Interval importance index to select relevant ATR-FTIR wavenumber Intervals for falsified drug classification

Gaussian process regression coupled with mRMR to predict adulterant concentration in cocaine

Street cocaine is often mixed with various substances that intensify its harmful effects. This paper proposes a framework to identify attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) intervals that best predict the concentration of adulterants in cocaine samples. Wavelengths are ranked according to their relevance through ReliefF and mRMR feature selection approaches, and an iterative process removes less relevant wavelengths based on the ranking suggested by each approach. Gaussian Process (GP) regression models are constructed after each wavelength removal and the prediction performance is evaluated using RMSE. The subset balancing a low RMSE value and a small percentage of retained wavelengths is chosen. The proposed framework was validated using a dataset consisting of 345 samples of cocaine with different amounts of levamisole, caffeine, phenacetin, and lidocaine. Averaged over the four adulterants, the GP regression coupled with the mRMR retained 1.07 % of the 662 original wavelengths, outperforming PLS and SVR regarding prediction performance.

Grade classification of human glioma using a convolutional neural network based on mid-infrared spectroscopy mapping

This study proposes a convolutional neural network (CNN)-based computer-aided diagnosis (CAD) system for the grade classification of human glioma by using mid-infrared (MIR) spectroscopic mappings. Through data augmentation of pixels recombination, the mappings in the training set increased almost 161 times relative to the original mappings. The pixels of the recombined mappings in the training set came from all of the one-dimensional (1D) vibrational spectroscopy of 62 (almost 80% of all 77 patients) patients at specific bands. Compared with the performance of the CNN-CAD system based on the 1D vibrational spectroscopy, we found that the mean diagnostic accuracy of the recombined MIR spectroscopic mappings at peaks of 2917 cm^-1 , 1539 cm^-1 and 1234 cm^-1 on the test set performed higher and the model also had more stable patterns. This research demonstrates that two-dimensional MIR mapping at a single frequency can be used by the CNN-CAD system for diagnosis and the research also gives a prompt that the mapping collection process can be replaced by a single-frequency IR imaging system, which is cheaper and more portable than a Fourier transform infrared microscopy and thus may be widely utilized in hospitals to provide meaningful assistance for pathologists in clinics.

Facing Counterfeit Medications in Sexual Medicine. A Systematic Scoping Review on Social Strategies and Technological Solutions

INTRODUCTION

The counterfeit phenomenon is a largely under-reported issue, with potentially large burden for healthcare. The market for counterfeit drugs used in sexual medicine, most notably type 5 phosphodiesterase inhibitors (PDE5i), is rapidly growing.

AIMS

To report the health risks associated with the use of counterfeit medications, the reasons driving their use, and the strategies enacted to contain this phenomenon.

METHODS

A systematic scoping review of the literature regarding counterfeit PDE5i was carried between January and June 2021, then updated in August 2021.

MAIN OUTCOME MEASURE

We primarily aimed to clarify the main drivers for counterfeit PDE5i use, the health risks associated, and the currently available strategies to fight counterfeiters.

RESULTS

One hundred thirty-one records were considered for the present scoping review. Production of fake PDE5i is highly lucrative and the lacking awareness of the potential health risks makes it a largely exploitable market by counterfeiters. Adulteration with other drugs, microbial contamination and unreliable dosages make counterfeit medications a cause of worry also outside of the sexual medicine scope. Several laboratory techniques have been devised to identify and quantify the presence of other compounds in counterfeit medications. Strategies aimed at improving awareness, providing antitampering packaging and producing non-falsifiable products, such as the orodispersible formulations, are also described.

CLINICAL IMPLICATIONS

Improving our understanding of the PDE5i counterfeit phenomenon can be helpful to promote awareness of this issue and to improve patient care.

STRENGTHS & LIMITATIONS

Despite the systematic approach, few clinical studies were retrieved, and data concerning the prevalence of counterfeit PDE5i use is not available on a global scale.

CONCLUSION

The counterfeit phenomenon is a steadily growing issue, with PDE5i being the most counterfeited medication with potentially large harmful effects on unaware consumers. Sansone A, Cuzin B, and Jannini EA. Facing Counterfeit Medications in Sexual Medicine. A Systematic Scoping Review on Social Strategies and Technological Solutions. Sex Med 2021;9:100437.

Selectivity of identification of compounds from the group of phosphodiesterase-5 inhibitors (PDE-5i) in falsified products from the Polish market using attenuated total reflectance Fourier transform infrared spectroscopy and X-ray powder diffraction

Falsified drugs are a growing problem and a great threat all over the world. Searching for easy, fast, not expensive and reproducible methods for identification of active pharmaceutical ingredients (APIs) is an important issue. The application of ATR-IR technique for identification of phosphodiesterase-5 inhibitors (PDE-5i) in falsified products from the Polish market has been demonstrated. Furthermore, the strategy of confirmation of the product's originality inaquick and easy way has been developed. The advantages of this application are: exceptional simplicity, very short measurement time, high selectivity inrelation to structurally similar substances and almost no sample preparation. After validation, developed strategy has been successfully introduced into routine analysis.

Combining wavelength importance ranking to the random forest classifier to analyze multiclass spectral data

Near Infrared (NIR) is a type of vibrational spectroscopy widely used in different areas to characterize substances. NIR datasets are comprised of absorbance measures on a range of wavelengths (λ). Typically noisy and correlated, the use of such datasets tend to compromise the performance of several statistical techniques; one way to overcome that is to select portions of the spectra in which wavelengths are more informative. In this paper we investigate the performance of the Random Forest (RF) classifier associated with several wavelength importance ranking approaches on the task of classifying product samples into categories, such as quality levels or authenticity. Our propositions are tested using six NIR datasets comprised of two or more classes of food and pharmaceutical products, as well as illegal drugs. Our proposed classification model, an integration of the χ² ranking score and the RF classifier, substantially reduced the number of wavelengths in the dataset, while increasing the classification accuracy when compared to the use of complete datasets. Our propositions also presented good performance when compared to competing methods available in the literature.

Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets

The Coronavirus pandemic caused by the novel SARS-CoV-2 has significantly impacted human health and the economy, especially in countries struggling with financial resources for medical testing and treatment, such as Brazil's case, the third most affected country by the pandemic. In this scenario, machine learning techniques have been heavily employed to analyze different types of medical data, and aid decision making, offering a low-cost alternative. Due to the urgency to fight the pandemic, a massive amount of works are applying machine learning approaches to clinical data, including complete blood count (CBC) tests, which are among the most widely available medical tests. In this work, we review the most employed machine learning classifiers for CBC data, together with popular sampling methods to deal with the class imbalance. Additionally, we describe and critically analyze three publicly available Brazilian COVID-19 CBC datasets and evaluate the performance of eight classifiers and five sampling techniques on the selected datasets. Our work provides a panorama of which classifier and sampling methods provide the best results for different relevant metrics and discuss their impact on future analyses. The metrics and algorithms are introduced in a way to aid newcomers to the field. Finally, the panorama discussed here can significantly benefit the comparison of the results of new ML algorithms.

Forensic analysis of anabolic steroids tablets composition using attenuated total reflection Fourier transform infrared microspectroscopy (µATR-FTIR) mapping

The use of falsified and unregistered drugs is a worldwide public health problem. Because these global market products usually do not follow the Good Manufacturing Practices required by health legislation, its composition may be completely different from the original or may contain relevant concentrations of impurities and toxic contaminants. Since anabolic steroids are among the main irregular therapeutic classes seized in Brazil, here we propose a new methodology for analyzing these products, in tablets form, using Attenuated Total Reflection Fourier Transform Infrared Microspectroscopy (µATR-FTIR) mapping. Spectra were acquired from solid tablets by attenuated total reflection, through point mapping methodology. In data processing, a characteristic absorption band for each Active Pharmaceutical Ingredient (API) was integrated and plotted to create its distribution map. This technique was applied in an unprecedented way for the forensic analysis of anabolic steroids and proved to be effective in distinguishing falsified products based on the detection of their APIs. It was possible to detect APIs in 26 out of 30 samples, five of which were classified as falsified only through µATR-FTIR analysis. We were able to create distribution maps of the detected substances associating the microspectroscopic results with characteristic band integration method, which can be used to detect substances and to study samples' homogeneity. We concluded that this methodology is promising for the analysis of anabolic steroid tablets, and can be used in a complementary way with techniques already consolidated in forensic laboratory routine for a better classification of questioned samples between authentic and falsified ones.

Applications of Fourier transform infrared spectroscopy to pharmaceutical preparations

Introduction: Various pharmaceutical preparations are widely used for clinical treatment. Elucidation of the mechanisms of drug release and evaluation of drug efficacy in biological samples are important in drug design and drug quality control.Areas covered: This review classifies recent applications of Fourier transform infrared (FTIR) spectroscopy in the field of medicine to comprehend drug release and diffusion. Drug release is affected by many factors of preparations, such as drug delivery system and microstructure polymorphism. The applications of FTIR imaging and nano-FTIR technique in biological samples lay a foundation for studying drug mechanism in vivo.Expert opinion: FTIR spectroscopy meets the research needs on preparations to understand the processes and mechanisms underlying drug release. The combination of attenuated total reflectance-FTIR imaging and nano-FTIR accompanied by chemometrics is a potent tool to overcome the deficiency of conventional infrared detection. FTIR shows an enormous potential in drug characterization, drug quality control, and bio-sample detection.