University of La Rioja

Background/Objectives: Pre-hospital delivery is an unpredictable event posing significant challenges for Emergency Medical Services (EMS) teams. Despite advances in perinatal care, emergency deliveries outside the hospital environment remain associated with increased maternal and neonatal risks. This study aimed to identify predictors of out-of-hospital delivery in EMS-attended labor cases and determinants of neonatal condition immediately after delivery. Methods: We conducted a retrospective analysis of 5097 EMS records of laboring women in Poland from August 2021 to January 2022, of which 2927 were included in the final study sample. Multivariate logistic regression models with multiple imputation for missing data were used to identify predictors of pre-hospital delivery and adverse neonatal condition (Apgar ≤ 7) in EMS-managed childbirths. Results: Pre-hospital delivery was strongly associated with second-stage labor (OR ≈ 535; p < 0.0001), ruptured membranes (OR ≈ 8.7; p < 0.0001), and fewer previous pregnancies (OR = 0.86; p = 0.018), and showed a trend with higher maternal heart rate (OR = 1.015; p = 0.083). Neonatal status classified as Apgar ≤ 7 was significantly associated with preterm birth (p < 0.0001), absence of fetal movements (OR ≈ 26.4; p = 0.025), and complications during pregnancy (p = 0.036). Complications during labor and lack of prenatal care were not significantly associated with increased risk of pre-hospital delivery in the model. Conclusions: Rupture of membranes, second-stage labor, and fewer previous pregnancies are significant predictors of pre-hospital delivery in EMS-managed cases. Absence of fetal movements and preterm gestation predict worse neonatal outcomes (Apgar ≤ 7). Early identification of these factors may enhance prehospital perinatal care and improve maternal and neonatal prognosis.

Abstract Although scientometric literature provides valuable insights into international collaborative networks, approaches that integrate interdisciplinary perspectives to study international co-authorship practices remain uncommon. To fill this gap, we enhance co-authorship network analysis by incorporating Fleck’s concepts of ‘thought-collective’ (or communities that have a shared body of knowledge, epistemology and practices) and ‘thought-style’ (particular ways of understanding concepts and ideas) as expressed through language. Specifically, we use network metrics and community detection algorithms to describe and interpret the configuration of networks, identify distinctive structural features and evaluate the significance and influence of four leading researchers in several subdisciplinary fields. Additionally, we use linguistic data mining techniques to explore how the thought-styles of the networks, as thought-collectives, are expressed semantically and discoursally in their co-authored articles. Our findings show that enhancing network analysis with linguistic analysis provides a better understanding of how information circulates in collaborative networks and how collective thinking is expressed through language in articles published in impact-factor journals. We also tentatively suggest how collective styles can influence researchers’ relevance and impact. These novel findings could inform researcher training programmes raising awareness of successful social interactions in international collaborative ecosystems.

Wastewater treatment plants (WWTPs) are recognized hotspots for the convergence and dissemination of antimicrobial-resistant bacteria (ARB) and antimicrobial resistance genes (ARGs) into the environment. Among ARB, carbapenem-resistant Enterobacterales (CR-E) and extended-spectrum β-lactamase-producing Escherichia coli and Klebsiella pneumoniae (ESBL-Ec/Kp) are of particular concern due to their clinical relevance. We characterized 30 CR-E and 176 ESBL-Ec/Kp isolates (two of them were both ESBL-producing and carbapenem-resistant) recovered from influent, intermediate, effluent, sludge, and downstream river samples collected from two WWTPs in northern Spain. Isolates were evaluated for resistance phenotypes against 12 antimicrobials, and β-lactamase-encoding genes were assessed by PCR and sequencing. Notably, among CR-E isolates, blaKPC-2 was the most prevalent (93%), followed by blaOXA-48-like, detected in two isolates from non-treated and pasteurized sludge; both isolates also carried blaCTX-M-15, a finding not previously reported specifically in sludge samples. Among ESBL-Ec/Kp, a broad diversity of ESBL genes was identified, including blaCTX-M group 1 (variants 1, 3, 15, 32, 55), blaCTX-M group 9 (variants 14, 27, 65, 97), blaSHV-12 and blaTEM-169. The most prevalent ESBL gene was blaCTX-M-15 (48.3%), followed by blaCTX-M-14, blaCTX-M-32, and blaSHV-12, detected in 10.8%, 8.5%, and 6.8% of isolates, respectively. CR-E and ESBL-Ec/Kp were found in all sample types and were still detectable at terminal stages, indicating persistence throughout treatment. These findings support the need to improve and optimize current wastewater treatment methods and underscore the importance of integrating culture-based and molecular methods into routine WWTP monitoring for early detection of microbiological hazards, although further research is still needed.

Face-to-face teaching was quickly moved to remote teaching following the introduction of a national lockdown in the United Kingdom (UK) on 23 March 2020 to tackle the coronavirus pandemic 2019 (COVID-19). In this context, De Montfort University (DMU, UK) expanded and adapted its pre-existing open-access virtual learning environment, named e-Biology (http://parasitology.dmu.ac.uk/ebiology/), to better support clinical biochemistry education in a remote setting. Originally created in 2017 to assist students' transition into biomedical science programmes, e-Biology was updated with specific modules for clinical biochemistry, including theoretical, laboratory, microscope, and case study components. This package has been used to teach final-year BSc Biomedical Science students since 2020/2021. Detailed analysis of scores of two multiple choice question tests distributed at the beginning (preScore; 39.3% and 41.4% successful, for all the cohort and paired students' exams, respectively) and end of the module (postScore; 41.8% and 45.3% successful), did show an improvement in students' overall performance but without statistical significance. However, this improvement showed statistical significance for the paired exams for the last cohort (2022/2023; from 34.0% to 46.8%; p < 0.05; n = 13/146). A total of 77.9% of respondents to the feedback questionnaire concurred that the mini-games and exercises within the e-practicals facilitated their learning and aided in their preparation for the unseen practical exam, while 13.0% neither agreed nor disagreed. These findings suggest that e-Biology was an effective tool for supporting the teaching and learning of applied clinical biochemistry remotely and may serve as a useful resource for blended and online education across STEM disciplines.

Reliable early-stage yield forecasts are essential in precision viticulture, enabling timely interventions such as harvest planning, canopy management, and crop load regulation. Since grape yield is directly related to the number and size of bunches, the early detection of inflorescences and bunches, carried out even before flowering, provides a valuable foundation for estimating potential yield far in advance of veraison. Traditional yield prediction methods are labor-intensive, subjective, and often restricted to advanced phenological stages. This study presents a deep learning-based approach for detecting grapevine inflorescences and bunches during early development, assessing how phenological stage and illumination conditions influence detection performance using the YOLOv11 architecture under commercial field conditions. A total of 436 RGB images were collected across two phenological stages (pre-bloom and fruit-set), two lighting conditions (daylight and artificial night-time illumination), and six grapevine cultivars. All images were manually annotated following a consistent protocol, and models were trained using data augmentation to improve generalization. Five models were developed: four specific to each condition and one combining all scenarios. The results show that the fruit-set stage under daylight provided the best performance (F1 = 0.77, R2 = 0.97), while for inflorescences, night-time imaging yielded the most accurate results (F1 = 0.71, R2 = 0.76), confirming the benefits of artificial lighting in early stages. These findings define optimal scenarios for early-stage organ detection and support the integration of automated detection models into vineyard management systems. Future work will address scalability and robustness under diverse conditions.

The study of non-phase modulation of different frequencies in the human electroencephalography (EEG) is revealing new mechanisms involved in information processing. In particular, it has been described that the alpha band, through a desynchronization of its non-phase component, could represent a mechanism for sensory gain in visual stimulus processing. One key question to address is whether this activity can be modulated (increased) by the recall of a previously memorized stimulus. The objective of this study is to answer this question by recording EEG activity with 58 electrodes and applying time-frequency analysis techniques (Temporal Spectral Evolution and the Hilbert Transform) in a sample of 27 human participants during a word recall task. The results of the study showed an increase in alpha phase modulation for recalled words compared to not recalled words, which included modulation of the P1 component. Additionally, alpha non-phase modulation also increased for recalled words, suggesting that the enhanced P1 component response could, in fact, be an indirect result of the attenuation of background neural noise, as proposed by the sensory gain hypothesis.

Abstract From the observational methodology approach, this study analyses definitive errors or losing blunders, i.e. errors that result in the loss of the game, in elite players at U8 level. An ad hoc observation instrument has been designed as a combination of field format and category systems, based on a thorough theoretical review of the internal logic of chess. The games were compiled in the ChessBase 17 program and analysed using Stockfish 16 NNUE via https://lichess.org/es. The moment in the game when the error occurs is extracted and recorded and coded using Lince software. The reliability of the records from the observation system developed was guaranteed by interobserver agreement, calculated using Cohen’s Kappa coefficient. This paper’s objective is achieved by means of the decision tree analysis technique, obtained using the CHAID procedure, taking the “impact of the error” as the predicted dimension. The results obtained have allowed us to conclude that the errors that lead to the loss of the game for elite U8 players are related to short-term calculation (tactical motifs, undefended pieces or checkmate) as opposed to long-term strategic errors.

The study aimed to assess whether the effect of nutritional risk score (NRS-2002) on the odds of in-hospital mortality would be modulated by sex and body mass index (BMI) in patients with heart failure with preserved ejection fraction (HFpEF). A retrospective analysis was conducted on 234 patients admitted with acute heart failure, in whom HFpEF was identified as the underlying diagnosis, during the period 08.2018-08.2020. Nutritional status was assessed using BMI and NRS2002. NRS-2002 is a validated screening tool recommended by ESPEN that evaluates nutritional risk based on recent weight loss, reduced dietary intake, severity of illness and age. Logistic regression models were used to evaluate the associations between these nutritional indices and in-hospital mortality. The models were adjusted for sex, age and comorbidities. Interactions between NRS2002, BMI and sex were also explored to assess whether the effect of nutritional status on mortality was modulated by these factors. The analysis revealed that male patients with elevated NRS2002 scores had significantly higher odds of in-hospital mortality (odds=47.512 at NRS2002=4 compared to odds=0.031 at NRS2002=1; BMI=28 in both cases). BMI negatively modulated the odds of death (OR=0.843, P=0.012) in the population sample. This effect was consistent across the sample regardless of NRS2002 score, as NRS2002 did not significantly influence the BMI-mortality relationship (P=0.289). Importantly, this relationship was observed only in male patients, as no such association between NRS2002 and mortality was found in women. In male patients with HFpEF, elevated NRS2002 scores showed significantly higher odds of in-hospital mortality. Higher BMI was generally associated with lower odds of mortality, with this protective effect remaining consistent in the population sample, regardless of the NRS2002 score.