DL-based reconstructions (26.2 ± 13.5) (p < 0.001). In the CNR assessment, the CNR between the lesion and adjacent or nearest muscle in the DL-based Fs-T2WI (16.8 ± 11.6) was significantly higher than that in the conventional Fs-T2WI (14.2 ± 12.9) (p < 0.001).

CONCLUSION: Dual-type DL-based image reconstruction by an effective denoising and super-resolution process successfully provided high image quality in head and neck Fs-T2WI with a shortened scan time compared to the conventional imaging method.

PMID:40038217 | DOI:10.1007/s11604-025-01756-y

Biochem Genet. 2025 Mar 4. doi: 10.1007/s10528-025-11071-w. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease linked to aging. Mitochondrial dysfunction in circulating T cells, often caused by disruption of mitochondrial DNA (mtDNA), may play a role in age-related conditions like IPF. In our previous study, we found high mtDNA mutational loads in blood lymphocytes from IPF patients, especially in regions critical for mtDNA expression. Since Complex I of the electron transport chain, partly encoded by mtDNA, is essential for energy production, we conducted a preliminary study on its activity. We found significantly reduced Complex I activity (p < 0.001) in lymphocytes from 40 IPF patients compared to 40 controls, which was positively correlated with lung function decline, specifically in functional vital capacity and diffusing capacity for carbon monoxide. These findings indicate that T cell mitochondrial dysfunction is associated with disease progression in IPF. Future work will explore the mechanisms linking T cell mitochondrial disruption with fibrosis, potentially uncovering new therapeutic targets.

PMID:40038177 | DOI:10.1007/s10528-025-11071-w

Intern Emerg Med. 2025 Mar 4. doi: 10.1007/s11739-025-03908-4. Online ahead of print.

ABSTRACT

Aortitis and periaortitis refer to the inflammation of the aortic wall and the surrounding tissues. Both conditions are associated with various diseases and express nonspecific manifestations. Early diagnosis and treatment are crucial to improve the prognosis of the disease. This study aimed to assess the causes and main clinical features of aortitis and periaortitis in patients from a single centre in Spain. Observational, retrospective study of patients diagnosed with aortitis or periaortitis at a Spanish referral center over the last decade. 134 patients (87 female; mean age of 55.1 ± 9.1 years) were recruited, 132 of which had aortitis and two periaortitis. Aortitis was associated with giant cell arteritis (n = 102), Takayasu's arteritis (n = 6), IgG4-related disease (n = 6), infectious diseases (n = 3), malignancy (n = 1), drugs (n = 1), isolated aortitis (n = 1), and other immune-mediated inflammatory diseases (IMIDs) (n = 12). IMIDs included were Sjögren's syndrome (n = 2), sarcoidosis (n = 2), rheumatoid arthritis (n = 2), axial spondyloarthritis (n = 2), inflammatory bowel disease (n = 1), primary biliary cirrhosis (n = 1), idiopathic pulmonary fibrosis (n = 1), and polyarteritis nodosa (n = 1). Periaortitis was due to idiopathic retroperitoneal fibrosis in both cases. Imaging techniques used for diagnosis included 18F-FDG PET/CT scan (n = 133), CT-angiography (n = 44), and/or MRI-angiography (n = 33). Polymyalgia rheumatica (52.2%) and asthenia (53.7%) were the most common manifestations, followed by limb claudication (23.9%) and inflammatory back pain (26.9%). Acute-phase reactants were typically increased. Aortitis is a common condition and may be associated with multiple non-infectious diseases. Its clinical presentation is often unspecific, requiring a high level of suspicion to get an early diagnosis and treatment.

PMID:40038164 | DOI:10.1007/s11739-025-03908-4

J Ethnopharmacol. 2025 Mar 2:119568. doi: 10.1016/j.jep.2025.119568. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Maimendong decoction (MMDD) originates from the ancient Chinese medical text Synopsis of the Golden Chamber and is a well-established remedy for treating lung diseases. It has demonstrated efficacy in the long-term clinical management of idiopathic pulmonary fibrosis (IPF); however, its underlying mechanisms remain unclear.

AIM OF THE STUDY: This study investigates whether MMDD alleviates IPF by reducing type 2 alveolar epithelial cell (AEC2) senescence and enhancing mitochondrial autophagy. It also explores whether these effects are mediated through the PTEN-induced putative kinase 1 (PINK1)/Parkinson juvenile disease protein 2 (Parkin) pathway.

MATERIALS AND METHODS: An IPF mouse model was established with bleomycin (BLM). Mice were administered MMDD, pirfenidone (PFD), or saline for 7 or 28 days. Body weight, lung coefficient, and lung appearance were monitored, and lung tissue pathology was assessed. The expression levels of p53, p21, p16, SA-β-gal activity, and senescence-associated secretory phenotype (SASP) markers were measured. Ultrastructural changes in AEC2 mitochondria were analyzed using transmission electron microscopy. Protein levels of autophagy markers sequestosome-1 and light chain 3 were assessed. The protein levels of PINK1, Parkin, and phosphorylated Parkin were further assessed using network pharmacology analysis and molecular docking technology.

RESULTS: MMDD alleviated BLM-induced IPF by improving body weight, lung appearance, and histopathological features. It reduced AEC2 senescence markers, including p53, p21, p16, SA-β-gal, and SASP, while enhancing mitochondrial autophagy and repairing mitochondrial damage. Network pharmacology and molecular docking identified PINK1 as a major target, and Western blot (WB) analysis confirmed that MMDD regulates the PINK1/Parkin signaling pathway in the treatment of IPF.

CONCLUSIONS: MMDD regulates the PINK1/Parkin signaling pathway, alleviates AEC2 senescence, and enhances mitochondrial autophagy, providing significant therapeutic potential for IPF treatment.

PMID:40037475 | DOI:10.1016/j.jep.2025.119568

BMC Genomics. 2025 Mar 4;26(1):215. doi: 10.1186/s12864-025-11250-4.

ABSTRACT

BACKGROUND: Integrating germplasm populations genotyped by different genotyping platforms via genotype imputation is a way to utilize accumulated genetic resources. In this study, we used 278 canola samples genotyped via whole-genome sequencing (WGS) at 10× coverage to evaluate the imputation accuracy of three imputation approaches. The optimal imputation methods were used to impute and integrate two Canola genotype datasets: a diverse canola collection genotyped by genotyping-by-sequencing via transcriptome (GBS-t) and a double haploid (DH) line collection genotyped with low-coverage WGS (skim-WGS). The genomic predictive ability (GP) and detection power of marker‒trait association (GWAS) of the combined population for blackleg resistance were evaluated.

RESULTS: The empirical imputation accuracy (r2) measured as the squared correlation between observed and imputed genotypes was moderate for Minimac3 when imputing from the GBS-t density to the WGS. The accuracy dramatically improved from 0.64 to 0.82 by removing SNPs with poor Minimac3-reported Rsq (Rsq < 0.2) quality statistics. The r2 for GLIMPSE was higher than that for Beagle when imputing from different low-coverage to full-coverage WGS. We imputed and integrated the diverse canola collection and the DH lines, and the combined population showed similar or slightly greater predictive ability (PA) for blackleg resistance traits than did each of the single populations with ~ 921 K SNPs. Higher marker-trait association (MTA) detection powers were indicated with the combined population; however, similar numbers of MTAs were discovered when each single population was combined in a meta-GWAS.

CONCLUSION: It is feasible to impute and integrate germplasms from different sequencing platforms for downstream analyses. However, genetic heterogeneity across populations could add complexity to the analysis. Increasing the sample size by combining datasets showed slightly greater predictive ability and greater detection power in GWASs in the present study.

PMID:40038585 | DOI:10.1186/s12864-025-11250-4

Commun Biol. 2025 Mar 4;8(1):361. doi: 10.1038/s42003-025-07796-4.

ABSTRACT

Chromosomal instability (CIN) is a hallmark of cancer that drives metastasis, immune evasion and treatment resistance. CIN may result from chromosome mis-segregation errors and excessive chromatin is frequently packaged in micronuclei (MN), which can be enumerated to quantify CIN. The assessment of CIN remains a predominantly manual and time-consuming task. Here, we present micronuclAI, a pipeline for automated and reliable quantification of MN of varying size and morphology in cells stained only for DNA. micronuclAI can achieve close to human-level performance on various human and murine cancer cell line datasets. The pipeline achieved a Pearson's correlation of 0.9278 on images obtained at 10X magnification. We tested the approach in otherwise isogenic cell lines in which we genetically dialed up or down CIN rates, and on several publicly available image datasets where we achieved a Pearson's correlation of 0.9620. Given the increasing interest in developing therapies for CIN-driven cancers, this method provides an important, scalable, and rapid approach to quantifying CIN on images that are routinely obtained for research purposes. We release a GUI-implementation for easy access and utilization of the pipeline.

PMID:40038430 | DOI:10.1038/s42003-025-07796-4

Geroscience. 2025 Mar 4. doi: 10.1007/s11357-025-01571-3. Online ahead of print.

ABSTRACT

A significant challenge in multi-omic geroscience research is the collection of high quality, fit-for-purpose biospecimens from a diverse and well-characterized study population with sufficient sample size to detect age-related changes in physiological biomarkers. The Dog Aging Project designed the precision cohort to study the mechanisms underlying age-related change in the metabolome, microbiome, and epigenome in companion dogs, an emerging model system for translational geroscience research. One thousand dog-owner pairs were recruited into cohort strata based on life stage, sex, size, and geography. We designed and built a novel implementation of the REDCap electronic data capture system to manage study participants, logistics, and biospecimen and survey data collection in a secure online platform. In collaboration with primary care veterinarians, we collected and processed blood, urine, fecal, and hair samples from 976 dogs. The resulting data include complete blood count, chemistry profile, immunophenotyping by flow cytometry, metabolite quantification, fecal microbiome characterization, epigenomic profile, urinalysis, and associated metadata characterizing sample conditions at collection and during lab processing. The project, which has already begun collecting second- and third-year samples from precision cohort dogs, demonstrates that scientifically useful biospecimens can be collected from a geographically dispersed population through collaboration with private veterinary clinics and downstream labs. The data collection infrastructure developed for the precision cohort can be leveraged for future studies. Most important, the Dog Aging Project is an open data project. We encourage researchers around the world to apply for data access and utilize this rich, constantly growing dataset in their own work.

PMID:40038157 | DOI:10.1007/s11357-025-01571-3

Plant Cell. 2025 Mar 4:koaf041. doi: 10.1093/plcell/koaf041. Online ahead of print.

ABSTRACT

Powdery mildews are obligate biotrophic fungi that manipulate plant metabolism to supply lipids to the fungus, particularly during fungal asexual reproduction when lipid demand is high. We found levels of leaf storage lipids (triacylglycerols, TAGs) are 3.5-fold higher in whole Arabidopsis (Arabidopsis thaliana) leaves with a 15-fold increase in storage lipids at the infection site during fungal asexual reproduction. Lipid bodies, not observable in uninfected mature leaves, were found in and external to chloroplasts in mesophyll cells underlying the fungal feeding structure. Concomitantly, thylakoid disassembly occurred and thylakoid membrane lipid levels decreased. Genetic analyses showed that canonical endoplasmic reticulum TAG biosynthesis does not support powdery mildew spore production. Instead, Arabidopsis chloroplast-localized DIACYLGLYCEROL ACYLTRANSFERASE 3 (DGAT3) promoted fungal asexual reproduction. Consistent with the reported AtDGAT3 preference for 18:3 and 18:2 acyl substrates, which are dominant in thylakoid membrane lipids, dgat3 mutants exhibited a dramatic reduction in powdery mildew-induced chloroplast TAGs, attributable to decreases in TAG species largely comprised of 18:3 and 18:2 acyl substrates. This pathway for TAG biosynthesis in the chloroplast at the expense of thylakoids provides insights into obligate biotrophy and plant lipid metabolism, plasticity and function. By understanding how photosynthetically active leaves can be converted into TAG producers, more sustainable and environmentally friendly plant oil production may be developed.

PMID:40037697 | DOI:10.1093/plcell/koaf041

Plant Physiol. 2025 Mar 4:kiaf066. doi: 10.1093/plphys/kiaf066. Online ahead of print.

NO ABSTRACT

PMID:40037614 | DOI:10.1093/plphys/kiaf066