J Clin Med. 2025 Feb 1;14(3):944. doi: 10.3390/jcm14030944.

ABSTRACT

Introduction: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are commonly prescribed for the management of type 2 diabetes mellitus (T2DM). However, the potential connection between GLP-1 RAs and the risk of pancreatitis presents a complex and nuanced issue. Although these drugs are effective in improving blood sugar control and cardiovascular health, their association with pancreatitis remains an area of concern. Our study aims to evaluate the association between the use of GLP-1 RAs, considered as a single class, and the risk of pancreatitis in a comorbidity-free subgroup of patients with type 2 diabetes mellitus (T2DM) in the United States. Methods: Data were retrieved from the TriNetX research database using the US Collaborative Network, which included information from 61 healthcare organizations within the U.S. Patients diagnosed with T2DM were categorized into two cohorts: one consisting of the patients prescribed with GLP-1 RAs and the other comprising patients who did not receive GLP-1 RAs. Of this class of medications, the agents analyzed were dulaglutide, lixisenatide, exenatide, liraglutide, and semaglutide. Using a 1:1 propensity score matching (PSM) model, we matched patients of both cohorts based on baseline demographics, comorbidities (hypertensive disorders, ischemic heart disease, gallstones, annular pancreas, alcohol use disorders, hypertriglyceridemia, hypercalcemia, cystic fibrosis, and cannabis use), medications known to cause drug-related pancreatitis, and laboratory values. Results: Of 969,240 patients with T2DM, 9.7% (93,608) were on GLP-1 RA, and 90.3% (875,632) were not. After PSM, the sample included 81,872 patients in each cohort. The risk of pancreatitis between the two groups was not statistically different between the two cohorts at 6 months at (0.1% vs. 0.1%, p = 0.04), and remained without significant increase with time; at 1 year (0.1% vs. 0.2%, p = 0.02), 3 years (0.2% vs. 0.3%, p = 0.001), and 5 years (0.3% vs. 0.4%, p < 0.001). The lifetime risk of developing pancreatitis in patients on GLP-1 RA was lower (0.3% vs. 0.4%, p < 0.001). Conclusions: In our comorbidity-free U.S.-based population with T2DM, the use of GLP-1 RAs did not increase their risk of pancreatitis. Their use was associated with a lower lifetime risk of pancreatitis.

PMID:39941615 | DOI:10.3390/jcm14030944

J Clin Med. 2025 Jan 30;14(3):907. doi: 10.3390/jcm14030907.

ABSTRACT

Background: Airway clearance techniques are a key element in the daily treatment of people with bronchiectasis. There are several methods and devices to assist in effective airway clearance. We investigated LibAirty, a novel medical device, and compared it with the common practice performed today. Methods: Twenty adults enrolled, and each one had three different treatments in a randomized order: a human respiratory physiotherapist, a High-Frequency Chest Wall Oscillator, and LibAirty with BiPAP. The outcome parameters were mucus weight and a questionnaire. Further studies were performed to investigate LibAirty with hypertonic saline (HS) inhalation and using the device as a standalone. Results: No adverse events were recorded. The sputum amount expectorated in all arms using LibAirty was 14.4 ± 11.1 g with BIPAP, 16.4 ± 7 g with HS, and 11.3 ± 4.1 g for the standalone treatment. For HFCWO, 4.45 ± 3.28 g was obtained, and for CPT, 15.9 ± 11.1 g was obtained. The amount obtained by LibAirty (all arms) was significantly higher than HFCWO. Conclusions: All arms of LibAirty were superior to HFCWO and similar to the human physiotherapist. Further studies should be performed to investigate the long-term effects of LibAirty.

PMID:39941578 | DOI:10.3390/jcm14030907

J Ophthalmic Inflamm Infect. 2025 Feb 13;15(1):8. doi: 10.1186/s12348-025-00465-x.

ABSTRACT

BACKGROUND: Microbial keratitis (MK) poses a substantial threat to vision and is the leading cause of corneal blindness. The outcome of MK is heavily reliant on immediate treatment following an accurate diagnosis. The current diagnostics are often hindered by the difficulties faced in low and middle-income countries where there may be a lack of access to ophthalmic units with clinical experts and standardized investigating equipment. Hence, it is crucial to develop new and expeditious diagnostic approaches. This study explores the application of deep learning (DL) in diagnosing and differentiating subtypes of MK using smartphone-captured images.

MATERIALS AND METHODS: The dataset comprised 889 cases of bacterial keratitis (BK), fungal keratitis (FK), and acanthamoeba keratitis (AK) collected from 2020 to 2023. A convolutional neural network-based model was developed and trained for classification.

RESULTS: The study demonstrates the model's overall classification accuracy of 83.8%, with specific accuracies for AK, BK, and FK at 81.2%, 82.3%, and 86.6%, respectively, with an AUC of 0.92 for the ROC curves.

CONCLUSION: The model exhibits practicality, especially with the ease of image acquisition using smartphones, making it applicable in diverse settings.

PMID:39946047 | DOI:10.1186/s12348-025-00465-x

Psychiatr Q. 2025 Feb 13. doi: 10.1007/s11126-024-10111-9. Online ahead of print.

ABSTRACT

Depression and suicidal thoughts are significant global health concerns typically diagnosed through clinical assessments, which can be constrained by issues of accessibility and stigma. However, current methods often face challenges with this variability and struggle to integrate different models effectively and generalize across different settings, leading to reduced effectiveness when applied to new contexts, resulting in less accurate outcomes. This research presents a novel approach to suicide and depression detection from social media (SADDSM) by addressing the challenges of variability and model generalization. The process involves four key stages: first, preprocessing the input data through stop word removal, tokenization, and stemming to improve text clarity; then, extracting relevant features such as TF-IDF, style features, and enhanced word2vec features to capture semantic relationships and emotional cues. A modified mutual information score is used for feature fusion, selecting the most informative features. Subsequently, deep learning models like RNN, DBN, and improved LSTM are stacked to form an ensemble model that boosts accuracy while reducing overfitting. The performance is further optimized using the Dwarf Updated Pelican optimization algorithm (DU-POA) to fine-tune model weights, achieving an impressive 0.962 accuracy at 90% training data, outperforming existing techniques.

PMID:39946018 | DOI:10.1007/s11126-024-10111-9

Med Phys. 2025 Feb 13. doi: 10.1002/mp.17624. Online ahead of print.

ABSTRACT

BACKGROUND: Obtaining accurate segmentation regions for liver cancer is of paramount importance for the clinical diagnosis and treatment of the disease. In recent years, a large number of variants of deep learning based liver cancer segmentation methods have been proposed to assist radiologists. Due to the differences in characteristics between different types of liver tumors and data imbalance, it is difficult to train a deep model that can achieve accurate segmentation for multiple types of liver cancer.

PURPOSE: In this paper, We propose a balance Dice Loss(BD Loss) function for balanced learning of multiple categories segmentation features. We also introduce a comprehensive method based on BD Loss to achieve accurate segmentation of multiple categories of liver cancer.

MATERIALS AND METHODS: We retrospectively collected computed tomography (CT) screening images and tumor segmentation of 591 patients with malignant liver tumors from West China Hospital of Sichuan University. We use the proposed BD Loss to train a deep model that can segment multiple types of liver tumors and, through a greedy parameter averaging algorithm (GPA algorithm) obtain a more generalized segmentation model. Finally, we employ model integration and our proposed post-processing method, which leverages inter-slice information, to achieve more accurate segmentation of liver cancer lesions.

RESULTS: We evaluated the performance of our proposed automatic liver cancer segmentation method on the dataset we collected. The BD loss we proposed can effectively mitigate the adverse effects of data imbalance on the segmentation model. Our proposed method can achieve a dice per case (DPC) of 0.819 (95%CI 0.798-0.841), significantly higher than baseline which achieve a DPC of 0.768(95%CI 0.740-0.796).

CONCLUSIONS: The differences in CT images between different types of liver cancer necessitate deep learning models to learn distinct features. Our method addresses this challenge, enabling balanced and accurate segmentation performance across multiple types of liver cancer.

PMID:39945728 | DOI:10.1002/mp.17624

J Acoust Soc Am. 2025 Feb 1;157(2):1162-1175. doi: 10.1121/10.0035830.

ABSTRACT

A robust multichannel speaker diarization and separation system is proposed by exploiting the spatiotemporal activity of the speakers. The system is realized in a hybrid architecture that combines the array signal processing units and the deep learning units. For speaker diarization, a spatial coherence matrix across time frames is computed based on the whitened Relative Transfer Functions of the microphone array. This serves as a robust feature for subsequent machine learning without the need for prior knowledge of the array configuration. A computationally efficient modified End-to-End Neural Diarization system in the Encoder-Decoder-based Attractor network is constructed to estimate the speaker activity from the spatial coherence matrix. For speaker separation, we propose the Global and Local Activity-driven Speaker Extraction network to separate speaker signals via speaker-specific global and local spatial activity functions. The local spatial activity functions depend on the coherence between the whitened Relative Transfer Functions of each time-frequency bin and the target speaker-dominant bins. The global spatial activity functions are computed from the global spatial coherence functions based on frequency-averaged local spatial activity functions. Experimental results have demonstrated superior speaker, diarization, counting, and separation performance achieved by the proposed system with low computational complexity compared to the pre-selected baselines.

PMID:39945646 | DOI:10.1121/10.0035830

Crit Rev Anal Chem. 2025 Feb 13:1-14. doi: 10.1080/10408347.2025.2463430. Online ahead of print.

ABSTRACT

Microbiomes significantly impact food flavor, food quality and human health. The development of omics technologies has revolutionized our understanding of the microbiome, the generated complex datasets, as well as their processing and interpretation need to be taken seriously. Currently, chemometrics has shown huge potential in omics data analysis, which is crucial to reveal the functional attributes and mechanisms of microbiomes in food nutrition and safety. However, various chemometric tools have their own characteristics, selecting appropriate technologies and performing multiomics data fusion analysis to improve the precision and reliability of food microbial investigations is still a huge challenge. In this review, we summarized the omics technologies used in food microbiome studies, overviewed the principle and applicability of chemometrics in omics, and discussed the challenges and prospects of chemometrics. The urgent need for chemometrics is to integrate deep learning (DL) and artificial intelligence algorithms to enhance its analytical capabilities and prediction accuracy. We hope this review will provide valuable insights of the integration of multiomics and bioinformatics combined with various chemometric techniques in data analysis for food microbial investigation. In the future, chemometrics combined with modern technologies for multiomics data analysis will further deepen our understanding of food microbiology and improve food safety.

PMID:39945579 | DOI:10.1080/10408347.2025.2463430

Med Phys. 2025 Feb 13. doi: 10.1002/mp.17680. Online ahead of print.

ABSTRACT

BACKGROUND: Accurate low-dose CT simulation is required to efficiently assess reconstruction and dose reduction techniques. Projection domain noise insertion requires proprietary information from manufacturers. Analytic image domain noise insertion methods are successful for linear reconstruction algorithms, however extending them to non-linear algorithms remains challenging. Emerging, deep-learning-based image domain noise insertion methods have potential, but few approaches have explicitly incorporated physics information and a texture-synthesis model to guide the generation of locally and globally correlated noise texture.

PURPOSE: We proposed a physics-informed model-based generative neural network for simulating scanner- and algorithm-specific low-dose CT exams (PALETTE). It is expected to provide an alternative to projection domain noise insertion methods in the absence of manufacturers' proprietary information and tools.

METHODS: PALETTE integrated a physics-based noise prior generation process, a Noise2Noisier sub-network, and a noise texture synthesis sub-network. The Noise2Noisier sub-network provided a bias prior, which, combined with the noise prior, served as the inputs to noise texture synthesis sub-network. Explicit regularizations in spatial and frequency domains were developed to account for noise spatial correlation and frequency characteristics. For proof-of-concept, PALETTE was trained and validated for a commercial iterative reconstruction algorithm (SAFIRE, Siemens Healthineers), using the paired routine and 25% dose images from CT phantoms (lateral size 30-40 cm; three training and four testing phantoms) and open-access patient cases (10 training and 20 testing cases). In phantom validation, noise power spectra (NPS) were compared in water background and tissue-mimicking inserts, using peak frequency and mean-absolute-error (MAE). In patient case evaluation, visual inspection and quantitative assessment were conducted on axial, coronal, and sagittal planes. Local and global noise texture were visually inspected in low-dose CT images and the difference images between routine and low dose. Noise levels in liver and fat were measured. Local and global 2D Fourier magnitude spectra of the difference images and the corresponding radial mean profiles were used to assess similarity in noise frequency components within tissues and entire field-of-view, using spectral correlation mapper (SCM) and spectral angle mapper (SAM). Several baseline neural network models (e.g., GAN) were included in the evaluation. Statistical significance was tested using a t-test for related samples.

RESULTS: PALETTE-derived NPS showed accurate noise peak frequency (PALETTE/reference: water 1.40/1.40 lp/cm; inserts 1.7/1.7lp/cm) and small MAE (≤0.65 HU2cm2). PALETTE created anatomy-dependent noise texture, showing realistic local and global granularity and streaks. No statistically significant difference was observed in noise levels (p > 0.05). Noise range was comparable across 3D image volume (PALETTE / reference):liver - [18.0, 53.4] / [19.3, 50.0] HU; fat - [11.7, 42.4] / [12.1, 41.3] HU. Percent absolute difference of local noise was small (mean ± standard deviation): liver 4.1%±3.1%, fat 4.6%±3.1%. Noise frequency distribution was close to the reference (mean per case): SCM ≥ 0.92, SAM ≤ 0.22. Additionally, PALETTE outperformed all baseline models in visual inspection and quantitative comparison.

CONCLUSION: PALETTE can provide high-quality image domain noise insertion for simulating accurate low-dose CT images created with a commercial non-linear reconstruction algorithm.

PMID:39945452 | DOI:10.1002/mp.17680

Proteins. 2025 Feb 13. doi: 10.1002/prot.26808. Online ahead of print.

ABSTRACT

The decomposition of a biomolecular complex into domains is an important step to investigate biological functions and ease structure determination. A successful approach to do so is the SPECTRUS algorithm, which provides a segmentation based on spectral clustering applied to a graph coding inter-atomic fluctuations derived from an elastic network model. We present SPECTRALDOM, which makes three straightforward and useful additions to SPECTRUS. For single structures, we show that high quality partitionings can be obtained from a graph Laplacian derived from pairwise interactions-without normal modes. For sets of homologous structures, we introduce a Multiple Sequence Alignment mode, exploiting both the sequence based information (MSA) and the geometric information embodied in experimental structures. Finally, we propose to analyze the clusters/domains delivered using the so-called D $$ D $$ -family-matching algorithm, which establishes a correspondence between domains yielded by two decompositions, and can be used to handle fragmentation issues. Our domains compare favorably to those of the original SPECTRUS, and those of the deep learning based method Chainsaw. Using two complex cases, we show in particular that SPECTRALDOM is the only method handling complex conformational changes involving several sub-domains. Finally, a comparison of SPECTRALDOM and Chainsaw on the manually curated domain classification ECOD as a reference shows that high quality domains are obtained without using any evolutionary related piece of information. SPECTRALDOM is provided in the Structural Bioinformatics Library, see http://sbl.inria.fr and https://sbl.inria.fr/doc/Spectral_domain_explorer-user-manual.html.

PMID:39945423 | DOI:10.1002/prot.26808

Vet Radiol Ultrasound. 2025 Mar;66(2):e70015. doi: 10.1111/vru.70015.

ABSTRACT

In this analytical cross-sectional method comparison study, we evaluated brain MR images in 30 dogs and cats with and without using a DICOM-based deep-learning (DL) denoising algorithm developed specifically for veterinary patients. Quantitative comparison was performed by measuring signal-to-noise (SNR) and contrast-to-noise ratios (CNR) on the same T2-weighted (T2W), T2-FLAIR, and Gradient Echo (GRE) MR brain images in each patient (native images and after denoising) in identical regions of interest. Qualitative comparisons were then conducted: three experienced veterinary radiologists independently evaluated each patient's T2W, T2-FLAIR, and GRE image series. Native and denoised images were evaluated separately, with observers blinded to the type of images they were assessing. For each image type (native and denoised) and pulse sequence type image, they assigned a subjective grade of coarseness, contrast, and overall quality. For all image series tested (T2W, T2-FLAIR, and GRE), the SNRs of cortical gray matter, subcortical white matter, deep gray matter, and internal capsule were statistically significantly higher on images treated with DL denoising algorithm than native images. Similarly, for all image series types tested, the CNRs between cortical gray and white matter and between deep gray matter and internal capsule were significantly higher on DL algorithm-treated images than native images. The qualitative analysis confirmed these results, with generally better coarseness, contrast, and overall quality scores for the images treated with the DL denoising algorithm. In this study, this DICOM-based DL denoising algorithm reduced noise in 1.5T MRI canine and feline brain images, and radiologists' perceived image quality improved.

PMID:39945204 | DOI:10.1111/vru.70015

Front Comput Neurosci. 2025 Jan 29;19:1513059. doi: 10.3389/fncom.2025.1513059. eCollection 2025.

ABSTRACT

Brain tumors are one of the major health threats to humans, and their complex pathological features and anatomical structures make accurate segmentation and detection crucial. However, existing models based on Transformers and Convolutional Neural Networks (CNNs) still have limitations in medical image processing. While Transformers are proficient in capturing global features, they suffer from high computational complexity and require large amounts of data for training. On the other hand, CNNs perform well in extracting local features but have limited performance when handling global information. To address these issues, this paper proposes a novel network framework, MUNet, which combines the advantages of UNet and Mamba, specifically designed for brain tumor segmentation. MUNet introduces the SD-SSM module, which effectively captures both global and local features of the image through selective scanning and state-space modeling, significantly improving segmentation accuracy. Additionally, we design the SD-Conv structure, which reduces feature redundancy without increasing model parameters, further enhancing computational efficiency. Finally, we propose a new loss function that combines mIoU loss, Dice loss, and Boundary loss, which improves segmentation overlap, similarity, and boundary accuracy from multiple perspectives. Experimental results show that, on the BraTS2020 dataset, MUNet achieves DSC values of 0.835, 0.915, and 0.823 for enhancing tumor (ET), whole tumor (WT), and tumor core (TC), respectively, and Hausdorff95 scores of 2.421, 3.755, and 6.437. On the BraTS2018 dataset, MUNet achieves DSC values of 0.815, 0.901, and 0.815, with Hausdorff95 scores of 4.389, 6.243, and 6.152, all outperforming existing methods and achieving significant performance improvements. Furthermore, when validated on the independent LGG dataset, MUNet demonstrated excellent generalization ability, proving its effectiveness in various medical imaging scenarios. The code is available at https://github.com/Dalin1977331/MUNet.

PMID:39944950 | PMC:PMC11814164 | DOI:10.3389/fncom.2025.1513059

Front Plant Sci. 2025 Jan 29;15:1486929. doi: 10.3389/fpls.2024.1486929. eCollection 2024.

ABSTRACT

Solanum rostratum Dunal (SrD) is a globally harmful invasive weed that has spread widely across many countries, posing a serious threat to agriculture and ecosystem security. A deep learning network model, TrackSolanum, was designed for real-time detection, location, and counting of SrD in the field. The TrackSolanmu network model comprises four modules: detection, tracking, localization, and counting. The detection module uses YOLO_EAND for SrD identification, the tracking module applies DeepSort for multi-target tracking of SrD in consecutive video frames, the localization module determines the position of the SrD through center-of-mass localization, and the counting module counts the plants using a target ID over-the-line invalidation method. The field test results show that for UAV video at a height of 2m, TrackSolanum achieved precision and recall of 0.950 and 0.970, with MOTA and IDF1 scores of 0.826 and 0.960, a counting error rate of 2.438%, and FPS of 17. For UAV video at a height of 3m, the model reached precision and recall of 0.846 and 0.934, MOTA and IDF1 scores of 0.708 and 0.888, a counting error rate of 4.634%, and FPS of 79. Thus, the TrackSolanum supports real-time SrD detection, offering crucial technical support for hazard assessment and precise management of SrD.

PMID:39944948 | PMC:PMC11814178 | DOI:10.3389/fpls.2024.1486929

Aging Cell. 2025 Feb 13:e14503. doi: 10.1111/acel.14503. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is an age-associated disease characterized by the irreversible accumulation of excessive extracellular matrix components by activated myofibroblasts (aMYFs). Following bleomycin administration in young mice, fibrosis formation associated with efficient resolution takes place limiting the clinical relevance of this model for IPF. In this study, we used aged mice in combination with bleomycin administration to trigger enhanced fibrosis formation and delayed resolution as a more relevant model for IPF. Alveolosphere assays were carried out to compare the alveolar resident mesenchymal niche activity for AT2 stem cells in young versus old mice. Lineage tracing of the Acta2+ aMYFs in old mice exposed to bleomycin followed by scRNAseq of the lineage-traced cells isolated during fibrosis formation and resolution was performed to delineate the heterogeneity of aMYFs during fibrosis formation and their fate during resolution. Integration of previously published similar scRNAseq results using young mice was carried out. Our results show that alveolar resident mesenchymal cells from old mice display decreased supporting activity for AT2 stem cells. Our findings suggest that the cellular and molecular mechanisms underlying the aMYFs formation and differentiation towards the Lipofibroblast phenotype are mostly conserved between young and old mice. In addition to persistent fibrotic signaling in aMYF from old mice during resolution, we also identified differences linked to interleukin signaling in old versus young alveolar fibroblast populations before and during bleomycin injury. Importantly, our work confirms the relevance of a subcluster of aMYFs in old mice that is potentially relevant for future management of IPF.

PMID:39945330 | DOI:10.1111/acel.14503

Front Genet. 2025 Jan 29;15:1496462. doi: 10.3389/fgene.2024.1496462. eCollection 2024.

ABSTRACT

INTRODUCTION: Idiopathic pulmonary fibrosis (IPF) is a rare but debilitating lung disease characterized by excessive fibrotic tissue accumulation, primarily affecting individuals over 50 years of age. Early diagnosis is challenging, and without intervention, the prognosis remains poor. Understanding the molecular mechanisms underlying IPF pathogenesis is crucial for identifying diagnostic markers and therapeutic targets.

METHODS: We analyzed transcriptomic data from lung tissues of IPF patients using two independent datasets. Differentially expressed genes (DEGs) were identified, and their functional roles were assessed through pathway enrichment and tissue-specific expression analysis. Protein-protein interaction (PPI) networks and co-expression modules were constructed to identify hub genes and their associations with disease severity. Machine learning approaches were applied to identify genes capable of differentiating IPF patients from healthy individuals. Regulatory signatures, including transcription factor and microRNA interactions, were also explored, alongside the identification of potential drug targets.

RESULTS: A total of 275 and 167 DEGs were identified across two datasets, with 67 DEGs common to both. These genes exhibited distinct expression patterns across tissues and were associated with pathways such as extracellular matrix organization, collagen fibril formation, and cell adhesion. Co-expression analysis revealed DEG modules correlated with varying IPF severity phenotypes. Machine learning analysis pinpointed a subset of genes with high discriminatory power between IPF and healthy individuals. PPI network analysis identified hub proteins involved in key biological processes, while functional enrichment reinforced their roles in extracellular matrix regulation. Regulatory analysis highlighted interactions with transcription factors and microRNAs, suggesting potential mechanisms driving IPF pathogenesis. Potential drug targets among the DEGs were also identified.

DISCUSSION: This study provides a comprehensive transcriptomic overview of IPF, uncovering DEGs, hub proteins, and regulatory signatures implicated in disease progression. Validation in independent datasets confirmed the relevance of these findings. The insights gained here lay the groundwork for developing diagnostic tools and novel therapeutic strategies for IPF.

PMID:39944354 | PMC:PMC11813903 | DOI:10.3389/fgene.2024.1496462

Expert Rev Respir Med. 2025 Feb 13. doi: 10.1080/17476348.2025.2467341. Online ahead of print.

ABSTRACT

INTRODUCTION: Idiopathic pulmonary fibrosis (IPF) isa chronic, progressive lung disease characterized by distorted alveolar structureand reduced lung compliance, and impaired ventilation-perfusion. Small airwaydisease (SAD) is often termed a 'quietzone' due to its asymptomatic nature. Around 30-40% of IPF patients exhibit SAD,which is associated with worse prognosis, higher fibrosis and emphysema scores,and elevated mortality risk. We used PubMed and Google Scholar for literaturesearch.

AREAS COVERED: This review explores thepathophysiology of small airways in IPF, focusing on 1. risk factors, includingage, gender, smoking and occupational dust exposure, and ozone. 2. Diagnosticchallenges: SAD is difficult to detect through traditional spirometry or high-resolutioncomputed tomography imaging due to resolutionlimitations. 3. Early physiologicalchanges of small airways include airway wall thickening, lumen distortion, andreduced terminal bronchioles, preceding microscopic fibrosis, occurs in the earlyprocess of IPF. 4. Pathological mechanisms: The review examines the underlyingmechanisms driving small airway disease in IPF.

EXPERT OPINION: A comprehensive approach is essential to improve the understanding andmanagement of SAD in IPF. Priorities include identifying therapeutic targets,advanced imaging and functional assessments. Forced oscillation technique should be introduced for early detection for smallairway abnormalities in IPF.

PMID:39943815 | DOI:10.1080/17476348.2025.2467341

J Clin Med. 2025 Jan 23;14(3):731. doi: 10.3390/jcm14030731.

ABSTRACT

Background: Transbronchial lung cryobiopsy (TBLC) has a high incidence of adverse events. This study aimed to investigate the relationship between the occurrence of these events and the condition of the pathology samples or pathological diagnosis in TBLC. Methods: We studied 102 patients who underwent TBLC for the diagnosis of interstitial lung disease. We analyzed the association between the condition or diagnosis of pathology samples and the occurrence of TBLC-related adverse events, including hemorrhage, pneumothorax, and acute exacerbation of interstitial lung disease. Results: The adverse events occurred in 19 patients (18.6%), of which hemorrhage was the most common (14 patients, 13.7%). The patients who experienced adverse events, especially hemorrhage, were less likely to have successful sampling with TBLC and showed lower diagnostic confidence in the pathology results. The diagnostic confidence was level A in 50 cases (49.0%) and level C in 23 cases (22.6%). TBLC-related adverse events, including hemorrhage, were significantly more common in patients with lower pathological confidence levels. Conclusions: TBLC-related adverse events, particularly hemorrhage, can lead to fewer successful samples and lower levels of diagnostic confidence.

PMID:39941401 | DOI:10.3390/jcm14030731

J Clin Med. 2025 Jan 22;14(3):714. doi: 10.3390/jcm14030714.

ABSTRACT

Background: Over the last few years, a few imaging studies have performed conventional transthoracic echocardiography (TTE) implemented with speckle tracking echocardiography (STE) for the assessment of biventricular mechanics in patients with non-advanced idiopathic pulmonary fibrosis (IPF). This systematic review and meta-analysis aimed at evaluating the overall effect of mild-to-moderate IPF on the main indices of biventricular systolic function assessed by TTE and STE. Methods: All imaging studies assessing right ventricular (RV)-global longitudinal strain (GLS), left ventricular (LV)-GLS, tricuspid annular plane systolic excursion (TAPSE), and left ventricular ejection fraction (LVEF) in IPF patients vs. healthy controls, selected from PubMed, Scopus, and EMBASE databases, were included. Continuous data (RV-GLS, LV-GLS, TAPSE, and LVEF) were pooled as standardized mean differences (SMDs) comparing the IPF group with healthy controls. The SMD of RV-GLS was calculated using the random-effect model, whereas the SMDs of LV-GLS, TAPSE, and LVEF were calculated using the fixed-effect model. Results: The full texts of 6 studies with 255 IPF patients and 195 healthy controls were analyzed. Despite preserved TAPSE and LVEF, both RV-GLS and LV-GLS were significantly, although modestly, reduced in the IPF patients vs. the controls. The SMD was large (-1.01, 95% CI -1.47, -0.54, p < 0.001) for RV-GLS, medium (-0.62, 95% CI -0.82, -0.42, p < 0.001) for LV-GLS, small (-0.42, 95% CI -0.61, -0.23, p < 0.001) for TAPSE, and small and not statistically significant (-0.20, 95% CI -0.42, 0.03, p = 0.09) for LVEF assessment. Between-study heterogeneity was high for the studies assessing RV-GLS (I2 = 80.5%), low-to-moderate for those evaluating LV-GLS (I2 = 41.7%), and low for those measuring TAPSE (I2 = 16.4%) and LVEF (I2 = 7.63%). The Egger's test yielded a p-value of 0.60, 0.11, 0.31, and 0.68 for the RV-GLS, LV-GLS, TAPSE, and LVEF assessment, respectively, indicating no publication bias. On meta-regression analysis, none of the moderators was significantly associated with effect modification for RV-GLS (all p > 0.05). The sensitivity analysis supported the robustness of the results. Conclusions: RV-GLS impairment is an early marker of subclinical myocardial dysfunction in mild-to-moderate IPF. STE should be considered for implementation in clinical practice for early detection of RV dysfunction in IPF patients without advanced lung disease.

PMID:39941384 | DOI:10.3390/jcm14030714

Int J Mol Sci. 2025 Feb 6;26(3):1378. doi: 10.3390/ijms26031378.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) has been associated with mitochondrial dysfunction. We investigated whether mitochondrial DNA variants in peripheral blood leukocytes (PBLs), which affect proteins of the respiratory chain and mitochondrial function, could be associated with an increased risk and poor prognosis of IPF. From 2020 to 2022, we recruited 36 patients (age: 75.3 ± 8.5; female: 19%) with IPF, and 80 control subjects (age: 72.3 ± 9.0; female: 27%). The mitochondrial genome of peripheral blood leukocytes was determined using next-generation sequencing. During a 45-month follow-up, 10 (28%) patients with IPF remained stable and the other 26 (72%) progressed, with 12 (33%) mortalities. IPF patients had more non-synonymous (NS) variants (substitution/deletion/insertion) in mitochondrial COX3 gene (coding for subunit 3 of complex IV of the respiratory chain), and more mitochondrial tRNA variants located in the anticodon (AC) stem, AC loop, variable loop, T-arm, and T-loop of the tRNA clover-leaf structure in PBLs than the control group. The succumbed IPF patients were older, had lower initial diffusion capacity, and higher initial fibrosis score on high-resolution computerized tomography (HRCT) than the alive group. NS variants in mitochondrial COX3 gene and tRNA variants in PBLs were associated with shorter survival. Our study shows that (1) leukocyte mitochondrial COX3 NS variants are associated with risk and prognosis of IPF; (2) leukocyte mitochondrial tRNA variants located in the AC stem, AC loop, variable loop, T-arm, and T-loop of the tRNA clover-leaf structure are associated with risk, and the presence of tRNA variants is associated with poor prognosis of IPF.

PMID:39941146 | DOI:10.3390/ijms26031378

Plant Mol Biol. 2025 Feb 13;115(2):31. doi: 10.1007/s11103-025-01554-0.

ABSTRACT

Folates are vital one carbon donors and acceptors for a whole range of key biochemical reactions, including the biosynthesis of DNA building blocks. Plants use one carbon metabolism as a jack of all trades in their growth and development. Depletion of folates impedes root growth in Arabidopsis thaliana, but the mechanistic basis behind this function is still obscure. A global transcriptomic study hinted that folate depletion may cause misregulation of cell cycle progression. However, investigations on a direct connection thereof are scarce. We confirmed the effect of methotrexate (MTX), a folate biosynthesis inhibitor, on the expression of cell cycle genes. Subsequently, we determined the effect of MTX on root morphology and cell cycle progression through phase-specific cell cycle reporter analyses. Our study reveals that folate depletion affects the expression of cell cycle regulatory genes in roots, thereby suppressing cell cycle progression. We confirmed, through DNA labelling by EdU, that MTX treatment leads to arrest in the S phase of meristematic cells, likely due to the lack of DNA precursors. Further, we noted an accumulation of the A-type CYCA3;1 cyclin at the root tip, suggesting a possible link with the observed loss of apical dominance. Overall, our study shows that the restricted cell division and cell cycle progression is one of the reasons behind the loss of primary root growth upon folate depletion.

PMID:39946030 | DOI:10.1007/s11103-025-01554-0

J Agric Food Chem. 2025 Feb 13. doi: 10.1021/acs.jafc.4c12651. Online ahead of print.

ABSTRACT

Bitter taste perception cautions humans against the ingestion of potentially toxic compounds. However, current knowledge about natural bitter substances and their activation of human bitter taste receptors (TAS2Rs) is biased toward substances from flowering plants, whereas other sources are underrepresented. Although numerous mushrooms taste bitter, the corresponding substances and receptors are unexplored. Three previously undescribed triterpene glucosides, named oligoporins D-F, together with the known oligoporins A and B, were isolated from Amaropostia stiptica. The structures of oligoporins D-F were determined using spectroscopic analyses. The isolated oligoporins and the bitter indolalkaloid infractopicrin from Cortinarius infractus were functionally screened with all TAS2Rs. For all compounds, at least one responding receptor was identified. Oligoporin D activated TAS2R46 already at a submicromolar concentration and thus belongs to the family of most potent bitter agonists. The addition of mushroom compounds to the list of cognate TAS2R activators lowers the existing bias of knowledge about bitter agonists.

PMID:39945763 | DOI:10.1021/acs.jafc.4c12651