Objective Photoelectric volumetric tracing(PPG)exhibits high sensitivity and specificity in flap monitoring.Deep learning(DL)is capable of automatically and robustly extracting features from raw data.In this study,we propose combining PPG with 1D convolutional neural networks(1D-CNN)to preliminarily explore the method's ability to distinguish the degree of embolism and to localize the embolic site in skin flap arteries.Methods Data were collected under normal conditions and various embolic scenarios by creating vascular emboli in a dermatome artery model and a rabbit dermatome model.These datasets were then trained,validated,and tested using 1D-CNN.Results As the degree of arterial embolization increased,the PPG amplitude upstream of the embolization site progressively increased,while the downstream amplitude progressively decreased,and the gap between the upstream and downstream amplitudes at the embolization site progressively widened.1D-CNN was evaluated in the skin flap arterial model and rabbit skin flap model,achieving average accuracies of 98.36%and 95.90%,respectively.Conclusion The combined monitoring approach of DL and PPG can effectively identify the degree of embolism and locate the embolic site within the skin flap artery.

Objective:To investigate the relationship between different obesity metabolic phenotypes and the incidence of new carotid artery plaque.Methods:The present study is a retrospective cohort study, collecting individuals from the Health Management Center of the Second Affiliated Hospital of Dalian Medical University who had two or more cervical vascular color ultrasound examinations and met the inclusion criteria from 2014 to 2022, and collected their baseline clinical data. According to whether the subjects were obese and had metabolic syndrome, they were divided into metabolically healthy non-obese group, metabolically unhealthy non-obese group, metabolically healthy obese group, and metabolically unhealthy obese group. The first physical examination time of the subjects was taken as the starting point of follow-up, and cervical vascular color ultrasound was performed during the follow-up physical examination, with the outcome event being carotid artery plaque. Kaplan-Meier survival curve analysis was used to analyze the cumulative incidence of carotid artery plaques in the four groups and log-rank test was performed, and a multifactorial Cox proportional hazards model was used to analyze the relationship between different obesity metabolic phenotypes and the risk of carotid artery plaque incidence.Results:A total of 4 890 subjects were enrolled, aged (45.4±9.6) years, and 2 754 (56.3%) males. The follow-up time was 1.14(0.93, 2.20) years. Compared with the other 3 obesity metabolic phenotypes, the incidence of carotid plaques in the metabolically unhealthy obesity group was the highest (15.4% (286/1 861)). Kaplan-Meier survival curve analysis showed that the cumulative incidence of carotid plaques in metabolically unhealthy obese subjects was about 2.962 times that of metabolically healthy non-obese subjects (log-rank P<0.001). Multivariate Cox regression results showed that the risk of carotid plaque in metabolically unhealthy obese subjects was 1.650 times that of metabolically healthy non-obese subjects (95% CI: 1.203-2.264, P=0.002). Conclusion:Metabolically unhealthy obesity phenotype is an independent risk factor for carotid plaque.

Objective:To study the correlation between different body weight metabolic phenotypes and their changes and new-onset hyperuricemia in physical examination population.Methods:This study was a retrospective cohort study. A total of 31 956 people who underwent routine physical examination and met the inclusion and exclusion criteria at the Health Management Center of the Second Affiliated Hospital of Dalian Medical University from January 1, 2014 to August 31, 2022 were selected as the study subjects to establish a dynamic physical examination cohort. The end point of follow-up was new-onset hyperuricemia or the end of follow-up period. Cox regression stepwise fitting model was used to analyze the risk of different body weight metabolic phenotypes and hyperuricemia, and stratified analysis was performed for gender. According to body weight metabolic phenotype, the subjects were divided into normal metabolism and normal weight(NMNW) group, normal metabolism and obesity (NMO) group, abnormal metabolism and normal weight (AMNW) group and abnormal metabolism and obesity (AMO) group. The risk of hyperuricemia was calculated according to the changes of body weight metabolic phenotype during the follow-up period. In the sensitivity analysis, the robustness of the results was verified by changing the diagnostic criteria for hyperuricemia, removing patients with hyperuricemia at the first year of follow-up, and removing subjects aged ≥65 years.Results:Compared with the NMNW group, the risk of hyperuricemia in the NMO group, AMNW group and AMO group increased by 78.9%, 61.3%, 115.4%, respectively ( χ2=272.88, 128.15, 496.12, all P<0.001). Patients who were initially classified as NMNW at baseline, if transitioned to NMO or AMO by the follow-up endpoint, their risk of hyperuricemia increased by 122.5% ( χ2=8.01, P<0.05) and 137.4% ( χ2=15.99, P<0.001), respectively. When the baseline AMNW group changed to AMO, the risk of hyperuricemia was increased by 119.2% ( χ2=6.63, P<0.05). For patients with AMO as baseline, if they turned into NMNW and AMNW at the end of follow-up, their risk of hyperuricemia would decrease by 58.3% ( χ2=43.67, P<0.001) and 27.2% ( χ2=16.07, P<0.001). Patients with a baseline of NMO who transitioned to NMNW and AMNW at the follow-up endpoint had their risk of developing hyperuricemia decreased by 36.7% ( χ2=25.35, P<0.001) and 30.9% ( χ2=9.70, P<0.05), respectively. Conclusions:The transition from metabolic health and non-overweight obesity to metabolic abnormalities and overweight obesity is associated with an increased risk of hyperuricemia, and improvements in metabolic health or weight are associated with a decreased risk of hyperuricemia.

Objective:To explore the relationship between weight change and the development of hyperuricemia (HUA) in adults receiving health checkups.Methods:A retrospective cohort study. A total of 37 722 subjects who underwent two or more health checkups at the Health Management Center of the Second Affiliated Hospital of Dalian Medical University from January 2014 to December 2022 were included, and the general information and laboratory findings at the time of the initial health checkups and follow-up were collected. Weight change was defined as the ratio of difference between the weight at the last follow-up and the baseline weight to baseline weight. The subjects were grouped with weight change: significant weight loss group (weight change ≤-5.0%), mild weight loss group (-5.0%

Objective:To investigate the association between different dietary patterns and subtypes of metabolic associated fatty liver disease(MAFLD).Methods:A total of 6 022 check-ups at the health management center of the Second Hospital of Dalian Medical University from January 2022 to March 2023 were selected as study subjects. MAFLD was categorised into three subtypes: overweight/obese type, metabolic disorder type, and diabetic type. Factor analysis was used to extract dietary patterns. Logistic regression was used to assess the impact of dietary patterns on MAFLD occurrence, constructing interaction models between dietary patterns intake and age, gender, and physical exercise levels. Results:Four dietary patterns were extracted based on feature sorting after factor analysis and were named as the high-quality protein pattern, the fruit-vegetable pattern, egg-aquatic pattern, and the processed meat pattern. Regression analysis of the unadjusted model showed that overweight/obese and diabetic types of MAFLD were negatively associated with the high-quality protein mode, while model-adjusted regression analysis showed that the processed meat pattern was positively associated with the risk of MAFLD, and fruit-vegetable pattern was positively associated with overweight/obese MAFLD( P<0.05). The results of subgroup analyses suggested that female( OR=1.55, 95% CI 1.14-2.15) with a high intake of pickle pattern had a higher risk of overweight/obese MAFLD than male( OR=1.18, 95% CI 1.02-1.49). Conclusion:High-quality protein pattern was negatively correlated with MAFLD, whereas fruit-vegetable pattern and processed meat pattern were positively correlated with MAFLD. Female with high consumption of processed meat pattern are more likely to develop overweight/obesity MAFLD compared with male. It is recommended that people with MAFLD reduce their intake of processed products and high-fructose food, and consume adequate amounts of high-quality protein food to maintain a balanced diet.

Objective:To investigate the relationship between obesity and incident chronic kidney disease(CKD) in a population undergoing health check-ups.Methods:This is a retrospective cohort study. A total of 31 251 participants who had at least 2 health physical examinations in the Health Management Center of the Second Affiliated Hospital of Dalian Medical University from January 2017 to December 2022 and met the inclusion criteria were selected. The participants were divided into normal body weight group, overweight group, and obese group according to baseline body mass index. Cox proportional hazard regression model was used to analyze the relationship between obesity and new-onset CKD, and the dose-response relationship between body mass index and CKD was analyzed with restricted cubic splines.Results:Multivariate Cox regression analysis showed that the risk of developing CKD increased by 13%( HR=1.13, 95% CI 1.01-1.25) and 55%( HR=1.55, 95% CI 1.36-1.76) in the overweight and obese group compared to the normal weight group. Subgroup analysis indicated that obese women had a higher risk of developing CKD compared to men. There was a " U-shaped" correlation between body mass index and CKD in male population, with the lowest risk of CKD occurring at body mass index of 19.6-24.2 kg/m 2. In women, the relationship between body mass index and CKD was approximately linear, with the risk of CKD gradually increasing when body mass index exceeded 22.5 kg/m 2. Conclusions:Obesity is an independent risk factor for new-onset CKD, and obese women have a higher risk of developing CKD than men. Regarding CKD prevention, men are advised to maintain a higher level of body weight within the normal range of body mass index, while women are encouraged to control their weight to a lower level within the normal body mass index range.

Objective:To explore the correlation between systemic immunity-inflammation index(SII) and hyperuricemia(HUA).Methods:Participants who had at least 3 health checkups in the Health Management Center of the Second Affiliated Hospital of Dalian Medical University from January 2014 to December 2022 were selected to construct a dynamic cohort. The SII, reflecting the inflammatory state of the body, was constructed using neutrophil, platelet, and lymphocyte counts. A Cox proportional hazard regression model was used to explore the association between SII and HUA in the overall population and different subgroups of the population, and sensitivity analysis was performed twice. Results:A total of 20 022 subjects were included, and the mean follow-up time was 3.67 years. After adjusting for confounding factors, each unit increase in the natural logarithm of SII(lnSII) was associated with a 24% increased risk of hyperuricemia( HR=1.24, 95% CI 1.16-1.32, P<0.001). As a categorical variable, compared with the lowest quartile array( Q1), the risk of HUA in the total population increased by 12%( HR=1.12, 95% CI 1.03-1.21, P=0.006), 14%( HR=1.14, 95% CI 1.06-1.24, P=0.001), 27%( HR=1.27, 95% CI 1.17-1.37, P<0.001) in Q2, Q3 and Q4 groups within the general population, respectively. All subgroup analysis and sensitivity analysis showed that SII was positively correlated with HUA. Conclusions:Elevated levels of SII significantly increase the risk of HUA. Assessing the body′s inflammatory status using SII can aid in risk screening and preventive management for individuals at high risk of HUA.

Objective:To establish an integrated model with KANO model and quality function deployment theory to determine the priority of measures in improving the quality of physical examination service.Methods:It was a cross-sectional study. A total of 196 physical examinees from the Health Management Center of the second affiliated Hospital of Dalian Medical University were selected by simple random sampling. Reliability test was used to analyze the reliability and validity of the questionnaire. KANO model was used to determine the importance of physical examination needs in health examination population. The quality function deployment model was used to create the house of quality and determine the priority of the importance of various service measures.Results:In the high important attribute requirements of physical examination, the final importance of emergency ability, outpatient time, professional and advanced equipment are 0.054, 0.052, 0.047 and 0.046 respectively. The measures that needed to be given priority to improve the quality of physical examination services were to improve the quality of medical services (absolute importance=107.5), strengthen skill assessment (absolute importance=70.1), define guidance, consultation and clinic identification (absolute importance=56.2), introduce advanced equipment and facilities (absolute importance=53.7), timely and accurate physical examination report (absolute importance=51.9) and interpretation of physical examination report (absolute importance=50.9).Conclusions:The physical examination center should give priority to the measures such as improving the medical level, strengthening the skill examination, introducing advanced equipment and facilities, defining the guidance of examination, consultation and consultation room identification, and strengthening service management.

Objective:To explore the correlation between changing trajectories of serum uric acid and the onset of nonalcoholic fatty liver disease (NAFLD).Methods:A longitudinal cohort study. Total of 3 353 subjects who had routine health examination every year from January 2017 to December 2019 in the Health Management Center of the Second Affiliated Hospital of Dalian Medical University and met the inclusion criteria were selected as the research subjects. Four different serum uric acid trajectory groups were determined by using the group-based trajectory model: the low stability group, medium stability group, medium-high stability group and high stability group. During the follow-up to December 2021, the differences in cumulative incidence of NAFLD in different serum uric acid trajectory groups were calculated and compared. Cox proportional hazard regression model was used to evaluate the hazard ratio ( HR) and 95% confidence interval ( CI) of the NAFLD onset in different serum uric acid trajectory groups. The dose-response relationship between baseline serum uric acid and NAFLD was evaluated by a restricted cubic spline regression model. Results:The cumulative incidence of NAFLD in two years was 10.77%, and the cumulative incidence increased with the rising trajectory of serum uric acid, it was the highestin the high stability group ( P<0.05). Compared that in the low stability group, the risk of NAFLD in the other three groups was as follows: 2.24 (95% CI: 1.59-3.14) in the medium stability group, 2.89 (95% CI: 1.92-4.33) in the medium-high stability group and 4.55 (95% CI:2.83-7.31) in the high stability group (all P<0.05). The risk of NAFLD gradually increased with the rising of serum uric acid level, and the cut-off value of serum uric acid for women and men was 260.32 μmol/L and 365.09 μmol/L, respectively. Conclusions:Long-term moderate and high levels of serum uric acid are independent risk factors for the occurrence of NAFLD. With the rising of serum uric acid trajectory, the risk of NAFLD increases. Attention should be paid to the longitudinal change trend of serum uric acid level in the prevention of NAFLD, and it should be controlled within lower level of the normal range.

Objective:To explore the application status and trends of the health examination data.Methods:Relevant literatures on health examination data were retrieved from Web of Science from 1 January 2007 to 30 June 2022 and China Knowledge Network Infrastructure from 1 January 1992 to 30 June 2022; and the literature and research contents were visually analyzed by using CiteSpace 6.1 bibliometrics software.Results:Total of 5 517 Chinese articles and 18 563 English articles were selected. The publication volume of health examination data in China increased year by year since 1992, and the international publication volume also showed an increasing trend. The Chinese literature focused on health management, osteoporosis, overweight, and physical examination subjects such as pilots, while the English literature focused on the study of obesity, care and functional cognition after the formation of a cohort of physical examination data. More co-citation papers related to health examination data were published in the journals from developed countries mainly in the United States and the United Kingdom, and the contents of the co-cited journals highly covered disease research on chronic diseases, such as cardiovascular disease, functional cognition and rehabilitation.Conclusions:The number of articles published on health examination data is increasing year by year, and in the future, more studies will use big data algorithms such as machine learning to measure the association between the risk of chronic diseases and related influencing factors.