Spectrum of opportunistic fungal lung co-infections in COVID-19: What the radiologist needs to know.

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.

Espectro de coinfecciones pulmonares fúngicas oportunistas en COVID-19: lo que el radiólogo debe saber / Spectrum of opportunistic fungal lung co-infections in COVID-19: What the radiologist needs to know

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)

Barriers to artificial intelligence implementation in radiology practice: What the radiologist needs to know.

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.

[Spectrum of opportunistic fungal lung co-infections in COVID-19: What the radiologist needs to know]. / Espectro de coinfecciones pulmonares fúngicas oportunistas en COVID-19: lo que el radiólogo debe saber.

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.

Dificultades en la implantación de la inteligencia artificial en la práctica radiológica: lo que el radiólogo necesita saber / Barriers to Artificial Intelligence implementation in radiology practice: What the Radiologist needs to know

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)

Epithelial dynamics in the epididymis: role in the maturation, protection, and storage of spermatozoa.

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.