MétaCan
Menu
Back to cohort
Record W2978598618 · doi:10.14740/jocmr3852

Acute Kidney Injury-Associated Systemic Inflammation Is Aggravated in Insulin-Dependent Diabetes Mellitus

2019· article· en· W2978598618 on OpenAlexvenueno aff
Daniel Patschan, Katrin Schwarze, Elvira Henze, Johanna Charlotte Hoffmann, Susann Patschan, Oliver Ritter, Gerhard A. Müller

Bibliographic record

VenueJournal of Clinical Medicine Research · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDiabetes mellitusInflammationSystemic inflammationImmune systemKidneyIschemiaInternal medicineAcute kidney injuryStreptozotocinEndocrinologyImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Acute kidney injury (AKI) significantly worsens the prognosis of hospitalized patients. Diabetes mellitus (DM) affects a growing number of individuals in the western world. DM subjects are at a higher risk for acquiring AKI during the stay at the hospital. The current study intended to quantify serum levels of specific immunomodulatory cytokines in diabetic mice suffering from AKI. METHODS: DM was induced in male C57/Bl6N mice by systemic injections of beta cell-toxic streptozotocin. Animals underwent bilateral renal ischemia (45 min) 6 weeks later. RESULTS: Post-ischemic diabetic mice showed significantly differing serum concentrations of the majority of all analytes as compared to untreated controls and non-diabetic (post-ischemic) animals. CONCLUSIONS: Together, our data suggest DM-associated immune activation in AKI. One may suppose that inadequate stimulation of the humoral/cellular immune response potentially contributes to the higher ischemia susceptibility of the organ in DM.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.096
GPT teacher head0.488
Teacher spread0.392 · 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 designObservational
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

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical Medicine ResearchSame topicAcute Kidney Injury ResearchFrench-language works237,207