Myelodysplastic Syndrome With Synchronous Gastric Cancer: When The Symptoms Suggest Something Else.

Although myelodysplastic syndromes have a clear definition in theory, the morphologic dysplasia associated with ineffective hematopoiesis may be subtle and difficult to recognize and can commonly be mimicked by systemic conditions, such as infections, autoimmune disorders, nutritional deficiencies, toxic factors and non-hematological malignancies. However, myelodysplastic syndromes may truly coexist with other systemic diseases, which can be masked when the patient's symptoms are attributed exclusively to myelodysplastic syndromes without further investigation. To better illustrate this, we herein describe two cases associated with synchronous gastric cancers.

Synchronous Oral Paracoccidioidomycosis And Pulmonary Tuberculosis In An Immunocompetent Patient.

Paracoccidioidomycosis (PCM) and tuberculosis (TB) are chronic granulomatous infectious diseases, in which the main form of contraction is through inhalation of the microorganism-Paracoccidioides brasiliensis and Mycobacterium tuberculosis. Oral involvement of PCM is observed in up to 70 % of the cases and usually presents clinically as ulcerations with granular surface showing tiny hemorrhagic areas. Oral presentation of TB is rare with prevalence smaller than 0.5 % of all cases. Clinical presentation of oral TB mainly consists of single ulcers with irregular limits and necrotic base. A 70-year-old immunocompetent man presented simultaneously oral PCM and pulmonary TB. Medical history revealed a previous diagnosis of pulmonary TB; however, even under treatment for TB, the patient remained with oral lesions and intense pulmonary fibrosis. The physician requested P. brasiliensis serological analysis, which resulted positive. Although the combination of PCM and TB has been reported in the literature, it is still considered an uncommon condition and their diagnosis may represent a challenge to healthcare professionals because of the similarity between their clinical and radiological presentations.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.