Lung cancer is one of the most prevalent malignant tumors,which incidence and mortality rates increasing annually.Development of drug resistance is a primary factor contributing to treatment failure.Tumor-associated macrophages(TAMs),as key immune cells within tumor microenvironment(TME),play a significant role in the emergence and progression of drug resistance in tu-mors.TAMs can polarize into two distinct subtypes,M1 and M2,in response to diverse signaling stimuli.Research indicates that M2 TAMs are closely associated with poor prognoses in lung cancer,facilitating drug resistance through mechanisms such as promoting angiogenesis,enabling immune evasion,inducing stem cell-like characteristics in tumors,modulating relevant signaling pathways,and secreting cytokines.Traditional Chinese medicine(TCM)is characterized by its multi-target approach and minimal toxic side effects,it has been shown to enhance tumor sensitivity to drugs,slow malignant progression,and extend patient survival.This paper reviews the relationship between TAMs and lung cancer drug resistance while summarizing current research on TCM and their active components that regulate TAM activity to mitigate drug resistance in lung cancer,aiming to provide new insights for targeting TAMs in this context.

Lung cancer is one of the most prevalent malignant tumors,which incidence and mortality rates increasing annually.Development of drug resistance is a primary factor contributing to treatment failure.Tumor-associated macrophages(TAMs),as key immune cells within tumor microenvironment(TME),play a significant role in the emergence and progression of drug resistance in tu-mors.TAMs can polarize into two distinct subtypes,M1 and M2,in response to diverse signaling stimuli.Research indicates that M2 TAMs are closely associated with poor prognoses in lung cancer,facilitating drug resistance through mechanisms such as promoting angiogenesis,enabling immune evasion,inducing stem cell-like characteristics in tumors,modulating relevant signaling pathways,and secreting cytokines.Traditional Chinese medicine(TCM)is characterized by its multi-target approach and minimal toxic side effects,it has been shown to enhance tumor sensitivity to drugs,slow malignant progression,and extend patient survival.This paper reviews the relationship between TAMs and lung cancer drug resistance while summarizing current research on TCM and their active components that regulate TAM activity to mitigate drug resistance in lung cancer,aiming to provide new insights for targeting TAMs in this context.

Objective:To explore the effect of core stability training on rehabilitation of patients after lumbar fusion.Methods:From June 2018 to December 2019, 90 patients with lumbar fusion admitted to Wuxi Second Hospital Affiliated to Nanjing Medical University were selected by convenience sampling. The patients were divided into control group and training group according to the method of random number table, with 45 cases in each group. The control group received routine postoperative rehabilitation nursing, while the training group carried out postoperative core stability training nursing. The rehabilitation effects of the two groups were observed.Results:The scores of Numerical Rating Scale (NRS) in the training group were lower than those in the control group at 2, 4, 12 and 24 weeks postoperatively, and the difference were statistical ( P<0.05) . The scores of Oswestry Disability Index (ODI) in the training group 12 and 24 weeks after operation were lower than those in the control group, with statistically significant differences ( P<0.05) . Conclusions:The core stability training nursing is helpful to improve the functional recovery after lumbar fusion, reduce the degree of postoperative pain, and then increase the rehabilitation effect of patients.

Objective:To construct and validate a nomogram model based on clinical factors and PET/CT metabolic parameters of 18F-FDG for predicting epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma. Methods:From January 2014 to January 2019, 114 patients (59 males, 55 females, age (60.0±10.8) years) with lung adenocarcinoma in the First Affiliated Hospital of Harbin Medical University were retrospectively enrolled. Clinical data (smoking status, tumor location, clinical stage and carcinoembryonic antigen (CEA) level), 18F-FDG PET/CT metabolic parameters (SUV max, metabolic tumor volume (MTV) and total lesion glycolysis (TLG)) and EGFR mutation status were analyzed. Patients were divided into training group (80 cases) and validation group (34 cases). In the training group, univariate analyses (independent-sample t test, Wilcoxon rank sum test, χ2 test or Fisher′s exact probability method) were used for categorical variables. Variables that showed significant differences between EGFR mutation group and wild type group were selected. Variance inflation factors (VIF) were calculated and the collinearity variables were deleted, and a nomogram model of optimal logistic model was constructed based on Akaike information criterion (AIC). The effect of the model was evaluated by the concordance index (C-index), sensitivity, specificity, accuracy, calibration and decision curve analysis (DCA) in the training group and the validation group. Results:Among 114 patients, 56 were with EGFR mutations and 58 were with EGFR wild type. In the training group, there were significant differences in gender (male/female: 14/26 vs 25/15; χ2=6.05, P=0.014), smoking status (with/without smoking history: 4/36 vs 22/18; χ2=18.46, P<0.001) and SUV max (5.72(3.90, 8.32) vs 8.09(4.56, 12.55); W=1 045.50, P=0.018) between EGFR mutation group and wild type group. However, there were no significant differences in other factors ( t=-0.54, χ2 values: 0.20 and 0.20, W values: 921.50 and 983.00, all P>0.05). The VIF of gender, smoking status and SUV max were all less than 10, and the nomogram model with three factors showed the minimum AIC (90.06). In the training group, C-index value of the model was 0.798 (95% CI: 0.699-0.897), with the sensitivity of 85.0%(34/40), the specificity of 70.0%(28/40) and the accuracy of 77.5%(62/80). In the validation group, C-index value was 0.854(95% CI: 0.725-0.984), with the sensitivity of 13/16, the specificity of 14/18, and the accuracy of 79.4%(27/34). The calibration curve and the goodness of fit test showed good calibration, and DCA showed that the model could benefit patients clinically within a large risk threshold range (training group: 0-0.59, validation group: 0-0.65). Conclusion:The nomogram model based on gender, smoking status and SUV max can be used to easily predict EGFR mutation status in lung adenocarcinoma.