Objective:To construct a risk prediction model for malnutrition in lung cancer patients undergoing chemotherapy and develop an online calculator to predict individual risk values.Methods:A convenience sampling method was used to select 386 lung cancer patients admitted to the Department of Medical Oncology at the Cancer Hospital of the Chinese Academy of Medical Sciences in Beijing from October 2022 to March 2023 as research subjects. They were divided into a well-nourished group and a malnourished group based on the presence or absence of malnutrition. Multivariate Logistic regression analysis was used to identify the influencing factors of malnutrition in lung cancer patients undergoing chemotherapy. The nomogram of the risk prediction model was drawn and the online calculator was developed using R Studio 4.3.2 software. The model was evaluated using the area under the receiver operating characteristic curve, sensitivity, specificity, Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis.Results:Among the 372 lung cancer patients undergoing chemotherapy, there were 237 males and 135 females, with 147 in the well-nourished group and 225 in the malnourished group. The incidence rate of malnutrition was 60.48%. Age 60 - 70 years [ OR(95% CI) = 1.74(1.03 - 2.93)], body mass index > 24.0 kg/m 2 [ OR(95% CI) = 0.58(0.36 - 0.93)], regular exercise [ OR(95% CI) = 0.53(0.33 - 0.85)], physical frailty [pre-frailty: OR(95% CI) = 2.74(1.61 - 4.65); frailty syndrome: OR(95% CI) = 2.67(1.37 - 5.22)], insomnia [ OR(95% CI) = 2.81(1.59 - 4.94)], and sarcopenia [ OR(95% CI) = 4.09(1.32 - 12.70)] were identified as independent influencing factors for malnutrition in lung cancer patients undergoing chemotherapy (all P<0.05). The area under the curve of the constructed prediction model was 0.76, with a sensitivity of 72%, specificity of 72%, a Hosmer-Lemeshow goodness-of-fit test P value of 0.594, and a mean absolute error of 0.018. Conclusions:The risk prediction model and online calculator for malnutrition in lung cancer patients undergoing chemotherapy constructed in this study demonstrated good performance, providing a reference for precise nutritional intervention and early screening in clinical practice.

Objective:To construct a risk prediction model for malnutrition in lung cancer patients undergoing chemotherapy and develop an online calculator to predict individual risk values.Methods:A convenience sampling method was used to select 386 lung cancer patients admitted to the Department of Medical Oncology at the Cancer Hospital of the Chinese Academy of Medical Sciences in Beijing from October 2022 to March 2023 as research subjects. They were divided into a well-nourished group and a malnourished group based on the presence or absence of malnutrition. Multivariate Logistic regression analysis was used to identify the influencing factors of malnutrition in lung cancer patients undergoing chemotherapy. The nomogram of the risk prediction model was drawn and the online calculator was developed using R Studio 4.3.2 software. The model was evaluated using the area under the receiver operating characteristic curve, sensitivity, specificity, Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis.Results:Among the 372 lung cancer patients undergoing chemotherapy, there were 237 males and 135 females, with 147 in the well-nourished group and 225 in the malnourished group. The incidence rate of malnutrition was 60.48%. Age 60 - 70 years [ OR(95% CI) = 1.74(1.03 - 2.93)], body mass index > 24.0 kg/m 2 [ OR(95% CI) = 0.58(0.36 - 0.93)], regular exercise [ OR(95% CI) = 0.53(0.33 - 0.85)], physical frailty [pre-frailty: OR(95% CI) = 2.74(1.61 - 4.65); frailty syndrome: OR(95% CI) = 2.67(1.37 - 5.22)], insomnia [ OR(95% CI) = 2.81(1.59 - 4.94)], and sarcopenia [ OR(95% CI) = 4.09(1.32 - 12.70)] were identified as independent influencing factors for malnutrition in lung cancer patients undergoing chemotherapy (all P<0.05). The area under the curve of the constructed prediction model was 0.76, with a sensitivity of 72%, specificity of 72%, a Hosmer-Lemeshow goodness-of-fit test P value of 0.594, and a mean absolute error of 0.018. Conclusions:The risk prediction model and online calculator for malnutrition in lung cancer patients undergoing chemotherapy constructed in this study demonstrated good performance, providing a reference for precise nutritional intervention and early screening in clinical practice.

ObjectiveTo validate the alleviating effect of Gegen Qinliantang （GGQLT） on insulin resistance in db/db diabetic mice by regulating the silent information regulator 1 （SIRT1）/forkhead transcription factor O1 （FoxO1） autophagy pathway. MethodSeventy-five SPF-grade spontaneous type 2 diabetic db/db mice and 15 control db/m mice were selected and maintained on regular feed for one week before measuring blood glucose. They were randomly divided into six groups， with 15 mice in each group. The groups included a normal group （physiological saline， 0.2 g·kg^-1）， a metformin group （0.2 g·kg^-1）， high-， medium-， and low-dose GGQLT groups （31.9， 19.1， 6.9 g·kg^-1）， and a model group （physiological saline， 0.2 g·kg^-1）. They were orally treated with corresponding drugs for eight weeks， once daily. Fasting blood glucose （FBG） was measured using a Roche glucometer. Serum levels of high-density lipoprotein cholesterol （HDL-C）， low-density lipoprotein cholesterol （LDL-C）， triglyceride （TG）， and total cholesterol （TC） were measured using an automated biochemical analyzer. Fasting serum insulin （INS） levels were determined using enzyme-linked immunosorbent assay （ELISA）， and the homeostasis model assessment of insulin resistance （HOMA-IR） was calculated. Western blot was used to detect the expression of Beclin-1， microtubule-associated protein 1 light chain 3 （LC3）， and SIRT1/FoxO1 autophagy pathway-related proteins in liver tissues. Immunohistochemistry was performed to assess the expression of SIRT1， FoxO1， Beclin-1， and LC3B proteins in liver tissues. Transmission electron microscopy was used to observe the formation of autophagosomes in the liver. ResultCompared with the normal group， the model group showed significant increases in FBG， FINS， HOMA-IR， TC， TG， LDL-C， and HDL-C levels （P<0.01）， and significant increases in the expression of SIRT1， Beclin-1， LC3， and FoxO1 proteins in liver tissues （P<0.01）. Transmission electron microscopy revealed the highest number of autophagosomes in the model group. Compared with the model group， the metformin group and the low-， medium-， and high-dose GGQLT groups showed significant decreases in serum FBG， FINS， HOMA-IR， TC， TG， LDL-C， and HDL-C levels （P<0.05， P<0.01）， significant decreases in the expression of SIRT1， Beclin-1， LC3 （P<0.05， P<0.01）， and up-regulated FoxO1 protein （P<0.01）. Transmission electron microscopy showed a reduction in the degree of autophagy in the treatment groups. Compared with the metformin group， the medium- and high-dose GGQLT groups showed significant decreases in FBG， FINS， and TG levels （P<0.01）， significant decreases in the expression of SIRT1， Beclin-1， and LC3 in liver tissues （P<0.05， P<0.01）， and reduced FoxO1 protein （P<0.01）. The high-dose GGQLT group showed reduced HOMA-IR， TC， LDL-C， and HDL-C levels （P<0.05， P<0.01）. Transmission electron microscopy revealed a significant reduction in autophagosomes in the medium- and high-dose GGQLT groups. ConclusionGGQLT can significantly improve glucose and lipid metabolism disorders， alleviate insulin resistance in db/db mice， and prevent and treat type 2 diabetes by activating the SIRT1/FoxO1 autophagy pathway.

Objective:To observe the adverse effects of skin toxicity after the administration of programmed cell death protein 1 (PD-1) inhibitor, summarize the nursing measures, and provide a basis for taking corresponding management measures.Methods:Totally 94 patients who were treated with PD-1 inhibitor in Cancer Hospital of Chinese Academy of Medical Sciences between February and October 2019 were selected by convenient sampling, among whom 37 cases (39.4%) with skin toxicity reaction were included as the research subjects. Their skin toxicity was observed and assessed. Meanwhile, health education, psychological counseling and nursing care for skin toxicity reaction were provided to them.Results:Of the 37 patients, 34 (91.9%) had G1 skin toxicity and were not treated with drugs. After the rash subsided, they were treated according to the original plan. 2 (5.4%) had G2 skin toxicity, whose rash subsided after drug treatment, with scattered pigmentation. They were treated according to the original plan. 1 (2.7%) had G4 skin toxicity. The medical staff from the Dermatology Department and the Infection Department were consulted. The body temperature was normal, and the skin and mucous membranes were ruptured, crusted and detached. The patient was discharged after the condition was improved.Conclusions:After receiving PD-1 inhibitor immunotherapy, patients may have different degrees of immune-related skin toxicity reactions, which requires clinicians and nurses to carry out standardized treatment and care to improve their quality of life.