ObjectiveTo analyze the processing mechanism underlying the enhanced effect of invigorating spleen and stopping diarrhea of soil-fried Atractylodis Macrocephalae Rhizoma（AMR） by analyzing the changes of microstructure， chemical composition and anti-ulcerative colitis（UC） activity before and after soil stir-frying. MethodsThe microstructure and elemental composition of AMR before and after soil stir-frying were analyzed by scanning electron microscopy-energy dispersive spectroscopy（SEM-EDS）， to investigate the differences in microstructure and the underlying causes. Ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS） coupled with UNIFI 1.9.2 natural product analysis platform were used to analyze and identify the chemical constituents in raw and soil-fried products， and multivariate statistical methods including principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA） were used to explore the differences and sources of chemical constituents between them. A dextran sulfate sodium（DSS）-induced UC mouse model was established. The method of disease activity index（DAI） was used to evaluate the severity of intestinal inflammation. Hematoxylin-eosin（HE） staining was used to observe the pathological changes of colon tissue， enzyme-linked immunosorbent assay（ELISA） was used to detect the levels of inflammatory factors， Real-time quantitative polymerase chain reaction（Real-time PCR） and Western blot were used to analyze the expressions of key genes and proteins involved in the intestinal mucosal barrier. The 16S rRNA sequencing was used to evaluate the diversity of intestinal flora， headspace gas chromatography-mass spectrometry（HS-GC-MS） was used to explore the levels of short-chain fatty acids（SCFAs） in feces. Base on the above findings， this paper investigated the effects of raw and soil-fried AMR on the biological， chemical， mechanical and immune barriers of model animals， and the differences in pharmacological effects and underlying mechanisms from the perspective of regulating the intestinal mucosal barrier in UC mice. ResultsSEM observation revealed numerous hearth soil particles on the surface of soil-fried AMR， accompanied by bubble-like bulges. At the same time， there were many cracks and folds on the surface of the hearth soil. EDS analysis revealed that the contents of Si， Al， Mg and Ca in soil-fried AMR were significantly higher than those of raw products， and these elements constituted the primary components of hearth soil. UPLC-Q-TOF-MS combined with database comparison was used to identify the chemical constituents of raw and soil-fried AMR. In positive ion mode， a total of 132 components were identified， primarily comprising three categories of terpenoids， polyphenols and amino acids. In negative ion mode， a total of 40 components were identified， primarily polyphenolic and glycoside compounds. Among them， the contents of sesquiterpenes and polyphenolic acids were changed significantly before and after processing. Soil-fried AMR could reduce the DAI score of UC mice， alleviate the shortening of colon length， reduce the levels of pro-inflammatory factors such as interleukin（IL）-17， IL-18， γ-interferon（IFN-γ） and tumor necrosis factor（TNF）-α in serum， increase the levels of anti-inflammatory factors such as secretory immunoglobulin A（sIgA）， IL-10， IL-4 and transforming growth factor-β（TGF-β） in serum， increase the expressions of key genes and proteins of intestinal mucosal barrier such as tight junction protein-1（ZO-1）， Occludin， Claudin-1 and mucin 2（MUC2） in colonic mucosa， and improve the disorders of intestinal flora diversity and the levels of SCFAs（P<0.05， P<0.01）. The raw and stir-fried products of AMR also exhibited the aforementioned effects， but they were weaker than the soil-fried products. Additionally， the auxiliary material hearth soil also had a certain pharmacodynamic effect. ConclusionSoil-fried AMR can enhance the protective effect on intestinal mucosal barrier in UC mice. These changes or heating-induced alterations in the microscopic structure and chemical composition of AMR may be attributed to the dual effects of adsorption of hearth soil.

ObjectiveTo analyze the processing mechanism underlying the enhanced effect of invigorating spleen and stopping diarrhea of soil-fried Atractylodis Macrocephalae Rhizoma（AMR） by analyzing the changes of microstructure， chemical composition and anti-ulcerative colitis（UC） activity before and after soil stir-frying. MethodsThe microstructure and elemental composition of AMR before and after soil stir-frying were analyzed by scanning electron microscopy-energy dispersive spectroscopy（SEM-EDS）， to investigate the differences in microstructure and the underlying causes. Ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS） coupled with UNIFI 1.9.2 natural product analysis platform were used to analyze and identify the chemical constituents in raw and soil-fried products， and multivariate statistical methods including principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA） were used to explore the differences and sources of chemical constituents between them. A dextran sulfate sodium（DSS）-induced UC mouse model was established. The method of disease activity index（DAI） was used to evaluate the severity of intestinal inflammation. Hematoxylin-eosin（HE） staining was used to observe the pathological changes of colon tissue， enzyme-linked immunosorbent assay（ELISA） was used to detect the levels of inflammatory factors， Real-time quantitative polymerase chain reaction（Real-time PCR） and Western blot were used to analyze the expressions of key genes and proteins involved in the intestinal mucosal barrier. The 16S rRNA sequencing was used to evaluate the diversity of intestinal flora， headspace gas chromatography-mass spectrometry（HS-GC-MS） was used to explore the levels of short-chain fatty acids（SCFAs） in feces. Base on the above findings， this paper investigated the effects of raw and soil-fried AMR on the biological， chemical， mechanical and immune barriers of model animals， and the differences in pharmacological effects and underlying mechanisms from the perspective of regulating the intestinal mucosal barrier in UC mice. ResultsSEM observation revealed numerous hearth soil particles on the surface of soil-fried AMR， accompanied by bubble-like bulges. At the same time， there were many cracks and folds on the surface of the hearth soil. EDS analysis revealed that the contents of Si， Al， Mg and Ca in soil-fried AMR were significantly higher than those of raw products， and these elements constituted the primary components of hearth soil. UPLC-Q-TOF-MS combined with database comparison was used to identify the chemical constituents of raw and soil-fried AMR. In positive ion mode， a total of 132 components were identified， primarily comprising three categories of terpenoids， polyphenols and amino acids. In negative ion mode， a total of 40 components were identified， primarily polyphenolic and glycoside compounds. Among them， the contents of sesquiterpenes and polyphenolic acids were changed significantly before and after processing. Soil-fried AMR could reduce the DAI score of UC mice， alleviate the shortening of colon length， reduce the levels of pro-inflammatory factors such as interleukin（IL）-17， IL-18， γ-interferon（IFN-γ） and tumor necrosis factor（TNF）-α in serum， increase the levels of anti-inflammatory factors such as secretory immunoglobulin A（sIgA）， IL-10， IL-4 and transforming growth factor-β（TGF-β） in serum， increase the expressions of key genes and proteins of intestinal mucosal barrier such as tight junction protein-1（ZO-1）， Occludin， Claudin-1 and mucin 2（MUC2） in colonic mucosa， and improve the disorders of intestinal flora diversity and the levels of SCFAs（P<0.05， P<0.01）. The raw and stir-fried products of AMR also exhibited the aforementioned effects， but they were weaker than the soil-fried products. Additionally， the auxiliary material hearth soil also had a certain pharmacodynamic effect. ConclusionSoil-fried AMR can enhance the protective effect on intestinal mucosal barrier in UC mice. These changes or heating-induced alterations in the microscopic structure and chemical composition of AMR may be attributed to the dual effects of adsorption of hearth soil.

ObjectiveTo investigate the associations of multiple glycolipid metabolic indicators and their cumulative abnormality burden with incident colorectal cancer risk in a general population-based prospective cohort, and to examine the mediating role of glycolipid metabolic abnormalities in the relationships between smoking, alcohol consumption, physical activity, and colorectal cancer incidence. MethodsA total of 17 897 eligible participants recruited from the Taizhou Cohort between 2011 and 2014 were included. Cox proportional hazards regression models were used to assess the associations of conventional and derived glycolipid indicators, as well as a glycolipid abnormality index constructed from total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, fasting plasma glucose, and insulin, with incident colorectal cancer risk. Restricted cubic spline models were applied to evaluate dose-response relationships for major indicators. Receiver operating characteristic curves were generated to compare predictive performance across indicators. Mediation analyses were conducted to assess the mediating effects of the glycolipid abnormality index on the associations of smoking, alcohol consumption, and physical activity with colorectal cancer incidence. ResultsDuring a median follow-up of 11.2 years, 102 incident colorectal cancer cases were identified, with an incidence density of 51.2 per 100 000 person-years. After adjustment for potential confounders, Cox proportional hazards regression analyses showed that decreased high-density lipoprotein cholesterol was associated with a 1.74-fold higher risk of colorectal cancer (HR=1.74, 95%CI: 1.03‒2.95), elevated low-density lipoprotein cholesterol was associated with a 1.78-fold higher risk (HR=1.78, 95%CI: 1.05‒3.02), and abnormal fasting plasma glucose was associated with a 1.86-fold higher risk (HR=1.86, 95%CI: 1.15‒3.02). Triglycerides and fasting plasma glucose showed an approximately linear increasing association with colorectal cancer risk in multivariable restricted cubic spline models. The glycolipid abnormality index showed a clear gradient association with colorectal cancer risk; participants with three or more abnormal indicators had a 3.08-fold higher risk than those without abnormalities (HR=3.08, 95%CI: 1.60‒5.92). The area under the curve was 0.795, higher than that of individual glycolipid indicators and other combined indices, and gender-stratified analyses showed generally consistent patterns. Glycolipid metabolic abnormalities partially mediated the associations of smoking, alcohol consumption, and physical activity with colorectal cancer incidence, with mediation proportions of 8.33%, 9.45%, and 9.04%, respectively. ConclusionGlycolipid metabolic abnormalities are associated with an increased risk of incident colorectal cancer. The glycolipid abnormality index shows an increasing relationship with colorectal cancer risk and demonstrates better discrimination, and it partially mediates the associations of smoking, alcohol consumption, and physical activity with colorectal cancer incidence in the overall population.

Objective:To assess intrafractional errors of the deformation of clinical target volumes (CTVs) during online adaptive radiotherapy (OART) for prostate cancer patients, aiming to provide a basis for online plan optimization.Methods:A retrospective analysis was conducted for 13 prostate cancer patients who received 1.5 T magnetic resonance imaging (MRI)-guided OART (8 Gy × 5 fractions, totaling 65 fractions) at the Department of Radiation Oncology, Cancer Hospital, Chinese Academy of Medical Sciences. The MRI images were collected at the beginning and end of various treatment fractions. Then, the CTVs and organs at risk (OARs) were delineated by the same radiation oncologist. After rigid registration and triangle mesh generation, the surface vertices were extracted. The deformable registration for the CTV surfaces was performed using the thin-plate spline robust point matching (TPS-RPM) algorithm, yielding vertex correspondences. Last, both systematic and random intrafractional errors of CTV deformation were calculated.Results:The average Hausdorff distance (HD) for deformable registration of treatment fractions was (1.68 ± 0.28) mm. The intrafractional systematic errors of CTV deformation were (0.25 ± 3.18) mm (anterior-posterior direction; A-P), (0.89 ± 3.85) mm (left-right direction, L-R), and (-1.98 ± 6.69) mm (superior-inferior direction, S-I). The intrafractional random errors of CTV deformation were determined at (-0.26 ± 1.89) mm (A-P), (-0.08 ± 0.88) mm (L-R), and (-0.04 ± 1.86) mm (S-I).Conclusions:During OART, CTV deformations primarily occur in the S-I direction. Therefore, it is necessary to consider the expanded size of margins in this direction during OART for prostate cancer.

Objective To investigate the application value of different doses of remifentanil combined with sevoflurane under pain threshold index(PTi)monitoring in ambulatory laparoscopic cholecystectomy.Methods 152 patients undergoing laparoscopic cholecystectomy under general anesthesia were selected from December 2023 to June 2024 at the Second Affiliated Hospital of Kunming Medical University.Patients were randomly divided into R1 group(n=38),R2 group(n=38),R3 group(n=38),and R4 group(n=38)using a random number table.On the basis of sevoflurane at 0.7 minimum alveolar concentration(MAC),patients in R1～R4 groups were combined with 0.1,0.2,0.3,and 0.4 μg/kg·min remifentanil,respectively.The changes in PTi at different time points,pre-and post-operative blood stress indicators[cortisol(Cor),norepinephrine(NE),and blood glucose(Glu)concentrations]were compared,and the incidence of intraoperative hypertension,hypotension,tachycardia,bradycardia,and postoperative nausea and vomiting were recorded.Results Compared with R1 group,PTi in R2,R3,and R4 groups decreased from the start of surgery(T3)to 5 min after extubation(T11)(P<0.05);compared with R2 group,PTi in R3 and R4 groups was lower at T3～T11(P<0.05);no statistically significant difference was found in PTi changes between R3 and R4 groups at different time points(P>0.05).Postoperative Cor,NE,and Glu concentrations showed statistically significant differences among the four groups(P<0.001),but no significant difference was found preoperatively(P>0.05).Compared with preoperative values,Cor,NE,and Glu levels significantly increased in all groups(P<0.001),with R1 group>R2 group>R4 group>R3 group.The incidence of intraoperative hypertension,hypotension,bradycardia,and tachycardia showed statistically significant differences(P<0.001),with R4>R1>R2=R3.The incidence of postoperative nausea and vomiting also showed statistically significant differences(P<0.001),with R1 group>R4 group>R2 group>R3 group.Conclusion Sevoflurane at 0.7 MAC combined with 0.3 μg/kg·min remifentanil provides good analgesic effects for patients undergoing ambulatory laparoscopic cholecystectomy,reduces stress response,and has high safety,making it worthy of clinical promotion.

Objective:To construct a general deep learning dose prediction model applicable to radiotherapy for head and neck tumors, establish design methods for artificial intelligence (AI)-assisted radiotherapy plan and evaluate the accuracy of prediction.Methods:Radiotherapy plans of 818 patients who received radiotherapy for head and neck cancers from January 2018 to June 2021 in Cancer Hospital of Chinese Academy of Medical Sciences were enrolled. Patients involved 17 types of common head and neck cancers, and the prescribed dose covered 5 kinds of dose gradients ranging from 54 Gy to 73.92 Gy. And 1-2 cases per each cancer type (31 cases in total) were randomly selected as the validation set, and the remaining 787 cases were used as the training set to build a deep learning head and neck radiotherapy generalized dose prediction model. Then based on the dose prediction results of this model, a program was written to automatically generate inverse optimization condition scripts, which were sent back to the treatment planning system to achieve AI-assisted radiotherapy plan design. Among the patients who received radiotherapy in our hospital from June 2021 to January 2022, 1 patient for each disease type (17 cases in total) was selected to evaluate the AI-assisted plan design program and evaluate its clinical feasibility using paired t-test. Results:Dose prediction model accuracy evaluation revealed that in the 31-case validation set, there was no statistical difference in the evaluation metrics of clinical concern for organs at risks, except for the D 1 cm3 prediction for spinal cord planning risk volume, which was statistically different compared with the clinical reference plan. The AI-assisted plan design program had higher plan quality metric scores (37.88±6.42) than manual plans (35.00±7.63) in 17 test cases ( t=-1.00, P=0.166). The number of manual adjustments to the inverse optimization conditions was reduced from (5.47±2.97) times to (2.76±1.00) times for the AI-assisted plan compared to the manual-only plan ( t=4.12, P<0.001). And the number of outlined dose shaping structures was reduced from 7.35±3.98 to 3.12±1.18 ( t=5.61, P<0.001). Conclusions:The unified universal model of dose prediction established for different head and neck cancers has high accuracy in dose prediction for all types of head and neck tumor plans. The AI-assisted planning method established in this pattern can reduce the clinical workload of physicists and improve the efficiency of their work.

Artificial intelligence (AI) technologies are being widely applied in radiotherapy. However, the integration of AI into clinical workflows of radiotherapy faces a series of challenges, such as poor model interpretability, domain shifts between clinical application and training data, and the inherent model uncertainties. Therefore, case-specific quality assurance (QA) is essential before deploying AI models in clinical practice. This paper reviews and summarizes QA methodologies for the application of AI models in radiotherapy across four key areas: image registration, image generation, region of interest segmentation, and treatment planning.

Objective:To assess intrafractional errors of the deformation of clinical target volumes (CTVs) during online adaptive radiotherapy (OART) for prostate cancer patients, aiming to provide a basis for online plan optimization.Methods:A retrospective analysis was conducted for 13 prostate cancer patients who received 1.5 T magnetic resonance imaging (MRI)-guided OART (8 Gy × 5 fractions, totaling 65 fractions) at the Department of Radiation Oncology, Cancer Hospital, Chinese Academy of Medical Sciences. The MRI images were collected at the beginning and end of various treatment fractions. Then, the CTVs and organs at risk (OARs) were delineated by the same radiation oncologist. After rigid registration and triangle mesh generation, the surface vertices were extracted. The deformable registration for the CTV surfaces was performed using the thin-plate spline robust point matching (TPS-RPM) algorithm, yielding vertex correspondences. Last, both systematic and random intrafractional errors of CTV deformation were calculated.Results:The average Hausdorff distance (HD) for deformable registration of treatment fractions was (1.68 ± 0.28) mm. The intrafractional systematic errors of CTV deformation were (0.25 ± 3.18) mm (anterior-posterior direction; A-P), (0.89 ± 3.85) mm (left-right direction, L-R), and (-1.98 ± 6.69) mm (superior-inferior direction, S-I). The intrafractional random errors of CTV deformation were determined at (-0.26 ± 1.89) mm (A-P), (-0.08 ± 0.88) mm (L-R), and (-0.04 ± 1.86) mm (S-I).Conclusions:During OART, CTV deformations primarily occur in the S-I direction. Therefore, it is necessary to consider the expanded size of margins in this direction during OART for prostate cancer.

Objective:To construct a general deep learning dose prediction model applicable to radiotherapy for head and neck tumors, establish design methods for artificial intelligence (AI)-assisted radiotherapy plan and evaluate the accuracy of prediction.Methods:Radiotherapy plans of 818 patients who received radiotherapy for head and neck cancers from January 2018 to June 2021 in Cancer Hospital of Chinese Academy of Medical Sciences were enrolled. Patients involved 17 types of common head and neck cancers, and the prescribed dose covered 5 kinds of dose gradients ranging from 54 Gy to 73.92 Gy. And 1-2 cases per each cancer type (31 cases in total) were randomly selected as the validation set, and the remaining 787 cases were used as the training set to build a deep learning head and neck radiotherapy generalized dose prediction model. Then based on the dose prediction results of this model, a program was written to automatically generate inverse optimization condition scripts, which were sent back to the treatment planning system to achieve AI-assisted radiotherapy plan design. Among the patients who received radiotherapy in our hospital from June 2021 to January 2022, 1 patient for each disease type (17 cases in total) was selected to evaluate the AI-assisted plan design program and evaluate its clinical feasibility using paired t-test. Results:Dose prediction model accuracy evaluation revealed that in the 31-case validation set, there was no statistical difference in the evaluation metrics of clinical concern for organs at risks, except for the D 1 cm3 prediction for spinal cord planning risk volume, which was statistically different compared with the clinical reference plan. The AI-assisted plan design program had higher plan quality metric scores (37.88±6.42) than manual plans (35.00±7.63) in 17 test cases ( t=-1.00, P=0.166). The number of manual adjustments to the inverse optimization conditions was reduced from (5.47±2.97) times to (2.76±1.00) times for the AI-assisted plan compared to the manual-only plan ( t=4.12, P<0.001). And the number of outlined dose shaping structures was reduced from 7.35±3.98 to 3.12±1.18 ( t=5.61, P<0.001). Conclusions:The unified universal model of dose prediction established for different head and neck cancers has high accuracy in dose prediction for all types of head and neck tumor plans. The AI-assisted planning method established in this pattern can reduce the clinical workload of physicists and improve the efficiency of their work.

Artificial intelligence (AI) technologies are being widely applied in radiotherapy. However, the integration of AI into clinical workflows of radiotherapy faces a series of challenges, such as poor model interpretability, domain shifts between clinical application and training data, and the inherent model uncertainties. Therefore, case-specific quality assurance (QA) is essential before deploying AI models in clinical practice. This paper reviews and summarizes QA methodologies for the application of AI models in radiotherapy across four key areas: image registration, image generation, region of interest segmentation, and treatment planning.