Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

To develop and optimize a simultaneous detection method of RotavirusA, Norovirus GI, GII, Sapovirus, human astrovirus, enteric adenoviruses and HBoV2 with GenomeLab GeXP analysis system. The sensitivity was verified to be 10(4) copies/microL with plasmids containing the viral targets in triplicate on different days, and no cross-reaction with enterovirus71, human Parechovirus and PicobirnavirusII was observed. Finally, we successfully developed a high throughout, rapid and maneuverable multiplex RT-PCR assay for simultaneous detection of seven viruses related with viral gastroenteritis, which provide a novel method for the molecular diagnosis of diarrhea-associated virus.

Download full-text PDF

Source

Publication Analysis

Top Keywords

novel method
8
simultaneous detection
8
method multiplex
4
multiplex detection
4
detection gastroenteritis-associated
4
gastroenteritis-associated viruses]
4
viruses] develop
4
develop optimize
4
optimize simultaneous
4
detection method
4

Similar Publications

This study presents a novel variable gain intermittent boundary control (VGIBC) approach for stabilizing delayed stochastic reaction-diffusion Cohen-Grossberg neural networks (SRDCGNN). In contrast to traditional constant gain intermittent boundary control (CGIBC) methods, the proposed VGIBC framework dynamically adjusts the control gain based on the operational duration within each control cycle, thereby improving adaptability to variations in work interval lengths. The time-varying control gain is designed using a piecewise interpolation method across work intervals, defined by a finite set of static gain matrices.

View Article and Find Full Text PDF

Region-guided attack on the segment anything model.

Neural Netw

September 2025

School of Electronic Science and Engineering, Nanjing University, China. Electronic address:

The Segment Anything Model (SAM) is a cornerstone of image segmentation, demonstrating exceptional performance across various applications, particularly in autonomous driving and medical imaging, where precise segmentation is crucial. However, SAM is vulnerable to adversarial attacks that can significantly impair its functionality through minor input perturbations. Traditional techniques, such as FGSM and PGD, are often ineffective in segmentation tasks due to their reliance on global perturbations that overlook spatial nuances.

View Article and Find Full Text PDF

Multimodal self-supervised retinal vessel segmentation.

Neural Netw

September 2025

Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China. Electronic address:

Automatic segmentation of retinal vessels from retinography images is crucial for timely clinical diagnosis. However, the high cost and specialized expertise required for annotating medical images often result in limited labeled datasets, which constrains the full potential of deep learning methods. Recent advances in self-supervised pretraining using unlabeled data have shown significant benefits for downstream tasks.

View Article and Find Full Text PDF

Ultrasensitive multifunctional biosensor integrating ECL quenching and DPV enhancement for early classification of thyroid cancer via BRAF V600E and microRNA-221 detection.

Biosens Bioelectron

September 2025

College of Chemistry, Jilin Province Research Center for Engineering and Technology of Spectral Analytical Instruments, Jilin University, Qianjin Street 2699, Changchun, 130012, China. Electronic address:

Papillary thyroid carcinoma (PTC) is the most prevalent form of thyroid cancer with a high incidence among endocrine malignancies. It tends to metastasize early in lymph nodes and differs markedly from other subtypes in biological behavior, clinical management, and prognosis. Therefore, accurately distinguishing PTC from other pathological subtypes is crucial for guiding diagnosis and treatment decisions.

View Article and Find Full Text PDF

Background: The interprofessional educational curriculum for patient and personnel safety is of critical importance, especially in the context of the COVID-19 pandemic, to prepare junior multiprofessional teams for emergency settings.

Objective: This study aimed to evaluate the effectiveness of an innovative interprofessional educational curriculum that integrated medical movies, massive open online courses (MOOCs), and 3D computer-based or virtual reality (VR) simulation-based interprofessional education (SimBIE) with team co-debriefing to enhance interprofessional collaboration and team performance using Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS). This study addressed 3 key questions.

View Article and Find Full Text PDF