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.

Objective:To develop an artificial intelligence (AI)-guided intravitreal injection robot system to accurately detect the injection point on the ocular surface and guide the robotic arm to complete the intravitreal injection positioning task through 3D position calculation.Methods:The Dikablis subset of the TEyeD dataset was used.Training set, testing set, and validation set were constructed by using equal interval sampling strategy.The system read the ocular surface color RGB image with an RGBD camera, then used a PatchCrop-Transformer-based injection point detection algorithm to detect and locate key points such as the pupil, iris, and eyelid in the image.Next, it extracted the local 3D point cloud data near the injection point based on the depth information obtained by the camera.Through principal component analysis (PCA) of the local area point cloud data, the injection point and injection direction were determined.The key information was then passed to the robotic arm system.The end of the robotic arm adopted a remote center of motion (RCM) mechanism.After solving the forward and inverse kinematics, the joint movement path was obtained, and the robotic arm was controlled to move to 2 cm above the injection point.After confirmation by the doctor, the insertion, injection, and withdrawal operations were completed to ensure the stability and repeatability of the injection process.The mean square error (MSE) of key points localization and the success detection rate (SDR) within different pixel error ranges (2, 5, and 10 pixels) of the study method were compared with those of the NFDP, SLPT, and StarLoss methods, and the effects of random weight enhancement, fixed weight enhancement, and no enhancement methods on the MSE of key points localization were evaluated.The repeatability and absolute positioning accuracy of the robotic arm system were also evaluated.Results:After adding random weight enhancement, the model of this study outperformed the fixed weight enhancement and no enhancement methods in both MSE and SDR.The MSEs of the model proposed in this study for overall eye, pupil, and iris localization were 4.25, 2.41, and 1.54, respectively, which were lower than those of the NFDP, StarLoss, and SLPT methods.Within the error ranges of 5 and 10 pixels, the SDRs of the model proposed in this study were 72.09% and 92.68%, respectively, which were higher than those of the NFDP, StarLoss, and SLPT methods.The single-axis repeatability errors and absolute positioning errors of the robotic arm were within ±5 μm.Conclusions:The AI-guided intravitreal injection robot system integrates RGBD images to achieve automatic recognition of the ocular injection point and high-precision motion control through RCM mechanism design and corresponding kinematic solution methods.

Objective To establish a combined model of spectral CT multi-parameters and clinical features to distinguish between gastric poorly cohesive carcinoma and tubular adenocarcinoma.Methods A total of 87 patients with gastric cancer confirmed by postoperative pathology were retrospectively selected,including 26 patients with poorly cohesive carcinoma and 61 patients with tubular adenocarcinoma.Predictors were identified by univariate and multivariate logistic regression analyses,and a combined model was established.The area under the curve(AUC)of receiver operating characteristic(ROC)curve was used to evaluate the differential diagnostic efficiency of the parameters and the model.The AUC was compared by DeLong method.Results The gender[odds ratio(OR)5.124,P=0.004],normalized iodine density in the arterial phase(nIoDAP)(OR 5.789,P=0.017),arterial enhancement fraction(AEF)(OR 7.007,P=0.002)and ΔIoD(OR 0.025,P=0.021)were identified as independent predictors for poorly cohesive carcinoma by logistic regression analysis.The AUC of combined model established by four variables in distinguishing poorly cohesive carcinoma and tubular adenocarcinoma was 0.837[95％confidence interval(CI)0.716-0.907],which was significantly higher than that of single tumor spectral CT parameters(P＜0.01).Conclusion The combined model based on patients'gender and tumor spectral CT parameters(nIoDAP,AEF and ΔIoD)can effectively distinguish gastric poorly cohesive carcinoma and tubular adenocarcinoma,providing a basis for gastric cancer patients'individualized treatment strategy.

Objective To understand the current situation and dynamic changing trends of healthcare-associated infection(HAI)in a tertiary first-class hospital in the northwest of Hunan Province from 2015 to 2024,and provide scientific basis for optimizing infection control strategies.Methods A single-day cross-sectional survey method was employed to investigate the HAI prevalence rates of hospitalized patients on the given survey day each year from 2015 to 2024.The standardized survey protocol on prevalence rate issued by the National Medical Institution Infec-tion Surveillance Network was strictly adhered,lanqingting real-time HAI monitoring management platform was adopted to retrieve cases from the hospital information system,and R4.2.2 was applied for statistical analysis.Results From 2015 to 2024,the prevalence rate of HAI decreased from 3.03％in 2015 to 1.76％in 2024(Z=-3.37,P＜0.001),and the HAI case prevalence rate decreased from 3.55％in 2015 to 2.20％in 2024(Z=-2.81,P=0.005).Department of critical care medicine continuously had the highest HAI case prevalence rate,which presented a downward trend over time(Z=-2.84,P=0.004).The main site of HAI was lower respiratory tract,accounting for 39.36％to 48.15％,bloodstream infection increased from 3.57％in 2015-2016 to 10.60％in 2023-2024(Z=2.41,P=0.016).A total of 302 strains of HAI pathogens were detected,including 212 strains(70.20％)of Gram-negative bacteria,mainly Pseudomonas aeruginosa(n=55,18.21％),Escherichia coli(n=45,14.90％),Acinetobacter baumannii(n=33,10.93％),and Klebsiella pneumoniae(n=31i,10.26％).65 strains(21.52％)of Gram-positive bacteria were identified,with Enterococcus faecium(n=19,6.29％)and Staphylococcus aureus(n=18,5.96％)accounting for the highest proportions.25 fungal strains(8.28％)were detected,mainly Candi-da albicans(n=11,3.64％).The use rate of antimicrobial agents showed a downward trend over the past decade(Z=-4.01,P＜0.001).Therapeutic antimicrobial use accounting for 82.42％,and its proportion increased over time(Z=6.02,P＜0.001).Prophylactic antimicrobial use accounted for 16.42％,showing a decreasing trend(Z=-2.75,P＜0.001).The pathogen detection rate presented an upward trend over the past decade(Z=13.01,P＜0.001).Conclusion The prevalence rate and case prevalence rate of HAI present a downward trend in this hospi-tal.In the future,it is necessary to establish a monitoring data-based dynamic analysis mechanism,achieve timely feedback and intervention in data monitoring,pay attention to high-risk links in department of critical care medicine,implement precise prevention and control mearsures,perform targeted prevention and control for lower respiratory tract,urinary tract,and bloodstream infection,optimize diagnosis and treatment processes,use antimicrobial agents rationally,and pay attention to the prevalence trend of Gram-negative bacteria.

Objective:To develop an artificial intelligence (AI)-guided intravitreal injection robot system to accurately detect the injection point on the ocular surface and guide the robotic arm to complete the intravitreal injection positioning task through 3D position calculation.Methods:The Dikablis subset of the TEyeD dataset was used.Training set, testing set, and validation set were constructed by using equal interval sampling strategy.The system read the ocular surface color RGB image with an RGBD camera, then used a PatchCrop-Transformer-based injection point detection algorithm to detect and locate key points such as the pupil, iris, and eyelid in the image.Next, it extracted the local 3D point cloud data near the injection point based on the depth information obtained by the camera.Through principal component analysis (PCA) of the local area point cloud data, the injection point and injection direction were determined.The key information was then passed to the robotic arm system.The end of the robotic arm adopted a remote center of motion (RCM) mechanism.After solving the forward and inverse kinematics, the joint movement path was obtained, and the robotic arm was controlled to move to 2 cm above the injection point.After confirmation by the doctor, the insertion, injection, and withdrawal operations were completed to ensure the stability and repeatability of the injection process.The mean square error (MSE) of key points localization and the success detection rate (SDR) within different pixel error ranges (2, 5, and 10 pixels) of the study method were compared with those of the NFDP, SLPT, and StarLoss methods, and the effects of random weight enhancement, fixed weight enhancement, and no enhancement methods on the MSE of key points localization were evaluated.The repeatability and absolute positioning accuracy of the robotic arm system were also evaluated.Results:After adding random weight enhancement, the model of this study outperformed the fixed weight enhancement and no enhancement methods in both MSE and SDR.The MSEs of the model proposed in this study for overall eye, pupil, and iris localization were 4.25, 2.41, and 1.54, respectively, which were lower than those of the NFDP, StarLoss, and SLPT methods.Within the error ranges of 5 and 10 pixels, the SDRs of the model proposed in this study were 72.09% and 92.68%, respectively, which were higher than those of the NFDP, StarLoss, and SLPT methods.The single-axis repeatability errors and absolute positioning errors of the robotic arm were within ±5 μm.Conclusions:The AI-guided intravitreal injection robot system integrates RGBD images to achieve automatic recognition of the ocular injection point and high-precision motion control through RCM mechanism design and corresponding kinematic solution methods.

Objective To understand the current situation and dynamic changing trends of healthcare-associated infection(HAI)in a tertiary first-class hospital in the northwest of Hunan Province from 2015 to 2024,and provide scientific basis for optimizing infection control strategies.Methods A single-day cross-sectional survey method was employed to investigate the HAI prevalence rates of hospitalized patients on the given survey day each year from 2015 to 2024.The standardized survey protocol on prevalence rate issued by the National Medical Institution Infec-tion Surveillance Network was strictly adhered,lanqingting real-time HAI monitoring management platform was adopted to retrieve cases from the hospital information system,and R4.2.2 was applied for statistical analysis.Results From 2015 to 2024,the prevalence rate of HAI decreased from 3.03％in 2015 to 1.76％in 2024(Z=-3.37,P＜0.001),and the HAI case prevalence rate decreased from 3.55％in 2015 to 2.20％in 2024(Z=-2.81,P=0.005).Department of critical care medicine continuously had the highest HAI case prevalence rate,which presented a downward trend over time(Z=-2.84,P=0.004).The main site of HAI was lower respiratory tract,accounting for 39.36％to 48.15％,bloodstream infection increased from 3.57％in 2015-2016 to 10.60％in 2023-2024(Z=2.41,P=0.016).A total of 302 strains of HAI pathogens were detected,including 212 strains(70.20％)of Gram-negative bacteria,mainly Pseudomonas aeruginosa(n=55,18.21％),Escherichia coli(n=45,14.90％),Acinetobacter baumannii(n=33,10.93％),and Klebsiella pneumoniae(n=31i,10.26％).65 strains(21.52％)of Gram-positive bacteria were identified,with Enterococcus faecium(n=19,6.29％)and Staphylococcus aureus(n=18,5.96％)accounting for the highest proportions.25 fungal strains(8.28％)were detected,mainly Candi-da albicans(n=11,3.64％).The use rate of antimicrobial agents showed a downward trend over the past decade(Z=-4.01,P＜0.001).Therapeutic antimicrobial use accounting for 82.42％,and its proportion increased over time(Z=6.02,P＜0.001).Prophylactic antimicrobial use accounted for 16.42％,showing a decreasing trend(Z=-2.75,P＜0.001).The pathogen detection rate presented an upward trend over the past decade(Z=13.01,P＜0.001).Conclusion The prevalence rate and case prevalence rate of HAI present a downward trend in this hospi-tal.In the future,it is necessary to establish a monitoring data-based dynamic analysis mechanism,achieve timely feedback and intervention in data monitoring,pay attention to high-risk links in department of critical care medicine,implement precise prevention and control mearsures,perform targeted prevention and control for lower respiratory tract,urinary tract,and bloodstream infection,optimize diagnosis and treatment processes,use antimicrobial agents rationally,and pay attention to the prevalence trend of Gram-negative bacteria.

Background: and Purpose Guillain–Barré syndrome (GBS) is an autoimmune-mediated disorder characterized by demyelinating or axonal injury of the peripheral nerve. Our aim is to determine whether serum amyloid A (SAA) is a biomarker of demyelinating injury and disease severity in patients with GBS. Methods: This study retrospectively enrolled 40 patients with either the demyelinating or axonal GBS and sex- and age-matched controls with other neurological diseases as well as healthy subjects. The demographic and clinical features at entry were collected. The serum levels of the SAA isoforms SAA1, SAA2, and SAA4 were determined in the patients with GBS and the controls using the enzyme-linked immunosorbent assay and analyzed for the associations between levels of different SAA isoforms and the clinical features of the patients. Results: The levels of SAA2 and SAA4 were significantly higher in patients with GBS than in both the other neurological disease controls and the healthy subjects (p<0.05 for all). The level of SAA1 did not differ between patients with GBS and the controls. The level of SAA2 was considerably higher in GBS patients with antecedent infection than in those without infection (p=0.020). The levels of different SAA isoforms were not associated with the disease severity or other clinical features of patients with GBS (p>0.05 for all). Conclusions Increased levels of SAA2 and SAA4 may only represent the acute inflammatory status and so cannot be utilized as biomarkers of the disease severity or demyelinating injury in patients with GBS.

Objective: To understand the relationship between working hours and occupational stress among front-line employees in manufacturing enterprises, so as to provide a basis for the prevention and control of occupational stress. Methods: Front-line employees from 5 manufacturing enterprises in Hangzhou City were selected from June to November 2024 using random cluster sampling. Demographic information and occupational information such as daily average working hours and weekly working hours, were collected through questionnaires. The effort-reward imbalance questionnaire was used to investigate occupational stress. The association between working hours and occupational stress was analyzed using a multivariable logistic regression model. Results: A total of 926 people were surveyed, among whom 572 were male, accounting for 61.77%, and 354 were female, accounting for 38.23%. The average age was (32.98±8.28) years. There were 515 people (55.62%) who had a monthly personal income of more than 5 000 yuan but less than 9 000 yuan. There were 353 people (38.12%) who had a working seniority of less than 5 years. There were 784 people (84.67%) who had an average daily working hours of >8 hours and 645 people (69.65%) who had a weekly working day of more than 5 days. There were 338 people (36.50%) on the shift work system, and 331 people (35.75%) worked night shifts. A total of 707 people were detected with occupational stress, with a detection rate of 76.35%. Multivariable logistic regression analysis showed that after adjusting for gender, age, educational level, marital status, number of children, only-child status, monthly personal income, working seniority, weekly working hours, assembly-line work, shift work system and night shift, compared with employees with an average daily working hours of ≤8 hours, the risk of occupational stress increased by 118.7% for those with an average daily working hours of >8-<10 hours (OR=2.187, 95%CI: 1.434-3.336) and by 248.4% for those with an average daily working hours of ≥10 hours (OR=3.484, 95%CI: 2.034-5.966). Conclusions The detection rate of occupational stress among front-line employees in manufacturing enterprises in Hangzhou City is high. Long average daily working hours can increase the risk of occupational stress among employees in manufacturing enterprises. It is recommended to reasonably arrange work intensity and working hours.