Retracted: Osteoprotegerin (OPG) Promotes Recruitment of Endothelial Progenitor Cells (EPCs) via CXCR4 Signaling Pathway to Improve Bone Defect Repair.

The Editors of Medical Science Monitor wish to inform you that the above manuscript has been retracted from publication due to concerns with the credibility and originality of the study, the manuscript content, and the Figure images. Reference: Rongfeng Zhang, Jianwei Liu, Shengpeng Yu, Dong Sun, Xiaohua Wang, Jingshu Fu, Jie Shen, Zhao Xie. Osteoprotegerin (OPG) Promotes Recruitment of Endothelial Progenitor Cells (EPCs) via CXCR4 Signaling Pathway to Improve Bone Defect Repair. Med Sci Monit, 2019; 25: 5572-5579. DOI: 10.12659/MSM.916838.

A Multi-Relational Graph Encoder Network for Fine-Grained Prediction of MiRNA-Disease Associations.

MicroRNAs (miRNAs) are critical in diagnosing and treating various diseases. Automatically demystifying the interdependent relationships between miRNAs and diseases has recently made remarkable progress, but their fine-grained interactive relationships still need to be explored. We propose a multi-relational graph encoder network for fine-grained prediction of miRNA-disease associations (MRFGMDA), which uses practical and current datasets to construct a multi-relational graph encoder network to predict disease-related miRNAs and their specific relationship types (upregulation, downregulation, or dysregulation). We evaluated MRFGMDA and found that it accurately predicted miRNA-disease associations, which could have far-reaching implications for clinical medical analysis, early diagnosis, prevention, and treatment. Case analyses, Kaplan-Meier survival analysis, expression difference analysis, and immune infiltration analysis further demonstrated the effectiveness and feasibility of MRFGMDA in uncovering potential disease-related miRNAs. Overall, our work represents a significant step toward improving the prediction of miRNA-disease associations using a fine-grained approach could lead to more accurate diagnosis and treatment of diseases.

CT-CBCT deformable registration using weakly-supervised artifact-suppression transfer learning network.

Objective.Computed tomography-cone-beam computed tomography (CT-CBCT) deformable registration has great potential in adaptive radiotherapy. It plays an important role in tumor tracking, secondary planning, accurate irradiation, and the protection of at-risk organs. Neural networks have been improving CT-CBCT deformable registration, and almost all registration algorithms based on neural networks rely on the gray values of both CT and CBCT. The gray value is a key factor in the loss function, parameter training, and final efficacy of the registration. Unfortunately, the scattering artifacts in CBCT affect the gray values of different pixels inconsistently. Therefore, the direct registration of the original CT-CBCT introduces artifact superposition loss.Approach. In this study, a histogram analysis method for the gray values was used. Based on an analysis of the gray value distribution characteristics of different regions in CT and CBCT, the degree of superposition of the artifact in the region of disinterest was found to be much higher than that in the region of interest. Moreover, the former was the main reason for artifact superposition loss. Consequently, a new weakly supervised two-stage transfer-learning network based on artifact suppression was proposed. The first stage was a pre-training network designed to suppress artifacts contained in the region of disinterest. The second stage was a convolutional neural network that registered the suppressed CBCT and CT.Main Results. Through a comparative test of the thoracic CT-CBCT deformable registration, whose data were collected from the Elekta XVI system, the rationality and accuracy after artifact suppression were confirmed to be significantly improved compared with the other algorithms without artifact suppression.Significance. This study proposed and verified a new deformable registration method with multi-stage neural networks, which can effectively suppress artifacts and further improve registration by incorporating a pre-training technique and an attention mechanism.

CasANGCL: pre-training and fine-tuning model based on cascaded attention network and graph contrastive learning for molecular property prediction.

MOTIVATION: Molecular property prediction is a significant requirement in AI-driven drug design and discovery, aiming to predict the molecular property information (e.g. toxicity) based on the mined biomolecular knowledge. Although graph neural networks have been proven powerful in predicting molecular property, unbalanced labeled data and poor generalization capability for new-synthesized molecules are always key issues that hinder further improvement of molecular encoding performance. RESULTS: We propose a novel self-supervised representation learning scheme based on a Cascaded Attention Network and Graph Contrastive Learning (CasANGCL). We design a new graph network variant, designated as cascaded attention network, to encode local-global molecular representations. We construct a two-stage contrast predictor framework to tackle the label imbalance problem of training molecular samples, which is an integrated end-to-end learning scheme. Moreover, we utilize the information-flow scheme for training our network, which explicitly captures the edge information in the node/graph representations and obtains more fine-grained knowledge. Our model achieves an 81.9% ROC-AUC average performance on 661 tasks from seven challenging benchmarks, showing better portability and generalizations. Further visualization studies indicate our model's better representation capacity and provide interpretability.

MVGCNMDA: Multi-view Graph Augmentation Convolutional Network for Uncovering Disease-Related Microbes.

MOTIVATION: Exploring the interrelationships between microbes and disease can help microbiologists make decisions and plan treatments. Predicting new microbe-disease associations currently relies on biological experiments and domain knowledge, which is time-consuming and inefficient. Automated algorithms are used to uncover the intrinsic link between microbes and disease. However, due to data noise and inadequate understanding of relevant biology, the efficient prediction of microbe-disease associations is still crucial. This study develops a multi-view graph augmentation convolutional network (MVGCNMDA) to predict potential disease-associated microbes. METHODS: First, we use two data augmentation methods, edge perturbation and node dropping, to remove the data noise in the preprocessing stage. Second, we calculate Gaussian interaction profile kernel similarity and cosine similarity. Therefore, the Graph Convolutional Network(GCN) can fully use multi-view features. Then, the multi-view features are fed into the multi-attention block to learn the weights of different features adaptively. Finally, the embedding results are obtained using a Convolutional Neural Network (CNN) combiner, and the matrix completion is used to predict the relationship between potential microbes and diseases. RESULTS: We test our model on the Human microbe-disease Association Database (HMDAD), Disbiome, and the Combined Dataset (Peryton and MicroPhenoDB). The area under PR curve (AUPR), area under ROC curve (AUC), F1 score, and RECALL value are calculated to evaluate the performance of the developed MVGCNMDA. The AUPR is 0.9440, AUC is 0.9428, F1 score is 0.9383, and RECALL value is 0.8858. The experiments show that our model can accurately predict potential microbe-disease associations compared with the state-of-the-art works on the global Leave-One-Out-Cross-Validation (LOOCV) and the fivefold Cross-Validation (fivefold CV). To further verify the effectiveness of the proposed graph data augmentation, we designed five different settings in the ablation study. Furthermore, we present two case studies that validate the prediction of the potential association between microbes and diseases by MVGCNMDA.

A knowledge-driven network for fine-grained relationship detection between miRNA and disease.

Increasing biological evidence indicated that microRNAs (miRNAs) play a vital role in exploring the pathogenesis of various human diseases (especially in tumors). Mining disease-related miRNAs is of great significance for the clinical diagnosis and treatment of diseases. Compared with the traditional experimental methods with the significant limitations of high cost, long cycle and small scale, the methods based on computing have the advantages of being cost-effective. However, although the current methods based on computational biology can accurately predict the correlation between miRNAs and disease, they can not predict the detailed association information at a fine level. We propose a knowledge-driven approach to the fine-grained prediction of disease-related miRNAs (KDFGMDA). Different from the previous methods, this method can finely predict the clear associations between miRNA and disease, such as upregulation, downregulation or dysregulation. Specifically, KDFGMDA extracts triple information from massive experimental data and existing datasets to construct a knowledge graph and then trains a depth graph representation learning model based on knowledge graph to complete fine-grained prediction tasks. Experimental results show that KDFGMDA can predict the relationship between miRNA and disease accurately, which is of far-reaching significance for medical clinical research and early diagnosis, prevention and treatment of diseases. Additionally, the results of case studies on three types of cancers, Kaplan-Meier survival analysis and expression difference analysis further provide the effectiveness and feasibility of KDFGMDA to detect potential candidate miRNAs. Availability: Our work can be downloaded from https://github.com/ShengPengYu/KDFGMDA.

The mixed layer modified radionuclide atmospheric diffusion based on Gaussian model.

Background: Atmospheric diffusion is often accompanied by complex meteorological conditions of inversion temperature. Methods: In response to the emergency needs for rapid consequence assessment of nuclear accidents under these complex meteorological conditions, a Gaussian diffusion-based model of radionuclide is developed with mixed layer modification. The inhibition effect of the inversion temperature on the diffusion of radionuclides is modified in the vertical direction. The intensity of the radionuclide source is modified by the decay constant. Results: The results indicate that the enhancement effect of the mixed layer on the concentration of radionuclides is reflected. The shorter the half-life of the radionuclide, the greater the effect of reducing the diffusion concentration. The Kincaid dataset validation in the Model Validation Kit (MVK) shows that, compared to the non-modified model, predictions of the modified model have an enhancement effect beyond 5 km, modulating the prediction values to be closer to the observation values. Conclusions: This development is consistent with the modification effects of the mixed layer. The statistical indicators show that the criteria of the modified model meet the criteria of the recommended model.

Radiological and clinical outcomes using induced membrane technique combined with bone marrow concentrate in the treatment of chronic osteomyelitis of immature patients.

AIMS: Treatment of chronic osteomyelitis (COM) for young patients remains a challenge. Large bone deficiencies secondary to COM can be treated using induced membrane technique (IMT). However, it is unclear which type of bone graft is optimal. The goal of the study was to determine the clinical effectiveness of bone marrow concentrator modified allograft (BMCA) versus bone marrow aspirate mixed allograft (BMAA) for children with COM of long bones. METHODS: Between January 2013 and December 2017, 26 young patients with COM were enrolled. Different bone grafts were applied to repair bone defects secondary to IMT procedure for infection eradication. Group BMCA was administered BMCA while Group BMAA was given BMAA. The results of this case-control study were retrospectively analyzed. RESULTS: Patient infection in both groups was eradicated after IMT surgery. As for reconstruction surgery, no substantial changes in the operative period (p = 0.852), intraoperative blood loss (p = 0.573), or length of hospital stay (p = 0.362) were found between the two groups. All patients were monitored for 12 to 60 months. The median time to bone healing was 4.0 months (interquartile range (IQR) 3.0 to 5.0; range 3 to 7) and 5.0 months (IQR 4.0 to 7.0; range 3 to 10) in Groups BMCA and BMAA, respectively. The time to heal in Group BMCA versus Group BMAA was substantially lower (p = 0.024). CONCLUSION: IMT with BMCA or BMAA may attain healing in large bone defects secondary to COM in children. The bone healing time was significantly shorter for BMCA, indicating that this could be considered as a new strategy for bone defect after COM treatment. Cite this article: Bone Joint Res 2021;10(1):31-40.

Investigation of biofilm production and its association with genetic and phenotypic characteristics of OM (osteomyelitis) and non-OM orthopedic Staphylococcus aureus.

BACKGROUND: Staphylococcus aureus is a primary pathogen of orthopedic infections. By mediating antimicrobial resistance, S. aureus biofilm plays an important role in the recalcitrance of orthopedic infections, especially for the intractable osteomyelitis (OM). This study investigated the relationship between biofilm production and various genetic or phenotypic characteristics among orthopedic S. aureus strains. METHODS: A total of 137 orthopedic S. aureus isolates were enrolled and divided into OM and non-OM groups. Biofilm production was evaluated using the crystal violet assay. Genetic and phenotypic characteristics including MRSA identification, MLST and spa typing, carriage of virulence genes, drug resistance, and patients' inflammatory responses indicators were characterized. The relationship between biofilm production and above-mentioned features was respectively analyzed among all isolates and compared between OM and non-OM isolates. RESULTS: Biofilm production presented no significant difference between OM (including 9 MRSA isolates) and non-OM (including 21 MRSA isolates) strains. We found that ST88, t377 and ST630-MSSA-t377 strains produced very strong biofilms, while MLST types of ST15, ST25, ST398, ST5, ST59 and spa types of t002, t2325, t437 tended to produce weaker biofilms. Strains with the following profiles produced stronger biofilms: fib(+)-hlgv(+)-lukED(+)-sei(-)-sem(-)-seo(-) for all isolates, sei(-)-sem(-)-seo(-) for OM isolates, and cna (+)-fib (+)-hlgv (+)-lukED (+)-seb(-)-sed(-) for non-OM isolates. In addition, not any single drug resistance was found to be related to biofilm production. We also observed that, among OM patients, strains with stronger biofilms caused weaker inflammatory responses. CONCLUSION: Some genetic or phenotypic characteristics of orthopedic strains were associated with biofilm production, and this association could be different among OM and non-OM strains. The results are of great significance for better understanding, evaluating and managing different kinds of biofilm-associated orthopedic infections, and provide potential targets for biofilm clearance.

An antibiotic cement-coated locking plate as a temporary fixation for treatment of infected bone defects: a new method of stabilization.

BACKGROUND: The induced membrane technique has achieved good clinical results in the treatment of infected bone defects, and external fixation is the main method, but it causes inconvenience and complications in patients. In this study, our objective was to investigate the outcomes of using an antibiotic cement-coated locking plate as a temporary internal fixation in the first stage of the surgical induced membrane technique for treating extremities with infected bone defects. METHODS: We retrospectively analysed patients with lower extremity infected bone defects in our department between January 2013 and December 2017. All patients were treated with the induced membrane technique. In the first stage, the defects were stabilized with an antibiotic cement-coated locking plate as a temporary fixation after debridement, and polymethyl methacrylate cement was implanted to induce the formation of a membrane. In the second stage, bone grafting rebuilt the bone defects after infection control, and the temporary fixation was changed to a stronger fixation. RESULTS: A total of 183 patients were enrolled, with an average follow-up duration of 32.0 (12-66) months. There were 154 males and 29 females with an average age of 42.8 (10-68) years. The infection sites included 81 femurs, 100 tibias and 2 fibulas. After the first stage of treatment (infection control), 16 (8.7%) patients had recurrence of infection. In terms of the incidence of complications, 4 patients had poor wound healing, 2 patients had fixation failure and 1 patient had femoral fracture due to a fall. After the second stage of treatment (bone reconstruction), there were 24 (13.1%) recurrences of infection, with a mean time of 9.9 months (range 0.5 to 36). Among them, 18 patients underwent bone grafting after re-debridement, 6 received permanent placement of antibiotic bone cement after debridement and 2 patients refused further treatment and chose amputation. Bone healing was achieved in 175 (95.9%) patients at the last follow-up, and the average time to bone union was 5.4 (4-12) months. CONCLUSIONS: Antibiotic cement-coated locking plates have good clinical effects in the control of bone infection, but attention must be paid to the possible difficulty of skin coverage when applied in calves.

Investigating clinical characteristics and prognostic factors in patients with chronic osteomyelitis of humerus.

BACKGROUND: Chronic osteomyelitis in the humerus, which has complex neuroanatomy and a good soft tissue envelope, represents a unique clinical challenge. However, there are relatively few related studies in the literature. This article retrospectively reviewed a large case series with the aims of sharing our management experiences and further determining factors associated with the outcomes. METHODS: Twenty-eight consecutive adult patients with a mean age of 36 years were identified by reviewing the osteomyelitis database of our clinic centre. The database was used to prospectively identify all osteomyelitis cases between 2013 and 2017, and all data then was retrospectively analysed. RESULTS: The mean follow-up period was 35 months (range 24-60). The aetiology was trauma in 43% (12) of the patients and haematogenous in 57% (16) of the patients, and Staphylococcus aureus was a solitary agent in 50% (14) of the patients. Host-type (Cierny's classification) was IA in 8, IIIB in 11 and IVB in 9 patients. All patients required debridement followed by the placement of a temporary antibiotic-impregnated cement spacer (rod). Seventeen patients received a cement-coated plate for internal fixation after debridement, and 13 patients needed bone grafts when the spacer was staged removed. All patients attained an infection-free bone healing state at the final follow-up. The final average DASH (disabilities of the arm, shoulder and hand) score was 18.14 ± 5.39, while 6 patients (two developed traumatic olecranarthritis, four developed radial nerve injuries) showed the lowest levels of limb function (p = 0.000) and were unemployed. Three patients (type I; significant difference between type I versus type III and type IV patients, p < 0.05) experienced recurrence after debridement and underwent a second revision, which was not related to the bone graft (p = 0.226) or plate fixation (p = 0.050). CONCLUSIONS: Humeral chronic osteomyelitis can be treated with general surgery and anti-infective therapy; medullary (type I) infection presents a challenge, and the antibiotic-coated cement plate provides favourable fixation without increasing recurrence of infections. Clinicians should be aware of potential iatrogenic nerve injuries when treating these patients with complicated cases, and an experienced surgeon may improve the outcome.

Osteoprotegerin (OPG) Promotes Recruitment of Endothelial Progenitor Cells (EPCs) via CXCR4 Signaling Pathway to Improve Bone Defect Repair.

BACKGROUND The aim of this study was to investigate the effect of using osteoprotegerin (OPG) to treat bone defects mediated by endothelial progenitor cell (EPC) recruitment and migration through the CXCR4 signaling pathway. MATERIAL AND METHODS The EPCs extracted from human peripheral blood were cultured in vitro and the expression of CXCR4 and its downstream p-AKT was monitored by the Western blot analysis after OPG treatment. Using the scratch wound healing test and Transwell assay, we assessed the variables influencing the effect of OPG on EPCs after pre-treatment with CXCR4 blocker (AMD3100) and PI3K blocker (Ly294002). After 4 weeks, the bone defect repair condition was estimated via micro-CT and staining with HE and Masson trichrome. Then, immunofluorescence staining was performed to assess angiogenesis in bone defects, while the expression of EPC marker and vascular endothelial growth factor receptor 2 (VEGFR2) was detected by immunohistochemical staining. RESULTS The EPCs treated with OPG had increased levels of CXCR4 and p-AKT. Moreover, the difference in EPC levels among groups in the scratch wound healing experiment and migration experiment indicated that the OPG treatment promoted cell migration and AMD3100 and LY294002 inhibited the function of OPG. In addition, OPG promoted angiogenesis and repair of bone defect in rats, and these effects were abolished by AMD3100 and LY294002 administration. CONCLUSIONS OPG enhanced the proliferation and migration of EPCs through the CXCR4 pathway and promoted angiogenesis and bone formation at bone defect sites.

Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs.

Increasing evidence has indicated that microRNAs(miRNAs) play vital roles in various pathological processes and thus are closely related with many complex human diseases. The identification of potential disease-related miRNAs offers new opportunities to understand disease etiology and pathogenesis. Although there have been numerous computational methods proposed to predict reliable miRNA-disease associations, they suffer from various limitations that affect the prediction accuracy and their applicability. In this study, we develop a novel method to discover disease-related candidate miRNAs based on Adaptive Multi-View Multi-Label learning(AMVML). Specifically, considering the inherent noise existed in the current dataset, we propose to learn a new affinity graph adaptively for both diseases and miRNAs from multiple similarity profiles. We then simultaneously update the miRNA-disease association predicted from both spaces based on multi-label learning. In particular, we prove the convergence of AMVML theoretically and the corresponding analysis indicates that it has a fast convergence rate. To comprehensively illustrate the prediction performance of our method, we compared AMVML with four state-of-the-art methods under different validation frameworks. As a result, our method achieved comparable performance under various evaluation metrics, which suggests that our method is capable of discovering greater number of true miRNA-disease associations. The case study conducted on thyroid neoplasms further identified a potential diagnostic biomarker. Together, the experimental results confirms the utility of our method and we anticipate that our method could serve as a reliable and efficient tool for uncovering novel disease-related miRNAs.

MCLPMDA: A novel method for miRNA-disease association prediction based on matrix completion and label propagation.

MiRNAs are a class of small non-coding RNAs that are involved in the development and progression of various complex diseases. Great efforts have been made to discover potential associations between miRNAs and diseases recently. As experimental methods are in general expensive and time-consuming, a large number of computational models have been developed to effectively predict reliable disease-related miRNAs. However, the inherent noise and incompleteness in the existing biological datasets have inevitably limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA-disease association prediction based on matrix completion and label propagation. Specifically, our method first reconstructs a new miRNA/disease similarity matrix by matrix completion algorithm based on known experimentally verified miRNA-disease associations and then utilizes the label propagation algorithm to reliably predict disease-related miRNAs. As a result, MCLPMDA achieved comparable performance under different evaluation metrics and was capable of discovering greater number of true miRNA-disease associations. Moreover, case study conducted on Breast Neoplasms further confirmed the prediction reliability of the proposed method. Taken together, the experimental results clearly demonstrated that MCLPMDA can serve as an effective and reliable tool for miRNA-disease association prediction.

A novel semi-supervised model for miRNA-disease association prediction based on [Formula: see text]-norm graph.

BACKGROUND: Identification of miRNA-disease associations has attracted much attention recently due to the functional roles of miRNAs implicated in various biological and pathological processes. Great efforts have been made to discover the potential associations between miRNAs and diseases both experimentally and computationally. Although reliable, the experimental methods are in general time-consuming and labor-intensive. In comparison, computational methods are more efficient and applicable to large-scale datasets. METHODS: In this paper, we propose a novel semi-supervised model to predict miRNA-disease associations via [Formula: see text]-norm graph. Specifically, we first recalculate the miRNA functional similarities as well as the disease semantic similarities based on the latest version of MeSH descriptors and HMDD. We then update the similarity matrices and association matrix iteratively in both miRNA space and disease space. The optimized association matrices from each space are combined together as the final output. RESULTS: Compared with four state-of-the-art prediction methods, our method achieved favorable performance with AUCs of 0.943 and 0.946 in both global LOOCV and local LOOCV, respectively. In addition, we carried out three types of case studies on five common human diseases, and most of the top 50 predicted miRNAs were confirmed to be associated with the investigated diseases by four databases dbDEMC, PheomiR, miR2Disease and miRwayDB. Specifically, our results provided potential evidence that miRNAs within the same family or cluster were likely to play functional roles together in given diseases. CONCLUSIONS: Taken together, the experimental results clearly demonstrated the utility of the proposed method. We anticipated that our method could serve as a reliable and efficient tool for miRNA-disease association prediction.

GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods.

Recently, increasing studies have shown that miRNAs are involved in the development and progression of various complex diseases. Consequently, predicting potential miRNA-disease associations makes an important contribution to understanding the pathogenesis of diseases, developing new drugs as well as designing individualized diagnostic and therapeutic approaches for different human diseases. Nonetheless, the inherent noise and incompleteness in the existing biological datasets have limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA-disease association prediction based on global linear neighborhoods (GLNMDA). Specifically, our method obtains a new miRNA/disease similarity matrix by linearly reconstructing each miRNA/disease according to the known experimentally verified miRNA-disease associations. We then adopt label propagation to infer the potential associations between miRNAs and diseases. As a result, GLNMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.867 and 0.929, respectively) and 5-fold cross validation (average AUC of 0.926). Case studies on five common human diseases further confirmed the utility of our method in discovering latent miRNA-disease pairs. Taken together, GLNMDA could serve as a reliable computational tool for miRNA-disease association prediction.

Clinical Staphylococcus argenteus Develops to Small Colony Variants to Promote Persistent Infection.

Staphylococcus argenteus is a novel staphylococcal species (also considered as a part of Staphylococcus aureus complex) that is infrequently reported on, and clinical S. argenteus infections are largely unstudied. Here, we report a persistent and recurrent hip joint infection case in which a S. argenteus strain and its small colony variants (SCVs) strain were successively isolated. We present features of the two S. argenteus strains and case details of their pathogenicity, explore factors that induce S. argenteus SCVs formation in the course of anti-infection therapy, and reveal potential genetic mechanisms for S. argenteus SCVs formation. S. argenteus strains were identified using phenotypic and genotypic methods. The S. argenteus strain XNO62 and SCV strain XNO106 were characterized using different models. S. argenteus SCVs were induced by the administration of amikacin and by chronic infection course based on the clinical case details. The genomes of both strains were sequenced and aligned in a pair-wise fashion using Mauve. The case details gave us important insights on the characteristics and therapeutic strategies for infections caused by S. argenteus and its SCVs. We found that strain XNO62 and SCV strain XNO106 are genetically-related sequential clones, the SCV strain exhibits reduced virulence but enhanced intracellular persistence compared to strain XNO62, thus promoting persistent infection. The induction experiments for S. argenteus SCVs demonstrated that high concentrations of amikacin greatly induce S. argenteus XNO62 to form SCVs, while a chronic infection of S. argenteus XNO62 slightly induces SCVs formation. Potential genetic mechanisms for S. argenteus SCVs formation were revealed and discussed based on genomic alignments. In conclusion, we report the first case of infection caused by S. argenteus and its SCVs strain. More attention should be paid to infections caused by S. argenteus and its SCVs, as they constitute a challenge to current therapeutic strategies. The problem of S. argenteus SCVs should be noticed, in particular when amikacin is used or in the case of a chronic S. argenteus infection.

Current data on extremities chronic osteomyelitis in southwest China: epidemiology, microbiology and therapeutic consequences.

The current study was designed to explore the epidemiology of extremities chronic osteomyelitis, its prognosis and the complications of the treatment methods being used in southwest China. The data from osteomyelitis patients treated at the Department of Orthopaedics, Southwest Hospital, China between May 2011 and September 2016 were collected and analysed. The study comprised 503 admitted patients, of which 416 males and 87 were females, with an average age of 40.15 ± 5.64 years. Approximately 356 cases were followed for more than 18 months; the average bone union time was 6.24 ± 0.76 months in 94.1% (335) patients, and infections were almost controlled in 93.8% patients. The rate of infection control with the induced membrane technique was higher than with the I-stage free bone graft. Iliac infection was the main complication of the induced membrane technique, and impaired joint activity was the main complication of I-stage free bone grafts. In southwest China, the incidence of haematogenous osteomyelitis, caused mainly by Staphylococcus aureus, remains very high. The speed of bone defect repair and the rate of infection control with the induced membrane technique were superior to those of I-stage free bone grafts. Internal fixation should be given priority because it offers reduced complications with no increase in the recurrence of infection.

Induced membrane technique: Advances in the management of bone defects.

Management of bone defects caused by trauma, osteomyelitis, and tumors is challenging, with many controversies over the optimal reconstruction method. Masquelet discovered induced membrane in management of large diaphyseal defects accidentally, and developed this technique with a concept of induced membrane. Induced membrane technique holds great potential for the reconstruction of bone defects, alternatively to manage this clinical challenge quiet easily. Induced membrane has unique structural characteristics and biological properties, which render this technique has an advantage of the time to bone healing is relatively independent of the length of bone defect. Herein, we reviewed the latest advances made in induced membrane technique and highlighted the concept of induced membrane in the management of bone defects.

Two stage management of Cierny-Mader type IV chronic osteomyelitis of the long bones.

INTRODUCTION: Cierny-Mader (C-M) type IV chronic osteomyelitis represents a complex clinical challenge with permeation of extensive bone and soft tissue involvement. Aggressive debridement through viable tissue margin includes en bloc resection improves the odds of eradication of infection, which creates large bone and soft tissue loss in treating this type of osteomyelitis. The potentially large defects increase reconstruction problems with traditional reconstruction technique. The newly staged induced membrane technique presents length-independent, potential as an alternative reconstruction method for segmental bone defects due to type IV chronic osteomyelitis. The purpose of this study was to assess the result and related factors of C-M type IV chronic osteomyelitis treated with staged methods of aggressive debridement and induced membrane technique. METHODS: From January 2012 to January 2014, 36 consecutive adult patients of C-M type IV chronic osteomyelitis were treated by this staged method in our clinical center with a minimum of 2-years follow-up. The clinical and imaging results were retrospectively analyzed. RESULTS: Five patients had a second debridement and eight needed a local flap transfer to cover the wound in the first stage. Patients formed a mean of 5.5cm (range: 2-10.9) segmental bone defect; Sixteen patients had autograft and twenty had autograft mixed allograft in the second stage. The mean follow-up time was 29.5 months (range: 24-45). No patients required amputation. Bone union was achieved in all patients. Clinical eradication of osteomyelitis was achieved in 35 (97%) patients, 35 (97%) patients were able to walk independently, and 31patients (86%) returned to work. Patients returned to a mean of 82% (46.3%-100%) lower extremity function. Bone union time was not dependent on the length of bone defect, but associated with the infection site (p=0.005) and age (p=0.005). CONCLUSIONS: Staged methods of aggressive debridement and induced membrane technique seems to be a simple, reliable and effective for the treatment of C-M type IV chronic osteomyelitis. Advanced age and poor soft tissue envelope may have adverse affects and are relative contraindications. The combined assessment and management of such patients with a plastic surgeon are advocated.