Pleomorphic adenoma rehabilitative treatment in growing up patient: a 20-years follow-up

OBJECTIVE: Although tumors of minor salivary glands are rare, the pleomorphic adenoma is the most common pathology among the benign neoplasm and can be found with high prevalence in the junction between hard palate and soft palate. Most of the maxillary tumors are surgically treated through either a total or partial maxillectomy. However, surgical defects lead to both clinical and psychologic disorders for the patient. A postoperative obturator prosthesis is a good option in patients who underwent maxillectomy. It allows to restore both masticatory and speaking functions, as well as aesthetic appearance. When reconstruction of the surgical site is possible, an implant-supported prosthesis can be considered to guarantee a better function and aesthetic's rehabilitation. CASE REPORT: This clinical report presents the prosthetic rehabilitation of a patient who underwent maxillectomy because of a pleomorphic adenoma of hard palate minor salivary glands. The patient was treated with a palatal obturator prosthesis first and with an implant-supported prosthesis after surgical site's reconstruction and complete healing. CONCLUSIONS: The rehabilitation of the patient after maxillectomy through both these devices was an excellent option and provided clinical benefits, improving the patient's quality of life, allowing the patient's reinsertion into societ

Valproic acid induces apoptosis, p16INK4A upregulation and sensitization to chemotherapy in human melanoma cells.

It is known that melanoma develops as a consequence of multifactorial alterations. To date several studies indicate the effective implication of p16 as a tumor suppressor gene with a major role in either the development or progression of human melanoma. Deregulation of melanoma cell growth has been widely associated with mutations in the p16-cyclin D/cdk4-pRb pathway. Recently anticancer therapies are focused on restoration of p16 CDK inhibitory function and other proteins unregulated in melanoma cell cycle pathway (e.g., c-myc, p27). A combined strategy for restoration of normal homeostasis in the melanoma skin with targeted delivery of apoptosis-inducing agents does not seems to be far obtained. New class of antitumoral agents are emerging: histone deacetylase (HDAC) inhibitors have attracted much interest because of their ability to arrest cell growth, induce cell differentiation, and in some cases, induce apoptosis of cancer cells. Recently, attention has been focused on the ability of HDAC inhibitors to induce perturbation in cell cycle regulatory protein (e.g., p21(CIP1)) and down-regulation of survival signalling pathway. In the present study, we have examined the effect of valproic acid (VPA) on M14 human melanoma cell line. Here we observed that VPA induces cell cycle arrest and apoptosis sensitising melanoma cells to cis-platin and etoposide treatment. IC(50) dose (2.99 mM) of VPA was able to induce G(1) arrest (up to 75%) in association with upregulation of p16, p21 and cyclin-D1 related to Rb ipo-phosphorilation. In addition VPA activated apoptosis (50%) in M14 cells, when given alone or in combination with antitumoral agents. The ability of valproic acid to reestablished the G(1) pathway in melanoma cells suggests a potential application of VPA in melanoma therapeutic protocols

Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods

Background The prediction of human gene–abnormal phenotype associations is a fundamental step toward the discovery of novel genes associated with human disorders, especially when no genes are known to be associated with a specific disease. In this context the Human Phenotype Ontology (HPO) provides a standard categorization of the abnormalities associated with human diseases. While the problem of the prediction of gene–disease associations has been widely investigated, the related problem of gene–phenotypic feature (i.e., HPO term) associations has been largely overlooked, even if for most human genes no HPO term associations are known and despite the increasing application of the HPO to relevant medical problems. Moreover most of the methods proposed in literature are not able to capture the hierarchical relationships between HPO terms, thus resulting in inconsistent and relatively inaccurate predictions. Results We present two hierarchical ensemble methods that we formally prove to provide biologically consistent predictions according to the hierarchical structure of the HPO. The modular structure of the proposed methods, that consists in a “flat” learning first step and a hierarchical combination of the predictions in the second step, allows the predictions of virtually any flat learning method to be enhanced. The experimental results show that hierarchical ensemble methods are able to predict novel associations between genes and abnormal phenotypes with results that are competitive with state-of- the-art algorithms and with a significant reduction of the computational complexity. Conclusions Hierarchical ensembles are efficient computational methods that guarantee biologically meaningful predictions that obey the true path rule, and can be used as a tool to improve and make consistent the HPO terms predictions starting from virtually any flat learning method. The implementation of the proposed methods is available as an R package from the CRAN repository

Gingival reactive lesions in orally rehabilitated patients by free revascularized flap

The aim is to discuss four cases of gingival reactive hyperplastic lesions in patients with a history of excision of oral neoplastic lesions and rehabilitation by a free revascularized flap of the iliac crest. One female and 3 male patients were referred due to the presence of exophytic lesions at the rehabilitated sites. The clinical examination revealed that the poor oral hygiene was the common trigger factor in all the cases, in addition to trauma from the upper left second molar in the first case, pericoronitis related to a partially erupted lower right third molar in the third case, and poor stability of an upper removable partial denture in the fourth case. All the cases were subjected to elimination of these suspected triggering factors, exclusion of dysplasia, excisional biopsy by CO2 laser, and five follow-up visits. The histological examination of all the cases confirmed the diagnosis of pyogenic granuloma. These presented cases suggest that the limitations in oral functions and maintaining the oral hygiene measures following the free revascularized flap reconstruction surgery probably played a role in the development of gingival reactive hyperplastic lesions with presence of trigger factors such as local trauma, chronic infection, or inadequate prosthesis

TELEMETRY-BASED RESOURCE SCALING AND TRAFFIC OPTIMIZATION IN CLOUD INTERCONNECTS

Software-defined cloud interconnect (SDCI) connections are established through different providers with specific or proprietary characteristics. However, no existing scaling technology guarantees the automatic provisioning and teardown of an entire network of circuits, from a software-defined wide area network (SD-WAN) router to the cross connect that is required to link to a cloud provider. Presented herein are techniques that not only abstract different implementations (including hypervisors, software images, releases etc.) but also provide for the ability to detect congestion and implement end-to-end remediation from branch devices to cloud workloads. The presented techniques optimize the costs of resources (such as SD-WAN routers and middle-mile connections) without compromising the level of service that is offered by different middle-mile providers; allow an SDCI’s automatic scaling of VMs and connections to be tied to the specific network SLA requirements of a user-application combination; and support networking solutions that enable a customer to build automated, scalable, and reliable interconnections that deliver richer cloud-based applications and services to enterprise customers while increasing operational efficiency

New Frontiers in the Catalytic Synthesis of Levulinic Acid: From Sugars to Raw and Waste Biomass as Starting Feedstock

Levulinic acid (LA) is one of the top bio-based platform molecules that can be converted into many valuable chemicals. It can be produced by acid catalysis from renewable resources, such as sugars, lignocellulosic biomass and waste materials, attractive candidates due to their abundance and environmentally benign nature. The LA transition from niche product to mass-produced chemical, however, requires its production from sustainable biomass feedstocks at low costs, adopting environment-friendly techniques. This review is an up-to-date discussion of the literature on the several catalytic systems that have been developed to produce LA from the different substrates. Special attention has been paid to the recent advancements on starting materials, moving from simple sugars to raw and waste biomasses. This aspect is of paramount importance from a sustainability point of view, transforming wastes needing to be disposed into starting materials for value-added products. This review also discusses the strategies to exploit the solid residues always obtained in the LA production processes, in order to attain a circular economy approac

Gene expression modeling through positive Boolean functions

In the framework of gene expression data analysis, the selection of biologically relevant sets of genes and the discovery of new subclasses of diseases at bio-molecular level represent two significant problems. Unfortunately, in both cases the correct solution is usually unknown and the evaluation of the performance of gene selection and clustering methods is difficult and in many cases unfeasible. A natural approach to this complex issue consists in developing an artificial model for the generation of biologically plausible gene expression data, thus allowing to know in advance the set of relevant genes and the functional classes involved in the problem. In this work we propose a mathematical model, based on positive Boolean functions, for the generation of synthetic gene expression data. Despite its simplicity, this model is sufficiently rich to take account of the specific peculiarities of gene expression, including the biological variability, viewed as a sort of random source. As an applicative example, we also provide some data simulations and numerical experiments for the analysis of the performances of gene selection methods