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Record W2899435144 · doi:10.1002/jcb.28044

D224V and S128Y mutation in FSHR<sub>ED</sub> influence thumb movement differentially: An intricate insight gained by short‐term molecular dynamics simulation

2018· article· en· W2899435144 on OpenAlexaff
Fatemeh Rahimi Gharemirshamlu, Maliha Afsar, Taseem A. Mokhdomi, Asif Amin, Shoiab Bukhari, Chundi Vinay Kumar, Kourosh Bamdad, Trupti Patel, Raies A. Qadri, Naveed Anjum Chikan, Nadeem Shabir

Bibliographic record

VenueJournal of Cellular Biochemistry · 2018
Typearticle
Languageen
FieldMedicine
TopicOvarian function and disorders
Canadian institutionsBrock University
Fundersnot available
KeywordsFollicle-stimulating hormone receptorBiologyReceptorFollicle-stimulating hormoneMutationHormoneInternal medicineEndocrinologyCell biologyGeneticsLuteinizing hormoneMedicineGene

Abstract

fetched live from OpenAlex

Follicle-stimulating hormone-follicle-stimulating hormone receptor (FSH-FSHR) interaction is one of the most thoroughly studied signaling pathways primarily because of being implicated in sexual reproduction in mammals by way of maintaining gonadal function and sexual fertility. Despite material advances in understanding the role of point mutations, their mechanistic basis in FSH-FSHR signaling is still confined to mystically altered behavior of sTYS335 (sulfated tyrosine) yet lacking a substantial theory. To understand the structural basis of receptor modulation, we choose two behaviorally contradicting mutations, namely S128Y (activating) and D224Y (inactivating), found in FSH receptor responsible for ovarian hyperstimulation syndrome and ovarian dysgenesis, respectively. Using short-term molecular dynamics simulations, the atomic scale investigations reveal that the binding pattern of sTYS with FSH and movement of the thumb region of FSHR show distinct contrasting patterns in the two mutants, which supposedly could be a critical factor for differential FSHR behavior in activating and inactivating mutations.

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 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 score0.026
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.005
GPT teacher head0.237
Teacher spread0.232 · 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.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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