Automated Intelligent Microscopy for the Recognition of Decoy Cells in Urine Samples of Kidney Transplant Patients

Automated Urine Cell Image Classification Model Using Chaotic Mixer Deep Feature Extraction

Microscopic examination of urinary sediments is a common laboratory procedure. Automated image-based classification of urinary sediments can reduce analysis time and costs. Inspired by cryptographic mixing protocols and computer vision, we developed an image classification model that combines a novel Arnold Cat Map (ACM)- and fixed-size patch-based mixer algorithm with transfer learning for deep feature extraction. Our study dataset comprised 6,687 urinary sediment images belonging to seven classes: Cast, Crystal, Epithelia, Epithelial nuclei, Erythrocyte, Leukocyte, and Mycete. The developed model consists of four layers: (1) an ACM-based mixer to generate mixed images from resized 224 × 224 input images using fixed-size 16 × 16 patches; (2) DenseNet201 pre-trained on ImageNet1K to extract 1,920 features from each raw input image, and its six corresponding mixed images were concatenated to form a final feature vector of length 13,440; (3) iterative neighborhood component analysis to select the most discriminative feature vector of optimal length 342, determined using a k-nearest neighbor (kNN)-based loss function calculator; and (4) shallow kNN-based classification with ten-fold cross-validation. Our model achieved 98.52% overall accuracy for seven-class classification, outperforming published models for urinary cell and sediment analysis. We demonstrated the feasibility and accuracy of deep feature engineering using an ACM-based mixer algorithm for image preprocessing combined with pre-trained DenseNet201 for feature extraction. The classification model was both demonstrably accurate and computationally lightweight, making it ready for implementation in real-world image-based urine sediment analysis applications.

Frequency of pre- and post-transplant infectious agents in kidney transplant patients

Purpose: Renal transplantation is the most important and successful treatment method for renal failure. In this study, it was aimed to investigate the frequency of Cytomegalovirus (CMV), BK virus (BKV) and bacterial agents in kidney transplant recipient (KTR)s before and in the first six months after transplantation. Materials and Methods: CMV and BKV were investigated by Real-time PCR in blood samples taken from patients who underwent kidney transplantation at the Organ Transplantation Center of our faculty, one week before the transplantation and in the first, third and sixth months after transplantation. Blood, urine, respiratory tract /wound (if necessary) cultures were performed. Decoy cells were evaluated in urine cytology. Results: The mean age of KTRs was 32.60±11.71 years, 28 (62.2%) were male. Donor origins were living related donors 39 (86.7%) and cadaveric 6 (13.3%). After transplantation, BKV was detected in 11/38 (28.9%) patients, CMV was found in 25/41 (60.9%) patients, and Decoy cell positivity was detected in 11/31 (35.4%) patients. While the highest rate of Real-time PCR positivities were in the third months and sixth months for BKV and first, month for CMV and gradually decreased towards the sixth month. Escherichia coli, Klebsiella pneumoniae, Candida nonalbicans, Enterococcus faecalis were most commonly grown in urine culture. Staphylococcus hominis, Streptecoccus epidermidis, were grown in blood culture. Acinetobacter baumannii, Klebsiella pneumoniae, Aspergillus fumigatus and Candida albicans grew in the culture of respiratory tract samples. Conclusion: Bacterial infections developed early in our KTRs. While the highest Real-time PCR positivity rate was in the third and sixth months for BKV, it was the first month for CMV and gradually decreased towards the sixth month. Decoy cell positivity may be also important for diagnosis of BKV infection in KTRs.

Sternheimer-Malbin Staining to Detect Decoy Cells in Urine of 213 Kidney Transplant Patients

BACKGROUND

Human polyoma virus-associated nephropathy frequently refers to allograft failure after kidney transplant. Thus, the early detection of viral activation is extremely important for these immunocompromised patients.

METHODS

Previously, urine polyoma virus-infected cells (decoy cells) were indicated as the virus action, usually screened by the routine papanicolaou cytology in renal biopsy, but these methods are complex and the positive rate is low. In this article, the direct microscopy observation method, Wright-Giemsa staining, and Sternheimer-Malbin (SM) staining were all used to screen the decoy cells in urine samples of 213 kidney transplant patients who had used immunosuppressive drugs.

RESULTS

Among them, decoy cells were detected in 40 cases (18.8%) by the direct observation method, 44 cases (20.7%) by Wright-Giemsa staining and 49 cases (23.0%) by SM staining. Furthermore, the most common polyoma viruses, BK and JC viruses, were also confirmed in 41 (83.7%) cases among these 49 decoy cell-positive samples. Importantly, compared with other decoy cell detection methods, SM staining is fast, easy to operate, and has a high positive rate.

CONCLUSION

Therefore, SM staining is recommended as a fast and effective method for screening urine decoy cells in kidney transplant patients.