Automated Detection
Automated Detection - Related Videos
08:58
Fully Automated Centrifugal Microfluidic Device for Ultrasensitive Protein Detection from Whole Blood
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Cited by 8 •
2016
This protocol demonstrates how to achieve femto molar detection sensitivity of proteins in 10 µL of whole blood within 30 min. This can be achieved by using electrospun nanofibrous mats integrated in a lab-on-a-disc, which offers high surface area as well as effective mixing and washing for enhanced signal-to-noise ratio.
06:45
Automated Joint Space Detection Improves Bone Segmentation Accuracy
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2025
The development of an automated joint space detection workflow enabled high-throughput segmentation of distinct murine hindpaw bones with >98% accuracy in wild-type animals. Flexible application to forepaws and paws with inflammatory-erosive arthritis was achieved, but with deprecated performance that warrants further optimization in future studies using publicly available data.
13:28
Automated Detection and Analysis of Exocytosis
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Cited by 14 •
2021
We developed automated computer vision software to detect exocytic events marked by pH-sensitive fluorescent probes. Here, we demonstrate the use of a graphical user interface and RStudio to detect fusion events, analyze and display spatiotemporal parameters of fusion, and classify events into distinct fusion modes.
08:06
Automated Acoustic Dispensing for the Serial Dilution of Peptide Agonists in Potency Determination Assays
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Cited by 1 •
2016
Peptide adsorption to plasticware during traditional tip-based serial dilutions can significantly impact potency determination and confound the understanding of structure-activity relationships used for lead identification and lead optimization phases of drug discovery. Here methods for automated acoustic non-contact serial dilution of peptide samples are described.
07:21
Automated, High-Throughput Detection of Bacterial Adherence to Host Cells
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Cited by 2 •
2021
Detection of host-bacterial pathogen interactions based on phenotypic adherence using high-throughput fluorescence labeling imaging along with automated statistical analysis methods enables rapid evaluation of potential bacterial interactions with host cells.
FAQs
The process begins by capturing a biological signal, such as fluorescence, an optical image, a molecular measurement, or an immunoassay response. Instruments and software convert that signal into digital data, which predefined rules or machine-learning models analyze. The system can then recognize targets including pathogens, antibodies, immune cells, or patterns associated with infection.
Predefined rules classify digital measurements according to criteria established before analysis, whereas machine-learning models analyze data using learned patterns. Both approaches support recognition of biological targets and infection-related patterns. The choice affects how the system interprets fluorescence, images, molecular results, or immunoassay responses, while the overall goal remains consistent and rapid analysis.
Quantitative analysis converts biological observations into measurable digital results that researchers can compare across samples or experimental conditions. In this context, it helps evaluate disease progression, treatment effects, and host responses to infection. Automated Detection also reduces reliance on continuous manual inspection, supporting more consistent measurements when studies involve large numbers of observations.
Relevant inputs include fluorescence signals, optical images, molecular measurements, and immunoassay responses. These different data types can support recognition of pathogens, antibodies, immune cells, or infection-related patterns. The input determines what biological information enters the digital analysis workflow, allowing the same broad approach to serve both immunological studies and infection-focused investigations.
A typical workflow starts with an instrument or assay producing a biological signal. That signal is converted into digital data, after which software or an algorithm applies predefined rules or a machine-learning model. The resulting analysis identifies or classifies the relevant target or pattern. Researchers can then use the output for quantitative analysis, screening, diagnosis, or surveillance.
Researchers apply it to high-throughput screening, diagnostic testing, pathogen surveillance, and quantitative analysis of host responses. It can help examine disease progression, assess treatment effects, and investigate emerging infectious threats. In immunology, the targets may include antibodies or immune cells; in infection research, the analysis may focus on pathogens or broader infection-related patterns.