The hottest DNA Substack posts right now

The SNT model can be improved by making the DNA repair period depend on the dose rate of radiation. This means that higher doses would take longer to repair, which fits better with the observed data.
There seems to be a limit to how much cancer can result from radiation, especially at high doses. Cells that are too damaged can't repair themselves, which might prevent cancer from developing.
Understanding how radiation affects the body is important for safety, especially for workers in nuclear plants. We need more data to ensure the current models aren’t overly cautious, which can lead to unnecessary costs.

The amount of DNA fragments in Covid-19 vaccines is relatively small compared to other particles.
DNA fragments are present in all vaccines and various medical treatments, and are kept at minimal levels for safety.
Human bodies have natural mechanisms to deal with foreign DNA and prevent integration into our genes.

Leverage points in systems can change mindsets or paradigms for achieving goals.
Risk management needs to be embedded early in innovation to avoid playing catch-up.
Building resilient core systems allows for adaptable growth and evolution.

Lydia Fairchild faced legal battles when doctors claimed she wasn't the mother of her children.
Lydia Fairchild's unique case revealed she was a human chimera with two sets of DNA.
Human chimerism can occur when zygotes merge, leading to a mix of DNA in one body.

A protein printer is a new technology that can convert digital bits into physical molecules, potentially revolutionizing biology.
The protein printer could make proteins without using DNA or cells, thus reducing costs significantly.
Engineering a protein printer involves reimagining the ribosome to quickly convert digital bits into physical molecules with high precision.

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Deep Learning is a key enabler for advancing single-cell sequencing technologies.
Single-cell sequencing allows us to explore the diversity of individual cells at a detailed level.
Tools using Deep Learning techniques are pivotal in analyzing single-cell RNA sequencing data.

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Illumina sequencing uses clusters of DNA fragments, which can create issues if not all the fragments are the same.
Unpatterned flowcells may have overlapping clusters leading to ambiguous results.
Patterned flowcells use Exclusion Amplification to prevent cluster overlap and improve sequencing accuracy.