The common respiratory viruses, including influenza A, influenza B, and newly emerging severe acute respiratory syndrome (SARS) viruses, may cause similar clinical symptoms. Therefore, differential diagnosis of these virus pathogens is frequently required for single clinical samples. In addition, there is an urgent need for noninfectious and stable RNA standards and controls for multivirus detection. In this study, reverse transcription-PCR (RT-PCR) targeting of the RNAs of influenza A and influenza B viruses and SARS coronavirus was performed, and the resulting products were spliced into a fragment which was packaged into armored RNA for use as a noninfectious, quantifiable synthetic substitute. Furthermore, in the present study we developed a multiplex real-time RT-PCR assay in which the armored RNA was used as an external positive control and the three RNA viruses could be detected simultaneously in a single reaction mix. The detection limit of the multiplex real-time PCR was 10 copies/l of armored RNA.
Osteoporosis is a significant global health concern that can be difficult to detect early due to a lack of symptoms. At present, the examination of osteoporosis depends mainly on methods containing dual-energy X-ray, quantitative CT, etc., which are high costs in terms of equipment and human time. Therefore, a more efficient and economical method is urgently needed for diagnosing osteoporosis. With the development of deep learning, automatic diagnosis models for various diseases have been proposed. However, the establishment of these models generally requires images with only lesion areas, and annotating the lesion areas is time-consuming. To address this challenge, we propose a joint learning framework for osteoporosis diagnosis that combines localization, segmentation, and classification to enhance diagnostic accuracy. Our method includes a boundary heat map regression branch for thinning segmentation and a gated convolution module for adjusting context features in the classification module. We also integrate segmentation and classification features and propose a feature fusion module to adjust the weight of different levels of vertebrae. We trained our model on a self-built dataset and achieved an overall accuracy rate of 93.3% for the three label categories (normal, osteopenia, and osteoporosis) in the testing datasets. The area under the curve for the normal category is 0.973; for the osteopenia category, it is 0.965; and for the osteoporosis category, it is 0.985. Our method provides a promising alternative for the diagnosis of osteoporosis at present.
Background. Transarterial chemoembolization (TACE) is a first-line treatment for patients with unresectable hepatocellular carcinoma (HCC). Owing to differences in its efficacy across individuals, determining the indicators of patient response to TACE and finding approaches to reversing nonresponse thereto are necessary. Methods. Transcriptome data were obtained from the GSE104580 dataset, in which patients were marked as having TACE response or nonresponse. We identified differentially expressed genes (DEGs) and performed Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. We screened genes with a prognostic value for TACE in the HIF-1 signaling pathway by univariate regression analysis. By using least absolute shrinkage and selection operator (LASSO) Cox regression, we established a multigene signature in GSE14520, which we verified using a drug sensitivity test. The Connectivity Map (CMap) database was used to find potential drugs to reverse nonresponse to TACE. Results. We constructed a prognostic signature consisting of three genes (erythropoietin (EPO), heme oxygenase 1 (HMOX1), and serine protease inhibitor 1 (SERPINE1)) that we validated by drug sensitivity test. After dividing patients treated with TACE into high- and low-risk groups based on this new signature, we showed that overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group and that the risk score was an independent predictor of OS in patients treated with TACE. Based on our CMap findings, we speculated that PD-184352, an inhibitor of mitogen-activated protein kinase (MEK), had potential as a drug treatment to reverse nonresponse to TACE. We confirmed this speculation by using PD-184352 in a cell promotion experiment in a TACE environment. Conclusion. We constructed a TACE-specific three-gene signature that could be used to predict HCC patients’ responses to and prognosis after TACE treatment. PD-184352 might have potential as a drug to improve TACE efficacy.
Purpose. Herbal medicine is one of crucial symbols of Chinese national medicine. Investigation on molecular responses of different herbal strategies against viral myocarditis is immeasurably conducive to targeting drug development in the current international absence of miracle treatment. Methods. Literature retrieval platforms were applied in the collection of existing empirical evidences for viral myocarditis-related single-herbal strategies. SwissTargetPrediction, Metascape, and Discovery Studio coordinating with multidatabases investigated underlying target genes, interactive proteins, and docking molecules in turn. Results. Six single-herbal medicines consisting of Huangqi (Hedysarum Multijugum Maxim), Yuganzi (Phyllanthi Fructus), Kushen (Sophorae Flavescentis Radix), Jianghuang (Curcumaelongae Rhizoma), Chaihu (Radix Bupleuri), and Jixueteng (Spatholobus Suberectus Dunn) meet the requirement. There were 11 overlapped and 73 unique natural components detected in these herbs. SLC6A2, SLC6A4, NOS2, PPARA, PPARG, ACHE, CYP2C19, CYP51A1, and CHRM2 were equally targeted by six herbs and identified as viral myocarditis-associated symbols. MCODE algorithm exposed the hub role of SRC and EGFR in strategies without Jianghuang. Subsequently, we learned intermolecular interactions of herbal components and their targeting heart-tissue-specific CHRM2, FABP3, TNNC1, TNNI3, TNNT2, and SCN5A and cardiac-myocytes-specific IL6, MMP1, and PLAT coupled with viral myocarditis. Ten interactive characteristics such as π-alkyl and van der Waals were modeled in which ARG111, LYS253, ILE114, and VAL11 on cardiac troponin (TNNC1-TNNI3-TNNT2) and ARG208, ASN106, and ALA258 on MMP1 fulfilled potential communicating anchor with ellagic acid, 5α, 9α-dihydroxymatrine, and leachianone g via hydrogen bond and hydrophobic interaction, respectively. Conclusions. The comprehensive outcomes uncover differences and linkages between six herbs against viral myocarditis through component and target analysis, fostering development of drugs.
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