Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:Based on the background of high-quality development,we analyze the operational efficiency of traditional Chinese medicine(TCM)hospitals in China's Yangtze River Economic Belt in 2021 and explore the impact of external environmental factors on operational efficiency,so as to provide a reference for promoting the high-quality development of TCM hospitals in the Yangtze River Economic Belt.Methods:The three-stage DEA model was used to analyze the operational efficiency of TCM hospitals in 11 provinces and cities in the Yangtze River Economic Zone in China in 2021.Results:After three-stage DEA analysis,the values of comprehensive efficiency,pure technical efficiency and scale efficiency of TCM hospitals in China's Yangtze River Economic Belt are 0.976,0.986 and 0.990,respectively.5 provinces and cities,Shanghai,Jiangsu,Hunan,Chongqing and Guizhou,are efficient before and after the adjustment,and the comprehensive efficiency of Zhejiang,Anhui,Hubei,Jiangxi,Sichuan and Yunnan have increased compared with that before the adjustment.Ranking of the average value of the comprehensive efficiency of TCM hospitals operation in the three major city clusters of the Yangtze River Economic Belt after adjustment:Chengdu-Chongqing city cluster(0.998)>city cluster in the Yangtze River Delta(0.964)>city cluster in the middle reaches of the Yangtze River(0.962).Conclusion:The operational efficiency of TCM hospitals in the Yangtze River Economic Zone has been underestimated,and the comprehensive efficiency is mainly affected by scale efficiency;there are differences in the operational efficiency of TCM hospitals in the three major urban agglomerations,and balanced development is needed between regions;the operational efficiency of TCM hospitals is affected by the external environment,and it is necessary to improve the external environment;it is necessary to strengthen the construction of digital and informatization of TCM,and to pay attention to the role of talents in TCM,so as to promote the high-quality development of TCM hospitals.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Microfluidic chips have revolutionized analytical sciences through miniaturization and high-throughput capabilities.However,conventional static devices are constrained by fixed architectures,functional rigidity,and high customization costs.In recent years,,the emerging configurable and reconfigurable microfluidic technologies have provided solutions for these limitations through dynamic adaptability.Configurable systems enable post-fabrication customization via modular assembly or boundary modification,offering cost-effective functional versatility.Reconfigurable microfluidics represents a more advanced paradigm,incorporating real-time decision-making and dynamic control through physical/virtual boundary adjustments during operation.These adaptive systems enable precise manipulation of microenvironments for applications ranging from single-cell manipulation to dynamic biochemical synthesis.In this review,a ″static-dynamic boundary″ framework to systematically analyze both technologies was proposed,and the design rationales,operational mechanisms,and implementation strategies were compared.The development history of these two techniques was introduced,and the applications demonstrated their transformative potential in developing intelligent lab-on-chip systems,while technical challenges in standardization and control interfaces were critically assessed.The development trend on integrating smart materials and AI-driven automation to advance next-generation adaptive microfluidic platforms was prospected.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

In view of the characteristics of H5N6 subtype avian influenza virus(AIV)that it has both high pathogenicity and the risk of cross-species transmission,posing a serious threat to the poultry farming industry and public health security,in order to effectively prevent and control the spread of H5N6 avian influenza,a rapid,sensitive and specific detection technolo-gy was established in this study.The specific monoclonal antibodies against the neuraminidase N6 protein of avian influenza A virus subtype H5N6 were obtained through hybridoma and monoclonal antibody technology.These antibodies were coupled and labeled with carboxyl-functionalized fluorescent quantum dots,along with previously prepared specific antibodies against the hemagglutinin H5 protein.A rapid fluorescence immunochromatographic detection method for the H5N6 subtype of avian influ-enza virus was established according to the principle of double-antibody sandwich immunochromatography.This method a-chieved a detection sensitivity of 1 ng/mL for recombinant hemagglutinin H5 subtype protein and 0.1 ng/mL for recombinant neuraminidase N6 subtype protein.Moreover,the method exhibited no cross-reactivity with other influenza subtypes or patho-gens,such as Newcastle disease(ND),infectious bronchitis(IB),and infectious laryngotracheitis(ILT),thus demonstrating good specificity.The method effectively identified the highly pathogenic avian influenza virus H5 subtype and directly distin-guished the H5N6 subtype with good accuracy.The fluorescent quantum dot immunochromatographic typing detection method established herein met the sensitivity,specificity,and accuracy requirements for H5N6 subtype detection,and can be further used for rapid detection of the H5 and H5N6 subtypes of avian influenza virus.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.