Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters in distinguishing pathological grades of stageⅠ invasive adenocarcinoma (IAC). Methods Clinical data of patients with clinical stageⅠ IAC admitted to Yantaishan Hospital Affiliated to Binzhou Medical University from October 2018 to May 2023 were retrospectively analyzed. Based on the 2021 WHO pathological grading criteria for lung adenocarcinoma, IAC was divided into gradeⅠ, grade Ⅱ, and grade Ⅲ. The differences in parameters among the groups were compared, and logistic regression analysis was used to evaluate the predictive efficacy of AI quantitative parameters for grade Ⅲ IAC patients. Parameters were screened using least absolute shrinkage and selection operator (LASSO) regression analysis. Three machine learning models were constructed based on these parameters to predict grade Ⅲ IAC and were internally validated to assess their efficacy. Nomograms were used for visualization. Results A total of 261 IAC patients were included, including 101 males and 160 females, with an average age of 27-88 (61.96±9.17) years. Six patients had dual primary lesions, and different lesions from the same patient were analyzed as independent samples. There were 48 patients of gradeⅠ IAC, 89 patients of grade Ⅱ IAC, and 130 patients of grade Ⅲ IAC. There were statitical differences in the AI quantitive parameters such as consolidation/tumor ratio (CTR), ect among the three goups. (P<0.05). Univariate analysis showed that the differences in all variables except age were statistically significant (P<0.05) between the group gradeⅠ+grade Ⅱand the group grade Ⅲ . Multivariate analysis suggested that CTR and CT standard deviation were independent risk factors for identifying grade Ⅲ IAC, and the two were negatively correlated. Grade Ⅲ IAC exhibited advanced TNM staging, more pathological high-risk factors, higher lymph node metastasis rate, and higher proportion of advanced structure. CTR was positively correlated with the proportion of advanced structures in all patients. This correlation was also observed in grade Ⅲ but not in gradeⅠand grade ⅡIAC. CTR and CT median value were selected by using LASSO regression. Logistic regression, random forest, and XGBoost models were constructed and validated, among which, the XGBoost model demonstrated the best predictive performance. Conclusion Cautious consideration should be given to grade Ⅲ IAC when CTR is higher than 39.48% and CT standard deviation is less than 122.75 HU. The XGBoost model based on combined CTR and CT median value has good predictive efficacy for grade Ⅲ IAC, aiding clinicians in making personalized clinical decisions.

Occupational pneumoconiosis remains the most common occupational disease in China, with occupational mineral dust exposure being its primary causative factor. Although national standards for online monitoring and early warning systems of coal mine dust concentrations have been established, national occupational health standards for rapid and online monitoring of dust concentration and particle size distribution in other industries are still limited. Among dust concentration sensor technologies, the light scattering method is the preferred choice for online dust monitoring owing to its wide measurement range and low cost. The beta-ray absorption method is mature but highly sensitive to humidity. The electrostatic induction method offers high sensitivity, simple structure, and low maintenance costs but exhibits high errors in low-concentration dust monitoring. The tapered element oscillating microbalance method is highly sensitive but costly. Multi-sensor data fusion technology can improve monitoring reliability, however, mature domestic products are not yet available. For monitoring dust particle size distribution, sieving and sedimentation methods are cumbersome. The aerodynamic method shows broad prospects in the online monitoring of respirable dust but has obvious measurement errors for larger dust particles. The use of optical measurement method is limited by dust morphology and is not suitable for monitoring coal dust particle size distribution. The electrical mobility method is primarily applicable to submicron dust. Future research should focus on promoting the application of monitoring technology for respirable dust particle size distribution in online monitoring of industrial dust. By integrating Internet of Things, data mining, and artificial intelligence technologies, along with multi-sensor data fusion and numerical simulation, dust concentration prediction models can be established to achieve accurate dust concentration monitoring and early warning of exceedances. The advancements of technologies will provide scientific support for the assessment of industrial dust hazards and the prevention and control of occupational pneumoconiosis.

Objective:To study the relationship between hemoglobin glycation index (HGI) and blood lipid indices such as low-density lipoprotein cholesterol (LDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), and plasma atherogenic index (AIP).Methods:This cross-sectional study included 16 049 participants from the Beijing Apple Garden community between December 2011 and August 2012. The subjects were divided into three groups based on the HGI quartile: low ( n=5 388), medium ( n=5 249), and high ( n=5 412). The differences in blood lipid indicators between different HGI groups were compared and multivariate logistic regression model was established to analyze the association between HGI and dyslipidemia. And multivariate logistic regression model was established to analyze the relationship between HGI and blood lipid indicators in different glucose metabolism populations. Results:There were 16 049 participants in all (mean age: 56 years), including 10 452 women (65.1%). They were classified into normal glucose tolerance (9 093 cases), prediabetes (4 524 cases), and diabetes (2 432 cases) based on glucose tolerance status. In the general population, with the increase of HGI, LDL-C, non-HDL-C, and AIP gradually increased (all P values for trends were <0.05), and the proportion of abnormalities increased significantly ( χ2=101.40, 42.91, 39.80; all P<0.001). A multivariate logistic regression model was established, which suggested a significant correlation between HGI and LDL-C, non-HDL-C, and AIP (all P<0.05), after adjusting for factors such as age, sex, fasting blood glucose, hypertension, body mass index, smoking, and alcohol consumption. In the overall population, normal glucose tolerance group, and diabetes group, HGI had the highest correlation with non-HDL-C ( OR values of 1.325, 1.678, and 1.274, respectively); in the prediabetes group, HGI had a higher correlation with LDL-C ( OR value: 1.510); and in different glucose metabolism groups, AIP and HGI were both correlated ( OR: 1.208-1.250), but not superior to non-HDL-C and LDL-C. Conclusion:HGI was closely related to LDL-C, non HDL-C, and AIP in the entire population and people with different glucose metabolism, suggesting that HGI may be a predictor of atherosclerotic cardiovascular disease.

Objective:To explore the relationship between glycosylated hemoglobin A 1c/high-density lipoprotein cholesterol ratio (HbA 1c/HDL-C) and urinary albumin-creatinine ratio (UACR) in Chinese adults. Methods:In this cross-sectional study, the clinical data of 43 820 community residents (age>40 years) from the Risk Evaluation of Cancers in Chinese Diabetic Individuals (REACTION study; March-December 2012) across eight centers (Liaoning, Guangdong, Shanghai, Gansu, Guangxi, Henan, Hubei, and Sichuan) in China were collected and analyzed. Participants were divided into three groups based on UACR levels:<10 mg/g, 10-30 mg/g, and >30 mg/g. The HbA 1c/HDL-C ratio was divided into four groups according to quartile division of the subjects: 1st quartile (Q1<3.79), 2nd quartile (3.79≤Q2<4.59), 3rd quartile (4.59≤Q3≤5.66), and 4th quartile (Q4>5.66). Multivariate ordinal logistic regression model was used to analyze the relationship between HbA 1c/HDL-C and UACR. Receiver operating characteristic (ROC) analysis was used to explore the predictive value of HbA 1c/HDL-C to UACR. Results:The 43 820 subjects included 13 452 (30.70%) male and 30 378 (69.30%) female patients, with an average age of (58.00±0.05) years. According to results of one-way analysis of variance analysis, the HbA 1c/HDL-C ratio was significantly associated with the risk of increased UACR ( F=495.73, P<0.001). After adjusting for clinically relevant confounding variables in logistic regression model, compared with participants with the lowest HbA 1c/HDL-C ratio (Q1), women with the highest HbA 1c/HDL-C ratio (Q4) had a 1.483-fold (95% CI 1.376-1.598, P<0.001) and men had a 1.161-fold (95% CI 1.019-1.323, P<0.001) increased risk of UACR. The ROC curve analysis showed that the area under the curve of HbA 1c/HDL-C for predicting increased UACR was 0.623 (95% CI 0.597-0.606), with a sensitivity of 60.18% and a specificity of 54.91%. The HbA 1c/HDL-C ratio showed the highest predictive value of all glycemic and lipidemic parameters. In individuals with well-controlled blood glucose (HbA 1c<6.5%) or lipid levels (HDL-C≥1.0 mmol/L), the HbA 1c/HDL-C ratio was still independently associated with the risk of increased UACR after adjusting for confounding variables [ OR(95% CI) of quartile 4: 1.563 (1.210-2.019, P=0.001) in participants with HbA 1c<6.5% and 1.822 (1.687-1.968, P<0.001) in participants with HDL-C≥1.0 mmol/L]. Conclusion:As a novel compound indicator for evaluating glucose homeostasis and dyslipidemia, the HbA 1c/HDL-C ratio was independently associated with increased UACR in the general population aged>40 years in China, which was superior to both glycemic and lipid parameters alone.

Background The threshold limit values (TLVs) established and regularly updated by the American Conference of Governmental Industrial Hygienists (ACGIH) are widely adopted and referenced globally, serving as a crucial reference for China's occupational exposure limits (OELs). It is necessary to track it regularly and compare it with China's OELs. Objective To compare the OELs stipulated in Occupational exposure limits for hazardous agents in the workplace—Part 1: Chemical hazardous agents (GBZ 2.1—2019) and the ACGIH TLVs (2024) and to provide references for subsequent formulation and revision of OELs in China. Methods The OELs specified in GBZ 2.1—2019 and the TLVs issued by ACGIH were used to establish a database using Microsoft Excel 2019 software. Cross verification was conducted through matching Chemical Abstracts Service Registry Numbers (CAS Rn) and both Chinese and English names to ensure accuracy. Then, comparisons and analyses were carried out based on the type of limit values, which were matched as follows: permissible concentration-time weighted average (PC-TWA) with threshold limit value-time weighted average (TLV-TWA), permissible concentration-short term exposure limit (PC-STEL) with threshold limit value-short term exposure limit (TLV-STEL), and maximum allowable concentration (MAC) with threshold limit value-ceiling (TLV-C). Comparisons included types, quantities, and sizes of limits. Results The GBZ 2.1—2019 OELs and the ACGIH TLVs (2024) were generally consistent in terms of types and definitions, but there were differences in the number and size of the limits. In terms of the number of limits, GBZ 2.1—2019 specified 365 OELs for 358 chemical hazardous agents, while ACGIH TLVs (2024) included 316 corresponding limits. Among these, 148 (46.9%) limits were consistent, 38 (12.0%) were basically consistent, and 130 (41.1%) were inconsistent. In terms of the size of the limits, out of the 130 inconsistent limits, 51 OELs were lower than the corresponding TLVs, 67 OELs were higher than the corresponding TLVs, and 12 were under different limit types. For some chemical hazardous agents, their OELs were significantly lower or higher than their TLVs. Conclusion Some of the OELs for chemical hazardous agents specified in GBZ 2.1—2019 are significantly lower or higher than the TLVs. For these chemical hazardous factors, it is recommended to prioritize their inclusion in research projects and to complete the revisions as soon as possible based on the latest scientific evidence.

Optimizing operation management mode is the core task to promote the high-quality development of public hospitals.Drawing on the typical experiences and practices of operation and management of representative in-ternational hospitals in the United States,the United Kingdom,Singapore and West China Hospital of Sichuan Univer-sity,Shanghai Jiao Tong University School of Medicine Affiliated Xinhua Hospital,Jilin University China-Japanese Union Hospital of Jilin University,and carrying out a full range of comparative analyses.Put forward the new situation of China's public hospital operations and management to establish a"big operations management"concept.By iden-tifying the operation management role,rationalizing the operation management organization structure and training operation management compound talents to discuss stablishing a committee system,integrating multi-departmental resources to form a scientific and sound problem identificaiton,feedback,consultation and improvement of working mechanism,and promote the high-quality development of publit hospitals.

Objective:To analyze the epidemiological characteristics of rabies in China from 2007 to 2023, and to provide reference evidence for tailoring strategies to facilitate the elimination of rabies in the country.Methods:Case data from 2007 to 2023 were obtained from China′s National Notifiable Infectious Disease Reporting Information System, and the spatial, temporal, and demographic features of cases were analyzed.Results:From 2007 to 2023, a total of 18 751 human rabies cases were reported in China, with an average annual incidence rate of 0.08 per 100 000. The average annual percentage change (AAPC) in incidence rate was -18.58% (95% CI: -21.32% to -15.75%, P<0.05), with three significant turning points in 2011, 2018, and 2021. Based on the trend of the epidemic, Chinese provinces can be roughly divided into five categories. The geographical range affected by rabies has decreased from 23 provinces and 984 counties (districts) in 2007 to 17 provinces and 101 counties (districts) in 2023. Since 2019, the high-incidence counties (districts) have been mainly concentrated in the southwestern part of Hunan, the southern part of Henan, and the western part of Anhui. Fourteen provinces have reported no cases for at least two consecutive years. Males (70.24%) and farmers (72.18%) were the main affected groups, and the proportion of cases aged 65 and above increased from 17.43% in 2007 to 36.07% in 2023. Conclusions:The incidence of rabies in China has changed from endemic in many areas to sporadic, with the remaining endemic regions mainly located in parts of the middle and lower reaches of the Yangtze River. The main vulnerable groups are middle-aged and elderly farmers. The current prevention and control measures can effectively curb the transmission of rabies, but the decline of cases has slowed down recently.

Objective:To explore the correlation of bone marrow polychonal plasma cell proportion (pPC% ) and clinical features in newly diagnosed multiple myeloma (NDMM) patients.Methods:A retrospective analysis of 317 patients with NDMM admitted to Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from January 2018 to January 2023 was performed. The results of the pPC% in all patients were clear. The relationship between the pPC% and clinical characteristics was analyzed.Results:A total of 317 patients were included, comprising 180 males and 137 females. The median age at diagnosis was 61 (26-91) years, and 55.8% were 60 years or older. The pPC% in the bone marrow of patients with NDMM was different in the DS, International Staging System (ISS), and revised ISS (R-ISS) stages ( P=0.002, 0.010, and 0.049, respectively), whereas no statistical difference in pPC% was observed among patients with different FISH risk stratigrams ( P=0.971). The correlation coefficient between pPC% and hemoglobin (HGB) at the first diagnosis in patients was 0.211 ( P<0.01). The correlation coefficients with serum calcium, serum creatinine, M protein level, and β 2-microglobulin were -0.141, -0.120, -0.181, and -0.207, respectively, and the results of the significance test were P=0.012, 0.033, 0.004, and 0.002, respectively, indicating a negative correlation. Compared with the patients with a pPC% of ≥2.5%, the group of patients with a pPC% of <2.5% had significantly higher levels of light chain, serum calcium, serum creatinine, M protein, and β 2-microglobulin at the initial diagnosis ( P<0.05) ; lower HGB level ( P<0.001) ; and a higher proportion of patients in ISS stage Ⅲ ( P=0.034) . Conclusion:In this study, the pPC% in patients with NDMM was associated with clinical features of good prognosis, including higher HGB, lower serum calcium, serum creatinine, M protein quantity, β 2-microglobulin, light chain involvement, lower proportion of advanced disease (DS stage and ISS stage Ⅲ), and clinical features showing lower tumor burden.

OBJECTIVE: This study explored whether thyroglobulin and thyroid disease prevalence rates were higher in pregnant Chinese women with a median urinary iodine concentration of 100-149 µg/L, compared with those with a median urinary iodine concentration of 150-249 μg/L maintained through sustainable universal salt iodization. METHODS: This was a cross-sectional study in which 812 healthy pregnant women were enrolled to collect samples of their household edible salt, urine, and blood during their routine antenatal care in the 18 counties in Fujian Province, China. The levels of salt iodine concentration, urinary iodine concentration (UIC), free triiodothyronine (FT3), free thyroid hormone (FT4), thyroid-stimulating hormone (TSH), thyroglobulin (Tg), thyroid peroxidase antibody and thyroglobulin antibody were assessed during the routine antenatal care visits. RESULTS: The median UIC (mUIC) in pregnant women was 130.8 μg/L (interquartile range = 91.5-198.1 μg/L) in the counties with an mUIC of 100-149 μg/L (Group I), and 172.0 μg/L (interquartile range = 123.5-244.4 μg/L) in the counties with an mUIC of 150-249 μg/L (Group II). Goiter prevalence and thyroid nodule detection rates showed no difference between Group I and Group II ( P > 0.05). Except for FT4 values, the TSH, FT4, FT3, Tg and Tg values > 40 (μg/L) and the thyroid diseases prevalence rate (TDR) showed no significant differences between Group I and Group II ( P > 0.05), whether or not iodine supplementation measures were taken. CONCLUSION Compared with an mUIC of 150-249 μg/L, not only there was no difference in thyroid morphology, but also the Tg value, rate of Tg values > 40 µg/L, and TDR were not higher in pregnant women in the counties with an mUIC of 100-149 μg/L achieved through sustainable universal salt iodization in Fujian Province, China.
Female ; Humans ; Pregnancy ; Cross-Sectional Studies ; Iodine/urine* ; Pregnant Women ; Sodium Chloride, Dietary ; Thyroglobulin ; Thyroid Gland ; Thyrotropin ; East Asian People

Objective:To investigate the various factors that contribute to self-reported hypoglycemia in elderly patients with type 2 diabetes mellitus.Methods:This is a prospective cohort study that included 1, 010 elderly patients with type 2 diabetes mellitus who were hospitalized in Tianjin Medical University Chu Hsien-I Memorial Hospital from April to September 2019.Patients were selected using the convenient sampling method and were between the ages of 60-90(66.7±5.3)years with a disease course of 0.5-40.0 years.Baseline data was collected through questionnaires and patients were followed up for six months.The study observed self-reported hypoglycemia events and analyzed the influencing factors of self-reported hypoglycemia using logistic regression.Results:Out of the 1, 010 patients who participated in the study, 901(89.2%)completed it.Of those who completed the study, 321(35.6%)reported experiencing hypoglycemia.The median frequency of hypoglycemia was found to be 3(2, 4)times.Several factors were found to be influential in self-reported hypoglycemia in elderly patients with type 2 diabetes.These factors included waist circumference, HbA1c, thiazolidinediones combined with insulin therapy, frequency of hypoglycemia in the past year, blood glucose prevention education, and fear of hypoglycemia.All of these factors had a significant impact on self-reported hypoglycemia(all P<0.05). Conclusions:Medical professionals should prioritize patients who are undergoing thiazolidinediones combined with insulin therapy and frequently experience hypoglycemia.They should address the patients' fear of hypoglycemia and provide them with comprehensive education.