In this study, we constructed a GLP-2R-HEK293 cell line and established a method for the determination of the in vitro biological activity of teduglutide based on HTRF, after optimizing experimental conditions and methodological verification. We also carried out relative potency detection of teduglutide pharmaceutical products using this method. The result showed that there was a quantitative-efficient relationship between the teduglutide activity and cAMP contents in GLP-2R-HEK293 cells, which conformed to four-parameter model. Method verification results of five concentrations of teduglutide (64%, 80%, 100%, 125% and 156%) met the requirements of the General Rules of Chinese Pharmacopoeia, 2020 edition, Volume IV (9401). We then analyzed the relative potency of three batches of teduglutide drug substances and three batches of drug products. The linearity, regression and parallelism of the obtained curves all fit the system suitability requirements. The relative potency of six batches of teduglutide was from 83% to 105%. In summary, the biological activity detection method established in this study was accurate, precise, simple and time-saving, which can be used for quality control of teduglutide pharmaceutical products.

Background::In vitro fertilization (IVF) has emerged as a transformative solution for infertility. However, achieving favorable live-birth outcomes remains challenging. Current clinical IVF practices in IVF involve the collection of heterogeneous embryo data through diverse methods, including static images and temporal videos. However, traditional embryo selection methods, primarily reliant on visual inspection of morphology, exhibit variability and are contingent on the experience of practitioners. Therefore, an automated system that can evaluate heterogeneous embryo data to predict the final outcomes of live births is highly desirable. Methods::We employed artificial intelligence (AI) for embryo morphological grading, blastocyst embryo selection, aneuploidy prediction, and final live-birth outcome prediction. We developed and validated the AI models using multitask learning for embryo morphological assessment, including pronucleus type on day 1 and the number of blastomeres, asymmetry, and fragmentation of blastomeres on day 3, using 19,201 embryo photographs from 8271 patients. A neural network was trained on embryo and clinical metadata to identify good-quality embryos for implantation on day 3 or day 5, and predict live-birth outcomes. Additionally, a 3D convolutional neural network was trained on 418 time-lapse videos of preimplantation genetic testing (PGT)-based ploidy outcomes for the prediction of aneuploidy and consequent live-birth outcomes.Results::These two approaches enabled us to automatically assess the implantation potential. By combining embryo and maternal metrics in an ensemble AI model, we evaluated live-birth outcomes in a prospective cohort that achieved higher accuracy than experienced embryologists (46.1% vs. 30.7% on day 3, 55.0% vs. 40.7% on day 5). Our results demonstrate the potential for AI-based selection of embryos based on characteristics beyond the observational abilities of human clinicians (area under the curve: 0.769, 95% confidence interval: 0.709–0.820). These findings could potentially provide a noninvasive, high-throughput, and low-cost screening tool to facilitate embryo selection and achieve better outcomes. Conclusions::Our study underscores the AI model’s ability to provide interpretable evidence for clinicians in assisted reproduction, highlighting its potential as a noninvasive, efficient, and cost-effective tool for improved embryo selection and enhanced IVF outcomes. The convergence of cutting-edge technology and reproductive medicine has opened new avenues for addressing infertility challenges and optimizing IVF success rates.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective To evaluate the risk of bias and reporting quality in randomized controlled trials(RCTs)of the Chinese medicine injection for acute cerebral infarction in the last five years.Methods RCTs literature on Chinese medicine injection in the treatment of acute cerebral infarction was systematically searched in CNKI,Wanfang Data,VIP,China Biology Medicine Database(CBM),PubMed,Embase and Cochrane Library from April 20,2018 to April 20,2023.The risk of bias and reporting quality of included RCTs were evaluated using the Cochrane Risk of Bias Tool(ROB 1.0)and CONSORT-CHM Formulas 2017,respectively.Results A total of 4 301 articles were retrieved,and 408 RCTs were included according to inclusion and exclusion criteria.The ROB evaluation results showed that the the majority of studies were rated as having an unclear risk of bias due to the lack of reporting on allocation concealment,blind method,trial registration information,and funding sources.The evaluation results of CONSORT-CHM Formulas 2017 showed that the number of reported papers of 17 items was greater than or equal to 50%,and the number of reported papers of 25 items was less than 10%,and most of the RCTs did not show the characteristics of TCM syndrome differentiation and treatment.Conclusion The quality of Chinese medicine injection in the treatment of acute cerebral infarction RCTs is generally low.It is recommended that researchers refer to the methodology design of RCTs and international reporting standards,improve the trial design,standardize the trial report,and highlight the characteristics of TCM syndrome differentiation and treatment.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

This study constructed a LHCGR-CRE-luc-HEK293 transgenic cell line according to the activation of the cAMP signaling pathway after recombinant human chorionic gonadotropin binding to the receptor. The biological activity of recombinant human chorionic gonadotropin was assayed using a luciferase assay system. The relative potency of the samples was calculated using four-parameter model. And the method conditions were optimized to validate the specificity, relative accuracy, precision and linearity of the method. The results showed that there was a quantitative potency relationship of human chorinonic gonadotropin (hCG) in the method and it was in accordance with the four-parameter curve. After optimization, the conditions were determined as hCG dilution concentration of 2.5 μg·mL^-1, dilution ratio of 1∶4, cell number of 10 000-15 000 cells/well, and induction time of 6 h. The method had good specificity, relative accuracy with relative bias ranging from -8.9% to 3.4%, linear regression equation correlation coefficient of 0.996, intermediate precision geometric coefficient of variation ranging from 3.3% to 15.0%, and linearity range of 50% to 200%. This study successfully established and validated a reporter gene method to detect hCG biological activity, which can be used for hCG biological activity assay and quality control.

Objective To observe the changes in serum a proliferation inducing ligand(a proliferation inducing ligand,APRIL)and N-myc downstream regulated gene 1(N-myc downstream regulated gene 1,NDRG1)levels,and analyze their diagnostic value for ovarian endometrioma(OEM).Methods From July 2021 to July 2022,132 patients with OEM who visited Zigong First People's Hospital were regarded as the observation group,and regular follow-up was conducted.According to the prognosis of these patients,they were grouped into the recurrence group(n=50)and the non recurrence group(n=82).Meanwhile,78 healthy individuals who had their medical checkups at the hospital during the same period were the control group.Enzyme linked immunosorbent assay(ELISA)was applied to detect serum APRIL and NDRG1 levels,and the general data of the recurrent and non recurrent groups were compared.Logistic regression analysis was applied to analyze the relevant factors affecting the prognosis of OEM.Pearson analysis was applied to explore the correlation between serum APRIL and NDRG1 levels in patients with OEM.Receiver operating characteristic(ROC)curve was applied to evaluate the diagnostic value of serum APRIL,NDRG1 levels and their combination for OEM.Results Compared with the control group,APRIL level(35.28±6.81ng/ml vs 26.37±3.19ng/ml)and NDRG1 level(124.39±15.67μg/L vs 9.67±10.82μg/L)in observation group were increased,and the differences were significant(t=10.864,17.278,all P<0.05).Compared with the non recurrence group,the serum levels of APRIL(40.38±7.88ng/ml vs 32.16±6.18ng/ml)and NDRG1(132.04±19.83μg/L vs 119.73±13.16μg/L)in the recurrence group were increased,and the differences were significant(t=6.668,4.287,all P<0.05).Logistic regression analysis showed that serum APRIL and NDRG1 levels were risk factors for the prognosis of patients with OEM(Waldχ2=11.839,28.437,all P＜0.001).Pearson method analysis results showed a positive correlation between serum APRIL level and NDRG1 level in patients with OEM(r=0.439,P<0.001).The area under the curve(AUC)of combined diagnosis of serum APRIL and NDRG1 levels in patients with OEM was 0.849,with a sensitivity and specificity of 73.95%and 85.37%,respectively,which was better than the single prediction of APRIL and NDRG1(Z =2.644,2.094,P=0.008,0.036).Conclusion The serum levels of APRIL and NDRG1 were increased in patients with OEM.The combination of the two has high clinical value in the diagnosis of OEM,which may be closely related to the prognosis of patients with OEM.

The identification of clinical questions for clinical practice guidelines of Chinese patent medicine （CPM） is important for subsequent evidence retrieval， evaluation of evidence quality， formation of recommendations. This paper described a methodological proposal for the identification of clinical questions for CPM guidelines to highlight the characteristics of Chinese patent medicine and reflect its effect in specific stage of the disease. Considering four aspects， namely， the drug of Chinese patent medicine （D）， the specific disease stage （S）， comparison （C）， and specific outcome （O）， DSCO framework has been proposed to formulate the clinical questions. Multi-source information through scientific research， policy or standard documents， and clinical data are suggested for collecting clinical questions， and clear selection criteria should be set to finalize the clinical questions to be addressed by the guideline. In addition， the above process needs to be transparently and publicly reported in order to ensure the clarity and completeness of the guidelines.