Objective:To study the clinicopathological features of adrenocortical oncocytic tumors (ACOT) and to compare the diagnostic values of Lin-Weiss-Bisceglia (LWB) score and Helsinki score.Methods:Forty-four cases of ACOT diagnosed at Beijing Friendship Hospital, China from March 2008 to July 2019 were histologically analyzed to evaluate their malignant potential (benign versus malignant) according to two scoring criteria. Immunohistochemical studies (EnVision method) were also used.Results:There were 23 males and 21 females with an average age of 46 years. Histologically, the tumor cells were arranged in trabecular, chrysanthemum-shaped, glandular and microcapsule structures, while clear cells were rare or absent. Most of the tumor cells were moderately atypical, and intranuclear inclusion bodies were conspicuous. Immunohistochemical staining showed that tumor cells were positive for Melan A, inhibin, Syn and calretinin. The average proliferation index was 3% in benign ACOT, about 5% in ACOT of malignant potential, and>20% in malignant ACOT. According to the LWB score, 61.4% (27/44) of the tumors were on the left side and had multiple lesions. The percentage of benign ACOT was 59.1% (26/44), malignant potential 6.8% (3/44), malignant 34.1% (15/44), respectively. Among the 15 malignant ACOT, the mitotic figures>5/50 HPF were found in 13 cases, necrosis in 11 cases and capsule invasion in 10 cases. According to the Helsinki score, 65.9% (29/44) of the tumors were benign, and 34.1% (15/44) were malignant. There was no significant difference between the two scoring standards ( P>0.05). During the follow-up of 9 to 144 months, 31 patients survived without disease and 13 patients relapsed or had metastasis. Conclusions:ACOT more likely be benign than malignant. The left side is more common. Malignant tumors are prone to recurrence and metastasis. The morphological parameters (high mitotic index, necrosis, and capsular invasion) in the LWB scoring standards combined with immunohistochemical parameters (Ki-67) in the Helsinki score are helpful for the diagnosis of malignant ACOT and are important predictors of poor prognosis.

The drug-target protein interaction prediction can be used for the discovery of new drug effects. Recent studies often focus on the prediction of an independent matrix filling algorithm, which apply a single algorithm to predict the drug-target protein interaction. The single-model matrix-filling algorithms have low accuracy, so it is difficult to obtain satisfactory results in the prediction of drug-target protein interaction. AdaBoost algorithm is a strong multiple classifier combination framework, which is proved by the past researches in classification applications. The drug-target interaction prediction is a matrix filling problem. Therefore, we need to adjust the matrix filling problem to a classification problem before predicting the interaction among drug-target protein. We make full use of the AdaBoost algorithm framework to integrate several weak classifiers to improve performance and make accurate prediction of drug-target protein interaction. Experimental results based on the metric datasets show that our algorithm outperforms the other state-of-the-art approaches and classical methods in accuracy. Our algorithm can overcome the limitations of the single algorithm based on machine learning method, exploit the hidden factors better and improve the accuracy of prediction effectively.

The colonization of microorganisms planted on the surface of teeth and restoration materials is the main cause of oral disease and treatment failure. How to improve the antibacterial properties of dental materials is a hot topic in dentistry. Nano-sized antibacterial materials have attracted much attention. Among them, metal and metal oxide nanoparticles are prominent due to their strong and broad-spectrum antibacterial activity. Thus, in recent years, many studies have used metal and metal oxide nanoparticles to develop antimicrobial dental materials for resin restoration, root canal therapy, orthodontic treatment, and implant surface and removable denture repair and have found that the antibacterial properties of nano-sized materials are significantly enhanced. However, the mechanical properties and esthetic properties of the modified materials are affected, so it is still necessary to explore appropriate modification methods. In addition, most of the experiments are carried out in vitro, which cannot accurately simulate the oral environment. Therefore, the antibacterial effect, cytotoxicity and immune response of these materials in vivo still need further research and exploration. This paper reviewed the potential antibacterial mechanisms and the safety of those nanoparticles and their applications in dentistry.