Background: There is a general assumption that periodontal disease is highly prevalent among patients with chronic renal failure undergoing hemodialysis. The aim of the study to estimate the frequency of periodontitis in patients on hemodialysis among a sample of the Egyptian population, as well as the correlation between different clinical parameters of periodontal status with serum creatinine and blood urea. This may rule out the bidirectional relationship between periodontitis and renal failure in patients on hemodialysis. Methods: The study was conducted on 263 hemodialysis patients (165 males and 98 females) at three dialysis centers in Benha Governorate, Egypt (Benha Hospital, Tukh hospital, Qalyub hospital). Periodontal parameters including plaque index (PI), gingival index (GI), clinical attachment level (CAL), and probing pocket depth (PPD) had been recorded in these patients. Serum urea and creatinine levels had been measured, the data had been collected and undergone statistical analysis. Results: Frequency of periodontitis was 85.6% with stage III is the most prevalent stage. There was a significant positive strong correlation between age and periodontitis stage (r_s = 0.707, p < 0.001). There was a positive correlation between clinical parameters and serum creatinine level. Conclusion: In the present study, a high frequency of periodontitis had been found among ESRD patients on hemodialysis in the severe form (stage III) periodontitis. There was a significant direct correlation between the severity of periodontitis and CAL with a duration of hemodialysis. There was a weak insignificant association between periodontal indices (PD, BOP, and plaque score) and duration of hemodialysis.

Objectives: This study aimed to assess the prevalence and risk factors associated with late xerostomia and hyposalivation in head and neck cancer (HNC) patients after radiotherapy (RT). Materials and methods: An observational, multicentric cross-sectional study was conducted on 260 HNC patients attending various radiation centers for follow up 1-year post-treatment. Clinical assessments included the Subjective Dry Mouth Score (SXI), Clinical Oral Dryness Score (CODS), and Unstimulated Salivary Flow Rate (UWS). Results: Xerostomia was reported by 78% of patients, with higher severity in those over 50 years (Mean ± SD: 13.53 ± 1.09). Women showed lower salivary flow (UWS: r = 0.556, p < 0.0001) and higher xerostomia scores (SXI: r = 0.337, CODS: r = 0.359) than men. Tumor site correlated strongly with xerostomia (SXI: r = 0.894, p < 0.001), with oral cavity tumors showing more severe effects than nasopharyngeal tumors. Higher RT dose and fraction were negatively associated with UWS ( r = -0.537, p < 0.0001) and positively correlated with SXI ( r = 0.293) and CODS ( r = 0.405, p < 0.0001). The regression models showed that xerostomia severity is significantly predicted by advanced tumor stage, female gender, older age, and higher radiation dose exposure. Conclusions: The study reveals a high prevalence of xerostomia and hyposalivation among HNC survivors. Increased xerostomia severity and decreased salivary flow were significantly associated with advanced tumor stage, higher radiation doses, and concurrent chemoradiotherapy. Clinical Relevance: Understanding risk factors can guide early interventions and personalized management to enhance long-term oral health outcomes.

Background: Future dental applications of artificial intelligence (AI) are anticipated to be widely adopted across all dental specialities. However, there are some concerns among many users about the accuracy of the given information. Therefore, this study aimed to investigate postgraduate’ dental students’ perception, usage, and concerns towards AI systems’ applications. Materials and methods: An online self-administered survey, consisting of 19 closed-ended questions in the English language, and a 3-point Likert-type scale was used to obtain a simple and straightforward response from participants in a “forced-choice” response format that was distributed to postgraduate dental students in the faculty of dentistry of multiple Universities. Results: Younger participants and BDS holders are more likely to use AI-based software (p < 0.001), as well as showing more optimism about AI’s potential to advance dentistry, whereas PhD holders are more skeptical about its integral role in healthcare (p < 0.001). Speciality influenced AI adoption significantly, with Endodontics showing the highest percentage (52.4% for 1 + years of AI usage; p = 0.006). Concerns about AI reliability and originality in research vary significantly by level of education and Speciality (p < 0.05). Younger participants show greater belief in AI’s potential for major advancements in dentistry (p < 0.001). Conclusions: Postgraduate dental students generally perceive AI positively, recognizing its potential to enhance care. Usage remains moderate, with higher adoption in specialities like Endodontics and Periodontics. Concerns include AI’s accuracy, ethical implications, and integration challenges, highlighting the need for further education and research. Clinical trial number: Not applicable.

Background: Patients with head and neck cancer (HNC) receiving radiation therapy (RT) have complications affecting both general and dental health. While RT is effective against HNC, it negatively impacts oral tissues, including changes in periodontal attachment. This study aimed to evaluate the frequency of periodontal disease among HNC patients after RT in a sample of the Egyptian population, as well as to assess the associated risk factors. Materials and methods: A prospective cross-sectional study was conducted on 189 HNC patients attending a radiation center in Cairo, Egypt. Eligible patients underwent a full periodontal examination including plaque index (PI), bleeding on probing (BOP), clinical attachment level (CAL), and probing pocket depth (PPD). Subsequently, Clinical Oral Dryness Score (CODS), salivary flow rate and body mass index (BMI) were assessed to examine associations with periodontal health. Results: Periodontal disease was diagnosed in 183 (96.8%) HNC patients. Among them, 174 (95.08%) cases were diagnosed as periodontitis and 9 (4.9%) as gingivitis. The distribution of periodontitis different stages was as follows: Stage I was found in 21 (12.07%) cases, Stage II in 30 (17.24%), Stage III in 55 (31.61%), and Stage IV in 68 (39.08%). The correlation between the number of RT fractions and periodontal disease severity was not statistically significant (p = 0.837). However, there was a strong negative correlation between periodontitis stage and both body mass index (BMI) and salivary flow rate. In contrast, strong positive correlations were observed between periodontitis severity and RT dose, plaque percentage, and Clinical Oral Dryness Score (CODS). Conclusions: The frequency of periodontitis in the studied sample of HNC patients post RT was 95.08%, reflecting a notably high burden. The most prevalent periodontitis stage was the severe form (Stage IV). Higher periodontitis severity was found to be positively associated with RT dose, plaque percentage, and CODS, suggesting factors to plan future investigation on preventive care in HNC patients. While causality cannot be inferred due to the study design, these findings could be useful in developing more effective clinical management strategies in future research. Trial registration: The study was retrospectively registered on 29/10/2024 at ClinicalTrials.gov (NCT06667362).

Background: The automated grading of clinical short-answer questions using large language models (LLMs) could alleviate faculty workload and improve the immediacy of feedback in dental education. However, evidence on the capacity of LLMs for rubric-based grading in dentistry remains limited. Therefore, this study aimed to compare the grading reliability and error patterns of two LLMs, ChatGPT-4 and the open-weight DeepSeek-3, against expert human evaluators. Materials and methods: In a retrospective cross-sectional study with comparative validation design, we analyzed 2,358 short-answer responses from 262 undergraduate dental students (across nine clinical questions). All responses were analyzed, then human-graded by three calibrated subject-matter experts (SME) (intraclass correlation coefficient [ICC] = 0.84) to provide a reference. Each LLM was provided a 12-point analytic rubric to guide the grading, but was not provided any prior examples of the grading task (i.e., a zero-shot prompt). We assessed agreement using ICC, Pearson correlation, Cohen’s kappa, and mixed-effects models, and examined error tiers (≤ 1, 2–3, > 3 points) across Bloom’s levels and response styles. Results: In this dataset, DeepSeek-3 obtained an ICC of 0.87 compared with ChatGPT-4 which obtained an ICC of 0.64. DeepSeek-3 matched exactly with human scores in 43.3% of cases and was within ± 1 point in 62.4%, compared with 35.5% and 44.1% for ChatGPT-4. High-error rates (> 3 points) were 7.5% for DeepSeek-3 vs. 26.9% for ChatGPT-4 (χ², p < 0.01). DeepSeek-3’s agreement was consistent across cognitive levels and response verbosity, while ChatGPT-4’s accuracy on higher-level and verbose responses was significantly lower ( p < 0.01). Both models exhibited an optimistic bias by over-scoring incorrect answers. Conclusions: DeepSeek-3 showed fewer large-magnitude errors and better agreement with human graders compared to ChatGPT-4, suggesting its potential value for large-scale AI-assisted assessment for dental education. Since both models can over-score on incorrect results, human-in-the-loop oversight is necessary for high-stakes applications. Further work should evaluate performance across more courses, institutions, and languages, as well as examine the effects of model calibration, bias reduction, and external validation before considering the broader integration of LLMs into dental education.

Purpose Radiation-induced oral mucositis (RIOM) severely impacts patients with head and neck cancer (HNC) undergoing radiotherapy, often leading to pain and malnutrition. L-arginine and glutamine are immune-enhancing amino acids with potential benefits in wound healing and inflammation control. This study evaluated the efficacy of L-arginine versus L-glutamine oral suspensions in managing RIOM. Methods In this triple-blind, randomized controlled trial, 69 HNC patients with RIOM were allocated to three groups (n = 23 each): Group I (L-arginine 5 g + maltodextrin 5 g), Group II (glutamine 5 g + maltodextrin 5 g), or Group III (maltodextrin 10 g). Outcomes, assessed at weeks 2, 5, and 7 of radiotherapy, included the WHO oral mucositis scale, Pain Visual Analogue Scale (Pain-VAS), body mass index (BMI), and Oral Health Impact Profile (OHIP-14) questionnaire. Results By week 5, WHO scale scores differed significantly among groups (p < 0.001), with arginine and glutamine groups exhibiting lower mucositis severity than the maltodextrin group. Pain-VAS scores at weeks 5 and 7 were significantly lower in the arginine and glutamine groups compared to maltodextrin (p = 0.004 and p < 0.001, respectively). By 7th week of radiotherapy, BMI was significantly decreased in the maltodextrin group than in either the arginine (p = 0.028) or glutamine (p = 0.001) groups, indicative of treatment-mediated weight loss. In contrast, the BMI over time in the arginine (p = 0.87) and glutamine (p = 0.170) groups were almost constant. This indicates that compared to maltodextrin alone, both amino acid supplements prevented a decline in BMI during radiotherapy. OHIP-14 scores improved significantly in the arginine and glutamine groups at weeks 5 and 7 (p < 0.001), indicating better quality of life. Conclusions Both L-arginine and glutamine significantly reduced RIOM severity, pain, and weight loss compared to maltodextrin, while improving quality of life in patients with head and neck cancer. Although no statistically significant difference was found between the two, a higher proportion of patients receiving L-arginine achieved complete healing by week 7, suggesting a potential late advantage. These findings support the use of both amino acids as viable options for symptom management during radiotherapy. Trial registration ClinicalTrials.gov (NCT06764420), registered 08/01/2024.