Tectonic infarct analysis: A computational tool for automated whole-brain infarct analysis from TTC-stained tissue

Santo, Briana; Ciecierska, Shiau-Sing K.; Sarayi, Seyyed Mostafa Mousavi Janbeh; Jenkins, TaJania D.; Baig, Ammad A; Monteiro, André; Koenigsknecht, Carmon; Pionessa, Donald; Gutierrez, Liza; King, R; Gounis, Matthew J.; Siddiqui, Adnan H.; Tutino, Vincent M.

doi:10.1016/j.heliyon.2023.e14837

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…To determine the degree of infarct in rats, TCC staining was quantified. 16 Metabolically active areas of the brain stain red when dehydrogenases in healthy tissue reduce TCC into a red compound, triphenylformazan. Inactive areas of the brain that do not metabolize TCC and are represented by white discoloration of the tissue.…”

Section: Brain Ttc Stainingmentioning

confidence: 99%

Evaluating Minocycline as a Neuroprotective Agent in an Aged Female Rat MCAO Stroke Model

Gutierrez

Tutino

Siddiqui

2023

Preprint

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The middle cerebral artery occlusion (MCAO) suture model is widely accepted ischemic stroke model. However, researchers routinely use young male rats, ignoring that stroke risk is increased in older, post-menopausal women. To this end, we implemented (120-minute) transient- and permanent-occlusion MCAO models in female retired-breeder rats, examining the endpoint across 1-30 days to identify the optimal time course for the model. We found that in both groups the physical infarct (measured by triphenyltetrazolium chloride -TTC staining), which is present initially, was not detectable 30 days post-MCAO (even if some neurologic symptoms persist). Across shorter time-points (namely 24 hours and 7 days) we found that neurologic scores generally reach a plateau/maximum at ~7 days, then infarct size gradually decreases over time for rats receiving a permanent MCAO. Across 3 transient occlusion times (60 minutes, 90 minutes, and 120 minutes), the longest gave the most robust result. Overall, the permeant and 120-minute transient MCAO evaluated at 7 days was optimal. Using these two models, we evaluated the neuroprotective qualities of the antibiotic, minocycline. We found that those in the treatment groups experienced a greater improvement in neurologic scores and a larger decrease in infarct size after daily treatment for seven days. This improvement was more prominent in the transiently occluded treatment group than in the permanently occluded group.

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Section: Brain Ttc Stainingmentioning

confidence: 99%

Evaluating Minocycline as a Neuroprotective Agent in an Aged Female Rat MCAO Stroke Model

Gutierrez

Tutino

Siddiqui

2023

Preprint

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“…After 30 minutes, the cerebrum was drained using towel tissue. The cerebrum was then photographed using a camera (Santo et al, 2023). The white area indicated the infarct area, measured using Image J Software.…”

Section: Brain Infarct Measurement and Morphometrymentioning

confidence: 99%

Brain Histopathological Changes After Treatment Using Calabash Fruit (Crescentia cujete L.) in Rat Model with Artificially Induced Ischemic Stroke

Hakim Hidayah,

Adi Prakoso,

Widyarini

2023

AAVS

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T he brain is an essential neurological system in humans and animals. Brain function can be destroyed through circulatory system disturbance, such as thrombus. Thrombus causes a significant decrease in the blood supply that not to carry nutrition, oxygen and other blood components to the brain tissue. Further, they promote severe stress within the neuronal tissue and cause neuronal death (Kuriakose and Xiao, 2020). In advance, it triggers partial infarction within brain tissue and causes ischemic stroke. An ischemic stroke is the highest prevalence in humans,

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Deep learning segmentation model for quantification of infarct size in pigs with myocardial ischemia/reperfusion

Braczko,

Skyschally,

Lieder

et al. 2024

Basic Res Cardiol

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Infarct size (IS) is the most robust end point for evaluating the success of preclinical studies on cardioprotection. The gold standard for IS quantification in ischemia/reperfusion (I/R) experiments is triphenyl tetrazolium chloride (TTC) staining, typically done manually. This study aimed to determine if automation through deep learning segmentation is a time-saving and valid alternative to standard IS quantification. High-resolution images from TTC-stained, macroscopic heart slices were retrospectively collected from pig experiments (n = 390) with I/R without/with cardioprotection to cover a wide IS range. Existing IS data from pig experiments, quantified using a standard method of manual and subsequent digital labeling of film-scan annotations, were used as reference. To automate the evaluation process with the aim to be more objective and save time, a deep learning pipeline was implemented; the collected images (n = 3869) were pre-processed by cropping and labeled (image annotations). To ensure their usability as training data for a deep learning segmentation model, IS was quantified from image annotations and compared to IS quantified using the existing film-scan annotations. A supervised deep learning segmentation model based on dynamic U-Net architecture was developed and trained. The evaluation of the trained model was performed by fivefold cross-validation (n = 220 experiments) and testing on an independent test set (n = 170 experiments). Performance metrics (Dice similarity coefficient [DSC], pixel accuracy [ACC], average precision [mAP]) were calculated. IS was then quantified from predictions and compared to IS quantified from image annotations (linear regression, Pearson’s r; analysis of covariance; Bland–Altman plots). Performance metrics near 1 indicated a strong model performance on cross-validated data (DSC: 0.90, ACC: 0.98, mAP: 0.90) and on the test set data (DSC: 0.89, ACC: 0.98, mAP: 0.93). IS quantified from predictions correlated well with IS quantified from image annotations in all data sets (cross-validation: r = 0.98; test data set: r = 0.95) and analysis of covariance identified no significant differences. The model reduced the IS quantification time per experiment from approximately 90 min to 20 s. The model was further tested on a preliminary test set from experiments in isolated, saline-perfused rat hearts with regional I/R without/with cardioprotection (n = 27). There was also no significant difference in IS between image annotations and predictions, but the performance on the test set data from rat hearts was lower (DSC: 0.66, ACC: 0.91, mAP: 0.65). IS quantification using a deep learning segmentation model is a valid and time-efficient alternative to manual and subsequent digital labeling.

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Tectonic infarct analysis: A computational tool for automated whole-brain infarct analysis from TTC-stained tissue

Cited by 3 publications

References 40 publications

Evaluating Minocycline as a Neuroprotective Agent in an Aged Female Rat MCAO Stroke Model

Evaluating Minocycline as a Neuroprotective Agent in an Aged Female Rat MCAO Stroke Model

Brain Histopathological Changes After Treatment Using Calabash Fruit (Crescentia cujete L.) in Rat Model with Artificially Induced Ischemic Stroke

Deep learning segmentation model for quantification of infarct size in pigs with myocardial ischemia/reperfusion

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