Humans ; Vascular Stiffness ; Coal Mining ; Occupational Diseases/epidemiology* ; Risk Factors ; Coal ; Miners

Based on data mining technology, the acupoints compatibility rules of acupuncture for depression diseases were explored. The randomized controlled trial (RCT) articles regarding acupuncture for depression diseases published from establishment of database to September 2nd, 2022 were searched in CNKI database, Wangfang database, VIP database, SinoMed database, PubMed, EMbase, Web of Science and Cochrane Library. The use frequency of acupoints, meridian tropism, selection of special acupoints and acupoint association rules for five common depression diseases, including primary depression, post-stroke depression, menopausal syndrome, psychoneurosis and anxiety disorder, were analyzed by Python programming language. Cytoscape software was used to analyze the acupoint association and the disease-acupoint co-occurrence network. As a result, totally 387 articles were included, and 319 acupoints prescriptions for the above five common depression diseases were extracted, involving 159 acupoints. The use frequency of acupoints was 2 574 times in total. The frequently-used acupoints were Baihui (GV 20), Sanyinjiao (SP 6), Taichong (LR 3), Neiguan (PC 6), Shenmen (HT 7), Yintang (GV 24⁺), Zusanli (ST 36), Hegu (LI 4), Sishencong (EX-HN 1) and Taixi (KI 3), etc. The frequently involved meridians were the governor vessel, foot-taiyang bladder meridian, foot-taiyin spleen meridian, and foot-jueyin liver meridian. The frequency of the special acupoints from high to low was crossing points, five-shu points, yuan-primary points, back-shu points, luo-connecting points, and eight confluent points, etc, which were often used in combination with "Baihui (GV 20)-Yintang (GV 24⁺)" (the highest degree of association). At the same time, the analysis of the co-occurrence network of depression diseases and acupoints showed that the core acupoints group of acupuncture for depression diseases were Baihui (GV 20), Taichong (LR 3), Shenmen (HT 7), Zusanli (ST 36), Neiguan (PC 6) and Sanyinjiao (SP 6). In conclusion, acupuncture treatment for depression diseases has gradually formed a rule of acupoint compatibility, with special acupoint as the main body and "unblocking the governor vessel, and regulating the spirit and qi " as the main therapeutic principle.
Acupuncture Points ; Acupuncture Therapy ; Data Mining ; Depression ; Meridians ; Randomized Controlled Trials as Topic

In the digital transformation of Chinese pharmaceutical industry, how to efficiently govern and analyze industrial data and excavate the valuable information contained therein to guide the production of drug products has always been a research hotspot and application difficulty. Generally, the Chinese pharmaceutical technique is relatively extensive, and the consistency of drug quality needs to be improved. To address this problem, we proposed an optimization method combining advanced calculation tools(e.g., Bayesian network, convolutional neural network, and Pareto multi-objective optimization algorithm) with lean six sigma tools(e.g., Shewhart control chart and process performance index) to dig deeply into historical industrial data and guide the continuous improvement of pharmaceutical processes. Further, we employed this strategy to optimize the manufacturing process of sporoderm-removal Ganoderma lucidum spore powder. After optimization, we preliminarily obtained the possible interval combination of critical parameters to ensure the P_(pk) values of the critical quality properties including moisture, fineness, crude polysaccharide, and total triterpenes of the sporoderm-removal G. lucidum spore powder to be no less than 1.33. The results indicate that the proposed strategy has an industrial application value.
Bayes Theorem ; Data Mining ; Drug Industry ; Powders ; Reishi ; Spores, Fungal

The aging society has led to a substantial increase in the number of clinical comorbidities. To meet the needs of comorbidity treatment, polypharmacy is widely used in clinical practice. However, polypharmacy has drawbacks such as treatment conflict. Same treatment of different diseases refers to treating different diseases with same treatment. Therefore, the principle of same treatment of different diseases can alleviate the problems caused by polypharmacy. Under the research background of precision medicine, it becomes possible to explore the mechanism of same treatment of different diseases and achieve its clinical application. However, drugs successfully developed in the past have revealed shortcomings in clinical use. To better interpret the mechanism of precision medicine for same treatment of different diseases, under the multi-dimensional attributes including dynamic space and time, omics was performed, and a new strategy of tensor decomposition was proposed. With the characteristics of complete data, tensor decomposition is advantageous in data mining and can fully grasp the connotation of precision treatment of different diseases with same treatment under dynamic spatiotemporal changes. This method is used for drug repositioning in some biocomputations. By taking advantage of the dimensionality reduction of tensor decomposition and integrating the dual influences of time and space, this study achieved accurate target prediction of same treatment of different diseases at each stage, and discovered the mechanism of precision medicine of same treatment for different diseases, providing scientific support for precision prescription and treatment of different diseases with same treatment in clinical practice. This study thus conducted preliminary exploration of the pharmacological mechanism of precision Chinese medicine treatment.
Humans ; Data Mining ; Medicine, East Asian Traditional ; Precision Medicine

The traditional Chinese medicine(TCM) enterprises have accumulated a large amount of product quality review(PQR) data. Mining these data can reveal the hidden knowledge in production and helps improve pharmaceutical manufacturing technology. However, there are few studies involving the mining of PQR data and thus enterprises lack the guidance to analyze the data. This study proposed a method to mine the PQR data, which consisted of 4 functional modules: data collection and preprocessing, risk classification of variables, risk evaluation by batches, and the regression analysis of quality. Further, we carried out a case study of the formulation process of a TCM product to illustrate the method. In the case study, the data of 398 batches of products during 2019-2021 were collected, which contained 65 process variables. The risks of variables were classified according to the process performance index. The risk of each batch was analyzed through short-term and long-term evaluation, and the critical variables with the strongest impact on the product quality were identified by partial least square regression. The results showed that 1 variable and 13 batches were of high risk, and the critical process variable was the quality of the intermediates. The proposed method enables enterprises to comprehensively mine the PQR data and helps to enhance the process understanding and improve the quality control.
Medicine, Chinese Traditional ; Drugs, Chinese Herbal ; Data Mining/methods* ; Quality Control ; Technology, Pharmaceutical

Objective: Differential flora and differential metabolites shared by the intestinal and respiratory tracts of rats were screened to analyze the possible role of changes in intestinal flora and metabolites in the progression of pneumoconiosis in rats. Methods: In April 2020, 18 SD rats were randomly divided into three groups (control group, coal mine dust group and silica group, 6 in each group) , rats in the coal mine dust group and silica group were perfused with 1 ml of 50 mg/ml coal mine well dust suspension and silica suspension by nontracheal exposure, respectively. While rats in the control group were perfused with an equal dose of sterilized normal saline. Twenty four weeks after dust staining, rat feces, throat swabs, and lung lavages were collected. 16SrDNA gene sequencing and UHPLC-QTOF-MS untargeted metabolomics were used to analyze the flora and metabolites in feces, throat swabs and lung lavage fluid of rats in each group, to screen for shared differential flora and shared differential metabolites in intestinal and respiratory tract, and the correlation analysis between the differential flora and metabolites was performed using Spearman's statistics. Results: Compared with the control group, a total of 9 species shared differential flora between intestinal and respiratory tract were screened at phylum level, and a total of 9 species shared differential genus between intestinal and respiratory tract were screened at genus level in the coal mine dust group, mainly Firmicutes, Actinobacteria, Streptococcus, Lactobacillus, etc. Compared with the control group, a total of 9 shared differential flora were screened at the phylum level, and a total of 5 shared differential genus were screened at the genus level in the silica group, mainly Proteobacteria, Actinobacteria, Allobactera, Mucilaginibacter, etc. Compared with the control group, a total of 7 shared differential metabolites were screened for up-regulation of Stigmatellin, Linalool oxide and Isoleucine-leucine in both intestinal and respiratory tract in the coal mine dust group. Compared with the control group , a total of 19 shared differential metabolites werescreened in the silica group, of which Diethanolamine, 1-Aminocyclopropanecarboxylic acid, Isoleucine-leucine, Sphingosine, Palmitic acid, D-sphinganine, 1, 2-dioleoyl-sn-glycero-3-phosphatidylcholine, and 1-Stearoyl-2-oleoyl-sn-glycerol 3-phosphocholine were up-regulated in both the intestinal and respiratory tract. Conclusion: There is a translocation of intestinal and respiratory flora in pneumoconiosis rats, and rats have an imbalance of lipid metabolism during the progression of pneumoconiosis.
Rats ; Animals ; Isoleucine ; Leucine ; Coal Mining ; Rats, Sprague-Dawley ; Pneumoconiosis ; Dust/analysis* ; Silicon Dioxide ; Coal

Objective: To construct and verify a light-weighted convolutional neural network (CNN), and explore its application value for screening the early stage (subcategory 0/1 and stage Ⅰ of pneumoconiosis) of coal workers' pneumoconiosis (CWP) from digital chest radiography (DR) . Methods: A total of 1225 DR images of coal workers who were examined at an Occupational Disease Prevention and Control Institute in Anhui Province from October 2018 to March 2021 were retrospectively collected. All DR images were collectively diagnosed by 3 radiologists with diagnostic qualifications and gave diagnostic results. There were 692 DR images with small opacity profusion 0/- or 0/0 and 533 DR images with small opacity profusion 0/1 to stage Ⅲ of pneumoconiosis. The original chest radiographs were preprocessed differently to generate four datasets, namely 16-bit grayscale original image set (Origin16), 8-bit grayscale original image set (Origin 8), 16-bit grayscale histogram equalized image set (HE16) and 8-bit grayscale histogram equalized image set (HE8). The light-weighted CNN, ShuffleNet, was applied to train the generated prediction model on the four datasets separately. The performance of the four models for pneumoconiosis prediction was evaluated on a test set containing 130 DR images using measures such as the receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, and Youden index. The Kappa consistency test was used to compare the agreement between the model predictions and the physician diagnosed pneumoconiosis results. Results: Origin16 model achieved the highest ROC area under the curve (AUC=0.958), accuracy (92.3%), specificity (92.9%), and Youden index (0.8452) for predicting pneumoconiosis, with a sensitivity of 91.7%. And the highest consistency between identification and physician diagnosis was observed for Origin16 model (Kappa value was 0.845, 95%CI: 0.753-0.937, P<0.001). HE16 model had the highest sensitivity (98.3%) . Conclusion: The light-weighted CNN ShuffleNet model can efficiently identify the early stages of CWP, and its application in the early screening of CWP can effectively improve physicians' work efficiency.
Humans ; Retrospective Studies ; Anthracosis/diagnostic imaging* ; Pneumoconiosis/diagnostic imaging* ; Coal Mining ; Neural Networks, Computer ; Coal

The underground environment is dark and humid, and it is easy to breed pathogenic microorganisms. A lump in the right lung of a coal mine underground transport worker was found druing occupational health examination. CT examination showed that the lump was located in the posterior segment of the upper lobe of the right lung, with point strip calcification, liquefaction necrosis, and proximal bronchial stenosis and occlusion. MRI examination FS-T(2)WI and DWI showed "target sign", annular low signal around the central high signal, and low mixed signal around the periphery, and annular high signal in the isosignal lesions on T(1)WI. Then the pulmonary aspergillus infection was confirmed by pathology.
Humans ; Coal ; Miners ; Pneumonia ; Lung ; Aspergillosis ; Coal Mining

Objective: To understand the exposure level of dust and noise in the mining industry and provide data support for revising policy for the prevention and control of occupational diseases. Methods: In May 2022, Data was collected through the National Surveillance Program for Occupational Hazards in the Workplace. Descriptive analysis was conducted for dust and noise levels by industry type and enterprise size from 7, 679 enterprises in the mining industry among 29 provincial regions nationwide. Results: The enterprises in the mining industry included in the National Surveillance Program for Occupational Hazards in the Workplace are mainly small and micro, accounting for 47.97% (3684/7679) and 30.00% (230/7679) respectively. The industry is mainly compred of employers in the non-metallic ming and beneficiation industry, accounting for 50.25% (3859/7679). Among the enterprises with silica dust, coal dust, and noise hazards, the proportion of enterprises where total dust concentration and noise intensity exceed the standard is higher than 50%. 30% of the posts are with an exposure level of silica dust, coal dust, and noise that exceeds the standard. The exceedance rate and the median of the time-weighted average concentration of total coal dust among large and medium-sized enterprises are higher than those among small and micro-sized enterprises (P<0.05) . Conclusion: The dust and noise hazards in the mining industry are lower than in the past in China, but more than 25% of workers are still at a high risk of occupational pneumoconiosis and noise deafness. Therefore, intervention and surveillance strategies should be strengthened in the future.
Humans ; Dust/analysis* ; Occupational Exposure/analysis* ; Occupational Health ; Coal ; Silicon Dioxide/analysis* ; Coal Mining

Objective: To understand the social security situation of current cases of pneumoconiosis in non-coal mine industries in Jiangsu Province, and to provide reference for the treatment and security work of pneumoconiosis patients. Methods: From January to October 2020, a follow-up survey was conducted on 4038 cases of pneumoconiosis in non-coal mine industries of the province from October 1949 to December 2019. The age, type of pneumoconiosis, industry type, and social security status of the patients were collected. Namely, work-related injury insurance, employer compensation, basic medical insurance for urban and rural residents, major illness insurance, etc. SPSS 19.0 was used for statistical description and analysis. Results: The cases of pneumoconiosis in non-coal mine industries in Jiangsu Province ranged in age from 36 to 105 (70.78±8.43) years old, and had been exposed to dust for 1 to 55 (19.27±9.29) years. Silicosis was the main form (3875 cases, 95.96%), and non-metallic mining and dressing industry was the main form (2618 cases, 64.83%). A total of 3991 cases (98.84%) of pneumoconiosis patients enjoyed social security, most of them were urban and rural residents with basic medical insurance (3624 cases, 89.75%), but there were still 47 patients without any social security. 15 cases (0.37%) enjoyed the subsistence allowance, with the monthly allowance amount ranging from 104 to 3960 yuan, with the average amount of 954.87 yuan/month. Conclusion: In Jiangsu Province, the proportion of pneumoconiosis patients in non-coal mine industries enjoying social security is relatively high, but there are still patients who do not enjoy any social security, and the difference in the amount of subsistence allowance is slightly larger. It is necessary to further improve the medical security of pneumoconiosis patients and improve their quality of life.
Humans ; Adult ; Middle Aged ; Aged ; Aged, 80 and over ; Social Security ; Prevalence ; Quality of Life ; Pneumoconiosis/epidemiology* ; Silicosis/epidemiology* ; Etoposide ; Ifosfamide ; Mesna ; Coal Mining ; China/epidemiology*