BMC Musculoskelet Disord. 2025 Mar 22;26(1):287. doi: 10.1186/s12891-025-08538-7.

ABSTRACT

OBJECTIVES: Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) present similar symptoms in the early stage, complicating their differentiation. This study aims to develop a classification model using radiomic features from MRI T2-weighted Dixon sequences to increase the accuracy of distinguishing DMD and BMD in the early disease stage.

METHODS: We retrospectively analysed MRI data from 62 patients aged 36-60 months with muscular dystrophy, including 41 with DMD and 21 with BMD. Radiomic features were extracted from in-phase, opposed-phase, water, fat, and postprocessed fat fraction images. We employed a deep learning segmentation method to segment regions of interest automatically. Feature selection included the Mann‒Whitney U test for identifying significant features, Pearson correlation analysis to remove collinear features, and the LASSO regression method to select features with nonzero coefficients. These selected features were then used in various machine learning algorithms to construct the classification model, and their diagnostic performance was compared.

RESULTS: Our proposed radiomic and machine learning methods effectively distinguished early DMD and BMD. The machine learning models significantly outperformed the radiologists in terms of accuracy (81.2-90.6% compared with 69.4%), specificity (71.0-86.0% compared with 19.0%), and F1 score (85.2-92.6% compared with 80.5%), while maintaining relatively high sensitivity (85.6-95.0% compared with 95.1%).

CONCLUSION: Radiomics based on Dixon sequences combined with machine learning methods can effectively distinguish between DMD and BMD in the early stages, providing a new and effective tool for the early diagnosis of these muscular dystrophies.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:40121488 | DOI:10.1186/s12891-025-08538-7

BMC Bioinformatics. 2025 Mar 22;26(1):88. doi: 10.1186/s12859-025-06101-8.

ABSTRACT

Binding proteins play a crucial role in biological systems by selectively interacting with specific molecules, such as DNA, RNA, or peptides, to regulate various cellular processes. Their ability to recognize and bind target molecules with high specificity makes them essential for signal transduction, transport, and enzymatic activity. Traditional experimental methods for identifying protein-binding peptides are costly and time-consuming. Current sequence-based approaches often struggle with accuracy, focusing too narrowly on proximal sequence features and ignoring structural data. This study presents Deep-ProBind, a powerful prediction model designed to classify protein binding sites by integrating sequence and structural information. The proposed model employs a transformer and evolutionary-based attention mechanism, i.e., Bidirectional Encoder Representations from Transformers (BERT) and Pseudo position specific scoring matrix -Discrete Wavelet Transform (PsePSSM -DWT) approach to encode peptides. The SHapley Additive exPlanations (SHAP) algorithm selects the optimal hybrid features, and a Deep Neural Network (DNN) is then used as the classification algorithm to predict protein-binding peptides. The performance of the proposed model was evaluated in comparison with traditional Machine Learning (ML) algorithms and existing models. Experimental results demonstrate that Deep-ProBind achieved 92.67% accuracy with tenfold cross-validation on benchmark datasets and 93.62% accuracy on independent samples. The Deep-ProBind outperforms existing models by 3.57% on training data and 1.52% on independent tests. These results demonstrate Deep-ProBind's reliability and effectiveness, making it a valuable tool for researchers and a potential resource in pharmacological studies, where peptide binding plays a critical role in therapeutic development.

PMID:40121399 | DOI:10.1186/s12859-025-06101-8

Sci Rep. 2025 Mar 22;15(1):9891. doi: 10.1038/s41598-025-94275-9.

ABSTRACT

In four-dimensional time series such as dynamic contrast-enhanced (DCE) MRI, motion between individual time steps due to the patient's breathing or movement leads to incorrect image analysis, e.g., when calculating perfusion. Image registration of the volumes of the individual time steps is necessary to improve the accuracy of the subsequent image analysis. Both groupwise and multiresolution registration methods have shown great potential for medical image registration. To combine the advantages of groupwise and multiresolution registration, we proposed a groupwise multiresolution network for deformable medical image registration. We applied our proposed method to the registration of DCE-MR images for the assessment of lung perfusion in patients with congenital diaphragmatic hernia. The networks were trained unsupervised with Mutual Information and Gradient L2 loss. We compared the groupwise networks with a pairwise deformable registration network and a published groupwise network as benchmarks and the classical registration method SimpleElastix as baseline using four-dimensional DCE-MR scans of patients after congenital diaphragmatic hernia repair. Experimental results showed that our groupwise network yields results with high spatial alignment (SSIM up to 0.953 ± 0.025 or 0.936 ± 0.028 respectively), medically plausible transformation with low image folding (|J| ≤ 0: 0.0 ± 0.0%), and a low registration time of less than 10 seconds for a four-dimensional DCE-MR scan with 50 time steps. Furthermore, our results demonstrate that image registration with the proposed groupwise network enhances the accuracy of medical image analysis by leading to more homogeneous perfusion maps.

PMID:40121309 | DOI:10.1038/s41598-025-94275-9

NPJ Digit Med. 2025 Mar 22;8(1):174. doi: 10.1038/s41746-025-01560-y.

ABSTRACT

Building accurate prediction models and identifying predictive biomarkers for treatment response in Muscle-Invasive Bladder Cancer (MIBC) are essential for improving patient survival but remain challenging due to tumor heterogeneity, despite numerous related studies. To address this unmet need, we developed an interpretable Graph-based Multimodal Late Fusion (GMLF) deep learning framework. Integrating histopathology and cell type data from standard H&E images with gene expression profiles derived from RNA sequencing from the SWOG S1314-COXEN clinical trial (ClinicalTrials.gov NCT02177695 2014-06-25), GMLF uncovered new histopathological, cellular, and molecular determinants of response to neoadjuvant chemotherapy. Specifically, we identified key gene signatures that drive the predictive power of our model, including alterations in TP63, CCL5, and DCN. Our discovery can optimize treatment strategies for patients with MIBC, e.g., improving clinical outcomes, avoiding unnecessary treatment, and ultimately, bladder preservation. Additionally, our approach could be used to uncover predictors for other cancers.

PMID:40121304 | DOI:10.1038/s41746-025-01560-y

Sci Rep. 2025 Mar 22;15(1):9972. doi: 10.1038/s41598-025-94464-6.

ABSTRACT

Recent data-driven deep learning methods for image reflection removal have made impressive progress, promoting the quality of photo capturing and scene understanding. Due to the massive consumption of computational complexity and memory usage, the performance of these methods degrades significantly while dealing with high-resolution images. Besides, most existing methods for reflection removal can only remove reflection patterns by downsampling the input image into a much lower resolution, resulting in the loss of plentiful information. In this paper, we propose a novel transformer-based framework for high-resolution image reflection removal, termed as the Laplacian pyramid-based component-aware transformer (LapCAT). LapCAT leverages a Laplacian pyramid network to remove high-frequency reflection patterns and reconstruct the high-resolution background image guided by the clean low-frequency background components. Guided by the reflection mask through pixel-wise contrastive learning, LapCAT designs a component-separable transformer block (CSTB) which removes reflection patterns from the background constituents through a reflection-aware multi-head self-attention mechanism. Extensive experiments on several benchmark datasets for reflection removal demonstrate the superiority of our LapCAT, especially the excellent performance and high efficiency in removing reflection from high-resolution images than state-of-the-art methods.

PMID:40121298 | DOI:10.1038/s41598-025-94464-6

Sci Rep. 2025 Mar 22;15(1):9924. doi: 10.1038/s41598-025-94437-9.

ABSTRACT

Medical image segmentation represents a pivotal and intricate procedure in the domain of medical image processing and analysis. With the progression of artificial intelligence in recent years, the utilization of deep learning techniques for medical image segmentation has witnessed escalating popularity. Nevertheless, the intricate nature of medical image poses challenges on the segmentation of diminutive targets is still in its early stages. Current networks encounter difficulties in addressing the segmentation of exceedingly small targets, especially when the number of training samples is limited. To overcome this constraint, we have implemented a proficient strategy to enhance lesion images containing small targets and constrained samples. We introduce a segmentation framework termed STS-Net, specifically designed for small target segmentation. This framework leverages the established capacity of convolutional neural networks to acquire effective image representations. The proposed STS-Net network adopts a ResNeXt50-32x4d architecture as the encoder, integrating attention mechanisms during the encoding phase to amplify the feature representation capabilities of the network. We evaluated the proposed network on four publicly available datasets. Experimental results underscore the superiority of our approach in the domain of medical image segmentation, particularly for small target segmentation. The codes are available at https://github.com/zlxokok/STSNet .

PMID:40121297 | DOI:10.1038/s41598-025-94437-9

Sci Rep. 2025 Mar 22;15(1):9905. doi: 10.1038/s41598-025-92872-2.

ABSTRACT

Predicting recurrence among early-stage hormone receptor-positive human epidermal growth factor receptor-negative breast cancer (HR+/HER2- BC) is crucial for guiding adjuvant therapy. However, studies are limited for patients with low recurrence risk. HR+/HER2- early-stage (T1-2N0-1) invasive BC patients who received definitive surgery and followed by endocrine therapy from four independent medical centers were included in this retrospective study. Patients from center 1 were used as derivation cohort, while those from other centers were combined as an external test cohort. A deep learning prognostic model, HERPAI, was developed based on Transformer to predict risk of invasive disease-free survival (iDFS) utilizing clinical and pathological predictors. The model performance was evaluated using C-index for the overall population and subgroups. Threshold for selecting 5-year recurrence risk > 10% was determined. Hazard ratio (HR) was estimated between risk groups for iDFS. A total of 6340 patients were included, of whom 5424 were assigned to the derivation cohort (training and validation [N = 4882] and internal test cohort [N = 542]), while 916 patients were utilized as external cohort. HERPAI yielded a C-index of 0.73 (95% CI 0.65-0.81), 0.73 (95% CI 0.62-0.85), and 0.68 (95% CI 0.60-0.77), in the validation, internal, and external test cohort, respectively. Consistent performances were observed for pre-specified subgroups. High-risk patients were associated with an increased risk of recurrence for validation (HR, 2.56 [95% CI 1.25-5.22], P = 0.01), internal test (HR, 2.52 [95% CI 0.97-6.57], P = 0.06) and external test (HR, 1.94 [95% CI 1.00-3.74], P = 0.049) cohort, respectively. HERPAI was a promising tool for selecting vulnerable early-stage HR+/HER2- BC patients who were at high-risk of recurrence. It could facilitate the prioritization of patients who may benefit more from escalating adjuvant treatment.

PMID:40121273 | DOI:10.1038/s41598-025-92872-2

Sci Rep. 2025 Mar 22;15(1):9914. doi: 10.1038/s41598-025-94267-9.

ABSTRACT

Accurate brain tumor segmentation is critical for clinical diagnosis and treatment planning. This study proposes an advanced segmentation framework that combines Multiscale Attention U-Net with the EfficientNetB4 encoder to enhance segmentation performance. Unlike conventional U-Net-based architectures, the proposed model leverages EfficientNetB4's compound scaling to optimize feature extraction at multiple resolutions while maintaining low computational overhead. Additionally, the Multi-Scale Attention Mechanism (utilizing [Formula: see text], and [Formula: see text] kernels) enhances feature representation by capturing tumor boundaries across different scales, addressing limitations of existing CNN-based segmentation methods. Our approach effectively suppresses irrelevant regions and enhances tumor localization through attention-enhanced skip connections and residual attention blocks. Extensive experiments were conducted on the publicly available Figshare brain tumor dataset, comparing different EfficientNet variants to determine the optimal architecture. EfficientNetB4 demonstrated superior performance, achieving an Accuracy of 99.79%, MCR of 0.21%, Dice Coefficient of 0.9339, and an Intersection over Union (IoU) of 0.8795, outperforming other variants in accuracy and computational efficiency. The training process was analyzed using key metrics, including Dice Coefficient, dice loss, precision, recall, specificity, and IoU, showing stable convergence and generalization. Additionally, the proposed method was evaluated against state-of-the-art approaches, surpassing them in all critical metrics, including accuracy, IoU, Dice Coefficient, precision, recall, specificity, and mean IoU. This study demonstrates the effectiveness of the proposed method for robust and efficient segmentation of brain tumors, positioning it as a valuable tool for clinical and research applications.

PMID:40121246 | DOI:10.1038/s41598-025-94267-9

Sci Rep. 2025 Mar 22;15(1):9942. doi: 10.1038/s41598-024-82909-3.

ABSTRACT

To develop a deep learning (DL) model based on MRI to predict muscle-invasive bladder cancer (MIBC). A total of 559 patients, including 521 patients in our center and 38 patients in external centers were collected from 2012 to 2023 to construct the DL model. In this study, the DL model was utilized to differentiate between MIBC and NMIBC based on three-channel image inputs, including original T2WI images, segmented bladder, and regions of interest. Inception V3 was employed for model construction. The accuracy, sensitivity (SN), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) for predicting MIBC by DL model were 92.4%, 94.7%, 91.5%, 81.8% and 97.7% in the validation set and 92.1%, 86.8%, 94.6%, 88.5% and 93.8% in the internal test set. In the external test set, these values were 81.6%, 57.1%, 87.1%, 50.0% and 90.0%. Additionally, the accuracy, SN, SP, PPV, and NPV for predicting MIBC were 93.5%, 100%, 93.4%, 11.1%, and 100% in VI-RADS 2; 80.0%, 66.7%, 87.2%, 73.7% and 82.9% in VI-RADS 3; 90.3%, 91.7%, 85.7%, 95.7%, 75.0% in VI-RADS 4. The accuracy, SN, and PPV were 93.9%, 93.9%, and 100% in VI-RADS 5. The DL model based on T2WI can effectively predict MIBC and serve as a valuable complement to VI-RADS 3.

PMID:40121216 | DOI:10.1038/s41598-024-82909-3

Respir Res. 2025 Mar 22;26(1):111. doi: 10.1186/s12931-025-03187-9.

ABSTRACT

The coronavirus disease 2019 (COVID-19) global pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, our understanding of SARS-CoV-2-induced inflammation in alveolar epithelial cells remains very limited. The contributions of intracellular insulin-like growth factor binding protein-2 (IGFBP2) to SARS-CoV-2 pathogenesis are also unclear. In this study, we have uncovered a critical role for IGFBP2, specifically in alveolar epithelial type 2 cells (AEC2), in the immunopathogenesis of COVID-19. Using bulk RNA sequencing, we show that IGFBP2 mRNA expression is significantly downregulated in primary AEC2 cells isolated from fibrotic lung regions from patients with COVID-19-acute respiratory distress syndrome (ARDS) compared to those with idiopathic pulmonary fibrosis (IPF) alone or IPF with a history of COVID-19. Using multicolor immunohistochemistry, we demonstrated that IGFBP2 and its selective ligands IGF1 and IGF2 were significantly reduced in AEC2 cells from patients with COVID-ARDS, IPF alone, or IPF with COVID history than in those from age-matched donor controls. Further, we demonstrated that lentiviral expression of Igfbp2 significantly reduced mRNA expression of proinflammatory cytokines-Tnf-α, Il1β, Il6, Stat3, Stat6 and chemokine receptors-Ccr2 and Ccr5-in mouse lung epithelial cells challenged with SARS-CoV-2 spike protein injury (S2; 500 ng/mL). Finally, we demonstrated higher levels of cytokines-TNF-α; IL-6 and chemokine receptor-CCR5 in AEC2 cells from COVID-ARDS patients compared to the IPF alone and the IPF with COVID history patients. Altogether, these data suggest that anti-inflammatory properties of IGFBP2 in AEC2 cells and its localized delivery may serve as potential therapeutic strategy for patients with COVID-19.

PMID:40121473 | DOI:10.1186/s12931-025-03187-9

BMC Bioinformatics. 2025 Mar 22;26(1):87. doi: 10.1186/s12859-025-06105-4.

ABSTRACT

BACKGROUND: Computational approaches to support rare disease diagnosis are challenging to build, requiring the integration of complex data types such as ontologies, gene-to-phenotype associations, and cross-species data into variant and gene prioritisation algorithms (VGPAs). However, the performance of VGPAs has been difficult to measure and is impacted by many factors, for example, ontology structure, annotation completeness or changes to the underlying algorithm. Assertions of the capabilities of VGPAs are often not reproducible, in part because there is no standardised, empirical framework and openly available patient data to assess the efficacy of VGPAs-ultimately hindering the development of effective prioritisation tools.

RESULTS: In this paper, we present our benchmarking tool, PhEval, which aims to provide a standardised and empirical framework to evaluate phenotype-driven VGPAs. The inclusion of standardised test corpora and test corpus generation tools in the PhEval suite of tools allows open benchmarking and comparison of methods on standardised data sets.

CONCLUSIONS: PhEval and the standardised test corpora solve the issues of patient data availability and experimental tooling configuration when benchmarking and comparing rare disease VGPAs. By providing standardised data on patient cohorts from real-world case-reports and controlling the configuration of evaluated VGPAs, PhEval enables transparent, portable, comparable and reproducible benchmarking of VGPAs. As these tools are often a key component of many rare disease diagnostic pipelines, a thorough and standardised method of assessment is essential for improving patient diagnosis and care.

PMID:40121479 | DOI:10.1186/s12859-025-06105-4

Sci Rep. 2025 Mar 22;15(1):9958. doi: 10.1038/s41598-025-94666-y.

ABSTRACT

The involvement of Cronobacter, which is frequently associated with meningitis and necrotizing enterocolitis, in human colorectal cancer remains unexplored. In this study, we isolate and characterize a novel strain of C. malonaticus designated PO3 from a fecal sample of a colon cancer patient and demonstrate its proliferative effects on colorectal cancer both in vitro and in vivo. The secretome of PO3 significantly promoted cell proliferation, as evidenced by increased cell viability, fluorescence intensity, and Ki-67 expression, without inducing cell death. Furthermore, using high-resolution mass spectrometry (HRMS), we identified a novel tryptic oncopeptide designated P506, in the PO3 secretome that promotes colorectal cancer. Synthetic P506 further stimulated human colorectal adenocarcinoma cell line HT-29 cell proliferation in a dose-dependent manner. Experiments with the BALB/c mouse model in vivo revealed that both the PO3 secretome and P506 contributed to the development of colorectal polyps and associated histological changes, including dysplasia and altered colonic architecture. These findings suggest that P506, a potent peptide from the PO3 secretome, may have oncogenic potential, promoting colorectal cancer progression.

PMID:40121280 | DOI:10.1038/s41598-025-94666-y

Eur J Pharmacol. 2025 Mar 20:177533. doi: 10.1016/j.ejphar.2025.177533. Online ahead of print.

ABSTRACT

BACKGROUND: The antibiotic salinomycin, a well-known cancer stem cell inhibitor, may impact the diversity of the intestinal microbiota in colorectal cancer (CRC) mice, which plays a pivotal role in shaping the immune system. This study explores the anti-cancer effects and mechanisms of combining salinomycin and fecal microbiota transplantation (FMT) in treating CRC.

METHODS: FMT was given via enema, while salinomycin was injected intraperitoneally into the CRC mouse model induced by azoxymethane/dextran sodium sulfate.

RESULTS: In CRC mice, a large number of LGR5-labeled cancer stem cells and severe disturbances in the intestinal microbiota were observed. Interestingly, salinomycin inhibited the proliferation of cancer stem cells without exacerbating the microbial disorder as expected. In comparison to salinomycin treatment, the combination of salinomycin and FMT significantly improved pathological damage and restored intestinal microbial diversity, which is responsible for shaping the anti-cancer immune microenvironment. The supplementation of FMT significantly increased the levels of propionic acid and butyric acid while also promoting the infiltration of CD8+ T cells and Ly6G+ neutrophils, as well as reducing F4/80+ macrophage recruitment. Notably, cytokines that were not impacted by salinomycin exhibited robust reactions to alterations in the gut microbiota. These included pro-inflammatory factors (IL6, IL12b, IL17, and IL22), chemokine-like protein OPN, and immunosuppressive factor PD-L1.

CONCLUSIONS: Salinomycin plays the role of "eliminating pathogenic qi," targeting cancer stem cells; FMT plays the role of "strengthening vital qi," reversing the intestinal microbiota disorder and enhancing anti-cancer immunity. They have a synergistic effect on the development of CRC.

PMID:40120791 | DOI:10.1016/j.ejphar.2025.177533

J Cyst Fibros. 2025 Mar 21:S1569-1993(25)00078-5. doi: 10.1016/j.jcf.2025.03.010. Online ahead of print.

ABSTRACT

BACKGROUND: CFTR modulators in post-transplant people with cystic fibrosis (pwCF) are less frequently used due to uncertainty regarding effectiveness and interactions with immunosuppressive agents. Elexacaftor/tezacaftor/ivacaftor (ETI) is a triple combination cystic fibrosis (CF) therapeutic with benefits in multiple organ systems where complications can impact lung transplant (LTx) outcomes, including malnutrition, diabetes, and sinus disease. ETI use in LTx recipients is variable.

METHODS: We conducted a pharmacokinetics (PK) study of concentrations of ETI parent compounds and the four major metabolites (M23-ELX, M1-TEZ, M1-IVA, M6-IVA) in a prospective non-randomized observational study, with all transplant participants concomitantly taking tacrolimus for LTx immunosuppression and excluded if taking any other medication with known interactions (e.g., azole antifungals) and compared to a non-transplant group of pwCF. We completed non-compartmental analysis (NCA) for both groups and compared the transplant to non-transplant PK parameters, as well as to published data from the manufacturer for non-transplant pwCF. Area under the curve (AUC), average concentrations (Cavg), minimum and maximum concentrations, clearance, and other parameters were determined.

RESULTS: Twelve transplant and fourteen non-transplant participants with CF completed the study. There were no significant differences between the mean values for any PK parameters for the transplant and non-transplant groups and no substantial differences in frequency of concentrations outside the therapeutic ranges in the two groups.

CONCLUSIONS: Our data suggest there are not significant differences in concentrations of ELX, TEZ, IVA, or their major human metabolites in LTx recipients compared to non-transplant pwCF.

PMID:40121139 | DOI:10.1016/j.jcf.2025.03.010

Trends Mol Med. 2025 Mar 21:S1471-4914(25)00034-6. doi: 10.1016/j.molmed.2025.02.001. Online ahead of print.

NO ABSTRACT

PMID:40121136 | DOI:10.1016/j.molmed.2025.02.001

Tissue Cell. 2025 Mar 19;95:102865. doi: 10.1016/j.tice.2025.102865. Online ahead of print.

ABSTRACT

The lungs are constantly subjected to enormous amounts of air and potentially transmitted agents, leading to a high incidence of severe and complex ailments urging the demand for defensive actions to maintain their regular function. Numerous studies have demonstrated how certain probiotics have many advantages including hindering pulmonary exacerbations in individuals with cystic fibrosis, which encourages the idea of combining them with approved antibiotics as a therapeutic choice for treatment patients with lung fibrosis who also have bacterial infections. This investigation aimed to test the possibility of a combination of Torulaspora delbrueckii as a probiotic with ciprofloxacin in an animal model having pulmonary fibrosis with a moderate load of Klebsiella pneumonia. Ninety adult male rats were split into six groups (15 rats/each): GI (control), GII (lung fibrosis), GIII (lung fibrosis infected by K. pneumonia), GIV (lung fibrosis infected by K. pneumonia then treated with ciprofloxacin), GV (lung fibrosis infected by K. pneumonia and fed with T. delbrueckii) and GVI (lung fibrosis infected by K. pneumonia then treated with combined therapy of ciprofloxacin and T. delbrueckii) for 28 days. Survival rate and bacterial load were determined in various experimental animal groups. Histological variations were examined as well as scanning electron microscopy. Gene expression, various levels of cytokines, redox enzymes, and oxidative stress markers were assessed and compared in different tested groups. The treatment using a combination of T. delbrueckii and ciprofloxacin is suggested as a new method to treat induced lung fibrosis in animals and infected with K. pneumonia as a possible option in complicated infection.

PMID:40120428 | DOI:10.1016/j.tice.2025.102865

Biochem Biophys Res Commun. 2025 Mar 14;758:151645. doi: 10.1016/j.bbrc.2025.151645. Online ahead of print.

ABSTRACT

Mycobacterium abscessus (M. abscessus) is an emerging, rapidly growing mycobacterium that causes chronic lung infection, particularly in patients with cystic fibrosis, as well as skin and soft tissue infections. Adipose tissue is recognized as an important niche that supports M. tuberculosis persistence. However, the dormancy, latency, and persistence mechanisms of M. abscessus in the host remain poorly understood. This study investigated how adipose tissue serves as a niche for M. abscessus using both a human adipose tissue ex vivo infection model and a murine adipose tissue in vivo infection model. M. abscessus infected not only the cytosol of adipocytes but also entered host lipid droplets, where it formed intracytoplasmic lipid inclusions in the bacterial cell. To our knowledge, this unique localization has never been reported for M. abscessus or any other mycobacterium. Within host lipid droplets, M. abscessus lost acid-fastness and gained Nile Red positivity. These results suggest that M. abscessus acquires a dormancy-like phenotype within host lipid droplets of adipocytes, potentially contributing to its persistence, virulence, and resistance to treatment. These findings provide valuable insights into mycobacterial persistence mechanisms and could offer a promising foundation for developing novel therapeutic approaches.

PMID:40120350 | DOI:10.1016/j.bbrc.2025.151645

BMJ Open Respir Res. 2025 Mar 22;12(1):e002327. doi: 10.1136/bmjresp-2024-002327.

ABSTRACT

INTRODUCTION: Breathlessness is common and impairs the quality of life of people with idiopathic pulmonary fibrosis (IPF) and non-IPF fibrotic interstitial lung diseases (ILD). We report the findings of a multicentre, fast-track (wait-list), mixed-methods, randomised controlled, feasibility study of a complex breathlessness intervention in breathless IPF and non-IPF fibrotic ILD patients.

METHODS: Breathless IPF and non-IPF fibrotic ILD patients were randomised to receive the intervention within 1 week (fast-track) or after 8 weeks (wait-list). The intervention comprised two face-to-face and one telephone appointment during a 3-week period covering breathing control, handheld fan-use, pacing and breathlessness management techniques, and techniques to manage anxiety. Feasibility and clinical outcomes were assessed to inform progression to, and optimal design for, a definitive trial. A qualitative substudy explored barriers and facilitators to trial and intervention delivery.

RESULTS: 47 patients (M:F 38:9, mean (SD) age 73.9 (7.2)) were randomised with a recruitment rate of 2.5 participants per month across three sites. The adjusted mean differences (95% CI) for key clinical outcomes at 4 weeks post randomisation were as follows: Chronic Respiratory Questionnaire breathlessness mastery domain (0.45 (-0.07, 0.97)); and numerical rating scales for 'worst' (-0.93 (-1.95, 0.10)), 'best' (-0.19 (-1.38, 1.00)), 'distress caused by' (-1.84 (-3.29, -0.39)) and 'ability to cope with' (0.71 (-0.57, 1.99)) breathlessness within the past 24 hours. The qualitative substudy confirmed intervention acceptability and informed feasibility and acceptability of study outcome measures.

CONCLUSION: A definitive trial of a complex breathlessness intervention in patients with IPF and non-IPF fibrotic ILD is feasible with preliminary data supporting intervention effectiveness.

TRIAL REGISTRATION NUMBER: ISRCTN13784514.

PMID:40121019 | DOI:10.1136/bmjresp-2024-002327

Aging Clin Exp Res. 2025 Mar 22;37(1):101. doi: 10.1007/s40520-025-03009-4.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is more likely to occur in the elderly population, and these patients often become depressed. It has been recognized that psychological disorders are not conducive to the control of many diseases. Thus, this study aims to determine whether alleviating depression can delay the progression of IPF and frailty in elderly patients with IPF.

METHODS: IPF patients over 60 years old were included in the study. None had a prior history of psychological disorders. All developed depression after being diagnosed with IPF. During the 12-month follow-up, some patients received anti-depression interventions and the rest didn't. Depression, IPF, frailty and peripheral inflammation at baseline and after follow-up were evaluated by indicators and scales such as BDI-II, FVC %pred, 6MWT, mMRC, CFS, TFI, SGRQ, K-BILD, IL-6, and TNF-α. Multivariate logistic regression was employed for data analysis.

RESULTS: There were 213 elderly patients with IPF. Among the 89 patients who received anti-depression interventions, the above-mentioned indicators and scales did not deteriorate during the follow-up period (P > 0.05). Among the remaining 124 patients, the FVC %pred, and 6MWT levels decreased, and the mMRC grade, CFS, TFI, SGRQ and K-BILD scores, and peripheral IL-6 and TNF-α levels increased during the follow-up period (P < 0.05).

DISCUSSION: Compared with non-intervened IPF patients, those receiving anti-depression interventions seemed to maintain a certain stability in IPF, frailty, and peripheral inflammation over a period.

CONCLUSION: Improving depression may help delay the deterioration of patients' IPF and frailty at certain stages.

TRIAL REGISTRATION: Registration on UMIN-CTR.

REGISTRATION NUMBER: UMIN000057161. Date of registration: February 27th, 2025.

PMID:40120048 | DOI:10.1007/s40520-025-03009-4

Fam Cancer. 2025 Mar 22;24(2):31. doi: 10.1007/s10689-025-00455-x.

ABSTRACT

Telomere biology disorders (TBDs) are a group of genetic conditions characterized by defects in telomere maintenance leading to multisystemic organ involvement and a predisposition to hematologic malignancies. The management of patients with TBDs who develop acute myeloid leukemia (AML) presents a significant challenge due to their limited bone marrow reserve and non-hematopoietic organ dysfunction. We present the case of a 45-year-old patient with a previously unrecognized TBD who presented with AML. The patient's history of longstanding cytopenias, idiopathic avascular necrosis, and pulmonary fibrosis were suggestive of a TBD, which was confirmed through telomere length testing and the presence of a TERT variant. Due to his underlying TBD, he was treated with dose-reduced azacitidine and venetoclax, adapting the approach commonly employed in elderly, co-morbid AML patients ineligible for intensive chemotherapy. This resulted in a complete remission with incomplete count recovery that has persisted for greater than 12 months to date. Aside from prolonged myelosuppression, the patient tolerated the regimen well with minimal toxicity. To our knowledge, this is the first report of the successful utilization of azacitidine and venetoclax as an AML treatment modality in TBD patients and underscores the potential of this regimen as an effective non-intensive treatment strategy for high grade myeloid neoplasms arising in the context of inherited bone marrow failure syndromes.

PMID:40119960 | DOI:10.1007/s10689-025-00455-x