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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.
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Objective:To evaluate the feasibility of a predictire model composed of non-specific test indexes in early diagnosis of gastric cancer.Methods:From the database of electronic medical record system of Shanghai Changhai Hospital, a total of 24 615 case records were included from January 1, 2010 to April 30, 2019, including 10 497 cases of gastric cancer, 5 198 cases of precancerous diseases, and 8 920 cases of health examination. Through stratified random sampling, the study population was divided into validation set, training set and test set. After data processing and quality control for all laboratory variables, the optimal machine learning algorithm and diagnostic efficiency grouping were selected through four machine learning algorithms, induding the gradient boosting decision tree, random forest, support vector machine, and artificial neural network, and the data were trained by backward stepwise regression method to build the best feature model.Result:In this study, a diagnostic model V22 consisting of 22 routine testing parameters was established. V22 could distinguish early gastric cancer from control group composed of healthy group and precancerous disease, AUC was 0.808, the sensitivity was 85.7%, and the specificity was 91.9%. For CEA negative gastric cancer, V22 also showed high diagnostic accuracy, AUC was 0.801.Conclusion:V22 was a valuable model for the diagnosis of gastric cancer. V22 was an auxiliary diagnostic model of gastric cancer with clinical application value, which could well distinguish early gastric cancer from the control group composed of healthy group and precancerous disease, and the detection rate of early gastric cancer was better than the traditional tumor marker CEA.
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Research on the application of artificial intelligence in laboratory medicine has become an important direction for laboratory development. However, there were still problems in the process of product application research and development of artificial intelligence technology, such as lack of interpretability of machine learning models, lack of talent teams, and many potential safety hazards. The reason for this may include low quality of data sets, deviations in research design, imperfect talent training mechanism, and inadequate legislation and supervision. In response to these reasons, the article proposes countermeasures, including establishing data entry and collection standards, formulating data labeling management standards, making model risk analysis, strengthening compound talent training, and improving supervision and management systems. Ensuring artificial intelligence products applied in the field of laboratory medicine could effectively improve the quality of medical services on the premise of improving the efficiency of diagnosis and reducing the rate of misdiagnosis and missed diagnosis.
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Objective: To investigate the effects and mechanism of osthole on proliferation and apoptosis in a hepatocellular carcinoma cell ( HCC) line Hep G2. Methods: Treated cells with osthole at different concentrations. Cell viability was measured by CCK8 assay and apoptosis was detected by flow cytometry. Western blot was performed for calculating the expression levels of proliferation-related, apoptosis-related proteins and PTEN. After pretreatment with bp V ( HOpic), cell proliferation and apoptosis were measured again. Results: Treatment with osthole ( 100 μmol/L) for 4 and 5 days inhibited cell viability of HCC markedly ( P＜0. 05, P＜0. 01). Osthole ( 150 μmol/L) decreased cell viability of HCC with a time-dependently manner and also decreased the expressions of Ki67 and PCNA ( P＜0. 05, P＜0. 01). Meanwhile, treatment with osthole ( 100 μmol/L, 150 μmol/L) induced apoptosis of HCC significantly coupled with increasing Bax and decreasing Bcl-2 ( P＜0. 05, P＜0. 01). In addition, osthole ( 100 μmol/L, 150 μmol/L) up-regulated the protein level of PTEN ( P＜0. 05, P＜ 0. 01). Furthermore, pretreatment with bp V ( HOpic) ( 1 μmol/L) notably reversed the inhibitory effect on proliferation and promotive effect on apoptosis of osthole ( P＜0. 05). Conclusion: Osthole inhibits cell proliferation and induces apoptosis of HCC by up-regulating the protein level of PTEN.
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The modern laboratory developed tests (LDTs) plays a very important role in the development of precision medicine and clinical laboratory diagnosis. The perfect LDTs inspection platform not only requires strict quality control system and management standards, but also requires technical support from high-end professional teams. By combing the development process of LDTs, this paper discusses the significance of developing LDTs in the context of precision medicine, the positive role played by the development of laboratory medicine and the cultivation of laboratory talents, and how to effectively develop LDTs.
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The modern laboratory developed tests (LDTs) plays a very important role in the development of precision medicine and clinical laboratory diagnosis. The perfect LDTs inspection platform not only requires strict quality control system and management standards, but also requires technical support from high-end professional teams. By combing the development process of LDTs, this paper discusses the significance of developing LDTs in the context of precision medicine, the positive role played by the development of laboratory medicine and the cultivation of laboratory talents, and how to effectively develop LDTs.
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Flow cytometry (FCM) is used for multi parameter and rapid quantitative analysis of biological particles,such as all cells,microorganisms and synthetic microspheres in fast line flow state.It is also a modern cell analysis technology for the separation of specific groups.In recent years,FCM has been applied in the field of assisted reproductive medicine.FCM plays an important role in the diagnosis of immune infertility and predicting the fertilization ability of sperm.This article aims to review FCM application in peripheral immune cell surface marker detection for infertility patients,and research on the structure and function of sperm cell.
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Objective:To investigate the inhibitory effect and its possible molecular mechanisms of MicroRNA-34a(miR-34a) on the human nasopharyngeal carcinoma CNE-2 cell line subcutaneous xenograft tumor in nude mice.Methods: The human nasopharyngeal carcinoma CNE-2 cell line was cultured in vitro.miR-34a and Scrambled miRNA recombinant plasmids were successfully established and stably transfected into CNE-2 cells.Fifteen six-week-old male nude mice were divided randomly into three groups:miR-34a group(5 mice) ,Scrambled miRNA group(5 mice) ,Blank control group(5 mice).Different CNE-2 cells were subcuta-neously injected on the back near right lower limb.Tumor volumes were examined every 7 days.Mice were executed on the 35 days,and the eventual average tumor volumes and weights were examined.Total RNA and protein were isolated from tumors,and the expression of miR-34a,CDK6,and Bcl-2 mRNA and protein were determined by qRT-PCR and western blot,respectively.Results: The relative expressions of miR-34a was significantly up-regulated in miR-34a transfected group compared to Scrambled miRNA transfected group (P (849.62±101.32) mm3 ,respectively,and the eventual average tumor weights in miR-34a group,Scrambled miRNA group and blank control group were(0.81±0.13)g,(1.47±0.21)g and(1.58±0.37)g,respectively.Both the eventual average tumor volumes and weights in miR-34a group were lower compared to the other two groups(P<0.05).qRT-PCR results revealed that the expression of miR-34a in miR-34a transfected group was significantly higher than in the other two groups,while the mRNA and protein expression of CDK6 and Bcl-2 were lower than the other two groups ( P<0.05 ) .Conclusion: miR-34a may inhibit the growth of human nasopharyngeal carcinoma CNE-2 cell line subcutaneous xenograft tumor in nude mice by down-regulating CDK6 and Bcl-2.
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Objective To evaluate the application of flow-through rapid hybridization technique and gene chip (HybriMax) on human papiUomavions (HPV) subtype in Chongqing.Methods Cervical tissue samples were taken under the colpnscope form 473 females who had cervical lesion for pathological analysis.The predictive value of HybriMax in cervical abnormality was compared with pathological results,which were used as golden standard.Resuits 13 different subtypes were found and total HPV positive rate was 63.0% (284/473) Among the 17 different subtypes ,the higher positive rote HPV subtypes were HPV16 (23.7%,112/473),HPV58 (12.7% ,60/473),HPV53(7.4% ,35/473).The HPV infection rates were higher with the worse d cervical lesion(X2=77.06,P＜0.01).Conclusions The most frequent subtypes of HPV infection in Chongqing cervical lesion were HPV 16,58.HybriMax was an effective method to detect HPV subtype in clinical.