Efficacy of oblique lumbar interbody fusion versus transforaminal lumbar interbody fusion in the treatment of lumbar degenerative diseases: a systematic review and meta-analysis.

INTRODUCTION: This meta-analysis aimed to compare the differences in postoperative efficacy between oblique lumbar interbody fusion (OLIF) and transforaminal lumbar interbody fusion (TLIF) in the treatment of lumbar degenerative diseases. MATERIALS AND METHODS: Strictly based on the search strategy, we searched the published papers on OLIF and TLIF for the treatment of lumbar degenerative diseases in PubMed, Embase, CINAHL, and Cochrane Library. A total of 607 related papers were retrieved, and 15 articles were finally included. The quality of the papers was evaluated according to the Cochrane systematic review methodology, and the data were extracted and meta-analyzed using Review manager 5.4 software. RESULTS: Through comparison, it was found that in the treatment of lumbar degenerative diseases, the OLIF group had certain advantages over the TLIF group in terms of intraoperative blood loss, hospital stay, visual analog scale (VAS) for leg pain (VAS-LP), Oswestry disability index (ODI), disc height (DH), foraminal height (FH), fused segmental lordosis (FSL), and cage height, and the differences were statistically significant. The results were similar in terms of surgery time, complications, fusion rate, VAS for back pain (VAS-BP) and various sagittal imaging indicators, and there was no significant difference. CONCLUSIONS: OLIF and TLIF can relieve low back pain symptoms in the treatment of lumbar degenerative diseases, but OLIF has certain advantages in terms of ODI and VAS-LP. In addition, OLIF has the advantages of minor intraoperative trauma and quick postoperative recovery.

The association between ADAM12 gene polymorphisms and osteoarthritis: an updated meta-analysis.

BACKGROUND: Osteoarthritis of the knee is an irreversible disease that causes great pain, and genetic factors play an important role in its occurrence and development. There have been many studies on the correlation between ADAM12 polymorphisms and genetic susceptibility to osteoarthritis, but the results remain inconclusive. METHODS: Papers from PubMed, Web of Science, EMbase, Springer, SCOPUS, Google Scholar and other databases were systematically retrieved with a cut-off of January 2022. All case-control studies on ADAM12 rs3740199, rs1871054, rs1044122, and rs1278279 polymorphisms and osteoarthritis were searched. Fixed or random effects models were used for pooled analysis with OR values and 95% confidence intervals (CI), and publication bias was assessed. In addition, the false-positive reporting probability test was used to assess the confidence of a statistically significant association. RESULTS: Eleven articles were included, which included 3332 patients with osteoarthritis and 5108 healthy controls. Meta-analysis showed that the rs1871054 polymorphism of ADAM12 was associated with osteoarthritis in dominant, recessive, allelic, and homozygote genetic models [C vs. T: OR = 1.34 95% CI (1.05, 1.71), P < 0.001]. Our subgroup analysis revealed an association between the ADAM12 polymorphism rs1871054 in Asians and osteoarthritis [C vs. T: OR = 1.61, 95% CI (1.25, 2.08), P < 0.001], albeit this was only for three studies. In addition, the ADAM12 polymorphism rs1871054 is associated with osteoarthritis in patients younger than 60 years of age [C vs. T: OR = 1.39, 95% CI (1.01, 1.92), P = 0.289]; however, the ADAM12 gene rs3740199, rs1044122, and rs1278279 site polymorphisms were not significantly. Furthermore, when assessing the confidence of the positive results, the positive results were found to be credible (except for Age < 60). CONCLUSION: Polymorphism at the rs1871054 site of ADAM12 is associated with genetic susceptibility to osteoarthritis, but rs3740199, rs1044122, and rs1278279 site polymorphisms are not.

Predicting MiRNA-Disease Associations by Graph Representation Learning Based on Jumping Knowledge Networks.

Growing studies have shown that miRNAs are inextricably linked with many human diseases, and a great deal of effort has been spent on identifying their potential associations. Compared with traditional experimental methods, computational approaches have achieved promising results. In this article, we propose a graph representation learning method to predict miRNA-disease associations. Specifically, we first integrate the verified miRNA-disease associations with the similarity information of miRNA and disease to construct a miRNA-disease heterogeneous graph. Then, we apply a graph attention network to aggregate the neighbor information of nodes in each layer, and then feed the representation of the hidden layer into the structure-aware jumping knowledge network to obtain the global features of nodes. The output features of miRNAs and diseases are then concatenated and fed into a fully connected layer to score the potential associations. Through five-fold cross-validation, the average AUC, accuracy and precision values of our model are 93.30%, 85.18% and 88.90%, respectively. In addition, for three case studies of the esophageal tumor, lymphoma and prostate tumor, 46, 45 and 45 of the top 50 miRNAs predicted by our model were confirmed by relevant databases. Overall, our method could provide a reliable alternative for miRNA-disease association prediction.