Search

Abstract Background In standard weaning from mechanical ventilation, a successful spontaneous breathing test (SBT) consisting of 30 min 8 cmH2O pressure-support ventilation (PSV8) without positive end-expiratory pressure (PEEP) is followed by extubation with continuous suctioning; however, these practices might promote derecruitment. Evidence supports the feasibility and safety of extubation without suctioning. Ultrasound can assess lung aeration and respiratory muscles. We hypothesize that weaning aiming to preserve lung volume can yield higher rates of successful extubation. Methods This multicenter superiority trial will randomly assign eligible patients to receive either standard weaning [SBT: 30-min PSV8 without PEEP followed by extubation with continuous suctioning] or lung-volume-preservation weaning [SBT: 30-min PSV8 + 5 cmH2O PEEP followed by extubation with positive pressure without suctioning]. We will compare the rates of successful extubation and reintubation, ICU and hospital stays, and ultrasound measurements of the volume of aerated lung (modified lung ultrasound score), diaphragm and intercostal muscle thickness, and thickening fraction before and after successful or failed SBT. Patients will be followed for 90 days after randomization. Discussion We aim to recruit a large sample of representative patients (N = 1600). Our study cannot elucidate the specific effects of PEEP during SBT and of positive pressure during extubation; the results will show the joint effects derived from the synergy of these two factors. Although universal ultrasound monitoring of lungs, diaphragm, and intercostal muscles throughout weaning is unfeasible, if derecruitment is a major cause of weaning failure, ultrasound may help clinicians decide about extubation in high-risk and borderline patients. Trial registration The Research Ethics Committee (CEIm) of the Fundació Unió Catalana d’Hospitals approved the study (CEI 22/67 and 23/26). Registered at ClinicalTrials.gov in August 2023. Identifier: NCT05526053.

Abstract Machine learning (ML) systems can model quantitative structure-property relationships (QSPR) using existing experimental data and make property predictions for new molecules. With the advent of modalities such as targeted protein degraders (TPD), the applicability of QSPR models is questioned and ML usage in TPD-centric projects remains limited. Herein, ML models are developed and evaluated for TPDs’ property predictions, including passive permeability, metabolic clearance, cytochrome P450 inhibition, plasma protein binding, and lipophilicity. Interestingly, performance on TPDs is comparable to that of other modalities. Predictions for glues and heterobifunctionals often yield lower and higher errors, respectively. For permeability, CYP3A4 inhibition, and human and rat microsomal clearance, misclassification errors into high and low risk categories are lower than 4% for glues and 15% for heterobifunctionals. For all modalities, misclassification errors range from 0.8% to 8.1%. Investigated transfer learning strategies improve predictions for heterobifunctionals. This is the first comprehensive evaluation of ML for the prediction of absorption, distribution, metabolism, and excretion (ADME) and physicochemical properties of TPD molecules, including heterobifunctional and molecular glue sub-modalities. Taken together, our investigations show that ML-based QSPR models are applicable to TPDs and support ML usage for TPDs’ design, to potentially accelerate drug discovery.

The use of hydrotherapy during childbirth has gained relevance due to the demand for natural childbirth and greater respect for the woman’s choice. Studies have shown benefits such as less use of epidural analgesia, increased ability to cope with pain, shorter labor, and a better overall birth experience. Objective: The main objective of this study was to generate further evidence on maternal and birth outcomes associated with the use of hydrotherapy during labor, specifically aiming to describe the effects of water immersion during all stages of labor (first, second, and third) on women. Methodology: A retrospective cohort study was carried out on a random sample of women who gave birth at the Costa del Sol Hospital between January 2010 and December 2020. The calculated sample size was 377 women and the data were extracted from their partograms. After data extraction, two groups were formed: one group used hydrotherapy during childbirth (n = 124), while the other group included women who did not use hydrotherapy during the childbirth process (n = 253). Results: The results highlight significant differences in pain perception, analgesia use, types of labor, and delivery times between the two groups. Women who did not use hydrotherapy reported higher pain perception, with a median (IQR) of 8 (7–9) on a numerical scale, compared to a median (IQR) of 6 (5–7) for the hydrotherapy group. Furthermore, the group without hydrotherapy required epidural analgesia in 40% of cases, while in the hydrotherapy group, it was only necessary in 20%. In terms of the type of delivery, the hydrotherapy group had more spontaneous vaginal deliveries compared to the non-hydrotherapy group, which had more operative vaginal deliveries. The overall duration of labor was longer in the hydrotherapy group, especially in women who arrived at the hospital late in labor. Conclusions: Hydrotherapy is associated with a longer time to delivery. Women with a higher pain tolerance tend to opt for hydrotherapy instead of epidural analgesia.

Silica-based scaffolds are promising in Tissue Engineering by enabling personalized scaffolds, boosting exceptional bioactivity and osteogenic characteristics. Moreover, silica materials are highly tunable, allowing for controlled drug release to enhance tissue regeneration. In this study, we developed a 3D printable silica material with controlled mesoporosity, achieved through the sol-gel reaction of tetraethyl orthosilicate (TEOS) at mild temperatures with the addition of different calcium concentrations. The resultant silica inks exhibited high printability and shape fidelity, while maintaining bioactivity and biocompatibility. Notably, the increased mesopore size enhanced the incorporation and release of large molecules, using cytochrome C as a drug model. Due to the varying surface charge of silica depending on the pH, a pH-dependent control release was obtained between pH 2.5 and 7.5, with maximum release in acidic conditions. Therefore, silica with controlled mesoporosity could be 3D printed, acting as a pH stimuli responsive platform with therapeutic potential.

Among the different layers of the cornea, the corneal endothelium, which is composed of corneal endothelial cells (CEC), plays a key role in the maintenance of cornea transparency. Based on the donor shortages and the limitations associated with transplantation, in this work we have developed collagen hydrogels with different patterned structures on the surface to provide topographies in ranges similar to the natural environment that CEC sense. This aimed at stimulating cells to maintain a typical CEC phenotype and provide alternatives to corneal transplantation. In this sense, we have elaborated curved collagen hydrogels (concave and convex) with three different topographies (50, 200 and 300 µm grooves), with the help of 3D printed mold and replicating the mold with the collagen hydrogel, onto which CEC were cultured in order to analyze its behavior. Flat hydrogels were used as controls. Cell morphology, cell circularity and gene expression of ATP1A1 and ZO-1 genes were analyzed after 3 and 6 days. Results showed an effect of the curvature and the topography compared to flat collagen hydrogels, showing higher expression of ZO-1 and ATP1A1 in curved non-patterned hydrogels at day 3. The patterned hydrogels did not have such a significant effect on gene expression compared to flat hydrogels, showing stronger effect of the curvature compared to the topography. The effect was opposite at day 6, showing higher gene expression at days 6 on the patterned hydrogels, especially for the ZO-1 gene. The gene expression results were in accordance with the cell morphology observed at the different time points, showing circularities closer to hexagon like morphology at shorter time points. Overall, this platform can serve as a system to culture cell under proper environment to further be able to transplant a CEC monolayer or to allow transplantation of thin collagen layers with cultured CEC.

IntroductionUnderstanding intestinal permeability is paramount for elucidating gastrointestinal health and pathology. The size and nature of the molecule traversing the intestinal barrier offer crucial insights into various acute and chronic diseases, as well as the evolution of some conditions. This study aims to assess the urinary excretion kinetics of gluten immunogenic peptides (u-GIP), a unique class of dietary peptides detectable in urine, in volunteers under controlled dietary conditions. This evaluation should be compared to established probes like lactulose, a non-digestible disaccharide indicative of paracellular permeability, and mannitol, reflecting transcellular permeability.MethodsFifteen participants underwent simultaneous ingestion of standardized doses of gluten (10 g), lactulose (10 g), and mannitol (1 g) under fasting conditions for at least 8 hours pre-ingestion and during 6 hours post-ingestion period. Urine samples were collected over specified time intervals. Excretion patterns were analyzed, and correlations between the lactulose-to-mannitol ratio (LMR) and u-GIP parameters were assessed.ResultsThe majority of u-GIP were detected within the first 12 hours post-ingestion. Analysis of the variability in cumulative excretion across two sample collection ranges demonstrated that lactulose and u-GIP exhibited similar onset and excretion dynamics, although GIP reached its maximum peak earlier than either lactulose or mannitol. Additionally, a moderate correlation was observed between the LMR and u-GIP parameters within the longest urine collection interval, indicating potential shared characteristics among permeability pathways. These findings suggest that extending urine collection beyond 6 hours may enhance data reliability.DiscussionThis study sheds light on the temporal dynamics of u-GIP in comparison to lactulose and mannitol, established probes for assessing intestinal permeability. The resemblance between u-GIP and lactulose excretion patterns aligns with the anticipated paracellular permeability pathway. The capacity to detect antigenic food protein fragments in urine opens novel avenues for studying protein metabolism and monitoring pathologies related to the digestive and intestinal systems.

Abstract The Ocean microbiome has a crucial role in Earth’s biogeochemical cycles. During the last decade, global cruises such as Tara Oceans and the Malaspina Expedition have expanded our understanding of the diversity and genetic repertoire of marine microbes. Nevertheless, there are still knowledge gaps regarding their diversity patterns throughout depth gradients ranging from the surface to the deep ocean. Here we present a dataset of 76 microbial metagenomes (MProfile) of the picoplankton size fraction (0.2–3.0 µm) collected in 11 vertical profiles covering contrasting ocean regions sampled during the Malaspina Expedition circumnavigation (7 depths, from surface to 4,000 m deep). The MProfile dataset produced 1.66 Tbp of raw DNA sequences from which we derived: 17.4 million genes clustered at 95% sequence similarity (M-GeneDB-VP), 2,672 metagenome-assembled genomes (MAGs) of Archaea and Bacteria (Malaspina-VP-MAGs), and over 100,000 viral genomic sequences. This dataset will be a valuable resource for exploring the functional and taxonomic connectivity between the photic and bathypelagic tropical and sub-tropical ocean, while increasing our general knowledge of the Ocean microbiome.

Targeted protein degradation (TPD) is a rapidly developing drug discovery technique with unique efficacy and target scope stemming from its degradation-based activity. Molecular glue degraders are a promising arm of TPD, as evidenced by the FDA-approved therapeutics within this class, the increasing number of degraders in clinical development, and their predisposition to drug-likeness. Cereblon (CRBN) glue degraders mediate target degradation by generating a neomorphic interface between CRBN and a protein of interest. While promising, the complicated nature of this CRBN-glue-target ternary complex makes the rational design of molecular glue degraders challenging. For other drug modalities, predictive modeling has been established to leverage existing activity data and generate quantitative structure-activity relationships (QSAR). However, the applicability of QSAR strategies for glues remains under-investigated. Herein, machine learning methodologies were developed to predict glue-mediated recruitment of CRBN to target proteins and achieved promising performance. Generated models leveraged more than a hundred internal screening campaigns across thousands of CRBN glues to predict glue-mediated recruitment of targets to CRBN. Our results show that recruitment activity of CRBN glue degraders can be modeled by machine learning, with 89 % of models producing an area under the receiver operating characteristic curve (ROC AUC) > 0.8 and 70 % of models producing a Matthew's correlation coefficient (MCC) > 0.2 for these primary screening data. Importantly, our findings also indicate that the combination of compound and protein descriptors in the so-called proteochemometric models improves performance, with >80 % of the models exhibiting higher ROC AUC and MCC values than per-target models only based on compound information. Hence, our investigations suggest that proteochemometric modeling is a successful approach for molecular glue degraders. The proposed machine learning strategies can aid compound prioritization based on recruitment efficacy and target selectivity, thus have the potential to facilitate the design and discovery of therapeutic CRBN molecular glues.

El objetivo de este trabajo es mostrar el proceso de construcción de un instrumento que mida la salud psicológica laboral de una organización a partir de la gestión adecuada de factores psicosociales en el ámbito laboral. Para llevar a cabo dicho objetivo se decidió elaborar un instrumento que tuviera en consideración una valoración positiva de las situaciones y factores a medir, teniendo en consideración que una de las claves de las organizaciones saludables son los procedimientos saludables. El cuestionario piloto estuvo integrado por 68 reactivos y se aplicó a una muestra de 259 trabajadores/as. Los ítems se presentaron en formato Likert, con afirmaciones positivas de los factores teóricos subyacentes, y con siete opciones. Se diseñaron los reactivos teniendo en cuenta las referencias más significativas en dicho conocimiento científico. El análisis de consistencia interna de la escala de Salud Psicológica Laboral mostró una fiabilidad de 0.97 (alpha de Cronbach). Los 68 ítems fueron factorializados utilizando el método de extracción de Componentes Principales, con rotación Oblimin. El índice KMO fue de 0.950, sugiriendo que los datos estaban suficientemente interrelacionados y era factible realizar análisis factorial. Asimismo, en la prueba de esfericidad de Bartlett se obtuvieron resultados significativos (chi-cuadrado=25402,24; p

Objectives: This study aimed to assess the real use of cefazolin for methicillin-susceptible Staphylococcus aureus (MSSA) infective endocarditis (IE) in the Spanish National Endocarditis Database (GAMES) and to compare it with antistaphylococcal penicillin (ASP). Methods: Prospective cohort study with retrospective analysis of a cohort of MSSA IE treated with cloxacillin and/or cefazolin. Outcomes assessed were relapse; intra-hospital, overall, and endocarditis-related mortality; and adverse events. Risk of renal toxicity with each treatment was evaluated separately. Results: We included 631 IE episodes caused by MSSA treated with cloxacillin and/or cefazolin. Antibiotic treatment was cloxacillin, cefazolin, or both in 537 (85%), 57 (9%), and 37 (6%) episodes, respectively. Patients treated with cefazolin had significantly higher rates of comorbidities (median Charlson Index 7, P

The objective of this study was to investigate the impact of nutrient starvation on the growth, biochemical, metabolomic, and biological traits of Lavandula viridis L’Hér and Thymus lotocephalus G. López and R. Morales in vitro cultures. In both species, a reduction in shoot growth and in the production of chlorophyll and carotenoids was observed in cultures grown under nutrient-deficient media (especially Fe and N) compared to those grown under control conditions. The highest levels of hydrogen peroxide and lipid peroxidation, two indicators of oxidative stress, were observed in L. viridis cultures grown under N deficiency and in T. lotocephalus under P and Fe limitation. The results demonstrated that nutrient deficiency led to a 72% and 62% increase in the quantified phenolic compounds in L. viridis and T. lotocephalus, respectively. The highest concentrations of the major compound in both species—rosmarinic acid—were observed in cultures grown under Mg-deficient (60.7 ± 1.0 mg/gDW) and Fe-deficient (50.0 ± 0.4 mg/gDW) conditions in L. viridis and T. lotocephalus, respectively. Furthermore, nutrient starvation enhanced the capacity of green extracts to inhibit three enzymes (tyrosinase, elastase, and hyaluronidase) associated with anti-aging and their antioxidant properties.

The shiitake mushroom has gained popularity in the last decade, ranking second in the world for mushrooms consumed, providing consumers with a wide variety of nutritional and healthy benefits. It is often not clear the origin of these mushrooms, so it becomes of great importance to the consumers. In this research, different machine learning algorithms were developed to determine the geographical origin of shiitake mushrooms (Lentinula edodes) consumed in Korea, based on experimental data reported in the literature (δ13C, δ15N, δ18O, δ34S, and origin). Regarding the origin of shiitake in three categories (Korean, Chinese, and mushrooms from Chinese inoculated sawdust blocks), the random forest model presents the highest accuracy value (0.940) and the highest kappa value (0.908) for the validation phase. To determine the origin of shiitake mushrooms in two categories (Korean and Chinese, including mushrooms from Chinese inoculated sawdust blocks in the latter ones), the support vector machine model is chosen as the best model due to the high accuracy (0.988) and kappa (0.975) values for the validation phase. Finally, to determine the origin in two categories (Korean and Chinese, but this time including the mushrooms from Chinese inoculated sawdust blocks in the Korean ones), the best model is the random forest due to its higher accuracy value (0.952) in the validation phase (kappa value of 0.869). The accuracy values in the testing phase for the best selected models are acceptable (between 0.839 and 0.964); therefore, the predictive capacity of the models could be acceptable for their use in real applications. This allows us to affirm that machine learning algorithms would be suitable modeling instruments to determine the geographical origin of shiitake.

The objective of this study was to analyze the chemical profile of extracts from different fruit parts of the perennial arborescent cactus Opuntia leucotricha by applying ultra-high-performance liquid chromatography–high-resolution mass spectrometry (UHPLC-HRMS) and to evaluate their biological properties (antioxidant and enzyme inhibitory capacities). Extracts were obtained from the fruit pulp (without or with seeds), seeds, and peel by ultrasound-assisted extraction (UAE) using the conventional solvent methanol 50% and two Natural Deep Eutectic Solvents (NADESs) (glycerol:urea, 1:1 and citric acid:sorbitol, 1:2). A total of 33 compounds were identified, including phenolics, fatty acids, and others. Phenolic acids were the most abundant class of phenolics identified in all fruit parts, with the highest concentration observed in the methanol extracts from peel (593.02 µg/gDW), followed by seed-containing pulp (69.03 µg/gDW), pulp (57.83 µg/gDW), and seeds (39.97 µg/gDW). The second most effective extractant was NADES 1 (glycerol:urea, 1:1), which was also successful in extracting compounds with antioxidant capacity. Overall, the extracts demonstrated considerable enzyme inhibitory activities, with the greatest effects observed against α-amylase and α-glucosidase. The results indicate that O. leucotricha fruits could be a promising source of bioactive compounds, and NADES a viable alternative to organic solvents for their industrial exploitation.

Television series enhance the social visibility of their content, as is the case with Queen Elizabeth II and The Crown. Netflix is the streaming television platform that has turned Peter Morgan’s successful series (2016) into a television icon where the monarch is the main protagonist, taking us on a biographical journey that mixes the historical and the political with fiction. The main character is made to seem more humane and is brought closer to the general public, all of which leads to a transmedia narrative. This research aims to analyze the content of the messages published on Twitter during the days surrounding the Queen’s State funeral in September 2022 and their connection with the series through the hashtag #TheCrown. The topics that have become trends worldwide are quantitatively analyzed, using different digital tools. The sample collected 1,489,279 tweets published during the days from the announcement of the death of Elizabeth II to the day of her funeral (from 8 to 19 September 2022). The results show nodes of connection between different players and linked communities to #TheCrown while offering the traffic generated by the hashtag with different nodes and edges.

Since the last century, it has been shown that dedifferentiated cells of Camellia sinensis can produce catechins and other secondary metabolites under in vitro conditions, with potential applications in the cosmetic, pharmaceutical and food industries. In this work, cell suspension cultures of a C. sinensis cell line (LSC-5Y) were established in a liquid medium in order to optimize the biomass productivity, catechin monomer (GC, EGC, C, EC, CG, and ECG) and alkaloid (TB and CAF) productivity. The following factors were evaluated: concentration of growth regulators (BA and IBA), inoculum size, age of the cell line, light exposure, and effect of biotic elicitors (MeJA and extracts of Ciborinia camelliae). GC, EGC, and ECG increased approximately 1.80-fold when the auxin IBA concentration was increased from 0.1 to 2.0 mg/L. In addition, better productivity of EGC, C, EC, and CAF was achieved by using inoculum densities between 50 and 100 g/L. Although lower inoculum densities (25 g/L) showed a higher growth rate (0.20 d−1), the use of inoculum densities higher than 25 g/L favors a 2–4-fold increase in total catechin (TC) productivity, with maximum productivity being reached after 21 days of culture. However, the cell line showed instability in TC productivity: in the short term (in three successive subcultures), the coefficient of variation was 32.80%, and catechin production capacity was 2.5 years with maximum productivity at 0.5 years. Finally, it was observed that ethanol, used as an elicitor solvent, has a strong elicitor effect capable of increasing the accumulation of catechins up to 5.24 times compared to the treatment without an elicitor.

El artículo plantea cómo se percibe el enfoque securitario de la política migratoria europea a través del discurso institucional y político de la Unión Europea (UE). Un discurso que hace hincapié en el control de las fronteras exteriores y el retorno de los inmigrantes como acción prioritaria en la lucha contra la “inmigración ilegal” –usando la terminología comunitaria– en la UE, convirtiendo así la figura del inmigrante en situación administrativa irregular en una “amenaza” para el territorio comunitario. Para responder al papel que desempeña la securitización en la interpretación del hecho migratorio, se han analizado documentos jurídicos-administrativos elaborados por el Parlamento Europeo, el Consejo de la UE, la Comisión Europea y el Consejo Europeo sobre inmigración durante el período 2000-2021.

PM742 (1), a new chemical entity, has been isolated from the sponge Discodermia du Bocage collected in the Pacific Ocean. This compound showed strong in vitro cytotoxicity against several human tumor cell lines as well as a tubulin depolymerization mechanism of action, which led us to conduct an extensive Structure-Activity-Relationship study through the synthesis of different analogs. As a result, a derivatively named PM534 (2) is currently in its first human Phase I clinical trial. Herein, we present a comprehensive review of the isolation, structural elucidation, and antitumor activities of the parent compound PM742.

Climate change is affecting all regions of the world, and the Mediterranean region is one of the most affected. Plants accumulate secondary metabolites as an adaptive response to stress circumstances. The present study investigated the effect of different abiotic factor conditions (drought, moderate heat, severe heat, salinity, and UV-B radiation) on the essential oil (EO) yield, composition (volatile profile), and biological activity (enzyme inhibition and antioxidant activity) of Lavandula viridis L’Hér. In general, the environmental conditions increased the extraction yield of EO. Eighty-two compounds were identified in the EO and environmental factors induced some quantitative changes in EO composition. Severe heat and salinity conditions increased the concentration of the two most abundant compounds, 1,8-cineole and camphor. Severe heat also increased the potential of EO to inhibit the enzymes butyrylcholinesterase and tyrosinase. Drought, salinity, and UV-B radiation promoted the ability of EO to inhibit acetylcholinesterase. In addition, heat and drought enhanced the antioxidant activity of EO. These results are relevant for exploring the potential of this EO for industrial applications, although future studies combining the factors studied are important to understand the influence of synergistic effects on the composition and bioactivity of the plant products obtained.

Physical inactivity represents a significant public health challenge globally. Mobile applications, particularly those utilizing augmented reality (AR), have emerged as innovative tools for promoting physical activity. However, a systematic evaluation of their efficacy is essential. This systematic review aims to evaluate and synthesize the evidence regarding the effectiveness and benefits of mobile applications with augmented reality in enhancing physical activity and improving health outcomes. A comprehensive search was conducted in Scopus, PubMed, WOS, and the Cochrane Library databases following PRISMA guidelines. Observational and interventional studies evaluating AR mobile applications for physical exercise were included, without restrictions on publication date or language. The search terms included “Mobile Applications”, “Augmented Reality”, “Physical Fitness”, “Exercise Therapy”, and “Health Behavior”. The methodological quality was assessed using the ROBINS tool. The review identified twelve eligible studies encompassing 5,534,661 participants. The findings indicated significant increases in physical activity and improvements in mental health associated with the use of AR applications, such as Pokémon GO. However, potential risk behaviors were also noted. The evidence suggests that AR interventions can effectively promote physical activity and enhance health. Nonetheless, further research is needed to address limitations and optimize their efficacy. Future interventions should be tailored to diverse cultural contexts to maximize benefits and mitigate risks. AR mobile applications hold promise for promoting physical activity and improving health outcomes. Strategies to optimize their effectiveness and address identified risks should be explored to fully realize their potential.

Abstract Explainable machine learning is increasingly used in drug discovery to help rationalize compound property predictions. Feature attribution techniques are popular choices to identify which molecular substructures are responsible for a predicted property change. However, established molecular feature attribution methods have so far displayed low performance for popular deep learning algorithms such as graph neural networks (GNNs), especially when compared with simpler modeling alternatives such as random forests coupled with atom masking. To mitigate this problem, a modification of the regression objective for GNNs is proposed to specifically account for common core structures between pairs of molecules. The presented approach shows higher accuracy on a recently-proposed explainability benchmark. This methodology has the potential to assist with model explainability in drug discovery pipelines, particularly in lead optimization efforts where specific chemical series are investigated.

A Python package for the analysis of dark-field X-ray microscopy (DFXM) and rocking curve imaging (RCI) data is presented. DFXM is a non-destructive diffraction imaging technique that provides three-dimensional maps of lattice strain and orientation. The darfix package enables fast processing and visualization of these data, providing the user with the essential tools to extract information from the acquired images in a fast and intuitive manner. These data processing and visualization tools can be either imported as library components or accessed through a graphical user interface as an Orange add-on. In the latter case, the different analysis modules can be easily chained to define computational workflows. Operations on larger-than-memory image sets are supported through the implementation of online versions of the data processing algorithms, effectively trading performance for feasibility when the computing resources are limited. The software can automatically extract the relevant instrument angle settings from the input files' metadata. The currently available input file format is EDF and in future releases HDF5 will be incorporated.

A pandemia de COVID-19 é um acontecimento stressante não normativo e um risco para o funcionamento psicológico dos adolescentes. No entanto, o modelo de risco-proteção postula que este depende da combinação de riscos e recursos. O objetivo foi analisar os potenciais factores de risco e de proteção em função do sexo, da idade e do estado, em estudantes com idades compreendidas entre os 13 e os 21 anos na Cidade do México e no Estado do México. O Inquérito de Perceção dos Jovens sobre a COVID-19 foi administrado em linha, consistindo em oito factores ou áreas (por exemplo, pressões escolares, dinâmica familiar, medidas preventivas, mudança de rotinas, socialização virtual, relaxamento) e um formulário de consentimento informado. As análises multivariadas (MANOVA) revelaram diferenças significativas na perceção da pandemia em função do sexo (FRoy=4,155; p ≤0; η=,080), da faixa etária (FRoy=4,889; p ≤0; η=,093) e do estatuto (FRoy=3,061; p =,002; η=,060) no que respeita ao medo de contágio, à mudança de rotinas e às pressões escolares. Verificaram-se também efeitos de interação sexo*estado (F= 2,281; p=.022; η=.046) e de interação idade*estado (F=2,748; p=.006; η=.054), bem como efeitos de sexo*idade*estado apenas no medo de contágio (F=3,147; p=.044; η=.016). Em conclusão, as mulheres obtiveram pontuações mais elevadas em todos os factores do que os homens, especialmente entre os 16 e os 18 anos. Estes resultados corroboram as conclusões anteriores e podem servir de base para programas de intervenção em função do género, da idade e do estatuto.

Academic and pharmaceutical industry research are both key for progresses in the field of molecular machine learning. Despite common open research questions and long-term goals, the nature and scope of investigations typically differ between academia and industry. Herein, we highlight the opportunities that machine learning models offer to accelerate and improve compound selection. All parts of the model life cycle are discussed, including data preparation, model building, validation, and deployment. Main challenges in molecular machine learning as well as differences between academia and industry are highlighted. Furthermore, application aspects in the design-make-test-analyze cycle are discussed. We close with strategies that could improve collaboration between academic and industrial institutions and will advance the field even further.

This study evaluated the efficacy and safety of two anesthetic agents, alfaxalone and propofol, on maternal physiological parameters (heart and respiratory rates, blood pressure, and temperature) on either ovariohysterectomies or cesarean sections in bitches. A total of 34 healthy and pyometra-affected females (classified as ASA II), were induced with IV propofol (4 mg/kg), while 35 females, both healthy and pyometra affected, were induced with IV alfaxalone (1 mg/kg). For cesarean sections, females (ASA II) were induced with propofol (n = 14) or alfaxalone (n = 14). Additionally, the neonatal viability and modified Apgar score were recorded at 5, 60, and 120 min post-delivery. There were no significant differences in the physiological parameters when comparing the use of propofol and alfaxalone in bitches undergoing ovariohysterectomies, regardless of their health status, nor when comparing cesarean sections. It was observed that bitches induced with propofol occasionally required an additional dose for maintenance of the anesthesia. Neonatal mortality rates were similar for both groups; however, alfaxalone was associated with higher neonatal viability as indicated by the Apgar scores. The findings suggest that both anesthetic protocols are effective and safe for use in canine reproductive surgeries, with no major differences in basic physiological parameters’ alteration or neonatal outcomes between the two agents.

Background: Although there is scientific evidence regarding the use of water immersion during labor, this evidence is primarily focused on the first stage of labor. There is limited scientific evidence on water immersion during the second stage of labor. Objective: The objective of this study was to conduct a comprehensive systematic review and synthesis of contemporary evidence related to water birth, with a specific focus on the second stage of labor. Methods: A systematic review of the scientific literature published between January 2018 and October 2023 was carried out. A synthesis of the results was conducted following the Synthesis without Meta-Analysis (SWiM) guidelines. PubMed, Scopus, and the Cochrane Library were utilized as information sources. The search strategy was designed using the keywords “immersion” and “parturition”, along with their relevant synonyms. Inclusion criteria encompassed studies employing randomized controlled trials (RCTs), systematic reviews, and quantitative and qualitative approaches focusing on pregnant women undergoing water immersion at any stage of the labor process. Results: Eleven articles were selected: two systematic reviews (one quantitative and one qualitative), five cohort studies, one case–control study, one cross-sectional observational study, and two qualitative studies. A thorough assessment of the methodology was performed using several specific tools: the Cochrane RoB 2 (Risk of Bias 2) tool for systematic reviews, JBI Critical Appraisal Checklist for Qualitative Research for qualitative studies, STROBE for observational descriptive studies, and CASPe for qualitative studies. The results provided fundamental insights that will contribute to conceptual standardization regarding the effects of water birth on maternal and fetal health. Additionally, a synthesis of the results was performed concerning types of delivery, analgesia use, pain perception, and maternal satisfaction with the water birth experience. Conclusions: In this study, we conclude that the results regarding delivery types, labor durations, and analgesia use found in the literature, along with statistically significant maternal/fetal effects, are crucial for making recommendations regarding the use of water during labor in any of its stages if the woman desires it safely.

This paper focuses on the concept of the impact area as the intersection between the common issues of the personal agenda and the public agenda. Expanding the concept of agenda-setting effects, which focuses mainly on the relation between the public and media agendas, we have examined the congruity of the public agenda and the personal agenda as a contingent condition for the strength of these effects. The concept also distinguishes between the top-priority issues of the public and personal agendas, setting out that the general public’s search for information about these top-priority issues highlights the need for orientation. We used data from Spain to focus on the national elections (2011, 2015 and 2019), plus the beginning of the pandemic (2020). The study includes a content analysis of the El País daily newspaper for the four periods and a variety of surveys and representative electoral barometers. To those data we have added an analysis of the main search topics on Google Trends for each of the four years. The results reveal that variations in the relationship between the public and personal agendas are linked to the strength of the agenda-setting effect between the media and public agendas.

IntroductionLoneliness is a distressful feeling that can affect mental and physical health, particularly among older adults. Cortisol, the primary hormone of the Hypothalamic-Pituitary-Adrenal axis (HPA-axis), may act as a biological transducer through which loneliness affects health. While most previous studies have evaluated the association between loneliness, as a unidimensional construct, and diurnal cortisol pattern, no research has examined this relationship discriminating between social and emotional loneliness in older adults. As sex differences in the negative mental health outcomes of loneliness have been reported, we also investigated whether diurnal cortisol indices and loneliness associations occur in a sex-specific manner.MethodsWe analyzed the diurnal cortisol- pattern in 142 community-dwelling, non-depressed, Caucasian older adults (55,6% female) aged 60-90. Social and emotional (family and romantic) loneliness scores were assessed using the Spanish version of the Social and Emotional Loneliness Scale for Adults (SELSA). Five salivary cortisol samples were used to capture key features of the diurnal cortisol pattern, including: awakening and bedtime cortisol levels, awakening response (CAR), post-awakening cortisol output (post-awakening cortisol [i.e., the area under the curve with reference to the ground: AUCG]), total diurnal cortisol release (AUCG), and diurnal cortisol slope (DCS).ResultsAfter controlling for sociodemographic variables, the hierarchical linear multiple regression analyses revealed that in male older adults, higher scores on social and family loneliness were associated with elevated awakening cortisol levels, total diurnal cortisol output, and a steeper diurnal cortisol slope (DCS). However, these associations were not observed in female older adults. In addition, feelings of romantic loneliness were positively associated with bedtime cortisol levels and AUCG in older males. Multilevel growth curve modeling showed that experiencing more social and emotional loneliness predicted higher diurnal cortisol output throughout the day in older male adults.DiscussionThe presence of sex differences in the relationship between cortisol indices and loneliness among older adults holds particular significance for diagnostic and screening procedures. Combining loneliness scales as screening tools with diurnal cortisol measures has the potential to be an effective and cost-efficient approach in identifying higher-risk individuals at early stages.

Abstract Background Patients with acute respiratory failure caused by cardiogenic pulmonary edema (CPE) may require mechanical ventilation that can cause further lung damage. Our aim was to determine the impact of ventilatory settings on CPE mortality. Methods Patients from the LUNG SAFE cohort, a multicenter prospective cohort study of patients undergoing mechanical ventilation, were studied. Relationships between ventilatory parameters and outcomes (ICU discharge/hospital mortality) were assessed using latent mixture analysis and a marginal structural model. Results From 4499 patients, 391 meeting CPE criteria (median age 70 [interquartile range 59–78], 40% female) were included. ICU and hospital mortality were 34% and 40%, respectively. ICU survivors were younger (67 [57–77] vs 74 [64–80] years, p

Abstract We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15–17, 2022. Fifteen lectures were presented during a virtual public event with speakers from industry, academia, and non-for-profit organizations. Twelve hundred and ninety students and academics from more than 60 countries. During the meeting, applications, challenges, and opportunities in drug discovery, de novo drug design, ADME-Tox (absorption, distribution, metabolism, excretion and toxicity) property predictions, organic chemistry, peptides, and antibiotic resistance were discussed. The program along with the recordings of all sessions are freely available at https://www.difacquim.com/english/events/2022-colloquium/ .

BackgroundLactobacillus coryniformis K8 CECT5711 has immune-modulating properties, enhances the immune response to viral antigens leading to the production of specific antibodies, and has anti-inflammatory activity, which may help to prevent uncontrolled inflammatory processes leading to respiratory and other organ failures. ObjectiveThe purpose of this study is to evaluate the effect of the consumption of a probiotic strain on the incidence and severity of COVID-19 in health personnel who carry out their professional work among patients with infection or suspected infection by SARS-CoV-2. MethodsThis is a double-blind randomized clinical trial in which the experimental group will receive a capsule of L coryniformis K8 per day (3×109 colony former units/day), and the control group will receive a daily placebo capsule consisting of maltodextrin. A sample size of 314 volunteers was calculated. Volunteers must meet the following inclusion criteria: older than 20 years and active health personnel caring for patients with COVID-19, including all professionals such as medical doctors, nurses, and caretakers at the 2 referral hospitals that treat patients with COVID-19. The main outcome of the clinical trial will be the incidence of symptomatic infection by SARS-CoV-2 in personnel who care for patients with suspected or confirmed COVID-19. ResultsThe study had to be extended to the 2 referral hospitals that treat patients with COVID-19 in the province of Granada (Andalusia, Spain); Hospital San Cecilio and Hospital Virgen de las Nieves. A total of 255 individuals met the inclusion criteria and were randomly assigned to one of the 2 groups. ConclusionsThe results of this randomized controlled trial will provide valuable information regarding the administration of L coryniformis K8 against COVID-19, including whether there are fewer infectious processes due to this virus or, in case of occurrence, whether the disease is milder in participants taking the probiotic strain. Trial RegistrationClinicalTrials.gov NCT04366180; http://www.clinicaltrials.gov/ct2/show/NCT04366180 International Registered Report Identifier (IRRID)RR1-10.2196/37857

The study of the phenolic compounds present in olive leaves (Olea europaea) is of great interest due to their health benefits. In this research, different machine learning algorithms such as RF, SVM, and ANN, with temperature, time, and volume as input variables, were developed to model the extract yield and the total phenolic content (TPC) from experimental data reported in the literature. In terms of extract yield, the neural network-based ANNZ-L model presents the lowest root mean square error (RMSE) value in the validation phase (9.44 mg/g DL), which corresponds with a mean absolute percentage error (MAPE) of 3.7%. On the other hand, the best model to determine the TPC value was the neural network-based model ANNR, with an RMSE of 0.89 mg GAE/g DL in the validation phase (MAPE of 2.9%). Both models obtain, for the test phase, MAPE values of 4.9 and 3.5%, respectively. This affirms that ANN models would be good modelling tools to determine the extract yield and TPC value of the ultrasound-assisted extraction (UAE) process of olive leaves under different temperatures, times, and solvents.

Prolonged COVID is a persistent condition following the initial COVID-19 infection, which is characterized by a variety of symptoms that may include fatigue, muscle pain, sleep disturbances, “brain fog”, respiratory, cardiovascular, digestive, neurological and dermatological symptoms. Physical therapy has been identified as a crucial aspect of the management of patients with long COVID, as it can help improve symptoms and overall physical function. The investigation of long COVID poses significant challenges due to the diversity and variability of symptoms, lack of clear diagnostic criteria, and limited understanding of the underlying mechanisms. The aim of this study is to conduct a systematic review of studies conducted in patients with long COVID in conjunction with interventions targeting respiratory function, particularly involving physical activity. To this end, we conducted a systematic review to analyze studies conducted on treatment programs for long COVID based on some form of physical activity. The protocol of the review was registered in the PROSPERO website, and the databases PubMed, Scopus, CINAHL and WOS were searched. Of the 62 initial articles, six were included in the review. The results obtained have positive implications for the advancement of physical activity as a therapeutic intervention for individuals with long COVID-19 and the conceptualization of evidence-based treatment protocols. Statistically significant results have been observed in studies of at least 6 weeks duration, in which inspiratory muscle training exercises are proposed. Further research is needed to better understand long COVID and develop effective treatment strategies.

The present work was conducted to evaluate the volatile profile of Ecuadorian Forastero, CCN-51, ETT103 and LR14 cocoa beans during traditional fermentation in laurel wood boxes followed by a sun-drying process. Fifty-six volatiles were identified with HS-SPME-GC–MS. Aldehydes, alcohols and ketones were the compounds that mainly characterized the fresh cocoa. The main compounds formed during the anaerobic fermentation step were esters and acids, while in the aerobic fermentation step, an increase in ester-, aldehyde- and acid-type compounds was observed. Finally, after the drying step, a notable increase in the acid (i.e., acetic acid) content was the predominant trend. According to the genotypes, ETT103 presented high contents of terpenes, alcohols, aldehydes and ketones and low contents of unfavorable acid compounds. The CCN-51 and LR14 (Trinitarian) varieties stood out for their highest amounts in acids (i.e., acetic acid) at the end of primary processing. Finally, the Forastero cocoa beans were highlighted for their low acid and high trimethylpyrazine contents. According to the chemometric and Venn diagram analyses, ETT-103 was an interestingly high-aromatic-quality variety for cocoa gourmet preparations. The results also showed the need for good control of the processing steps (using prefermentative treatments, starter cultures, etc.) on Ecuadorian genotypes of Trinitarian origin.

Abstract Background It remains elusive how the characteristics, the course of disease, the clinical management and the outcomes of critically ill COVID-19 patients admitted to intensive care units (ICU) worldwide have changed over the course of the pandemic. Methods Prospective, observational registry constituted by 90 ICUs across 22 countries worldwide including patients with a laboratory-confirmed, critical presentation of COVID-19 requiring advanced organ support. Hierarchical, generalized linear mixed-effect models accounting for hospital and country variability were employed to analyse the continuous evolution of the studied variables over the pandemic. Results Four thousand forty-one patients were included from March 2020 to September 2021. Over this period, the age of the admitted patients (62 [95% CI 60–63] years vs 64 [62–66] years, p

Las tesis doctorales (TD) representan la culminación en la formación de posgrado y también significan el punto de partida en la trayectoria académica de los investigadores júnior. Los estudios de posgrado sobre periodismo permiten realizar investigaciones de amplio calado sobre diferentes elementos marcados por la realidad. La obtención del título de doctor mediante la elaboración de un trabajo de investigación constituye una oportunidad para conocer más a fondo la formación de posgrado en periodismo de las universidades españolas. Los resultados de este trabajo se basan en las tesis defendidas en universidades españolas que contemplan estudios de comunicación con 2784 monografías censadas entre el 2007 y el 2018. Concretamente, esta investigación se centra exclusivamente en las tesis vinculadas al ámbito del periodismo, defendidas entre el 2014 y el 2018, con 506 TD, de las cuales 67 también tienen referencias directas a Latinoamérica. El análisis de los datos evidencia que el periodismo es uno de los temas centrales en el campo de la comunicación; los documentos son las principales unidades de análisis y las técnicas de investigación predominantes se ciñen a los análisis de contenido y de discursos. Metodológicamente, se aprovecha el repositorio de acceso abierto de MapCom (https://mapcom.es/) para analizar la formulación de los objetos de estudio, los enfoques epistemológicos y los métodos de investigación.

BackgroundProsthetic valve endocarditis (PVE) is a serious infection associated with high mortality that often requires surgical treatment.MethodsStudy on clinical characteristics and prognosis of a large contemporary prospective cohort of prosthetic valve endocarditis (PVE) that included patients diagnosed between January 2008 and December 2020. Univariate and multivariate analysis of factors associated with in-hospital mortality was performed.ResultsThe study included 1354 cases of PVE. The median age was 71 years with an interquartile range of 62-77 years and 66.9% of the cases were male. Patients diagnosed during the first year after valve implantation (early onset) were characterized by a higher proportion of cases due to coagulase-negative staphylococci and Candida and more perivalvular complications than patients detected after the first year (late onset). In-hospital mortality of PVE in this series was 32.6%; specifically, it was 35.4% in the period 2008-2013 and 29.9% in 2014-2020 (p = 0.031). Variables associated with in-hospital mortality were: Age-adjusted Charlson comorbidity index (OR: 1.15, 95% CI: 1.08-1.23), intracardiac abscess (OR:1.78, 95% CI:1.30-2.44), acute heart failure related to PVE (OR: 3. 11, 95% CI: 2.31-4.19), acute renal failure (OR: 3.11, 95% CI:1.14-2.09), septic shock (OR: 5.56, 95% CI:3.55-8.71), persistent bacteremia (OR: 1.85, 95% CI: 1.21-2.83) and surgery indicated but not performed (OR: 2.08, 95% CI: 1.49-2.89). In-hospital mortality in patients with surgical indication according to guidelines was 31.3% in operated patients and 51.3% in non-operated patients (pConclusionsNot performing cardiac surgery in patients with PVE and surgical indication, according to guidelines, has a significant negative effect on in-hospital mortality. Strategies to better discriminate patients who can benefit most from surgery would be desirable.

Abstract The Coronavirus Disease of 2019 (COVID-19) pandemic caused by SARS-CoV-2 led the Spanish government to impose a national lockdown in an attempt to control the spread of the infection. Mobility restrictions and the requirement of a medical prescription for serological testing for COVID-19 were included among the control measures. Under this scenario, between April 15th and June 15th, 2020, we performed an observational study including 449 individuals allowed to be tested according to the governmental restrictions, i.e. fulfilling the following prescription requirements: manifestation of COVID-19-compatible symptoms, contact with a confirmed COVID-19 patient, or employment as an essential worker, including health care workers, firefighters and public safety personnel such as police. Importantly, a relevant feature of the studied cohort was that none of the participants had been hospitalized. We analyzed SARS-CoV-2 IgG seropositivity in this specific cohort, uncovering intrinsic features of great demographic interest. The overall rate of IgG seropositivity was 33.69% (95% CI: 29.27–38.21). This frequency was comparable among the different participant occupations. A RT-PCR positive test, contact with a household member previously tested positive and the presence of COVID-19-compatible symptoms were positively associated with IgG + results. Among these symptoms, ageusia/anosmia was positively and independently associated with SARS-CoV-2 IgG seropositivity, while odynophagia was inversely associated. However, fever, ageusia/anosmia and asthenia were the most frequent symptoms described by IgG + subjects. Therefore, our data illustrate how specific cohorts display particular characteristics that should be taken into account when studying population-wide SARS-CoV-2 seroprevalence and key defining symptoms of COVID-19.

Background: Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care. Methods: We retrieved data on patients with COVID-19 admitted to an intensive care unit (ICU) between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit (RISC-19-ICU) registry. We applied the Extreme Gradient Boosting (XGBoost) algorithm to the data to predict as a binary outcome the increase or decrease in patients’ Sequential Organ Failure Assessment (SOFA) score on day 5 after ICU admission. The model was iteratively cross-validated in different subsets of the study cohort. Results: The final study population consisted of 675 patients. The XGBoost model correctly predicted a decrease in SOFA score in 320/385 (83%) critically ill COVID-19 patients, and an increase in the score in 210/290 (72%) patients. The area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model (0.86 vs. 0.69, P

Summary: Graph neural networks (GNNs) recursively propagate signals along the edges of an input graph, integrate node feature information with graph structure, and learn object representations. Like other deep neural network models, GNNs have notorious black box character. For GNNs, only few approaches are available to rationalize model decisions. We introduce EdgeSHAPer, a generally applicable method for explaining GNN-based models. The approach is devised to assess edge importance for predictions. Therefore, EdgeSHAPer makes use of the Shapley value concept from game theory. For proof-of-concept, EdgeSHAPer is applied to compound activity prediction, a central task in drug discovery. EdgeSHAPer’s edge centricity is relevant for molecular graphs where edges represent chemical bonds. Combined with feature mapping, EdgeSHAPer produces intuitive explanations for compound activity predictions. Compared to a popular node-centric and another edge-centric GNN explanation method, EdgeSHAPer reveals higher resolution in differentiating features determining predictions and identifies minimal pertinent positive feature sets.

The banana is a tropical fruit characterized by its composition of healthy and nutritional compounds. This fruit is part of traditional Ecuadorian gastronomy, being consumed in a wide variety of ways. In this context, unripe Red Dacca banana samples and those submitted to different traditional Ecuadorian heating treatments (boiling, roasting, and baking) were evaluated to profile their phenolic content by ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) and the antioxidant activity by ORAC, ABTS, and DPPH assays. A total of sixty-eight phenolic compounds were identified or tentatively identified in raw banana and treated samples, highlighting the content in flavonoids (flavan-3-ols with 88.33% and flavonols with 3.24%) followed by the hydroxybenzoic acid family (5.44%) in raw banana samples. The total phenolic compound content significantly decreased for all the elaborations evaluated, specifically from 442.12 mg/100 g DW in fresh bananas to 338.60 mg/100 g DW in boiled (23.41%), 243.63 mg/100 g DW in roasted (44.90%), and 109.85 mg/100 g DW in baked samples (75.15%). Flavan-3-ols and flavonols were the phenolic groups most affected by the heating treatments, while flavanones and hydroxybenzoic acids showed higher stability against the heating treatments, especially the boiled and roasted samples. In general, the decrease in phenolic compounds corresponded with a decline in antioxidant activity, evaluated by different methods, especially in baked samples. The results obtained from PCA studies confirmed that the impact of heating on the composition of some phenolic compounds was different depending on the technique used. In general, the heating processes applied to the banana samples induced phytochemical modifications. Even so, they remain an important source of bioactive compounds for consumers.

Rare diseases, also known as orphan diseases, are medical conditions that affect a small percentage of the population [...]

Abstract Machine learning is widely applied in drug discovery research to predict molecular properties and aid in the identification of active compounds. Herein, we introduce a new approach that uses model-internal information from compound activity predictions to uncover relationships between target proteins. On the basis of a large-scale analysis generating and comparing machine learning models for more than 200 proteins, feature importance correlation analysis is shown to detect similar compound binding characteristics. Furthermore, rather unexpectedly, the analysis also reveals functional relationships between proteins that are independent of active compounds and binding characteristics. Feature importance correlation analysis does not depend on specific representations, algorithms, or metrics and is generally applicable as long as predictive models can be derived. Moreover, the approach does not require or involve explainable or interpretable machine learning, but only access to feature weights or importance values. On the basis of our findings, the approach represents a new facet of machine learning in drug discovery with potential for practical applications.

Abstract Background Uncertainty about the optimal respiratory support strategies in critically ill COVID-19 patients is widespread. While the risks and benefits of noninvasive techniques versus early invasive mechanical ventilation (IMV) are intensely debated, actual evidence is lacking. We sought to assess the risks and benefits of different respiratory support strategies, employed in intensive care units during the first months of the COVID-19 pandemic on intubation and intensive care unit (ICU) mortality rates. Methods Subanalysis of a prospective, multinational registry of critically ill COVID-19 patients. Patients were subclassified into standard oxygen therapy ≥10 L/min (SOT), high-flow oxygen therapy (HFNC), noninvasive positive-pressure ventilation (NIV), and early IMV, according to the respiratory support strategy employed at the day of admission to ICU. Propensity score matching was performed to ensure comparability between groups. Results Initially, 1421 patients were assessed for possible study inclusion. Of these, 351 patients (85 SOT, 87 HFNC, 87 NIV, and 92 IMV) remained eligible for full analysis after propensity score matching. 55% of patients initially receiving noninvasive respiratory support required IMV. The intubation rate was lower in patients initially ventilated with HFNC and NIV compared to those who received SOT (SOT: 64%, HFNC: 52%, NIV: 49%, p = 0.025). Compared to the other respiratory support strategies, NIV was associated with a higher overall ICU mortality (SOT: 18%, HFNC: 20%, NIV: 37%, IMV: 25%, p = 0.016). Conclusion In this cohort of critically ill patients with COVID-19, a trial of HFNC appeared to be the most balanced initial respiratory support strategy, given the reduced intubation rate and comparable ICU mortality rate. Nonetheless, considering the uncertainty and stress associated with the COVID-19 pandemic, SOT and early IMV represented safe initial respiratory support strategies. The presented findings, in agreement with classic ARDS literature, suggest that NIV should be avoided whenever possible due to the elevated ICU mortality risk.

A mindfulness-based intervention (MBI) to address emotional factors that may negatively impact the fluency of the subjects was implemented. A MBI consisting of 5 weekly 2-h sessions was designed and applied to two stuttering cases. The goals were to lower stuttering and to reduce anxiety through the acquisition of mindfulness skills. Participants were asked to video-record a speaking task and to fill out anxiety and mindfulness skills self-reports pre and post-intervention. Both participants diminished stuttering error rates from severe (participant 1) and moderate (participant 2) to mild. Participant 2, who showed high pre-treatment anxiety, showed a significant reduction. A year-follow-up revealed that therapeutic gains in anxiety, mindfulness skills, and stuttering were successfully maintained in both participants. This is the first study focused on emotional aspects of stuttering showing long-term improvements through a MBI. MBI programs could serve as a promising complement for stuttering treatments.

Abstract Background The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. Methods Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient’s factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. Results The database included a total of 2022 patients (mean age 64 [IQR 5–71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10–17]) and SOFA score (5 [IQR 3–7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age ( 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications. Conclusion The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice.

This article shows the use of Twitter that the main official spokespersons of the Spanish government made during the first weeks of the pandemic, with the aim of analyzing how government health campaigns were managed during the exceptional period of the state of alarm to deal with the COVID-19 pandemic and whether the instructions in terms of institutional management of communication to combat the infodemic set by the World Health Organization (WHO) were followed. This research considers the diffusion of official information in different phases of the first three months of the government’s action (102 days) from the outbreak of COVID-19 in Spain (March 2020) and how it developed its approach to crisis communication using the Twitter accounts of the President of the Spanish government (@sanchezcastejon), front-line leaders and the Ministry of Health (@sanidadgob), the main public institution responsible for health crisis management with the hashtags #EsteVirusLoParamosUnidos and #COVID-19. The results of a sample of 750 tweets reveal how the official sources used a model of online communication with a particular emphasis on informative and motivational tweets from leaders aimed at audiences (media and the general public). At the same time, there is also an instructive function about the pandemic towards audiences (general public and companies), with the Ministry and health authorities playing a key, proactive role in an attempt to achieve informative transparency to mitigate the pandemic and infodemic.

Cocoa (Theobroma cacao L.) is one of the most widely consumed edible seeds in the world affected by on-farm processing. This study investigated the effect of different drying techniques, namely oven drying (OD), sun drying (SD), and a modification of sun drying using black plastic sheeting (SBPD), on the volatile profile of fine-flavor and bulk cocoa varieties analyzed by HS-SPME-GC-MS. A total of sixty-four volatile compounds were identified in fresh and dried cocoa. As expected, the volatile profile was clearly modified after the drying step, showing strong differences among cocoa varieties, this factor and its interaction with the drying technique having greater influence according to the ANOVA simultaneous component analysis. A principal component analysis revealed a close relationship between the volatile content of bulk cocoa samples dried by the OD and SD techniques, whereas slight volatile modifications were perceived among fine-flavor samples dried using the three different techniques under study. Overall, the results provide the basis for the potential application of the simplest inexpensive SBPD technique in order to accelerate the drying process of sun drying and produce cocoa with similar (fine-flavor cocoa) or improved (bulk cocoa) aromatic quality to that formed using the traditional SD or the small-scale OD.