The Incorporation of Etanercept into a Porous Tri-Layer Scaffold for Restoring and Repairing Cartilage Tissue

Morphology and transverse alignment of the patella have no effect on knee gait characteristics in healthy Chinese adults over the age of 40 years

Background: The influence of patella morphology and horizontal alignment on knee joint kinematics and kinetics remains uncertain. This study aimed to assess patella morphology and transverse alignment in relation to knee kinetics and kinematics in individuals without knee conditions. A secondary objective was to investigate the impact of femur and tibia alignment and shape on knee gait within this population. Patients and methods: We conducted a prospective collection of data, including full-leg anteroposterior and skyline X-ray views and three-dimensional gait data, from a cohort comprising 54 healthy individuals aged 40 years and older. Our study involved correlation and logistic regression analyses to examine the influence of patella, femur, and tibia morphology and alignment on knee gait. Results: The patellar tilt angle or the patella index did not show any significant relationships with different aspects of gait in the knee joint, such as velocity, angle, or moment (p > 0.05, respectively). Using multivariate logistic regression analysis, we found that the tibiofemoral angle and the Q angle both had a significant effect on the adduction angle (OR = 1.330, 95%CI 1.033-1.711, p = 0.027; OR = 0.475, 95%CI 0.285-0.792, p = 0.04; respectively). The primary variable influencing the knee adduction moment was the tibiofemoral angle (OR = 1.526, 95% CI 1.125-2.069, p = 0.007). Conclusion: In healthy Chinese individuals aged over 40, patella morphology and transverse alignment do not impact knee gait. However, the femoral-tibial angle has a big impact on the knee adduction moment.

Polymeric biomaterials: Advanced drug delivery systems in osteoarthritis treatment

Polymeric biomaterials have emerged as a highly promising candidate for drug delivery systems (DDS), exhibiting significant potential to enhance the therapeutic landscape of osteoarthritis (OA) therapy. Their remarkable capacity to manifest desirable physicochemical attributes, coupled with their excellent biocompatibility and biodegradability, has greatly expanded their utility in pharmacotherapeutic applications. Nevertheless, an urgent necessity exists for a comprehensive synthesis of the most recent advances in polymeric DDS, providing valuable guidance for their implementation in the context of OA therapy. This review is dedicated to summarizing and examining recent developments in the utilization of polymeric DDS for OA therapy. Initially, we present an overview of the intricate pathophysiology characterizing OA and underscore the prevailing limitations inherent to current treatment modalities. Subsequently, we introduce diverse categories of polymeric DDS, including hydrogels, nanofibers, and microspheres, elucidating their inherent advantages and limitations. Moreover, we discuss and summarize the delivery of bioactive agents through polymeric biomaterials for OA therapy, emphasizing key findings and emerging trends. Finally, we highlight prospective directions for advancing polymeric DDS, offering a promising approach to enhance their translational potential for OA therapy.

Pharmacognostic standardization and machine learning-based investigations on Akebia quinata and Akebia trifoliata

Currently, Akebiae Caulis is being used in clinical practice, but there are few reseaches on its different varieties. To ensure the accuracy and effectiveness of clinical practice, this study distinguished the Akebia quinata (Thunb.) Decne. and Akebia trifoliata (Thunb.) Koidz, using organoleptic evaluation, microscopic observation, fluorescence reaction, physicochemical properties, thin-layer chromatography, IR spectroscopy, HPLC, four machine learning models, and in vitro antioxidant methods. Analysis of the powders of these two varieties using optical microscopy revealed the presence of starch granules, cork cells, crystal fibers, scalariform vessels, and wood fibers. Scanning electron microscopy revealed the presence of scalariform vessels, pitted vessels, wood fibers, and calcium oxalate crystals. Several tissues, including the cork layer, fiber population, cortex, phloem, pith, xylem, and ray, were found in the transverse section. In addition, thin-layer chromatography was used to identify two components: oleanolic acid and calceolarioside B; 11 common peaks were identified in 15 batches of SAQ and 5 batches of SAT by using HPLC. Support vector machine, BP neural networks, and GA-bp neural networks were able to predict 100% accurately of the different origins of stem of Akebia quinate (Thunb.) Decne (SAQ) and Akebia trifoliata (Thunb.) Koidz (SAT). Extreme learning machine achieved a correct rate of 87.5%. Meanwhile, Fourier-transform infrared spectroscopy fingerprint identified nine characteristic absorption peaks of the secondary metabolites of SAQ and SAT. 2,2-Diphenyl-1-1-picrylhydrazyl experiment revealed that the IC50 values of SAQ and SAT extracts were 155.49 and 128.75 μg/ml, respectively. For the 2,2'-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) assay, the IC50 value of SAT extract was found to be 269.24 μg/ml, which was lower than that of SAQ extract (IC50  = 358.99 μg/ml). This study successfully used different methods to differentiate between A. quinata (Thunb.) Decne. and A. trifoliata (Thunb.) Koidz., to help decide on which type to use for clinical application.

Identification and validation of hub genes and potential drugs involved in osteoarthritis through bioinformatics analysis

Purpose: Osteoarthritis (OA) is a common degenerative disease, which still lacks specific therapeutic drugs. Synovitis is one of the most important pathological process in OA. Therefore, we aim to identify and analyze the hub genes and their related networks of OA synovium with bioinformatics tools to provide theoretical basis for potential drugs. Materials and methods: Two datasets were obtained from GEO. DEGs and hub genes of OA synovial tissue were screened through Gene Ontology (GO) annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment as well as protein-protein interaction (PPI) network analysis. Subsequently, the correlation between expression of hub genes and ferroptosis or pyroptosis was analyzed. CeRNA regulatory network was constructed after predicting the upstream miRNAs and lncRNAs. The validation of hub genes was undertook through RT-qPCR and ELISA. Finally, potential drugs targeting pathways and hub genes were identified, followed by the validation of the effect of two potential drugs on OA. Results: A total of 161 commom DEGs were obtained, of which 8 genes were finally identified as hub genes through GO and KEGG enrichment analysis as well as PPI network analysis. Eight genes related to ferroptosis and pyroptosis respectively were significantly correlated to the expression of hub genes. 24 miRNAs and 69 lncRNAs were identified to construct the ceRNA regulatory network. The validation of EGR1, JUN, MYC, FOSL1, and FOSL2 met the trend of bioinformatics analysis. Etanercept and Iguratimod reduced the secretion of MMP-13 and ADAMTS5 of fibroblast-like synoviocyte. Conclusion: EGR1, JUN, MYC, FOSL1, and FOSL2 were identified as hub genes in the development of OA after series of bioinformatics analysis and validation. Etanercept and Iguratimod seemed to have opportunities to be novel drugs for OA.

NIR-II live imaging study on the degradation pattern of collagen in the mouse model

The degradation of collagen in different body parts is a critical point for designing collagen-based biomedical products. Here, three kinds of collagens labeled by second near-infrared (NIR-II) quantum dots (QDs), including collagen with low crosslinking degree (LC), middle crosslinking degree (MC) and high crosslinking degree (HC), were injected into the subcutaneous tissue, muscle and joints of the mouse model, respectively, in order to investigate the in vivo degradation pattern of collagen by NIR-II live imaging. The results of NIR-II imaging indicated that all tested collagens could be fully degraded after 35 days in the subcutaneous tissue, muscle and joints of the mouse model. However, the average degradation rate of subcutaneous tissue (k = 0.13) and muscle (k = 0.23) was slower than that of the joints (shoulder: k = 0.42, knee: k = 0.55). Specifically, the degradation rate of HC (k = 0.13) was slower than LC (k = 0.30) in muscle, while HC showed the fastest degradation rate in the shoulder and knee joints. In summary, NIR-II imaging could precisely identify the in vivo degradation rate of collagen. Moreover, the degradation rate of collagen was more closely related to the implanted body parts rather than the crosslinking degree of collagen, which was slower in the subcutaneous tissue and muscle compared to the joints in the mouse model.