A Genetic Algorithm Based Feature Selection for Classification of Brain MRI Scan Images Using Random Forest Classifier

A brain tumour is a mass of tissue that is formed by a gradual addition of anomalous cells and it is important to classify brain tumours from the magnetic resonance imaging (MRI) for treatment. Magnetic Resonance Imaging is a useful imaging technique that is widely used by physicians to investigate different pathologies. After a long clinical research, it is proved to be harmless. Improvement in computing power has introduced Computer Aided Diagnosis (CAD) which can efficiently work in an automated environment. Diagnosis or classification accuracy of such a CAD system is associated with the selection of features. This paper proposes an enhanced brain MRI image classifier targeting two main objectives, the first is to achieve maximum classification accuracy and second is to minimize the number of features for classification. Feature selection is performed using Genetic Algorithm (GA) while classifiers used are Random forest Classifier

Potential drug target from breast milk Lactobacillus against vaginal pathogens

The term “Probiotics” refers to the micro-organisms that confers health benefits to hosts when administered in adequate amounts. In this work, Lactobacillus was isolated from breast milk of  a 26 yr old women and was treated against vaginal pathogens by varying in different concentration (50µl, 40µl and 30µl). Identification of Lactobacillus was carried out by motility, gram staining and biochemical test. The antibacterial effects of the Lactobacillus against vaginal pathogens were carried out by disc Agar diffusion method and Antibiotic sensitivity test was also analyzed for the pathogens. The antimicrobial activity of the sample revealed that the Lactobacillus isolated from breast milk showed significant effectively against vaginal pathogens especially higher for Klebsiella pneumonia. GC-MS was carried out to identify bioactive compounds, followed by the identification of novel bioactive compounds in the corresponding fraction. The main aim is to assess the probiotic nature of Lactobacillus in preventing cervical pathogens by studying the effectiveness of antimicrobial activity against vaginal pathogens by identifying the effective compounds by GC-MS and they may widened up the panorama in research and may act as a promising natural human source based drug in medical field without taking any chemical drugs which cause side effects

KSAN (KISAN SOIL ANALYSING NETWORK)

 The whole world is developing into a digitalizing world, whereas the uses of smart phones are rapidly increasing and mobile applications are created in many fields. As well as the field of agriculture is also developing in mobile application.  In order to help the farmers in need of help, we have researched some information about mobile application and created an app based on soil analysis. Soil analysis is a major Problem of agriculture & farming, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted soil analyzing, Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted soil analyzing on a massive global scale

Comparative analysis of treatment outcomes in osteoarthritis knee: integrating physiotherapy and medication versus mono-therapies

Background: Osteoarthritis (OA) of the knee is a prevalent degenerative joint disorder that significantly impacts patients' mobility and quality of life. Effective management of knee OA is crucial to alleviate symptoms and improve daily functioning. This study aims to conduct a comparative analysis of treatment outcomes for knee OA by evaluating three distinct therapeutic approaches: a combination of physiotherapy and medication, physiotherapy alone and medication alone, all supplemented with routine daily activities. Methods: The research involves a cohort of patients diagnosed with knee OA, divided into three groups, each receiving one of the specified treatments. Outcome measures include pain reduction, assessed through the Visual Analog Scale (VAS); functional mobility, evaluated using the Timed Up and Go (TUG) test; and overall quality of life, measured by the Knee Injury and Osteoarthritis Outcome Score (KOOS). Results: Preliminary findings suggest that patients receiving the integrated treatment of physiotherapy and medication show significantly greater improvements in pain relief and functional mobility compared to those undergoing mono-therapies. The combination approach appears to leverage the synergistic effects of both modalities, offering a more comprehensive management strategy. Physiotherapy alone also demonstrates notable benefits in enhancing mobility and reducing pain, while medication primarily provides symptomatic relief. Conclusions: This study underscores the importance of a multidisciplinary approach in treating knee OA, highlighting that integrated treatment plans may offer superior outcomes. These findings aim to inform clinical practice, suggesting that combining physiotherapy with medication can optimize therapeutic efficacy, improve patient quality of life and potentially alter the standard care protocols for knee OA. Further research is warranted to substantiate these results and explore long-term benefits

Structural and molecular interrogation of intact biological systems

Obtaining high-resolution information from a complex system, while maintaining the global perspective needed to understand system function, represents a key challenge in biology. Here we address this challenge with a method (termed CLARITY) for the transformation of intact tissue into a nanoporous hydrogel-hybridized form (crosslinked to a three-dimensional network of hydrophilic polymers) that is fully assembled but optically transparent and macromolecule-permeable. Using mouse brains, we show intact-tissue imaging of long-range projections, local circuit wiring, cellular relationships, subcellular structures, protein complexes, nucleic acids and neurotransmitters. CLARITY also enables intact-tissue in situ hybridization, immunohistochemistry with multiple rounds of staining and de-staining in non-sectioned tissue, and antibody labelling throughout the intact adult mouse brain. Finally, we show that CLARITY enables fine structural analysis of clinical samples, including non-sectioned human tissue from a neuropsychiatric-disease setting, establishing a path for the transmutation of human tissue into a stable, intact and accessible form suitable for probing structural and molecular underpinnings of physiological function and disease

Stock Price Predictions with LSTM-ARIMA Hybrid Model under Neutrosophic Treesoft sets with MCDM interaction

The stock market is regarded as volatile, complex, tumultuous, and dynamic. Forecasting stock performance has proven to be a challenging endeavour due to its increasing need for investment and growth prospects. At the forefront of machine learning, deep learning models facilitate the straightforward and efficient exploration and identification of optimal stocks, the hybrid forecasting models (LSTM and ARIMA) are used in the prediction of stock increase. This paper incorporated the MCDM technique to determine the optimal stocks for investment. The Analytic Hierarchy Process (AHP) is used to assign weights to various financial factors. These weights are then used by the Technique for Order Preference by Similarity to Ideal solution (TOPSIS) technique, which is a component Multi Criteria Decision Making method (MCDM), to compute and rank the optimal stocks for investment. Stock analysis involves considering numerous criteria and sub-criteria, which might lead to an unsuitable answer. To address this uncertainty, we utilize Neutrosophic Treesoft sets, which primarily handle numerous criteria, sub-criteria, and an increased number of sub-sub-criteria. Given a larger number of criteria, we will be capable of providing a precise solution to the problem. Furthermore, the definitions of fuzzy treesoft sets and neutrosophic treesoft sets have been presented for the first time. A plotly graph is generated to compare the real and projected stock prices for all the equities. All these are implemented using the program language python, which seems to be simple and easily understandable when compared to the other programming languages like Julia, MATLAB and so on. This hybrid methodology facilitates the forecast of stock prices, the ranking of stocks based on several financial and non-financial factors using AHP and TOPSIS, and the visualization of the outcomes

Frequency of polymorphic variants in corticotropin releasing hormone receptor 1, glucocorticoid induced 1 and Fc fragment of IgE receptor II genes in healthy and asthmatic Tamilian population

Background: Asthma is a chronic airway inflammatory disease characterized by increased hyper-responsiveness and recurrent episodes of reversible obstructions. Asthma pharmacogenomic studies report significant association of single nucleotide polymorphisms (SNPs) in genes corticotropin releasing hormone receptor 1 (CRHR1), Fc fragment of IgE receptor II (FCER2) and glucocorticoid induced 1 (GLCCI1) with inhaled corticosteroid (ICS) response. The present study was aimed to establish the allelic and genotypic frequencies of polymorphisms rs242941, rs28364072 & rs37972 in CRHR1, FCER2 and GLCCI1 genes, respectively in Tamilian healthy population and asthma patients and to compare with established frequencies of global populations.Methods: The study groups consisted of healthy volunteers and persistent asthma patients who were drug naïve or without ICS treatment in the last ≥2 months, attending JIPMER hospital (n=111 and 78, respectively). SNP genotyping was done using PCR-RFLP (polymerase chain reaction-restriction fragment length polymorphism) and real time-PCR methods.Results: Allelic and genotypic frequencies for all the studied variants found to be in hardy-weinberg equilibrium with minor allele frequencies (MAF) of rs 242941, rs 28364072 and rs 37972 at 0.51, 0.33 and 0.38, respectively, in healthy population. No significant difference in gene frequencies was obtained between healthy control and asthma patient groups. Significant difference in allele frequencies was observed between Tamilian healthy and specific global populations. West African frequency was found to be significantly different for all 3 SNPs (p<0.0001).Conclusions: MAF of rs 242941, rs 28364072 and rs 37972 were 0.51, 0.33 and 0.38, respectively in Tamilian population which were significantly different from various global populations. The frequency distribution found helps to further with ICS response association studies in larger cohorts of asthma patients

Self-reported medication side effects in an older cohort living independently in the community - the Melbourne Longitudinal Study on Health Ageing (MELSHA) : cross-sectional analysis of prevalence and risk factors

Background Medication side effects are an important cause of morbidity, mortality and costs in older people. The aim of our study was to examine prevalence and risk factors for self-reported medication side effects in an older cohort living independently in the community.Methods The Melbourne Longitudinal Study on Healthy Ageing (MELSHA), collected information on those aged 65 years or older living independently in the community and commenced in 1994. Data on medication side effects was collected from the baseline cohort (n = 1000) in face-to-face baseline interviews in 1994 and analysed as cross-sectional data. Risk factors examined were: socio-demographics, health status and medical conditions; medication use and health service factors. Analysis included univariate logistic regression to estimate unadjusted risk and multivariate logistic regression analysis to assess confounding and estimate adjusted risk.Results Self-reported medication side effects were reported by approximately 6.7% (67/1000) of the entire baseline MELSHA cohort, and by 8.5% (65/761) of those on medication. Identified risk factors were increased education level, co-morbidities and health service factors including recency of visiting the pharmacist, attending younger doctors, and their doctor\u27s awareness of their medications. The greatest increase in risk for medication side effects was associated with liver problems and their doctor\u27s awareness of their medications. Aging and gender were not risk factors.Conclusion Prevalence of self-reported medication side effects was comparable with that reported in adults attending General Practices in a primary care setting in Australia. The prevalence and identified risk factors provide further insight and opportunity to develop strategies to address the problem of medication side effects in older people living independently in the community setting. <br /

Prosthetics for Lower Limb Amputation

The Chapter will include a brief note on Amputation, Particularly Lower Limb Amputation (LLA), Levels and Causes of LLA. Importance of Prosthetics for LLA are explained in detail. The types of Prosthesis, Application (Donning & Doffing) of prosthesis are included in this chapter. Diagrammatic representation of the prosthesis are added too. Bio mechanical component is explained in detail within this chapter. The advantages and disadvantages of each and every Lower limb Prosthesis are clearly mentioned. Moreover, the Gait analysis & Training after the application of prosthesis are discussed. The reader will get a complete picture of Prosthetics for Lower limb Amputation by going through this chapter for lower limb prosthesis