Application of Chromatography Methods for the Separation and Detection of Certain Benzodiazepine Drugs in Forensic Sample |Chapter 6 | Pharmaceutical Research: Recent Advances and Trends Vol. 7

Benzodiazepines (BZDs) are widely used in pharmacotherapy as antiepileptic, muscle relaxant, hypnotic, and anesthetic inductors. These drugs were once an occasional problem, today they have become much more common. Due to the structural similarity of the specimens encountered by the forensic laboratory, an array of instruments is needed to correctly identify these substances. Any of the BZDs can be identified by combining the results obtained with different mobile phases. For pharmaceutical sample preparation, each type of BZD tablet was accurately weighed and powdered in a mortar. Ten milligrams were taken and dissolved in 10 mL of methanol and sonicated for five minutes. Forensic Scientists are required to identify an ever-increasing and more complex assortment of drugs and related compounds. Rapid, sensitive, and specific thin-layer chromatography (TLC), high-performance liquid chromatography (HPLC), and gas chromatography-mass spectrometry (GC-MS) methods were utilized for the analysis of certain benzodiazepines (BZDs). HPLC proposes a cost-efficient method with the ruggedness and consistency necessary for forensic testing and consequently is widely used in forensic laboratories today. GC-MS is one of the most commonly used techniques for the identification and quantitation of forensic drug samples. As a “hyphenated” technique, it combines the separation power of a GC with the analyte specificity of a spectroscopic technique, provided that vastly specific spectral data on individual compounds in a complex mixture often devoid of prior separation. We succeeded in the separation of the encountered BZD drug diazepam in the forensic sample. The method was validated for linearity, accuracy, precision, and limit of detection. This method could be potentially applied in the analysis of forensic samples from drug-facilitated sexual assault cases. A competent forensic toxicologist relies on their own case experience as well as the unique state of affairs of each case under assessment.

 

Author (s) Details

Karuppasamy Gurusamy
Regional Forensic Science Laboratory, Government of Tamil Nadu, Madurai – 20, India and Department of Chemistry, Raja Doraisingam Government Arts College, Government of Tamil Nadu, Sivagangai, India.

A. Cyril
Department of Chemistry, Raja Doraisingam Government Arts College, Government of Tamil Nadu, Sivagangai, India.

Subbiah Thangadurai
Department of Chemistry, Government Arts and Science College, Government of Tamil Nadu, Sivakasi – 24, India..

 

Please see the book here:- https://doi.org/10.9734/bpi/prrat/v7/1627

The Design of a Smart Prototype Security System for Wildlife Conservation | Chapter 3 | Science and Technology: Developments and Applications Vol. 8

The exploitation of natural resources through poaching and plundering has a significant impact on the environment, society, economy, and culture. Namibia has recently witnessed a surge in wildlife crime, as evidenced by the reported poaching incidents in the iconic Etosha Game Park in 2022, where 61 black and 26 white rhinos were killed. Elephants, rhinos, and pangolins are currently the three main high-value targets for poachers due to their ivory, horns, scales, and flesh. Despite the Namibian government's efforts to combat poaching, the illegal killing of wild animals persists, which negatively impacts natural diversity and the national tourism industry. This paper proposes an innovative, intelligent system to facilitate real-time detection and monitoring of wild animals and timely detection of poaching activities. The system is designed to assist national parks and private game parks in identifying and combating poaching in real-time by utilising off-the-shelf components.

 

Author (s) Details

 

Smita Francis
Faculty of Engineering, Namibia University of Science and Technology, Windhoek, Namibia.

 

Tega Paulus
Department of Electrical and Computer Engineering, Faculty of Engineering, Namibia University of Science and Technology, Windhoek, Namibia.

 

Please see the book here:- https://doi.org/10.9734/bpi/stda/v8/2587

Detecting Presence of Chlamydia trachomatis in Donated Blood at Regional Blood Transfusion Centre, Eldoret, Kenya | Chapter 3 | Recent Updates in Disease and Health Research Vol. 8

 

 

Background and Aim: C. trachomatis is globally a major cause of sexually transmitted diseases (STDs) including Chlamydia. It causes various contaminations in donated blood. This study aims to detect Chlamydia trachomatis in donated blood at the Regional Blood Transfusion center, Eldoret, Kenya.

Methodology: A random selection of blood samples was done for this study for a period of one month from July 24th 2017 to August 24th 2017. The prevalence of C. trachomatis was also determined with the help of the demographic questionnaire that is mandatory to be filled by the donor before any blood donation. Blood bags were numbered 1-87 and picked amongst unnumbered blood bags by each donor in order of donation. The samples obtained were subjected to centrifugation and serum used in the immunoassay test by using the Chlamydia trachomatis IgG/IgM Elisa test kit.

Results: In this study, a greater percentage of the samples were from the donors between 20-29 years of age (60.9%), while the least was below 20 years old group. This study showed that C. trachomatis was present in 8% (7 samples) of the blood samples analyzed and 92% were found to be negative. The age group between 20 to 29 years old had the greatest percentage of those with C. trachomatis.

Conclusion: With 8% of the samples testing positive for Chlamydia trachomatis this indicated that some of the blood donated to the Regional Transfusion Center is contaminated.

 

Author(s)details:-

 

P. Chemutai
Department of Biological Sciences, University of Eastern Africa, Baraton, P.O.Box – 2500, Eldoret – 30100, Kenya.

 

F. Ramesh
Department of Biological Sciences, University of Eastern Africa, Baraton, P.O.Box – 2500, Eldoret – 30100, Kenya.

 

Frank K. Onyango
Department of Biological Sciences, University of Eastern Africa, Baraton, P.O.Box – 2500, Eldoret – 30100, Kenya.

 

Please See the book here :- https://doi.org/10.9734/bpi/rudhr/v8/223

Data Bias Detection in Machine Learning Using Various Statistical Measures | Chapter 11 | Advances and Challenges in Science and Technology Vol. 7

 The study explains numerous mathematical techniques for bias labeling. The techniques are secondhand with twofold and multinomial dafigta. Artificial intelligence (AI) is a broad term referring to machines or calculatings that mimic human intelligence. Machine learning (ML), a subdomain of up-to-date AI, creates rules established training dossier. The Bias detection of dossier and model are essential in today’s scenarios. The lightened data will give better forecast results. Good predictions help institutions to take better decisions.

Author(s) Details:

Harshaprabha N. Shetty,
Accenture Technology, Bengaluru, India.

Sony Asampalli,
Accenture Technology, Hyderabad, India.

Prabhu Vara Prasad Bonam,
Accenture Technology, Hyderabad, India.

Please see the link here: https://stm.bookpi.org/ACST-V7/article/view/12208

Study on Dynamic Detection of Reinforced Concrete Bridge Damage by Finite Element Model Updating | Chapter 11 | Advanced Aspects of Engineering Research Vol. 15

 The purpose of this research is to detect faults in reinforced concrete bridges using an MMUM mathematical model updating approach based on incomplete test data. A suitable finite element model of a repaired bridge is created. The diagnostics can be used to locate and detect problems in a reinforced concrete bridge. As a result, modal testing techniques were employed to monitor the progress of analytical predictions. In addition, a finite time centred space scheme has been created to solve a set of equations in a bridge model that can easily accommodate finite element matrices. The approach is utilised in this study to detect damage and existing cracks in a restored bridge in real time. The method was put to the test on a repaired bridge at Oued Oumazer, Algeria. Identification approaches have been proven to be capable of detecting the precise location of damaged portions that need to be repaired while avoiding the finite element model's mass, stiffness, and loading errors.

Author (S) Details

Mohammed Lamine Moussaoui
Faculty of Mechanical and Chemical Engineering, University of Sciences and Technology Houari Boumediene, Algiers 16111, Algeria.

Mohamed Chabaat
Faculty of Civil Engineering, University of Sciences and Technology Houari Boumediene, Algiers 16111, Algeria.

Abderrahmane Kibboua
National Earthquake Engineering Research Center (CGS), Hussein Dey, Algiers 16000, Algeria.

View Book :- https://stm.bookpi.org/AAER-V15/article/view/1747

Studies on Red Blood Cells (RBCs) and White Blood Cells (WBCs) Detection by Image Processing| Chapter 6 | Newest Updates in Physical Science Research Vol. 7

 The most typical way of identifying and counting red and white blood cells involves manual operations on a microscope, which are set up by a laboratory worker with their own experience. We will create a computer programme to recognise and identify the recommended objects based on their pattern in this study. In terms of cell detection, our proposed method is quite effective. Red Blood Cells (RBCs) and White Blood Cells (WBCs) are the proposed objects (WBCs). An approach proposed by Viola and Jones will be used to identify and classify blood cells.  To improve the accuracy of the learning algorithm's error, the Adaboost (adaptive boosting) method will be used.  By displaying the number and time spent by cells discovered, the output of the suggested programme demonstrates that all of the categories of cells stated can be detected and classified properly.

Author(s) Details

Irwan Rahadi, M.Sc
Department of Statistics, Universitas Hamzanwadi, Lombok, Indonesia.

Meechoke Choodoung
Department of Mathematics, Mahidol University, Bangkok, Thailand.

Arunsri Choodoung
Center of Medical Laboratory Service, Mahidol University, Bangkok, Thailand.

View Book :- https://stm.bookpi.org/NUPSR-V7/article/view/1291