a) Oral Cancer (OC) and OPMD. Specifically, the primary research interests include:
i) Exploring strategies to enhance the tumor cell-killing effectiveness of second-generation antimitotics. The goal is to offer a fresh opportunity for these antimitotics to undergo further clinicaltrials and, ideally, become part of cancer therapy protocols.
ii) Targeting mitosis for enhancing immunotherapy in OC. This chemo-immunotherapy strategy aimsto overcome resistance to immune checkpoint blockade, providing a pioneering approach to oral cancer treatment.
iii) Studying the effect of compounds with antitumor activity in the cell bioenergetics status, and in the reverse of the multidrug resistance phenotype. We intend to use different metabolic modulators,targeting cell energetic metabolism in different steps. Their potential to be used in combined treatments will be assessed.
iv) Screening and determining the antitumor activity of derivatives of natural compounds like flavonoids. Namely, their ability to overcome the resistance of cancer to therapies will be addressed, a major drawback for cancer cure. At the end, we hope to identify promising compounds to use in the fight against cancer.
v) Discover and implement predictive and prognostic Biomarkers in early diagnosis of OPMD and OC. We aim to evaluate of multiple biomarkers panels assisted by AI for automatized predictive/prognostic score results. This will benefit from comparative pathology research using animal models in collaboration researchers from CECAV/UTAD.vi) Development of innovative adjunct diagnostic technologies on OPMD and early OC. This includes3 important projects:-Involving populational OC screenings in communities of the north of Portugal) we aim to validate the BlueStain® diagnostic tool, combining the speed and innovation of staining with digital pathology and AI algorithm.-The Use of Contact endoscopy and NBI in early diagnosis of OPMD and OC, a multicentric project collaborating with the AORN"A. Cardarelli"Hospital (Italy) and industry partners (Olympus/Storz), and using AI for an automatized diagnosis result in real time in a non-invasive way.-AI-enabled rapid oral cancer assay qMIDS project, aiming the evaluation of the rapid PCR genetic test in a multicentric collaboration including KCL London and the Clinical Research HUB (UNIPRO).We wish the integration of a researcher fully expert on bioinformatics and AI research for associate to these projects.
b) Periodontal diseases. Our main interests are focusing on:
i) Role of metalloproteinases in periodontal disease. This study, involves the detection of 13 cytokines with a multiplex kit, and by flow cytometry, which could help define other biomarkers (collaboration with Minho U.).
ii) Identifying periodontopathogens in the Portuguese population and relating them to systemic diseases. The group (in collaboration with USC) intends to evaluate new bacterial associations with the 16S rDNA gene amplification sequence.
iii) Integrate periodontal health metrics with indicators of other systemic diseases to create predictivemodels, through statistical and AI techniques in collaboration with the ALGORITMI Center (Minho U.), using advanced analysis of metagenomic and multiplex inflammatory markers.
iv) Development of innovative and disruptive periodontal diagnostic tool, in partnership with CESPU Diagnóstico.
a) Oral Cancer (OC) and OPMD. Specifically, the primary research interests include:
i) Exploring strategies to enhance the tumor cell-killing effectiveness of second-generation antimitotics. The goal is to offer a fresh opportunity for these antimitotics to undergo further clinicaltrials and, ideally, become part of cancer therapy protocols.
ii) Targeting mitosis for enhancing immunotherapy in OC. This chemo-immunotherapy strategy aimsto overcome resistance to immune checkpoint blockade, providing a pioneering approach to oral cancer treatment.
iii) Studying the effect of compounds with antitumor activity in the cell bioenergetics status, and in the reverse of the multidrug resistance phenotype. We intend to use different metabolic modulators,targeting cell energetic metabolism in different steps. Their potential to be used in combined treatments will be assessed.
iv) Screening and determining the antitumor activity of derivatives of natural compounds like flavonoids. Namely, their ability to overcome the resistance of cancer to therapies will be addressed, a major drawback for cancer cure. At the end, we hope to identify promising compounds to use in the fight against cancer.
v) Discover and implement predictive and prognostic Biomarkers in early diagnosis of OPMD and OC. We aim to evaluate of multiple biomarkers panels assisted by AI for automatized predictive/prognostic score results. This will benefit from comparative pathology research using animal models in collaboration researchers from CECAV/UTAD.vi) Development of innovative adjunct diagnostic technologies on OPMD and early OC. This includes3 important projects:-Involving populational OC screenings in communities of the north of Portugal) we aim to validate the BlueStain® diagnostic tool, combining the speed and innovation of staining with digital pathology and AI algorithm.-The Use of Contact endoscopy and NBI in early diagnosis of OPMD and OC, a multicentric project collaborating with the AORN"A. Cardarelli"Hospital (Italy) and industry partners (Olympus/Storz), and using AI for an automatized diagnosis result in real time in a non-invasive way.-AI-enabled rapid oral cancer assay qMIDS project, aiming the evaluation of the rapid PCR genetic test in a multicentric collaboration including KCL London and the Clinical Research HUB (UNIPRO).We wish the integration of a researcher fully expert on bioinformatics and AI research for associate to these projects.
b) Periodontal diseases. Our main interests are focusing on:
i) Role of metalloproteinases in periodontal disease. This study, involves the detection of 13 cytokines with a multiplex kit, and by flow cytometry, which could help define other biomarkers (collaboration with Minho U.).
ii) Identifying periodontopathogens in the Portuguese population and relating them to systemic diseases. The group (in collaboration with USC) intends to evaluate new bacterial associations with the 16S rDNA gene amplification sequence.
iii) Integrate periodontal health metrics with indicators of other systemic diseases to create predictivemodels, through statistical and AI techniques in collaboration with the ALGORITMI Center (Minho U.), using advanced analysis of metagenomic and multiplex inflammatory markers.
iv) Development of innovative and disruptive periodontal diagnostic tool, in partnership with CESPU Diagnóstico.