Characterization of a Low-Energy Cyclotron-Based Proton Beam for Preclinical Radiobiological Studies.

Aptamer-guided upconversion nanoconstructs enable proximity-dependent and precise photodynamic therapy for MRSA-infected wounds.

Glioblastoma is the most aggressive and malignant tumor of the central nervous system. Current treatment options, including surgical excision, radiotherapy, and chemotherapy, have Limited efficacy, with a median survival rate of approximately 15months. To develop novel therapeutics, it is crucial to understand the underlying molecular mechanisms driving glioblastoma. However, obtaining healthy tissue counterparts for comparison is often challenging due to the critical nature of the tissues and the tumor's location. This study aimed to compare the transcriptomic profiles of glioblastoma tissues with those of adjacent healthy tissues, to elucidate key pathways and identify upstream regulators involved in glioblastoma pathogenesis. Twenty-six pairs of glioblastoma tissues and their adjacent healthy tissues were obtained during surgery. The tumor and healthy origins were confirmed through histopathological examination. Twelve pairs were analyzed via transcriptome analysis by using the Ion GeneStudio S5 system. Ingenuity pathway analysis was performed to identify the associated pathways and upstream regulators. Selected 51 upstream regulators were analyzed using qRT-PCR. Three pairs were excluded from the RNA-sequencing (RNA-seq) analysis due to similarities between normal and tumor tissues. The dysregulated pathways were primarily associated with neuronal connections and neurotransmitter pathways. The expression patterns of upstream regulators were consistent with RNA-seq results. Molecular changes linked to the initiation of tumors can begin at an early stage, potentially preceding the appearance of clinical symptoms. The dysregulated pathways were particularly associated with specific brain tissue types. The expression of upstream regulators was consistent across both methods; however, their functional roles need further investigation.

Single-cell profiling of T/NK cell glycosylation and interaction networks during liver cirrhosis.

Leveraging deep learning semantic segmentation for imaging coral skeletons.

Biomimetic villi-crypt scaffold-on-chip with tunable mechanical properties for intestinal epithelium modeling.

Dual-band terahertz graphene–MXene–silver metasurface biosensor for brain tumor screening and early detection using machine learning

Enhancing adverse drug event extraction and summarization for cancer drugs through large language models.

Assessment of proton versus photon therapy in the reduction of lymphopaenia in thoracic cancers: A scoping review.

A dual-lumen microcatheter for minimizing particle reflux during embolization: Proof-of-concept with multiphysics simulations.

Unveiling the therapeutic potential of copper nanoparticles for cancer treatment: a systematic review.

Targeted microwave sensitizers reprogram cancer-associated fibroblasts via nitric oxide delivery to potentiate hepatocellular carcinoma therapy.

Vitamin A-modified melanin nanoparticles for photoacoustic imaging-guided photothermal therapy of hepatic fibrosis.

Investigation of the impact of image-guided radiotherapy selection on image registration results and non-coding RNAs for cervical cancer patients.

A pH-responsive layered double hydroxide nanoradiosensitizer for bone metastasis tumor.

Silicosis is the most common occupational lung disease caused by respirable crystalline silica inhalation, with limited therapeutic options. Cellular senescence plays a critical role in the pathogenesis of lung diseases, while the role of senescent macrophages in silicosis remains unclear. Single-cell RNA sequencing (scRNA-seq) of healthy and silicosis human and mouse lung tissues revealed that activating transcription factor 3 (ATF3)-mediated macrophage senescence is closely linked to silicosis progression. Mechanistically, Sirtuin 6 (SIRT6)-mediated ATF3 deacetylation enhanced its nuclear transport and subsequently activated mitochondria-localized glutamic acid-rich protein (MGARP) transcription, thereby causing mitochondrial dysfunction and macrophage senescence. Senescent macrophages promoted fibroblast activation via the secreted phosphoprotein 1 (SPP1)-cluster of differentiation 44 (CD44) signaling pathway. Furthermore, the nuclear transport protein importin α and the molecular chaperone protein heat shock protein 70 (HSP70) competitively bound to ATF3, preventing its lysosomal degradation while promoting its nuclear import during macrophage senescence. Moreover, the small-molecule inhibitor Itraconazole, which targets the binding site of ATF3 and importin α, could reduce ATF3 nuclear entry, macrophage senescence, and pulmonary fibrosis (PF). Collectively, our study provided insights into the mechanism by which deacetylated ATF3 facilitates silicosis progression via increased nuclear transport and macrophage senescence, and indicated potential therapeutic targets for PF.

Manual interpretation of gastrointestinal (GI) endoscopy images is a subjective and time-intensive process, often leading to variability between clinicians and the risk of missed abnormalities. To address these challenges, we investigate the use of StyleGAN2-ADA as a generative model for unsupervised anomaly detection in gastrointestinal endoscopy images. StyleGAN2-ADA combines adaptive discriminator augmentation with style-based generative modelling, enabling the model to capture both global anatomical structures and fine-grained mucosal textures. In this work, the performance of StyleGAN2-ADA is empirically compared with the widely used f-AnoGAN framework to assess its suitability for anomaly detection in gastrointestinal endoscopy images. The model was trained and evaluated on the HyperKvasir dataset, a large and diverse collection of endoscopic images representing both normal and pathological findings. Our method achieved a ROC-AUC of 0.945 and an F1 score of 0.847, outperforming the established f-AnoGAN baseline in anomaly discrimination. These results highlight the ability of StyleGAN2-ADA to model complex multimodal distributions of healthy tissue and to emphasize abnormal regions as reconstruction defects. Clinically, this framework has the potential to support early detection of GI diseases and to integrate into real-time endoscopy workflows, reducing cognitive load for physicians, assisting less experienced clinicians, and accelerating biopsy decision-making.

Machine-learning (ML) driven molecular diagnostics based on omics data has a potential to revolutionize personalized medicine. However, implementation of ML into diagnostic protocols is hindered by methodological challenges which often lead to inflated performance assessment of models during development followed by poor performance of these models in implementation phase. Here, we aimed to develop and validate a pan-cancer classification framework based on DNA methylation data, that addresses methodological challenges of omics data powered ML. We curated a primary dataset of DNA methylation profiles for 10756 samples, that included 54 healthy and cancer tissue types and validation dataset comprising data for 2306 samples from 28 independent studies. The classification framework was build using custom biomarkers selection strategy based on effect size metric that considers variance and class imbalance. The ML models were trained, tuned and evaluated using nested cross-validation approach. Local outlier factor algorithm was built into the inference pipelines to identify and filter samples displaying technical or biological anomalies. Additionally, for methodological validation of our framework we used methylation profiles for 3905 central nervous system (CNS) tumors. We found that relatively simple ML models outperformed complex algorithms such as deep neural network. A logistic regression classifier achieved a balanced accuracy (BACC) of 0.90 to classify 54 cancer and healthy tissue types using methylation levels at 1208 CpG sites. Similarly, our CNS tumor classifier also based on logistic regression algorithm reached a BACC of 0.94 across 59 CNS tumor subtypes. The anomaly filtering improved performance across all categories of samples tested. Our study demonstrates that DNA methylation profiling, when combined with carefully controlled ML practices allows for development of robust solutions that might substantially increase the efficacy of oncological diagnosis. Finally, we deployed our inference pipelines for public access via secure web platform - https://opp.pum.edu.pl/ .

The high molecular heterogeneity of breast cancer (BC) poses a significant challenge for its classification and biological characterization. Despite numerous efforts, conventional immunohistochemical techniques and traditional mass spectrometry (MS) have failed to provide an exhaustive characterization of tumor subtypes. This limitation is likely due to the loss of spatial information, which significantly impacts the interpretation of the results. In this study, we present a matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) approach that spatially integrates three multiomics layers, including lipids, N-glycans, and tryptic peptides, on the same tissue microarray (TMA) section with BC and normal tissue cores. The analysis of individual layers and their integration demonstrates the potential of multiomics MALDI-MSI in discriminating between healthy and tumor tissues and in capturing molecular differences associated with different subtypes of BC. Specifically, the approach adopted highlighted the significant contribution of lipids and glycans to characterizing breast tumor subtypes. The proteomic layer provides complementary information on the proliferative state and biological heterogeneity of the tumors, clearly distinguishing between the healthy and neoplastic conditions. Overall, this proof-of-concept study demonstrates the potential of spatial multiomics MALDI-MSI as a tool for a more in-depth characterization of BC subtypes, laying the groundwork for future applications on larger sample cohorts.

Oral lichen planus (OLP) is a chronic immune-mediated inflammatory disease that affects the oral mucosa, with tongue lesions being clinically significant due to their association with oral cancer risk and taste dysfunction. However, the layer-specific molecular heterogeneity of the basal layer (BL), spinous layer (SL) and lamina propria (LP) in tongue OLP remains poorly characterized, limiting mechanistic understanding and therapeutic development. We used spatial proteomics combined with laser capture microdissection to analyse layer-specific protein expression in tongue OLP lesions (with lymphocyte infiltration) and adjacent nonlesional tissues from three patients. Histopathological validation via immunohistochemistry was performed on samples from an expanded cohort of 10 patients with OLP and 10 healthy tongue dorsum tissue samples. Multiplex fluorescent staining was used to analyse the spatial associations between keratin 17 (KRT17) and relevant immune cells and cytokines. In vitro functional assays, including tumour necrosis factor/interferon (IFN)-γ or lipopolysaccharide (LPS)-induced inflammation models, scratch wound healing, apoptosis detection and KRT17 knockdown, were done in human oral keratinocytes (HOKs) to explore the mechanistic roles of key proteins. A total of 4434 proteins were identified, revealing distinct layer-specific dysregulation: 183 upregulated and 98 downregulated proteins in the BL; 262 upregulated and 330 downregulated in the LP; and 91 upregulated and 112 downregulated in the SL. In the BL, immune activation markers (lymphocyte cytosolic protein 1, RAC2, KRT17) were enriched, while metabolic enzymes (argininosuccinate synthetase 1, phosphoglycerate dehydrogenase) were suppressed. The LP showed upregulated proinflammatory mediators [coronin-1A, interleukin (IL)-16] and downregulated extracellular matrix proteins (tenascin X, keratin 9). The SL exhibited disrupted differentiation markers (transglutaminase 3, keratin 13) and innate immune molecules (IFN-induced GTP-binding protein, IFN-stimulated gene 15). Functional enrichment analyses confirmed layer-specific pathogenic axes: immune-metabolic crosstalk in the BL, inflammatory-stromal loops in the LP and differentiation defects in the SL. Multiplex staining revealed spatial correlation between KRT17, T-cell infiltration and IL-1β. In vitro, KRT17 knockdown attenuated LPS-induced inflammation (IL-6, IL-1β), was accompanied by restored HOK migration and reduced apoptosis. Our study uncovers layer-specific molecular landscapes in tongue OLP and identifies KRT17 as a candidate implicated in immune-epithelial crosstalk, providing novel insights into pathogenesis and a potential direction for future therapeutic exploration.