Gene regulatory network analysis identifies MYL1, MDH2, GLS, and TRIM28 as the principal proteins in the response of mesenchymal stem cells to Mg2+ ions.

Magnesium (Mg)-based implants have emerged as a promising alternative for orthopedic applications, owing to their bioactive properties and biodegradability. As the implants degrade, Mg2+ ions are released, influencing all surrounding cell types, especially mesenchymal stem cells (MSCs). MSCs are vital for bone tissue regeneration, therefore, it is essential to understand their molecular response to Mg2+ ions in order to maximize the potential of Mg-based biomaterials. In this study, we conducted a gene regulatory network (GRN) analysis to examine the molecular responses of MSCs to Mg2+ ions. We used time-series proteomics data collected at 11 time points across a 21-day period for the GRN construction. We studied the impact of Mg2+ ions on the resulting networks and identified the key proteins and protein interactions affected by the application of Mg2+ ions. Our analysis highlights MYL1, MDH2, GLS, and TRIM28 as the primary targets of Mg2+ ions in the response of MSCs during 1-21 days phase. Our results also identify MDH2-MYL1, MDH2-RPS26, TRIM28-AK1, TRIM28-SOD2, and GLS-AK1 as the critical protein relationships affected by Mg2+ ions. By offering a comprehensive understanding of the regulatory role of Mg2+ ions on MSCs, our study contributes valuable insights into the molecular response of MSCs to Mg-based materials, thereby facilitating the development of innovative therapeutic strategies for orthopedic applications.

Artificial intelligence in emergency medicine. A systematic literature review.

Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies. METHODS: The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms. FINDINGS AND DISCUSSION: The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction. CONCLUSION: Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.

Enhanced lead phytoextraction and soil health restoration through exogenous supply of organic ligands: Geochemical modeling".

Phytoremediation of lead (Pb) contaminated soil is a green technology to reduce Pb exposure and root exudates-derived organic acids play a vital role in this treatment process. In this study, Pb hyperaccumulator Pelargonium hortorum was chosen to investigate root-induced organic acid secretions and their subsequent role in Pb phytoextraction. In the first step, root exudation of P. hortorum was investigated in hydroponic experiments (0.2X Hoagland solution) under control and Pb stress conditions. Possible chemical interactions between Pb and the observed root exudates were then analyzed using Visual MINTEQ modeling. In the next step, the effects of the exogenous application of organic acids on Pb phytoextraction and soil enzymatic activities were studied in a pot experimental setup. Results indicated significant exudation of malic acid > citric acid > oxalic acid > tartaric acid in root exudates of P. hortorum under 50 mg L-1 Pb. Visual MINTEQ modeling results revealed that organic acids directly affect Pb dissolution in the nutrient solution by modulation of solution pH. Experimental results revealed that malic acid and citric acid significantly increased available Pb contents (7.2- and 6.7-folds) in the soil with 1500 mg kg-1 Pb contamination. Whereas, in shoot and root, the highest increase in Pb concentration was observed with citric acid (2.01-fold) and malic (3.75-fold) supplements, respectively. Overall, Pb uptake was notably higher when malic acid was applied (2.8-fold) compared to other organic acids, followed by citric acid (2.7-fold). In the case of soil enzymatic activities, oxalic acid significantly improved dehydrogenase, alkaline phosphatase, and microbial biomass by 1.6-, 1.4- and 1.3-folds, respectively. The organic acids were successful in reviving enzyme activity in Pb-contaminated soil, and might thus be used for long-term soil regeneration.

On Methodological and Technological Challenges for Proactive Health Management in Smart Homes.

Health management in smart homes has advanced during the last years. With proactive health management in such environments further progress for health prevention and care is to be expected. Challenges for proactive health management in three areas are summarized and briefly discussed: pattern recognition and machine learning, information privacy and user-oriented design, and sensor-enhanced health information systems architectures.