Predicting Cellular Rejection of Renal Allograft Based on the Serum Proteomic Fingerprint.

Kidney transplantation is an essential medical procedure that significantly enhances the survival rates and quality of life for patients with end-stage kidney disease. However, despite advancements in immunosuppressive therapies, allograft rejection remains a leading cause of organ loss. Notably, predictions of cellular rejection processes primarily rely on biopsy analysis, which is not routinely performed due to its invasive nature. The present work evaluates if the serum proteomic fingerprint, as acquired by Fourier Transform Infrared (FTIR) spectroscopy, can predict cellular rejection processes. We analyzed 28 serum samples, corresponding to 17 without cellular rejection processes and 11 associated with cellular rejection processes, as based on biopsy analyses. The leave-one-out-cross validation procedure of a Naïve Bayes model enabled the prediction of cellular rejection processes with high sensitivity and specificity (AUC > 0.984). The serum proteomic profile was obtained in a high-throughput mode and based on a simple, rapid, and economical procedure, making it suitable for routine analyses and large-scale studies. Consequently, the current method presents a high potential to predict cellular rejection processes translatable to clinical scenarios, and that should continue to be explored.

Surrogate biomarkers identification for neuropsychiatric lupus by proteomic fingerprint technology / 中华风湿病学杂志

Objective To identify biomarkers in cerebrospinal fluid (CSF) by proteomic technology and develop a diagnostic model for neuropsychiatric lupus (NPSLE).Methods CSF proteomic spectra of 27 patients with NPSLE before and after treatment,and 27 controls including 17 patients with scoliosis,and 10 SLE patients without neuropsychiatric manifestation (non-NPSLE) were generated by matrix assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) combined with weak cationic exchange (WCX) magnetic beads.Data were analyzed with t test,non-parametric Kruskal-Wallis H test or Wilcoxon sign-rank test.A decision tree model for NPSLE classification was built based on the discriminating peaks.In addition,CSF samples of 12 patients with NPSLE,12 patients with lumbar disc herniation and 9 patients with other neurological conditions were employed as blind test group to verify the accuracy of the model.Results Twelve discriminating mass-to-charge (m/z) peaks were identified between NPSLE and controls.The diagnostic decision tree model,built with a panel of m/z peaks 8595,7170,7661,7740 and 5806,recognized NPSLE with the sensitivity and specificity of 92.6％ and 92.6％ based on training group samples,91.7％ and 85.7％ based on blind test group,respectively.Conclusion Potential CSF NPSLE biomarkers are identified by proteomic technology,the novel diagnostic model is sensitive and relatively specific for the diagnosis of NPSLE.