Association of circulating visfatin level and metabolic fatty liver disease: An updated meta-analysis and systematic review.

BACKGROUND: The rate of incidence of metabolic dysfunction-related fatty liver disease (MAFLD) has rapidly increased globally in recent years, but early diagnosis is still a challenge. The purpose of this systematic review and meta-analysis is to identify visfatin for early diagnosis of MAFLD. METHODS: We strictly adhered to the relevant requirements of Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The systematic search was conducted in 7 sources (PubMed, Embase, Cochrane Library, CNKI, Wanfang, CBM, and ClinicalTrials.gov) until February 2024. The meta-analysis was performed using Stata 12. Outcomes were expressed in the form of standardized mean difference (SMD) and 95% confidence interval and were analyzed using meta-analysis. RESULTS: The results showed that there was no significant difference in circulating visfatin levels between patients with MAFLD and controls (SMD = 0.13 [-0.34, 0.60]). However, the outcomes indicated that the level of circulating visfatin was significantly higher in MAFLD patients in the Middle Eastern subgroup (SMD = 0.45 [0.05, 0.85]) and in the obese patient subgroup (SMD = 1.05 [0.18, 1.92]). No publication bias was detected, and sensitivity analysis confirmed the stability of the outcomes. CONCLUSION: The serum visfatin levels of MAFLD patients did not differ significantly from those of controls. However, visfatin concentrations in serum were statistically higher within Middle Eastern or obese MAFLD patients compared to controls. There is a need for further research to investigate visfatin's potential as a biomarker for MAFLD.

CTFusion: CNN-transformer-based self-supervised learning for infrared and visible image fusion.

Infrared and visible image fusion (IVIF) is devoted to extracting and integrating useful complementary information from muti-modal source images. Current fusion methods usually require a large number of paired images to train the models in supervised or unsupervised way. In this paper, we propose CTFusion, a convolutional neural network (CNN)-Transformer-based IVIF framework that uses self-supervised learning. The whole framework is based on an encoder-decoder network, where encoders are endowed with strong local and global dependency modeling ability via the CNN-Transformer-based feature extraction (CTFE) module design. Thanks to the development of self-supervised learning, the model training does not require ground truth fusion images with simple pretext task. We designed a mask reconstruction task according to the characteristics of IVIF, through which the network can learn the characteristics of both infrared and visible images and extract more generalized features. We evaluated our method and compared it to five competitive traditional and deep learning-based methods on three IVIF benchmark datasets. Extensive experimental results demonstrate that our CTFusion can achieve the best performance compared to the state-of-the-art methods in both subjective and objective evaluations.