Objective To develop an artificial intelligence (AI)-driven lung cancer database by structuring and standardizing clinical data, enabling advanced data mining for lung cancer research, and providing high-quality data for real-world studies. Methods Building on the extensive clinical data resources of the Department of Thoracic Surgery at Peking Union Medical College Hospital, this study utilized machine learning techniques, particularly natural language processing (NLP), to automatically process unstructured data from electronic medical records, examination reports, and pathology reports, converting them into structured formats. Data governance and automated cleaning methods were employed to ensure data integrity and consistency. Results As of September 2024, the database included comprehensive data from 18 811 patients, encompassing inpatient and outpatient records, examination and pathology reports, physician orders, and follow-up information, creating a well-structured, multi-dimensional dataset with rich variables. The database’s real-time querying and multi-layer filtering functions enabled researchers to efficiently retrieve study data that meet specific criteria, significantly enhancing data processing speed and advancing research progress. In a real-world application exploring the prognosis of non-small cell lung cancer, the database facilitated the rapid analysis of prognostic factors. Research findings indicated that factors such as tumor staging and comorbidities had a significant impact on patient survival rates, further demonstrating the database’s value in clinical big data mining. Conclusion The AI-driven lung cancer database enhances data management and analysis efficiency, providing strong support for large-scale clinical research, retrospective studies, and disease management. With the ongoing integration of large language models and multi-modal data, the database’s precision and analytical capabilities are expected to improve further, providing stronger support for big data mining and real-world research of lung cancer.

Objective:To explore the method for selecting characteristic prices of Chinese patent medicines based on informa-tion entropy theory.It involves analyzing the connotative differences among various price indicators and utilizing information entropy metrics to validate the scientific rigor of characteristic price selection so as to optimize the pricing model for Chinese patent medi-cines and improve the accuracy of price evaluation.Methods:A correlation analysis and information entropy calculation are con-ducted on the median price of the smallest preparation unit,average daily cost,and average course cost of TCM.It compares the information diversity and uncertainty of different pricing indicators.Results:The average daily cost exhibits the highest information diversity and uncertainty among all the pricing indicators examined.Conclusion:It is recommended that the average daily cost be used as the dependent variable for characteristic prices in TCM pricing research.This choice plays an important role in optimizing TCM pricing models and enhancing the accuracy of price evaluation.

Objective:To establish a scientific,systematic,and objective indicator system for the characteristic price variables of Traditional Chinese Patent Medicines(TCPM),providing a reference framework for the pricing mechanism of TCPM.Methods:The brainstorming method was initially used to screen related variable indicators.The Nominal Group Technique(NGT)and Delphi methods were applied to gather expert opinions,and SPSS 28.0 was employed for data statistical analysis.It led to the development of a TCPM characteristic price variable indicator system consisting of 6 dimensions,14 characteristic variables and 26 measurement indicators.Results:The authority coefficient of the experts exceeded 0.7,indicating the representativeness of the results.Expert opinions were generally concentrated.Based on the collected opinions and statistical analysis,the scope of selected TCPM characteristic price variables was preliminarily established.Conclusion:The TCPM characteristic price variable indicator system was initially developed.However,due to the complexity of the pricing mechanism and divergent expert opinions,further qualitative and quantitative research methods,along with a dynamic adjustment mechanism,are needed to verify and refine the system.

The National Essential Medicines System could protect public health and ensure access to essential medications.Although the current selection methods for China's National Essential Medicines Lists(NEMLs)are becoming more scientific and standardized,there are still problems such as much emphasis on expert experience and the lack of transparency of decision-making basis.To address these issues,it proposes an evidence-based decision-making pathway for NEMLs selection guided by clinical value.This approach ensures a strong integration of evidence and decision-making,offering valuable insights for improving the adjustment procedures and selection criteria of the NEMLs in China.

Objective:To investigate the iodine nutrition status of children aged 8 - 10 and pregnant women in Xining City, Qinghai Province.Methods:From 2019 to 2021, a stratified cluster sampling method was used to divide 7 counties (districts) under the jurisdiction of Xining City, Qinghai Province into 5 sampling areas according to east, west, south, north, and center each year. One township (town, street) was selected from each area. Forty non boarding students aged 8 to 10 from each primary school (half male and half female, age balanced) and 20 pregnant women from each township (town, street) location were selected to collect edible salt samples at home and a random urine sample to measure salt iodine and urinary iodine level. B-ultrasound was used to measure thyroid volume in children and the goiter rate was calculated.Results:A total of 6 534 samples of household edible salt were collected from children and pregnant women, with an average salt iodine concentration of 25.58 mg/kg. The coverage rate of iodized salt was 97.50% (6 371/6 534), and the qualified iodized salt consumption rate was 89.46% (5 845/6 534). A total of 4 362 urine samples were collected from children, with a median urinary iodine level of 183.10 μg/L. The difference between different years was statistically significant ( H = 20.27, P < 0.001). A total of 2 169 urine samples were collected from pregnant women, with a median urinary iodine level of 168.90 μg/L. The difference between different years was statistically significant ( H = 107.09, P < 0.001). A total of 3 336 cases of thyroid gland examination were conducted in children, including 33 cases of thyroid enlargement, with a goiter rate of 0.99%. There was a statistically significant difference between different years (χ 2 = 15.00, P < 0.001). Conclusion:From 2019 to 2021, children aged 8 to 10 and pregnant women in Xining City are at an appropriate level of iodine, and the achievements in prevention and treatment of iodine deficiency disorders still need to be continuously consolidated.

Objective:To investigate the iodine nutrition status of children aged 8 - 10 and pregnant women in Xining City, Qinghai Province.Methods:From 2019 to 2021, a stratified cluster sampling method was used to divide 7 counties (districts) under the jurisdiction of Xining City, Qinghai Province into 5 sampling areas according to east, west, south, north, and center each year. One township (town, street) was selected from each area. Forty non boarding students aged 8 to 10 from each primary school (half male and half female, age balanced) and 20 pregnant women from each township (town, street) location were selected to collect edible salt samples at home and a random urine sample to measure salt iodine and urinary iodine level. B-ultrasound was used to measure thyroid volume in children and the goiter rate was calculated.Results:A total of 6 534 samples of household edible salt were collected from children and pregnant women, with an average salt iodine concentration of 25.58 mg/kg. The coverage rate of iodized salt was 97.50% (6 371/6 534), and the qualified iodized salt consumption rate was 89.46% (5 845/6 534). A total of 4 362 urine samples were collected from children, with a median urinary iodine level of 183.10 μg/L. The difference between different years was statistically significant ( H = 20.27, P < 0.001). A total of 2 169 urine samples were collected from pregnant women, with a median urinary iodine level of 168.90 μg/L. The difference between different years was statistically significant ( H = 107.09, P < 0.001). A total of 3 336 cases of thyroid gland examination were conducted in children, including 33 cases of thyroid enlargement, with a goiter rate of 0.99%. There was a statistically significant difference between different years (χ 2 = 15.00, P < 0.001). Conclusion:From 2019 to 2021, children aged 8 to 10 and pregnant women in Xining City are at an appropriate level of iodine, and the achievements in prevention and treatment of iodine deficiency disorders still need to be continuously consolidated.

Objective:To explore the method for selecting characteristic prices of Chinese patent medicines based on informa-tion entropy theory.It involves analyzing the connotative differences among various price indicators and utilizing information entropy metrics to validate the scientific rigor of characteristic price selection so as to optimize the pricing model for Chinese patent medi-cines and improve the accuracy of price evaluation.Methods:A correlation analysis and information entropy calculation are con-ducted on the median price of the smallest preparation unit,average daily cost,and average course cost of TCM.It compares the information diversity and uncertainty of different pricing indicators.Results:The average daily cost exhibits the highest information diversity and uncertainty among all the pricing indicators examined.Conclusion:It is recommended that the average daily cost be used as the dependent variable for characteristic prices in TCM pricing research.This choice plays an important role in optimizing TCM pricing models and enhancing the accuracy of price evaluation.

Objective:To establish a scientific,systematic,and objective indicator system for the characteristic price variables of Traditional Chinese Patent Medicines(TCPM),providing a reference framework for the pricing mechanism of TCPM.Methods:The brainstorming method was initially used to screen related variable indicators.The Nominal Group Technique(NGT)and Delphi methods were applied to gather expert opinions,and SPSS 28.0 was employed for data statistical analysis.It led to the development of a TCPM characteristic price variable indicator system consisting of 6 dimensions,14 characteristic variables and 26 measurement indicators.Results:The authority coefficient of the experts exceeded 0.7,indicating the representativeness of the results.Expert opinions were generally concentrated.Based on the collected opinions and statistical analysis,the scope of selected TCPM characteristic price variables was preliminarily established.Conclusion:The TCPM characteristic price variable indicator system was initially developed.However,due to the complexity of the pricing mechanism and divergent expert opinions,further qualitative and quantitative research methods,along with a dynamic adjustment mechanism,are needed to verify and refine the system.

The National Essential Medicines System could protect public health and ensure access to essential medications.Although the current selection methods for China's National Essential Medicines Lists(NEMLs)are becoming more scientific and standardized,there are still problems such as much emphasis on expert experience and the lack of transparency of decision-making basis.To address these issues,it proposes an evidence-based decision-making pathway for NEMLs selection guided by clinical value.This approach ensures a strong integration of evidence and decision-making,offering valuable insights for improving the adjustment procedures and selection criteria of the NEMLs in China.

Cytopharmaceutical based on macrophages is a breakthrough in the field of targeted drug delivery. However, it remains a challenge to localize and control drug release while retaining macrophage activity and exerting its immunotherapeutic effect. Herein, a localized light-triggered release macrophage cytopharmaceutical (USIP@M) was proposed, which could utilize the tumor targeting and immunotherapy effects of macrophages to reverse the immune suppression of tumor microenvironment (TME). Amphiphilic block copolymers with ultraviolet (UV)-responsive o-nitrobenzyl groups were synthesized and co-loaded with sorafenib (SF), IMD-0354 (IMD), and upconverting nanoparticles (UCNPs), which were then taken up by macrophages, and the targeted delivery of drugs was realized by using the tumor tropism of macrophages. UCNPs converted near-infrared light with strong penetrability and high safety into UV light, which promoted the photoresponsive depolymerization of block copolymers and production of exosomes from USIP@M, accelerated drug efflux and maintained the activity of macrophages. IMD simultaneously polarized carrier macrophages and tumor-associated macrophages to exert the antitumor effect of macrophages, enhance T cell immunity, and alleviate the immunosuppressive state of TME. Synergistically with the chemotherapeutic effect of SF, it could effectively kill tumors. In conclusion, based on the localized light-triggered release strategy, this study constructed a novel macrophage cytopharmaceutical that could localize and control drug release while retaining the activity of macrophages and exerting its immunotherapeutic effect, which could effectively treat solid tumors.