Diagnostic Efficacy of Ultrasound, Cytology, and BRAFV600E Mutation Analysis and Their Combined Use in Thyroid Nodule Screening for Papillary Thyroid Microcarcinoma

BackgroundUltrasound, cytology, and BRAFV600E mutation analysis were applied as valuable tools in the differential diagnosis of thyroid nodules. The aim of the present study was to evaluate the diagnostic efficiency of the three methods and their combined use in screening for papillary thyroid microcarcinoma (PTMC).MethodsA total of 1,081 patients with 1,157 thyroid nodules (0.5–1 cm in maximum diameter) classified as thyroid imaging reporting and data system (TIRADS) 4–5 were recruited. All patients underwent ultrasound, fine-needle aspiration (FNA) examination, and an additional BRAFV600E mutation test. TIRADS and Bethesda System for Reporting Thyroid Cytopathology (BSRTC) were adopted to judge the ultrasound and cytological results. The receiver operating characteristic (ROC) curve was established to assess the diagnostic values of different methods.ResultsOf the 1,157 nodules, 587 were benign and 570 were PTMCs. BRAFV600E mutation test had highest sensitivity (85.4%), specificity (97.1%), accuracy (91.4%), and area under the ROC curve (Az) value (0.913) among the three methods. The combination of BSRTC and BRAFV600E mutation analysis yielded a considerably high sensitivity (96.0%), accuracy (94.3%), and negative predictive value (95.9%) than either BSRTC or BRAFV600E mutation alone (P < 0.0001 for all comparisons). Of all the methods, the combined use of the three methods produced the best diagnostic performance (Az = 0.967), which was significantly higher than that (Az = 0.943) for the combination of BSRTC and BRAFV600E mutation (P < 0.0001). The diagnostic accuracy of the molecular method in the 121 nodules with indeterminate cytology was 90.1% (109/121), which was significantly higher than that of TIRADS classification, 74.4% (90/121) (P = 0.002).ConclusionThe combined use of ultrasound, cytology, and BRAFV600E mutation analysis is the most efficient and objective method for diagnosing PTMC. Both BRAFV600E mutation and TIRADS classification are potentially useful adjuncts to differentiate thyroid nodules, especially indeterminate samples classified as BSRTC III.

Origin and evolution of HIV-1 subtype A6

Background HIV outbreaks in the Former Soviet Union (FSU) countries were characterized by repeated transmission of the HIV variant AFSU, which is now classified as a distinct subtype A sub-subtype called A6. The current study used phylogenetic/phylodynamic and signature mutation analyses to determine likely evolutionary relationship between subtype A6 and other subtype A sub-subtypes. Methods For this study, an initial Maximum Likelihood phylogenetic analysis was performed using a total of 553 full-length, publicly available, reverse transcriptase sequences, from A1, A2, A3, A4, A5, and A6 sub-subtypes of subtype A. For phylogenetic clustering and signature mutation analysis, a total of 5961 and 3959 pol and env sequences, respectively, were used. Results Phylogenetic and signature mutation analysis showed that HIV-1 sub-subtype A6 likely originated from sub-subtype A1 of African origin. A6 and A1 pol and env genes shared several signature mutations that indicate genetic similarity between the two subtypes. For A6, tMRCA dated to 1975, 15 years later than that of A1. Conclusion The current study provides insights into the evolution and diversification of A6 in the backdrop of FSU countries and indicates that A6 in FSU countries evolved from A1 of African origin and is getting bridged outside the FSU region.

Phylogenetic and full-length genome mutation analysis of SARS-CoV-2 in Indonesia prior to COVID-19 vaccination program in 2021

Background Indonesia has started the big project of COVID-19 vaccination program since 13 January 2021 by employing the first shot of vaccine to the President of Indonesia as the outbreak and rapid transmission of COVID-19 have endangered not only Indonesian but the global health and economy. This study aimed to investigate the full-length genome mutation analysis of 166 Indonesian SARS-CoV-2 isolates as of 12 January 2021. Results All data of the isolates were extracted from the Global Initiative on Sharing All Influenza Data (GISAID) EpiCoV database. CoVsurver platform was employed to investigate the full-length genome mutation analysis of all isolates. This study also focused on the phylogeny analysis in unlocking the mutation of S protein in Indonesian SARS-CoV-2 isolates. WIV04 isolate that was originated from Wuhan, China was used as the virus reference according to the CoVsurver default. The result showed that a full-length genome mutation analysis of 166 Indonesian SARS-CoV-2 isolates was successfully generated. Every single mutation in S protein was described and then visualized by utilizing BioRender platform. Furthermore, it also found that D614G mutation appeared in 103 Indonesian SARS-CoV-2 isolates. Conclusions To sum up, this study helped to observe the spread of COVID-19 transmission. However, it also proposed that the epidemiological surveillance and genomics studies might be improved on COVID-19 pandemic in Indonesia.

Integrative Bioinformatics approaches to therapeutic gene target selection in various cancers for Nitroglycerin

Integrative Bioinformatics analysis helps to explore various mechanisms of Nitroglycerin activity in different types of cancers and help predict target genes through which Nitroglycerin affect cancers. Many publicly available databases and tools were used for our study. First step in this study is identification of Interconnected Genes. Using Pubchem and SwissTargetPrediction Direct Target Genes (activator, inhibitor, agonist and suppressor) of Nitroglycerin were identified. PPI network was constructed to identify different types of cancers that the 12 direct target genes affected and the Closeness Coefficient of the direct target genes so identified. Pathway analysis was performed to ascertain biomolecules functions for the direct target genes using CluePedia App. Mutation Analysis revealed Mutated Genes and types of cancers that are affected by the mutated genes. While the PPI network construction revealed the types of cancer that are affected by 12 target genes this step reveals the types of cancers affected by mutated cancers only. Only mutated genes were chosen for further study. These mutated genes were input into STRING to perform NW Analysis. NW Analysis revealed Interconnected Genes within the mutated genes as identified above. Second Step in this study is to predict and identify Upregulated and Downregulated genes. Data Sets for the identified cancers from the above procedure were obtained from GEO Database. DEG Analysis on the above Data sets was performed to predict Upregulated and Downregulated genes. A comparison of interconnected genes identified in step 1 with Upregulated and Downregulated genes obtained in step 2 revealed Co-Expressed Genes among Interconnected Genes. NW Analysis using STRING was performed on Co-Expressed Genes to ascertain Closeness Coefficient of Co-Expressed genes. Gene Ontology was performed on Co-Expressed Genes to ascertain their Functions. Pathway Analysis was performed on Co-Expressed Genes to identify the Types of Cancers that are influenced by co-expressed genes. The four types of cancers identified in Mutation analysis in step 1 were the same as the ones that were identified in this pathway analysis. This further corroborates the 4 types of cancers identified in Mutation analysis. Survival Analysis was done on the co-expressed genes as identified above using Survexpress. BIOMARKERS for Nitroglycerin were identified for four types of cancers through Survival Analysis. The four types of cancers are Bladder cancer, Endometrial cancer, Melanoma and Non-small cell lung cancer.