Comparative Study of Popular Data Mining Algorithms

Data Science is an appealing field , in the present world due to advancement of science as there is huge assortment of data which exist in numerous forms . Such data must be handled with care and store safely so that it can be retrieved as per needs. Some of the popular or commonly used algorithms are Apriori algorithm, K Means Clustering, Support Vector machines(SVM) and Association Rule Mining algorithms. This paper focus on the above mentioned algorithms and a comparison is made in terms of Technique, Time Utilization Software taking real time data examples

Principal component analysis of gait and cycling experiments: Crosstalk error reduction and corrected knee axes

Crosstalk is a leading source of error in motion analysis [1-2]. Due to incorrect flexion axis direction that develops from marker placement error, crosstalk results in a strong, anatomically incorrect correlation between flexion-extension (FE) and adduction-abduction (AA) motions [1-2]. Thus, crosstalk limits the ability of biomechanical models to reflect the “true” motion of the knee. Principal Component Analysis (PCA) has been proposed as a post-hoc correction for crosstalk in prior gait studies [1-2]; however, previous studies have not proposed a method to determine PCA corrected knee axes. Further, it is not clear how PCA should be implemented in motion analysis studies that involve several exercises, on the same subjects, involving a relatively high range of flexion angles. The long-term goal of this study is to determine accurate knee kinematics in a variety of exercises performed by the same subjects. This study tests two hypotheses: (1) PCA corrects for crosstalk between FE and AA angles in gait and cycling and (2) PCA corrected knee axes are similar for gait and cycling. The aims are to (1) determine PCA corrected knee angles in gait and cycling for the same subjects and their corresponding FE-AA correlations, (2) develop and implement an algorithm for determining PCA corrected knee FE and AA axes, and (3) compare the PCA corrected FE and AA axes for the same subjects to determine if they are similar in gait and cycling

Comprehensive mutations analyses of FTO (fat mass and obesity-associated gene) and their effects on FTO’s substrate binding implicated in obesity

An excessive amount of fat deposition in the body leads to obesity which is a complex disease and poses a generic threat to human health. It increases the risk of various other diseases like diabetes, cardiovascular disease, and multiple types of cancer. Genomic studies have shown that the expression of the fat mass obesity (FTO) gene was highly altered and identified as one of the key biomarkers for obesity. This study has been undertaken to investigate the mutational profile of the FTO gene and elucidates its effect on the protein structure and function. Harmful effects of various missense mutations were predicted using different independent tools and it was observed that all mutations were highly pathogenic. Molecular dynamics (MD) simulations were performed to study the structure and function of FTO protein upon different mutations and it was found that mutations decreased the structure stability and affected protein conformation. Furthermore, a protein residue network analysis suggested that the mutations affected the overall residues bonding and topology. Finally, molecular docking coupled with MD simulation suggested that mutations affected FTO substrate binding by changing the protein-ligand affinity. Hence, the results of this finding would help in an in-depth understanding of the molecular biology of the FTO gene and its variants and lead to the development of effective therapeutics against associated diseases and disorders

Chronic pain evaluation in breast cancer patients using the Self-Report Leeds Assessment of Neuropathic Symptoms and Signs (S-LANSS): a single center cross-sectional retrospective study

Background: Breast cancer is the most common cancer in India, and the number of survivors has increased over the last few years. Pain is one of the most common symptoms during cancer treatment due to either the disease itself or adverse effects of treatment. The available data suggests that breast cancer patients have a high prevalence of neuropathic pain. Patients and methods: A cross sectional observational study was done at the Department of Radiation Oncology, between November 2021 to June 2022. The patients were admitted and screened for participation, non-metastatic post operative breast cancer on regular follow up for 2 years after their last chemotherapy or radiotherapy and not having any chronic neuropathy disease and the Self-Report Leeds Assessment of Neuropathic Symptoms and Signs (S-LANSS) pain scale was used to assess the neuropathy pain status of patients. Patients’ demographics, clinical characteristics, and treatment of surgery, radiation therapy, and chemotherapy were collected and the comparison of the pain score between the patients was analysed. Results: Total of 149 patients were included in the study. S-LANSS score was calculated in the study population and more than 61% of participants reported a score equal or greater than 12, suggesting a predominant neuropathic pain component. Autonomic dysfunction, thermal pain, and allodynia were more prevalent in patients who underwent mastectomies compared to breast-conserving surgery. Whereas the dysesthesia and autonomic dysfunction score was higher in only the anthracycline group. Conclusions: The most important index for quality of life in cancer patients is the presence of persistent chronic pain and it is important to classify it accordingly in order to provide the best management. Using the S-LANSS score, the pattern of neuropathic pain can be determined early which leads to early intervention

Chronic pain evaluation in breast cancer patients using the Self-Report Leeds Assessment of Neuropathic Symptoms and Signs (S-LANSS): a single center cross-sectional retrospective study

© 2024 The Authors. Published by VM Media Group. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.5603/PMPI.a2023.0027Background: Breast cancer is the most common cancer in India, and the number of survivors has increased over the last few years. Pain is one of the most common symptoms during cancer treatment due to either the disease itself or the adverse effects of treatment. The available data suggests that breast cancer patients have a high prevalence of neuropathic pain. Patients and methods: A cross-sectional observational study was done at the Department of Radiation Oncology, between November 2021 to June 2022. The patients were admitted and screened for participation, non-metastatic post-operative breast cancer on regular follow-up for 2 years after their last chemotherapy or radiotherapy and not having any chronic neuropathy disease and the Self-Report Leeds Assessment of Neuropathic Symptoms and Signs (S-LANSS) pain scale was used to assess the neuropathy pain status of patients. Patients’ demographics, clinical characteristics, and treatment of surgery, radiation therapy, and chemotherapy were collected and the comparison of the pain scores between the patients was analysed. Results: A total of 149 patients were included in the study. S-LANSS score was calculated in the study population and more than 61% of participants reported a score equal to or greater than 12, suggesting a predominant neuropathic pain component. Autonomic dysfunction, thermal pain, and allodynia were more prevalent in patients who underwent mastectomies compared to breast-conserving surgery. Whereas the dysesthesia and autonomic dysfunction score was higher in only the anthracycline group. Conclusions: The most important index for quality of life in cancer patients is the presence of persistent chronic pain and it is important to classify it accordingly in order to provide the best management. Using the S-LANSS score, the pattern of neuropathic pain can be determined early which leads to early intervention.This study did not receive any financial support/funding.Published onlin

Strategic Business Performance Management Tool Using Business Intelligence for Decision Making

Business Intelligence is decision support system which is driven by data. Handling data is very significant in Business Intelligence and Analytics. This paper focus on Business Intelligence, it’s importance, type of tools used, trends of BI, how it differs from Business Analytics(BA), BI advantages, disadvantages and it’s applications in the real world