Saliva

The MET-SELS study aims to revolutionize how we identify individuals for lung cancer screening by moving beyond the limitations of self-reported smoking history. Currently, eligibility for low-dose CT (LDCT) scans relies on "pack-years"-a metric often compromised by recall bias, under-reporting, and an inability to account for the biological nuances of smoke inhalation or environmental exposure. Consequently, current criteria miss nearly half of incidental lung cancers. To bridge this gap, the study investigates DNA methylation as a stable, objective "biological footprint" of smoking. Unlike short-term biomarkers like nicotine or CO levels, specific epigenetic changes in genes such as AHRR and F2RL3 persist long after cessation and correlate accurately with cumulative tobacco exposure. Led by Professor Dr. Annemiek Snoeckx and a multidisciplinary team at UZA and the Centre for Medical Genetics, the research will analyze saliva samples from two groups: roughly 900 participants from the ZORALCS screening trial and 150 volunteers. By comparing saliva-derived genomic signatures against both self-reported data and professional interviews, the team aims to validate a panel of methylation markers that can pinpoint high-risk individuals with far greater precision. The ultimate vision for MET-SELS is to implement a population-based "saliva-first" triage system, similar to the FIT test used for colorectal cancer. In this model, high-risk candidates would provide a saliva sample at home; only those with a confirmed epigenetic risk profile would be invited for a LDCT scan, significantly increasing the yield of early-stage lung cancer detection while streamlining healthcare resources.

This research study examines recovery after a sports-related concussion or mild head injury. Many athletes experience lingering problems with thinking, balance, and mood months after a concussion. We are testing a new home-based program called BraW-Day™ (Brain \& Walk Exercise Every Day). This 14-day program uses a mobile app on your smartphone and combines simple brain exercises (like counting backwards) with walking exercises. The idea is that doing these tasks together may help your brain and body recover more effectively. What You'll Do: * Two in-person visits at UNLV (baseline and after 14 days) for assessments * Daily 15-minute exercises at home using the BraW-Day app * Provide a small saliva sample for biomarker testing * Complete brief questionnaires about your symptoms and health Potential Benefits: You may experience improvements in thinking, balance, or mood. Even if you don't benefit directly, your participation will help researchers understand how to better support athletes recovering from concussion. Who Can Join: * Ages 18-40 * Had a sports-related concussion or mild head injury within the past 3-12 months * Have a smartphone and can safely do light walking and simple tasks This study is registered at ClinicalTrials.gov and approved by the UNLV Institutional Review Board to protect your safety and rights.

Head and neck squamous cell carcinoma (HNSCC) are malignant tumors originating from the epithelial mucosa of the upper aerodigestive tract. The oral cavity is the most frequent location of HNSCC (oral squamous cell carcinoma: OSCC). Tobacco use and alcohol consumption are the greatest risk factors. The Hauts de France region has one of the highest incidence rates of OSCC. The overall survival of patients with OSCC remains low, with a 5-year overall survival rate of around 60%. In addition to the oncological prognosis, OSCCs and their treatment have a significant impact on the quality of life of patients. An early diagnosis of OSCC is recommended, but it remains difficult. It can be for example challenging to diagnose OSCC in a context of oral premalignant lesions. Identifying objective biomarkers of malignancy would be an advantage and would allow better progress in the field of precision medicine and surgery for these tumors. The investigators propose to establish the diagnostic use of an optimized DNA methylation profile detected in the saliva of OSCC patients by comparing these epigenetic marks before and after tumor resection. The investigators will construct a consolidated signature of 4 genes whose DNA is subject to methylation and gene expression is restricted to cancer cells, by crossing TCGA analysis with single-cell analysis (single-cell RNA sequencing). The investigators propose to analyse DNA methylation of the corresponding genes in the saliva of n=30 OSCC patients recruited for primary surgical resection in the Department of Maxillofacial Surgery vs controls. In addition, the investigators will examine the methylation profiles before / after complete excisional surgery of OSCC. This pilot study will aim to validate the analysis of DNA methylation markers in saliva of OSCC, with the aim of improving the diagnostic precision of OSCC and, secondly, to compare these markers before and after treatment by primary surgery.

Periodontitis is a major public health issue in China: it is responsible for loss of masticatory function in 60 million older adults, and 400-500 million adults are on the same disease trajectory. In addition, gingivitis and early-stage periodontitis are highly prevalent in all age groups. The Lancet 2021 burden of disease study provides worrying projections for China's oral health, with a 47.8% increase in advanced-stage periodontitis and a 217% increase in edentulism by the year 2050. The numbers are not manageable by the Chinese health system unless a series of coordinated actions are implemented: i) health education promoting oral hygiene in school and the workplace; ii) effective AI-based self-detection strategies and accurate identification of high-risk subjects; iii) efficient treatment modalities; and iv) reorganization of the health system. We have developed, patented, and validated a self-detection AI-based screening test for the general population through an app. It is based on a few validated questions and the performance of a lateral flow immunoassay to detect activated matrix metalloproteinase 8 (aMMP8). The algorithm enables accurate self-detection of severe periodontitis. The system, however, cannot identify subjects without clinically evident periodontitis (subjects who present with superficial inflammation consistent with gingivitis and incipient periodontitis) who will develop the disease, which, therefore, should be the target of early interventions. This limitation is due to insufficient knowledge of the process that turns superficial inflammation (gingivitis) into periodontitis. This limitation is apparent in the recently published NIH-sponsored American diagnostic trial results to detect periodontitis onset biomarkers (and progression). In their study, Teles et al. (2024) show that almost 24% of gingivitis subjects progress to periodontitis over a 12-month period but failed to identify salivary or serum biomarkers. Similarly, our recently completed study (Li et al. in preparation) did not identify highly accurate biomarkers for disease onset and progression. Importantly, the American and our study have tested putative biomarkers identified based on the current crude knowledge of the disease process. Gaps in fundamental knowledge are now apparent and limit our ability to detect periodontitis early. In addition, the current crude differential diagnosis based on clinical examination with a periodontal probe with millimeter markings cannot accurately differentiate gingivitis from early-stage periodontitis, complicating the ground truth definition (gold standard). In the current study, we propose implementing a multi-omics approach to test the ability to discriminate a mixed population of clinically undifferentiable gingivitis and stage I periodontitis into two or more clusters. In this biomarker discovery phase, we plan to use multiple state-of-the-art methods: i) laser scanning microdissection proteomics of tissue biopsies, ii) conventional salivary proteomics, iii) tissue biopsy transcriptomics, and iv) shotgun microbiome analysis. The methods will be applied in an agnostic approach to test the following hypotheses: 1. It is possible to identify two or more clusters of subjects from a mixed population of gingivitis and stage I periodontitis subjects. 2. The clusters differ based on host-derived biomarkers and/or microbiome factors and the risk of progression to periodontitis. 3. The biomarker pathways and microbial virulence factors among subjects identified according to the different approaches used to explore disease biology are generally consistent. 4. It is possible to identify a limited set of biomarkers that can be used to predict periodontitis onset and thus target early interventions for this high-risk population.

The use of a novel topical mucosa composition (XCM-OM118) comprising 2-(trimethylazaniumyl) acetate; (2R,3R,4S)-pentane-1,2,3,4,5-pentol; Hexadecanoic acid; (9Z, 12Z)-octadeca-9,12-dienoic acid; octadecanoic acid; (Z)-hexadec-9-enoic acid; (Z)-octadec-9-enoic acid) delivered as a gel and a mouthwash is to be studied in regard to its effect on the incidence of severe oral mucositis in Head \& Neck cancer radiated patients. Patient reported outcome measures seem to be an effective tool to obtain a greater knowledge of the physical and emotional state of patients, being used in this study to assess quality of life of Head \& Neck cancer radiated patients.

Study Design: A randomized controlled trial with two parallel arms and an allocation ratio of 1:1. Setting: The study will be conducted in the orthodontic department of Riyadh Elm University hospitals in Riyadh City, Saudi Arabia. Participants: Patients undergoing fixed or clear aligner orthodontic treatment referred to REU dental hospital will be randomly allocated to either the clear aligner group or the fixed orthodontic appliance group. Intervention: Prior to orthodontic treatment, all patients will receive phase I periodontal therapy and oral hygiene instructions. Fixed orthodontic appliances will be bonded using metallic brackets with a 0.022-inch slot and 0.014-inch NiTi archwires. Clear aligner patients will receive Invisalign® treatment. Outcomes and Saliva Collection: Salivary samples will be collected using the spitting method at baseline (T0) and follow-up time points according to the study protocol. Salivary flow rate, pH, and buffering capacity will be assessed as described in the proposal. Randomization and Blinding: Randomization will be performed using a random number generator with allocation concealment via opaque envelopes. The investigators involved in outcome assessment and data analysis will be blinded. Ethical Considerations: The study will be submitted to the Institutional Review Board at Riyadh Elm University and conducted in accordance with IRB policies.

This observational study aims to develop and validate a novel, AI-assisted electrochemical sensor platform for saliva-based diagnostics in periodontitis. Periodontitis is a chronic inflammatory disease affecting the gums and supporting tissues of the teeth. Despite its high global prevalence, early diagnosis remains challenging because the disease often progresses silently until irreversible damage has occurred. Saliva offers a promising, non-invasive diagnostic medium that reflects both oral and systemic health. However, its biological complexity and variability have limited its clinical use. This project addresses these challenges by combining advanced electrochemical sensing with artificial intelligence (AI) and synthetic data generation to improve diagnostic precision and reliability. The study involves the collection of saliva samples from adult participants with diagnosed periodontitis and from healthy controls. The samples will be analyzed using a modular sensor platform equipped with multiple electrodes that detect electrochemical signals from a wide range of salivary biomarkers. The sensor data will then be processed using machine learning models trained on both real and synthetic data to classify disease states. The main goals are to: Evaluate the performance of the electrochemical sensor array for saliva analysis. Develop and validate AI-based algorithms for detecting and differentiating between healthy and diseased samples. Generate feasibility data supporting future clinical implementation of saliva-based diagnostics for periodontitis. This interdisciplinary project combines expertise in clinical dentistry, biomedical engineering, and computer science. It is conducted in collaboration between Linköping University and Malmö University, with patient sampling carried out at an affiliated dental clinic. The study is expected to result in a working proof-of-concept device that enables real-time, non-invasive detection of periodontitis at the point of care. By enabling earlier diagnosis and more personalized treatment, this technology may transform periodontal care and serve as a foundation for future saliva-based diagnostics targeting other oral and systemic diseases.

Dental implants have been on the market for several years and they are routinely used to replace single/multiple missing teeth with a high success rate. However, there is still a limited number of studies comparing the influence of timing of implant placement on wound healing. In addition, there is no data available on the signaling pathways and the expression of healing biomarkers involved in the early stages of osseointegration after immediate implant placement (IP) or delayed implant placement (DP). The primary objective of this study is to describe changes in the expression of inflammatory, angiogenesis and osseous biomarkers of saliva at 1, 3, 7, 15 and 30 days and of PICF at 3, 7, 15 and 30 days after immediate implant placement (IP) compared with delayed placement (DP).