BACKGROUND: Advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the medical field and transformed translational medicine. These technologies enable more accurate disease trajectory models while enhancing patient-centered care. However, challenges such as heterogeneous datasets, class imbalance, and scalability remain barriers to achieving optimal predictive performance. METHODS: This study proposes a novel AI-based framework that integrates Gradient Boosting Machines (GBM) and Deep Neural Networks (DNN) to address these challenges. The framework was evaluated using two distinct datasets: MIMIC-IV, a critical care database containing clinical data of critically ill patients, and the UK Biobank, which comprises genetic, clinical, and lifestyle data from 500,000 participants. Key performance metrics, including Accuracy, Precision, Recall, F1-Score, and AUROC, were used to assess the framework against traditional and advanced ML models. RESULTS: The proposed framework demonstrated superior performance compared to classical models such as Logistic Regression, Random Forest, Support Vector Machines (SVM), and Neural Networks. For example, on the UK Biobank dataset, the model achieved an AUROC of 0.96, significantly outperforming Neural Networks (0.92). The framework was also efficient, requiring only 32.4 s for training on MIMIC-IV, with low prediction latency, making it suitable for real-time applications. CONCLUSIONS: The proposed AI-based framework effectively addresses critical challenges in translational medicine, offering superior predictive accuracy and efficiency. Its robust performance across diverse datasets highlights its potential for integration into real-time clinical decision support systems, facilitating personalized medicine and improving patient outcomes. Future research will focus on enhancing scalability and interpretability for broader clinical applications.

• Klíčová slova
• Artificial intelligence, Clinical decision support, Disease prediction, Machine learning, Translational medicine,
• MeSH
• lidé MeSH
• neuronové sítě MeSH
• péče orientovaná na pacienta * MeSH
• strojové učení * MeSH
• translační biomedicínská věda * metody   MeSH
• translační biomedicínský výzkum * MeSH
• umělá inteligence * MeSH
• výsledek terapie MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

The foundations of cell reprogramming were laid by Yamanaka and co-workers, who showed that somatic cells can be reprogrammed into pluripotent cells (induced pluripotency). Since this discovery, the field of regenerative medicine has seen advancements. For example, because they can differentiate into multiple cell types, pluripotent stem cells are considered vital components in regenerative medicine aimed at the functional restoration of damaged tissue. Despite years of research, both replacement and restoration of failed organs/ tissues have remained elusive scientific feats. However, with the inception of cell engineering and nuclear reprogramming, useful solutions have been identified to counter the need for compatible and sustainable organs. By combining the science underlying genetic engineering and nuclear reprogramming with regenerative medicine, scientists have engineered cells to make gene and stem cell therapies applicable and effective. These approaches have enabled the targeting of various pathways to reprogramme cells, i.e., make them behave in beneficial ways in a patient-specific manner. Technological advancements have clearly supported the concept and realization of regenerative medicine. Genetic engineering is used for tissue engineering and nuclear reprogramming and has led to advances in regenerative medicine. Targeted therapies and replacement of traumatized , damaged, or aged organs can be realized through genetic engineering. Furthermore, the success of these therapies has been validated through thousands of clinical trials. Scientists are currently evaluating induced tissue-specific stem cells (iTSCs), which may lead to tumour-free applications of pluripotency induction. In this review, we present state-of-the-art genetic engineering that has been used in regenerative medicine. We also focus on ways that genetic engineering and nuclear reprogramming have transformed regenerative medicine and have become unique therapeutic niches.

• Klíčová slova
• Genetic engineering, induced pluripotency, nuclear reprogramming, regenerative medicine, somatic cell nuclear transfer, stem cells.,
• MeSH
• genetické inženýrství MeSH
• indukované pluripotentní kmenové buňky metabolismus   cytologie   MeSH
• lidé MeSH
• přeprogramování buněk * MeSH
• regenerativní lékařství * MeSH
• tkáňové inženýrství MeSH
• vývojová biologie MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

The human ovarian granulosa cells (GCs) surround the oocyte and form the proper architecture of the ovarian follicle. The ability of GCs to proliferate and differentiate in the conditions of in vitro culture has been proven. However, there is still a large field for extensive investigation of molecular basics, as well as marker genes, responsible for these processes. This study aimed to find the new marker genes, encoding proteins that regulate human GCs in vitro capability for proliferation and differentiation during long-term primary culture. The human follicular GCs were collected from hyper-stimulated ovarian follicles during IVF procedures and transferred to a long-term in vitro culture. The culture lasted for 30 days, with RNA samples isolated at days 1, 7, 15, 30. Transcriptomic analysis was then performed with the use of Affymetrix microarray. Obtained results were then subjected to bioinformatical evaluation and sorting. After subjecting the datasets to KEGG analysis, three differentially expressed ontology groups "cell differentiation" (GO:0030154), "cell proliferation" (GO:0008283) and "cell-cell junction organization" (GO:0045216) were chosen for further investigation. All three of those ontology groups are involved in human GCs' in vitro lifespan, proliferation potential, and survival capability. Changes in expression of genes of interest belonging to the chosen GOs were validated with the use of RT-qPCR. In this manuscript, we suggest that VCL, PARVA, FZD2, NCS1, and COL5A1 may be recognized as new markers of GC in vitro differentiation, while KAT2B may be a new marker of their proliferation. Additionally, SKI, GLI2, FERMT2, and CDH2 could also be involved in GC in vitro proliferation and differentiation processes. We demonstrated that, in long-term in vitro culture, GCs exhibit markers that suggest their ability to differentiate into different cells types. Therefore, the higher expression profile of these genes may also be associated with the induction of cellular differentiation processes that take place beyond the long-term primary in vitro culture.

• Klíčová slova
• Differentiation, Granulosa cells, Microarrays, Proliferation, Stem cells,
• MeSH
• adhezní spoje metabolismus   MeSH
• buněčná adheze genetika   MeSH
• buněčná diferenciace genetika   MeSH
• dospělí MeSH
• folikulární buňky cytologie   metabolismus   MeSH
• kultivované buňky MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• ovarium cytologie   MeSH
• proliferace buněk genetika   MeSH
• upregulace * MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH