Pancreatic acinar cell carcinoma (PACC) is a rare exocrine tumor of the pancreas with distinct clinical and pathological features. In recent years, advancements in molecular biology techniques have led to a deeper understanding of the molecular mechanisms underlying PACC. Progress in imaging, endoscopic, and molecular diagnostic technologies has improved the early detection rate of PACC. The primary treatment modalities for PACC include surgical resection, chemotherapy, targeted therapy and immunotherapy; however, the therapeutic efficacy still requires further improvement. This article reviews the current research status of PACC, covering its epidemiology, pathological characteristics, molecular alterations, diagnostic methods, and treatment strategies, and discusses the controversies and future directions in PACC research.

The traditional web version of cancer screening information management platform can not fully meet the complex requirements of current early cancer diagnosis and treatment pro-grams,due to its functional limitation,low efficiency of data processing and difficulties in up-grading screening acceptability and effectiveness.This paper analyzes the informational require-ment for cancer early diagnosis and early treatment programs;introduces the design and construc-tion of an intellectualized management information platform;elaborates the roles of digital intelli-gence technology in the whole-process management,including intelligent health management;emphasizes the importance of improving the supervision and evaluation system for its high-quality development.The paper also discusses economic barriers and other issues related to the applica-tion of digital intelligence technology.

The traditional web version of cancer screening information management platform can not fully meet the complex requirements of current early cancer diagnosis and treatment pro-grams,due to its functional limitation,low efficiency of data processing and difficulties in up-grading screening acceptability and effectiveness.This paper analyzes the informational require-ment for cancer early diagnosis and early treatment programs;introduces the design and construc-tion of an intellectualized management information platform;elaborates the roles of digital intelli-gence technology in the whole-process management,including intelligent health management;emphasizes the importance of improving the supervision and evaluation system for its high-quality development.The paper also discusses economic barriers and other issues related to the applica-tion of digital intelligence technology.

AIM: To establish the quality standard of Agui Yangxue Tablets (Radix Angelicae sinensis, Radix Codonopsis, Rhizoma Atractylodis macrocephalae, etc.) METHODS: Radix Astragali, Radix et Rhizoma Glycyrrhizae and Radix Rehmanniae praeparata were identified by TLC. The content of ferulic acid and paeoniflorin was determined by HPLC. The HPLC condition of ferulic acid was as follows: column was Diamonsil~ TM C_ 18 (4.6 mm?200 mm,5 ?m), the mobile phase consisted of acetonitrile -0.085% phosphoric acid (22 ∶ 78), the UV detection wavelength was at 322 nm, the flow rate was 1.0 mL/min with column temperature at 35 ?C . The HPLC condition of paeoniflorin was as follows: column was Diamonsil~ TM C_ 18 (4.6 mm?200 mm,5 ?m), the mobile phase consisted of acetonitrile -0.1% phosphoric acid (15 ∶ 85), the UV detection wavelength was at 229 nm, the flow rate was 1.0 mL/min with column temperature at 25 ?C . RESULTS: The developed TLC spots were quite clear. The calibration curve of ferulic acid was linear with 0.012 9-0.154 8 ?g(r=0.999 7). The average recovery was 99.32% with RSD of 1.74% (n=9). The calibration curve of paeoniflorin was linear with 0.106 8-1.602 0 ?g(r= 0.999 9). The average recovery was 98.68% with RSD of 0.98 (n=9). CONCLUSION: The established TLC and HPLC methods are exclusive, reproducible and suitable for the quality control of Agui Yangxue Tables.