Tundra Space

Tundra Space

Clinical Research Directory

Browse clinical research sites, groups, and studies.

2 clinical studies listed.

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Oesophagitis

Tundra lists 2 Oesophagitis clinical trials. Each listing includes eligibility criteria, study locations, and direct links to research sites in the Tundra directory.

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NOT YET RECRUITING

NCT07183267

Using Red Light Therapy to Ease Skin Side Effects and Mouth Side Effects, in Children and Young People Aged 0 to 16 Years Old, Receiving Radiotherapy

The goal of this interventional study is to see if the daily use of red light therapy called photobiomodulation can help sore skin and sore mouths in children having radiotherapy. All children aged 0-16 years old who are receiving either proton or photon radiotherapy treatment, except to the brain only, will be asked if they would like to join the study. The main questions it aims to answer are: 1. Does the use of red light therapy help the skin side effects in children and young people undergoing proton or photon therapy? 2. Does the use of red light therapy help the mouth side effects in children and young people undergoing proton or photon therapy in the head and neck area? Researchers will compare the patients enrolled on to the study with a like for like historic patient to see if red light therapy improves the sore skin and sore mouths caused by proton or photon therapy. Participants will have a daily treatment with red light therapy alongside their daily proton or photon treatment fraction.

Gender: All

Ages: 0 Years - 16 Years

Updated: 2026-03-19

1 state

Radio Dermatitis
Mucositis Oral
Oesophagitis
RECRUITING

NCT06822413

Raman Spectroscopy-Based Deep Learning Model for Early Pan-Cancer Early Diagnosis

The goal of this observational study is to explore whether a Raman-based, deep learning-assisted approach can be used to develop an effective method for early pan-cancer screening. The study includes healthy individuals, patients at risk of cancer, and patients with diagnosed cancers. The main questions it aims to answer are: * Evaluating the deep-learning model's accuracy and specificity in identifying cancer-specific features in Raman spectral data and determining whether this method can accurately classify patients based on risk. * Identifying which model is more adaptable to the Raman spectrum * Providing an interpretable analysis of the model-generated diagnosis Participants are already being diagnosed and follow-up to determine the type of cancer.

Gender: All

Updated: 2025-04-24

3 states

Cancer Diagnosis
Liver Cancer, Adult
Cancer Screening
+14