Lung cancer stands as the primary cause of cancer-related mortalities globally, presenting a severe menace to human health. In individuals with non-small cell lung cancer (NSCLC), Kirsten rat sarcoma viral oncogene (KRAS) mutations serve as crucial oncogenic drivers. NSCLC with KRASG12C mutation is among the most prevalent subtypes. Currently, the detection methods for KRAS mutations predominantly concentrate on polymerase chain reaction (PCR) and sequencing platforms. The diverse derivative technologies of these two platforms each exhibit distinct merits and demerits in terms of testing performance and detection throughput, and find significant applications in tissue biopsy and liquid biopsy. In targeted therapies, KRASG12C targeted drugs, including Sotorasib, Adagrasib, Fulzerasib, Garsorasib, and Glecirasib, have demonstrated certain therapeutic efficacies in clinical trials and have obtained marketing approval. To tackle drug resistance and enhance patient's prognoses, combination therapeutic strategies that integrate targeted agents with chemotherapy, immune checkpoint inhibitors, Src homology region 2 domain-containing phosphatase 2 (SHP2) inhibitors, and epidermal growth factor receptor (EGFR) monoclonal antibodies have emerged. This paper systematically reviews the advancements in the diagnosis and targeted therapy of NSCLC with KRASG12C mutation, aiming to offer a reference for the selection of clinical treatment regimens and subsequent research.
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Humans ; Carcinoma, Non-Small-Cell Lung/drug therapy* ; Lung Neoplasms/drug therapy* ; Proto-Oncogene Proteins p21(ras)/genetics* ; Mutation ; Molecular Targeted Therapy

Objective To establish and validate the diagnostic model of ferroptosis genes for acute myocardial infarction (AMI) based on bioinformatics. Methods Five AMI gene expression data were obtained from Gene Expression Omnibus (GEO), namely GSE66360, GSE48060, GSE60993, GSE83500, GSE34198. Among them, GSE66360 was used as the training set to perform differential analysis, and intersection of differential genes and ferroptosis genes was taken to obtain differentially expressed ferroptosis genes in AMI. GO and KEGG enrichment analyses were performed using Metascape website. Subsequently, random forest (RF) algorithm was used to screen out key genes with high classification performance according to the Keeny coefficient score, and artificial neural network (ANN) diagnostic model of AMI ferroptosis feature gene was constructed by model group GSE83500. The area under the receiver operating characteristic curve (AUC) of 10-fold cross-validation was used to evaluate the performance and generalization ability of the model, and 3 external independent datasets were used to verify the diagnostic performance of this model. The single sample gene set enrichment analysis was used to explore the difference in immune cell infiltration between infarcted myocardium and normal myocardium after AMI. In addition, correlation analysis between immune cells and key genes was also conducted. Finally, potential drugs that would prevent and treat AMI by regulating ferroptosis were screened out from the Coremin Medical platform. Results A total of 16 differentially expressed ferroptosis genes were obtained in the training set, GO enrichment analysis showed that they mainly participated in biological functions such as cellular response to biological stimuli and chemical stress, and regulation of interleukin 17. KEGG enrichment analysis showed that these genes were significantly enriched in NOD-like receptor signaling pathway, programmed cell necrosis, Leishmaniasis and other pathways. Four genes with good classification performance were screened out using RF algorithm, namely EPAS1, SLC7A5, FTH1, and ZFP36. The results of 10-fold cross-validation showed that the minimum AUC value was 0.746, the maximum value was 0.906, and the average value was 0.805. The AUC of the ANN model was 0.859, and the AUC values of the three independent validation sets were 0.763 (GSE48060), 0.673 (GSE60993), 0.698 (GSE34198). Immune cell infiltration found that macrophages, mast cells and monocytes were significantly active after AMI. Correlation analysis found that there were positive correlations between 4 key genes and activated dendritic cells, eosinophils and γδT cells. A total of 20 potential western medicines were predicted which could prevent and treat AMI by regulating ferroptosis, and the predicted potential Chinese medicine was mainly heat-clearing and detoxifying and blood-activating and removing blood stasis drugs. Conclusion The identified AMI ferroptosis genes by bioinformatics method have certain diagnostic significance, which provides a reference for disease diagnosis and treatment.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To study echinococcosis in the population and canine Echinococcus infection in Yushu City, Qinghai Province, and to explore the current epidemic situation and main transmission species of Echinococcus. Methods:In June 2023, a multi-stage sampling method was used to select 2 villages each in Shanglaxiu Township and Longbao Town, Yushu City, Qinghai Province. Each village included at least 100 permanent residents who had lived locally for at least 1 year and were 2 years old or older as the survey subjects. Enzyme-linked immunosorbent assay (ELISA) was used to detect serum antibodies against Echinococcus larvae in the population, and B-mode ultrasound was used for abdominal organ scanning. Meanwhile, on the main roads of Shanglaxiu Township and Longbao Town, canine feces were collected in designated areas at intervals. ELISA was used to detect the antigen of canine fecal Echinococcus, and PCR was used to detect the types of parasites ( Echinococcus multilocularis, Echinococcus granulosus and Echinococcus shiquicus). Results:A total of 511 residents were investigated in Yushu City, and the positive rate of serum Echinococcus larvae antibodies in the population was 26.22% (134/511), and the detection rate of echinococcosis B-mode ultrasound was 1.37% (7/511). Among them, the detection rates of B-mode ultrasound for cystic echinococcosis (CE) and alveolar echinococcosis (AE) were 1.17% (6/511) and 0.20% (1/511), respectively. The positive rate of Echinococcus antigen in 543 canine feces detected by ELISA was 12.89% (70/543). PCR was used to test 497 canine feces, and the detection rate of Echinococcus was 3.02% (15/497). Among them, the detection rate of Echinococcus multilocularis was higher than that of Echinococcus granulosus [2.82% (14/497) vs 0.20% (1/497)], and the difference was statistically significant (χ 2 = 11.44, P < 0.001). No Echinococcus shiquicus was detected. Conclusions:The positive rates of Echinococcus larvae antibodies in the population and canine Echinococcus antigen in Yushu City, Qinghai Province are both relatively high. There is a mixed epidemic of CE and AE, with Echinococcus multilocularis being the main species.

Objective To understand the current status of knowledge-attitude-practice(KAP)regarding risk of medication safety behavior among cancer patients in western Yunnan and the related influencing factors,and to provide a basis for conducting rational medication education.Methods Using an approximate random sampling method,a KAP survey on medication safety behavior was conducted among inpatients and outpatients with cancer(aged over 18 years)in multiple hospitals in western Yunnan through online and offline questionnaires.Multiple linear regression analysis was used to explore the impact of different characteristics of the research objects on the KAP scores concerning medication knowledge,behavior,and attitude.Results A total of 512 valid questionnaires were collected.The average scores of medication knowledge,attitude,and behavior among cancer patients were(82.53±23.07),(41.98±8.64),and(67.88±10.08),respectively.Medication knowledge and behavior were at a good level,while medication attitude was at a qualified level.The multiple linear regression results showed that the educational level was a significant influencing factor of the medication knowledge and attitude scores(P＜0.001).Conclusion The overall medication safety among cancer patients in western Yunnan is in a good state,however,there is a need for improvement in medication attitudes.Educational level is the main factor affecting medication safety risk.Pharmacy services should be focused on cancer patients with lower educational levels to enhance their medication safety.

Objective To analyze cysteinylaspartate specific proteinase-3,caspase-7,caspase-9,AMP activated protein kinase(AMPK)in the diagnosis and prognosis evaluation of sepsis.Methods Sixty patients with sepsis from the Affiliated Hospital of Xiangnan University from March 2020 to March 2023 were selected as the observation group,and another 60 healthy individuals from the physical examination center of the hospital during the same period were selected as the control group.Acute physiology and chronic health evaluationⅡ(APACHEII)score was used to evaluate the severity of the patient's condition.Serum caspase-3,caspase-7,caspase-9,AMPK and APACHEII scores were detected,and compared between the two groups.These parameters were compared with serum Caspase-3,caspase-7,caspase-9,AMPK and APACHEII scores in different disease severity groups.The patients were divided into survival group(50 cases)and death group(10 cases)according to prognosis.Serum Caspase-3,caspase-7,caspase-9,AMPK and APACHEII scores were compared between the two groups,and the correlation between serum caspase-3,caspase-7,caspase-9,AMPK and APACHEII scores were analyzed by Pearson.Receiver operating characteristic(ROC)was drawn,the area under the curve(AUC)was calculated,the predictive efficacy of serum caspase-3,caspase-7,caspase-9 and AMPK in patients death with sepsis were analyzed.Results The serum levels of caspase-3,caspase-7,caspase-9,AMPK,and APACHEII score in the observation group were higher than those in the control group,and the differences were statistically significant(t=19.501～44.219,all P<0.05).The serum caspase-3,caspase-7,caspase-9,AMPK,and APACHEII scores in the sep-tic shock group were higher than those in the severe and mild groups,and the differences were statistically significant(t=2.091～6.946,all P<0.05).The serum caspase-3,caspase-7,caspase-9,AMPK,and APACHEII scores of the death group were higher than those of the survival group,and the differences were statistically significant(t=4.576～8.835,all P<0.05).Serum caspase-3,caspase-7,caspase-9 and AMPK were positively correlated with APACHEII scores(r=0.382～0.406,all P<0.05).The AUC of combined detection of serum caspase-3,caspase-7,caspase-9,and AMPK for predicting death in sepsis patients was 0.863,with 95%confidence interval of 0.805-0.958.which was greater than that of single indicator detection,and the difference is statistical-ly significant(Z=2.912～2.294,all P<0.05).Conclusion Serum caspase-3,caspase-7,caspase-9,and AMPK in sepsis patients showed abnormally high expressions,which were closely related to disease severity and prognosis.Combined detection could improve the sensitivity and specificity of death prediction,which has certain reference value.

Objective To understand the current status of knowledge-attitude-practice(KAP)regarding risk of medication safety behavior among cancer patients in western Yunnan and the related influencing factors,and to provide a basis for conducting rational medication education.Methods Using an approximate random sampling method,a KAP survey on medication safety behavior was conducted among inpatients and outpatients with cancer(aged over 18 years)in multiple hospitals in western Yunnan through online and offline questionnaires.Multiple linear regression analysis was used to explore the impact of different characteristics of the research objects on the KAP scores concerning medication knowledge,behavior,and attitude.Results A total of 512 valid questionnaires were collected.The average scores of medication knowledge,attitude,and behavior among cancer patients were(82.53±23.07),(41.98±8.64),and(67.88±10.08),respectively.Medication knowledge and behavior were at a good level,while medication attitude was at a qualified level.The multiple linear regression results showed that the educational level was a significant influencing factor of the medication knowledge and attitude scores(P＜0.001).Conclusion The overall medication safety among cancer patients in western Yunnan is in a good state,however,there is a need for improvement in medication attitudes.Educational level is the main factor affecting medication safety risk.Pharmacy services should be focused on cancer patients with lower educational levels to enhance their medication safety.

OBJECTIVE To explore and evaluate the machine learning model for prediction of bacterial bloodstream infections established based on routine test data.METHODS By means of retrospective survey,a total of 5 421 pa-tients who were hospitalized in 3 medical institutions from Jan.2015 to Dec.2022 were recruited as the research subjects,1 914 of whom were assigned as the bloodstream infection group,and 3 507 were assigned as the non-bloodstream infection group.The baseline data including gender and age and the results of routine laboratory tests were collected from the enrolled patients.The 3 types of machine learning algorithms,logistic regression,support vector machine and random forest,were respectively used for the screening of the optimal prediction model;the contribution of feature variables to the predictive capability of the model was interpreted through SHAP.The fea-ture variables of the model were optimized by using recursive feature elimination method,and the predictive effi-ciency of the model was evaluated by the area under the curve(AUC)of receiver operating characteristic(ROC)curves.RESULTS Totally 26 variables involving age,gender and blood routine test indexes were included.The random forest was chosen as the optimal machine learning algorithm for the establishment of prediction model for bloodstream infections,and the accuracy of the model was 0.709,with the AUC 0.706.The result of SHAP ex-planation indicated that the age,hematokrit and erythrocyte volume distribution width-CV had remarkable effect on the model's making right decisions.17 variables of the prediction model showed more remarkable effect than 26 variable on distinguishing from the gram-positive bacteria bloodstream infections from the gram-negative bacteria bloodstream infections,with the AUC 0.715,the sensitivity 0.701,the specificity 0.632.CONCLUSIONS The prediction model that is established based on the blood routine test indexes by machine learning algorithm can pre-dict the bacterial bloodstream infection.Meanwhile,the feature selection strategy can further improve the predic-tive efficiency of the model on basis of lowering the dimensionality.

OBJECTIVE To explore and evaluate the machine learning model for prediction of bacterial bloodstream infections established based on routine test data.METHODS By means of retrospective survey,a total of 5 421 pa-tients who were hospitalized in 3 medical institutions from Jan.2015 to Dec.2022 were recruited as the research subjects,1 914 of whom were assigned as the bloodstream infection group,and 3 507 were assigned as the non-bloodstream infection group.The baseline data including gender and age and the results of routine laboratory tests were collected from the enrolled patients.The 3 types of machine learning algorithms,logistic regression,support vector machine and random forest,were respectively used for the screening of the optimal prediction model;the contribution of feature variables to the predictive capability of the model was interpreted through SHAP.The fea-ture variables of the model were optimized by using recursive feature elimination method,and the predictive effi-ciency of the model was evaluated by the area under the curve(AUC)of receiver operating characteristic(ROC)curves.RESULTS Totally 26 variables involving age,gender and blood routine test indexes were included.The random forest was chosen as the optimal machine learning algorithm for the establishment of prediction model for bloodstream infections,and the accuracy of the model was 0.709,with the AUC 0.706.The result of SHAP ex-planation indicated that the age,hematokrit and erythrocyte volume distribution width-CV had remarkable effect on the model's making right decisions.17 variables of the prediction model showed more remarkable effect than 26 variable on distinguishing from the gram-positive bacteria bloodstream infections from the gram-negative bacteria bloodstream infections,with the AUC 0.715,the sensitivity 0.701,the specificity 0.632.CONCLUSIONS The prediction model that is established based on the blood routine test indexes by machine learning algorithm can pre-dict the bacterial bloodstream infection.Meanwhile,the feature selection strategy can further improve the predic-tive efficiency of the model on basis of lowering the dimensionality.

Objective:To study echinococcosis in the population and canine Echinococcus infection in Yushu City, Qinghai Province, and to explore the current epidemic situation and main transmission species of Echinococcus. Methods:In June 2023, a multi-stage sampling method was used to select 2 villages each in Shanglaxiu Township and Longbao Town, Yushu City, Qinghai Province. Each village included at least 100 permanent residents who had lived locally for at least 1 year and were 2 years old or older as the survey subjects. Enzyme-linked immunosorbent assay (ELISA) was used to detect serum antibodies against Echinococcus larvae in the population, and B-mode ultrasound was used for abdominal organ scanning. Meanwhile, on the main roads of Shanglaxiu Township and Longbao Town, canine feces were collected in designated areas at intervals. ELISA was used to detect the antigen of canine fecal Echinococcus, and PCR was used to detect the types of parasites ( Echinococcus multilocularis, Echinococcus granulosus and Echinococcus shiquicus). Results:A total of 511 residents were investigated in Yushu City, and the positive rate of serum Echinococcus larvae antibodies in the population was 26.22% (134/511), and the detection rate of echinococcosis B-mode ultrasound was 1.37% (7/511). Among them, the detection rates of B-mode ultrasound for cystic echinococcosis (CE) and alveolar echinococcosis (AE) were 1.17% (6/511) and 0.20% (1/511), respectively. The positive rate of Echinococcus antigen in 543 canine feces detected by ELISA was 12.89% (70/543). PCR was used to test 497 canine feces, and the detection rate of Echinococcus was 3.02% (15/497). Among them, the detection rate of Echinococcus multilocularis was higher than that of Echinococcus granulosus [2.82% (14/497) vs 0.20% (1/497)], and the difference was statistically significant (χ 2 = 11.44, P < 0.001). No Echinococcus shiquicus was detected. Conclusions:The positive rates of Echinococcus larvae antibodies in the population and canine Echinococcus antigen in Yushu City, Qinghai Province are both relatively high. There is a mixed epidemic of CE and AE, with Echinococcus multilocularis being the main species.