Nurses' perceptions of the performance of nurse managers and senior hospital management during the COVID-19 pandemic.

BACKGROUND: The first waves of the COVID-19 pandemic had a negative impact on health systems and health professionals, due to the high number of cases and a lack of preparation. The aim of this study was to understand how nurses working in hospital units and in intensive care perceived the performance of nurse managers and senior hospital management during the first two waves of the pandemic. METHODS: The phenomenological approach proposed by Giorgi was used to investigate perceptions of the performance of nurse managers and senior hospital management during the first two waves of the COVID-19 pandemic in Spain. Fourteen clinical nurses who worked on the front line in inpatient units or intensive care units of the Health Services of Extremadura and Madrid in the first (March-April 2020) and second (October-November 2020) waves of the COVID-19 pandemic participated in this study. The data was collected through semi-structured interviews, following a script of themes, in a theoretical sample of nurses who were worked during the pandemic. RESULTS: Two main themes emerged from the analysis of the data: (1) perceptions about the performance of nurse managers and senior hospital managers during the first and second waves of the pandemic (health system failure; belief that senior hospital management professionals could have managed the pandemic better; recognizing the efforts of middle management (nursing supervisors); insufficient institutional support) and (2) strategies employed by nurses to compensate for the weaknesses in pandemic management. CONCLUSIONS: The clinical nurses perceived that the nurse managers demonstrated better management of the pandemic than the hospital's senior management, which they attribute to their proximity, empathy, accessibility, and ability to mediate between them and the senior management. The nurses also believe that the senior management of the hospitals was to blame for organisational failures and the poor management of the pandemic.

A Dynamic Fitting Strategy for Physiological Models: A Case Study of a Cardiorespiratory Model for the Simulation of Incremental Aerobic Exercise.

Using mathematical models of physiological systems in medicine has allowed for the development of diagnostic, treatment, and medical educational tools. However, their complexity restricts, in most cases, their application for predictive, preventive, and personalized purposes. Although there are strategies that reduce the complexity of applying models based on fitting techniques, most of them are focused on a single instant of time, neglecting the effect of the system's temporal evolution. The objective of this research was to introduce a dynamic fitting strategy for physiological models with an extensive array of parameters and a constrained amount of experimental data. The proposed strategy focused on obtaining better predictions based on the temporal trends in the system's parameters and being capable of predicting future states. The study utilized a cardiorespiratory model as a case study. Experimental data from a longitudinal study of healthy adult subjects undergoing aerobic exercise were used for fitting and validation. The model predictions obtained in a steady state using the proposed strategy and the traditional single-fit approach were compared. The most successful outcomes were primarily linked to the proposed strategy, exhibiting better overall results regarding accuracy and behavior than the traditional population fitting approach at a single instant in time. The results evidenced the usefulness of the dynamic fitting strategy, highlighting its use for predictive, preventive, and personalized applications.

Optimization of 3D Synthetic Scaffolds for Neuronal Tissue Engineering Applications.

The increasing prevalence of neurodegenerative diseases has spurred researchers to develop advanced 3D models that accurately mimic neural tissues. Hydrogels stand out as ideal candidates as their properties closely resemble those of the extracellular matrix. A critical challenge in this regard is to comprehend the influence of the scaffold's mechanical properties on cell growth and differentiation, thus enabling targeted modifications. In light of this, a synthesis and comprehensive analysis of acrylamide-based hydrogels incorporating a peptide has been conducted. Adequate cell adhesion and development is achieved due to their bioactive nature and specific interactions with cellular receptors. The integration of a precisely controlled physicochemical hydrogel matrix and inclusion of the arginine-glycine-aspartic acid peptide sequence has endowed this system with an optimal structure, thus providing a unique ability to interact effectively with biomolecules. The analysis fully examined essential properties governing cell behavior, including pore size, mechanical characteristics, and swelling ability. Cell-viability experiments were performed to assess the hydrogel's biocompatibility, while the incorporation of grow factors aimed to promote the differentiation of neuroblastoma cells. The results underscore the hydrogel's ability to stimulate cell viability and differentiation in the presence of the peptide within the matrix.

Mimicking the extracellular matrix by incorporating functionalized graphene into hybrid hydrogels.

The efficient functionalization of graphene with sulfonic groups using a sustainable approach facilitates the interaction of biomolecules with its surface. The inclusion of these graphene sheets inside a photopolymerized acrylamide-based hydrogel provides a 3D scaffold with viscoelastic behaviour closer to that found in natural tissues. Cell-culture experiments and differentiation assays with SH-SY5Y cells showed that these hybrid hydrogels are non-cytotoxic, thus making them potentially useful as scaffold materials mimicking the extracellular environment.

Microlearning through TikTok in Higher Education. An evaluation of uses and potentials.

While social media is evolving rapidly, understanding its underlying and persistent features with the potential to support high-quality learning would provide opportunities to enhance competence acquisition and collaborative work in higher education. Moreover, the adoption of tools that students already use in their everyday lives facilitates the integration of new forms of learning. In this context, we have developed an initiative to disseminate content through TikTok in three modules of the Bachelor's Degree in Nursing course, with the aim of promoting quality learning through these microlearning environments. To this end, we have implemented these learning environments and evaluated the users' perceptions, as well as their level of acceptance of the technology according to the Technology Acceptance Model. Overall, our results show high levels of satisfaction with regard to engagement and the content generated, as well as in terms of the acceptance of the technology. Our results do not show gender-specific variations, but we did detect slight variations depending on the subject in which the microlearning tool was deployed. Although for the most part these variations do not change the participants' assessment of their experience, it will be necessary in the future to determine the underlying reasons for these variations. In addition, our results suggest that it is possible to design a content creation system to promote quality learning through microlearning that can be transferred to other subjects, at least in the Bachelor's Degree in Nursing. Supplementary Information: The online version contains supplementary material available at 10.1007/s10639-023-11904-4.

A Novel Strategy to Fit and Validate Physiological Models: A Case Study of a Cardiorespiratory Model for Simulation of Incremental Aerobic Exercise.

Applying complex mathematical models of physiological systems is challenging due to the large number of parameters. Identifying these parameters through experimentation is difficult, and although procedures for fitting and validating models are reported, no integrated strategy exists. Additionally, the complexity of optimization is generally neglected when the number of experimental observations is restricted, obtaining multiple solutions or results without physiological justification. This work proposes a fitting and validation strategy for physiological models with many parameters under various populations, stimuli, and experimental conditions. A cardiorespiratory system model is used as a case study, and the strategy, model, computational implementation, and data analysis are described. Using optimized parameter values, model simulations are compared to those obtained using nominal values, with experimental data as a reference. Overall, a reduction in prediction error is achieved compared to that reported for model building. Furthermore, the behavior and accuracy of all the predictions in the steady state were improved. The results validate the fitted model and provide evidence of the proposed strategy's usefulness.

The interactions of alcohol and cocaine regulate the expression of genes involved in the GABAergic, glutamatergic and endocannabinoid systems of male and female rats.

Although the pharmacological and behavioural interactions between cocaine and alcohol are well established, less is known about how polyconsumption of these drugs affects the neurotransmitter systems involved in their psychoactive effects and in particular, in the process of addiction. Here, rats of both sexes at two stages of development were studied under a chronic regime of intravenous cocaine and/or alcohol administration. Brain samples from the medial prefrontal cortex, nucleus accumbens, hippocampus and amygdala were extracted to analyse the mRNA expression of genes encoding subunits of the GABA, NMDA and AMPA receptors, as well as the expression of the CB1 receptor, and that of enzymes related to the biosynthesis and degradation of endocannabinoids. Moreover, two synaptic scaffold proteins related to GABA and NMDA receptors, gephyrin and PSD-95, were quantified in Western blots. Significant interactions between cocaine and alcohol were common, affecting the GABAergic and endocannabinoid systems in the medial prefrontal cortex and amygdala of young adults, whereas such interactions were evident in the glutamatergic and endocannabinoid systems in adults, as well as a more pronounced sex effect. Significant interactions between these drugs affecting the scaffold proteins were evident in the medial prefrontal cortex and nucleus accumbens of young adults, and in the nucleus accumbens and amygdala of adults, but not in the hippocampus. These results highlight the importance of considering the interactions between cocaine and alcohol on neurotransmitter systems in the context of polyconsumption, specifically when treating problems of abuse of these two substances.

Rationale and Methods of Evaluation for ACHO, A New Virtual Assistant to Improve Therapeutic Adherence in Rural Elderly Populations: A User-Driven Living Lab.

Low therapeutic adherence is a concern for health professionals as it decreases therapeutic efficiency while increasing costs, especially in elderly populations. To increase therapeutic adherence in elderly populations, the technology applied in the medical devices that are used must be adapted to improve usability. This paper outlines the rationale behind, and methods applied to assess the usability of, ACHO (Assistant on Care and Health Offline), a voice assistant that provides elderly patients with reminders of medical appointments to attend and when they need to take their medication. This work is a descriptive, cross-sectional, observational study, and will include a three-phase (analysis, testing and refinement) multidimensional usability analysis of an initial prototype, in the setting of a user-driven Living Lab, which enables the needs and characteristics of the end users to be identified and incorporated into the prototype with each iteration, in which a multidisciplinary team of researchers and users will participate as co-creators. This methodology will allow us to develop a better prototype, increasing usability and, thus, increasing therapeutic adherence.

Relationship between exclusive breastfeeding and brain-derived neurotrophic factor in children.

OBJECTIVE: A positive relationship between breastfeeding and brain-derived neurotrophic factor (BDNF) in infants has been suggested due to the presence of BDNF in human milk. This study aimed to determine the relationship between exclusive breastfeeding and BDNF serum levels in Spanish schoolchildren. METHODS: A cross-sectional analysis including 202 schoolchildren, aged eight to 11 years, from Cuenca, Spain, was conducted. Information on sociodemographic and anthropometric variables, sexual maturation, birth weight and exclusive breastfeeding ('no exclusive breastfeeding', and exclusive breastfeeding for ≤6 and >6 months), and BDNF serum levels using an ELISA method were obtained. Covariance analyses (ANCOVA) were conducted to examine the relationship between serological BDNF and exclusive breastfeeding after controlling for potential confounders. RESULTS: ANCOVA models showed no significant differences in BDNF levels between children who were exclusively breastfed for more than six months versus those who were not (p > 0.05). No significant differences were observed by age group (eight to nine years versus 10 to 11 years; p > 0.05). Additionally, no clear negative trend in BDNF serum levels according to sexual maturation categories was found (p > 0.05). CONCLUSION: These findings suggest that exclusive breastfeeding does not have a significant positive association on BDNF from eight to 11 years, since children who were exclusively breastfed did not have significantly higher BDNF levels than those who were not exclusively breastfed. Likewise, BDNF levels were not found to be negatively affected by hormonal development. Future research should examine the influence of exclusive breastfeeding on BDNF over the different developmental stages.