ABSTRACT
Fungal lung co-infections associated with COVID-19 may occur in severely ill patients or those with underlying co-morbidities, and immunosuppression. The most common invasive fungal infections are caused by aspergillosis, mucormycosis, pneumocystis, cryptococcus, and candida. Radiologists integrate the clinical disease features with the CT pattern-based approach and play a crucial role in identifying these co-infections in COVID-19 to assist clinicians to make a confident diagnosis, initiate treatment and prevent complications.
Subject(s)
COVID-19 , Coinfection , Mycoses , Pneumonia , Humans , COVID-19/complications , Coinfection/diagnostic imaging , Coinfection/complications , Mycoses/etiology , Mycoses/microbiology , Lung/diagnostic imaging , RadiologistsABSTRACT
Las coinfecciones pulmonares fúngicas asociadas a la COVID-19 pueden ocurrir en pacientes gravemente enfermos o con comorbilidades subyacentes e inmunosupresión. Las infecciones fúngicas invasivas más comunes son causadas por aspergilosis, mucormicosis, y las debidas a Pneumocystis, criptococo y cándida. Los radiólogos integran las características clínicas de la enfermedad con el enfoque basado en patrones de TAC y desempeñan un papel crucial en la identificación de estas coinfecciones en la COVID-19 para ayudar a los médicos a realizar un diagnóstico seguro, iniciar el tratamiento y prevenir complicaciones.(AU)
Fungal lung co-infections associated with COVID-19 may occur in severely ill patients or those with underlying co-morbidities, and immunosuppression. The most common invasive fungal infections are caused by aspergillosis, mucormycosis, pneumocystis, cryptococcus, and candida. Radiologists integrate the clinical disease features with the CT pattern-based approach and play a crucial role in identifying these co-infections in COVID-19 to assist clinicians to make a confident diagnosis, initiate treatment and prevent complications.(AU)
Subject(s)
Humans , Severe acute respiratory syndrome-related coronavirus , Coronavirus Infections , Betacoronavirus , Pandemics , Radiologists , Lung Diseases, Fungal , Pneumocystis , Cryptococcus , Candida , Aspergillosis , Radiology , Diagnostic Imaging , Radiology Department, HospitalABSTRACT
Artificial Intelligence has the potential to disrupt the way clinical radiology is practiced globally. However, there are barriers that radiologists should be aware of prior to implementing Artificial Intelligence in daily practice. Barriers include regulatory compliance, ethical issues, data privacy, cybersecurity, AI training bias, and safe integration of AI into routine practice. In this article, we summarize the issues and the impact on clinical radiology.
Subject(s)
Artificial Intelligence , Radiology , Humans , Privacy , RadiologistsABSTRACT
Fungal lung co-infections associated with COVID-19 may occur in severely ill patients or those with underlying co-morbidities, and immunosuppression. The most common invasive fungal infections are caused by aspergillosis, mucormycosis, pneumocystis, cryptococcus, and candida. Radiologists integrate the clinical disease features with the CT pattern-based approach and play a crucial role in identifying these co-infections in COVID-19 to assist clinicians to make a confident diagnosis, initiate treatment and prevent complications.
ABSTRACT
La inteligencia artificial (IA) ofrece la posibilidad de cambiar la práctica de la radiología clínica en todo el mundo. Sin embargo, existen dificultades que los radiólogos deben conocer antes de aplicar la inteligencia artificial en la práctica diaria. Estas dificultades incluyen cuestiones de cumplimiento de la legislación, cuestiones éticas, aspectos relacionados con la privacidad de los datos y la ciberseguridad, el sesgo de aprendizaje automático y la integración segura de la IA en la práctica habitual. En este artículo, resumimos estas cuestiones y su repercusión en la radiología clínica.(AU)
Artificial Intelligence has the potential to disrupt the way clinical radiology is practiced globally. However, there are barriers that radiologists should be aware of prior to implementing Artificial Intelligence in daily practice. Barriers include regulatory compliance, ethical issues, data privacy, cybersecurity, AI training bias, and safe integration of AI into routine practice. In this article, we summarize the issues and the impact on clinical radiology.(AU)
Subject(s)
Artificial Intelligence , Technology, Radiologic , Radiologists , Artificial Intelligence/ethics , Machine Learning , RadiologyABSTRACT
Epithelial cells line the lumen of tubular organs and are key players in their respective functions. They establish a unique luminal environment by providing a protective barrier and by performing vectorial transport of ions, nutrients, solutes, proteins, and water. Complex intercellular communication networks, specific for each organ, ensure their interaction with adjacent epithelial and non-epithelial cells, allowing them to respond to and modulate their immediate environment. In the epididymis, several epithelial cell types work in a concerted manner to establish a luminal acidic milieu that is essential for the post-testicular maturation and storage of spermatozoa. The epididymis also prevents autoimmune responses against auto-antigenic spermatozoa, while ensuring protection against ascending and blood pathogens. This is achieved by a network of immune cells that are in close contact and interact with epithelial cells. This review highlights the coordinated interactions between spermatozoa, basal cells, principal cells, narrow cells, clear cells, and immune cells that contribute to the maturation, protection, selection, and storage of spermatozoa in the lumen of the epididymis.
