Function (Oxf). 2025 May 16:zqaf020. doi: 10.1093/function/zqaf020. Online ahead of print.

ABSTRACT

Previous studies reported that opioids depress breathing by inhibiting respiratory neural networks in the brainstem. The effects of opioids on sensory inputs regulating breathing are less studied. This study examined the effects of fentanyl and sufentanil on carotid body neural activity, a crucial sensory regulator of breathing. Both opioids stimulated carotid body afferent nerve activity and increased glomus cell [Ca2+]i levels. RNA sequencing and immunohistochemistry revealed a high abundance of κ opioid receptors (KORs) in carotid bodies, but no µ or δ opioid receptors. A KOR agonist, like fentanyl, stimulated carotid body afferents, while a KOR antagonist blocked carotid body activation by fentanyl and KOR agonist. In unanesthetized rats, fentanyl initially stimulated breathing, followed by respiratory depression. A KOR agonist stimulated breathing without respiratory inhibition, and this effect was absent in carotid body-denervated rats. Combining fentanyl with a KOR agonist attenuated respiratory depression in rats with intact carotid body but not in carotid body denervated rats. These findings highlight previously uncharacterized activation of carotid body afferents by fentanyl via KORs as opposed to depression of brainstem respiratory neurons by µ opioid receptors and suggest that KOR agonists might counteract the central depressive effects of opioids on breathing.

PMID:40378144 | DOI:10.1093/function/zqaf020

J Chem Inf Model. 2025 May 16. doi: 10.1021/acs.jcim.5c00462. Online ahead of print.

ABSTRACT

Although there are over 100,000 distinct human metabolites, their biological significance is often not fully appreciated. Metabolites can reshape the protein pockets to which they bind by COLIG formation, thereby influencing enzyme kinetics and altering the monomer-multimer equilibrium in protein complexes. Binding a common metabolite to a set of protein monomers or multimers results in metabolic entanglements that couple the conformational states and functions of nonhomologous, nonphysically interacting proteins that bind the same metabolite. These shared metabolites might provide the collective behavior responsible for protein pathway formation. Proteins whose binding and functional behavior is modified by a set of metabolites are termed an "entabolon"─a portmanteau of metabolic entanglement and metabolon. 55%-60% (22%-24%) of pairs of nonenzymatic proteins that likely bind the same metabolite have a p-value that they are in the same pathway, which is <0.05 (0.0005). Interestingly, the most populated pairs of proteins common to multiple pathways bind ancient metabolites. Similarly, we suggest how metabolites can possibly activate, terminate, or preclude transcription and other nucleic acid functions and may facilitate or inhibit the binding of nucleic acids to proteins, thereby influencing transcription and translation processes. Consequently, metabolites likely play a critical role in the organization and function of biological systems.

PMID:40378093 | DOI:10.1021/acs.jcim.5c00462

Cell Rep. 2025 May 14;44(5):115703. doi: 10.1016/j.celrep.2025.115703. Online ahead of print.

ABSTRACT

Radiotherapy is a pillar of breast cancer treatment; however, it remains unclear how radiotherapy modulates the tumor microenvironment. We investigated this question in a cohort of 20 patients with estrogen-receptor positive (ER+) breast tumors who received neoadjuvant radiotherapy. Tumor biopsies were collected before and 7 days postradiation. Single-cell DNA sequencing (scDNA-seq) and scRNA-seq were conducted on 8 and 11 patients, respectively, at these two time points. The scRNA data showed increased infiltration of naive-like CD4 T cells and an early, activated CD8 T cell population following radiotherapy. Radiotherapy also eliminated existing cytotoxic T cells and resulted in myeloid cell increases. In tumor cells, the scDNA-seq data showed a high genomic selection of subclones in half of the patients with high ER expression, while the remaining number had low genomic selection and an interferon response. Collectively, these data provide insight into the impact of radiotherapy in ER+ breast cancer patients.

PMID:40378044 | DOI:10.1016/j.celrep.2025.115703

Proc Natl Acad Sci U S A. 2025 May 20;122(20):e2420132122. doi: 10.1073/pnas.2420132122. Epub 2025 May 16.

ABSTRACT

Anther dehiscence is the process that facilitates pollen release from mature anthers in flowering plants. Despite its crucial importance to reproduction, the underlying developmental mechanism and its integration with environmental cues remain poorly understood. Establishing noninvasive, controlled humidity treatments of Arabidopsis thaliana flowers, we show here that high humidity prevents anthers from opening. Manipulation of stomatal densities alters dehiscence dynamics, suggesting a contribution of controlled transpiration. Furthermore, analyses of subcellular markers revealed the occurrence of a developmentally prepared and environmentally triggered programmed cell death (PCD) process in specific anther tissues, epidermis and endothecium. Notably, genetic inhibition of PCD delays anther dehiscence, whereas precocious PCD induction promotes it. Our data reveal a rapid PCD execution process modulated by ambient humidity as instrumental for timely pollen release in the flowering plant Arabidopsis.

PMID:40377996 | DOI:10.1073/pnas.2420132122

Proc Natl Acad Sci U S A. 2025 May 20;122(20):e2418279122. doi: 10.1073/pnas.2418279122. Epub 2025 May 16.

ABSTRACT

The switch from precursor cell proliferation to onset of differentiation in adult stem cell lineages must be carefully regulated to produce sufficient progeny to maintain and repair tissues, yet prevent overproliferation that may enable oncogenesis. In the Drosophila male germ cell lineage, spermatogonia produced by germ line stem cells undergo a limited number of transit amplifying mitotic divisions before switching to the spermatocyte program that sets up meiosis and eventual spermatid differentiation. The number of transit amplifying divisions is set by accumulation of the bag-of-marbles (Bam) protein to a critical threshold. In bam mutants, spermatogonia proliferate through several extra rounds of mitosis and then die without becoming spermatocytes. Here, we show that a key role of Bam for the mitosis to differentiation switch is repressing expression of Held Out Wings (how), homolog of mammalian Quaking. Knockdown of how in germ cells was sufficient to allow spermatogonia mutant for bam or its partner benign gonial cell neoplasm to differentiate, while forced expression of nuclear-targeted How protein in spermatogonia wild-type for bam resulted in continued proliferation at the expense of differentiation. Our findings suggest that Bam targets how RNA for degradation by acting as an adapter to recruit the CCR4-NOT deadenylation complex via binding its subunit, Caf40. As How is itself an RNA-binding protein with roles in RNA processing, our findings reveal that the switch from proliferation to meiosis and differentiation in the Drosophila male germ line adult stem cell lineage is regulated by a cascade of RNA-binding proteins.

PMID:40377994 | DOI:10.1073/pnas.2418279122

PLoS One. 2025 May 16;20(5):e0321785. doi: 10.1371/journal.pone.0321785. eCollection 2025.

ABSTRACT

BACKGROUND: In 2017, reports of adverse drug reactions worldwide reached an estimated 35 million.Chemotherapeutic agents were one of the most often implicated pharmacological classes in inducing adverse drug reactions. Adverse drug reactions increase the overall expense and mortality. Adverse drug reactions increase morbidity, mortality, hospitalization rate and financial expenses. Therefore, this study intended to assess chemotherapy-related adverse drug reactions and associated factors among adult cancer patients.

PATIENTS AND METHOD: A facility-based prospective observational study was conducted from July 2022 to October 2022 at Jimma Medical Center's oncology unit. A standard data collection tool (Naranjo's algorithm, modified Hartwig's severity scale, and modified Schumock-Thornton criteria) was used for assessment of causality, severity, and preventability of adverse reactions, respectively. Socio-demographic profile and any adverse drug reactions reported were collected separately. The data was collected by one pharmacist and two nurses after giving training. Data was entered into Epidata version 4.6.0 and analyzed by SPSS version 25. Bivariate and multivariable logistic regression was conducted to identify independent predictors of the pattern of adverse drug reaction occurrence. A P-value of 0.05 was taken as statistically significant.

RESULT: Out of 154 patients enrolled in the study, 66.2% were female. The mean age of patients was 41.20 ± 13.54 years. From the total, 98 (63.6%) cases developed a total of 198 adverse drug reactions. Out of them, 59.2% were female. The most commonly encountered adverse drug reactions were nausea and vomiting (33.8%) and hair loss (29.3%). Most of the reactions were probable (61.1%) in causality, mild (66.2%) in severity, and not preventable (43.9%) in nature. Female sex (AOR = 1.054; 95% CI= (1.021-1.087); P = 0.001), number of chemotherapy treatments (AOR = 3.33; 95% CI= (1.301-8.52); P = 0.012), and elderly age (AOR = 3.065; 95% CI= (1.01-9.296); P = 0.048) were associated with occurrences of adverse drug reactions.

CONCLUSION: We can deduce from the data that adverse drug reactions are a significant concern for patients undergoing chemotherapy, with nearly two-thirds experiencing ADRs. The most common reactions are nausea and vomiting, which are mostly mild and probable. Age, gender, and the use of several chemotherapy drugs were associated with an increased risk of adverse drug reactions. Hence all concerned bodies should make an effort for early detection and take preventive measure of chemotherapy-related adverse drug reactions. Where feasible, use chemotherapy protocols with alower risk of ADRs. Evaluate dose adjustments for elderly patients. Implement protocols for risk assessment before initiating chemotherapy.

PMID:40378362 | DOI:10.1371/journal.pone.0321785

PLoS One. 2025 May 16;20(5):e0322378. doi: 10.1371/journal.pone.0322378. eCollection 2025.

ABSTRACT

OBJECTIVES: Brentuximab Vedotin (BV) is a novel antibody-drug conjugate (ADC) approved for the treatment of classical Hodgkin's lymphoma and systemic anaplastic large cell lymphoma. However, as a relatively new therapeutic agent, the long-term safety profile and adverse event (AE) profile of BV require further investigation. This study aimed to identify significant and unexpected AEs associated with BV using data from the FDA Adverse Event Reporting System (FAERS) and the Japanese Adverse Drug Event Report (JADER) databases.

METHODS: Data on BV-related AEs were extracted from the FAERS and JADER databases. Signal detection was performed using the reporting odds ratio (ROR) and 95% confidence intervals (95% CI). Risk signals were categorized according to system organ classes (SOCs) and preferred terms (PTs) as defined by the Medical Dictionary for Regulatory Activities (MedDRA) version 26.0. In addition, the onset times of BV-related AEs were analyzed.

RESULTS: Between 2004 and 2023, a total of 19,279 and 2,561 AEs related to BV were recorded in the FAERS and JADER databases, respectively. At the SOC level, prominent signals in the FAERS database included blood and lymphatic system disorders, benign, malignant, and unspecified neoplasms (including cysts and polyps), as well as congenital, familial, and genetic disorders. In the JADER database, the most notable signals involved benign, malignant, and unspecified neoplasms, blood and lymphatic system disorders, and nervous system disorders. At the PT level, the top five signals in the FAERS database were peripheral motor neuropathy, peripheral sensory neuropathy, pneumocystis jirovecii pneumonia, febrile bone marrow aplasia, and polyneuropathy. Unexpected AEs included febrile bone marrow aplasia and Guillain-Barré syndrome. In the JADER database, the top five signals included peripheral motor neuropathy, peripheral sensory neuropathy, bacterial gastroenteritis, febrile neutropenia and pneumonia, with unexpected AEs such as left ventricular dysfunction, cardiomegaly, retinal detachment, and marasmus. The median onset time of AEs was 22 days (interquartile range [IQR] 7-81 days) in FAERS and 27 days (IQR 7-73 days) in JADER.

CONCLUSION: The signal detection results from the FAERS and JADER databases highlight the importance of monitoring significant and unexpected AEs associated with BV, particularly in the early stages of treatment. These findings contribute to enhancing the post-marketing safety profile of BV and offer valuable insights for clinical risk management strategies.

PMID:40378127 | DOI:10.1371/journal.pone.0322378

J Chem Inf Model. 2025 May 16. doi: 10.1021/acs.jcim.5c00435. Online ahead of print.

ABSTRACT

Drug repositioning, which identifies novel therapeutic applications for existing drugs, offers a cost-effective alternative to traditional drug development. However, effectively capturing the complex relationships between drugs and diseases remains challenging. We present HGCL-DR, a novel heterogeneous graph contrastive learning framework for drug repositioning that effectively integrates global and local feature representations through three key components. First, we introduce an improved heterogeneous graph contrastive learning approach to model drug-disease relationships. Second, for local feature extraction, we employ a bidirectional graph convolutional network with a subgraph generation strategy in the bipartite drug-disease association graph, while utilizing a graph diffusion process to capture long-range dependencies in drug-drug and disease-disease relation graphs. Third, for global feature extraction, we leverage contrastive learning in the heterogeneous graph to enhance embedding consistency across different feature spaces. Extensive experiments on four benchmark data sets using 10-fold cross-validation demonstrate that HGCL-DR consistently outperforms state-of-the-art baselines in both AUPR, AUROC, and F1-score metrics. Ablation studies confirm the significance of each proposed component, while case studies on Alzheimer's disease and breast neoplasms validate HGCL-DR's practical utility in identifying novel drug candidates. These results establish HGCL-DR as an effective approach for computational drug repositioning.

PMID:40377926 | DOI:10.1021/acs.jcim.5c00435

Urologiia. 2025 May;(2):121-127.

ABSTRACT

OBJECTIVE: to assess the needs and attitudes of urologists regarding the use of technologies related to artificial intelligence, particularly the web platform "Sechenov.AI_nephro", in the surgical treatment of patients with renal parenchymal neoplasms.

MATERIALS AND METHODS: a qualitative study was conducted through in-depth interviews. A questionnaire was developed for the interviews, including 14 categories of questions covering various aspects of the use of artificial intelligence (AI) aimed at optimizing preoperative planning for patients with renal parenchymal neoplasms. The study involved 8 urologists with extensive experience in the surgical treatment of patients with renal parenchymal neoplasms.

RESULTS: the survey results highlight the growing interest in the implementation of AI technologies in medical practice.

CONCLUSION: in-depth interviews among urologists in Russia showed that there is a high interest in AI developments in urological practice. At the same time, successful integration of technologies requires overcoming several obstacles, including training specialists and ensuring data security. The "Sechenov.AI_nephro" platform has the potential to become an important tool in optimizing preoperative planning, but its success will depend on the readiness of physicians for new technologies and support from the medical community.

PMID:40377592

Nucleic Acids Res. 2025 May 16:gkaf414. doi: 10.1093/nar/gkaf414. Online ahead of print.

ABSTRACT

We present the CellHit web server (https://cellhit.bioinfolab.sns.it/), a web-based platform designed to predict and analyze cancer patients' responsiveness to drugs using transcriptomic data. By leveraging extensive pharmacogenomics datasets from the Genomics of Drug Sensitivity in Cancer v1 and v2 (GDSC) and Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) and transcriptomic data from the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas Program (TCGA). CellHit integrates a computational pipeline for preprocessing, gene imputation, and robust alignment between patient and cell line transcriptomic data with pre-trained SOTA models for drug sensitivity prediction. The pipeline employs batch correction, enhanced Celligner methodology, and Parametric UMAP for stable and actionable alignment. The intuitive interface requires no programming expertise, offering interactive visualizations, including low-dimensional embeddings and drug sensitivity heatmaps for the input transcriptomic samples. Results feature contextual metadata, SHAP-based feature importance, and transcriptomic neighbors from reference datasets, simplifying interpretation and hypothesis generation. CellHit provides precomputed predictions across TCGA samples and offers the ability to run custom analyses online on input samples, democratizing precision oncology by enabling rapid, interpretable predictions accessible the research community.

PMID:40377071 | DOI:10.1093/nar/gkaf414

Stat Med. 2025 May;44(10-12):e70082. doi: 10.1002/sim.70082.

ABSTRACT

Group sequential designs in clinical trials allow for interim efficacy and futility monitoring. Adjustment for baseline covariates can increase power and precision of estimated effects. However, inconsistently applying covariate adjustment throughout the stages of a group sequential trial can result in inflation of type I error, biased point estimates, and anticonservative confidence intervals. We propose methods for performing correct interim monitoring, estimation, and inference in this setting that avoid these issues. We focus on two-arm trials with simple, balanced randomization and continuous outcomes. We study the performance of our boundary, estimation, and inference adjustments in simulation studies. We end with recommendations about the application of covariate adjustment in group sequential designs.

PMID:40377247 | DOI:10.1002/sim.70082

Int J Comput Assist Radiol Surg. 2025 May 16. doi: 10.1007/s11548-025-03390-5. Online ahead of print.

ABSTRACT

PURPOSE: Trauma-induced rib fractures are a common injury. The number and characteristics of these fractures influence whether a patient is treated nonoperatively or surgically. Rib fractures are typically diagnosed using CT scans, yet 19.2-26.8% of fractures are still missed during assessment. Another challenge in managing rib fractures is the interobserver variability in their classification. Purpose of this study was to develop and assess an automated method that detects rib fractures in CT scans, and classifies them according to the Chest Wall Injury Society (CWIS) classification.

METHODS: 198 CT scans were collected, of which 170 were used for training and internal validation, and 28 for external validation. Fractures and their classifications were manually annotated in each of the scans. A detection and classification network was trained for each of the three components of the CWIS classifications. In addition, a rib number labeling network was trained for obtaining the rib number of a fracture. Experiments were performed to assess the method performance.

RESULTS: On the internal test set, the method achieved a detection sensitivity of 80%, at a precision of 87%, and an F1-score of 83%, with a mean number of FPPS (false positives per scan) of 1.11. Classification sensitivity varied, with the lowest being 25% for complex fractures and the highest being 97% for posterior fractures. The correct rib number was assigned to 94% of the detected fractures. The custom-trained nnU-Net correctly labeled 95.5% of all ribs and 98.4% of fractured ribs in 30 patients. The detection and classification performance on the external validation dataset was slightly better, with a fracture detection sensitivity of 84%, precision of 85%, F1-score of 84%, FPPS of 0.96 and 95% of the fractures were assigned the correct rib number.

CONCLUSION: The method developed is able to accurately detect and classify rib fractures in CT scans, there is room for improvement in the (rare and) underrepresented classes in the training set.

PMID:40377883 | DOI:10.1007/s11548-025-03390-5

Infection. 2025 May 16. doi: 10.1007/s15010-025-02555-3. Online ahead of print.

ABSTRACT

INTRODUCTION: Severe respiratory infections pose a major challenge in clinical practice, especially in older adults. Body composition analysis could play a crucial role in risk assessment and therapeutic decision-making. This study investigates whether obesity or sarcopenia has a greater impact on mortality in patients with severe respiratory infections. The study focuses on the National Pandemic Cohort Network (NAPKON-SUEP) cohort, which includes patients over 60 years of age with confirmed severe COVID-19 pneumonia. An innovative approach was adopted, using pre-trained deep learning models for automated analysis of body composition based on routine thoracic CT scans.

METHODS: The study included 157 hospitalized patients (mean age 70 ± 8 years, 41% women, mortality rate 39%) from the NAPKON-SUEP cohort at 57 study sites. A pre-trained deep learning model was used to analyze body composition (muscle, bone, fat, and intramuscular fat volumes) from thoracic CT images of the NAPKON-SUEP cohort. Binary logistic regression was performed to investigate the association between obesity, sarcopenia, and mortality.

RESULTS: Non-survivors exhibited lower muscle volume (p = 0.043), higher intramuscular fat volume (p = 0.041), and a higher BMI (p = 0.031) compared to survivors. Among all body composition parameters, muscle volume adjusted to weight was the strongest predictor of mortality in the logistic regression model, even after adjusting for factors such as sex, age, diabetes, chronic lung disease and chronic kidney disease, (odds ratio = 0.516). In contrast, BMI did not show significant differences after adjustment for comorbidities.

CONCLUSION: This study identifies muscle volume derived from routine CT scans as a major predictor of survival in patients with severe respiratory infections. The results underscore the potential of AI supported CT-based body composition analysis for risk stratification and clinical decision making, not only for COVID-19 patients but also for all patients over 60 years of age with severe acute respiratory infections. The innovative application of pre-trained deep learning models opens up new possibilities for automated and standardized assessment in clinical practice.

PMID:40377852 | DOI:10.1007/s15010-025-02555-3

Insights Imaging. 2025 May 16;16(1):107. doi: 10.1186/s13244-025-01966-y.

ABSTRACT

OBJECTIVES: This study aimed to develop and validate a clinical-radiomics deep learning (DL) model based on MRI for endometrial cancer (EC) molecular subtypes classification.

METHODS: This multicenter retrospective study included EC patients undergoing surgery, MRI, and molecular pathology diagnosis across three institutions from January 2020 to March 2024. Patients were divided into training, internal, and external validation cohorts. A total of 386 handcrafted radiomics features were extracted from each MR sequence, and MoCo-v2 was employed for contrastive self-supervised learning to extract 2048 DL features per patient. Feature selection integrated selected features into 12 machine learning methods. Model performance was evaluated with the AUC.

RESULTS: A total of 526 patients were included (mean age, 55.01 ± 11.07). The radiomics model and clinical model demonstrated comparable performance across the internal and external validation cohorts, with macro-average AUCs of 0.70 vs 0.69 and 0.70 vs 0.67 (p = 0.51), respectively. The radiomics DL model, compared to the radiomics model, improved AUCs for POLEmut (0.68 vs 0.79), NSMP (0.71 vs 0.74), and p53abn (0.76 vs 0.78) in the internal validation (p = 0.08). The clinical-radiomics DL Model outperformed both the clinical model and radiomics DL model (macro-average AUC = 0.79 vs 0.69 and 0.73, in the internal validation [p = 0.02], 0.74 vs 0.67 and 0.69 in the external validation [p = 0.04]).

CONCLUSIONS: The clinical-radiomics DL model based on MRI effectively distinguished EC molecular subtypes and demonstrated strong potential, with robust validation across multiple centers. Future research should explore larger datasets to further uncover DL's potential.

CRITICAL RELEVANCE STATEMENT: Our clinical-radiomics DL model based on MRI has the potential to distinguish EC molecular subtypes. This insight aids in guiding clinicians in tailoring individualized treatments for EC patients.

KEY POINTS: Accurate classification of EC molecular subtypes is crucial for prognostic risk assessment. The clinical-radiomics DL model outperformed both the clinical model and the radiomics DL model. The MRI features exhibited better diagnostic performance for POLEmut and p53abn.

PMID:40377781 | DOI:10.1186/s13244-025-01966-y

Environ Monit Assess. 2025 May 16;197(6):657. doi: 10.1007/s10661-025-14066-7.

ABSTRACT

Remote sensing (RS) data is extensively used in the observation and management of surface water and the detection of water bodies for studying ecological and hydrological processes. Small waterbodies are often neglected because of their tiny presence in the image, but being very large in numbers, they significantly impact the ecosystem. However, the detection of small waterbodies in satellite images is challenging because of their varying sizes and tones. In this work, a geospatial artificial intelligence (GeoAI) approach is proposed to detect small water bodies in RS images and generate a spatial map of it along with area statistics. The proposed approach aims to detect waterbodies of different shapes and sizes including those with vegetation cover. For this purpose, a deep neural network (DNN) is trained using the Indian Space Research Organization's (ISRO) Cartosat-3 multispectral satellite images, which effectively extracts the boundaries of small water bodies with a mean precision of 0.92 and overall accuracy over 96%. A comparative analysis with other popular existing methods using the same data demonstrates the superior performance of the proposed method. The proposed GeoAI approach efficiently generates a map of small water bodies automatically from the input satellite image which can be utilized for monitoring and management of these micro water resources.

PMID:40377752 | DOI:10.1007/s10661-025-14066-7

Curr Opin Neurol. 2025 May 19. doi: 10.1097/WCO.0000000000001378. Online ahead of print.

ABSTRACT

PURPOSE OF REVIEW: To summarize recent advancements in artificial intelligence-driven lesion segmentation and novel neuroimaging modalities that enhance the identification and characterization of multiple sclerosis (MS) lesions, emphasizing their implications for clinical use and research.

RECENT FINDINGS: Artificial intelligence, particularly deep learning approaches, are revolutionizing MS lesion assessment and segmentation, improving accuracy, reproducibility, and efficiency. Artificial intelligence-based tools now enable automated detection not only of T2-hyperintense white matter lesions, but also of specific lesion subtypes, including gadolinium-enhancing, central vein sign-positive, paramagnetic rim, cortical, and spinal cord lesions, which hold diagnostic and prognostic value. Novel neuroimaging techniques such as quantitative susceptibility mapping (QSM), χ-separation imaging, and soma and neurite density imaging (SANDI), together with PET, are providing deeper insights into lesion pathology, better disentangling their heterogeneities and clinical relevance.

SUMMARY: Artificial intelligence-powered lesion segmentation tools hold great potential for improving fast, accurate and reproducible lesional assessment in the clinical scenario, thus improving MS diagnosis, monitoring, and treatment response assessment. Emerging neuroimaging modalities may contribute to advance the understanding MS pathophysiology, provide more specific markers of disease progression, and novel potential therapeutic targets.

PMID:40377692 | DOI:10.1097/WCO.0000000000001378

Eur Radiol. 2025 May 16. doi: 10.1007/s00330-025-11673-3. Online ahead of print.

ABSTRACT

OBJECTIVES: Clinical assessment of lymphedema, particularly for lymphedema severity and fluid-fibrotic lesions, remains challenging with traditional methods. We aimed to develop and validate a deep learning segmentation tool for automated tissue component analysis in lower extremity CT scans.

MATERIALS AND METHODS: For development datasets, lower extremity CT venography scans were collected in 118 patients with gynecologic cancers for algorithm training. Reference standards were created by segmentation of fat, muscle, and fluid-fibrotic tissue components using 3D slicer. A deep learning model based on the Unet++ architecture with an EfficientNet-B7 encoder was developed and trained. Segmentation accuracy of the deep learning model was validated in an internal validation set (n = 10) and an external validation set (n = 10) using Dice similarity coefficient (DSC) and volumetric similarity (VS). A graphical user interface (GUI) tool was developed for the visualization of the segmentation results.

RESULTS: Our deep learning algorithm achieved high segmentation accuracy. Mean DSCs for each component and all components ranged from 0.945 to 0.999 in the internal validation set and 0.946 to 0.999 in the external validation set. Similar performance was observed in the VS, with mean VSs for all components ranging from 0.97 to 0.999. In volumetric analysis, mean volumes of the entire leg and each component did not differ significantly between reference standard and deep learning measurements (p > 0.05). Our GUI displays lymphedema mapping, highlighting segmented fat, muscle, and fluid-fibrotic components in the entire leg.

CONCLUSION: Our deep learning algorithm provides an automated segmentation tool enabling accurate segmentation, volume measurement of tissue component, and lymphedema mapping.

KEY POINTS: Question Clinical assessment of lymphedema remains challenging, particularly for tissue segmentation and quantitative severity evaluation. Findings A deep learning algorithm achieved DSCs > 0.95 and VS > 0.97 for fat, muscle, and fluid-fibrotic components in internal and external validation datasets. Clinical relevance The developed deep learning tool accurately segments and quantifies lower extremity tissue components on CT scans, enabling automated lymphedema evaluation and mapping with high segmentation accuracy.

PMID:40377677 | DOI:10.1007/s00330-025-11673-3

Urologiia. 2024 Dec;(6):5-11.

ABSTRACT

AIM: to explore the development of a computer-aided diagnosis (CAD) system based on deep learning (DL) neural networks aimed at minimizing human error in PI-RADS grading and supporting medical decision-making.

MATERIALS AND METHODS: This retrospective multicenter study included a cohort of 136 patients, comprising 108 cases of PCa (PI-RADS score 4-5) and 28 cases of benign conditions (PI-RADS score 1-2). The 3D U-Net architecture was applied to process T2-weighted images (T2W), diffusion-weighted images (DWI), and dynamic contrast-enhanced images (DCE). Statistical analysis was conducted using Python libraries to assess diagnostic performance, including sensitivity, specificity, Dice similarity coefficients, and the area under the receiver operating characteristic curve (AUC).

RESULTS: The DL-CAD system achieved an average accuracy of 78%, sensitivity of 60%, and specificity of 84% for detecting lesions in the prostate. The Dice similarity coefficient for prostate segmentation was 0.71, and the AUC was 81.16%. The system demonstrated high specificity in reducing false-positive results, which, after further optimization, could help minimize unnecessary biopsies and overtreatment.

CONCLUSION: The DL-CAD system shows potential in supporting clinical decision-making for patients with clinically significant PCa by improving diagnostic accuracy, particularly in minimizing intra- and inter-observer variability. Despite its high specificity, improvements in sensitivity and segmentation accuracy are needed, which could be achieved by using larger datasets and advanced deep learning techniques. Further multicenter validation is required for accelerated integration of this system into clinical practice.

PMID:40377545

Curr Med Imaging. 2025 May 15. doi: 10.2174/0115734056360665250506115221. Online ahead of print.

ABSTRACT

BACKGROUND: Due to differences in subjective experience and professional level among doctors, as well as inconsistent diagnostic criteria, there are issues with the accuracy and reliability of single imaging diagnosis results for knee joint injuries.

OBJECTIVE: To address these issues, magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound (US) are adopted in this article for ensemble learning, and deep learning (DL) is combined for automatic analysis.

METHODS: By steps such as image enhancement, noise elimination, and tissue segmentation, the quality of image data is improved, and then convolutional neural networks (CNN) are used to automatically identify and classify injury types. The experimental results show that the DL model exhibits high sensitivity and specificity in the diagnosis of different types of injuries, such as anterior cruciate ligament tear, meniscus injury, cartilage injury, and fracture.

RESULTS: The diagnostic accuracy of anterior cruciate ligament tear exceeds 90%, and the highest diagnostic accuracy of cartilage injury reaches 95.80%. In addition, compared with traditional manual image interpretation, the DL model has significant advantages in time efficiency, with a significant reduction in average interpretation time per case. The diagnostic consistency experiment shows that the DL model has high consistency with doctors' diagnosis results, with an overall error rate of less than 2%.

CONCLUSION: The model has high accuracy and strong generalization ability when dealing with different types of joint injuries. These data indicate that combining multiple imaging technologies and the DL algorithm can effectively improve the accuracy and efficiency of diagnosing sports injuries of knee joints.

PMID:40377156 | DOI:10.2174/0115734056360665250506115221

Nucleic Acids Res. 2025 May 16:gkaf392. doi: 10.1093/nar/gkaf392. Online ahead of print.

ABSTRACT

Antisense oligonucleotides (ASOs) are a promising class of gene therapies that can modulate the gene expression. However, designing ASOs manually is resource-intensive and time-consuming. To address this, we introduce a user-friendly web server for ASOptimizer, a deep learning-based computational framework for optimizing ASO sequences and chemical modifications. Given a user-provided ASO sequence, the web server systematically explores modification sites within the nucleic acid and returns a ranked list of promising modification patterns. With an intuitive interface requiring no expertise in deep learning tools, the platform makes ASOptimizer easily accessible to the broader research community. The web server is freely available at https://asoptimizer.s-core.ai/.

PMID:40377084 | DOI:10.1093/nar/gkaf392