Int J Artif Organs. 2025 Aug 9:3913988251359522. doi: 10.1177/03913988251359522. Online ahead of print.

ABSTRACT

Lung cancer (LC) is one of the leading causes of cancer related deaths worldwide and early recognition is critical for enhancing patient outcomes. However, existing LC detection techniques face challenges such as high computational demands, complex data integration, scalability limitations, and difficulties in achieving rigorous clinical validation. This research proposes an Enhanced Hyper Tuning Deep Learning (EHTDL) model utilizing bioinspired algorithms to overcome these limitations and improve accuracy and efficiency of LC detection and classification. The methodology begins with the Smooth Edge Enhancement (SEE) technique for preprocessing CT images, followed by feature extraction using GLCM-based Texture Analysis. To refine the features and reduce dimensionality, a Hybrid Feature Selection approach combining Grey Wolf optimization (GWO) and Differential Evolution (DE) is employed. Precise lung segmentation is performed using Mask R-CNN to ensure accurate delineation of lung regions. A Deep Fractal Edge Classifier (DFEC) is introduced, consisting of five fractal blocks with convolutional layers and pooling to progressively learn LC characteristics. The proposed EHTDL model achieves remarkable performance metrics, including 99% accuracy, 100% precision, 98% recall, and 99% F1-score, demonstrating its robustness and effectiveness. The model's scalability and efficiency make it suitable for real-time clinical application offering a promising solution for early LC detection and significantly enhancing patient care.

PMID:40781973 | DOI:10.1177/03913988251359522

Med Phys. 2025 Aug;52(8):e17976. doi: 10.1002/mp.17976.

ABSTRACT

BACKGROUND: Quantitative measures of dopamine transporter (DaT) uptake in the caudate, putamen, and globus pallidus (GP) derived from DaT-single-photon emission computed tomography (SPECT) images are being investigated as biomarkers to diagnose, assess disease status, and track the progression of Parkinsonism. Reliable quantification from DaT-SPECT images requires performing attenuation compensation (AC), typically with a separate x-ray CT scan. Such CT-based AC (CTAC) has multiple challenges, a key one being the non-availability of x-ray CT components on many clinical SPECT systems. Even when a CT is available, the additional CT scan leads to increased radiation dose, costs, and complexity; potential quantification errors due to SPECT-CT misalignment; and higher training and regulatory requirements.

PURPOSE: To overcome the challenges with the requirement of a CT scan for AC in DaT SPECT, we develop a transmission-less AC method for DaT SPECT and validate the method in a clinically realistic setting using an in silico imaging trial.

METHOD: Integrating concepts from physics and deep learning (DL), we propose a DL-based transmission-less AC method for DaT-SPECT (DaT-CTLESS). In this method, an initial attenuation map reconstructed from scatter-energy window projection is segmented into different regions using a U-net-based network trained on CT scans. Each region is assigned a predefined attenuation coefficient, yielding an attenuation map for AC. An in silico imaging trial, titled ISIT-DaT, was designed to evaluate the performance of DaT-CTLESS on the regional uptake quantification task. In this trial, DaT SPECT scans of a virtual patient population, curated from CT and MR images of real patients, were generated with simulated SPECT scanners from two vendors. The Society of Nuclear Medicine (SNM) guidelines suggest using a uniform attenuation map for AC (UAC) when a CT scan is unavailable. Thus, the primary objective of ISIT-DaT was to assess whether the correlation between the activity uptake obtained using DaT-CTLESS and CTAC, as quantified using the intraclass correlation coefficient (ICC), was higher than the correlation between UAC and CTAC. Secondary objectives included evaluating DaT-CTLESS on the task of distinguishing patients with normal versus reduced DaT-specific binding ratio (SBR) of putamen, evaluating the repeatability of DaT-CTLESS in a test-retest study, assessing the generalizability across two SPECT scanners, evaluating DaT-CTLESS using fidelity-based figures of merit (FoMs), and evaluating the sensitivity of DaT-CTLESS to intra-regional uptake heterogeneity. Finally, we compared DaT-CTLESS with two other deep-learning transmission-less AC methods on regional uptake quantification across different training dataset sizes.

RESULTS: In the ISIT-DaT trial, data from 150 virtual patients were used to train, and another 47 were used to evaluate the DaT-CLTESS method. We observed that DaT-CTLESS yielded a significantly higher correlation with CTAC than the correlation between UAC and CTAC on the regional DaT uptake quantification task. Further, DaT-CLTESS had an excellent agreement with CTAC (ICC: 0.96, 95% CI: [0.94, 0.97], p < 0.05) on this task, significantly outperformed UAC in distinguishing patients with normal versus reduced putamen SBR, and on fidelity-based FoMs, yielded good generalizability across two scanners, was generally insensitive to intra-regional uptake heterogeneity, demonstrated good repeatability in the test-retest study, exhibited robust performance even as the size of the training data was reduced, and generally outperformed the other considered DL methods on the task of quantifying regional uptake across different training dataset sizes.

CONCLUSION: The proposed DaT-CTLESS method, as evaluated in ISIT-DaT trial, was observed to yield reliable performance for transmission-less AC in DaT-SPECT, providing a strong motivation for further clinical evaluation.

PMID:40781836 | DOI:10.1002/mp.17976

Med Phys. 2025 Aug;52(8):e17990. doi: 10.1002/mp.17990.

ABSTRACT

BACKGROUND: CT lung imaging protocols need to be optimized. This claim is especially important due to the possible introduction of low-dose CT (LDCT) for lung cancer screening. Given the incorporation of non-linear reconstructions and post-processing, the use of phantoms that consider task-based evaluation is needed. This is also true for acceptance and continuous QC use.

PURPOSE: To present and validate a lung-CT hybrid phantom composed of two setups, one for task-based image quality metrics and the other anthropomorphic.

METHODS: The task-based metrics setup was based on the well-known Mercury phantom and the anthropomorphic setup named Freddie (from Figure of Merit Performance evaluation of Detectability in Diagnostic CT Imaging Equipment) was designed with the same basic dimensions of the Mercury phantom, but including pieces and materials for mimicking chest structures, such as tracheobronchial tree and lung parenchyma. This setup allows the inclusion of pieces of different sizes to mimic ground-glass opacities, and sub-solid and solid lung nodules. The validation of the phantom adopted three methods: comparative evaluation of the attenuation properties and the corresponding Hounsfield Units (HU) values of the selected materials; image assessment according to five chest radiologists and eight non-radiologists' observations (reader study), and measurement of task-based metrics. Images of both setups were acquired using two clinical thorax protocols, both using automatic tube current modulation (TCM). Two x-ray filter combinations were adopted. The images were reconstructed using a deep learning-based algorithm.

RESULTS: The agreement of nominal and observed HU values in the task-based setup was within 15%, except for three (TangoBlack+, VeroClear, and HIPS) of the materials employed in the phantom construction, at some beam energies. In the reader study, synthetic solid nodules printed in VeroClear received average Likert scores 4.0 (range 3.0-4.0) from radiologists and 3 (range 2.6-3.8) from non-radiologists, printed in TangoBlack+ received an average Likert score of 3.9 (range 3.8-4.2) from radiologists and 4.0 (range 3.8-4.4) from non-radiologists, while those printed in HIPS scored an average Likert of 3.8 (range 3.3-3.9) from radiologists and 3.3 (range 3.1-3.3) from non-radiologists. The synthetic ground-glass opacities (GGO) nodules manufactured in EVA received an average Likert score of 3.8 (range 2.8-4.6) from radiologists and 4.3 (range 3.6-4.8) from non-radiologists. The task-based setup demonstrated detectability index variations across protocols influenced by the dose levels, voltage, and x-ray beam filtration used.

CONCLUSIONS: The novelty of the proposed design is concentrated on the possibility of associating the response of the task-based setup (Mercury) with a patient-based setup (Freddie) in a unique phantom. This hybrid design enhances the potential to apply the detectability index for optimizing CT protocols in clinical scenarios.

PMID:40781832 | DOI:10.1002/mp.17990

Med Phys. 2025 Aug;52(8):e18002. doi: 10.1002/mp.18002.

ABSTRACT

BACKGROUND: Cherenkov imaging enables real-time visualization of megavoltage X-ray or electron beam delivery to the patient during radiation therapy (RT). Bio-morphological features, such as vasculature, seen in these images are patient-specific signatures that can be used for verification of positioning and motion management that are essential to precise RT treatment. However, no concerted analysis of this biological feature-based tracking has been utilized until now because of the slow speed and accuracy of conventional image processing for feature segmentation.

PURPOSE: This study aims to demonstrate the first deep learning framework for such an application, achieving video frame rate processing.

MATERIALS AND METHODS: To address the challenge of limited annotation of bio-morphological features in Cherenkov images, a transfer learning strategy was applied. A fundus photography dataset including 20,529 patch retina images with ground-truth vessel annotation was used to pre-train a ResNet based segmentation framework. Subsequently, a small Cherenkov dataset (1483 images from 212 treatment fractions of 19 breast cancer patients) with known annotated vasculature masks was used to fine-tune the model for accurate segmentation prediction.

RESULTS: The well-trained model was tested on clinical Cherenkov dataset which was not used in fine-tune steps. This deep learning framework achieved consistent and rapid segmentation of Cherenkov-imaged bio-morphological features on a test dataset containing 19 patients (179 images), including subcutaneous veins, scars, and pigmented skin. The average segmentation by the model achieved a Dice score of 0.85 and required less than 0.7 ms processing time per instance.

CONCLUSIONS: The model demonstrated outstanding consistency against input image variances and speed compared to conventional manual segmentation methods, laying the foundation for online segmentation in real-time monitoring in a prospective setting.

PMID:40781822 | DOI:10.1002/mp.18002

Med Phys. 2025 Aug;52(8):e18016. doi: 10.1002/mp.18016.

ABSTRACT

BACKGROUND: One-tissue compartment model (1TCM) kinetic parameters calculated from dynamic [11C]aceta te cardiac PET/CT imaging can assess cardiac function and assist clinical diagnosis. However, the long acquisition time of dynamic data hinders its clinical application.

PURPOSE: This study proposed a deep learning-based method for the generation of [11C]acetate 1TCM kinetic parametric images with shortened dynamic PET data, aiming to explore the feasibility of reducing the time required for parametric analysis.

METHODS: A spatial-temporal cascaded network (STCN), consisting of two convolutional modules and one Transformer module, was proposed to generate parametric images K1, k2, and vb. The STCN was trained and tested on [11C]acetate dataset (training/testing: 40 subjects/17 subjects) using 10 frames of dynamic data acquired within the first 10 min of scanning. The parametric images fitted from 40 min of dynamic data using non-linear least squares (NLLS) are considered the reference standard (RS). A temporal loss was incorporated into the training process by integrating the kinetic model. The performance of the STCN was evaluated using normalized root-mean-square error (NRMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM). Weighted Akaike information criterion (WAIC) and coefficient of variance (CoV) were calculated within the myocardial region to evaluate the model's goodness-of-fit and the parameter's degree of variability. The proposed method was compared with NLLS and multilinear least squares fitted on 10 min of dynamic data (CM_10 and MLM_10). Three deep learning-based methods, that is, U-Net, Pix2pix, and CycleGAN, were also trained for comparison. Furthermore, ablation experiments were performed to assess the contribution of individual components of the STCN to the generation of parametric images.

RESULTS: The STCN achieved the best PSNR and SSIM for k2 and vb parametric images (PSNR: 25.718 ± 2.635 and 32.230 ± 4.090; SSIM: 0.864 ± 0.056 and 0.944 ± 0.041, respectively). The PSNR for the K1 images generated by STCN was lower than that generated by the Pix2pix model (28.927 ± 2.956 vs. 28.930 ± 2.705). The 1TCM parameters obtained by STCN achieved an average WAIC of 635.64 ± 38.44 in the myocardial region. No significant difference in CoV within the myocardium was found between RS and parametric images derived from STCN. The ablation study results demonstrated that our proposed model architecture and specialized loss functions could improve the quality of the generated parametric images in NRMSE, PSNR and SSIM.

CONCLUSIONS: The result of the present study shows that the proposed STCN can generate 1TCM parametric images using only 10 min of dynamic [¹¹C]acetate PET data, demonstrating its potential for calculating cardiac [11C]acetate PET 1TCM kinetic parameters in clinical practice.

PMID:40781790 | DOI:10.1002/mp.18016

Med Phys. 2025 Aug;52(8):e18036. doi: 10.1002/mp.18036.

ABSTRACT

BACKGROUND: Magnetic Particle Imaging (MPI) is an emerging technology used to visualize the motion of magnetic nanoparticles in biological tissues. Due to the complexity of the physical behavior of nanoparticles, it is challenging to reconstruct high-quality MPI images from MPI signals. Traditional reconstruction methods, such as system matrix and x-space, are either extremely time-consuming or result in very blurry images. Recently, generative models have been employed for MPI image reconstruction and have achieved success. However, these models still have limitations in obtaining high-quality MPI images.

PURPOSE: Here, we propose a novel multi-task generative method to realize high-quality MPI image reconstruction.

METHODS: In this method, the generative model simultaneously undertakes two MPI image processing tasks: reconstruction and segmentation. The main task of image reconstruction generates MPI images, while the auxiliary task of image segmentation guides the main task to focus on key areas of objects in MPI images during the generation process.

RESULTS: Our experimental results showed that the proposed multi-task model, with its superior generalization ability, outperforms both traditional MPI reconstruction methods and single-task generative methods. Our results also suggested that the tasks of image generation and image segmentation significantly promote each other during the MPI image reconstruction.

CONCLUSION: In our current study, we propose a deep-learning based multi-task method to realize high-quality MPI (Magnetic Particle Imaging) image reconstruction. As far as we know, it is the first attempt to perform feature sharing on MPI image reconstruction and segmentation in medical image analysis.

PMID:40781733 | DOI:10.1002/mp.18036

BMC Geriatr. 2025 Aug 8;25(1):602. doi: 10.1186/s12877-025-06250-8.

ABSTRACT

OBJECTIVES: Older patients with hip fractures face a patient safety threat due to multimorbidity, polypharmacy, potentially inappropriate medications (PIMs), and adverse drug reactions (ADRs, refer to adverse and unrelated reactions to drugs administered at normal doses for the prevention, diagnosis, treatment of diseases, or regulation of physiological functions). Therefore, optimizing drug therapy through drug review and prescription intervention is essential. The primary aim of this study was to assess the efficacy of pharmacist intervention in mitigating ADRs in older inpatients with hip fractures.

METHODS: This study employed a cross-sectional design. It included patients aged 75 years or older who were hospitalized for hip fractures and received multi-disciplinary treatment (MDT) between December 2021 and March 2024. Intervention comprised clinical pharmacist-led drug reviews focusing on PIMs and recommendations for minimizing any unnecessary medications during hospitalization. If the pharmacist-led intervention was accepted by the MDT, the inpatients were regarded as the intervention group; otherwise, they were regarded as the control group. The 2023 AGS/Beers criteria was utilized to assess the PIM status of inpatients with hip fractures. Medscape was used to evaluate drug-drug interactions (DDIs). Quantitative ADRs indicators, including hypoglycemia and hypotension, were assessed via the monitoring of biochemical parameters. Qualitative ADRs indicators, such as rash and delirium, were evaluated based on the observation of patients' clinical manifestations. Causality between drugs and ADRs was systematically assessed based on the standardized criteria established by the WHO-UMC (Uppsala Monitoring Center), and multivariate logistic regression was employed to identify the risk factors associated ADRs. The primary outcome was a significant reduction in the incidence of ADRs. Secondary outcomes included decreases in the prevalence of PIMs and the incidence of serious DDIs.

RESULTS: A total of 106 inpatients were included in the analysis. Following pharmacist-led drug review and deprescribing intervention, the mean number of prescribed medicines in the intervention group was significantly less than that in the control group [8.89 (SD, 2.20) vs. 11.38 (SD, 2.99), P < 0.01]. Additionally, the mean number of PIMs was significantly lower in the intervention group compared to the control group [1.11 (SD, 0.93) vs. 1.82 (SD, 1.30), P = 0.01]. Furthermore, the incidence of ADRs in the intervention group was significantly lower than that in the control group [17.14% (n = 6) vs. 69.01% (n = 49), P < 0.01]. The logistic regression further demonstrated that renal insufficiency (OR: 3.44, 95% CI: 1.22, 9.70, P = 0.02) was the independent risk factor for ADRs in older inpatients with hip fractures, and pharmacist-led deprescribing intervention significantly reduced the incidence of ADRs (OR: 0.14, 95% CI: 0.04, 0.48, P = 0.02).

CONCLUSIONS: The clinical pharmacist-led deprescribing intervention, designed to reduce the use of PIMs not only significantly decreased the number of prescribed drugs, but also markedly reduced the incidence of ADRs among older inpatients with hip fractures.

PMID:40781701 | DOI:10.1186/s12877-025-06250-8

BMC Cancer. 2025 Aug 8;25(1):1289. doi: 10.1186/s12885-025-14696-7.

ABSTRACT

OBJECTIVE: This study sought to comprehensively evaluate the safety profile of tremelimumab and durvalumab in patients with hepatocellular carcinoma (HCC) by examining adverse drug reaction (ADR) documented in the Food and Drug Administration's Adverse Event Reporting System (FAERS) database.

METHODS: Data from the FAERS database spanning from the first quarter of 2004 to the first quarter of 2025 were extracted, filtered, and standardized. The Reporting Odds Ratio (ROR) and Bayesian Confidence Propagation Neural Network (BCPNN) method were utilized to analyze the signal strength and identify potential ADRs.

RESULTS: A total of 574 cases and 1,021 ADR reports were identified. Gastrointestinal disorders (15.65%, n = 188), hepatobiliary disorders (12.16%, n = 146), and general disorders and administration site conditions (11.74%, n = 141) emerged as the most frequently reported categories by system organ class (SOC). Death, diarrhea, and malignant neoplasm progression were identified as the three most common preferred terms (PT) based on reporting frequency. Immune-mediated ADRs with elevated ROR and BCPNN values were prevalent across multiple systems, particularly within the gastrointestinal (528.22, 9.03), hepatic (863.82, 9.70), cardiac (365.24, 8.49), endocrine (321.26, 8.31), and dermatological (744.71, 9.51) domains.

CONCLUSION: The study underscored the intricate safety profile of tremelimumab and durvalumab in HCC patients, characterized predominantly by immune-mediated toxicities and significant gastrointestinal and hepatobiliary risks. Utilization of drugs necessitated meticulous patient selection and a multidisciplinary management approach to optimize patient outcomes and mitigate ADRs.

PMID:40781661 | DOI:10.1186/s12885-025-14696-7

BMC Med. 2025 Aug 8;23(1):467. doi: 10.1186/s12916-025-04301-w.

ABSTRACT

BACKGROUND: Chronic inflammatory cardiomyopathy (infl-CMP) is a long-term sequela caused by the chronicity of acute myocarditis, especially fulminant myocarditis (FM). Hydroxychloroquine (HCQ) may benefit these patients by inhibiting the excessive inflammatory response.

METHODS: In this multicenter, randomized trial, we evaluated the efficacy and safety of HCQ in patients with chronic infl-CMP after FM. The primary outcome of the trial was a composite of the cardiovascular outcomes of time to cardiovascular death or heart transplant, hospitalization for heart failure or recurrence of myocarditis, permanent pacemaker, or implantable cardioverter defibrillator implantation. Secondary outcomes were changes in left ventricular ejection fraction (LVEF), left ventricular internal diastolic diameter (LVIDd), plasma levels of high-sensitivity cardiac troponin I (hs-cTnI), N-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein (hs-CRP), and erythrocyte sedimentation rate (ESR) from baseline to 12 months.

RESULTS: Fifty patients were randomized to receive HCQ combined with prednisolone (PDN) or PDN monotherapy for 12 months. Compared to PDN monotherapy, HCQ combined with PDN therapy reduced the primary composite outcome [hazard ratio (HR) = 0.28, 95% confidence interval (CI) = 0.11-0.71] and had significant changes in the increase of LVEF and the decrease of LVIDd, hs-cTnI, NT-proBNP, and hs-CRP in patients with infl-CMP. No serious drug-related adverse events were recorded in either group, indicating an acceptable safety profile. Furthermore, HCQ combined with PDN significantly reduced the levels of 16 plasma cytokines to levels comparable to healthy controls.

CONCLUSIONS: Twelve months of HCQ combined with PDN therapy significantly improved the prognosis and heart function, inhibited inflammation, and had acceptable safety in patients with infl-CMP after FM.

TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05961202.

PMID:40781621 | DOI:10.1186/s12916-025-04301-w

Am J Hum Genet. 2025 Aug 7;112(8):1778-1791. doi: 10.1016/j.ajhg.2025.06.014.

ABSTRACT

Identification of drug-repurposing targets with genetic and biological support is an economically and temporally efficient strategy for improving the treatment of diseases. We employed a cross-disciplinary approach to identify potential therapeutics for the prevention of metabolic-dysfunction-associated steatotic liver disease (MASLD) in at-risk individuals by using humans as a model organism. We identified 212 putative candidate genes associated with MASLD by using data from a large multi-ancestry genetic association study, of which 158 (74.5%) were previously unreported. From this set, we identified 57 genes that encode for druggable protein targets and for which the effects of increasing genetically predicted gene expression on MASLD risk align with the function of that drug on the protein target. We then used We then evaluated these potential targets for evidence of efficacy by using Mendelian randomization, pathway analysis, and protein structural modeling. Through these approaches, we present compelling evidence to suggest that the activation of FADS1 by icosapent ethyl, as well as S1PR2 by fingolimod, could be a promising therapeutic strategy for MASLD prevention.

PMID:40780050 | DOI:10.1016/j.ajhg.2025.06.014

J Pharmacol Exp Ther. 2025 Jul 15;392(9):103660. doi: 10.1016/j.jpet.2025.103660. Online ahead of print.

ABSTRACT

Equilibrative nucleoside transporters (ENTs) facilitate the equilibrative movement of nucleosides and nucleobases across cell membranes in a sodium-independent manner. ENT1 (SLC29A1) and ENT2 (SLC29A2) also transport nucleoside analogs and can affect the pharmacokinetics and pharmacodynamics of drugs used in cancer, viral infections, and inflammatory disorders. ENT1 and ENT2 may be differentiated functionally by their sensitivity to inhibition by nitrobenzylthioinosine (NBMPR), and we used this difference in NBMPR sensitivity to create a HeLa-based ENT2 inhibition assay. We then screened a library of 1600 diverse compounds composed of drugs and natural products for inhibition against ENT1 and ENT2, selecting a subset of compounds for side-by-side comparison of dose-response studies. We used these screening data to build machine learning models for ENT1 and ENT2 inhibition, employing dataset balancing and conformal prediction to adjust for the asymmetrical nature of the data. A random forest model predicted a prospective test set of 44 additional molecules (from the MedChem Express Drug Repurposing Library [2700 compounds]) as potential ENT1 inhibitors with 59% accuracy. This resulted in the identification of the Food and Drug Administration-approved drugs isradipine, avanafil, and istradefylline as inhibitors of ENT1. These new experimental and computational methods and models for these clinically relevant transporters can be used to evaluate drug-transporter interactions early in drug discovery, before testing in vivo. SIGNIFICANCE STATEMENT: Recent regulatory guidance have suggest the inclusion of the equilibrative nucleoside transporters (eg, ENT1 and ENT2) as transporters with emerging clinical relevance for in vitro and in vivo assessment. We have screened over 1600 diverse molecules, allowing us to build machine learning models that in turn were further used to make predictions to validate the models. Our combined experimental and machine learning approach resulted in the identification of multiple Food and Drug Administration-approved medications as inhibitors of ENT1 or ENT2.

PMID:40779912 | DOI:10.1016/j.jpet.2025.103660

Pharmacogenomics J. 2025 Aug 8;25(5):24. doi: 10.1038/s41397-025-00382-1.

ABSTRACT

Depression affects around 10% of the Swiss population. While SSRIs are commonly prescribed, only 30-40% of patients achieve remission. Pharmacogenetic (PGx) factors may explain part of this high rate of SSRI treatment failure. This study examined antidepressant (AD) switching among Swiss patients using escitalopram, focusing on whether they switched to ADs with PGx dosing guidelines (PGx AD) or ADs without PGx dosing guidelines (non-PGx ADs). Data from Swiss health insurance records identified 41 275 patients who used escitalopram between July 2020 and June 2022. While 6.4% (n = 2 638) switched to another antidepressant, only 35.4% of these opted for a PGx AD. Men, younger adults showed higher switching rates, whereas patients on antipsychotic medications switched less. Individuals younger than 20 years old and women were more likely to switch to PGx AD whereas the elderly were less likely to switch to PGx AD.

PMID:40781213 | DOI:10.1038/s41397-025-00382-1

ESMO Open. 2025 Aug 7;10(8):105539. doi: 10.1016/j.esmoop.2025.105539. Online ahead of print.

ABSTRACT

BACKGROUND: Clonal hematopoiesis of indeterminate potential (CHIP) and mosaic chromosomal alterations (mCAs) have been linked to increased risks of cardiovascular disease (CVD) and mortality. CHIP and mCAs may also then contribute to CVD in cancer patients. Our objective was to investigate the prevalence of CHIP mutations and mCAs in cancer patients, their co-occurrence, and the associated CVD outcomes across different cancer types.

PATIENTS AND METHODS: We carried out a case-control analysis of CHIP and mCA on the risks of CVD-related outcomes using the UK Biobank. Somatic CHIP mutations were identified from whole-exome sequencing and mCAs from genotyping data among patients diagnosed with cancers. Logistic regression and Cox proportional hazards models were used to assess the associations between CHIP mutations, mCAs, and CVD outcomes, and overall mortality.

RESULTS: Overall, 2701 patients (5.5%) harbored CHIP mutations. Increasing age, current smoking, and chemotherapy exposure were associated with higher odds of CHIP mutations and mCAs. Co-occurrence of CHIP and mCAs was observed in 695 patients (25.7% of those with CHIP mutations). Loss of the Y chromosome (LOY) was inversely correlated with CHIP mutations among men [odds ratio (OR) 0.65, 95% confidence interval (CI) 0.57-0.74, P < 0.001] whereas loss of the X chromosome (LOX) was positively correlated with CHIP mutations among women (OR 1.24, 95% CI 1.03-1.49, P = 0.03). CHIP mutations were associated with an increased risk of incident CVD [hazard ratio (HR) 1.07, 95% CI 1.02-1.13, P = 0.004] and overall mortality (HR 1.31, 95% CI 1.22-1.40, P < 0.001). Notably, there was no synergistic impact of CHIP mutations co-occurring with mCAs (LOY/LOX) on considered outcomes.

CONCLUSIONS: CHIP mutations and mCAs are prevalent in cancer patients and are associated with significant increases in cardiovascular risk and mortality, with variations across cancer types. These findings underscore the importance of considering clonal hematopoiesis in the clinical management of cancer patients to mitigate cardiovascular risks.

PMID:40779931 | DOI:10.1016/j.esmoop.2025.105539

BMC Med. 2025 Aug 9;23(1):464. doi: 10.1186/s12916-025-04330-5.

NO ABSTRACT

PMID:40781313 | DOI:10.1186/s12916-025-04330-5

BMJ Case Rep. 2025 Aug 8;2009:bcr2007119503. doi: 10.1136/bcr.2007.119503.

NO ABSTRACT

PMID:40780940 | DOI:10.1136/bcr.2007.119503

Eur Respir J. 2025 Aug 8;66(2):2501122. doi: 10.1183/13993003.01122-2025. Print 2025 Aug.

NO ABSTRACT

PMID:40780850 | DOI:10.1183/13993003.01122-2025

BMJ Open Respir Res. 2025 Aug 7;12(1):e002784. doi: 10.1136/bmjresp-2024-002784.

ABSTRACT

BACKGROUND: T2*-weighted oxygen-enhanced MRI (T2*-OE-MRI) may directly assess pulmonary ventilation using oxygen as an inhaled tracer gas. It has shown promise in healthy volunteers (HVs) and cystic fibrosis but has yet to be demonstrated in patients with chronic obstructive pulmonary disease (COPD).

RESEARCH QUESTION: To determine the feasibility and repeatability of T2*-OE-MRI in patients with COPD. To assess correlations between T2*-OE-MRI measurements of pulmonary ventilation, pulmonary function tests (PFTs) and measures of functional limitation.

STUDY DESIGN AND METHODS: 13 patients with mild-to-severe COPD and 13 HVs underwent PFTs, lung clearance index (LCI) measurement, incremental exercise test (patients only) and two lung MRI scans at 3 T. For T2*-OE-MRI, participants were fitted with a non-rebreathing face mask and given 100% oxygen during image acquisition.

RESULTS: Patients (age: 63 (55-72) years, forced expiratory volume in 1 s (FEV1): 63 (36-79) %predicted, median (IQR)) had evidence of pulmonary gas trapping, small airway disease (SAD) and ventilation heterogeneity. During T2*-OE-MRI, the magnitude of the percentage difference between mean signal intensity at normoxia and hyperoxia (percent signal enhancement (PSE)) and the enhancing fraction (EF) were lower in patients versus HVs (2.77 (2.19-4.19) vs 5.34 (4.33-5.61) % and 0.74 (0.66-0.77) vs 0.89 (0.82-0.94), respectively, both p<0.001). Intraclass correlation coefficient values indicated moderate (0.74) and good (0.80) repeatability for PSE and EF, respectively. PSE and EF significantly correlated with FEV1, LCI and SAD indices, and in COPD, they correlated with measures of exercise capacity, dynamic hyperinflation and dyspnoea intensity during exercise.

INTERPRETATION: In patients with COPD, T2*-OE-MRI is feasible and repeatable and provides regional information on pulmonary ventilation that is linked with physiological measures of disease severity, functional limitation and exertional dyspnoea.

PMID:40780846 | DOI:10.1136/bmjresp-2024-002784

Sci Rep. 2025 Aug 8;15(1):29048. doi: 10.1038/s41598-025-12566-7.

ABSTRACT

The telomerase enzyme is essential for telomere maintenance. Pathogenic variants in telomere-associated genes have been associated with critical telomere shortening, resulting in telomere biology disorders (TBD) such as bone marrow failure, idiopathic pulmonary fibrosis, and dyskeratosis congenita. The TBDs are clinically heterogeneous and families with TBD often experience an earlier onset and increased symptom severity for each generation. Consensus guidelines have identified certain genetic variants as pathogenic or likely pathogenic, but many are classified as variants of uncertain significance (VUS) in the absence of additional supporting evidence. The pathogenicity of a VUS in genes encoding the telomerase complex could be evaluated by in vitro telomerase activity (TA) measurement. We have developed a functional TA assay in patient-derived T-cells based on the Telomeric Repeat Amplification Protocol (TRAP) combined with qPCR. TA was significantly lower in six TBD patients with a TERT or TERC variant compared to controls (0.11 versus 0.54, p < 0.001). Four patients had a TA of more than three standard deviations below the mean of controls, strongly supporting pathogenicity of the variants. In summary, functional analysis of TA in patient-derived cells could support pathogenic evaluation in clinical diagnostics and reduce the number of reported VUS for TBD patients.

PMID:40781257 | DOI:10.1038/s41598-025-12566-7

Eur J Pharmacol. 2025 Aug 6:178043. doi: 10.1016/j.ejphar.2025.178043. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a kind of chronic and progressive interstitial lung disease of unclear aetiology. A key aspect of IPF is transforming growth factor-β1 (TGF-β1)-induced mitophagy dysfunction and senescence in lung fibroblasts. Mesenchyme homeobox 1 (MEOX1) is a critical transcription factor in the regulation of cell differentiation. However, the role of MEOX1 in the pathogenesis of lung fibrosis and mitophagy has not been clarified. In this study, RNA-sequencing analysis was employed to identify the differentially expressed genes in TGF-β1-treated lung fibroblasts and IPF lung tissue. In vivo, the mouse model of lung fibrosis was established by intratracheal injection of bleomycin (BLM), and fibroblast-specific knockdown of MEOX in mice was achieved by intratracheal injection of adeno-associated viruse-shMEOX1. And in vitro experiments were also carried out on human lung fibroblasts. Our results indicated that fibroblast-specific knockdown of MEOX1 protected mice from BLM-induced pulmonary fibrosis, connective tissue growth factor (CTGF) expression, and lung fibroblast activation, as well as mitophagy deficiency and senescence. In vitro, MEOX1 knockdown abolished TGF-β1-induced mitophagy deficiency by downregulating CTGF expression, thereby inhibiting senescence, over-activation and collagen production in lung fibroblasts. Furthermore, we also found that TGF-β1 upregulated the expression of MEOX1 through the NOX4-ROS-Smad pathway. In conclusion, MEOX1 knockdown may ameliorate pulmonary fibrosis by regulating mitophagy and senescence and may be a potential therapeutic target for IPF and other types of interstitial lung diseases.

PMID:40780596 | DOI:10.1016/j.ejphar.2025.178043