Comprehensive review of adnexal masses in a tertiary care hospital

Background: Adnexal masses can be either be a physiological luteal cyst, a benign tubo-ovarian mass or a malignancy. The signs and symptoms along with tumour markers and imaging modalities are considered to differentiate between a benign and a malignant adnexal mass. Adnexal masses in pregnancy can be asymptomatic or can present with acute abdomen in cases of ectopic pregnancy and torsion. The aim was to study the prevalence of various histopathologic types of adnexal masses in different age groups. Methods: This was a retrospective study carried out in the department of obstetrics and gynecology in a tertiary care hospital from May-2019 to April-2022. Women with sonographically diagnosed adnexal mass were evaluated. Data regarding ultrasound findings, tumour markers, RMI score and the management done were recorded from medical record charts. Descriptive statistics was applied and results shown in the form of frequencies and percentages. Results: Among 31 study participants, the most common presentation was pain abdomen. Majority (93.5%) patients had benign adnexal pathology and 6.45% had malignant pathology. The most common ovarian pathology encountered was Benign surface epithelial tumours (48.4%). Early diagnosis of 2 tubal ectopic and 1 ovarian ectopic pregnancy was made and managed conservatively. Conclusions: Early diagnosis and intervention is helpful in adolescent girls to conserve their ovarian function. Early diagnosis of ectopic pregnancy in stable patients can be managed conservatively. A high RMI should raise a suspicion of malignancy

Texture Feature Based Analysis of Segmenting Soft Tissues from Brain CT Images using BAM type Artificial Neural Network

Soft tissues segmentation from brain computed tomography image data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue among different patients and in many cases, similarity between tumor and normal tissue. A computer software system is designed for the automatic segmentation  of brain CT images. Image analysis methods were applied to the images of 30 normal and 25 benign,25 malignant images. Textural features extracted from the gray level co-occurrence matrix of the brain CT images and bidirectional associative memory were employed for the design of the system. Best classification accuracy was achieved by four textural features and BAM type ANN classifier. The proposed system provides new textural information and segmenting normal and benign, malignant tumor images, especially in small tumor regions of CT images efficiently and accurately with lesser computational time. Keywords: Bidirectional Associative Memory classifier(BAM), Computed Tomography (CT), Gray Level Co-occurrence Matrix (GLCM), Artificial Neural Network (ANN)

Evaluation and prioritization of rice production practices and constraints under temperate climatic conditions using Fuzzy Analytical Hierarchy Process (FAHP)

Due to overwhelming complex and vague nature of interactions between multiple factors describing agriculture, Multi-Criteria Decision Making (MCDM) methods are widely used from farm to fork to facilitate systematic and transparent decision support, figure out multiple decision outcomes and equip decision maker with confident decision choices in order to choose best alternative. This research proposes a Fuzzy Analytical Hierarchy Process (FAHP) based decision support to evaluate and prioritize important factors of rice production practices and constraints under temperate climatic conditions and provides estimate of weightings, which measure relative importance of critical factors of the crop under biotic, abiotic, socio-economic and technological settings. The results envisage that flood, drought, water logging, late sali, temperature and rainfall are important constraints. However, regulating transplantation time; maintaining planting density; providing training to the educated farmers; introducing high productive varieties like Shalimar Rice-1 and Jhelum; better management of nutrients, weeds and diseases are most important opportunities to enhance rice production in the region. Therefore, the proposed system supplements farmers with precise decision information about important rice production practices, opportunities and constraints

(R1514) Nano Continuous Mappings via Nano M Open Sets

Nano M open sets are a union of nano θ semi open sets and nano δ pre open sets. The properties of nano M open sets with their interior and closure operators are discussed in a previous paper. In this paper, we discuss about nano M-continuous and nano M-irresolute functions are introduced in a nano topological spaces along with their continuous and irresolute mappings. Also, nano M-open and nano M-closed functions are introduced and compare with their near open and closed mappings in a nano topological spaces. Further, nano M homeomorphism is also discussed in nano topological spaces. Also, we discuss nano e-Cts, nano e-Irr, nano eo and nano ec functions and nano eHom in nano topological spaces. Also some of their properties are well discussed

Economic analysis of integrated farming systems in the Kuttanad region of Kerala state, India: A case study

Agriculture, with its allied sectors, is unquestionably the largest livelihood provider in India. According to Committee on Doubling of Farmers’ Income Report, the average annual earning of a small and marginal farmer household was Rs 79,779 in 2015-16 and indicates that 86% of farmer households earn only 9% of total income and rest of the farmers earn 91% of total income. Integrated farming system practised mostly by small and marginal farmers, is a viable option for increasing farm income. The present study was undertaken to identify the farming systems practised by small and marginal holdings in Kuttanad region of Kerala state, India and also attempts to assess the profitability of these farms and suggest optimal farm plans using linear programming technique. The study revealed that rice + fish and Coconut + Banana+ Dairy cow + Poultry+ Goat were the most profitable farming systems with a benefit cost ratio of 2.63 and 2.86, respectively. The resource allocation in the existing plan was sub-optimal. The optimisation of resource use led to maximization of net returns, indicating the potential for realising greater income. The net returns of rice + fish increased from Rs. 181724 to Rs. 220010 in the optimal plan. The study also suggests the extent to which net returns can be increased with additional units of constraint resources viz., land/labour. The net returns in FS IV can be increased by Rs.286177.9 per additional acreage of land allotted.  Thus, the farmers in Kuttanad can increase their income by optimal resource allocation and by deploying additional units of land or labour

IMPACT OF DELETERIOUS NON-SYNONYMOUS SINGLE NUCLEOTIDE POLYMORPHISMS OF CYTOKINE GENES ON NON-CLASSICAL HYDROGEN BONDS PREDISPOSING TO CARDIOVASCULAR DISEASE: AN IN SILICO APPROACH

  Objective: Cardiovascular disease (CVD) is a leading cause of death worldwide. Malfunctioning of genes that are responsible for several inflammatory processes is the major cause for its initiation. Cytokine genes are one such group of genes that are involved in the development of CVD. Hence, the prediction of potential point mutations in these genes is important for diagnostic purposes. Such mutations result in altered protein structure and function when compared to neutral ones.Methods: In this study, interleukin factor 6 (IL6), tumor necrosis factor α (TNF-α), interleukin factor 4 (IL4), and interferon gamma have been analyzed using sorting intolerant from tolerant and PolyPhen 2.0 tools.Results: Several single nucleotide polymorphisms (SNPs), in IL6, TNF-α, and IL4, are found to be potentially deleterious. In addition, bond analysis has also been performed on these SNPs. It has been predicted that L119P and R196H of IL6 as well as K87T and T181N of TNF-α are potential ns-SNP's that may cause structural and functional variations in the corresponding proteins. The hydrogen and Cation-Pi bond analysis performed in this study provides molecular-based evidence that support the predicted deleterious potential of such SNPs for these CVD candidate genes along with other conventional in silico tools.Conclusion: The study testifies the importance of adopting a computational approach to narrow down potential point mutants for disease predictions