OBJECTIVE To investigate the current status of self-medication behaviors and the demand preferences for pharmaceutical services among residents in Xining， providing a basis for developing pharmaceutical service intervention strategies tailored to regional characteristics. METHODS A self-designed questionnaire， developed based on literature review and revised after expert panel discussion and a pre-survey， was used to conduct anonymous surveys among residents purchasing medicines at 12 retail pharmacies in Xining area from April 2023 to April 2024. Descriptive analysis， Chi-square test， and Spearman correlation analysis were employed to analyze the characteristics and influencing factors of residents’ self-medication behaviors， and pharmaceutical service needs. RESULTS A total of 859 valid questionnaires were collected， with 605 respondents （70.43%） reporting self-medication behaviors. The primary reason for self-medication was mild symptoms （46.94%）. The main conditions treated were gastrointestinal discomfort （38.51%） and cold， fever， headache （35.37%）. Chinese patent medicines were the most commonly used （58.68%）. Drug selection relied mainly on recommendations by pharmacy staff （52.07%）， and retail pharmacies were the primary source of medicines （65.95%）. Dosage and administration were determined primarily by referring to the drug package insert （67.27%）， while a minority relied on personal experience （9.92%）. Notably， 20.33% of respondents never read the package insert before medication， with a higher tendency observed among ethnic minorities， farmers， and individuals with chronic diseases （P＜0.05）. While 65.29% of respondents reported partially understanding the insert content， those aged ≥60 years， ethnic minorities， and individuals with chronic diseases qhsrmyy-28） were more likely to report being completely unable to understand it （P＜0.05）. Self-medication was “often effective” for 52.73% of respondents， whereas 7.77%“ often experienced adverse drug reactions”； farmers were more prone to poor efficacy or adverse reactions （P＜0.05）. A majority （72.89%） of respondents residents believed it necessary to learn about self- medication knowledge， and 47.11% preferred to obtain pharmaceutical services through WeChat public accounts or mobile applications （APP）. CONCLUSIONS Self-medication is common among residents in Xining area. Issues such as neglecting to read package inserts and relying on personal experience are evident. It is essential to develop science popularization programs suitable for the characteristics of the local population and utilize digital platforms like WeChat public accounts and APP to disseminate self- medication knowledge， thereby enhancing residents’ awareness and capacity for rational medication use.

The plants of the genus Caragana Fabr. are desert plants of the Leguminosae family, which are widely used in traditional ethnomedicine, with the effects of nourishing Yin and nourishing blood, dispelling wind and removing dampness, clearing heat and detoxifying toxins. The anti-inflammatory effect of Caragana Fabr. has attracted much attention, the research on the related mechanism has made some progress, but it lacks systematic collation. By summarising the anti-inflammatory effects of the active ingredients of Caragana Fabr., the authors found that they have good anti-inflammatory activities on different inflammatory cell models in vitro, and have good improvement effects on rheumatoid arthritis, colitis, complex nephritis, and acute lung injury and other disease models. The anti-inflammatory effects of the active components of this genus mainly include the reduction of the levels of a variety of pro-inflammatory factors, and the signalling pathways involved mainly include TLR4/NF-κB, TLR4/MAPK, TLR4/NF-κB/IRF3, JAK/STAT-1, ERK/STAT-1 and so on. Therefore, the paper mainly reviews the research progress on the anti-inflammatory effects and mechanisms of the Caragana Fabr. plants and their active components according to disease types, with a view to providing reference for the in-depth study of their anti-inflammatory activities and the development of new products with related functions.

The establishment of mouse models is critical for discovering the biological targets of tumorigenesis and cancer development, preclinical trials of targeted drugs, and formulation of personalized therapeutic regimens. Currently, the patient-derived xenograft (PDX) model is considered a reliable animal tumor model because of its ability to retain the characteristics of the primary tumor at the histopathological, molecular, and genetic levels, and to preserve the tumor microenvironment. The application of the PDX model has promoted in-depth research on tumors in recent years, focusing on drug development, tumor target discovery, and precise treatment of patients. However, there are still some common questions. This review introduces the latest research progress and common questions regarding tumors with high mortality rates, focusing on their application in targeted drug screening and the formulation of personalized medical strategies. The challenges faced, improvement methods, and future development of the PDX model in tumor treatment applications are also discussed. This article provides technical guidance and comprehensive expectations for anti-cancer drug screening and clinical personalized therapy.

Objective:To investigate the value of a deep learning radiomics (DLR) nomogram model based on intra-tumoral and peri-tumoral MRI features for differentiating IgG4-related ophthalmic disease (IgG4-ROD) from orbital mucosa-associated lymphoid tissue (MALT) lymphoma.Methods:This was a case-control study. The clinical and imaging data of 233 patients pathologically confirmed with either IgG4-ROD or orbital MALT lymphoma were retrospective collected between January 2020 and December 2024 from the Second Hospital of Jilin University (Center 1) and Beijing Tongren Hospital, Capital Medical University (Center 2). Patients from Center 1 ( n=158) were used as the training cohort, while those from Center 2 ( n=75) served as the validation cohort. Among the cases, 102 were IgG4-ROD (70 in training, 32 in validation) and 131 were orbital MALT lymphoma (88 in training, 43 in validation). Univariate and multivariate logistic regression analyses were used to identify independent clinical imaging predictors and build a clinical imaging model. Based on T 1WI, T 2WI, and diffusion weighted images, intra-tumoral regions were manually delineated, a 2 mm peri-tumoral margin was automatically generated, and both regions were combined as a single region of interest for radiomics feature extraction. Deep learning features were extracted using a ResNet-50 backbone, and after feature selection and dimensionality reduction, a DLR model was constructed. The clinical imaging features and DLR features were integrated to build a combined nomogram model. Model performance in differentiating IgG4-ROD from orbital MALT lymphoma was assessed using receiver operating characteristic curves, calibration curves, and decision curve analysis. The area under the curve (AUC) were compared using the DeLong test. Results:Bilateral orbital involvement ( OR=1.983, 95% CI 1.166-2.843, P=0.046) and extraocular muscle involvement ( OR=1.246, 95% CI 1.079-1.764, P=0.015) were identified as independent predictors for distinguishing IgG4-ROD from orbital MALT lymphoma and were used to construct the clinical model. Fourteen features (9 radiomics and 5 deep learning features) were selected for the DLR model, and a nomogram was developed. In the training set, the AUCs for the clinical model, DLR model, and nomogram were 0.762 (95% CI 0.712-0.812), 0.865 (95% CI 0.822-0.908), and 0.943 (95% CI 0.909-0.953), respectively. In the validation set, the AUCs were 0.733 (95% CI 0.675-0.791), 0.823 (95% CI 0.762-0.884), and 0.924 (95% CI 0.902-0.958), respectively. The nomogram showed significantly higher AUCs than those of the clinical and DLR models alone (training set: Z=3.92, 2.87, P0.001, P=0.004; validation set: Z=3.25, 2.46, P=0.001, 0.014). Calibration curves indicated good agreement between predicted and actual IgG4-ROD incidence, and decision curve analysis demonstrated the highest net benefit for the nomogram. Conclusion:A nomogram that incorporates both intra-tumoral and peri-tumoral DLR features and clinical imaging characteristics demonstrates excellent performance in distinguishing IgG4-ROD from orbital MALT lymphoma.

Background and purpose:Thyroid cancer is the most common malignant endocrine tumor,particularly prevalent among the Asian population.The overall survival for thyroid cancer patients is relatively high,but there are significant survival differences among patients.Based on long-term hospital-based cancer registry database,this study analyzed the 10-year observed overall survival(OS)rate of thyroid cancer cases and the distribution of causes of death,providing real-world evidences to further survival management of thyroid cancer in China.Methods:A total of 55343 thyroid cancer patients who underwent treatment at Fudan University Shanghai Cancer center from 2005 to 2021 were included in this study.Clinical information and the follow-up endpoint data were collected through medical records review,telephone visits and death registry data linkage.The last follow-up date was October 31,2024.Kaplan-Meier method was applied in evaluating the OS rate,and survival data were described by different subgroups as age group,gender,treatment period,tumor staging and pathological characteristics.The standardized mortality ratio(SMR)and absolute excess risk(AER)were calculated using general Shanghai population as the reference,and the mortality risk was described by gender,age at diagnosis and histological subtype.Results:With a median follow-up time of 63.01 months,the overall 1-,3-,5-and 10-year OS rates of thyroid cancer patients were 99.67%(95%CI:99.62%-99.72%),99.11%(95%CI:99.03%-99.19%),98.48%(95%CI:98.36%-98.60%)and 95.81%(95%CI:95.50%-96.11%),respectively.The 10-year OS rates of stage Ⅰ,Ⅱ,Ⅲ and Ⅳ were 97.99%(95%CI:97.70%-98.28%),89.80%(95%CI:87.24%-92.37%),77.84%(95%CI:70.76%-84.92%)and 62.95%(95%CI:55.37%-70.54%),respectively.The differences in OS among patients with different age,gender and histological classification were significant.1256(2.27%)deaths occurred,of which 18.63%,50.88%and 7.32%were attributable to thyroid cancer,other cancers and cardiovascular disease(CVD),respectively.Compared with the general population,patients with different subtypes of thyroid cancer had higher all-cause mortality rates,progressively increasing with papillary,follicular,medullary and anaplastic thyroid carcinoma/poorly differentiated carcinoma.Compared with general population,the death risk was 2.24 times higher in papillary thyroid cancer patients(95%CI:2.06-2.44),9.94 times higher in follicular thyroid cancer patients(95%CI:6.79-14.09),12.16 times higher in medullary thyroid cancer patients(95%CI:8.05-17.69),and the highest risk was observed in patients with anaplastic thyroid carcinoma/poorly differentiated carcinoma[SMR=79.67(95%CI:58.38-106.31),AER=766.01/1 000 person-years].Conclusion:The 10-year long survival data and cause of death for thyroid cancer patients with different histological types were reported in China based on a large single institution hospital-based cancer registry database.Staging and histological characteristics were the most important factors directly affected the survival.Early diagnosis and individualized treatment are crucial for improving prognosis.

Objective:To investigate the value of a deep learning radiomics (DLR) nomogram model based on intra-tumoral and peri-tumoral MRI features for differentiating IgG4-related ophthalmic disease (IgG4-ROD) from orbital mucosa-associated lymphoid tissue (MALT) lymphoma.Methods:This was a case-control study. The clinical and imaging data of 233 patients pathologically confirmed with either IgG4-ROD or orbital MALT lymphoma were retrospective collected between January 2020 and December 2024 from the Second Hospital of Jilin University (Center 1) and Beijing Tongren Hospital, Capital Medical University (Center 2). Patients from Center 1 ( n=158) were used as the training cohort, while those from Center 2 ( n=75) served as the validation cohort. Among the cases, 102 were IgG4-ROD (70 in training, 32 in validation) and 131 were orbital MALT lymphoma (88 in training, 43 in validation). Univariate and multivariate logistic regression analyses were used to identify independent clinical imaging predictors and build a clinical imaging model. Based on T 1WI, T 2WI, and diffusion weighted images, intra-tumoral regions were manually delineated, a 2 mm peri-tumoral margin was automatically generated, and both regions were combined as a single region of interest for radiomics feature extraction. Deep learning features were extracted using a ResNet-50 backbone, and after feature selection and dimensionality reduction, a DLR model was constructed. The clinical imaging features and DLR features were integrated to build a combined nomogram model. Model performance in differentiating IgG4-ROD from orbital MALT lymphoma was assessed using receiver operating characteristic curves, calibration curves, and decision curve analysis. The area under the curve (AUC) were compared using the DeLong test. Results:Bilateral orbital involvement ( OR=1.983, 95% CI 1.166-2.843, P=0.046) and extraocular muscle involvement ( OR=1.246, 95% CI 1.079-1.764, P=0.015) were identified as independent predictors for distinguishing IgG4-ROD from orbital MALT lymphoma and were used to construct the clinical model. Fourteen features (9 radiomics and 5 deep learning features) were selected for the DLR model, and a nomogram was developed. In the training set, the AUCs for the clinical model, DLR model, and nomogram were 0.762 (95% CI 0.712-0.812), 0.865 (95% CI 0.822-0.908), and 0.943 (95% CI 0.909-0.953), respectively. In the validation set, the AUCs were 0.733 (95% CI 0.675-0.791), 0.823 (95% CI 0.762-0.884), and 0.924 (95% CI 0.902-0.958), respectively. The nomogram showed significantly higher AUCs than those of the clinical and DLR models alone (training set: Z=3.92, 2.87, P0.001, P=0.004; validation set: Z=3.25, 2.46, P=0.001, 0.014). Calibration curves indicated good agreement between predicted and actual IgG4-ROD incidence, and decision curve analysis demonstrated the highest net benefit for the nomogram. Conclusion:A nomogram that incorporates both intra-tumoral and peri-tumoral DLR features and clinical imaging characteristics demonstrates excellent performance in distinguishing IgG4-ROD from orbital MALT lymphoma.

Background and purpose:Thyroid cancer is the most common malignant endocrine tumor,particularly prevalent among the Asian population.The overall survival for thyroid cancer patients is relatively high,but there are significant survival differences among patients.Based on long-term hospital-based cancer registry database,this study analyzed the 10-year observed overall survival(OS)rate of thyroid cancer cases and the distribution of causes of death,providing real-world evidences to further survival management of thyroid cancer in China.Methods:A total of 55343 thyroid cancer patients who underwent treatment at Fudan University Shanghai Cancer center from 2005 to 2021 were included in this study.Clinical information and the follow-up endpoint data were collected through medical records review,telephone visits and death registry data linkage.The last follow-up date was October 31,2024.Kaplan-Meier method was applied in evaluating the OS rate,and survival data were described by different subgroups as age group,gender,treatment period,tumor staging and pathological characteristics.The standardized mortality ratio(SMR)and absolute excess risk(AER)were calculated using general Shanghai population as the reference,and the mortality risk was described by gender,age at diagnosis and histological subtype.Results:With a median follow-up time of 63.01 months,the overall 1-,3-,5-and 10-year OS rates of thyroid cancer patients were 99.67%(95%CI:99.62%-99.72%),99.11%(95%CI:99.03%-99.19%),98.48%(95%CI:98.36%-98.60%)and 95.81%(95%CI:95.50%-96.11%),respectively.The 10-year OS rates of stage Ⅰ,Ⅱ,Ⅲ and Ⅳ were 97.99%(95%CI:97.70%-98.28%),89.80%(95%CI:87.24%-92.37%),77.84%(95%CI:70.76%-84.92%)and 62.95%(95%CI:55.37%-70.54%),respectively.The differences in OS among patients with different age,gender and histological classification were significant.1256(2.27%)deaths occurred,of which 18.63%,50.88%and 7.32%were attributable to thyroid cancer,other cancers and cardiovascular disease(CVD),respectively.Compared with the general population,patients with different subtypes of thyroid cancer had higher all-cause mortality rates,progressively increasing with papillary,follicular,medullary and anaplastic thyroid carcinoma/poorly differentiated carcinoma.Compared with general population,the death risk was 2.24 times higher in papillary thyroid cancer patients(95%CI:2.06-2.44),9.94 times higher in follicular thyroid cancer patients(95%CI:6.79-14.09),12.16 times higher in medullary thyroid cancer patients(95%CI:8.05-17.69),and the highest risk was observed in patients with anaplastic thyroid carcinoma/poorly differentiated carcinoma[SMR=79.67(95%CI:58.38-106.31),AER=766.01/1 000 person-years].Conclusion:The 10-year long survival data and cause of death for thyroid cancer patients with different histological types were reported in China based on a large single institution hospital-based cancer registry database.Staging and histological characteristics were the most important factors directly affected the survival.Early diagnosis and individualized treatment are crucial for improving prognosis.

Objective To screen key autophagy-related genes in alcoholic hepatitis (AH) and investigate potential biomarkers and therapeutic targets for AH. Methods Two AH gene chips in Gene Expression Omnibus (GEO) and autophagy-related data sets obtained from MSigDB and GeneCards databases were used, and the key genes were verified and obtained by weighted gene co-expression network analysis (WGCNA). The screened key genes were subject to gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) and immune infiltration analyses. Messenger RNA (mRNA)- microRNA (miRNA) network was constructed to analyze the expression differences of key autophagy-related genes during different stages of AH, which were further validated by real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) in the liver tissues of AH patients and mice. Results Eleven autophagy-related genes were screened in AH (EEF1A2, CFTR, SOX4, TREM2, CTHRC1, HSPB8, TUBB3, PRKAA2, RNASE1, MTCL1 and HGF), all of which were up-regulated. In the liver tissues of AH patients and mice, the relative expression levels of SOX4, TREM2, HSPB8 and PRKAA2 in the AH group were higher than those in the control group. Conclusions SOX4, TREM2, HSPB8 and PRKAA2 may be potential biomarkers and therapeutic targets for AH.

Huocao(a traditional Chinese herbal medicine) moxibustion is a characteristic technology in Yi medicine suitable for cold-dampness diseases. Huocao, as the moxibustion material, is confusedly used in clinical practice and little is known about its quality control. In this study, UPLC method was used to establish the chemical fingerprint of non-volatile components in Huocao, and the contents of eight phenolic acids such as chlorogenic acid were determined. Multivariate statistical analysis was performed to obtain the indicator components of Huocao for quality evaluation, and thus a comprehensive evaluation system for the quality of Huocao was built. The UPLC fingerprints of 49 batches of Huocao were established, and there were 20 common peaks, of which eight phenolic acids including neochlorogenic acid and chlorogenic acid were identified. Except for three batches of Huocao, the similarity of the other 46 batches was higher than 0.89, suggesting that the established fingerprint method could be used for quality control of the medicinal herb. The correlation coefficient between entropy weight score of the eight phenolic acids and comprehensive fingerprint score in Huocao was 0.875(P<0.01), which indicated that the eight phenolic acids could be used as indicator components for the quality evaluation of Huocao. Furthermore, in multivariate statistical analysis on the common peaks of fingerprint and the contents of the eight phenolic acids, chlorogenic acid, isochlorogenic acid A and isochlorogenic acid C were screened to be the indicator components. The results revealed that the proposed method achieved a simple and accurate quality control of Huocao based on UPLC fingerprint and multi-component content determination, which provided useful data for establishing the quality standard of Huocao.
Chlorogenic Acid ; Entropy ; Hydroxybenzoates ; Quality Control

OBJECTIVE: To rapidly identify the two morphologies and chemical properties of similar herbal medicines, Blumea riparia and B. megacephala as the basis for chemical constituent analysis. METHODS: UPLC-Q-Exactive-MS/MS was utilized for profiling and identification of the constituents in B. riparia and B. megacephala. Chemical pattern recognition (CPR) was further used to compare and distinguish the two herbs and to identify their potential characteristic markers. Then, an HPLC method was established for quality evaluation. RESULTS: A total of 93 constituents are identified, including 54 phenolic acids, 35 flavonoids, two saccharides, one phenolic acid glycoside, and one other constituent, of which 67 were identified in B. riparia and B. megacephala for the first time. CPR indicates that B. riparia and B. megacephala samples can be distinguished from each other based on the LC-MS data. The isochlorogenic acid A to cryptochlorogenic acid peak area ratio calculated from the HPLC chromatograms was proposed as a differentiation index for distinguishing and quality control of B. riparia and B. megacephala. CONCLUSION This study demonstrates significant differences between B. riparia and B. megacephala in terms of chemical composition. The results provide a rapid and simple strategy for the comparison and evaluation of the quality of B. riparia and B. megacephala.