OBJECTIVE: Our study aimed to assess phytochemical of C.nutans leaves, isolate breast cancer stem cells and determine the cytotoxic effects of the ethanolic extract and water extract of C.nutans leaves on breast cancer stem cells at 24, 48, and 72 h of observation.

METHODS: We underwent the cytotoxic test by using MTT assay and isolated breast cancer stem cells by using MACS and validated them by mammosphere test.

RESULTS: We found alkaloids, flavonoids, glycosides and tannins in simplicia and all extracts. BCSCs was valid with the diameter of the mammosphere BCSCs was > 60 μm. The IC50 values of 100%, 60%, 40%, 20% EE, and WE of C.nutans leaves were 227.30; 46.05; 31.12; 98.54, and 16.16 μg/ml respectively in the first 24 hours. In administering WE of C.nutans leaves, BCSCs viability was decreased at 24,48 and 72 hours of observation, namely 69.29±26%; 75.82 ± 21.02% and 38.94±9.34 % (p < 0.0001).

INTRODUCTION: Artificial intelligence (AI) is a relatively new technology that has widespread use in dentistry. The AI technologies have primarily been used in dentistry to diagnose dental diseases, plan treatment, make clinical decisions, and predict the prognosis. AI models like convolutional neural networks (CNN) and artificial neural networks (ANN) have been used in endodontics to study root canal system anatomy, determine working length measurements, detect periapical lesions and root fractures, predict the success of retreatment procedures, and predict the viability of dental pulp stem cells. Methodology. The literature was searched in electronic databases such as Google Scholar, Medline, PubMed, Embase, Web of Science, and Scopus, published over the last four decades (January 1980 to September 15, 2021) by using keywords such as artificial intelligence, machine learning, deep learning, application, endodontics, and dentistry.

RESULTS: The preliminary search yielded 2560 articles relevant enough to the paper's purpose. A total of 88 articles met the eligibility criteria. The majority of research on AI application in endodontics has concentrated on tracing apical foramen, verifying the working length, projection of periapical pathologies, root morphologies, and retreatment predictions and discovering the vertical root fractures.

METHODOLOGY: An electronic search was conducted in PubMed / Medline, Scopus, Google Scholar, and Web of Science databases until January 2023 to retrieve related studies. "Root canal morphology," "Saudi Arabia," "Micro-CT," and "cone-beam computed tomography" were used as keywords. A modified version of previously published risk of bias assessment tool was used to determine the quality assessment of included studies.

RESULTS: The literature search revealed 47 studies that matched the criteria for inclusion, out of which 44 studies used cone beam computed tomography (CBCT) and three were micro-computed tomography (micro-CT) studies. According to the modified version of risk of bias assessment tool, the studies were categorized as low, moderate, and high risk of bias. A total of 47,612 samples were included which comprised of either maxillary teeth (5,412), or mandibular teeth (20,572), and mixed teeth (21,327). 265 samples were used in micro-CT studies while 47,347 teeth samples were used in CBCT studies. Among the CBCT studies, except for three, all the studies were retrospective studies. Frequently used imaging machine and software were 3D Accuitomo 170 and Morita's i-Dixel 3D imaging software respectively. Minimum and maximum voxel sizes were 75 and 300 μm, Vertucci's classification was mostly used to classify the root canal morphology of the teeth. The included micro-CT studies were in-vitro studies where SkyScan 1172 X-ray scanner was the imaging machine with pixel size ranging between 13.4 and 27.4 μm. Vertucci, Ahmed et al. and Pomeranz et al. classifications were applied to classify the root canal morphology.

MATERIAL AND METHODS: A sample of 85 patients diagnosed with superficial bladder tumours was selected to be used in fitting the non-mixture cure model. In order to estimate the parameters of the suggested model, which takes into account the presence of a cure rate, censored data, and covariates, we utilized the maximum likelihood estimation technique using R software version 3.5.7.

RESULT: Upon conducting a comparison of various parametric models fitted to the data, both with and without considering the cure fraction and without incorporating any predictors, the EE distribution yields the lowest AIC, BIC, and HQIC values among all the distributions considered in this study, (1191.921/1198.502, 1201.692/1203.387, 1195.851/1200.467). Furthermore, when considering a non-mixture cure model utilizing the EE distribution along with covariates, an estimated ratio was obtained between the probabilities of being cured for placebo and thiotepa groups (and its 95% confidence intervals) were 0.76130 (0.13914, 6.81863).

METHODS: It was a cross-sectional study. The Malay elderly aged 60 years and above were selected through convenient sampling to give a total of 230 respondents. The Depression, Anxiety, and Stress Scale (DASS-21) was used to assess the symptoms of depression, anxiety, and stress. Bivariate analyses were performed using chi-square tests and multiple logistic regression analyses were conducted to determine the association between the factors and each of the mental health statuses assessed.

RESULTS: The results showed that the prevalence of depression, anxiety, and stress among the elderly respondents was 27.8%, 22.6%, and 8.7%, respectively. The significant factors for depression were single elderly (Adjusted OR = 3.27, 95%CI 1.66, 6.44), living with family (Adjusted OR = 4.98, 95%CI 2.05, 12.10), and poor general health status (Adjusted OR = 2.28, 95%CI 1.20, 4.36). Living with family was the only significant factor for anxiety (Adjusted OR = 2.68, 95%CI 1.09, 6.57). There was no significant factor for stress.