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Gold nanoparticles (GNPs) have gained significant attention as multifunctional agents in biomedical applications, particularly for enhancing radiotherapy. Their advantages, including low toxicity, high biocompatibility, and excellent conductivity, make them promising candidates for improving treatment outcomes across various radiation sources, such as femtosecond lasers, X-rays, Cs-137, and proton beams. However, a deeper understanding of their precise mechanisms in radiotherapy is essential for maximizing their therapeutic potential. This review explores the role of GNPs in enhancing reactive oxygen species (ROS) generation through plasmon-induced hot electrons or radiation-induced secondary electrons, leading to cellular damage in organelles such as mitochondria and the cytoskeleton. This additional pathway enhances radiotherapy efficacy, offering new therapeutic possibilities. Furthermore, we discuss emerging trends and future perspectives, highlighting innovative strategies for integrating GNPs into radiotherapy. This comprehensive review provides insights into the mechanisms, applications, and potential clinical impact of GNPs in cancer treatment.- Book : 15(4)
- Pub. Date : 2025
- Page : pp.317-317
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2025
Lithium is a metal with a highly promising outlook for future global demand. Its industrial processing relies on two primary methods: production from brines through solar evaporation ponds and production from rock sources via flotation, roasting, and subsequent leaching. Chile is currently the world’s second-largest producer of lithium, surpassed only by Australia. However, Chile’s lithium production process is significantly advantaged by the exceptionally high lithium concentration in the Salar de Atacama—the highest in the world—and the region’s high solar radiation, which enables the most cost-effective solar evaporation process globally. Despite these comparative advantages, Chile’s lithium production has stagnated in recent years. This stagnation can be attributed to the need for more flexible legislation surrounding the lithium industry or an increase in the number of CEOLs (Lithium Exploitation Contracts) to regain its position as the leading global producer of lithium. Furthermore, increased investment in national universities and research centers is essential to foster the development and implementation of new, clean technologies for future projects. By addressing these challenges, Chile has the potential to solidify its role as a key player in the global lithium market while promoting sustainable industrial practices.- Book : 14(2)
- Pub. Date : 2025
- Page : pp.33-33
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2025
AbstractBackgroundInput data curation and model training are essential, but time‐consuming steps in building a deep‐learning (DL) auto‐planning model, ensuring high‐quality data and optimized performance. Ideally, one would prefer a DL model that exhibits the same high‐quality performance as a trained model without the necessity of undergoing such time‐consuming processes. That goal can be achieved by providing models that have been trained on a given dataset and are capable of being fine‐tuned for other ones, requiring no additional training.PurposeTo streamline the process for producing an automated right‐sided breast (RSB) treatment planning technique adapting a DL model originally trained on left‐sided breast (LSB) patients via treatment planning system (TPS) specific tools only, thereby eliminating the need for additional training.MethodsThe adaptation process involved the production of a predicted dose (PD) for the RSB by swapping from left‐to‐right the symmetric structures in association with the tuning of the initial LSB model settings for each of the two steps that follow the dose prediction: the predict settings for the post‐processing of the PD (ppPD) and the mimic settings for the dose mimicking, respectively. Thirty patients were involved in the adaptation process: Ten manual plans were chosen as ground truth for tuning the LSB model settings, and the adapted RSB model was validated against 20 manual plans. During model tuning, PD, ppPD, and mimicked dose (MD) were iteratively compared to the manual dose according to the new RSB model settings configurations. For RSB model validation, only MD was involved in the planning comparison. Subsequently, the model was applied to 10 clinical patients. Manual and automated plans were compared using a site‐specific list of dose‐volume requirements.ResultsPD for the RSB model required substantial corrections as it differed significantly from manual doses in terms of mean dose to the heart (+11.1 Gy) and right lung (+4.4 Gy), and maximum dose to the left lung (+6.4 Gy) and right coronary (+11.5 Gy). Such discrepancies were first addressed by producing a ppPD always superior to the manual dose by changing or introducing new predict settings. Second, the mimic settings were also reformulated to ensure a MD not inferior to the manual dose. The final adapted version of the RSB model settings, for which MD was found to be not significantly different than the manual dose except for a better right lung sparing (‐1.1 Gy average dose), was retained for the model validation. In RSB model validation, a few significant—yet not clinically relevant—differences were noted, with the right lung being more spared in auto‐plans (‐0.6 Gy average dose) and the maximum dose to the left lung being lower in the manual plans (‐0.8 Gy). The clinical plans returned dose distributions not significantly different than the validation plans.ConclusionThe proposed technique adapts a DL model initially trained for LSB cancer for right‐sided patients. It involves swapping the dose predictions from left to right and adjusting model settings, without the need for additional training. This technique—specific to a TPS—could be transposed to other TPS platforms.- Book : ()
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2025
Abstract
It is well known that aging affects many systems in the body. The digestive system is one of the systems most affected by aging. In our study, we examined the effects of young plasma treatment on cell proliferation, growth factors, immune defense and histological parameters in the jejunum of aged male rats. For this purpose, aged male Sprague Dawley rats (24 months, n = 7) were treated with pooled plasma (0.5 ml/day, intravenously for 30 days) collected from young (5 weeks, n = 51) rats. Aged rats that received young plasma treatment were grouped as the experimental group, while aged rats formed the control group. At the end of the experiment, the jejunums of the groups were collected and histological parameters such as villus height, crypt depth, total mucosal thickness and surface absorption areas were measured and compared. In addition, cell proliferation index and proliferation intensity in the crypt glands of the jejunum were evaluated with proliferating cell nuclear antigen and expressions of growth factors such as insulin-like growth factor I (IGF-I) and its receptor (IGF-IR) expression and effects of immunoglobulin A (IgA), which plays a role in the defense of the digestive system against microorganisms, were examined. In the experimental group, an increase in histological parameters, IGF-R and IGF-IR expression, proliferation density, proliferation index and IgA expression density and IgA cell count were observed compared to the control group. These results suggest that young plasma treatment has a positive effect on the digestive system and may be a potential therapeutic for tissue regeneration.- Book : 26(2)
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2025
The reduced-activation high-entropy alloys (RAHEAs) have promising applications in advanced nuclear systems due to their low activation, excellent mechanical properties and radiation resistance. However, compared to the conventional high-entropy alloys (HEAs), the relatively small datasets of RAHEAs pose challenges for alloy design by using conventional machine learning (ML) methods. In this work, we proposed a framework by incorporating symbolic regression (SR) and domain adaptation to improve the accuracy of property prediction based on the small datasets of RAHEAs. The conventional HEA datasets and RAHEA datasets were classified as source and target domains, respectively. SR was used to generate features from element-based features in the source domains. The domain-invariant features related to hardness were captured and used to construct the ML model, which significantly improved the prediction accuracy for both HEAs and RAHEAs. The normalized root mean square error decreases by 24% for HEAs and 30% for RAHEAs compared to that of the models trained with element-based features. The proposed framework can achieve accurate and robust prediction on small datasets with interpretable domain-invariant features. This research paves the way for efficient material design under small dataset scenarios.- Book : 5(1)
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2025
ABSTRACTTumor necrosis factor receptor‐associated factor‐6 (TRAF6) is a well‐established upstream regulator of the IKK complex, essential for the modulation of the NF‐κB (nuclear factor kappa B) signaling pathway. Aberrant activation of TRAF6 has been strongly implicated in the pathogenesis of various cancers, including hepatocellular carcinoma (HCC). The speckle type BTB/POZ protein (SPOP), an E3 ubiquitin ligase substrate‐binding adapter, constitutes a significant component of the CUL3/SPOP/RBX1 complex, which is closely linked to tumorigenesis. In this study, we demonstrated that the E3 ubiquitin ligase SPOP shielded TRAF6 from proteasomal degradation, leading to the hyperactivation of the NF‐κB pathway. Notably, a liver cancer‐associated S119N mutation in SPOP resulted in a failure to mediate the ubiquitination and subsequent degradation of TRAF6. Moreover, both gain‐of‐function and loss‐of‐function experiments revealed that SPOP inhibits the proliferation and invasion of HCC cells through the TRAF6‐NF‐κB axis in vitro and in vivo. Taken together, our findings elucidate the underpinning mechanism by which SPOP negatively regulates the stability of the TRAF6 oncoprotein, thus offering a new therapeutic target for HCC intervention.- Book : ()
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