Anticipating tumor metastasis by circulating tumor cells captured by acoustic microstreaming.

Detecting an ALK fusion gene in patients with non-small cell lung cancer (NSCLC) could provide evidence to guide individualized therapy. The 5′/3′ imbalance strategy for quantitative reverse transcription-PCR (RT-qPCR) was developed to detect ALK fusion genes in circulating tumor RNA (ctRNA) of NSCLC patients. This method was validated in patients with the ALK fusion gene confirmed by next generation sequencing (NGS). The amount of the ALK fusion gene detected by the new method ranged from 33.2 to 987.4, (mean 315.2), in the patients confirmed to have the ALK fusion gene (+). This is much higher than the amount of fusion gene detected in the patients who are negative for the ALK fusion gene (−). The amount detected in the ALK fusion gene (−) samples ranged from 0.36 to 13.04, (mean 4.58). In 188 NSCLC patients, the specificity and sensitivity of the method was compared to that of the FISH method. About 10.64% of the patients showed higher ALK fusion gene expression, and were classified as ALK fusion gene (+). This is identical to the percentage of patients detected by the FISH method to be ALK fusion gene (+). The cutoff value for diagnosis of ALK fusion (+) is 32.9 as determined by this method. A new RT-PCR method using a 5′/3′ imbalance strategy was developed, with high specificity and sensitivity, for detection of the ALK fusion gene in ctRNA of NSCLC patients. This method can rapidly detect ALK fusion genes in patients, which will be helpful for guiding targeted therapy, particularly the individualized usage of TKIs in these patients.

Background: The SARS-CoV-2 RNA was detected positive again after discharged from hospital in some COVID-19 patients, with or without clinical symptoms such as fever or dry cough. Methods: 1008 severe COVID-19 patients, with SARS-CoV-2 RNA positive detected with the mixed specimen of nasopharyngeal swab and oropharyngeal swab by real-time fluorescence quantitative PCR (RT-qPCR), were selected to monitor SARS-CoV-2 RNA with the 12 types of specimens by RT-qPCR during hospitalization. All of 20 discharged cases with COVID-19 were selected to detect SARS-CoV-2 RNA in isolation period with 7 types of specimens by RT-qPCR before releasing the isolation period. Results: Of the enrolled 1008 severe patients, the nasopharyngeal swab specimens showed the highest positive rate of SARS-CoV-2 RNA (71.06%), followed by alveolar lavage fluid (66.67%), oropharyngeal swab (30.77%), sputum (28.53%), urine (16.30%), blood (12.5%), stool (12.21%), anal swab (11.22%) and corneal secretion (2.99%), and SARS-CoV-2 RNA couldn’t be detected in other types of specimen in this study. Of the 20 discharged cases during the isolation period, the positive rate of SARS-CoV-2 RNA was 30% (6/20): 2 cases were positive in sputum at the eighth and ninth day after discharge, respectively, 1 case was positive in nasopharynx swab at the sixth day after discharge, 1 case was positive in anal swab at the eighth day after discharge, and 1 case was positive in 3 specimens (nasopharynx swab, oropharynx swab and sputum) simultaneously at the fourth day after discharge, and no positive SARS-CoV-2 RNA was detected in other specimens including stool, urine and blood at the discharged patients. Conclusions: SARS-CoV-2 RNA should be detected in multiple specimens, such as nasopharynx swab, oropharynx swab, sputum, and if necessary, stool and anal swab specimens should be performed simultaneously at discharge when the patients were considered for clinical cure and before releasing the isolation period.

Purpose: This study screens for deafness gene mutations in newborns in the Northwest China population. Method: The 9 sites of 4 common deafness genes ( GJB2, GJB3, SLC26A 4, and mt 12S rRNA ) were detected by bloodspot-based gene chip array in 2,500 newborns. Results: We detected mutations of the 4 genes in 101 (4.04%) newborns; particularly, 0.20% detected the double mutations. In the Hui population, 4.58% of the newborns tested positive for mutations, whereas 4.01% of Han newborns tested positive for mutations. The detective rates are as follows: 1.44% for GJB2 235delC, 1.08% for SLC26A4 IVS7-2A>G, 0.48% for GJB2 299 _300delAT, 0.28% for SLC26A4 2168A>G, 0.2% for mt 12S rRNA 1555A>G, and 0.16% for GJB3 538C>T. The 31.25% (5/16) of infants with GJB2 235delC, 50% (3/6) with GJB2 299_300delAT, and 25% (3/12) with SLC26A4 IVS7-2A>G showed abnormal hearing when tested; only 1 double mutation case received the hearing test, and this infant showed abnormality in both ears on the hearing test. Conclusions: High mutation rates in the common deafness genes were detected in newborns in Northwest China. Our study is helpful in understanding the deafness genomic epidemiology and also provides evidence for prenatal and postnatal care as well as policy making on population health in the region.

Background: Chronic Obstructive Pulmonary Disease (COPD), a complex respiratory disorder, results from genetic and environmental factors. Uncovering its genetic basis is vital for diagnostics and treatment. Robust genetic analysis is essential to establish a causal link. Methods: Genome-wide DNA methylation analysis was performed using the Illumina Infinium HumanMethylation850 BeadChip in peripheral blood from 8 COPD patients and 8 healthy smoking controls. Differentially methylated genes (DMGs) were cross-analyzed with differentially expressed genes (DEGs) identified from the Gene Expression Omnibus (GEO) dataset GSE38974 (23 COPD, 9 controls). Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) networks were utilized to identify COPD-associated hub genes. Mendelian randomization (MR) analysis examined the causal relationship between hub genes and COPD. The expression of selected hub genes was validated through RT-qPCR (80 COPD, 62 controls), immunohistochemistry, and Western blot analyses (10 COPD and 10 controls). Results: We found 10,593 DMGs and 646 DEGs associated with COPD. These genes were compared with WGCNA module genes, and the Protein-Protein Interaction (PPI) network interaction diagram was drawn, thereby identifying five Hub genes: PPIB, HSPA2, PRPF19, FKBP10 and DOHH. The expression levels of DOHH, FKBP10, PPIB and PRPF19 are higher in COPD, while the expression level of HSPA2 is lower. MR results indicate a potential causal relationship between PRPF19, PPIB and COPD. RT-qPCR, immunohistochemistry and Western blot experiments verified that the expression of PRPF-19 and PPIB was up-regulated in peripheral blood and lung tissue, which was consistent with the results of bioinformatics analysis. Conclusion: Our findings suggest that PRPF19 and PPIB may serve as promising diagnostic biomarkers in COPD. Further studies are required to fully elucidate their roles in COPD pathogenesis.