Background: Cultured meat holds significant potential as a pivotal solution for producing safe, sustainable, and high-quality protein to meet the growing demands of the global population. However, scaling this technology requires innovative bioengineering approaches integrated with software methods to assess the growth of cell cultures. This study aims to develop a technology for 3D printing a hybrid meat product and subsequently design a finely tuned You Only Look Once (YOLO) model for detecting and counting lipoblasts, fibroblasts, and myogenic cells. Methods: Cultures of multipotent mesenchymal stem cells (MMSCs) and fibroblasts were obtained from the domestic rabbit Oryctolagus cuniculus domesticus. Standard protocols were employed to induce adipogenic and myogenic differentiation from MMSCs. Fibroblasts were isolated from skin biopsy samples. The 3D printing process utilized bioinks. The engineering approach involved the development of a unique print head integrated into a 3D printer. Confocal and transmission electron microscopy of the cells within the construct was performed. A dataset of digital images of lipoblasts, myogenic cells, and fibroblasts was created. Four models based on the YOLOv8-seg architecture were trained on annotated images, implemented in the Telegram bot. Results: Stable cultures of lipoblasts, myogenic cells, and fibroblasts were obtained. 3D-printed tissue constructs composed of rabbit cells, sodium alginate, and sunflower protein were successfully fabricated. A unique print head for a 3D printer was assembled. Confocal microscopy confirmed cell viability within the tissue construct. Ultrastructural analysis revealed dense intercellular contacts and high metabolic activity. The resulting product replicated the organoleptic and structural properties of natural meat. In the IT segment, the single-class model trained on lipoblasts achieved metrics of recall 85%, precision 77%, and mean Average Precision at IoU threshold 0.50 (mAP50) 79%, which improved in the multiclass model to recall 92%, precision 92%, and mAP50 81%. The IT solution was implemented in a Telegram bot capable of detecting and counting different cell types. Conclusions: A 3D tissue construct was achieved. Detailed microscopic analysis demonstrated cell viability and high metabolic activity within the polymerized alginate hydrogel. The engineered tissue product presents a potential alternative to natural meat. Additionally, the trained neural network models, implemented in a Telegram bot, proved effective in monitoring culture growth and identifying cell types in digital images across three cell cultures. As a result, we developed four YOLOv8 models and demonstrated that the multiclass model outperforms the single-class model. However, all models exhibited reduced accuracy in high-density cultures, where overlapping cells led to undercounting.

Traumatic brain injury (TBI) is one of the most common forms of neurotrauma, accompanied by significant disruptions in neuronal homeostasis and intercellular communication. A key protein involved in these processes is connexin 43 (Cx43), which facilitates the formation of gap junctions in the astrocytic network. In this study, using confocal and immunofluorescence microscopy, ultrastructural analysis, and molecular modeling, we investigated the dynamics of Cx43 expression and structural changes in neuroglia during various post-traumatic periods following TBI. It was shown that in the acute phase, 24 h post-injury, there is a reduction in Cx43 expression, accompanied by apoptotic neuronal degradation, disruption of nuclear NeuN localization, and destruction of cellular ultrastructure. By 7 days post-injury, a significant increase in Cx43 levels was observed, along with the formation of protein aggregates associated with pronounced reactive astrogliosis. Peripheral blood analysis revealed persistent neutrophilia, lymphopenia, and reduced monocyte levels, reflecting a systemic inflammatory response and immunosuppression, which was corroborated by a custom-trained neural network-based computer vision model. Linear regression and correlation analyses further identified a strong positive association between normalized monocyte levels and Cx43 expression, a moderate negative correlation with lymphocytes, and no significant correlation with neutrophils. Using a custom-built computer vision model, we confirmed these hematological trends and detected subtle changes, such as early increases in platelet counts, that were not captured by manual evaluation. The model demonstrated strong performance in classifying common blood cell types and proved to be a valuable tool for monitoring dynamic post-traumatic shifts in blood. Molecular dynamics modeling of Cx43 identified a pH-dependent mechanism of conformational reorganization under post-traumatic acidosis, mediated by the interaction between protonated His142 and Glu103. This mechanism mimics the structural consequences of the pathogenic E103K mutation and may play a critical role in the neurotoxic effects of Cx43 in TBI. These findings highlight the complexity of Cx43 regulation under traumatic conditions and its potential significance as a target for neuroprotective therapy.

Detection of volatile organic compounds in exhaled air is a promising approach to non-invasive and scalable gastric cancer screening. This work proposes a new approach for the detection of volatile organic compounds by analyzing odor-evoked calcium responses in the rat olfactory bulb. We estimate the feasibility of gastric cancer biomarker detection added to the exhaled air of healthy participants. Our detector consists of a convolutional encoder and a similarity-based classifier over encoder outputs. To minimize overfitting on a small available training set, we involve a pre-training where the encoder is trained on synthetic data representing spatiotemporal patterns similar to real calcium responses in the olfactory bulb. We estimate the classification accuracy of exhaled air samples by matching their encodings with encodings of calibration samples of two classes: 1) exhaled air and 2) a mixture of exhaled air with the cancer biomarker. On our data, the accuracy increased from 0.68 on real data up to 0.74 if pre-training on synthetic data is involved. Our work is focused on proving the feasibility of proposed new approach rather than on comparing its efficiency with existing methods. Such detection is often performed with an electronic nose, but its output becomes unstable over time due to a sensor drift. In contrast to the electronic nose, rats can robustly detect low concentrations of biomarkers over lifetime. The feasibility of gastric cancer biomarker detection in exhaled air by bio-hybrid system is shown. Pre-training of neural models for images analysis increases the accuracy of detection.

This work introduces a novel and specialized dataset of high-resolution fluorescence microscopic images focused on astrocytic gap junctions, aiming to get insights into intercellular communication in both healthy and pathological brain conditions. The dataset includes 20 z-stack image series -- 10 from human glioblastoma tissue and 10 from healthy rat brain tissue -- each containing between 24 and 126 optical slices. These images were acquired using standardized protocols following immunofluorescence labeling with antibodies against connexin 43 (Cx43), enabling visualization of gap junction localization at the subcellular level. The dataset is uniquely tailored to support detailed morphological and quantitative analyses of gap junction networks, featuring metadata including species, age, sex, imaging parameters, tissue characteristics, and localization of gap junctions in images provided by bounding boxes and image masks. Its structure facilitates comparative studies across physiological states and species, enhancing translational and evolutionary perspectives on astrocytic connectivity. Given the labor-intensive nature of manual gap junction quantification and 3D statistical analysis for Z-stacks, this dataset serves as a resource for the development of machine learning tools capable of automating the detection and analysis of Cx43-positive signals.