Background and aims: Hair analysis for drug detection is one of the widely accepted imperative techniques in the field of forensic toxicology. The current study was designed to investigate the efficacy of chromatography for detection of drugs of abuse in hair.Method: A comprehensive review of articles from last two decades on hair analyses via PubMed and similar resources was performed. Issues concerning collection, decontamination and analytical techniques are summarised. Physiochemical nature of hair, mechanism of drug incorporation and its stability in hair are briefly discussed. Furthermore, various factors affecting results and interpretation are elucidated.Result: A hair sample is chosen over traditional biological samples such blood, urine, saliva or tissues due to its inimitable ability to provide a longer time frame for drug detection. Its collection is almost non-invasive, less cumbersome and does not involve any specialised training/expertise. Recent advances in analytical technology have resulted in better sensitivity, reproducibility and accuracy, thus providing a new arena of scientific understanding and test interpretation.Conclusion: Though recent studies have yielded many insights into drug binding and drug incorporation in hair, the major challenge in hair analysis lies in the interpretation of results, which may be affected by external contamination and thus lead to false-positives. Therefore, there is a need for more sensitive and selective analysis methods to be developed in order to minimise factors that induce the effect of melanin, age and so on, and this would certainly provide a new dimension to hair analysis and its applications.

Detection of drugs from hair samples has become an imperative technique in forensic toxicological analysis. In this study, hair samples were collected from 20 cannabis users undergoing treatment at a rehabilitation center at different time intervals. Hair samples were cleaned and digested, followed by extraction and quantification of THC by GC–MS. At LOD of 0.1ng/mg of THC, the concentration ranged from 0.16 to 2.3ng/mg (mean, 0.95ng/mg). Results indicate that THC is detectable after 3months of last drug intake

BackgroundThe detection of drugs ingested by humans by analysing hair samples for their presence is now one of the imperative techniques in the field of forensic toxicology. A number of cases pertaining to the abuse of opium or its derivatives have been registered under the NDPS Act over the last few years.ObjectiveThis study was conducted with the objective of detecting levels of morphine accumulated in the human hair over a period of time.MethodAll samples analysed showed the presence of detectable levels of morphine, even 90days after ingestion by the abusers. The concentration of morphine ranged from 0.26 to 2.2ng/mg (mean, 1.33ng/mg) and the LOD was 0.1ng/mg.ResultHair samples were collected at different time intervals from 40 opium abusers undergoing treatment at a de-addiction center. The samples were first collected on the day of an individual’s admission to the center followed by a second round of collection 45days after the date of admission and finally, 90days after the date of admission. After each round of sample collection, the hair samples were cleaned and digested, followed by extraction of morphine and its quantification via GC–MS.ConclusionIt can be concluded that once morphine gets incorporated into the hair, it will remain there for at least 3months and this finding can be used to prove chronic intoxication in an individual, aid in solving drug-facilitated crimes and child custody cases, can be useful in conducting postmortem drug screening, drug testing in the workplace, etc

Background: Remarkable diversity of skin tones among Indians ranging from pale pinkish to dark brown appears to be an ideal choice for the assessment of skin pigment variation. Aim: The present study was designed to assess the variation observed in melanin and erythema indices among north Indians. Material and Methods: Skin reflectance data (n = 574) was collected from six diverse populations of north India using DermaSpectrometer (DSM II ColorMeter) followed by statistical analysis to investigate the impact of geographical location and gender on constitutive skin pigmentation. Results: The melanin index (MI) varied between 17.93 and 56.92 (Mean (M) = 35.80 ± 6.26) whereas the erythema index (EI) varied between 4.92 and 18.82 (M = 10.48 ± 2.68). MI and EI of females were found to be significantly lower than males (P < 0.001). Geographical location exhibited a significant association with MI and EI (P < 0.001). Furthermore, we have noted a positive correlation between MI and EI (P < 0.001). Conclusion: The study has refined our understanding of skin pigmentation variation among north Indians in terms of significant association with geographical location {MI: F_(5,568) = 31.07, P < 0.001; EI: F_(5,568) = 73.37, P < 0.001} and gender {MI: t₍₃₈₆₎ = −4.06, P < 0.001; EI: t₍₃₈₆₎ = −11.96, P < 0.001} and rendered opportunities for further studies.

Quantum algorithms for predictive analytics is important across various sectors such as finance, healthcare, marketing and supply chain management where accurate predictions can be used to optimise operations and decision-making processes. This chapter addresses Specific quantum machine learning algorithms such as Quantum Support Vector Machines (QSVM), Quantum K-nearest Neighbors (QKNN) and Quantum Neural Networks (QNN). These algorithms enhance classification, regression and complex predictive tasks by accelerating training processes and managing large feature spaces effectively. And, to this, quantum optimization algorithms like Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) are mentioned for their role in optimising model parameters and improving prediction accuracy. In addition, it discusses quantum data encoding methods, such as Quantum Fourier Transform (QFT) and amplitude encoding. Along with this, it acknowledges significant challenges such as hardware limitations, algorithmic complexity and practical integration issues.