Programa de Mentoría: acompañando tus primeros pasos hacia la excelencia científica / Mentoring Programme: Guiding your first steps towards scientific excellence

El primer Programa de Mentoría de SEMICYUC tiene como objetivo apoyar la carrera investigadora de los miembros más jóvenes de la Sociedad. Como beneficios añadidos está la adquisición de nuevas capacidades de investigación y/o clínicas, incrementar la capacidad de reflexión y fomentar el desarrollo de la próxima generación de líderes en la investigación. Este proyecto no sería posible sin el equipo excepcional de mentores o expertos investigadores dispuestos a emprender el viaje con los jóvenes aprendices. El presente artículo expone las bases de dicho programa, además de proponer futuros cambios en haz de una mejora continua (AU)

SEMICYUC's first Mentoring Programme aims to support the research careers of the Society's youngest members. Added benefits include acquiring new research and/or clinical skills, increasing the ability of critical thought, and fostering the development of the next generation of research leaders. This project would not be possible without the exceptional team of mentors or research experts willing to embark on the journey with the young trainees. This article sets out the foundations of such a programme and proposes future changes for continuous improvement (AU)

Mentoring Programme: Guiding your first steps towards scientific excellence.

SEMICYUC's first Mentoring Programme aims to support the research careers of the Society's youngest members. Added benefits include acquiring new research and/or clinical skills, increasing the ability of critical thought, and fostering the development of the next generation of research leaders. This project would not be possible without the exceptional team of mentors or research experts willing to embark on the journey with the young trainees. This article sets out the foundations of such a programme and proposes future changes for continuous improvement.

Genetic profile in patients with complicated acute aortic syndrome: the GEN-AOR study.

INTRODUCTION AND OBJECTIVES: Genetic testing is becoming increasingly important for diagnosis and personalized treatments in aortopathies. Here, we aimed to genetically diagnose a group of acute aortic syndrome (AAS) patients consecutively admitted to an intensive care unit and to explore the clinical usefulness of AAS-associated variants during treatment decision-making and family traceability. METHODS: We applied targeted next-generation sequencing, covering 42 aortic diseases genes in AAS patients with no signs consistent with syndromic conditions. Detected variants were segregated by Sanger sequencing in available family members. Demographic features, risk factors and clinical symptoms were statistically analyzed by Fisher or Fisher-Freeman-Halton Exact tests, to assess their relationship with genetic results. RESULTS: Analysis of next-generation sequencing data in 73 AAS patients led to the detection of 34 heterozygous candidate variants in 14 different genes in 32 patients. Family screening was performed in 31 relatives belonging to 9 families. We found 13 relatives harboring the family variant, of which 10 showed a genotype compatible with the occurrence of AAS. Statistical tests revealed that the factors associated with a positive genetic diagnosis were the absence of hypertension, lower age, family history of AAS and absence of pain. CONCLUSIONS: Our findings broaden the spectrum of the genetic background for AAS. In addition, both index patients and studied relatives benefited from the results obtained, establishing the most appropriate level of surveillance for each group. Finally, this strategy could be reinforced by the use of stastistically significant clinical features as a predictive tool for the hereditary character of AAS. CLINICALTRIALS: gov (Identifier: NCT04751058).

A Validated Ontology for Metareasoning in Intelligent Systems.

Metareasoning suffers from the heterogeneity problem, in which different researchers build diverse metareasoning models for intelligent systems with comparable functionality but differing contexts, ambiguous terminology, and occasionally contradicting features and descriptions. This article presents an ontology-driven knowledge representation for metareasoning in intelligent systems. The proposed ontology, called IM-Onto, provides a visual means of sharing a common understanding of the structure and relationships between terms and concepts. A rigorous research method was followed to ensure that the two main requirements of the ontology (integrity based on relevant knowledge and acceptance by researchers and practitioners) were met. The high accuracy rate indicates that most of the knowledge elements in the ontology are useful information for the integration of multiple types of metareasoning problems in intelligent systems.

Agroclimatic Metrics for the Main Stone Fruit Producing Areas in Spain in Current and Future Climate Change Scenarios: Implications From an Adaptive Point of View.

Stone fruit production has enormous economic importance in Spain. Cultivation locations for these fruit species (i.e., peach, apricot, plum, and sweet cherry) cover wide and climatically diverse geographical areas within the country. Climate change is already producing an increase in average temperatures with special intensity in certain areas like the Mediterranean ones. These changes lead to a decrease in the accumulated chill, which can have a profound impact on the phenology of Prunus species like stone fruits due to, e.g., difficulties to cover the chilling requirements to break endodormancy, the occurrence of late frost events, or abnormal early high temperatures. All these factors can severely affect fruit production and quality and therefore provoke very negative consequences from the socio-economic point of view in the incumbent regions. Thus, characterization of current cultivation areas in terms of agroclimatic variables (e.g., chill and heat accumulation and probabilities of frost and early abnormal heat events), based on data from 270 weather stations for the past 20 years, is carried out in this work to produce an informative picture of the current situation. Besides, future climatic projections from different global climate models (data retrieved from the Meteorological State Agency of Spain-AEMET) up to 2065 for two Representative Concentration Pathway scenarios (i.e., RCP4.5 and RCP8.5) are also analyzed. Using the current situation as a baseline and considering the future scenarios, information on the current and future adaptive suitability of the different species/cultivars to the different growing areas can be inferred. This information could be the basis of a decision support tool to help the different stakeholders to take optimal decisions regarding current and future stone fruit or other temperate species cultivation in Spain.

Classification of Basal Cell Carcinoma in Ex Vivo Confocal Microscopy Images from Freshly Excised Tissues Using a Deep Learning Algorithm.

Ex vivo confocal microscopy (EVCM) generates digitally colored purple-pink images similar to H&E without time-consuming tissue processing. It can be used during Mohs surgery for rapid detection of basal cell carcinoma (BCC); however, reading EVCM images requires specialized training. An automated approach using a deep learning algorithm for BCC detection in EVCM images can aid in diagnosis. A total of 40 BCCs and 28 negative (not-BCC) samples were collected at Memorial Sloan Kettering Cancer Center to create three training datasets: (i) EVCM image dataset (663 images), (ii) H&E image dataset (516 images), and (iii) a combination of the two datasets. A total of seven BCCs and four negative samples were collected to create an EVCM test dataset (107 images). The model trained with the EVCM dataset achieved 92% diagnostic accuracy, similar to the H&E model (93%). The area under the receiver operator characteristic curve was 0.94, 0.95, and 0.94 for EVCM-, H&E-, and combination-trained models, respectively. We developed an algorithm for automatic BCC detection in EVCM images (comparable accuracy to dermatologists). This approach could be used to assist with BCC detection during Mohs surgery. Furthermore, we found that a model trained with only H&E images (which are more available than EVCM images) can accurately detect BCC in EVCM images.

Climate-Aware and IoT-Enabled Selection of the Most Suitable Stone Fruit Tree Variety.

The application of new technologies such as the Internet of Things offers the opportunity to improve current agricultural development, facilitate daily tasks, and turn farms into efficient and sustainable production systems. The use of these new technologies enables the digital transformation process demanded by the sector and provides agricultural collectives with more optimized analysis and prediction tools. Due to climate change, one of the farm industry's problems is the advance or decay in the cycle of stone fruit trees. The objective is to recommend whether a specific area meets the minimum climatic requirements for planting certain stone fruit trees based on climatic data and bioclimatic indicators. The methodology used implements a large amount of meteorological data to generate information on specific climatic conditions and interactions on crops. In this work, a pilot study has been carried out in the Region of Murcia using an IoT platform. We simulate scenarios for the development of stone fruit varieties better adapted to the environment. Based on the standard, open interfaces, and protocols, the platform integrates heterogeneous information sources and interoperability with other third-party solutions to exchange and exploit such information.

Myopic LASIK and LASEK in patients with preoperative mean central keratometry ≥47.50 D.

PURPOSE: To evaluate whether patients who undergo myopic LASIK or laser epithelial keratomileusis (LASEK) with preoperative central keratometry ≥47.50 diopters (D) are at higher risk for developing corneal ectasia postoperatively. METHODS: Records of 32 patients (54 eyes) who underwent myopic LASIK or LASEK between May 2002 and July 2005 with preoperative mean central keratometry ≥47.50 D were retrospectively reviewed. Patients eligible for the study were offered free follow-up. Last follow-up was at least 3 years after surgery. All procedures were performed with the Technolas Keracor 217z excimer laser (Bausch & Lomb). RESULTS: Twenty-four patients (40 eyes) had LASIK and 8 patients (14 eyes) had LASEK. At last follow-up, no patient had developed ectasia. CONCLUSIONS: This study suggests that patients with preoperative central keratometry ≥47.50 D as the only parameter evaluated should not be excluded from corneal refractive surgery due to increased risk of postoperative ectasia. However, when this preoperative finding is accompanied by other topographic abnormalities, treatment options should be carefully evaluated.

¿Deberían repetirse las pruebas diagnósticas de la infección por el VIH en el segundo y tercer trimestres del embarazo? / Do the diagnostic tests of HIV infection must be repeated in second and third trimester of pregnancy?

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