Improving the nitrogen use efficiency (NUE) of Brassica napus is of significant importance for achieving the national goal of zero growth in chemical fertilizer application and ensuring the green development of the rapeseed industry. This study aims to explore the effects of the nitrate transporter gene BnaNRT1.5s on the nitrogen transport and NUE of B. napus, providing excellent genetic resources for the development of nitrogen-efficient B. napus varieties. The spatiotemporal expression of BnaA05.NRT1.5 as a key nitrogen responsive gene was profiled by qRT-PCR at different growth stages and for different tissue samples of B. napus 'Westar'. Subcellular localization was employed to examine its expression pattern in the cells. Additionally, CRISPR/Cas9 was used to create BnaNRT1.5s knockout lines, which were subjected to hydroponic experiments under high nitrogen (12.0 mmol/L) and low nitrogen (0.3 mmol/L) conditions. After the seedlings were cultivated for 21 days, root and shoot samples were collected for weighing, nitrogen content determination, xylem sap nitrate content assessment, and calculation of total nitrogen and NUE. The B. napus nitrate transporter BnaA05.NRT1.5 was localized to the cell membrane. During the seedling and early bolting stages, BnaA05.NRT1.5 was predominantly expressed in roots, while it was highly expressed in old leaves and mature silique skin during the reproductive stage. Compared with the wild type, the mutant BnaNRT1.5s showed significant increases in the dry weight and total nitrogen of seedlings under both high and low nitrogen conditions. Under low nitrogen conditions, NUE in the roots of BnaNRT1.5s significantly improved. Notably, under both high and low nitrogen conditions, the nitrate content in the shoots of BnaNRT1.5s decreased significantly, while that in the roots increased significantly, resulting in a significantly decreased shoot-to-root nitrate content ratio. BnaNRT1.5s is involved in regulating the transport of nitrate from the roots to the shoots, and its mutation enhances nitrogen absorption and utilization in B. napus seedlings, promoting seedling growth. This study not only provides references for understanding the physiological and molecular mechanisms by which BnaNRT1.5s regulates NUE but also offers valuable genetic resources for improving NUE in B. napus.
Brassica napus/genetics* ; Anion Transport Proteins/metabolism* ; Nitrogen/metabolism* ; Nitrate Transporters ; Plant Proteins/metabolism* ; Nitrates/metabolism* ; Gene Expression Regulation, Plant ; Biological Transport

Objective To investigate the association between the TyG index,its modified variants,and the risk of developing colorectal cancer(CRC).Methods This study included a total of 93,177 participants from the 2006 Kailuan Group health examination cohort.Participants were categorized into four quartiles(Q1-Q4)according to their TyG and modified TyG indices.Follow-up began at the baseline examination,with incident CRC as the primary outcome.Participants were censored at the time of CRC diagnosis,death,or the end of the study,whichever occurred first.The dose-response relationship between TyG and its modified indices and the risk of CRC was evalu-ated using restricted cubic splines(RCS)in conjunction with Cox proportional hazards regression models,yielding hazard ratios(HRs)and 95%confidence intervals(CIs).To compare the strength of associations between TyG and its modified versions(TyG-BMI,TyG-WC,TyG-WHR,TyG-WHtR,TyG-WWI)and CRC risk,HRs for CRC per one standard deviation increase in each index were calculated and compared.Results Both the TyG index and its modified variants demonstrated a significant dose-response relationship with the risk of CRC incidence.Specifically,for the TyG index,each 1-standard deviation(SD)increase was associated with a 1.17-fold(95%CI:1.09～1.27)higher risk of CRC.Compared with the first quartile(Q1),the third quartile(Q3)and fourth quartile(Q4)exhibited a 1.25-fold(95%CI:1.01～1.55)and 1.26-fold(95%CI:1.01～1.57)increased risk,respectively.For TyG-BMI,each 1-SD increase was linked to a 1.20-fold(95%CI:1.07～1.35)elevated CRC risk.Compared with Q1,Q3 and Q4 showed a 1.32-fold(95%CI:1.06～1.64)and 1.51-fold(95%CI:1.21～1.88)increase,respectively.Regarding TyG-WC,each 1-SD increment was associated with a 1.22-fold(95%CI:1.13～1.32)higher CRC risk,with Q3 and Q4 showing a 1.35-fold(95%CI:1.08～1.70)and 1.56-fold(95%CI:1.24～1.96)increased risk compared to Q1.For TyG-WHtR,each 1-SD increase was associated with a 1.24-fold(95%CI:1.08-1.42)higher CRC risk.Compared with Q1,Q2,Q3,and Q4 demonstrated a 1.31-fold(95%CI:1.03～1.66),1.55-fold(95%CI:1.23～1.95),and 1.60-fold(95%CI:1.27～2.02)increase,respectively.In the case of TyG-WHR,each 1-SD increase was associated with a 1.19-fold(95%CI:1.10～1.29)higher CRC risk,with Q4 showing a 1.42-fold(95%CI:1.14～1.77)increased risk compared to Q1.Finally,for TyG-WWI,each 1-SD increase was associated with a 1.22-fold(95%CI:1.13～1.32)elevated CRC risk,with both Q3 and Q4 showing a 1.58-fold increase(Q3:95%CI:1.26～1.98;Q4:95%CI:1.25～1.99).Stratified analyses by sex and age consistently revealed significant associations between the TyG index and its modified variants and CRC risk.Furthermore,these indices were independently associated with the incidence of both colon cancer and rectal cancer.Conclusions(1)Elevated levels of the TyG index and its modified variants are independent risk factors for CRC.(2)Both the TyG index and its modified forms demonstrate a significant dose-response association with the incidence of CRC.(3)Among the modified TyG indices,TyG-WWI,TyG-WHtR,TyG-BMI,TyG-WC,and TyG-WHR showed stronger correlations with CRC risk compared to the original TyG index.

Objective:To develop a preoperative differentiation model for colorectal mucinous adenocarcinoma and non-mucinous adenocarcinoma using a combination of contrast-enhanced CT radiomics and deep learning methods.Methods:This is a retrospective cohort study. Clinical data of colorectal cancer patients confirmed by postoperative pathological examination were retrospectively collected from January 2016 to December 2023 at Shanghai General Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Center 1, n=220) and the First Affiliated Hospital of Bengbu Medical University (Center 2, n=51). Among them, there were 108 patients diagnosed with mucinous adenocarcinoma, including 55 males and 53 females, with an age of (68.4±12.2) years (range: 38 to 96 years); and 163 patients diagnosed with non-mucinous adenocarcinoma, including 96 males and 67 females, with an age of (67.9±11.0) years (range: 43 to 94 years). The cases from Center 1 were divided into a training set ( n=156) and an internal validation set ( n=64) using stratified random sampling in a 7∶3 ratio, and the cases from Center 2 were used as an independent external validation set ( n=51). Three-dimensional tumor volume of interest was manually segmented on venous-phase contrast-enhanced CT images. Radiomics features were extracted using PyRadiomics, and deep learning features were extracted using the ResNet-18 network. The two sets of features were then combined to form a joint feature set. The consistency of manual segmentation was assessed using the intraclass correlation coefficient. Feature dimensionality reduction was performed using the Mann-Whitney U test and the least absolute shrinkage and selection operator regression. Six machine learning algorithms were used to construct models based on radiomics features, deep learning features, and combined features, including support vector machine, logistic regression, random forest, extreme gradient boosting, k-nearest neighbors, and decision tree. The discriminative performance of each model was evaluated using receiver operating characteristic curves, the area under the curve (AUC), DeLong test, and decision curve analysis. Results:After feature selection, 22 features with the most discriminative value were finally retained, among which 12 were traditional radiomics features and 10 were deep learning features. In the internal validation set, the Random Forest algorithm based on the combined features model achieved the best performance (AUC=0.938, 95% CI: 0.875 to 0.984), which was superior to the single-modality radiomics feature model (AUC=0.817, 95% CI: 0.702 to 0.913, P=0.048) and the deep learning feature model (AUC=0.832, 95% CI: 0.727 to 0.926, P=0.087); in the independent external validation set, the Random Forest algorithm with the combined features model maintained the highest discriminative performance (AUC=0.891, 95% CI: 0.791 to 0.969), which was superior to the single-modality radiomics feature model (AUC=0.770, 95% CI: 0.636 to 0.890, P=0.045) and the deep learning feature model (AUC=0.799, 95% CI: 0.652 to 0.911, P=0.169). Conclusion:The combined model based on radiomics and deep learning features from venous-phase enhanced CT demonstrates good performance in the preoperative differentiation of colorectal mucinous from non-mucinous adenocarcinoma.

Objective To enhance medical service efficiency and optimize healthcare resource utilization,our hospital developed a novel one-stop integrated medical service model combining pre-hospitalization with day surgery.Methods Starting in August 2022,a Class A Tertiary Hospital in Guangzhou implemented a multi-dimensional collaborative mechanism:1.Process reengineering:Standardized workflows shifted preoperative tests and anesthesia evaluations to pre-hospitalization.2.Resource in-tegration:Established a one-stop medical service center as a comprehensive service coordination hub,integrating deposit pay-ment,examination scheduling,testing,medical check-ups,bed allocation,and"one-click admission"into a unified diagnostic and treatment service chain.3.Closed-loop management:Streamlined workflow from outpatient evaluation to follow-up.Results By 2024 vs 2022:Increased annual discharges by 30,000+cases.Reduced preoperative hospitalization by 0.4 days.Im-proved bed occupancy(+10.72％)and turnover(+10.86％).Achieved 94％patient satisfaction.Conclusion This model enhances bed efficiency,reduces hospitalization delays,and offers a scalable framework for healthcare optimization,demonstra-ting both social and operational benefits.

Objective Construction and Practice of a Whole-Process Digitalized Hemodialysis Management System.Methods ① Integrate device data through the Internet of Things(IoT)to enable automatic collection of dialysis parameters and real-time validation of quality control rules.② Build an"intelligent error prevention system"based on Data Analysis,realizing the transformation from patient management to digital quality control management in hemodialysis treatment.③ Implement a closed-loop management system for the entire patient dialysis process.Results The system reduced clinicians' time spent manu-ally filling and verifying documentation by 75％,and increased the adequacy compliance rate for maintenance dialysis patients to 100％per quarter and other goals.Conclusion Traditional hemodialysis management relies on manual records,leading to issues such as fragmented data,delayed quality control,material waste,and high risks of hospital-acquired infections.The entire process,from patient appointment to post-dialysis summary management lacks coordination,and manual calculation of key indica-tors is prone to errors,with insufficient timeliness in quality surveillance warnings.The construction of an information system can break down operational silos and enable"data-driven decision-making",and provides a replicable model for the digital manage-ment of hemodialysis.

Objective To investigate the association between the TyG index,its modified variants,and the risk of developing colorectal cancer(CRC).Methods This study included a total of 93,177 participants from the 2006 Kailuan Group health examination cohort.Participants were categorized into four quartiles(Q1-Q4)according to their TyG and modified TyG indices.Follow-up began at the baseline examination,with incident CRC as the primary outcome.Participants were censored at the time of CRC diagnosis,death,or the end of the study,whichever occurred first.The dose-response relationship between TyG and its modified indices and the risk of CRC was evalu-ated using restricted cubic splines(RCS)in conjunction with Cox proportional hazards regression models,yielding hazard ratios(HRs)and 95%confidence intervals(CIs).To compare the strength of associations between TyG and its modified versions(TyG-BMI,TyG-WC,TyG-WHR,TyG-WHtR,TyG-WWI)and CRC risk,HRs for CRC per one standard deviation increase in each index were calculated and compared.Results Both the TyG index and its modified variants demonstrated a significant dose-response relationship with the risk of CRC incidence.Specifically,for the TyG index,each 1-standard deviation(SD)increase was associated with a 1.17-fold(95%CI:1.09～1.27)higher risk of CRC.Compared with the first quartile(Q1),the third quartile(Q3)and fourth quartile(Q4)exhibited a 1.25-fold(95%CI:1.01～1.55)and 1.26-fold(95%CI:1.01～1.57)increased risk,respectively.For TyG-BMI,each 1-SD increase was linked to a 1.20-fold(95%CI:1.07～1.35)elevated CRC risk.Compared with Q1,Q3 and Q4 showed a 1.32-fold(95%CI:1.06～1.64)and 1.51-fold(95%CI:1.21～1.88)increase,respectively.Regarding TyG-WC,each 1-SD increment was associated with a 1.22-fold(95%CI:1.13～1.32)higher CRC risk,with Q3 and Q4 showing a 1.35-fold(95%CI:1.08～1.70)and 1.56-fold(95%CI:1.24～1.96)increased risk compared to Q1.For TyG-WHtR,each 1-SD increase was associated with a 1.24-fold(95%CI:1.08-1.42)higher CRC risk.Compared with Q1,Q2,Q3,and Q4 demonstrated a 1.31-fold(95%CI:1.03～1.66),1.55-fold(95%CI:1.23～1.95),and 1.60-fold(95%CI:1.27～2.02)increase,respectively.In the case of TyG-WHR,each 1-SD increase was associated with a 1.19-fold(95%CI:1.10～1.29)higher CRC risk,with Q4 showing a 1.42-fold(95%CI:1.14～1.77)increased risk compared to Q1.Finally,for TyG-WWI,each 1-SD increase was associated with a 1.22-fold(95%CI:1.13～1.32)elevated CRC risk,with both Q3 and Q4 showing a 1.58-fold increase(Q3:95%CI:1.26～1.98;Q4:95%CI:1.25～1.99).Stratified analyses by sex and age consistently revealed significant associations between the TyG index and its modified variants and CRC risk.Furthermore,these indices were independently associated with the incidence of both colon cancer and rectal cancer.Conclusions(1)Elevated levels of the TyG index and its modified variants are independent risk factors for CRC.(2)Both the TyG index and its modified forms demonstrate a significant dose-response association with the incidence of CRC.(3)Among the modified TyG indices,TyG-WWI,TyG-WHtR,TyG-BMI,TyG-WC,and TyG-WHR showed stronger correlations with CRC risk compared to the original TyG index.

Objective To enhance medical service efficiency and optimize healthcare resource utilization,our hospital developed a novel one-stop integrated medical service model combining pre-hospitalization with day surgery.Methods Starting in August 2022,a Class A Tertiary Hospital in Guangzhou implemented a multi-dimensional collaborative mechanism:1.Process reengineering:Standardized workflows shifted preoperative tests and anesthesia evaluations to pre-hospitalization.2.Resource in-tegration:Established a one-stop medical service center as a comprehensive service coordination hub,integrating deposit pay-ment,examination scheduling,testing,medical check-ups,bed allocation,and"one-click admission"into a unified diagnostic and treatment service chain.3.Closed-loop management:Streamlined workflow from outpatient evaluation to follow-up.Results By 2024 vs 2022:Increased annual discharges by 30,000+cases.Reduced preoperative hospitalization by 0.4 days.Im-proved bed occupancy(+10.72％)and turnover(+10.86％).Achieved 94％patient satisfaction.Conclusion This model enhances bed efficiency,reduces hospitalization delays,and offers a scalable framework for healthcare optimization,demonstra-ting both social and operational benefits.

Objective:To develop a preoperative differentiation model for colorectal mucinous adenocarcinoma and non-mucinous adenocarcinoma using a combination of contrast-enhanced CT radiomics and deep learning methods.Methods:This is a retrospective cohort study. Clinical data of colorectal cancer patients confirmed by postoperative pathological examination were retrospectively collected from January 2016 to December 2023 at Shanghai General Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Center 1, n=220) and the First Affiliated Hospital of Bengbu Medical University (Center 2, n=51). Among them, there were 108 patients diagnosed with mucinous adenocarcinoma, including 55 males and 53 females, with an age of (68.4±12.2) years (range: 38 to 96 years); and 163 patients diagnosed with non-mucinous adenocarcinoma, including 96 males and 67 females, with an age of (67.9±11.0) years (range: 43 to 94 years). The cases from Center 1 were divided into a training set ( n=156) and an internal validation set ( n=64) using stratified random sampling in a 7∶3 ratio, and the cases from Center 2 were used as an independent external validation set ( n=51). Three-dimensional tumor volume of interest was manually segmented on venous-phase contrast-enhanced CT images. Radiomics features were extracted using PyRadiomics, and deep learning features were extracted using the ResNet-18 network. The two sets of features were then combined to form a joint feature set. The consistency of manual segmentation was assessed using the intraclass correlation coefficient. Feature dimensionality reduction was performed using the Mann-Whitney U test and the least absolute shrinkage and selection operator regression. Six machine learning algorithms were used to construct models based on radiomics features, deep learning features, and combined features, including support vector machine, logistic regression, random forest, extreme gradient boosting, k-nearest neighbors, and decision tree. The discriminative performance of each model was evaluated using receiver operating characteristic curves, the area under the curve (AUC), DeLong test, and decision curve analysis. Results:After feature selection, 22 features with the most discriminative value were finally retained, among which 12 were traditional radiomics features and 10 were deep learning features. In the internal validation set, the Random Forest algorithm based on the combined features model achieved the best performance (AUC=0.938, 95% CI: 0.875 to 0.984), which was superior to the single-modality radiomics feature model (AUC=0.817, 95% CI: 0.702 to 0.913, P=0.048) and the deep learning feature model (AUC=0.832, 95% CI: 0.727 to 0.926, P=0.087); in the independent external validation set, the Random Forest algorithm with the combined features model maintained the highest discriminative performance (AUC=0.891, 95% CI: 0.791 to 0.969), which was superior to the single-modality radiomics feature model (AUC=0.770, 95% CI: 0.636 to 0.890, P=0.045) and the deep learning feature model (AUC=0.799, 95% CI: 0.652 to 0.911, P=0.169). Conclusion:The combined model based on radiomics and deep learning features from venous-phase enhanced CT demonstrates good performance in the preoperative differentiation of colorectal mucinous from non-mucinous adenocarcinoma.

Objective Construction and Practice of a Whole-Process Digitalized Hemodialysis Management System.Methods ① Integrate device data through the Internet of Things(IoT)to enable automatic collection of dialysis parameters and real-time validation of quality control rules.② Build an"intelligent error prevention system"based on Data Analysis,realizing the transformation from patient management to digital quality control management in hemodialysis treatment.③ Implement a closed-loop management system for the entire patient dialysis process.Results The system reduced clinicians' time spent manu-ally filling and verifying documentation by 75％,and increased the adequacy compliance rate for maintenance dialysis patients to 100％per quarter and other goals.Conclusion Traditional hemodialysis management relies on manual records,leading to issues such as fragmented data,delayed quality control,material waste,and high risks of hospital-acquired infections.The entire process,from patient appointment to post-dialysis summary management lacks coordination,and manual calculation of key indica-tors is prone to errors,with insufficient timeliness in quality surveillance warnings.The construction of an information system can break down operational silos and enable"data-driven decision-making",and provides a replicable model for the digital manage-ment of hemodialysis.

Objective To explore the progress on the application of in vivo drug analysis techniques in clinical pharmacy work. Methods Relevant literature was reviewed to provide an overview of the characteristics of clinical samples, common in vivo drug analysis methods used in the clinic, the application and existing problems of in vivo drug analysis in clinical pharmacy. Results and Conclusion In recent years, with the increasing demand for individualized and precise treatment in clinical practice and the continuous development of analytical techniques, in vivo drug analysis techniques have been widely used in clinical pharmacy work, which have become one of the important auxiliary techniques to promote rational clinical drug use, improve individualized treatment and reduce the occurrence of adverse reactions. However, in the actual application, there were still problems such as the invasive blood sampling that hinders sampling, the weak ability to interpret drug monitoring results and clinical testing methods that still need to be improved. These problems should be taken seriously and continuously improved and solved in the subsequent research and application.