Dietary Immunoglobulin Y by Targeting Both GbpB and GtfB Enhances the Anticaries Effect in Rats.

OBJECTIVE: The purpose of this work was to develop an anti-CAT-SYI immunoglobulin Y (IgY) antibody that targeted both GtfB (glucosyltransferase B) and GbpB (glucan-binding protein B) and test its anticaries properties in rats. METHODS: A new CAT-SYI fusion gene was created utilising functional DNA fragments from the GtfB and GbpB genes. The recombinant antigens, comprising the fused CAT-SYI antigen, GtfB, and GbpB, were expressed and purified using a prokaryotic expression and purification system. The purified recombinant antigens were utilised to immunise laying hens against particular IgY production. The biological activities of these particular IgY antibodies were then assessed both in vitro and in vivo, including their capacity to suppress biofilm formation and tooth caries. RESULTS: Results indicated that these produced IgY antibodies demonstrated a high antibody titer (>0.1 µg/mL) and could precisely recognise and bind to their respective antigens. Furthermore, it was discovered that these specific IgY antibodies successfully bind to Streptococcus mutans and significantly reduce biofilm development. After 8 weeks of ingesting antigen-specific IgY meals, comprising anti-GtfB IgY and anti-GbpB IgY, the severity of dental caries was dramatically reduced in S mutans-infected Sprague-Dawley rats (P < .01). Anti-CAT-SYI IgY therapy significantly reduced tooth cavities by 89.0% in vivo (P < .05) compared to other treatment groups. CONCLUSIONS: The anti-CAT-SYI IgY, a multitarget antibody that targets both GtfB and GbpB, displayed excellent inhibitory effects against S mutans, making it a promising targeted method with improved anticaries efficacy and significant application opportunities.

The isolation and analysis of fetal nucleated red blood cells using multifunctional microbeads with a nanostructured coating toward early noninvasive prenatal diagnostics.

Prenatal diagnostics holds great significance for pregnant women desiring healthy babies. Fetal nucleated red blood cells (fNRBCs), bearing the complete genome of the fetus, have been regarded as an important biomarker for noninvasive prenatal diagnostics (NIPD). The high-performance detection and enrichment of fNRBCs from maternal blood, especially during early pregnancy, is urgently needed for NIPD, which, unfortunately, remains a big challenge for early-pregnancy fNRBC isolation. In this study, we developed an innovative platform based on silica microbeads for fNRBC isolation and release in early pregnancy. Microbeads were coated with self-assembled MnO2 nanoparticles (SiO2@MnO2) and then modified with a specific antibody. Benefiting from the three-dimensional nanostructure of the MnO2 nanoparticles, the isolation efficiency of the fNRBCs was enhanced. Subsequently, fNRBCs were released via dissolving the MnO2-nanoparticle coating using oxalic acid. We successfully isolated fNRBCs from the maternal peripheral blood samples of 20 pregnant women in the early pregnancy period, ranging from 41 to 62 gestational days. More importantly, the fetal origin of isolated cells was confirmed via fluorescent in situ hybridization and short tandem repeat analysis. This platform based on SiO2@MnO2 microbeads has verified the existence of fNRBCs in early-pregnancy maternal blood and is a promising approach for NIPD in early pregnancy.

A Novel Evaluation Model for a Mixed-Reality Surgical Navigation System: Where Microsoft HoloLens Meets the Operating Room.

HoloLens-based mixed-reality surgical navigation system (MR-SNS) technology has made great progress. However, the methodology for evaluating users' perceptions concerning the safety, comfort, and efficiency of MR-SNS is still in its infancy. This study was intended to develop a method to systematically evaluate an existing MR-SNS system during actual clinical applications. This method differs from other existing methods currently used in industry, education, and device maintenance. Based on analytical hierarchy process theory and ergonomics evaluation methods, in this article, we propose a novel multicriteria evaluation model for a HoloLens-based MR-SNS. The model includes factors such as comfort, safety, and effectiveness, and is performed in an actual clinical application. A comprehensive experimental platform and scoring system that can analyze all indicators was built. The validation test showed no statistically significant differences in the accuracy of the 3 different movement patterns (P = .95, P > .05). However, the static pattern showed the best accuracy. In addition, no significant difference (P = .68, P > .05) in accuracy was found under 4 kinds of illuminance. A comparison of the results of this evaluation model and the input from experts who use the HoloLens-based MR-SNS in hospitals, indicated that this model has good precision (100%), recall (80%), and F1-measure (88.89%). The results highlighted the full efficacy of the proposed model in determining whether this system can be used in clinical trials to provide indicators for preliminary ex ante feasibility studies. This article describes the lessons learned from conducting this evaluation study of MR-SNS as part of the design process.

[Comparative study on evaluation algorithms for neck muscle fatigue based on surface electromyography signal].

The purpose of this study is to compare the differences among neck muscle fatigue evaluation algorithms and to find a more effective algorithm which can provide a human factor quantitative evaluation method for neck muscle fatigue during bending over the desk. We collected surface electromyography signal of sternocleidomastoid muscle of 15 subjects using wireless physiotherapy Bio-Radio when they bent over the desk using memory pillows for 12 minutes. Five algorithms including mean power frequency, spectral moments ratio, discrete wavelet transform, fuzzy approximation entropy and the complexity algorithms were used to calculate the corresponding muscle fatigue index. The least squares method was used to calculate the corresponding coefficient of determination R2 and slope k of the linear regression of the muscle fatigue metric. The coefficient of determination R2 evaluates anti-interference ability of algorithms. The maximum vertical distance Lmax which is obtained by the Kolmogorov-Smirnov test for the slopes k evaluates the ability to distinguish fatigue of algorithms. The results indicate that in the aspect of anti-interference ability, the fuzzy approximation entropy has the largest R2 when using memory pillows with different heights. When the fuzzy approximate entropy is compared with average power frequency or the discrete wavelet transform, the differences are significant ( P < 0.05). In terms of distinguishing the degree of fatigue, the approximate entropy is still the largest, with a maximum of 0.496 7. Fuzzy approximation entropy is superior to other algorithms in ability of anti-interference and distinguishing fatigue. Therefore, fuzzy approximation entropy can be used as a better evaluation algorithm in the evaluation of cervical muscle fatigue.