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Record W6987547840

Targeting Alpha-Synuclein using chemically modified nucleic acid scaffolds

2024· dissertation· en· W6987547840 on OpenAlexaff

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsiNano Medical (Canada)
Fundersnot available
KeywordsNucleic acidAptamerDNASystematic evolution of ligands by exponential enrichmentRNASmall moleculeScaffoldBinding selectivityOligonucleotide
DOInot available

Abstract

fetched live from OpenAlex

Parkinson’s disease is the second most prevalent neurodegenerative disease and a rising problem worldwide, where the lack of early and specific diagnosis is causing significant concerns. To date, diagnosis relies on clinical determination of symptoms that may develop decades after the disease’s onset. Biomarkers reflecting the progression of Parkinson’s disease have arisen as potential diagnostic targets, such as -Synuclein (-SN) aggregates. -SN oligomers are small aggregated species which play a key role in disease initiation and progression. These appear promising biomarkers for early detection, although specific targeting remains challenging. Nucleic acid technology has transpired in diagnostic applications due to its unique properties of self-assembly and sequence programmability, facilitated by Watson-Crick base pairing, along with the possibility of easy chemical functionalisation. Nucleic acids are commonly applied in biosensing and target recognition, as they can be utilised to build various architectures, either binding a target specifically, such as aptamers, or scaffolding known binding partners. This thesis presents three projects approaching specific targeting of -SN by employing the functionalisation of nucleic acids. In the first project, RNA was modified in a site-specific manner using small molecules reported to interact with -SN. The modifications were incorporated, using the RNA as a spatially designed scaffold or employing systematic evolution of ligands by exponential enrichment (SELEX) for specific targeting of -SN. The incorporation of modifications did increase interactions of -SN, though specificity was not obtained. The second project applied the conjugation of known -SN binding partners to DNA oligonucleotides, spatially scaffolding the binding partners, such as a nanobody, a peptide or DNA aptamers to obtain increased binding through multimerisation. Multimerisation of the nanobody resulted in an increased binding affinity. The final project approached the employment of modified RNA in standard applications, such as fibrillation assays and histochemistry. Small -SN binding molecules were conjugated to RNA to study if the conjugation affected binding properties or fibrillation of -SN. And whether modified RNA could be utilised as an imaging agent of human tissue containing pathological -SN. The application of modified RNA did not yield conclusive results when investigating -SN, as none of the RNA constructs exhibited the desired specificity. Collectively, these projects demonstrate the potential of chemically modifying nucleic acids by altering the sequence in a site-specific manner or utilising oligonucleotides to scaffold binding partners and detection agents. Nucleic acid scaffolds can display a diverse range of molecules with high spatial control, making them suitable for applications in bioimaging and molecular targeting.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.287
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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