This study investigates stress recognition using galvanic skin response (GSR) and photoplethysmography (PPG) data and machine learning, with a new focus on air raid sirens as a stressor. It bridges laboratory and real-world conditions and highlights the reliability of wearable sensors in dynamic, high-stress environments such as war and conflict zones. The study involves 37 participants (20 men, 17 women), aged 20–30, who had not previously heard an air raid siren. A 70 dB “S-40 electric siren” (400–450 Hz) was delivered via headphones. The protocol included a 5 min resting period, followed by 3 min “no-stress” phase, followed by 3 min “stress” phase, and finally a 3 min recovery phase. GSR and PPG signals were recorded using Shimmer 3 GSR+ sensors on the fingers and earlobes. A single session was conducted to avoid sensitization. The workflow includes signal preprocessing to remove artifacts, feature extraction, feature selection, and application of different machine learning models to classify the “stress “and “no-stress” states. As a result, the best classification performance was shown by the k-Nearest Neighbors model, achieving 0.833 accuracy. This was achieved by using a particular combination of heart rate variability (HRV) and GSR features, which can be considered as new indicators of siren-induced stress.

The share of chronic odontogenic rhinosinusitis is 40% among all chronic rhinosinusitis. Using automated information systems for differential diagnosis will improve the efficiency of decision-making by doctors in diagnosing chronic odontogenic rhinosinusitis. Therefore, this study aimed to develop an intelligent decision support system for the differential diagnosis of chronic odontogenic rhinosinusitis based on computer vision methods. A dataset was collected and processed, including 162 MSCT images. A deep learning model for image segmentation was developed. A 23 convolutional layer U-Net network architecture has been used for the segmentation of multi-spiral computed tomography (MSCT) data with odontogenic maxillary sinusitis. The proposed model is implemented in such a way that each pair of repeated 3 × 3 convolutions layers is followed by an Exponential Linear Unit instead of a Rectified Linear Unit as an activation function. The model showed an accuracy of 90.09%. To develop a decision support system, an intelligent chatbot allows the user to conduct an automated patient survey and collect patient examination data from several doctors of various profiles. The intelligent information system proposed in this study made it possible to combine an image processing model with a patient interview and examination data, improving physician decision-making efficiency in the differential diagnosis of Chronic Odontogenic Rhinosinusitis. The proposed solution is the first comprehensive solution in this area.

Precancerous processes of the female reproductive system continue to be an issue that is being actively studied, despite the successes of recent years. One of the key prognostic factors is the characterization of the receptor set, primarily the mammary gland and endometrium, namely estrogen (ER), progesterone (PR), epidermal growth factor-2 (HER2) receptors. Purpose – to identify the features of the distribution of estrogen, progesterone and epidermal growth factor receptors in the tissues of the endometrium, cervix and mammary gland with a combination of HIV infection and alcohol abuse. Fifty women of reproductive age from 20 to 40 years were studied. After routine conduction and immunohistochemical staining, ER and PR expression were determined by a modified D.Allred scale. HER2 expression was determined by a semi-quantitative method. The obtained digital data were statistically processed. A morphometric study indicates that the combination of HIV infection and alcohol abuse significantly affects the distribution of receptors in the female reproductive system. To a greater extent, the overall score is reduced by reducing the number of cellular elements with the expression of ER and PR. HER2 receptor expression is significantly reduced in the endometrium and mammary gland. These changes may be facilitated by atrophic changes in these organs, which occurs in immunodeficiency. According to the D.Allred scale, the mean score decreases from 5.45±0.63 to 3.10±0.88 in the endometrium, from 4.68±0.65 to 2.47±0.58 in the cervix, from 6.57±0.67 to 3.57±0.77 in the mammary gland (p<0.05). HER2 expression decreased from 3.24±0.47 to 2.21±0.23 in the endometrium, from 3.43±0.66 to 2.36±0.32 in the mammary gland (p<0.05). The development of immunodeficiency is characterized by the restructuring of female reproductive organs with a change in the receptor set of the endometrium, cervix and mammary gland and a decrease in both the qualitative and quantitative composition of the receptors, which may serve as a prerequisite for the development of a triple-negative phenomenon in tumors of these organs.

Despite the importance of image assessment in the biomedical field (radiation diagnosis, otolaryngology, dentistry, pathology, etc.) there is no clearly defined unified image processing algorithm to date, the result of examination and interpretation of these images often depends on the physician's qualifications and experience. The purpose of our study was implementation of the computer vision system for evaluation of CT images for obtaining impersonal real data. Thus, in the course of the study, a number of medical goals were solved, which included the need to measure bone thickness on radiograms, tomograms, SCTs and other images. This is especially relevant for measuring the minimum thickness, as a thin bone can be easily damaged by a medical instrument during surgery (for example, damage to the upper wall of the maxillary sinus with damage to the orbit). The suggested method could be used for automated measurement of bone thickness of the walls of the paranasal sinuses. The obtained results can be implemented in the work of doctors in a number of specialties (therapists, radiologists, otolaryngologists, dentists, ophthalmologists, neurosurgeons) for study many biomedical images.

Today 3D modeling with AI involvement is a promising direction in all branches of science, in particular in medicine and especially in otolaryngology. The study involved 100 subjects without any abnormalities of the ENT organs. The main anatomical structures of the ostiomeatal complex, the sizes of which were determined, were the uncinated process, the middle nasal concha and the natural anastomosis between the nasal cavity and the anterior group of the paranasal sinuses. On the basis of the study, it can be assumed that the structure of the ostiomeatal complex components during 3D modeling is as close as possible when measured in vivo by means of endoscopic surgical treatment.