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Public attitudes about genetics appear to depend on the local context. We analyzed survey responses obtained in 2015 from 293 residents of Luján, a city in the province of Buenos Aires, Argentina, who self-assessed their knowledge about genetics and their trust in genetic tests. The survey integrated a larger research project for which consenting adult participants shared demographic and genealogical information and provided saliva samples for genetic ancestry analyses. Participants reported little knowledge but high trust in genetic testing when questioned about knowledge and trust. Well-known media stories of DNA-based forensic genetic investigations to identify the victims of state repression during the military dictatorship may have contributed to the high self-assessment of their genetic knowledge expressed by some participants, regardless of educational attainment. Our analysis provides information that could be used as a baseline to begin unraveling the current level of public trust in genetics in a region of the Global South where genetic testing has become widespread, but people’s knowledge of and trust in genetics remain poorly studied.

Jon Zabaleta,1 Borja Aguinagalde,1 Iker López,1 Stephany M Laguna,1 Mikel Mendoza,2 Ainhoa Galardi,2 Luis Matey,3,4 Andrea Larrañaga,4 Gorka Baqueriza,5 Ander Izeta61Thoracic surgery service, Donostia University Hospital, IIS Biodonostia, San Sebastian, Spain; 2Radiology service, Donostia Universitary Hospital, San Sebastian, Spain; 3Additive Manufacturing, Ceit-IK4, San Sebastian, Spain; 4School of Engineering, Tecnun-University of Navarra, Pamplona, Spain; 5Additive Manufacturing, Tknika-Basque Centre of Research and Applied Innovation in Vocational Education and Training; 6Tissue Engineering Group, IIS Biodonostia, San Sebastian, SpainIntroduction: In recent years, the use of 3D printing in medicine has grown exponentially, but the use of 3D technology has not been equally adopted by the different medical specialties. Published 3D printing activity in general thoracic surgery is scarce and has been mostly limited to case reports. The aim of this report was to reflect on the results and lessons learned from a newly created multidisciplinary and multicenter 3D unit of the Spanish Society of Thoracic Surgery (SECT).Methods: This is a pilot study to determine the feasibility and usefulness of printing 3D models for patients with thoracic malignancy or airway complications, based on real data. We designed a point-of-care 3D printing workflow involving thoracic surgeons, radiologists with experience in intrathoracic pathology, and engineers with experience in additive manufacturing.Results: In the first year of operation we generated 26 three-dimensional models out of 27 cases received (96.3%). In 9 cases a virtual model was sufficient for optimal patient handling, while in 17 cases a 3D model was printed. Per pathology, cases were classified as airway stenosis after lung transplantation (7 cases, 25.9%), tracheal pathology (7 cases, 25.9%), chest tumors (6 cases, 22.2%) carcinoid tumors (4 cases, 14.8%), mediastinal tumors (2 cases, 7.4%) and Pancoast tumors (one case, 3.7%).Conclusion: A multidisciplinary 3D laboratory is feasible in a hospital setting, and working as a multicenter group increases the number of cases and diversity of pathologies thus providing further opportunity to study the benefits of the 3D printing technology in general thoracic surgery.Keywords: 3d printing, thoracic surgery, multidisciplinary group, preoperative study

This article is oriented to the degradation of nickel in an ionic state at laboratory level from synthetic water made with nickel sulfate, using the electrocoagulation process with aluminum cathodes and modifying this process by the addition of the Fenton reagent, which results from the combination of hydrogen peroxide (H2O2) and ferrous sulfate (FeSO4) being this reagent a catalyst and oxo-coagulant agent, The efficiency of this reagent will be compared with the typical treatment with aluminum sulfate, which is a typical process based on ion exchange/coagulation at the same percentage concentrations as the Fenton reagent. For this purpose, the optimum conditions of the advanced electrocoagulation process were determined, which consisted of determining the concentrations of Fenton’s reagent at concentrations of 150 ppm, 300 ppm, and 450 ppm, in addition to the operating variables such as pH of 8 and 10, voltage of 17.5 V and 19 V and their reaction time, which were compared with aluminum sulfate at 300 ppm, 600 ppm, and 900 ppm. The results obtained with respect to the typical treatment were 0% nickel degradation. However, with the advanced oxidation treatment, an average reduction of 97.5% was found at the conditions of 19 V, pH 10, and Fenton 150 ppm in a time of 30 min.

Objective: To compare the predictive performance of three different mathematical models for first‐trimester screening of pre‐eclampsia (PE), which combine maternal risk factors with mean arterial pressure (MAP), uterine artery pulsatility index (UtA‐PI) and serum placental growth factor (PlGF), and two risk‐scoring systems. Methods: This was a prospective cohort study performed in eight fetal medicine units in five different regions of Spain between September 2017 and December 2019. All pregnant women with singleton pregnancy and a non‐malformed live fetus attending their routine ultrasound examination at 11 + 0 to 13 + 6 weeks' gestation were invited to participate in the study. Maternal characteristics and medical history were recorded and measurements of MAP, UtA‐PI, serum PlGF and pregnancy‐associated plasma protein‐A (PAPP‐A) were converted into multiples of the median (MoM). Risks for term PE, preterm PE (< 37 weeks' gestation) and early PE (< 34 weeks' gestation) were calculated according to the FMF competing‐risks model, the Crovetto et al. logistic regression model and the Serra et al. Gaussian model. PE classification was also performed based on the recommendations of the National Institute for Health and Care Excellence (NICE) and the American College of Obstetricians and Gynecologists (ACOG). We estimated detection rates (DR) with their 95% CIs at a fixed 10% screen‐positive rate (SPR), as well as the area under the receiver‐operating‐characteristics curve (AUC) for preterm PE, early PE and all PE for the three mathematical models. For the scoring systems, we calculated DR and SPR. Risk calibration was also assessed. Results: The study population comprised 10 110 singleton pregnancies, including 32 (0.3%) that developed early PE, 72 (0.7%) that developed preterm PE and 230 (2.3%) with any PE. At a fixed 10% SPR, the FMF, Crovetto et al. and Serra et al. models detected 82.7% (95% CI, 69.6–95.8%), 73.8% (95% CI, 58.7–88.9%) and 79.8% (95% CI, 66.1–93.5%) of early PE; 72.7% (95% CI, 62.9–82.6%), 69.2% (95% CI, 58.8–79.6%) and 74.1% (95% CI, 64.2–83.9%) of preterm PE; and 55.1% (95% CI, 48.8–61.4%), 47.1% (95% CI, 40.6–53.5%) and 53.9% (95% CI, 47.4–60.4%) of all PE, respectively. The best correlation between predicted and observed cases was achieved by the FMF model, with an AUC of 0.911 (95% CI, 0.879–0.943), a slope of 0.983 (95% CI, 0.846–1.120) and an intercept of 0.154 (95% CI, –0.091 to 0.397). The NICE criteria identified 46.7% (95% CI, 35.3–58.0%) of preterm PE at 11% SPR and ACOG criteria identified 65.9% (95% CI, 55.4–76.4%) of preterm PE at 33.8% SPR. Conclusions: The best performance of screening for preterm PE is achieved by mathematical models that combine maternal factors with MAP, UtA‐PI and PlGF, as compared to risk‐scoring systems such as those of NICE and ACOG. While all three algorithms show similar results in terms of overall prediction, the FMF model showed the best performance at an individual level. © 2024 International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

The 2012 East Indian Ocean earthquake (Mw 8.6), so far the largest intraoceanic plate strike-slip event ever recorded, modulated tectonic tremors in the Cascadia subduction zone. The rate of tremor activity near Vancouver Island increased by about 1.5 times from its background level during the passage of seismic waves of this earthquake. In most cases of dynamic modulation, large-amplitude and long-period surface waves stimulate tremors. However, in this case even the small stress change caused by body waves generated by the 2012 earthquake modulated tremor activity. The tremor modulation continued during the passage of the surface waves, subsequent to which the tremor activity returned to background rates. Similar tremor modulation is observed during the passage of the teleseismic waves from the Mw 8.2 event, which occurs about 2 h later near the Mw 8.6 event. We show that dynamic stresses from back-to-back large teleseismic events can strongly influence tremor sources.

Concerning preventive maintenance plans for heritage timber buildings, computational methods are pioneering knowledge for the implementation of new preservation approaches in heritage structure management. In this context, fuzzy logic and random forest methodologies manage both data obtained from professional experts and data obtained in situ from the buildings themselves. This kind of digital procedure can harmonize the outcomes of building assessments because slight variations in the evaluation of input parameters do not produce a significant dispersion over the model's output. Preventive conservation strategies require cooperation among qualified experts who examine multidisciplinary knowledge related to heritage properties. Thus, new digital protocols and procedures that help decision makers prioritize preventive interventions and avoid corrective actions are paramount in minimizing the irreparable loss of properties. The main aim of this research is a new approach to two computational management systems: fuzzy logic and random forest. The outcomes of this study will be useful to stakeholders who are responsible for the maintenance of heritage buildings, as this methodology reduces the probability of failure and uncertainty during decision-making. The instruments derived will establish mitigation strategies oriented toward proactive future maintenance programs for heritage timber buildings in southern Chile. [ABSTRACT FROM AUTHOR]

The hemoparasite Trypanosomaequiperdum belongs to the Trypanozoon subgenus and includes several species that are pathogenic to animals and humans in tropical and subtropical areas across the world. As with all eukaryotic organisms, Ca2+ is essential for these parasites to perform cellular processes thus ensuring their survival across their life cycle. Despite the established paradigm to study proteins related to Ca2+ homeostasis as potential drug targets, so far little is known about Ca2+ entry into trypanosomes. Therefore, in the present study, the presence of a plasma membrane Ca2+-channel in T. equiperdum (TeCC), activated by sphingosine and inhibited by verapamil, is described. The TeCC was cloned and analyzed using bioinformatic resources, which confirmed the presence of several domains, motifs, and a topology similar to the Ca2+ channels found in higher eukaryotes. Biochemical and confocal microscopy assays using antibodies raised against an internal region of human L-type Ca2+ channels indicate the presence of a protein with similar predicted molar mass to the sequence analyzed, located at the plasma membrane of T. equiperdum. Physiological assays based on Fura-2 signals and Mn2+ quenching performed on whole parasites showed a unidirectional Ca2+ entry, which is activated by sphingosine and blocked by verapamil, with the distinctive feature of insensitivity to nifedipine and Bay K 8644. This suggests a second Ca2+ entry for T. equiperdum, different from the store-operated Ca2+ entry (SOCE) previously described. Moreover, the evidence presented here for the TeCC indicates molecular and pharmacological differences with their mammal counterparts, which deserve further studies to evaluate the potential of this channel as a drug target. [ABSTRACT FROM AUTHOR]

Objective: Effective first‐trimester screening for pre‐eclampsia (PE) can be achieved using a competing‐risks model that combines risk factors from the maternal history with multiples of the median (MoM) values of biomarkers. A new model using artificial intelligence through machine‐learning methods has been shown to achieve similar screening performance without the need for conversion of raw data of biomarkers into MoM. This study aimed to investigate whether this model can be used across populations without specific adaptations. Methods: Previously, a machine‐learning model derived with the use of a fully connected neural network for first‐trimester prediction of early (< 34 weeks), preterm (< 37 weeks) and all PE was developed and tested in a cohort of pregnant women in the UK. The model was based on maternal risk factors and mean arterial blood pressure (MAP), uterine artery pulsatility index (UtA‐PI), placental growth factor (PlGF) and pregnancy‐associated plasma protein‐A (PAPP‐A). In this study, the model was applied to a dataset of 10 110 singleton pregnancies examined in Spain who participated in the first‐trimester PE validation (PREVAL) study, in which first‐trimester screening for PE was carried out using the Fetal Medicine Foundation (FMF) competing‐risks model. The performance of screening was assessed by examining the area under the receiver‐operating‐characteristics curve (AUC) and detection rate (DR) at a 10% screen‐positive rate (SPR). These indices were compared with those derived from the application of the FMF competing‐risks model. The performance of screening was poor if no adjustment was made for the analyzer used to measure PlGF, which was different in the UK and Spain. Therefore, adjustment for the analyzer used was performed using simple linear regression. Results: The DRs at 10% SPR for early, preterm and all PE with the machine‐learning model were 84.4% (95% CI, 67.2–94.7%), 77.8% (95% CI, 66.4–86.7%) and 55.7% (95% CI, 49.0–62.2%), respectively, with the corresponding AUCs of 0.920 (95% CI, 0.864–0.975), 0.913 (95% CI, 0.882–0.944) and 0.846 (95% CI, 0.820–0.872). This performance was achieved with the use of three of the biomarkers (MAP, UtA‐PI and PlGF); inclusion of PAPP‐A did not provide significant improvement in DR. The machine‐learning model had similar performance to that achieved by the FMF competing‐risks model (DR at 10% SPR, 82.7% (95% CI, 69.6–95.8%) for early PE, 72.7% (95% CI, 62.9–82.6%) for preterm PE and 55.1% (95% CI, 48.8–61.4%) for all PE) without requiring specific adaptations to the population. Conclusions: A machine‐learning model for first‐trimester prediction of PE based on a neural network provides effective screening for PE that can be applied in different populations. However, before doing so, it is essential to make adjustments for the analyzer used for biochemical testing. © 2023 International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

Objective: To develop a model for the prediction of adverse perinatal outcome in growth‐restricted fetuses requiring delivery before 28 weeks in order to provide individualized patient counseling. Methods: This was a retrospective multicenter cohort study of singleton pregnancies with antenatal suspicion of fetal growth restriction requiring delivery before 28 weeks' gestation between January 2010 and January 2020 in six tertiary public hospitals in the Barcelona area, Spain. Separate predictive models for mortality only and mortality or severe neurological morbidity were created using logistic regression from variables available antenatally. For each model, predictive performance was evaluated using receiver‐operating‐characteristics (ROC)‐curve analysis. Predictive models were validated externally in an additional cohort of growth‐restricted fetuses from another public tertiary hospital with the same inclusion and exclusion criteria. Results: A total of 110 cases were included. The neonatal mortality rate was 37.3% and, among the survivors, the rate of severe neurological morbidity was 21.7%. The following factors were retained in the multivariate analysis as significant predictors of mortality: magnesium sulfate neuroprotection, gestational age at birth, estimated fetal weight, male sex and Doppler stage. This model had a significantly higher area under the ROC curve (AUC) compared with a model including only gestational age at birth (0.810 (95% CI, 0.730–0.889) vs 0.695 (95% CI, 0.594–0.795); P = 0.016). At a 20% false‐positive rate, the model showed a sensitivity, negative predictive value and positive predictive value of 66%, 80% and 66%, respectively. For the prediction of the composite adverse outcome (mortality or severe neurological morbidity), the model included: gestational age at birth, male sex and Doppler stage. This model had a significantly higher AUC compared with a model including only gestational age at birth (0.810 (95% CI, 0.731–0.892) vs 0.689 (95% CI, 0.588–0.799); P = 0.017). At a 20% false‐positive rate, the model showed a sensitivity, negative predictive value and positive predictive value of 55%, 63% and 74%, respectively. External validation of both models yielded similar AUCs that did not differ significantly from those obtained in the original sample. Conclusions: Estimated fetal weight, fetal sex and Doppler stage can be combined with gestational age to improve the prediction of death or severe neurological sequelae in growth‐restricted fetuses requiring delivery before 28 weeks. This approach may be useful for parental counseling and decision‐making. © 2023 International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]