Functional Anatomy of the Kidney

Sands, Jeff M.; Verlander, Jill W.

doi:10.1016/b978-0-08-046884-6.00801-0

Cited by 4 publications

(3 citation statements)

References 176 publications

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“…The vessel networks of the whole imaged liver block can also be extracted after image thresholding, as shown in Figure q. Figure r shows the reconstructed 3D histological image of a mouse kidney, and Figure s,t shows two virtual H&E images at different depths in the kidney, showing cortex and papillary structures, , respectively.…”

Section: Resultsmentioning

confidence: 99%

Deep Learning Enables Rapid Whole-Organ Histological Imaging with Ultraviolet-Excited Sectioning Tomography

Kang,

Yu,

Zhang

et al. 2023

ACS Photonics

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Three-dimensional (3D) histopathology involves the microscopic examination of a specimen, which plays a vital role in studying tissue's 3D structures and the signs of diseases. However, acquiring high-quality histological images of a whole organ is extremely time-consuming (e.g., several weeks) and laborious, as the organ has to be sectioned into hundreds or thousands of slices for imaging. Besides, the acquired images are required to undergo a complicated image registration process for 3D reconstruction. Here, by incorporating a recently developed vibratome-assisted block-face imaging technique with deep learning, we developed a pipeline termed HistoTRUST that can rapidly and automatically generate subcellular whole organ's virtual hematoxylin and eosin (H&E) stained histological images, which can be reconstructed into 3D by simple image stacking (i.e., without registration). The performance and robustness of HistoTRUST have been successfully validated by imaging all six organs (e.g., brain, heart, liver, lung, kidney, and spleen). The imaging process for a whole organ takes hours to days, depending on the volume of imaged samples. The generated 3D dataset has the same color tune as the traditional H&E stained histological images. Therefore, the virtual H&E stained images can be directly analyzed by pathologists. HistoTRUST has a high potential to serve as a new standard in providing 3D histology for research or clinical applications.

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Section: Resultsmentioning

confidence: 99%

Deep Learning Enables Rapid Whole-Organ Histological Imaging with Ultraviolet-Excited Sectioning Tomography

Kang,

Yu,

Zhang

et al. 2023

ACS Photonics

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“…Fig.4ris the reconstructed 3D histological image of a mouse kidney, and Fig.4s,t show two virtual H&E images at different depths in the kidney, showing cortex and papillary structures23,24 , respectively.…”

mentioning

confidence: 99%

Automated whole-organ histological imaging assisted with ultraviolet-excited sectioning tomography and deep learning

Kang

Zhang

et al. 2023

Preprint

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Three-dimensional (3D) histopathology involves the microscopic examination of a specimen, which plays a vital role in studying tissue's 3D structures and the signs of diseases. However, acquiring high-quality histological images of a whole organ is extremely time-consuming (e.g., several weeks) and laborious, as the organ has to be sectioned into hundreds or thousands of slices for imaging. Besides, the acquired images are required to undergo a complicated image registration process for 3D reconstruction. Here, by incorporating a recently developed vibratome-assisted block-face imaging technique with deep learning, we developed a pipeline termed HistoTRUST that can rapidly and automatically generate subcellular whole organ's virtual hematoxylin and eosin (H&E) stained histological images which can be reconstructed into 3D by simple image stacking (i.e., without registration). The performance and robustness of HistoTRUST have been successfully validated by imaging all vital mouse organs (brain, liver, kidney, heart, lung, and spleen) within 1-3 days depending on the size. The generated 3D dataset has the same color tune as the traditional H&E stained histological images. Therefore, the virtual H&E stained images can be directly analyzed by pathologists. HistoTRUST has a high potential to serve as a new standard in providing 3D histology for research or clinical applications.

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“…They are particularly sensitive to xenobiotics actions and constitute theirs main route of excretion, be it drugs, toxins or toxic substances (Beneditti et al, 2003). Metabolites derived drugs excreted by kidney can cause cellular damage by disrupting its functioning (Sands and Verlande., 2010). The proximal tubule and renal interstitium are the major targets of nephrotic substances (Lee and al.,2013).…”

Section: Introductionmentioning

confidence: 99%

Nephrotoxicity assessment of the hydroethanolic leaf extract of Ziziphus mauritiana LAM (Rhamnaceae) in mammalians.

2017

Int. J. Adv. Res. Biol. Sci

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Ziziphus mauritiana LAM is traditionally used in the treatment of various ailments. The purpose of this study was to check out the effect of the hydroethanolic leaf extract of Ziziphus mauritiana LAM on Wistar rats kidney functions. Determination of LD50 and the evaluation of the haematological, biochemical parameters followed by the histopathological assessment of kidney tissues in treated rats was carried out. The acute toxicity study showed a low toxicity with LD50 > 20000 mg /Kg. However, chronic treatment of rats resulted in a significant rise of platelet counts in both sexes of animals at experimental doses. Moreover, a significant increase of neutrophils in male was recorded but decreased in female rats at high doses. Biochemical analysis revealed significant increase of Urea and Creatinine in both sexes of rats followed by a decrease of uric acid in male rats at a dose of 1200mg / Kg. there was no significant difference in blood electrolytes except for Ca2+ at experimental doses in female rats compared to the control. Histopathological examination confirmed biochemical tests with presence of tissue damages. This study demonstrates the ability of the extract to cause thrombocytosis and induce kidney tissue damages in rats.

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Functional Anatomy of the Kidney

Cited by 4 publications

References 176 publications

Deep Learning Enables Rapid Whole-Organ Histological Imaging with Ultraviolet-Excited Sectioning Tomography

Deep Learning Enables Rapid Whole-Organ Histological Imaging with Ultraviolet-Excited Sectioning Tomography

Automated whole-organ histological imaging assisted with ultraviolet-excited sectioning tomography and deep learning

Nephrotoxicity assessment of the hydroethanolic leaf extract of Ziziphus mauritiana LAM (Rhamnaceae) in mammalians.

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