The development of biomaterials, stem cells and bioactive factors has led to cartilage tissue engineering becoming a promising tactic to repair cartilage defects. Various polymer three-dimensional scaffolds that provide an extracellular matrix (ECM) mimicking environment play an important role in promoting cartilage regeneration. In addition, numerous growth factors have been found in the regenerative process. However, it has been elucidated that the uncontrolled delivery of these factors cannot fully exert regenerative potential and can also elicit undesired side effects. Considering the complexity of the ECM, neither scaffolds nor growth factors can independently obtain successful outcomes in cartilage tissue engineering. Therefore, collectively, an appropriate combination of growth factors and scaffolds have great potential to promote cartilage repair effectively; this approach has become an area of considerable interest in recent investigations. Of late, an increasing trend was observed in cartilage tissue engineering towards this combination to develop a controlled delivery system that provides adequate physical support for neo-cartilage formation and also enables spatiotemporally delivery of growth factors to precisely and fully exert their chondrogenic potential. This review will discuss the role of polymer scaffolds and various growth factors involved in cartilage tissue engineering. Several growth factor delivery strategies based on the polymer scaffolds will also be discussed, with examples from recent studies highlighting the importance of spatiotemporal strategies for the controlled delivery of single or multiple growth factors in cartilage tissue engineering applications.

Metal-organic frameworks have become promising stimuli-responsive agents to release the loaded therapeutic agents in the target site to achieve more precise drug delivery due to their high drug loading, excellent biocompatibility, and high stimuli-responsiveness.

Bone regeneration following trauma, tumor resection, infection, or congenital disease is challenging. Diabetes mellitus (DM) is a metabolic disease characterized by hyperglycemia. It can result in complications affecting multiple systems including the musculoskeletal system. The increased number of diabetes-related fractures poses a great challenge to clinical specialties, particularly orthopedics and dentistry. Various pathological factors underlying DM may directly impair the process of bone regeneration, leading to delayed or even non-union of fractures. This review summarizes the mechanisms by which DM hampers bone regeneration, including immune abnormalities, inflammation, reactive oxygen species (ROS) accumulation, vascular system damage, insulin/insulin-like growth factor (IGF) deficiency, hyperglycemia, and the production of advanced glycation end products (AGEs). Based on published data, it also summarizes bone repair strategies in diabetic conditions, which include immune regulation, inhibition of inflammation, reduction of oxidative stress, promotion of angiogenesis, restoration of stem cell mobilization, and promotion of osteogenic differentiation, in addition to the challenges and future prospects of such approaches.

Background: Post-operative mortality risk assessment for geriatric patients with hip fractures (HF) is a challenge for clinicians. Early identification of geriatric HF patients with a high risk of post-operative death is helpful for early intervention and improving clinical prognosis. However, a single significant risk factor of post-operative death cannot accurately predict the prognosis of geriatric HF patients. Therefore, our study aims to utilize a machine learning approach, random forest algorithm, to fabricate a prediction model for post-operative death of geriatric HF patients. Methods: This retrospective study enrolled consecutive geriatric HF patients who underwent treatment for surgery. The study cohort was divided into training and testing datasets at a 70:30 ratio. The random forest algorithm selected or excluded variables according to the feature importance. Least absolute shrinkage and selection operator (Lasso) was utilized to compare feature selection results of random forest. The confirmed variables were used to create a simplified model instead of a full model with all variables. The prediction model was then verified in the training dataset and testing dataset. Additionally, a prediction model constructed by logistic regression was used as a control to evaluate the efficiency of the new prediction model. Results: Feature selection by random forest algorithm and Lasso regression demonstrated that seven variables, including age, time from injury to surgery, chronic obstructive pulmonary disease (COPD), albumin, hemoglobin, history of malignancy, and perioperative blood transfusion, could be used to predict the 1-year post-operative mortality. The area under the curve (AUC) of the random forest algorithm-based prediction model in training and testing datasets were 1.000, and 0.813, respectively. While the prediction tool constructed by logistic regression in training and testing datasets were 0.895, and 0.797, respectively. Conclusions: Compared with logistic regression, the random forest algorithm-based prediction model exhibits better predictive ability for geriatric HF patients with a high risk of death within post-operative 1 year.

Background:Recently, there is an increasing interest in the therapeutic potential of platelet-rich plasma (PRP) for bone fracture treatment. Nevertheless, the effect of PRP for bone fracture treatment remains controversial and is still a matter of discussion. Therefore, we performed a systematic review to evaluate the efficacy and safety of PRP injection for treatment of bone fracture.Methods:The main bibliographic databases, including Medline, PubMed, Embase, Web of Science, and the Cochrane library, were comprehensively searched for studies focusing on the application of platelet-rich plasma (PRP) on bone fracture treatment. All relevant articles were screened for eligibility and subdivided into the preclinical and clinical studies. Data were extracted and presented systematically.Results:Finally, twenty-sixin vitropreclinical studies (basic studies), ninein vivopreclinical studies (animal studies), and nine clinical studies, met the selection criteria, and were included in the present systematic review. Preclinical studies showed an overall positive effect of PRP on osteoblast-like cellsin vitroand bone healing in animal models. The most used treatment for bone fracture in animal and clinical studies is fixation surgery combined with PRP injection. The clinical studies reported PRP shortened bony healing duration, and had no positive effect on improving the healing rate of closed fractures. However, the results of functional outcomes are controversial. Additionally, compared with control group, PRP would not increase the rate of postoperative wound infection.Conclusion:The present systematic review confirmed the continuing interests of PRP as an additional treatment for bone fracture. Preclinical studies highlighted the potential value of PRP as promising therapy for bone fracture. However, the preclinical evidence did not translate into a similar result in the clinical studies. In addition, types of fractures and procedures of PRP preparation are heterogeneous in enrolled studies, which might result in controversial results. Meanwhile, characteristics of PRP, such as platelet concentration, the numbers of leukocytes, still need to be determined and further research is required.

Objective: chordomas are rare bone tumors with few therapeutic options. Skull base and sacrum are the two most common origin sites. Immunotherapies are emerging as the most promising approaches to fight various cancers. This study tends to identify new cell surface targets for immunotherapeutic options of skull base chordomas.Methods: we profiled 45 skull base chordoma clinical samples by immunohistochemistry for the expression of six CAR-Targets (PD-L1, B7-H3, B7-H4, VISTA, HER2 and HER3). In addition, we generated B7-H3 targeted CAR-T-cells and evaluated their antitumor activities in vitro.Results: We found that B7-H3 was positively stained in 7 out of 45 (16%) chordoma samples and established an expression hierarchy for these antigens (B7-H3 > HER3 > PD-L1 > HER2 = VISTA = B7-H4). We then generated a B7-H3 targeted CAR vector and demonstrated that B7-H3-CAR-T-cells recognized antigen positive cells and exhibited significant antitumor effects, including suppression of tumor spheroid formation, CAR-T-cell activation and cytokine secretion.Conclusions: Our results support B7-H3 might serve as a promising target for CAR-T-cell therapies against chordomas.