Advances in experimental medicine and biology最新文献

Mobilization of critically ill patients within 72 hours of admission is associated with improved outcomes. Recently, the predictive value of regional cerebral oxygen saturation (rSO₂) measured via near-infrared spectroscopy (NIRS) has been emphasized. This study aimed to evaluate the differences in rSO₂ values depending on the availability of mobilization within 72 hours in the ICU. Eighty patients admitted to the emergency center between June 2020 and December 2022 were analyzed. Patients were assessed based on whether they could be mobilized within 72 hours (early mobilization group, EM) or later (non-early mobilization group, non-EM). During mobilization, prefrontal rSO₂ values were monitored. Regarding patient background, significant differences were noted between the groups, including delayed release in non-EM patients. rSO₂ values varied significantly, with the lowest values in the first half of end-sitting in both groups (non-EM 56.5 ± 4.8%, EM 58.6 ± 4.3%, p < 0.05). The rSO₂ value was also lower in the non-EM group than in the EM group (P < 0.014). A weak correlation was observed between rSO₂ and the number of days to first mobilization (r = -0.251, p = 0.025). The rSO₂ value may serve as a potential marker to guide the timing of mobilization in ICU patients.

A theoretical framework is presented to clarify the role of architectural and structural forces in ion selectivity. It expresses the relative free energy of bound ions in terms of a reduced subsystem corresponding to the local degrees of freedom coupled to the rest of the protein. The latter is separated into a first contribution that includes all the forces keeping the ion and the coordinating ligands confined to a microscopic sub-volume but do not prevent the ligands from adapting to a smaller ion, while the second contribution regroups the remaining forces that serve to dictate the precise geometry of the coordinating ligands best adapted to a given ion. The theoretical framework makes it possible to delineate two important limiting cases. In the limit where the geometric forces are dominant (rigid binding site), selectivity is controlled via the cavity size according to the familiar "snug-fit" mechanism of host-guest chemistry. In the limit where the geometric forces are negligible, the ion and ligands behave as a dynamical "confined droplet" that is free and adapt to the ion's size. In this case, selectivity is controlled by the number and the chemical type of ion-coordinating ligands.

The fat body plays a vital role in the proper functioning of invertebrates, contributing to their metabolic processes and resilience. It serves as the central tissue for metabolism integrating signals, regulating molting and metamorphosis and producing hormones that govern the overall body function and immune system protein synthesis. Thanks to this multifunctionality, the fat body is responsible for the metabolism of basic compounds: lipids, carbohydrates and proteins, storing them in the form of reserves (protecting against weather conditions, starving, etc.) and "post-trials" metabolites. In this tissue the remodeling process takes place, which enables the metamorphosis of insects. Also, the fat body is the place of the synthesis of immune proteins, some hormones, pheromones and vitellogenin. Understanding its physiology has, therefore, become an important element of research on insects in the context of general health.

Background: Determining optical properties of biological tissues enables critical clinical insights, e.g., quantifying hemoglobin oxygenation in preterm infants or detecting malignant tissues in cancer diagnostics. In applications such as neonatal monitoring or endoscopic imaging, measurements at short distances (less than 1 cm) are essential due to space constraints.

Aim: This study aims to investigate the relationship between the tissue optical properties and the reflectance at short distances using a combination of experimental data and a modified Monte Carlo (MC) simulation.

Materials and methods: Twelve phantoms with different optical properties were created using silicone and validated using the commercial frequency domain near infrared spectroscopy system ISS Imagent. Reflectance measurements were conducted at precise source-detector separations ranging from 1.5 mm to 5 mm. Modified MC simulations incorporating the modified Henyey-Greenstein (MHG) and Gegenbauer (GB) phase functions were employed and simulated and measured data were compared.

Results: The reflectance data exhibited a clear dependence on absorption and scattering coefficients. The MHG and GB models provided better fits to experimental data compared to the traditional Henyey-Greenstein (HG) model. The median value of the intraclass correlation coefficient (ICC) among all the investigated separation distances and optical properties of 0.982 for MHG and 0.979 for GB confirms higher agreement with experimental data by using these phase functions compared to HG (ICC = 0.978).

Conclusion: The modified MC simulation enabled us to better simulate the experimental data. The MHG and GB models offer improved accuracy over traditional HG models, thus advancing optical imaging and diagnostic applications especially for measuring the peripheral oxygenation for preterm babies.

Free energy calculations play a vital role in understanding protein behaviour at the molecular level. From protein folding and stability to ligand binding and enzyme catalysis, these calculations provide quantitative insights that are indispensable for both basic research and practical applications in fields such as drug discovery, protein engineering, and biotechnology. Due to the complex energy landscapes of proteins, traditional molecular dynamics simulations often fail to explore rare events or overcome high-energy barriers effectively. Enhanced sampling techniques, such as metadynamics, umbrella sampling, or replica exchange molecular dynamics, were developed to address these challenges, allowing for more efficient exploration of conformational space and improved accuracy in free energy predictions. These methods accelerate the sampling of relevant states and transitions, making it feasible to capture rare but biologically significant events. Recently, machine learning has also begun playing a crucial role in enhancing sampling efficiency, reducing the need for extensive computational resources. As computational power continues to increase and machine learning techniques are integrated with enhanced sampling algorithms, the scope and accuracy of free energy calculations will significantly improve, opening new avenues for more precise understanding and prediction of molecular interactions and biological processes.

This chapter explores the pivotal roles of planar lipid bilayers and liposomes in advancing our understanding of protein function. These artificial membrane systems serve as essential models that mimic natural cellular environments, providing a versatile platform for studying protein-lipid interactions. We delve into methods for assembling planar lipid bilayers and liposomes, examining their composition and diversity, and the techniques employed to integrate membrane proteins for functional analysis. We consider the influence of osmotic dynamics on membrane behavior and protein interactions through pressure gradients and review studies of signal transduction in artificial membrane systems aimed at elucidating how proteins interact with lipids to mediate cellular communication. We also discuss the contributions of these systems to structural studies, involving techniques like cryo-electron microscopy and X-ray crystallography, to reveal high-resolution insights into membrane protein conformations. Discussing challenges and limitations, we review the biological relevance and technical constraints that shape experimental outcomes. Looking forward, we consider innovations in lipid bilayer model systems and new research directions that promise to expand their utility. This chapter underscores the continued importance of these systems as indispensable tools in unraveling the complexities of protein function within biological membranes.

Undergraduate nursing education is an important starting point for promoting patient safety in the areas of knowledge, attitude, and skills in the preparation of future nurses. Nursing students are considered an integral and necessary component of the health care system. Therefore, exposing them to the concept of patient safety will help new graduates in the workplace to become ambassadors for promoting a culture of safety which will be key to reducing patient mortality and the level of adverse events actions.The purpose of this research is to investigate the attitudes of nursing students towards patient safety. A qualitative study was conducted with audio-recorded, face-to-face, semistructured interviews lasting an average of 15 min.The "under study" population consists of students of the Nursing Department of the University of Thessaly and was obtained by convenience sampling.The number of interviews (n = 17) conducted was determined after data saturation was reached. From the responses of the participants, important conclusions about patient safety emerge. The term patient safety mainly refers to the protection of the patient at a physical and psychological level, including falls, medication errors, and other risks. Factors affecting patient safety include staff training, working conditions, and collaboration. Nurse education is highlighted as important in preparing for safe care. Finally, reporting and recording errors are considered vital to improving the quality of care, although many factors such as fear dominate the decision to report errors. Overall, patient safety is a vital aspect of nursing practice and requires professional education, collaboration, and commitment to error reporting.

Preeclampsia is a multifaceted pregnancy syndrome that significantly contributes to maternal and neonatal morbidity and mortality. Characterized by hypertension, proteinuria, and multi-organ involvement, it is influenced by genetic, immunological, and environmental factors. Recent research has highlighted the role of epigenetic modifications, such as DNA methylation, histone modifications, and microRNA regulation, in the pathogenesis of preeclampsia. This study examines how these epigenetic mechanisms impact gene expression in the placenta, contributing to abnormal trophoblastic invasion, immune maladaptation, and endothelial dysfunction observed in preeclampsia. Focus is given to the role of altered DNA methylation patterns, such as those observed in the HSD11B2 and IGF2 genes, which could serve as potential biomarkers for early diagnosis. Understanding these epigenetic changes offers opportunities for developing novel diagnostic tools and therapeutic interventions, with the potential to improve pregnancy outcomes for affected women and their infants.

With opioids being increasingly prescribed and illicit opioids being misused, substance use disorder has become a growing public health concern. The impacts of the opioid epidemic have been devastating, especially for pregnant people, infants, and children. Pre- and perinatal opioid exposure is complex. Opioids affect multiple body systems and have detrimental effects on the placenta, brain, and immune system. Pharmacological properties make each class of opioid unique, thereby compounding effects on development based on the type, receptors engaged, or combination of drugs used. Accordingly, animal models are necessary to elucidate the mechanisms, pathways, and developmental processes affected by opioid exposure during and after pregnancy. However, the complexity of opioid use in humans means that preclinical modeling is also complicated with variation by species type, duration, and timing of exposure, and combinations of opioids studied. In this chapter, we present a summary of numerous, intricate preclinical models of perinatal opioid exposure. Specifically, we discuss (1) the inherent variability and difficulty in modeling complex patterns of opioid use by pregnant and peripartum people, (2) provide background on opioids and their receptors, and (3) present evidence for long-term changes in brain structure and function secondary to prenatal opioid exposure. Together, we emphasize the significant immunological, structural, and cognitive changes documented in animals and humans after opioid exposure to highlight the potential for translatability and illustrate a path forward for improved mechanistic and therapeutic discovery.

This chapter provides an introduction and overview of animal models that have been used to investigate the teratogenic effects of alcohol. Since the first model was developed in 1899, prenatal alcohol exposure (PAE) has been studied in species ranging from invertebrates to primates. Here, we contextualize and outline critical experimental considerations, including blood alcohol concentration, timing of exposure, and routes of ethanol administration. Detailed comparisons of vertebrate and invertebrate models, particularly rodents, guinea pigs, and non-human primates, highlight their translational relevance and limitations in replicating human gestational processes, and the pathophysiology of fetal alcohol spectrum disorder (FASD). This chapter also examines behavioral outcomes across motor, executive, cognitive, and social domains, illustrating how PAE disrupts neural development and function throughout the lifespan. Collectively, we emphasize the importance of recognizing the pros and cons when selecting an animal model and experimental paradigm.