Implementation science (IS) aims to systematically analyze and address the real-world gaps from evidence to practice and the influencing factors of the context. It is necessary to carry out qualitative research to gather relevant implementation outcomes. Nevertheless, traditional qualitative analysis has issues such as consuming a great deal of time and energy, and it is unable to promptly provide the crucial data required for implementation science research. The Rapid Qualitative Analysis (RQA) method, through semi-structured interviews and the adoption of techniques such as immediate data condensation and matrix analysis, can effectively shorten the cycle of qualitative data collection and data processing. RQA can promptly identify social determinants of health such as structural barriers, facilitators, and the behavioral characteristics of target groups. It provides a real-time basis for public health decision-making, the interpretation of complex social phenomena, and the process and effectiveness evaluation of research projects. Although RQA is difficult to conduct in-depth theoretical analysis based on grounded theory, its efficiency and flexibility make it the preferred tool for large-scale and time-sensitive research. Thus, it has been widely applied in implementation science research. This paper sorts out the core concepts and commonly used technical methods of RQA, as well as the differences between RQA and traditional qualitative analysis. It also explores the applications of RQA in intervention optimization, process evaluation, and implementation outcome evaluation. By integrating specific cases, this paper clarifies its application value in the field of implementation science. In the future, it is advisable to explore the integration of RQA with technologies such as artificial intelligence and big data, in order to bridge the gap between the transformation of scientific research achievements into practice. Under circumstances of limited resources or tight time constraints, RQA can be used to efficiently conduct implementation science research, providing convenient and scientific methodological and technical support for accelerating evidence-based practice.

Traditional Chinese medicine （TCM） clinical trials face challenges such as low participant compliance， insufficient geographical coverage， and cost-effectiveness imbalances. Decentralized clinical trials （DCT）， enabled by digital technology for remote data collection and monitoring， offer a new direction for TCM clinical trial research. This article systematically reviews three novel clinical trial design models. Combining the holistic concept and indivi-dualized treatment characteristics of TCM， it analyzes the challenges currently faced in TCM DCT practice， including the digitization and standardization of TCM theory， data security， privacy protection and patient engagement difficu-lties， insufficient ethical review and regulatory system adaptation， inadequate personnel training， and a shortage of interdisciplinary talent. Addressing these challenges， the article proposes methodological recommendations for DCT implementation that align with the principles of TCM diagnosis and treatment. These recommendations include promoting the intelligentization and standardization of TCM practices， constructing a full-chain data security and privacy protection system， improving the ethical framework and clarifying regulatory responsibilities， and cultivating and building interdisciplinary talent and capabilities， which provide theoretical and technical references for establishing standardized DCT practices in TCM.

Traditional Chinese medicine （TCM） clinical trials face challenges such as low participant compliance， insufficient geographical coverage， and cost-effectiveness imbalances. Decentralized clinical trials （DCT）， enabled by digital technology for remote data collection and monitoring， offer a new direction for TCM clinical trial research. This article systematically reviews three novel clinical trial design models. Combining the holistic concept and indivi-dualized treatment characteristics of TCM， it analyzes the challenges currently faced in TCM DCT practice， including the digitization and standardization of TCM theory， data security， privacy protection and patient engagement difficu-lties， insufficient ethical review and regulatory system adaptation， inadequate personnel training， and a shortage of interdisciplinary talent. Addressing these challenges， the article proposes methodological recommendations for DCT implementation that align with the principles of TCM diagnosis and treatment. These recommendations include promoting the intelligentization and standardization of TCM practices， constructing a full-chain data security and privacy protection system， improving the ethical framework and clarifying regulatory responsibilities， and cultivating and building interdisciplinary talent and capabilities， which provide theoretical and technical references for establishing standardized DCT practices in TCM.

Protein liquid-liquid phase separation (LLPS), a pivotal phenomenon intricately linked to cellular processes, is regulated by various other proteins. However, there is still a lack of high-throughput methods for screening protein regulators of LLPS in target proteins. Here, we developed a CRISPR/Cas9-based screening method to identify protein phase separation regulators by integrating bimolecular fluorescence complementation (BiFC) and fluorescence-activated cell sorting (FACS). Using this newly developed method, we screened the RNA-binding proteins that regulate PABPN1 phase separation and identified the tumor suppressor QKI as a promoter of PABPN1 phase separation. Furthermore, QKI exhibits decreased expression levels and diminished nuclear localization in colorectal cancer cells, resulting in reduced PABPN1 phase separation, which, in turn, promotes alternative polyadenylation (APA), cell proliferation, and migration in colorectal cancer.
Humans ; Colorectal Neoplasms/genetics* ; RNA-Binding Proteins/genetics* ; Poly(A)-Binding Protein I/genetics* ; CRISPR-Cas Systems ; Flow Cytometry ; Cell Proliferation ; Cell Line, Tumor ; Cell Movement

Objective:To develop a predictive model for adverse pregnancy outcomes in patients with pregnancy-related acute kidney injury (Pr-AKI) using machine learning methods.Methods:This study was a single-center retrospective study. Patients with Pr-AKI in the First Affiliated Hospital of Xinjiang Medical University from January 2013 to December 2020 were included. Demographic characteristics, laboratory parameters, and fetal outcomes for comparative analysis between adverse pregnancy outcome group and favorable pregnancy outcome group were collected. Adverse pregnancy outcomes were defined as the occurrence of any one or more of the following events: stillbirth, perinatal death, preterm birth (reaching 28 weeks but less than 37 weeks), and low birth weight (< 2.5 kg). Conversely, an ideal pregnancy outcome was defined as the absence of any adverse pregnancy outcome events. The dataset was randomly divided into a training set (70%) and a validation set (30%). Logistic regression, decision tree, random forest, K-nearest neighbor, support vector machine, and lightweight gradient boosting algorithms were employed on the training set to develop predictive models for adverse pregnancy outcomes in patients with Pr-AKI. Receiver operating characteristic curves were plotted, and the area under the curves ( AUC) were calculated. Recall, precision, accuracy, and F1 scores were used to evaluate the predictive performance of each model. The optimal machine learning model was selected for subsequent analysis. Predictive model variables were screened and compressed by visualizing SHAP (SHapley additive exPlanations) with recursive feature regression. Furthermore, the efficacy of each model was evaluated through calibration curves and clinical decision curves. The optimal predictive model was selected for internal validation using the validation set, and data of in-hospital Pr-AKI patients (72 cases) in the hospital from January 2021 to June 2023 were collected for validation (time series validation set). Results:A total of 458 pregnancies in 441 patients were included in the present analysis, among which 277 cases (60.5%) resulted in adverse pregnancy outcomes. Utilizing the training set, 21 feature variables were selected for model construction. Among the 6 models, the random forest model performed the best ( AUC=0.860, recall=0.784, precision=0.813, F1-score=0.790, accuracy=0.806). With subsequent feature refinement proceeding, a total of 12 clinical indicators were selected to construct the model. Among them, proteinuria, systolic blood pressure, and the highest serum creatinine were the top three related factors, and the other related factors included: severe preeclampsia, baseline serum creatinine, serum albumin, diastolic blood pressure, aspartate aminotransferase, blood uric acid, white blood cell count, serum cystatin C, and cholesterol. Among various machine learning models, the random forest model demonstrated optimal net benefits and the widest clinical utility range, showing robust performance in both internal validation set ( AUC=0.80) and the time series validation set ( AUC=0.72). Conclusions:In this study, different machine learning algorithms are successfully applied to develop predictive models for adverse pregnancy outcomes in patients with Pr-AKI. The random forest model is translated into a clinically applicable tool, providing a reference for the convenient and rapid identification of adverse pregnancy outcomes in Pr-AKI patients.

Objective:To develop a predictive model for adverse pregnancy outcomes in patients with pregnancy-related acute kidney injury (Pr-AKI) using machine learning methods.Methods:This study was a single-center retrospective study. Patients with Pr-AKI in the First Affiliated Hospital of Xinjiang Medical University from January 2013 to December 2020 were included. Demographic characteristics, laboratory parameters, and fetal outcomes for comparative analysis between adverse pregnancy outcome group and favorable pregnancy outcome group were collected. Adverse pregnancy outcomes were defined as the occurrence of any one or more of the following events: stillbirth, perinatal death, preterm birth (reaching 28 weeks but less than 37 weeks), and low birth weight (< 2.5 kg). Conversely, an ideal pregnancy outcome was defined as the absence of any adverse pregnancy outcome events. The dataset was randomly divided into a training set (70%) and a validation set (30%). Logistic regression, decision tree, random forest, K-nearest neighbor, support vector machine, and lightweight gradient boosting algorithms were employed on the training set to develop predictive models for adverse pregnancy outcomes in patients with Pr-AKI. Receiver operating characteristic curves were plotted, and the area under the curves ( AUC) were calculated. Recall, precision, accuracy, and F1 scores were used to evaluate the predictive performance of each model. The optimal machine learning model was selected for subsequent analysis. Predictive model variables were screened and compressed by visualizing SHAP (SHapley additive exPlanations) with recursive feature regression. Furthermore, the efficacy of each model was evaluated through calibration curves and clinical decision curves. The optimal predictive model was selected for internal validation using the validation set, and data of in-hospital Pr-AKI patients (72 cases) in the hospital from January 2021 to June 2023 were collected for validation (time series validation set). Results:A total of 458 pregnancies in 441 patients were included in the present analysis, among which 277 cases (60.5%) resulted in adverse pregnancy outcomes. Utilizing the training set, 21 feature variables were selected for model construction. Among the 6 models, the random forest model performed the best ( AUC=0.860, recall=0.784, precision=0.813, F1-score=0.790, accuracy=0.806). With subsequent feature refinement proceeding, a total of 12 clinical indicators were selected to construct the model. Among them, proteinuria, systolic blood pressure, and the highest serum creatinine were the top three related factors, and the other related factors included: severe preeclampsia, baseline serum creatinine, serum albumin, diastolic blood pressure, aspartate aminotransferase, blood uric acid, white blood cell count, serum cystatin C, and cholesterol. Among various machine learning models, the random forest model demonstrated optimal net benefits and the widest clinical utility range, showing robust performance in both internal validation set ( AUC=0.80) and the time series validation set ( AUC=0.72). Conclusions:In this study, different machine learning algorithms are successfully applied to develop predictive models for adverse pregnancy outcomes in patients with Pr-AKI. The random forest model is translated into a clinically applicable tool, providing a reference for the convenient and rapid identification of adverse pregnancy outcomes in Pr-AKI patients.

Objective This study investigated the epidemic characteristics and spatio-temporal dynamics of hemorrhagic fever with renal syndrome (HFRS) in Qingdao City,China. Methods Information was collected on HFRS cases in Qingdao City from 2010 to 2022. Descriptive epidemiologic,seasonal decomposition,spatial autocorrelation,and spatio-temporal cluster analyses were performed. Results A total of 2,220 patients with HFRS were reported over the study period,with an average annual incidence of 1.89/100,000 and a case fatality rate of 2.52％. The male:female ratio was 2.8:1. 75.3％ of patients were aged between 16 and 60 years old,75.3％ of patients were farmers,and 11.6％ had both "three red" and "three pain" symptoms. The HFRS epidemic showed two-peak seasonality:the primary fall-winter peak and the minor spring peak. The HFRS epidemic presented highly spatially heterogeneous,street/township-level hot spots that were mostly distributed in Huangdao,Pingdu,and Jiaozhou. The spatio-temporal cluster analysis revealed three cluster areas in Qingdao City that were located in the south of Huangdao District during the fall-winter peak. Conclusion The distribution of HFRS in Qingdao exhibited periodic,seasonal,and regional characteristics,with high spatial clustering heterogeneity. The typical symptoms of "three red" and"three pain" in patients with HFRS were not obvious.

Objective:To understand the performance of public health risk assessment in emergencies of institutions for disease control and prevention at different levels in China, and provide suggestions for the improvement of public health risk assessment.Methods:A self-administered survey was conducted in professionals involved in public health risk assessment in emergencies from national institution, provincial institutions and some prefectural institutions for disease control and prevention (1-2 prefectural institutions were selected using convenience sampling in each province) between March and April in 2021.Results:A total of 79 institutions for disease control and prevention were investigated, including 1 national institution, 32 provincial institutions and 46 prefectural institutions. By April 2021, all the 79 institutions surveyed had conducted risk assessment of public health emergencies, in which 61 (77.2%) had established departments responsible for the public health risk assessment, i.e. emergency management office or communicable disease prevention and control office (section), and regular risk assessment mechanisms. The main sources of information for public health risk assessment were public health surveillance systems, including the National Notifiable Diseases Reporting System (100.0%) and Public Health Emergencies Management Information System (97.5%). Compared with the provincial institutions, the prefectural institutions were more likely to use specific disease surveillance systems (84.8% vs. 62.5%; χ2=5.09, P=0.024). The risk management recommendations made by 43 institutions for disease control and prevention (54.4%) after the risk assessment were accepted by the superior health administrative departments and used in epidemic prevention and control. Conclusions:Public health risk assessment in emergencies has been widely carried out by national, provincial and prefectural institutions for disease control and prevention in China. Specialized departments and mechanisms have been established, but the information sources are still confined to public health surveillance systems and the application of the risk assessment results still needs to be further improved.

OBJECTIVE: To ensure the supply of prevention materials in the tertiary public hospitals in prefecturelevel cities, and to make the process of allocating prevention materials more scientific and reasonable. METHODS: Open the green passage, simplify the procurement process, carry out emergency procurement of related materials, ensure timely delivery of prevention materials, distribute them at different levels, and strengthen the warehouse management of prevention materials. RESULTS: The scheme of emergancy supplies was constantly improved, and the supply of prevention materials was completed with good quality. CONCLUSIONS Using scientific and efficient management methods, the supply of prevention materials in medical institutions has been guaranteed, which has experience and reference significance for the prevention and control of similar public health emergencies in the future.
Emergencies ; Humans ; Public Health ; Tertiary Care Centers

Objective:To observe the effect of CoCl2on the cisplatin sensitivity of human ovarian cancer SKOV3cells, and to clarify the possible mechanism.Methods:The SKOV3cells were cultured in vitro and randomly divided into control group, CoCl2 group, cisplatin (DDP) group and CoCl2 combined with DDP (combination) group.The cells in CoCl2group were cultured in normal cell medium for 20hafter cultured in 200μmol·L-1 CoCl2for 4h, the cells in DDP group were cultured in normal cell medium containing 10mg·L-1 DDP for 24h, and the cells in combination group were cultured in 10mg·L-1 DDP for 20hafter cultured in 200μmol·L-1 CoCl2for 4h.The survival rates of SKOV3cells in various groups were detected by MTT method, and the positive expression intensities of hypoxia-inducible factor-1α (HIF-1α) and inducible nitric oxide synthase (iNOS) in the cells in various groups were detected by immunofluorescence method.Rhod 2-AM fluorescence probe was used to observe the levels of Ca2+in mitochondria in the cells in various groups.Western blotting method was used to observe the expression levels of cytochrome C (cyto C) , cysteinyl aspartase 3 (caspase 3) and cleaved cysteinyl aspartase 3 (cleaved caspase 3) .Muse○R apoptosis assay kit was used to detect the apoptotic rates of cells in various groups.Results:Compared with control group, the survival rate of the cells in CoCl2group had no significant change (P＞0.05) , and the survival rates of the cells in DDP and combination groups were decreased (P＜0.05) ;the survival rate in combination group was higher than that in DDP group (P＜0.05) .Compared with control group, the positive expression intensities of HIF-1αin CoCl2and combination groups were increased (P＜0.05) .Compared with control group, the positive expressions of iNOS in DDP and combination groups were increased (P＜0.05) .The Ca2+levels in the cells in DDP group and combination groups were higher than that in control group (P＜0.05) and the Ca2+level in DDP group was higher than that in combination group (P＜0.05) .Compared with control group, the expression levels of cyto C, caspase 3and cleaved caspase 3proteins in the SKOV3cells in CoCl2group had no significant changes (P＞0.05) , and the expression levels of cyto C, caspase 3and cleaved caspase 3in DDP group were increased significantly (P＜0.05) ;compared with DDP group, they were lower than those in combination group (P＜0.05) .Compared with control group, the apoptotic rate of SKOV3cells in DDP group was increased significantly (P＜0.05) ;the apoptotic rate of SKOV3cells in combination group was lower than that in DDP group (P＜0.05) .Conclusion:CoCl2can redece the mitochondrial apoptosis of human ovarian cancer SKOV3cells by inhibiting the DDP-induced enhancement of iNOS expression and decrease the sensitivity of SKOV3cells to cisplatin.