Peoples' Friendship University of Russia

Photocatalysis is a promising sustainable technology to remove organic pollution and convert solar energy into chemical energy. Titanium dioxide has drawn extensive attention in this field owing to its high activity under UV light, good chemical stability, large availability, low price and low toxicity. However, the poor quantum efficiency derived from fast electron/hole recombination, the limited utilization of sunlight, and a weak reducing ability still hinder its practical application. Among the modification strategies of TiO₂ to enhance its performance, the construction of heterojunctions with other semiconductors is a powerful and versatile way to maximise the separation of photogenerated charge carriers and steer their transport toward enhanced efficiency and selectivity. Here, the research progress and current status of TiO₂ modification are reviewed, focusing on heterojunctions. A rapid evolution of the understanding of the different charge transfer mechanisms is witnessed from traditional type II to the recently conceptualised S-scheme. Particular attention is paid to different synthetic approaches and interface engineering methods designed to improve and control the interfacial charge transfer, and several cases of TiO₂ heterostructures with metal oxides, metal sulfides and carbon nitride are discussed. The application hotspots of TiO₂-based photocatalysts are summarized, including hydrogen generation by water splitting, solar fuel production by CO₂ conversion, and the degradation of organic water pollutants. Hints about less studied and emerging processes are also provided. Finally, the main issues and challenges related to the sustainability and scalability of photocatalytic technologies in view of their commercialization are highlighted, outlining future directions of development.

The Banff classification is useful for diagnosing renal transplant rejection. However, it has limitations due to subjectivity and varying concordance in physicians' assessments. Artificial intelligence (AI) can help standardize research, increase objectivity and accurately quantify morphological characteristics, improving reproducibility in clinical practice. This study aims to develop an AI-based solutions for diagnosing acute kidney transplant rejection by introducing automated evaluation of prognostic morphological patterns. The proposed approach aims to help accurately distinguish borderline changes from rejection. We trained a deep-learning model utilizing a fine-tuned Mask R-CNN architecture which achieved a mean Average Precision value of 0.74 for the segmentation of renal tissue structures. A strong positive nonlinear correlation was found between the measured infiltration areas and fibrosis, indicating the model's potential for assessing these parameters in kidney biopsies. The ROC analysis showed a high predictive ability for distinguishing between ci and i scores based on infiltration area and fibrosis area measurements. The AI model demonstrated high precision in predicting clinical scores which makes it a promising AI assisting tool for pathologists. The application of AI in nephropathology has a potential for advancements, including automated morphometric evaluation, 3D histological models and faster processing to enhance diagnostic accuracy and efficiency.

Applications of Artificial Intelligence in medical informatics solutions risk sharing have social value. At a time of ever-increasing cost for the provision of medicines to citizens, there is a need to restrain the growth of health care costs. The search for computer technologies to stop or slow down the growth of costs acquires a new very important and significant meaning. We discussed the two information technologies in pharmacotherapy and the possibility of combining and sharing them, namely the combination of risk-sharing agreements and Machine Learning, which was made possible by the development of Artificial Intelligence (AI). Neural networks could be used to predict the outcome to reduce the risk factors for treatment. AI-based data processing automation technologies could be also used for risk-sharing agreements automation.