Well Artifact Detection
Well Artifact Detection - Related Videos
06:42
A Multi-hole Cryovial Eliminates Freezing Artifacts when Muscle Tissues are Directly Immersed in Liquid Nitrogen
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This protocol describes a procedure to freeze muscle tissues by plunging them directly into liquid nitrogen. This protocol also highlights a new cryovial that can avoid the "blanket effect" of nitrogen gas when liquid nitrogen contacts the tissue surface of a specimen.
10:23
Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
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This article details simultaneous electroencephalogram and functional magnetic resonance imaging (EEG-fMRI) recording procedures that can be used in both clinical and research settings. EEG processing procedures to remove imaging artifacts for clinical review are also included. This study focuses on the example of epilepsy during the interictal period.
10:07
Measurement of Dynamic Scapular Kinematics Using an Acromion Marker Cluster to Minimize Skin Movement Artifact
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This report presents details of how to adopt the acromion marker cluster method of obtaining scapular kinematics when using a passive marker motion capture device. As has been described in the literature, this method provides a robust, non-invasive, three-dimensional, dynamic and valid measurement of scapular kinematics, minimizing skin movement artifact.
06:58
Synthesis of an In vivo MRI-detectable Apoptosis Probe
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Early detection of apoptosis may identify at-risk cell populations in a variety of diseases. Here we demonstrate a method to link an early apoptosis-detection protein (Annexin V) to a MRI-detectable iron oxide nanoparticle (SPIO). This method may be extended to other proteins of interest to generate MRI-detectable molecular imaging probes.
07:20
A Novel Approach to Overcome Movement Artifact When Using a Laser Speckle Contrast Imaging System for Alternating Speeds of Blood Microcirculation
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Cited by 11 •
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This study introduces a novel technique for the measurement and analysis of alternating speeds of blood microcirculation in a single experiment using a laser speckle contrast imager.
FAQs
The method compares each well with expected spatial and statistical patterns across the plate or assay. A measurement becomes more concerning when it deviates from neighboring wells, replicate behavior, or the overall distribution rather than simply being unusually high or low. This comparison helps separate technical irregularities from genuine biological differences in bioengineered experiments.
Bubbles, uneven liquid handling, evaporation, contamination, edge effects, and imaging problems can all produce abnormal measurements or visible features. These causes may alter well intensity, appearance, or consistency without reflecting the biology under study. Recognizing their distinct physical or spatial signatures helps researchers interpret flagged wells and identify weaknesses in the experimental setup.
Spatial patterns reveal whether irregularities cluster at plate edges, follow liquid-handling locations, or affect particular regions. Plate-layout checks add experimental context by showing whether flagged wells share a treatment, control, or replicate arrangement. Together, these analyses help determine whether an apparent result reflects a technical pattern or a biologically meaningful response.
A practical workflow begins by collecting well measurements or images, then comparing them with expected spatial and statistical patterns. Researchers flag deviations, examine their appearance and distribution, and compare affected wells with replicates and the plate layout. The resulting characterization can guide quality control decisions and protocol adjustments before a larger experiment proceeds.
Researchers can apply it during quality control of microplates, culture plates, and other bioengineered assay platforms, especially before committing resources to larger experiments. Early screening identifies wells that may distort results and reveals recurring technical problems. This supports more reliable cell-based assays by allowing protocols or handling practices to be refined in advance.
By identifying wells affected by bubbles, evaporation, contamination, handling variation, edge effects, or imaging problems, the analysis prevents technical noise from being interpreted as biological variation. Automated image analysis, intensity thresholds, replicate comparisons, and plate-layout checks provide complementary evidence. The outcome is stronger data quality and a clearer basis for refining cell-based assay protocols.