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Record W2151462602 · doi:10.1681/asn.2008010098

Molecular Determinants of Magnesium Homeostasis

2008· review· en· W2151462602 on OpenAlexfundno aff
R. Todd Alexander, Joost G.J. Hoenderop, ReneCombining Acute Accent J. Bindels

Bibliographic record

VenueJournal of the American Society of Nephrology · 2008
Typereview
Languageen
FieldNursing
TopicMagnesium in Health and Disease
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsHomeostasisMagnesiumMedicineEndocrinologyInternal medicineChemistry

Abstract

fetched live from OpenAlex

The past decade has witnessed multiple advances in our understanding of magnesium (Mg(2+)) homeostasis. The discovery that mutations in claudin-16/paracellin-1 or claudin-19 are responsible for familial hypomagnesemia with hypercalciuria and nephrocalcinosis provided insight into the molecular mechanisms governing paracellular transport of Mg(2+). Our understanding of the transcellular movement of Mg(2+) was similarly enhanced by the realization that defects in transient receptor potential melastatin 6 (TRPM6) cause hypomagnesemia with secondary hypocalcemia. This channel regulates the apical entry of Mg(2+) into epithelia. In so doing, TRPM6 alters whole-body Mg(2+) homeostasis by controlling urinary excretion. Consequently, investigation into the regulation of TRPM6 has increased. Acid-base status, 17beta estradiol, and the immunosuppressive agents FK506 and cyclosporine affect plasma Mg(2+) levels by altering TRPM6 expression. A mutation in epithelial growth factor is responsible for isolated autosomal recessive hypomagnesemia, and epithelial growth factor activates TRPM6. A defect in the gamma-subunit of the Na,K-ATPase causes isolated dominant hypomagnesemia by altering TRPM6 activity through a decrease in the driving force for apical Mg(2+) influx. We anticipate that the next decade will provide further detail into the control of the gatekeeper TRPM6 and, therefore, overall whole-body Mg(2+) balance.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.347
Teacher spread0.314 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations143
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Society of NephrologySame topicMagnesium in Health and DiseaseFrench-language works237,207