A retrospective study using machine learning to develop predictive model to identify urinary infection stones in vivo.

Preoperative diagnosis of urinary infection stones is difficult, and accurate detection of stone composition can only be performed ex vivo. To provide guidance for better perioperative management and postoperative prevention of infection stones, we developed a machine learning model for preoperative identification of infection stones in vivo. The clinical data of patients with urolithiasis who underwent surgery in our hospital from January 2011 to December 2015 and January 2017 to December 2021 were retrospectively analyzed. A total of 2565 patients were included in the study, and 1168 eligible patients with urinary calculi were randomly divided into training set (70%) and test set (30%). Five machine learning algorithms (Support Vector Machine (SVM), Multilayer Perceptron (MLP), Decision Tree (DT), Random Forest Classifier (RFC), and Adaptive Boost (AdaBoost)) and 14 preoperative variables were used to construct the prediction model. The performance measure was the area under the receiver operating characteristic curve (AUC) of the validation set. The importance of 14 features in each prediction model for predicting infection stones was analyzed. A total of 89 patients (5.34%) with infection stones were included in the validation set. All the five prediction models showed strong discrimination in the validation set (AUC: 0.689-0.772). AdaBoost model was selected as the final model (AUC: 0.772(95% confidence interval, 0.657-0.887); Sensitivity: 0.522; Specificity: 0.902), UC positivity, and urine pH value were two important predictors of infection stones. We developed a predictive model through machine learning that can quickly identify infection stones in vivo with good predictive performance. It can be used for risk assessment and decision support of infection stones, optimize the disease management of urinary calculi and improve the prognosis of patients.

LncRNA nuclear-enriched abundant transcript 1 shuttled by prostate cancer cells-secreted exosomes initiates osteoblastic phenotypes in the bone metastatic microenvironment via miR-205-5p/runt-related transcription factor 2/splicing factor proline- and glutamine-rich/polypyrimidine tract-binding protein 2 axis.

Prostate cancer (PCa) patients commonly present with osteoblastic-type bone metastasis. Exosomes derived from tumor cells possess biological significance and can mediate intercellular communication in the tumor microenvironment. Long noncoding RNA (lncRNA) nuclear-enriched abundant transcript 1 (NEAT1) is also implicated in the stability in tumorigenesis and the development of PCa, but the underlying mechanism remains elusive. Hence, the current study set out to investigate the physiological mechanisms by which exosomes-encapsulated NEAT1 affects the progression of PCa. First, after isolation, we found PCa cell-derived exosomes induced the osteogenic differentiation of human bone marrow-derived mesenchymal stem cells (hBMSCs). Besides, NEAT1 in PCa cells could be transferred into hBMSCs via exosomes. Further gain- and loss-of-function experimentation revealed that NEAT1 acted as a competing endogenous RNA (ceRNA) of microRNA (miR)-205-5p to upregulate the runt-related transcription factor 2 (RUNX2) levels. Moreover, NEAT1 could promote the RUNX2 expression via the splicing factor proline- and glutamine-rich (SFPQ)/polypyrimidine tract-binding protein 2 (PTBP2) axis. Functional assays uncovered that NEAT1 shuttled by PCa-exosomes facilitated the activity of alkaline phosphatase (ALP) and mineralization of extracellular matrix, and continuously upregulated the levels of RUNX2, ALP, alpha-1 type 1 collagen, and osteocalcin by regulating RUNX2, to induce the osteogenic differentiation of hBMSCs. Furthermore, in vivo experimentation confirmed that upregulated NEAT1 induced osteogenesis. Collectively, our findings indicated that PCa-derived exosomes-loaded NEAT1 upregulated RUNX2 to facilitate the osteogenesis of hBMSCs by competitively binding to miR-205-5p via the SFPQ/PTBP2 axis, therefore providing a potential therapeutic target to treat osteogenesis of hBMSCs in PCa. PCa cells secrete exosomes containing NEAT1, and NEAT1 exerts effects on osteogenic differentiation of hBMSCs in PCa. NEAT1 shuttled by PCa-derived exosomes could be transferred into hBMSCs, where NEAT1 exerted inductive properties in osteogenic differentiation of hBMSCs through the upregulation of RUNX2 by competitively binding to miR-205-5p and regulating SFPQ/PTBP2 in vitro and in vivo.

A Nomogram for Predicting Intraoperative Hemodynamic Instability in Patients With Pheochromocytoma.

Purpose: Surgical removal of pheochromocytoma (PCC), including open, laparoscopic, and robot-assisted adrenalectomy, is the cornerstone of therapy, which is associated with high risk of intraoperative and postoperative life-threatening complications due to intraoperative hemodynamic instability (IHD). This study aims to develop and validate a nomogram based on clinical characteristics as well as computed tomography (CT) features for the prediction of IHD in pheochromocytoma surgery. Methods: The data from 112 patients with pheochromocytoma were collected at a single center between January 1, 2010, and December 31, 2019. Clinical and radiological features were selected with the least absolute shrinkage and selection operator regression analysis to predict IHD then constitute a nomogram. The performance of the nomogram was assessed in terms of discrimination, calibration, and clinical utility. Results: Age, tumor shape, Mayo Adhesive Probability score, laterality, necrosis, body mass index, and surgical technique were identified as risk predictors of the presence of IHD. The nomogram was then developed using these seven variables. The model showed good discrimination with a C-index of 0.773 (95% CI, 0.683-0.862) and an area under the receiver operating characteristic curve (AUC) of 0.739 (95% CI, 0.642-0.837). The calibration plot suggested good agreement between predicted and actual probabilities. Besides, calibration was tested with the Hosmer-Lemeshow test (P = 0.961). The decision curve showed the clinical effectiveness of the nomogram. Conclusions: Our nomogram based on clinical and CT parameters could facilitate the treatment strategy according to assessment of the risk of IHD in patients with pheochromocytoma.

Characterization and Management of Juxtaglomerular Cell Tumor: Analysis of 9 Cases and Literature Review

Aims: Juxtaglomerular cell tumor is a rare kidney tumor. This study aimed to report the clinic features of juxtaglomerular cell tumor and our treatment experience. Methods: The medical records of 9 juxtaglomerular cell tumor patients treated in our hospital from 1997 to 2017 were retrospectively reviewed. Clinical characteristics, immunohistochemical findings, treatments and outcomes were collected. Results: The mean age of 9 patients was 24±8.1 years (range: 18-37). All cases had symptoms of hypertension, hyperaldosteronism, high plasma renin, high plasma angiotensin II. Four cases had hypokalemia. The renal masses were found by enhanced contrast tomography in all patients. One case received ultrasound-guided ablation and was clinically diagnosed with juxtaglomerular cell tumor. Among the remaining 8 cases, 2 cases received nephrectomy while 6 underwent partial nephrectomy. The 8 cases were pathologically diagnosed with juxtaglomerular cell tumor. Immunohistochemical findings showed that juxtaglomerular cell tumor was positive for vimentin, CD34, and actin but negative for chromogranin A. After treatment, all the patients had normal levels of blood pressure, serum renin activity, potassium, and aldosterone. No patients had tumor progress or metastasis within a median follow-up period of 94 (range: 33-241) months. Conclusion: Hypertension combined with hyperaldosteronism and hypokalemia secondary to high plasma renin activity are the typical symptoms of juxtaglomerular cell tumor. Partial nephrectomy is an optimal treatment for juxtaglomerular cell tumor.

Human bone marrow mesenchymal stem cells-derived microRNA-205-containing exosomes impede the progression of prostate cancer through suppression of RHPN2.

BACKGROUND: Human bone marrow mesenchymal stem cells (hBMSCs) are implicated in cancer initiation and metastasis, sometimes by releasing exosomes that mediate cell communication by delivering microRNAs (miRNAs). This study aimed to investigate the physiological mechanisms by which exosomal miR-205 derived from hBMSCs may modulate the growth of prostate cancer cells. METHODS: Microarray-based gene expression profiling of prostate cancer was adopted to identify differentially expressed genes and regulatory miRNAs, which identified the candidates RHPN2 and miR-205 as the study focus. Then the binding affinity between miR-205 and RHPN2 was identified using in silico analysis and luciferase activity detection. Prostate cancer cells were co-cultured with exosomes derived from hBMSCs treated with either miR-205 mimic or miR-205 inhibitor. Subsequently, prostate cancer cell proliferation, invasion, migration, and apoptosis were detected in vitro. The effects of hBMSCs-miR-205 on tumor growth were investigated in vivo. RESULTS: miR-205 was downregulated, while RHPN2 was upregulated in prostate cancer cells. RHPN2 was a target of miR-205, and upregulated miR-205 inhibited prostate cancer cell proliferation, invasion, and migration and promoted apoptosis by targeting RHPN2. Next, experiments demonstrated that hBMSCs-derived exosomes carrying miR-205 contributed to repressed prostate cancer cell proliferation, invasion, and migration and enhanced apoptosis. Furthermore, in vivo assays confirmed the inhibitory effects of hBMSCs-derived exosomal miR-205 on prostate cancer. CONCLUSION: The hBMSCs-derived exosomal miR-205 retards prostate cancer progression by inhibiting RHPN2, suggesting that miR-205 may present a predictor and potential therapeutic target for prostate cancer.