Search

Purpose: Previous studies highlight paraspinal muscles' significance in spinal stability. This study aims to assess paraspinal muscle predictiveness for postoperative recurrent lumbar disc herniation (PRLDH) after lumbar disc herniation patients undergo percutaneous endoscopic transforaminal discectomy (PETD). Patients and Methods: Retrospectively collected data from 232 patients undergoing PETD treatment at our institution between January 2020 and January 2023, randomly allocated into training (60%) and validation (40%) groups. Utilizing Lasso regression and multivariable logistic regression, independent risk factors were identified in the training set to construct a Nomogram model. Internal validation employed Enhanced Bootstrap, with Area Under the ROC Curve (AUC) assessing accuracy. Calibration was evaluated through calibration curves and the Hosmer-Lemeshow goodness-of-fit test. Decision curve analysis (DCA) and clinical impact curve (CIC) were employed for clinical utility analysis. Results: Diabetes, Modic changes, and ipsilesional multifidus muscle skeletal muscle index (SMI) were independent predictive factors for PRLDH following PETD (P< 0.05). Developed Nomogram model based on selected predictors, uploaded to a web page. AUC for training: 0.921 (95% CI 0.872– 0.970), validation: 0.900 (95% CI 0.828– 0.972), respectively. The Hosmer-Lemeshow test yielded χ2=5.638/6.259, P=0.688/0.618, and calibration curves exhibited good fit between observed and predicted values. DCA and CIC demonstrate clinical net benefit for both models at risk thresholds of 0.02– 1.00 and 0.02– 0.80. Conclusion: The Nomogram predictive model developed based on paraspinal muscle parameters in this study demonstrates excellent predictive capability and aids in personalized risk assessment for PRLDH following PETD. [ABSTRACT FROM AUTHOR]

Aiming at the problem of traffic data distribution shift caused by new unknown attacks in the industrial Internet, a malicious traffic identification method based on neighborhood filtering and stable learning was proposed to enhance the effectiveness and robustness of the existing graph neural network model in identifying known malicious traffic. Firstly, the graph structure of the traffic data was modeled to capture the topological relationship and interaction mode in communication behavior. Secondly, the traffic subgraph was divided based on the neighborhood filtering mechanism of biased sampling to eliminate the pseudo-homogeneity between communication behaviors. Finally, the statistical independence of high-dimensional traffic features was realized by applying graph representation learning and stable learning strategies, combined with adaptive sample weighting and collaborative loss optimization methods. The experimental results on two benchmark datasets show that compared with the baseline method, the recognition performance of the proposed method is increased by more than 2.7% in the new unknown attack scenario, which shows its high efficiency and practicability in the industrial Internet environment. [ABSTRACT FROM AUTHOR]

β-thalassemia is an inherited blood disease caused by reduced or inadequate β-globin synthesis due to β-globin gene mutation. Our previous study developed a gene-edited mice model (β654-ER mice) by CRISPR/Cas9-mediated genome editing, targeting both the βIVS2-654 (C > T) mutation site and the 3ʹ splicing acceptor site at 579 and corrected abnormal β-globin mRNA splicing in the β654-thalassemia mice. Herein, we further explored the therapeutic effect of the hematopoietic stem cells (HSCs) from β654-ER mice on β-thalassemia by consecutive HSC transplantation. The results indicated that HSC transplantation derived from gene-edited mice can significantly improve the survival rate of mice after lethal radiation doses and effectively achieve hematopoietic reconstruction and long-term hematopoiesis. Clinical symptoms, including hematologic parameters and tissue pathology of transplanted recipients, were significantly improved compared to the non-transplanted β654 mice. The therapeutic effect of gene-edited HSC transplantation demonstrated no significant difference in hematological parameters and tissue pathology compared with wild-type mouse-derived HSCs. Our data revealed that HSC transplantation from gene-edited mice completely recovered the β-thalassemia phenotype. Our study systematically investigated the therapeutic effect of HSCs derived from β654-ER mice on β-thalassemia and further confirmed the efficacy of our gene-editing approach. Altogether, it provided a reference and primary experimental data for the clinical usage of such gene-edited HSCs in the future. [ABSTRACT FROM AUTHOR]

Background: LentiGlobin BB305 is a self‐inactivating lentiviral vector carrying a human β‐globin expressing cassette for treating β‐thalassemia. Initially, a 2 × 250 bp chicken Locus Control Region fragment of cHS4, functioning as an insulator, was placed at its ΔU3, which was removed after the first clinical trial led by a French team to avoid abnormal splicing, etc. This action could potentially lead to an increasing risk of the transcriptional read‐through rate driven by the β‐globin promoter to a significant level, posing a biosafety risk in clinical trials. Methods: In the present study, a read‐through reducing agent (C‐U+ or WPRE) was designed to be placed at the 3′ UTR of the β‐globin gene. The Enhancer Activities and/or Transcriptional Read‐Through (EATRT) rate at the mRNA level and the protein expression level regarding lentiviral preparation titer were examined. Results: We found that the insertion of the element (C‐U+ or WPRE) reduced the EATRT effectively by 53% or 41%, respectively. C‐U+ has less impact on virus package efficiency. Furthermore, there was no significant difference in the protein expression level after the C‐U+ or WPRE insertion. Conclusions: The results of the present study show that inserting C‐U+ or WPRE before the polyA sequence of the BB305 would reduce the EATRT rate at no cost of its expressing efficacy and viral preparation titers. Thus, we present an alternative improvement for a safer lentiviral vector for β‐thalassemia clinical trials. [ABSTRACT FROM AUTHOR]

β 654 -thalassemia is caused by a point mutation in the second intron (IVS-II) of the β-globin gene that activates a cryptic 3' splice site, leading to incorrect RNA splicing. Our previous study demonstrated that when direct deletion of the β 654 mutation sequence or the cryptic 3' splice site in the IVS-II occurs, correct splicing of β-globin mRNA can be restored. Herein, we conducted an in-depth analysis to explore a more precise gene-editing method for treating β 654 -thalassemia. A single-base substitution of the cryptic 3' acceptor splice site was introduced in the genome of a β 654 -thalassemia mouse model using clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9(Cas9)-mediated homology-directed repair (HDR). All of the HDR-edited mice allow the detection of correctly spliced β-globin mRNA. Pathological changes were improved compared with the nonedited β 654 mice. This resulted in a more than twofold increase in the survival rate beyond the weaning age of the mice carrying the β 654 allele. The therapeutic effects of this gene-editing strategy showed that the typical β-thalassemia phenotype can be improved in a dose-dependent manner when the frequency of HDR is over 20%. Our research provides a unique and effective method for correcting the splicing defect by gene editing the reactive splicing acceptor site in a β 654 mouse model.

Background: The investigation of UV-induced fluorescence imaging coupled with machine learning was conducted to non-destructively detect the total volatile basic nitrogen (TVB-N) of frozen-whole-round tilapia (FWRT) during freezing and thawing. The UV-induced fluorescence images of FWRT at the wavelength of 365 nm were acquired by self-developed fluorescence image acquisition system. In total, 169 color and texture features based on RGB, hue-saturation-intensity and L*a*b* color spaces and gray level co-occurrence matrix were extracted, respectively. Successive projections algorithm (SPA) was employed to select the optimal 16 features to achieve feature dimension reduction modeling. With full and extracted features as input, the models of partial least squares regression (PLSR), least-squares support vector machine (LSSVM) and convolutional neural network (CNN) were established for TVB-N prediction., Results: Results indicated that the full features-based CNN performed better than SPA based prediction models (SPA-PLSR and SPA-LSSVM). The CNN model was determined to be the optimal with an RP2 value of 0.9779, RMSEP value of 1.1502 × 10 -2  g N kg -1 and RPD value of 6.721 for TVB-N content predictiin., Conclusion: The CNN method based on UV fluorescence imaging technology has potential for quality and safety detection of FWRT. © 2023 Society of Chemical Industry., (© 2023 Society of Chemical Industry.)