OSCR

Effect of Caffeine on Cell Death, Oxidative Stress, and Microglial Morphology in a Ferret Organotypic Brain Slice Model of Hypoxia-Ischemia.

Code ↔ Paper

3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 3 matches
  1. [1] § 2. Materials and Methods › 2.7. Automated Nuclei Counting ↔ analysis/cell_counter.py, lines 123–161 · score 0.89 · min_sigma, num_sigma, threshold_rel, max_sigma, relative intensity, overlap
  2. [2] § 2. Materials and Methods › 2.7. Automated Nuclei Counting ↔ train_rdf_model.py, lines 4–59 · score 0.63 · ideal radius, weighted intensity, trained, filtered, perimeter, sigma
  3. [3] § 2. Materials and Methods › 2.7. Automated Nuclei Counting ↔ fit_pca.py, lines 132–223 · score 0.60 · ideal radius, weighted intensity, fit, perimeter, sigma, eccentricity

Paper

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The authors' code

Python · 285 lines · 9.4 KB · MIT · 1 match

  1. #!/usr/bin/env python
  2. # -*- coding: utf-8 -*-
  4. """Identifies cells in a preprocessed microscopy image"""
  6. from math import sqrt
  8. # from typing import Any, Optional
  9. import warnings
  11. import numpy as np
  12. import matplotlib.pyplot as plt
  13. from scipy.signal import find_peaks
  15. # from skimage.io import imread, imshow
  16. # from skimage.color import rgb2gray, label2rgb
  17. from skimage.feature import blob_dog, blob_log # , blob_doh
  19. # from skimage.morphology import erosion, dilation, opening, closing
  20. # from skimage.measure import label, regionprops
  22. from .utils import pixels_in_radius, calc_weighted_intensity
  23. from utils.models import Cell
  26. def apply_blob_log_REDUNDANT(img: np.ndarray, **kwargs) -> np.ndarray:
  27. """Blob detection using Laplacian of Gaussian (log)
  29. Arguments:
  30. img: cleaned and preprocesed image. Should be masked to reduce false positives
  32. Kwargs:
  33. min_sigma: float, minimum sigma value, default = 2
  34. max_sigma: float, maximum sigma value, default = 8
  35. num_sigma: int, number of sigmas to test = 20
  36. threshold: float, absolute threshold for cell detection, default = 0.1
  37. overlap: float, amount cells can overlap, default = 0.5,
  39. Returns:
  40. (n x 3) array with n rows of (i, j, sigma) representing the coordinates of each
  41. blob and the gaussian value of sigma that detected the blob
  42. """
  43. params = {
  44. "min_sigma": 2,
  45. "max_sigma": 8,
  46. "num_sigma": 20,
  47. "threshold": 0.1,
  48. "threshold_rel": 0,
  49. "overlap": 0.6,
  50. }
  51. params.update(kwargs)
  53. blobs_log = blob_log(
  54. img,
  55. **params,
  56. )
  57. # blobs_log[:, 2] *= sqrt(2)
  59. if False:
  60. plt.close("all")
  61. fig = plt.figure()
  62. ax = fig.gca()
  63. ax.imshow(img, origin="lower", cmap="gray")
  64. for i in range(blobs_log.shape[0]):
  65. [y, x] = blobs_log[i, :2]
  66. r = blobs_log[i, 2]
  67. c = plt.Circle((x, y), r, color="r", linewidth=2, fill=False, alpha=0.6)
  68. ax.add_patch(c)
  69. plt.show()
  70. plt.close(fig)
  72. return blobs_log
  75. def apply_blob_dog(img: np.ndarray) -> np.ndarray:
  76. """Blob detection using Difference of Gaussian (dog)
  78. More computationally efficient than Laplacian of Gaussian
  80. Arguments:
  81. img: cleaned and preprocesed image. Should be masked to reduce false positives
  83. Returns:
  84. (n x 3) array with n rows of (x, y, sigma) representing the coordinates of each
  85. blob and the gaussian value of sigma that detected the blob
  86. """
  87. blobs_dog = blob_dog(img, max_sigma=30, threshold=0.1)
  88. blobs_dog[:, 2] = blobs_dog[:, 2] * sqrt(2)
  90. return blobs_dog
  93. def find_blob_params(
  94. img: np.ndarray, blobs_info: np.ndarray
  95. ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
  96. """Finds pixels
  98. Args:
  99. img: image
  100. blobs_info: (n x 3) array of [i, j, sigma/radius]
  102. Returns:
  103. average intensity,
  104. weighted intensity,
  105. total intensity,
  106. """
  107. n, _ = np.shape(blobs_info)
  108. avg_intensity = np.zeros(n, dtype=np.float64)
  109. weighted_intensity = np.zeros(n, dtype=np.float64)
  110. total_intensity = np.zeros(n, dtype=np.float64)
  112. for i in range(n):
  113. center = (blobs_info[i, 0], blobs_info[i, 1])
  114. intensities, dists = pixels_in_radius(img, center, blobs_info[i, 2])
  115. if len(intensities) > 0:
  116. avg_intensity[i] = np.sum(intensities)
  117. weighted_intensity[i] = calc_weighted_intensity(intensities, dists)
  118. total_intensity[i] = np.mean(intensities)
  120. return (avg_intensity, weighted_intensity, total_intensity)
  123. def optimize_blob_kwargs(meta: dict, max_value: float) -> dict:
  124. """Optimize blob parameters"""
  125. x_px_width, y_px_width, _ = meta["axes_calibration"]
  126. x_px_width = float(x_px_width)
  127. y_px_width = float(y_px_width)
  128. if abs(x_px_width - y_px_width) > 1e-8:
  129. print("Confirm axes_calibration is stored as (x,y,z), not (i,j,k)")
  130. warnings.warn(
  131. f"different x, y dimensions ({x_px_width}, {y_px_width})",
  132. category=UserWarning,
  133. )
  135. px_width = np.mean([x_px_width, y_px_width])
  136. if abs(px_width - 0.8631674575031096) > 1e-3:
  137. warnings.warn(
  138. "Blob kwargs are only optimized for a pixel width of ~0.86 micrometers",
  139. category=UserWarning,
  140. )
  142. # All of these values are chosen semi-arbitrarily and can be adjusted.
  143. # The scaling factors are intended to convert pixel length (um) back to some number
  144. # of pixels.
  145. min_sigma_ = 2.2 # microns
  146. max_sigma_ = 4.2 # microns
  147. n_step_ = round((max_sigma_ - min_sigma_) / 0.1) + 1
  148. params = { # expected pixel edge length is 0.8631674575031096 micrometers
  149. "min_sigma": float(min_sigma_ / px_width), # 1.5 (init: 2 @ 0.86 -> 2.3)
  150. "max_sigma": float(max_sigma_ / px_width), # 4.5 (init: 8 @ 0.86 -> 9.3)
  151. # "min_sigma": float(2 * px_width), # 2 @ 0.86 -> 2.3
  152. # "max_sigma": float(8 * px_width), # 8 @ 0.86 -> 9.3
  153. # "min_sigma": float(2.1 * px_width), # 2 @ 0.86 -> 2.3
  154. # "max_sigma": float(9.3 * px_width), # 8 @ 0.86 -> 9.3
  155. "num_sigma": n_step_, # previously 25
  156. "threshold": 0.005 * max_value, # absolute intensity a peak must exceed
  157. "threshold_rel": 0, # relative intensity, between 0 and 1
  158. "overlap": 0.7, # 0.6 @ 0.86 -> 0.695
  159. }
  161. return params
  164. def filter_cells(cells_found: np.ndarray, criteria: np.ndarray) -> np.ndarray:
  165. """Filter cells
  167. Args:
  168. cells_found: array of (n_cells x 3)
  169. criteria: measure by which to filter cells
  171. Return:
  172. indices of cells to include
  173. """
  174. n_cells, _ = np.shape(cells_found)
  175. hist, bins = np.histogram(criteria, bins=len(np.unique(criteria)))
  177. x_vals = bins[:-1]
  179. peaks, info = find_peaks(hist)
  181. if len(peaks) == 0:
  182. thresh = 0.0
  183. elif len(peaks) == 1:
  184. thresh = float(peaks[0] / 2)
  185. else:
  186. pk0 = int(peaks[0])
  187. pk1 = int(peaks[1])
  188. thresh = x_vals[np.argmin(hist[pk0:pk1+1]) + pk0]
  190. indices = np.arange(n_cells, dtype=np.int64)[criteria > thresh]
  192. return indices
  195. def find_cells(
  196. img: np.ndarray, img_norm: np.ndarray, meta: dict, include_all: bool = False
  197. ) -> list | tuple[list, dict]:
  198. """Finds cells within preprocessed image
  200. Parameters:
  201. ----------
  202. img: preprocessed image
  204. meta: dict of meta info for image
  207. Returns:
  208. -------
  209. list of Cell models
  211. Optionally, returns this dict:
  212. - all_cells: np.ndarray (n x 3) of [i, j, sigma] for every blob detected
  213. - all_avg_intensities: np.ndarray (n,) of average intensity within sigma for each
  214. blob
  215. - all_weighted_intensities: np.ndarray (n,) of weighted average intensity of each
  216. blob
  217. - all_total_intensities: np.ndarray (n,) of total intensity within sigma for each
  218. blob
  219. - acceptable_indices: np.ndarray (m,) of cell indices deemed true positives
  220. - cells: np.ndarray (m x 3) of [i, j, sigma] for "acceptable" blobs
  221. - avg_intensities: np.ndarray (m,) of average intensity within sigma for
  222. "acceptable" blobs
  223. - weighted_intensities: np.ndarray (m,) of weighted average intensity of
  224. "acceptable" blobs
  225. - total_intensities: np.ndarray (m,) of total intensity within sigma for
  226. "acceptable" blobs
  227. """
  228. # _compare_blob_methods(masked_img, mask)
  229. px_height, px_width, _ = meta["axes_calibration"]
  230. px_height = float(px_height)
  231. px_width = float(px_width)
  232. blob_kwargs = optimize_blob_kwargs(meta, np.max(img.copy().flatten()))
  233. cells_found = blob_log(img, **blob_kwargs)
  234. avg_intensity, weighted_intensity, total_intensity = find_blob_params(
  235. img_norm, cells_found
  236. )
  238. # filter cells to remove some false positives
  239. acceptable_indices = filter_cells(cells_found, total_intensity)
  241. cells = []
  242. for i, idx in enumerate(acceptable_indices):
  243. cells.append(
  244. Cell(
  245. uid=i,
  246. center=tuple(cells_found[idx, :2].astype(np.int64).tolist()),
  247. sigma_px=float(cells_found[idx, 2]),
  248. avg_intensity=float(avg_intensity[idx]),
  249. weighted_intensity=float(weighted_intensity[idx]),
  250. total_intensity=float(total_intensity[idx]),
  251. pixel_height=px_height,
  252. pixel_width=px_width,
  253. image_dims=tuple(meta["voxel_count"][:2]),
  254. )
  255. )
  257. # if False:
  258. # fig, axes = plot_blobs(
  259. # (img + 50),
  260. # cells_found[acceptable_indices, :],
  261. # side_by_side=True,
  262. # show=False,
  263. # )
  264. # radii = cells_found[:, -1].copy().flatten()
  265. # fig1, ax1 = plot_cell_histogram(
  266. # radii, avg_intensity, weighted_intensity, total_intensity, show=False
  267. # )
  268. # plt.show()
  269. # plt.close()
  271. if include_all:
  272. d = {
  273. "all_cells": cells_found.copy(),
  274. "all_avg_intensities": avg_intensity.copy(),
  275. "all_weighted_intensities": weighted_intensity.copy(),
  276. "all_total_intensities": total_intensity.copy(),
  277. "acceptable_indices": acceptable_indices.copy(),
  278. "cells": np.array(cells_found[acceptable_indices, :]),
  279. "avg_intensities": np.array(avg_intensity[acceptable_indices]),
  280. "weighted_intensities": np.array(weighted_intensity[acceptable_indices]),
  281. "total_intensities": np.array(total_intensity[acceptable_indices]),
  282. }
  283. return (cells, d)
  285. return cells

cell_counter.py at commit 72057f8, under MIT · at the source

Overview

Authors: Olivia C. Brandon1, Kylie A. Corry1, Zheyu Ruby Jin2, Kate F. DiNucci1, Matthew J. Magoon3, Nels Schimek2, Daniel H. Moralejo1, Sandra E. Juul1,4, Patrick M. Boyle3, Elizabeth A. Nance2,4, Thomas R. Wood1,4, Sarah E. Kolnik1
ORCID iDs: Olivia C. Brandon, Kylie A. Corry, Zheyu Ruby Jin, Kate F. DiNucci, Matthew J. Magoon, Sandra E. Juul, Elizabeth A. Nance, Thomas R. Wood, Sarah E. Kolnik
  1. Division of Neonatology, University of Washington, Seattle, WA 98195, USA; (O.C.B.); (K.A.C.); (K.F.D.); (D.H.M.); (S.E.J.); (T.R.W.)
  2. Department of Chemical Engineering, University of Washington, Seattle, WA 98195, USA; (Z.R.J.); (N.S.); (E.A.N.)
  3. Department of Bioengineering, University of Washington, Seattle, WA 98195, USA; (M.J.M.); (P.M.B.)
  4. Institute on Human Development and Disability, University of Washington, Seattle, WA 98195, USA
Institutions: University of Washington (United States)
Journal: NeuroSci, volume 7, issue 4, article 79
Dates: received 31 May 2026; accepted 7 July 2026; published online 10 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/neurosci7040079 · PMID 42496326 · PMCID PMC13398045 · OpenAlex W7167929975
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (organism), cellular / molecular (subfield)
Methods: Smoothing, state filtering, decompositions, Machine learning, Statistics, fMRI & imaging
Keywords: caffeine, neonatal, hypoxia–ischemia, organotypic slice culture, ferret
Topic: Adenosine and Purinergic Signaling (Physiology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: University of Washington
Citations: not cited yet (Europe PMC); 52 references in the paper

Abstract

Brain injury after hypoxia–ischemia (HI) is the leading cause of morbidity and mortality in term and near-term neonates worldwide. The ferret is a promising translational model to study HI due to its gyrified brain and white-to-gray matter ratio that more closely resembles humans compared to rodents. Caffeine, an adenosine A2A receptor (A2AR) antagonist, shows neuroprotective potential after HI, but its effects have not been fully characterized. We sought to evaluate caffeine’s effect on neuronal cell death, cytotoxicity, and inflammatory and oxidative stress markers in a term-equivalent ferret organotypic brain slice model of HI. Slices were cultured for 72 h, exposed to two hours of oxygen–glucose deprivation (OGD), and randomized to OGD alone, OGD with caffeine (20 or 50 mg/L), or OGD with caffeine and an A2AR agonist. Healthy slices served as controls. Outcomes included global cell death, regional cell death, microglial morphology, and expression of inflammatory and oxidative stress genes (46–48 slices/group for cell death assays and 18 slices/group for imaging, balanced by sex). Caffeine 50 mg/L significantly reduced global cell death compared to OGD (p = 0.02), and this effect persisted despite co-administration of an A2AR agonist (p = 0.01), suggesting that protection was not primarily mediated through A2AR signaling. Caffeine also did not change regional pyknotic nuclei counts (p > 0.05). Caffeine altered microglial morphology, increasing the proportion of microglia with features characteristic of control conditions. OGD significantly increased expression of inflammatory and oxidative stress-related genes (p < 0.05) compared with control slices, whereas caffeine did not significantly alter gene expression. In summary, caffeine partially reversed global cell death after OGD and altered microglial morphology. Larger, higher-powered studies are needed to further investigate caffeine’s effects on neonatal HI.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

MattM719/NeuroCellCounter

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 72057f8233c9bb728db757530b356ca192be6b84, 6 May 2026
Languages: Python (40)
Size: 45 files, 40 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (31 files), Matplotlib (17 files), scikit-image (12 files), pandas (9 files), scikit-learn (7 files), SciPy (6 files), ImageJ / Fiji (1 file), Pillow (1 file), SHAP (1 file), xarray (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
42 files
At the source: github.com/MattM719/NeuroCellCounter

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 40 scripts, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

The data described in the manuscript and/or analyzed during the current study will be made available upon reasonable request from the corresponding author. The publicly available code for nuclei counts is available at https://github.com/MattM719/NeuroCellCounter. Accessed on 1 June 2026.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 5 keywords, 1 funder, 49 references.

Cite

This paper

Brandon, O. C., Corry, K. A., Jin, Z. R., DiNucci, K. F., Magoon, M. J., Schimek, N., Moralejo, D. H., Juul, S. E., Boyle, P. M., Nance, E. A., Wood, T. R., & Kolnik, S. E. (2026). Effect of Caffeine on Cell Death, Oxidative Stress, and Microglial Morphology in a Ferret Organotypic Brain Slice Model of Hypoxia-Ischemia. NeuroSci, 7(4), 79. https://doi.org/10.3390/neurosci7040079

BibTeX

@article{brandon2026effect,
author = {Brandon, Olivia C. and Corry, Kylie A. and Jin, Zheyu Ruby and DiNucci, Kate F. and Magoon, Matthew J. and Schimek, Nels and Moralejo, Daniel H. and Juul, Sandra E. and Boyle, Patrick M. and Nance, Elizabeth A. and Wood, Thomas R. and Kolnik, Sarah E.},
title = {{Effect of Caffeine on Cell Death, Oxidative Stress, and Microglial Morphology in a Ferret Organotypic Brain Slice Model of Hypoxia-Ischemia}},
journal = {NeuroSci},
year = {2026},
month = jul,
volume = {7},
number = {4},
pages = {79},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2673-4087},
doi = {10.3390/neurosci7040079},
url = {https://doi.org/10.3390/neurosci7040079},
pmid = {42496326},
pmcid = {PMC13398045}
}

RIS

TY - JOUR
AU - Brandon, Olivia C.
AU - Corry, Kylie A.
AU - Jin, Zheyu Ruby
AU - DiNucci, Kate F.
AU - Magoon, Matthew J.
AU - Schimek, Nels
AU - Moralejo, Daniel H.
AU - Juul, Sandra E.
AU - Boyle, Patrick M.
AU - Nance, Elizabeth A.
AU - Wood, Thomas R.
AU - Kolnik, Sarah E.
TI - Effect of Caffeine on Cell Death, Oxidative Stress, and Microglial Morphology in a Ferret Organotypic Brain Slice Model of Hypoxia-Ischemia
T2 - NeuroSci
J2 - NeuroSci
PY - 2026
DA - 2026/07/10
VL - 7
IS - 4
SP - 79
SN - 2673-4087
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/neurosci7040079
UR - https://doi.org/10.3390/neurosci7040079
LA - en
ER -

CSL-JSON

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"id": "10.3390/neurosci7040079",
"type": "article-journal",
"title": "Effect of Caffeine on Cell Death, Oxidative Stress, and Microglial Morphology in a Ferret Organotypic Brain Slice Model of Hypoxia-Ischemia",
"container-title": "NeuroSci",
"author": [
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"family": "Brandon",
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{
"family": "Jin",
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},
{
"family": "DiNucci",
"given": "Kate F."
},
{
"family": "Magoon",
"given": "Matthew J."
},
{
"family": "Schimek",
"given": "Nels"
},
{
"family": "Moralejo",
"given": "Daniel H."
},
{
"family": "Juul",
"given": "Sandra E."
},
{
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"given": "Patrick M."
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{
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{
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}
],
"container-title-short": "NeuroSci",
"volume": "7",
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"page": "79",
"DOI": "10.3390/neurosci7040079",
"PMID": "42496326",
"PMCID": "PMC13398045",
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"issued": {
"date-parts": [
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