Objective To investigate the efficacy and toxicities of gemcitabine and cisplatin as a chemother-apy regimen for patients with advanced non-small cell lung cancer (NSCLC). Methods Thirty-five patients with NSCLC were enrolled in this study. C, emeitabine was given on day 1 and 8 at a dose of 1000 mg/m~2 and cisplatin at a dose of 25 mg/m~2 on day 1 to 3. The chemotherapy was repeated every 28 days, after 2 cycles for evaluating response. Results Complete response (CR), partial response (PR) ,stable disease (SD) and progressive disease (PD) were observed in 0,14,16 and 5 cases, respectively, with a response rate (RR) of 40. 0%. The RR in initial treatment group was found more than that in the retreatment group (52. 2% vs 16.7% ,P<0. 05).The main toxicities were tol-erable, which included myelosuppression, nausea, vomiting, and liver damage. Conclusion Gemcitabine combined with cisplatin is effective and safe in the treatment of NSCLC, especially in the initial treatment patients.

Telomerase expression and telomere maintenance are critical for long-term cell proliferation and survival, and they play important roles in development, aging, and cancer. Cumulating evidence has indicated that regulation of the rate-limiting subunit of human telomerase reverse transcriptase gene (hTERT) is a complex process in normal cells and many cancer cells. In addition to a number of transcriptional activators and repressors, the chromatin environment and epigenetic status of the endogenous hTERT locus are also pivotal for its regulation in normal human somatic cells and in tumorigenesis.
Animals ; Cell Transformation, Neoplastic ; Chromatin ; genetics ; metabolism ; DNA Methylation ; Epigenomics ; Gene Expression Regulation, Enzymologic ; Humans ; Mice ; Mice, Transgenic ; Promoter Regions, Genetic ; Telomerase ; genetics ; Telomere ; enzymology ; Transcription, Genetic

OBJECTIVE: To explore the biomarkers of tinnitus in vestibular schwannoma patients using electroencephalographic (EEG) microstate technology. METHODS: The EEG and clinical data of 41 patients with vestibular schwannoma were collected. All the patients were evaluated by SAS, SDS, THI and VAS scales. The EEG acquisition time was 10-15 min, and the EEG data were preprocessed and analyzed using MATLAB and EEGLAB software package. RESULTS: Of the 41 patients with vestibular schwannoma, 29 patients had tinnitus and 12 did not have tinnitus, and their clinical parameters were comparable. The average global explanation variances of the non-tinnitus and tinnitus groups were 78.8% and 80.1%, respectively. The results of EEG microstate analysis showed that compared with those without tinnitus, the patients with tinnitus had an increased frequency (P=0.033) and contribution (P=0.028) of microstate C. Correlation analysis showed that THI scale scores of the patients were negatively correlated with the duration of microstate A (R=-0.435, P=0.018) and positively with the frequencies of microstate B (R=0.456, P=0.013) and microstate C (R=0.412, P=0.026). Syntax analysis showed that the probability of transition from microstate C to microstate B increased significantly in vestibular schwannoma patients with tinnitus (P=0.031). CONCLUSION EEG microstate features differ significantly between vestibular schwannoma patients with and without tinnitus. This abnormality in patients with tinnitus may reflect the potential abnormality in the allocation of neural resources and the transition of brain functional activity.
Humans ; Neuroma, Acoustic/complications* ; Electroencephalography ; Patients ; Probability

Objective To create risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on machine learning algorithms, so as to provide insights into early identification of imported malaria cases in Jiangsu Province. Methods Case investigation, first symptoms and time of initial diagnosis of imported malaria patients in Jiangsu Province in 2019 were captured from Infectious Disease Report Information Management System and Parasitic Disease Prevention and Control Information Management System of Chinese Center for Disease Control and Prevention. The risk predictive models of healthcare-seeking delay among imported malaria patients were created with the back propagation (BP) neural network model, logistic regression model, random forest model and Bayesian model using thirteen factors as independent variables, including occupation, species of malaria parasite, main clinical manifestations, presence of complications, severity of disease, age, duration of residing abroad, frequency of malaria parasite infections abroad, incubation period, level of institution at initial diagnosis, country of origin, number of individuals travelling with patients and way to go abroad, and time of healthcare-seeking delay as a dependent variable. Logistic regression model was visualized using a nomogram, and the nomogram was evaluated using calibration curves. In addition, the efficiency of the four models for prediction of risk of healthcare-seeking delay among imported malaria patients was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). The importance of each characteristic was quantified and attributed by using SHAP to examine the positive and negative effects of the value of each characteristic on the predictive efficiency. Results A total of 244 imported malaria patients were enrolled, including 100 cases (40.98%) with the duration from onset of first symptoms to time of initial diagnosis that exceeded 24 hours. Logistic regression analysis identified a history of malaria parasite infection [odds ratio (OR) = 3.075, 95% confidential interval (CI): (1.597, 5.923)], long incubation period [OR = 1.010, 95% CI: (1.001, 1.018)] and seeking healthcare in provincial or municipal medical facilities [OR = 12.550, 95% CI: (1.158, 135.963)] as risk factors for delay in seeking healthcare among imported malaria cases. BP neural network modeling showed that duration of residing abroad, incubation period and age posed great impacts on delay in healthcare-seek among imported malaria patients. Random forest modeling showed that the top five factors with the greatest impact on healthcare-seeking delay included main clinical manifestations, the way to go abroad, incubation period, duration of residing abroad and age among imported malaria patients, and Bayesian modeling revealed that the top five factors affecting healthcare-seeking delay among imported malaria patients included level of institutions at initial diagnosis, age, country of origin, history of malaria parasite infection and individuals travelling with imported malaria patients. ROC curve analysis showed higher overall performance of the BP neural network model and the logistic regression model for prediction of the risk of healthcare-seeking delay among imported malaria patients (Z = 2.700 to 4.641, all P values < 0.01), with no statistically significant difference in the AUC among four models (Z = 1.209, P > 0.05). The sensitivity (71.00%) and Youden index (43.92%) of the logistic regression model was higher than those of the BP neural network (63.00% and 36.61%, respectively), and the specificity of the BP neural network model (73.61%) was higher than that of the logistic regression model (72.92%). Conclusions Imported malaria cases with long duration of residing abroad, a history of malaria parasite infection, long incubation period, advanced age and seeking healthcare in provincial or municipal medical institutions have a high likelihood of delay in healthcare-seeking in Jiangsu Province. The models created based on the logistic regression and BP neural network show a high efficiency for prediction of the risk of healthcare-seeking among imported malaria patients in Jiangsu Province, which may provide insights into health management of imported malaria patients.