This study aims to systematically review and identify the related influencing factors for the recurrence of diabetic foot ulcers (DFUs)in diabetic patients. We searched PUBMED, EMBASE, Web of Science, Cochrane Library, China Biology Medicine (CBM), China National Knowledge Infrastructure (CNKI), Wan Fang and VIP databases to identify eligible studies published before March 31, 2022 to collect case-control studies or cohort studies on the related influencing factors for the recurrence of DFUs. Two reviewers independently screened the literature, and extracted data. Also, they assessed the risk of bias of the included studies using the Newcastle-Ottawa Scale. A meta-analysis was performed using RevMan5.4.1 software. 20 studies were included; 4238 patients were enrolled, in which 1567 were in the DFU recurrence group and 2671 were in the non-recurrent DFU group. Risk factors for the recurrence of DFUs included diabetic peripheral neuropathy (odds ratio [OR] = 4.05, 95% CI, 2.50-6.58, P < 0.00001), peripheral vascular disease (OR = 3.94, 95% CI, 2.65-5.84, P < 0.00001), poor blood glucose control (OR = 3.27, 95% confidence interval [CI], 2.79-3.84, P < 0.00001), plantar ulcer (OR = 3.66, 95% CI, 2.06-6.50, P < 0.00001), osteomyelitis (OR = 7.17, 95% CI, 2.29-22.47, P = 0.0007), smoking (OR = 1.98, 95% CI, 1.65-2.38, P < 0.00001), history of amputation (OR = 11.96, 95%CI, 4.60-31.14, P < 00001), multidrug-resistant bacterial infection (OR = 3.61, 95%CI, 3.13-4.17, P < 0.00001), callus (OR = 5.70, 95%CI, 1.36-23.89, P = 0.02), previous diabetic foot ulcer (OR = 4.10, 95% CI, 2.58-6.50, P < 0.00001), duration of previous diabetic foot ulcer >60d (OR = 1.02, 95% CI, 1.00-1.03, P = 0.004), history of vascular intervention (OR = 3.20, 95% CI, 2.13-4.81, P < 0.00001) and Wagner grade III/IV (OR = 4.40, 95% CI, 2.21-8.78, P < 0.0001). However, no significant differences were found in age, duration of diabetes, body mass index, total cholesterol or foot deformity. Recurrence of diabetic foot ulcers is affected by a variety of factors. Thus, we should focus on high-risk groups and take targeted interventions as soon as possible to reduce the recurrence rate of DFUs, because of the
The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.
A large amount of time series data is being generated every day in a wide range of sensor application domains. The symbolic aggregate approximation (SAX) is a well-known time series representation method, which has a lower bound to Euclidean distance and may discretize continuous time series. SAX has been widely used for applications in various domains, such as mobile data management, financial investment, and shape discovery. However, the SAX representation has a limitation: Symbols are mapped from the average values of segments, but SAX does not consider the boundary distance in the segments. Different segments with similar average values may be mapped to the same symbols, and the SAX distance between them is 0. In this paper, we propose a novel representation named SAX-BD (boundary distance) by integrating the SAX distance with a weighted boundary distance. The experimental results show that SAX-BD significantly outperforms the SAX representation, ESAX representation, and SAX-TD representation.
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