RESUMEN
Sensing of noxious heat has been reported to be mediated by TRPV1, TRPA1, TRPM3, and ANO1 in mice, and this is redundant so that the loss of one receptor is at least partially compensated for by others. We have established an infusion-based human heat pain model. Heat-induced pain probed with antagonists for the four receptors did not match the redundancy found in mice. In healthy participants, only TRPV1 contributes to the detection of noxious heat; none of the other three receptors are involved. TRPV1 inhibition reduced the pain at all noxious temperatures, which can also be seen as an increase in the temperature that causes a particular level of pain. However, even if the TRPV1-dependent shift in heat detection is about 1°C, at the end of the temperature ramp to 52°C, most heat-induced pain remains unexplained. This difference between species reopens the quest for the molecular safety net for the detection of noxious heat in humans.
Asunto(s)
Calor , Canales Catiónicos TRPV , Sensación Térmica , Humanos , Canales Catiónicos TRPV/antagonistas & inhibidores , Canales Catiónicos TRPV/metabolismo , Masculino , Adulto , Animales , Femenino , Ratones , Estudios Cruzados , Dolor , Canal Catiónico TRPA1/metabolismo , Canal Catiónico TRPA1/genética , Canal Catiónico TRPA1/antagonistas & inhibidores , Adulto JovenRESUMEN
Jupyter Notebooks have transformed the communication of data analysis pipelines by facilitating a modular structure that brings together code, markdown text, and interactive visualizations. Here, we extended Jupyter Notebooks to broaden their accessibility with Appyters. Appyters turn Jupyter Notebooks into fully functional standalone web-based bioinformatics applications. Appyters present to users an entry form enabling them to upload their data and set various parameters for a multitude of data analysis workflows. Once the form is filled, the Appyter executes the corresponding notebook in the cloud, producing the output without requiring the user to interact directly with the code. Appyters were used to create many bioinformatics web-based reusable workflows, including applications to build customized machine learning pipelines, analyze omics data, and produce publishable figures. These Appyters are served in the Appyters Catalog at https://appyters.maayanlab.cloud. In summary, Appyters enable the rapid development of interactive web-based bioinformatics applications.
