Remote temperature sensing in microelectronics: optical thermometry using dual-center phosphors.

Remote thermal sensing has emerged as a temperature detection technique for tasks in which standard contact thermometers cannot be used due to environment or dimension limitations. One of such challenging tasks is the measurement of temperature in microelectronics. Here, optical thermometry using co-doped and mixed dual-center Gd2O3:Tb3+/Eu3+samples were realized. Ratiometric approach based on monitoring emission intensities of Tb3+(5D4-7F5) and Eu3+(5D0-7F2) transition provided sensing in the range of 30 °C-80 °C. Dispersion system type only slightly affected relative sensitivity, accuracy and precision. The applicability of phosphors synthesized to be utilized as remote optical thermometers for microelectronics has been proved with an example on a surface mount resistor and microcontroller.

Development and validation of an artificial intelligence model for predicting de novo distant bone metastasis in breast cancer: a dual-center study.

OBJECTIVE: Breast cancer has become the most prevalent malignant tumor in women, and the occurrence of distant metastasis signifies a poor prognosis. Utilizing predictive models to forecast distant metastasis in breast cancer presents a novel approach. This study aims to utilize readily available clinical data and advanced machine learning algorithms to establish an accurate clinical prediction model. The overall objective is to provide effective decision support for clinicians. METHODS: Data from 239 patients from two centers were analyzed, focusing on clinical blood biomarkers (tumor markers, liver and kidney function, lipid profile, cardiovascular markers). Spearman correlation and the least absolute shrinkage and selection operator regression were employed for feature dimension reduction. A predictive model was built using LightGBM and validated in training, testing, and external validation cohorts. Feature importance correlation analysis was conducted on the clinical model and the comprehensive model, followed by univariate and multivariate regression analysis of these features. RESULTS: Through internal and external validation, we constructed a LightGBM model to predict de novo bone metastasis in newly diagnosed breast cancer patients. The area under the receiver operating characteristic curve values of this model in the training, internal validation test, and external validation test1 cohorts were 0.945, 0.892, and 0.908, respectively. Our validation results indicate that the model exhibits high sensitivity, specificity, and accuracy, making it the most accurate model for predicting bone metastasis in breast cancer patients. Carcinoembryonic Antigen, creatine kinase, albumin-globulin ratio, Apolipoprotein B, and Cancer Antigen 153 (CA153) play crucial roles in the model's predictions. Lipoprotein a, CA153, gamma-glutamyl transferase, α-Hydroxybutyrate dehydrogenase, alkaline phosphatase, and creatine kinase are positively correlated with breast cancer bone metastasis, while white blood cell ratio and total cholesterol are negatively correlated. CONCLUSION: This study successfully utilized clinical blood biomarkers to construct an artificial intelligence model for predicting distant metastasis in breast cancer, demonstrating high accuracy. This suggests potential clinical utility in predicting and identifying distant metastasis in breast cancer. These findings underscore the potential prospect of developing economically efficient and readily accessible predictive tools in clinical oncology.

Dual-center co-doped and mixed ratiometric LuVO4:Nd3+/Yb3+nanothermometers.

During last decade luminescence thermometry has become a widely studied research field due to its potential applications for real time contactless temperature sensing where usual thermometers cannot be used. Special attention is paid to the development of accurate and reliable thermal sensors with simple reading. To address existing problems of ratiometric thermometers based on thermally-coupled levels, LuVO4:Nd3+/Yb3+thermal sensors were studied as a proof-of-concept of dual-center thermometer obtained by co-doping or mixture. Both approaches to create a dual-center sensor were compared in terms of energy transfer efficiency, relative sensitivity, and temperature resolution. Effect of excitation mechanism and Yb3+doping concentration on thermometric performances was also investigated. The best characteristics ofSr = 0.34% K-1@298 K and ΔT = 0.2 K were obtained for mixed phosphors upon host excitation.