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Diabetes mellitus is a metabolic disorder that can occur before pregnancy, be detected during pregnancy, or develop during pregnancy. Therapeutic modalities available today significantly facilitate glycoregulation during pregnancy and childbirth. This review presents different insulin regimens, as well as the advantages and disadvantages of oral antidiabetic agents use with a special focus on hypoglycemia. The importance of maintaining optimal glycemic levels and educating patients in blood glucose self-measurement is explained.

Copper is an essential trace element and is an integral part of many metabolic and other chemical processes in cells and tissues. Copper-based nanoparticles (CuNPs) have wide industrial and engineering applications, as catalysts, sensing materials, part of superconductors, storage systems, and phototermal / thermoelectric materials. In recent years, their potential use in biology and medicine has also contributed to the increased interest by scientific community. Unfortunately, many aspects of CuNP interactions with cells and tissues remain unknown. It is unclear if, in a living organism, CuNPs behave the same way as conventional metallic copper. Possible impact of CuNPs on the functioning of organs such as liver, kidney and brain is of particular concern. This concise review aims to cover recent research on potential CuNP toxicity, with the emphasis on neurotoxocity, nephrotoxicity and hepatotoxicity. Also, potential implications of CuNPs toxicity in fundamental and clinical medicine are discussed.

With the rapid development of nanotechnology during the past several years, attention has been focused on metallic nanomaterials, due to their specific physical and chemical characteristics. Zinc oxide nanoparticles (ZnO NPs) have numerous potential applications in industry, as a part of various consumer products, but also in medical research. Anticancer properties of ZnO NPs have been suggested in cell cultures, however, the precise mechanism responsible for their activity in these conditions remains elusive. Cytotoxicity and genotoxicity of ZnO NPs are also unclear. Apart from cancer research, ZnO NPs are today widely researched in almost all areas of fundamental medicine. In this short review, we discuss recently published articles on ZnO NPs applications in cellular physiology, pathology, neurosciences and oncology.

Recent findings have suggested that iron oxide nanoparticles (IONPs) have some exceptional chemical characteristics which make them useful in both experimental physiology and cell biology research. These nanoparticles might be applied as drug delivery systems for anti-cancer and other medications. Also, IONPs might be a valuable part of many novel bioassays in various fundamental medical fields. In recent years, several studies have indicated that IONPs may have certain cytotoxic and genotoxic potential in living systems. During in vitro conditions, IONPs might induce generation of reactive oxygen species and cause oxidative stress in some cell populations. The toxicity of IONPs is not yet fully understood, and additional research is needed to confirm if IONPs have detrimental effects on human health. This short article focuses on the recent developments and trends in the fields of nanomedicine and nanobiology regarding iron oxide nanomaterials and their application in fundamental medical disciplines such as experimental physiology. We discuss our previously published works on structural effects of IONPs and other metallic nanoparticles on cell nucleus in in vitro conditions. We also describe our recent findings regarding the impact of IONPs on certain biophysical properties of subcellular components.

Facebook is one the most popular social networking sites in the world. The social aspect and perceiving oneself in the context of relationships seem to be crucial in the analysis of Facebook use. In recent years the number of Facebook users has been increasing, which is related to the problem of Facebook intrusion. Machiavellianism and selected interdependence agency indicators (conformity, relatedness, and autonomy) are associated with social functioning, which is an important aspect of Facebook intrusion. The aim of the study was to check how Machiavelianism and the interdependent agency indicators mentioned above affect Facebook intrusion and whether Facebook intrusion decreases satisfaction with life. The participants in the online study were 715 Polish Facebook users. We administered the Facebook Intrusion Scale, Conformity, Relatedness, and Autonomy Scales, and the Satisfaction With Life Scale. The results show that the selected interdependence agency indicators predict Facebook intrusion. Our results revealed that a high level of Machiavellianism was related to a high level of Facebook intrusion in women. They also show that conformity is positively and autonomy negatively related to Facebook intrusion and that Facebook decreases reduces satisfaction with life. The presented research thus contributes to knowledge about Facebook usage patterns. [ABSTRACT FROM AUTHOR]

Developing sustainable and effective treatments for chronic venous insufficiency (CVI) is crucial. In this study, we propose an innovative restorative approach utilizing hydrogels derived from the decellularized extracellular matrix (dECM) of cadaveric vascular tissues, adipose-derived stem cells (ADSCs), and gold nanoparticles (AuNPs). This therapeutic method leverages waste valorization by repurposing discarded cadaveric tissues from slaughterhouse livestock. The dECM hydrogels, enriched with ADSCs and AuNPs, offer a biocompatible scaffold that supports cellular differentiation and vascular integrity. Our approach addresses the limitations of current allo-, auto-, and xenograft methods by enhancing integration and functionality while potentially reducing costs through sustainable practices. This study explores functionalized hydrogel formulation solely generated from agri-food waste, gelation mechanisms, and preliminary cost-effectiveness, presenting a promising new avenue for treating early-stage varicose veins that can ultimately be translated to human models using discarded tissues. [ABSTRACT FROM AUTHOR]

In this study, we propose an innovative concept that applies an AI-based approach using the random forest algorithm integrated with fractal and discrete wavelet transform features of nuclear chromatin. This strategy could be employed to identify subtle structural changes in cells that are in the early stages of programmed cell death. The code for the random forest model is developed using the Scikit-learn library in Python and includes hyperparameter tuning and cross-validation to optimize performance. The suggested input data for the model are chromatin fractal dimension, fractal lacunarity, and three wavelet coefficient energies obtained through high-pass and low-pass filtering. Additionally, the code contains several methods to assess the performance metrics of the model. This model holds potential as a starting point for designing simple yet advanced AI biosensors capable of detecting apoptotic cells that are not discernible through conventional microscopy techniques. [ABSTRACT FROM AUTHOR]

Fractal analysis (FA) is a contemporary computational technique that can assist in identifying and assessing nuanced structural alterations in cells and tissues after exposure to certain toxic chemical agents. Its application in toxicology may be particularly valuable for quantifying structural changes in cell nuclei during conventional microscopy assessments. In recent years, the fractal dimension and lacunarity of cell nuclei, considered among the most significant FA features, have been suggested as potentially important indicators of cell damage and death. In this study, we demonstrate the feasibility of developing a random forest machine learning model that employs fractal indicators as input data to identify yeast cells treated with oxidopamine (6-hydroxydopamine, 6-OHDA), a powerful toxin commonly applied in neuroscience research. The model achieves notable classification accuracy and discriminatory power, with an area under the receiver operating characteristics curve of more than 0.8. Moreover, it surpasses alternative decision tree models, such as the gradient-boosting classifier, in differentiating treated cells from their intact counterparts. Despite the methodological challenges associated with fractal analysis and random forest training, this approach offers a promising avenue for the continued exploration of machine learning applications in cellular physiology, pathology, and toxicology. [ABSTRACT FROM AUTHOR]