ObjectiveTo observe the dose of methadone and the compliance of patients to the methadone maintenance treatment(MMT).MethodsWe analyzed the status of 69 patients who were addicted to opiate medication and 44 patients who dropped out in our clinic until July 31,2010.ResultsThere was no significant relationship between patients' urine test and the dose.Crime caused the patients who dropped out.The percentage of positive urine did not decline as the dose increased,but the rate of patients who dropped and the number of positive urine test showed a significant correlation( r =0.523 P =0.000).Crime was the main reason that affected the compliance to MMT and caused dropping out.ConclusionThe use of MMT dose should be individualized.

Objective To observe therapeutic effects of acupoint catgut embedding therapy on ulcerative colitis.Methods 55 cases of ulcerative colitis were randomly recruited into a treatment group and a control group. The treatment group was treated with catgut embedding, and the control group was treated with oral administration of salicylazosul fapyridine. Results The therapeutic effect of the treatment group was better than those of the control group (P=0.002＜0.05). Conclusion Acupoint catgut embedding therapy has a better therapeutic effect on ulcerative colitis.

Adenocarcinoma ; Aged ; Antineoplastic Agents ; pharmacology ; Apoptosis ; drug effects ; Cell Line, Tumor ; Cisplatin ; pharmacology ; Dexamethasone ; pharmacology ; Female ; Humans ; Lung Neoplasms ; Male ; Middle Aged ; Tumor Cells, Cultured

Objective:To screen out the differentially regulated metabolites by the analysis of serum metabolic fingerprints, and to provide potential biomarkers for diagnosis of lung cancer.Methods:A total of 228 subjects were enrolled in Changhai Hospital from January 27, 2021 to June 4, 2021, including 97 newly diagnosed lung cancer patients and 131 healthy individuals. Serum samples were collected from the enrolled cohort according to a standard procedure, and the enrolled cohort was divided into a training set and a completely independent validation set by stratified random sampling. The metabolic fingerprints of serum samples were collected by previously developed nano-assisted laser desorption/ionization mass spectrometry (nano-LDI MS). After age and gender matching of the training set, a diagnostic model based on serum metabolic fingerprints was established by machine learning algorithm, and the classification performance of the model was evaluated by receiver operating characteristic (ROC) curve.Results:Serum metabolic fingerprint for each sample was obtained in 1 minute using a novel nano-LDI MS, with consumption of only 1 μl original serum sample. For the training set, the area under ROC curve (AUC) of the constructed classifier for diagnosis of lung cancer was 0.92 (95% CI 0.87-0.97), with a sensitivity of 89% and specificity of 89%. For the independent validation set, the AUC reached 0.96 (95% CI 0.90-1.00) with a sensitivity of 91% and specificity of 94%, which showed no significant decrease compared to training set. We also identified a biomarker panel of 5 metabolites, demonstrating a unique metabolic fingerprint of lung cancer patients. Conclusion:Serum metabolic fingerprints and machine learning were combined to establish a diagnostic model, which can be used to distinguish between lung cancer patients and healthy controls. This work sheds lights on the rapid metabolic analysis for clinical application towards in vitro diagnosis.

Background::Clinical opportunistic screening is a cost-effective cancer screening modality. This study aimed to establish an easy-to-use diagnostic model serving as a risk stratification tool for identification of individuals with malignant gastric lesions for opportunistic screening.Methods::We developed a questionnaire-based diagnostic model using a joint dataset including two clinical cohorts from northern and southern China. The cohorts consisted of 17,360 outpatients who had undergone upper gastrointestinal endoscopic examination in endoscopic clinics. The final model was derived based on unconditional logistic regression, and predictors were selected according to the Akaike information criterion. External validation was carried out with 32,614 participants from a community-based randomized controlled trial.Results::This questionnaire-based diagnostic model for malignant gastric lesions had eight predictors, including advanced age, male gender, family history of gastric cancer, low body mass index, unexplained weight loss, consumption of leftover food, consumption of preserved food, and epigastric pain. This model showed high discriminative power in the development set with an area under the receiver operating characteristic curve (AUC) of 0.791 (95% confidence interval [CI]: 0.750–0.831). External validation of the model in the general population generated an AUC of 0.696 (95% CI: 0.570–0.822). This model showed an ideal ability for enriching prevalent malignant gastric lesions when applied to various scenarios.Conclusion::This easy-to-use questionnaire-based model for diagnosis of prevalent malignant gastric lesions may serve as an effective prescreening tool in clinical opportunistic screening for gastric cancer.