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Record W4210423138 · doi:10.1007/s11568-009-9084-7

Chemical genomics and molecular medicine

2008· article· en· W4210423138 on OpenAlexaff
R Ramanan, Biplab Bhattacharjee, Amit Kumar, Zahra Hassan, Kumar Jeevan, M. Suresh, M Saleem Farooqi, Prabhat Arya, Pratibha Nallari, T Tanjore, Maithili V. N. Dokuparti, Urooge Boda, P Pranati, Advithi Rangaraju, P Kelkar, R. K. Jain, C. Narsimhan

Bibliographic record

VenueGenomic Medicine · 2008
Typearticle
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsSteacie Institute for Molecular SciencesOntario Institute for Cancer Research
Fundersnot available
KeywordsGenomicsGenomic medicineComputational biologyBiologyGeneticsGenomeGene

Abstract

fetched live from OpenAlex

Alzheimer's disease (AD) is a progressive neurodegenerative disease that, in its most common form, occurs as dementia in people over 65 years old although early onset form also exists. Currently it afflicts 24 million people worldwide. The disease is characterized in the brain by abnormal clumps (amyloid plaques) and tangled bundles of fibers (neurofibrillary tangles) composed of misplaced proteins. Three neurotransmitters commonly affected by AD are acetylcholine, serotonin, and nor epinephrine. The cholinergic system-the nerve cell system in the brain that uses acetylcholine as a neurotransmitter-is the most dramatic of the neurotransmitter systems affected by Alzheimer's disease. Acetylcholinesterase is one of the most crucial enzymes that hydrolyze acetylcholine in the brain to choline and acetic acid at cholinergic synapses, leading to nerve response and function. Potentiation of central cholinergic activity has been proposed as a therapeutic approach for improving the cognitive function in patients with Alzheimer's disease (AD). Increasing the acetylcholine concentration in the brain by modulating/inhibiting acetylcholinesterase (AchE) activity is among the most promising therapeutic strategies. In our studies attempts were made to produce potential natural acetylcholinesterase inhibitors. More than eight hundred compounds were taken from different natural sources. Initial in-silico screening of molecules was based on docking score between acetylcholinesterase and compounds taken from the natural sources. The hits were further analyzed and narrowed by applying Lipinski's rule-of-five analysis. Molecules with high ability to cross blood brain barrier were taken for energy minimization and quantitative structure activity studies. The biological activity of promising compounds was predicted by using PASS prediction analysis. Ferulic acid and Caffeic acid found in garlic showed very good IC50 values and satisfied the standard drug-like properties with remarkable pharmacokinetics and pharmacodynamics abilities. Thus we hypothesize that ferulic acid and caffeic acid which is obtained from garlic can be used as an effective acetylcholinesterase inhibitors in the early stage of Alzheimer's disease. Wet lab studies were conducted on the Swiss Albino mice as the animal model to check the inhibition of acetycholinestrerase by ferulic acid and caffeic acid.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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 teacher head, not a consensus.

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
Published2008
Admission routes1
Has abstractyes

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