Introduction: Analysis of smile is imperative in the diagnosis and treatment planning phases of aesthetic dentistry.Aim: To evaluate the components of smile among students of a dental institution.Methods: Frontal view digital photographs with posed smile of 157 dental students were assessed using Adobe Photoshop7.0. Smile characteristics evaluated included; smile line, smile arc, smile design, upper lip curvature, labiodental relationship and number of teeth displayed. Data were analyzed using SPSS version 23.0. Pearson chi-square test was used to determine the gender based differences for various parameters.Results: Average smile line (43.3%), consonant smile arcs (45.2%), cuspid smiles (45.9%), upward lip curvature (43.9%), maxillary anterior teeth not covered by lower lip (60.5%) and teeth displayed up to first premolars (35.7%). Gender based differences were not statistically significant except for smile arc (pvalue = 0.02) and number of teeth displayed (pvalue < 0.001). There was a significant relationship between lip curvature and smile pattern (pvalue < 0.001) and lip curvature and smile arc (pvalue = 0.01) revealing that upward lip curvature was associated with commissure type smiles and consonant smile arcs.Conclusions: The smile characteristics should be considered before beginning the aesthetic treatment of the patient to obtain adequate results in oral rehabilitation.

Objective: This study underscores the transformative role of Artificial Intelligence (AI) in healthcare, particularly the promising applications of Large Language Models (LLMs) in the delivery of post-operative dental care. The aim is to evaluate the performance of an embedded GPT model and its comparison with ChatGPT-3.5 turbo. The assessment focuses on aspects like response accuracy, clarity, relevance, and up-to-date knowledge in addressing patient concerns and facilitating informed decision-making. Material and methods: An embedded GPT model, employing GPT-3.5-16k, was crafted via GPT-trainer to answer postoperative questions in four dental specialties including Operative Dentistry & Endodontics, Periodontics, Oral & Maxillofacial Surgery, and Prosthodontics. The generated responses were validated by thirty-six dental experts, nine from each specialty, employing a Likert scale, providing comprehensive insights into the embedded GPT model’s performance and its comparison with GPT3.5 turbo. For content validation, a quantitative Content Validity Index (CVI) was used. The CVI was calculated both at the item level (I-CVI) and scale level (S-CVI/Ave). To adjust I-CVI for chance agreement, a modified kappa statistic (K*) was computed. Results: The overall content validity of responses generated via embedded GPT model and ChatGPT was 65.62% and 61.87% respectively. Moreover, the embedded GPT model revealed a superior performance surpassing ChatGPT with an accuracy of 62.5% and clarity of 72.5%. In contrast, the responses generated via ChatGPT achieved slightly lower scores, with an accuracy of 52.5% and clarity of 67.5%. However, both models performed equally well in terms of relevance and up-to-date knowledge. Conclusion: In conclusion, embedded GPT model showed better results as compared to ChatGPT in providing post-operative dental care emphasizing the benefits of embedding and prompt engineering, paving the way for future advancements in healthcare applications.

Objectives: 1- To compare the effectiveness of 1% Hydrogen peroxide, 0.2% Povidone-Iodine, 2% hypertonic saline and a novel solution Neem extract (Azardirachta indica) in reducing intra-oral viral load in COVID-19 positive patients. 2- To determine the salivary cytokine profiles of IL-2, IL-4, IL-6, IL-10, TNF-α, IFN-γ and IL- 17 among COVID-19 patients subjected to 1% Hydrogen peroxide, 0.2% Povidone-Iodine, 2% hypertonic saline or Neem extract (Azardirachta indica) based gargles. Trial design: This will be a parallel group, quadruple blind-randomised controlled pilot trial with an add on laboratory based study. Participants: A non-probability, purposive sampling technique will be followed to identify participants for this study. The clinical trial will be carried out at the Aga Khan University Hospital (AKUH), Karachi, Pakistan. The viral PCR tests will be done at main AKUH clinical laboratories whereas the immunological tests (cytokine analysis) will be done at the Juma research laboratory of AKUH. The inclusion criteria are laboratory-confirmed COVID-19 positive patients, male or female, in the age range of 18-65 years, with mild to moderate disease, already admitted to the AKUH. Subjects with low Glasgow coma score, with a history of radiotherapy or chemotherapy, who are more than 7 days past the onset of COVID- 19 symptoms, or intubated or edentulous patients will be excluded. Patients who are being treated with any form of oral or parenteral antiviral therapy will be excluded, as well as patients with known pre-existing chronic mucosal lesions such as lichen planus. Intervention and comparator: Group A (n=10) patients on 10 ml gargle and nasal lavage using 0.2% Povidone-Iodine (Betadiene® by Aviro Health Inc./ Pyodine® by Brooks Pharma Inc.) for 20-30 seconds, thrice daily for 6 days. Group B (n=10) patients will be subjected to 10 ml gargle and nasal lavage using 1% Hydrogen peroxide (HP® by Karachi Chemicals Products Inc./ ActiveOxy® by Boumatic Inc.) for 20-30 seconds, thrice daily for 6 days. Group C will comprised of (n=10) subjects on 10ml gargle and nasal lavage using Neem extract solution (Azardirachta indica) formulated by Karachi University (chemistry department laboratories) for 20-30 seconds, thrice daily for 6 days. Group D (n=10) patients will use 2% hypertonic saline (Plabottle® by Otsuka Inc.) gargle and nasal lavage for a similar time period. Group E (n=10) will serve as positive controls. These will be given simple distilled water gargles and nasal lavage for 20-30 seconds, thrice daily for six days. For nasal lavage, a special douche syringe will be provided to each participant. Its use will be thoroughly explained by the data collection officer. After each use, the patient is asked not to eat, drink, or rinse their mouth for the next 30 minutes. Main outcomes: The primary outcome is the reduction in the intra-oral viral load confirmed with real time quantitative PCR. Randomisation: The assignment to the study group/ allocation will be done using the sealed envelope method under the supervision of Clinical Trial Unit (CTU) of Aga Khan University, Karachi, Pakistan. The patients will be randomised to their respective study group (1:1:1:1:1 allocation ratio) immediately after the eligibility assessment and consent administration is done. Blinding (masking): The study will be quadruple-blinded. Patients, intervention provider, outcome assessor and the data collection officer will be blinded. The groups will be labelled as A, B, C, D or E. The codes of the intervention will be kept in lock & key at the CTU and will only be revealed at the end of study or if the study is terminated prematurely. Numbers to be randomised (sample size): As there is no prior work on this research question, so no assumptions for the sample size calculation could be made. The present study will serve as a pilot trial. We intend to study 50 patients in five study groups with 10 patients in each study group. For details, please refer to Fig. 1 for details. Trial Status: Protocol version is 7.0, approved by the department and institutional ethics committees and clinical trial unit of the university hospital. Recruitment is planned to start as soon as the funding is sanctioned. The total duration of the study is expected to be 6 months i.e. August 2020-January 2021. Trial registration: This study protocol was registered at www.clinicaltrials.gov on 10 April 2020 NCT04341688. Full protocol: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol. The study protocol has been reported in accordance with the Standard Protocol Items: Recommendations for Clinical Interventional Trials (SPIRIT) guidelines (Additional file 2).

Introduction: Journal clubs are integral to continuing medical education, promoting critical thinking and evidence-based learning. However, inconsistent engagement, reliance on faculty expertise, and the complexity of research articles can limit their effectiveness. Generative Artificial Intelligence (Gen AI), particularly Large Language Models (LLMs) offers a potential solution, but general-purpose LLMs may generate inaccurate responses (“hallucinations”). Retrieval-Augmented Generation (RAG) mitigates this by integrating AI-generated content with curated knowledge sources, ensuring more accurate and contextually relevant responses. This study explores the development and preliminary evaluation of a RAG-enhanced LLM to support journal club discussions. Materials and methods: A specialized LLM was deployed using Microsoft Azure’s GPT-4o. A vector database was created by embedding journal club articles using text-embedding-ada-002 (Version 2) for efficient information retrieval. A dedicated website provided user-friendly access. The study followed a design-based research (DBR) approach, engaging residents and faculty who interacted with the LLM before and during journal club sessions. Data collection included focus group discussions (FGDs) and questionnaires assessing engagement, usability, and impact. Results: The study involved a total of 13 residents and three faculty members as participants. 50% of residents reported a positive experience, while the rest had a neutral response, citing both advantages and limitations. The LLM improved article summarization, query responses, and engagement as reported by residents. Moreover, the faculty observed enhanced discussion quality and preparation whereas overall challenges included the need for precise prompts and occasional misleading responses. Conclusion: The study highlights the potential of a RAG-enhanced LLM to improve journal club engagement and learning. Future advancements in AI and open-source models may enhance accessibility, warranting further research.

Simulation-based learning (SBL) and augmented reality (AR) /virtual reality (VR) are increasingly adapted and investigated globally to aid traditional teaching methods of clinical skills in several fields of clinical dentistry. This cross-sectional study was, therefore, aimed to assess the availability of such technology to Prosthodontics postgraduate trainees in Pakistan, as well as their introspective views regarding the effectiveness of adapting to simulation-based learning methods. Total population sampling yielded a sample of 200 participants. Weblink of a pre-validated questionnaire designed using REDCap 0.001 version 11.0.3 software was disseminated to Prosthodontics postgraduate trainees across Pakistan with the ethical review board’s approval. Trainees were inquired regarding the availability of simulation-based learning and augmented/virtual reality technology in their centres and whether they receive regular training. The authors further explored trainees’ perceptions regarding the utility and effectiveness of adopting simulation-based training in the postgraduate education system. Frequencies of categorical responses were reported. Differences in data from all provinces of Pakistan were studied with Fisher’s exact. Ninety completed responses were analysed with SPSS 23.0. A few trainees (n = 18, 20%) were familiar with Augmented Reality. No participant reported having Augmented Reality in their centre. Though simulation-based learning was available to 38 (42.2%) of participants across Pakistan, with differences in distribution among provinces (p = 0.001), only 24% of them received regular simulator training. Participants believe that SBL can reduce procedure-related risks (72.2%). Combining simulation-based learning with traditional teaching was favoured by 81.1% of participants. A varied availability of SBL was observed across provinces, whereas AR was unavailable to Prosthodontics postgraduate trainees in Pakistan. Stakeholders and regulatory bodies should continually strive to incorporate technological advancements of simulation-based learning and Augmented/Virtual Reality in teaching strategies to improve Prosthodontics postgraduate students’ academic and clinical training quality. Not applicable due to the cross-sectional nature of the study.

Due to the overlapping aetiology of Gestational Diabetes Mellitus (GDM) and Periodontal disease (PD), which are prevalent metabolic disorder and chronic inflammatory disorder in pregnant women respectively, they are often at risk of developing both diseases simultaneously. This study aims to evaluate the association of periodontal disease and gestational diabetes mellitus among post-partum women who delivered within 24 h at private tertiary care hospital, Karachi, Pakistan. Analytical cross sectional study with sample size of 178 by non- probability purposive sampling, a total of 101 postpartum women (57%) were diagnosed with periodontal disease and 50 (28%) were GDM positive. Of those who had PD, 35% (n = 35/101) were GDM positive. An insignificant association of the prevalence ratio of GDM in women with periodontal disease was found. [PR = 1.7; 95% CI: 0.2–3.2; p-value 0.07] A statistically significant association was found between the prevalence ratio of GDM in women with obesity. It was 2.6 times compared to women who were not obese (p value < 0.01, 95% CI: 1.3–5.1). There is insignificant association found between the prevalence ratio of GDM in women with periodontal disease in our setting. Women who are overweight or tend to gain weight should be closely monitored and guided to take dietary measures.