Engineered extracellular vesicles enable high-efficient delivery of intracellular therapeutic proteins.

Developing an intracellular delivery system is of key importance in the expansion of protein-based therapeutics acting on cytosolic or nuclear targets. Recently, extracellular vesicles (EVs) have been exploited as next-generation delivery modalities due to their natural role in intercellular communication and biocompatibility. However, fusion of protein of interest to a scaffold represents a widely-used strategy for cargo enrichment in EVs, which could compromise t the stability and functionality of cargo. Herein, we report intracellular delivery via EV-based approach (IDEA) that efficiently packages and delivers native proteins both in vitro and in vivo without the use of a scaffold. As a proof-of-concept, we applied the IDEA to deliver cyclic GMP-AMP synthase (cGAS), an innate immune sensor. The results showed that cGAS-carrying EVs activated interferon signaling and elicited enhanced antitumor immunity in multiple syngeneic tumor models. Combining cGAS EVs with immune checkpoint inhibition further synergistically boosted antitumor efficacy in vivo. Mechanistically, scRNA-seq demonstrated that cGAS EVs mediated significant remodelling of intratumoral microenvironment, revealing a pivotal role of infiltrating neutrophils in the antitumor immune milieu. Collectively, IDEA, as a universal and facile strategy, can be applied to expand and advance the development of protein-based therapeutics.

The Explanation of Photopic Luminous Efficiency Curve by Using Both of the Cones' Optical Fiber Coupling Effects and the Absorption of L Cones.

In this paper, we build four-part cone models to explore the coupling effect of seven cone fiber couplers. Moreover, this is the first study of the coupling effect of four layers of biological couplers in animals and other biological lives. We simulate the four layers cone couplers by using the beam propagation method, and we assume the input beam is located at the outer fiber of the central cone. Our simulation results showed that there are two wavelength regions (short and long wavelength regions) with the strongest coupling, where the most power of input optical powers of the central cones will transfer to the six surrounding cones after transmitting through the four layers of cone couplers. However, within a wavelength region of ±75 nm near to the peak wavelengths, located in the yellow-green wavelength range, the splitting ratios at the output of the outer segment of the central cone are always greater than the sum of the splitting ratios of the six surrounding cones. These cone couplers may play an important role in color preprocessing (e.g., doing opponent color processing partially). The cone fiber coupler effect and light absorption of cones are considered separately in our models. By taking account of both the cone fiber coupling effect and absorption of outer segment of L cone, we find the multiplication of the relative optical power of cone couplers, the spectral sensitivity data of the L cone, and a normalized coefficient that matches with the photopic luminous efficiency of the human eye well. This is the attempt to use both the cone fiber coupling effect and the absorption of L cones to explain the photopic luminous efficiency. The splitting ratios of the central cones are greater than 80% at peak wavelengths located in the yellow-green wavelength range, and this can help to explain why the human eye is more sensitive to green light.

Correlation Analysis Combined with a Floating Reference Measurement to Improve the Prediction Accuracy of Glucose in Scattering Media.

Noninvasive sensing of blood glucose based on near-infrared (NIR) spectroscopy is a research hotspot in the biomedical field. However, its accuracy is severely limited by the weak specific signal of glucose and the strong background variations caused by other constituents in the blood, the measuring instrument, and the environment. In this paper, special source-detector distances, defined as the floating reference position, are used to conduct relative measurements and correct for background variations. These floating reference positions are chosen so that the diffuse reflectance is not sensitive to the change in glucose concentration due to the combined effects of absorption and scattering. Nine 10% intralipid samples with glucose concentrations in the range of 1000-5000 mg dL-1 at an interval of 500 mg dL-1 were prepared. Using a custom-built, continuously moving, spatially resolving, double-fiber measurement system with a superluminescent diode (SLD) as the light source, the diffuse reflectance of intralipid samples containing glucose under different source-detector distances (0.2-5 mm, with intervals of 0.2 mm) were collected. Then, a correlation analysis between the spectra and the glucose concentration was carried out to determine the floating reference position and the optimal measuring position. The signal in the floating reference position was used to correct the background variation because it contains the same systematic drift and interference as the signal in the optimal measuring position. The results showed that the correlation between the diffuse reflectance and the glucose concentration was increased significantly compared with traditional correction by subtracting the nearest spectrum of pure 10% intralipid solution. The correlation between the diffuse reflectance and the concentration of glucose is significantly increased, which indicated that the combination of the correlation analysis and a floating reference is able to eliminate the influence of background variations.

Quantitative assessment of the effect of cholesterol on blood glucose measurement using near infrared spectroscopy and a method for error reduction.

BACKGROUND AND OBJECTIVE: There is a growing body of evidence suggesting that the accurate measurement of blood glucose concentration can be perturbed by many factors. Current literature is limited in describing the influence of cholesterol on non-invasive blood glucose measurements by near-infrared spectroscopy (NIRS). This study aims to investigate the influence of cholesterol on blood glucose measurement through clinical oral glucose tolerance test (OGTT) and NIRS. Further, a method to reduce the prediction errors induced by cholesterol is proposed, facilitating the clinical application of non-invasive blood glucose sensing by NIRS. STUDY DESIGN/MATERIAL AND METHODS: We obtained clinical data of glucose and cholesterol concentrations at specific time points (0, 0.5, 1, 2, and 3 h) during OGTTs from 115 subjects. The subjects were grouped into: Norm for normal control, IGT for Impaired Glucose Tolerance, and Diabetes. In addition, spectral data between 1200 and 1800 nm were collected from 130 phantom samples, which are separated into seven groups depending on glucose and cholesterol levels. Statistical methods including One Sample T-test (OSTT), Pearson Correlation Analysis(PCA), and Unary Linear Regression (ULR) were used to analyze clinical data and spectral data to determine the relationship between glucose and cholesterol concentrations with the time course of OGTT. Reference wavelength-based method (RWM) was introduced to diminish the influence of cholesterol on glucose measurement and further the prediction error induced by cholesterol was reduced when using partial least square (PLS) model. RESULTS: Clinical results statistically show that there is a strong negative correlation between the changes of glucose and cholesterol concentrations in the diabetes group. The spectra of cholesterol exhibit similar absorbance peaks to those of glucose within NIR range. PLS modelling results demonstrate that glucose prediction is influenced by cholesterol concentrations in a calibration model. Furthermore, a model expression (ΔCg=0.0356Cc+1.0129 R(2) = 0.993) is fitted to quantitatively describe the glucose prediction increment (ΔCg) due to cholesterol concentration (Cc). The results show that glucose prediction accuracy can be improved up to 38.36% by using RWM when using NIRS. CONCLUSIONS: The cholesterol has an effect on blood glucose sensing. RWM is useful to help realize non-invasive blood glucose sensing by NIRS.