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OBJECTIVE: Cold atmospheric plasma (CAP) has shown an ability to promote wound healing by modulating biological processes without causing thermal damage. This scoping review aimed to evaluate the effectiveness of CAP application in the oral wound healing process. DESIGN: An electronic literature search was conducted using PubMed/Medline, Embase, Web of Science, Scopus, and grey literature (Google Scholar). The search included all articles published up to October 11, 2023. Only studies focusing on the different CAP types' effects on oral cavity wounds or cells were included in the review. RESULTS: This review analyzed 13 studies including seven cell culture studies, one animal study, and five human studies (three in vivo and two ex vivo). The findings from the reviewed articles suggest that CAP may have therapeutic potential. It can maintain cell viability and influence gene expression, accelerate wound healing, and modulate inflammation-related cytokines. DBD plasma exhibited time-sensitive effects on cellular behavior and microplasma irradiation positively impacted cell count, biochemical profiles, and cellular migration. CONCLUSION: The application of CAP has been shown to have a positive impact on the healing of oral wounds in cell culture, animal, and human studies.
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Statement of the Problem: Gag reflex is among the most common problems during dental and endoscopic procedures. Benzydamine hydrochloride is a non-steroidal anti-inflammatory medication and a local anesthetic that might be useful in reducing the gag reflex. Purpose: This study aimed to evaluate the effects of benzydamine hydrochloride mouthwash on the intensity of the gag reflex. Materials and Method: In this randomized clinical trial study, 30 participants aged 21-26 with a gag trigger point index (GTPI) higher than 2 were divided into 2 groups. In the case group, 15 ml of 0.15% benzydamine hydrochloride mouthwash was gargled for 1 minute, and after 10 minutes GTPI test was conducted. In the control group, 4 puffs of 10% lidocaine spray were applied to the mucosa of the targeted area, and after 5 minutes, GTPI was measured. Participants were asked about the taste and smell of the medications. Results: GTPI was significantly reduced in both groups. In the lidocaine group, the GTPI score changed from 4.47 to 2.00 (p< 0.001), and that for the benzydamine group was 4.20 to 1.47 (p< 0.001). The variance rate of the gag reflex was -2.73 and -2.47 in the benzydamine group and lidocaine group, respectively. However, this reduction was not statistically significant between the two groups. Moreover, benzydamine mouthwash has a significantly better taste and smell than lidocaine (p= 0.001). Conclusion: The results of this study showed that benzydamine mouthwash could be used quite effectively in reducing the gag reflex.
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BACKGROUND: Iran will soon have an aging population. Healthcare providers must consider factors affecting the quality of life for those 60 and older. Understanding oral health as one of these factors can improve the elderly's quality of life. Oral health-related quality of life (OHRQoL) is a crucial reflection of individuals' general well-being and their overall quality of life linked to health. This study aimed to evaluate the OHRQoL among elderly Iranians referring to Azad University of Medical Sciences in Tehran. METHODS: In this cross-sectional study, 171 individuals over 65 (93 men and 78 women) were evaluated using OHIP-14 and GOHAI-12 questionnaires to analyze their quality of life. Gender, age, systemic diseases, using medications and dentures, the number of remaining teeth, and oral lesions were recorded. Salivary flow and xerostomia were analyzed with the spitting method and xerostomia index questionnaire, respectively. Also, four main flavor solutions were used to evaluate the taste perception. Data were analyzed using PASS11 and p value < .05 was the significance level. RESULTS: Based on OHIP-14 and GOHAI-12, Iranian older people's quality of life can be affected by cardiovascular diseases, hypertension, xerostomia, and the number of remaining teeth (p < .05). The results also showed a significant correlation between the OHIP-14 and GOHAI-12 scores (p < .001). Both indexes revealed that the number of remaining teeth, xerostomia, salivary flow, and taste perception greatly influenced participants' quality of life. More remaining teeth, improved salivary flow, and better perception of sweetness and sourness were all linked to a higher quality of life, while increased xerostomia, reduced salivary flow, and bitter taste perception were linked to a decline in overall well-being. Cardiovascular diseases, hypertension, and medication use were also found to significantly impact quality of life. CONCLUSION: This study's results indicate that cardiovascular diseases, hypertension, dry mouth, and tooth loss can negatively impact the elderly's quality of life. So, improving both systemic and oral health is vital for enhancing life quality in this age group.
Subject(s)
Oral Health , Quality of Life , Humans , Cross-Sectional Studies , Iran , Male , Female , Aged , Surveys and Questionnaires , Aged, 80 and over , Xerostomia/psychology , Dental ClinicsABSTRACT
OBJECTIVE: This study aimed to review and synthesize studies using artificial intelligence (AI) for classifying, detecting, or segmenting oral mucosal lesions on photographs. MATERIALS AND METHOD: Inclusion criteria were (1) studies employing AI to (2) classify, detect, or segment oral mucosa lesions, (3) on oral photographs of human subjects. Included studies were assessed for risk of bias using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A PubMed, Scopus, Embase, Web of Science, IEEE, arXiv, medRxiv, and grey literature (Google Scholar) search was conducted until June 2023, without language limitation. RESULTS: After initial searching, 36 eligible studies (from 8734 identified records) were included. Based on QUADAS-2, only 7% of studies were at low risk of bias for all domains. Studies employed different AI models and reported a wide range of outcomes and metrics. The accuracy of AI for detecting oral mucosal lesions ranged from 74 to 100%, while that for clinicians un-aided by AI ranged from 61 to 98%. Pooled diagnostic odds ratio for studies which evaluated AI for diagnosing or discriminating potentially malignant lesions was 155 (95% confidence interval 23-1019), while that for cancerous lesions was 114 (59-221). CONCLUSIONS: AI may assist in oral mucosa lesion screening while the expected accuracy gains or further health benefits remain unclear so far. CLINICAL RELEVANCE: Artificial intelligence assists oral mucosa lesion screening and may foster more targeted testing and referral in the hands of non-specialist providers, for example. So far, it remains unclear if accuracy gains compared with specialized can be realized.