Semi-automatic proximal humeral trabecular bone density assessment tool: technique application and clinical validation.

PURPOSE: This study aimed to apply a newly developed semi-automatic phantom-less QCT (PL-QCT) to measure proximal humerus trabecular bone density based on chest CT and verify its accuracy and precision. METHODS: Subcutaneous fat of the shoulder joint and trapezius muscle were used as calibration references for PL-QCT BMD measurement. A self-developed algorithm based on a convolution map was utilized in PL-QCT for semi-automatic BMD measurements. CT values of ROIs used in PL-QCT measurements were directly used for phantom-based quantitative computed tomography (PB-QCT) BMD assessment. The study included 376 proximal humerus for comparison between PB-QCT and PL-QCT. Two sports medicine doctors measured the proximal humerus with PB-QCT and PL-QCT without knowing each other's results. Among them, 100 proximal humerus were included in the inter-operative and intra-operative BMD measurements for evaluating the repeatability and reproducibility of PL-QCT and PB-QCT. RESULTS: A total of 188 patients with 376 shoulders were involved in this study. The consistency analysis indicated that the average bias between proximal humerus BMDs measured by PB-QCT and PL-QCT was 1.0 mg/cc (agreement range - 9.4 to 11.4; P > 0.05, no significant difference). Regression analysis between PB-QCT and PL-QCT indicated a good correlation (R-square is 0.9723). Short-term repeatability and reproducibility of proximal humerus BMDs measured by PB-QCT (CV: 5.10% and 3.41%) were slightly better than those of PL-QCT (CV: 6.17% and 5.64%). CONCLUSIONS: We evaluated the bone quality of the proximal humeral using chest CT through the semi-automatic PL-QCT system for the first time. Comparison between it and PB-QCT indicated that it could be a reliable shoulder BMD assessment tool with acceptable accuracy and precision. This study developed and verify a semi-automatic PL-QCT for assessment of proximal humeral bone density based on CT to assist in the assessment of proximal humeral osteoporosis and development of individualized treatment plans for shoulders.

Epidemiology and Risk Factors for Revision Total Knee Arthroplasty in Chinese Patients: A Retrospective Study in Changchun, Jilin Province, China.

BACKGROUND Over the past decades, total knee arthroplasty (TKA) in China has increased substantially. Owing to a lack of a joint registry, there is restricted information concerning the epidemiology of TKA failures in China. We aimed to (1) investigate the etiology of TKA failures in a cohort of Chinese patients and (2) determine the related demographic and anthropometric risk factors in Jilin, China, to have a look at the actual situation. MATERIAL AND METHODS A total of 1927 primary and 109 revision TKAs performed between April 2014 and May 2022 were analyzed in this retrospective study. Patient demographics and anthropometric measures, the interval from primary TKA to revision procedures, and the mechanisms for primary TKA failure were evaluated. A chi-square test, unpaired t test, and multivariate logistic regression were used to investigate the relationships between different factors and TKA failures. RESULTS The leading failure mechanism was infection (53.3%), followed by aseptic loosening (21.5%), stiffness (15.0%), instability (3.7%), malposition (2.8%), periprosthetic fractures (2.8%), and extensor mechanism disruption (0.9%). Infection (59.7%) was the main reason for early revision. Aseptic loosening (43.3%) was the leading cause of late revision. The male ratio in infection patients was higher (35.1% vs 20.6%). The smoking rate in patients with revision and infection was higher (18.9%, 23.9% vs 7%) than in primary patients. There was no difference in BMI between groups. CONCLUSIONS The leading cause of revision TKA in Jilin, China, was infection, followed by aseptic loosening and stiffness. Sex and smoking history were associated with TKA failures in this region.

Trends in prevalence of arthritis by race among adults in the United States, 2011-2018.

BACKGROUND: There is currently a lack of comprehensive prevalence information on arthritis and its various classifications among adults in the U.S., particularly given the notable absence of detailed data regarding the Asian population. We examined the trends in the prevalence of arthritis, including osteoarthritis (OA), rheumatoid arthritis (RA), psoriatic arthritis (PsA), and other types of arthritis, among U.S. adults by race between 2011 and 2018. METHODS: We analyzed data from the National Health and Nutrition Examination Survey (NHANES), spanning from 2011 to 2018. Our study focused on a nationally representative sample of U.S. adults aged 20 and older. Participants who answered "y es" to the research question "Doctors ever said you had arthritis?" were classified as having arthritis. Further classification into specific diseases was based on responses to the question "Which type of arthritis was it?" with options including "OA or degenerative arthritis, " "RA, " "PsA, " or "Other. " RESULTS: We analyzed 22,566 participants from NHANES (2011-2018), averaging 44.8 years, including 10,927 males. The overall arthritis prevalence rose significantly from 22.98% (95% CI: 21.47-24.55%) in 2011-12 to 27.95% (95% CI: 26.20-29.76%) in 2017-18 (P for trend < 0.001). OA increased from 12.02% (95% CI: 10.82-13.35%) in 2011 to 14.93% (95% CI: 13.47-16.51%) in 2018 (P for trend < 0.001). RA and PsA remained stable (P for trend = 0.220 and 0.849, respectively), while other arthritis rose from 2.03% (95% CI: 1.54-2.67%) in 2011-12 to 3.14% (95% CI: 2.56-3.86%) in 2017-18 (P for trend = 0.001). In Whites, Asians, and other races , arthritis and RA prevalence increased significantly (P for trend < 0.05). OA and other arthritis rose in Whites and other races (P for trend < 0.05), but no significant change occurred in the black population. The prevalence of PsA remained stable across all racial groups, with no statistically significant changes. CONCLUSIONS: In this nationally representative U.S. adult survey spanning 2011 to 2018, we identified a rising prevalence trend in arthritis, OA, and other arthritis, with notable variations among different racial groups.

Development and Validation of an Automated Classification System for Osteonecrosis of the Femoral Head Using Deep Learning Approach: A Multicenter Study.

BACKGROUND: Accurate classification can facilitate the selection of appropriate interventions to delay the progression of osteonecrosis of the femoral head (ONFH). This study aimed to perform the classification of ONFH through a deep learning approach. METHODS: We retrospectively sampled 1,806 midcoronal magnetic resonance images (MRIs) of 1,337 hips from 4 institutions. Of these, 1,472 midcoronal MRIs of 1,155 hips were divided into training, validation, and test datasets with a ratio of 7:1:2 to develop a convolutional neural network model (CNN). An additional 334 midcoronal MRIs of 182 hips were used to perform external validation. The predictive performance of the CNN and the review panel was also compared. RESULTS: A multiclass CNN model was successfully developed. In internal validation, the overall accuracy of the CNN for predicting the severity of ONFH based on the Japanese Investigation Committee classification was 87.8%. The macroaverage values of area under the curve (AUC), precision, recall, and F-value were 0.90, 84.8, 84.8, and 84.6%, respectively. In external validation, the overall accuracy of the CNN was 83.8%. The macroaverage values of area under the curve, precision, recall, and F-value were 0.87, 79.5, 80.5, and 79.9%, respectively. In a human-machine comparison study, the CNN outperformed or was comparable to that of the deputy chief orthopaedic surgeons. CONCLUSION: The CNN is feasible and robust for classifying ONFH and correctly locating the necrotic area. These findings suggest that classifying ONFH using deep learning with high accuracy and generalizability may aid in predicting femoral head collapse and clinical decision-making.

Deep Learning Approach for Diagnosing Early Osteonecrosis of the Femoral Head Based on Magnetic Resonance Imaging.

BACKGROUND: The diagnosis of early osteonecrosis of the femoral head (ONFH) based on magnetic resonance imaging (MRI) is challenging due to variability in the surgeon's experience level. This study developed an MRI-based deep learning system to detect early ONFH and evaluated its feasibility in the clinic. METHODS: We retrospectively evaluated clinical MRIs of the hips that were performed in our institution from January 2019 to June 2022 and collected all MRIs diagnosed with early ONFH. An advanced convolutional neural network (CNN) was trained and optimized; then, the diagnostic performance of the CNN was evaluated according to its accuracy, sensitivity, and specificity. We also further compared the CNN's performance with that of orthopaedic surgeons. RESULTS: Overall, 11,061 images were retrospectively included in the present study and were divided into three datasets with ratio 7:2:1. The area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of the CNN model for identifying early ONFH were 0.98, 98.4, 97.6, and 98.6%, respectively. In our review panel, the averaged accuracy, sensitivity, and specificity for identifying ONFH were 91.7, 87.0, and 94.1% for attending orthopaedic surgeons; 87.1, 84.0, and 89.3% for resident orthopaedic surgeons; and 97.1, 96.0, and 97.9% for deputy chief orthopaedic surgeons, respectively. CONCLUSION: The deep learning system showed a comparable performance to that of deputy chief orthopaedic surgeons in identifying early ONFH. The success of deep learning diagnosis of ONFH might be conducive to assisting less-experienced surgeons, especially in large-scale medical imaging screening and community scenarios lacking consulting experts.

Automatic detection of early osteonecrosis of the femoral head from various hip pathologies using deep convolutional neural network: a multi-centre study.

PURPOSE: The aim of this study was to develop a deep convolutional neural network (DCNN) for detecting early osteonecrosis of the femoral head (ONFH) from various hip pathologies and evaluate the feasibility of its application. METHODS: We retrospectively reviewed and annotated hip magnetic resonance imaging (MRI) of ONFH patients from four participated institutions and constructed a multi-centre dataset to develop the DCNN system. The diagnostic performance of the DCNN in the internal and external test datasets was calculated, including area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, and F1 score, and gradient-weighted class activation mapping (Grad-CAM) technique was used to visualize its decision-making process. In addition, a human-machine comparison trial was performed. RESULTS: Overall, 11,730 hip MRI segments from 794 participants were used to develop and optimize the DCNN system. The AUROC, accuracy, and precision of the DCNN in internal test dataset were 0.97 (95% CI, 0.93-1.00), 96.6% (95% CI: 93.0-100%), and 97.6% (95% CI: 94.6-100%), and in external test dataset, they were 0.95 (95% CI, 0.91- 0.99), 95.2% (95% CI, 91.1-99.4%), and 95.7% (95% CI, 91.7-99.7%). Compared with attending orthopaedic surgeons, the DCNN showed superior diagnostic performance. The Grad-CAM demonstrated that the DCNN placed focus on the necrotic region. CONCLUSION: Compared with clinician-led diagnoses, the developed DCNN system is more accurate in diagnosing early ONFH, avoiding empirical dependence and inter-reader variability. Our findings support the integration of deep learning systems into real clinical settings to assist orthopaedic surgeons in diagnosing early ONFH.

Fully Automatic Knee Joint Segmentation and Quantitative Analysis for Osteoarthritis from Magnetic Resonance (MR) Images Using a Deep Learning Model.

BACKGROUND We aimed to develop and evaluate a deep learning-based method for fully automatic segmentation of knee joint MR imaging and quantitative computation of knee osteoarthritis (OA)-related imaging biomarkers. MATERIAL AND METHODS This retrospective study included 843 volumes of proton density-weighted fat suppression MR imaging. A convolutional neural network segmentation method with multiclass gradient harmonized Dice loss was trained and evaluated on 500 and 137 volumes, respectively. To assess potential morphologic biomarkers for OA, the volumes and thickness of cartilage and meniscus, and minimal joint space width (mJSW) were automatically computed and compared between 128 OA and 162 control data. RESULTS The CNN segmentation model produced reasonably high Dice coefficients, ranging from 0.948 to 0.974 for knee bone compartments, 0.717 to 0.809 for cartilage, and 0.846 for both lateral and medial menisci. The OA-related biomarkers computed from automatic knee segmentation achieved strong correlation with those from manual segmentation: average intraclass correlations of 0.916, 0.899, and 0.876 for volume and thickness of cartilage, meniscus, and mJSW, respectively. Volume and thickness measurements of cartilage and mJSW were strongly correlated with knee OA progression. CONCLUSIONS We present a fully automatic CNN-based knee segmentation system for fast and accurate evaluation of knee joint images, and OA-related biomarkers such as cartilage thickness and mJSW were reliably computed and visualized in 3D. The results show that the CNN model can serve as an assistant tool for radiologists and orthopedic surgeons in clinical practice and basic research.

A Systematic Review of Risk Factors for Anterior Cruciate Ligament Reconstruction Failure.

Although most studies have introduced risk factors related to anterior cruciate ligament reconstruction failure, studies on combinations of high-risk factors are rare. To provide a systematic review of the risk factors of anterior cruciate ligament reconstruction failure to guide surgeons through the decision-making process, an extensive literature search was performed of the Medline, Embase and Cochrane Library databases. Studies published between January 1, 2009, and September 19, 2019, regarding the existing evidence for risk factors of anterior cruciate ligament reconstruction failure or graft failure were included in this review. Study quality was evaluated with the quality index. Ultimately, 66 articles met our criteria. There were 46 cases classified as technical factors, 21 cases as patient-related risk factors, and 14 cases as status of the knee joint. Quality assessment scores ranged from 14 to 24. This systematic review provides a comprehensive summary of the risk factors for anterior cruciate ligament reconstruction failure, including technical factors, patient-related factors, and the factors associated with the status of the knee joint. Emphasis should be placed on avoiding these high-risk combinations or correcting modifiable risk factors during preoperative planning to reduce the rate of graft rupture and anterior cruciate ligament reconstruction failure.

Retraction Note: Glycol chitosan incorporated retinoic acid chlorochalcone (RACC) nanoparticles in the treatment of Osteosarcoma.

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1186/s12944-020-01416-2.

Customized Knee Prosthesis in Treatment of Giant Cell Tumors of the Proximal Tibia: Application of 3-Dimensional Printing Technology in Surgical Design.

BACKGROUND We explored the application of 3-dimensional (3D) printing technology in treating giant cell tumors (GCT) of the proximal tibia. A tibia block was designed and produced through 3D printing technology. We expected that this 3D-printed block would fill the bone defect after en-bloc resection. Importantly, the block, combined with a standard knee joint prosthesis, provided attachments for collateral ligaments of the knee, which can maintain knee stability. MATERIAL AND METHODS A computed tomography (CT) scan was taken of both knee joints in 4 patients with GCT of the proximal tibia. We developed a novel technique - the real-size 3D-printed proximal tibia model - to design preoperative treatment plans. Hence, with the application of 3D printing technology, a customized proximal tibia block could be designed for each patient individually, which fixed the bone defect, combined with standard knee prosthesis. RESULTS In all 4 cases, the 3D-printed block fitted the bone defect precisely. The motion range of the affected knee was 90 degrees on average, and the soft tissue balance and stability of the knee were good. After an average 7-month follow-up, the MSTS score was 19 on average. No sign of prosthesis fracture, loosening, or other relevant complications were detected. CONCLUSIONS This technique can be used to treat GCT of the proximal tibia when it is hard to achieve soft tissue balance after tumor resection. 3D printing technology simplified the design and manufacturing progress of custom-made orthopedic medical instruments. This new surgical technique could be much more widely applied because of 3D printing technology.

Glycol chitosan incorporated retinoic acid chlorochalcone (RACC) nanoparticles in the treatment of Osteosarcoma.

BACKGROUND: Osteosarcoma is the most common of all the bone malignancies and accounts for 30-80% of the primary skeletal sarcomas. The overall survival rate of patients with osteosarcoma is < 20% suggesting poor prognosis. METHODS: The present study demonstrates the effect of retinoic acid chlorochalcone (RACC) incorporated glycol chitosan (GC) nanoparticle transfection in osteosarcoma cells. MG-63 and Saos-2 osteosarcoma cells were transfected with various concentrations of RACC-incorporated GC nanoparticle for 24 h. The effect on cell proliferation, Ezh2 expression, apoptosis, cell cycle arrest, cell migration and invasiveness, Akt phosphorylation and local tumour growth and metastases were studied. RESULTS: MG-63 and Saos-2 osteosarcoma cells on RACC-incorporated GC nanoparticle transfection for 24 h showed a concentration-dependent inhibition of cell proliferation. Of the various concentrations of RACC tested, the effective concentration started from 5 µM with an IC50 of 20 µM. Wound healing assay also showed that RACC-incorporated GC nanoparticles inhibited migration of tumor cells more effectively compared to the parent RA. RACC transfection resulted in inhibition of cell proliferation, Ezh2 expression inhibition, apoptosis through mitochondrial pathway by decrease in membrane potential and release of cytochrome c and cell cycle arrest in the G0/G1 phase. The invasiveness of cells treated with 5 and 20 µM RACC was decreased by 49 and 76% respectively, compared to the control. RACC-treated mice showed significantly lower number of metastases compared to that in the control mice. CONCLUSIONS: Thus, RACC-incorporated glycol chitosan nanoparticle strategy can be promising for the treatment of osteosarcoma.

Improved method for planning intramedullary guiding rod entry point in total knee arthroplasty.

BACKGROUND: To study the accuracy of using the deepest point of the intercondylar notch (DPIN) as a reference point for femoral intramedullary (IM) guiding rod entrance in total knee arthroplasty (TKA) with 3-D reconstruction in Chinese subjects. METHODS: A total of 50 normal femurs in 25 Chinese subjects (mean age 25.6 ± 2.9 years; range 18-29 years) were chosen from the lower extremities computed tomography digital imaging and communications in medicine (DICOM) database for this study. The DICOM data were imported into Mimics 10.0 software. A cylinder (radius = 4 mm; length = 20 cm) was used to simulate ideal insertion of a IM guiding rod into the femoral canal. DPIN was taken as a reference point for calculating the relative position of the rod's entry point. RESULTS: The mean rod entry point position in the coronal plane was 2.94 ± 1.12 mm (range 0.79-4.91 mm) medial and 6.01 ± 2.09 mm (range 2.49-9.51 mm) anterior to the DPIN, with no significant difference between sides. All potential angle errors were below 2°. CONCLUSION: The results of this study show that the DPIN can serve as a reference for surgeons using an IM guide system in TKA.

Segmental femoral fracture malunion: evidence and prognostic analysis of medical intervention in the third century BC.

We examined the remains of an individual who was unearthed from the Tuchengzi site and was believed to be from the Warring States period in China. The remains exhibited segmental femoral fracture. We aimed to deduce the cause of fracture, medical interventions, healing process, and motion behavior after fracture healing using several techniques, including macroscopic observation, computed tomography (CT), and finite element analysis. Based on the morphology of the long bones, it appeared that the individual was male. The fractures resulted in an adduction angle of 5.47° and an anterior flexion angle of 21.34° in the proximal femur, while the femoral neck anteversion angle had been replaced by a retroversion angle of 10.74°. Additionally, the distal femur formed an abnormal anterior convex angle of 144.60°. CT revealed mature callus formation and visible trabecular bundles. The finite element analysis indicated that the maximum von Mises stress in the femur was 17.44 MPa during standing and 96.46 MPa during walking. We suggest that medical practitioners in the Warring States period possessed a good knowledge of thigh anatomy, enabling them to perform fracture reduction and fixation. Reasonable medical intervention facilitated fracture healing and load recovery. Satisfactory fracture healing ensured that the individual could engage in normal standing and walking activities after rehabilitation.

Is diet related to osteoarthritis? A univariable and multivariable Mendelian randomization study that investigates 45 dietary habits and osteoarthritis.

Background: Diet is a safe intervention for many chronic diseases as a modifiable lifestyle. However, the potential causal effect of many dietary intake habits on the risk of osteoarthritis has not been fully understood. The purpose of this study was to reveal the potential causal relationship of 45 genetically predicted dietary intakes with osteoarthritis and its subtypes. Methods: Data on 45 dietary intakes were obtained from the UK Biobank study of approximately 500,000 participants, and data on six osteoarthritis-related phenotypes were obtained from the Genetics of Osteoarthritis Consortium study of 826,690 participants. We performed univariable Mendelian randomization (MR), multivariable MR and linkage disequilibrium score regression (LDSC) analyses. Results: In univariate analyses, 59 potential associations between diet and osteoarthritis were found. After false discovery rate (FDR) correction and sensitivity analyses, 23 reliable causal evidence were identified. In multivariate analyses, controlling separately for the effects of body mass index, total body bone mineral density, and smoking status, eight robust causal relationships remained: Muesli intake was negatively associated with knee osteoarthritis, spine osteoarthritis and total knee replacement. Dried fruit intake had a negative association with osteoarthritis of knee and total knee replacement. Eating cheese may reduce the risk of osteoarthritis in the knee and spine. And alcohol usually taken with meals was associated with a reduced risk of total knee replacement. LDSC analyses showed significant genetic correlations between all exposures and their corresponding outcomes, respectively, in these eight causal relationships. Conclusion: Evidence of dietary effects on osteoarthritis is provided in our study, which has important implications for the prevention, management, and intervention of osteoarthritis in common sites through rational dietary modification.

Development and clinical validation of deep learning for auto-diagnosis of supraspinatus tears.

BACKGROUND: Accurately diagnosing supraspinatus tears based on magnetic resonance imaging (MRI) is challenging and time-combusting due to the experience level variability of the musculoskeletal radiologists and orthopedic surgeons. We developed a deep learning-based model for automatically diagnosing supraspinatus tears (STs) using shoulder MRI and validated its feasibility in clinical practice. MATERIALS AND METHODS: A total of 701 shoulder MRI data (2804 images) were retrospectively collected for model training and internal test. An additional 69 shoulder MRIs (276 images) were collected from patients who underwent shoulder arthroplasty and constituted the surgery test set for clinical validation. Two advanced convolutional neural networks (CNN) based on Xception were trained and optimized to detect STs. The diagnostic performance of the CNN was evaluated according to its sensitivity, specificity, precision, accuracy, and F1 score. Subgroup analyses were performed to verify its robustness, and we also compared the CNN's performance with that of 4 radiologists and 4 orthopedic surgeons on the surgery and internal test sets. RESULTS: Optimal diagnostic performance was achieved on the 2D model, from which F1-scores of 0.824 and 0.75, and areas under the ROC curves of 0.921 (95% confidence interval, 0.841-1.000) and 0.882 (0.817-0.947) were observed on the surgery and internal test sets. For the subgroup analysis, the 2D CNN model demonstrated a sensitivity of 0.33-1.000 and 0.625-1.000 for different degrees of tears on the surgery and internal test sets, and there was no significant performance difference between 1.5 and 3.0 T data. Compared with eight clinicians, the 2D CNN model exhibited better diagnostic performance than the junior clinicians and was equivalent to senior clinicians. CONCLUSIONS: The proposed 2D CNN model realized the adequate and efficient automatic diagnoses of STs, which achieved a comparable performance of junior musculoskeletal radiologists and orthopedic surgeons. It might be conducive to assisting poor-experienced radiologists, especially in community scenarios lacking consulting experts.

[Corrigendum] Gene silencing of NOB1 by lentivirus suppresses growth and migration of human osteosarcoma cells.

Subsequently to the publication of this paper, an interested reader drew to the authors' attention that, on p. 2174 in the Materials and methods section (subsection "Lentivirus production and lentiviral transduction"), the sequence presented for the shRNA targeting the gene NOB1 appeared to conform with the sequence that would have been predicted to target PNO1, according to a blastn search. The authors have checked their original paper, and realize that the sequence of this shRNA was written incorrectly in the paper; the sequence for the shRNA targeting the gene NOB1 should have been written as: GCTTGCACTCACATACCAGTTCTCGAG- AACTGGTATGTGAGTGCAAGC. Furthermore, the published version of Fig. 5A on p. 2178 contained a pair of overlapping panels, such that data were apparently derived from the same original source even though they were intended to show the results from differently performed experiments. After having re­examined their original data, the authors have realized that a pair of data panels were inadvertently incorporated into this figure incorrectly; specifically, the centre panel of the Lv­shCon group and the right­hand panel of the Lv­shNOB1 group. The revised version of Fig. 5, showing the correct images for the abovementioned pair of data panels in Fig. 5A, is shown opposite. Note that these errors did not significantly affect either the results or the conclusions reported in this paper, and all the authors agree to this corrigendum. Furthermore, the authors thank the Editor of Molecular Medicine Reports for allowing them the opportunity to publish this corrigendum, and apologize to the readership for any inconvenience caused. [Molecular Medicine Reports 9: 2173­2179, 2014; DOI: 10.3892/mmr.2014.2119].

Performance optimization of biomimetic ant-nest silver nanoparticle coatings for antibacterial and osseointegration of implant surfaces.

Infection prevention and bone-implant integration remain major clinical challenges. Silver nanoparticle (AgNPs) bone-implant coatings have received extensive attention. Balancing the toxicity and antibacterial properties of AgNP coatings has become a significant problem. In this study, inspired by the structure of the ant-nest, a polyetherimide (PEI) coating with ant-nest structure was prepared, aiming to realize the structural modification of the AgNPs coating. AgNPs were loaded in the inner porous area of the PEI ant-nest coating, avoiding direct contact between AgNPs and cells. The nanopores on the surface of the coating ensured the orderly release of silver ions. SEM, FTIR, XPS, and XRD experiments confirmed that the PEI ant-nest coating was successfully prepared. Interestingly, in the PEI ant-nest coating, Ag+ showed a steady increase in the release trend within 28 days, and there was no early burst release phenomenon. In -vivo experiments showed a good control effect for local infection. In order to improve the osteogenic properties of the materials, 45S5 bioactive glasses (BG) were loaded to achieve further osseointegration. In general, this natural ant-nest-inspired surface modification coating for orthopedic prostheses provides a new strategy for balancing the antibacterial and toxic effects of AgNP coatings.

Injectable thermosensitive black phosphorus nanosheet- and doxorubicin-loaded hydrogel for synergistic bone tumor photothermal-chemotherapy and osteogenesis enhancement.

Removing residual tumor cells around bone tissue and promoting bone defect repair pose significant challenges after osteosarcoma resection. Herein, we designed an injectable multifunctional hydrogel therapeutic platform for synergistic photothermal chemotherapy of tumors and promoting osteogenesis. In this study, the black phosphorus nanosheets (BPNS) and doxorubicin (DOX) were encapsulated in an injectable chitosan-based hydrogel (BP/DOX/CS). The BP/DOX/CS hydrogel exhibited excellent photothermal effects under NIR irradiation due to incorporating BPNS. The prepared hydrogel has good drug-loading capacity and can continuously release DOX. In addition, K7M2-WT tumor cells are effectively eliminated under the combined effect of chemotherapy and photothermal stimulation. Furthermore, the BP/DOX/CS hydrogel has good biocompatibility and promotes osteogenic differentiation of MC3T3-E1 cells by releasing phosphate. In vivo results also confirmed that the BP/DOX/CS hydrogel can be injected at the tumor site to eliminate the tumor efficiently without systemic toxicity. This easily prepared multifunctional hydrogel with a synergistic photothermal-chemotherapy effect has excellent potential for clinically treating bone-related tumors.

Mild photothermal therapy assist in promoting bone repair: Related mechanism and materials.

Achieving precision treatment in bone tissue engineering (BTE) remains a challenge. Photothermal therapy (PTT), as a form of precision therapy, has been extensively investigated for its safety and efficacy. It has demonstrated significant potential in the treatment of orthopedic diseases such as bone tumors, postoperative infections and osteoarthritis. However, the high temperatures associated with PTT can lead to certain limitations and drawbacks. In recent years, researchers have explored the use of biomaterials for mild photothermal therapy (MPT), which offers a promising approach for addressing these limitations. This review provides a comprehensive overview of the mechanisms underlying MPT and presents a compilation of photothermal agents and their utilization strategies for bone tissue repair. Additionally, the paper discusses the future prospects of MPT-assisted bone tissue regeneration, aiming to provide insights and recommendations for optimizing material design in this field.