OBJECTIVE To explore the pharmacokinetic characteristics of 3 blood-absorbed components of Xiangshao sanjie oral liquid in rats with hyperplasia of mammary gland （HMG）. METHODS Female SD rats were divided into control group and HMG group according to body weight， with 6 rats in each group. The HMG group was given estrogen+progesterone to construct HMG model. After modeling， two groups were given 1.485 g/kg of Xiangshao sanjie oral liquid （calculated by crude drug） intragastrically， once a day， for 7 consecutive days. Blood samples were collected before the first administration （0 h）， and at 5， 15， 30 minutes and 1， 2， 4， 8， 12， 24 hours after the last administration， respectively. Using chlorzoxazone as the internal standard， the plasma concentrations of ferulic acid， paeoniflorin and rosmarinic acid in rats were detected by UPLC-Q/TOF-MS. The pharmacokinetic parameters [area under the drug time curve （AUC0－24 h， AUC0－∞）， mean residence time （MRT0－∞）， half-life （t1/2）， peak time （tmax）， peak concentration （cmax）] were calculated by the non-atrioventricular model using Phoenix WinNonlin 8.1 software. RESULTS Compared with the control group， the AUC0－24 h， AUC0－∞ and cmax of ferulic acid in the HMG group were significantly increased （P＜0.05）； the AUC0－24 h， AUC0－∞ ， MRT0－∞ ， t1/2 and cmax of paeoniflorin increased， but there was no significant difference between 2 groups （P＞0.05）； the AUC0－24 h and MRT0-∞ of rosmarinic acid were significantly increased or prolonged （P＜0.05）. C ONCLUSIONS In HMG model rats， the exposure of ferulic acid， paeoniflorin and rosmarinic acid in Xiangshao sanjie oral liquid all increase， and the retention time of rosmarinic acid is significantly prolonged.

Objective To explore the research hotspots and development trends in the field of cancer treatment in the past decade. Methods The CNKI and Web of Science Core Collection databases were searched for Chinese and English articles related to cancer treatment published over the last 10 years. Bibliometric research methods were employed, including keyword cluster analysis of published literature. Results A total of 45 455 Chinese articles and 866 958 English articles were retrieved. Combining the visualization analysis results and the current research dilemma of tumor treatment revealed that the current research hotspots of tumor treatment domestically and internationally can primarily focus on four key areas. In the realm of targeted therapy, efforts are directed towards the discovery of new drug targets, overcoming resistance to targeted therapy, and the development of monoclonal antibodies and antibody–drug conjugates. In the field of immunotherapy, the emphasis lies in enhancing the response rate to immune checkpoint inhibitors, determining the mechanisms behind resistance to immunotherapy, and improving the safety of treatment. The research in traditional Chinese medicine (TCM) covers evidence-based evaluation studies on TCM treatment, the identification of populations that can gain the most benefit from TCM, and strategies for improving the quality of life. In the area of novel drug development, cutting-edge technologies, such as organoid-based screening for anticancer drugs, synthetic biology, and artificial intelligence, are under investigation. Conclusion New targeted drugs, immune efficacy improvement, multidisciplinary integration, nano-delivery, and TCM innovation are the key research directions in the field of tumor therapy in the future.

Objective To explore the prognostic impact and clinical application value of therapeutic plasma exchange(TPE)intervention timing and liver injury periodization in patients with exertional heat stroke(EHS).Methods Data of 127 EHS patients from the First Medical Center of the General Hospital of the People′s Liberation Army from January 2011 to December 2023 were collected,then divided into the death group and the survival group based on therapeutic outcomes and into 5 stages according to the dynamic changes of ALT,AST,TBIL and DBIL.According to propensity score matching analysis,11 patients in the survival group and 12 patients in the death group were included in the statistical analysis,and 20 of them were treated with TPE.The changes in indicators and clinical outcomes before and after TPE were observed,in order to evaluate the impact of intervention timing on prognosis.Results Among the 23 patients,14 had no liver injury or could progress to the repair phase,resulting in 3 deaths(with the mortality rate of 21.43%),while 9 patients failed to pro-gress to the repair phase,resulting in 9 deaths(with the mortality rate of 100%),with significant differences(P＜0.05).The mortality rate of the first TPE intervention before the third stage of liver injury was 23.08%(3/13),while that of interven-tion after reaching or exceeding the third stage was 85.71%(6/7),and the difference was statistically significant(P＜0.05).Conclusion TPE should be executed actively in EHS patients combined with liver injury before the third phase to lock its pathological and physiological processes,thereby improving prognosis and reducing mortality.

Objective:This study aimed to investigate the correlation between the levels of serum amyloid A protein (SAA) and apolipoprotein A-Ⅰ (ApoA-Ⅰ) with the severity and prognosis of septic patients, in order to find new clinical prognostic markers for sepsis patients.Methods:This study prospectively included patients admitted to the intensive care unit of Northern Jiangsu People's Hospital from September 2021 to February 2022. Patients were diagnosed with sepsis according to the Sepsis-3 criteria and aged between 18 and 80 years old. Peripheral venous blood samples were collected at 0 h, 24 h, and 72 h after inclusion in the study, measured the levels of ApoA-Ⅰ and SAA, and the 72 h ΔSAA and 72 h ΔApoA-Ⅰwere calculated.. Patient demographics, laboratory parameters, acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) scores, sequential organ failure assessment scores, etc., were recorded. Patients were divided into survival and death groups based on outcomes, and were divided into shock and non-shock groups based on the presence of shock. Logistic regression was used to combine ApoA-I and SAA to establish a new combined index. Receiver Operating Characteristic curve analysis was performed to evaluate the predictive value of SAA, ApoA-Ⅰ, 72 h ΔApoA-Ⅰ, 72 h ΔSAA and the combined SAA and ApoA-Ⅰ for the prognosis of sepsis patients.Results:A total of 108 patients were included in the analysis, with 48 cases in the non-septic shock group and 60 cases in the septic shock group; 77 cases in the survival group and 31 cases in the death group. There were statistically significant differences in SAA and ApoA-Ⅰ levels at each time point between the shock and non-shock groups (all P<0.05), as well as between the death and survival groups (all P<0.05). SAA levels at each time point were positively correlated with APACHEⅡ scores (all P<0.001), while ApoA-Ⅰ levels at each time point were negatively correlated with APACHEⅡ scores (all P<0.01). SAA levels could predict the risk of death in sepsis patients, with the highest area under curve (AUC) value at 24 h SAA (AUC=0.713, P=0.001), sensitivity was 65.3%, and specificity was 72.7% for predicting 28-day mortality in sepsis. ApoA-Ⅰ levels at each time point could also predict the risk of death in sepsis patients, with the highest AUC value at 72 h ApoA-Ⅰ (AUC=0.743, P<0.001), sensitivity was 69.4%, and specificity was 77.1% for predicting 28-day survival in sepsis. The combined detection of 24 h SAA and 72 h ApoA-Ⅰ increased the AUC value (AUC=0.758, P<0.05), but the Z test showed that the prediction of death risk in patients with sepsis was not significantly higher than that of a single index ( P>0.05). Conclusions:Serum levels of SAA and ApoA-Ⅰ could reflect the severity of sepsis in patients and serve as independent indicators for predicting the prognosis of sepsis patients. The overall diagnostic efficacy of the combined SAA and ApoA-Ⅰ was not significantly different from that of a single index.

Objective:To describe the detail sampling design,weighting,instruments,filed procedures and quality control methods of the epidemiological survey on dementia among community residents in Tongliao City.Methods:A three-stage disproportionate probability sampling design was used to investigate the inhabitants aged 65 years and over in Tongliao City,Inner Mongolia Autonomous Region.The 10/66 Dementia Research Group(10/66 DRG)assessment instruments were used to diagnose dementia,using computer-assisted personal interview mode in the selected older people.Comprehensive quality control methods were implemented throughout the field-work.Results:A total of 166 villages or communities were sampled from nine counties or districts in Tongliao Cit-y.Totally 4 345 older people were interviewed with 96.2％response rate.By calculating sampling design weights,non-response adjustment weights and post-stratification adjustment weights,these weights were multiplied and per-formed trimming adjustment and standardization adjustment to generate final weights.The 171 interviewers were well-trained and qualified to carry out filed interview.Quality control methods included computer data check,audio record check,and telephone check in order to ensure the quality of the survey.Conclusion:This survey is imple-mented using a rigorous sampling design and timely quality control methods,and uses the 10/66 DRG assessment instruments with satisfactory international validity and reliability as survey instruments,which has international cross-cultural comparability.It provides a valid and feasible methodology of epidemiological survey on dementia for further studies in different regions in China.

0n December 31,2021,the Chinese Nursing Association released the group standard"nursing care of patients with infusion of vasoactive agents(T/CNAS 22-2021)",which outlines the fundamental requirements for intravenous infusion of vasoactive drugs and standardizes the evaluation,administration,and monitoring.This article provides an interpretation of the key parts and sections of the standard to ensure nursing safety during the administration of vasoactive drugs,aiming to reduce complications.Additionally,it serves as a crucial reference for nurses during the administration of the medication.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.