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Expression of DNA-damage response genes after exposure to high LET particles used in BNCT in glioblastoma cells with altered radiosensitivity
Abstract DNA-dependent protein kinase catalytic subunit (DNA-PKcs) plays a central role in the repair of double-strand breaks (DSBs), but its deficiency alters the broader DNA damage response in glioblastoma cells exposed to α particle irradiation. Here, we investigated transcriptional changes in DNA repair pathways and cellular radiosensitivity in two isogenic glioblastoma cell lines differing in DNA-PKcs status: M059J (DNA-PKcs-deficient) and M059K (DNA-PKcs-proficient). Using pathway-focused qPCR, we profiled 30 genes involved in key DNA repair pathways and evaluated cell survival by clonogenic and MTT assays. M059J cells, despite DNA-PKcs deficiency, exhibited comparable survival fractions and higher metabolic activity than DNA-PKcs-proficient M059K cells following α particle irradiation. Irradiated M059J cells exhibited broad transcriptional upregulation of genes involved in double-strand break repair, single-strand break repair, mismatch repair, and nucleotide excision repair, reflecting compensatory activation of multiple repair mechanisms. In contrast, M059K cells displayed a restricted response, characterized primarily by strong PRKDC upregulation, the gene encoding DNA-PKcs. These findings highlight the pivotal role of DNA-PKcs status in shaping the DNA damage response and radiosensitivity of glioblastoma cells. Targeting compensatory repair pathways in DNA-PKcs-deficient tumors may offer novel strategies for radiosensitization in glioblastoma therapy.
Comprehensive Theoretical, Spectroscopic, Solvent, Topological and Antimicrobial investigation of 5-Chloro-6-fluoro-2-(2-pyrazinyl)-1H-benzimidazole
Pan immune inflammation value improves MELD 3.0 for mortality prediction in critically ill cirrhotic patients
Immature platelet fraction and bone marrow findings in hematology
Early Montessori education shows delayed benefits for mathematical problem-solving in a 5-year longitudinal randomized controlled trial
Abstract Early childhood education may have the potential to narrow the income achievement gap. However, studies have shown substantial intervention fadeout, calling for rigorous assessment of the long-term effects of early childhood programs. In a randomized controlled study, Courtier et al. (2021) found that an adapted Montessori curriculum led to larger reading gains in kindergarten than conventional public education in France. Participants from that intervention were recovered five years later while in conventional classrooms and once again tested on academic, cognitive, and social skills ( N = 97; M age =10–11). Children who benefited from the adapted Montessori curriculum no longer showed better reading skills than their peers (d=-0.07). However, they outperformed their peers on math problem-solving (d = 0.58), an effect not present in kindergarten.
Circulating MicroRNAs do not provide a diagnostic benefit over tissue biopsy in patients with brain metastases
Personalized intensity modulation radiation therapy auto-planning for esophageal cancer
Integrated assessment of greenhouse gas emissions in extensive livestock farming systems
Unlocking the hidden pathways: river channel delineation and reservoir characterization in Shurijeh Formation, Kopeh Dagh Basin
Fusing LandTrendr BCI and machine learning for spoil dump mapping
Multicomponent intervention increases nutrition knowledge scores in rural China
Efficient and scalable training set generation for automated pollen monitoring with Hirst-type samplers
Abstract Automated pollen detection is essential for ecological monitoring, allergy forecasting, and biodiversity research. However, existing methods rely heavily on manual or semi-automated annotations, limiting scalability and broader applicability. We introduce a highly automated training dataset generation pipeline that combines one-shot detection with systematic refinement, producing tens of thousands of high-quality annotations from bright-field microscopy while significantly reducing manual effort and annotation costs. Using multi-regional datasets from France, Hungary, and Sweden, we trained object detection models on seven pollen taxa and evaluated their performance on both external pure and mixed species slides and real-world airborne samples. We assessed the reusability of pretrained vision models for pollen detection, aiming to reduce the need for extensive retraining. Using linear probing, we identified foundational Vision Transformers (ViTs) as the most effective feature extractors and integrated them into Faster R-CNN detection models. We benchmarked these models against ResNet50, a widely adopted backbone in biological imaging. On held-out regions of the training datasets, our models achieved high performance in both classification and detection tasks. On independent reference slides from other datasets, ViTs continued to outperform ResNet50 in classification. However, in full object detection and under real deployment conditions, ResNet50-based models remained competitive and achieved the highest accuracy for detecting Ambrosia , a major allergen with public health significance. Cross-dataset generalization remains a challenge, underscoring the need for domain adaptation techniques such as stain normalization and data augmentation. This study establishes a scalable framework for AI-assisted pollen monitoring, supporting large-scale slide digitization and enabling applications in long-term ecological research, allergen surveillance, and automated biodiversity assessment.