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Background: Mycoplasma pneumoniae can be divided into different subtypes on the basis of the sequence differences of adhesive protein P1, but the relationship between different subtypes, macrolide resistance and clinical manifestations are still unclear. In the present study, we established a molecular beacon based real-time polymerase chain reaction (real-time PCR) p1 gene genotyping method, analyzed the macrolide resistance gene mutations and the relationship of clinical characteristics with the genotypes.Methods: A molecular beacon based real-time PCR p1 gene genotyping method was established, the mutation sites of macrolide resistance genes were analyzed by PCR and sequenced, and the relationship of clinical characteristics with the genotypes was analyzed.Results: The detection limit was 1-100 copies/reaction. No cross-reactivity was observed in the two subtypes. In total, samples from 100 patients with positive M. pneumoniae detection results in 2019 and 2021 were genotyped using the beacon based real-time PCR method and P1-1 M. pneumoniae accounted for 69.0%. All the patients had the A2063G mutation in the macrolide resistance related 23S rRNA gene. Novel mutations were also found, which were C2622T, C2150A, C2202G and C2443A mutations. The relationship between p1 gene genotyping and the clinical characteristics were not statistically related.Conclusion: A rapid and easy clinical application molecular beacon based real-time PCR genotyping method targeting the p1 gene was established. A shift from type 1 to type 2 was found and 100.0% macrolide resistance was detected. Our study provided an efficient method for genotyping M. pneumoniae, valuable epidemiological monitoring information and clinical treatment guidance to control high macrolide resistance. [ABSTRACT FROM AUTHOR]

The purpose of this study is to better understand the role of interleukin 35 (IL35) in esophageal carcinoma by comparing the mRNA level in Barrett's esophageal mucosa and in matched normal squamous mucosa and to understand how the diagnosis model works with two other genes: hepatocyte nuclear factor 1B (HNF1B) and cAMP responsive element binding protein 3‐like 1 (CREB3L1). By comparing carcinoma tissue and normal tissue samples, we extracted all the differentially expressed mRNAs. The bioinformatics analysis resulted in the discovery of three prominent genes. Eventually, the three genes were utilized to train a deep‐learning model. An additional wet experiment was conducted to validate the effect of IL35. All the differentially expressed genes were enriched into nine groups, each of which has specific biological functions. Given that the three significant genes HNF1B, CREB3L1, and IL35 as diagnostic features, a deep‐learning model was constructed, reaching an accuracy of 93% in the training set and 87% in the test set. Our findings suggest that IL35, along with the other two signatures, can distinguish esophageal tumor samples from normal samples precisely. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to achieve early and accurate diagnosis of lung cancer and long-term monitoring of the therapeutic response.Methods: We downloaded GSE20189 from GEO database as analysis data. We also downloaded human lung adenocarcinoma RNA-seq transcriptome expression data from the TCGA database as validation data. Finally, the expression of all of the genes underwent z test normalization. We used ANOVA to identify differentially expressed genes specific to each stage, as well as the intersection between them. Two methods, correlation analysis and co-expression network analysis, were used to compare the expression patterns and topological properties of each stage. Using the functional quantification algorithm, we evaluated the functional level of each significantly enriched biological function under different stages. A machine-learning algorithm was used to screen out significant functions as features and to establish an early diagnosis model. Finally, survival analysis was used to verify the correlation between the outcome and the biomarkers that we found.Results: We screened 12 significant biomarkers that could distinguish lung cancer patients with diverse risks. Patients carrying variations in these 12 genes also presented a poor outcome in terms of survival status compared with patients without variations.Conclusions: We propose a new molecular-based noninvasive detection method. According to the expression of the stage-specific gene set in the peripheral blood of patients with lung cancer, the difference in the functional level is quantified to realize the early diagnosis and prediction of lung cancer. [ABSTRACT FROM AUTHOR]

Myocardial Infarction (MI) is a fatal cardiovascular system disease. At present, the diagnosis of MI patients is mainly based on the patient's clinical manifestations, dynamic changes in electrocardiogram (ECG), and changes in myocardial enzymes. ECG is insufficient to diagnose an acute coronary syndrome or acute myocardial infarction, since ST-segment deviation might be also present in other conditions, such as acute pericarditis and early repolarization patterns. Given the low specificity and effectiveness of the current diagnostic strategies, an accurate diagnostic approach based on the level of gene expression is urgently needed in the clinic. We compared the gene's expression between MI patients and normal samples. The RNAseq data were downloaded from the GEO database. Differentially expressed genes underwent a feature selection process, and the signatures were selected to train a machine-learning model. In this study, we identified the risk genes associated with MI as signatures and uses the SVM to establish a diagnostic model. The accuracy of the model on discovery data is 0.87, which significantly improves the diagnostic efficiency of early detection of MI patients (MIPs). Two independent datasets were applied to verify the diagnostic model. Our model can effectively distinguish the control group from the disease group. We used risk genes to construct a diagnostic model for MI diagnosis, which can effectively distinguish MIPs from normal samples in the both of the discovery data and validation data. In the validation data, we found that percutaneous coronary intervention could indeed reverse MI to a certain extent, and the gene expression level of patients treated with percutaneous coronary intervention (PCI) was closer to the normal state. • We compared the gene's expression between MI patients and normal samples and found the some risk genes associated with MI. • We used risk genes to construct a diagnostic model for MI diagnosis, which can effectively distinguish MIPs from normal samples in the both of the discovery data and validation data. • In the validation data, we found that percutaneous coronary intervention could indeed reverse MI, which the gene expression level of patients was closer to the normal state. • We have constructed a myocardial infarction diagnostic model and verified it in patients with myocardial infarction. [ABSTRACT FROM AUTHOR]