University of Valladolid

Aims To identify whether there are differences in knowledge regarding the management of patients with respiratory stomas among nurses working in hospitals with an advanced practice tracheostomy service compared to those without it. Design Descriptive, cross‐sectional, comparative, analytical survey study. Method The study was conducted from January to March 2023 in four tertiary care hospitals, two of which have an advanced practice tracheostomy service. A self‐administered questionnaire was designed, consisting of 16 questions about nurses' specialised training in caring for tracheostomy patients. The study adhered to the STROBE checklist. Statistical analyses were performed using SPSS (24.0) from IBM. Results Nurses in hospitals with a dedicated tracheostomy service obtained a higher mean score (7.1/10) and demonstrated greater anxiety when managing patients with stomas ( p < 0.001), as well as an increased willingness to undergo specific training ( p = 0.017) to reduce their lack of self‐confidence. Conclusions A higher level of anxiety in the management of tracheostomised patients and a greater interest in receiving specific training have been observed among nurses in hospitals with advanced practice services (APTS), despite having greater training. Therefore, institutions should commit to incorporating advanced practice nurses and continuing education in the approach to ostomies among their professionals. Implications for the Profession and/or Patient Care Implementing ongoing training programmes and specific tracheostomy services or units in hospitals would enable nurses to provide high‐quality care for patients with respiratory stomas. Reporting Method The study adhered to the STROBE checklist. Patient or Public Contribution Neither patients nor the public were involved in the design or conduct of this research. Nurses participated exclusively in data collection.

Articles 86 to 92 of MiCA promote a simplified regime to control unlawful disclosure, insider dealing and possible manipulation or market abuses in issuing and trading crypto assets. The tendency to assimilate the regulation with the regulatory background of the ordinary financial market is hardly avoidable. However, the most coherent option is probably to use these references as an ex-post mechanism and not as ex-ante requirements when the ESMA guidelines don’t declare analogous application of Market Abuse Regulations.

Background Despite muscle power derived from the 5‐rep sit‐to‐stand (STS) test having been demonstrated to be a valuable biomarker in older individuals, there is limited information regarding muscle power derived from the 30‐s STS test, a widely used test in the clinical setting. This study aimed (i) to compare relative 30‐s STS power values between older men and women, (ii) to identify cut‐off points for low relative 30‐s STS power, (iii) to compare the prevalence of low relative STS power between sexes and (iv) to evaluate the association of low relative 30‐s STS power with adverse conditions in older people. Methods A total of 1475 community‐dwelling older adults (65–98 years; 45% men) from the Toledo Study for Healthy Aging were included. Relative STS power was assessed using the 30‐s STS test and the Alcazar's equation. Adverse health conditions considered encompassed frailty, depression, disability in basic (BADL) and instrumental activities of daily living (IADL), cognitive impairment and low habitual gait speed (HGS). Results Relative STS power decreased linearly at an average rate of 1.0% year⁻¹ in men and 1.5% year⁻¹ in women. The cut‐off points for low relative STS power were 2.53 and 2.01 W·kg⁻¹ for men and women, respectively. The prevalence of low relative STS power was significantly lower in older men compared with older women (43.5% vs. 50.0%, respectively; p = 0.005). In men, low relative STS power was associated with frailty (OR [95% CI] = 4.4 [2.4–8.0]), cognitive impairment (OR [95% CI] = 1.7 [1.0–2.7]), disability in BADL (OR [95% CI] = 4.5 [1.5–13.8]) and low HGS (OR [95% CI] = 3.4 [1.9–5.9]). In women, low relative STS power was associated with frailty (OR [95% CI] = 5.2 [3.5–7.7]), disability in BADL (OR [95% CI] = 4.3 [1.8–9.9]) and IADL (OR [95% CI] = 3.1 [2.2–4.3]) and low HGS (OR [95% CI] = 6.1 [2.8–13.1]). No associations were found between low relative STS power and disability in IADL or depression in men, nor between low relative STS power and cognitive impairment or depression in women. Conclusion Relative STS power decreased with increasing age in both men and women. The provided sex‐specific cut‐off points for low relative STS power using the 30‐s STS test adequately identified older people with frailty and were associated with an increased risk of experiencing adverse conditions.

Indane‐based molecules are effective scaffolds for different pharmaceutical products, so it is relevant to analyze the relation between structure and functionality in indane derivatives. Here, we have characterized the conformational landscape and molecular structure of 1‐aminoindane in the gas phase using chirped‐excitation Fourier‐transform microwave spectroscopy and computational methods. The rotational spectrum confirmed the presence of two conformers, which were identified based on their rotational constants and 14N nuclear quadrupole coupling tensor elements. The observed conformers share the cyclopentane puckering and amino equatorial configuration but differ in the orientation of the amino group hydrogens. The spectral analysis further allowed the observation of all monosubstituted 13C and 15N isotopologues in natural abundance for the most stable isomer, allowing a precise structural determination for this species. Structural information was derived using the substitution (rs) and effective vibrational ground state (r0) methods, revealing that the structure of 1‐aminoindane is very similar to that of indane. A calculation of the potential energy surface along the pathway for the conversion between the most stable equatorial species permitted to rationalize the non‐observation of additional conformers via molecular relaxation during the adiabatic expansion. The computational results include ab initio (MP2) and DFT methods (B3LYP, wB97X‐D and M06‐2X).

This research explores the social response to disclosures and conversations about mental health on social media, which is a pioneering and innovative approach. Unlike previous studies, which focused predominantly on psychopathological aspects, this study explores how communities react to conversations about mental health on Instagram, one of the favourite social media platforms among young people, breaking new ground not only in the Spanish context, but also on a global scale, filling a gap in international research. The study created a novel corpus by collecting and labelling comments on Instagram posts related to celebrity mental health disclosures, categorising them by polarity (positive, negative, neutral) and stigma. Additionally, the research implements machine learning algorithms to detect stigma and polarity in mental health disclosures on Instagram. While traditional techniques like Support Vector Machine (SVM) and RF (Random Forest) displayed decent performance with lower computational loads, advanced deep learning and BERT (Bidirectional Encoder Representation from Transformers) algorithms achieved outstanding results. In fact, BERT models achieve around 96% accuracy in polarity and stigma detection, while deep learning models achieve 80% for polarity and 87% for stigma, very high accuracy metrics. This research contributes significantly to understanding the impact of mental health discussions on social media, offering insights that can reduce stigma and raise awareness. Artificial intelligence can be used for more responsible use of social media and effective management of mental health problems in digital environments.

This auto-netnographic study explores the impact of neoliberal ideologies on teacher identity within the context of public education in Spain, with a particular focus on how social networks and media, such as Twitter and Facebook, shape and reflect these dynamics. Combining narrative introspection with thematic analysis of online content, the study examines how social media amplifies neoliberal values, including individualism, commodification and competitive appraisal, influencing teacher subjec-tivity and solidarity. By situating the findings within the Spanish educational landscape, the study highlights how localised socio-political dynamics intersect with global neoliberal trends. The results reveal that social media serves as both a platform for ideological critique and a site where collective identities are fragmented, contributing to the erosion of solidarity amongst educators. Ultimately, this research advocates for reclaiming public education values through critical reflection and dialogue, promoting a more equitable and humane educational system.

Luminescent materials doped with rare-earth (RE) ions have emerged as powerful tools in thermometry, offering high sensitivity and accuracy. However, challenges remain, particularly in maintaining efficient luminescence at elevated temperatures. This study investigates the thermometric properties of BiVO4: Yb³⁺/Er³⁺ (BVO: Er/Yb) nanophosphors synthesized via the sol–gel method. Structural, morphological, and optical analyses confirm the high purity and monoclinic crystal structure of the materials. Dual-mode luminescence under UV and near-infrared (NIR) excitation is explored, revealing complex thermal dynamics. The distinct performances of these luminescent thermometers, in terms of thermal sensitivity and temperature uncertainty, were evaluated in the non-saturation regime in both down-shifting (DS) and up-conversion (UC) processes. Utilizing fluorescence intensity ratio (LIR) measurements, we quantified absolute and relative sensitivities, as well as temperature uncertainties, over a temperature range of 300–450 K. Temperature sensing was based on the LIR of green emission bands arising from the thermally coupled ²H11/2 → ⁴I15/2 and ⁴S3/2 → ⁴I15/2 transitions of Er³⁺. The maximum absolute sensitivity (Sa) reached 60 × 10⁻⁴ K⁻¹ at 388 K under 975 nm excitation (UC) and 56 × 10⁻⁴ K⁻¹ at 400 K under 325 nm excitation (DS). Notably, for both excitation modes, the relative sensitivity (Sr) decreased consistently with increasing temperature, peaking at 0.908% K⁻¹ and 0.87% K⁻¹ at 300 K, and gradually declining to 0.4% K⁻¹ and 0.39% K⁻¹ at 450 K for the DS and UC processes, respectively. Temperature resolution (δT) also varied with temperature, increasing from 0.55 K to 1.23 K as the temperature rose from 300 to 450 K under 325 nm excitation. A comparable trend was observed for δT under 975 nm excitation. These findings underscore the potential of BVO: Er/Yb nanophosphors as versatile and effective luminescent thermometers for a broad range of applications.

Arthropods play key roles in ecosystems as pollinators or as food resources for many birds. The decline in arthropods in farmland due to agricultural intensification is related to negative population trends in farmland birds. Semi‐natural areas such as fallow land are valuable habitats for arthropod communities in farmland, but the potential of these areas to boost biodiversity greatly depends on their management. We used a field experiment to explore the mechanisms behind the effects of mechanical management on arthropod communities in high conservation value fallow land. We used GLMMs to explore changes in arthropod abundance after treatment application, and pSEM to discriminate direct effects of treatments from indirect effects mediated by changes in vegetation structure. Tillage had stronger negative effects than vegetation shredding on total arthropod, spider and bee abundance, which were mediated by a reduction in vegetation height, green cover and flowering. Coleoptera biomass did not vary between treatments. The differences between treatments disappeared from 2 to 3 months after treatment application for total arthropod and spider biomass, but not for Orthoptera and bees. Synthesis and applications. Low‐intensity mechanical management of fallows has short‐term negative effects on arthropod abundance. For conservation purposes, fallow management should combine untreated fallow fields (or strips within fields) to boost arthropod communities, with low‐intensity management to create suitable breeding habitats for steppe birds.

An accurate prediction of wind power generation is crucial for optimizing the integration of wind energy into the power grid, ensuring energy reliability. This research focuses on enhancing the accuracy of wind power generation forecasts by combining data from mesoscale and reanalysis models with Machine Learning (ML) approaches. We utilized WRF forecast data alongside ERA5 reanalysis data to estimate wind power generation for a wind farm located at Valladolid, Spain. The study evaluated the performance of ML models based on WRF and ERA5 data individually, as well as a combined model using inputs from both datasets. The hybrid model combining WRF and ERA5 data with ML resulted in a 15% improvement in root mean square error (RMSE) and a 10% increase in R2${R}^2$ compared with standalone models, providing a more reliable 1‐h forecast of wind power generation. Additionally, the availability of data over time was addressed: WRF provides the advantage of projecting data into the future, whereas ERA5 offers retrospective data.

In this paper we analyze a dynamic Cournot oligopoly to study the relationship between competition and green innovation. Firms face a tax on emissions and react to this tax investing in an abatement technology. The tax is given by the feedback Stackelberg equilibrium of a dynamic policy game between a regulator and a polluting oligopoly where environmental damages depend on the pollution stock. For constant marginal damages, we find that firms’ R&D investment increases monotonically with the number of firms in the industry because competition increases the tax. This effect is explained by the fact that the tax can be decomposed in two terms, one negative that reflects the divergence between the price and the marginal revenue because of the market power of firms, and another positive that reflects the divergence between the social valuation of the pollution stock and the private valuation. When the number of firms in the industry increases, the absolute value of the first term decreases and the tax increases, leading to more investment. Moreover, as in this case firms increase their stock of abatement capital, net emissions decrease causing a reduction of the pollution stock.

This editorial explores the dynamic psychiatric research field by focusing on interdisciplinary approaches to understand the complexity of mental disorders by placing particular emphasis on schizophrenia. It highlights the need to integrate findings from diverse scientific disciplines, such as neuroscience, computational modeling and genomics, to unravel the multifaceted nature of these conditions. The potential of interdisciplinary research to transform our knowledge and the treatment of psychiatric disorders is underscored by moving beyond traditional models and developing more nuanced frameworks to more effectively address these complexities. Thus by combining perspectives from different fields, significant advancements are expected in the diagnosis, treatment and prevention of mental disorders like schizophrenia, and will open new research and clinical practice avenues in psychiatry.

Background: Hip fractures are prevalent among the elderly and impose a significant burden on healthcare systems due to the associated high morbidity and costs. The increasing use of intramedullary nails for hip fracture fixation has inadvertently introduced risks; these implants can alter bone elasticity and create stress concentrations, leading to peri-implant fractures. The aim of this study is to investigate the outcomes of peri-implant hip fractures, evaluate the potential causes of such fractures, determine the type of treatment provided, assess the outcomes of said treatments, and establish possible improvement strategies. Methods: We conducted a retrospective observational study on 33 patients with peri-implant hip fractures (PIFs) who underwent surgical management at Río Hortega University Hospital from 2010 to 2022. The collected data included demographics, initial fracture characteristics, the peri-implant fracture classification, implant details, surgical outcomes, functional scores, and complications. Functional capacity was evaluated using the Parker Mobility Score (PMS). Results: The cohort (91% female, mean age 87.6 years) included 34 peri-implant fractures. The mean time from the initial fracture to the PIF was 47.2 months (nine patients developed PIFs within 2 months). Most fractures (76%) were managed with implant removal and the insertion of a long intramedullary nail, with cement augmentation in 31% of cases. The mean surgical time was 102 min, and the average hospital stay was 9.6 days. Postoperative complications occurred in 27%, with a perioperative mortality rate of 9%. Functional capacity showed a significant decline, with an average PMS loss of 4.16 points. Mortality at one year post-PIF was 36%, rising to 83% at five years. Radiographic consolidation was observed in 72% of cases at an average of 6.04 months, though 24% of patients died before consolidation. Statistically significant correlations were found for PMS pre-index fracture (PMS1: r = 0.354, p < 0.05), pre-PIF (PMS2: r = 0.647, p < 0.001), and post-PIF (PMS3: r = 0.604, p < 0.001). Conclusions: Peri-implant hip fractures present complex challenges due to their surgical difficulty and impact on patient mobility and survival. Successful management requires individualized treatment based on fracture type, implant positioning, and patient factors. These findings underscore the need for preventive measures, particularly in implant choice and techniques like overlapping and interlocking constructs, to minimize the secondary fracture risk.

RET gene is a driver of thyroid cancer (TC) tumorigenesis. The incidence of TC has increased worldwide in the last few decades, both in medullary and follicular‐derived subtypes. Several drugs, including multikinase and selective inhibitors, have been explored. Selpercatinib and pralsetinib are selective RET inhibitors that have shown clear clinical benefits for patients in the LIBRETTO and ARROW trials, respectively. Currently, their development and application in clinical practice are ongoing. However, its efficacy in different RET pathogenic variants has not yet been well established. Although selpercatinib and pralsetinib achieved a high ORR, no data are available regarding the differences in tumor responses of both TC groups according to RET pathogenic variants. Clinical trials and literature have analyzed the efficacy of selective RET inhibitors with a special interest in the most common variants. A review of LIBRETTO and ARROW trials was made regarding the change in tumor size depending on the pathogenic variants. M918T pathogenic variant resulted in a higher complete response rate. Patients who underwent fusion had the highest ORR (objective response rate). MKi‐treated patients did not exhibit significant differences from untreated patients. Different RET pathogenic variants are not biomarkers of RETi response in TC. Selpercatinib showed a tendency to achieve a complete response. All patients with RET pathogenic variants should receive treatment with selpercatinib or pralsetinib at any moment of the therapeutic schedule owing to off‐target inhibition and toxicity. Therefore, new targets for drug sensitivity and resistance should be explored.

The Internet of Things (IoT) is integral to modern infrastructure, enabling connectivity among a wide range of devices from home automation to industrial control systems. With the exponential increase in data generated by these interconnected devices, robust anomaly detection mechanisms are essential. Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns. This paper presents a novel approach utilizing generative adversarial networks (GANs) for anomaly detection in IoT systems. However, optimizing GANs involves tuning hyper-parameters such as learning rate, batch size, and optimization algorithms, which can be challenging due to the non-convex nature of GAN loss functions. To address this, we propose a five-dimensional Gray wolf optimizer (5DGWO) to optimize GAN hyper-parameters. The 5DGWO introduces two new types of wolves: gamma (γ) for improved exploitation and convergence, and theta (θ) for enhanced exploration and escaping local minima. The proposed system framework comprises four key stages: 1) preprocessing, 2) generative model training, 3) autoencoder (AE) training, and 4) predictive model training. The generative models are utilized to assist the AE training, and the final predictive models (including convolutional neural network (CNN), deep belief network (DBN), recurrent neural network (RNN), random forest (RF), and extreme gradient boosting (XGBoost)) are trained using the generated data and AE-encoded features. We evaluated the system on three benchmark datasets: NSL-KDD, UNSW-NB15, and IoT-23. Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives. The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics, including accuracy, recall, precision, root mean square error (RMSE), and convergence trend. The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD, UNSW-NB15, and IoT-23 datasets, with values of 0.24, 1.10, and 0.09, respectively. Additionally, it attained the highest accuracy, ranging from 94% to 100%. These results suggest a promising direction for future IoT security frameworks, offering a scalable and efficient solution to safeguard against evolving cyber threats.

Sound-based uroflowmetry (SU) offers a non-invasive alternative to traditional uroflowmetry (UF) for evaluating lower urinary tract dysfunctions, enabling home-based testing and reducing the need for clinic visits. This study compares SU and UF in estimating urine flow rate and voided volume in 50 male volunteers (aged 18–60), with UF results from a Minze uroflowmeter as the reference standard. Audio signals recorded during voiding were segmented and machine learning algorithms (gradient boosting, random forest, and support vector machine) estimated flow parameters from three devices: Ultramic384k, Mi A1 smartphone, and Oppo smartwatch. The mean absolute error for flow rate estimation were 2.6, 2.5 and 2.9 ml/s, with R² values of 84%, 83%, and 79%, respectively. Analysis of the Ultramic384k’s frequency range showed that the 0–8 kHz band contained 83% of significant components, suggesting higher sampling frequencies are unnecessary. A 1000 ms segment size was optimal for balancing computational efficiency and accuracy. Lin’s concordance coefficients for urine flow and voided volume using the smartwatch (0–8 kHz, 1000 ms) were 0.9 and 0.85, respectively, demonstrating that SU is a reliable, cost-effective alternative to UF for estimating key uroflowmetry parameters, with added patient convenience.