Summary
Background/purpose
The present study explores whether photodynamic therapy (PDT)-induced apoptosis can increase the number of tolerogenic regulatory T cells (Treg) and limit collateral tissue damage.
Methods
BALB/c mice were vaccinated subcutaneously three times with PDT-induced apoptotic or thaw-frozen, necrotic non-infected autologous macrophages (MΦ). Two weeks after the last vaccination, mice were infected intradermally with 106 promastigotes of Leishmania major.
Results
Mice that received PDT-induced apoptotic MΦ had fewer parasites and higher numbers of Treg than mice vaccinated with thaw-frozen necrotic MΦ or phosphate-buffered saline (PBS). Interleukin (IL)-4 and IL-6 were significantly suppressed, while IL-10 was increased in mice that received the PDT-induced apoptotic MΦ. The role of Treg in this process was confirmed through Treg transfer from vaccinated to naïve mice. Mice receiving CD4+CD25+ cells from mice vaccinated with PDT-induced apoptotic MΦ showed smaller lesions 3 weeks after infection and lower parasitic burdens than mice that received Tregs from mice of thaw-frozen necrotic MΦ or PBS groups. These changes were mediated by the depletion of CD3+CD8+ and NKT cells and increased levels of IL-12p70 and interferon-γ, IL-10, and TGF-β in the cutaneous leishmaniasis lesions.
Conclusion
Vaccination with apoptotic MΦ-induced tolerogenic Treg cells that limited collateral tissue damage and diminished parasitic burden.
The spectral analysis based on laser-induced breakdown spectroscopy (LIBS) is an effective approach to carbon concentration monitoring. In this work, a novel LIBS-based method, together with a system designed independently, was developed for carbon monitoring. The experiments were conducted in two modes: static and dynamic. In static monitoring, gases in three scenarios were selected to represent different carbon concentrations, based on which measurements of carbon concentrations were performed through a mathematical model. Then, K-nearest Neighbors (KNN) was adopted for classification, and its accuracy could reach 99.17%, which can be applied for the identification of gas composition and pollution traceability. In dynamic monitoring, respiration and fossil fuel combustion were selected because of their important roles in increasing carbon concentration. In addition, the simulation of combustion degree was performed by the radial basis function (RBF) based on the spectral information, where the accuracy reached 96.41%, which is the first time that LIBS is proposed to be used for combustion prediction. The innovative approach derived from LIBS and machine learning algorithms is fast, online, and in-situ, showing far-reaching application prospects in real-time monitoring of carbon concentrations.
As an important physical quantity to describe the resistance of fluid to flow, viscosity is an essential property of fluids in fluid mechanics, chemistry, medicine, as well as hydraulic engineering. While traditional measurement methods, including the rotating-cylinder method, capillary tube method and falling sphere method, have significant drawbacks especially in terms of accuracy, response time and the sample must be made to move. In this work, a novel Beer-Lambert law-based method was proposed for the viscosity measurement. Specifically, this work demonstrates that viscosity can be quantitatively reflected by spectral line intensity, and castor oil was selected due to its viscous temperature properties (viscosity has been accurately measured under different temperature), and its transmission spectrum at different temperatures ranging from 10 to 50°C was detected firstly. Then, the principal component analysis (PCA) was employed to obtain the intrinsic features of the transmission spectrum. Finally, the processed data was utilized to train and verify the radial basis function (RBF) neural network. As a result, the accuracy of the predictions conducted by means of the RBF reached 98.45%, which indicates the complicated and non-linear relationships between spectra formation and viscosity can be depicted well by RBF. The results show that the real-time in-situ optical detection method adopted in this work represents a great leap forward in the viscosity measurement, which fundamentally reforms the traditional viscosity measurement methods.
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