Differentiation of Bone Mesenchymal Stem Cells Into Vascular Endothelial Cell-Like Cells Using Functionalized Single-Walled Carbon Nanotubes.

Carbon nanotubes (CNTs) are a promising bioactive scaffold for bone regeneration because of their superior mechanical and biological properties. Vascularization is crucial in bone tissue engineering, and insufficient vascularization is a long-standing problem in tissue-engineered scaffolds. However, the effect of CNTs on vascularization is still minimal. In the current study, pristine single-walled carbon nanotubes (SWNTs) were purified to prepare different ratios of SWNTs/EDC composites, and their surface morphology and physicochemical properties of SWNTs/EDC were studied. Furthermore, the effect of SWNTs/EDC on vascularization was investigated by inducing the differentiation of bone mesenchymal stem cells (BMSCs) into vascular endothelial cell-like cells (VEC-like cells). Results showed that SWNTs/EDC composite was successfully prepared, and EDC was embedded in the SWNTs matrix and uniformly distributed throughout the composites. The AFM, FTIR spectra, and Raman results confirmed the formation of SWNTs/EDC composites. Besides, the surface topography of the SWNTs/EDC composites presents a rough surface, which may positively affect cell function. In vitro cell culture revealed that SWNTs and SWNTs/EDC composites exhibited excellent biocompatibility and bioactivity. The SWNTs/EDC composite at mass/volume ratios 1:10 had the best enhancement of proliferation and differentiation of BMSCs. Moreover, after culture with SWNTs/EDC composite, approximately 78.3% ± 4.2% of cultured cells are double-positive for FITC-UEA-1 and DiI-Ac-LDL double staining. Additionally, the RNA expression of representative endothelial cell markers VEGF, VEGF-R2, CD31, and vWF in the SWNTs/EDC composite group was significantly higher than those in the control and SWNTs group. With the limitation of our study, the results suggested that SWNTs/EDC composite can promote BMSCs differentiation into VEC-like cells and positively affect angiogenesis and bone regeneration.

Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer's Disease: A Systematic Review and Meta-Analysis.

Alzheimer's disease (AD) is the most common form of dementia. Currently, only symptomatic management is available, and early diagnosis and intervention are crucial for AD treatment. As a recent deep learning strategy, generative adversarial networks (GANs) are expected to benefit AD diagnosis, but their performance remains to be verified. This study provided a systematic review on the application of the GAN-based deep learning method in the diagnosis of AD and conducted a meta-analysis to evaluate its diagnostic performance. A search of the following electronic databases was performed by two researchers independently in August 2021: MEDLINE (PubMed), Cochrane Library, EMBASE, and Web of Science. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to assess the quality of the included studies. The accuracy of the model applied in the diagnosis of AD was determined by calculating odds ratios (ORs) with 95% confidence intervals (CIs). A bivariate random-effects model was used to calculate the pooled sensitivity and specificity with their 95% CIs. Fourteen studies were included, 11 of which were included in the meta-analysis. The overall quality of the included studies was high according to the QUADAS-2 assessment. For the AD vs. cognitively normal (CN) classification, the GAN-based deep learning method exhibited better performance than the non-GAN method, with significantly higher accuracy (OR 1.425, 95% CI: 1.150-1.766, P = 0.001), pooled sensitivity (0.88 vs. 0.83), pooled specificity (0.93 vs. 0.89), and area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) (0.96 vs. 0.93). For the progressing MCI (pMCI) vs. stable MCI (sMCI) classification, the GAN method exhibited no significant increase in the accuracy (OR 1.149, 95% CI: 0.878-1.505, P = 0.310) or the pooled sensitivity (0.66 vs. 0.66). The pooled specificity and AUC of the SROC in the GAN group were slightly higher than those in the non-GAN group (0.81 vs. 0.78 and 0.81 vs. 0.80, respectively). The present results suggested that the GAN-based deep learning method performed well in the task of AD vs. CN classification. However, the diagnostic performance of GAN in the task of pMCI vs. sMCI classification needs to be improved. Systematic Review Registration: [PROSPERO], Identifier: [CRD42021275294].

Effects of photobiomodulation therapy on implant stability and postoperative recovery: a systematic review and meta-analysis.

The purpose of this study was to systemically analyse the effects of photobiomodulation therapy (PBMT) on implant stability and postoperative recovery. Electronic searches on MEDLINE (PubMed), Cochrane Library, EMBASE, and Web of Science were completed independently by two researchers in February 2021, and a manual search was performed for the references of the included articles. The primary outcome was implant stability. The secondary outcome was postoperative recovery, including postoperative pain, recovery of peri-implant hard tissue (marginal bone loss and bone mineral density), facial swelling, and peri-implant clinical parameters. Twenty studies were finally obtained (17 randomised controlled, and 3 controlled clinical studies). Meta-analysis revealed that PBMT increased implant stability at 10 days after insertion (MD 2.27, 95% CI: 0.40 to 4.13, P = 0.020), and reduced marginal bone loss at 6 months after insertion (MD -0.16, 95% CI: -0.23 to -0.08, P < 0.001). However, no significant improvements were noted in implant stability two weeks (P = 0.070), three weeks (P = 0.090), six weeks (P = 0.050), and 12 weeks (P = 0.080) after insertion. Qualitative analysis suggested that PBMT could not alleviate postoperative pain, increase bone mineral density, or improve peri-implant clinical parameters. It was effective only in reducing facial swelling. This study suggests that the effects of PBMT on implant stability and postoperative recovery may be limited.

Effects of platelet concentrates on implant stability and marginal bone loss: a systematic review and meta-analysis.

BACKGROUND: Osseointegration is essential for the success and stability of implants. Platelet concentrates were reported to enhance osseointegration and improve implant stability. The purpose of this review is to systematically analyze the effects of platelet concentrates on implant stability and marginal bone loss. METHODS: Two researchers independently performed searches in the following databases (last searched on 21 July 2021): MEDLINE (PubMed), Cochrane Library, EMBASE, and Web of Science. In addition, a manual search was carried out on references of relevant reviews and initially included studies. All randomized controlled trials (RCTs) and controlled clinical trials (CCTs) on the application of platelet concentrates in the implant surgery procedure were included. The risk of bias of RCTs and CCTs were assessed with a revised Cochrane risk of bias tool for randomized trials (RoB 2.0) and the risk of bias in non-randomized studies-of interventions (ROBINS-I) tool, respectively. Meta-analyses on implant stability and marginal bone loss were conducted. Researchers used mean difference or standardized mean difference as the effect size and calculated the 95% confidence interval. In addition, subgroup analysis was performed based on the following factors: type of platelet concentrates, method of application, and study design. RESULTS: Fourteen studies with 284 participants and 588 implants were included in the final analysis. 11 studies reported implant stability and 5 studies reported marginal bone level or marginal bone loss. 3 studies had high risk of bias. The meta-analysis results showed that platelet concentrates can significantly increase implant stability at 1 week (6 studies, 302 implants, MD 4.26, 95% CI 2.03-6.49, P < 0.001) and 4 weeks (8 studies, 373 implants, MD 0.67, 95% CI 0.46-0.88, P < 0.001) after insertion, significantly reduced marginal bone loss at 3 months after insertion (4 studies, 95 implants, mesial: MD - 0.33, 95% CI - 0.46 to - 0.20, P < 0.001; distal: MD - 0.38, 95% CI - 0.54 to - 0.22, P < 0.001). However, the improvement of implant stability at 12 weeks after insertion was limited (P = 0.10). Subgroup analysis showed that PRP did not significantly improve implant stability at 1 week and 4 weeks after insertion (P = 0.38, P = 0.17). Platelet concentrates only placed in the implant sites did not significantly improve implant stability at 1 week after insertion (P = 0.20). CONCLUSIONS: Platelet concentrates can significantly improve implant stability and reduce marginal bone loss in the short term. Large-scale studies with long follow-up periods are required to explore their long-term effects and compare effects of different types. TRIAL REGISTRATION: This study was registered on PROSPERO, with the Registration Number being CRD42021270214.

MPI-based bioinformatic analysis and co-inhibitory therapy with mannose for oral squamous cell carcinoma.

Mannose induces tumor cell apoptosis and inhibits glucose metabolism by accumulating intracellularly as mannose 6-phosphate while the drug sensitivity of tumors is negatively correlated with mannose phosphate isomerase gene (MPI) expression. In this study, we performed a first attempt to explore the relationship between the targeted gene MPI and immune infiltration and genetic and clinical characteristics of head and neck squamous carcinoma (HNSC) using computational algorithms and bioinformatic analysis, and further to verify the co-inhibition effects of mannose with genotoxicity, immune responses, and microbes dysbiosis in oral squamous cell carcinoma (OSCC) in vitro and in vivo. Our results found that patients with lower MPI expression had higher survival rate. The enhancement of MPI expression was in response to DNA damage gene, and ATM inhibitor was verified as a potential drug with a synergistic effect with mannose on HSC-3. In the HNSC, infiltrated immunocytes CD8+ T cell and B cell were the significantly reduced risk cells, while IL-22 and IFN-γ showed negative correlation with MPI. Finally, mannose could reverse immunophenotyping caused by antibiotics in mice, resulting in the decrease of CD8+ T cells and increase of myeloid-derived suppressor cells (MDSCs). In conclusion, the MPI gene showed a significant correlation with immune infiltration and genetic and clinical characteristics of HNSC. The treatment of ATM inhibitor, immune regulating cells of CD8+ T cells and MDSCs, and oral microbiomes in combination with mannose could exhibit co-inhibitory therapeutic effect for OSCC.

Advancing diagnostic performance and clinical applicability of deep learning-driven generative adversarial networks for Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disease that severely affects the activities of daily living in aged individuals, which typically needs to be diagnosed at an early stage. Generative adversarial networks (GANs) provide a new deep learning method that show good performance in image processing, while it remains to be verified whether a GAN brings benefit in AD diagnosis. The purpose of this research is to systematically review psychoradiological studies on the application of a GAN in the diagnosis of AD from the aspects of classification of AD state and AD-related image processing compared with other methods. In addition, we evaluated the research methodology and provided suggestions from the perspective of clinical application. Compared with other methods, a GAN has higher accuracy in the classification of AD state and better performance in AD-related image processing (e.g. image denoising and segmentation). Most studies used data from public databases but lacked clinical validation, and the process of quantitative assessment and comparison in these studies lacked clinicians' participation, which may have an impact on the improvement of generation effect and generalization ability of the GAN model. The application value of GANs in the classification of AD state and AD-related image processing has been confirmed in reviewed studies. Improvement methods toward better GAN architecture were also discussed in this paper. In sum, the present study demonstrated advancing diagnostic performance and clinical applicability of GAN for AD, and suggested that the future researchers should consider recruiting clinicians to compare the algorithm with clinician manual methods and evaluate the clinical effect of the algorithm.