Med Phys. 2025 Mar 29. doi: 10.1002/mp.17794. Online ahead of print.

ABSTRACT

BACKGROUND: Conventional proton dose calculation methods are either time- and resource-intensive, like Monte Carlo (MC) simulations, or they sacrifice accuracy, as seen with analytical methods. This trade-off between computational efficiency and accuracy highlights the need for improved dose calculation approaches in clinical settings.

PURPOSE: This study aims to develop a deep-learning-based model that calculates dose-to-water (DW) and dose-to-medium (DM) using patient anatomy and proton spot map (PSM), achieving approaching MC-level accuracy with significantly reduced computation time. Additionally, the study seeks to generalize the model to different treatment sites using transfer learning.

METHODS: A SwinUNetr model was developed using 259 four-field prostate proton stereotactic body radiation therapy (SBRT) plans to calculate patient-specific DW and DM distributions from CT and projected PSM (PPSM). The PPSM was created by projecting PSM into the CT scans using spot coordinates, stopping power ratio, beam divergence, and water-equivalent thickness. Fine-tuning was then performed for the central nervous system (CNS) site using 84 CNS plans. The model's accuracy was evaluated against MC simulation benchmarks using mean absolute error (MAE), gamma analysis (2% local dose difference, 2-mm distance-to-agreement, 10% low dose threshold), and relevant clinical indices on the test dataset.

RESULTS: The trained model achieved a single-field dose calculation time of 0.07 s on a Nvidia-A100 GPU, over 100 times faster than MC simulators. For the prostate site, the best-performing model showed an average MAE of 0.26 ± 0.17 Gy and a gamma index of 92.2% ± 3.1% in dose regions above 10% of the maximum dose for DW calculations, and an MAE of 0.30 ± 0.19 Gy with a gamma index of 89.7% ± 3.9% for DM calculations. After transfer learning for CNS plans, the model achieved an MAE of 0.49 ± 0.24 Gy and a gamma index of 90.1% ± 2.7% for DW computations, and an MAE of 0.47 ± 0.25 Gy with a gamma index of 85.4% ± 7.1% for DM computations.

CONCLUSIONS: The SwinUNetr model provides an efficient and accurate method for computing dose distributions in proton therapy. It also opens the possibility of reverse-engineering PSM from DW, potentially speeding up treatment planning while maintaining accuracy.

PMID:40156258 | DOI:10.1002/mp.17794

Ultrason Imaging. 2025 Mar 29:1617346251319410. doi: 10.1177/01617346251319410. Online ahead of print.

ABSTRACT

Exploring the clinical significance of employing deep learning methodologies on ultrasound images for the development of an automated model to accurately identify pleomorphic adenomas and Warthin tumors in salivary glands. A retrospective study was conducted on 91 patients who underwent ultrasonography examinations between January 2016 and December 2023 and were subsequently diagnosed with pleomorphic adenoma or Warthin's tumor based on postoperative pathological findings. A total of 526 ultrasonography images were collected for analysis. Convolutional neural network (CNN) models, including ResNet18, MobileNetV3Small, and InceptionV3, were trained and validated using these images for the differentiation of pleomorphic adenoma and Warthin's tumor. Performance evaluation metrics such as receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value were utilized. Two ultrasound physicians, with varying levels of expertise, conducted independent evaluations of the ultrasound images. Subsequently, a comparative analysis was performed between the diagnostic outcomes of the ultrasound physicians and the results obtained from the best-performing model. Inter-rater agreement between routine ultrasonography interpretation by the two expert ultrasonographers and the automatic identification diagnosis of the best model in relation to pathological results was assessed using kappa tests. The deep learning models achieved favorable performance in differentiating pleomorphic adenoma from Warthin's tumor. The ResNet18, MobileNetV3Small, and InceptionV3 models exhibited diagnostic accuracies of 82.4% (AUC: 0.932), 87.0% (AUC: 0.946), and 77.8% (AUC: 0.811), respectively. Among these models, MobileNetV3Small demonstrated the highest performance. The experienced ultrasonographer achieved a diagnostic accuracy of 73.5%, with sensitivity, specificity, positive predictive value, and negative predictive value of 73.7%, 73.3%, 77.8%, and 68.8%, respectively. The less-experienced ultrasonographer achieved a diagnostic accuracy of 69.0%, with sensitivity, specificity, positive predictive value, and negative predictive value of 66.7%, 71.4%, 71.4%, and 66.7%, respectively. The kappa test revealed strong consistency between the best-performing deep learning model and postoperative pathological diagnoses (kappa value: .778, p-value < .001). In contrast, the less-experienced ultrasonographer demonstrated poor consistency in image interpretations (kappa value: .380, p-value < .05). The diagnostic accuracy of the best deep learning model was significantly higher than that of the ultrasonographers, and the experienced ultrasonographer exhibited higher diagnostic accuracy than the less-experienced one. This study demonstrates the promising performance of a deep learning-based method utilizing ultrasonography images for the differentiation of pleomorphic adenoma and Warthin's tumor. The approach reduces subjective errors, provides decision support for clinicians, and improves diagnostic consistency.

PMID:40156239 | DOI:10.1177/01617346251319410

J Forensic Sci. 2025 Mar 29. doi: 10.1111/1556-4029.70034. Online ahead of print.

ABSTRACT

With the rapid development of diffusion models, AI generation technology can now generate very realistic images. If such AI-generated images are used as evidence, they may threaten judicial fairness. Taking the adversarial identification of images generated by Craiyon V3 software as an example, this paper studies the adversarial identification methods for AI-generated image software. First, an AI-generated image set containing 18,000 images is constructed using Craiyon V3; then, an AI-generated image detection model based on deep learning is selected, and a score-based likelihood ratio method is introduced to evaluate the strength of evidence. Experimental results show that the proposed method achieves an accuracy of over 99% on multiple threshold classifiers including Swin-Transformer, ResNet-18, and so on, and the fitted likelihood ratio model also passes a series of validation criteria including Tippett plots. The research results of this paper are expected to be applied to judicial practice in the future, providing judges with a reliable and powerful decision-making basis, and laying a foundation for further exploration of AI-generated image identification methods.

PMID:40156229 | DOI:10.1111/1556-4029.70034

BMC Cancer. 2025 Mar 28;25(1):561. doi: 10.1186/s12885-025-13951-1.

ABSTRACT

BACKGROUND: Differentiating chondroid tumors is crucial for proper patient management. This study aimed to develop a deep learning model (DLM) for classifying enchondromas, atypical cartilaginous tumors (ACT), and high-grade chondrosarcomas using CT images.

METHODS: This retrospective study analyzed chondroid tumors from two independent cohorts. Tumors were segmented on CT images. A 2D convolutional neural network was developed and tested using split-sample and geographical validation. Four radiologists blinded to patient data and the DLM results with various levels of experience performed readings of the external test dataset for comparison. Performance metrics included accuracy, sensitivity, specificity, and area under the curve (AUC).

RESULTS: CTs from 344 patients (175 women; age = 50.3 ± 14.3 years;) with diagnosed enchondroma (n = 124), ACT (n = 92) or high-grade chondrosarcoma (n = 128) were analyzed. The DLM demonstrated comparable performance to radiologists (p > 0.05), achieving an AUC of 0.88 for distinguishing enchondromas from chondrosarcomas and 0.82 for differentiating enchondromas from ACTs. The DLM and musculoskeletal expert showed similar performance in differentiating ACTs from high-grade chondrosarcomas (p = 0.26), with an AUC of 0.64 and 0.56, respectively.

CONCLUSIONS: The DLM reliably differentiates benign from malignant cartilaginous tumors and is particularly useful for the differentiation between ACTs and Enchondromas, which is challenging based on CT images only. However, the differentiation between ACTs and high-grade chondrosarcomas remains difficult, reflecting known diagnostic challenges in radiology.

PMID:40155859 | DOI:10.1186/s12885-025-13951-1

Sci Rep. 2025 Mar 28;15(1):10764. doi: 10.1038/s41598-025-95425-9.

NO ABSTRACT

PMID:40155745 | DOI:10.1038/s41598-025-95425-9

Nat Methods. 2025 Mar 28. doi: 10.1038/s41592-025-02626-1. Online ahead of print.

ABSTRACT

Protein sequence design in the context of small molecules, nucleotides and metals is critical to enzyme and small-molecule binder and sensor design, but current state-of-the-art deep-learning-based sequence design methods are unable to model nonprotein atoms and molecules. Here we describe a deep-learning-based protein sequence design method called LigandMPNN that explicitly models all nonprotein components of biomolecular systems. LigandMPNN significantly outperforms Rosetta and ProteinMPNN on native backbone sequence recovery for residues interacting with small molecules (63.3% versus 50.4% and 50.5%), nucleotides (50.5% versus 35.2% and 34.0%) and metals (77.5% versus 36.0% and 40.6%). LigandMPNN generates not only sequences but also sidechain conformations to allow detailed evaluation of binding interactions. LigandMPNN has been used to design over 100 experimentally validated small-molecule and DNA-binding proteins with high affinity and high structural accuracy (as indicated by four X-ray crystal structures), and redesign of Rosetta small-molecule binder designs has increased binding affinity by as much as 100-fold. We anticipate that LigandMPNN will be widely useful for designing new binding proteins, sensors and enzymes.

PMID:40155723 | DOI:10.1038/s41592-025-02626-1

Sci Rep. 2025 Mar 28;15(1):10786. doi: 10.1038/s41598-025-95281-7.

ABSTRACT

Photovoltaic power plant outputs depend on the daily global solar radiation (DGSR). The main issue with DGSR data is its lack of precision. The potential unavailability of DGSR data for several sites can be attributed to the high cost of measuring instruments and the intermittent nature of time series data due to equipment malfunctions. Therefore, DGSR prediction research is crucial nowadays to produce photovoltaic power. Different artificial neural network (ANN) models will give different DGSR predictions with varying levels of accuracy, so it is essential to compare the different ANN model inputs with various sets of meteorological stochastic variables. In this study, radial basis function neural network (RBFNN), long short-term memory neural network (LSTMNN), modular neural network (MNN), and transformer model (TM) are developed to investigate the performances of these algorithms for the DGSR prediction using different combinations of meteorological stochastic variables. These models employ five stochastic variables: wind speed, relative humidity, minimum, maximum, and average temperatures. The mean absolute relative error for the transformer model with input variables as average, maximum, and minimum temperatures is 1.98. ANN models outperform traditional models in predictive accuracy.

PMID:40155686 | DOI:10.1038/s41598-025-95281-7

Sci Rep. 2025 Mar 28;15(1):10741. doi: 10.1038/s41598-025-94905-2.

ABSTRACT

The kidney plays a vital role in maintaining homeostasis, but lifestyle factors and diseases can lead to kidney failures. Early detection of kidney disease is crucial for effective intervention, often challenging due to unnoticeable symptoms in the initial stages. Computed tomography (CT) imaging aids specialists in detecting various kidney conditions. The research focuses on classifying CT images of cysts, normal states, stones, and tumors using a hyperparameter fine-tuned approach with convolutional neural networks (CNNs), VGG16, ResNet50, CNNAlexnet, and InceptionV3 transfer learning models. It introduces an innovative methodology that integrates finely tuned transfer learning, advanced image processing, and hyperparameter optimization to enhance the accuracy of kidney tumor classification. By applying these sophisticated techniques, the study aims to significantly improve diagnostic precision and reliability in identifying various kidney conditions, ultimately contributing to better patient outcomes in medical imaging. The methodology implements image-processing techniques to enhance classification accuracy. Feature maps are derived through data normalization and augmentation (zoom, rotation, shear, brightness adjustment, horizontal/vertical flip). Watershed segmentation and Otsu's binarization thresholding further refine the feature maps, which are optimized and combined using the relief method. Wide neural network classifiers are employed, achieving the highest accuracy of 99.96% across models. This performance positions the proposed approach as a high-performance solution for automatic and accurate kidney CT image classification, significantly advancing medical imaging and diagnostics. The research addresses the pressing need for early kidney disease detection using an innovative methodology, highlighting the proposed approach's capability to enhance medical imaging and diagnostic capabilities.

PMID:40155680 | DOI:10.1038/s41598-025-94905-2

Sci Rep. 2025 Mar 28;15(1):10761. doi: 10.1038/s41598-025-95118-3.

ABSTRACT

Global horizontal irradiance prediction is essential for balancing the supply-demand and minimizing the energy costs for effective integration of solar photovoltaic system in electric power grid. However, its stochastic nature makes it difficult to get accurate prediction results. This study aims to develop a hybrid deep learning model that integrates a Convolutional Neural Network and Stacked Long Short-Term Memory (CNN-SLSTM) to predict the daily average global solar irradiance using real time meteorological parameters and daily solar irradiance data recorded in the study site. First, we have selected 14 significant relevant features from the dataset using recursive feature elimination techniques. The hyperparameters of the developed models are optimized using metaheuristic algorithm, a Slime Mould Optimization method. The efficacy of the model performance is evaluated using tenfold cross validation techniques. By using statistical performances metrics, the predictive performance of the developed model is compared with Gated Recurrent Unit, LSTM, CNN-LSTM, SLSTM and machine learning regressor models like Support Vector Machine, Decision Tree, and Random Forest. From the experimental results, the developed CNN-SLSTM model outperformed other models with a MSE, R2 and Adj_R2 of 0.0359, 0.9790 and 0.9789, respectively.

PMID:40155655 | DOI:10.1038/s41598-025-95118-3

Sci Data. 2025 Mar 28;12(1):523. doi: 10.1038/s41597-025-04874-4.

ABSTRACT

The recent surge in electric vehicles (EVs), driven by a collective push to enhance global environmental sustainability, has underscored the significance of exploring EV charging prediction. To catalyze further research in this domain, we introduce UrbanEV - an open dataset showcasing EV charging space availability and electricity consumption in a pioneering city for vehicle electrification, namely Shenzhen, China. UrbanEV offers a rich repository of charging data (i.e., charging occupancy, duration, volume, and price) captured at hourly intervals across an extensive six-month span for over 20,000 individual charging stations. Beyond these core attributes, the dataset also encompasses diverse influencing factors like weather conditions and spatial proximity. Comprehensive experiments have been conducted to showcase the predictive capabilities of various models, including statistical, deep learning, and transformer-based approaches, using the UrbanEV dataset. This dataset is poised to propel advancements in EV charging prediction and management, positioning itself as a benchmark resource within this burgeoning field.

PMID:40155635 | DOI:10.1038/s41597-025-04874-4

Nat Rev Genet. 2025 Mar 28. doi: 10.1038/s41576-025-00828-z. Online ahead of print.

ABSTRACT

Transcriptome sequencing revolutionized the analysis of gene expression, providing an unbiased approach to gene detection and quantification that enabled the discovery of novel isoforms, alternative splicing events and fusion transcripts. However, although short-read sequencing technologies have surpassed the limited dynamic range of previous technologies such as microarrays, they have limitations, for example, in resolving full-length transcripts and complex isoforms. Over the past 5 years, long-read sequencing technologies have matured considerably, with improvements in instrumentation and analytical methods, enabling their application to RNA sequencing (RNA-seq). Benchmarking studies are beginning to identify the strengths and limitations of long-read RNA-seq, although there remains a need for comprehensive resources to guide newcomers through the intricacies of this approach. In this Review, we provide a comprehensive overview of the long-read RNA-seq workflow, from library preparation and sequencing challenges to core data processing, downstream analyses and emerging developments. We present an extensive inventory of experimental and analytical methods and discuss current challenges and prospects.

PMID:40155769 | DOI:10.1038/s41576-025-00828-z

Nat Commun. 2025 Mar 28;16(1):3042. doi: 10.1038/s41467-025-58073-1.

ABSTRACT

Mycobacteria harbor a proteasome that was acquired by Actinobacteria through horizontal gene transfer and that supports the persistence of the human pathogen Mycobacterium tuberculosis within host macrophages. The core particle of the proteasome (20S CP) associates with ring-shaped activator complexes to degrade protein substrates. One of these is the bacterial proteasome activator Bpa that stimulates the ATP-independent proteasomal degradation of the heat shock repressor HspR. In this study, we determine the cryogenic electron microscopy 3D reconstruction of the complex between Bpa and its natural substrate HspR at 4.1 Å global resolution. The resulting maps allow us to identify regions of Bpa that interact with HspR. Using structure-guided site-directed mutagenesis and in vitro biochemical assays, we confirm the importance of the identified residues for Bpa-mediated substrate recruitment and subsequent proteasomal degradation. Additionally, we show that the dodecameric Bpa ring associates asymmetrically with the heptameric α-rings of the 20S CP, adopting a conformation resembling a hinged lid, while still engaging all seven docking sites on the proteasome.

PMID:40155375 | DOI:10.1038/s41467-025-58073-1

J Dermatol. 2025 Mar 29. doi: 10.1111/1346-8138.17706. Online ahead of print.

ABSTRACT

The concept of immune reconstitution inflammatory syndrome (IRIS) has recently been applied to patients with non-HIV infection with immune fluctuations. However, quantitative criteria to diagnose non-HIV IRIS have not been established. Similarly, immune-related adverse events (irAEs) caused by immune checkpoint inhibitors (ICIs) are also caused by immune fluctuations. No study has directly compared the immunological indicators of non-HIV IRIS and irAEs. Thus, we investigated whether irAEs can be included in non-HIV IRIS. We aimed to search for diagnostic biomarkers for non-HIV IRIS and to compare the immunopathogenesis of non-HIV IRIS and irAEs based on immunological indicators. We selected drug-induced hypersensitivity syndrome/drug reaction with eosinophilia and systemic symptoms (DIHS/DRESS) and dipeptidyl peptidase-4 inhibitor-associated bullous pemphigoid (DPP4i-BP) as underlying diseases of non-HIV IRIS. Blood cell counts, cytokines or chemokines, and herpesvirus-derived DNA in saliva were quantified and compared between IRIS/irAE-positive and -negative as well as non-HIV IRIS and irAEs groups. The DPP4i-BP group had a shorter incubation time to IRIS onset than the DIHS/DRESS group; the irAE group had a longer incubation time than the DIHS/DRESS group. A higher neutrophil-to-lymphocyte ratio and serum interferon gamma inducible protein 10 levels could be potential biomarkers of IRIS and irAEs onset; however, no useful cut-off values for diagnosis were indicated. Meanwhile, the transition of regulatory T cells (Tregs) from the baseline to the onset of IRIS or irAEs differed between IRIS in DIHS/DRESS and irAEs. Only the DIHS/DRESS group showed an increase of Epstein-Bar virus (EBV) (p < 0.0001) and human herpesvirus 6 (p < 0.05) positivity in saliva at the onset of IRIS compared to that at baseline. Although non-HIV IRIS and irAEs have a small number of common immunological indicators, the dynamics of Tregs, cytokines, or chemokines and positivity of herpesvirus-derived DNA in saliva differ, suggesting that non-HIV IRIS and irAEs should remain as separate entities.

PMID:40156255 | DOI:10.1111/1346-8138.17706

BMC Med. 2025 Mar 28;23(1):188. doi: 10.1186/s12916-025-04021-1.

ABSTRACT

BACKGROUND: Acute Type A Aortic Dissection (aTAAD) is a severe and life-threatening condition. While animal studies have suggested that ketorolac could slow the progression of aortic aneurysms and dissections, clinical data on its efficacy in aTAAD patients remain limited. This study seeks to evaluate the safety and effectiveness of ketorolac in this patient group.

METHODS: Patients were randomly assigned to receive either ketorolac or a placebo (0.9% saline). Treatment began at least 2 h prior to surgery (60 mg ketorolac or 2 ml saline administered once intramuscularly) and continued for 48 h post-surgery (30 mg ketorolac or 1 ml saline administered intramuscularly twice daily). The primary endpoints included assessing the safety and efficacy of ketorolac in improving the prognosis of aTAAD, focusing on mortality and organ malperfusion syndrome. Secondary endpoints included drug-related adverse events, blood test results, and other postoperative outcomes.

RESULTS: Of 179 patients who underwent aTAAD repair, 110 met the inclusion criteria and were randomized into two groups of 55. One patient discontinued the intervention due to erythroderma on the first postoperative day, leaving 54 patients in the ketorolac group and 55 in the placebo group for analysis. No significant differences were found in the primary endpoints. However, the ketorolac group showed lower intraoperative bleeding (median: 1.8 L vs. 2.0 L, P = 0.03), shorter intensive care unit (ICU) stays (median: 6.5 days vs. 8 days, P = 0.04), and lower total hospital costs (median: ¥170,430 vs. ¥187,730, P = 0.03).

CONCLUSIONS: Short-term ketorolac therapy did not alter the primary outcome but was associated with reduced intraoperative bleeding, shorter ICU stays, and potentially lower hospitalization costs. It demonstrates safety and a certain degree of effectiveness during the perioperative period. These findings suggest that ketorolac could be a viable option for perioperative management in patients with aTAAD.

TRIAL REGISTRATION: The trial was registered at the Chinese Clinical Trial Register ( www.chictr.org.cn , No: ChiCTR2300074394).

PMID:40156036 | DOI:10.1186/s12916-025-04021-1

BMC Geriatr. 2025 Mar 28;25(1):205. doi: 10.1186/s12877-025-05851-7.

ABSTRACT

BACKGROUND: Globally, falls are considered a serious healthcare problem for aged care residents. Fall-risk-increasing drugs (FRIDs) are medications that can increase the risk of falling, given their adverse effects. Medication reviews are advocated to identify potentially inappropriate use of FRIDs. However, their impact on clinical and resident-centered outcomes is unclear. This study explored aged care residents' use of FRIDs and the content of medication review reports concerning these.

METHODS: A retrospective cross-sectional study of medication review reports completed between 1st July 2021 and 30th June 2022 was conducted. Statistical descriptive analysis was used to examine the use of FRIDs (defined as medications listed in the Screening Tool of Older Persons Prescriptions in older adults with high fall risk (STOPPFall)). The resident's medicine experience, identified drug-related problems (DRPs), and related recommendations concerning FRIDs were explored via content analysis. For recommendations to deprescribe FRIDs, clinical situations detailed in the reports were compared to those presented in STOPPFall.

RESULTS: Medication review reports relating to 966 residents were analysed. Of these residents, 83.2% (n = 804) used FRIDs, with 31.2% (n = 301) taking three or more FRIDs. In total, pharmacists made recommendations concerning 2635 identified DRPs, of which 19.7% (n = 520) were the potentially inappropriate use of FRIDs and deprescribing was recommended. The clinical situation for which deprescribing was most frequently recommended was the use of a FRID for an indication of limited clinical benefit 37.9% (n = 197). The clinical situation was not detailed for 130 (25.0%) recommendations to deprescribe FRIDs, and only three reports included the resident's viewpoint on deprescribing.

CONCLUSIONS: FRID use was found to be highly prevalent among aged care residents. Pharmacists frequently identified opportunities to deprescribe FRIDs. However, reports often omitted resident viewpoints and the clinical grounds for deprescribing. Using resident-centered communication in medication review reports could improve their impact on FRID use and resident outcomes.

PMID:40155803 | DOI:10.1186/s12877-025-05851-7

Biomedicines. 2025 Mar 13;13(3):707. doi: 10.3390/biomedicines13030707.

ABSTRACT

Backgroung/objectives: Diffuse large B-cell lymphoma (DLBCL) is the most frequent subtype of malignant lymphoma and is a heterogeneous disease with various gene and chromosomal abnormalities. The development of novel therapeutic treatments has improved DLBCL prognosis, but patients with early relapse or refractory disease have a poor outcome (with a mortality of around 40%). Metabolic reprogramming is a hallmark of cancer cells. Fatty acid (FA) metabolism is frequently altered in cancer cells and recently emerged as a critical survival path for cancer cell survival. Methods: We first performed the metabolic characterization of an extended panel of DLBCL cell lines, including lipid droplet content. Then, we investigated the effect of drugs targeting FA metabolism on DLBCL cell survival. Further, we studied how the combination of drugs targeting FA and either mitochondrial metabolism or mTOR pathway impacts on DLBCL cell death. Results: Here, we reveal, using a large panel of DLBCL cell lines characterized by their metabolic status, that targeting of FA metabolism induces massive DLBCL cell death regardless of their OxPhos or BCR/glycolytic subtype. Further, FA drives resistance of DLBCL cell death induced by mitochondrial stress upon treatment with either metformin or L-asparaginase, two FDA-approved antimetabolic drugs. Interestingly, combining inhibition of FA metabolism with that of the mTOR oncogenic pathway strongly potentiates DLBCL cell death. Conclusion: Altogether, our data highlight the central role played by FA metabolism in DLBCL cell survival, independently of their metabolic subtype, and provide the framework for the use of drugs targeting this metabolic vulnerability to overcome resistance in DLBCL patients.

PMID:40149683 | PMC:PMC11940118 | DOI:10.3390/biomedicines13030707

Cancer Lett. 2025 Mar 26:217660. doi: 10.1016/j.canlet.2025.217660. Online ahead of print.

ABSTRACT

Medulloblastoma (MB) represents the most common malignant central nervous system tumor in childhood. The nervous system plays a critical role in the progression of MB, with interactions between the nervous system and cancer significantly influencing oncogenesis, tumor growth, invasion, stemness, and metabolism. These interactions also regulate angiogenesis, metastatic dissemination, the tumor immune microenvironment, and drug resistance. Investigating the nervous system-MB axis holds promise for identifying diagnostic markers, prognostic biomarkers, and therapeutic targets. It also provides insights into the molecular mechanisms underlying MB and informs the development of novel therapeutic strategies. This review summarizes the latest advancements in understanding the interplay between the nervous system and MB, including the role of glial cells in MB and the potential of drug repurposing targeting nervous system components for MB treatment. These findings underscore promising diagnostic and therapeutic opportunities for MB management. Additionally, we outline future research directions in neurosciences that may pave the way for innovative therapeutic approaches and deepen our understanding of this complex disease.

PMID:40154912 | DOI:10.1016/j.canlet.2025.217660

Ann Pharm Fr. 2025 Mar 26:S0003-4509(25)00047-1. doi: 10.1016/j.pharma.2025.03.005. Online ahead of print.

ABSTRACT

OBJECTIVES: The key objective of present research is to effectively treat lung cancer with repurposed celecoxib while overcoming challenges such as solubility, bioavailability, non-selectivity, and negative effects by delivering celecoxib through nanostructured lipid carriers via the parenteral route.

METHODS: Celecoxib-laden nanostructured lipid carriers were manufactured by melt-emulsification ultrasonication approach and optimized through Box-Behnken Design. The celecoxib nanostructured lipid carriers were examined for particle size, % entrapment efficiency, zeta potential, in vitro release, cytotoxicity, stability, etc. Results: The optimized celecoxib nanostructured lipid carriers displayed a % entrapment efficiency of 91.69±4.9% and particle size of 132.1±6.8 nm with a polydispersity index of 0.41±0.06, and a zeta potential of -39.1 ± 3.0 mV. Notably, celecoxib nanostructured lipid carriers exhibited better and controlled celecoxib release at phosphate buffer solution pH 6.8 than pH 7.4, revealing the tumor-targeting potential of nanostructured lipid carriers. Also, the release of celecoxib from nanostructured lipid carriers was controlled for 48 h, indicating reduced chances of systemic toxicity. The in vitro cytotoxicity against A549 cells of celecoxib nanostructured lipid carriers was 1.5-fold greater than that of pure celecoxib, confirming significant anti-lung cancer effectiveness. Further, the celecoxib-loaded nanostructured lipid carriers remained stable for twelve weeks at cold and ambient temperatures.

CONCLUSION: Thus, the given research concludes that parenteral administration of nanostructured lipid carriers could be a harmless, efficient, and novel choice to treat lung cancer using repurposed celecoxib.

PMID:40154777 | DOI:10.1016/j.pharma.2025.03.005

Cell Genom. 2025 Mar 21:100820. doi: 10.1016/j.xgen.2025.100820. Online ahead of print.

ABSTRACT

Most variants identified from genome-wide association studies (GWASs) are non-coding and regulate gene expression. However, many risk loci fail to colocalize with expression quantitative trait loci (eQTLs), potentially due to limited GWAS and eQTL analysis power or cellular heterogeneity. Population-scale single-cell RNA-sequencing (scRNA-seq) datasets are emerging, enabling mapping of eQTLs in different cell types (sc-eQTLs). Compared to eQTL data from bulk tissues (bk-eQTLs), sc-eQTL datasets are smaller. We propose a joint model of bk-eQTLs as a weighted sum of sc-eQTLs (JOBS) from constituent cell types to improve power. Applying JOBS to One1K1K and eQTLGen data, we identify 586% more eQTLs, matching the power of 4× the sample sizes of OneK1K. Integrating sc-eQTLs with GWAS data creates an atlas for 14 immune-mediated disorders, colocalizing 29.9% or 32.2% more loci than using sc-eQTL or bk-eQTL alone. Extending JOBS, we develop a drug-repurposing pipeline and identify novel drugs validated by real-world data.

PMID:40154479 | DOI:10.1016/j.xgen.2025.100820

J Infect Public Health. 2025 Mar 19;18(6):102763. doi: 10.1016/j.jiph.2025.102763. Online ahead of print.

ABSTRACT

BACKGROUND: Leprosy and tuberculosis caused by Mycobacterium leprae and Mycobacterium tuberculosis, respectively, are chronic infections with significant public health implications. While leprosy affects the skin and peripheral nerves and tuberculosis primarily targets the lungs, both diseases involve systemic immune responses. This study integrates transcriptomic analysis cheminformatics and molecular dynamics simulations to identify molecular mechanisms and potential therapeutic targets.

METHODS: Transcriptomic datasets were analyzed to identify dysregulated genes and pathways. Pathway enrichment tissue-specific and bulk RNA-seq expression analyses provided biological context. System biology networks revealed regulatory hub genes and molecular docking studies evaluated CHEMBL compounds as potential therapeutics. Molecular dynamics (MD) simulations assessed the stability of top ligand-protein complexes through RMSD RMSF and MM-GBSA free energy calculations.

RESULTS: Gene expression analysis identified 13 core dysregulated genes, including HSP90AA1 MAPK8IP3 and ZMPSTE24. Tissue-specific expression localized pivotal genes to lung tissues and immune cells with HSP90AA1 highly expressed in alveolar macrophages and epithelial cells. HSP90AA1 gene emerged as a central hub gene with 96 interactions involved in stress response pathways. Docking studies identified CHEMBL3653862 and CHEMBL3653884 with strong binding affinities (-10.16 to -12.69 kcal/mol) interacting with Asp93 and Tyr139. MD simulations confirmed binding stability with RMSD fluctuations within 2.1-3.5 Å and MM-GBSA energy values supporting ligand-protein stability.

CONCLUSION: This study identifies HSP90AA1 as a potential drug target in leprosy and tuberculosis. Findings support host-directed therapy approaches and highlight the importance of computational modeling in accelerating drug discovery. The study provides a foundation for future experimental validation, including in vitro and in vivo testing to advance drug repurposing strategies for these chronic infections.

PMID:40153981 | DOI:10.1016/j.jiph.2025.102763