Upconversion and NIR-II luminescent rare earth nanoparticles combined with machine learning for cancer theranostics.

How to develop contrast agents for cancer theranostics is a meaningful and challenging endeavor, and rare earth nanoparticles (RENPs) may provide a possible solution. In this study, we initially modified RENPs through the application of photodynamic agents (ZnPc) and targeted the bevacizumab antibody for cancer theranostics, which was aimed at improving the therapeutic targeting and efficacy. Subsequently, we amalgamated anthocyanin with the modified RENPs, creating a potential cancer diagnosis platform. When the spectral data were obtained from the composite of cells, the crucial information was extracted through a competitive adaptive reweighted sampling feature algorithm. Then, we employed a machine learning classification model and classified both the individual spectral data and fused spectral data to accurately predict distinctions between breast cancer and normal tissue. The results indicate that the amalgamation of fusion techniques with machine learning algorithms provides highly precise predictions for molecular-level breast cancer detection. Finally, in vitro and in vivo experiments were carried out to validate the near-infrared luminescence and therapeutic effectiveness of the modified nanomedicine. This research not only underscores the targeted effects of nanomedicine but also demonstrates the potent synergy between optical spectral technology and machine learning. This innovative approach offers a comprehensive strategy for the integrated treatment of breast cancer.

Fluorescence-Recovered Wearable Hydrogel Patch for In Vitro Detection of Glucose Based on Rare-Earth Nanoparticles.

The physiological state of the human body can be indicated by analyzing the composition of sweat. In this research, a fluorescence-recovered wearable hydrogel patch has been designed and realized which can noninvasively monitor the glucose concentration in human sweat. Rare-earth nanoparticles (RENPs) of NaGdF4 doped with different elements (Yb, Er, and Ce) are synthesized and optimized for better luminescence in the near-infrared second (NIR-II) and visible region. In addition, RENPs are coated with CoOOH of which the absorbance has an extensive peak in the visible and NIR regions. The concentration of H2O2 in the environment can be detected by the fluorescence recovery degree of CoOOH-modified RENPs based on the fluorescence resonance energy transfer effect. For in vivo detection, the physiological state of oxidative stress at tumor sites can be visualized through its fluorescence in NIR-II with low background noise and high penetration depth. For the in vitro detection, CoOOH-modified RENP and glucose oxidase (GOx) were doped into a polyacrylamide hydrogel, and a patch that can emit green upconversion fluorescence under a 980 nm laser was prepared. Compared with the conventional electrochemical detection method, the fluorescence we presented has higher sensitivity and linear detection region to detect the glucose. This improved anti-interference sweat patch that can work in the dark environment was obtained, and the physiological state of the human body is conveniently monitored, which provides a new facile and convenient method to monitor the sweat status.

A stable and quantitative method for dimensionality reduction of aortic centerline.

Aortic dissection (AD) is a fatal aortic disease with high mortality. Assessing the morphology of the aorta is critical for diagnostic and surgical decisions. Aortic centerline projection methods have been used to evaluate the morphology of the aorta. However, there is a big difference between the current model of primary plane projection (PPP) and the actual shape of individuals, which is not conducive to morphological statistical analysis. Finding a method to compress the three-dimensional information of the aorta into two dimensions is helpful to clinical decision-making. In this paper, the evaluation parameters, including contour length (CL), enclosure area, and the sum of absolute residuals (SAR), were introduced to objectively evaluate the optimal projection plane rather than artificial subjective judgment. Our results showed that the optimal projection plane could be objectively characterized by the three evaluation parameters. As the morphological criterion, SAR is optimal among the three parameters. Compared to the optimal projection plane selected by traditional PPP, our method has better AD discrimination in the analysis of aortic tortuosity, and is conducive to the clinical operation of AD. Thus, it has application prospects for the preprocessing techniques for the geometric morphology analysis of AD.