OBJECTIVE To explore and establish a full-cycle management model for drug traceability codes that aligns with national policy requirements and the practical needs of healthcare institutions， thereby enhancing the refinement of drug management and the level of medication safety. METHODS A tripartite strategy integrating “hardware deployment， system transformation， and process re-engineering” was adopted. This involved the introduction of intelligent identification devices （personal digital assistant， high-definition industrial reader）， the modification of the hospital information system interface， and the re-engineering of workflows （drug warehousing， dispensing and distribution， drug withdrawal， uploading to the insurance platform） to achieve comprehensive， informatized collection and association of drug traceability codes throughout all stages. RESULTS A full-cycle management model for drug traceability codes was successfully established， realizing the goals of making drugs “traceable to their source， trackable in their distribution， and accountable in their responsibility”. The patient waiting time for medication dispensing before and after the implementation was [3.08（1.67，5.58）] min and [3.28（1.77，5.98）] min， respectively. Among them， the patient waiting time under the pre-preparation mode was [3.60（2.13，6.35）] min and [3.50（2.03，6.30）] min， respectively； the patient waiting time under the real-time mode was [2.05（0.83，4.03）] min and [2.78（1.18，5.38）] min， respectively； the number of dispensing errors was 3， 0， respectively； the staffing of relevant positions had not been increased. CONCLUSIONS The drug traceability code management model constructed from a full-cycle perspective effectively meets national policy requirements. It provides data support for refined hospital management and offers solid technical and procedural safeguards for ensuring patient medication safety and strengthening medical insurance fund supervision， demonstrating practical value.

Objective:To explore the construction and efficacy of cranial magnetic resonance imaging(MRI)classification model based on attention mechanism in type 2 diabetes patients with cognitive impairment.Methods:The case data of 100 patients with type 2 diabetes who were treated in the General Hospital of North China Petroleum Administration Bureau from June 2022 to January 2024 were retrospectively selected.A total of 100 MRI images of cranial FLAIR_LongTR sequence with cognitive impairment(32 cases)and those without cognitive impairment(68 cases)were respectively collected.The images of the above two kinds of samples were horizontally and vertically translated to expand to 1000 samples,respectively.The samples were randomly divided into training samples(n=700)and test samples(n=300)as the ratio of 7:3 according to affine transformation data augmentation method.Then,the attention mechanism model was established to test the images with full scan of the test samples.The ability of the attention mechanism system in screening cognitive impairment was analyzed according to the method of setting threshold value.The 100 MRI images of cranial FLAIR_LongTR sequence of patients in our hospital from January 2024.From January to May 2024 were used as a verification set to verify the diagnostic value of attention mechanism.Results:With the increasing of iteration times,the sample loss of training and verification of attention mechanism model gradually decreased and tended toward stability,and the accuracy of training set and verification set gradually increased and tended toward stability.In the attention mechanism model,the average loss rate of training samples was 10.024%,and that of test samples was 15.247%.In the attention mechanism model,the average accuracy of training samples was 99.078%,and the average accuracy of test samples was 99.753%.Receiver operating characteristic(ROC)curves showed that the area under curve(AUC)of attention mechanism model was 0.998,which can better diagnose cognitive impairment of patients with type 2 diabetes than the resNET model(AUC=0.656)(Z=3.437,P<0.001).Conclusion:The constructed cranial MRI classification model by using attention mechanism has favorable diagnostic value for cognitive impairment in patients with type 2 diabetes.

Objective:To explore the construction and efficacy of cranial magnetic resonance imaging(MRI)classification model based on attention mechanism in type 2 diabetes patients with cognitive impairment.Methods:The case data of 100 patients with type 2 diabetes who were treated in the General Hospital of North China Petroleum Administration Bureau from June 2022 to January 2024 were retrospectively selected.A total of 100 MRI images of cranial FLAIR_LongTR sequence with cognitive impairment(32 cases)and those without cognitive impairment(68 cases)were respectively collected.The images of the above two kinds of samples were horizontally and vertically translated to expand to 1000 samples,respectively.The samples were randomly divided into training samples(n=700)and test samples(n=300)as the ratio of 7:3 according to affine transformation data augmentation method.Then,the attention mechanism model was established to test the images with full scan of the test samples.The ability of the attention mechanism system in screening cognitive impairment was analyzed according to the method of setting threshold value.The 100 MRI images of cranial FLAIR_LongTR sequence of patients in our hospital from January 2024.From January to May 2024 were used as a verification set to verify the diagnostic value of attention mechanism.Results:With the increasing of iteration times,the sample loss of training and verification of attention mechanism model gradually decreased and tended toward stability,and the accuracy of training set and verification set gradually increased and tended toward stability.In the attention mechanism model,the average loss rate of training samples was 10.024%,and that of test samples was 15.247%.In the attention mechanism model,the average accuracy of training samples was 99.078%,and the average accuracy of test samples was 99.753%.Receiver operating characteristic(ROC)curves showed that the area under curve(AUC)of attention mechanism model was 0.998,which can better diagnose cognitive impairment of patients with type 2 diabetes than the resNET model(AUC=0.656)(Z=3.437,P<0.001).Conclusion:The constructed cranial MRI classification model by using attention mechanism has favorable diagnostic value for cognitive impairment in patients with type 2 diabetes.

Objective:To analyze the epidemiological and clinical characteristics of respiratory pathogens in China.Methods:This study was a cross-sectional study, which encompassed 19 core units of the clinical pathogen network and established a three-tiered clinical pathogen surveillance system. Thirty respiratory samples were collected every two weeks from various units from January to December 2024, and the clinical and pathogen diagnostic information were gathered. A total of 11 864 samples were tested using this system. The tier-1 clinical pathogen surveillance system covered influenza A virus (Flu-A), influenza B virus (Flu-B), respiratory syncytial virus (RSV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The tier-2 clinical pathogen surveillance system focused on 18 key respiratory pathogens. The tier-3 clinical pathogen surveillance system further clarified whether any emerging infectious diseases had occurred.Results:The tier-1 clinical pathogen surveillance system showed Flu-A predominated in December, Flu-B predominated in January, SARS-CoV-2 peaked in March and August, whereas RSV circulated sporadically throughout the year. Geographic trends were broadly consistent across the seven major regions, although Flu-A detection in December was notably higher in Northeast China (48.1%(111/231)) and East China (36.2%(148/409)), and RSV detection was concentrated in the Northwest and South China from January to March. Data from the tier-2 clinical pathogen surveillance system indicated that Streptococcus pneumoniae, Mycoplasma pneumoniae, rhinovirus, and adenovirus were detected year-round, of these, Streptococcus pneumoniae and rhinovirus showed elevated positive detection rates from August to September, while adenovirus peaked in January. Legionella pneumophila was not detected throughout the year, and other pathogens fluctuated throughout the year without a consistent pattern. The predominant etiologic agents of pediatric pneumonia were Mycoplasma pneumoniae (35.0%(105/300)), rhinovirus (25.7%(77/300)), and adenovirus (17.3%(52/300)), whereas adult pneumonia was mainly caused by Streptococcus pneumoniae (10.5%(29/277)), Staphylococcus aureus (6.9%(19/277)), Mycoplasma pneumoniae (6.9%(19/277)), and Flu-A (6.1%(17/277)). The tier-3 clinical pathogen surveillance system did not identify any emerging respiratory pathogens. Conclusion:Respiratory pathogens in China in 2024 exhibit distinct temporal and spatial distribution patterns and vary among different populations.

Objective:To investigate and validate the performance of a dosiomics model that utilized 3D dose distribution to forecast radiation-induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC) patients following intensity-modulated radiotherapy (IMRT).Methods:Clinical data of 3578 patients diagnosed with NPC admitted to Jiangsu Cancer Hospital from January 2011 to December 2021 were retrospectively analyzed. According to the inclusion and exclusion criteria, 97 NPC patients who developed RTLI were assigned into the case group. A 1:1 propensity score matching (PSM) method was used to match 97 NPC patients without RTLI as the control group. Patients were assigned into the training cohort ( n=135) and the validation cohort ( n=59) at a 7:3 ratio by simple random method. Dosiomics features were extracted from the patients' three-dimensional dose distribution maps. Spearman rho and the least absolute shrinkage and selection operator regression were used to select dosiomics features. Clinical features were collected and screened by univariate and multivariate analyses. Eight machine learning classifiers were then trained to build dosiomics models and clinical models, respectively. The area under the ROC curve (AUC), sensitivity, and specificity were calculated to compare the predictive performance of the dosiomics and clinical models. Multivariate analysis was conducted using logistic regression to assess the influencing factors, while comparisons of the ROC curves between two different models were performed using the DeLong test. Results:A total of 1130 dosiomics features were extracted from the three-dimensional dose distribution maps, and 14 features were retained for model building after feature selection. The model based on the support vector machine (SVM) classifier achieved the highest AUC value of 0.977 (95% CI: 0.949-1.000) in the validation cohort, with an AUC of 1.000 (95% CI: 1.000-1.000) in the training cohort. By conducting univariate and multivariate analyses of the patients' clinical features, 2 clinical features were retained to build the clinical model. The model based on the SVM classifier achieved the optimal AUC value of 0.667 (95% CI: 0.523-0.810) in the validation cohort, with an AUC of 0.804 (95% CI: 0.730-0.878) in the training cohort. DeLong test showed that the difference between the dosiomics and clinical models was statistically significant ( P<0.05). Conclusion:The dosiomics model based on 3D dose distribution yields high predictive performance for RTLI in NPC patients after IMRT, which surpasses the clinical feature model, providing a new approach for early clinical prediction of RTLI.

BACKGROUND: Disease-modifying therapies have been approved for the treatment of relapsing multiple sclerosis (RMS). The present study aims to examine the safety of teriflunomide in Chinese patients with RMS. METHODS: This non-randomized, multi-center, 24-week, prospective study enrolled RMS patients with variant (c.421C>A) or wild type ABCG2 who received once-daily oral teriflunomide 14 mg. The primary endpoint was the relationship between ABCG2 polymorphisms and teriflunomide exposure over 24 weeks. Safety was assessed over the 24-week treatment with teriflunomide. RESULTS: Eighty-two patients were assigned to variant ( n  = 42) and wild type groups ( n  = 40), respectively. Geometric mean and geometric standard deviation (SD) of pre-dose concentration (variant, 54.9 [38.0] μg/mL; wild type, 49.1 [32.0] μg/mL) and area under plasma concentration-time curve over a dosing interval (AUC tau ) (variant, 1731.3 [769.0] μg∙h/mL; wild type, 1564.5 [1053.0] μg∙h/mL) values at steady state were approximately similar between the two groups. Safety profile was similar and well tolerated across variant and wild type groups in terms of rates of treatment emergent adverse events (TEAE), treatment-related TEAE, grade ≥3 TEAE, and serious adverse events (AEs). No new specific safety concerns or deaths were reported in the study. CONCLUSION: ABCG2 polymorphisms did not affect the steady-state exposure of teriflunomide, suggesting a similar efficacy and safety profile between variant and wild type RMS patients. REGISTRATION NCT04410965, https://clinicaltrials.gov .
Humans ; Crotonates/adverse effects* ; Toluidines/adverse effects* ; Nitriles ; Hydroxybutyrates ; Female ; Male ; Adult ; ATP Binding Cassette Transporter, Subfamily G, Member 2/genetics* ; Middle Aged ; Multiple Sclerosis, Relapsing-Remitting/genetics* ; Prospective Studies ; Young Adult ; Neoplasm Proteins/genetics* ; East Asian People

Objective To study the inhibitory effect of secondary metabolites of Pseudomonas aeruginosa(PA)on Candida albicans(CA)and to explore some of the mechanisms.Methods PA and CA strains were i-solated from clinical specimens from the hospital.Then,PA strains with inhibitory effects on CA were screened through cross-line test and co-incubation test,and crude extracts of PA secondary metabolites were prepared,and were tested together with pyocyanin,phenazine-1-carboxylic acid,1-hydroxyphenazine,and 3-ox-ododecyl-l-homoserine lactone(3-oxo-HSL).The inhibitory effects of various PA secondary metabolites on CA were determined through minimum inhibitory concentration test,minimum bactericidal concentration test,time-sterilization curve measurement,and XTT method activity measurement test,and some mechanisms by which PA secondary metabolites inhibited CA were explored.Results The strongest inhibitory effect on CA was 1-hydroxyphenazine,and at a concentration of 6.250 μg/mL,the relative activity of CA decreased to 0.00%.Next were pyocyanin and PA crude extract,and the relative fungal activity of CA decreased to 0.00%at concentrations of 200 and 100 μg/mL.1-hydroxyphenazine,pyocyanin,3-oxo-HSL and PA crude extract all had inhibitory effects on the formation of CA hyphae.Reactive oxygen species(ROS)were generated in CA cells treated with 1-hydroxyphenazine,phenazine 1-carboxylic acid,pyocyanin,and PA crude extract,and the highest levels of ROS were induced by pyocyanin and 1-hydroxyphenazine.Conclusion Phenazine secondary metabolites 1-hydroxyphenazine and pyocyanin have significant inhibitory effects on the growth and activity of CA,and both induce the highest amount of ROS.The quorum-sensing signal molecule 3-oxo-HSL have no in-hibitory effect on CA growth,but have a significant inhibitory effect on the formation of fungal hyphae.

Objective:To analyze the epidemiological and clinical characteristics of respiratory pathogens in China.Methods:This study was a cross-sectional study, which encompassed 19 core units of the clinical pathogen network and established a three-tiered clinical pathogen surveillance system. Thirty respiratory samples were collected every two weeks from various units from January to December 2024, and the clinical and pathogen diagnostic information were gathered. A total of 11 864 samples were tested using this system. The tier-1 clinical pathogen surveillance system covered influenza A virus (Flu-A), influenza B virus (Flu-B), respiratory syncytial virus (RSV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The tier-2 clinical pathogen surveillance system focused on 18 key respiratory pathogens. The tier-3 clinical pathogen surveillance system further clarified whether any emerging infectious diseases had occurred.Results:The tier-1 clinical pathogen surveillance system showed Flu-A predominated in December, Flu-B predominated in January, SARS-CoV-2 peaked in March and August, whereas RSV circulated sporadically throughout the year. Geographic trends were broadly consistent across the seven major regions, although Flu-A detection in December was notably higher in Northeast China (48.1%(111/231)) and East China (36.2%(148/409)), and RSV detection was concentrated in the Northwest and South China from January to March. Data from the tier-2 clinical pathogen surveillance system indicated that Streptococcus pneumoniae, Mycoplasma pneumoniae, rhinovirus, and adenovirus were detected year-round, of these, Streptococcus pneumoniae and rhinovirus showed elevated positive detection rates from August to September, while adenovirus peaked in January. Legionella pneumophila was not detected throughout the year, and other pathogens fluctuated throughout the year without a consistent pattern. The predominant etiologic agents of pediatric pneumonia were Mycoplasma pneumoniae (35.0%(105/300)), rhinovirus (25.7%(77/300)), and adenovirus (17.3%(52/300)), whereas adult pneumonia was mainly caused by Streptococcus pneumoniae (10.5%(29/277)), Staphylococcus aureus (6.9%(19/277)), Mycoplasma pneumoniae (6.9%(19/277)), and Flu-A (6.1%(17/277)). The tier-3 clinical pathogen surveillance system did not identify any emerging respiratory pathogens. Conclusion:Respiratory pathogens in China in 2024 exhibit distinct temporal and spatial distribution patterns and vary among different populations.

Objective:To investigate and validate the performance of a dosiomics model that utilized 3D dose distribution to forecast radiation-induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC) patients following intensity-modulated radiotherapy (IMRT).Methods:Clinical data of 3578 patients diagnosed with NPC admitted to Jiangsu Cancer Hospital from January 2011 to December 2021 were retrospectively analyzed. According to the inclusion and exclusion criteria, 97 NPC patients who developed RTLI were assigned into the case group. A 1:1 propensity score matching (PSM) method was used to match 97 NPC patients without RTLI as the control group. Patients were assigned into the training cohort ( n=135) and the validation cohort ( n=59) at a 7:3 ratio by simple random method. Dosiomics features were extracted from the patients' three-dimensional dose distribution maps. Spearman rho and the least absolute shrinkage and selection operator regression were used to select dosiomics features. Clinical features were collected and screened by univariate and multivariate analyses. Eight machine learning classifiers were then trained to build dosiomics models and clinical models, respectively. The area under the ROC curve (AUC), sensitivity, and specificity were calculated to compare the predictive performance of the dosiomics and clinical models. Multivariate analysis was conducted using logistic regression to assess the influencing factors, while comparisons of the ROC curves between two different models were performed using the DeLong test. Results:A total of 1130 dosiomics features were extracted from the three-dimensional dose distribution maps, and 14 features were retained for model building after feature selection. The model based on the support vector machine (SVM) classifier achieved the highest AUC value of 0.977 (95% CI: 0.949-1.000) in the validation cohort, with an AUC of 1.000 (95% CI: 1.000-1.000) in the training cohort. By conducting univariate and multivariate analyses of the patients' clinical features, 2 clinical features were retained to build the clinical model. The model based on the SVM classifier achieved the optimal AUC value of 0.667 (95% CI: 0.523-0.810) in the validation cohort, with an AUC of 0.804 (95% CI: 0.730-0.878) in the training cohort. DeLong test showed that the difference between the dosiomics and clinical models was statistically significant ( P<0.05). Conclusion:The dosiomics model based on 3D dose distribution yields high predictive performance for RTLI in NPC patients after IMRT, which surpasses the clinical feature model, providing a new approach for early clinical prediction of RTLI.

Objective:To explore the chain mediating effect of anxiety and flow experience on perceived stress and mobile phone addiction in nursing college students.Methods:In December 2021, a cross-sectional design survey was conducted on 4 179 freshmen and sophomores in a nursing college in Heilongjiang Province. The Chinese perceived stress scale, generalized anxiety disorder-7, flow state scale, and mobile phone addiction tendency scale were selected separately to assess perceived stress, anxiety symptoms, flow experience and mobile phone addiction. SPSS 26.0 software was used for descriptive analysis, independent sample t-test, Spearman correlation analysis, and AMOS 24.0 software was used for mediating effect test. Results:(1) Among the 3 050 nursing students, there were 714(23.41%) students who were addicted to mobile phones. (2) Spearman correlation analysis indicated that perceived stress(27.31±9.56) was positively correlated with anxiety(7.00(1.00, 10.00), r=0.441, P<0.05), flow experience((12.00±3.40), r=0.517, P<0.05), and mobile phone addiction((42.42±13.05), r=0.476, P<0.05).Anxiety was positively correlated with flow experience ( r=0.430, P<0.01) and mobile phone addiction ( r=0.538, P<0.01).Flow experience was positively correlated with mobile phone addiction ( r=0.490, P<0.01). (3) Anxiety and flow experience played seperate mediating and chain mediating roles between perceived stress and mobile phone addiction, accounting for 26.06%(0.165/0.633), 23.54%(0.149/0.633) and 3.48%(0.022/0.633) of the total effect. Conclusion:Perceived stress not only directly affects the mobile phone addiction of nursing students, but also indirectly affects mobile phone addiction through the independent and chain mediating effects of anxiety and flow experience.