AIM:To explore the promoting action of chloroquine on the anti-proliferation effect of dexametha-sone on acute lymphoblastic leukemia cells .METHODS:CCK-8 assay was used to assess the viability of the dexametha-sone-resistant human acute lymphoblastic leukemia CEM-C1 cell line treated with the combination of chloroquine and dexa-methasone .Western blotting , quantitative real-time PCR and LysoTracker Red staining were utilized to examine the mecha-nism.RESULTS:Combination of chloroquine and dexamethasone significantly inhibited the proliferation of CEM -C1 cells compared with control group (P<0.01).The combination of chloroquine and dexamethasone increased the abundance of glucocorticoid receptor and inhibited lysosomal function , while lysosomal inhibitor bafilomycin A 1 also increased glucocorti-coid signaling .CONCLUSION:Dexamethasone combined with chloroquine triggers an anti-proliferation effect on CEM-C1 cells via a lysosome-mediated pathway .

OBJECTIVE:To establish an HPLC method for the detection of Prednisone acetate, Hydrocortisone, Dexamethasone acetate, Piroxicam, Diclofenac sodium, Indometacin and Ibuprofen, the 7 chemical constituents in antirheumatic traditional Chinese medicine. METHODS: The chromatographic separation was performed on a Hypersil BDS C18 column (200 mm?4.6 mm,5 ?m) using methanol-1 g?L-1KH2PO4(58∶42) (adjusted to pH 4.0?0.05 with H3PO4) as the mobile phase at a flow rate of 1.0 mL?min-1. The detective wavelength was set at 220 nm. RESULTS: Seven chemical constituents were all well-separated with good linearity within respective range of sample size. CONCLUSION: The method is rapid and convenient, and can be used for the quantitative and qualitative analyses of 7 chemical constituents in antirheumatic traditional Chinese medicine and to detect whether there is addition of chemical constituents in antirheumatic traditional Chinese medicines.

Objective:To analyze the research hotspots and trends in the field of Traditional Chinese Medicine (TCM) for cervical cancer at home and abroad in recent 30 years through visualization method.Methods:By setting "cervical cancer" and "TCM" as the research subjects, the articles were retrieved from CNKI, Wanfang, VIP, SinoMed, PubMed, and Web of Science Core database (WoS) from January 1, 1992 to January 1, 2022. Citespace 5.8 R3 software was used to conduct visual analysis on the authors, institutions and keywords included in the articles.Results:A total of 1 123 Chinese articles and 573 English articles were included. The number of articles issued showed a gradual upward trend; the author with the largest number of Chinese publications was Jin Zhe, and the author with the largest number of English publications was Chen Guo. The institution with the largest number of Chinese publications was Beijing University of Chinese Medicine; the institution with the largest number of English publications was Chinese Academy of Medical Sciences. Keywords co-occurrence of the top 3 Chinese keywords in the network frequency (excluding the search words) were "cervical erosion", "urinary retention" and "Yunnan Baiyao"; English keywords were "HeLa cell", "bibliometric", and "information monitoring system". Chinese keywords formed 11 clusters and 25 emergent words; English keywords formed 9 clusters and 17 emergent words.Conclusions:The research content in the field of TCM of cervical cancer about knowledge map in the past 30 years at home and abroad is rich. TCM can play the role of antagonizing human papillomavirus, enhancing immune function, regulating TCM constitution, improving quality of life and so on. At present, the research hotspot is the mechanism exploration of cervical cancer and the curative effect research of clinical complications, and the research trend is clinical curative effect research.

The inherent clinical uncertainties, substantial costs, and small patient cohorts of orphan drugs limit the applicability of randomized controlled trial (RCT)-based health technology assessments (HTAs) in guiding coverage criteria, sustainable financing models, and equitable reimbursement frameworks for medical financial assistance policies for rare diseases.The digital transformation in healthcare system leads to solutions to the challenges in designing the policy by using data-driven decision-making. This article summarizes the decision-making issues in policy design, discusses the current status and trends of digital transformation, and analyzes the important new opportunities for AI-driven policy design for medical financial assistance policies for rare diseases. Decision-making that is digital intelligence driven and using techniques such as big data analytics and real-world research methods will enhance targeting efficiency, improve the quality of financing, and realize the performance-based reimbursement in the medical financial assistance, providing significant value in facilitating the policy reform and development for rare diseases healthcare.