Evaluating a method of creating artificial caries in typodont teeth for teaching Class III cavity preparations.

OBJECTIVES: The purpose of the study was to report a step-by-step process of creating artificial caries typodont teeth and to determine the perception and efficacy of their use in preclinical operative training. METHODS: Artificial caries material comprised of commercially available hide glue and chocolate powder for more realistic coloring was embedded into the distolingual of #9 ModuPRO plastic typodont teeth. First-year dental students having no clinical experience in excavating Class III cavity preparations were divided into two groups. Group BA prepared conventional typodont teeth (CTT) first, then artificial caries typodont teeth. Group AB prepared the ACT first, then CTT. The preps were scored employing a rubric used in the operative dentistry course class. A feedback questionnaire was conducted to rate students' satisfaction regarding the use of ACT and CTT. The Mann-Whitney U-test was used to compare the scores between groups ACT-CTT and CTT-ACT and the Chi-Square test was used to evaluate the positive and negative questionnaire responses. RESULTS: The two groups showed no significant difference in grades and no significant changes in their scores regardless of which order they prepped the teeth (P > 0.05). The questionnaire heavily favored the use of artificial caries typodont teeth (P < 0.05). CONCLUSIONS: The artificial caries typodont teeth protocol described in this study was feasible when implemented at the preclinical laboratory instruction level with positive questionnaire feedback from dental students.

Differential Regulation of Intracisternally Injected Angiotensin II-Induced Mechanical Allodynia and Thermal Hyperalgesia in Rats.

The present study examined the underlying mechanisms of mechanical allodynia and thermal hyperalgesia induced by the intracisternal injection of angiotensin (Ang) II. Intracisternal Ang II injection decreased the air puff threshold and head withdrawal latency. To determine the operative receptors for each distinct type of pain behavior, we intracisternally injected Ang II receptor antagonists 2 h after Ang II injection. Losartan, an Ang II type 1 receptor (AT1R) antagonist, alleviated mechanical allodynia. Conversely, PD123319, an Ang II type 1 receptor (AT2R) antagonist, blocked only thermal hyperalgesia. Immunofluorescence analyses revealed the co-localization of AT1R with the astrocyte marker GFAP in the trigeminal subnucleus caudalis and co-localization of AT2R with CGRP-positive neurons in the trigeminal ganglion. Intracisternal pretreatment with minocycline, a microglial inhibitor, did not affect Ang II-induced mechanical allodynia, whereas L-α-aminoadipate, an astrocyte inhibitor, significantly inhibited Ang II-induced mechanical allodynia. Furthermore, subcutaneous pretreatment with botulinum toxin type A significantly alleviated Ang II-induced thermal hyperalgesia, but not Ang II-induced mechanical allodynia. These results indicate that central Ang II-induced nociception is differentially regulated by AT1R and AT2R. Thus, distinct therapeutic targets must be regulated to overcome pain symptoms caused by multiple underlying mechanisms.

Uncertainty-Aware Portfolio Management With Risk-Sensitive Multiagent Network.

As deep neural networks (DNNs) have gained considerable attention in recent years, there have been several cases applying DNNs to portfolio management (PM). Although some researchers have experimentally demonstrated its ability to make a profit, it is still insufficient to use in real situations because existing studies have failed to answer how risky investment decisions are. Furthermore, even though the objective of PM is to maximize returns within a risk tolerance, they overlook the predictive uncertainty of DNNs in the process of risk management. To overcome these limitations, we propose a novel framework called risk-sensitive multiagent network (RSMAN), which includes risk-sensitive agents (RSAs) and a risk adaptive portfolio generator (RAPG). Standard DNNs do not understand the risks of their decision, whereas RSA can take risk-sensitive decisions by estimating market uncertainty and parameter uncertainty. Acting as a trader, this agent is trained via reinforcement learning from dynamic trading simulations to estimate the distribution of reward and via unsupervised learning to assess parameter uncertainty without labeled data. We also present an RAPG that can generate a portfolio fitting the user's risk appetite without retraining by exploiting the estimated information from the RSAs. We tested our framework on the U.S. and Korean real financial markets to demonstrate the practicality of the RSMAN.