Accurate identification of sleep stages is essential for understanding sleep physiology and its role in neurological and behavioral research. Manual scoring of polysomnographic data, while reliable, is time-intensive and prone to variability. This study presents a novel Python-based algorithm for automated vigilance state scoring using single-channel electroencephalogram (EEG) recordings from rats and mice. The algorithm employs artifact processing, multi-band frequency analysis, and Gaussian mixture model (GMM)-based clustering to classify wakefulness, non-rapid, and rapid eye movement sleep (NREM and REM sleep, respectively). Combining narrow and broad frequency bands across the delta, theta, and sigma ranges, it uses a majority voting system to enhance accuracy, with tailored preprocessing and voting criteria improving REM detection. Validation on datasets from 10 rats and 10 mice under standard conditions showed sleep–wake state detection accuracies of 92% and 93%, respectively, closely matching manual scoring and comparable to existing methods. REM sleep detection accuracies of 89% (mice) and 91% (rats) align with previously reported (85–90%). Processing a full day of EEG data within several minutes, the algorithm is advantageous for large-scale and longitudinal studies. Its open-source design, flexibility, and scalability make it a robust, efficient tool for automated rodent sleep scoring, advancing research in standard experimental conditions, including aging and sleep deprivation.

Introduction: The olfactory system in chronic animal experiments is studied according to the records of bioelectric activity recorded with microelectrode arrays located on the dorsal surface of the olfactory bulb. It is believed that the response of the olfactory bulb bioelectric activity to the odorant presentation is associated with fluctuations synchronized by respiration and belonging to the range of so-called gamma rhythms. Purpose: To develop a method of automatic search for time boundaries of odor-induced gamma rhythm patterns in the bioelectric activity of an olfactory bulb. Results: The complexity of automatic search for odor-induced patterns are due to masking them by spontaneous oscillations of gamma rhythms unrelated to the olfactory system activity. Besides, the modal frequency of the patterns is a priori unknown and changes dynamically during an experiment on an anesthetized animal. The Hausdorff distance is used as a cost function of the search. In order to increase its sensitivity, an adaptive band-pass filter was synthesized based on a multichannel singular spectrum analysis. Its passband in the analyzed time sample corresponds best to the spectrum of the required patterns. The results of experiments on rats are presented. The use of band-pass filters based on the Fourier transform with parameters unchanged during the experiment due to the non-stationary nature of the frequency characteristics of the desired patterns does not ensure their effective search. In particular, when using the Butterworth band-pass filter in the search algorithm, significant errors were observed in determining the time boundaries of the epochs of patterns, and omissions of patterns. But the use of a synthesized adaptive band-pass filter provided reliable automatic search for patterns and determination of their time boundaries with a high accuracy. Algorithm failures in this case were observed only in rat motion artifacts. Practical relevance: The developed method extends the existing tools used to study the olfactory system of a living organism.

It is known that the characteristics of spontaneous and evoked neuronal activity undergo changes over time. Typically, these changes are studied in acute and subacute experimental settings (e.g., pharmacological anesthesia, sleep-wake cycles). Long-term (weeks to months) recording of activity from identified neurons presents considerable methodological challenges. Consequently, questions regarding whether such changes occur, their nature, and their impact on the functional properties of neurons and neuronal populations require further investigation. To our knowledge, such studies at the level of olfactory bulb (OB) neurons—particularly in relation to the perception of ultra-low-concentration odorants—have not been conducted to date. The aim of this work was to perform a longitudinal study of the characteristics of spontaneous and evoked activity (triggered by presentation of sulcatone vapor at ultra-low concentration, ~1 × 10 g/cm) in identified mitral/bundle (M/B) neurons of the OB. To achieve this, M/B neuron activity in the OB was periodically recorded (no more than once weekly) under xylazine-tiletamine-zolazepam anesthesia in 5 rats over at least 1 month (maximum: 121 days). Analysis showed that the characteristics of both spontaneous and odorant-evoked activity in M/B neurons remain stable over extended periods. This finding is significant for research aimed at developing biohybrid sensory system technologies for identifying target substances at ultra-low concentrations.

Introduction. One of the leading places in the structure of socially significant diseases in the Russian Federation is occupied by malignant neoplasms, among which cancer of the trachea, bronchi and lung occupies the first place in terms of prevalence. Screening for lung cancer is not carried out at the federal level in Russia; recommended screening measures include questionnaires to identify risk factors for the development of the disease (including occupational) and the formation of dispensary observation groups. The study aims to assess the prognostic value of a new method of biohybrid screening of lung cancer by exhaled air. Materials and methods. Scientists conducted a study at the National Medical Research Center of Oncology of the Ministry of Health, Russian Federation, with the participation of 24 conditionally healthy volunteers and 5 patients diagnosed with lung cancer in the early stages of the disease. The design of the study is simultaneous (on-line) with blinding; experts estimated the risk of systematic errors at 10 points on the QUADAS scale. The authors examined exhaled air samples on a gas analyzer (biohybrid detector) of the BGS-APK 02SZZ model, in which the sensor were animals — gray domesticated rats (Rattus norvegicus) with microelectrode matrices implanted into the olfactory bulb, and which were in the internal (ventilated) space of the analyzer in a state of drug anesthesia. The conclusion about the presence or absence of a risk of lung cancer was formed by an artificial neural network of the analyzer, at the input of which bioelectric signals from each microelectrode were received, which were preprocessed using a system for recording the focal activity of the olfactory analyzer. The specialists confirmed the presence or absence of a risk of lung cancer by a reference method, according to computed tomography of the chest in accordance with the Lung Imaging Reporting and Data System (LungRADS™). Results. During the study, experts determined the effectiveness indicators of the method of biohybrid lung cancer screening by exhaled air: sensitivity (64%), specificity (87%), prognosticality of negative and positive conclusions about the presence/absence of disease risk (82% and 72%, respectively). Conclusion. The safety, efficiency and throughput of the investigated method, together with the reproducibility of the analyzer's conclusions, meet the criteria of practicability to ensure the first (population-based) stage of lung cancer screening.