ChatGPT es un estudiante por encima de la media en la Facultad de Medicina de la Universidad de Zaragoza y un colaborador excelente en la elaboración de material docente / ChatGPT is an above-average student at the Faculty of Medicine of the University of Zaragoza and an excellent collaborator in the development of teaching materials

Introducción y objetivo: La inteligencia artificial se halla plenamente presente en nuestras vidas. En educación las posibilidades de su uso son infinitas, tanto para alumnos como para docentes. Material y métodos: Se ha explorado la capacidad de ChatGPT a la hora de resolver preguntas tipo test a partir del examen de la asignatura Procedimientos Diagnósticos y Terapéuticos Anatomopatológicos de la primera convocatoria del curso 2022-2023. Además de comparar su resultado con el del resto de alumnos presentados, se han evaluado las posibles causas de las respuestas incorrectas. Finalmente, se ha evaluado su capacidad para realizar preguntas de test nuevas a partir de instrucciones específicas. Resultados: ChatGPT ha acertado 47 de las 68 preguntas planteadas, obteniendo una nota superior a la de la media y mediana del curso. La mayor parte de preguntas falladas presentan enunciados negativos, utilizando las palabras «no», «falsa» o «incorrecta» en su enunciado. Tras interactuar con él, el programa es capaz de darse cuenta de su error y cambiar su respuesta inicial por la correcta. Finalmente, ChatGPT sabe elaborar nuevas preguntas a partir de un supuesto teórico o bien de una simulación clínica determinada. Conclusiones: Como docentes estamos obligados a explorar las utilidades de la inteligencia artificial, e intentar usarla en nuestro beneficio. La realización de tareas que suponen un consumo de tipo importante, como puede ser la elaboración de preguntas tipo test para evaluación de contenidos, es un buen ejemplo. (AU)

Introduction and objective: Artificial intelligence is fully present in our lives. In education, the possibilities of its use are endless, both for students and teachers. Material and methods: The capacity of ChatGPT has been explored when solving multiple choice questions based on the exam of the subject «Anatomopathological Diagnostic and Therapeutic Procedures» of the first call of the 2022-23 academic year. In addition, to comparing their results with those of the rest of the students presented the probable causes of incorrect answers have been evaluated. Finally, its ability to formulate new test questions based on specific instructions has been evaluated. Results: ChatGPT correctly answered 47 out of 68 questions, achieving a grade higher than the course average and median. Most failed questions present negative statements, using the words «no», «false» or «incorrect» in their statement. After interacting with it, the program can realize its mistake and change its initial response to the correct answer. Finally, ChatGPT can develop new questions based on a theoretical assumption or a specific clinical simulation. Conclusions: As teachers we are obliged to explore the uses of artificial intelligence and try to use it to our benefit. Carrying out tasks that involve significant consumption, such as preparing multiple-choice questions for content evaluation, is a good example. (AU)

[ChatGPT is an above-average student at the Faculty of Medicine of the University of Zaragoza and an excellent collaborator in the development of teaching materials]. / ChatGPT es un estudiante por encima de la media en la Facultad de Medicina de la Universidad de Zaragoza y un colaborador excelente en la elaboración de material docente.

INTRODUCTION AND OBJECTIVE: Artificial intelligence is fully present in our lives. In education, the possibilities of its use are endless, both for students and teachers. MATERIAL AND METHODS: The capacity of ChatGPT has been explored when solving multiple choice questions based on the exam of the subject «Anatomopathological Diagnostic and Therapeutic Procedures¼ of the first call of the 2022-23 academic year. In addition, to comparing their results with those of the rest of the students presented the probable causes of incorrect answers have been evaluated. Finally, its ability to formulate new test questions based on specific instructions has been evaluated. RESULTS: ChatGPT correctly answered 47 out of 68 questions, achieving a grade higher than the course average and median. Most failed questions present negative statements, using the words «no¼, «false¼ or «incorrect¼ in their statement. After interacting with it, the program can realize its mistake and change its initial response to the correct answer. Finally, ChatGPT can develop new questions based on a theoretical assumption or a specific clinical simulation. CONCLUSIONS: As teachers we are obliged to explore the uses of artificial intelligence and try to use it to our benefit. Carrying out tasks that involve significant consumption, such as preparing multiple-choice questions for content evaluation, is a good example.

Gorlin-Like Phenotype in a Young Girl With a De Novo PTCH2 Variant Mutation of Uncertain Significance.

ABSTRACT: Gorlin syndrome, also known as basal cell nevus syndrome, is an autosomal dominant genetic disorder that predisposes humans to tumors. In most cases, this syndrome results from inactivating mutations in the patched homologue 1 gene. Basal cell carcinomas are one of the main characteristics of this syndrome and serve as a major diagnostic criterion. Gorlin syndrome shows a variable phenotype, and recently, other less common mutations in the suppressor of fused homologue or patched homologue 2 genes have been documented in individuals with this syndrome. We present the case of a patient with early-onset basal cell carcinomas and a mild Gorlin syndrome phenotype, attributed to a de novo patched homologue 2 variant of uncertain significance, which has not been previously reported in the literature.

Human intestinal spirochetosis: an entity associated with sexual transmitted infections.

INTRODUCTION: Human intestinal spirochetosis (HIE) is a poorly studied clinical entity with variable clinical manifestations. However, in recent years it has gained special relevance because an increasing number of cases have been described in people living with HIV (PWH) and in patients with a history of sexually transmitted infections (STI) or immunosuppression. METHODS: Retrospective review of all HIE cases identified in a tertiary level hospital (Hospital Universitario la Paz, Madrid) between 2014 and 2021. RESULTS: 36 Cases of HIE were identified. Most cases corresponded to males (94%) with a median age of 45 years. 10 patients (29.4%) were PWH and 20 (56%) were men who had sex with men. Although the clinical manifestations were very heterogeneous, the most frequent was chronic diarrhea (47%), and up to 25% of the subjects had clinical proctitis. 39% percent of patients had been diagnosed with an STI in the previous two years, this characteristic being more frequent in PWH (90% vs. 28%; p < 0.01) than in patients without HIV infection. The STI most frequently associated with a diagnosis of HIE was syphilis (31%). CONCLUSION: HIE is frequently diagnosed with other STIs and affects mostly men who have sex with men, which supports that this entity could be considered as a new STI.

Stratification of radiosensitive brain metastases based on an actionable S100A9/RAGE resistance mechanism.

Whole-brain radiotherapy (WBRT) is the treatment backbone for many patients with brain metastasis; however, its efficacy in preventing disease progression and the associated toxicity have questioned the clinical impact of this approach and emphasized the need for alternative treatments. Given the limited therapeutic options available for these patients and the poor understanding of the molecular mechanisms underlying the resistance of metastatic lesions to WBRT, we sought to uncover actionable targets and biomarkers that could help to refine patient selection. Through an unbiased analysis of experimental in vivo models of brain metastasis resistant to WBRT, we identified activation of the S100A9-RAGE-NF-κB-JunB pathway in brain metastases as a potential mediator of resistance in this organ. Targeting this pathway genetically or pharmacologically was sufficient to revert the WBRT resistance and increase therapeutic benefits in vivo at lower doses of radiation. In patients with primary melanoma, lung or breast adenocarcinoma developing brain metastasis, endogenous S100A9 levels in brain lesions correlated with clinical response to WBRT and underscored the potential of S100A9 levels in the blood as a noninvasive biomarker. Collectively, we provide a molecular framework to personalize WBRT and improve its efficacy through combination with a radiosensitizer that balances therapeutic benefit and toxicity.

A tree-based machine learning model to approach morphologic assessment of malignant salivary gland tumors.

Malignant salivary gland tumors represent a challenge for pathologists due to their low frequency and morphologic overlap. In recent years machine learning techniques have been applied to the field of pathology to improve diagnostic performance. In the present work, we fitted a machine learning algorithm to approach the diagnosis of malignant salivary gland tumors. Twelve morphologic variables were scored across 115 samples representing the most commonly encountered malignant salivary gland tumors. The sample was randomly split into a discovery and validation set. A recursive partitioning algorithm was used to systematically screen and organize candidate variables into a classification tree using the discovery set. A cross-validation strategy was used to tune the algorithm hyperparameters. Inter-observer concordance was calculated by independent evaluation of 26 randomly selected cases. The five-tiered tree built, required the evaluation of 6 morphological variables. Basaloid appearance, presence of mucous cells, necrosis, cribriform pattern, clear cells and keratinization were selected by the algorithm to build the tree. This diagnostic tool correctly classified 89.9% and 84.6% of the samples in the discovery and validation sets respectively. Misclassification pattern was consistent between both sets. Misclassified tumors belonged to one of three histologic types: epithelial-myoepithelial, polymorphous and mucoepidermoid carcinomas. Other histotypes demonstrated perfect recall in both the discovery and validation sets. Overall inter-observer concordance was good, with median kappa scores between the expert evaluator and training pathologists being 0.81. Overall, our classification tool developed using a recursive partitioning algorithm can effectively guide the morphological approach to malignant salivary gland tumors.