In this study, the mechanical and thermophysical properties of carbon fiber-reinforced polyethersulfone are investigated. To enhance the interfacial interaction between carbon fibers and the polymer matrix, the surface modification of carbon fibers by thermal oxidation is conducted. By means of AFM and X-ray spectroscopy, it is determined that surface modification changes the morphology and chemical composition of carbon fibers. It is shown that surface modification dramatically increases the mechanical properties of the composites. Thus, flexural strength and the E-modulus of the composites reinforced with modified fibers reached approximately 962 MPa and 60 GPa, respectively, compared with approximately 600 MPa and 50 GPa for the composites reinforced with the initial ones. The heat deflection temperatures of the composites reinforced with the initial and modified fibers were measured. It is shown that composites reinforced with modified fibers lose their stability at temperatures of about 211 °C, which correlates with the glass transition temperature of the PES matrix. The thermal conductivity of the composites with different fiber content is investigated in two directions: in-plane and transverse to layers of carbon fibers. The obtained composites had a relatively high realization of the thermal conductive properties of carbon fibers, up to 55–60%.

The structure of self-reinforced composites (SRCs) based on ultra-high molecular weight polyethylene (UHMWPE) was studied by means of Wide-Angle X-ray Scattering (WAXS), X-ray tomography, Raman spectroscopy, Scanning Electron Microscopy (SEM) and in situ tensile testing in combination with advanced processing tools to determine the correlation between the processing conditions, on one hand, and the molecular structure and mechanical properties, on the other. SRCs were fabricated by hot compaction of UHMWPE fibers at different pressure and temperature combinations without addition of polymer matrix or softener. It was found by WAXS that higher compaction temperatures led to more extensive melting of fibers with the corresponding reduction of the Herman’s factor reflecting the degree of molecular orientation, while the increase of hot compaction pressure suppressed the melting of fibers within SRCs at a given temperature. X-ray tomography proved the absence of porosity while polarized light Raman spectroscopy measurements for both longitudinal and perpendicular fiber orientations showed qualitatively the anisotropy of SRC samples. SEM revealed that the matrix was formed by interlayers of molten polymer entrapped between fibers in SRCs. Moreover, in situ tensile tests demonstrated the increase of Young’s modulus and tensile strength with increasing temperature.

Studying cell settlement in the three-dimensional structure of synthetic biomaterials over time is of great interest in research and clinical translation for the development of artificial tissues and organs. Tracking cells as physical objects improves our understanding of the processes of migration, homing, and cell division during colonisation of the artificial environment. In this study, the 3D environment had a direct effect on the behaviour of biological objects. Recently, deep learning-based algorithms have shown significant benefits for cell segmentation tasks and, furthermore, for biomaterial design optimisation. We analysed the primary LHON fibroblasts in an artificial 3D environment after adeno-associated virus transduction. Application of these tools to model cell homing in biomaterials and to monitor cell morphology, migration and proliferation indirectly demonstrated restoration of the normal cell phenotype after gene manipulation by AAV transduction. Following the 3Rs principles of reducing the use of living organisms in research, modeling the formation of tissues and organs by reconstructing the behaviour of different cell types on artificial materials facilitates drug testing, the study of inherited and inflammatory diseases, and wound healing. These studies on the composition and algorithms for creating biomaterials to model the formation of cell layers were inspired by the principles of biomimicry.

Cellular senescence is a heterogeneous and dynamic state characterised by stable proliferation arrest, macromolecular damage and metabolic remodelling. Although markers such as SA-β-galactosidase staining, yH2AX foci and p53 activation are widely used as de facto standards, they are imperfect and differ in terms of sensitivity, specificity and dependence on context. We present a multifactorial imaging platform integrating scanning electron, flow cytometry and high-resolution confocal microscopy. This allows us to identify senescence phenotypes in three in vitro models: replicative ageing via serial passaging; dose-graded genotoxic stress under serum deprivation; and primary fibroblasts from young and elderly donors. We present a multimodal imaging framework to characterise senescence-associated phenotypes by integrating LysoTracker and MitoTracker microscopy and SA-β-gal/FACS, p16INK4a immunostaining provides independent confirmation of proliferative arrest. Combined nutrient deprivation and genotoxic challenge elicited the most pronounced and concordant organelle alterations relative to single stressors, aligning with age-donor differences. Our approach integrates structural and functional readouts across modalities, reducing the impact of phenotypic heterogeneity and providing reproducible multiparametric endpoints. Although the framework focuses on a robustly validated panel of phenotypes, it is extensible by nature and sensitive to distributional shifts. This allows both drug-specific redistribution of established markers and the emergence of atypical or transient phenotypes to be detected. This flexibility renders the platform suitable for comparative studies and the screening of senolytics and geroprotectors, as well as for refining the evolving landscape of senescence-associated states.

The development of neural networks has made the introduction of multimodal systems inevitable. Computer vision methods are still not widely used in biological research, despite their importance. It is time to recognize the significance of advances in feature extraction and real-time analysis of information from cells. Teacherless learning for the image clustering task is of great interest. In particular, the clustering of single cells is of great interest. This study will evaluate the feasibility of using latent representation and clustering of single cells in various applications in the fields of medicine and biotechnology. Of particular interest are embeddings, which relate to the morphological characterization of cells. Studies of C2C12 cells will reveal more about aspects of muscle differentiation by using neural networks. This work focuses on analyzing the applicability of the latent space to extract morphological features. Like many researchers in this field, we note that obtaining high-quality latent representations for phase-contrast or bright-field images opens new frontiers for creating large visual-language models. Graph structures are the main approaches to non-Euclidean manifolds. Graph-based segmentation has a long history, e.g., the normalized cuts algorithm treated segmentation as a graph partitioning problem—but only recently have such ideas merged with deep learning in an unsupervised manner. Recently, a number of works have shown the advantages of hyperbolic embeddings in vision tasks, including clustering and classification based on the Poincaré ball model. One area worth highlighting is unsupervised segmentation, which we believe is undervalued, particularly in the context of non-Euclidean spaces. In this approach, we aim to mark the beginning of our future work on integrating visual information and biological aspects of individual cells to multimodal space in comparative studies in vitro.

Background: Immune aging is a complex process involving various cellular changes, such as a myeloid bias, decreased functional activity of immune cells, accumulation of senescent cells, and alterations in serum levels of bactericidal humoral factors. As believed, these changes contribute to increased susceptibility of older adults to infectious diseases. Myeloid cells are considered the first line of defense against bacterial invasion. However, it remains unclear whether the protective functions of myeloid cells diminish in active older adults and whether potential age-related changes are evolutionarily conserved across primates. Methods: In this study, myeloid cell populations from peripheral blood and bone marrow of cynomolgus macaques and human peripheral blood were analyzed across a broad age range for phenotypic and functional characteristics, e.g., E. coli phagocytosis, secretion of proinflammatory factors, genetic instability, and signs of cellular aging. Results: Despite minor interspecies phenotypic differences in granulocyte populations, both the quantity and functions of myeloid cells were remarkably stable during aging in both species. Myeloid cells maintained genetic stability, and high SA-β-Gal activity was observed, likely reflecting metabolic traits rather than age-related changes. Importantly, a predominant and age-independent role of humoral factors, rather than cellular mechanisms, was identified in the initial control of bacterial infection. Conclusions: These findings suggest that innate immune functions remain stable for a long time during aging in both species.