By analyzing the current application of multi-modal data in the diagnosis of gastric "inflammation-cancer" transformation， this study explored the feasibility and strategies for constructing a disease-syndrome integrated diagnosis and treatment system. Based on traditional Chinese medicine （TCM） phenomics， we proposed utilizing multi-modal data from literature research， cross-sectional studies， and cohort follow-ups， combined with artificial intelligence technology， to establish a multi-dimensional diagnostic and treatment index system. This approach aims to uncover the complex pathogenesis and transformation patterns of gastric "inflammation-cancer" progression. Additionally， by dynamically collecting TCM four-diagnostic information and modern medical diagnostic information through a long-term follow-up system， we developed three major modules including information extraction， multi-modal phenotypic modeling， and information output， to make it enable real-world clinical data-driven long-term follow-up and treatment of chronic atrophic gastritis. This system can provide technical support for clinical diagnosis， treatment evaluation， and research， while also offering insights and methods for intelligent TCM diagnosis.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: To analyze the trajectories of body mass index for age z-score (BAZ) and its influencing factors among children with congenital hypothyroidism (CH) based on latent class growth modeling (LCGM), so as to provide the evidence for improving treatment measures and optimizing growth management among children with CH. Methods Children with CH aged 0 to 3 years from the Newborn Disease Screening Center of Shanxi Children's Hospital (Shanxi Maternal and Child Health Hospital) between 2017 and 2022 were selected as the research subjects. Basic information, height and weight data from 3 to 36 months of age, age at treatment initiation, thyroid-stimulating hormone (TSH) levels at diagnosis, and family information were retrospectively collected. BAZ for children with CH at each month of age was calculated based on the WHO Child Growth Standards. The trajectories of BAZ were analyzed using LCGM, and factors affecting the trajectories of BAZ among children with CH were analyzed using a multinomial logistic regression model. Methods: Children with CH aged 0 to 3 years from the Newborn Disease Screening Center of Shanxi Children's Hospital (Shanxi Maternal and Child Health Hospital) between 2017 and 2022 were selected as the research subjects. Basic information, height and weight data from 3 to 36 months of age, age at treatment initiation, thyroid-stimulating hormone (TSH) levels at diagnosis, and family information were retrospectively collected. BAZ for children with CH at each month of age was calculated based on the WHO Child Growth Standards. The trajectories of BAZ were analyzed using LCGM, and factors affecting the trajectories of BAZ among children with CH were analyzed using a multinomial logistic regression model. Results: A total of 299 children with CH were included. There were 140 boys (46.82%) and 159 girls (53.18%). The median of BAZ was 0.50 (interquartile range, 1.68). The LCGM analysis categorized the subjects into three groups: the persistent high-growth pattern group with 24 cases (8.03%), the slow-growth pattern group with 39 cases (13.04%), and the appropriate-growth pattern group with 236 cases (78.93%). Multinomial logistic regression analysis showed that compared to the children with CH in the appropriate-growth pattern group, those who started treatment at the age of 30 to 60 days (OR=0.109, 95%CI: 0.016-0.732; OR=0.166, 95%CI: 0.032-0.852) had a lower risk of persistent high-growth and slow-growth patterns; CH children with TSH levels of 50 to 150 mIU/L at diagnosis (OR=3.554, 95%CI: 1.201-10.514) and those whose paternal had a senior high school/technical secondary school education (OR=2.975, 95%CI: 1.003-8.823) exhibited a higher risk of the persistent high-growth pattern. Conversely, CH children whose paternal reproductive age was 30 to 35 years (OR=0.166, 95%CI: 0.034-0.806) had a lower risk of the persistent high-growth pattern. Conclusions The BAZ trajectory of children with CH aged 0 to 3 years exhibited three patterns: persistent high-growth, slow-growth, and appropriate-growth. The persistent high-growth and slow-growth patterns were associated with treatment timing, TSH levels at diagnosis, paternal reproductive age, and paternal education level. It is recommended to strengthen early treatment interventions and provide family follow-up guidance.

Objective To evaluate the bioequivalence of test product and reference product in a single dose of amoxicillin clavulanate potassium tablet under fasting and fed conditions in healthy volunteers.Methods An open label,randomized,single dose,four-period,crossover bioequivalence study was designed.Fasting and postprandial tests were randomly divided into 2 administration sequence groups according to 1:1 ratio,amoxicillin clavulanate potassium tablet test product or reference product 375 mg,oral administration separately,liquid chromatography tanden mass spectrometry was applied to determine the concentration of amoxicillin and clavulanate potassium in plasma of healthy subjects after fasting or fed administration,while Phoenix WinNonlin 8.2 software were used for pharmacokinetics(PK)parameters calculation and bioequivalence analysis.Results Healthy subjects took the test product and the reference product under fasting condition,the main PK parameters of amoxicillin are as follows:Cmax were(5 075.57±1 483.37)and(5 119.86±1 466.73)ng·mL-1,AUC0_twere(1.32 × 104±2 163.76)and(1.30 × 104±1 925.11)ng·mL-1,AUC0-∞were(1.32 × 104±2 175.40)and(1.31 ×104±1 935.86)ng·mL-1;the main PK parameters of clavulanic acid are as follows:Cmax were(3 298.27±1 315.23)and(3 264.06±1 492.82)ng·mL-1,AUC0-twere(7 690.06±3 053.40)and(7 538.39±3 155.89)ng·mL-1,AUC0-∞were(7 834.81±3 082.61)and(7 671.67±3 189.31)ng·mL-1;the 90％confidence intervals of Cmax,AUC0-tand AUC0-∞ after logarithmic conversion of amoxicillin and clavulanate potassium of the two products were all within 80.00％-125.00％.Healthy subjects took the test and reference product under fed condition,the main PK parameters of amoxicillin are as follows:Cmax were(4 514.08±1 324.18)and(4 602.82±1 366.48)ng·mL-1,AUC0-twere(1.15 × 104±1 637.95)and(1.15 × 104±1 665.69)ng·mL-1,AUC0-∞ were(1.16 × 104±1 646.26)and(1.15 × 104±1 607.20)ng·mL-1;the main PK parameters of clavulanic acid are as follows:Cmax were(2 654.75±1 358.29)and(2 850.51±1 526.31)ng·mL-1,AUC0-twere(5 882.82±2 930.06)and(6 161.28±3 263.20)ng·mL-1,AUC0-∞ were(6 022.70±2 965.05)and(6 298.31±3 287.63)ng·mL-1;the 90％confidence intervals of Cmax,AUC0-t and AUC0-∞ after logarithmic conversion of amoxicillin and clavulanate potassium of the two products were all within 80.00％-125.00％.Conclusion The two formulations were bioequivalent to healthy adult volunteers under fasting and fed conditions.

Objective To observe the pharmacokinetic effect of amoxicillin and clavulanate potassium tablets on amoxicillin in Chinese healthy subjects under fasting and high fat and high calorie diet.Methods 71 healthy subjects were given a single dose of amoxicillin potassium clavulanate tablets(0.375 g)on fasting or high fat diet,and venous blood samples were collected at different time points.The concentrations of amoxicillin in human plasma were determined by HPLC-MS/MS method,and the pharmacokinetic parameters were calculated by non-atrioventricular model using PhoenixWinNonlin 8.0 software.Results The main pharmacokinetic parameters of amoxicillin potassium clavulanate tablets after fasting and high fat diet were(5 105.00±1 444.00),(4 593.00±1 327.00)ng·mL-1,and postprandial-fasting ratio 89.40％,90％confidence interval(79.55％-100.19％);t1/2 were(1.52±0.16),(1.39±0.22)h;AUC0-t were(12 969.00±1 841.00),(11 577.00±1 663.00)ng·mL-1·h,and postdietary/fasting ratio 89.20％,90％confidence interval(83.92％-94.28％);AUC0-∞ were(13 024.00±1 846.00),(11 532.00±1 545.00)ng·mL-1·h,and postprandial-fasting ratio 88.60％,90％confidence interval(83.48％-93.50％).The median Tmax(range)were 1.63(0.75,3.00)and 2.50(0.75,6.00)h,respectively,and the Tmax of postprandial medication was delayed(P＜0.01).Conclusion Compared with fasting condition,amoxicillin Tmax was significantly delayed after high fat diet,while Cmax,AUC0-t and AUC0-∞ were not significantly changed,indicating that food could delay the absorption of amoxicillin,but did not affect the degree of absorption.

Objective To evaluate the bioequivalence of metronidazole tablet and reference formulation in Chinese healthy subjects.Methods A single-dose,two-cycle,randomized,open,self-crossover trial was designed with 48 healthy subjects randomly assigned to fasting or postprandial group.For each group,a single oral dose of metronidazole tablet(200 mg)or a reference preparation(200 mg)per cycle were enrolled.The concentration of metronidazole in plasma was measured by high performance liquid chromatography tandem mass spectrometry(HPLC-MS/MS).The non-compartmental model was applied to calculate the pharmacokinetic parameters for bioequivalence analysis via SAS 9.3 software.Results The main pharmacokinetic parameters of test and reference metronidazole tablets in the fasting group were as follows,the Cmax were(4 855.00±1 383.97)and(4 799.13±1 195.32)ng·h·mL-1;the AUC0-t were(54 834.68±12 697.88)and(55 931.35±11 935.28)ng·h·mL-1;the AUC0-∞ were(56 778.09±13 937.76)and(57 922.83±13 260.54)ng·h·mL-1;the Tmax were respectively 1.17 and 1.00 h;t1/2 were(8.99±1.76)and(9.11±1.73)h,respectively.The ratio of the geometric mean and its 90％confidence intervals(CI)of Cmax,AUC0-t and AUC0-∞ were all within the equivalent interval of 80.00％-125.00％.As for postprandial conditions,the main pharmacokinetic parameters of test and reference metronidazole tablets were as follows,the Cmax were(4 057.08±655.08)and(4 044.17±773.98)ng·h·mL-1;the AUC0-t were(55 956.42±12 228.12)and(55 121.04±11 784.55)ng·h·mL-1;the AUC0-∞ were(58 212.83±13 820.00)and(57 350.38±13 229.46)ng·h·mL-1;the Tmax were 2.50 and 2.25 h;the t1/2 were(9.37±1.68)and(9.37±1.79)h,respectively.The ratio of the geometric mean and 90％CI of Cmax,AUC0-t and AUC0-∞ were all within the equivalent interval of 80.00％-125.00％.Conclusion The two preparations were bioequivalent to Chinese healthy adult volunteers under both fasting and fed conditions.