Animal experimentation constitutes a critical link between basic research and clinical application, making its research quality and translational efficiency paramount. Although considerable progress has been made in standardizing operational procedures and ethical guidelines, the standardization of outcome evaluation systems has significantly lagged, creating a key bottleneck that constrains the quality of biomedical research and evidence synthesis. This deficiency is manifested by pronounced heterogeneity in outcome selection across similar studies, incomplete methodological reporting, and disparate criteria for result interpretation, which severely impairs the comparability of findings and the evidence integration. To cope with this challenge, this paper systematically introduces a mature methodological tool from clinical research–the core outcome set (COS)–and explores its construction strategies and application potential in the field of animal experimentation. Given the extensive diversity of animal experiments, a pragmatic strategy of "focusing on key areas, implementing phased pilots, and promoting gradual expansion" should be adopted. This approach prioritizes the development of domain-specific COS for disease areas characterized by high research volume, urgent translational needs, and well-established animal models. A multi-source integration pathway for COS development is detailed, comprising systematic literature searches, methodological appraisals, and expert consensus, with the feasibility of leveraging artificial intelligence (AI) to enhance efficiency also being examined. The development and promotion of such COS are not intended to restrict scientific exploration; rather, they aim to establish a new, tiered evaluation paradigm consisting of "core outcomes" (mandatory), "recommended outcomes" (encouraged), and "exploratory outcomes" (optional). This framework is expected not only to enhance research quality through standardization and to adhere to the "3R" principles but also to accelerate the accumulation of high-quality evidence. This, in turn, provides a solid foundation for higher-level evidence synthesis, ultimately facilitating the effective translation of basic research findings into clinical practice and providing an essential methodological framework for scientific advancement in relevant disciplines.

Animal experiments are essential tools in biomedical research, serving as a bridge between basic research and clinical trials. Systematic reviews and meta-analyses (SRs/MAs) of animal experiments are crucial methods for integrating evidence from animal experiment, which can facilitate the translation of findings into clinical research, reduce translational risks, and promote resource integration in basic research. With the continuous development of the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology, its application in SRs/MAs of animal experiments has gained increasing attention. This article first outlines the principles and specific applications of the GRADE methodology in SRs/MAs of animal experiments, including qualitative descriptive systematic reviews, meta-analyses, and network meta-analyses. It then deeply analyzes the misuse of the GRADE methodology in practice, including incorrect evidence grading, improper classification of evidence, misapplication in qualitative systematic reviews, inconsistencies between the documentation of the upgrading and downgrading process and results, and inappropriate use for making recommendations. Furthermore, this article comprehensively discusses the factors influencing the grading of evidence certainty in SRs/MAs of animal experiments, including the impact of bias risk, indirectness, inconsistency, imprecision, and publication bias on evidence downgrading, as well as the role of large effect sizes and cross-species consistency in evidence upgrading. Finally, in response to the issues discussed, improvement strategies are proposed, including further research and optimization of the GRADE methodology for SRs/MAs of animal experiments, the development of reporting guidelines tailored to the characteristics of SRs/MAs in animal experiment research, and enhanced professional training for researchers in the GRADE methodology. This article aims to improve the quality of evidence in SRs/MAs of animal experiments, strengthen their reliability in clinical decision-making, and promote the more efficient translation of findings from animal experiment research into clinical practice.

Radiotherapy is main method in treating cervical cancer,and the rapid advancement of artificial intelligence(AI)technique is providing entirely new solutions for radiotherapy for cervical cancer.The AI means that is represented by deep learning is deeply integrating into the whole process of diagnosis,treatment and management for cervical cancer,which can promote intelligent and precise development of radiotherapy workflows.Currently,the applied cores of AI in radiotherapy for cervical cancer include image registration,target delineation,optimization of radiotherapy planning and risk assessment,which can significantly enhance efficiency and precision of treatment.But,AI is facing some challenges in clinical applications include data quality,and algorithm's robustness and interpretability at the same time.Depended on the above analyses,this paper systematically reviewed the frontier applications and progress in practice of AI in radiotherapy for cervical cancer,which especially analyzed technical advantages and limitations of AI in key link,and explored its development path and coping strategy in clinical promotion and standard application in future.It is purpose to provide theoretical references for clinical practice of precise and accurate radiotherapy for cervical cancer.

Objective To establish a Nomogram model based on serum tumor markers to assess the risk of pleural metastasis in patients with stage Ⅲ lung cancer,and to validate the constructed model.Methods A to-tal of 140 patients with stage Ⅲ lung cancer in the hospital from January 2022 to October 2024 were selected as the research subjects.They were divided into the metastasis group(36 cases)and the non-metastasis group(104 cases)based on whether pleural metastasis occurred.Clinical data of patients in the two groups were col-lected,and the levels of serum tumor markers[carcinoembryonic antigen(CEA),cancer antigen(CA)125,CA15-3,CA19-9]of patients in the two groups were detected simultaneously.The risk factors affecting pleural metastasis in patients with stage Ⅲ lung cancer were screened,and the risk of pleural metastasis in patients with stage Ⅲ lung cancer was evaluated by constructing a Nomogram model based on serum tumor markers,and the constructed prediction model was validated.Results The proportion of smoking history,maximum tumor diameter≥4 cm,lymph node metastasis and low differentiation in metastatic group were higher than those in non-metastatic group(P＜0.05).Compared with non-metastatic group,serum CEA,CA125,CA15-3 and CA19-9 levels in metastatic group were higher(P＜0.05).Multiuariate Logistic regression analysis showed that lymph node metastasis,low differentiation and abnormal increase of CEA,CA125,CA15-3 and CA19-9 levels were independent risk factors for pleural metastasis in stage Ⅲ lung cancer patients(P＜0.05).The area under the receiver operating characteristic curve of the Nomogram model constructed based on serum tumor markers was 0.896(95％CI:0.812-0.991),which proved that the model had good predic-tive efficacy.The calibration curve of the model confirmed that there was a good agreement between the pre-dicted risk and the actual risk,and the Hosmer-Lemeshow goodness of fit curve test x2=1.602,P=0.638.The results of the decision curve analysis show that this model could achieve a relatively high net benefit with-in the range of 0％to 80％.Conclusion The Nomogram model based on serum tumor markers can effectively evaluate the risk of pleural metastasis in patients with stage Ⅲ lung cancer,and has good calibration,goodness of fit and clinical practicability.

This paper reviews gamified mobile applications, summarizes their current application status in nurse training for pressure injury management, and analyzes the functions, uses, and limitations of pressure injury management mobile applications. The aim is to provide a reference for the development of gamified mobile applications for pressure injury management in China.

Objective To compare the clinical outcomes of the clomiphene citrate(CC)dual stimulation protocol and the luteal phase ovarian stimulation protocol after natural cycle oocytes retrieved in POSEIDON patients with low expected prognosis.Methods The clinical data from patients who underwent in vitro fertilization(IVF)or intracytoplasmic sperm injection(ICSI)at the Reproductive Center of the Third Affiliated Hospital of Zhengzhou University from May 2016 to December 2023 were analyzed retrospectively.According to the POSEI-DON criteria,including POSEIDON group 3 and group 4,which were treated with the CC dual stimulation protocol(the CC group)and the luteal-phase ovarian stimulation protocol after the natural cycle oocytes retrieval(the natural cycle group).The basic data and clinical outcomes of the two ovulation protocols were analyzed,and the main observations included the number of oocytes retrieved,the cumulative clinical pregnancy rate and the cumulative live birth rate,and explored the impact of ovulation promotion regimens on clinical outcomes through binary logis-tic regression.Results On the whole,there was no significant difference in the total number of oocytes retrieved,cumulative clinical pregnancy rate and live birth rate between the CC group and the natural cycle group(all P>0.05),And in POSEIDON group 4,the numbers of oocytes retrieved in the follicular phase of the CC group were higher than those in the natural cycle group,whereas the number of oocytes retrieved in the luteal phase of natural cycle group were higher,and the difference was statistically significant(P<0.05).After logistic regression analysis,the ovulation stimulation protocols did not affect the cumulative clinical pregnancy rate and live birth rate of POSEI-DON 3 and 4 group(P>0.05).Conclusions For the POSEIDON patients with a low expected prognosis,the clinical effectiveness of the luteal phase ovarian stimulation protocol after natural cycle oocytes retrieved is not inferior to that of the CC dual stimulation protocol.In POSEIDON group 4,on the premise that the AMH levels of patients in the natural cycle group were lower than those in the CC group,who underwent natural cycle ovulation followed by luteal phase induction achieved similar clinical outcomes to those of the CC dual stimulation protocol group.Therefore,the luteal phase ovarian stimulation protocol after natural cycle oocytes retrieved should be more clinically recommended for POSEIDON group 4 patients.

This paper reviews gamified mobile applications, summarizes their current application status in nurse training for pressure injury management, and analyzes the functions, uses, and limitations of pressure injury management mobile applications. The aim is to provide a reference for the development of gamified mobile applications for pressure injury management in China.

Objective To compare the clinical outcomes of the clomiphene citrate(CC)dual stimulation protocol and the luteal phase ovarian stimulation protocol after natural cycle oocytes retrieved in POSEIDON patients with low expected prognosis.Methods The clinical data from patients who underwent in vitro fertilization(IVF)or intracytoplasmic sperm injection(ICSI)at the Reproductive Center of the Third Affiliated Hospital of Zhengzhou University from May 2016 to December 2023 were analyzed retrospectively.According to the POSEI-DON criteria,including POSEIDON group 3 and group 4,which were treated with the CC dual stimulation protocol(the CC group)and the luteal-phase ovarian stimulation protocol after the natural cycle oocytes retrieval(the natural cycle group).The basic data and clinical outcomes of the two ovulation protocols were analyzed,and the main observations included the number of oocytes retrieved,the cumulative clinical pregnancy rate and the cumulative live birth rate,and explored the impact of ovulation promotion regimens on clinical outcomes through binary logis-tic regression.Results On the whole,there was no significant difference in the total number of oocytes retrieved,cumulative clinical pregnancy rate and live birth rate between the CC group and the natural cycle group(all P>0.05),And in POSEIDON group 4,the numbers of oocytes retrieved in the follicular phase of the CC group were higher than those in the natural cycle group,whereas the number of oocytes retrieved in the luteal phase of natural cycle group were higher,and the difference was statistically significant(P<0.05).After logistic regression analysis,the ovulation stimulation protocols did not affect the cumulative clinical pregnancy rate and live birth rate of POSEI-DON 3 and 4 group(P>0.05).Conclusions For the POSEIDON patients with a low expected prognosis,the clinical effectiveness of the luteal phase ovarian stimulation protocol after natural cycle oocytes retrieved is not inferior to that of the CC dual stimulation protocol.In POSEIDON group 4,on the premise that the AMH levels of patients in the natural cycle group were lower than those in the CC group,who underwent natural cycle ovulation followed by luteal phase induction achieved similar clinical outcomes to those of the CC dual stimulation protocol group.Therefore,the luteal phase ovarian stimulation protocol after natural cycle oocytes retrieved should be more clinically recommended for POSEIDON group 4 patients.

Radiotherapy is main method in treating cervical cancer,and the rapid advancement of artificial intelligence(AI)technique is providing entirely new solutions for radiotherapy for cervical cancer.The AI means that is represented by deep learning is deeply integrating into the whole process of diagnosis,treatment and management for cervical cancer,which can promote intelligent and precise development of radiotherapy workflows.Currently,the applied cores of AI in radiotherapy for cervical cancer include image registration,target delineation,optimization of radiotherapy planning and risk assessment,which can significantly enhance efficiency and precision of treatment.But,AI is facing some challenges in clinical applications include data quality,and algorithm's robustness and interpretability at the same time.Depended on the above analyses,this paper systematically reviewed the frontier applications and progress in practice of AI in radiotherapy for cervical cancer,which especially analyzed technical advantages and limitations of AI in key link,and explored its development path and coping strategy in clinical promotion and standard application in future.It is purpose to provide theoretical references for clinical practice of precise and accurate radiotherapy for cervical cancer.

Objective:To evaluate the accuracy of continuous photoplethysmography algorithms for atrial fibrillation diagnosis and atrial fibrillation burden evaluation via wearable devices.Methods:This study was a self-controlled prospective cohort study. A total of 254 consecutive inpatients were recruited from the Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University from September 2022 to November 2022. All participants were required to wear two devices at the same time: (1) an electrocardiogram (ECG) watch for acquisition of photoplethysmography (watch-recorded-photoplethysmography, W-PPG) and electrocardiogram (watch-recorded-electrocardiogram, W-ECG); (2) an ECG patch for acquisition of electrocardiogram (patch-recorded-electrocardiogram, P-ECG). The results were measured in 30 s data segments and individual participants, separately, which were calculated for sensitivity and specificity by comparing them with the results of expert-read ECG according to the receiver operating characteristic curve. Four groups were separated according to the proportion of the atrial fibrillation burden, and the difference of atrial fibrillation burden from algorithm and expert-read ECG was calculated.Results:All 254 subjects completed the study. The mean age of participants was (63.04±11.04) years old, 99 (38.98%) of them were female, and 97 (38.19%) patients had persistent atrial fibrillation. Expert-read ECG results were taken as standard criteria in all calculations. The P-ECG algorithm had a sensitivity of 94.86% (95% CI: 94.81%-94.91%) and a specificity of 99.30% (95% CI: 99.28%-99.31%) when measured in data segments. The W-PPG algorithm had a sensitivity of 96.07% (95% CI: 95.97%-96.18%) and a specificity of 98.62% (95% CI: 98.59%-98.65%). When measured in terms of individual participants, the P-ECG algorithm had a sensitivity of 92.55% (95% CI: 87.57%-95.71%) and a specificity of 96.39% (95% CI: 93.45%-98.09%), while the W-PPG algorithm had a sensitivity of 93.71% (95% CI: 88.75%-96.67%) and a specificity of 89.62% (95% CI: 85.61%-92.65%). When measured in terms of a single acquisition of W-ECG records, the W-ECG algorithm had a sensitivity of 92.04% (95% CI: 88.14%-94.78%) and a specificity of 96.19% (95% CI: 94.35%-97.47%). For atrial fibrillation burden assessment, the difference between the W-PPG analysis results and the expert-read ECG results was less than 2% in all burden distribution intervals. Conclusions:Continuous photoplethysmography algorithm applied to wearable devices to detect atrial fibrillation is a feasible strategy. Taking expert-read ECG results as standard, continuous monitoring of PPG by a smartwatch is highly accurate for atrial fibrillation diagnosis and can also be used to effectively complete atrial fibrillation burden assessment.