Background: Identifying dysmorphic red blood cells (RBCs) is critical for diagnosing glomerular diseases, as distinguishing glomerular from non-glomerular hematuria may reduce reliance on invasive diagnostics such as kidney biopsy. We aimed to enhance urinary RBC morphological classification by employing machine learning (ML) to analyze UF-5000 scattergram data. Methods: RBCs in urine samples (N = 185) were classified as dysmorphic or isomorphic based on microscopic findings. UF-5000 scattergrams were quantified to generate 20 statistical features and used to train a ML model in DataRobot (v9.1) with an automated pipeline, five-fold cross-validation, and LogLoss-based selection. Performance was evaluated in an independent cohort (N = 1,093). Accuracy was defined as concordance with microscopy findings. Areas under ROC curves (AUROCs) and diagnostic metrics are reported with 95% confidence intervals (CIs). Results: Among conventional UF-5000 parameters, the small RBC/total RBC ratio was the strongest predictor (AUROC 0.97, 95% CI 0.94–0.99). Scattergram-derived features indicated that RBC size-related parameters were crucial for identifying dysmorphic RBCs. The ML model alone demonstrated superior accuracy over UF-5000 RBC-Info alone (concordance 95.2% vs. 92.1%; AUROC 0.95 [0.94–0.97] vs. 0.92 [0.91–0.94]). Logical (OR/ AND) combinations of the ML model with RBC-Info outperformed RBC-Info alone (OR: concordance 92.7%, AUROC 0.93 [0.92–0.95]; AND: concordance 94.6%, AUROC 0.94 [0.93–0.96]). Conclusions A scattergram-based ML model improves the accuracy and reliability of urinary RBC morphological classification based on UF-5000 scattergrams and may help reduce reliance on invasive diagnostics. Prospective, multicenter studies should validate generalizability and assess integration into routine workflows.

Prostate stem cell antigen (PSCA) is a glycosylphosphatidylinositol-anchored cell surface antigen with an organ-dependent expression pattern in cancers; e.g., up-regulated in prostate cancer and down-regulated in gastric cancer. Previously it was reported that PSCA is not expressed in the normal pancreas but aberrantly expressed in pancreatic cancer. In this present study, we identified PSCA expression in islets of the pancreas by immunohistochemistry, which was co-localized with four islet-cell markers: insulin, glucagon, somatostatin and pancreatic polypeptide. In our investigation of the transcription start site of PSCA, we found a non-coding splicing variant of PSCA as well as authentic PSCA transcripts in mRNA samples from a normal pancreas. Both the transcripts were also identified in several pancreatic cancer cell lines. We previously reported that PSCA expression is correlated to the methylation status of the enhancer region in gastric and gallbladder cancer cell lines but not in pancreatic cancer cell lines, suggesting that PSCA expression is regulated in a diff erent mode in pancreatic cancer from that in gastric and gallbladder cancers.
Antigens, Surface ; Cell Line ; Gallbladder Neoplasms ; Glucagon ; Immunohistochemistry ; Insulin ; Islets of Langerhans ; Methylation ; Pancreas ; Pancreatic Neoplasms ; Pancreatic Polypeptide ; Prostate ; Prostatic Neoplasms ; RNA, Messenger ; Somatostatin ; Stem Cells ; Stomach Neoplasms ; Transcription Initiation Site