Unperturbed islet α‐cell function examined in mouse pancreas tissue slices
Bibliographic record
Abstract
Critical investigation into α-cell biology in health and diabetes has been sparse and at times inconsistent because of the technical difficulties with employing conventional strategies of isolated islets and dispersed single cells. An acute pancreas slice preparation was developed to overcome the enzymatic and mechanical perturbations inherent in conventional islet cell isolation procedures. This preparation preserves intra-islet cellular communication and islet architecture in their in situ native state. α-Cells within tissue slices were directly assessed by patch pipette and electrophysiologically characterized. The identity of the patched cells was confirmed by biocytin dye labelling and immunocytochemistry. α-Cells in mouse pancreas slices exhibited well-described features of I(Na) (excitable at physiological membrane potential), I(KATP), small cell size, low resting membrane conductance, and inducible low and high voltage-activated I(Ca), the latter correlating with exocytosis determined by capacitance measurements. In contrast to previous reports, our large unbiased sampling of α-cells revealed a wide range distribution of all of these parameters, including the amount of K(ATP) conductance, Na+ and Ca2+ current amplitudes, and capacitance changes induced by a train of depolarization pulses. The proposed pancreas slice preparation in combination with standard patch-clamping technique allowed large sampling and rapid assessment of α-cells, which revealed a wide distribution in α-cell ion channel properties. This specific feature explains the apparent inconsistency of previous reports on these α-cell ion channel properties. Our innovative approach will enable future studies into elucidating islet α-cell dysregulation occurring during diabetes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".