Renal cell carcinoma (RCC) represents the sixth most frequently diagnosed cancer in men and is asymptomatic, being detected mostly incidentally. The apparition of symptoms correlates with advanced disease, aggressive histology, and poor outcomes. The development of the Surface-Enhanced Raman Scattering (SERS) technique opened the way for investigating and detecting small molecules, especially in biological liquids such as serum or blood plasma, urine, saliva, and tears, and was proposed as a simple technique for the diagnosis of various diseases, including cancer. In this study, we investigated the use of serum label-free SERS combined with two multivariate analysis tests: Principal Component Analysis combined with Linear Discriminate Analysis (PCA-LDA) and Supported Vector Machine (SVM) for the discrimination of 50 RCC cancer patients from 45 apparently healthy donors. In the case of LDA-PCA, we obtained a discrimination accuracy of 100% using 12 principal components and a quadratic discrimination function. The accuracy of discrimination between RCC stages was 88%. In the case of the SVM approach, we obtained a training accuracy of 100%, a validation accuracy of 92% for the discrimination between RCC and controls, and an accuracy of 81% for the discrimination between stages. We also performed standard statistical tests aimed at improving the assignment of the SERS vibration bands, which, according to our data, are mainly due to purinic metabolites (uric acid and hypoxanthine). Moreover, our results using these assignments and Student’s t-test suggest that the main differences in the SERS spectra of RCC patients are due to an increase in the uric acid concentration (a conclusion in agreement with recent literature), while the hypoxanthine concentration is not statistically different between the two groups. Our results demonstrate that label-free SERS combined with chemometrics holds great promise for non-invasive and early detection of RCC. However, more studies are needed to validate this approach, especially when combined with other urological diseases.

The advent of Surface-Enhanced Raman Scattering (SERS) has enabled the exploration and detection of small molecules, particularly in biological fluids such as serum, blood plasma, urine, saliva, and tears. SERS has been proposed as a simple diagnostic technique for various diseases, including cancer. Renal cell carcinoma (RCC) ranks as the sixth most commonly diagnosed cancer in men and is often asymptomatic, with detection occurring incidentally. The onset of symptoms typically aligns with advanced disease, aggressive histology, and unfavorable prognosis, and therefore new methods for an early diagnosis are needed. In this study, we investigated the utility of label-free SERS in urine, coupled with two multivariate analysis approaches: Principal Component Analysis combined with Linear Discriminant Analysis (PCA-LDA) and Support Vector Machine (SVM), to discriminate between 50 RCC patients and 44 healthy donors. Employing LDA-PCA, we achieved a discrimination accuracy of 100% using 13 principal components, and an 88% accuracy in discriminating between different RCC stages. The SVM approach yielded a training accuracy of 100%, a validation accuracy of 99% for discriminating between RCC and controls, and an 80% accuracy for discriminating between stages. The comparative analysis of raw and normalized SERS spectral data shows that while raw data disclose relative concentration variations in urine metabolites between the two classes, the normalization of spectral data significantly improves the accuracy of discrimination. Moreover, the selection of principal components with markedly distinct scores between the two classes serves to alleviate overfitting risks and reduces the number of components employed for discrimination. We obtained the accuracy of the discrimination between the RCC patients cases and healthy donors of 90% for three PCs and a linear discrimination function, and a 88% accuracy of discrimination between stages using six PCs, mitigating practically the risk of overfitting and increasing the robustness of our analysis. Our findings underscore the potential of label-free SERS of urine in conjunction with chemometrics for non-invasive and early RCC detection.

Background: eHealth can help replicate the benefits of conventional surgical prehabilitation programs and overcome organizational constraints related to human resources and health care–related costs. Objective: We aimed to assess the impact of an optimized perioperative program using a personalized mobile app designed for preparation and recovery after radical prostatectomy (RP). Methods: We report on a series of 122 consecutive robot-assisted RP before and after the implementation of the betty.care app (cohort A: standard of care, n=60; cohort B: optimized program, n=62). The primary end point was continence recovery, defined as “0 or 1 safety pad per day” at 6 weeks after surgery. Secondary end points were length of stay, same-day discharge, complications, readmissions, and number of days alive and out of hospital within 30 days from surgery. Results: Both cohorts were comparable in terms of age, prostate-specific antigen, prostate volume, and disease aggressiveness. Intraoperative parameters (lymph node dissection, operative time, and bilateral nerve-sparing surgery) were comparable in both groups, except for blood loss, which was significantly higher in cohort B (182 vs 125 cc; P=.008). The 6-week continence rate was improved in cohort B in both univariable and multivariable analyses (92% vs 75%; P=.01). There were trends favoring cohort B for all secondary end points with a minimal 30% benefit compared with cohort A. Grade 2 or more complications occurred less frequently in cohort B (13% vs 3.2%; P=.042). Same-day discharge and readmission rates were 35% and 53% (P=.043), and 3.3% and 1.6% (P=.54) in cohorts A and B, respectively. Mean length of stay was reduced by 0.2 days in cohort B (0.58 vs 0.78 days; P=.10). The main limitation was the absence of randomization. Conclusions: The implementation of a mobile app that provides a holistic approach to the perioperative period, integrating prehabilitation, rehabilitation, and remote monitoring, could lead to the improvement of important functional outcomes after RP and could replicate an on-site prehabilitation program. Multicenter validation is needed.

Background: Before the advent of fibrinolytic therapy as a gold standard method of care for cases of acute ischemic stroke in Romania, issues regarding legal medicine aspects involved in this area of medical expertise were already presented and, in the majority of cases, the doctors seem to be unprepared for these situations. Main text: The present research illustrates some of the cases in which these aspects were involved, that adressed a clinical center having 6 years of professional experience in the application of fibrinolytic treatment for stroke. The following cases report either situations in which the afore mentioned therapy was not rightfully administrated or legal aspects regarding the obtainment of informed consent. Conclusion: Obtaining informed consent is a mandatory procedure, which takes time, to the detriment of application of fibrinolytic treatment.

The aim of this study is to evaluate a new user-friendly scoring system, namely the BETTY score, that aims to predict 30-day patient outcomes after surgery. In this first description, we rely on a population of prostate cancer patients undergoing robot-assisted radical prostatectomy. The BETTY score includes the patient’s American Society of Anesthesiologists score, the body mass index, and intraoperative data, including operative time, estimated blood loss, any major intraoperative complications, hemodynamic, and/or respiratory instability. There is an inverse relationship between the score and severity. Three clusters assessing the risk of postoperative events were defined: low, intermediate, and high risk of postoperative events. A total of 297 patients was included. The median length of hospital stay was 1 day (IQR1-2). Unplanned visits, readmissions, any complications, and serious complications occurred in 17.2%, 11.8%, 28.3%, and 5% of cases, respectively. We found a statistically significant correlation between the BETTY score and all endpoints analyzed (all p ≤ 0.01). A total of 275, 20, and 2 patients were classified as low-, intermediate-, and high-risk according to the BETTY scoring system, respectively. Compared with low-risk patients, patients at intermediate-risk were associated with worse outcomes for all endpoints analyzed (all p ≤ 0.04). Future studies, in various surgical subspecialties, are ongoing to confirm the usefulness of this easy-to-use score in routine.