IEEE Trans Vis Comput Graph. 2024 Jun 20;PP. doi: 10.1109/TVCG.2024.3413857. Online ahead of print.

ABSTRACT

Accurately determining the clinical positions for each tooth is essential in orthodontics, while most existing solutions heavily rely on inefficient manual design. In this paper, we present the LETA, a dual-branch Latent Encoding based 3D Tooth Alignment. Our system takes as input the segmented individual 3D tooth meshes in the Intra-oral Scanner (IOS) dental surfaces, and automatically predicts the proper 3D pose transformation for each tooth. LETA includes three components: an Encoder that learns a latent code of dental pointcloud, a Projector that transforms the latent code of misaligned teeth to predicted aligned ones, and a Solver to estimate the transformation between different dental latent codes. A key novelty of LETA is that we extract the features from the ground truth (GT) aligned teeth to guide network learning during training. To effectively learn tooth features, our Encoder employs an improved point-wise convolutional operation and an attention-based network to extract local shape features and global context features respectively. Extensive experimental results on a large-scale dataset with 9,868 IOS surfaces demonstrate that LETA can achieve state-of-the-art performance. A further clinical applicability study reveals that our method can reduce orthodontists' workload over 60% compared to starting tooth alignment from scratch, demonstrating the strong potential of deep learning for future digital dentistry.

PMID:40184274 | DOI:10.1109/TVCG.2024.3413857

PLoS One. 2025 Apr 4;20(4):e0322319. doi: 10.1371/journal.pone.0322319. eCollection 2025.

ABSTRACT

[This corrects the article DOI: 10.1371/journal.pone.0289535.].

PMID:40184393 | DOI:10.1371/journal.pone.0322319

Cell Rep. 2025 Apr 3;44(4):115502. doi: 10.1016/j.celrep.2025.115502. Online ahead of print.

ABSTRACT

The endoplasmic reticulum (ER) is structurally and functionally diverse, yet how its functions are organized within morphological subdomains is incompletely understood. Utilizing TurboID-based proximity labeling and CRISPR knockin technologies, we map the proteomic landscape of the human ER network. Sub-organelle proteomics reveals enrichments of proteins into ER tubules, sheets, and the nuclear envelope. We uncover an ER-enriched actin-binding protein, calmin/CLMN, and define it as an ER-actin tether that localizes to focal adhesions adjacent to ER tubules. Mechanistically, we find that CLMN depletion perturbs adhesion disassembly, actin dynamics, and cell movement. CLMN-depleted cells display decreased polarization of ER-plasma membrane contacts and calcium signaling factor STIM1 and altered calcium signaling near ER-actin interfaces, suggesting that CLMN influences calcium signaling to facilitate F-actin/adhesion dynamics. Collectively, we map the sub-organelle proteome landscape of the ER, identify CLMN as an ER-actin tether, and describe a non-canonical mechanism by which ER tubules engage actin to regulate cell migration.

PMID:40184252 | DOI:10.1016/j.celrep.2025.115502

STAR Protoc. 2025 Apr 3;6(2):103724. doi: 10.1016/j.xpro.2025.103724. Online ahead of print.

ABSTRACT

Myocardial infarction (MI) and myocardial ischemia-reperfusion injury (MIRI) are major pathological conditions in cardiovascular disease, requiring in-depth study for effective therapy development. Here, we present a detailed protocol for establishing a mouse model using the squeeze technique to simulate MI and MIRI. Key steps include isoflurane-induced anesthesia, left anterior descending artery (LAD) ligation, and real-time monitoring. Additionally, we describe procedures for histological analysis, offering a comprehensive approach to investigating disease mechanisms and potential therapeutic strategies. For complete details on the use and execution of this protocol, please refer to Gao et al.1.

PMID:40184247 | DOI:10.1016/j.xpro.2025.103724

Biofouling. 2025 Apr 4:1-18. doi: 10.1080/08927014.2025.2486250. Online ahead of print.

ABSTRACT

Urinary infections caused by Staphylococcus aureus are commonly associated with urinary catheterization and often result in severe complications. Given this problem, the objective of the study was to investigate the preventive action of promethazine (PMT) against the formation of methicillin-resistant Staphylococcus aureus (MRSA) biofilms when impregnated in urinary catheters. For this purpose, techniques such as broth microdilution, checkerboard, impregnation on urinary catheter fragments, flow cytometry assays and scanning electron microscopy were employed. PMT exhibited antimicrobial activity with Minimum Inhibitory Concentration (MIC) values ranging from 171 to 256 µg/mL, predominantly additive interaction in combination with oxacillin (OXA) and vancomycin (VAN), and a reduction in cell viability of biofilms formed and forming by methicillin-sensitive and -resistant S. aureus. Morphological alterations, damage to the membrane, and genetic material of cells treated with promethazine were also observed. The results demonstrated that PMT can be classified as a promising antimicrobial agent for use in the antibacterial coating of long-term urinary devices.

PMID:40183686 | DOI:10.1080/08927014.2025.2486250

J Virol. 2025 Apr 4:e0223424. doi: 10.1128/jvi.02234-24. Online ahead of print.

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) generally hijacks the cellular machinery of host cells for survival. However, how SARS-CoV-2 employs the host's deubiquitinase to facilitate virus replication remains largely unknown. In this study, we identified the host deubiquitinase USP22 as a crucial regulator of the expression of SARS-CoV-2 nucleocapsid protein (SARS-CoV-2 NP), which is essential for SARS-CoV-2 replication. We demonstrated that SARS-CoV-2 NP proteins undergo ubiquitination-dependent degradation in host cells, while USP22 interacts with SARS-CoV-2 NP and downregulates K63-linked polyubiquitination of SARS-CoV-2 NP, thereby protecting SARS-CoV-2 NP from degradation. Importantly, we further revealed that sulbactam, an antibiotic, can reduce USP22 protein levels, eventually promoting the degradation of SARS-CoV-2 NP in vitro and in vivo. This study reveals the mechanism by which SARS-CoV-2-encoded NP protein employs host deubiquitinase for virus survival and provides a potential strategy to fight against SARS-CoV-2 infection.IMPORTANCESevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid protein (SARS-CoV-2 NP) plays a pivotal role in viral infection by binding to viral RNA, stabilizing the viral genome, and promoting replication. However, the interactions between SARS-CoV-2 NP and host intracellular proteins had not been elucidated. In this study, we provide evidence that SARS-CoV-2 NP interacts with the deubiquitinase USP22 in host cells, which downregulates SARS-CoV-2 NP ubiquitination. This reduction in ubiquitination effectively prevents intracellular degradation of SARS-CoV-2 NP, thereby enhancing its stability, marking USP22 as a potential target for antiviral strategies. Additionally, our findings indicate that sulbactam significantly decreases the protein levels of USP22, thereby reducing SARS-CoV-2 NP levels. This discovery suggests a novel therapeutic pathway in which sulbactam could be repurposed as an antiviral agent, demonstrating how certain antibiotics might contribute to antiviral treatment. This work thus opens avenues for drug repurposing and highlights the therapeutic potential of targeting host pathways to inhibit viral replication.

PMID:40183543 | DOI:10.1128/jvi.02234-24

Acta Myol. 2025 Mar;44(1):23-27. doi: 10.36185/2532-1900-1026.

ABSTRACT

OBJECTIVES: Non-dystrophic myotonias (NDM) are rare diseases due to mutations in the voltage-gated sodium (Nav1.4) and chloride (ClC-1) channels expressed in skeletal muscle fibers. We provide an up-to-date review of pharmacological treatments available for NDM patients and experimental studies aimed at identifying alternative treatments and at better understanding the mechanisms of actions.

METHODS: Literature research was performed using PubMed and ClinicalTrial.gov.

RESULTS: Today, the sodium channel blocker mexiletine is the drug of choice for treatment of NDM. Alternative drugs include other sodium channel blockers and the carbonic anhydrase inhibitor acetazolamide. Preclinical studies suggest that activators of ClC-1 channels or voltage-gated potassium channels may have antimyotonic potential.

CONCLUSIONS: An increasing number of antimyotonic drugs would help to design a precision therapy to address personalized treatment of myotonic individuals.

PMID:40183437 | DOI:10.36185/2532-1900-1026

Pediatr Pulmonol. 2025 Apr;60(4):e71057. doi: 10.1002/ppul.71057.

ABSTRACT

INTRODUCTION: Primary Ciliary Dyskinesia (PCD) is a rare genetic disorder characterized by abnormalities in the motile cilia. Diagnosis could be hard to make, but genetic analysis could be important for the diagnosis and for defining prognosis.

AIM OF THE STUDY: To evaluate the clinical, ultrastructural, and molecular characteristics of a cohort of PCD subjects.

MATERIALS AND METHODS: The study cohort included PCD patients enrolled in two Italian centers. Clinical data were retrospectively collected consulting medical records. All patients underwent nasal brushing and peripheral blood sampling for ultrastructural analysis of motile cilia and genetic testing, respectively.

RESULTS: A total of 39 patients with PCD were enrolled (median age 25.5 years, range 2.5-54.3 years). All patients showed common clinical features, which included SIT in 22/39 (56.4%), chronic rhinitis in 31/39 (79.5%), chronic sinusitis in 26/37 (66.7%), chronic cough in 32/39 (82.1%), and neonatal respiratory distress in 46.2% (18/39). The genetic defect was identified in 27/39 patients (69.2%), while a diagnostic ultrastructure was found in 27/35 (77.1%). Assessing genotype-phenotype correlations, subjects with biallelic pathogenic variants in CCDC39 and CCDC40 genes had a significantly lower forced expiratory volume in the first second of exhalation value (p = 0.017) than subjects with pathogenic variants in DNAH5 or in other PCD-related genes.

CONCLUSIONS: Our study further highlights the high heterogeneity of ultrastructural defects and genetics characterizing patients with PCD, as well as providing additional evidence that patients with biallelic pathogenic variants in CCDC39 or CCDC40 display a worse clinical phenotype than patients with pathogenic variants in other PCD genes.

PMID:40183288 | DOI:10.1002/ppul.71057

Neuroradiology. 2025 Apr 4. doi: 10.1007/s00234-025-03605-1. Online ahead of print.

ABSTRACT

BACKGROUND: Cerebrovascular reactivity (CVR) assesses vascular health in various brain conditions, but CVR measurement requires a challenge to cerebral perfusion such as the administration of acetazolamide(ACZ), thus limiting widespread use. We determined whether generative adversarial networks (GANs) can create CVR images from baseline pre-ACZ arterial spin labeling (ASL) MRI.

METHODS: This study included 203 Moyamoya cases with a total of 3248 pre- and post-ACZ ASL Cerebral Blood Flow (CBF) images. Reference CVRs were generated from these CBF slices. From this set, 2640 slices were used to train a Pixel-to-Pixel GAN consisting of a generator and discriminator network, with the remaining 608 slices reserved as a testing set. Following training, the pre-ACZ CBF in the testing set was introduced to the trained model to generate synthesized CVR. The quality of the synthesized CVR was evaluated with structural similarity index(SSI), spatial correlation coefficient(SCC), and the root mean squared error(RMSE), compared with reference CVR. The segmentations of the low CVR regions were compared using the Dice similarity coefficient (DSC). Reference and synthesized CVRs in single-slice and individual-hemisphere settings were reviewed to assess CVR status, with Cohen's Kappa measuring consistency.

RESULTS: The mean SSIs of the CVR of training and testing sets were 0.943 ± 0.019 and 0.943 ± 0.020. The mean SCCs of the CVR of training and testing sets were 0.988 ± 0.009 and 0.987 ± 0.011. The mean RMSEs of the CVR are 0.077 ± 0.015 and 0.079 ± 0.018. Mean DSC of low CVR area of testing sets was 0.593 ± 0.128. Visual interpretation yielded Cohen's Kappa values of 0.896 and 0.813 for the training and testing sets in the single-slice setting, and 0.781 and 0.730 in the individual-hemisphere setting.

CONCLUSIONS: Synthesized CVR by GANs from baseline ASL without challenge may be a useful alternative in detecting vascular deficits in clinical applications when ACZ challenge is not feasible.

PMID:40183965 | DOI:10.1007/s00234-025-03605-1

Eur J Nucl Med Mol Imaging. 2025 Apr 4. doi: 10.1007/s00259-025-07248-5. Online ahead of print.

ABSTRACT

PURPOSE: To address heterogeneity in prostate cancer (PCa) pathological grading, we developed an interpretable multimodal fusion model integrating 18F prostate-specific membrane antigen (18F-PSMA)-targeted positron emission tomography/computed tomography (18F-PSMA-PET/CT) imaging features with clinical variables for predicting post-surgical ISUP grade (psISUP ≥ 4 vs. < 4).

METHODS: This retrospective study analyzed 222 patients with PCa (2020-2024) undergoing 18F-PSMA PET/CT. We constructed a deep transfer learning framework incorporating radiomic features from PET/CT and clinical parameters. Model performance was validated against three established methods and preoperative biopsy Gleason scores. Additionally, SHapley Additive exPlanations (SHAP) values elucidated feature contributions, and a radiomic nomogram was developed for clinical translation.

RESULTS: The fusion model achieved superior discrimination in psISUP grading (test set area under the curve (AUC) = 0.850, 95% confidence interval [CI] 0.769-0.932; validation set AUC = 0.833, 95% CI 0.657-1.000), significantly outperforming preoperative Gleason scores. SHAP analysis identified PSMA uptake heterogeneity and PSA density as key predictive features. The nomogram demonstrated clinical interpretability through visualised risk stratification.

CONCLUSION: Our deep learning-based multimodal fusion model enables accurate preoperative prediction of aggressive PCa pathology (ISUP ≥ 4), potentially optimising surgical planning and personalised therapeutic strategies. The interpretable framework enhances clinical trustworthiness in artificial intelligence-assisted decision-making.

PMID:40183953 | DOI:10.1007/s00259-025-07248-5

Eur J Nucl Med Mol Imaging. 2025 Apr 4. doi: 10.1007/s00259-025-07215-0. Online ahead of print.

ABSTRACT

PURPOSE: This study aims to develop and evaluate deep learning models for the detection and classification of hypermetabolic lung lesions into four categories: benign, lung cancer, pulmonary lymphoma, and metastasis. These categories are defined by their pathological origin, clinical relevance, and therapeutic implications.

METHODS: A lesion localisation model was first developed using manually annotated PET/CT images. For classification, a multi-dimensional joint network was employed, incorporating both image patches and two-dimensional projections. Classification performance was quantified by metrics like accuracy, and compared to that of a radiomics model. Additionally, false-positive segmentations were manually reviewed and analysed for clinical evaluation.

RESULTS: The study retrospectively included 647 cases (409 males/238 females) over more than 8 years from five centres, divided into an internal dataset (426 cases from Shanghai Ruijin Hospital), an external test set I (151 cases from four other institutions), and an external test set II (70 cases from a new imaging device). The localisation model achieved detection rates of 81.19%, 75.48%, and 77.59% on the internal, external test set I, and external test set II, respectively. The classification model outperformed the radiomics approach, with area-under-curves of 88.4%, 80.7%, and 66.6%, respectively. Most false-positive segmentations were clinically acceptable, corresponding to suspicious lesions in adjacent regions, particularly lymph nodes.

CONCLUSION: Deep learning models based on PET/CT imaging can effectively detect, segment, and classify hypermetabolic lung lesions, and identify suspicious adjacent lesions. These results highlight the potential of artificial intelligence in clinical decision-making and lung disease diagnosis.

PMID:40183951 | DOI:10.1007/s00259-025-07215-0

Med Phys. 2025 Apr 4. doi: 10.1002/mp.17808. Online ahead of print.

ABSTRACT

BACKGROUND: Meningiomas are the most common primary intracranial tumors in adults. Low-grade meningiomas have a low recurrence rate, whereas high-grade meningiomas are highly aggressive and recurrent. Therefore, the pathological grading information is crucial for treatment, as well as follow-up and prognostic guidance. Most previous studies have used radiomics or deep learning methods to extract feature information for grading meningiomas. However, some radiomics features are pixel-level features that can be influenced by factors such as image resolution and sharpness. Additionally, deep learning models that perform grading directly from MRI images often rely on image features that are ambiguous and uncontrollable, which reduces the reliability of the results to a certain extent.

PURPOSE: We aim to validate that combining medical features with deep neural networks can effectively improve the accuracy and reliability of meningioma grading.

METHODS: We construct a SNN-Tran model for grading meningiomas by analyzing medical features including tumor volume, peritumoral edema volume, dural tail sign, tumor location, the ratio of peritumoral edema volume to tumor volume, age and gender. This method is able to better capture the complex relationships and interactions in the medical features and enhance the reliability of the prediction results.

RESULTS: Our model achieve an accuracy of 0.875, sensitivity of 0.886, specificity of 0.847, and AUC of 0.872. And the method is superior to the deep learning, radiomics and SOTA methods.

CONCLUSION: We demonstrate that combining medical features with SNN-Tran can effectively improve the accuracy and reliability of meningioma grading. The SNN-Tran model excel in capturing long-range dependencies in the medical feature sequence.

PMID:40183528 | DOI:10.1002/mp.17808

Technol Cancer Res Treat. 2025 Jan-Dec;24:15330338251333018. doi: 10.1177/15330338251333018. Epub 2025 Apr 4.

ABSTRACT

IntroductionEarly identification of patients at risk for toxicity induced by radiotherapy (RT) is essential for developing personalized treatments and mitigation plans. Preclinical models with relevant endpoints are critical for systematic evaluation of normal tissue responses. This study aims to determine whether attention-based vision transformers can classify MR images of irradiated and control mice, potentially aiding early identification of individuals at risk of developing toxicity.MethodC57BL/6J mice (n = 14) were subjected to 66 Gy of fractionated RT targeting the oral cavity, swallowing muscles, and salivary glands. A control group (n = 15) received no irradiation but was otherwise treated identically. T2-weighted MR images were obtained 3-5 days post-irradiation. Late toxicity in terms of saliva production in individual mice was assessed at day 105 after treatment. A pre-trained vision transformer model (ViT Base 16) was employed to classify the images into control and irradiated groups.ResultsThe ViT Base 16 model classified the MR images with an accuracy of 69%, with identical overall performance for control and irradiated animals. The ViT's model predictions showed a significant correlation with late toxicity (r = 0.65, p < 0.01). One of the attention maps from the ViT model highlighted the irradiated regions of the animals.ConclusionsAttention-based vision transformers using MRI have the potential to predict individuals at risk of developing early toxicity. This approach may enhance personalized treatment and follow-up strategies in head and neck cancer radiotherapy.

PMID:40183426 | DOI:10.1177/15330338251333018

Cancer Biomark. 2025 Mar;42(3):18758592241311184. doi: 10.1177/18758592241311184. Epub 2025 Apr 4.

ABSTRACT

BackgroundIn this research, we explore the application of Convolutional Neural Networks (CNNs) for the development of an automated cancer detection system, particularly for MRI images. By leveraging deep learning and image processing techniques, we aim to build a system that can accurately detect and classify tumors in medical images. The system's performance depends on several stages, including image enhancement, segmentation, data augmentation, feature extraction, and classification. Through these stages, CNNs can be effectively trained to detect tumors in MRI images with high accuracy. This automated cancer detection system can assist healthcare professionals in diagnosing cancer more quickly and accurately, improving patient outcomes. The integration of deep learning and image processing in medical diagnostics has the potential to revolutionize healthcare, making it more efficient and accessible.MethodsIn this paper, we examine the failure of semantic segmentation by predicting the mean intersection over union (mIoU), which is a standard evaluation metric for segmentation tasks. mIoU calculates the overlap between the predicted segmentation map and the ground truth segmentation map, offering a way to evaluate the model's performance. A low mIoU indicates poor segmentation, suggesting that the model has failed to accurately classify parts of the image. To further improve the robustness of the system, we introduce a deep neural network capable of predicting the mIoU of a segmentation map. The key innovation here is the ability to predict the mIoU without needing access to ground truth data during testing. This allows the system to estimate how well the model is performing on a given image and detect potential failure cases early in the process. The proposed method not only predicts the mIoU but also uses the expected mIoU value to detect failure events. For instance, if the predicted mIoU falls below a certain threshold, the system can flag this as a potential failure, prompting further investigation or triggering a safety mechanism in the autonomous vehicle. This mechanism can prevent the vehicle from making decisions based on faulty segmentation, improving safety and performance. Furthermore, the system is designed to handle imbalanced data, which is a common challenge in training deep learning models. In autonomous driving, certain objects, such as pedestrians or cyclists, might appear much less frequently than other objects like vehicles or roads. The imbalance can cause the model to be biased toward the more frequent objects. By leveraging the expected mIoU, the method can effectively balance the influence of different object classes, ensuring that the model does not overlook critical elements in the scene. This approach offers a novel way of not only training the model to be more accurate but also incorporating failure prediction as an additional layer of safety. It is a significant step forward in ensuring that autonomous systems, especially self-driving cars, operate in a safe and reliable manner, minimizing the risk of accidents caused by misinterpretations of visual data. In summary, this research introduces a deep learning model that predicts segmentation performance and detects failure events by using the mIoU metric. By improving both the accuracy of semantic segmentation and the detection of failures, we contribute to the development of more reliable autonomous driving systems. Moreover, the technique can be extended to other domains where segmentation plays a critical role, such as medical imaging or robotics, enhancing their ability to function safely and effectively in complex environments.Results and DiscussionBrain tumor detection from MRI images is a critical task in medical image analysis that can significantly impact patient outcomes. By leveraging a hybrid approach that combines traditional image processing techniques with modern deep learning methods, this research aims to create an automated system that can segment and identify brain tumors with high accuracy and efficiency. Deep learning techniques, particularly CNNs, have proven to be highly effective in medical image analysis due to their ability to learn complex features from raw image data. The use of deep learning for automated brain tumor segmentation provides several benefits, including faster processing times, higher accuracy, and more consistent results compared to traditional manual methods. As a result, this research not only contributes to the development of advanced methods for brain tumor detection but also demonstrates the potential of deep learning in revolutionizing medical image analysis and assisting healthcare professionals in diagnosing and treating brain tumors more effectively.ConclusionIn conclusion, this research demonstrates the potential of deep learning techniques, particularly CNNs, in automating the process of brain tumor detection from MRI images. By combining traditional image processing methods with deep learning, we have developed an automated system that can quickly and accurately segment tumors from MRI scans. This system has the potential to assist healthcare professionals in diagnosing and treating brain tumors more efficiently, ultimately improving patient outcomes. As deep learning continues to evolve, we expect these systems to become even more accurate, robust, and widely applicable in clinical settings. The use of deep learning for brain tumor detection represents a significant step forward in medical image analysis, and its integration into clinical workflows could greatly enhance the speed and accuracy of diagnosis, ultimately saving lives. The suggested plan also includes a convolutional neural network-based classification technique to improve accuracy and save computation time. Additionally, the categorization findings manifest as images depicting either a healthy brain or one that is cancerous. CNN, a form of deep learning, employs a number of feed-forward layers. Additionally, it functions using Python. The Image Net database groups the images. The database has already undergone training and preparation. Therefore, we have completed the final training layer. Along with depth, width, and height feature information, CNN also extracts raw pixel values.We then use the Gradient decent-based loss function to achieve a high degree of precision. We can determine the training accuracy, validation accuracy, and validation loss separately. 98.5% of the training is accurate. Similarly, both validation accuracy and validation loss are high.

PMID:40183298 | DOI:10.1177/18758592241311184

Bioinformatics. 2025 Apr 4:btaf150. doi: 10.1093/bioinformatics/btaf150. Online ahead of print.

ABSTRACT

MOTIVATION: Precision medicine leverages patient-specific multimodal data to improve prevention, diagnosis, prognosis and treatment of diseases. Advancing precision medicine requires the non-trivial integration of complex, heterogeneous and potentially high-dimensional data sources, such as multi-omics and clinical data. In the literature several approaches have been proposed to manage missing data, but are usually limited to the recovery of subsets of features for a subset of patients. A largely overlooked problem is the integration of multiple sources of data when one or more of them are completely missing for a subset of patients, a relatively common condition in clinical practice.

RESULTS: We propose miss-Similarity Network Fusion (miss-SNF), a novel general-purpose data integration approach designed to manage completely missing data in the context of patient similarity networks. Miss-SNF integrates incomplete unimodal patient similarity networks by leveraging a non-linear message-passing strategy borrowed from the SNF algorithm. Miss-SNF is able to recover missing patient similarities and is "task agnostic", in the sense that can integrate partial data for both unsupervised and supervised prediction tasks. Experimental analyses on nine cancer datasets from The Cancer Genome Atlas (TCGA) demonstrate that miss-SNF achieves state-of-the-art results in recovering similarities and in identifying patients subgroups enriched in clinically relevant variables and having differential survival. Moreover, amputation experiments show that miss-SNF supervised prediction of cancer clinical outcomes and Alzheimer's disease diagnosis with completely missing data achieves results comparable to those obtained when all the data are available.

AVAILABILITY AND IMPLEMENTATION: miss-SNF code, implemented in R, is available at https://github.com/AnacletoLAB/missSNF.

SUPPLEMENTARY INFORMATION: Supplementary information are available at Bioinformatics online.

PMID:40184204 | DOI:10.1093/bioinformatics/btaf150

Metab Brain Dis. 2025 Apr 4;40(4):169. doi: 10.1007/s11011-025-01585-y.

ABSTRACT

Amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD) share overlapping molecular mechanisms, including estrogen signaling dysregulation, oxidative stress, and neuroinflammation. Standard treatments often lead to adverse effects due to unintended cross-talk with the estrogen signaling pathway. Identifying key regulatory genes and bioactive plant-derived compounds that modulate estrogen signaling without interfering with standard therapies offers a promising neuroprotective strategy. A network medicine and systems biology approach was used, beginning with the screening of 29 medicinal plants for ALS and 49 for PD, identifying 12 shared plants with neuroprotective potential. Bioactive compounds were screened for gene, protein, and pathway interactions, leading to target prediction (846 ALS-related and 690 PD-related targets) and disease association mining, which identified 93 overlapping genes (OGs). Protein-protein interaction (PPI) network analysis and MCODE clustering revealed ESR1, EGFR, and SRC as key hub-bottleneck (HB) genes, further validated via differential gene expression analysis. Gene ontology (GO) and pathway enrichment analyses revealed significant enrichment in estrogen signaling confirming the involvement of HB genes in neurodegenerative disease progression. Differential expression analysis confirmed ESR1 upregulation in ALS but downregulation in PD, suggesting a converse disease-specific regulatory pattern. Gene regulatory network (GRN) analysis identified hsa-miR-145-5p (ALS) and hsa-miR-181a-5p (PD) as key regulators, while FOXC1, GATA2, and TP53 emerged as crucial transcription factors (TFs) influencing disease progression. Molecular docking and MD simulations validated strong and stable interactions of Eupalitin (CYP19A1, -9.0 kcal/mol), Hesperetin (ESR1, -8.1 kcal/mol), and Sumatrol (PIK3CA, -8.9 kcal/mol). These phytochemicals, derived from Rosmarinus officinalis, Artemisia scoparia, Ocimum tenuiflorum, and Indigofera tinctoria, maintained stable hydrogen bonding and hydrophobic interactions for over 30% of a 25 ns simulation, supporting their therapeutic potential. The identification of ESR1, EGFR, and SRC as key targets, alongside estrogen signaling involvement, highlights the need for targeted nutraceutical interventions. These findings pave the way for safer, plant-based therapies that mitigate neurodegeneration while preserving estrogen signaling integrity, offering a promising adjuvant strategy alongside existing treatments.

PMID:40184012 | DOI:10.1007/s11011-025-01585-y

Chromosome Res. 2025 Apr 4;33(1):5. doi: 10.1007/s10577-025-09764-4.

ABSTRACT

DNA topoisomerase II beta (TOP2B) is required for correct execution of certain developmental transcriptional programs and for signal-induced transcriptional activation, including transcriptional activation by nuclear hormone ligands such as retinoic acid. In addition, TOP2B is enriched at genomic locations occupied by CCCTC-Binding factor (CTCF) and cohesin (RAD21). suggesting a role in chromosome looping and/or establishing or maintaining aspects of chromosome 3D structure. This led us to investigate the effect of TOP2B inactivation on patterns of intra- and inter- chromosomal interaction that reflect the 3D architecture of the genome. Using the retinoic acid responsive SH-SY5Y neuroblastoma cell line model, we had previously demonstrated many gene expression changes upon retinoic acid treatment and upon deletion of TOP2B. We report here that these expression changes in TOP2B null versus WT cells are accompanied by surprisingly subtle changes in local chromosome organization. However, we do observe quantitative changes in chromosome organization on a megabase scale. First, lack of TOP2B did affect compartment strength changes that occur upon ATRA treatment. Second, we observe an excess of very long-range interactions, reminiscent of interactions seen in mitotic cells, suggesting the possibility that in the absence of TOP2B some mitotic interactions are retained. Third, we see quantitative changes in centromere-telomere interactions, again indicating global changes at the megabase and chromosome level. These data support the surprising conclusion that TOP2B has only a minor role in chromosome dynamics and organization.

PMID:40183884 | DOI:10.1007/s10577-025-09764-4

Immunology. 2025 Apr 4. doi: 10.1111/imm.13923. Online ahead of print.

ABSTRACT

Inflammatory bowel disease (IBD), comprising ulcerative colitis (UC) and Crohn's disease (CD) and irritable bowel syndrome (IBS) are distinct gastrointestinal disorders. Mycobacterium avium subspecies paratuberculosis (MAP) is implicated in IBD pathogenesis, while the roles of human endogenous retroviruses (HERVs) are under investigation. We aimed (a) to investigate whether the levels of humoral response to MAP-3865c, HERV-K envelope and HERV-W envelope against the epitopes in IBD/IBS patients; (b) to determine the frequency of micronuclei in IBD patients and (c) to evaluate the possible correlation between genomic damage and humoral response. This study investigates antibody titres against MAP 3865c, HERV-K env and HERV-W env in plasma from 102 IBD, 20 IBS patients and 92 healthy controls (HCs). Micronuclei (MNi) frequency in IBD patients is assessed, correlating with humoral responses and patient genotype profiles. IBD patients exhibited elevated antibody responses to MAP 3865c, with those carrying the GA genotype for TNF-α showing higher anti-MAP 3865c IgG levels. A significant positive correlation was observed between MNi frequency and the humoral response against MAP 3865c in IBD patients. Higher antibody responses to HERV-K env were detected in both IBD and IBS patients compared to HCs, with significant positive correlations found between MAP 3865c and HERV-K env peptide responses in IBD patients. HERV-W env antibody levels were higher in IBS patients than in HCs. Our findings highlight the association between UC and CD and immune responses targeting MAP and HERV-Kenv. Specific genetic profiles may exacerbate inflammation, potentially amplifying genetic damage observed in IBD patients, as indicated by MNi frequencies.

PMID:40183428 | DOI:10.1111/imm.13923

Medicine (Baltimore). 2025 Jan 3;104(1):e40958. doi: 10.1097/MD.0000000000040958.

ABSTRACT

RATIONALE: Taxanes, derived from Taxus chinesnsis, stabilize microtubules and include drugs like Paclitaxel, Docetaxel, and Nab-paclitaxel. These are commonly used to treat various malignant tumors. However, Taxane-drug-induced cystoid macular edema (TDICME) is a rare and often under-recognized complication.

PATIENT CONCERNS: A male patient, aged sixty-three, who was diagnosed with poorly differentiated gastric adenocarcinoma, experienced a progressive decline in visual acuity in both eyes after a 4-month course of nab-paclitaxel therapy.

DIAGNOSES: Upon Fundus examination, bilateral cystoid macular edema (CME) was seen.

INTERVENTIONS: Undergo treatment with carbonic anhydrase inhibitors and discontinue the use of nab-paclitaxel.

OUTCOMES: After eleven days of treatment with carbonic anhydrase inhibitors, the patient reported significant improvement in visual acuity. Furthermore, CME was completely resolved in both eyes 8 weeks after stopping nab-paclitaxel.

LESSONS: This case highlights the potential therapeutic effectiveness of topical carbonic anhydrase inhibitors in treating TDICME. Our findings underscore the importance of monitoring and addressing ocular side effects in patients undergoing Taxane therapy, ultimately contributing to enhanced patient quality of life and treatment outcomes.

PMID:40184125 | DOI:10.1097/MD.0000000000040958

Front Public Health. 2025 Mar 20;13:1450625. doi: 10.3389/fpubh.2025.1450625. eCollection 2025.

ABSTRACT

INTRODUCTION: The role of public health has evolved from addressing infectious diseases to encompass non-communicable diseases. Individuals with genetic disorders and rare diseases constitute a particularly vulnerable population, requiring tailored public health policies, practical implementation strategies, and a long-term vision to ensure sustainable support. Given the prolonged duration and significant costs often associated with these conditions, comprehensive, patient-centered, and cost-effective approaches are essential to safeguard their physical and mental well-being.

AIMS: To summarize definitions and concepts related to health, public health, rare diseases, and to highlight the role of integrating public health interventions into routine care in improving patient outcomes. Hemophilia was selected as an exemplary rare disease due to its significant lifetime treatment costs and the recent approval and pricing of its gene therapy as the world's most expensive drug, highlighting the critical importance of public health policies in ensuring equitable access to care and treatment.

METHODS: A narrative literature review was conducted between July 2023 and December 2024, searching PubMed, Google Scholar, and Google for various topics related to rare diseases, public health, and hemophilia.

RESULTS: Public health can play an important role in improving the health outcomes of people with rare diseases by implementing conceptual and applied models to accomplish a set of objectives. Over the past two decades, legislative and regulatory support in high income countries (HICs) has facilitated the development and approval of diagnostics and treatments for several rare diseases leading to important advancements. In contrast, many low- and middle-income countries (LMICs) face obstacles in enacting legislation, developing regulations, and implementing policies to support rare disease diagnosis and treatment. More investment and innovation in drug discovery and market access pathways are still needed in both LMICs and HICs. Ensuring the translation of public health policies into regulatory measures, and in turn implementing, and regularly evaluating these measures to assess their effectiveness is crucial. In the case of hemophilia, public health can play a pivotal role.

CONCLUSION: Enhancing public health surveillance, policies, and interventions in hemophilia and other rare diseases can bridge data gaps, support access to equitable treatment, promote evidence-based care, and improve outcomes across the socioeconomic spectrum.

PMID:40182514 | PMC:PMC11965367 | DOI:10.3389/fpubh.2025.1450625