Postoperative assessment of malignant melanoma aggressiveness for further treatment personalization

The aim of the study was to develop, implement and evaluate a method for predicting the aggressiveness of primary melanoma after surgical removal.It was established that the method for predicting tumor aggressiveness allows to determine the degree of aggressiveness, life expectancy, and to identify patients with poor prognosis in order to individualize treatment. The survival rate of patients was found to depend on the degree of aggressiveness of the tumor. A group of patients with stages 0-IIa (16,4 %) and tumor aggressiveness Grade II was identified as having a potentially high risk of progression, which can help individualize treatment for this category of patients. Using the method for predicting disease progression may potentially expand the scope of indications for further personalized treatment.

A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI

Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adult patients with a median survival of around one year. Prediction of survival outcomes in GBM patients could represent a huge step in treatment personalization. The objective of this study was to develop machine learning (ML) algorithms for survival prediction of GBM patient. We identified a radiomic signature on a training-set composed of data from the 2019 BraTS challenge (210 patients) from MRI retrieved at diagnosis. Then, using this signature along with the age of the patients for training classification models, we obtained on test-sets AUCs of 0.85, 0.74 and 0.58 (0.92, 0.88 and 0.75 on the training-sets) for survival at 9-, 12- and 15-months, respectively. This signature was then validated on an independent cohort of 116 GBM patients with confirmed disease relapse for the prediction of patients surviving less or more than the median OS of 22 months. Our model insured an AUC of 0.71 (0.65 on train). The Kaplan–Meier method showed significant OS difference between groups (log-rank p = 0.05). These results suggest that radiomic signatures may improve survival outcome predictions in GBM thus creating a solid clinical tool for tailoring therapy in this population.

Pharmacogenetic approach to methotrexate-related toxicity prediction in psoriasis

Methotrexate is a highly effective for psoriasis, but the use of methotrexate may be limited by its adverse effects. Approximately 1030% of patients treated with methotrexate experience adverse drug reactions, leading to the therapy discontinuation. Patient genetics can play a significant role in the interindividual variability of drug response. There is a growing body of literature on allelic variants of various genes that are assosiated with methotrexate toxicity. Pharmacogenetic studies may establish how patients genotype affect the safety of methotrexate. Treatment Data shows that to predict the risk of methotrexate-induced toxicity it is necessary to take into account the interindividual variability in methotrexate pharmacokinetics, which may be determined by the presence of single-nucleotide polymorphisms of genes encoding methotrexate carrier proteins and enzymes of its biotransformation. The activity of transporter proteins affects the drugs in the blood plasma and peripheral tissues, thereby determining its toxicity. The review was aimed is to summarize the current knowledge on pharmacogenetic polymorphisms that may affect the variability of methotrexate-related toxicity. Evaluation of such promising candidates for predictors of methotrexate-related toxicity risk could be used in psoriasis treatment personalization.

Targeting DNA Damage Response and Repair to Enhance Therapeutic Index in Cisplatin-Based Cancer Treatment

Platinum-based chemotherapies, such as cisplatin, play a large role in cancer treatment. The development of resistance and treatment toxicity creates substantial barriers to disease control, yet. To enhance the therapeutic index of cisplatin-based chemotherapy, it is imperative to circumvent resistance and toxicity while optimizing tumor sensitization. One of the primary mechanisms by which cancer cells develop resistance to cisplatin is through upregulation of DNA repair pathways. In this review, we discuss the DNA damage response in the context of cisplatin-induced DNA damage. We describe the proteins involved in the pathways and their roles in resistance development. Common biomarkers for cisplatin resistance and their utilization to improve patient risk stratification and treatment personalization are addressed. Finally, we discuss some of the current treatments and future strategies to circumvent the development of cisplatin resistance.

A Mechanistic DNA Repair and Survival Model (Medras): Applications to Intrinsic Radiosensitivity, Relative Biological Effectiveness and Dose-Rate

Variations in the intrinsic radiosensitivity of different cells to ionizing radiation is now widely believed to be a significant driver in differences in response to radiotherapy. While the mechanisms of radiosensitivity have been extensively studied in the laboratory, there are a lack of models which integrate this knowledge into a predictive framework. This paper presents an overview of the Medras model, which has been developed to provide a mechanistic framework in which different radiation responses can be modelled and individual responses predicted. This model simulates the repair of radiation-induced DNA damage, incorporating the overall kinetics of repair and its fidelity, to predict a range of biological endpoints including residual DNA damage, mutation, chromosome aberration, and cell death. Validation of this model against a range of exposure types is presented, including considerations of varying radiation qualities and dose-rates. This approach has the potential to inform new tools to deliver mechanistic predictions of radiation sensitivity, and support future developments in treatment personalization.

Evolution of International Psychiatry

International psychiatry is currently facing serious challenges triggered by the global economic crisis and the COVID-19 pandemic. These global events lead to the need to broaden our nosographic and therapeutic horizons, and to make use of the newest psychological approaches and the latest neuroscience acquisitions. The focus should be on the psychological consequences of the pandemic, not only on people suffering from mental disorders, but also on the general population, for which the risk of developing psychic symptoms appears to be increased. A population that needs special attention is that of health workers involved in the management of the pandemic. In facing these problems, psychiatry today can use numerous new clinical applications and technologies in the fields of precision medicine. These include genomics, neuroimaging, and microbiomics, which can also be integrated with each other through machine learning systems. They can provide new contributions both in treatment personalization and in the evolution of nosographic systems. Besides this, the contribution of psychotherapies and dynamic and clinical psychology appears to be indispensable for a complete understanding of the clinical and personological aspects of patients. This journal aims to include innovative studies deriving from original, clinical, and basic research in the fields of mental health, precision psychiatry, genomics, neuroimaging, neuropsychopharmacology, and dynamic and clinical psychology.