Proc Natl Acad Sci U S A. 2025 Apr 8;122(14):e2412818122. doi: 10.1073/pnas.2412818122. Epub 2025 Apr 1.

ABSTRACT

The tumor microenvironment (TME) encompasses various cell types, blood and lymphatic vessels, and noncellular constituents like extracellular matrix (ECM) and cytokines. These intricate interactions between cellular and noncellular components contribute to the development of a malignant TME, such as immunosuppressive, desmoplastic, angiogenic conditions, and the formation of a niche for cancer stem cells, but there is limited understanding of the specific subtypes of stromal cells involved in this process. Here, we utilized p16-CreERT2-tdTomato mouse models to investigate the signaling networks established by senescent cancer stromal cells, contributing to the development of a malignant TME. In pancreatic ductal adenocarcinoma (PDAC) allograft models, these senescent cells were found to promote cancer fibrosis, enhance angiogenesis, and suppress cancer immune surveillance. Notably, the selective elimination of senescent cancer stromal cells improves the malignant TME, subsequently reducing tumor progression in PDAC. This highlights the antitumor efficacy of senolytic treatment alone and its synergistic effect when combined with conventional chemotherapy. Taken together, our findings suggest that the signaling crosstalk among senescent cancer stromal cells plays a key role in the progression of PDAC and may be a promising therapeutic target.

PMID:40168129 | DOI:10.1073/pnas.2412818122

Mol Ecol Resour. 2025 Apr 1:e14110. doi: 10.1111/1755-0998.14110. Online ahead of print.

ABSTRACT

The percomorph fish clade Gobioidei is a suborder that comprises over 2200 species distributed in nearly all aquatic habitats. To understand the genetics underlying their species diversification, we sequenced and annotated the genome of the loach goby, Rhyacichthys aspro, an early-diverging group, and compared it with nine additional Gobioidei species. Within Gobioidei, the loach goby possesses the smallest genome at 594 Mb, and a rise in species diversity from early-diverging to more recently diverged lineages is mirrored by enlarged genomes and a higher presence of transposable elements (TEs), particularly DNA transposons. These DNA transposons are enriched in genic and regulatory regions and their copy number increase is strongly correlated with substitution rate, suggesting that DNA repair after transposon excision/insertion leads to nearby mutations. Consequently, the proliferation of DNA transposons might be the crucial driver of Gobioidei diversification and adaptability. The loach goby genome also points to mechanisms of ecological adaptation. It contains relatively few genes for lateral line development but an overrepresentation of synaptic function genes, with genes putatively under selection linked to synapse organisation and calcium signalling, implicating a sensory system distinct from other Gobioidei species. We also see an overabundance of genes involved in neurocranium development and renal function, adaptations likely connected to its flat morphology suited for strong currents and an amphidromous life cycle. Comparative analyses with hill-stream loaches and the European eel reveal convergent adaptations in body shape and saltwater balance. These findings shed new light on the loach goby's survival mechanisms and the broader evolutionary trends within Gobioidei.

PMID:40168108 | DOI:10.1111/1755-0998.14110

Clin Exp Dermatol. 2025 Apr 1:llaf147. doi: 10.1093/ced/llaf147. Online ahead of print.

ABSTRACT

This is a secondary analysis of a multicentre randomised controlled trial of ciclosporin and methotrexate in children and young people (CYP) with severe atopic dermatitis (AD). Longitudinal trough ciclosporin and erythrocyte methotrexate polyglutamates (MTX-PG) concentrations were measured to evaluate their associations with treatment response and adverse events. Both ciclosporin (4 mg/kg/day) and methotrexate (0.4 mg/kg/week) led to a significant reduction in disease severity scores over the 36-week treatment period. Higher trough ciclosporin concentrations were associated with lower disease severity scores and may serve as a useful tool for therapeutic drug monitoring of ciclosporin in CYP with AD. However, in contrast to a previously published study, steady-state erythrocyte-MTX-PG concentrations showed no significant association with treatment response. Drug concentrations were comparable between patients with and without drug-related adverse events.

PMID:40168525 | DOI:10.1093/ced/llaf147

Methods Mol Biol. 2025;2914:323-332. doi: 10.1007/978-1-0716-4462-1_22.

ABSTRACT

Selecting a fluid near an affected organ can improve the likelihood of identifying a biomarker panel from pathological tissue. Cerebrospinal fluid (CSF), in close contact with the brain, is a valuable source of biomarkers for neurological disorders due to the inaccessibility of brain tissue. Moreover, the altered CSF proteome identified in neurological diseases can facilitate the repurposing of drugs already used for other therapeutic purposes. In this context, Connectivity Map (CMap) is a valuable tool as it provides information on compounds and gene modifications that can be utilized to reverse specific pathological signatures. Analyzing CSF differential proteomics through the CMap framework offers an efficient and cost-effective approach to identifying potential novel therapies for neurodegenerative diseases.

PMID:40167927 | DOI:10.1007/978-1-0716-4462-1_22

ACS Biomater Sci Eng. 2025 Apr 1. doi: 10.1021/acsbiomaterials.5c00068. Online ahead of print.

ABSTRACT

Drug repurposing is an attractive route for finding new therapeutics for brain cancers such as glioblastoma. Local administration of drugs to brain tumors or the postsurgical resection cavity holds promise to deliver a high dose to the target site with minimal off-target effects. Drug delivery systems aim to sustain the release of the drug at the target site but typically exhibit drawbacks such as a poor safety profile, uncontrolled/rapid drug release, or poor control over synthesis parameters/material dimensions. Herein, we analyzed the antidepressant vortioxetine and showed in vitro that it causes a greater loss of viability in glioblastoma cells than it does to normal primary human astrocytes. We developed a new droplet microfluidic-based emulsion method to reproducibly produce vortioxetine-loaded poly(lactic-co-glycolic) acid (PLGA) microspheres with tight size control (36.80 ± 1.96 μm). The drug loading efficiency was around 90% when 9.1% (w/w) drug was loaded into the microspheres, and drug release could be sustained for three to 4 weeks. The vortioxetine microspheres showed robust antiglioblastoma efficacy in both 2D monolayer and 3D spheroid patient-derived glioblastoma cells, highlighting the potential of combining an antidepressant with sustained local delivery as a new therapeutic strategy.

PMID:40167528 | DOI:10.1021/acsbiomaterials.5c00068

Am J Med Genet C Semin Med Genet. 2025 Apr 1:e32138. doi: 10.1002/ajmg.c.32138. Online ahead of print.

ABSTRACT

Rare diseases impact approximately 1 in 10 people worldwide, and yet, less than 5% of all rare diseases currently have an approved treatment option available. This is due to many challenges unique to rare diseases, including small, diverse patient populations, the cost of drug development that is not proportionate to the number of patients who could potentially benefit from treatment, and difficulty with clinical trial design to validate new therapeutics. As a result, drug repurposing has become an increasingly promising option for finding treatment options for rare diseases. First described in 2018, Bachmann-Bupp Syndrome (BABS) is a rare neurodevelopmental disorder that is caused by gain-of-function variants in the ornithine decarboxylase (ODC1) gene and is characterized by developmental delay, hypotonia, and alopecia. Through collaboration and the use of a unique drug repurposing strategy, the first patient identified with BABS was treated with the repurposed drug eflornithine, also known as α-difluoromethylornithine (DFMO), in just 16 months. Currently, five additional patients with BABS are being treated with DFMO. This model of drug repurposing of an FDA-approved drug for use in another indication can serve as an example of what is possible in the scope of other rare diseases, specifically in other polyaminopathies.

PMID:40167220 | DOI:10.1002/ajmg.c.32138

Pediatr Pulmonol. 2025 Apr;60(4):e71076. doi: 10.1002/ppul.71076.

ABSTRACT

BACKGROUND: The effects of CFTR modulators, particularly elexacaftor/tezacaftor/ivacaftor (ETI), on exercise capacity in people with cystic fibrosis (pwCF) remain unclear, with no data available on their impact within the context of an exercise intervention. Therefore, this study aimed to assess the effects of an exercise intervention on exercise capacity in adults with CF, comparing those treated with and without ETI.

METHODS: A total of 56 adult pwCF participated in this quasi-experimental study as part of a rehabilitation program, which included a 3.5-week exercise intervention. The program involved five weekly 45-min sessions, including endurance training on a cycle ergometer. VO2 peak and Wpeak were the primary outcomes used to assess changes in exercise capacity.

RESULTS: The intervention significantly increased VO2 peak and Wpeak in all pwCF, regardless of ETI use, with similar improvements between groups. PwCF with lower baseline fitness (VO2 peak ≤ 81%pred) showed greater improvements than those with higher fitness (VO2 peak ≥ 82%pred). ppFEV1 remained unchanged, while BMI increased in both groups. Notably, the ETI group spent significantly more time in physical activity (PA) at hard and very hard intensities compared to the non-ETI group. Additionally, a positive correlation was observed between PA intensity and VO2 peak and Wpeak in the ETI group.

CONCLUSION: Independent of ETI treatment, adult pwCF improve their exercise capacity by participating in a regular exercise program. ETI treatment appears to enhance time spent in higher PA intensities. Despite the effectiveness of CFTR modulators, regular PA and exercise remain essential to maintain and improve exercise capacity in pwCF.

PMID:40167900 | DOI:10.1002/ppul.71076

Disabil Rehabil. 2025 Apr 1:1-9. doi: 10.1080/09638288.2025.2484779. Online ahead of print.

ABSTRACT

Purpose: Adherence to airway clearance therapy (ACT) among individuals with cystic fibrosis (CF) is often inconsistent. This study aims to explore the perceptions of adults with CF regarding their experiences with ACT and what influences their selection of specific ACTs. Findings may help inform clinician approaches to patient care and ACT. Materials and Methods: A qualitative descriptive study was conducted using individual, semi-structured interviews. Eight participants [six male and two female, median (min-max) age 42.5 (27-52)] were purposively recruited from the Toronto Adult CF Centre at St. Michael's Hospital, Unity Health Toronto. Results: Four key themes were generated from participants' accounts. First, they described the intensive nature of CF self-management and its influence on their perceptions and selection of ACT techniques. Second, they emphasized the importance of healthcare professional guidance in treatment decisions. Third, physical health status, exercise, and CF transmembrane conductance regulator modulator therapy also shaped participants' self-management approaches. Lastly, their social context influenced how they navigated self-management, which evolved over time. Conclusion: This study shows that ACT technique selection is influenced by various evolving needs across the lifespan. Understanding the role that patient experiences play in ACT technique selection may help clinicians personalize recommendations and promote patient-centred care.

PMID:40167245 | DOI:10.1080/09638288.2025.2484779

Biopsychosoc Sci Med. 2025 Mar 24. doi: 10.1097/PSY.0000000000001387. Online ahead of print.

ABSTRACT

OBJECTIVE: An emerging body of evidence suggests that psychological flexibility may be an important and underexamined determinant of overall psychological functioning. The chronic nature of Cystic Fibrosis (CF) may require a greater level of flexibility to navigate complex and dynamic health concerns in an increasingly aging population.

METHODS: We examined associations between psychological flexibility, coping styles, psychological grit, and negative affectivity (anxiety and depressive symptoms) from baseline assessments of randomized trial among adults with CF. Regression models controlling for age, gender, income, psychotropic medication use and pulmonary function were used to characterize associations between psychological flexibility, coping styles and negative affect.

RESULTS: A total of 124 individuals were included in analyses, 74 (60%) of whom were taking a psychotropic medication. Depressive (BDI-II=18.6 [SD=9.9]) and anxious (BAI=13.8 [SD=9.3]) symptoms were both elevated. Greater levels of psychological flexibility were associated with lower negative affect, such that individuals reporting less cognitive fusion (B=-0.59, P<0.001) and greater psychological acceptance (B=-0.51. P<0.001) exhibited lesser levels of anxiety and depressive symptoms. Psychological flexibility was the most robust correlate of negative affect after accounting for other coping variables (B=-0.50, P<0.001) and this association was not moderated by FEV1/FVC levels.

CONCLUSIONS: Psychological flexibility is robustly associated with decreased negative affect among individuals with CF, independent of background and clinical characteristics.

PMID:40167140 | DOI:10.1097/PSY.0000000000001387

ACS Sens. 2025 Apr 1. doi: 10.1021/acssensors.4c03621. Online ahead of print.

ABSTRACT

Lung cancer exhibits strong heterogeneity, and its early diagnosis and precise subtyping are of great importance, as they can increase the ability to deliver personalized medicines by tailoring therapy regimens. Tissue biopsy, albeit the gold standard, is invasive, costly and provides limited information about the tumor and its molecular landscape. Exosomes, as promising biomarkers for lung cancer, are a heterogeneous collection of membranous vesicles containing tumor-specific information for liquid biopsy to identify lung cancer subtypes. However, the small size, complex structure, and heterogeneous molecular features of exosomes pose significant challenges for their effective isolation and analysis. Herein, we report a deep learning-driven microfluidic chip with surface-enhanced Raman scattering (SERS) readout to characterize the differences in exosomes for the early diagnosis and molecular subtyping of non-small cell lung cancer (NSCLC). This integration comprises a processing unit for exosome capture and enrichment using polystyrene microspheres (PS) binding gold nanocubes (AuNCs) and anti-CD-9 antibody (denoted as PACD), and an optical sensing unit to trap the PACD and detect SERS signals from these exosomes. This system achieved a maximum trapping efficiency of 85%, and could distinguish three different NSCLC cell lines from the normal cell line with an overall accuracy of 97.88% and an area under the curve (AUC) of over 0.95 for each category. This work highlights the combined power of deep learning, SERS, and microfluidics in realizing the capture, detection, and analysis of exosomes from biological matrices, which may pave the way for clinical exosome-based cancer diagnosis and prognostication in the future.

PMID:40167999 | DOI:10.1021/acssensors.4c03621

Radiol Med. 2025 Apr 1. doi: 10.1007/s11547-025-02006-x. Online ahead of print.

ABSTRACT

PURPOSE: This study aims to evaluate the effectiveness of combining transvaginal ultrasound (US)-based radiomics and deep learning model for the accurate differentiation between benign and malignant ovarian tumors in large-scale studies.

MATERIALS AND METHODS: A multicenter retrospective study collected grayscale and color US images of ovarian tumors. Patients were divided into training, internal, and external validation groups. Models including a convolutional neural networks (CNN), optimal radiomics, and a combined model were constructed and evaluated for predictive performance using area under curve (AUC), sensitivity, and specificity. The DeLong test compared model AUCs with O-RADS and expert assessments.

RESULTS: 3193 images from 2078 patients were analyzed. The CNN achieved AUCs of 0.970 (internal) and 0.959 (external), respectively. Optimal radiomic model achieved AUCs of 0.949 (internal) and 0.954 (external), respectively. The combined CNN-radiomics model attained the highest AUC of 0.977 (internal) and 0.972 (external), respectively, outperforming individual models, O-RADS, and expert methods (p < 0.05).

CONCLUSIONS: The combined CNN-radiomics model using transvaginal US images provides more accurate and reliable ovarian tumor diagnosis, enhancing malignancy prediction and offering clinicians a more precise diagnostic tool.

PMID:40167932 | DOI:10.1007/s11547-025-02006-x

Discov Oncol. 2025 Apr 1;16(1):438. doi: 10.1007/s12672-025-02212-z.

ABSTRACT

Artificial intelligence (AI) marks a frontier in histopathologic analysis shift towards the clinic, becoming a mainstream choice to interpret histological images. Surveying studies assessing AI applications in histopathology from 2013 to 2024, we review key methods (including supervised, unsupervised, weakly supervised and transfer learning) in deep learning-based pattern recognition in computational histopathology for diagnostic and prognostic purposes. Deep learning methods also showed utility in identifying a wide range of genetic mutations and standard pathology biomarkers from routine histology. This survey of 41 primary studies also encompasses key regions of AI applicability in histopathology in a multi-cancer review while marking prospects to introduce AI into the clinical setting with key examples including Swarm Learning and Data Fusion.

PMID:40167870 | DOI:10.1007/s12672-025-02212-z

Opt Lett. 2025 Apr 1;50(7):2390-2393. doi: 10.1364/OL.549356.

ABSTRACT

To meet rapidly growing communication demands, researchers have focused on structured light-based shift keying techniques. However, higher-order modes are prone to large diffraction divergence and are easily perturbed. In this study, we experimentally demonstrate what we believe to be a novel coherence shaping method for petal-like structures of optical vortices, enabling the generation of non-diffraction interference states between completely coherent and incoherent states. In addition, we propose a coherence shift keying (CSK) scheme enabled by deep learning, and a well-trained model can achieve a high recognition accuracy (>0.997) of interference states under practical conditions, including complex environments. Further experimental validation has confirmed that the minimum achievable visibility-level bandwidth is 0.02. This study provides a new, to the best of our knowledge, platform for low-order structured light mode-based high-capacity and encrypted shift keying communication systems.

PMID:40167728 | DOI:10.1364/OL.549356

Abdom Radiol (NY). 2025 Apr 1. doi: 10.1007/s00261-025-04895-y. Online ahead of print.

ABSTRACT

PURPOSE: To investigate the value of deep learning (DL) in T2-weighted imaging (T2DL) of the bladder regarding acquisition time (TA), image quality, and diagnostic confidence compared to standard T2-weighted turbo-spin-echo (TSE) imaging (T2S).

METHODS: We prospectively enrolled a total of 28 consecutive patients for the evaluation of bladder cancer. T2S and T2DL sequences in three planes were performed for each participant, and acquisition time was compared between the two acquisition protocols. The image evaluation was conducted independently by two radiologists using a 5-point Likert scale for artifacts, noise, overall image quality, and diagnostic confidence, with 5 indicating the best quality. Additionally, T2 scoring based on Vesical Imaging-Reporting and Data System (VI-RADS) was performed by two readers.

RESULTS: Compared to T2S, the acquisition time of T2DL was reduced by 49.4% in the axial and by 43.8% in the coronal and sagittal orientations. The severity and impact of artifacts and noise levels were superior in T2DL versus T2S (both p < 0.05). The overall image quality in T2DL was demonstrated to be higher compared to that in T2S in axial and sagittal imaging (both p < 0.05). The diagnostic confidence and T2 scoring of both sequences in all planes did not differ (p > 0.05).

CONCLUSIONS: Our study preliminarily demonstrated the feasibility of T2-weighted imaging with DL reconstruction of bladder MR in clinical practice. T2DL achieved a reduction in acquisition time, superior lesion detectability, and overall image quality with similar diagnostic confidence and T2 score compared to the standard T2 TSE sequence.

PMID:40167648 | DOI:10.1007/s00261-025-04895-y

Abdom Radiol (NY). 2025 Apr 1. doi: 10.1007/s00261-025-04886-z. Online ahead of print.

ABSTRACT

PURPOSE: To develop and validate a deep learning-based feature ensemble model using multiparametric magnetic resonance imaging (MRI) for predicting tumor budding (TB) grading in patients with rectal cancer (RC).

METHODS: A retrospective cohort of 458 patients with pathologically confirmed rectal cancer (RC) from three institutions was included. Among them, 355 patients from Center 1 were divided into two groups at a 7:3 ratio: the training cohort (n = 248) and the internal validation cohort (n = 107). An additional 103 patients from two other centers served as the external validation cohort. Deep learning models were constructed for T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) based on the CrossFormer architecture, and deep learning features were extracted. Subsequently, a feature ensemble module based on the attention mechanism of Transformer was used to capture spatial interactions between different imaging sequences, creating a multiparametric ensemble model. The predictive performance of each model was evaluated using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA).

RESULTS: The deep learning model based on T2WI achieved AUC values of 0.789 (95% CI: 0.680-0.900) and 0.720 (95% CI: 0.591-0.849) in the internal and external validation cohorts, respectively. The deep learning model based on DWI had AUC values of 0.806 (95% CI: 0.705-0.908) and 0.772 (95% CI: 0.657-0.887) in the internal and external validation cohorts, respectively. The multiparametric ensemble model demonstrated superior performance, with AUC values of 0.868 (95% CI: 0.775-0.960) in the internal validation cohort and 0.839 (95% CI: 0.743-0.935) in the external validation cohort. DeLong test showed that the differences in AUC values among the models were not statistically significant in both the internal and external test sets (P > 0.05). The DCA curve demonstrated that within the 10-80% threshold range, the fusion model provided significantly higher clinical net benefit compared to other models.

CONCLUSION: Compared to single-sequence deep learning models, the attention mechanism-based multiparametric MRI fusion model enables more effective individualized prediction of TB grading in RC patients. It offers valuable guidance for treatment selection and prognostic evaluation while providing imaging-based support for personalized postoperative follow-up adjustments.

PMID:40167646 | DOI:10.1007/s00261-025-04886-z

Abdom Radiol (NY). 2025 Apr 1. doi: 10.1007/s00261-025-04887-y. Online ahead of print.

ABSTRACT

OBJECTIVES: To train and validate segmentation models for automated segmentation of gallbladder cancer (GBC) lesions from contrast-enhanced CT images.

MATERIALS AND METHODS: This retrospective study comprised consecutive patients with pathologically proven treatment naïve GBC who underwent a contrast-enhanced CT scan at four different tertiary care referral hospitals. The training and validation cohort comprised CT scans of 317 patients (center 1). The internal test cohort comprised a temporally independent cohort (n = 29) from center 1 (internal test 1). The external test cohort comprised CT scans from three centers [ (n = 85)]. We trained the state-of-the-art 2D and 3D image segmentation models, SAM Adapter, MedSAM, 3D TransUNet, SAM-Med3D, and 3D-nnU-Net, for automated segmentation of the GBC. The models' performance for GBC segmentation on the test datasets was assessed via dice score and intersection over union (IoU) using manual segmentation as the reference standard.

RESULTS: The 2D models performed better than 3D models. Overall, MedSAM achieved the highest dice and IoU scores on both the internal [mean dice (SD) 0.776 (0.106) and mean IoU 0.653 (0.133)] and external [mean dice (SD) 0.763 (0.098) and mean IoU 0.637 (0.116)] test sets. Among the 3D models, TransUNet showed the best segmentation performance with mean dice (SD) and IoU (SD) of 0.479 (0.268) and 0.356 (0.235) in the internal test and 0.409 (0.339) and 0.317 (0.283) in the external test sets. The segmentation performance was not associated with GBC morphology. There was weak correlation between the dice/IoU and the size of the GBC lesions for any segmentation model.

CONCLUSION: We trained 2D and 3D GBC segmentation models on a large dataset and validated these models on external datasets. MedSAM, a 2D prompt-based foundational model, achieved the best segmentation performance.

PMID:40167645 | DOI:10.1007/s00261-025-04887-y

Abdom Radiol (NY). 2025 Apr 1. doi: 10.1007/s00261-025-04897-w. Online ahead of print.

ABSTRACT

Artificial intelligence (AI) may have the potential to improve existing diagnostic challenges in endometriosis imaging. To better direct future research, this descriptive review summarizes the general landscape of AI applications in endometriosis imaging. Articles from PubMed were selected to represent different approaches to AI applications in endometriosis imaging. Current endometriosis imaging literature focuses on AI applications in ultrasound (US) and magnetic resonance imaging (MRI). Most studies use US data, with MRI studies being limited at present. The majority of US studies employ transvaginal ultrasound (TVUS) data and aim to detect deep endometriosis implants, adenomyosis, endometriomas, and secondary signs of endometriosis. Most MRI studies evaluate endometriosis disease diagnosis and segmentation. Some studies analyze multi-modal methods for endometriosis imaging, combining US and MRI data or using imaging data in combination with clinical data. Current literature lacks generalizability and standardization. Most studies in this review utilize small sample sizes with retrospective approaches and single-center data. Existing models only focus on narrow disease detection or diagnosis questions and lack standardized ground truth. Overall, AI applications in endometriosis imaging analysis are in their early stages, and continued research is essential to develop and enhance these models.

PMID:40167644 | DOI:10.1007/s00261-025-04897-w

JASA Express Lett. 2025 Apr 1;5(4):040801. doi: 10.1121/10.0036312.

ABSTRACT

A generative deep learning framework is introduced for ray-based ocean acoustic tomography (OAT), an inverse problem for estimating sound speed profiles (SSP) based on arrival-times measurements between multiple acoustic transducers, which is typically ill-posed. This framework relies on a robust low-dimensional parametrization of the expected SSP variations using a variational autoencoder and a linear dynamical model as further regularization. This framework was tested using SSP variations simulated by a regional ocean model with submesoscale permitting horizontal resolution and various transducer configurations spanning the upper ocean over short propagation ranges and was found to outperform conventional linear least squares formulations of OAT.

PMID:40167492 | DOI:10.1121/10.0036312

J Synchrotron Radiat. 2025 May 1. doi: 10.1107/S1600577525001833. Online ahead of print.

ABSTRACT

In bone-imaging research, in situ synchrotron radiation micro-computed tomography (SRµCT) mechanical tests are used to investigate the mechanical properties of bone in relation to its microstructure. Low-dose computed tomography (CT) is used to preserve bone's mechanical properties from radiation damage, though it increases noise. To reduce this noise, the self-supervised deep learning method Noise2Inverse was used on low-dose SRµCT images where segmentation using traditional thresholding techniques was not possible. Simulated-dose datasets were created by sampling projection data at full, one-half, one-third, one-fourth and one-sixth frequencies of an in situ SRµCT mechanical test. After convolutional neural networks were trained, Noise2Inverse performance on all dose simulations was assessed visually and by analyzing bone microstructural features. Visually, high image quality was recovered for each simulated dose. Lacunae volume, lacunae aspect ratio and mineralization distributions shifted slightly in full, one-half and one-third dose network results, but were distorted in one-fourth and one-sixth dose network results. Following this, new models were trained using a larger dataset to determine differences between full dose and one-third dose simulations. Significant changes were found for all parameters of bone microstructure, indicating that a separate validation scan may be necessary to apply this technique for microstructure quantification. Noise present during data acquisition from the testing setup was determined to be the primary source of concern for Noise2Inverse viability. While these limitations exist, incorporating dose calculations and optimal imaging parameters enables self-supervised deep learning methods such as Noise2Inverse to be integrated into existing experiments to decrease radiation dose.

PMID:40167487 | DOI:10.1107/S1600577525001833

J Clin Lab Anal. 2025 Apr 1:e70026. doi: 10.1002/jcla.70026. Online ahead of print.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive and irreversible interstitial lung disease with a complex pathogenesis involving multiple immune cells. This study investigates the relationship between immune cells and IPF using Mendelian randomization (MR) analysis.

METHODS: A two-sample MR analysis was performed using genome-wide association studies (GWAS) and immune cell databases by R software. We analyzed data from 1028 European individuals with IPF, focusing on 731 immune traits. The primary method of analysis was inverse variance weighting (IVW), supplemented with sensitivity analyses, including MR-Egger regression and MR-PRESSO, to detect and correct for pleiotropy.

RESULTS: The MR analysis identified six immune panels and 23 immune traits significantly associated with IPF, including five traits that increase and 18 traits that decrease IPF risk. Notable traits increasing IPF risk included switched memory B-cells (OR = 1.27, p = 0.0029) and IgD- CD38dim B-cells (OR = 1.08, p = 0.0449). Traits associated with a reduced IPF risk included central memory CD4+ T-cells (%CD4+, OR = 0.96, p = 0.0489), CD20 on naive-mature B-cells (OR = 0.94, p = 0.0499), and CD33br HLA-DR+ absolute count (AC) (OR = 0.93, p = 0.0489). There was no significant causal relationship between IPF disease and some immune traits (p > 0.05).

CONCLUSION: This study suggests a potential causal link between specific immune cell traits and the development of IPF, providing new insights into the disease's immunological mechanisms. Future research should focus on validating these findings in larger, more diverse populations to inform drug development and therapeutic strategies.

PMID:40167279 | DOI:10.1002/jcla.70026