Comparing external fixators and intramedullary nailing for treating open tibia fractures: a meta-analysis of randomized controlled trials.

BACKGROUND: External fixators (EFs) and intramedullary nailing (IMN) are two effective methods for open tibial fractures. However, both methods have advantages and disadvantages, and the optimal surgical approach remains controversial. Thus, we performed a meta-analysis of randomized controlled trials (RCTs) to compare EF with IMN to evaluate their efficacy and safety. METHODS: A systematic study of the literature was conducted in relevant studies published in PubMed, Embase, the Cochrane Library, Web of Science, CNKI, CBM, Wanfang and Weipu from database inception to April 2022. All eligible literature was critically appraised for methodological quality via the Cochrane's collaboration tool. The primary outcome measurements included postoperative superficial infection, postoperative deep infection, union time, delayed union, malunion, nonunion, and hardware failure. RESULTS: Nine RCTs involving 733 cases were included in the current meta-analysis. The pooled results suggested that cases in the IMN group had a significantly lower postoperative superficial infection rate [risk ratio (RR) = 2.84; 95% confidence interval (CI) = 1.83 to 4.39; P < 0.00001)] and malunion rate (RR = 3.05; 95% CI = 2.06 to 4.52; P < 0.00001) versus EF, but IMN had a significantly higher hardware failure occurrence versus EF (RR = 0.38; 95% CI = 0.17 to 0.83; P = 0.02). There were no significant differences in the postoperative deep infection rate, union time, delayed union rate or nonunion rate between the two groups (p > 0.05). CONCLUSIONS: Compared to EF, IMN had a significantly lower risk of postoperative superficial infection and malunion in patients with open tibial fractures. Meanwhile, IMN did not prolong the union time and increased the risk of the deep infection rate, delayed union rate and nonunion rate but had a higher hardware failure rate. The reanalysis of union time showed that it was significantly shorter in the IMN group than in the EF group after excluding the study with significant heterogeneity during sensitivity analysis. Therefore, IMN is recommended as a preferred method of fracture fixation for patients with open tibial fractures, but more attention should be given to the problem of hardware failure.

The Cuproptosis-Related Long Noncoding RNA Signature Predicts Prognosis and Tumour Immune Analysis in Osteosarcoma.

Background: Cuprotopsis is a type of programmed cell death discovered in recent years. Long noncoding RNAs (lncRNAs) play an important regulatory role in programmed cell death. The effect of cuproptosis-related lncRNAs on osteosarcoma is unknown. Our work, based on cuproptosis-related lncRNAs, proposes a gene signature to assess the prognosis of patients with osteosarcoma. Methods: Osteosarcoma gene expression data from The Cancer Genome Atlas (TCGA), clinical features of osteosarcoma and RNA sequencing data of normal adipose tissue were obtained from the UCSC Xena database. A cuproptosis-related lncRNA risk model was established to calculate the risk score. At the same time, cluster analysis, clinicopathological analysis, functional enrichment analysis, and prediction of compounds with potential therapeutic value were evaluated. We analyzed whether there was a correlation between the risk score and tumour immunity. RT-qPCR was used to verify the expression level of lncRNA. Results: Nine lncRNAs (AC124798.1, AC006033.2, AL450344.2, AL512625.2, LINC01060, LINC00837, AC004943.2, AC064836.3, and AC100821.2) were identified to create a risk model and indicate the prognosis of patients with osteosarcoma. The high-risk group had a worse prognosis than the low-risk group. Analysis of clinicopathological features, principal component analysis, receiver operating characteristic curve, c-index curve, and comparative analysis of models proved that the model is reliable. Functional enrichment analysis suggests that the risk score may correlate with cell energy metabolism and tumour-related biological function. Three potentially therapeutic compounds have been predicted. These analyses may be beneficial to the treatment of osteosarcoma in the future. RT-qPCR verified the expression level of three lncRNA (LINC01060, NKILA, and SNHG8). Conclusions: Cuproptosis-related lncRNAs have a strong relationship with osteosarcoma patients. Nine lncRNA models can effectively forecast the prognosis of osteosarcoma and may play a significant role in the individualized treatment of osteosarcoma patients in the future.

Optimizing Top- Multiclass SVM via Semismooth Newton Algorithm.

Top- performance has recently received increasing attention in large data categories. Advances, like a top- multiclass support vector machine (SVM), have consistently improved the top- accuracy. However, the key ingredient in the state-of-the-art optimization scheme based upon stochastic dual coordinate ascent relies on the sorting method, which yields complexity. In this paper, we leverage the semismoothness of the problem and propose an optimized top- multiclass SVM algorithm, which employs semismooth Newton algorithm for the key building block to improve the training speed. Our method enjoys a local superlinear convergence rate in theory. In practice, experimental results confirm the validity. Our algorithm is four times faster than the existing method in large synthetic problems; Moreover, on real-world data sets it also shows significant improvement in training time.

Improving Sparsity and Scalability in Regularized Nonconvex Truncated-Loss Learning Problems.

The truncated regular -loss support vector machine can eliminate the excessive number of support vectors (SVs); thus, it has significant advantages in robustness and scalability. However, in this paper, we discover that the associated state-of-the-art solvers, such as difference convex algorithm and concave-convex procedure, not only have limited sparsity promoting property for general truncated losses especially the -loss but also have poor scalability for large-scale problems. To circumvent these drawbacks, we present a general multistage scheme with explicit interpretation regarding SVs as well as outliers. In particular, we solve the general nonconvex truncated loss minimization through a sequence of associated convex subproblems, in which the outliers are removed in advance. The proposed algorithm can be regarded as a structural optimization attempt carefully considering sparsity imposed by the nonconvex truncated losses. We show that this general multistage algorithm offers sufficient sparsity especially for the truncated -loss. To further improve the scalability, we propose a linear multistep algorithm by employing a single iteration of coordinate descent to monotonically decrease the objective function at each stage and a kernel algorithm by using the Karush-Kuhn-Tucker conditions to cheaply find most part of the outliers for the next stage. Comparison experiments demonstrate that our methods have superiority in sparsity as well as efficiency in scalability.

Stochastic learning via optimizing the variational inequalities.

A wide variety of learning problems can be posed in the framework of convex optimization. Many efficient algorithms have been developed based on solving the induced optimization problems. However, there exists a gap between the theoretically unbeatable convergence rate and the practically efficient learning speed. In this paper, we use the variational inequality (VI) convergence to describe the learning speed. To this end, we avoid the hard concept of regret in online learning and directly discuss the stochastic learning algorithms. We first cast the regularized learning problem as a VI. Then, we present a stochastic version of alternating direction method of multipliers (ADMMs) to solve the induced VI. We define a new VI-criterion to measure the convergence of stochastic algorithms. While the rate of convergence for any iterative algorithms to solve nonsmooth convex optimization problems cannot be better than O(1/√t), the proposed stochastic ADMM (SADMM) is proved to have an O(1/t) VI-convergence rate for the l1-regularized hinge loss problems without strong convexity and smoothness. The derived VI-convergence results also support the viewpoint that the standard online analysis is too loose to analyze the stochastic setting properly. The experiments demonstrate that SADMM has almost the same performance as the state-of-the-art stochastic learning algorithms but its O(1/t) VI-convergence rate is capable of tightly characterizing the real learning speed.

Recursive support vector machines for dimensionality reduction.

The usual dimensionality reduction technique in supervised learning is mainly based on linear discriminant analysis (LDA), but it suffers from singularity or undersampled problems. On the other hand, a regular support vector machine (SVM) separates the data only in terms of one single direction of maximum margin, and the classification accuracy may be not good enough. In this letter, a recursive SVM (RSVM) is presented, in which several orthogonal directions that best separate the data with the maximum margin are obtained. Theoretical analysis shows that a completely orthogonal basis can be derived in feature subspace spanned by the training samples and the margin is decreasing along the recursive components in linearly separable cases. As a result, a new dimensionality reduction technique based on multilevel maximum margin components and then a classifier with high accuracy are achieved. Experiments in synthetic and several real data sets show that RSVM using multilevel maximum margin features can do efficient dimensionality reduction and outperform regular SVM in binary classification problems.