2 results about "Oxyhemoglobins" patented technology

This invention discloses a method for preparing a core-shell microneedle patch loaded with dual nanoparticles, comprising: preparing silica nanoparticles loaded with oxyhemoglobin and dihydroporphyrin e6; preparing calcium carbonate nanoparticles loaded with aPD-1 antibody; dispersing the silica nanoparticles loaded with oxyhemoglobin and dihydroporphyrin e6 in a polyvinyl alcohol solution to prepare the shell structure of the microneedles; preparing a polymer hydrogel; dispersing the calcium carbonate nanoparticles loaded with aPD-1 antibody in the polymer hydrogel to prepare the core structure of the microneedles; and casting the polyvinyl alcohol solution onto the backing of the microneedles to finally obtain the core-shell microneedle patch loaded with dual nanoparticles. The core-shell microneedle patch loaded with dual nanoparticles prepared by this invention can deliver nanoparticles to the tumor site, where the nanoparticles respond to the tumor microenvironment and activate the body's own immune system, thereby achieving a therapeutic effect on tumors.

A functional near-infrared spectral signal enhancement and classification method based on spatiotemporal autocorrelation and attention encoding is proposed to address the limitations of limited sample size and insufficient generalization ability of classification models in functional near-infrared spectral signal data. This method is applicable to the auxiliary diagnosis of neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD). The method first preprocesses the raw near-infrared spectral data, extracting oxyhemoglobin signals and calculating spatial autocorrelation parameters, channel-level temporal autocorrelation parameters, and the eigenvalue distribution of the functional connectivity matrix. Then, enhanced data is generated based on the spatiotemporal autocorrelation model. Finally, the raw and enhanced data are input into the STEAFNet deep learning model for accurate classification. This invention generates high-quality enhanced data, expands the sample size, and maintains high fidelity. Combined with deep learning, it improves classification accuracy and generalization ability, providing technical support for clinical applications.