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Fulltext Optimisation of polymerase chain reaction conditions for detection of mineralization markers in isolated odontoblasts from human teeth

Wafa' Zahari, Ong, Wei Shen, Tan, Hong Jin, Azlina Ahmad, Khairul Bariah Ahmad Amin Noordin, Saaid Al Shehadat

Archives of Orofacial Sciences, 2017;12(1):45-54.
MyJurnal

The present study aimed to determine the best polymerase chain reaction (PCR) conditions for
amplification of odontoblast markers; alkaline phosphatase (ALP), dentin matrix protein 1 (DMP1), dentin
sialophosphoprotein (DSPP) and osteopontin (OPN). Informed consent was obtained from the individuals
prior to tooth extraction. RNA was extracted from odontoblasts obtained from extracted teeth using
innuPREP RNA Mini kit (Analytik Jena, Germany). Five selected target factors in enhancing PCR: primer
concentration, extension time, number of cycles, annealing time, and annealing temperature were
manipulated to yield the correct size of amplicons. One step reverse transcriptase PCR reactions were
performed using MyTaq One-Step RT-PCR kit (Bioline, USA) with a C1000 Thermal Cycler (Bio-Rad, USA)
in a 25 µL reaction, keeping the amount of 2 ng/µL RNA, 0.25 µL reverse transcriptase, 0.5 µL RiboSafe
Rnase inhibitor and 1X MyTaq One-Step Mix, constant. The optimal conditions were determined to be
400nM of primers for DMP1 and DSPP, 200 nM for ALP and OPN; 30 seconds of extension time and 35
PCR cycles for all genes; 10 seconds of annealing time for ALP, DMP1 and DSPP, 7 seconds for OPN. The
annealing temperature were 56.4°C for ALP, 58.6°C for DMP1, 52.7°C for DSPP, and 56.3°C for OPN,
respectively. The optimized PCR protocols produced the correct size of odontoblast markers.
Fulltext Assessing airflow limitation among smokers in a primary care setting

Chean KY, Rahim FF, Chin JS, Choi XL, Liew KW, Tan CC, et al.

Malays J Med Sci, 2018 May;25(3):78-87.
PMID: 30899189 MyJurnal DOI: 10.21315/mjms2018.25.3.8

Background: Many smokers have undiagnosed chronic obstructive pulmonary disease (COPD), and yet screening for COPD is not recommended. Smokers who know that they have airflow limitation are more likely to quit smoking. This study aims to identify the prevalence and predictors of airflow limitation among smokers in primary care.
Methods: Current smokers ≥ 40 years old who were asymptomatic clinic attendees in a primary care setting were recruited consecutively for two months. We used a two-step strategy. Step 1: participants filled in a questionnaire. Step 2: Assessment of airflow limitation using a pocket spirometer. Multiple logistic regression was utilised to determine the best risk predictors for airflow limitation.
Results: Three hundred participants were recruited. Mean age was 58.35 (SD 10.30) years old and mean smoking history was 34.56 pack-years (SD 25.23). One in two smokers were found to have airflow limitation; the predictors were Indian ethnicity, prolonged smoking pack-year history and Lung Function Questionnaire score ≤ 18. Readiness to quit smoking and the awareness of COPD were low.
Conclusions: The high prevalence of airflow limitation and low readiness to quit smoking imply urgency with helping smokers to quit smoking. Identifying airflow limitation as an additional motivator for smoking cessation intervention may be considered. A two-step case-finding method is potentially feasible.
Study site: primary care clinic (outpatient clinic), Pulau Pinang, Malaysia
Fulltext External Validation of an AI mHealth Tool for Gingivitis Detection among Older Adults at Daycare Centers: A Pilot Study

Chau RCW, Cheng ACC, Mao K, Thu KM, Ling Z, Tew IM, et al.

Int Dent J, 2025 Jan 25.
PMID: 39864975 DOI: 10.1016/j.identj.2025.01.008

OBJECTIVES: Periodontal disease is a significant public health concern among older adults due to its relationship with tooth loss and systemic health disease. However, there are numerous barriers that prevent older adults from receiving routine dental care, highlighting the need for innovative screening tools at the community level. This pilot study aimed first, to evaluate the accuracy of GumAI, a new mHealth tool that uses AI and smartphones to detect gingivitis, and the user acceptance of personalized oral hygiene instructions provided through the new tool, among older adults in day-care community centers.
METHODS: Participants were invited from 3 day-care community centers. Intraoral photographs were captured and assessed by both GumAI (test) and a panel consisting of 2 calibrated periodontists and a dentist (benchmark). Mean sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and F1 score were calculated to determine GumAI's diagnostic performance in comparison to the benchmark. User acceptance with this tool was assessed using 2 Rasch Theory-based 5-point Likert-type questions.
RESULTS: 44 participants were recruited out of 80 invited older adults. GumAI demonstrated a sensitivity of 0.93 and specificity of 0.50 compared to the panel's assessments, with a PPV of 0.90 and NPV of 0.56. The accuracy and F1 scores were 0.85 and 0.91, respectively. All participants expressed high acceptance of the process.
CONCLUSION: GumAI demonstrates high sensitivity, PPV, accuracy, and F1 score compared to the panel's assessments but falls relatively short in specificity and NPV. Despite this, the tool was highly accepted by older adults, indicating its potential to enhance gingivitis detection and oral hygiene management in community settings. Further refinements are necessary to improve specificity and validate usability measures.
CLINICAL RELEVANCE: This study may pave the way for broader applications of mHealth systems in community settings, enabling greater health coverage and addressing oral health disparities.

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