Background: Contemporary techniques for repair of acute anterior cruciate ligament (ACL) rupture have been receiving renewed interest recently because of reports of good outcomes. Methods: A literature search of PUBMED, MEDLINE, EMBASE, and the Cochrane Library was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Only RCTs published in English and comparing clinical outcomes of ACL repair versus reconstruction were included. Outcomes were evaluated using the International Knee Documentation Committee subjective score, Lysholm score, Tegner activity scale, visual analog scale pain score, anterior laxity, Lachman test, hop tests, knee injury and osteoarthritis outcome score, extension deficit, revision rate, and re-rupture rate. Statistical analysis was performed with Review Manager 5.4 and Stata 14.0. Two-tailed P < .05 was considered statistically significant. Results: Four RCTs (with a total of 293 patients) that met the eligibility criteria were included in this review. Over short-term follow-up, none of the studies found significant differences between the repair groups and reconstruction groups with respect to International Knee Documentation Committee, Lysholm, Tegner, visual analog scale, anterior laxity, Lachman test, re-rupture rate, extension deficit, and performance of 3 hop tests ( P > .05). In both groups, the hop tests scores were >90%. Conclusion: ACL repair and ACL reconstruction appear to provide comparable short-term outcomes. The low revision rate after primary repair is encouraging. For patients with ACL injury, current repair techniques such as dynamic intraligamentary stabilization and bridge-enhanced ACL repair may be an effective alternative to reconstruction.
In the past few years, immunotherapy of tumors has become an extensive research hotspot, and the value of IKZF family genes in the tumor microenvironment has also been increasingly recognized. However, the expression of the IKAROS family zinc finger 3 (IKZF3) gene in human head and neck squamous cell carcinoma (HNSCC) and its prognostic value were not reported for the main subset until now. In the present study, we analyzed the relationship between IKZF3 gene expression and the survival of HNSCC patients. To evaluate the potential of IKZF3 as a prognostic biomarker for HNSCC comprehensively, multiple online analysis tools, including UALCAN, cBioPortal, GEPIA, WebGestalt, String, Genomic Data Commons, and TIMER databases were utilized in our study. We observed that the HNSCC patients with higher IKZF3 expression tended to exhibit longer overall survival. Univariate and multivariate Cox regression analyses indicated that age and grade were independent prognostic indicators in HNSCC. Moreover, Gene Ontology and KEGG function enrichment analyses showed that several pathways in HNSCC might be pivotal pathways regulated by IKZF3, which revealed that IKZF3 was probably participating in the occurrence and development of HNSCC. Furthermore, the hypomethylation of the IKZF3 gene was closely associated with genes that observed mutation in HNSCC. IKZF3 was significantly correlated with several immune cells in HNSCC (e.g., CD8+ T cell, CD4+ cell, and dendritic cell). We explored the potential prognostic values and roles of the IKZF3 in HNSCC, revealing that IKZF3 was probably a novel and reliable prognostic biomarker for patients with HNSCC.
The purpose of this study was to investigate the clinical value of CT angiography (CTA) images processed by the segmentation denoising technique based on deep convolution neural network algorithm in the diagnosis of abdominal aortic aneurysm (AAA) and the detection of disease changes. A total of 98 patients with ruptured AAA were retrospectively selected as the study subjects. Patients were grouped according to whether the CTA images were optimized, the images receiving artificial intelligence segmentation and denoising were set as the observation group, and the CTA images without optimization were set as the control group. The detection and diagnosis effects of CTA images before and after the treatment were compared. The surgical results were used as the standard to analyze the diagnostic effect, and the maximum diameter measurement results of AAA and the proportion results of intraluminal thrombus (ILT) were compared. Although the sensitivity and accuracy of diagnosis in the observation group (97.73% and 94.9%) were higher than those in the control group (95.45% and 92.86%), there was no significant statistical significance ( P > 0.05 ). When the diameter of AAA was no less than 5 cm, all results showed that the coverage percentage of intraluminal thrombus (ILT) was over 50%. When the diameter of AAA was less than 5 cm, only 55.56% of the results showed that the percentage of ILT coverage was over 50%, with considerable differences ( P > 0.05 ). According to the results of the study, it was found that there was a certain relationship between the thrombus coverage of the abdominal aortic wall and the growth rate of AAA. The deep convolution neural network algorithm had a certain effect on the treatment of CTA, but it is not obvious. However, CTA had a better clinical diagnostic effect on AAA.
This study was to analyze the impacts of the image segmentation model and computed tomography angiography (CTA) on the clinical diagnosis of aortic constriction under the background of artificial intelligence. In this study, 126 patients with congenital aortic constriction (CAC) diagnosed by surgery were selected as the research objects and routine digital subtraction angiography (DSA) and CTA were performed. Then, the traditional active contour model (AC model) was optimized based on the local area information to construct a new image segmentation model for intelligent segmentation and reconstruction of the CTA images of patients. The results revealed that compared with the AC model and the image segmentation model based on region growth (RG model) obtained from angiography segmentation, the algorithm constructed in this study showed a smaller segmentation range for angiography images and more accurate segmentation results. The quantitative data results suggested that the evolution times and running time of the constructed model were less than those of the AC and RG models P < 0.05 . Based on the gold standard of DSA examination results, there were 122 correctly diagnosed cases, 3 missed diagnosed cases, and 1 misdiagnosed by CTA, so the diagnosis coincidence rate was 96.83%. Compared with DSA, the average inner diameter and average pressure difference of patients with precatheter, paracatheter, and postcatheter type were not greatly different in CTA P > 0.05 . The CTA examination suggested there were 154 cases with intracardiac structural abnormalities, with a detection rate of 86.52%; there were 32 cases of cardiac-vascular connection abnormalities, with a detection rate of 100%; and there were 79 extracardiac vascular abnormalities, with the detection rate of 95.18%. It indicated that the optimized image segmentation model based on local area information proposed in this paper has excellent segmentation performance for CT angiography images and has good segmentation effect and efficiency. The CTA based on the artificial intelligence image segmentation model showed a better diagnostic effect on abnormal heart-vascular connection and abnormal extracardiac blood vessels and can be used as an effective examination method for clinical diagnosis of CAC.
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