Background: Breast cancer is the prevailing malignancy among women, exhibiting a discernible escalation in incidence within our nation; hormone receptor‐positive (HR+) human epidermal growth factor receptor 2‐negative (HER2−) breast cancer is the most common subtype. In this study, we aimed to search for a non‐invasive, specific, blood‐based biomarker for the early detection of luminal A breast cancer through proteomic studies.Methods: To explore new potential plasma biomarkers, we applied data‐independent acquisition (DIA), a technique combining liquid chromatography and tandem mass spectrometry, to quantify breast cancer‐associated plasma protein abundance from a small number of plasma samples in 10 patients with luminal A breast cancer, 10 patients with benign breast tumors, and 10 healthy controls.Results: The proteomes of 30 participants in all cohorts were analyzed using the DIA method, and a total of 517 proteins and 3584 peptides were quantified. We found that there were significant differences in plasma protein expression profiles between breast cancer patients and non‐breast cancer patients, and breast cancer was mainly related to lipid metabolism pathways. Finally, the optimal protein combinations for the diagnosis of breast cancer were PON3, IGLV3‐10, and IGHV3‐73 through multi‐model analysis, which had a high prediction accuracy for breast cancer (AUC = 0.92), and the model could also distinguish breast cancer from HC (AUC = 0.92) and breast cancer from benign breast tumor (AUC = 0.91).Conclusions: The study revealed proteomic signatures of patients with luminal A breast cancer, identified multiple differential proteins, and identified three plasma proteins as potential diagnostic biomarkers for breast cancer. It provides a reference for the screening of biomarkers for breast cancer.

Background. The differences in the antihypertensive treatment with angiotensin type II receptor blockers (ARBs) may be attributed to polymorphisms in genes involving drug-targeted receptor and drug metabolism. The present study aimed to investigate whether the antihypertensive effect of the ARB drug valsartan was associated with angiotensin II type 1 receptor (AGTR1) gene polymorphism (A1166 C) and cytochrome P450 enzyme 2C9 (CYP2C9) gene polymorphism (CYP2C9∗3). Methods. 281 patients with hypertension who received valsartan monotherapy in the past month were included in this retrospective study. Polymerase chain reaction-melting curve analysis was performed to genotype the AGTR1 and CYP2C9 gene polymorphisms. Based on the systolic blood pressure (SBP) and diastolic blood pressure (DBP) at the time of visit, the patients were divided into well-controlled group (n = 144, SBP/DBP <140/90 mmHg) and poorly controlled group (n = 137, SBP/DBP ≥140/90 mmHg). Results. Older age, decreased history of drinking, a higher proportion of mild-to-moderate hypertension, lower alanine aminotransferase levels, and higher high-density lipoprotein cholesterol levels were observed in the well-controlled group than the poorly controlled group. Higher frequencies of the C allele and AC + CC genotype of AGTR1 A1166C were detected in the well-controlled than the poorly controlled patients ( = 0.005 and = 0.006). After adjustment for demographic and environmental factors, the CC + AC genotype of AGTR1 A1166C was markedly linked to better hypertension control with valsartan treatment compared to the AA genotype (odds ratio: 2.836, 95% confidence interval: 1.199–6.705, = 0.018). No significant difference was observed in the allele or genotype distribution of CYP2C9∗3 polymorphism between well-controlled and poorly controlled patients. Conclusions. The current data suggested that the AGTR1 A1166 C polymorphism may be associated with the antihypertensive effect of valsartan, and carriers with AC and CC genotypes may have a better antihypertensive efficacy response to valsartan treatment.

Background: The SARS-CoV-2 Omicron strain has multiple immune-escape mutations in the spike protein receptor-binding domain (RBD). Rapid detection of these mutations to identify Omicron and its lineages is essential for guiding public health strategies and patient treatments. We developed a two-tube, four-color assay employing asymmetric polymerase chain reaction (PCR)-based melting curve analysis to detect Omicron mutations and discriminate the BA.1, BA.2, BA.4/5, and BA.2.75 lineages. Methods: The presented technique involves combinatory analysis of the detection of six fluorescent probes targeting the immune-escape mutations L452R, N460K, E484A, F486V, Q493R, Q498R, and Y505H within one amplicon in the spike RBD and probes targeting the ORF1ab and N genes. After protocol optimization, the analytical performance of the technique was evaluated using plasmid templates. Sensitivity was assessed based on the limit of detection (LOD), and reliability was assessed by calculating the intra- and inter-run precision of melting temperatures (T_ms). Specificity was assessed using pseudotyped lentivirus of common human respiratory pathogens and human genomic DNA. The assay was used to analyze 40 SARS-CoV-2–positive clinical samples (including 36 BA.2 and 4 BA.4/5 samples) and pseudotyped lentiviruses of wild-type and BA.1 viral RNA control materials, as well as 20 SARS-CoV-2–negative clinical samples, and its accuracy was evaluated by comparing the results with those of sequencing. Results: All genotypes were sensitively identified using the developed method with a LOD of 39.1 copies per reaction. The intra- and inter-run coefficients of variation for the T_ms were ≤ 0.69% and ≤ 0.84%, with standard deviations ≤ 0.38 °C and ≤ 0.41 °C, respectively. Validation of the assay using known SARS-CoV-2–positive samples demonstrated its ability to correctly identify the targeted mutations and preliminarily characterize the Omicron lineages. Conclusion: The developed assay can provide accurate, reliable, rapid, simple and low-cost detection of the immune-escape mutations located in the spike RBD to detect the Omicron variant and discriminate its lineages, and its use can be easily generalized in clinical laboratories with a fluorescent PCR platform.

Objective. A sensitive and specific multiplex fluorescence rapid detection method was established for simultaneous detection of SARS-CoV-2, influenza A virus, and influenza B virus in a self-made device within 30min, with a minimum detection limit of 200 copies/mL. Methods. Based on the genome sequences of SARS-CoV-2, influenza A virus (FluA), and influenza B virus (FluB) with reference to the Chinese Center for Disease Control and Prevention and related literature, specific primers were designed, and a multiplex fluorescent PCR system was established. The simultaneous and rapid detection of SARS-CoV-2, FluA, and FluB was achieved by optimizing the concentrations of Taq DNA polymerase as well as primers, probes, and Mg²⁺. The minimum detection limits of the nucleic acid rapid detection system for SARS-CoV-2, FluA, and FluB were evaluated. Results. By optimizing the amplification system, the N enzyme with the best amplification performance was selected, and the optimal concentration of Mg²⁺ in the multiamplification system was 3mmol/L; the final concentrations of SARS-CoV-2 NP probe and primer were 0.15μmol/L and 0.2μmol/L, respectively; the final concentrations of SARS-CoV-2 ORF probe and primer were both 0.15μmol/L; the final concentrations of FluA probe and primer were 0.2μmol/L and 0.3μmol/L, respectively; the final concentrations of FluB probe and primer were 0.15μmol/L and 0.25μmol/L, respectively. Conclusion. A multiplex real-time quantitative fluorescence RT-PCR system for three respiratory viruses of SARS-CoV-2, FluA, and FluB was established with a high amplification efficiency and sensitivity reaching 200 copies/mL for all samples. Combined with the automated microfluidic nucleic acid detection system, the system can achieve rapid detection in 30minutes.

Objectives. To establish and evaluate the analytical and clinical performance of the Flash20 SARS-CoV-2 nucleic acid rapid detection system free of RNA extraction. Methods. The limit of detection (LoD) was determined using a negative nasopharyngeal swab matrix spiked with different concentrations of SARS-CoV-2 virus; a total of 734,337 reference sequences of viral genomes from GenBank were used for the in-silico analysis to assess the inclusivity of the assay. The specificity of the system was evaluated by testing 27 medically relevant organisms. A total of 115 clinical specimens were collected and tested on the Flash20 SARS-CoV-2 detection system and with an FDA-approved comparator test to assess the clinical performance of the system. Results. The LoD of the Flash20 SARS-CoV-2 detection system is 250 copies/mL with a positive rate 90 (n=20); alignments results showed that over 99 identity of the primer and probe of the Flash20 SARS-CoV-2 nucleic acid rapid detection system to the available SARS-CoV-2 sequences; the omicron samples tested 100 positive. None of the 27 organisms showed cross-reactivity with the Flash20 SARS-CoV-2 nucleic acid rapid detection system. Among all the 215 clinical samples, the Flash20 SARS-CoV-2 nucleic acid rapid detection system exhibits a high sensitivity of 99.24 (131/132) and 100 (83/83) specificity. Conclusion. The nucleic acid rapid detection system provides sensitive and accurate detection of SARS-CoV-2 free of RNA extraction. The high sensitivity and short time to results of approximately 35minutes may impact earlier infection control and disease management.

Background: Mitochondrial DNA m.3243A>G variant causes maternally inherited diabetes with deafness. We developed a droplet digital PCR (dPCR) assay to quantify m.3243A>G heteroplasmy and investigated its prevalence in Chinese Han early‐onset diabetes patients. Methods: The dPCR method was validated using plasmid mixtures (10 ⁴ copies) for sensitivity, specificity, dynamic range (10–10 ⁵ copies/μL), accuracy, and precision. We screened 1506 unselected early‐onset diabetes patients, analyzing blood and urinary sediment DNA. This population comprised 1474 diagnosed type 2 diabetes patients (97.88%), 31 diagnosed type 1 diabetes patients (2.06%), and one patient with mitochondrial diabetes (0.07%). The dPCR results were compared with Sanger sequencing and next generation sequencing (NGS) to evaluate the accuracy of heteroplasmy rate quantification. Results: The dynamic range spans from 10 to 10 ⁵ copies/μL. The assay detected heteroplasmy as low as 0.05% with 100% specificity. The method can reliably quantify the heteroplasmy rate at 0% to 100% for an input copy number of 10 ⁴ . Using the dPCR method, we identified 0.80% ( n = 12) had >5% m.3243A>G heteroplasmy in peripheral blood and/or urinary sediment, and 0.40% ( n = 6) had 1%–5% among the early‐onset diabetes patients. The dPCR approach was more sensitive than Sanger sequencing. When compared with NGS results, the dPCR method accurately genotyped m.3243A>G and quantified its heteroplasmy rate in clinical samples. Furthermore, it demonstrated that urine is a reliable noninvasive sample source for determining the m.3243A>G heteroplasmy rate. Conclusion: The developed method can provide an accurate, reliable, rapid, simple, and low‐cost evaluation of the heteroplasmy rate of m.3243A>G for both peripheral blood and urinary sediment. Thus, it is highly promising as a genetic screening method for early‐onset diabetes related to m.3243A>G.