Multiplex digital PCR: a superior technique to qPCR for the simultaneous detection of duck Tembusu virus, duck circovirus, and new duck reovirus

Duck Tembusu virus (DTMUV), duck circovirus (DuCV), and new duck reovirus (NDRV) have seriously hindered the development of the poultry industry in China. To detect the three pathogens simultaneously, a multiplex digital PCR (dPCR) was developed and compared with multiplex qPCR in this study. The multiplex dPCR was able to specifically detect DTMUV, DuCV, and NDRV but not amplify Muscovy duck reovirus (MDRV), Muscovy duck parvovirus (MDPV), goose parvovirus (GPV), H4 avian influenza virus (H4 AIV), H6 avian influenza virus (H6 AIV), and Newcastle disease virus (NDV). The standard curves showed excellent linearity in multiplex dPCR and qPCR and were positively correlated. The sensitivity results showed that the lowest detection limit of multiplex dPCR was 1.3 copies/μL, which was 10 times higher than that of multiplex qPCR. The reproducibility results showed that the intra- and interassay coefficients of variation were 0.06–1.94%. A total of 173 clinical samples were tested to assess the usefulness of the method; the positive detection rates for DTMUV, DuCV, and NDRV were 18.5, 29.5, and 14.5%, respectively, which were approximately 4% higher than those of multiplex qPCR, and the kappa values for the clinical detection results of multiplex dPCR and qPCR were 0.85, 0.89, and 0.86, indicating that the two methods were in excellent agreement

Analysis of bronchoscopy characteristics for 729 female patients with lung cancer

Background and objective The aim of this article is to study features of the bronchoscopy signs in female lung cancer patients. Methods The bronchoscopy data of 729 female lung cancer patients enrolled between January 1994 and June 2007 was analyzed, retrospectively. Results Most of the patients were middle-aged female (57.0%), then were the elderly (28.5%), and the youth composed much lower (14.0%). The most common histopathology was adenocarcinoma (42.8%), followed by squamous cell carcinoma (23.9%) and small cell carcinoma (19.2%), and all of them increased in the past few years. The female lung cancers were more in the right lung (P<0.05), and the upper lobes (P<0.05). Among 729 female lung cancer patients, 92.0% had apparent signs. Most of adenocarcinoma had infiltrative changes (P<0.05), but most of squamous cell carcinoma and small cell carcinoma had proliferative changes (P<0.05). The most common sing of bronchoscopy in patients with atelectasis was proliferative changes (P<0.05), but the most common sings of bronchoscopy in patients with pleural effusion was infiltrative changes (P<0.05). Conclusion This study suggests brochoscopy is an important approach in diagnosis of female lung cancer. Paying more attention to the lung cancer of female patients and examining with bronchoscopy would be helpful for earlier diagnosis

A Quadruplex qRT-PCR for Differential Detection of Four Porcine Enteric Coronaviruses

Porcine epidemic diarrhea virus (PEDV), transmissible gastroenteritis virus (TGEV), porcine deltacoronavirus (PDCoV), and swine acute diarrhea syndrome coronavirus (SADS-CoV) are four identified porcine enteric coronaviruses. Pigs infected with these viruses show similar manifestations of diarrhea, vomiting, and dehydration. Here, a quadruplex real-time quantitative PCR (qRT-PCR) assay was established for the differential detection of PEDV, TGEV, PDCoV, and SADS-CoV from swine fecal samples. The assay showed extreme specificity, high sensitivity, and excellent reproducibility, with the limit of detection (LOD) of 121 copies/&mu;L (final reaction concentration of 12.1 copies/&mu;L) for each virus. The 3236 clinical fecal samples from Guangxi province in China collected between October 2020 and October 2022 were evaluated by the quadruplex qRT-PCR, and the positive rates of PEDV, TGEV, PDCoV, and SADS-CoV were 18.26% (591/3236), 0.46% (15/3236), 13.16% (426/3236), and 0.15% (5/3236), respectively. The samples were also evaluated by the multiplex qRT-PCR reported previously by other scientists, and the compliance rate between the two methods was more than 99%. This illustrated that the developed quadruplex qRT-PCR assay can provide an accurate method for the differential detection of four porcine enteric coronaviruses

Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection.

By 2040, ~100 million people will have glaucoma. To date, there are a lack of high-efficiency glaucoma diagnostic tools based on visual fields (VFs). Herein, we develop and evaluate the performance of 'iGlaucoma', a smartphone application-based deep learning system (DLS) in detecting glaucomatous VF changes. A total of 1,614,808 data points of 10,784 VFs (5542 patients) from seven centers in China were included in this study, divided over two phases. In Phase I, 1,581,060 data points from 10,135 VFs of 5105 patients were included to train (8424 VFs), validate (598 VFs) and test (3 independent test sets-200, 406, 507 samples) the diagnostic performance of the DLS. In Phase II, using the same DLS, iGlaucoma cloud-based application further tested on 33,748 data points from 649 VFs of 437 patients from three glaucoma clinics. With reference to three experienced expert glaucomatologists, the diagnostic performance (area under curve [AUC], sensitivity and specificity) of the DLS and six ophthalmologists were evaluated in detecting glaucoma. In Phase I, the DLS outperformed all six ophthalmologists in the three test sets (AUC of 0.834-0.877, with a sensitivity of 0.831-0.922 and a specificity of 0.676-0.709). In Phase II, iGlaucoma had 0.99 accuracy in recognizing different patterns in pattern deviation probability plots region, with corresponding AUC, sensitivity and specificity of 0.966 (0.953-0.979), 0.954 (0.930-0.977), and 0.873 (0.838-0.908), respectively. The 'iGlaucoma' is a clinically effective glaucoma diagnostic tool to detect glaucoma from humphrey VFs, although the target population will need to be carefully identified with glaucoma expertise input