Analysis of fetal dose using Optically Simulated Luminescence Dosimeter and ion chamber in randophantom for various radiotherapy techniques.

To analyse the fetal dose in all three trimesters in patients treated for brain tumors during pregnancy, a modified rando phantom representing three different trimesters was used with provisions for insertion of ion-chamber and Optically Simulated Luminescence Dosimeter (OSLD). The measurement regions were chosen at the level of fundus, umbilicus and pubis. Seven different treatment plans with 6FF and 6FFF beam energies were generated. Treating pregnant patients with brain tumors is safe irrespective of planning modalities except 3DCRT plan where the dose is 10.24 cGy.

An integrative machine learning framework for classifying SEER breast cancer.

Breast cancer is the commonest type of cancer in women worldwide and the leading cause of mortality for females. The aim of this research is to classify the alive and death status of breast cancer patients using the Surveillance, Epidemiology, and End Results dataset. Due to its capacity to handle enormous data sets systematically, machine learning and deep learning has been widely employed in biomedical research to answer diverse classification difficulties. Pre-processing the data enables its visualization and analysis for use in making important decisions. This research presents a feasible machine learning-based approach for categorizing SEER breast cancer dataset. Moreover, a two-step feature selection method based on Variance Threshold and Principal Component Analysis was employed to select the features from the SEER breast cancer dataset. After selecting the features, the classification of the breast cancer dataset is carried out using Supervised and Ensemble learning techniques such as Ada Boosting, XG Boosting, Gradient Boosting, Naive Bayes and Decision Tree. Utilizing the train-test split and k-fold cross-validation approaches, the performance of various machine learning algorithms is examined. The accuracy of Decision Tree for both train-test split and cross validation achieved as 98%. In this study, it is observed that the Decision Tree algorithm outperforms other supervised and ensemble learning approaches for the SEER Breast Cancer dataset.

Dosimetric evaluation of hybrid and volumetric-modulated arc therapy plan for left-sided chest wall irradiation in MONACO treatment planning system.

Background and Objective: Radiation induced toxicities in heart and lungs are diminishes the survival rate of cancer patients. The purpose of this study is to evaluate the dosimetric parameters of hybrid plans for chest wall irradiation of left breast carcinoma patients using Monaco treatment planning system and compare with the volumetric-modulated arc therapy (VMAT) treatment plans. Materials and Methods: Fifteen carcinoma left breast patients were randomly selected to evaluate the advantage of hybrid plan over VMAT. Hybrid plans were generated with 70% and 30% dose contribution from forward intensity-modulated radiotherapy and VMAT, respectively, whereas VMAT had been done with full prescription. Conformity and homogeneity indices were evaluated for target coverage between hybrid and VMAT plans. Results: Hybrid plan has proved its superiority over VMAT in terms of better organ at risk sparing and lesser low dose spillage and at the same time providing comparable target coverage. In low-dose spectrum, VMAT showed higher dose-volume than the hybrid plan. The maximum variation was found to be 44.75% at 7 Gy and the minimum dose difference was observed at 1 Gy (6.02%). Conclusion: This study suggests that the hybrid plan could be a better option for left-sided chest wall irradiation in regular clinical practice.

Tumor grading model employing geometric analysis of histopathological images with characteristic nuclei dictionary.

Histopathological study has been shown to improve diagnosis of various disease classifications effectively as any disease condition is correlated to characteristic set of changes in the tissue structure. This study aims at developing an automated neural network system for grading brain tumors (Glioblastoma Multiforme) from histopathological images within the Whole Slide Images (WSI) of hematoxylin and eosin (H&E) stains with significant accuracy. Hematoxylin channels are extracted from the histopathological image patches using color de-convolution. Cell nuclei are precisely segmented using three level Otsu thresholding. From each segmented image, nuclei boundaries are extracted to extract nucleus level features based on their shape and size. Geometric features including ellipse eccentricities, nucleus perimeter, area, and polygon edge counts are extracted using geometric algorithms to define the nuclei boundaries of the segmented image. These features are collected for a large number of nuclei and the nuclei are clustered using the K-Means algorithm in order to create a dictionary. One of the major contributions involves the creation of dictionary of a fixed number of representative cell nuclei to speed up patch level classification. This optimal dictionary is used for clustering extracted cell nuclei and a fixed length histogram of counts on different types of nuclei is obtained. The proposed system has been tested with a total of 239600 TCGA patches of GBM and 206000 patches of LGG collected from GDC data portal and it showed good diagnosis performance with auto-classification accuracy of 97.2% compared to other state-of-art methods. Our results on segmentation and classification are encouraging, with better attainment with regard to precision and accuracy in contrast with previous models. The auto grading proposed system will act as a potential guide for pathologists to make more accurate decisions.

Fuzzy based algorithms to predict MicroRNA regulated protein interaction pathways and ranking estimation in Arabidopsis thaliana.

In living organisms, the MicroRNAs act as an important role by controlling regulatory mechanisms, and likely manipulating the output of numerous protein-coding genes. Several computational databases, algorithms and tools have been developed to discover the miRNA target genes. But, the existing methods obtain poorer results in identification of miRNA target gene. Hence in this research work, integrated prediction scores is used to identify the microRNA target interactions and hybrid fuzzy algorithms are used to make final predictions. The proposed algorithms such as Fuzzy, Fuzzy + Support Vector Machine (SVM) and Fuzzy + SVM + Random Forest (RF) are used to conduct prediction by majority voting and it is compared with the existing techniques such as SVM, RF and Neural Network (NN) to demonstrate the performance of the proposed algorithms. Furthermore, the ranking features are estimated using the Arabidopsis thaliana microRNA sequence. From the experimental results, it is inferred that the proposed Fuzzy + SVM + RF algorithm performs superior than the existing ones.

PATSIM: Prediction and analysis of protein sequences using hybrid Knuth-Morris Pratt (KMP) and Boyer-Moore (BM) algorithm.

In phylogenomic profiling, the genomic context based methods are based on the observation that two or more proteins having the same pattern of presence or absence in many diverse genomes most likely have a functional link. In this research work, a tool (PATSIM) has been developed to predict the protein patterns based on the SOPM tool. In this tool, the secondary structure for CATH database protein sequences, predicted by the SOPM (Self Optimized Prediction Method) server is passed as input to fulfill objectives such as, (i) Predict the Amino Acid Pattern using the proposed Hybrid KMP and BM algorithm, (ii) Predict the physiochemical properties such as Hydrophobic Non-Polar ALKYL Amino Acid groups, Hydrophobic Non-Polar AROMATIC Amino Acid groups, Hydrophilic Polar Neutral Amino Acid groups, Hydrophilic Polar Acidic Amino Acid groups and Hydrophilic Polar Basic Amino Acid groups of protein sequence, (iii) Predict the secondary structure of protein where the structure of protein sequence is unknown, and (iv) Similarity analysis of protein sequence (structure unknown) with the CATH database. From the results, it is inferred that this tool effectively predicts the similarity between the sequences and also identifies the protein patterns for four secondary structural classes, namely Alpha Helix (h), Beta Sheet (e), Turn (t) and Coil (c). Based on the experimental results, it is inferred that this tool identifies the physiochemical properties of the protein sequence in an effective manner. The source code and its documentation for the PATSIM tool is freely available in the GitHub public repository (https://github.com/manimkn89/Protein-Sequence-Analysis).

Bacterial Foraging Optimization -Genetic Algorithm for Multiple Sequence Alignment with Multi-Objectives.

This research work focus on the multiple sequence alignment, as developing an exact multiple sequence alignment for different protein sequences is a difficult computational task. In this research, a hybrid algorithm named Bacterial Foraging Optimization-Genetic Algorithm (BFO-GA) algorithm is aimed to improve the multi-objectives and carrying out measures of multiple sequence alignment. The proposed algorithm employs multi-objectives such as variable gap penalty minimization, maximization of similarity and non-gap percentage. The proposed BFO-GA algorithm is measured with various MSA methods such as T-Coffee, Clustal Omega, Muscle, K-Align, MAFFT, GA, ACO, ABC and PSO. The experiments were taken on four benchmark datasets such as BAliBASE 3.0, Prefab 4.0, SABmark 1.65 and Oxbench 1.3 databases and the outcomes prove that the proposed BFO-GA algorithm obtains better statistical significance results as compared with the other well-known methods. This research study also evaluates the practicability of the alignments of BFO-GA by applying the optimal sequence to predict the phylogenetic tree by using ClustalW2 Phylogeny tool and compare with the existing algorithms by using the Robinson-Foulds (RF) distance performance metric. Lastly, the statistical implication of the proposed algorithm is computed by using the Wilcoxon Matched-Pair Signed- Rank test and also it infers better results.

Detection of overlapping protein complexes in gene expression, phenotype and pathways of Saccharomyces cerevisiae using Prorank based Fuzzy algorithm.

Proteins show their functional activity by interacting with other proteins and forms protein complexes since it is playing an important role in cellular organization and function. To understand the higher order protein organization, overlapping is an important step towards unveiling functional and evolutionary mechanisms behind biological networks. Most of the clustering algorithms do not consider the weighted as well as overlapping complexes. In this research, Prorank based Fuzzy algorithm has been proposed to find the overlapping protein complexes. The Fuzzy detection algorithm is incorporated in the Prorank algorithm after ranking step to find the overlapping community. The proposed algorithm executes in an iterative manner to compute the probability of robust clusters. The proposed and the existing algorithms were tested on different datasets such as PPI-D1, PPI-D2, Collins, DIP, Krogan Core and Krogan-Extended, gene expression such as GSE7645, GSE22269, GSE26923, pathways such as Meiosis, MAPK, Cell Cycle, phenotypes such as Yeast Heterogeneous and Yeast Homogeneous datasets. The experimental results show that the proposed algorithm predicts protein complexes with better accuracy compared to other state of art algorithms.

Interval-value Based Particle Swarm Optimization algorithm for cancer-type specific gene selection and sample classification.

Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM), K-nearest neighbor (KNN), Interval Valued Classification (IVC) and the improvised Interval Value based Particle Swarm Optimization (IVPSO) algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions.

In vitro susceptibility of filamentous fungi from mycotic keratitis to azole drugs.

OBJECTIVE: The in vitro antifungal activities of azole drugs viz., itraconazole, voriconazole, ketoconazole, econazole and clotrimazole were investigated in order to evaluate their efficacy against filamentous fungi isolated from mycotic keratitis. METHODS: The specimen collection was carried out from fungal keratitis patients attending Aravind eye hospital and Post-graduate institute of ophthalmology, Coimbatore, India and was subsequently processed for the isolation of fungi. The dilutions of antifungal drugs were prepared in RPMI 1640 medium. Minimum inhibitory concentrations (MICs) were determined and MIC50 and MIC90 were calculated for each drug tested. RESULTS: A total of 60 fungal isolates were identified as Fusarium spp. (n=30), non-sporulating moulds (n=9), Aspergillus flavus (n=6), Bipolaris spp. (n=6), Exserohilum spp. (n=4), Curvularia spp. (n=3), Alternaria spp. (n=1) and Exophiala spp. (n=1). The MICs of ketoconazole, clotrimazole, voriconazole, econazole and itraconazole for all the fungal isolates ranged between 16 µg/mL and 0.03 µg/mL, 4 µg/mL and 0.015 µg/mL, 8 µg/mL and 0.015 µg/mL, 8 µg/mL and 0.015 µg/mL and 32 µg/mL and 0.06 µg/mL respectively. From the MIC50 and MIC90 values, it could be deciphered that in the present study, clotrimazole was more active against the test isolates at lower concentrations (0.12-5 µg/mL) when compared to other drugs tested. CONCLUSION: The results suggest that amongst the tested azole drugs, clotrimazole followed by voriconazole and econazole had lower MICs against moulds isolated from mycotic keratitis.

The quenching effect of silver nanoparticles on 2-amino-3-bromo-1, 4-naphthoquinone using fluorescence spectroscopy.

Nanoparticles of noble metals belong to the most extensively studied colloidal systems in the field of nanoscience and nanotechnology. Silver nanoparticles of different sizes have been prepared with the chemical reduction method using sodium borohydride and characterized using optical absorption technique. Using optical absorption and fluorescence emission studies, the photo physical properties of 2-amino-3-bromo1, 4-naphthoquinone (ABNQ) on silver nanoparticle have been studied. Concentration of the silver nanoparticle has been evaluated and the particle size dependent interaction between silver nanoparticles and ABNQ has been studied. The fluorescence quantum yield of ABNQ with and without silver nanoparticles has been calculated. The Stern-Volmer quenching constants and the molar absorptivity have been evaluated.

Relationship of in vitro susceptibility to moxifloxacin and in vivo clinical outcome in bacterial keratitis.

BACKGROUND: For bacterial infections, the susceptibility to antibiotics in vitro has been associated with clinical outcomes in vivo, although the importance of minimum inhibitory concentration (MIC) has been debated. In this study, we analyzed the association of MIC on clinical outcomes in bacterial corneal ulcers, while controlling for organism and severity of disease at presentation. METHODS: Data were collected as part of a National Eye Institute-funded, randomized, controlled trial (the Steroids for Corneal Ulcers Trial [SCUT]). All cases enrolled in SCUT had a culture-positive bacterial corneal ulcer and received moxifloxacin. The MIC to moxifloxacin was measured by E test. Outcomes included best spectacle-corrected visual acuity, infiltrate/scar size, time to re-epithelialization, and corneal perforation. RESULTS: Five hundred patients with corneal ulcers were enrolled in the trial, and 480 were included in this analysis. The most commonly isolated organisms were Streptococcus pneumoniae and Pseudomonas aeruginosa. A 2-fold increase in MIC was associated with an approximately 0.02 logMAR decrease in visual acuity at 3 weeks, approximately 1 letter of vision loss on a Snellen chart (0.019 logMAR; 95% confidence interval [CI], .0040-.033; P = .01). A 2-fold increase in MIC was associated with an approximately 0.04-mm larger infiltrate/scar size at 3 weeks (0.036 mm; 95% CI, .010-.061; P = .006). After controlling for organism, a higher MIC was associated with slower time to re-epithelialization (hazards ratio, 0.92; 95% CI, .86-.97; P = .005). CONCLUSIONS: In bacterial keratitis, a higher MIC to the treating antibiotic is significantly associated with worse clinical outcomes, with approximately 1 line of vision loss per 32-fold increase in MIC. CLINICAL TRIALS REGISTRATION: NCT00324168.

Comparison of homogeneity indices for quantitative evaluation of dose homogeneity for IMRT treatments of head and neck cancers.

The purpose of this study was to evaluate different homogeneity indices for IMRT of head and neck cancers and to suggest the best representative homogeneity index for quantitative measure of dose homogeneity. In this study 3 different homogeneity indices were evaluated for 22 head and neck cancer patients receiving dynamic IMRT treatments on Clinac-DHX linear accelerator with 6 MV photon beam. IMRT planning was carried out with Helios software on Eclipse treatment planning system. H index, HI index and S index proposed by Yoon (2007) were calculated for these patients. The H-index, HI index and S index values varied between 1.024 to 1.112, 4.03 to 16.9 and 0.94 to 3.43 respectively. H index values for patient 5 and 10 are identical (H index-1.06) though different in DVH distributions but the S index values for these patients are different (1.36 and 2.01). Similarly the HI index for the patient 11 and 16 are identical in spite of different DVH distributions but S index values are different for patient 11 and 16. Since the S index represents the whole DVH curve unlike the conventional indices which depends on dose at a point, it is the better method to quantify the dose homogeneity. These results indicate that H and HI indices do not provide the accurate dose homogeneity information, but the S indices uniquely provide quantitative information about the dose homogeneity.

Eugenol inhibits cell proliferation via NF-κB suppression in a rat model of gastric carcinogenesis induced by MNNG.

The modulation of intracellular nuclear factor-kappaB (NF-κB) signaling pathway involved in the deregulated expression of cell proliferation and cell cycle regulatory molecules is a pragmatic approach for chemoprevention. Eugenol (4-allyl-1-hydroxy-2-methoxybenzene), a natural phenolic constituent of oils of cloves is known to possess attractive remedial features. In the present study, we investigated the modulatory effects of eugenol on NF-κB signaling in a rat model of gastric carcinogenesis induced by N-methyl-N(')-nitro-N-nitrosoguanidine (MNNG) by analysing the expression of nuclear factor-kappaB (NF-κB) family members ((NF-κB (p50 and p65), inhibitor of kappaB alpha (IκBα), phosphorylated IκBα (p-IκBα), IκB kinase ß (IKKß)) and the NF-κB target genes that promote (e.g., cyclin D1, cyclin B and PCNA) or inhibit (e.g., p53, p21, and Gadd45) cell proliferation and cell survival. MNNG-induced gastric tumours were characterized by NF-κB activation that correlated with upregulation of IKKß, and phosphorylation and degradation of IκBα. Furthermore, upregulation of cyclins and PCNA with downregulation of p21, p53, and Gadd45 suggested that the proliferative advantage in gastric carcinomas is dependent on elevated constitutive NF-κB activity. Administration of eugenol significantly reduced the incidence of MNNG-induced gastric tumours by suppressing NF-κB activation and modulating the expression of NF-κB target genes that regulate cell proliferation and cell survival. The targeting of NF-κB signaling pathway by eugenol may have a significant impact on chemopreventive and therapeutic approaches for cancer.

Spectral investigations on N-(2-methylthiophenyl)-2-hydroxy-1-naphthaldimine by silver nanoparticles: quenching.

The photo-physical properties of N-(2-methylthiophenyl)-2-hydroxy-1-naphthaldimine (NMTHN) on silver nano particles have been investigated using optical absorption and fluorescence emission techniques. Silver nanoparticles of different sizes have been prepared by two different methods. The increases in size of the silver nanoparticles cause a decrease in the quenching of fluorescence of NMTHN. Stern-Volmer quenching constants and the association constants have also been calculated.

Evaluation of Azadirachta indica leaf fractions for in vitro antioxidant potential and protective effects against H2O2-induced oxidative damage to pBR322 DNA and red blood cells.

We evaluated the protective effects of subfractions of the ethyl acetate fraction (EAF) and the methanolic fraction (MF) from the crude ethanolic extract (CEE) of Azadirachta indica A. Juss (neem) leaves against various free radicals and hydrogen peroxide (H2O2)-induced oxidative damage to red blood cells (RBCs) and pBR322 DNA. Neem leaf fractions reduced DPPH(*), ABTS(*+), superoxide (O(*-)), hydroxyl (OH(*)), and nitric oxide radicals to nonradical forms in a concentration-dependent manner. Treatment with the benzene insoluble fraction from EAF (EBIF), the chloroform insoluble fraction from EAF (ECIF), the chloroform insoluble fraction from MF (MCIF), and the ethyl acetate insoluble fraction from MF (MEIF) significantly mitigated H2O2-induced oxidative damage to RBCs and pBR322 DNA. Although we found low in vitro free radical scavenging activity for the benzene insoluble fraction from EAF (EBSF), the chloroform soluble fraction from EAF (ECSF), the chloroform soluble fraction from MF (MCSF), and the ethyl acetate soluble fraction from MF (MESF), these fractions showed no effect on H2O2-induced lipid peroxidation and pBR322 DNA damage. High-performance liquid chromatography (HPLC) and TLC-Iatroscan analysis revealed that the greater efficacy of EBIF, ECIF, MCIF, and MEIF may be due to the presence of more polar compounds such as nimbolide and quercetin. Our studies suggest that the antioxidant and protective effects of active neem leaf fractions against H2O2-induced lipid peroxidation and pBR322 DNA damage can be attributed to their ability to inhibit various free radicals.

Chemoprevention of rat mammary carcinogenesis by Azadirachta indica leaf fractions: modulation of hormone status, xenobiotic-metabolizing enzymes, oxidative stress, cell proliferation and apoptosis.

We evaluated the chemopreventive potential of the ethyl acetate fraction (EAF) and methanolic fraction (MF) of Azadirachta indica (neem) leaf on 7,12-dimethylbenz[a]anthracene (DMBA)-induced rat mammary carcinogenesis. Estradiol and estrogen receptor status, xenobiotic-metabolizing enzyme activities, redox status, DNA and protein modifications, and the expression of cell proliferation, and apoptosis related proteins in the mammary gland and liver were used as biomarkers of chemoprevention. Administration of both EAF and MF at a dose of 10mg/kg bw effectively suppressed tumour incidence. Chemoprevention by neem leaf fractions was associated with modulation of hormone and receptor status, xenobiotic-metabolising enzymes, and lipid and protein oxidation, with upregulation of antioxidants, inhibition of oxidative DNA damage, protein modification, and cell proliferation, and induction of apoptosis. However EAF rich in constituent phytochemicals was more effective than MF in modulating multiple molecular targets. These results provide evidence for the chemopreventive efficacy of neem leaf fractions in the rat mammary tumour model.

Combinatorial chemopreventive effect of Azadirachta indica and Ocimum sanctum on oxidant-antioxidant status, cell proliferation, apoptosis and angiogenesis in a rat forestomach carcinogenesis model.

INTRODUCTION: We investigated the combinatorial chemopreventive efficacy of Azadirachta indica (AI) and Ocimum sanctum (OS) against N-methyl-N'-nitro-N-nitrosoguanidine (MNNG)-induced gastric carcinogenesis, based on changes in oxidant-antioxidant status, cell proliferation, apoptosis and angiogenesis. METHODS: Male Wistar rats were assigned to four groups. Rats in groups 1 and 2 received MNNG (150 mg/kg body weight i.g.) three times with a gap of two weeks in between the treatment. Group 2 rats additionally received ethanolic AI (100 mg/kg body weight i.g.) and OS (150 mg/kg body weight i.g.) leaf extract three times per week for 26 weeks. Group 3 animals were given AI and OS leaf extract alone, whereas group 4 served as the control. RESULTS: Lipid and protein oxidation and status of the antioxidants, superoxide dismutases, catalase, reduced glutathione (GSH) and GSH-dependent enzymes together with markers of proliferation (proliferating cell nuclear antigen [PCNA], glutathione S-transferase-Pi [GST-P]), invasion (cytokeratin [CK]), angiogenesis (vascular endothelial growth factor [VEGF]) and apoptosis (Bcl-2, Bax, cytochrome C and caspase-3) were used to biomonitor chemoprevention. Rats administered MNNG developed forestomach carcinomas that displayed low lipid and protein oxidation coupled to enhanced antioxidant activities, and overexpression of PCNA, GST-P, CK, VEGF and Bcl-2 with downregulation of Bax, cytochrome C and caspase-3. Coadministration of AI and OS extract suppressed MNNG-induced gastric carcinomas accompanied by modulation of the oxidant-antioxidant status, inhibition of cell proliferation and angiogenesis, and induction of apoptosis. CONCLUSION: The results of the present study suggest that chemoprevention by AI and OS combination may be mediated by their antioxidant, antiangiogenic, antiproliferative and apoptosis inducing properties.