Automated Death Scoring
Automated Death Scoring - Related Videos
10:06
An Introduction to Cell Death
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2023
Necrosis, apoptosis, and autophagic cell death are all manners in which cells can die, and these mechanisms can be induced by different stimuli, such as cell injury, low nutrient levels, or signaling proteins. Whereas necrosis is considered to be an “accidental” or unexpected form of cell death, evidence exists that apoptosis and autophagy are both programmed and “planned” by cells.In this introductory video, JoVE highlights key discoveries pertaining to cell death, including recent work done...
06:55
Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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When randomized controlled trials are not feasible, a comprehensive health care data source like the Military Health System Data Repository provides an attractive alternative for retrospective analyses. Incorporating mortality data from the national death index and balancing differences between groups using propensity weighting helps reduce biases inherent in retrospective designs.
04:25
Neuronal Death Assay to Evaluate Cell Death in Ex Vivo Epilepsy Model
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This video demonstrates the technique of assessing neuronal death in rhinal cortex-hippocampus organotypic slices. Neuronal death occurs via evolving epileptic seizures — resembling in vivo epilepsy — and is detected through dual-staining of the dead cells using propidium iodide and immunofluorescence.
01:06
Introduction to z Scores
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2023
A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one. z scores help...
01:17
z Scores and Area Under the Curve
0 Views •
2023
z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of zero.
FAQs
The computational decision depends on measurable indicators such as movement, morphology, image-based viability signals, or combinations of these inputs. A predefined threshold can classify a specimen when its signal crosses a chosen boundary, while machine-learning classification uses learned patterns to separate living from nonliving states. This makes the timing rule explicit and repeatable across observations.
Reliability depends on whether the selected signal remains distinguishable as specimens lose viability. Time-lapse images can provide movement or morphological information, while viability signals offer another basis for classification. The chosen threshold or classification pattern determines how observations are converted into death records, so the signal must support a clear distinction between living and nonliving states.
Manual observation requires people to inspect specimens and decide when characteristic signs of life have been lost, which can introduce observer variability. Automated death scoring applies predefined thresholds or machine-learning classifications to recorded observations instead. This computational approach supports more consistent measurements, larger experimental scale, and finer temporal resolution when many specimens or repeated time points are involved.
A typical workflow begins by collecting time-lapse images or other observations that capture movement, morphology, or viability signals. The computational system then evaluates those data using predefined thresholds or machine-learning classifications, identifies when each specimen loses characteristic signs of life, and records the resulting measurements. These records can be analyzed as quantitative data rather than relying only on manual notes.
It is useful when experiments need repeated, quantitative records of survival or loss of viability across many observations. In lifespan studies, it can track when specimens die; in toxicity studies, it can record biological effects; and in treatment-response experiments, it can compare how specimens respond over time. The same approach also supports disease-related investigations.
Automated death scoring can be applied to cell populations, model organisms, and other biological systems when their viability can be represented through images, movement, morphology, or viability signals. By converting those observations into consistent records, it helps researchers compare outcomes across specimens and time points. This supports biological studies requiring scale, reproducibility, or detailed timing of viability loss.