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.

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.

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.

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 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.

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.

BACKGROUND: Small cell lung cancer (SCLC) with high c-Myc expression is prone to relapse and metastasis, leading to extremely low survival rate. Cyclin-dependent kinases 4 and 6 (CDK4/6) inhibitor Abemaciclib plays a key role in the treatment of tumors, but the effects and mechanisms on SCLC remain unclear. This study was to analyze the effect and molecular mechanism of Abemaciclib in inhibiting proliferation, migration and invasion of SCLC with high c-Myc expression, with a view to expanding a new direction for reducing the recurrence and metastasis. METHODS: Proteins interacting with CDK4/6 were predicted using the STRING database. The expressions of CDK4/6 and c-Myc in 31 cases of SCLC cancer tissues and paired adjacent normal tissues were analyzed by immunohistochemistry. The effects of Abemaciclib on the proliferation, invasion and migration of SCLC were detected by CCK-8, colony formation assay, Transwell and migration assay. Western blot was used to detect the expressions of CDK4/6 and related transcription factors. Flow cytometry was used to analyze the effects of Abemaciclib on the cell cycle and checkpoint of SCLC. RESULTS: The expression of CDK4/6 was associated with c-Myc by STRING protein interaction network. c-Myc can directly modalize achaete-scute complex homolog 1 (ASCL1), neuronal differentiation 1 (NEUROD1) and Yes-associated protein 1 (YAP1). Moreover, CDK4 and c-Myc regulate the expression of programmed cell death ligand 1 (PD-L1). Immunohistochemistry showed that the expressions of CDK4/6 and c-Myc in cancer tissues were higher than those in adjacent tissues(P<0.0001). CCK-8, colony formation assay, Transwell and migration assay verified that Abemaciclib could effectively inhibit the proliferation, invasion and migration of SBC-2 and H446OE(P<0.0001). Western blot analysis further showed that Abemaciclib not only inhibited CDK4 (P<0.05) and CDK6 (P<0.05), but also affected c-Myc (P<0.05), ASCL1 (P<0.05), NEUROD1 (P<0.05) and YAP1 (P<0.05), which are related to SCLC invasion and metastasis. Flow cytometry showed that Abemaciclib not only inhibited the cell cycle progression of SCLC cells (P<0.0001), but also significantly increased PD-L1 expression on SBC-2 (P<0.01) and H446OE (P<0.001). CONCLUSIONS Abemaciclib significantly inhibits the proliferation, invasion, migration and cell cycle progression of SCLC by inhibiting the expressions of CDK4/6, c-Myc, ASCL1, YAP1 and NEUROD1. Abemaciclib can also increase the expression of PD-L1 in SCLC.
Humans ; Small Cell Lung Carcinoma ; B7-H1 Antigen ; Sincalide ; Lung Neoplasms ; Neoplasm Recurrence, Local ; Transcription Factors ; Adaptor Proteins, Signal Transducing ; Cell Proliferation

Background Phenolic compounds may adversely affect human health, but the current relevant studies are mostly limited to the impact of single phenolic compound exposure on human health, and there is still a lack of studies on the population-based association between combined exposure to multiple common phenolic compounds and dyslipidemia. Objective To explore the association of phenolic compound combined exposure and dyslipidemia based on principal component analysis-random forest (PCA-RF) strategy. Methods The data were from the National Health and Nutrition Examination Survey (2013–2016). A total of 1301 adult residents aged ≥ 20 years with complete information on demographics and lifestyle, urine phenol concentrations (bisphenol A, bisphenol F, bisphenol S, triclocarban, benzophenone, and triclosan), and serum concentrations of total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were included in this study. The concentrations of six urinary phenolic compounds were determined by solid phase extraction coupled with high performance liquid chromatography and tandem mass spectrometry, and the lipid indicators were determined by enzymatic methods. Principal component analysis combined with random forest model was used for model construction. First, principal component analysis was performed on 18 original variables including 6 phenolic compounds and 12 basic characteristic indicators, and then random forest model was established with dyslipidemia and its four evaluation indicators as dependent variables and the extracted principal components as independent variables, respectively. Results The PCA-RF analysis showed that bisphenol A, bisphenol F, and benzophenone may be important factors for dyslipidemia in the study subjects; bisphenol A, bisphenol F, and triclosan may be important factors for TC level in the study subjects; bisphenol A, bisphenol F, triclocarban, and benzophenone may be important factors for TG level in the study subjects; bisphenol A may be an important factor for LDL-C level in the study subjects; bisphenol F and benzophenone may be important factors for HDL-C level in the study subjects. Conclusion Phenolic compound exposure may be an important risk factor for the development of dyslipidemia. PCA-RF strategy can be effectively used to explore the association between phenolic compound exposure and dyslipidemia in the population.