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Background: Currently, the gadolinium retention in the brain after the use of contrast agents is studied by T1‐weighted magnetic resonance imaging (MRI) (T1w) and T1 mapping. The former does not provide easily quantifiable data and the latter requires prolonged scanning and is sensitive to motion. T2 mapping may provide an alternative approach. Animal studies of gadolinium retention are complicated by repeated intravenous (IV) dosing, whereas intraperitoneal (IP) injections might be sufficient. Hypothesis: T2 mapping will detect the changes in the rat brain due to gadolinium retention, and IP administration is equivalent to IV for long‐term studies. Study Type: Prospective longitudinal. Animal Model: A total of 31 Sprague–Dawley rats administered gadodiamide IV (N = 8) or IP (N = 8), or saline IV (N = 6) or IP (N = 9) 4 days per week for 5 weeks. Field strength/sequences: A 7 T, T1w, and T2 mapping. Assessment: T2 relaxation and image intensities in the deep cerebellar nuclei were measured pre‐treatment and weekly for 5 weeks. Then brains were assessed for neuropathology (N = 4) or gadolinium content using inductively coupled plasma mass spectrometry (ICP‐MS, N = 12). Statistical tests: Repeated measures analysis of variance with post hoc Student–Newman–Keuls tests and Hedges' effect size. Results: Gadolinium was detected by both approaches; however, T2 mapping was more sensitive (effect size 2.32 for T2 vs. 0.95 for T1w), and earlier detection (week 3 for T2 vs. week 4 for T1w). ICP‐MS confirmed the presence of gadolinium (3.076 ± 0.909 nmol/g in the IV group and 3.948 ± 0.806 nmol/g in the IP group). There was no significant difference between IP and IV groups (ICP‐MS, P = 0.109; MRI, P = 0.696). No histopathological abnormalities were detected in any studied animal. Conclusion: T2 relaxometry detects gadolinium retention in the rat brain after multiple doses of gadodiamide irrespective of the route of administration. Evidence Level 1 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Abstract Background Urine from male Sprague-Dawley rats 25, 40, and 80 days old was analyzed by NMR and UPLC/MS. The effects of data normalization procedures on principal component analysis (PCA) and quantitative analysis of NMR-based metabonomics data were investigated. Additionally, the effects of age on the metabolic profiles were examined by both NMR and UPLC/MS analyses. Results The data normalization factor was shown to have a great impact on the statistical and quantitative results indicating the need to carefully consider how to best normalize the data within a particular study and when comparing different studies. PCA applied to the data obtained from both NMR and UPLC/MS platforms reveals similar age-related differences. NMR indicated many metabolites associated with the Krebs cycle decrease while citrate and 2-oxoglutarate, also associated with the Krebs cycle, increase in older rats. Conclusion This study compared four different normalization methods for the NMR-based metabonomics spectra from an age-related study. It was shown that each method of normalization has a great effect on both the statistical and quantitative analyses. Each normalization method resulted in altered relative positions of significant PCA loadings for each sample spectra but it did not alter which chemical shifts had the highest loadings. The greater the normalization factor was related to age, the greater the separation between age groups was observed in subsequent PCA analyses. The normalization factor that showed the least age dependence was total NMR intensity, which was consistent with UPLC/MS data. Normalization by total intensity attempts to make corrections due to dietary and water intake of the individual animal, which is especially useful in metabonomics evaluations of urine. Additionally, metabonomics evaluations of age-related effects showed decreased concentrations of many Krebs cycle intermediates along with increased levels of oxidized antioxidants in urine of older rats, which is consistent with current theories on aging and its association with diminishing mitochondrial function and increasing levels of reactive oxygen species. Analysis of urine by both NMR and UPLC/MS provides a comprehensive and complementary means of examining metabolic events in aging rats.

Neurotoxicity studies are important in the preclinical stages of drug development process, because exposure to certain compounds that may enter the brain across a permeable blood brain barrier damages neurons and other supporting cells such as astrocytes. This could, in turn, lead to various neurological disorders such as Parkinson's or Huntington's disease as well as various dementias. Toxicity assessment is often done by pathologists after these exposures by qualitatively or semiquantitatively grading the severity of neurotoxicity in histopathology slides. Quantification of the extent of neurotoxicity supports qualitative histopathological analysis and provides a better understanding of the global extent of brain damage. Stereological techniques such as the utilization of an optical fractionator provide an unbiased quantification of the neuronal damage; however, the process is time‐consuming. Advent of whole slide imaging (WSI) introduced digital image analysis which made quantification of neurotoxicity automated, faster and with reduced bias, making statistical comparisons possible. Although automated to a certain level, simple digital image analysis requires manual efforts of experts which is time‐consuming and limits analysis of large datasets. Digital image analysis coupled with a deep learning artificial intelligence model provides a good alternative solution to time‐consuming stereological and simple digital analysis. Deep learning models could be trained to identify damaged or dead neurons in an automated fashion. This review has focused on and discusses studies demonstrating the role of deep learning in segmentation of brain regions, toxicity detection and quantification of degenerated neurons as well as the estimation of area/volume of degeneration. Neurotoxicity studies are important in preclinical stages of drug development process because exposure to certain compounds may damage neurons and other supporting cells in brain. Advent of whole slide imaging introduced digital image analysis, which made quantification of neurotoxicity automated, faster and reduced bias, making statistical comparisons possible. Digital image analysis coupled with deep learning provides a useful solution to time‐consuming simple digital analysis. This review has focused on studies demonstrating the role of deep learning in neurotoxicity analysis. [ABSTRACT FROM AUTHOR]