J Neural Eng. 2025 Feb 26. doi: 10.1088/1741-2552/adba8a. Online ahead of print.

ABSTRACT

Brain-computer interface (BCI) is a cutting-edge technology that enables interaction with external devices by decoding human intentions, and is highly valuable in the fields of medical rehabilitation and human-robot collaboration. The technique of decoding motor intent for motor execution (ME) based on electroencephalographic (EEG) signals is in the feasibility study stage. There are still insufficient studies on the accuracy of motor execution EEG signal recognition in between-subjects classification to reach the level of realistic applications. This paper aims to investigate EEG signal-driven hand movement recognition by analyzing low-frequency time-domain (LFTD) information. Experiments with four types of hand movements, two force parameter (extraction and pushing) tasks, and a four-target directional displacement task were designed and executed, and the EEG data from thirteen healthy volunteers was collected. Sliding window approach is used to expand the dataset in order to address the issue of EEG signal overfitting. Furtherly, CNN-BiLSTM model, an end-to-end serial combination of a Bidirectional Long Short-Term Memory Network (BiLSTM) and Convolutional Neural Network (CNN) is constructed to classify the raw EEG data to recognize the hand movement. According to experimental data, the model is able to categorize four types of hand movements, extraction movements, pushing movements, and four target direction displacement movements with an accuracy of 99.14%±0.49%, 99.29%±0.11%, 99.23%±0.60%, and 98.11%± 0.23%, respectively. Furthermore, comparative tests conducted with alternative deep learning models (LSTM, CNN, EEGNet, CNN-LSTM) demonstrates that the CNN-BiLSTM model is with practicable accuracy in terms of EEG-based hand movement recognition and its parameter decoding.

PMID:40009879 | DOI:10.1088/1741-2552/adba8a

Invest Radiol. 2025 Feb 25. doi: 10.1097/RLI.0000000000001168. Online ahead of print.

ABSTRACT

OBJECTIVE: The 3-dimensional (3D) double echo steady state (DESS) magnetic resonance imaging sequence can image knee cartilage with high, isotropic resolution, particularly at high and ultra-high field strengths. Advanced undersampling techniques with high acceleration factors can provide the short acquisition times required for clinical use. However, the optimal undersampling scheme and its limits are unknown.

MATERIALS AND METHODS: High-resolution isotropic (reconstructed voxel size: 0.3 × 0.3 × 0.3 mm3) 3D DESS images of 40 knees in 20 volunteers were acquired at 7 T with varying undersampling factors (R = 4-30) and schemes (regular: GRAPPA, CAIPIRINHA; incoherent: compressed sensing [CS]), whereas the remaining imaging parameters were kept constant. All imaging data were reconstructed with deep learning (DL) algorithms. Three readers rated image quality on a 4-point Likert scale. Four-fold accelerated GRAPPA was used as reference standard. Incidental cartilage lesions were graded on a modified Whole-Organ Magnetic Resonance Imaging Score (WORMS). Friedman's analysis of variance characterized rating differences. The interreader agreement was assessed using κ statistics.

RESULTS: The quality of 16-fold accelerated CS images was not rated significantly different from that of 4-fold accelerated GRAPPA and 8-fold accelerated CAIPIRINHA images, whereas the corresponding data were acquired 4.5 and 2 times faster (01:12 min:s) than in 4-fold accelerated GRAPPA (5:22 min:s) and 8-fold accelerated CAIPIRINHA (2:22 min:s) acquisitions, respectively. Interreader agreement for incidental cartilage lesions was almost perfect for 4-fold accelerated GRAPPA (κ = 0.91), 8-fold accelerated CAIPIRINHA (κ = 0.86), and 8- to 16-fold accelerated CS (κ = 0.91).

CONCLUSIONS: Our results suggest significant advantages of incoherent versus regular undersampling patterns for high-resolution 3D DESS cartilage imaging with high acceleration factors. The combination of CS undersampling with DL reconstruction enables fast, isotropic, high-resolution acquisitions without apparent impairment of image quality. Since DESS specific absorption rate values tend to be moderate, CS DESS with DL reconstruction promises potential for high-resolution assessment of cartilage morphology and other musculoskeletal anatomies at 7 T.

PMID:40009727 | DOI:10.1097/RLI.0000000000001168

PLoS One. 2025 Feb 26;20(2):e0318992. doi: 10.1371/journal.pone.0318992. eCollection 2025.

ABSTRACT

In the acquisition of Magnetic Resonance (MR) images shorter scan times lead to higher image noise. Therefore, automatic image denoising using deep learning methods is of high interest. In this work, we concentrate on image denoising of MR images containing line-like structures such as roots or vessels. In particular, we investigate if the special characteristics of these datasets (connectivity, sparsity) benefit from the use of special loss functions for network training. We hereby translate the Perceptual Loss to 3D data by comparing feature maps of untrained networks in the loss function. We tested the performance of untrained Perceptual Loss (uPL) on 3D image denoising of MR images displaying brain vessels (MR angiograms - MRA) and images of plant roots in soil. In this study, 536 MR images of plant roots in soil and 450 MRA images are included. The plant root dataset is split to 380, 80, and 76 images for training, validation, and testing. The MRA dataset is split to 300, 50, and 100 images for training, validation, and testing. We investigate the impact of various uPL characteristics such as weight initialization, network depth, kernel size, and pooling operations on the results. We tested the performance of the uPL loss on four Rician noise levels (1%, 5%, 10%, and 20%) using evaluation metrics such as the Structural Similarity Index Metric (SSIM). Our results are compared with the frequently used L1 loss for different network architectures. We observe, that our uPL outperforms conventional loss functions such as the L1 loss or a loss based on the Structural Similarity Index Metric (SSIM). For MRA images the uPL leads to SSIM values of 0.93 while L1 and SSIM loss led to SSIM values of 0.81 and 0.88, respectively. The uPL network's initialization is not important (e.g. for MR root images SSIM differences of 0.01 occur across initializations, while network depth and pooling operations impact denoising performance slightly more (SSIM of 0.83 for 5 convolutional layers and kernel size 3 vs. 0.86 for 5 convolutional layers and kernel size 5 for the root dataset). We also find that small uPL networks led to better or comparable results than using large networks such as VGG (e.g. SSIM values of 0.93 and 0.90 for a small and a VGG19 uPL network in the MRA dataset). In summary, we demonstrate superior performance of our loss for both datasets, all noise levels, and three network architectures. In conclusion, for images containing line-like structures, uPL is an alternative to other loss functions for 3D image denoising. We observe that small uPL networks have better or equal performance than very large network architectures while requiring lower computational costs and should therefore be preferred.

PMID:40009630 | DOI:10.1371/journal.pone.0318992

PLoS One. 2025 Feb 26;20(2):e0310992. doi: 10.1371/journal.pone.0310992. eCollection 2025.

ABSTRACT

With the dramatic increase in the number of published papers and the continuous progress of deep learning technology, the research on name disambiguation is at a historic peak, the number of paper authors is increasing every year, and the situation of authors with the same name is intensifying, therefore, it is a great challenge to accurately assign the newly published papers to their respective authors. The current mainstream methods for author disambiguation are mainly divided into two methods: feature-based clustering and connection-based clustering, but none of the current mainstream methods can efficiently deal with the author name disambiguation problem, For this reason, this paper proposes the author name ablation method based on the relational graph heterogeneous attention neural network, first extract the semantic and relational information of the paper, use the constructed graph convolutional embedding module to train the splicing to get a better feature representation, and input the constructed network to get the vector representation. As the existing graph heterogeneous neural network can not learn different types of nodes and edge interaction, add multiple attention, design ablation experiments to verify its impact on the network. Finally improve the traditional hierarchical clustering method, combined with the graph relationship and topology, using training vectors instead of distance calculation, can automatically determine the optimal k-value, improve the accuracy and efficiency of clustering. The experimental results show that the average F1 value of this paper's method on the Aminer dataset is 0.834, which is higher than other mainstream methods.

PMID:40009590 | DOI:10.1371/journal.pone.0310992

Biochem Cell Biol. 2025 Feb 26. doi: 10.1139/bcb-2024-0287. Online ahead of print.

ABSTRACT

Estrogen (E2) regulates the differentiation and proliferation of mammary progenitor cells by modulating the transcription of multiple genes. One of the genes that is downregulated by E2 is SOX2, a transcription factor associated with stem and progenitor cells that is overexpressed during breast tumourigenesis. To elucidate the mechanisms underlying E2-mediated SOX2 repression, we investigated epigenome and transcriptome changes following short- and long-term E2 exposure in breast cancer cells. We found that short-term E2 exposure reduces chromatin accessibility at the downstream SOX2 SRR134 enhancer, decreasing SOX2 expression. In contrast, long-term E2 exposure completely represses SOX2 transcription while maintaining accessibility at the SRR124-134 enhancer cluster, keeping it poised for reactivation. This repression was accompanied by widespread epigenome and transcriptome changes associated with commitment towards a more differentiated and less invasive luminal phenotype. Finally, we identified a role for the transcription factor NFIB in this process, suggesting it collaborates with the estrogen receptor to mediate SOX2 repression and genome-wide epigenome accessibility changes.

PMID:40009831 | DOI:10.1139/bcb-2024-0287

Sci Adv. 2025 Feb 28;11(9):eadv2067. doi: 10.1126/sciadv.adv2067. Epub 2025 Feb 26.

ABSTRACT

3D organization of the genome plays a critical role in regulating gene expression. How 3D-genome organization differs among different cell types and relates to cell type-dependent transcriptional regulation remains unclear. Here, we used genome-scale DNA and RNA imaging to investigate 3D-genome organization in transcriptionally distinct cell types in the mouse cerebral cortex. We uncovered a wide spectrum of differences in the nuclear architecture and 3D-genome organization among different cell types, ranging from the size of the cell nucleus to higher-order chromosome structures and radial positioning of chromatin loci within the nucleus. These cell type-dependent variations in nuclear architecture and chromatin organization exhibit strong correlations with both the total transcriptional activity of the cell and transcriptional regulation of cell type-specific marker genes. Moreover, we found that the methylated DNA binding protein MeCP2 promotes active-inactive chromatin segregation and regulates transcription in a nuclear radial position-dependent manner that is highly correlated with its function in modulating active-inactive chromatin compartmentalization.

PMID:40009678 | DOI:10.1126/sciadv.adv2067

Proc Natl Acad Sci U S A. 2025 Mar 4;122(9):e2417695122. doi: 10.1073/pnas.2417695122. Epub 2025 Feb 26.

ABSTRACT

Advances in sequencing technology have unveiled examples of nucleus-encoded polycistrons, once considered rare. Exclusively polycistronic transcripts are prevalent in green algae, although the mechanism by which multiple polypeptides are translated from a single transcript is unknown. Here, we used bioinformatic and in vivo mutational analyses to evaluate competing mechanistic models for translation of bicistronic mRNAs in green algae. High-confidence manually curated datasets of bicistronic loci from two divergent green algae, Chlamydomonas reinhardtii and Auxenochlorella protothecoides, revealed a preference for weak Kozak-like sequences for ORF 1 and an underrepresentation of potential initiation codons before the ORF 2 start codon, which are suitable conditions for leaky ribosome scanning to allow ORF 2 translation. We used mutational analysis in A. protothecoides to test the mechanism. In vivo manipulation of the ORF 1 Kozak-like sequence and start codon altered reporter expression at ORF 2, with a weaker Kozak-like sequence enhancing expression and a stronger one diminishing it. A synthetic bicistronic dual reporter demonstrated inversely adjustable activity of green fluorescent protein expressed from ORF 1 and luciferase from ORF 2, depending on the strength of the ORF 1 Kozak-like sequence. Our findings demonstrate that translation of multiple ORFs in green algal bicistronic transcripts is consistent with episodic leaky scanning of ORF 1 to allow translation at ORF 2. This work has implications for the potential functionality of upstream open reading frames (uORFs) found across eukaryotic genomes and for transgene expression in synthetic biology applications.

PMID:40009642 | DOI:10.1073/pnas.2417695122

Eur J Nucl Med Mol Imaging. 2025 Feb 26. doi: 10.1007/s00259-025-07165-7. Online ahead of print.

ABSTRACT

PURPOSE: Respiratory motion during PET acquisition may produce lesion blurring. Ultra-fast 20-second breath-hold (U2BH) PET reduces respiratory motion artifacts, but the shortened scanning time increases statistical noise and may affect diagnostic quality. This study aims to denoise the U2BH PET images using a deep learning (DL)-based method.

METHODS: The study was conducted on two datasets collected from five scanners where the first dataset included 1272 retrospectively collected full-time PET data while the second dataset contained 46 prospectively collected U2BH and the corresponding full-time PET/CT images. A robust and data-efficient DL method called mask vision transformer (Mask-ViT) was proposed which, after fine-tuned on a limited number of training data from a target scanner, was directly applied to unseen testing data from new scanners. The performance of Mask-ViT was compared with state-of-the-art DL methods including U-Net and C-Gan taking the full-time PET images as the reference. Statistical analysis on image quality metrics were carried out with Wilcoxon signed-rank test. For clinical evaluation, two readers scored image quality on a 5-point scale (5 = excellent) and provided a binary assessment for diagnostic quality evaluation.

RESULTS: The U2BH PET images denoised by Mask-ViT showed statistically significant improvement over U-Net and C-Gan on image quality metrics (p < 0.05). For clinical evaluation, Mask-ViT exhibited a lesion detection accuracy of 91.3%, 90.4% and 91.7%, when it was evaluated on three different scanners.

CONCLUSION: Mask-ViT can effectively enhance the quality of the U2BH PET images in a data-efficient generalization setup. The denoised images meet clinical diagnostic requirements of lesion detectability.

PMID:40009163 | DOI:10.1007/s00259-025-07165-7

Abdom Radiol (NY). 2025 Feb 26. doi: 10.1007/s00261-025-04849-4. Online ahead of print.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) exhibits a high recurrence rate, and early recurrence significantly jeopardizes patient prognosis, necessitating reliable methods for early recurrence prediction.

METHODS: Utilizing multi-institutional data and integrating deep learning (DL) techniques, we established a neural network based on DenseNet capable of concurrently processing patients' triphasic enhanced CT scans. By incorporating an attention mechanism, the model automatically focuses on regions that significantly impact patient survival. Performance metrics were first evaluated using the concordance index (C-index), calibration curves, and decision curves based on the training and validation cohorts. Finally, class activation map (CAM) techniques were employed to visualize the regions of interest identified by the model. After model construction, five-fold cross-validation was performed to assess overfitting risks and further evaluate model stability.

RESULTS: We retrospectively collected data from 302 cases across five centers, including patients who underwent Partial Hepatectomy between December 2016 and December 2022. During model development, 180 patients from Institution I formed the training cohort, while the remaining patients comprised the validation cohort. The area under the ROC curve (AUC) for two-year outcomes was 0.797 in the validation cohort. Calibration curves, survival curves, and decision curve analysis (DCA) demonstrated the model's robust performance. CAMs revealed that the model primarily focuses on intra-abdominal solid organs, consistent with clinical experience. After model development, datasets were merged for cross-validation. The best model achieved a C-index of 0.774 in the validation cohort, with five-fold cross-validation yielding an average C-index of 0.778. The 95% confidence interval (CI) for the C-index, derived from cross-validation, ranged from 0.762 to 0.793.

CONCLUSION: Our DL-based enhanced CT network shows promise in predicting early recurrence in patients, representing a potential new strategy for early recurrence prediction in HCC.

PMID:40009155 | DOI:10.1007/s00261-025-04849-4

Med Biol Eng Comput. 2025 Feb 26. doi: 10.1007/s11517-025-03327-9. Online ahead of print.

ABSTRACT

Implant dentistry is the standard of care for the replacement of missing teeth. It is a complex process where cone-beam computed tomography (CBCT) images are analyzed by the dentist to determine the implants' length, diameter, and position, and angulation diameter, position, and angulation taking into consideration the prosthodontic treatment plan, bone morphology, and position of adjacent vital anatomical structures. This traditional procedure is time-consuming and relies heavily on the dentist's knowledge and expertise, which makes it subject to human errors. This study presents a two-stage framework for the placement of dental implants. The first stage utilizes YOLOv11 for the detection of fiducial markers and adjacent bone within 2D slices of 3D CBCT images. In the second stage, classification and regression are applied to extract the apical and occlusal coordinates of the implants and to predict the implants' intra-osseous length and intra-osseous diameter. YOLOv11 achieved a 59% F-score in the marker detection phase. The mean absolute error for the implant position prediction ranged from 11.931 to 15.954. The classification of the intra-osseous diameter showed 76% accuracy, and the intra-osseous length showed an accuracy of 59%. Our results were reviewed by an expert prosthodontist and deemed promising.

PMID:40009142 | DOI:10.1007/s11517-025-03327-9

Radiol Artif Intell. 2025 Feb 26:e240303. doi: 10.1148/ryai.240303. Online ahead of print.

ABSTRACT

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a deep learning (DL) model that derives aligned strain values from cine (noncontrast) cardiac MRI and evaluate performance of these values to predict myocardial fibrosis in patients with Duchenne muscular dystrophy (DMD). Materials and Methods This retrospective study included 139 male patients with DMD who underwent cardiac MRI examinations at a single center between February 2018 and April 2023. A DL pipeline was developed to detect five key frames throughout the cardiac cycle, and respective dense deformation fields, allowing for phase-specific strain analysis across patients and from one key frame to the next. Effectiveness of these strain values in identifying abnormal deformations associated with fibrotic segments was evaluated in 57 patients (15.2 ± 3.1 years), and reproducibility was assessed in 82 patients (12.8 ± 2.7 years), comparing our method with existing feature-tracking and DL-based methods. Statistical analysis compared strain values using t tests, mixed models, and 2000+ ML models, reporting accuracy, F1 score, sensitivity, and specificity. Results DL-based aligned strain identified five times more differences (29 versus 5, P < .01) between fibrotic and nonfibrotic segments compared with traditional strain values and identified abnormal diastolic deformation patterns often missed by traditional methods. Additionally, aligned strain values enhanced performance of predictive models for myocardial fibrosis detection, improving specificity by 40%, overall accuracy by 17%, and accuracy patients with preserved ejection fraction by 61%. Conclusion The proposed aligned strain technique enables motion-based detection of myocardial dysfunction on contrast free cardiac MRI, facilitating detailed interpatient strain analysis, and allowing precise tracking of disease progression in DMD. ©RSNA, 2025.

PMID:40008976 | DOI:10.1148/ryai.240303

J Comput Assist Tomogr. 2025 Feb 26. doi: 10.1097/RCT.0000000000001734. Online ahead of print.

ABSTRACT

The development of novel image reconstruction algorithms has been pivotal in enhancing image quality and reducing radiation dose in computed tomography (CT) imaging. Traditional techniques like filtered back projection perform well under ideal conditions but fail to generate high-quality images under low-dose, sparse-view, and limited-angle conditions. Iterative reconstruction methods improve upon filtered back projection by incorporating system models and assumptions about the patient, yet they can suffer from patchy image textures. The emergence of artificial intelligence (AI), particularly deep learning, has further advanced CT reconstruction. AI techniques have demonstrated great potential in reducing radiation dose while preserving image quality and noise texture. Moreover, AI has exhibited unprecedented performance in addressing challenging CT reconstruction problems, including low-dose CT, sparse-view CT, limited-angle CT, and interior tomography. This review focuses on the latest advances in AI-based CT reconstruction under these challenging conditions.

PMID:40008975 | DOI:10.1097/RCT.0000000000001734

J Chem Inf Model. 2025 Feb 26. doi: 10.1021/acs.jcim.4c02264. Online ahead of print.

ABSTRACT

Osteoporosis is a systemic microstructural degradation of bone tissue, often accompanied by fractures, pain, and other complications, resulting in a decline in patients' life quality. In response to the increased incidence of osteoporosis, related drug discovery has attracted more and more attention, but it is often faced with challenges due to long development cycle and high cost. Deep learning with powerful data processing capabilities has shown significant advantages in the field of drug discovery. With the development of technology, it is more and more applied to all stages of drug discovery. In particular, large models, which have been developed rapidly recently, provide new methods for understanding disease mechanisms and promoting drug discovery because of their large parameters and ability to deal with complex tasks. This review introduces the traditional models and large models in the deep learning domain, systematically summarizes their applications in each stage of drug discovery, and analyzes their application prospect in osteoporosis drug discovery. Finally, the advantages and limitations of large models are discussed in depth, in order to help future drug discovery.

PMID:40008920 | DOI:10.1021/acs.jcim.4c02264

Chin Med J (Engl). 2025 Feb 26. doi: 10.1097/CM9.0000000000003489. Online ahead of print.

ABSTRACT

Artificial intelligence (AI), particularly deep learning, has demonstrated remarkable performance in medical imaging across a variety of modalities, including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), and pathological imaging. However, most existing state-of-the-art AI techniques are task-specific and focus on a limited range of imaging modalities. Compared to these task-specific models, emerging foundation models represent a significant milestone in AI development. These models can learn generalized representations of medical images and apply them to downstream tasks through zero-shot or few-shot fine-tuning. Foundation models have the potential to address the comprehensive and multifactorial challenges encountered in clinical practice. This article reviews the clinical applications of both task-specific and foundation models, highlighting their differences, complementarities, and clinical relevance. We also examine their future research directions and potential challenges. Unlike the replacement relationship seen between deep learning and traditional machine learning, task-specific and foundation models are complementary, despite inherent differences. While foundation models primarily focus on segmentation and classification, task-specific models are integrated into nearly all medical image analyses. However, with further advancements, foundation models could be applied to other clinical scenarios. In conclusion, all indications suggest that task-specific and foundation models, especially the latter, have the potential to drive breakthroughs in medical imaging, from image processing to clinical workflows.

PMID:40008785 | DOI:10.1097/CM9.0000000000003489

Appl Biochem Biotechnol. 2025 Feb 26. doi: 10.1007/s12010-025-05200-9. Online ahead of print.

ABSTRACT

Colorectal cancer (CRC) is one of the common deadliest cancers worldwide. In Malaysia, the numbers of new CRC cases were horrific and worrisome. Telomerase is both prognostic indicator and predictor of carcinogenesis in CRC patients. Berberine, a telomerase inhibitor, was used in clinical trials and metabolomic studies; however, the association of telomerase with metabolites and metabolic pathways was not fully understood. Colorectal cancer cell line HCT 116 was cultured and treated with 10.54 µg/mL berberine. The cells were harvested at different time points to conduct subsequent analyses. The methods used in this research were real time-polymerase chain reaction (RT-PCR) to assess RNA expressions; Western blot to determine protein levels; TELOTAGGG Telomerase PCR ELISA to determine relative telomerase activity (RTA); 4',6-diamidino-2-phenylindole (DAPI) staining to determine percentage of nuclei damage; fluorescence microscopy for cell area; spectrophotometric potassium iodide assay for intracellular hydrogen peroxide concentration [H2O2]; as well as liquid chromatography mass spectrometry (LCMS) and tandem mass spectrometry (MS/MS) to investigate the intracellular metabolites. Partial least square-discriminant analysis (PLS-DA) score plot exhibited an improved separation compared to principal component analysis (PCA) when metabolomic data analysis of HCT 116 at various berberine treatment durations was conducted. Time and berberine treatment had an impact on RTA in HCT 116. RTA was discovered to be positively and negatively correlated to 14 and 2 metabolites, respectively. Glutamic acid was consistently found correlated to RTA. Other four metabolites, i.e., MG(14:0), [3-[hydroxy(phosphonooxy)phosphoryl]oxyphenyl] phosphono hydrogen phosphate), (3S,6S)-6-[[(3S,6R)-6-[(2S,3S,5S)-2,5-diiodo-4-methoxy-6-methyloxan-3-yl]oxy-3,4,5-trihydroxyoxan-2-yl]methoxy]-3,4,5-trihydroxyoxane-2-carboxylic acid, and 1-[5-O-(5'-adenylyloxyphosphonyl)-beta-D-ribofuranosyl]-5-amino-1H-imidazole-4-carboxamide, were newly discovered to be connected to RTA in HCT 116. Four metabolic pathways that majorly affected shared glutamic acid and glutamine. Nitrogen metabolism, D-glutamine and D-glutamate metabolism, glyoxylate and dicarboxylate metabolism, and aminoacyl-tRNA biosynthesis have been identified to be associated with RTA. Network analyses hinted that glutamic acid was also associated with oxidative stress mechanism. The multiple roles glutamic acid acted in diverse metabolic pathways and interaction networks emphasized the importance of glutamic acid in HCT 116 regarding RTA. This research establishes the association between RTA and several chosen RNAs, proteins, metabolites, and oxidative stress mechanisms, consequential in morphological alteration in HCT 116, to expand the knowledge of the intricate biological relationships and telomerase mechanism in CRC.

PMID:40009339 | DOI:10.1007/s12010-025-05200-9

Mol Oncol. 2025 Feb 26. doi: 10.1002/1878-0261.70005. Online ahead of print.

ABSTRACT

Glioblastoma (GBM) is the most aggressive and lethal type of glioma, characterized by aberrant expression of noncoding RNAs including circular RNAs (circRNAs). CircRNAs may impact cellular processes by interacting with other molecules-like RNA-binding proteins (RBPs). The diagnostic value of circRNA and circRNA/RBP complexes is still largely unknown. To explore circRNA and RBP transcript expression in GBM, we performed and further analyzed RNA-seq data from GBM patients' primary and recurrent tumor samples. We identified circRNAs differentially expressed in primary tumors, the circRNA progression markers in recurrent GBM samples, and the expression profile of RBP genes. Furthermore, we demonstrated the clinical potential of circRNAs and RBPs in GBM and proposed them as stratification markers in de novo assembled tumor subtypes. Additionally, we experimentally validated the subcellular localization of select circRNAs and their interactions with FUS. Subsequently, we showed that circARID1A may play a role in promoting GBM cell proliferation. Overall, we described circRNA-RBP interactions that could play a regulatory role in gliomagenesis and GBM progression and provided a list of molecular players in GBM for further extensive studies.

PMID:40008750 | DOI:10.1002/1878-0261.70005

CNS Neurosci Ther. 2025 Feb;31(2):e70294. doi: 10.1111/cns.70294.

ABSTRACT

OBJECTIVE: To identify and validate causal protein targets that may serve as potential therapeutic interventions for both the onset and progression of Parkinson's disease (PD) through integrative proteomic and genetic analyses.

METHOD: We utilized large-scale plasma and brain protein quantitative trait loci (pQTL) datasets from the deCODE Health study and the Religious Orders Study/Rush Memory and Aging Project (ROS/MAP), respectively. Proteome-wide association studies (PWAS) were conducted using the OTTERS framework for plasma proteins and the FUSION tool for brain proteins, examining associations with PD onset and three progression phenotypes: composite, motor, and cognitive. Significant protein associations (FDR-corrected p < 0.05) from PWAS were further validated using summary-based Mendelian randomization (SMR), colocalization analyses, and reverse Mendelian randomization (MR) to establish causality. Phenome-wide Mendelian randomization (PheW-MR) was performed to assess potential side effects across 679 disease traits when targeting these proteins to reduce PD-related phenotype risk by 20%. Additionally, we conducted cellular distribution-based clustering using gene expression data from the Allen Brain Atlas (ABA) to explore the distribution of key proteins across brain regions, constructed protein-protein interaction (PPI) networks via the STRING database to explore interactions among proteins, and evaluated the druggability of identified targets using the DrugBank database to identify opportunities for drug repurposing.

RESULT: Our analyses identified 25 candidate proteins associated with PD phenotypes, including 16 plasma proteins linked to PD progression (10 cognitive, 4 motor, and 3 composite) and 9 plasma proteins associated with PD onset. Notably, GPNMB was implicated in both plasma and brain tissues for PD onset. PheW-MR revealed predominantly beneficial side effects for the identified targets, with 83.7% of associations indicating positive outcomes and 16.3% indicating adverse effects. Cellular clustering categorized candidate targets into three distinct expression profiles across brain cell types using ABA. PPI network analysis highlighted one key interaction cluster among the proteins for PD cognitive progression and PD onset. Druggability assessment revealed 15 out of 25 proteins had repurposing opportunities for PD treatment.

CONCLUSION: We have identified 25 causal protein targets associated with the onset and progression of PD, providing new insights into the research and development of treatment strategies for PD.

PMID:40008429 | DOI:10.1111/cns.70294

Front Pharmacol. 2025 Feb 11;16:1525029. doi: 10.3389/fphar.2025.1525029. eCollection 2025.

ABSTRACT

Computational drug repositioning, serving as an effective alternative to traditional drug discovery plays a key role in optimizing drug development. This approach can accelerate the development of new therapeutic options while reducing costs and mitigating risks. In this study, we propose a novel deep learning-based framework KGRDR containing multi-similarity integration and knowledge graph learning to predict potential drug-disease interactions. Specifically, a graph regularized approach is applied to integrate multiple drug and disease similarity information, which can effectively eliminate noise data and obtain integrated similarity features of drugs and diseases. Then, topological feature representations of drugs and diseases are learned from constructed biomedical knowledge graphs (KGs) which encompasses known drug-related and disease-related interactions. Next, the similarity features and topological features are fused by utilizing an attention-based feature fusion method. Finally, drug-disease associations are predicted using the graph convolutional network. Experimental results demonstrate that KGRDR achieves better performance when compared with the state-of-the-art drug-disease prediction methods. Moreover, case study results further validate the effectiveness of KGRDR in predicting novel drug-disease interactions.

PMID:40008124 | PMC:PMC11850324 | DOI:10.3389/fphar.2025.1525029

Heliyon. 2025 Feb 5;11(3):e42476. doi: 10.1016/j.heliyon.2025.e42476. eCollection 2025 Feb 15.

ABSTRACT

Predicting drug-target binding affinity via in silico methods is crucial in drug discovery. Traditional machine learning relies on manually engineered features from limited data, leading to suboptimal performance. In contrast, deep learning excels at extracting features from raw sequences but often overlooks essential biological context features, hindering effective binding prediction. Additionally, these models struggle to capture global and local feature distributions efficiently in protein sequences and drug SMILES. Previous state-of-the-art models, like transformers and graph-based approaches, face scalability and resource efficiency challenges. Transformers struggle with scalability, while graph-based methods have difficulty handling large datasets and complex molecular structures. In this paper, we introduce InceptionDTA, a novel drug-target binding affinity prediction model that leverages CharVec, an enhanced variant of Prot2Vec, to incorporate both biological context and categorical features into protein sequence encoding. InceptionDTA utilizes a multi-scale convolutional architecture based on the Inception network to capture features at various spatial resolutions, enabling the extraction of both local and global features from protein sequences and drug SMILES. We evaluate InceptionDTA across a range of benchmark datasets commonly used in drug-target binding affinity prediction. Our results demonstrate that InceptionDTA outperforms various sequence-based, transformer-based, and graph-based deep learning approaches across warm-start, refined, and cold-start splitting settings. In addition to using CharVec, which demonstrates greater accuracy in absolute predictions, InceptionDTA also includes a version that employs simple label encoding and excels in ranking and predicting relative binding affinities. This versatility highlights how InceptionDTA can effectively adapt to various predictive requirements. These results emphasize the promise of our approach in expediting drug repurposing initiatives, enabling the discovery of new drugs, and contributing to advancements in disease treatment.

PMID:40007773 | PMC:PMC11850134 | DOI:10.1016/j.heliyon.2025.e42476

Cell J. 2025 Feb 23;26(10):619-621. doi: 10.22074/cellj.2025.2037024.1635.

ABSTRACT

Endometriosis, a benign gynecological disorder affecting 10-15% of women during their reproductive years, is characterized by the growth of endometrial tissue outside the uterus. Despite its prevalence, the exact pathophysiology of this disease remains poorly understood. Current treatments, including surgery and hormonal therapies, often have limited efficacy and may be associated with significant side effects. In recent years, drug repurposing has emerged as a promising strategy for managing endometriosis. This approach capitalizes on the molecular similarities between endometriosis and certain cancers, particularly the role of proteins such as fibronectin. By targeting these shared molecular pathways, researchers are exploring the potential of repurposing existing drugs, especially anticancer agents, to treat endometriosis. This strategy promises to provide more effective and less invasive treatment options for patients with endometriosis. Preliminary studies have shown the potential of anticancer drugs in inhibiting disease progression and alleviating symptoms. However, further clinical trials are necessary to confirm these findings and establish the precise role of anticancer drugs in the management of endometriosis.

PMID:40007448 | DOI:10.22074/cellj.2025.2037024.1635