Immunohistochemistry UCP1 and Lipid Droplets Images
Bibliographic record
Abstract
<b>Hematoxylin and eosin (H&E) staining protocol and lipid droplet analysis</b><br><br>BAT was collected from naked mole-rats treated in normoxia (n = 12), or 1 or 3 hrs of hypoxia (n = 11 each) and samples prepared as described previously<sup>1</sup>. Briefly, interscapular BAT (iBAT) was dissected, cleaned to remove any white adipose, muscle or connective tissues, and then fixed in 10% formalin overnight. iBAT was then ethanol dehydrated and stored in 70% ethanol prior to paraffin embedding. Paraffin embedded tissue was sectioned to the largest surface area and used for H&E staining. After H&E staining, Mirax Viewer Image software (version 1.6) was used to analyze the sections in a ZEISS-MIRAX Midi Slide scanning system (Zeiss Microimaging, Oberkochen, Germany, and 3DTech, Budapest, Hungary) and digital images were acquired at 20x magnification. Images were extracted using Aperio ImageScope software (version 12.3.3; Leica Biosystems), and then were processed using Zeiss software ZEN 3.2 (Zen Lite; Carl Zeiss Canada Ltd. Toronto, Canada). ROIs were carefully selected in each image using the rectangular selective tool bar in the ZEN 3.2 software, making sure to avoid blood vessels and edges of the tissue in the ROI selected. All the ROI images produced were converted to a TIFF format. Then the images were analyzed by a blinded researcher using FIJI (ImageJ; NIH). The range thresholding that was applied across all replicate images in all conditions was 200-255 and the range of lipid droplet area analyzed was between 1-1000µm². The scale of each image processed was calibrated by adding a scale according to the ROI analyzed. Finally, the total lipid droplet area of each image was measured, and a percent lipid area was calculated using ImageJ and as described elsewhere<sup>2</sup>.<br><br><b><br></b><b>Immunohistochemistry staining of interscapular BAT of NMR</b><br>IHC staining was performed on formalin fixed paraffin embedded tissue sections using the Leica Bond™ system using a modification of protocol F that eliminates the post primary step when using rabbit antibodies on rat tissue. Sections were pre-treated using sodium citrate buffer (pH 6.0, epitope retrieval solution 1) for 20 minutes. The sections were then incubated using a 1:1000 dilution of Rabbit UCP1 for 30 minutes at room temperature and detected using an HRP conjugated compact polymer system. Slides were then stained using DAB as the chromogen, counterstained with Hematoxylin, mounted and cover slipped.<br><br><br><b><br></b><b>UCP1 positive cell manual counting of IHC-stained NMR interscapular BAT sections</b><br>IHC UCP1 DAB-stained images for three conditions (Normoxia, Hypoxia 1hr & 3hrs) were converted into TIFF format and then opened with FIJI (ImageJ; NIH). For each replicate, 2 sections of 2000x2000 pixels were taken and then further analyzed. With FIJI (ImageJ; NIH), Plugin>Analyze>Cell Counter> Cell counter was selected to count the UCP1 positive and negative cells manually. The images were counted manually by selecting UCP1 positive cells and negative cells and then the data was exported to an excel spreadsheet. The UCP1 positive cells were divided by the total number of cells in each section to obtain the percentage of UCP1 positive cells which was graphed. Statistical analysis was made including the ANOVA one-way test and a t-test multiple comparison.<br>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.862 | 0.001 |
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 teacher head, 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".