New Molecular Markers for Prostate Cancer Diagnosis.

PURPOSE: Prostate cancer (PCa) is the second most commonly diagnosed cancer and the sixth leading cause of cancer death among men worldwide. Biomarkers are an important tool in the early detection of PCa. Prostate-specific antigen (PSA) is one of the oldest biomarkers for the early detection of PCa. Digital rectal exam (DRE) is another screening test for PCa detection, which is considered as an irritating experience for patients. Biopsy is still the most reliable method for PCa diagnosis; however, patients are prone to complications. Therefore, developing non-invasive and accurate methods for PCa screening seems urgent to avoid unnecessary biopsies. There has been remarkable development in PCa molecular biomarkers discovery, largely through progress in omics technologies. Due to the many benefits of liquid biopsies, a significant set of PCa diagnostic kits have been developed using urine samples. Despite the unique benefits of these kits, there are still many challenges to their widespread use in clinics. Here, we have reviewed the latest developments of PCa biomarkers in liquid biopsies. METHODS: Literature on biomarkers for diagnosis of PCa was reviewed during the past two decades. RESULTS: PSA, PHI, PCA3, and 4K score are among the commonly used markers for PCa diagnosis which have been used over a long-moderate length of time with multiple studies on their performance. We performed a review of their performance. Newer markers are among RNA and DNA markers. Multiple non-coding RNAs (mi-RNAs) were reviewed and their performance on Pca diagnosis was reviewed. Long noncoding RNAs (Lnc RNAs) including PlncRNA-1, HOTAIR, SchLAP-1, MALAT1, MEG3, and PRCAT17.3 were summarized. mRNA markers including TMPRSS2:ERG, and HOXC6 were presented. DNA-based markers including PTEN, HOXB13, and BRCA2 were reviewed. Finally, the use of CircRNAs was reviewed for PCa diagnosis. CONCLUSION: Many reviewed RNA-based biomarkers have promising results in the diagnosis of PCa.

The predictive value of cardiac MRI strain parameters in hypertrophic cardiomyopathy patients with preserved left ventricular ejection fraction and a low fibrosis burden: a retrospective cohort study.

Background: Prompt interventions prevent adverse events (AE) in hypertrophic cardiomyopathy (HCM). We evaluated the pattern and the predictive role of feature tracking (FT)-cardiac magnetic resonance (CMR) imaging parameters in an HCM population with a normal left ventricular ejection fraction (LVEF) and a low fibrosis burden. Methods: The CMR and clinical data of 170 patients, consisting of 142 HCM (45 ± 15.7 years, 62.7% male) and 28 healthy (42.2 ± 11.26 years, 50% male) subjects, who were enrolled from 2015 to 2020, were evaluated. HCM patients had a normal LVEF with a late gadolinium enhancement (LGE) percentage below 15%. Between-group differences were described, and the potent predictors of AE were determined. A P-value below 0.05 was considered significant. Results: LV global longitudinal, circumferential, and radial strains (GLS, GCS, and GRS, respectively) and the LV myocardial mass index (MMI) were different between the healthy and HCM cases (all Ps < 0.05). Strains were significantly impaired in the HCM patients with a normal MMI. A progressive decrease in LVGLS and a distinct fall in LVGCS were noted with a rise in MMI. AE were predicted by LVGLS, LVGCS, and the LGE percentage, and LVGCS was the single robust predictor (HR, 1.144; 95% CI, 1.080-1.212; P = 0.001). An LVGCS below 16.2% predicted AE with 77% specificity and 58% sensitivity. Conclusions: LV strains were impaired in HCM patients with a normal EF and a low fibrosis burden, even in the presence of a normal MMI. CMR parameters, especially FT-CMR values, predicted AE in our HCM patients.

Efficacy of intranasal ketamine and midazolam for pediatric sedation: A double-blind, randomized clinical trial.

BACKGROUND: Pediatric patients feel significant fear and anxiety when undergoing surgeries. The ideal drug and its administration route have not been found yet. The aim of this study was to compare the efficacy and safety of intranasal (IN) ketamine and midazolam as premedication in children. METHODS: We studied 71 eligible pediatric patients undergoing elective urologic surgeries, aged 2 to 6 years. The degree of sedation and separation scores was compared between the two groups. Additionally, hemodynamic parameters, before premedication, after induction of anesthesia, and during surgery were documented and compared between two groups. Postoperatively, any side effect was recorded as well. RESULTS: Finally, the data from 71 children were analyzed. Recovery time was significantly longer in group K (ketamine) compared to group M (midazolam); 27.86±4.42 vs 38.19± 6.67 minutes respectively (P=0.01). No significant difference was observed in terms of sedation score between two groups of K & M; 3.29±0.78 vs 3 ±0.71 respectively (P=0.17), and not regarding separation score; 2.51±0.61 & 2.31±0.52 respectively (P=0.01). Vital signs were kept within the physiological limits in both groups with no marked fluctuations. CONCLUSION: To produce sedation in young children, both midazolam and ketamine were effective and safe by IN route.

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks.

Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high workload conditions. Although a laboratory test is the current routine diagnostic tool, it is time-consuming, imposing a high cost and requiring a well-equipped laboratory for analysis. Computed tomography (CT) has thus far become a fast method to diagnose patients with COVID-19. However, the performance of radiologists in diagnosis of COVID-19 was moderate. Accordingly, additional investigations are needed to improve the performance in diagnosing COVID-19. In this study is suggested a rapid and valid method for COVID-19 diagnosis using an artificial intelligence technique based. 1020 CT slices from 108 patients with laboratory proven COVID-19 (the COVID-19 group) and 86 patients with other atypical and viral pneumonia diseases (the non-COVID-19 group) were included. Ten well-known convolutional neural networks were used to distinguish infection of COVID-19 from non-COVID-19 groups: AlexNet, VGG-16, VGG-19, SqueezeNet, GoogleNet, MobileNet-V2, ResNet-18, ResNet-50, ResNet-101, and Xception. Among all networks, the best performance was achieved by ResNet-101 and Xception. ResNet-101 could distinguish COVID-19 from non-COVID-19 cases with an AUC of 0.994 (sensitivity, 100%; specificity, 99.02%; accuracy, 99.51%). Xception achieved an AUC of 0.994 (sensitivity, 98.04%; specificity, 100%; accuracy, 99.02%). However, the performance of the radiologist was moderate with an AUC of 0.873 (sensitivity, 89.21%; specificity, 83.33%; accuracy, 86.27%). ResNet-101 can be considered as a high sensitivity model to characterize and diagnose COVID-19 infections, and can be used as an adjuvant tool in radiology departments.