Tumors of Maxillary Salivary Glands

AIM OF THE STUDY: Salivary gland tumors constitute 3% of all head and neck malignancies. Tumors of minor salivary gland constitute 9-23%. Minor salivary glands are distributed throughout the oral cavity and also in paranasal sinuses. Tumors of maxillary salivary glands are heterogeneous in nature, the etiology of which remains unclear.The aim of this study is to evaluate the surgical,surgical and radiotherapeutic outcome, recurrence and prognosis in patients with maxillary salivary glands tumors. METHODOLOGY: This study is a prospective and retrospective study of tumors of maxillary salivary glands. The study spreads from 2008-2014. Fifteen patients were treated in our institution, eight patients were males and six patients were females. Adenoid cystic carcinoma represented 6 cases. Mucoepidermoid carcinoma represented 3 cases. Pleomorphic adenoma and inverted papilloma represented 2 cases each. Polymorphous low grade adenocarcinoma and carcinoma EX pleomorphic adenoma represented 1 case each. 11 cases underwent maxillectomy, 4 cases underwent wide local excision. The patients were followed up and details regarding surgical, surgical radiotherapeutic outcomes, recurrence and prognosis were evaluated. RESULTS: Palatal tumors are more aggressive. Adenoid cystic carcinoma is associated with distant metastasis and perineural invasion. Mucoepidermoid carcinoma is associated with recurrence. Clear 3-dimensional surgical margins results in good prognosis. T-stage does not influence the surgical outcome. The tumor involvement of nerve, bone, soft tissue and the grade of tumor influence the surgical outcome. CONCLUSION: Any palatal tumor must be considered as minor salivary gland tumor until proven otherwise. Earlier the treatment initiated better is the prognosis. Radiotherapy must be included in the treatment of minor salivary gland tumors, so as not to miss any therapeutic benefits, even though controversies exist

Automatic detection of tuberculosis using VGG19 with seagull-algorithm.

Due to various reasons, the incidence rate of communicable diseases in humans is steadily rising, and timely detection and handling will reduce the disease distribution speed. Tuberculosis (TB) is a severe communicable illness caused by the bacterium Mycobacterium-Tuberculosis (M. tuberculosis), which predominantly affects the lungs and causes severe respiratory problems. Due to its significance, several clinical level detections of TB are suggested, including lung diagnosis with chest X-ray images. The proposed work aims to develop an automatic TB detection system to assist the pulmonologist in confirming the severity of the disease, decision-making, and treatment execution. The proposed system employs a pre-trained VGG19 with the following phases: (i) image pre-processing, (ii) mining of deep features, (iii) enhancing the X-ray images with chosen procedures and mining of the handcrafted features, (iv) feature optimization using Seagull-Algorithm and serial concatenation, and (v) binary classification and validation. The classification is executed with 10-fold cross-validation in this work, and the proposed work is investigated using MATLAB® software. The proposed research work was executed using the concatenated deep and handcrafted features, which provided a classification accuracy of 98.6190% with the SVM-Medium Gaussian (SVM-MG) classifier

A customized VGG19 network with concatenation of deep and handcrafted features for brain tumor detection

Brain tumor (BT) is one of the brain abnormalities which arises due to various reasons. The unrecognized and untreated BT will increase the morbidity and mortality rates. The clinical level assessment of BT is normally performed using the bio-imaging technique, and MRI-assisted brain screening is one of the universal techniques. The proposed work aims to develop a deep learning architecture (DLA) to support the automated detection of BT using two-dimensional MRI slices. This work proposes the following DLAs to detect the BT: (i) implementing the pre-trained DLAs, such as AlexNet, VGG16, VGG19, ResNet50 and ResNet101 with the deep-features-based SoftMax classifier; (ii) pre-trained DLAs with deep-features-based classification using decision tree (DT), k nearest neighbor (KNN), SVM-linear and SVM-RBF; and (iii) a customized VGG19 network with serially-fused deep-features and handcrafted-features to improve the BT detection accuracy. The experimental investigation was separately executed using Flair, T2 and T1C modality MRI slices, and a ten-fold cross validation was implemented to substantiate the performance of proposed DLA. The results of this work confirm that the VGG19 with SVM-RBF helped to attain better classification accuracy with Flair (>99%), T2 (>98%), T1C (>97%) and clinical images (>98%)

Rapid isolation of high molecular weight DNA from single dry preserved adult beetle of Cryptolaemus montrouzieri for polymerase chain reaction (PCR) amplification

For studying genetic diversity in populations of predatory coccinellid, Cryptolaemus montrouzieri Mulsant (Coccinellidae: Coleoptera), our attempts to isolate high quality DNA from individual adult beetle using several previously reported protocols and even modifications were quite unsuccessful as the insect size was small and was preserved at -20°C as dry specimen. Here we describe a simple, rapid and efficient method of isolating high-quality intact genomic DNA with reduced protein contamination for polymerase chain reaction (PCR) amplification from a single, dry preserved specimen of adult Cryptolaemus. The procedure features macerating and mixing the single adult specimen of Cryptoalemus with cationic detergent cetyltrimethylammonium bromide (CTAB) in the homogenization buffer, two chloroform-isoamylalcohol extractions and an alcohol precipitation. RNA contamination was eliminated with RNAse treatment. The purity of DNA was high since the A260/A280 ratio ranged from 1.78 to 1.97. The isolated DNA was used as template for PCR, and the results were evaluated by comparing with different preserved samples.Key words: Rapid isolation, quality DNA, dry preserved specimens, Cryptolaemus montrouzieri

Evaluation of gellan gum fluid gels as modified release oral liquids

Oral liquids are often preferred for drug administration to patients for whom swallowing is difficult, however formulating modified release versions can be challenging. A potential route to achieve modified release in oral liquids is by using fluid (sheared) gels formed by introducing a shear field during gelation in gel-forming biopolymers. These fluid gels can act as pourable viscoelastic fluids but retain true gel micro/nano structure. Here, we have demonstrated that fluid gels have potential as paediatric oral liquids preventing release of ibuprofen in simulated gastric fluid. Subsequent release at pH 7.4 was affected by the duration of exposure and magnitude of acid pH with a linear relationship between onset of release and the preceding acidic exposure duration. Delayed release was a result of increasing gel stiffness, a consequence of the acidity of the initial release media and exposure time. A much faster release rate was measured when exposure time in acid was 10 min compared with 60 min. This study highlights the potential to design fluid gels that are tuned to have a specified stiffness at a particular pH and exposure time. This could enable the preparation oral liquids with modified release behaviour

Priprava i in vitro karakterizacija plutajućih zrnaca acetohidroksamske kiseline za iskorjenjivanje H. pylori

Gellan based floating beads of acetohydroxamic acid (AHA) were prepared by the ionotropic gellation method to achieve controlled and sustained drug release for treatment of Helicobacter pylori infection. The prepared beads were evaluated for diameter, surface morphology and encapsulation efficiency. Formulation parameters like concentrations of gellan, chitosan, calcium carbonate and the drug influenced the in vitro drug release characteristics of beads. Drug and polymer interaction studies were carried out using differential scanning calorimetry. Chitosan coating increased encapsulation efficiency of the beads and reduced the initial burst release of the drug from the beads. Kinetic treatment of the drug release data revealed a matrix diffusion mechanism. Prepared floating beads showed good antimicrobial activity (in vitro H. pylori culture) as potent urease inhibitors. In conclusion, an oral dosage form of floating gellan beads containing AHA may form a useful stomach site specific drug delivery system for the treatment of H. pylori infection.Metodom ionotropskog želiranja pripravljena su plutajuća zrnca acetohidroksamske kiseline (AHA) na bazi gelana za kontrolirano i usporeno oslobađanje ljekovite tvari, namijenjena za liječenje infekcija uzrokovanih Helicobacter pylori. Pripravljenim zrncima proučavani su dijametar, površinska morfologija i sposobnost inkapsuliranja. Koncentracija gelana, kitozana, kalcijeva karbonata i ljekovite tvari utjecala je na oslobađanje in vitro. Interakcija između ljekovite tvari i polimera praćena je diferencijalnom pretražnom kalorimetrijom. Oblaganje zrnaca kitozanom povećalo je učinkovitost inkapsuliranja i smanjilo početno naglo oslobađanje. Oslobađanje ljekovite tvari slijedilo je mehanizam difuzije matriksa. Plutajuća zrnca s AHA pokazala su antimikrobno djelovanje in vitro na kulturi H. pylori kao snažni inhibitori ureaze. Može se zaključiti da su plutajuća zrnca s AHA na bazi gelana pogodna za specifičnu isporuku u želucu te korisna u terapiji infekcija uzrokovanih H. pylori

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Brain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance-Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon's-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation procedures considered in this article. The ANFIS classifier obtained an accuracy of 94.51% on the used ISLES and real clinical images. (C) 2019 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences

Formulation and evaluation of floating mucoadhesive alginate beads for targetingHelicobacter pylori

Objectives: There are various obstacles in the eradication of Helicobacter.pylori (H. pylori) infections, including low antibiotic levels and poor accessibility of the drug at the site of the infection. This study describes the preparation and characterisation of novel floating-mucoadhesive alginate beads loaded with clarithromycin (CMN) for delivery to the gastric mucosa to improve the eradication of this micro-organism. Methods: Calcium alginate beads were prepared by ionotropic gelation. The formulation was modified through addition of oil and coating with chitosan in order to improve floating, mucoadhesion and modify drug release. Key findings: SEM confirmed the sphericity of the beads with X-ray microtomography (XμMT) showing the 3D structure of the beads with the layered internal structure of the bead and the even distribution of the drug within the bead. This formulation combined two gastro-retentive strategies and these formulations produced excellent in vitro floating, mucoadhesive and drug release characteristics. Enhanced stability of the beads in phosphate buffer raises a potential for the modified formulations to be targeted to regions of higher pH within the gastrointestinal tract with a higher pH. Drug release from these beads was sustained through an unstirred mucin layer simulating in vivo conditions under which the H. pylori resides in the gastric mucosa. Conclusions: This novel formulation will ensure retention for a longer period in the stomach than conventional formulations and control drug release, ensuring high local drug concentrations, leading to improved eradication of the bacteria

Deep-learning framework to detect lung abnormality - A study with chest X-Ray and lung CT scan images

Lung abnormalities are highly risky conditions in humans. The early diagnosis of lung abnormalities is essential to reduce the risk by enabling quick and efficient treatment. This research work aims to propose a Deep-Learning (DL) framework to examine lung pneumonia and cancer. This work proposes two different DL techniques to assess the considered problem: (i) The initial DL method, named a modified AlexNet (MAN), is proposed to classify chest X-Ray images into normal and pneumonia class. In the MAN, the classification is implemented using with Support Vector Machine (SVM), and its performance is compared against Softmax. Further, its performance is validated with other pre-trained DL techniques, such as AlexNet, VGG16, VGG19 and ResNet50. (ii) The second DL work implements a fusion of handcrafted and learned features in the MAN to improve classification accuracy during lung cancer assessment. This work employs serial fusion and Principal Component Analysis (PCA) based features selection to enhance the feature vector. The performance of this DL frame work is tested using benchmark lung cancer CT images of LIDC-IDRI and classification accuracy (97.27%) is attained. (c) 2019 Elsevier B.V

Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network

Hepatic granuloma develops in the early stage of liver cirrhosis which can seriously injury liver health. At present, the assessment of medical microscopic images is necessary for various diseases and the exploiting of artificial intelligence technology to assist pathology doctors in pre-diagnosis is the trend of future medical development. In this article, we try to classify mice liver microscopic images of normal, granuloma-fibrosis 1 and granuloma-fibrosis2, using convolutional neural networks (CNNs) and two conventional machine learning methods: support vector machine (SVM) and random forest (RF). On account of the included small dataset of 30 mice liver microscopic images, the proposed work included a preprocessing stage to deal with the problem of insufficient image number, which included the cropping of the original microscopic images to small patches, and the disorderly recombination after cropping and labeling the cropped patches In addition, recognizable texture features are extracted and selected using gray the level co-occurrence matrix (GLCM), local binary pattern (LBP) and Pearson correlation coefficient (PCC), respectively. The results established a classification accuracy of 82.78% of the proposed CNN based classifiers to classify 3 types of images. In addition, the confusion matrix figures out that the accuracy of the classification results using the proposed CNNs based classifiers for the normal class, granuloma-fibrosisl, and granuloma-fibrosis2 were 92.5%, 76.67%, and 79.17%, respectively. The comparative study of the proposed CNN based classifier and the SVM and RF proved the superiority of the CNNs showing its promising performance for clinical cases