Herbal Compounds as an Antidote against Snake Bite

Snakebites have been declared a neglected health problem that must be considered a national disease of the WHO[world health organisation]. Asian countries like India have high snakebite death rates due to short antidotes and poorly equipped doctors. In today's scenario, local resources like herbs need to be used to prepare cheap antidotes and often available to victims. Snake bites should be viewed as an emergency problem and require additional national guidelines, doctor training, expertise, and human concentration for effective and timely treatment-measures to be taken to ensure the availability and mass production of antidotes. Currently available, antidotes have problems with storage, manufacture, and aspects of the results. Attention should be paid to the natural compound Gedunin with antitoxic effects. To determine Gedunin's therapeutic efficacy well-designed clinical research is required. This article emphasizes and proves the therapeutic effectiveness of the herbal plant active ingredient Gedunin against snakebites.

Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network

Stereotactic treatments are today the reference techniques for the irradiation of brain metastases in radiotherapy. The dose per fraction is very high, and delivered in small volumes (diameter <1 cm). As part of these treatments, effective detection and precise segmentation of lesions are imperative. Many methods based on deep-learning approaches have been developed for the automatic segmentation of gliomas, but very little for that of brain metastases. We adapted an existing 3D convolutional neural network (DeepMedic) to detect and segment brain metastases on MRI. At first, we sought to adapt the network parameters to brain metastases. We then explored the single or combined use of different MRI modalities, by evaluating network performance in terms of detection and segmentation. We also studied the interest of increasing the database with virtual patients or of using an additional database in which the active parts of the metastases are separated from the necrotic parts. Our results indicated that a deep network approach is promising for the detection and the segmentation of brain metastases on multimodal MRI.