University of Castilla-La Mancha

Pan‐cyclin‐dependent‐kinase (CDK) inhibitors are a new class of targeted therapies that can act on multiple CDKs, with dinaciclib being one of the most promising compounds. Although used as a monotherapy, an interesting approach could be to combine it with radiotherapy. Here, we show that dinaciclib increases radiosensitivity in some experimental models of lung and colon cancer (A549 or HCT 116) but not in others (H1299 or HT‐29). Dinaciclib did not alter serine‐protein kinase ATM signalling or cell cycle profiling after ionising‐radiation exposure, which have been described for other CDK inhibitors. Interestingly, in terms of apoptosis, although the combination renders a clear increase, no potentiation of the ionising‐radiation‐induced apoptosis was observed. Mechanistically, inhibition of CDK12 by dinaciclib diminishes BRCA1 expression, which decreases homologous recombination (HR) and probably promotes the nonhomologous end joining repair process (NHEJ), which ultimately promotes the induction of ionising‐radiation‐associated cellular senescence in a TP53‐dependent manner, explaining the lack of effect observed in some experimental models. In conclusion, our report proposes a molecular mechanism, based on the signalling axis CDK12–BRCA1, involved in this newly identified therapeutic effect of dinaciclib, although other players implicated in HR should not be discarded. In addition, our data provide a rationale for more selective and personalised chemo/radiotherapy treatment according to the genetic background of the tumour.

Amphiphilic dendrons or Janus dendrimers self-assembling into nanoscale vesicles offer promising avenues for drug delivery. Triazine-carbosilane dendrons have shown great potential for the intracellular delivery of rose bengal, additionally enhancing...

We are in the midst of a revolution in the fields of neuroanatomy and electron microscopy. The monumental advancements in the neuroscience field during the last decade have led to unprecedented scientific discoveries about our brain and to the development of new technologies and applications that have significantly contributed to such advances. Conventional applications of transmission electron microscopy have revolutionized neurosciences and are critical for determining the fine morpho-functional characterization of brain cells and their connections. Electron microscopy has progressively evolved toward the development of both more sensitive approaches to unravel the bidimensional subcellular localization of proteins and tools that allow for the three-dimensional characterization of different nerve cells and their connections. The development of new technological advances in two- and three-dimensional electron microscopy to study and map the brain has led to the development of essential tools to decipher the complexity of the brain. For two-dimensional, the sodium dodecyl sulfate-digested freeze-fracture replica labeling technique is a technique with the main goal of chemically identifying the structural components viewed in freeze-fracture replicas and has significant advantages over conventional immunoelectron microscopic techniques for revealing the subcellular organization of proteins along the neuronal surface in the brain. For three-dimensional, volume electron microscopy methods can be applied to structural studies of cell components and organelles, just as conventional transmission electron microscopy has been traditionally applied, but with advantages derived from the possibility of three-dimensional visualization and analysis. The development of volume electron microscopy has greatly facilitated the study of brain structure and connectivity at the synaptic level. Dedicated software tools for the analysis of highly complex connectivity patterns in three dimension are evolving in parallel, allowing the extraction of relevant information from large datasets. Moreover, by applying these new methodologies, the field of pathology is expected to advance, potentially with the identification of the pathogenesis generating these diseases. This review aims to present the possibilities and fundamentals of two- and three-dimensional electron microscopy for high-resolution ultrastructural analyses of neurons and their connections. These technological tools have improved the ability to study the brain, thus providing new insights into brain structure and function.

Adipocytes expand massively to accommodate excess energy stores and protect the organism from lipotoxicity. Adipose tissue expandability is at the center of disorders such as obesity and lipodystrophy; however, little is known about the relevance of adipocyte biomechanics on the etiology of these conditions. Here, we show in male mice in vivo that the adipocyte plasma membrane undergoes caveolar domain reorganization upon lipid droplet expansion. As the lipid droplet grows, caveolae disassemble to release their membrane reservoir and increase cell surface area, and transfer specific caveolar components to the LD surface. Adipose tissue null for caveolae is stiffer, shows compromised deformability, and is prone to rupture under mechanical compression. Mechanistically, phosphoacceptor Cav1 Tyr14 is required for caveolae disassembly: adipocytes bearing a Tyr14Phe mutation at this residue are stiffer and smaller, leading to decreased adiposity in vivo; exhibit deficient transfer of Cav1 and EHD2 to the LD surface, and show distinct Cav1 molecular dynamics and tension adaptation. These results indicate that Cav1 phosphoregulation modulates caveolar dynamics as a relevant component of the homeostatic mechanoadaptation of the differentiated adipocyte.

We analyzed the interindividual heterogeneity in health responses to a supervised high-intensity interval training (HIIT) program in individuals with metabolic syndrome (MetS). Two-hundred and sixty-four adults with overweight/obesity (56.3±7.3 y, body mass index, 32.3±4.7 kg·m ⁻² ), and MetS were randomized to a standard health care non-exercise group (CONT group, N=58) or standard health care plus HIIT (EXER group, N=206). HIIT intervention was performed on a cycloergometer thrice a week (43 min·session ⁻¹ ). The change in MetS components (i.e., MetS z score), cardiorespiratory fitness (VO 2PEAK ), maximal cycling power (W PEAK ), and body weight/composition was assessed in both groups before (0 weeks) and after the intervention (16 weeks). Individual responses in the EXER group were considered attributable to HIIT when the improvements were larger than twice the typical error (>2 TE). TE was calculated using pre- and post-intervention data from the time-matched CONT group. The percent of participants that improved MetS z score beyond 2TE was 51% driven by reductions in blood pressure (45%) and waist circumference (48%). Blood lipids and glucose response were only 21% and 16% (participants improving beyond 2TE). Sixty percent of individuals that improved MetS z score, also improved VO 2PEAK (r=−0.013; P=0.86) while 85% of individuals improving MetS z score also improved W PEAK (r=0.151; P=0.03). In summary, health providers can expect that a 16- week HIIT program would indisputably improve MetS in approximately 50% of individuals completing the program. Lastly, W PEAK better predicts which individuals would improve MetS than VO 2PEAK when the direct assessment of the five MetS factors is not feasible.

Background High systolic blood pressure is one of the leading global risk factors for mortality, contributing significantly to cardiovascular diseases. Despite advances in treatment, a large proportion of patients with hypertension do not achieve optimal blood pressure control. Arterial stiffness (AS), measured by pulse wave velocity (PWV), is an independent predictor of cardiovascular events and overall mortality. Various antihypertensive drugs exhibit differential effects on PWV, but the extent to which these effects vary depending on individual patient characteristics is not well understood. Given the complexity of selecting the most appropriate antihypertensive medication for reducing PWV, machine learning (ML) techniques offer an opportunity to improve personalized treatment recommendations. Objective This study aims to develop an ML model that provides personalized recommendations for antihypertensive medications aimed at reducing PWV. The model considers individual patient characteristics, such as demographic factors, clinical data, and cardiovascular measurements, to identify the most suitable antihypertensive agent for improving AS. Methods This study, known as the RIGIPREV study, used data from the EVA, LOD-DIABETES, and EVIDENT studies involving individuals with hypertension with baseline and follow-up measurements. Antihypertensive drugs were grouped into classes such as angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), β-blockers, diuretics, and combinations of diuretics with ACEIs or ARBs. The primary outcomes were carotid-femoral and brachial-ankle PWV, while the secondary outcomes included various cardiovascular, anthropometric, and biochemical parameters. A multioutput regressor using 6 random forest models was used to predict the impact of each antihypertensive class on PWV reduction. Model performance was evaluated using the coefficient of determination (R2) and mean squared error. Results The random forest models exhibited strong predictive capabilities, with internal validation yielding R2 values between 0.61 and 0.74, while external validation showed a range of 0.26 to 0.46. The mean squared values ranged from 0.08 to 0.22 for internal validation and from 0.29 to 0.45 for external validation. Variable importance analysis revealed that glycated hemoglobin and weight were the most critical predictors for ACEIs, while carotid-femoral PWV and total cholesterol were key variables for ARBs. The decision tree model achieved an accuracy of 84.02% in identifying the most suitable antihypertensive drug based on individual patient characteristics. Furthermore, the system’s recommendations for ARBs matched 55.3% of patients’ original prescriptions. Conclusions This study demonstrates the utility of ML techniques in providing personalized treatment recommendations for antihypertensive therapy. By accounting for individual patient characteristics, the model improves the selection of drugs that control blood pressure and reduce AS. These findings could significantly aid clinicians in optimizing hypertension management and reducing cardiovascular risk. However, further studies with larger and more diverse populations are necessary to validate these results and extend the model’s applicability.

Alcohols from biological waste sources or renewable electricity (electrofuels) are gaining attention in hard-to-decarbonize sectors such as transport. Adding alcohol to conventional fuels has positive environmental effects on automotive applications, requiring minimal engine adjustments. Employing a combination of terahertz (THz) and gigahertz (GHz) spectroscopies, a comprehensive analysis of model fitting is presented for diesel-like fuels, pure alcohols (ethanol and n-butanol), and alcohol-fuel blends. Through the integration of data from both spectroscopic techniques, new Debye parameters are introduced to improve the accuracy of fitting for various fuels. This research demonstrates that THz spectroscopy alone is valuable for reasonable fits, particularly for alcohols. However, integrating THz and GHz spectroscopies leads to improved fitting, and to better potential to understand the behavior of fuel properties. In addition, the effect of alcohol concentration on the dielectric constant spectra in blends was investigated, highlighting the importance of molecular interactions. The results reveal a linear relationship between fitted parameters and alcohol content in the blends. However, the study acknowledges limitations, including challenges in achieving satisfactory fits at low alcohol concentrations and the necessity for assumptions in the modeling process. These findings provide a basis for future research and advances in fuel property modeling.

We report on the synthesis, characterization and photobehavior of single crystalline non‐stoichiometric hydrogen‐bonded organic frameworks (NS‐HOFs). NS‐HOFs (BTNT‐1) with various composition ratios were successfully obtained as single crystals from two analogue tetratopic carboxylic acids, possessing naphthalene and benzothiadiazole cores NTTA and BTTA, respectively. The heterogeneous distribution of the components was thoroughly confirmed by time‐resolved fluorescence microscopy and focused synchrotron X‐ray radiation techniques, for the first time. The versatile fluorescence of BTNT‐1 behavior depends on the composition ratio and distribution of the component in the single crystals. We observed not only fluorescence bands with various colors such as purple, blue, green and white, depending on the composition ratio of both components in BTNT‐1, but also different emission bands from a single crystal. Furthermore, we provide details on their emission lifetimes following the composition, emission color and targeted region on the crystal. These results demonstrate unique and versatile optical properties of carboxylic acid‐based NS‐HOFs and provide a concept of creating functional mixed porous materials capable of different and tunable optical properties.

We report on the synthesis, characterization and photobehavior of single crystalline non‐stoichiometric hydrogen‐bonded organic frameworks (NS‐HOFs). NS‐HOFs (BTNT‐1) with various composition ratios were successfully obtained as single crystals from two analogue tetratopic carboxylic acids, possessing naphthalene and benzothiadiazole cores NTTA and BTTA, respectively. The heterogeneous distribution of the components was thoroughly confirmed by time‐resolved fluorescence microscopy and focused synchrotron X‐ray radiation techniques, for the first time. The versatile fluorescence of BTNT‐1 behavior depends on the composition ratio and distribution of the component in the single crystals. We observed not only fluorescence bands with various colors such as purple, blue, green and white, depending on the composition ratio of both components in BTNT‐1, but also different emission bands from a single crystal. Furthermore, we provide details on their emission lifetimes following the composition, emission color and targeted region on the crystal. These results demonstrate unique and versatile optical properties of carboxylic acid‐based NS‐HOFs and provide a concept of creating functional mixed porous materials capable of different and tunable optical properties.

First predicted by Richtmyer in 1960 and experimentally confirmed by Meshkov in 1969, the Richtmyer–Meshkov instability (RMI) is crucial in fields such as physics, astrophysics, inertial confinement fusion and high-energy-density physics. These disciplines often deal with strong shocks moving through condensed materials or high-pressure plasmas that exhibit non-ideal equations of state (EoS), thus requiring theoretical models with realistic fluid EoS for accurate RMI simulations. Approximate formulae for asymptotic growth rates, like those proposed by Richtmyer, are helpful but rely on heuristic prescriptions for compressible materials. These prescriptions can sometimes approximate the RMI growth rate well, but their accuracy remains uncertain without exact solutions, as the fully compressible RMI growth rate is influenced by both vorticity deposited during shock refraction and multiple sonic wave refractions. This study advances previous work by presenting an analytic, fully compressible theory of RMI for reflected shocks with arbitrary EoS. It compares theoretical predictions with heuristic prescriptions using ideal gas, van der Waals gas and three-term constitutive equations for simple metals, the latter being analysed with detailed and simplified ideal-gas-like EoS. We additionally offer an alternative explicit approximate formula for the asymptotic growth rate. The comprehensive model also incorporates the effects of constant-amplitude acoustic waves at the interface, associated with the D'yakov–Kontorovich instability in shocks.

Correction for ‘Mn-ferrite nanoparticles as promising magnetic tags for radiofrequency inductive detection and quantification in lateral flow assays’ by Vanessa Pilati et al. , Nanoscale Adv. , 2024, 6 , 4247–4258, https://doi.org/10.1039/D4NA00445K.

This chapter discusses the concept of cross-functional teams (CFTs), highlighting their development and application, mainly in manufacturing organizations, as an essential tool for sustained performance for the business. Furthermore, it provides a valuable analysis of how CFTs have developed over the years, their importance, and benefits, particularly the essential factors that have positively influenced CFTs. Additionally, it provides a valuable analysis of the shortcomings of the functional departments and the associated conventional teams in the workplace, which are being replaced by CFTs—teams integrating different disciplines and skills, seeking greater collaboration in the workplace. In this respect, the chapter provides an evolution and the relevant research at each stage, covering various decades, beginning from 1990–2000 and highlighting the relevant research streams up to recent years. Each decade captures the critical focus of CFTs for the manufacturing industry, such setbacks as effectiveness and efficacy resulting from a lack of authoritative capacity on the part of CFT members. In the subsequent decade, increasing globalization and CFTs led to the new challenges of managing multiculturalism. In recent years, the new challenges have been the impact of the complex and novel business problems associated with technology and the associated benefits and lessons. Thus, organizations’ need for CFTs cannot be overemphasized in a competitive world. Finally, the chapter addresses the complex nature within which the CFTs operate, which differs from conventional teams. It highlights factors influencing well-functioning CFTs and some examples of organizations with benefits of CFTs in place.

Providing user-friendly software for groups with special needs is crucial. However, there is a lack of specific evaluation methods and techniques adapted to the characteristics and needs of some users, such as those with autism spectrum disorder (ASD). The main objective of this work is to validate a UX evaluation methodology that focuses specifically on the use of systems, products, and services by adults with ASD through a specific use case. This methodology was developed by taking into account the perspectives of UX experts and ASD professionals on the one hand and potential users on the other. It comprises a three-stage validated process based on the UX/ASD model. It focuses on assessing the user experience of systems used by adults with ASD by considering specific tools and techniques for effective evaluation, in contrast to the existing UX methodologies for evaluating software applications for users with ASD that are limited to the use of generic usability analysis tools. The choice of the PlanTEA application is motivated by the fact that it is designed specifically for users with ASD to assist them in their visits to medical centers by planning and anticipating those situations. However, it has not been evaluated from the ASD user experience point of view. Applying this specific methodology for users with ASD has provided significant feedback for improving their satisfaction and perception of PlanTEA before making it available to society. Moreover, the methodology has been proven to be a powerful tool in the whole process of the UX evaluation of PlanTEA, allowing for more comprehensive UX assessments and reporting.

The aim of this study is to explore the existence of different segments of future university students on the basis of their university selection criteria and to analyse the extent to which the segments identified differ as regards the students’ perceptions of the reliability of university communication tools. This has been done using 605 usable questionnaires, while the segmentation method employed was a latent class cluster analysis. Future students were divided into five clusters based on their university selection criteria: (1) high academic performance; (2) high academic performance but economy and word of mouth (WOM); (3) unconcerned; (4) independence from parents; and (5) overinformed. The differences in the perceptions of the reliability of universities’ communication tools among the clusters identified were studied using the bias-adjusted three-step approach. Students in the cluster labelled ‘high academic performance but economy and WOM’ tended to rate the university’s communication tools as more reliable, while those included in the cluster labelled ‘independence from parents’ found them to be less reliable. This paper contributes to the marketing theory for higher education by modelling the heterogeneity in the students’ choices. This makes it possible to cover one of the main gaps in the literature on higher education.

Small and medium-sized enterprises (SMEs) play a crucial role in supporting the country’s economy. However, Spanish SMEs have not developed their full potential. It is thus essential for managers to seek and adopt better strategies to be more successful. In today’s turbulent environments, digital transformation strategy is considered an important strategic competitive resource. Given its importance, managers are encouraged to develop their capabilities to deal with digitilization efficiently, leading them to be more innovative and obtain higher levels of organizational effectiveness. There is extensive research in the literature on the effect of digitalization strategy on organizational effectiveness, with it being considered a composite construct. Little research has been conducted, however, to examine the independent effects on its economic, human resources and internationalization dimensions. Based on the Input-Mediator-Output framework, this study aims to highlight the mechanisms through which digital transformation strategy improves organizational effectiveness. Specifically, this article examines (1) the direct effect of SMEs’ digital transformation strategy and organizational effectiveness on their economic, human resources and internationalization dimensions, and (2) the mediating role of firm innovation in these relationships. To test our hypotheses, we randomly selected SMEs operating in Spain from the INE 2022 database. The selection framework used was the SABI database developed by Faedpyme (2023). Once the data were filtered, information was obtained for 1,113 SMEs. To test our hypothesis, we used structural equation modeling (SEM) based on partial least squares (PLS) and Smart PLS 4.0.9.9. PLS-SEM is a robust statistical technique that allows for the positing of direct and mediated relationships simultaneously between the study variables. Our results revealed that digital transformation strategy has a positive impact on the economic, human resources and internationalization dimensions of organizational effectiveness. Furthermore, we found that firm innovation partially mediates the positive influence of digital transformation strategy and the three dimensions of organizational effectiveness. Thus, SMEs that wish to improve their effectiveness should orient their strategy towards higher levels of firm innovation. Testing this model contributes to the literature in two ways. First, it sheds new light on how digital transformation strategy fosters higher levels of organizational effectiveness in SMEs, specifically, in its economic, human resources and internationalization dimensions, which have not hitherto been analyzed in the literature. Second, our results add to the organizational effectiveness theory, which is dominated by research on large firms. They also extend the evidence that digital transformation strategy and firm innovation are positive for organizational effectiveness in SMEs.

Living in a modern society driven by data underscores the significance of Human-data interaction (HDI). HDI is at the intersection of computer science, statistics, sociology, psychology, and behavioral studies, and is crucial in a landscape where seemingly ’free’ products often capitalize on users as commodities. It is important to ensure robust protection mechanisms for personal data, as the quality and ease of human interaction with the surrounding data shape our knowledge. The research explores HDI to understand how people perceive and interact with data, with the aim of improving decision-making and refining interaction within this context. The text has been improved to adhere to the following characteristics: objectivity, comprehensibility and logical structure, conventional structure, clear and objective language, format, formal register, structure, balance, precise word choice, and grammatical correctness. The primary goal is to generate insights that facilitate informed decision-making by understanding human engagement with data. In addition, our goal is to improve interaction within the context of HDI. To establish a knowledge foundation, we conducted a systematic review of HDI research over the past decade, consolidating essential knowledge. The article outlines the introduction of HDI, details the methodology of the systematic review, and presents the results obtained. The systematic study provides comprehensive insights into HDI, addressing key research questions. The findings illuminate human interaction with data, contributing to nuanced understandings of information dissemination and user engagement. The results offer a valuable resource for future studies, providing a well-rounded perspective on HDI. This article contributes an introductory exploration of HDI, outlines systematic study methodology, and presents outcomes to answer research questions. The importance of understanding human data interaction for informed decision-making is underscored by the findings. The synthesized knowledge can serve as a foundational stepping stone for future research in the dynamic realm of HDI.