Immanuel Kant Baltic Federal University

A general approach was developed to the synthesis of 3-amino-4-arylquinolin-2(1H)-ones and 3-amino-4-arylquinoline-2(1H)-thiones, based on simple and readily available reagents, with good overall yields of up to 76%. A broad variety of aryl and heteroaryl substituents introduced at the C-4 position of the quinoline system was achieved. The route can also be used for the synthesis of 4-alkyl derivatives, as shown in the example of 4-methylquinolines. Solutions of some synthesized compounds emit in the blue-violet region of the visible spectrum with fluorescence quantum yields of up to 0.33 upon UV irradiation.

A high-resolution nonlinear numerical model is used to simulate propagation of internal gravity waves (IGWs) from the troposphere to the upper atmosphere. This simulation takes into account background wind profiles containing critical levels at which the horizontal wind velocity becomes equal to the horizontal phase speed of IGW. According to traditional linear theories of atmospheric waves, near critical levels the vertical wavelength approaches zero, which should lead to a strong dissipation of IGWs propagating from the troposphere and may significantly decrease their ampli-tudes in the upper atmosphere. The wave sources in the model are defined as vertical velocity perturbations propagating along the Earth surface. The mean horizontal wind in the atmosphere is approximated by the Gaussian profile with a maximum at an altitude of 50 km. We analyze the spectra of wave fields near criti-cal levels and at a distance from them. It has been found that the instability of waves near critical levels intensifies the energy transition from primary IGWs propagating from surface sources to secondary wave modes. This causes an increase in spectral peaks at wavelengths shorter than the horizontal length of primary IGW. Therefore, above critical levels, spectral modes with shorter horizontal wavelengths begin to prevail with increasing altitude, and the amplitudes of these secondary waves at the same altitudes can exceed the amplitudes of analogous primary IGW propagating in the absence of critical levels in the middle atmosphere.

The global incidence and mortality rates of pancreatic cancer have been on the rise in recent decades. At the same time, pancreatic cancer occupies a leading position among malignant neoplasms, which are often first detected at advanced stages, which significantly worsens the prognosis for patients. In this regard, it is important to create automated systems capable of accurately segmenting and diagnosing pancreatic tumors at an early stage. A diagnostic model based on U-Net is proposed and evaluated, aimed at improving the accuracy of automatic segmentation of medical images for segmentation of the pancreas and identification of malignant changes in computed tomography (CT) images. The diagnostic model of the algorithm was based on the U-Net architecture, and image texture indices were used as control features. The neural network algorithm was trained using CT data from 310 patients with pancreatic neoplasms. Segmentation datasets from 280 pancreatic cancer studies from an open source, Memorial Sloan Kettering Cancer Center, was used to segment medical images and train the algorithm. Manual segmentation of medical images obtained at Regional Clinical Hospital of the Kaliningrad was performed using specialized three-dimensional (3D) Slicer software: CT images of 30 patients with pancreatic neoplasms. Diagnoses in the patient group were confirmed by morphological diagnostics, including examination of biopsy samples and/or surgical material. Four types of indicators were used to quantify segmentation results: Dice similarity coefficient (DSC), accuracy, sensitivity, and specificity. The diagnostic model achieved an accuracy of 88% in the classification of pancreatic cancer, and the DSC segmentation accuracy factor was 70%, demonstrating sensitivity of 98% and specificity of 98%. The results obtained emphasize the effectiveness of CT image analysis for the diagnosis of pancreatic malignancies using a neural network algorithm that uses textural features of medical images. In the course of the work, a number of limitations were identified: obtaining false positive results during segmentation, a limited number of medical images and clinical data that may affect the representativeness of the developed diagnostic model. Nevertheless, the results show the prospects for integrating artificial intelligence (AI) technologies into clinical practice, which can significantly improve diagnostic efficiency. The use of neural network algorithms based on the textural features of medical images has significant potential in the field of clinical decision support systems. Despite certain limitations in the course of work, the using textural features for training neural networks will increase the accuracy of diagnosis of pancreatic malignancies and will play an important role in determining patient management strategies and monitoring the effectiveness of prescribed treatment methods. It is necessary to further study additional textural characteristics, utilize 3D models, and include more clinical data to improve the accuracy of the diagnostic model when differentiating malignant tumors from healthy tissues.

Decades ago, the introduction of GC-MS marked a significant advancement in primary plant metabolite studies. Here, in our review, we will delve into critical aspects of the workflow, spanning the selection of an analytical platform, sample preparation, analytical acquisition, and data processing and interpretation. The exceptional separation capabilities of GC, characterized by remarkable chromatographic resolution, render it ideal for analysis of the complex plant metabolome, including the separation of isomeric compounds. The diversity of analytical platforms allows the investigation of plant metabolomes using targeted and non-targeted approaches. GC-MS, equipped with efficient extraction methods and reliable derivatization protocols for semi- and non-volatile compounds, enables qualitative and quantitative analysis of these molecules. The stability of derivatives forms the foundation for the robustness and reproducibility of GC-MS methods, and their mass spectra provide characteristic fragments for confident identification and sensitive quantification of individual metabolites. There has been key progress in the advancement of GC-MS approaches to studying plant metabolism. However, the presence of artifacts during GC-MS analysis, particularly during derivatization, is a challenge that requires careful validations, which frequently necessitate additional investigations. The feasible solutions that were achieved to overcome the limitations in GC-MS-based studies are a particular focus of the present discussion.

HighlightsWhat are the main findings?Transient, region-specific microglial 5-HTR expression;Upregulated microglial 5-HT2b in Tph2−/− rats parallels BDNF levels.What are the implications of the main findings?Temporal differences in 5-HTR signaling may drive adaptive responses to serotonin deficiency, with BDNF compensating at the later stage;Interplay between microglial serotonin and BDNF highlights potential targets for interventions in neuroinflammation.Growing evidence suggests that psychiatric disorders are characterized by a prolonged inflammatory state, which may influence the efficacy of compounds targeting serotonin. Serotonin is a key signaling molecule in neuroplasticity of the adult hippocampus and involved in antidepressant action. Recent in vitro studies indicate the neurotransmitter may also facilitate the response to inflammation and potentially modulate microglial function towards neuroprotection. Using Tph2−/− rats depleted of brain serotonin, we examined microglial expression of various serotonin receptors (5-HTRs) in vivo in both the hippocampus and prefrontal cortex and assessed mRNA levels of cytokines and brain-derived neurotrophic factor (BDNF). We observed age-dependent and region-specific gene expression of 5-HTRs on sorted microglia, paralleling changes in BDNF signaling, especially with 5-HT2b. Notably, both 5-HT2b and BDNF expression in the hippocampus was significantly upregulated in the absence of brain serotonin. Our data indicate distinct roles of 5-HTR subtypes in early network formation (5-HT1b, 5-HT5b) and in the response to endogenous changes (5-HT2b, 5-HT5a). Understanding serotonin–microglia interplay could offer therapeutic insights into the maintenance of mood via brain–immune cell interactions.

The article analyzes the education system for ethnic minorities in the People's Republic of China through the lens of the social governance concept. The central research problem is finding a balance between the policy of forming "The unity of the Chinese nation" and the constitutionally guaranteed rights of ethnic groups to preserve their linguistic and cultural diversity. The author traces the evolution of the PRC's ethnic educational policy from 1949 to the present, highlighting a shift from the model dominant during the Mao era – which can be characterized by the Confucian concept of "uniformity without unity" (tong er bu he) – to the contemporary model of "unity in diversity" (he er bu tong). Based on the theoretical framework of "vertical interaction", a hybrid governance model is analyzed, in which the Party and the government as the subject retain hierarchical control but delegate some authority and the responsibility for preserving their identity to ethnic groups as the object. As a result, the ways of implementing the "unity and diversity" model within the four key strategies of modern ethnic education – multiculturalism, cultural transformation, ethnic identity, and social equality – have been identified and characterized.