Rapid Microwave Tissue Processing and Staining Method Using a Kitchen Microwave Oven in Histopathology Laboratory

Shivkumar, VitaladevuniBalasubramanyam; Shirbhate, PranavNaresh; Deshmukh, Abhay Vilas

doi:10.4103/mjbl.mjbl_47_22

Cited by 2 publications

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Optimizing Spinal Infection Classification in Whole-Slide Images via Graph Convolutional Network and Model Uncertainty Integration

Liu,

Cheng,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

Background Spinal infections such as pyogenic spondylitis, spinal tuberculosis, and brucellar spondylitis are severe conditions that can lead to significant spinal damage and chronic pain. Whole-slide imaging (WSI) provides valuable visual information in pathological diagnoses. However, owing to the complexity and high dimensionality of WSI data, traditional manual diagnostic methods are often time-consuming and prone to errors. Therefore, developing an automated image analysis method is crucial to enhance the diagnostic accuracy and efficiency of WSI for spinal infections. Methods This study employed a novel framework that combines Graph Convolutional Networks (GCNs) with uncertainty quantification techniques to classify WSI images of spinal infections. A graph was constructed from segmented regions of the WSI, where nodes represented segmented pathological features and edges represented spatial relationships. The model was trained using a dataset of 422 cases from a provincial center for disease control and prevention and annotated for tuberculosis, brucellosis, and purulent spondylitis. The performance metrics were accuracy, precision, recall, and F1 scores. Results The integrated GCN model demonstrated a classification accuracy of 87%, recall of 85%, and F1 score of 0.86. Comparative analyses revealed that the GCN model exhibited a 10% higher performance than that of traditional CNN models. Moreover, the GCN model effectively quantified uncertainty and enhanced confidence in diagnostic decisions. Conclusions Integrating GCNs with model uncertainty enhances the accuracy and reliability of WSI image classification in pathology. This method significantly improves the capture of spatial relationships and identification of pathological features of spinal infections, offering a robust framework for supporting diagnostic and therapeutic decisions in medical practice.

show abstract

Optimizing Spinal Infection Classification in Whole-Slide Images via Graph Convolutional Network and Model Uncertainty Integration

Liu,

Cheng,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Beneficial effect of Alhagi maurorum on rats submitted to sulfadimidine-induced kidney injury

Alkhafaji,

Alharbi

2024

Rev. Clin. Pharmacol. Pharmacokinet., Int. Ed.

View full text Add to dashboard Cite

Alhagi maurorum is one of the many plants that have proven effectiveness in folklore medicine and that are still utilized to treat disease or disorders, thanks to their phytochemical compounds and other secondary metabolites. Sulfadimidine, chemical known as 4-amino-N-(4,6-dimethylpyrimidin-2-yl)benzene-sulfonamide, is an antibacterial drug that has side-effects on organs such as the kidney. In this study, the unwanted acute effect of this sulfonamide and of its metabolites was recorded in the form of rat interstitial nephritis and as an increase in creatinine and blood urea nitrogen (BUN) levels. Results showed a significant (P<0.05) decrease in BUN levels in rat groups treated with the ethanolic extract of Alhagi maurorum as a therapy, but there were no significant differences ob¬served in terms of the creatinine levels in these groups. The undertaken histological study revealed an almost normal histological appearance of the kidneys in the two groups of rats that were treated with the plant extract as a therapy after the damage that occurred as a result of the drug injection (interstitial nephritis, infiltration lympho¬cytes, and mild tubular atrophy). Our study suggests a potential benefit from natural plants in the treatment of drug-related adverse effects.

show abstract

Rapid Microwave Tissue Processing and Staining Method Using a Kitchen Microwave Oven in Histopathology Laboratory

Cited by 2 publications

References 27 publications

Optimizing Spinal Infection Classification in Whole-Slide Images via Graph Convolutional Network and Model Uncertainty Integration

Optimizing Spinal Infection Classification in Whole-Slide Images via Graph Convolutional Network and Model Uncertainty Integration

Beneficial effect of Alhagi maurorum on rats submitted to sulfadimidine-induced kidney injury

Contact Info

Product

Resources

About