Mid-term outcomes of microfragmented adipose tissue plus arthroscopic surgery for knee osteoarthritis: A randomized, active-control, multicenter clinical trial.

BACKGROUND: Osteoarthritis (OA) is the most prevalent form of degenerative whole-joint disease. Before the final option of knee replacement, arthroscopic surgery was the most widely used joint-preserving surgical treatment. Emerging regenerative therapies, such as those involving platelet-rich plasma, mesenchymal stem cells, and microfragmented adipose tissue (MFAT), have been pushed to the forefront of treatment to prevent the progression of OA. Currently, MFAT has been successfully applied to treat different types of orthopedic diseases. AIM: To assess the efficacy and safety of MFAT with arthroscopic surgery in patients with knee OA (KOA). METHODS: A randomized, multicenter study was conducted between June 2017 and November 2022 in 10 hospitals in Zhejiang, China. Overall, 302 patients diagnosed with KOA (Kellgren-Lawrence grades 2-3) were randomized to the MFAT group (n = 151, were administered MFAT following arthroscopic surgery), or the control group (n = 151, were administered hyaluronic acid following arthroscopic surgery). The study outcomes were changes in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score, the visual analog scale (VAS) score, the Lequesne index score, the Whole-Organ Magnetic Resonance Imaging Score (WORMS), and safety over a 24-mo period from baseline. RESULTS: The changes in the WOMAC score (including the three subscale scores), VAS pain score, and Lequesne index score at the 24-mo mark were significantly different in the MFAT and control groups, as well as when comparing values at the posttreatment visit and those at baseline (P < 0.001). The MFAT group consistently demonstrated significant decreases in the WOMAC pain scores and VAS scores at all follow-ups compared to the control group (P < 0.05). Furthermore, the WOMAC stiffness score, WOMAC function score, and Lequesne index score differed significantly between the groups at 12 and 24 mo (P < 0.05). However, no significant between-group differences were observed in the WORMS at 24 mo (P = 0.367). No serious adverse events occurred in both groups. CONCLUSION: The MFAT injection combined with arthroscopic surgery treatment group showed better mid-term clinical outcomes compared to the control group, suggesting its efficacy as a therapeutic approach for patients with KOA.

Population pharmacokinetics and clinical outcomes of polymyxin B in paediatric patients with multidrug-resistant Gram-negative bacterial infections.

BACKGROUND: Current polymyxin B dosing in children relies on scant data. OBJECTIVES: To build a population pharmacokinetic (PK) model for polymyxin B in paediatric patients and assess the likely appropriateness of different dosages. METHODS: A total of 19 paediatric patients were enrolled to receive intravenous polymyxin B (1.33-2.53 mg/kg/day), and the median age was 12.5 (range 3.2-17.8) years. Serial plasma samples were collected at steady-state and modelled by population PK analysis. Clinical efficacy and nephrotoxicity of polymyxin B treatment were also assessed. RESULTS: PK data were adequately described by a two-compartment model with first-order elimination, and weight was a significant covariate of polymyxin B clearance. Clinical success occurred in 14 of 19 patients (73.7%) and only one patient developed acute kidney injury. The 28 day mortality was 10.5% (2/19). The steady-state polymyxin B exposure was 36.97 ±â€Š9.84 mg·h/L, lower than the therapeutic exposure of 50-100 mg·h/L. With the AUC24h/MIC target of 50, the dosage of 1.5-3.0 mg/kg/day had a probability of target attainments over 90% when MICs were <0.5 mg/L. CONCLUSIONS: Dose adjustment of polymyxin B needs to consider the MIC of infecting pathogens. Current polymyxin B dosing for paediatric patients may be acceptable when MICs are <0.5 mg/L.

Comparison between zero-profile and cage plate devices in the treatment of single-level cervical spondylopathy.

OBJECTIVE: To compare the new zero-profile (Zero-P) fusion cage with regular cage and plate (CP) in the treatment of anterior cervical single-level cervical degenerative diseases. METHODS: Patients operated from January 2013 to August 2018 were enrolled. They were divided into the Zero-P group (n = 74 cases) and CP groups (n = 116 cases). Follow-up assessment was at 1, 3, 12, and 24 months after surgery, including the incidence of dysphagia, visual analogue scale (VAS) score, Japanese orthopaedic association (JOA)score, C2-C7 Cobb angle, intervertebral disc height (IDH) and adjacent joint degeneration. RESULTS: The operation time and blood loss of patients in Zero-P group were less than those in the CP group, and there was no difference in hospitalization time. All 190 patients were followed up for 24 to 72 months, with an average of 35.29 months. In terms of clinical outcomes, vas and JOA scores of the two groups were significantly improved at one month and the last follow-up. The incidence of dysphagia in the Zero-P group was lower than that in the CP group. On radiological effects, Cobb angle and IDH showed significant correction in both groups, but the degeneration rate of adjacent joints in the Zero-P group was lower than the CP group. CONCLUSIONS: In ACDF, the clinical and radiological results of Zero-P and CP devices are satisfactory, but Zero-P cage may be superior in operation time, blood loss, the incidence of dysphagia and adjacent joint degeneration.

Development of a Machine Learning-Based Autophagy-Related lncRNA Signature to Improve Prognosis Prediction in Osteosarcoma Patients.

BACKGROUND: Osteosarcoma is a frequent bone malignancy in children and young adults. Despite the availability of some prognostic biomarkers, most of them fail to accurately predict prognosis in osteosarcoma patients. In this study, we used bioinformatics tools and machine learning algorithms to establish an autophagy-related long non-coding RNA (lncRNA) signature to predict the prognosis of osteosarcoma patients. METHODS: We obtained expression and clinical data from osteosarcoma patients in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. We acquired an autophagy gene list from the Human Autophagy Database (HADb) and identified autophagy-related lncRNAs by co-expression analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the autophagy-related lncRNAs were conducted. Univariate and multivariate Cox regression analyses were performed to assess the prognostic value of the autophagy-related lncRNA signature and validate the relationship between the signature and osteosarcoma patient survival in an independent cohort. We also investigated the relationship between the signature and immune cell infiltration. RESULTS: We initially identified 69 autophagy-related lncRNAs, 13 of which were significant predictors of overall survival in osteosarcoma patients. Kaplan-Meier analyses revealed that the 13 autophagy-related lncRNAs could stratify patients based on their outcomes. Receiver operating characteristic curve analyses confirmed the superior prognostic value of the lncRNA signature compared to clinically used prognostic biomarkers. Importantly, the autophagy-related lncRNA signature predicted patient prognosis independently of clinicopathological characteristics. Furthermore, we found that the expression levels of the autophagy-related lncRNA signature were significantly associated with the infiltration levels of different immune cell subsets, including T cells, NK cells, and dendritic cells. CONCLUSION: The autophagy-related lncRNA signature established here is an independent and robust predictor of osteosarcoma patient survival. Our findings also suggest that the expression of these 13 autophagy-related lncRNAs may promote osteosarcoma progression by regulating immune cell infiltration in the tumor microenvironment.

An Immune-Related Long Non-Coding RNA Signature to Predict the Prognosis of Ewing's Sarcoma Based on a Machine Learning Iterative Lasso Regression.

The aim of this study was to construct a new immune-associated long non-coding RNA (lncRNA) signature to predict the prognosis of Ewing sarcoma (ES) and explore its molecular mechanisms. We downloaded transcriptome and clinical prognosis data from the Gene Expression Omnibus (GSE17679, which included 88 ES samples and 18 matched normal skeletal muscle samples), and used it as a training set to identify immune-related lncRNAs with different expression levels in ES. Univariable Cox regression was used to screen immune-related lncRNAs related to ES prognosis, and an immune-related lncRNA signature was constructed based on machine learning iterative lasso regression. An external verification set was used to confirm the predictive ability of the signature. Clinical feature subgroup analysis was used to explore whether the signature was an independent prognostic factor. In addition, CIBERSORT was used to explore immune cell infiltration in the high- and low-risk groups, and to analyze the correlations between the lncRNA signature and immune cell levels. Gene set enrichment and variation analyses were used to explore the possible regulatory mechanisms of the immune-related lncRNAs in ES. We also analyzed the expression of 17 common immunotherapy targets in the high- and low-risk groups to identify any that may be regulated by immune-related lncRNAs. We screened 35 immune-related lncRNAs by univariate Cox regression. Based on this, an immune-related 11-lncRNA signature was generated by machine learning iterative lasso regression. Analysis of the external validation set confirmed its high predictive ability. DPP10 antisense RNA 3 was negatively correlated with resting dendritic cell, neutrophil, and γδ T cell infiltration, and long intergenic non-protein coding RNA 1398 was positively correlated with resting dendritic cells and M2 macrophages. These lncRNAs may affect ES prognosis by regulating GSE17721_CTRL_VS_PAM3CSK4_12H_BMDC_UP, GSE2770_IL4_ACT_VS_ACT_CD4_TCELL_48H_UP, GSE29615_CTRL_VS_DAY3_ LAIV_IFLU_VACCINE_PBMC_UP, complement signaling, interleukin 2-signal transducer and activator of transcription 5 signaling, and protein secretion. The immune-related 11-lncRNA signature may also have regulatory effects on the immunotherapy targets CD40 molecule, CD70 molecule, and CD276 molecule. In conclusion, we constructed a new immune-related 11-lncRNA signature that can stratify the prognoses of patients with ES.

Screening of osteoarthritis diagnostic markers based on immune-related genes and immune infiltration.

Osteoarthritis (OA) is a chronic degenerative disease of the bone and joints. Immune-related genes and immune cell infiltration are important in OA development. We analyzed immune-related genes and immune infiltrates to identify OA diagnostic markers. The datasets GSE51588, GSE55235, GSE55457, GSE82107, and GSE114007 were downloaded from the Gene Expression Omnibus database. First, R software was used to identify differentially expressed genes (DEGs) and differentially expressed immune-related genes (DEIRGs), and functional correlation analysis was conducted. Second, CIBERSORT was used to evaluate infiltration of immune cells in OA tissue. Finally, the least absolute shrinkage and selection operator logistic regression algorithm and support vector machine-recurrent feature elimination algorithm were used to screen and verify diagnostic markers of OA. A total of 711 DEGs and 270 DEIRGs were identified in this study. Functional enrichment analysis showed that the DEGs and DEIRGs are closely related to cellular calcium ion homeostasis, ion channel complexes, chemokine signaling pathways, and JAK-STAT signaling pathways. Differential analysis of immune cell infiltration showed that M1 macrophage infiltration was increased but that mast cell and neutrophil infiltration were decreased in OA samples. The machine learning algorithm cross-identified 15 biomarkers (BTC, PSMD8, TLR3, IL7, APOD, CIITA, IFIH1, CDC42, FGF9, TNFAIP3, CX3CR1, ERAP2, SEMA3D, MPO, and plasma cells). According to pass validation, all 15 biomarkers had high diagnostic efficacy (AUC > 0.7), and the diagnostic efficiency was higher when the 15 biomarkers were fitted into one variable (AUC = 0.758). We developed 15 biomarkers for OA diagnosis. The findings provide a new understanding of the molecular mechanism of OA from the perspective of immunology.

An immune-related gene signature for determining Ewing sarcoma prognosis based on machine learning.

PURPOSE: Ewing sarcoma (ES) is one of the most common malignant bone tumors in children and adolescents. The immune microenvironment plays an important role in the development of ES. Here, we developed an optimal signature for determining ES patient prognosis based on immune-related genes (IRGs). METHODS: We analyzed the ES gene expression profile dataset, GSE17679, from the GEO database and extracted differential expressed IRGs (DEIRGs). Then, we conducted functional correlation and protein-protein interaction (PPI) analyses of the DEIRGs and used the machine learning algorithm-iterative Lasso Cox regression analysis to build an optimal DEIRG signature. In addition, we applied ES samples from the ICGC database to test the optimal gene signature. We performed univariate and multivariate Cox regressions on clinicopathological characteristics and optimal gene signature to evaluate whether signature is an important prognostic factor. Finally, we calculated the infiltration of 24 immune cells in ES using the ssGSEA algorithm, and analyzed the correlation between the DEIRGs in the optimal gene signature and immune cells. RESULTS: A total of 249 DEIRGs were screened and an 11-gene signature with the strongest correlation with patient prognoses was analyzed using a machine learning algorithm. The 11-gene signature also had a high prognostic value in the ES external verification set. Univariate and multivariate Cox regression analyses showed that 11-gene signature is an independent prognostic factor. We found that macrophages and cytotoxic, CD8 T, NK, mast, B, NK CD56bright, TEM, TCM, and Th2 cells were significantly related to patient prognoses; the infiltration of cytotoxic and CD8 T cells in ES was significantly different. By correlating prognostic biomarkers with immune cell infiltration, we found that FABP4 and macrophages, and NDRG1 and Th2 cells had the strongest correlation. CONCLUSION: Overall, the IRG-related 11-gene signature can be used as a reliable ES prognostic biomarker and can provide guidance for personalized ES therapy.

Development of a novel immune-related genes prognostic signature for osteosarcoma.

Immune-related genes (IRGs) are responsible for osteosarcoma (OS) initiation and development. We aimed to develop an optimal IRGs-based signature to assess of OS prognosis. Sample gene expression profiles and clinical information were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases. IRGs were obtained from the ImmPort database. R software was used to screen differentially expressed IRGs (DEIRGs) and functional correlation analysis. DEIRGs were analyzed by univariate Cox regression and iterative LASSO Cox regression analysis to develop an optimal prognostic signature, and the signature was further verified by independent cohort (GSE39055) and clinical correlation analysis. The analyses yielded 604 DEIRGs and 10 hub IRGs. A prognostic signature consisting of 13 IRGs was constructed, which strikingly correlated with OS overall survival and distant metastasis (p < 0.05, p < 0.01), and clinical subgroup showed that the signature's prognostic ability was independent of clinicopathological factors. Univariate and multivariate Cox regression analyses also supported its prognostic value. In conclusion, we developed an IRGs signature that is a prognostic indicator in OS patients, and the signature might serve as potential prognostic indicator to identify outcome of OS and facilitate personalized management of the high-risk patients.

GRB10 and E2F3 as Diagnostic Markers of Osteoarthritis and Their Correlation with Immune Infiltration.

This study aimed to find potential diagnostic markers for osteoarthritis (OA) and analyze the role of immune cells infiltration in this pathology. We used OA datasets from the Gene Expression Omnibus database. First, R software was used to identify differentially expressed genes (DEGs) and perform functional correlation analysis. Then least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination algorithms were used to screen and verify the diagnostic markers of OA. Finally, CIBERSORT was used to evaluate the infiltration of immune cells in OA tissues, and the correlation between diagnostic markers and infiltrating immune cells was analyzed. A total of 458 DEGs were screened in this study. GRB10 and E2F3 (AUC = 0.962) were identified as diagnostic markers of OA. Immune cell infiltration analysis found that resting mast cells, T regulatory cells, CD4 memory resting T cells, activated NK cells, and eosinophils may be involved in the OA process. In addition, GRB10 was correlated with NK resting cells, naive CD4 + T cells, and M1 macrophages, while E2F3 was correlated with resting mast cells. In conclusion, GRB10 and E2F3 can be used as diagnostic markers of osteoarthritis, and immune cell infiltration plays an important role in the occurrence and progression of OA.

[Role of immunologic abnormality in recurrent parotitis in children].

OBJECTIVE: To investigate the relationship between immune function and the recurrent parotitis (RP) for children. METHODS: The children diagnosed as RP were divided into two groups: aged under 6y and over 6y and the immune function were measured and compared with that of normal children. RESULTS: For RP children the ratio of CD4+ T cell in over 6y group was significantly lower than that in under 6y group (P<0.05), while IgG value in over 6y group was higher than that in under 6y group (P<0.05). Compared with normal children, RP children in under 6y group had higher CD8+ T cell ratio and IgG, IgE, IgA and C3 value (P<0.01) and lower CD4+ T cell ratio (P<0.01), while RP children over 6y group, they had higher CD8+ T cell ratio, IgE value (P<0.01) and C3 (P<0.05), lower CD4

[CT assist in surgical design for the posterior pharyngeal flap].

OBJECTIVE: To establish a method for assist design of the posterior pharyngeal flap (PPF). METHODS: The width, length of velopharyngeal space in repose and phonation of "i" in 26 cases with VPI were measured with CT. The width and length of PPF needed, the location of the pedicle and the contact position with the soft palate were analyzed. RESULTS: The width of velopharyngeal space in repose ranged from 15.10 to 37.25 mm, with the average of 27.01 mm, the length was 12.54 to 30.39 mm, with the average of 22.10 mm. The width in phonation of "i" was 9.11 to 34.06 mm, with the average of 17.69 mm, the position of the pedicle wasall located at the level of the first cervical vertebra or above and the contact area of the flap with the soft palate was in the middle posterior 1/3 mostly. CONCLUSION: As a method to quantitatively evaluate velopharyngeal function, CT can assist in design for the PPF.