12 research outputs found

    Histopathological classification of ovarian neoplasm: a retrospective study of 612 cases from a regional cancer centre, Odisha, India

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    Background: Ovarian cancer is the leading cause of cancer related death amongst women in India. Identification of histological types helps to predict tumor behaviour and further appropriate management. Aims and objectives is to study the histopathological parameters of ovarian tumor.Methods: This retrospective study was done on patients who presented ovarian mass and subsequently underwent surgery in a Regional Cancer Centre, Odisha, over a period of three years from January 2016 to December 2018.  All datas such as age, site, gross findings and histological tumor types were retrieved from pathology and analyzed using MS Excel worksheet.Results: A total 612 cases of ovarian tumor were included for study. Non-neoplastic to neoplastic tumor ratio was 1:7.74. Surface epithelial tumors comprised the majority of tumors, accounting for 452 cases (83.39%). Malignant lesions were predominant in this series 416 cases (76%). Majority of borderline tumors were of mucinous subtype 20 (76.92%). The Mean±SD ages of all benign comprising, borderline and malignant tumors were 47.4±11.9, 44.9±14.3 and 46.9±13, respectively. On the basis of two tired grading system, high grade malignant serous tumors were maximum, 226 (74.34%). Ovarian surface involvement, omental invasion, uterine invasion, LVSI, capsular invasion and pelvic lymph node involvement was observed in 146 (35.26%), 106 (25.6%), 12 (2.89%), 70 (16.9%), 6 (1.44%) and 12 (2.89%) respectively. According to the FIGO staging system, among primary malignant tumor, 58% patients were presented in late stage (III and IV).Conclusions: The high incidence of malignant ovarian tumor with late presentation was observed in our study. So, further study is warranted to elucidate the major factors in our population

    Equivalence of DES and AES Algorithm with Cellular Automata

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    In this paper we present the equivalence of the operations involved in DES and AES algorithm with operations of cellular automata. We identify all the permutation and substitution operations involved in DES and AES algorithm and compare these operations with the cellular automata rules. Then we find that permutation operations involved in DES and AES are equivalent to linear cellular automata rules providing diffusion property of cryptography whereas substitution operations involved in DES and AES are equivalent to non linear cellular automata rules providing the confusion property of cryptography. Hence instead of using operations involved in DES and AES algorithm, we can apply linear as well as non-linear cellular automata rules in cryptography for better security and parallel processing

    Encryption and Decryption algorithm using two dimensional cellular automata rules in Cryptography

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    In this paper we present a new encryption and decryption algorithm for block cipher based on the linear (periodic boundary-PB) and nonlinear cellular automata rules. First we apply non linear CA rules (complements) to both plain text and key. Then PB CA rule is applied to the above results separately followed by the XOR operation of above results. After that the result of XOR operation is fed to substitution box(S-box) and again PB CA rules are applied followed by SBox. The decryption process is carried out just similar to that of encryption but in the reverse way. Both the process of encryption and decryption is performed for 8 number of rounds in order to avoid the dependency between the plain text and cipher text so that the our proposed algorithm is more secure than that of AES and DES algorithms

    STUDY OF CLINICAL, ETIOLOGICAL AND ECHOCARDIOGRAPHIC PROFILE OF ATRIAL FIBRILLATION IN A TERTIARY CARE HOSPITAL

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    Aims and Objectives: The present study was aimed in analyzing the clinical presentations, etiological influences, and echocardiographic structures of cases of atrial fibrillation (AF) in SCB MCH, Cuttack. Methods: A cross-sectional research performed on AF cases admitted in SCB MCH, Cuttack, during the period from May 2021 to November 2022. Detailed history, thorough clinical examination and transthoracic echocardiography, has been carried out among study population. Results: Average age of cases was observed as 49.56±17.64 years. Among 100 cases, females were found to be 63% whereas males as 37%. Most of the cases were suffered due to more than one symptoms of severity. About 59% of cases showed rheumatic heart disease (RHD) out of total 100 cases. Among RHD cases, 43% cases were female and the male cases as 16%. Hypertension and coronary artery disease formed the furthermost communal reasons. Mean left atrial (LA) dimension between the study populations was found to be 4.49±0.57 cm. Conclusion: This study concluded that dyspnea seems to be the most common observed symptom; RHD forms the major communal reasons for AF, preceded by hypertension. Mitral valve seems to be involved among all cases of RHD. In major cases, LA enlargement has been reported; therefore, this forms the prognosticator of AF. LA enlargement cases seem to be further susceptible to progress to LA appendage clot

    Cold Tolerance Mechanisms in Mungbean (<i>Vigna radiata</i> L.) Genotypes during Germination

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    Mungbean or greengram (Vigna radiata) is an important legume crop well known for its high protein with nitrogen-fixing abilities. However, the severe yield loss in mungbean occurs due to susceptibility to low temperatures at all stages of plant growth including germination and is a serious concern for its cultivation and productivity. To select cold-tolerant genotypes, a germination-based screening at 10 °C was performed in a total of 204 germplasms. The study showed that cold stress of the initial 8-days during seedling establishment imposed a negative impact throughout the life of mungbean genotypes, which were reflected in the vegetative and reproductive phase (plant height, days to 50% flowering and pods/plant, seeds/pod, yield/plant, and 100-seed weight). The biplot analysis showed that parameters such as germination rate index, Timson’s index, mean germination time, and coefficient of the velocity of germination are the key influential germination parameters for identifying cold tolerance in the seedling stage. Identified cold-tolerant genotype (PAU911) retained higher rootlet number, leaf area, and increased chlorophyll, carotenoid, and malondialdehyde (MDA) content at 10 °C. Based on the confocal microscopic study, it is noticed that the stomatal density, open pore percentage, and trichome density were significant differences in seedlings exposed to cold stress as compared to non-stress. On the basis of matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) analysis, it is observed that a new protein identified as TETRATRICOPEPTIDE-REPEAT THIOREDOXIN-LIKE1 (TTL1) (UNIPROT Identifier: LOC106762419) which highly correlated with the cold stress response of in the cold-tolerant genotype. Our study identifies a noble member, TTL1, whose expression has a positive role in cold tolerance response at the protein level in V. radiata. This study will help breeding programs with regard to the sustainable growth of mungbean

    Naringenin and quercetin – potential anti-HCV agents for NS2 protease targets

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    <div><p>Nonstructural proteins of hepatitis C virus had drawn much attention for the scientific fraternity in drug discovery due to its important role in the disease. 3D structure of the protein was predicted using molecular modelling protocol. Docking studies of 10 medicinal plant compounds and three drugs available in the market (control) with NS2 protease were employed by using rigid docking approach of AutoDock 4.2. Among the molecules tested for docking study, naringenin and quercetin revealed minimum binding energy of − 7.97 and − 7.95 kcal/mol with NS2 protease. All the ligands were docked deeply within the binding pocket region of the protein. The docking study results showed that these compounds are potential inhibitors of the target; and also all these docked compounds have good inhibition constant, vdW+Hbond+desolv energy with best RMSD value.</p></div

    Assessing the prediction of type 2 diabetes risk using polygenic and clinical risk scores in South Asian study populations

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    Background: Genome-wide polygenic risk scores (PRS) have shown high specificity and sensitivity in predicting type 2 diabetes (T2D) risk in Europeans. However, the PRS-driven information and its clinical significance in non-Europeans are underrepresented. We examined the predictive efficacy and transferability of PRS models using variant information derived from genome-wide studies of Asian Indians (AIs) (PRS AI ) and Europeans (PRS EU ) using 13,974 AI individuals. Methods: Weighted PRS models were constructed and analyzed on 4602 individuals from the Asian Indian Diabetes Heart Study/Sikh Diabetes Study (AIDHS/SDS) as discovery/training and test/validation datasets. The results were further replicated in 9372 South Asian individuals from UK Biobank (UKBB). We also assessed the performance of each PRS model by combining data of the clinical risk score (CRS). Results: Both genetic models (PRS AI and PRS EU ) successfully predicted the T2D risk. However, the PRS AI revealed 13.2% odds ratio (OR) 1.80 [95% confidence interval (CI) 1.63–1.97; p  = 1.6 × 10 −152 ] and 12.2% OR 1.38 (95% CI 1.30–1.46; p  = 7.1 × 10 −237 ) superior performance in AIDHS/SDS and UKBB validation sets, respectively. Comparing individuals of extreme PRS (ninth decile) with the average PRS (fifth decile), PRS AI showed about two-fold OR 20.73 (95% CI 10.27–41.83; p  = 2.7 × 10 −17 ) and 1.4-fold OR 3.19 (95% CI 2.51–4.06; p  = 4.8 × 10 −21 ) higher predictability to identify subgroups with higher genetic risk than the PRS EU . Combining PRS and CRS improved the area under the curve from 0.74 to 0.79 in PRS AI and 0.72 to 0.75 in PRS EU . Conclusion: Our data suggest the need for extending genetic and clinical studies in varied ethnic groups to exploit the full clinical potential of PRS as a risk prediction tool in diverse study populations
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