BACKGROUND: Spicy food consumption has been reported to be inversely associated with mortality from multiple diseases. However, the effect of spicy food intake on the incidence of vascular diseases in the Chinese population remains unclear. This study was conducted to explore this association. METHODS: This study was performed using the large-scale China Kadoorie Biobank (CKB) prospective cohort of 486,335 participants. The primary outcomes were vascular disease, ischemic heart disease (IHD), major coronary events (MCEs), cerebrovascular disease, stroke, and non-stroke cerebrovascular disease. A Cox proportional hazards regression model was used to assess the association between spicy food consumption and incident vascular diseases. Subgroup analysis was also performed to evaluate the heterogeneity of the association between spicy food consumption and the risk of vascular disease stratified by several basic characteristics. In addition, the joint effects of spicy food consumption and the healthy lifestyle score on the risk of vascular disease were also evaluated, and sensitivity analyses were performed to assess the reliability of the association results. RESULTS: During a median follow-up time of 12.1 years, a total of 136,125 patients with vascular disease, 46,689 patients with IHD, 10,097 patients with MCEs, 80,114 patients with cerebrovascular disease, 56,726 patients with stroke, and 40,098 patients with non-stroke cerebrovascular disease were identified. Participants who consumed spicy food 1-2 days/week (hazard ratio [HR] = 0.95, 95% confidence interval [95% CI] = [0.93, 0.97], P <0.001), 3-5 days/week (HR = 0.96, 95% CI = [0.94, 0.99], P = 0.003), and 6-7 days/week (HR = 0.97, 95% CI = [0.95, 0.99], P = 0.002) had a significantly lower risk of vascular disease than those who consumed spicy food less than once a week ( Ptrend <0.001), especially in those who were younger and living in rural areas. Notably, the disease-based subgroup analysis indicated that the inverse associations remained in IHD ( Ptrend = 0.011) and MCEs ( Ptrend = 0.002) risk. Intriguingly, there was an interaction effect between spicy food consumption and the healthy lifestyle score on the risk of IHD ( Pinteraction = 0.037). CONCLUSIONS Our findings support an inverse association between spicy food consumption and vascular disease in the Chinese population, which may provide additional dietary guidance for the prevention of vascular diseases.
Humans ; Male ; Female ; Prospective Studies ; Middle Aged ; Aged ; Vascular Diseases/etiology* ; Risk Factors ; China/epidemiology* ; Adult ; Proportional Hazards Models ; Cerebrovascular Disorders/epidemiology* ; East Asian People

By reviewing modern research and integrating clinical practice， this paper elucidates the correlation between the traditional Chinese medicine theory of pathological accumulation from collateral obstruction and gap junction intercellular communication （GJIC）， as well as its theoretical connotation and clinical application in tumor prevention and treatment. Physiologically， gap junction and collateral channels share similarities in structural distribution， substance exchange and information transmission. Pathologically， metabolic coupling mediated by dysfunctional gap junction resembles collaterals stagnation， forming the basis of tumor pathogenesis. The establishment of heterotypic gap junction parallels collateral hyperactivity， contributing to tumor metastasis. The post-translational modifications （PTMs） disorder of connexins is similar to the deficiency of collaterals， serving as a driver of tumor progression. Clinically， tumor treatment should follow the pathomechanism of collateral obstruction leading to pathological accumulation. In the early stage， detoxifying and unblocking collaterals can restore intercellular communication and inhibit tumorigenesis； in the progressive stage， calming hyperactivity and suppressing aberrant collateral pathways can prevent metastasis by interrupting heterotypic gap junction formation； and in the terminal stage， supporting vital qi and modulating PTMs of connexins can help delay tumor progression.

Objective:To develop an intelligent surveillance system for identifying upper gastrointestinal high-risk patients and assigning surveillance intervals, and to verify its efficacy.Methods:The endoscopic and pathological reports of 23 035 patients undergoing endoscopy at Renmin Hospital of Wuhan University from January to October 2021 were collected retrospectively. A training set of 17 934 patients (January to August) and a test set of 5 101 patients (September to October) were established. Keywords in the endoscopic and pathological reports were extracted by the intelligent surveillance system, and high-risk patients were automatically identified and classified into 7 risk levels. Then the standardized surveillance intervals were assigned based on the guideline. Guideline-based surveillance intervals assigned by expert endoscopists based on endoscopic and pathological reports were used as the golden standard. The accuracy of the intelligent surveillance system was calculated. Of the patients within the test set, 189 were hospitalized and the surveillance intervals given by physicians could be obtained from the electronic health records. The accuracy of the intelligent surveillance system with that of physicians from different departments was compared. Then 67 patients were randomly selected from 189 patients by simple random sampling to evaluate the adjunctive effect of the system in assigning surveillance intervals among 3 endoscopists.Results:The overall accuracy of the intelligent surveillance system in identifying upper gastrointestinal high-risk patients was 99.94% (5 098/5 101), and that of assigning surveillance intervals to correctly included patients was 100.00% (534/534). The intelligent surveillance system achieved significantly higher accuracy compared with all physicians from different departments [98.94% (187/189) VS 35.45% (67/189), χ2=118.01, P<0.001] as well as physicians from department of gastroenterology [100.00% (117/117) VS 24.79% (29/117), χ2=86.01, P<0.001]. With the assistance of the intelligent surveillance system, the endoscopists' accuracy of assigning surveillance intervals to 67 patients was significantly improved [55.22% (111/201) VS 22.39% (45/201), χ2=58.68, P<0.001]. Conclusion:The intelligent surveillance system can accurately identify upper gastrointestinal high-risk patients and assign surveillance intervals according to risk levels, which can alleviate the workload of doctors and improve the follow-up rate of patients.

Objective:To develop an artificial intelligence (AI) calculation system for the effective withdrawal time of colonoscopy and to evaluate its clinical application value.Methods:First, 17 118 colonoscopy pictures from Renmin Hospital of Wuhan University were used for training and testing to establish a deep convolutional neural network model to recognize various colonoscopy fields. Then this model was integrated with the internal and external recognition model and cecum recognition model developed by the research group to create an AI system for automatic calculation of the effective withdrawal time. Finally, 944 colonoscopy videos from the Endoscopy Center of Renmin Hospital of Wuhan University from July 1, 2020 to October 10, 2020 were included in a retrospective analysis. AI automatic computing system was used to calculate the effective withdrawal time, and 89 of them were manually calculated to evaluate the accuracy of the AI automatic computing system. The remaining 855 cases were divided into two groups according to AI calculations, namely, the effective withdrawal time <6 min group ( n=615) and the effective withdrawal time ≥6 min group ( n=240), and the differences in the overall detection rate of adenoma and polyp were compared and analyzed. Results:The accuracy of AI automatic calculation system for effective withdrawal time reached 92.1% (82/89). The overall adenoma detection rate in the group with effective withdrawal time ≥6 min was 37.5% (90/240), that in the group with effective withdrawal time <6 min was 19.0% (117/615), and the difference was statistically significant ( χ2=32.11, P<0.001). The overall polyp detection rate in the group with effective withdrawal time ≥6 min was 75.0% (180/240), and that in the group with effective withdrawal time <6 min was 45.2% (278/615), with statistical significance ( χ2=61.62, P<0.001). Conclusion:AI automatic computing system can accurately calculate the effective withdrawal time of colonoscopy, and can be used to monitor the effective withdrawal time of clinical colonoscopy. In addition, effective withdrawal time ≥6 min can effectively improve the detection rate of adenoma and polyps.

Objective:To evaluate the ability of ENDOANGEL artificial intelligence system to predict invasion depth and differentiation status of early gastric cancer using more diverse multi-center videos, and to test the performance of the new system upgraded from ENDOANGEL.Methods:Based on the completed 2020 man-machine competition for early gastric cancer diagnosis using single-center videos, the second man-machine competition was conducted in 2022, involving 30 endoscopists from 30 hospitals across 10 Chinese provinces. A multi-center video cohort was retrospectively collected from 12 institutions in 8 provinces/municipalities in China. The study proceeded in 3 stages. First, the ENDOANGEL was re-tested on multi-center videos, its performance on single and multi-center videos was compared, then the ENDOANGEL was upgraded to ENDOANGEL-2022. Second, the second man-machine competition was conducted between ENDOANGEL-2022 and 30 endoscopists using multi-center videos, and the performance between ENDOANGEL-2022, ENDOANGEL and endoscopists on multi-center videos were compared. Third, the ENDOANGEL-2022 was re-tested on the single-center videos previously collected in 2020, its performance on single and multi-center videos was also compared.Results:Compared with the performance on single-center videos, the sensitivity of ENDOANGEL for predicting submucosal invasion of early gastric cancer decreased significantly [18.18% (2/11) VS 70.00% (7/10), P=0.030], but demonstrated comparable ability to predict undifferentiated type of early gastric cancer ( P>0.05). On multi-center videos, in the respect of predicting submucosal invasion of early gastric cancer, the sensitivity of ENDOANGEL-2022 was higher than that of ENDOANGEL [40.00% (4/10) VS 18.18% (2/11), P=0.361], but inferior to that of 30 endoscopists [40.00% VS 52.04% (95% CI: 43.70%-60.38%), P<0.001]. The specificity of ENDOANGEL-2022 was lower than that of ENDOANGEL [82.86% (29/35) VS 100.00% (34/34), χ2=4.41, P=0.036] and higher than that of 30 endoscopists [82.86% VS 68.97% (95% CI: 60.83%-77.11%), P=0.018], the accuracy of ENDOANGEL-2022 was lower than that of ENDOANGEL [73.33% (33/45) VS 80.00% (36/45), χ2=0.56, P=0.455] and higher than that of 30 endoscopists [73.33% VS 65.30% (95% CI: 60.61%-69.99%), P=0.018]. In the respect of predicting undifferentiated type of early gastric cancer, the sensitivity of ENDOANGEL-2022 was higher than that of ENDOANGEL [71.43% (5/7) VS 57.14% (4/7), P>0.999] and 30 endoscopists [71.43% VS 63.11% (95% CI: 55.58%-70.64%), P=0.031], the specificity of ENDOANGEL-2022 was lower than that of ENDOANGEL [76.32% (29/38) VS 78.95% (30/38), χ2=0.08, P=0.783] and higher than that of 30 endoscopists [76.32% VS 65.27% (95% CI: 59.10%-71.44%), P=0.004],the accuracy of ENDOANGEL-2022 was similar to that of ENDOANGEL [75.56% (34/45) VS 75.56% (34/45), χ2=0.00, P>0.999] and higher than that of 30 endoscopists [75.56% VS 65.10% (95% CI: 59.96%- 70.24%), P<0.001]. Compared with performance in single center videos, the sensitivity [40.00% VS 60.00%(6/10), P=0.656], specificity [82.86% VS 93.75% (15/16), χ2=0.37, P=0.542] and accuracy [73.33% VS 80.77% (21/26), χ2=0.50, P=0.479] of ENDOANGEL-2022 for predicting submucosal invasion of early gastric cancer decreased; in predicting undifferentiated type of early gastric cancer, the sensitivity of ENDOANGEL-2022 increased [71.43% VS 37.50% (3/8), P=0.315], while the specificity [76.32% VS 100.00% (18/18), χ2=3.48, P=0.062] and accuracy [75.56% VS 80.77% (21/26), χ2=0.26, P=0.612] decreased. Conclusion:Multi-center cases introduce greater heterogeneity that may reduce artificial intelligence prediction accuracy, but the artificial intelligence system still outperforms endoscopists.

Objective:To explore status and influencing factors of surveillance in colorectal post-polypectomy patients.Methods:Patients who underwent colorectal polypectomy in Renmin Hospital of Wuhan University between April 1, 2019 and June 30, 2019 were retrospectively studied. The surveillance information was obtained through electronic health record and telephone call. Status and influencing factors of surveillance in colorectal post-polypectomy patients were evaluated. Logistic regression model was used for multivariate analysis to determine independent risk factors influencing surveillance.Results:A total of 268 colorectal post-polypectomy patients and their surveillance information were reviewed, of whom 153 (57.09%) patients received surveillance colonoscopy, and 115 (42.91%) patients did not. Univariate analysis showed that the source of patients (outpatients VS inpatients, χ 2=5.68, P=0.017), department (others VS department of gastroenterology, χ 2=6.64, P=0.010), and the number of polyps (1/(2~4)/≥5, χ2=7.32, P=0.026) influenced the outcome of surveillance. Logistic regression model indicated that department of gastroenterology ( P=0.039, OR=2.12, 95% CI:1.04-4.34), risk level 3 ( P=0.040, OR=1.92, 95% CI:1.03-3.58) and the number of polyps ≥5 ( P=0.016, OR=2.89, 95% CI:1.22-6.83) were independent risk factors influencing surveillance. Conclusion:Patients visit the department of gastroenterology or had a risk level 3 or ≥5 polyps are more likely to opt for surveillance following the procedure.

Objective:To investigate the correlationship between an artificial intelligence-based e-Boston bowel preparation scale (e-BBPS) system score and the adenoma miss rate.Methods:Colonoscopy images of 4 373 patients at the Endoscopy Center of Renmin Hospital of Wuhan University from December 21, 2017 to December 31, 2019 were collected for model training. Patients who underwent colonoscopy at the Eighth Affiliated Hospital of Sun Yat-sen University from October 8, 2021 to November 9, 2022 were prospectively included. Patient's bowel preparation was evaluated by the e-BBPS system and endoscopists based on BBPS score. If both the endoscopists and e-BPPS system believed that the bowel preparation was sufficient, the patient immediately proceeded to a second colonoscopy. Otherwise, the patient underwent bowel preparation again. The differences in adenoma and polyp miss rate between the qualified group (e-BBPS system score ≤3) and the unqualified group (e-BBPS system score >3) were compared.Results:The adenoma miss rate in the qualified group was significantly lower than that in the unqualified group [26.72% (62/232) VS 42.53% (37/87), χ2=7.384， P=0.007, OR=2.029 (95% CI: 1.212-3.396)], and the polyp miss rate in the qualified group was significantly lower than that in the unqualified group [27.28% (195/702) VS 41.24% (113/274), χ2=16.539， P<0.001, OR=1.825 (95% CI: 1.363-2.443)]. Conclusion:The deep learning-based e-BBPS system demonstrates accuracy and reliability in bowel preparation assessment, offering potential to standardize the process of evaluating bowel preparation and reduce missed lesions.

Objective To compare the effects of perfusion tube and pressure air gun on endoscopic channels drying by using the stripped endoscope model,so as to explore the application value of perfusion tube in endoscopic channels drying.Methods After the standard manual cleaning and disinfection process,the stripped endoscope model was randomly divided into group A and group B.Each group was subjected to 30 tests.Group A was injected with a perfusion tube,and group B was injected with a pressure air gun.The liquid residues in the endoscope tubes were observed visually and with a magnifying lens,and the time of thorough drying of the endoscope channels and the physical fatigue of the operators were compared between the two groups.Results The average drying time of the group A was(366.47±75.74)s,the average drying time of the group B was(338.40±65.81)s,there was no significant difference between the two groups(t=-1.53,P=0.131).The fatigue degree of the group A was lower than group B(t=16.79,P＜0.01).Conclusion There is no statistical difference in the air delivery time between the perfusion tube and the pressure air gun drying endoscopic tube,and the operator's physical fatigue is light when the perfusion tube is used,so it is recommended to use the perfusion tube drying endoscopic tube in clinic.

AIM:To evaluate the application effec-tiveness of a Bayesian mixture model based on the principal stratum strategy for estimating the com-plier average causal effect(CACE)in clinical trials with non-compliance.METHODS:Using a non-infe-riority randomized controlled trial investigating a novel drug for primary type 2 diabetes mellitus(non-inferiority margin:-0.4)as a case study,the primary analysis applied a Bayesian mixture model under the monotonicity assumption to estimate CACE of between-group differences in glycated he-moglobin(HbA1c)changes within the compliant stratum,followed by non-inferiority testing.Sensi-tivity analyses included a Bayesian mixture model relaxing the monotonicity assumption and compar-ing results with per-protocol set(PPS)analysis.RE-SULTS:In the primary analysis,the posterior mean of CACE for HbA1c change in the compliant stratum was 0.081％,with a one-sided 97.5％credible inter-val lower bound of-0.124,exceeding the non-infe-riority margin(-0.4％),supporting the non-inferiori-ty efficacy of the novel drug in the compliant stra-tum(P(H1|Data)=1).Consistent findings were ob-served in PPS analyses(estimated effect:0.136％;one-sided 97.5％credible interval lower bound:-0.069％),further validating methodological robust-ness.CONCLUSION:In clinical trials with noncom-pliance as an intercurrent event,the Bayesian mix-ture model under the principal stratum strategy ef-fectively adjusts for compliance-related bias and yields conservative,robust estimates of causal ef-fects,supporting its value in efficacy evaluation un-der complex compliance scenarios.

Objective:To construct and validate an artificial intelligence diagnostic system based on multi-feature integration for diagnosing gastric whitish neoplastic lesions under white-light endoscopy.Methods:Gastroscopic images from Renmin Hospital of Wuhan University and the Seventh Medical Center of Chinese PLA General Hospital were collected from November 2012 to July 2021. A total of 823 images of gastric whitish lesions from 267 patients were finally selected. Five white-light endoscopic features associated with gastric whitish lesions were selected through a literature search, including lesion location, boundary clarity, surface texture, roundness, and depression status. Images with manually annotated features were used to train machine learning models, with the optimal model selected as the multi-feature fitting diagnostic system, which assigned diagnostic weights to each feature. A conventional deep learning model was trained with the same dataset. The diagnostic performance of the two models were compared, and eight endoscopists of varying expertise were invited to participate in human-machine comparisons.Results:Accuracy, sensitivity, and specificity of the multi-feature fitting diagnostic system were 82.11% (101/123), 78.43% (40/51), and 84.72% (61/72), respectively. Feature weights in descending order were depression (0.71), lesion location (0.11), surface roughness (0.08), boundary clarity (0.06), and subcircular shape (0.04). The diagnostic accuracy of the system was significantly higher than that of non-expert endoscopists (82.11% VS 74.31%, Z=-2.785, P=0.008) and comparable to that of expert endoscopists (82.11% VS 83.20%, Z=-0.696, P=0.700). There was no significant difference in accuracy between the multi-feature fitting diagnostic system and the traditional deep learning model [82.11% (101/123) VS 82.93% (102/123), P=1.000]. Conclusion:The feature-weighted artificial intelligence diagnostic system for gastric whitish neoplastic lesions demonstrates clinically relevant diagnostic accuracy under white-light endoscopy.