Masseteric-facial anastomosis has gained popularity in recent days compared to the facial-hypoglossal anastomosis. Masseteric nerve has numerous advantages like its proximity to the facial nerve, stronger motor impulse, its reliability, low morbidity in harvesting and sacrificing the nerve and faster re-innervation that is achievable in most patients. The present case series demonstrate the surgical technique and the effectiveness of the masseteric nerve as donor for early facial reanimation. Between January 2017 and February 2019, 6 patients (2 male, 4 female) with iatrogenic unilateral complete facial paralysis (grade VI, House Brackmann scale) who underwent masseteric-facial nerve anastomosis were included in the study. The time interval between the onset of paralysis and surgery ranged from 4 to 18 months (mean 8.5 months). In all patients pre-operative electromyography had facial mimetic muscle fibrillation potentials. All patients underwent end to end anastomosis except for one patient where greater auricular interposition graft was used. In all cases, the facial muscles showed earliest sign of recovery at 2-5 months. These movements were first noticed on the cheek musculature when the patients activated their masseter muscle. Eye movements started appearing at 6-9 months (in 3 cases) and forehead movements at 18 months (in 1 case). According to the modified House-Brackmann grading scale, one patient had Grade I function, two patients had Grade II function, and three had Grade V function. There was no morbidity except one patient who underwent interposition graft had numbness in the ear lobule. None of the patients could feel the loss of masseteric nerve function. Masseteric facial nerve anastomosis is a versatile, powerful early facial dynamic reanimation tool with almost negligible morbidity compared to other neurotization procedures for patients with complete facial nerve paralysis.

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The virtual classroom environment is created using virtual reality that enables multiple students to enter as if in a real class but with better learning environment. Conventional learning is currently limited in the current model of textbook teaching. An interactive and visual environment provided for learning enhances the rate at which the student grasps concepts. Even though many modern online teaching methods are available today, it is not possible to check whether a student is paying attention or not. Technology is evolving at a very fast rate, and this research is an apt integration of two modern technologies: machine learning and virtual reality, so as to increase the quality of education for students. A shared VR environment, optimised for learning, will be created. Students can wear a head-mounted display and select an avatar for themselves, which will be seen by other students and teachers. The VR environment is created using Unity3D software. Students will also have to wear an EEG scanner on their heads. The output of this scanner will be fed to the machine learning subpart. Neural networks are used to identify whether the student is paying attention or not. If a student is not paying attention, the teacher will be informed about it, with a message near the student's avatar. It has many advantages over traditional learning techniques, like usage of multiple senses and inclusivity for differently abled students.

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