There has been an increase in human studies of exposure to endocrine-disrupting chemicals (EDCs). In this follow-up to a previous study, we analyzed new data to determine whether or not there was substantial human evidence linking the exposures and outcomes of the prior study. Perfluoroalkyl chemicals have been linked to an increased risk of childhood and adult obesity, impaired glucose tolerance, gestational diabetes, low birth weight, poor sperm quality, polycystic ovary syndrome, endometriosis, and breast cancer. There is substantial evidence supporting these associations. Children exposed prenatally to bisphenol A, organophosphate insecticides, and polybrominated flame retardants are more likely to develop cognitive impairments and attention-deficit hyperactivity disorder. Although a comprehensive evaluation of the frequency and intensity of these exposure-result links is required, a growing body of evidence suggests that immediate action be taken to reduce exposure to environmental toxicants (EDCs).
Our environment has become contaminated enormously with different industrial chemicals that could disturb the natural mechanism of hormones like their secretion, transport etc., in our body. These industrial effluents are called as endocrine disruptors (EDs) and they mainly affect through polluted water and food. These chemicals (EDs) have the ability to stay in the environment for years and to travel vast distances due to their long half-life. Plastics, fungicides, pesticides, medications, plasticizers, lubricants, and so on are only few examples of the wide range of potential environmental contaminants (EDs). Due to the complexity of the situation, it is difficult to understand the precise mechanism of action of these EDs, which cause disruptions in hormone production or epigenetic pathways as well as in thyroid receptors (TRs), oestrogen receptors (ER), androgen receptors (AR) and aryl hydrocarbon receptors (AhR). The research advancements in this area is mandatory to get more information about EDs, their potential and their mode of action and removal of these chemicals from environment.
Keywords: Endocrine Disrupting Chemicals EDCs, toxicity mechanism, mixture interaction.
MOLECULAR MECHANISMS OF BIOSYNTHESIS & PHARMACEUTICAL ACTIVITIES OF MARINE NATURAL PRODUCTS: AN OVERVIEW AND FUTURE OUTLOOK FOR DRUG DISCOVERY
Abstract:
Author/s:
Yousaf Khana* Abdul Sattara, Syed Amin ullaha, Zia Ur Rehman Panizaib, Madeeha Bibic, Saif Ud Din Panizai d
aDepartment of Chemistry, COMSATS University Islamabad, 45550, Islamabad Pakistan.
bDepartment of Chemistry, University of Baluchistan Quetta, Pakistan. cDepartment of Chemistry, Hazara University Mansehra, Pakistan.
dDepartment of Bio-Chemistry, Abasyn University Islamabad, Pakistan.
Email Address: Corresponding Author: yousaf7n@gmail.com.
Page Nos:
36-50
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HEART DISEASE PREDICTION SYSTEM USING MACHINE LEARNING ALGORITHMS
Abstract:
One of the most difficult problems in the medical field nowadays is the prediction of heart disease. Approximately in the current period, one person dies from heart disease every minute. In the sphere of healthcare, the process must be automated to reduce risks and notify the patient well in advance because predicting cardiac illness is a challenging task. In this study, the UCI machine learning repository’s heart disease data-set is used.
In the current project, five well-known machine learning algorithms—decision tree, Random forest, Support Vector Machine, logistic regression, and k-neighbor —are being evaluated for accuracy by using machine learning, which learns from natural events and data and uses biological variables like cholesterol, blood pressure, sex, and age as evaluating data. Finding the most accurate way to predict cardiac illnesses is the study’s main objective.
The performance of five well-known machine learning algorithms is thus analyzed in this work to give a comparison study. In comparison to other ML algorithms used, the testing results demonstrate that the support vector machine approach has the highest accuracy of 85%.
Keywords— Decision tree, Support vector machine, linear regression, decision tree, naïve bayes, supervised, unsupervised, reinforced.
Author/s:
Muhammad Abrar1, Muhammad Sadique2
1Department of Computer Science GPGC Mansehra, Pakistan
2Department of Computer Science GPGC Mansehra, Pakistan
Page Nos:
51-75