Clinical Research Directory

Curcumin-Based Photodynamic Therapy in Epidermolysis Bullosa: Wound Healing, Quality of Life, and Salivary Biomarkers

Epidermolysis bullosa (EB) is a rare condition that causes extreme fragility of the skin and mucous membranes, leading to the formation of painful blisters. It can be hereditary (HEB) or autoimmune (AEB), and its diagnosis requires invasive procedures such as biopsies. Saliva emerges as a promising alternative for diagnosis and monitoring, as it is easy to collect and contains relevant biomarkers. The disease has no cure, and care focuses on improving the daily lives of those affected. Lesions in oral soft tissues are common and affect functions such as chewing and speech. Photodynamic therapy (PDT), especially with curcumin, has shown positive results in treating oral lesions in other conditions due to its antimicrobial and anti-inflammatory properties. However, it has not yet been specifically studied in people with EB. The primary objective of this study is to evaluate the efficacy of PDT with curcumin in repairing oral lesions in people with EB. Secondary objectives are to assess the impact of photodynamic therapy on the quality of life of people with EB and to identify potential salivary biomarkers and their correlation with the current gold-standard markers of EB. Participant selection and research will be carried out at the Reference Center for Neurodevelopment, Care and Rehabilitation of Children (NINAR), in São Luís, Maranhão, in July 2025. Children, adolescents and adults diagnosed with EB who feed orally, as well as people without EB, will be included in the study. A single calibrated evaluator will be responsible for administering the questionnaires, performing the clinical examination, collecting saliva, and carrying out PDT. Demographic and socioeconomic information will be collected from participants and guardians. Dietary intake will be assessed using a 77-item food frequency questionnaire (FFQ) validated by ELSA-BRASIL (Chor et al., 2013). The following clinical data will be collected: dental caries, using the ICDAS system \[scores 0 (healthy tooth), 5 (visible dentinal cavity) and 6 (extensive cavity)\] (Ismail et al., 2007); molar-incisor hypomineralization (MIH), using the SES-MIH index (Cabral et al., 2019); soft tissue lesions (ulcers, vesicles, bullae), coloration (whitish, yellowish, reddish, etc.) and location (lips, tongue, palate, buccal mucosa, gingiva, etc.). Unstimulated saliva will be collected from EB participants at the NINAR facility and from non-EB participants (control) at the Ana Lúcia Chaves Fecury Clinical School. Saliva samples will be obtained using a 1 mL syringe between 7:00 and 10:00 AM, stored in Eppendorf tubes under refrigeration and subsequently in an ultrafreeze unit (-80°C) at the University of Ceuma. PDT will be performed with 0.1% curcumin gel, applied to the oral mucosal lesions for 5 minutes, followed by irradiation with a blue LED (Radii-CAL CX, 440/480 nm) for one and a half minutes. The procedure will be repeated for three consecutive days. Pain intensity will be monitored before and for seven days after treatment using the Wong-Baker FACES Scale (0 to 5). To assess the impact of treatment on the quality of life of children, the short-form Parental-Caregiver Perceptions Questionnaire (P-CPQ) will be completed by their parents or guardians. For adolescents (aged 12 and above) and adults, the Oral Health Impact Profile-14 (OHIP-14) will be applied. Both questionnaires will be administered before the 1st, 2nd, and 3rd PDT sessions. The reparative efficacy of PDT on lesions will be measured through clinical evaluation. Biochemical analyses will be performed at the laboratories of the Federal University of Uberlândia (UFU), including: metabolite extraction, mass spectrometry (ESI-MS and HPLC-MS), spectroscopy (ATR-FTIR), chemometric analysis, and identification of salivary biomarkers with the aid of artificial intelligence algorithms. Data will be subjected to descriptive analysis of qualitative variables (absolute and relative frequency) and quantitative variables (mean, standard deviation, median, and interquartile range). Statistical tests will be applied for intragroup comparison regarding treatment reparative efficacy and quality of life impact (before and after treatment days) and between groups (with and without EB) regarding salivary biomarkers. Statistical analyses will be conducted at a 5% significance level. SPSS for Windows (Version 20.0; SPSS Inc., Chicago) will be used for data analysis.

Characteristics of Patients With Recessive Dystrophic Epidermolysis Bullosa

Recessive dystrophic epidermolysis bullosa (RDEB) is a disease caused by genetic mutations in the gene for type VII collagen. Patients with RDEB develop large, severely painful blisters and open wounds from minor trauma to their skin. We are screening subjects with RDEB to evaluate characteristics of the subjects and their cells in order to develop new strategies of therapy and determine whether subjects could be candidates for treatment studies.

Self-Assembled Skin Substitute for the Treatment of Epidermolysis Bullosa

Single patient study. Patient diagnosed with dystrophic epidermolysis bullosa presenting chronic open wounds that are not responding to dressings, topical preparations (antimicrobials, antibiotics) and systemic agents (anti-inflammatory antibacterials). The Self-Assembled Skin Substitutes will be used to cover wounds.

Artificial Intelligence Patient App for RDEB SCCs

In this study, an artificial intelligence model to detect squamous cell carcinomas (SCC) on photos of recessive dystrophic epidermolysis bullosa (RDEB) skin is developed. The ultimate goal is to integrate this model into an app for patients and physicians, to help detect SCCs in RDEB early. SCCs which rapidly metastasize are the main cause of death in adults with RDEB. The earlier an SCC is recognized, the easier it can be removed and the better the outcome. AI leverages computer science to perform tasks that typically require human intelligence and has recently been used to identify skin cancers based on images. We are currently developing an AI approach for early detection of SCC and distinction of malignancy from chronic wounds and other RDEB skin findings. The aim is to create a web application for patients with RDEB to upload images of their skin and get an output as to SCC present/ no SCC. This will be especially valuable for patients with difficult access to medical expertise and those who are hesitant to allow full skin examination at each visit, often because of fear of biopsies. Thus, this project will directly benefit patients by allowing early recognition of SCCs and will empower patients and their families by providing a home use tool. So far, the study team has mainly used professional images (photographs taken in hospital settings by physicians, nurses, and clinical photographers) of both SCCs in RDEB and images of RDEB skin without SCC to develop and train the AI model. The images that are expected in a real-life setting will mostly be pictures taken by patients or family members with their phones or digital cameras. These images have different properties regarding resolution, focus, lighting, and backgrounds. Incorporating such images will be crucial in the upcoming phases of model development-testing and validation-for the web application be a success for patients.

A Natural History Study of Corneal Abrasions in Patients With Dystrophic Epidermolysis Bullosa (DEB)

This study is a non-interventional, observational study that will evaluate the natural history of corneal abrasions in patients with Dystrophic Epidermolysis Bullosa (DEB).

Study of the Blood and Skin Immunological Profile of Patients With Recessive Dystrophic Epidermolysis Bullosa: in Vivo Analysis and the Impact of Placental Stem Cells in Vitro

Patients with recessive dystrophic epidermolysis bullosa (RDEB) suffer from acute and chronic post-bullous wounds along with impaired skin healing. These issues are attributed not only to mucocutaneous fragility and abnormal healing directly related to quantitative and/or qualitative constitutional abnormalities of collagen VII but also to a contingent cutaneous and systemic inflammatory component. This inflammatory aspect contributes to the perpetuation of skin lesions and delayed healing. Our primary objective is to define the systemic immunological/inflammatory signature of patients with RDEB with an aim to develop a strategy that involves using stem cells with high immunomodulatory/anti-inflammatory capacity such as allogeneic placental stem cells (WJ-MSCs and trophoblasts).