AJNR Am J Neuroradiol. 2025 Jan 23. doi: 10.3174/ajnr.A8493. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Whether differences in the O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status of glioblastoma (GBM) are reflected in MRI markers remains largely unknown. In this work, we analyze the ADC in the perienhancing infiltration zone of GBM according to the corresponding MGMT status by using a novel distance-resolved 3D evaluation.

MATERIALS AND METHODS: One hundred one patients with IDH wild-type GBM were retrospectively analyzed. GBM was segmented in 3D with deep learning. Tissue with FLAIR hyperintensity around the contrast-enhanced tumor was divided into concentric distance-resolved subvolumes. Mean ADC was calculated for the 3D tumor core and for the distance-resolved volumes around the core. Differences in group mean ADC between patients with MGMT promoter methylated (mMGMT, n = 43) and MGMT promoter unmethylated (uMGMT, n = 58) GBM was analyzed with Wilcoxon signed rank test.

RESULTS: For both mMGMT and uMGMT GBM, mean ADC values around the tumor core significantly increased as a function of distance from the core toward the periphery of the perienhancing FLAIR hyperintensity (approximately 10% increase within 5 voxels with P < 001). While group mean ADC in the tumor core was not significantly different, the distance-resolved ADC profile around the core was approximately 10% higher in mMGMT than in uMGMT GBM (P < 10-8 at 5 voxel distance from the tumor core).

CONCLUSIONS: Distance-resolved volumetric ADC analysis around the tumor core reveals tissue signatures of GBM imperceptible to the human eye on conventional MRI. The different ADC profiles around the core suggest epigenetically influenced differences in perienhancing tissue characteristics between mMGMT and uMGMT GBM.

PMID:39848779 | DOI:10.3174/ajnr.A8493

J Hepatol. 2025 Jan 21:S0168-8278(25)00018-2. doi: 10.1016/j.jhep.2025.01.011. Online ahead of print.

ABSTRACT

BACKGROUND & AIMS: Accurate multi-classification is the prerequisite for reasonable management of focal liver lesions (FLLs). Ultrasound is the common image examination, but lacks accuracy. Contrast enhanced ultrasound (CEUS) offers better performance, but highly relies on experience. Therefore, we aimed to develop a CEUS-based artificial intelligence (AI) model for FLL multi-classification and evaluate its performance in multicenter clinical tests.

METHODS: Since January 2017 to December 2023, CEUS videos, immunohistochemical biomarkers and clinical information of solid FLLs>1cm in adults were collected from 52 centers to build and test the model. It aimed to classify FLLs into six types: hepatocellular carcinoma, hepatic metastasis, intrahepatic cholangiocarcinoma, hepatic hemangioma, hepatic abscess and others. First, Module-Disease, Module-Biomarker and Module-Clinic were built in training set A and validation set. Then, three modules were aggregated as Model-DCB in training set B and internal test set. Model-DCB performance was compared with CEUS and MRI radiologists in three external test sets.

RESULTS: In total 3725 FLLs from 52 centers were divided into training set A (n=2088), validation set (n=592), training set B (n=234), internal test set (n=110), external test set A (n=113), B (n=276) and C (n=312). In external test sets A, B and C, Model-DCB all achieved significantly better performance (Accuracy from 0.85 to 0.86) than junior CEUS-radiologists (0.59-0.73), and comparable to senior CEUS-radiologists (0.79-0.85) and senior MRI-radiologists (0.82-0.86). In multiple subgroup analyses on demographic characteristics, tumor characteristics and ultrasound devices, its accuracy ranged from 0.79 to 0.92.

CONCLUSIONS: CEUS-based Model-DCB provides accurate multi-classification of FLLs. It holds promise to benefit a wide range of population, especially for patients in remote suburban areas who have difficulty accessing MRI.

IMPACT AND IMPLICATIONS: Ultrasound is the most common image examination for screening focal liver lesions (FLLs), but it lacks accuracy for multi-classification, which is the prerequisite for reasonable management. Contrast enhanced ultrasound (CEUS) offers better diagnostic performance, but highly relies on work experience of radiologists. We develop a CEUS-based model (Model-DCB) can assist junior CEUS radiologists to achieve comparable diagnostic ability to senior CEUS radiologists and senior MRI radiologists. The combination of ultrasound device, CEUS examination and Model-DCB enables even patients in remote areas to obtain excellent diagnostic performance through examination by junior radiologists.

CLINICAL TRIAL: NCT04682886.

PMID:39848548 | DOI:10.1016/j.jhep.2025.01.011

Drug Alcohol Depend. 2025 Jan 15;268:112554. doi: 10.1016/j.drugalcdep.2025.112554. Online ahead of print.

ABSTRACT

BACKGROUND: The hypothalamus is involved in stress regulation and reward processing, with its various nuclei exhibiting unique functions and connections. However, human neuroimaging studies on the hypothalamic subregions are limited in drug addiction. This study examined the volumes and functional connectivity of hypothalamic subregions in individuals with alcohol use disorder (AUD).

METHOD: The study included 24 male patients with AUD who had maintained abstinence and 24 healthy male controls, all of whom underwent brain structural and resting-state functional magnetic resonance imaging. The hypothalamus was segmented into five subunits using a deep learning-based algorithm, with comparisons of volumes and functional connectivity (FC) between the two groups. The relationships between these measures and alcohol-related characteristics were examined in the AUD group.

RESULTS: Findings indicated lower volumes in the anterior-superior (corrected-p < 0.001) and tuberal-superior subunits (corrected-p = 0.002) and altered FC of these and the anterior-inferior subunit among AUD patients (corrected-p < 0.05). Moreover, greater disease severity and a longer history of heavy drinking correlated with lower volumes in the anterior-superior (r = -0.42, p = 0.045) and tuberal-superior subregions (r = -0.61, p = 0.013), respectively. Conversely, a longer abstinence duration was associated with larger volumes in the anterior-superior (r = 0.56, p = 0.008) and tuberal-superior subunits (r = 0.40, p = 0.048) and with higher FC between the tuberal-superior hypothalamus and the thalamus, caudate, and anterior cingulate cortex (r = 0.55, p = 0.014).

CONCLUSIONS: Our results suggest that specific regional alterations within the hypothalamus, particularly the superior subregions, are associated with AUD, and more importantly, that these alterations may be reversible with prolonged abstinence.

PMID:39848134 | DOI:10.1016/j.drugalcdep.2025.112554

Comput Methods Programs Biomed. 2025 Jan 20;261:108606. doi: 10.1016/j.cmpb.2025.108606. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is a significant cause of life-threatening heart disease due to its potential to lead to stroke and heart failure. Although deep learning-assisted diagnosis of AF based on ECG holds significance in clinical settings, it remains unsatisfactory due to insufficient consideration of noise and redundant features. In this work, we propose a novel multiscale feature-enhanced gating network (MFEG Net) for AF diagnosis.

METHOD: The network integrates multiscale convolution, adaptive feature enhancement (FE), and dynamic temporal processing. The multiscale convolution helps capture global and local information. The FE module consists of a soft-threshold residual shrinkage component, a dilated convolution module, and a Squeeze-and-Excitation (SE) module, eliminating redundant features and emphasizing effective features. The design allows the network to focus on the most relevant AF features, thereby enhancing its robustness and accuracy in the presence of noise and irrelevant information. The dynamic temporal module helps the network learn and recognize the time dependence associated with AF. The novel design endows the model with excellent robustness to cope with random noise in real-world environments.

RESULT: Compared with the state-of-the-art methods, our model exhibits excellent classification performance with an accuracy of 0.930, an F1 score of 0.883, and remarkable resilience to noise interference on the PhysioNet Challenge 2017 dataset. Moreover, the model was trained on the CinC2017 database and validated on the CPSC2018 database and AFDB database, achieving accuracies of 0.908 and 0.938, respectively.

CONCLUSION: The excellent classification performance of MFEG Net, coupled with its robustness in processing noisy electrocardiogram signals, makes it a powerful method for automatic atrial fibrillation detection. This method has made significant progress over state-of-the-art methods and may alleviate the burden of manual diagnosis for clinical doctors.

PMID:39847993 | DOI:10.1016/j.cmpb.2025.108606

Int J Biol Macromol. 2025 Jan 21:140204. doi: 10.1016/j.ijbiomac.2025.140204. Online ahead of print.

ABSTRACT

Colorectal cancer (CRC) affects the population worldwide, occupying the first place in terms of death and incidence. Synthetic peptides (SPs) emerged as alternative molecules due to their activity and low toxicity. Proteomic analysis of PepGAT-treated HCT-116 cells revealed a decreased abundance of proteins involved in ROS metabolism and energetic metabolisms, cell cycle, DNA repair, migration, invasion, cancer aggressiveness, and proteins involved in resistance to 5-FU. PepGAT induced earlier ROS and apoptosis in HCT-116 cells, cell cycle arrest, and inhibited HCT-116 migration. PepGAT enhances the action of 5-FU against HCT-116 cells by dropping down 6-fold the 5-FU toward HCT-116 and reduces its toxicity for non-cancerous cells. These findings strongly suggest the multiple mechanisms of action displayed by PepGAT against CRC cells and its potential to either be studied alone or in combination with 5-FU to develop new studies against CRC and might develop new drugs against it.

PMID:39848367 | DOI:10.1016/j.ijbiomac.2025.140204

J Cyst Fibros. 2025 Jan 22:S1569-1993(25)00006-2. doi: 10.1016/j.jcf.2025.01.007. Online ahead of print.

ABSTRACT

BACKGROUND: Reproductive life planning is key, now that people with cystic fibrosis (pwCF) may live into their 60s. This study explores contraceptive use, pregnancy trends, and whether concomitant cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapy reduces contraceptive effectiveness.

METHODS: Females with CF aged 18-45 years from 10 U.S. CF centers completed a self-administered web-based questionnaire. Pregnancy rates were calculated by linear-mixed models with a logit link detected associations with contraception and modulator use.

RESULTS: A total of 561 pwCF (median age of 29 years [IQR 24.9-35.8] years) completed the survey. Most participants (n = 499, 89%) used modulators, and almost all (n = 555, 99%) used contraception. Condoms (n = 448, 80%) and oral contraceptive pills (n = 363, 65%) were the most prevalent methods used. One-third (n = 189, 34%) reported ever being pregnant. Of those reporting pregnancies (n = 319), about half (n = 151, 48%) were unintended. Pregnancy was significantly associated with age (20-29 years or 30-39 years), partner cohabitation (aOR 21.5, 95% CI 5.1 to 91.1), and non-hormonal contraceptive use (aOR 5.1, 95% CI 1.1 to23.0). Among pwCF cohabitating with a partner, modulator use was positively associated with pregnancy (OR 1.8, 95% CI 1.3 to 2.6) (p = 0.0008).

CONCLUSIONS: Despite almost universal contraceptive use, unintended pregnancy among pwCF is common. Likelihood of pregnancy is increased among CFTR modulator users who are partnered, although CFTR modulators themselves do not appear to decrease hormonal contraceptive effectiveness. Patient education about contraception is an increasingly critical aspect of CF care.

PMID:39848844 | DOI:10.1016/j.jcf.2025.01.007

Int J Comput Assist Radiol Surg. 2025 Jan 23. doi: 10.1007/s11548-024-03315-8. Online ahead of print.

ABSTRACT

PURPOSE: Semantic segmentation and landmark detection are fundamental tasks of medical image processing, facilitating further analysis of anatomical objects. Although deep learning-based pixel-wise classification has set a new-state-of-the-art for segmentation, it falls short in landmark detection, a strength of shape-based approaches.

METHODS: In this work, we propose a dense image-to-shape representation that enables the joint learning of landmarks and semantic segmentation by employing a fully convolutional architecture. Our method intuitively allows the extraction of arbitrary landmarks due to its representation of anatomical correspondences. We benchmark our method against the state-of-the-art for semantic segmentation (nnUNet), a shape-based approach employing geometric deep learning and a convolutional neural network-based method for landmark detection.

RESULTS: We evaluate our method on two medical datasets: one common benchmark featuring the lungs, heart, and clavicle from thorax X-rays, and another with 17 different bones in the paediatric wrist. While our method is on par with the landmark detection baseline in the thorax setting (error in mm of 2.6 ± 0.9 vs. 2.7 ± 0.9 ), it substantially surpassed it in the more complex wrist setting ( 1.1 ± 0.6 vs. 1.9 ± 0.5 ).

CONCLUSION: We demonstrate that dense geometric shape representation is beneficial for challenging landmark detection tasks and outperforms previous state-of-the-art using heatmap regression. While it does not require explicit training on the landmarks themselves, allowing for the addition of new landmarks without necessitating retraining.

PMID:39849288 | DOI:10.1007/s11548-024-03315-8

Neurosurg Rev. 2025 Jan 24;48(1):78. doi: 10.1007/s10143-025-03236-3.

ABSTRACT

Currently, the World Health Organization (WHO) grade of meningiomas is determined based on the biopsy results. Therefore, accurate non-invasive preoperative grading could significantly improve treatment planning and patient outcomes. Considering recent advances in machine learning (ML) and deep learning (DL), this meta-analysis aimed to evaluate the performance of these models in predicting the WHO meningioma grade using imaging data. A systematic search was performed in PubMed/MEDLINE, Embase, and the Cochrane Library for studies published up to April 1, 2024, and reporting the performance metrics of the ML models in predicting of WHO meningioma grade using imaging studies. Pooled area under the receiver operating characteristics curve (AUROC), specificity, and sensitivity were estimated. Subgroup and meta-regression analyses were performed based on a number of potential influencing variables. A total of 32 studies with 15,365 patients were included in the present study. The overall pooled sensitivity, specificity, and AUROC of ML methods for prediction of tumor grade in meningioma were 85% (95% CI, 79-89%), 87% (95% CI, 81-91%), and 93% (95% CI, 90-95%), respectively. Both the type of validation and study cohort (training or test) were significantly associated with model performance. However, no significant association was found between the sample size or the type of ML method and model performance. The ML predictive models show a high overall performance in predicting the WHO meningioma grade using imaging data. Further studies on the performance of DL algorithms in larger datasets using external validation are needed.

PMID:39849257 | DOI:10.1007/s10143-025-03236-3

Med Biol Eng Comput. 2025 Jan 24. doi: 10.1007/s11517-024-03279-6. Online ahead of print.

ABSTRACT

Major depressive disorder (MDD) is a significant neurological disorder that imposes a substantial burden on society, characterized by its high recurrence rate and associated suicide risk. Clinical diagnosis, which relies on interviews with psychiatrists and questionnaires used as auxiliary diagnostic tools, lacks precision and objectivity in diagnosing MDD. To address these challenges, this study proposes an assessment method based on EEG. It involves calculating the phase lag index (PLI) in alpha and gamma bands to construct functional brain connectivity. This method aims to find biomarkers to assess the severity of MDD and suicidal ideation. The convolutional inception with shuffled attention network (CISANET) was introduced for this purpose. The study included 61 patients with MDD, who were classified into mild, moderate, and severe levels based on depression scales, and the presence of suicidal ideation was evaluated. Two paradigms were designed for the study, with EEG analysis focusing on 32 selected electrodes to extract alpha and gamma bands. In the gamma band, the classification accuracy reached 77.37% in the visual paradigm and 80.12% in the auditory paradigm. The average accuracy in classifying suicidal ideation was 93.60%. The findings suggest that gamma bands can be used as potential biomarkers differentiating illness severity and identifying suicidal ideation of MDD, and that objective assessment methods can effectively assess MDD The objective assessment method can effectively assess the severity of MDD and identify suicidal ideation of MDD patients, which provides a valuable theoretical basis for understanding the biological characteristics of MDD.

PMID:39849234 | DOI:10.1007/s11517-024-03279-6

J Imaging Inform Med. 2025 Jan 23. doi: 10.1007/s10278-025-01402-z. Online ahead of print.

ABSTRACT

Parkinson's disease (PD), a degenerative disorder of the central nervous system, is commonly diagnosed using functional medical imaging techniques such as single-photon emission computed tomography (SPECT). In this study, we utilized two SPECT data sets (n = 634 and n = 202) from different hospitals to develop a model capable of accurately predicting PD stages, a multiclass classification task. We used the entire three-dimensional (3D) brain images as input and experimented with various model architectures. Initially, we treated the 3D images as sequences of two-dimensional (2D) slices and fed them sequentially into 2D convolutional neural network (CNN) models pretrained on ImageNet, averaging the outputs to obtain the final predicted stage. We also applied 3D CNN models pretrained on Kinetics-400. Additionally, we incorporated an attention mechanism to account for the varying importance of different slices in the prediction process. To further enhance model efficacy and robustness, we simultaneously trained the two data sets using weight sharing, a technique known as cotraining. Our results demonstrated that 2D models pretrained on ImageNet outperformed 3D models pretrained on Kinetics-400, and models utilizing the attention mechanism outperformed both 2D and 3D models. The cotraining technique proved effective in improving model performance when the cotraining data sets were sufficiently large.

PMID:39849204 | DOI:10.1007/s10278-025-01402-z

J Imaging Inform Med. 2025 Jan 23. doi: 10.1007/s10278-025-01386-w. Online ahead of print.

ABSTRACT

Accurate wound segmentation is crucial for the precise diagnosis and treatment of various skin conditions through image analysis. In this paper, we introduce a novel dual attention U-Net model designed for precise wound segmentation. Our proposed architecture integrates two widely used deep learning models, VGG16 and U-Net, incorporating dual attention mechanisms to focus on relevant regions within the wound area. Initially trained on diabetic foot ulcer images, we fine-tuned the model to acute and chronic wound images and conducted a comprehensive comparison with other state-of-the-art models. The results highlight the superior performance of our proposed dual attention model, achieving a Dice coefficient and IoU of 94.1% and 89.3%, respectively, on the test set. This underscores the robustness of our method and its capacity to generalize effectively to new data.

PMID:39849203 | DOI:10.1007/s10278-025-01386-w

J Imaging Inform Med. 2025 Jan 23. doi: 10.1007/s10278-025-01390-0. Online ahead of print.

ABSTRACT

While radiation hazards induced by cone-beam computed tomography (CBCT) in image-guided radiotherapy (IGRT) can be reduced by sparse-view sampling, the image quality is inevitably degraded. We propose a deep learning-based multi-view projection synthesis (DLMPS) approach to improve the quality of sparse-view low-dose CBCT images. In the proposed DLMPS approach, linear interpolation was first applied to sparse-view projections and the projections were rearranged into sinograms; these sinograms were processed with a sinogram restoration model and then rearranged back into projections. The sinogram restoration model was modified from the 2D U-Net by incorporating dynamic convolutional layers and residual learning techniques. The DLMPS approach was trained, validated, and tested on CBCT data from 163, 30, and 30 real patients respectively. Sparse-view projection datasets with 1/4 and 1/8 of the original sampling rate were simulated, and the corresponding full-view projection datasets were restored via the DLMPS approach. Tomographic images were reconstructed using the Feldkamp-Davis-Kress algorithm. Quantitative metrics including root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and feature similarity (FSIM) were calculated in both the projection and image domains to evaluate the performance of the DLMPS approach. The DLMPS approach was compared with 11 state-of-the-art (SOTA) models, including CNN and Transformer architectures. For 1/4 sparse-view reconstruction task, the proposed DLMPS approach achieved averaged RMSE, PSNR, SSIM, and FSIM values of 0.0271, 45.93 dB, 0.9817, and 0.9587 in the projection domain, and 0.000885, 37.63 dB, 0.9074, and 0.9885 in the image domain, respectively. For 1/8 sparse-view reconstruction task, the DLMPS approach achieved averaged RMSE, PSNR, SSIM, and FSIM values of 0.0304, 44.85 dB, 0.9785, and 0.9524 in the projection domain, and 0.001057, 36.05 dB, 0.8786, and 0.9774 in the image domain, respectively. The DLMPS approach outperformed all the 11 SOTA models in both the projection and image domains for 1/4 and 1/8 sparse-view reconstruction tasks. The proposed DLMPS approach effectively improves the quality of sparse-view CBCT images in IGRT by accurately synthesizing missing projections, exhibiting potential in substantially reducing imaging dose to patients with minimal loss of image quality.

PMID:39849201 | DOI:10.1007/s10278-025-01390-0

Nat Methods. 2025 Jan 23. doi: 10.1038/s41592-024-02503-3. Online ahead of print.

ABSTRACT

Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of these data complicates analysis of spatial gene expression patterns. We address this issue by deriving a topographic map of a tissue slice-analogous to a map of elevation in a landscape-using a quantity called the isodepth. Contours of constant isodepths enclose domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in expression. We develop GASTON (gradient analysis of spatial transcriptomics organization with neural networks), an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gradients and piecewise linear expression functions that model both continuous gradients and discontinuous variation in gene expression. We show that GASTON accurately identifies spatial domains and marker genes across several tissues, gradients of neuronal differentiation and firing in the brain, and gradients of metabolism and immune activity in the tumor microenvironment.

PMID:39849132 | DOI:10.1038/s41592-024-02503-3

Sci Rep. 2025 Jan 23;15(1):2961. doi: 10.1038/s41598-025-87309-9.

ABSTRACT

Feature matching in computer vision is crucial but challenging in weakly textured scenes due to the lack of pattern repetition. We introduce the SwinMatcher feature matching method, aimed at addressing the issues of low matching quantity and poor matching precision in weakly textured scenes. Given the inherently significant local characteristics of image features, we employ a local self-attention mechanism to learn from weakly textured areas, maximally preserving the features of weak textures. To address the issue of incorrect matches in scenes with repetitive patterns, we use a cross-attention and positional encoding mechanism to learn the correct matches of repetitive patterns in two scenes, achieving higher matching precision. We also introduce a matching optimization algorithm that calculates the spatial expected coordinates of local two-dimensional heat maps of correspondences to obtain the final sub-pixel level matches. Experiments indicate that, under identical training conditions, the SwinMatcher outperforms other standard methods in pose estimation, homography estimation, and visual localization. It exhibits strong robustness and superior matching in weakly textured areas, offering a new research direction for feature matching in weakly textured images.

PMID:39849068 | DOI:10.1038/s41598-025-87309-9

Sci Rep. 2025 Jan 23;15(1):3000. doi: 10.1038/s41598-024-77118-x.

ABSTRACT

With the global population surpassing 8 billion, waste production has skyrocketed, leading to increased pollution that adversely affects both terrestrial and marine ecosystems. Public littering, a significant contributor to this pollution, poses severe threats to marine life due to plastic debris, which can inflict substantial ecological harm. Additionally, this pollution jeopardizes human health through contaminated food and water sources. Given the annual global plastic consumption of approximately 475 million tons and the pervasive issue of public littering, addressing this challenge has become critically urgent. The Surveillance and Waste Notification (SAWN) system presents an innovative solution to combat public littering. Leveraging surveillance cameras and advanced computer vision technology, SAWN aims to identify and reduce instances of littering. Our study explores the use of the MoViNet video classification model to detect littering activities by vehicles and pedestrians, alongside the YOLOv8 object detection model to identify individuals responsible through facial recognition and license plate detection. Collecting appropriate data for littering detection presented significant challenges due to its unavailability. Consequently, project members simulated real-life littering scenarios to gather the required data. This dataset was then used to train different models, including LRCN, CNN-RNN, and MoViNets. After extensive testing, MoViNets demonstrated the most promising results. Through a series of experiments, we progressively improved the model's performance, achieving accuracy rates of 93.42% in the first experiment, 95.53% in the second, and ultimately reaching 99.5% in the third experiment. To detect violators' identities, we employed YOLOv8, trained on the KSA vehicle plate dataset, achieving 99.5% accuracy. For face detection, we utilized the Haar Cascade from the OpenCV library, known for its real-time performance. Our findings will be used to further enhance littering behavior detection in future developments.

PMID:39848984 | DOI:10.1038/s41598-024-77118-x

ISA Trans. 2025 Jan 13:S0019-0578(25)00013-8. doi: 10.1016/j.isatra.2025.01.012. Online ahead of print.

ABSTRACT

Existing cross-domain mechanical fault diagnosis methods primarily achieve feature alignment by directly optimizing interdomain and category distances. However, this approach can be computationally expensive in multi-target scenarios or fail due to conflicting objectives, leading to decreased diagnostic performance. To avoid these issues, this paper introduces a novel method called domain feature disentanglement. The key to the proposed method lies in computing domain features and embedding domain similarity into neural networks to assist in extracting cross-domain invariant features. Specifically, the neural network architecture designed based on information theory can disentangle key features from multiple entangled latent variables. It employs the concept of contrastive learning to extract domain-relevant information from each data point and uses the Wasserstein distance to determine the similarity relationships across all domains. By informing the neural network of domain similarity relationships, it learns how to extract cross-domain invariant features through adversarial learning Eight multi-target domain adaptation tasks were set up on two public datasets, and the proposed method achieved an average diagnostic accuracy of 96.82%, surpassing six other advanced domain adaptation methods, demonstrating its superiority.

PMID:39848906 | DOI:10.1016/j.isatra.2025.01.012

Sci Rep. 2025 Jan 23;15(1):3007. doi: 10.1038/s41598-025-87474-x.

ABSTRACT

The traditional Chinese medicine compound preparation known as Jinbei Oral Liquid (JBOL) consists of 12 herbs, including Astragalus membranaceus (Fisch.) Bge, Codonopsis pilosula (Franch.) Nannf, et al. Having been used for over 30 years in the treatment of pulmonary diseases, JBOL was evaluated in this study in order to assess its effect on idiopathic pulmonary fibrosis as well as its safety (ChiCTR2000035351, Chictr.org.cn.09/08/2020). A double-blind, multicenter, randomized, proof-of-concept trial was conducted to assess the efficacy of oral JBOL 40 ml and Corbrin Capsules 1 g compared to a placebo and Corbrin Capsules in patients with idiopathic pulmonary fibrosis (IPF). Over a 26-week period, patients received the active treatment or placebo three times daily, in a 1:1 ratio. This clinical study uses a randomized method, with a cycle of every 4 patients. TCM doctors at or above the deputy director level of the research center conduct TCM dialectics on IPF patients. To assess efficacy, over the duration of the trial, we measured serial changes in a composite indicator encompassing time to first acute exacerbation of IPF (first hospitalization or death due to respiratory cause), total lung capacity (TLC) (mL), predicted forced vital capacity (FVC%), forced vital capacity (FVC) (mL), predicted diffusing capacity of the lungs for carbon monoxide (predicted DLco%), 6-minute walk distance (6MWD), St. George's Respiratory Questionnaire (SGRQ) total score, and arterial oxygen partial pressure (PaO2) from baseline to week 26 versus placebo. A total of 103 patients were screened, and 72 received the study medication. Of these, 68 patients were included in the analysis set, with 34 receiving JBOL and 34 receiving a placebo. After 26 weeks, a statistically significant reduction in total lung capacity (TLC) was observed for the JBOL group, with a change of 136 mL compared to -523 mL for the placebo group (difference 659 mL, 95% CI -1215 to -104 mL, p = 0.02). The study found that the change in FVC% predicted was - 1.48% and - 3.58% for the JBOL and placebo groups, respectively (difference of 2.10%, 95% CI -7.13 to 2.93, p = 0.41). Additionally the differences between the two groups in changes in FVC (mL), DLCO % predicted, PaO2 (mmHg) measures were - 67 mL (95% CI -238 to 104), -7.74% (95% CI -17.26 to 1.79), and - 3.57 mmHg (95% CI -10.02 to 2.87), respectively. Treatment with JBOL compared to placebo resulted in sequential changes in acute exacerbation, with no significant difference in SGRQ scores. It was not found that there was a statistically significant difference between the JBOL and placebo groups in TEAE reporting and serious TEAE reporting. Compared to the placebo group, there was a statistically significant reduction (p < 0.021) in TLC (mL) after 26 weeks for JBOL. The rates of FVC % predicted, FVC, DLCO % predicted, and PaO2 in the group treatment with JBOL were numerically lower than those in the placebo treatment group, although these differences did not reach statistical significance. JBOL exhibited comparable safety to placebo. This study has preliminarily shown the efficacy and safety of JBOL for IPF, but this is an exploratory clinical trial, more patient-involved studies should be needed in the near future.Trial registration: Chictr.org.cn ChiCTR2000035351; the trial was prospective clinical studies registered on August 9, 2020.

PMID:39849152 | DOI:10.1038/s41598-025-87474-x

Thorax. 2025 Jan 22:thorax-2024-222754. doi: 10.1136/thorax-2024-222754. Online ahead of print.

NO ABSTRACT

PMID:39848685 | DOI:10.1136/thorax-2024-222754

Int J Biochem Cell Biol. 2025 Jan 21:106739. doi: 10.1016/j.biocel.2025.106739. Online ahead of print.

ABSTRACT

Lung fibrosis, including idiopathic pulmonary fibrosis (IPF), is a complex and devastating disease characterised by the progressive scarring of lung tissue leading to compromised respiratory function. Aberrantly activated fibroblasts deposit extracellular matrix components into the surrounding lung tissue, impairing lung function and capacity for gas exchange. Both genetic and epigenetic factors have been found to play a role in the pathogenesis of lung fibrosis, with emerging evidence highlighting the interplay between these two regulatory mechanisms. This review provides an overview of the current understanding of the interplay between genetics and epigenetics in lung fibrosis. We discuss the genetic variants associated with susceptibility to lung fibrosis and explore how epigenetic modifications such as DNA methylation, histone modifications, and non-coding RNA expression contribute to disease. Insights from genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS) are integrated to explore the molecular mechanisms underlying lung fibrosis pathogenesis. We also discuss the potential clinical implications of genetics and epigenetics in lung fibrosis, including the development of novel therapeutic targets. Overall, this review highlights the importance of considering both genetic and epigenetic factors in the understanding and management of lung fibrosis.

PMID:39848439 | DOI:10.1016/j.biocel.2025.106739

Sci Rep. 2025 Jan 23;15(1):2948. doi: 10.1038/s41598-025-86854-7.

ABSTRACT

Many cellular patterns exhibit a reaction-diffusion component, suggesting that Turing instability may contribute to pattern formation. However, biological gene-regulatory pathways are more complex than simple Turing activator-inhibitor models and generally do not require fine-tuning of parameters as dictated by the Turing conditions. To address these issues, we employ random matrix theory to analyze the Jacobian matrices of larger networks with robust statistical properties. Our analysis reveals that Turing patterns are more likely to occur by chance than previously thought and that the most robust Turing networks have an optimal size, consisting of only a handful of molecular species, thus significantly increasing their identifiability in biological systems. Broadly speaking, this optimal size emerges from a trade-off between the highest stability in small networks and the greatest instability with diffusion in large networks. Furthermore, we find that with multiple immobile nodes, differential diffusion ceases to be important for Turing patterns. Our findings may inform future synthetic biology approaches and provide insights into bridging the gap to complex developmental pathways.

PMID:39849094 | DOI:10.1038/s41598-025-86854-7