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The paper summarizes and analyzes the progress of research on the use of intangible cultural heritage in food packaging design through the number of articles, keywords, research objects, research perspectives and ideas. The study analyzes the application of intangible cultural heritage in food packaging design from three aspects: principles, strategies, and methods. It also explores the future development directions, suggesting that future research can guide design practice through theoretical studies and validate design practice using big data models.

"Objective To investigate the effect of visceral fat area (VFA) on carotid atherosclerotic plaque in patients with type 2 diabetes mellitus(T2DM). Methods From August 2018 to August 2023, 683 T2DM patients included in the database of the National Metabolic Management Center (MMC) were selected from the Department of Endocrinology, the Affiliated Geriatric Hospital of Nanjing Medical University. Medical history, physical examination, serum biochemical indicators, VFA and other data were collected from the study subjects. According to the presence or absence of carotid atherosclerotic plaque, all patients were divided into T2DM patients with plaque (n=342) and T2DM patients without plaque (n=341), and statistical analysis was performed. Results There were statistically significant differences in age, body mass index (BMI), waist circumference, diastolic blood pressure (DBP), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), abdominal subcutaneous fat area (SFA) and VFA between the two groups (PConclusion Age and VFA are independent influencing factors for atherosclerotic plaque formation in patients with T2DM，and as a new obesity assessment index, VFA can better predict and assess the risk of atherosclerotic plaque formation in T2DM patients."

Objective To investigate the the clinical application of triglyceride-glucose (TyG) index and its correlation with atherosclerosis in patients with type 2 diabetes mellitus (T2DM). Methods From January 2020 to December 2022，499 patients with T2DM were screened from the inpatients and physical examination populations of Nanjing Jiangbei Hospital. Based on the presence or absence of atherosclerotic plaques， the subjects were divided into T2DM with plaques group (n=249) and T2DM without plaques group (n=250). The subjects underwent medical history collection，physical examination，and serum biochemical testing. The TyG index was calculated，and the correlation between TyG and common risk factors for T2DM atherosclerosis was statistically analyzed. Results The age，systolic blood pressure (SBP)，triglyceride (TG)，total cholesterol (TC)，fasting blood glucose (FBG)，glycosylated hemoglobin (HbA1c)，and apolipoprotein A of the T2DM without plaques group were lower than those in the T2DM with plaques group, the diastolic blood pressure (DBP) was higher than that in the T2DM with plaques group (P＜0.05). The TyG index in the T2DM with plaques group was higher than that in the T2DM without plaques group (11.52±0.37 vs 9.34±0.48, t=56.803, P＜0.01). TyG index was positively correlated with age, SBP, TG, TC, low-density lipoprotein cholesterol (LDL-C), uric acid (UA), and HbA1c (P＜0.05)，and negatively correlated with high-density lipoprotein cholesterol (HDL-C) and fasting C-peptide (P＜0.05). The area under ROC curve of TyG index for predicting T2DM atherosclerosis was 0.611 ，with a best cut-off value of 10.21，sensitivity of 84.8%, and specificity of 62.2%. Conclusion TyG index is closely related to multiple risk factors for atherosclerosis in patients with T2DM. TyG index combined with risk factors for atherosclerosis can comprehensively assess the risk of T2DM atherosclerosis development.

The autoantibody in patients’ serum can act as a biomarker for diagnosing cancer, and the differences in autoantibodies are significantly correlated with the changes in their target proteins. In this study, 16 renal cancer (RC) patients were assigned to the disease group, and 16 healthy people were assigned to the healthy control (HC) group. The human proteome microarray consisting of>19,500 proteins was used to examine the differences in IgG and IgM autoantibodies in sera between RC and HC. The comparative analysis of the microarray results shows that 101 types of IgG and 25 types of IgM autoantibodies are significantly higher in RC than in HC. Highly responsive autoantibodies can be candidate biomarkers (e.g., anti-KCNAB2 IgG and anti-RCN1 IgM). Extensive enzyme-linked immunosorbent assay (ELISA) was performed to screen sera in 72 RC patients and 66 healthy volunteers to verify the effectiveness of the new autoantibodies. The AUCs of anti-KCNAB2 IgG and anti-GAPDH IgG were 0.833 and 0.753, respectively. KCNAB2 achieves high protein expression, and its high mRNA level is confirmed to be an unfavorable prognostic marker in clear cell renal cell carcinoma (ccRCC) tissues. This study suggests that the high-throughput human proteome microarray can effectively screen autoantibodies in serum as candidate biomarkers, and their corresponding target proteins can lay a basis for the in-depth investigation into renal cancer.

Abstract Background Sarcopenia has been confirmed as a poor prognostic indicator of lung cancer. However, the lack of abdominal computed tomography (CT) hindered the application to assess the status of sarcopenia. The purpose of this study was to assess the ability of chest CT radiomics combined with machine learning classifiers to identify sarcopenia in advanced non‐small cell lung cancer (NSCLC) patients. Methods This study retrospectively analyzed CT images of 99 patients with NSCLC. Skeletal muscle radiomics were extracted from a single axial slice of the chest CT scan at the 12th thoracic vertebrae level. In total, 854 radiomic and clinical features were obtained from each patient. Feature selection was conducted with FeatureSelector module, optimal key features were fed into the lightGBM classifier for model construction, and Bayesian optimization was adopted to tune hyperparameters. The model's performance was evaluated by specificity, sensitivity, accuracy, precision, F1‐score, Matthew's correlation coefficient (MCC), Cohen's kappa coefficient (Kappa), and AUC. Results A total of 40 patients were found to have sarcopenia. Five optimal features were selected. In the base lightGBM model, the specificity, sensitivity, accuracy, precision, F1‐score, AUC, MCC, Kappa of validation set were 0.889, 0.750, 0.833, 0.818, 0.783, 0.819, 0.649, 0.648, respectively. After Bayesian hyperparameter tuning, the optimized lightGBM model achieved better prediction performance, and the corresponding values were 0.944, 0.833, 0.900, 0.909, 0.870, 0.889, 0.791, 0.789, respectively. Conclusions Chest CT‐based radiomics has the potential to identify sarcopenia in NSCLC patients with the lightGBM classifier, and the optimal lightGBM model via Bayesian hyperparameter tuning demonstrated better performance. Key points Significant findings of the study Our study demonstrates that chest CT‐based radiomics combined with lightGBM classifier has the ability to identify sarcopenia in NSCLC patients. What this study adds Skeletal muscle radiomics would be a potential biomarker for sarcopenia identity in NSCLC patients.