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.

Five flavonoid glycosides were isolated from the methanol and ethyl acetate fractions of the ethanol extract of Diphylleia sinensi by using various chromatographic methods, including silica gel, MCI gel, Sephadex LH-20, ODS and semi-preparative HPLC. The structures of the isolated compounds were identified as diphyflavonoid A (1), diphyflavonoid B (2), quercetin-3-O-β-D-glucopyranoside (3), kaempferol-3-O-β-D-glucopyranoside (4), kaempferol-3-O-(6″-O-acetyl)-β-D-glucopyranoside (5) by spectroscopy methods (1D NMR, 2D NMR, UV, IR, and MS). Compounds 1 and 2 were two new flavonoid glycosides, and compounds 3 and 5 were isolated from the genus Diphylleia for the first time.

Objective:To investigate the efficacy and prognosis of different surgical approaches in sporadic medullary thyroid carcinoma.Methods:A retrospective analysis was conducted on 101 patients with sporadic medullary thyroid carcinoma (MTC) who underwent surgical treatment at the Department of Thyroid Surgery, China-Japan Union Hospital of Jilin University, from Feb. 2009 to Nov. 2023. The patients included 36 males and 75 females, with a male-to-female ratio of 1:2.1. The median age of the patients was 47 years old, with an age range of 21 to 72 years old. The study divided participants into two groups based on their surgical methods: an observation group (78 cases) and a control group (23 cases). The observation group received surgical methods in accordance with expert consensus, while the control group did not. The study compared the efficacy and prognosis of the two groups.Results:Statistical differences were found between the two groups in terms of stage II and III in TNM staging, intraoperative frozen pathological findings, number of lymph node resections in the central group, number of lymph node metastases in the central group, number of lymph node resections in the lateral cervical region, postoperative follow-up time, and five-year postoperative serum procalcitonin (Ctn) levels ( P<0.05) .Both groups of patients obtained a significant decrease in Ctn after surgical treatment. In the observation group, Ctn was at the remission level in 57 cases (73.1%), at the stable level in 13 cases (16.7%), and at the progression level in 8 cases (10.2%), while in the control group, Ctn was at the remission level in 20 cases (86.9%), at the progression level in 3 cases (13.1%), and there were no patients at the stable level after the operation.One patient (1.3 per cent) in the observation group had a recurrence after surgery; Two patients (8.7 per cent) in the control group had a recurrence. Conclusions:Standardised and thorough surgery can maximise the clearance of metastatic lymph nodes, effectively reduce the recurrence rate, achieve better efficacy, and improve the long-term prognosis of patients without increasing the risk of surgery and postoperative complications.

Objective To investigate the clinical and imaging features of extralobar pulmonary sequestration(ELS)with torsion.Methods The clinical and imaging data,surgical records and pathological results of four ELS children with torsion were analyzed ret-rospectively.Results All four children presented with abdominal pain,and all CT scans showed soft tissue masses on the medial side of the lower lobe,and there were 2 masses with long fusculine-shaped in the right pleural cavity,2 round masses in the left pleural cavity,all of which were accompanied by pleural effusion and poor ventilation of adjacent lung lobes.There were 1 case without enhance-ment,1 case with mild enhancement,and 2 cases with simple marginal linear enhancement.There were no patients with definitive supplying artery and 3 cases with peripheral post-intercostal dilated veins.Intraoperative ELS combined with torsion was shown,and the nutrient artery were derived from the thoracic aorta.All ELS showed bleeding and necrosis according to the pathological results,and 1 case was complicated with congenital pulmonary airway malformation(type 2).Conclusion ELS combined with torsion is mostly abdominal pain as the first symptom,and there are soft tissue mass adjacent to the lower lobe with pleural effusion on the affected side as the imaging features with no,mild or marginal linear enhancement,with no supplying artery,and with intracostal posterior venous expansion.

Objective:To construct of evaluation index system and theory-skill-humanity (TSH) model for core competence of radiation specialist nurses, to provide reference for clinical training of radiation specialist nurses.Methods:In January 2023, the draft of the evaluation index system of core competence for radiation specialist nurses was development by literature reviews and semi-structured interviews; from February to March 2023, 20 experts were consulted with 2 rounds of Delphi consultations to determine the index system and determine indicator weights based on the importance assigned by experts to each indicator.Results:Among 20 experts, 17 were female and 3 were male, aged 35-65 (46.48 ± 8.19) years old. The effective recovery rate of the questionnaires was 100.0% (20/20) and 95.0% (19/20) in the first and second round, respectively. The authority coefficient of the experts was 0.937 in the second round, the Kendall coefficients of concordance in the first-level indexes, second-level indexes and third-level indexes were 0.448, 0.407, 0.464, respectively, the difference was statistically significant ( χ2 = 53.60, 148.81, 58.18, all P<0.001). The final evaluation index system of the core competence of radiation specialist nurses included 3 first-level indicators, 11 second-level indicators and 56 third-level indicators. Conclusions:The evaluation index system of the core competence of radiation specialist nurses constructed based on TSH model in the present study has certain feasibility and practicality, which can be used as the basis for the training and evaluation of radiation specialized nurses.