M2 macrophage-derived exosomes for bone regeneration: A systematic review.

OBJECTIVE: This systematic review aims to evaluate existing evidence to investigate the therapeutic efficacy of M2 macrophage-derived exosomes in bone regeneration. DESIGN: A comprehensive search between 2020 and 2024 across PubMed, Web of Science, and Scopus was conducted using a defined search strategy to identify relevant studies regarding the following question: "What is the impact of M2 macrophage-derived exosomes on bone regeneration?". Controlled in vitro and in vivo studies were included in this study. The SYRCLE tool was used to evaluate the risk of bias in the included animal studies. RESULTS: This review included 20 studies published. Seven studies were selected for only in vitro analysis, whereas 13 studies underwent both in vitro and in vivo analyses. The in vivo studies employed animal models, including 163 C57BL6 mice and 73 Sprague-Dawley rats. Exosomes derived from M2 macrophages were discovered to be efficacious in promoting bone regeneration and vascularization in animal models of bone defects. These effects were primarily confirmed through morphological and histological assessments. This remarkable outcome is attributed to the regulation of multiple signaling pathways, as evidenced by the findings of 11 studies investigating the involvement of miRNAs in this intricate process. In addition, in vitro studies observed positive effects on cell proliferation, migration, osteogenesis, and angiogenesis. Heterogeneity in study methods hinders direct comparison of results across studies. CONCLUSION: M2 macrophage-derived exosomes demonstrate remarkable potential for promoting bone regeneration. Further research optimizing their application and elucidating the underlying mechanisms can pave the way for clinical translation.

Efficacy and safety of adoptive T-cell therapy in treating cytomegalovirus infections post-haematopoietic stem cell transplantation: A systematic review and meta-analysis.

Cytomegalovirus (CMV) infection poses significant risks in allogeneic haematopoietic stem cell transplant (allo-HSCT) recipients. Despite advances in antiviral therapies, issues such as drug resistance, side effects, and inadequate immune reconstitution remain. This systematic review and meta-analysis aim to evaluate the efficacy and safety of adoptive cell therapy (ATC) in managing CMV infections in allo-HSCT recipients. Adhering to preferred reporting items for systematic reviews and meta-analyses guidelines, we conducted a comprehensive database search through July 2023. A systematic review and meta-analysis were conducted on studies involving HSCT patients with CMV infections treated with ATC. The primary outcome was the response rate to ATC, and secondary outcomes included adverse events associated with ATC. The Freeman-Tukey transformation was applied for analysis. In the meta-analysis of 40 studies involving 953 participants, ATC achieved an overall integrated response rate of 90.16%, with a complete response of 82.59% and a partial response of 22.95%. ATC source, HLA matching, steroid intake, and age group markedly influenced response rates. Donor-derived T-cell treatments exhibited a higher response rate (93.66%) compared to third-party sources (88.94%). HLA-matched patients demonstrated a response rate of 92.90%, while mismatched patients had a lower rate. Children showed a response rate of 83.40%, while adults had a notably higher rate of 98.46%. Adverse events were minimal, with graft-versus-host disease occurring in 24.32% of patients. ATC shows promising response rates in treating CMV infections post-HSCT, with an acceptable safety profile. However, to establish its efficacy conclusively and compare it with other antiviral treatments, randomised controlled trials are essential. Further research should prioritise such trials over observational and one-arm studies to provide robust evidence for clinical decision-making.

Quantile regression shrinkage and selection via the Lqsso.

Quantile regression has recently received a considerable attention due to its remarkable development in enriching the variety of regression models. Many efforts have been made to blend different penalty and loss function to extend or develop novel regression models that are unique from different perspectives. Bearing in mind that the lasso quantile regression model ignores the randomness of the realizations in the penalty part, we propose a new penalty for the quantile regression models. Similar to the adaptive lasso quantile regression model, the proposed model simultaneously does estimation and variable selection tasks. We call the new model 'lqsso-QR', standing for the least quantile shrinkage and selection operator quantile regression. In this article, we present a sufficient and necessary condition for the variable selection of the lasso quantile regression to enjoy the consistent property. We show that the lqsso-QR follows oracle properties under some mild conditions. From computational perspective, we apply an efficient algorithm, originally developed for the lasso quantile regression. Using simulation studies, we elaborate on the superiority of the proposed model compared with other lasso-type penalties, especially regarding relative prediction error. Also, an application of our method to a real-life data; the rat eye data, is reported.

Regression shrinkage and selection via least quantile shrinkage and selection operator.

Over recent years, the state-of-the-art lasso and adaptive lasso have aquired remarkable consideration. Unlike the lasso technique, adaptive lasso welcomes the variables' effects in penalty meanwhile specifying adaptive weights to penalize coefficients in a different manner. However, if the initial values presumed for the coefficients are less than one, the corresponding weights would be relatively large, leading to an increase in bias. To dominate such an impediment, a new class of weighted lasso will be introduced that employs all aspects of data. That is to say, signs and magnitudes of the initial coefficients will be taken into account simultaneously for proposing appropriate weights. To allocate a particular form to the suggested penalty, the new method will be nominated as 'lqsso', standing for the least quantile shrinkage and selection operator. In this paper, we demonstate that lqsso encompasses the oracle properties under certain mild conditions and delineate an efficient algorithm for the computation purpose. Simulation studies reveal the predominance of our proposed methodology when compared with other lasso methods from various aspects, particularly in ultra high-dimensional condition. Application of the proposed method is further underlined with real-world problem based on the rat eye dataset.