BackgroundDespite their high accuracy to recognize oral potentially malignant disorders (OPMDs) with cancer risk, non‐invasive oral assays are poor in discerning whether the risk is high or low. However, it is critical to identify the risk levels, since high‐risk patients need active intervention, while low‐risk ones simply need to be follow‐up. This study aimed at developing a personalized computational model to predict cancer risk level of OPMDs and explore its potential web application in OPMDs screening.MethodsEach enrolled patient was subjected to the following procedure: personal information collection, non‐invasive oral examination, oral tissue biopsy and histopathological analysis, treatment, and follow‐up. Patients were randomly divided into a training set (N = 159) and a test set (N = 107). Random forest was used to establish classification models. A baseline model (model‐B) and a personalized model (model‐P) were created. The former used the non‐invasive scores only, while the latter was incremented with appropriate personal features.ResultsWe compared the respective performance of cancer risk level prediction by model‐B, model‐P, and clinical experts. Our data suggested that all three have a similar level of specificity around 90%. In contrast, the sensitivity of model‐P is beyond 80% and superior to the other two. The improvement of sensitivity by model‐P reduced the misclassification of high‐risk patients as low‐risk ones. We deployed model‐P in web.opmd-risk.com, which can be freely and conveniently accessed.ConclusionWe have proposed a novel machine‐learning model for precise and cost‐effective OPMDs screening, which integrates clinical examinations, machine learning, and information technology.
In this randomized, controlled clinical trial to compare efficacy and safety, 41 patients with labial discoid lupus erythematosus (DLE) were randomized to 2 groups, either receiving tacrolimus 0.03% ointment (n = 22) or triamcinolone acetonide 0.1% cream (n = 19). Each patient was treated with 3, 2, and 1 daily doses in the first, second, and third weeks, respectively, for 1 course. After the 3 week treatment, patients with complete disappearance of erosion were followed up for 3 months. After the 3 week application, 20 participants in the tacrolimus group and 19 in the triamcinolone acetonide group completed the study. The rates of complete response were 70% and 89.5% in tacrolimus-treated and triamcinolone acetonide-treated patients, respectively, with no significant difference (P = .235). Reduction in erosion and erythema showed no significant difference between groups (P > .05). Final reduction in reticulation areas and numeric rating scale scores were significantly greater in the tacrolimus group than in the triamcinolone acetonide group (P = .013; P = .048, respectively). Only 1 patient receiving tacrolimus presented with slight discomfort. There was no significant difference in 3 month recurrence rate between the groups (P > .05). Topical tacrolimus is considered as effective as triamcinolone acetonide for the management of labial DLE.
One important mission of strategic defense is to develop an integrated layered Ballistic Missile Defense System (BMDS). Motivated by the queueing theory, we presented a work for the representation, modeling, performance simulation, and channels optimal allocation of the layered BMDS M/M/N queueing systems. Firstly, in order to simulate the process of defense and to study the Defense Effectiveness (DE), we modeled and simulated the M/M/N queueing system of layered BMDS. Specifically, we proposed the M/M/N/N and M/M/N/C queueing model for short defense depth and long defense depth, respectively; single target channel and multiple target channels were distinguished in each model. Secondly, we considered the problem of assigning limited target channels to incoming targets, we illustrated how to allocate channels for achieving the best DE, and we also proposed a novel and robust search algorithm for obtaining the minimum channel requirements across a set of neighborhoods. Simultaneously, we presented examples of optimal allocation problems under different constraints. Thirdly, several simulation examples verified the effectiveness of the proposed queueing models. This work may help to understand the rules of queueing process and to provide optimal configuration suggestions for defense decision-making.
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