The predictive value of earnings, cash flows and accruals in the period surrounding the Asian financial crisis: Evidence from Hong Kong, Malaysia, Singapore and Thailand

10.1111/j.1467-646X.2005.00116.xJournal of International Financial Management and Accounting163165-19

Model-based design analysis for programmable remote center of motion in minimally invasive surgery

10.1109/RAMECH.2010.55132092010 IEEE Conference on Robotics, Automation and Mechatronics, RAM 201084-8

A bioabsorbable microclip for laryngeal microsurgery: Design and evaluation

10.1016/j.actbio.2012.03.051Acta Biomaterialia872835-284

Development of a microclip for laryngeal microsurgery: Initial animal studies

10.1002/lary.23280Laryngoscope12281809-1814LARY

Real-time simulation of bleeding and smoke effects in virtual reality environment

10.2316/P.2013.791-131Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2013276-28

Caries Risk Prediction Models in a Medical Health Care Setting

Despite development of new technologies for caries control, tooth decay in primary teeth remains a major global health problem. Caries risk assessment (CRA) models for toddlers and preschoolers are rare. Among them, almost all models use dental factors (e.g., past caries experience) to predict future caries risk, with limited clinical/community applicability owing to relatively uncommon dental visits compared to frequent medical visits during the first year of life. The objective of this study was to construct and evaluate risk prediction models using information easily accessible to medical practitioners to forecast caries at 2 and 3 y of age. Data were obtained from the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) mother-offspring cohort. Caries was diagnosed using modified International Caries Detection and Assessment System criteria. Risk prediction models were constructed using multivariable logistic regression coupled with receiver operating characteristic analyses. Imputation was performed using multiple imputation by chained equations to assess effect of missing data. Caries rates at ages 2 y (n = 535) and 3 y (n = 721) were 17.8% and 42.9%, respectively. Risk prediction models predicting overall caries risk at 2 and 3 y demonstrated area under the curve (AUC) (95% confidence interval) of 0.81 (0.75-0.87) and 0.79 (0.74-0.84), respectively, while those predicting moderate to extensive lesions showed 0.91 (0.85-0.97) and 0.79 (0.73-0.85), respectively. Postimputation results showed reduced AUC of 0.75 (0.74-0.81) and 0.71 (0.67-0.75) at years 2 and 3, respectively, for overall caries risk, while AUC was 0.84 (0.76-0.92) and 0.75 (0.70-0.80), respectively, for moderate to extensive caries. Addition of anterior caries significantly increased AUC in all year 3 models with or without imputation (all P < 0.05). Significant predictors/protectors were identified, including ethnicity, prenatal tobacco smoke exposure, history of allergies before 12 mo, history of chronic maternal illness, maternal brushing frequency, childbearing age, and so on. Integrating oral-general health care using medical CRA models may be promising in screening caries-susceptible infants/toddlers, especially when medical professionals are trained to "lift the lip" to identify anterior caries lesions

Caries Risk Prediction Models in a Medical Health Care Setting

10.1177/0022034520913476Journal of Dental Research997787 - 796JDREAGUSTO (Growing up towards Healthy Outcomes

Application of an ex-vivo drug sensitivity platform towards achieving complete remission in a refractory T-cell lymphoma

10.1038/s41408-020-0276-7Blood Cancer Journal101