Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

OBJECTIVE To study the anti-fatigue effects of differently-cultivated Astragalus extract in a hypoxic environment of the plateau and explore the related mechanisms.METHODS Fifty-six male KM mice were randomly divided into the hypoxic swimming control(HSC)group,imitation wild Astragalus extract(IWA)430,860 and 1 720 mg·kg-1 groups,and cultivated Astragalus extract(CA)463,925 and 1 850 mg·kg-1 groups.The drug was administered by gavage once daily for 15 days,while body mass was monitored every three days.After 15 days of gavage,the mice were subjected to load swimming(5%body weight)in a hypobaric chamber(simulating a 4 000 m altitude),with exhaus-tive swimming time measured to identify the optimal dosage.Following randomization,fifty male KM mice were assigned to five groups:normoxic control(NC),hypoxic control(HC),HSC,IWA 860 and CA 925 mg·kg-1.All groups underwent daily gavage for 15 d before 90 min non-weight-bearing swimming was conducted in the HSC,IWA 860 and CA 925 mg·kg-1 groups within a hypobaric chamber,followed by immediate measurement of muscle strength.Hematoxylin-eosin(HE)staining was used to observe the histopathological changes in liver and gastrocnemius muscle tissues.Blood urea nitrogen(BUN),blood glucose(BG)and serum lactic acid(LA),glutathione(GSH),glutathione peroxidase(GSH-Px),malondialdehyde(MDA),superoxide dismutase(SOD),liver glycogen(LG)and muscle glycogen(MG)in livers and muscles,and total antioxidant capacity(T-AOC)and reactive oxygen species(ROS)in muscles were measured by commercial kits.Taurine and hypotaurine were measured by HPLC.Enzyme-linked immunosorbent assay(ELISA)was used for cysteine sulfenic acid decarboxylase(CSAD)measure-ment.Western blotting was used to detect protein expressions of phosphatidylinositol 3-kinase(PI3K),protein kinase B(Akt),nuclear factor E2-related factor 2(Nrf2),and heme oxygenase-1(HO-1)in skeletal muscles.RESULTS Compared with the HSC group,the swimming time was prolonged in IWA 463,IWA 860,CA 925 and CA 1 850 mg·kg-1 groups.Compared with the HSC group,the muscle strength of mice in the IWA 860 mg·kg-1 group and the CA 925 mg·kg-1 group was significantly increased,histo-pathological damage in the liver and gastrocnemius muscle was reduced,serum levels of LA and BUN were significantly decreased,levels of BG,LG and MG were significantly increased,levels of GSH,GSH-Px and SOD were significantly increased,contents of MDA were significantly decreased,expressions of CSAD were significantly increased in liver tissue,contents of GSH,T-AOC,taurine and hypotaurine were significantly increased,levels of ROS were significantly decreased,and protein expressions of PI3K,Akt,Nrf2,HO-1 were significantly upregulated in muscle tissues.CONCLUSION Under simulated high-altitude hypoxic conditions,extracts of Astragalus membranaceus cultivated by two methods consis-tently exhibit anti-fatigue effects.Its mechanisms may be mitigating oxidative stress,augmenting taurine and hypotaurine metabolic regulation,and activating PI3K/Akt and Nrf2/HO-1 signaling pathways.IWA has a better anti-fatigue effect than CA.

OBJECTIVE To study the anti-fatigue effects of differently-cultivated Astragalus extract in a hypoxic environment of the plateau and explore the related mechanisms.METHODS Fifty-six male KM mice were randomly divided into the hypoxic swimming control(HSC)group,imitation wild Astragalus extract(IWA)430,860 and 1 720 mg·kg-1 groups,and cultivated Astragalus extract(CA)463,925 and 1 850 mg·kg-1 groups.The drug was administered by gavage once daily for 15 days,while body mass was monitored every three days.After 15 days of gavage,the mice were subjected to load swimming(5%body weight)in a hypobaric chamber(simulating a 4 000 m altitude),with exhaus-tive swimming time measured to identify the optimal dosage.Following randomization,fifty male KM mice were assigned to five groups:normoxic control(NC),hypoxic control(HC),HSC,IWA 860 and CA 925 mg·kg-1.All groups underwent daily gavage for 15 d before 90 min non-weight-bearing swimming was conducted in the HSC,IWA 860 and CA 925 mg·kg-1 groups within a hypobaric chamber,followed by immediate measurement of muscle strength.Hematoxylin-eosin(HE)staining was used to observe the histopathological changes in liver and gastrocnemius muscle tissues.Blood urea nitrogen(BUN),blood glucose(BG)and serum lactic acid(LA),glutathione(GSH),glutathione peroxidase(GSH-Px),malondialdehyde(MDA),superoxide dismutase(SOD),liver glycogen(LG)and muscle glycogen(MG)in livers and muscles,and total antioxidant capacity(T-AOC)and reactive oxygen species(ROS)in muscles were measured by commercial kits.Taurine and hypotaurine were measured by HPLC.Enzyme-linked immunosorbent assay(ELISA)was used for cysteine sulfenic acid decarboxylase(CSAD)measure-ment.Western blotting was used to detect protein expressions of phosphatidylinositol 3-kinase(PI3K),protein kinase B(Akt),nuclear factor E2-related factor 2(Nrf2),and heme oxygenase-1(HO-1)in skeletal muscles.RESULTS Compared with the HSC group,the swimming time was prolonged in IWA 463,IWA 860,CA 925 and CA 1 850 mg·kg-1 groups.Compared with the HSC group,the muscle strength of mice in the IWA 860 mg·kg-1 group and the CA 925 mg·kg-1 group was significantly increased,histo-pathological damage in the liver and gastrocnemius muscle was reduced,serum levels of LA and BUN were significantly decreased,levels of BG,LG and MG were significantly increased,levels of GSH,GSH-Px and SOD were significantly increased,contents of MDA were significantly decreased,expressions of CSAD were significantly increased in liver tissue,contents of GSH,T-AOC,taurine and hypotaurine were significantly increased,levels of ROS were significantly decreased,and protein expressions of PI3K,Akt,Nrf2,HO-1 were significantly upregulated in muscle tissues.CONCLUSION Under simulated high-altitude hypoxic conditions,extracts of Astragalus membranaceus cultivated by two methods consis-tently exhibit anti-fatigue effects.Its mechanisms may be mitigating oxidative stress,augmenting taurine and hypotaurine metabolic regulation,and activating PI3K/Akt and Nrf2/HO-1 signaling pathways.IWA has a better anti-fatigue effect than CA.

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Background and objective Cystic lung cancer,a special type of lung cancer,has been paid more and more attention.The most common pathological type of cystic lung cancer is adenocarcinoma.The invasiveness of cystic lung adenocarcinoma is vital for the selection of clinical treatment and prognosis.The aim of this study is to analyze the multiple clinical features of cystic lung adenocarcinoma,explore the independent risk factors of its invasiveness,and establish a risk pre-diction model.Methods A total of 129 cases of cystic lung adenocarcinoma admitted to the Department of Thoracic Surgery of the First Affiliated Hospital of Nanjing Medical University from January 2021 to July 2022 were retrospectively analyzed and divided into pre-invasive group[atypical adenomatous hyperplasia(AAH),adenocarcinoma in situ(AIS)and minimally invasive adenocarcinoma(MIA)]and invasive group[invasive adenocarcinoma(IAC)]according to pathological findings.There were 47 cases in the pre-invasive group,including 19 males and 28 females,with an average age of(51.23±14.96)years.There were 82 cases in the invasive group,including 60 males and 22 females,with an average age of(61.27±11.74)years.Mul-tiple clinical features of the two groups were collected,including baseline data,imaging data and tumor markers.Univariate analysis,LASSO regression and multivariate Logistic regression analysis were used to screen out the independent risk factors of the invasiveness of cystic lung adenocarcinoma,and the risk prediction model was established.Results In univariate analysis,age,gender,smoking history,history of emphysema,neuron-specific enolase(NSE),number of cystic airspaces,lesion di-ameter,cystic cavity diameter,nodule diameter,solid components diameter,cyst wall nodule,smoothness of cyst wall,shape of cystic airspace,lobulation,short burr sign,pleural retraction,vascular penetration and bronchial penetration were statisti-cally different between the pre-invasive group and invasive groups(P<0.05).The above variables were processed by LASSO regression dimensionality reduction and screened as follows:age,gender,smoking history,NSE,number of cystic airspaces,lesion diameter,cystic cavity diameter,cyst wall nodule,smoothness of cyst wall and lobulation.Then the above variables were included in multivariate Logistic regression analysis.Cyst wall nodule(P=0.035)and lobulation(P=0.001)were found to be independent risk factors for the invasiveness of cystic lung adenocarcinoma(P<0.05).The prediction model was established as follows:P=e^x/(1+e^x),x=-7.927+1.476* cyst wall nodule+2.407* lobulation,and area under the curve(AUC)was 0.950.Conclusion Cyst wall nodule and lobulation are independent risk factors for the invasiveness of cystic lung adenocarcinoma,which have certain guiding significance for the prediction of the invasiveness of cystic lung adenocarcinoma.

Background::Copper and zinc are involved in the development of multiple malignancies; yet, epidemiological evidence on hepatocellular carcinoma (HCC) is limited. This study aimed to investigate the association between dietary intake and serum levels of copper and zinc with the risk of HCC.Methods::A total of 434 case-control pairs matched for sex and age (±1 year) were included in this study. Cases with newly diagnosed HCC were from the Guangdong Liver Cancer Cohort (GLCC) study, and healthy controls were from the Guangzhou Nutrition and Health Study (GNHS). A semi-quantitative 79-item food frequency questionnaire (FFQ) was used to assess habitual dietary intakes of copper and zinc. Serum levels of copper and zinc were measured by using inductively coupled plasma mass spectrometry. The copper (Cu)/ zinc (Zn) ratio was computed by dividing copper levels by zinc levels. Conditional logistic regression models were performed to calculate the odds ratio (OR) and 95% confidence intervals (CI) for per 1 standard deviation increase (per-SD increase) in copper and zinc levels.Results::Higher dietary intake (OR per-SD increase = 0.65, 95% CI: 0.44, 0.96, Ptrend = 0.029) and serum levels of zinc (OR per-SD increase = 0.11, 95% CI: 0.04, 0.30, Ptrend <0.001) were both associated with a lower risk of HCC. Subgroup analyses showed that the inverse association was only pronounced in men but not in women ( Pinteraction = 0.041 for dietary zinc intake and 0.010 for serum zinc levels). Serum copper levels (OR per-SD increase = 2.05, 95% CI: 1.39, 3.03, Ptrend = 0.020) and serum Cu/Zn ratio (OR per-SD increase = 6.53, 95% CI: 2.52, 16.92, Ptrend <0.001) were positively associated with HCC risk, while dietary copper intake and dietary Cu/Zn ratio were not associated with HCC risk. Conclusion::Zinc may be a protective factor for HCC, especially among men, but the effects of copper on HCC risk are not clear.

Objective:To carry out evidence-based nursing for postoperative exercise rehabilitation of patients with vertebral fractures, develop review indicators, analyze obstacles and facilitators in evidence-based nursing practice, and formulate reform strategies.Methods:Guided by the evidence-based nursing practice model of the Joanna Briggs Institute, this study incorporated evidence of postoperative exercise rehabilitation in patients with vertebral fractures and developed review items. Based on baseline review, the obstacles and facilitators in evidence-based nursing practice were analyzed, and reform strategies were formulated.Results:A total of 29 evidence items were included, and 16 review indicators were formulated. Only seven indicators had a compliance rate of 100%, two indicators had a compliance rate of 0, and the compliance rate of the remaining indicators was between 11% and 95%. The obstacles in evidence-based nursing practice came from three levels: system, practitioner, and patient. The facilitators included strong leadership and organizational abilities, close cooperation between medical and nursing teams, and so on. Corresponding change strategies were developed based on obstacles and facilitators.Conclusions:There is a certain gap between the evidence of exercise rehabilitation and evidence-based nursing practice in postoperative patients with vertebral fractures. We should make full use of facilitators to overcome obstacles and implement reform strategies.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective:To analyze the diagnostic value of sex hormone combined with carcinoembryonic antigen on lymph node metastasis in breast cancer patients.Methods:52 cases of breast cancer patients who underwent surgical resection were collected and divided into non-metastasis group and metastases group after axillary lymph node ultrasonography. The blood samples was collected from patients and the levels of serum estradiol, testosterone, progesterone and carcinoembryonic antigen were detected; The diagnostic value of the above indexes in patients with lymph node metastasis was analyzed; Logistic risk regression model was used to analyze the independent risk factors for lymph node metastasis after surgical resection.Results:There were significant differences between the non-metastatic group and the metastatic group in the largest tumor diameter and menopause (all P<0.05), but there were no significant differences among other general data (all P>0.05). The serum estradiol level in the non-metastatic group was (153.97±35.55) pg/ml, the progesterone level was (0.33±0.05) ng/ml, and the carcinoembryonic antigen level was (11.44±3.77) ng/ml, while the estradiol level in the metastatic group was (207.19±52.11) pg/ml ( t=4.13, P<0.001), progesterone level (0.38±0.04) ng/ml ( t=4.01, P<0.001), carcinoembryonic antigen level (15.41±3.46) ng/ml ( t=3.94, P<0.001). The above three indicators were significantly increased in patients in the transfer group. The area under the curve of estradiol was 0.83, the area under the curve of progesterone was 0.80, the area under the curve of carcinoembryonic antigen was 0.77, the area under the curve of the combination of the three was 0.85, and the area under the curve of the combination of the three was the largest. Logistic risk regression model showed that estradiol, progesterone, and carcinoembryonic antigen levels were independent risk factors affecting lymph node metastasis in breast cancer patients (all P<0.05) . Conclusion:The sex hormone estradiol, progesterone combined with carcinoembryonic antigen has a high diagnostic value for lymph node metastasis in patients with breast cancer, and can independently predict the occurrence of lymph node metastasis in breast cancer patients.