Objective To explore the prescription patterns and core medication characteristics of traditional Chinese medicine(TCM)in the real-world treatment of non-small cell lung cancer(NSCLC).Methods Outpatient prescription data for NSCLC patients treated in the department of respiratory medicine at the First Affiliated Hospital of Henan University of Chinese Medicine from January 1,2020,to December 31,2023,were extracted through the Hospital Information System(HIS)to establish a standardized Chinese herbal medicine database.Latent structure models were constructed using Lantern 5.0 software,while association rule analysis was conducted with SPSS Modeler 18.0 software.Additionally,clustering analysis of high-frequency herbs was performed using SPSS 26.0 software to systematically analyse the laws of medicine identification and administration.Results A total of 620 TCM prescriptions involving 329 herbs were included,with a cumulative frequency of 10,461 herb applications.The most frequently used herbs were Citri Reticulatae pericarpium,Poria,Codonopsis radix,and Astragali radix.Tonifying and replenishing herbs constituted the primary category,with warm-natured and sweet-flavored herbs predominating.Most herbs targeted the lung meridian.Twenty core prescriptions,such as Xuefu Zhuyu decoction were identified.Core herb combinations included"Ligustri Lucidi fructus-Glycyrrhizae radix et rhizoma-Astragali radix"and"Solani nigri herba-Coicis semen-Astragali radix,"primarily reflecting therapeutic strategies of supplementing Qi,nourishing yin,detoxifying,and dispersing toxins.Conclusion TCM prescriptions for NSCLC predominantly utilize sweet and warm-natured herbs,adhering to the principle of strengthening the body's resistance and eliminating pathogenic factors.Therapeutic approaches integrate methods such as supplementing qi and nourishing yin,clearing heat and detoxifying,and activating blood circulation and resolving stasis.Core prescriptions are derived from classical formulas like Xuefu Zhuyu decoction and Baihe Gujin decoction through syndrome differentiation.The prescription patterns and core herb groups revealed by real-world data mining provide evidence-based insights for optimizing clinical treatment strategies for NSCLC.

Objective To explore the prescription patterns and core medication characteristics of traditional Chinese medicine(TCM)in the real-world treatment of non-small cell lung cancer(NSCLC).Methods Outpatient prescription data for NSCLC patients treated in the department of respiratory medicine at the First Affiliated Hospital of Henan University of Chinese Medicine from January 1,2020,to December 31,2023,were extracted through the Hospital Information System(HIS)to establish a standardized Chinese herbal medicine database.Latent structure models were constructed using Lantern 5.0 software,while association rule analysis was conducted with SPSS Modeler 18.0 software.Additionally,clustering analysis of high-frequency herbs was performed using SPSS 26.0 software to systematically analyse the laws of medicine identification and administration.Results A total of 620 TCM prescriptions involving 329 herbs were included,with a cumulative frequency of 10,461 herb applications.The most frequently used herbs were Citri Reticulatae pericarpium,Poria,Codonopsis radix,and Astragali radix.Tonifying and replenishing herbs constituted the primary category,with warm-natured and sweet-flavored herbs predominating.Most herbs targeted the lung meridian.Twenty core prescriptions,such as Xuefu Zhuyu decoction were identified.Core herb combinations included"Ligustri Lucidi fructus-Glycyrrhizae radix et rhizoma-Astragali radix"and"Solani nigri herba-Coicis semen-Astragali radix,"primarily reflecting therapeutic strategies of supplementing Qi,nourishing yin,detoxifying,and dispersing toxins.Conclusion TCM prescriptions for NSCLC predominantly utilize sweet and warm-natured herbs,adhering to the principle of strengthening the body's resistance and eliminating pathogenic factors.Therapeutic approaches integrate methods such as supplementing qi and nourishing yin,clearing heat and detoxifying,and activating blood circulation and resolving stasis.Core prescriptions are derived from classical formulas like Xuefu Zhuyu decoction and Baihe Gujin decoction through syndrome differentiation.The prescription patterns and core herb groups revealed by real-world data mining provide evidence-based insights for optimizing clinical treatment strategies for NSCLC.

Objective:To investigate the predictive value of differential distribution of peripheral lymphocyte subsets before and after the first 131I treatment on the therapeutic response to 131I treatment in patients with papillary thyroid cancer (PTC). Methods:A retrospective study was conducted on 46 PTC patients (16 males, 30 females, age 20-77 years) who underwent total thyroidectomy and received 131I treatment between January 2021 and August 2021 in First Hospital of Shanxi Medical University. Peripheral blood lymphocyte subsets (T, B, CD4 + T, CD8 + T, natural killer (NK), helper T (Th)1, Th2, Th17, and regulatory T (Treg) cells) were measured 1-2 d before and 30 d after 131I treatment. Based on serological and imaging evidence, therapeutic response at 6-12 months post- 131I therapy was categorized as either excellent response (ER) or non-excellent response (NER). Differences of preablative stimulated thyroglobulin (psTg) and clinical baseline characteristics between two groups were assessed by using independent-sample t test, paired t test, or Mann-Whitney U test. Predictive value of lymphocyte subsets before and after 131I treatment for therapeutic response was assessed through logistic regression analysis, ROC curve analysis, and decision curve analysis (DCA). Results:In ER group ( n=33) and NER group ( n=13), most lymphocyte subsets showed different degrees of reduction 30 d after 131I treatment compared to before 131I treatment, such as T, B, CD4 + T and Th1 cells in ER group, as well as T, B, CD4 + T, Th1, Th2, Th17, and Treg cells in NER group ( t values: 2.41-9.57, all P<0.05). Before 131I treatment, NER group had significantly higher levels of psTg, Th2, Th17, and Treg cells compared to the ER group ( t values: from -3.32 to -2.48, U=29.00, all P<0.05). After 131I treatment, most of lymphocyte subsets in NER group (T, B, CD4 + T, CD8 + T, Th1 and Treg cells) showed higher trend than those in ER group but without statistical significances ( t values: from -1.12 to -0.06, all P>0.05). Th2 cells before 131I treatment (odds ratio ( OR)=25.00, 95% CI: 1.36-459.10, P=0.030) was identified as a risk factor for NER. ROC curve analysis indicated that AUCs of psTg and Th2 cells for predicting therapeutic response were 0.932 and 0.790, respectively, which was 0.958 for the combined psTg and Th2 cells. DCA showed that within the threshold probability range of 10%-60%, the curves for psTg, Th2 cells, and the combined psTg and Th2 cells were all higher than the extreme curve, suggesting good effect. Conclusions:Most lymphocyte subsets decrease to varying degrees, and NER group shows a significant decrease 30 d after 131I treatment. Th2 cells may be a risk factor for poor response to 131I treatment, providing a certain value in predicting the therapeutic response to 131I treatment.

Objective:To visualize and analyze the literature related to nurses′ psychological contract at home and abroad by CiteSpace software, discuss the development trend, and to provide reference for subsequent research.Methods:Literature related to nurses′ psychological contract collected by China National Knowledge Infrastructure and Web of Science from the establishment of the database to August 31, 2023 were retrieved. CiteSpace software was used to visually process the authors, institutions, keywords, publication trends and other types of nodes. Combined with the visualization map, the research status and hot trends of nurses′ psychological contract were analyzed.Results:A total of 175 articles in English and 126 articles in Chinese were included. The annual publication volume showed a zigzag upward trend. Chen Furong′s team published the most Chinese literature, while Rodwell J′s team published the most English literature. Shanxi Medical University and Monash University were the research institutions with the largest number of publications in Chinese and English respectively. Domestic research hotspots mainly focused on 5 directions: the status quo investigation, influence factors, intervention strategies, the impact on performance behavior, the content and structure of nurses′ psychological contract. Foreign research hotspots mainly focused on 3 directions: the influence factors, intervention strategies and the impact on performance behavior.Conclusions:The overall number of papers published in the field of nurses′psychological contract research at home and abroad is small, and the depth and breadth of the research are far from enough. The cooperation between authors and institutions should be strengthened, the research direction should be more diversified, and the mediation effect analysis and influence mechanism research of psychological contract should be paid attention to, so as to construct a multipath nurses′psychological contract system complied with national condition.

Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection with an extremely high mortality rate,and it is the main risk factor for acute lung injury(ALI).However,the pathophysiology and pathogenesis of sepsis-associated ALI are not fully understood,and effective drugs are extremely limited.Therefore,it is urgent that we explore the pathogenesis of sepsis-associated ALI and attempt to discover effective intervention measures to improve the prognosis of sepsis-associated ALI patients.In recent years,ferroptosis has been considered closely related to the pathological and physiological processes of sepsis-associated ALI,and inhibiting related cell ferroptosis can effectively slow down the occurrence and development of the disease.In this paper,therapeutic strategies targeting ferroptosis in related cells are reviewed to provide a reference for future research on ferroptosis in sepsis-associated ALI and provide a new perspective on potential treatments.

Health science popularization is one of the key factors for patients with differentiated thyroid carcinoma treated with 131I to achieve positive survival outcomes. This paper reviews the general situation of health science popularization, the necessity of health science popularization for differentiated thyroid carcinoma treated with 131I and the current implementation situation at home and abroad, in order to provide reference for comprehensive health science popularization for differentiated thyroid carcinoma treated with 131I in clinical practice.

Radioactive iodine therapy is a safe and effective postoperative treatment for patients with differentiated thyroid cancer. Since 131I is a radionuclide, the patient becomes a living radioactive source after receiving 131I treatment, causing radioactive damage to the surrounding population and the environment. This article reviews the necessity of radiation safety guidance for differentiated thyroid cancer patients undergoing 131I treatment and the content of radiation safety guidance during hospitalization and after discharge, aiming to provide reasonable guidance on radiation safety for these patients. It provides a reference for the formulation of radiation safety management plans in line with national conditions in the future.

Objective:To establish a risk prediction model for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) using regression analysis and verify the model.Methods:The risk factors and acute exacerbation of 1 326 patients with chronic obstructive pulmonary disease (COPD) who entered the stable phase and followed up for 6 months in the four completed multi-center large-sample randomized controlled trials were retrospectively analyzed. Using the conversion-random number generator, about 80% of the 1 326 cases were randomly selected as the model group ( n = 1 074), and about 20% were the verification group ( n = 252). The data from the model group were selected, and Logistic regression analysis was used to screen independent risk factors for AECOPD, and an AECOPD risk prediction model was established; the model group and validation group data were substituted into the model, respectively, and the receiver operating characteristic (ROC) curve was drawn to verify the effectiveness of the risk prediction model in predicting AECOPD. Results:There were no statistically significant differences in general information (gender, smoking status, comorbidities, education level, etc.), body mass index (BMI) classification, lung function [forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), etc.], disease status (the number and duration of acute exacerbation in the past year, duration of disease, etc.), quality of life scale [COPD assessment test (CAT), etc.] and clinical symptoms (cough, chest tightness, etc.) between the model group and the validation group. It showed that the two sets of data had good homogeneity, and the cases in the validation group could be used to verify the effectiveness of the risk prediction model established through the model group data to predict AECOPD. Logistic regression analysis showed that gender [odds ratio ( OR) = 1.679, 95% confidence interval (95% CI) was 1.221-2.308, P = 0.001], BMI classification ( OR = 0.576, 95% CI was 0.331-1.000, P = 0.050), FEV1 ( OR = 0.551, 95% CI was 0.352-0.863, P = 0.009), number of acute exacerbation ( OR = 1.344, 95% CI was 1.245-1.451, P = 0.000) and duration of acute exacerbation ( OR = 1.018, 95% CI was 1.002-1.034, P = 0.024) were independent risk factors for AECOPD. A risk prediction model for AECOPD was constructed based on the results of regression analysis: probability of acute exacerbation ( P) = 1/(1+ e- x), x = -3.274 + 0.518×gender-0.552×BMI classification + 0.296×number of acute exacerbation + 0.018×duration of acute exacerbation-0.596×FEV1. The ROC curve analysis verified that the area under ROC curve (AUC) of the model group was 0.740, the AUC of the verification group was 0.688; the maximum Youden index of the model was 0.371, the corresponding best cut-off value of prediction probability was 0.197, the sensitivity was 80.1%, and the specificity was 57.0%. Conclusion:The AECOPD risk prediction model based on the regression analysis method had a moderate predictive power for the acute exacerbation risk of COPD patients, and could assist clinical diagnosis and treatment decision in a certain degree.

Objective? To design a satisfaction scale on nutrition support nursing service for inpatients, and establish a structural equation model of inpatient satisfaction with nutrition support nursing service in central city area to test the scale, so as to construct a tool for evaluating the satisfaction with nutrition support nursing service. Methods? This study selected 18 indicators affecting the satisfaction with nutrition support nursing services of inpatients based on the European Customer Satisfaction Index (ECSI), the American Customer Satisfaction Index (ACSI) Model, and the China Customer Satisfaction Index Model. From September to December of 2017, a convenience sampling investigation was carried out among the inpatients receiving nutritional support in a Class?Ⅲ Grade A medical institution. Through factor analysis, the 18 impact indicators were categorized into 6 dimensions, namely, perception of nutrition support nursing quality, nursing expectations, nutrient support nursing brands, nutrient support nursing service satisfaction, patient nutrient support nursing loyalty, patient nutrient support nursing complaints. Results? A structural equation model for satisfaction of nursing support services for inpatients was constructed, and the rationality of the 6 dimensions was verified. The Cronbach's α indexes of the 6 dimensions were all equal or greater than 0.80. KMO test coefficient was 0.745. Bartlett spherical test results showed χ2=3 402.132 (P＜0.01). The reasonableness of the six dimensions was verified, and the fitting effect of the model was good (χ2/df=1.416, RMS of residual=0.030, goodness of fit index=0.945, adjusting goodness of fit index=0.927, standard fitting index=0.947, value-added fitting index=0.984, comparing fitting index=0.984, root mean square of approximate error=0.035). Conclusions? With this model, nurse managers can identify the factors affecting the satisfaction of patients receiving nutritional support, and propose continuous improvement strategies that will help improve the effectiveness of nursing services, and improve patient satisfaction with inpatient nutritional support, so as to provide reference for the research of satisfaction model for inpatients.

Objective To construct the evaluation index system of the core competence of the nurses in the nuclear medicine department and provide a scientific assessment tool for the core competence evaluation of nurses in the nuclear medicine discipline. Methods Based on the theory of core competence, we selected indexes and determined the weight of them by using literature retrieval, experts interview, Delphi expert consultation and the analytic hierarchy process(AHP) in order to analyze the concentration degree of expert opinion. The coordination degree of expert opinion was analyzed with the coefficient of variation (CV) and coordination coefficient. Finally, the weight of each index was calculated by the AHP. Results Expert enthusiasm: the effective recovery rate of the two rounds of expert consultation was 100%, and a total of 12 experts made personal suggestions. Expert authority analysis: judge coefficient (Ca=0.932) and familiarity degree coefficient (Cs=0.865), the expert's authorities (Cr=0.879). Expert opinion coordination degree: two rounds of expert consultation was CV< 0.25. The expert opinion and modification situation finally formed 5 primary indicators, 18 secondary indicators and 47 tertiary indicators. Conclusions The evaluation index system of core competency of nuclear medicine nurses is highly scientific and reliable, which provides a reference for core competency training of nurses in nuclear medicine.