This study aimed to comprehensively examine the association of gallstones, cholecystectomy, and cancer risk. Multivariable logistic regressions were performed to estimate the observational associations of gallstones and cholecystectomy with cancer risk, using data from a nationwide cohort involving 239 799 participants. General and gender-specific two-sample Mendelian randomization (MR) analysis was further conducted to assess the causalities of the observed associations. Observationally, a history of gallstones without cholecystectomy was associated with a high risk of stomach cancer (adjusted odds ratio (aOR)=2.54, 95% confidence interval (CI) 1.50-4.28), liver and bile duct cancer (aOR=2.46, 95% CI 1.17-5.16), kidney cancer (aOR=2.04, 95% CI 1.05-3.94), and bladder cancer (aOR=2.23, 95% CI 1.01-5.13) in the general population, as well as cervical cancer (aOR=1.69, 95% CI 1.12-2.56) in women. Moreover, cholecystectomy was associated with high odds of stomach cancer (aOR=2.41, 95% CI 1.29-4.49), colorectal cancer (aOR=1.83, 95% CI 1.18-2.85), and cancer of liver and bile duct (aOR=2.58, 95% CI 1.11-6.02). MR analysis only supported the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer. This study added evidence to the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer, highlighting the importance of cancer screening in individuals with gallstones.
Humans ; Mendelian Randomization Analysis ; Gallstones/complications* ; Female ; Male ; Cholecystectomy/statistics & numerical data* ; Middle Aged ; Risk Factors ; Aged ; Adult ; Neoplasms/etiology* ; Stomach Neoplasms/epidemiology*

Objective:Middle ear cholesteatoma is a common otolaryngological disease, and traditional diagnostic methods have certain limitations. This study aims to construct a computer-aided diagnosis model for middle ear cholesteatoma based on integrated convolutional neural networks (CNNs) to improve diagnostic accuracy and efficiency.Methods:Firstly, Data were collected from patients who visited the Department of Otorhinolaryngology Head and Neck Surgery at the First People′s Hospital of Yinchuan between January 2020 and December 2021. 8 000 temporal bone CT images were collected, including 5 000 images diagnosed pathologically as middle ear cholesteatoma and 3 000 normal images. A five-fold cross-validation method was used to divide the dataset into training and testing sets. Next, a transfer learning approach was used to initialize model parameters, and the AlexNet, GoogleNet, and ResNet networks were pre-trained to extract deep features from the images. Then, the Softmax classification algorithm was applied to classify the features, resulting in three independent classifiers. These classifiers were combined using an ensemble learning method with a weighted voting approach to obtain the final diagnostic results. Finally, the model was evaluated by comparing the ensemble classifier with individual classifiers to assess its accuracy, precision, sensitivity, specificity, and diagnostic time, and a comparison with low-mid-and high-experience physician groups was conducted to comprehensively evaluate the model′s diagnostic performance.Results:The experimental results showed that the model achieved an accuracy of 88.8%(178/200), precision of 92.9%,(112/120) sensitivity of 89.8%(108/120), and specificity of 88.1%(70/80). The average diagnostic time for individual patient temporal bone CT images was reduced to 2-3 seconds. Compared to the diagnostic results from low-mid-and high-experience physician groups, the model demonstrated significant advantages and effectively assisted clinicians in making rapid and accurate middle ear cholesteatoma diagnoses.Conclusion:The proposed middle ear cholesteatoma diagnostic model based on integrated convolutional neural networks exhibits high recognition accuracy and rapid diagnostic speed, significantly improving clinical diagnostic efficiency, especially in early screening and auxiliary diagnosis, making it of considerable value in clinical practice.

Objective:Middle ear cholesteatoma is a common otolaryngological disease, and traditional diagnostic methods have certain limitations. This study aims to construct a computer-aided diagnosis model for middle ear cholesteatoma based on integrated convolutional neural networks (CNNs) to improve diagnostic accuracy and efficiency.Methods:Firstly, Data were collected from patients who visited the Department of Otorhinolaryngology Head and Neck Surgery at the First People′s Hospital of Yinchuan between January 2020 and December 2021. 8 000 temporal bone CT images were collected, including 5 000 images diagnosed pathologically as middle ear cholesteatoma and 3 000 normal images. A five-fold cross-validation method was used to divide the dataset into training and testing sets. Next, a transfer learning approach was used to initialize model parameters, and the AlexNet, GoogleNet, and ResNet networks were pre-trained to extract deep features from the images. Then, the Softmax classification algorithm was applied to classify the features, resulting in three independent classifiers. These classifiers were combined using an ensemble learning method with a weighted voting approach to obtain the final diagnostic results. Finally, the model was evaluated by comparing the ensemble classifier with individual classifiers to assess its accuracy, precision, sensitivity, specificity, and diagnostic time, and a comparison with low-mid-and high-experience physician groups was conducted to comprehensively evaluate the model′s diagnostic performance.Results:The experimental results showed that the model achieved an accuracy of 88.8%(178/200), precision of 92.9%,(112/120) sensitivity of 89.8%(108/120), and specificity of 88.1%(70/80). The average diagnostic time for individual patient temporal bone CT images was reduced to 2-3 seconds. Compared to the diagnostic results from low-mid-and high-experience physician groups, the model demonstrated significant advantages and effectively assisted clinicians in making rapid and accurate middle ear cholesteatoma diagnoses.Conclusion:The proposed middle ear cholesteatoma diagnostic model based on integrated convolutional neural networks exhibits high recognition accuracy and rapid diagnostic speed, significantly improving clinical diagnostic efficiency, especially in early screening and auxiliary diagnosis, making it of considerable value in clinical practice.

Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients′suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.

Background/Aims: Low educational attainment is a well-established risk factor for nonalcoholic fatty liver disease (NAFLD) in developed areas. However, the association between educational attainment and the risk of NAFLD is less clear in China. Methods: A cross-sectional study including over 200,000 Chinese adults across mainland China was conducted. Information on education level and lifestyle factors were obtained through standard questionnaires, while NAFLD and advanced fibrosis were diagnosed using validated formulas. Outcomes included the risk of NAFLD in the general population and high probability of fibrosis among patients with NAFLD. Logistic regression analysis was employed to estimate the risk of NAFLD and fibrosis across education levels. A causal mediation model was used to explore the potential mediators. Results: Comparing with those receiving primary school education, the multi-adjusted odds ratios (95% confidence intervals) for NAFLD were 1.28 (1.16 to 1.41) for men and 0.94 (0.89 to 0.99) for women with college education after accounting for body mass index. When considering waist circumference, the odds ratios (95% CIs) were 0.94 (0.86 to 1.04) for men and 0.88 (0.80 to 0.97) for women, respectively. The proportions mediated by general and central obesity were 51.00% and 68.04% for men, while for women the proportions were 48.58% and 32.58%, respectively. Furthermore, NAFLD patients with lower educational attainment showed an incremental increased risk of advanced fibrosis in both genders. Conclusions In China, a low education level was associated with a higher risk of prevalent NAFLD in women, as well as high probability of fibrosis in both genders.

Objective:To investigate the distribution of allergens in patients with allergic rhinitis （AR） in Ningxia, and provide theoretical data for the prevention and treatment of AR in this region. Methods:A total of 1664 patients diagnosed with AR in the Otorhinolaryngology Head and Neck Surgery Department of Yinchuan First People's Hospital Outpatient Clinic from January 2018 to December 2021 were retrospectively collected. Use the allergen sIgE antibody detection kit （immunoblotting method） to detect inhalation and ingestion allergens in patients.Results: ①Among all AR patients, 1 158 cases were detected positive, resulting in the detection rate was 69.59%; ②The detection rate of inhalation allergen was 65.87%, and the detection rate of ingestion allergen was 19.83%; ③Mugwort was the most sensitive allergen, and 76.32% of the patients having a positive grade ≥3; ④Out of the patients, 294 cases （25.39%） were allergic to only one allergen, 244 cases （21.07%） were allergic to two allergens, and 620 cases （53.54%） were allergic to three or more allergens; ⑤During different seasons, the highest number of positive allergens detected was in the summer, with 968 cases （83.59%）. Mugwort was the main allergen during this season （69.01%）. After the COVID-19 epidemic, the total positive rate of sIgE tests in AR patients decreased compared to before, and the difference was statistically significant （P<0.001）; ⑥Mugwort, dog epithelium, mold combination, egg, peanut, soybean, Marine fish combination and fruit combination all showed statistically significant differences between different gender groups （P<0.05）; ⑦Common ragweed, mugwort, dust mite combination, cockroach, egg, milk, Marine fish combination, shrimp, fruit combination and nut combination all showed statistically significant differences among different age groups （P<0.05）; ⑧There were statistically significant differences in hay dust among different ethnic groups （P<0.05）. Conclusion:Artemisia argyi is the main allergen in Ningxia, and the distribution characteristics of different allergens are influenced by treatment season, the COVID-19 epidemic, gender, age, ethnicity, and other factors, showing certain distribution patterns and rules.
Allergens ; Artemisia ; COVID-19 ; Retrospective Studies ; Rhinitis, Allergic ; Skin Tests ; Humans ; Male ; Female

Objective To evaluate the role of preoperative serological indexes in predicting long-term survival and tumor recurrence of hepatocellular carcinoma (HCC) patients after liver transplantation, aiming to explore its significance in expanding the Milan criteria. Methods Clinical data of 669 recipients undergoing liver transplantation for HCC were retrospectively analyzed. The optimal cut-off value was calculated by the receiver operating characteristic (ROC) curve. The risk factors affecting the overall survival and recurrence-free survival rates of HCC patients after liver transplantation were identified by univariate and multivariate regression analyses. The correlation between preoperative serum liver enzymes and pathological characteristics in HCC patients was analyzed. The predictive values of alpha-fetoprotein (AFP) combined with γ -glutamyl transferase (GGT) and different liver transplant criteria for the survival and recurrence of HCC patients after liver transplantation were compared. Results Exceeded Milan criteria, total tumor diameter (TTD) > 8 cm, AFP > 200 ng/mL and GGT > 84 U/L were the independent risk factors for the overall survival and recurrence-free survival rates of HCC patients after liver transplantation (all P < 0.05). Correlation analysis showed that preoperative serum GGT level was correlated with TTD, number of tumor, venous invasion, microsatellite lesions, capsular invasion, tumor, node, metastasis (TNM) stage, Child-Pugh score and exceeded Milan criteria (all P < 0.05). Milan-AFP-GGT-TTD (M-AGT) criteria were proposed by combining Milan criteria, TTD with serum liver enzyme indexes (AFP and GGT). The 5-year overall survival and recurrence-free survival rates of HCC recipients who met the M-AGT criteria (111 cases of exceeded Milan criteria) were significantly higher than those who met Hangzhou criteria (both P < 0.05), whereas had no significant difference from their counterparts who met the University of California at San Francisco (UCSF) criteria (both P > 0.05). Conclusions Preoperative serological indexes of AFP and GGT could effectively predict the long-term survival and tumor recurrence of HCC patients after liver transplantation. Establishing the M-AGT criteria based on serological indexes contributes to expanding the Milan criteria, which is convenient and feasible.

Objective: The relationship between serum uric acid (SUA) levels and glycemic indices, including plasma glucose (FPG), 2-hour postload glucose (2h-PG), and glycated hemoglobin (HbA1c), remains inconclusive. We aimed to explore the associations between glycemic indices and SUA levels in the general Chinese population. Methods: The current study was a cross-sectional analysis using the first follow-up survey data from The China Cardiometabolic Disease and Cancer Cohort Study. A total of 105,922 community-dwelling adults aged ≥ 40 years underwent the oral glucose tolerance test and uric acid assessment. The nonlinear relationships between glycemic indices and SUA levels were explored using generalized additive models. Results: A total of 30,941 men and 62,361 women were eligible for the current analysis. Generalized additive models verified the inverted U-shaped association between glycemic indices and SUA levels, but with different inflection points in men and women. The thresholds for FPG, 2h-PG, and HbA1c for men and women were 6.5/8.0 mmol/L, 11.0/14.0 mmol/L, and 6.1/6.5, respectively (SUA levels increased with increasing glycemic indices before the inflection points and then eventually decreased with further increases in the glycemic indices). Conclusion An inverted U-shaped association was observed between major glycemic indices and uric acid levels in both sexes, while the inflection points were reached earlier in men than in women.
Aged ; Asian Continental Ancestry Group ; Blood Glucose/analysis* ; China/epidemiology* ; Cohort Studies ; Diabetes Mellitus/blood* ; Female ; Glucose Tolerance Test ; Glycated Hemoglobin A/analysis* ; Glycemic Index ; Humans ; Male ; Middle Aged ; Uric Acid/blood*

Air Pollution ; China ; Humans ; Meteorological Concepts ; Outpatients ; Particulate Matter/analysis* ; Skin Diseases/epidemiology*