Physiology and pathophysiology of the Stomatognathic System

Recommended Prerequisites

The student should master the knowledge acquired in the subjects of General Human Anatomy, Head and Neck Anatomy, Dental Anatomy, General Human Physiology; must be proficient in English.

Teaching Methods

T: On-line lessons, with a method of expository basis, previous organizer, evolving according to the principle of gradual elaboration. They are interactive, sometimes with role-playing.

P: The haptic simulation and use of new technology instruments by the students is previously demonstrated by the teachers, and also whenever it is considered necessary. Case discussion and problem-solving training are provided. Students fill in assessment sheets in each class and are made aware of ethical obligations and the usefulness of these new technologies for the digital transition in dentistry.

Learning Outcomes

Precision practice in dentistry, based on the use of Machine Learning to optimize preventive and therapeutic strategies (stratification, prognosis and prediction); use of scanner, 3D printing and manufacture of CAD-CAM gutters; clinical agility, based on the understanding of the stomatognathic system, pain, sleep, disruptive mechanisms of normofunction; recognition of biological adaptation mechanisms, Nervous System action and mechanisms of return to homeostasis; to integrate knowledge of neurochemical processes, namely the role of neurotransmitters in human behavior; critical ability to re-evaluate clinical performance; Use artificial intelligence for diagnostic imaging; to use new technologies in the evaluation of pain, muscular activity, neurological and sleep. (ultramobile ultrasound; QST; laser; functional rm (bolt, dti, spectroscopy) and pet scan; apps.

Work Placement(s)

No

Syllabus

Pathophysiology- pain and sleep and assessment of its activity with new technologies: ultramobile ultrasound; QST;laser; apps(face-to-face content);
Analysis of brain activity with functional neuroimaging tests, such as functional MRI (bolt, dti, spectroscopy) and pet scan(face-to-face content);
Optimization of preventive and therapeutic strategies, with concepts of stratification, prognosis and prediction through Machine Learning(face-to-face content);
Adapting the organization/clinic to a digital flow: monitoring and improving the patient's experience with new technologies(on-line content);
Patient stratification and artificial Intelligence(on-line content);
Promoting the improvement of patients' digital literacy(on-line content);
Dental telemedicine for the treatment of orofacial pain and sleep disorders(face-to-face content);
3D printing and manufacture of CAD-CAM splints(face-to-face content);
Tissue regeneration (on-line content).

Head Lecturer(s)

Maria João Pascoal Rodrigues

Assessment Methods

Assessment
Exam: 50.0%
Continuous assessment according to e-log book : 50.0%

Bibliography

MacEachern, S.J., Forkert, N.D.(2020).Machine learning for precision medicine.Genome, 64(4), 416-425. https://doi.org/10 1139/gen-2020-0131
Van Spil, W.E.,Bierma-Zeinstra, S.M.A., Deveza, L.A., Arden, N.K.,Bay-Jensen, A.C.,Kraus,V.B., et al. (2020).A consensus-based framework for conducting and reporting osteoarthritis phenotype research. Arthritis Res Ther, 22(1), 54 doi:10.1186/s13075-020-2143-0

Zhang, X.D.(2015)Precision Medicine, Personalized Medicine, Omics and Big Data:Concepts and Relationships. J Pharmacogenomics Pharmacoproteomics,6, e144 doi:10.4172/2153-0645.1000e144
Mier, W., Mier, D. (20§15).Advantages in functional imaging of the brain. Front Hum Neurosci, 19(9), 249. doi:10.3389/fnhum.2015.00249.PMID:26042013;PMCID:PMC4436574

Lin, C.S. (2014).Brain signature of chronic orofacial pain: a systematic review and meta-analysis on neuroimaging research of trigeminal neuropathic pain and temporomandibular joint disorders. PLoS One,9(4), e94300. doi:10.1371/journal.pone.0094300