The development of a novel artificial heart valve with outstanding durability and safety has remained a challenge since the first mechanical heart valve entered the market 65 years ago. Recent progress in high-molecular compounds opened new horizons in overcoming major drawbacks of mechanical and tissue heart valves (dysfunction and failure, tissue degradation, calcification, high immunogenic potential, and high risk of thrombosis), providing new insights into the development of an ideal artificial heart valve. Polymeric heart valves can best mimic the tissue-level mechanical behavior of the native valves. This review summarizes the evolution of polymeric heart valves and the state-of-the-art approaches to their development, fabrication, and manufacturing. The review discusses the biocompatibility and durability testing of previously investigated polymeric materials and presents the most recent developments, including the first human clinical trials of LifePolymer. New promising functional polymers, nanocomposite biomaterials, and valve designs are discussed in terms of their potential application in the development of an ideal polymeric heart valve. The superiority and inferiority of nanocomposite and hybrid materials to non-modified polymers are reported. The review proposes several concepts potentially suitable to address the above-mentioned challenges arising in the R&D of polymeric heart valves from the properties, structure, and surface of polymeric materials. Additive manufacturing, nanotechnology, anisotropy control, machine learning, and advanced modeling tools have given the green light to set new directions for polymeric heart valves.

Complex health-care data will be growing exponentially as we enter the era of “big data”. Interpretation and analysis of a large amount of medical data for rapid and personalized decision-making require artificial intelligence-driven technologies and data mining techniques. Over the past decade, a rapid transition to the analysis, treatment and monitoring of kidney stone disease using the artificial intelligence (AI) with machine learning algorithms and artificial neural networks ensures the development of precise support systems. Machine learning algorithms able to predict the location of stones using computed tomography scans or ultrasound images, determine their chemical composition, recurrence rate based on demographic and genetic variables, spontaneous ureteral calculous passage, and treatment outcomes. Recent studies use rather simple machine learning methods and small training sets that reduce their accuracy, sensitivity, and specificity and limit their routine application. Therefore, the development of complex architectures, using multilayer artificial neural networks and convolutional neural networks, and integration other heterogeneous variables will improve the resultant predictive accuracy ensuring their introduction in real clinical practice.

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Introduction. Nowadays the standard of care for locally advanced and metastatic urothelial carcinoma (UC) is a combination of platinum-based drugs. However, such a therapy is characterized with high toxicity and selective efficacy. So, the question of the optimal alternative to the first line of therapy and the choice of drugs for the second line of therapy is currently relevant.Immune checkpoint inhibitors have revolutionized the treatment of UC. Nevertheless, despite the fact that initially the drugs of this series showed a fairly high efficacy as a second-line therapy for metastatic UC, at present there is no unambiguous opinion about the correct tactics of their use. There is also no consensus on the predictive value of PD-L1 biomarkers and their significance in determining treatment tactics.Aim. To evaluate the efficacy and tolerability of first-line atezolizumab therapy in 22 patients with unresectable forms of UC.Materials and methods. The experience of the State Clinical Hospital named after D.D. Pletnev on the example of 22 patients with advanced UC who received first-line therapy with atezolizumab 1200 mg intravenously once every 21 days until progression or intolerable toxicity. Efficacy was assessed according to RECIST 1.1 criteria.Results and discussion. Median follow-up 16.3 months. The objective response rate (ORR) is estimated at 72.7%, 95% CI. A complete response according to RECIST 1.1 criteria was observed in 5 patients (22.7%). Median time to first response was 2.2 months (range 1.5-5.7), late responses (at 5 and 5.7 months) required space in 2 patients. Median progression-free survival was 5.2 months (95% CI) in all patients. Median overall survival (OS) 18.5 months (95% CI). Specific application-related events were required in 10 (45.4%) cases. All the side effects were managed by standard symptomatic therapy. The dosage of atezoli-zumab was reduced in 7 (32%) cases. Immune-mediated adverse events were reported in 5 (23%) patients. No patient received systemic non-corticosteroid immunomodulatory agents for immune-mediated events. 2 (9%) patients received corticosteroids.Conclusions. Atezolizumab has shown high efficacy in the first line of treatment for advanced UC.

The standards of treatment for metastatic renal cell carcinoma (mRCC) have changed significantly from unsuccessful attempts of radiation and cytostatic therapy to the encouraging results of targeted therapy and specific immunotherapy. Sunitinib has got into the practice in 2006, and now it`s one of the most studied and approved. Sunitinib is one of the first oral targeted drugs for RCC. It affects such receptors as VEGFR1, 2, 3; PDGFR, FGFR, c-KIT, and RET, which take part in the pathologic angiogenesis, tumor growth, and metastasizing. Moreover, sunitinib stimulates the growth and development of lymphatic vessels, that deliver immunocytes to the tumor. The advantage of sunitinib over non-specific immunotherapy has been proven by Motzer et al. The randomized trials COMPARZ, RECORD-3, and SWITCH have confirmed that sunitinib is more effective than several targeted drugs (pazopanib, everolimus, and sorafenib respectively) as the first line of treatment for mRCC. The randomized trial of the 3rd phase CARMENA has demonstrated the importance of sunitinib monotherapy for mRCC of intermediate and poor prognosis. In general, sunitinib has been proven to be an effective first-line drug for mRCC, as it`s evidenced in the comprehensive metaanalysis of real-world data and randomized controlled trials published between 2000 and 2017. Nowadays, despite the success of the immunotherapeutic direction, tyrosine kinase inhibitors, and particularly sunitinib, rightfully remain the standard for mRCC of favourable prognosis, the treatment option for worse prognosis in case of contraindications for other methods of therapy, and it` s also used in subsequent therapy lines.