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Optimization of antitumor radiotherapy represents an urgent issue, as approximately half of the patients diagnosed with cancer undergo radiotherapy during their treatment. Proton therapy is potentially more efficient than traditional X-ray radiotherapy due to fundamental differences in physics of dose deposition, leading to better targeting of tumors and less collateral damage to healthy tissue. There is increasing interest in the use of non-radioactive radiosensitizing tumor-specific nanoparticles the use of which can boost the performance of proton therapy. Such nanoparticles are small volumes of a sensitizer, such as boron-10 or various metal oxides, enclosed in a polymer layer containing tumor-specific antibodies, which allows for their targeted delivery to malignant cells. Furthermore, a combination of proton therapy with antiangiogenic therapy that normalizes tumor-associated microvasculature may yield further synergistic increase in overall treatment efficacy.

We have developed a spatially distributed mathematical model simulating the growth of a non-invasive tumor undergoing treatment by fractionated proton therapy with nanosensitizers and antiangiogenic therapy. The modeling results suggest that the most effective way to combine these treatment modalities should strongly depend on the tumor cells’ proliferation rate and their intrinsic radiosensitivity. Namely, a combination of antiangiogenic therapy with proton therapy, regardless of whether radiosensitizing nanoparticles are used, benefits treatment efficacy of rapidly growing tumors as well as radioresistant tumors with moderate growth rate. In these cases, administration of proton therapy simultaneously with antiangiogenic drugs after the initial single injection of nanosensitizers is the most effective option among those analyzed. Conversely, for slowly growing tumors, maximization of the number of nanosensitizer injections without antiangiogenic therapy proves to be a more efficient option, with enhancement in treatment efficacy growing with the increase of tumor radiosensitivity. However, the results also show that the overall efficacy of proton therapy is likely to increase only modestly with the addition of nanosensitizers and antiangiogenic drugs.

In recent decades, the introduction of biophotonics and quantum electronics advance into medical practice led to the development of new diagnostic and therapeutic approaches for many diseases. In the field of oncology, photodynamic therapy (PDT) is successfully used today in the treatment of various types of cancer. Along with further improvement of PDT, the development of direct laser therapy is currently underway, in which the generation of singlet oxygen molecules ($^{1}$О$_2^{}$) in cancer cells occurs under NIR laser irradiation with a wavelength of $\lambda=1267$ nm without the need to introduce photosensitizers into the patient's body. For the purpose of a~theoretical investigation of the direct effect of NIR laser irradiation on cancer cells and the description of a~large set of experimental data, a mathematical model has been developed. The model includes the main cellular processes activated in cancer cells by NIR laser irradiation that determine the effectiveness of its cytotoxic effect on cancer cells. As a result of modeling, the rate of $^{1}$О$_2^{}$ generation under NIR laser irradiation was estimated, and the kinetics of active oxygen species (ROS) molecules was described. The ROS degradation due to the action of the antioxidant system of cell protection was taken into account in the model. It was shown that NIR laser irradiation induces lipid peroxidation that leads to cellular membrane damage and cell death through ferroptosis. As a result of modeling, it was established that a cascade of free-radical and enzymatic reactions of ROS transformation and accumulation leads to a prolonged response of cervical adenocarcinoma cells HeLa to the action of laser irradiation with $\lambda=1267$ nm, during which oxidative stress develops, causing cancer cell death through apoptosis and ferroptosis.

In this paper the various methods of machine classification of scientific texts by thematic sections on the example of publications in specialized medical journals published by Springer are compared. The corpus of texts was studied in five sections: pharmacology/toxicology, cardiology, immunology, neurology and oncology. We considered both classification methods based on the analysis of annotations and keywords, and classification methods based on the processing of actual texts. Methods of Bayesian classification, reference vectors, and reference letter combinations were applied. It is shown that the method of classification with the best accuracy is based on creating a library of standards of letter trigrams that correspond to texts of a certain subject. It is turned out that for this corpus the Bayesian method gives an error of about 20%, the support vector machine has error of order 10%, and the proximity of the distribution of three-letter text to the standard theme gives an error of about 5%, which allows to rank these methods to the use of artificial intelligence in the task of text classification by industry specialties. It is important that the support vector method provides the same accuracy when analyzing annotations as when analyzing full texts, which is important for reducing the number of operations for large text corpus.