Archive for the 'SciLit' Category

Huntington disease – PubMed

March 29, 2026

https://pubmed.ncbi.nlm.nih.gov/27188817/

ASO v RNAi v siRNA

QT:{{”

how a specific toxic conformation might be favoured
within the expanded polyQ of monomeric HTT exon1
is unclear37,47. More-complex conformational effects in
monomeric HTT exon1 linked to polyQ repeat length
are formally possible but challenging to establish37,49. By
contrast, the widely reported ability of HTT exon1 to
readily form a variety of aggregated structures presents
an array of plausible candidates that might mediate toxicity (see below)37. This aggregation links Huntington
disease to other neurodegenerative diseases that feature
a protein aggregation component, including Alzheimer
disease, Parkinson disease, amyotrophic lateral sclerosis
and spongiform encephalopathies.

bind to HTT mRNA selectively and target it for degradation
by cellular mechanisms. When the agent is a short
interfering RNA (siRNA) or microRNA, the HTT
mRNA is degraded by cytoplasmic RNA-induced silencing
complex (RISC) — a process known as RNA interference
(RNAi). Alternatively, a single-stranded modified
DNA molecule or antisense oligonucleotide (ASO) can
be used to direct the transcript for degradation by
nuclear ribonuclease H.
“}}

Bates, G. P., Dorsey, R., Gusella, J. F., Hayden, M. R., Kay, C., Leavitt, B. R., Nance, M., Ross, C. A., Scahill, R. I., Wetzel, R., Wild, E. J., & Tabrizi, S. J. (2015). Huntington disease. Nature Reviews Disease Primers, 1(1), 15005.
https://doi.org/10.1038/nrdp.2015.5

from G search {{

Yes, amyloid fibrils in Huntington’s disease (HD) contain a specific protein—the mutated huntingtin (Htt) protein. These fibrils are formed specifically from the N-terminal exon 1 fragment of the mutant protein, which contains an expanded polyglutamine (polyQ) tract that forms the amyloid core.
….
Although they contain the mutant protein, the amyloid fibrils in HD are distinct from those in Alzheimer’s (A
) or Parkinson’s (
-synuclein) diseases.

}}

A Programmable Dual-RNA–Guided DNA Endonuclease in Adaptive Bacterial Immunity | Science

March 28, 2026

https://www.science.org/doi/10.1126/science.1225829

Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., & Charpentier, E. (2012). A programmable Dual-RNA–Guided DNA
endonuclease in adaptive bacterial immunity. Science, 337(6096), 816–821. https://doi.org/10.1126/science.1225829

Towards end-to-end automation of AI research | Nature

March 28, 2026

https://www.nature.com/articles/s41586-026-10265-5

Lu, C., Lu, C., Lange, R. T., Yamada, Y., Hu, S., Foerster, J., Ha, D., & Clune, J. (2026). Towards end-to-end automation of AI research. Nature, 651(8107), 914–919. https://doi.org/10.1038/s41586-026-10265-5

cost of privacy

March 14, 2026

game theory papers

https://www.science.org/doi/10.1126/sciadv.abe9986

Wan, Z., Vorobeychik, Y., Xia, W., Liu, Y., Wooders, M., Guo, J., Yin, Z., Clayton, E. W., Kantarcioglu, M., & Malin, B. A. (2021). Using game theory to thwart multistage privacy intrusions when sharing data. Science Advances, 7(50), eabe9986.
https://doi.org/10.1126/sciadv.abe9986

Guo, J., Clayton, E. W., Kantarcioglu, M., Vorobeychik, Y., Wooders, M., Wan, Z., Yin, Z., & Malin, B. A. (2023). A game theoretic approach to balance privacy risks and familial benefits. Scientific Reports, 13(1), 6932. https://doi.org/10.1038/s41598-023-33177-0

They seem to be more focused on the cost to the attacker

Human hippocampal neurogenesis in adulthood, ageing and Alzheimer’s disease

March 1, 2026

Interesting paper on the Aging Brain. Featured in NY Times.

Nature https://www.nature.com/articles/s41586-026-10169-4

Disouky, A., Sanborn, M. A., Sabitha, K. R., Mostafa, M. M., Ayala, I. A., Bennett, D. A., Lu, Y., Zhou, Y., Keene, C. D., Weintraub, S., Gefen, T., Mesulam, M., Geula, C., Maienschein-Cline, M., Rehman, J., & Lazarov, O. (2026). Human hippocampal neurogenesis in adulthood, ageing and Alzheimer’s disease. Nature.
https://doi.org/10.1038/s41586-026-10169-4

Huntington disease | Nature Reviews Disease Primers

February 22, 2026

https://www.nature.com/articles/nrdp20155

nrdp20155.pdf

Bates, G. P., Dorsey, R., Gusella, J. F., Hayden, M. R., Kay, C., Leavitt, B. R., Nance, M., Ross, C. A., Scahill, R. I., Wetzel, R., Wild, E. J., & Tabrizi, S. J. (2015). Huntington disease. Nature Reviews Disease Primers, 1(1), 15005.
https://doi.org/10.1038/nrdp.2015.5

Toward practical high-capacity low-maintenance storage of digital information in synthesised DNA – PMC

February 15, 2026

https://pmc.ncbi.nlm.nih.gov/articles/PMC3672958/

Goldman, N., Bertone, P., Chen, S., Dessimoz, C., LeProust, E. M., Sipos, B., & Birney, E. (2013). Towards practical, high-capacity, low-maintenance information storage in synthesized DNA. Nature, 494(7435), 77–80. https://doi.org/10.1038/nature11875

Nature medcine “A minimally invasive dried blood spot biomarker test for the detection of Alzheimer’s dis ease pathology”

January 26, 2026

QT:{{”
The DROP-AD project investigates the potential of dried plasma spot (DPS) and dried blood spot (DBS) analysis, derived from capillary blood, for detecting AD biomarkers, including phosphorylated tau at amino acid 217 (p-tau217), glial fibrillary acidic protein and neurofilament light. …. Similarly, we demonstrated the successful detection of glial fibrillary acidic protein and neurofilament light with strong correlations between DBS and DPS, respectively, using paired venous plasma samples.
“}}

Might find this paper very interesting. Just published this month in Nature Medicine. “A minimally invasive dried blood spot biomarker test for the detection of Alzheimer’s disease pathology.”

A minimally invasive dried blood spot biomarker test for the detection of Alzheimer’s disease pathology – Nature Medicine
https://www.nature.com/articles/s41591-025-04080-0

Xpresso

January 25, 2026

https://xpresso.gs.washington.edu/

Agarwal V, Shendure J. Predicting mRNA abundance directly from genomic sequence using deep convolutional neural networks. 2020. Cell Reports 31 (7), 107663.

Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation | Nature Genetics

January 25, 2026

https://github.com/calico/borzoi

N&V – https://www.nature.com/articles/s41588-025-02154-w

https://www.nature.com/articles/s41588-024-02053-6#:~:text=This%20paper%20proposes%20a%20new%20machine%2Dlearning%20model%2C,that%20drive%20RNA%20expression%20and%20post%2Dtranscriptional%20regulation QT:{{” ere, we introduce Borzoi, a model that learns to predict cell-type-specific and tissue-specific RNA-seq coverage from DNA sequence. “}}

Linder, J., Srivastava, D., Yuan, H., Agarwal, V., & Kelley, D. R. (2025). Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation. Nature Genetics, 57(4), 949–961.
https://doi.org/10.1038/s41588-024-02053-6