Maxillofacial reconstruction in a pediatric patient with Osteosarcoma

Osteosarcoma is a bone tumor that consists of malignant cells that produce immature bone. Is a bone tumor that develops during periods of rapid growth in adolescents and young adults. It is the most common type of bone cancer in children and adolescents. The diagnosis and treatment of patients with osteosarcoma requires a multidisciplinary team approach. Resection of maxillary tumours remains a surgical challenge due to the possible aesthetic and functional secuelae. We present herein the case of a 15 year-old female with an osteoblastic osteosarcoma of the left maxilla. It was treated with eight cycles of neoadjuvant chemotherapy, followed by a total left maxillectomy. Resection was performed through a modified Ferguson-Weber approach, using a titanium mesh to reconstruct the orbital base and the maxillary process. A palatal obturator was placed at the same time. The use of a three-dimensional model by stereolithography is extremely helpful in planning and performing the maxillectomy, as well as the facial reconstructio

A single case report of granular cell tumor of the tongue successfully treated through 445 nm diode laser

Oral granular cell tumor (GCT) is a relatively rare, benign lesion that can easily be misdiagnosed. Particularly, the presence of pseudoepitheliomatous hyperplasia might, in some cases, lead to the hypothesis of squamous cell carcinoma. Surgical excision is the treatment of choice. Recurrence has been reported in up to 15% of cases treated with conventional surgery. Here, we reported a case of GCT of the tongue in a young female patient, which was successfully treated through 445 nm diode laser excision. Laser surgery might reduce bleeding and postoperative pain and may be associated with more rapid healing. Particularly, the vaporization effect on remnant tissues could eliminate GCT cells on the surgical bed, thus hypothetically leading to a lower rate of recurrence. In the present case, complete healing occurred in 1 week, and no recurrence was observed after 6 months. Laser surgery also allows the possibility to obtain second intention healing. Possible laser-induced histopathological artifacts should be carefully considered

Ep-CAM (MOC-31) expression in tooth germ and ameloblastoma

Ep-CAM, a transmembrane glycoprotein expressed in most epithelium in normal conditions, has diverse roles in these tissues, including in cell adhesion, proliferation, differentiation, cell cycle regulation, migration and intracellular signaling. It is also over-expressed in most malignant neoplasia, participating in the initiation, progression, and metastatic dissemination of the tumor. The expression and roles of this protein in oral neoplasia, particularly in odontogenic tumors, remain unestablished. The objective of this study consisted in analyzing the expression of this protein in ameloblastoma and tooth germ

EGFR, CD10 and proliferation marker Ki67 expression in ameloblastoma: possible role in local recurrence

<p>Abstract</p> <p>Background</p> <p>Ameloblastoma is an odontogenic neoplasm characterized by local invasiveness and tendency towards recurrence.</p> <p>Aims</p> <p>Studying the role played by EGFR, CD10 and Ki67 in the recurrence of ameloblastoma.</p> <p>Methods</p> <p>This study was carried out on 22 retrospective cases of mandibular ameloblastoma from the period from Jan 2002 to Jan 2008 with follow up period until Jan 2011 (3 to 8 years follow up peroid). Archival materials were obtained from pathology department, Mansoura university. Paraffin sections of tumor tissue from all cases were submitted for routine H&E stains and immunohistochemistry using EGFR, CD10 and Ki67 monoclonal antibodies. Statistical analysis using of clinical data for all patients, tumor type, EGFR, CD10 and Ki67 expression in relation to recurrence were evaluated.</p> <p>Results</p> <p>Among the 22 cases, 10 cases were males and 12 were females with sex ratio 1:1.2. Age ranged from 34 to 59 years old with a mean age 44.18 year. Five cases showed local recurrence within studied period and proved by biopsy. No statistically significant relation was found between local recurrence and patient age, tumor size, tumor type, EGFR expression. There was a significant relation between CD10 expression as well as Ki67 labelling index and recurrence (P value = 0.003, 0.000 respectively).</p> <p>Conclusion</p> <p>Evaluation of CD10 and Ki67 status together with conventional histological evaluation can help in providing more information about the biologic behavior of the tumor, while EGFR could be a target of an expanding class of anticancer therapies.</p> <p>Since ameloblastomas are EGFR-positive tumors, anti-EGFR agents could be considered to reduce the size of large tumors and to treat unresectable tumors that are in close proximity to vital structures.</p> <p>Virtual Slides</p> <p>The virtual slide(s) for this article can be found here:</p> <p><url>http://www.diagnosticpathology.diagnomx.eu/vs/1902106905645651</url></p

Artificial intelligence within the interplay between natural and artificial computation:Advances in data science, trends and applications

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.BMS - Pfizer(U01 AG024904). Spanish Ministry of Science, projects: TIN2017-85827-P, RTI2018-098913-B-I00, PSI2015-65848-R, PGC2018-098813-B-C31, PGC2018-098813-B-C32, RTI2018-101114-B-I, TIN2017-90135-R, RTI2018-098743-B-I00 and RTI2018-094645-B-I00; the FPU program (FPU15/06512, FPU17/04154) and Juan de la Cierva (FJCI-2017–33022). Autonomous Government of Andalusia (Spain) projects: UMA18-FEDERJA-084. Consellería de Cultura, Educación e Ordenación Universitaria of Galicia: ED431C2017/12, accreditation 2016–2019, ED431G/08, ED431C2018/29, Comunidad de Madrid, Y2018/EMT-5062 and grant ED431F2018/02. PPMI – a public – private partnership – is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc

Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.MCIU - Nvidia(UMA18-FEDERJA-084

The <i>N</i>-myristoylome of <i>Trypanosoma cruzi</i>

Protein N-myristoylation is catalysed by N-myristoyltransferase (NMT), an essential and druggable target in Trypanosoma cruzi, the causative agent of Chagas’ disease. Here we have employed whole cell labelling with azidomyristic acid and click chemistry to identify N-myristoylated proteins in different life cycle stages of the parasite. Only minor differences in fluorescent-labelling were observed between the dividing forms (the insect epimastigote and mammalian amastigote stages) and the non-dividing trypomastigote stage. Using a combination of label-free and stable isotope labelling of cells in culture (SILAC) based proteomic strategies in the presence and absence of the NMT inhibitor DDD85646, we identified 56 proteins enriched in at least two out of the three experimental approaches. Of these, 6 were likely to be false positives, with the remaining 50 commencing with amino acids MG at the N-terminus in one or more of the T. cruzi genomes. Most of these are proteins of unknown function (32), with the remainder (18) implicated in a diverse range of critical cellular and metabolic functions such as intracellular transport, cell signalling and protein turnover. In summary, we have established that 0.43–0.46% of the proteome is N-myristoylated in T. cruzi approaching that of other eukaryotic organisms (0.5–1.7%)

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Measurement of ϒ production in pp collisions at √s = 2.76 TeV

The production of ϒ(1S), ϒ(2S) and ϒ(3S) mesons decaying into the dimuon final state is studied with the LHCb detector using a data sample corresponding to an integrated luminosity of 3.3 pb−1 collected in proton–proton collisions at a centre-of-mass energy of √s = 2.76 TeV. The differential production cross-sections times dimuon branching fractions are measured as functions of the ϒ transverse momentum and rapidity, over the ranges pT &#60; 15 GeV/c and 2.0 &#60; y &#60; 4.5. The total cross-sections in this kinematic region, assuming unpolarised production, are measured to be σ (pp → ϒ(1S)X) × B ϒ(1S)→μ+μ− = 1.111 ± 0.043 ± 0.044 nb, σ (pp → ϒ(2S)X) × B ϒ(2S)→μ+μ− = 0.264 ± 0.023 ± 0.011 nb, σ (pp → ϒ(3S)X) × B ϒ(3S)→μ+μ− = 0.159 ± 0.020 ± 0.007 nb, where the first uncertainty is statistical and the second systematic