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The purpose of this study was to examine the differences between the use of a posterior interosseous artery (PIA) flap and an anterolateral thigh (ALT) flap for post-traumatic, medium-sized soft tissue reconstruction of the hand based on flap characteristics, postoperative complications, and aesthetic outcomes. From October, 2010 to March, 2016, 62 patients undergoing soft tissue reconstruction of the hand with 30 PIA flaps and 32 ALT flaps were included in this study. The 62 patients were divided into the PIA flap group and the ALT flap group. The differences between the 2 groups were analyzed. The 62 patients included 52 males and 10 females, and the mean age at the time of surgery was 41 years. The flap failure rate was 13.3% (4/30) in the PIA flap group and 9.4% (3/32) in the ALT flap group. No significant differences in flap failure rate, recipient site complication rate, or donor site complication rate were observed between the 2 groups. However, the operative time (136 min vs 229 min) and aesthetic outcomes (flap bulk swelling, 0 cases vs 31 cases) were statistically significantly different. Both the pedicled PIA flap and the free ALT flap were comparable for the reconstruction of post-traumatic, medium-sized soft tissue defects of the hand according to the evaluated outcomes of postoperative complications. Based on the surgical characteristics of the flap and the evaluation of aesthetic outcomes, the pedicled PIA flap was significantly superior to the free ALT flap.

With people’s pursuit of music art, a large number of singers began to analyze the trend of music in the future and create music works. Firstly, this study introduces the theory of music pop trend analysis, big data mining technology, and related algorithms. Then, the autoregressive integrated moving (ARIM), random forest, and long-term and short-term memory (LSTM) algorithms are used to establish the image analysis and prediction model, analyze the music data, and predict the music trend. The test results of the three models show that when the singer’s songs are analyzed from three aspects: collection, download, and playback times, the LSTM model can predict well the playback times. However, the LSTM model also has some defects. For example, the model cannot accurately predict some songs with large data fluctuations. At the same time, there is no big data gap between the playback times predicted by the ARIM model image analysis and the actual playback times, showing the allowable error fluctuation range. A comprehensive analysis shows that compared with the ARIM algorithm and random forest algorithm, the LSTM algorithm can predict the music trend more accurately. The research results will help many singers create songs according to the current and future music trends and will also make traditional music creation more information-based and modern.

Background Triglyceride (TG) to high-density lipoprotein cholesterol (HDL-C) ratio and triglyceride glucose index (TyG) have been recommended as surrogate markers for nonalcoholic fatty liver disease (NAFLD). In the present study, we aimed to compare the ability of TG/HDL-C, TyG and TyG-HDL-C, a new indicator deriving from TyG and HDL-C, in identifying NAFLD in Chinese population. Methods A total of 11,524 subjects who participated in a health checkup program were included. NAFLD was diagnosed by ultrasonography. Results The prevalence rates of NAFLD progressively increased across the quartiles of TG/HDL-C, TyG and TyG-HDL-C (P for tend < 0.0001). After adjustment for confounding factors, the three indicators were all independently associated with the risk of NAFLD. The odds ratios (ORs) for NAFLD of the second to fourth TyG-HDL-C quartiles compared to the first quartile were 2.24 (1.92–2.62), 4.24 (3.64–4.94) and 8.96 (7.64–10.50), respectively. There was no difference in areas under the ROC curve (AUCs) of the three indicators in men, regardless of body weight. AUCs of TyG were significantly higher than thats of TG/HDL-C in overweight and obese women (P < 0.0001), with the optimal cut-off point of 8.56 (sensitivity = 74.4 %, specificity = 60.9 %). In normal or low body weight women, TyG-HDL-C, with a AUC of 0.827 (0.810–0.843), was better than the other two indictors, with AUCs of 0.813 (0.796–0.830), 0.815 (0.797–0.831), respectively (P = 0.0015). The optimal cut-off point of TyG-HDL-C in normal or low body weight women was 6.8 (sensitivity = 78.1%, specificity = 72.1 %). Conclusions TG/HDL-C is as effective as TyG in predicting NAFLD in Chinese men. TyG is superior to TG/HDL-C in identifying NFAFLD in Chinese overweight and obese women. While in Chinese normal and low weight women, TyG-HDL-C is a better predictor for NAFLD.