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Archives of Pathology & Laboratory... Oct 2022There is no standardized process for utilization of periodic acid-Schiff during intraoperative frozen sections to identify fungal organisms.
CONTEXT.—
There is no standardized process for utilization of periodic acid-Schiff during intraoperative frozen sections to identify fungal organisms.
OBJECTIVE.—
To develop a rapid staining process for fresh tissue with periodic acid-Schiff during intraoperative consultation and develop an appropriate control block.
DESIGN.—
Muscle tissue was inoculated with 2 species of fungus (Aspergillus fumigatus and Paecilomyces spp) and grown at 3 different temperatures for 72 hours. Inoculated tissue was embedded in optimal cutting temperature compound, cut, and stained using a modified periodic acid-Schiff stain. The optimal control was determined for future use as the standard control. Multiple control slides were cut and stained, using successively shorter time intervals for each step. The staining process that provided accurate results in the shortest amount of time was deemed ultra-rapid periodic acid-Schiff. This method was validated by carryover studies and clinical specimens.
RESULTS.—
Paecilomyces spp incubated at 30°C for 72 hours was the most optimal positive control, with numerous yeast and hyphal forms. The fastest staining process involved 2 minutes of periodic acid and Schiff reagent and 10 dips of light green solution. Tap water was as effective as distilled water. Validation was successfully achieved. Clinical cases all stained identical to formalin-fixed, paraffin-embedded tissue stained with hematoxylin-eosin and periodic acid-Schiff.
CONCLUSIONS.—
Ultra-rapid periodic acid-Schiff provides fast and reliable identification of fungal organisms on fresh tissue. Development of a concurrent positive control allows for quality control and validation.
Topics: Coloring Agents; Eosine Yellowish-(YS); Formaldehyde; Frozen Sections; Fungi; Hematoxylin; Humans; Methyl Green; Periodic Acid; Staining and Labeling; Water
PubMed: 35104313
DOI: 10.5858/arpa.2021-0273-OA -
Ageing Research Reviews May 2016Brain ageing in mice leads to the progressive appearance and expansion of degenerative granular structures frequently referred as "PAS granules" because of their... (Review)
Review
Brain ageing in mice leads to the progressive appearance and expansion of degenerative granular structures frequently referred as "PAS granules" because of their positive staining with periodic acid-Schiff (PAS). PAS granules are present mainly in the hippocampus, although they have also been described in other brain areas such as piriform and entorhinal cortices, and have been observed in other mammals than mice, like rats and monkeys. PAS granules have been identified as a wide range of brain deposits related to numerous neurodegenerative diseases, such as amyloid deposits, neurofibrillary tangles, Lafora bodies, corpora amylacea and polyglucosan bodies, and these identifications have generated controversy and particular theories about them. We have recently reported the presence of a neo-epitope in mice hippocampal PAS granules and the existence of natural IgM auto-antibodies directed against the neo-epitope in the plasma of the animals. The significance of the neo-epitope and the autoantibodies is discussed in this review. Moreover, we observed that the IgM anti-neo-epitope is frequently present as a contaminant in numerous commercial antibodies and is responsible of a considerable amount of false positive immunostainings, which may produce misinterpretations in the identification of the granules. Now that this point has been clarified, this article reviews and reconsiders the nature and physiopathological significance of these degenerative granules. Moreover, we suggest that neo-epitopes may turn into a useful brain-ageing biomarker and that autoimmunity could become a new focus in the study of age-related degenerative processes.
Topics: Aging; Animals; Autoimmunity; Epitopes; Hippocampus; Humans; Immunohistochemistry; Mice; Neurodegenerative Diseases; Periodic Acid; Plaque, Amyloid
PubMed: 26970374
DOI: 10.1016/j.arr.2016.03.001 -
Archives of Pathology & Laboratory... Dec 2021
Topics: Humans; Melanoma; Nevus; Nevus, Pigmented; Periodic Acid; Skin Neoplasms; beta Catenin
PubMed: 34818422
DOI: 10.5858/arpa.2021-0301-LE -
The American Journal of Pathology Jan 2023Convolutional neural network (CNN)-based image analysis applications in digital pathology (eg, tissue segmentation) require a large amount of annotated data and are...
Convolutional neural network (CNN)-based image analysis applications in digital pathology (eg, tissue segmentation) require a large amount of annotated data and are mostly trained and applicable on a single stain. Here, a novel concept based on stain augmentation is proposed to develop stain-independent CNNs requiring only one annotated stain. In this benchmark study on stain independence in digital pathology, this approach is comprehensively compared with state-of-the-art techniques including image registration and stain translation, and several modifications thereof. A previously developed CNN for segmentation of periodic acid-Schiff-stained kidney histology was used and applied to various immunohistochemical stainings. Stain augmentation showed very high performance in all evaluated stains and outperformed all other techniques in all structures and stains. Without the need for additional annotations, it enabled segmentation on immunohistochemical stainings with performance nearly comparable to that of the annotated periodic acid-Schiff stain and could further uphold performance on several held-out stains not seen during training. Herein, examples of how this framework can be applied for compartment-specific quantification of immunohistochemical stains for inflammation and fibrosis in animal models and patient biopsy specimens are presented. The results show that stain augmentation is a highly effective approach to enable stain-independent applications of deep-learning segmentation algorithms. This opens new possibilities for broad implementation in digital pathology.
Topics: Deep Learning; Coloring Agents; Periodic Acid; Neural Networks, Computer; Image Processing, Computer-Assisted; Kidney
PubMed: 36309103
DOI: 10.1016/j.ajpath.2022.09.011 -
International Journal of Biological... Dec 2020Bioadhesives have a potential to modulate the wound closure process with significant biological outcomes. However, none of the currently commercialized adhesives are...
Bioadhesives have a potential to modulate the wound closure process with significant biological outcomes. However, none of the currently commercialized adhesives are satisfactory in their performance. It is a challenging task to develop an adhesive system that can work on wet surface and enhances tissue repair and closure. In this study, we have fabricated a series of gelatin-dopamine (Gel-dop) conjugates and studied their adhesive properties after being chemically crosslinked using sodium periodate. The designed material was assessed for its adhesive properties including tensile, lap shear and peeling study by varying the degree of dopamine substitution. It was observed that the adhesive property has a direct correlation with increase in dopamine content until reaching a maximum and then a subsequent decrease. We tested the adhesive strength of the different formulations by varying the degree of substitution and compared against fibrin glue, which is considered as the gold standard of adhesives. The formulation with a moderate substitution degree demonstrated the optimal adhesive property than those formulations with lower and larger substitution degree. Further, the in vitro cytotoxicity study showed that this tunable Gel-dop adhesives are to non-cytotoxic, indicating a potential use in clinic applications. This study illustrates that adhesiveness can be regulated by changing the degree of dopamine substitution.
Topics: Adhesiveness; Animals; Benzoquinones; Catechols; Cell Adhesion; Cell Survival; Cross-Linking Reagents; Dopamine; Fibrin Tissue Adhesive; Gelatin; Hydrogels; Materials Testing; Oxygen; Periodic Acid; Pressure; Rheology; Shear Strength; Skin; Surface Properties; Swine; Tensile Strength; Tissue Adhesives
PubMed: 32721461
DOI: 10.1016/j.ijbiomac.2020.07.195 -
Journal of the American Society of... Jan 2021Nephropathologic analyses provide important outcomes-related data in experiments with the animal models that are essential for understanding kidney disease...
BACKGROUND
Nephropathologic analyses provide important outcomes-related data in experiments with the animal models that are essential for understanding kidney disease pathophysiology. Precision medicine increases the demand for quantitative, unbiased, reproducible, and efficient histopathologic analyses, which will require novel high-throughput tools. A deep learning technique, the convolutional neural network, is increasingly applied in pathology because of its high performance in tasks like histology segmentation.
METHODS
We investigated use of a convolutional neural network architecture for accurate segmentation of periodic acid-Schiff-stained kidney tissue from healthy mice and five murine disease models and from other species used in preclinical research. We trained the convolutional neural network to segment six major renal structures: glomerular tuft, glomerulus including Bowman's capsule, tubules, arteries, arterial lumina, and veins. To achieve high accuracy, we performed a large number of expert-based annotations, 72,722 in total.
RESULTS
Multiclass segmentation performance was very high in all disease models. The convolutional neural network allowed high-throughput and large-scale, quantitative and comparative analyses of various models. In disease models, computational feature extraction revealed interstitial expansion, tubular dilation and atrophy, and glomerular size variability. Validation showed a high correlation of findings with current standard morphometric analysis. The convolutional neural network also showed high performance in other species used in research-including rats, pigs, bears, and marmosets-as well as in humans, providing a translational bridge between preclinical and clinical studies.
CONCLUSIONS
We developed a deep learning algorithm for accurate multiclass segmentation of digital whole-slide images of periodic acid-Schiff-stained kidneys from various species and renal disease models. This enables reproducible quantitative histopathologic analyses in preclinical models that also might be applicable to clinical studies.
Topics: Algorithms; Animals; Deep Learning; Diagnosis, Computer-Assisted; Disease Models, Animal; Image Processing, Computer-Assisted; Kidney; Kidney Diseases; Kidney Glomerulus; Male; Mice; Mice, Inbred C57BL; Neural Networks, Computer; Pattern Recognition, Automated; Periodic Acid; Reproducibility of Results; Schiff Bases; Translational Research, Biomedical
PubMed: 33154175
DOI: 10.1681/ASN.2020050597 -
The American Journal of Pathology Oct 2022In kidney transplant biopsies, both inflammation and chronic changes are important features that predict long-term graft survival. Quantitative scoring of these features...
In kidney transplant biopsies, both inflammation and chronic changes are important features that predict long-term graft survival. Quantitative scoring of these features is important for transplant diagnostics and kidney research. However, visual scoring is poorly reproducible and labor intensive. The goal of this study was to investigate the potential of convolutional neural networks (CNNs) to quantify inflammation and chronic features in kidney transplant biopsies. A structure segmentation CNN and a lymphocyte detection CNN were applied on 125 whole-slide image pairs of periodic acid-Schiff- and CD3-stained slides. The CNN results were used to quantify healthy and sclerotic glomeruli, interstitial fibrosis, tubular atrophy, and inflammation within both nonatrophic and atrophic tubuli, and in areas of interstitial fibrosis. The computed tissue features showed high correlation with Banff lesion scores of five pathologists (A.A., A.Dend., J.H.B., J.K., and T.N.). Analyses on a small subset showed a moderate correlation toward higher CD3 cell density within scarred regions and higher CD3 cell count inside atrophic tubuli correlated with long-term change of estimated glomerular filtration rate. The presented CNNs are valid tools to yield objective quantitative information on glomeruli number, fibrotic tissue, and inflammation within scarred and non-scarred kidney parenchyma in a reproducible manner. CNNs have the potential to improve kidney transplant diagnostics and will benefit the community as a novel method to generate surrogate end points for large-scale clinical studies.
Topics: Atrophy; Biomarkers; Biopsy; Fibrosis; Graft vs Host Disease; Humans; Inflammation; Kidney; Kidney Transplantation; Neural Networks, Computer; Periodic Acid
PubMed: 35843265
DOI: 10.1016/j.ajpath.2022.06.009 -
Analytical Chemistry Dec 2018NMR-based metabolomics is a powerful tool to comprehensively monitor chemical processes in biological systems. Key to its success is the accurate and complete...
NMR-based metabolomics is a powerful tool to comprehensively monitor chemical processes in biological systems. Key to its success is the accurate and complete metabolite identification and quantification. Due to the inherent complexity of most metabolic mixtures, NMR peak overlap can make data analysis of 1D or even 2D NMR spectra challenging, especially for the H spectral region from 3.2-4.5 ppm that is dominated by carbohydrates and their derivatives. To address this problem, we present an effective method for carbohydrate signal removal in complex metabolomics samples by oxidation via the addition of sodium periodate (NaIO). In an optional step, reaction products can be removed with hydrazide beads. The treated samples show substantially simplified 1D and 2D NMR spectra with their carbohydrate peaks removed, whereas noncarbohydrate peaks remain mostly unaffected. This allows the unrestricted detection of those metabolites that are otherwise obscured by carbohydrate signals. The method was first tested for metabolite model mixtures and then applied to urine and serum samples. It revealed a significant number of noncarbohydrates that were made unambiguously observable and identifiable by this method. The proposed protocol is simple and it is suitable for high-throughput sample treatment for the comprehensive metabolite identification in a broad range of samples.
Topics: Body Fluids; Carbohydrates; Humans; Magnetic Resonance Spectroscopy; Metabolome; Metabolomics; Oxidation-Reduction; Periodic Acid
PubMed: 30474970
DOI: 10.1021/acs.analchem.8b04482 -
British Medical Journal May 1969
Topics: Child; Humans; Leukemia, Lymphoid; Periodic Acid; Prognosis; Staining and Labeling
PubMed: 4181618
DOI: 10.1136/bmj.2.5655.513-a -
Acta Tropica Jun 2018Strongyloidiasis is an important helminthiasis affecting million people worldwide. The aim of this study was to use sodium metaperiodate (MP) treatment to... (Review)
Review
Strongyloidiasis is an important helminthiasis affecting million people worldwide. The aim of this study was to use sodium metaperiodate (MP) treatment to immunochemically characterize Strongyloides venezuelensis filariform larvae and use MP-treated heterologous antigen to detect IgG and subclasses in serum. Samples from individuals with definitive diagnosis of strongyloidiasis (n = 50), other parasitic diseases (n = 60) and negative endemic (n = 50) were tested. TG-ROC and two-way ANOVA were applied. MP-treatment resulted on differential localization of carbohydrates at larval structure and no carbohydrate content in saline extract (SE). Electrophoretic profiles were similar before and after treatment. ELISA sensitivity and specificity were: 90%; 88.2% for SE and 92.0%; 94.6% for MP, respectively. When using MP treated antigen we observed reduction in IgG1 and IgG3 detection in strongyloidiasis group and decrease of cross reactions in control groups. Our data demonstrate the role of carbohydrate residues in cross reactions and on the recognition of anti-Strongyloides IgG and its subclasses.
Topics: Animals; Antigens, Helminth; Glycosylation; Humans; Immunoglobulin G; Larva; Periodic Acid; Strongyloides; Strongyloidiasis
PubMed: 29454735
DOI: 10.1016/j.actatropica.2018.02.001