Monitoring moving bio-objects is currently of great interest for both fundamental and practical research. The advent of deep-learning algorithms has made it possible to automate the qualitative and quantitative analysis of the behavior of bio-objects recorded in video format. When processing such data, it is necessary to consider additional factors, such as background noise in the frame, the speed of the bio-object, and the need to reflect information about the previous (past) and subsequent (future) pose of the bio-object in one video frame. The preprocessed dataset must be suitable for verification by experts. This article proposes a method for preprocessing data to identify the behavior of a bio-object, a clear example of which is experiments on laboratory animals with the collection of video data. The method is based on combining information about a behavioral event presented in a sequence of frames with the addition of a native image and subsequent boundary detection using the Sobel filter. The resulting representation of a behavioral event is easily perceived by both human experts and neural networks of various architectures. The article presents the results of training several neural networks on the obtained dataset and proposes an effective neural network architecture (F1-score = 0.95) for identifying discrete events of biological objects’ behavior.

Non-steroidal anti-inflammatory drugs (NSAIDs) are an important class of medications; however, they have some drawbacks. We are developing a new NSAID with pronounced anti-inflammatory and analgesic activities and a very low toxicity—(Z)-3-(2-oxo-2-(p-tolyl)ethylidene)piperazin-2-one (piron). In this work, we describe the synthesis of the main metabolite of piron—(Z)-4-(2-(3-oxopiperazin-2-ylidene)acetyl)benzoic acid. The anti-inflammatory activity of the synthesized metabolite was determined in vivo.

The biological activity of extracts from Cirsium heterophyllum leaves is of interest. Traditional medicine and preliminary studies indicate that this raw material may have the potential for anti-inflammatory activity. The aim of this study was to evaluate the anti-inflammatory activity of an aqueous extract of Cirsium heterophyllum leaves. We studied the anti-inflammatory activity of aqueous extracts of Cirsium heterophyllum leaves harvested in Vasilyevka village, Perm district, Perm territory (kray), Russia. The extracts were standardized for the content of flavonoids in terms of cynaroside. To study the anti-inflammatory activity, we used carrageenan-induced paw edema model in outbred ICR (CD-1) mice and outbred Wistar rats. Diclofenac was used as a reference drug. The test substances were dissolved in a 2% starch solution and injected intraperitoneally to the animals. The paw volume was measured hydrometrically and photometrically. The study has shown that the aqueous extracts of Cirsium heterophyllum have a mild effect compared to diclofenac. In mice, the percentage of inhibition of edema at the 4th hour was 19% (p-value = 0.0310) and 20% (p-value = 0.0130) for photometry and hydrometry, respectively. In rats, the percentage of edema inhibition was 47% (p-value = 0.0300) at the 1st hour and 28.7% (p-value = 0.0170) at 3 hours for hydrometry; photometry did not confirm these results. The results suggest that the aqueous extract of Cirsium heterophyllum has a mild activity in comparison with diclofenac, which may be due to the mutual action of the extract substances when applied intraperitoneally, as well as the presence of polysaccharides in the aqueous extract. Further research is required to determine whether the anti-inflammatory activity of its aqueous extract might be better combined with wound healing effects when applied topically.

The observation of moving bio-object s is currently a topic of great interest in both fundamental and practical research. To illustrate, when developing and testing pharmaceuticals and vaccines, it is imperative to study their impact on the physiological state and behavior of an animal or human subject. The advent of deep learning algorithms has enabled the automation of qualitative and quantitative analysis of the behavior of bio-objects recorded in video format. It is essential that video data undergoes the appropriate preprocessing prior to training deep learning models. It is essential to consider additional factors, such as the background noise in the frame, the rapidity of bio-object movement, and the necessity to reflect information about the previous (past) and subsequent (future) pose of the bio-object in a single video frame. Moreover, the preprocessed dataset should be suitable for verification by its experts. This paper proposes a method of data preprocessing for the identification of bio-object behavior, with the laboratory animal experiments involving video data collection serving as an illustrative example. The method is based on the combination of information about a behavior event presented in a sequence of frames, with the addition of the native image and further boundary extraction by Sobel filter. Consequently, the behavior event representation obtained is readily comprehensible for both human experts and neural networks of varying architectures. The paper presents the outcomes of training multiple neural networks on the acquired data set and proposes an effective neural network architecture (accuracy = 0.95) for the identification of discrete behavior events of bio-objects.