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Cruz J, Sáez-Hernández R, Armenta S, Morales-Rubio AE, Cervera ML. 3D-printed portable device for illicit drug identification based on smartphone-imaging and artificial neural networks. Talanta 2024; 276:126217. [PMID: 38759361 DOI: 10.1016/j.talanta.2024.126217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 05/03/2024] [Accepted: 05/05/2024] [Indexed: 05/19/2024]
Abstract
In this manuscript, a 3D-printed analytical device has been successfully developed to classify illicit drugs using smartphone-based colorimetry. Representative compounds of different families, including cocaine, 3,4-methylenedioxy-methamphetamine (MDMA), amphetamine and cathinone derivatives, pyrrolidine cathinones, and 3,4-methylenedioxy cathinones, have been analyzed and classified after appropriate reaction with Marquis, gallic acid, sulfuric acid, Simon and Scott reagents. A picture of the colored products was acquired using a smartphone, and the corrected RGB values were used as input data in the chemometric treatment. ANN using two active layers of nodes (6 nodes in layer 1 and 2 nodes in layer 2) with a sigmoidal transfer function and a minimum strict threshold of 0.50 identified illicit drug samples with a sensitivity higher than 83.4 % and a specificity of 100 % with limits of detection in the microgram range. The 3D printed device can operate connected to a rechargeable lithium-ion cell portable battery, is inexpensive, and requires minimal training. The analytical device has been able to discriminate the analyzed psychoactive substances from cutting and mixing agents, being a useful tool for law enforcement agents to use as a screening method.
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Affiliation(s)
- J Cruz
- EUSS School of Engineering, Pg. Sant Joan Bosco, 74, 08017, Barcelona, Spain.
| | - R Sáez-Hernández
- Department of Analytical Chemistry, University of Valencia, Dr. Moliner 50, 46100, Burjassot, Valencia, Spain
| | - S Armenta
- Department of Analytical Chemistry, University of Valencia, Dr. Moliner 50, 46100, Burjassot, Valencia, Spain
| | - A E Morales-Rubio
- Department of Analytical Chemistry, University of Valencia, Dr. Moliner 50, 46100, Burjassot, Valencia, Spain
| | - M L Cervera
- Department of Analytical Chemistry, University of Valencia, Dr. Moliner 50, 46100, Burjassot, Valencia, Spain
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Zhong Y, Shen C, Xi X, Luo Y, Ding P, Luo L. Multitask joint learning with graph autoencoders for predicting potential MiRNA-drug associations. Artif Intell Med 2023; 145:102665. [PMID: 37925217 DOI: 10.1016/j.artmed.2023.102665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 06/14/2023] [Accepted: 09/14/2023] [Indexed: 11/06/2023]
Abstract
The occurrence of many diseases is associated with miRNA abnormalities. Predicting potential drug-miRNA associations is of great importance for both disease treatment and new drug discovery. Most computation-based approaches learn one task at a time, ignoring the information contained in other tasks in the same domain. Multitask learning can effectively enhance the prediction performance of a single task by extending the valid information of related tasks. In this paper, we presented a multitask joint learning framework (MTJL) with a graph autoencoder for predicting the associations between drugs and miRNAs. First, we combined multiple pieces of information to construct a high-quality similarity network of both drugs and miRNAs and then used a graph autoencoder (GAE) to learn their embedding representations separately. Second, to further improve the embedding quality of drugs, we added an auxiliary task to classify drugs using the learned representations. Finally, the embedding representations of drugs and miRNAs were linearly transformed to obtain the predictive association scores between them. A comparison with other state-of-the-art models shows that MTJL has the best prediction performance, and ablation experiments show that the auxiliary task can enhance the embedding quality and improve the robustness of the model. In addition, we show that MTJL has high utility in predicting potential associations between drugs and miRNAs by conducting two case studies.
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Affiliation(s)
- Yichen Zhong
- School of Computer Science, University of South China, Hengyang 421001, China
| | - Cong Shen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410083, China
| | - Xiaoting Xi
- School of Computer Science, University of South China, Hengyang 421001, China
| | - Yuxun Luo
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411105, China
| | - Pingjian Ding
- School of Computer Science, University of South China, Hengyang 421001, China
| | - Lingyun Luo
- School of Computer Science, University of South China, Hengyang 421001, China.
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Affiliation(s)
- Farid Belialov
- Department of Gerontology, Geriatrics, and Clinical Pharmacology, Russian Medical Academy of Continuous Professional Education, Yubileiny 100/4, Irkutsk, 664079, Russia.
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Mohd Yusof N, Muda AK, Pratama SF, Carbo-Dorca R. Amphetamine-type stimulants (ATS) drug classification using shallow one-dimensional convolutional neural network. Mol Divers 2021; 26:1609-1619. [PMID: 34338915 DOI: 10.1007/s11030-021-10289-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 07/27/2021] [Indexed: 10/20/2022]
Abstract
Amphetamine-type stimulants (ATS) drug analysis and identification are challenging and critical nowadays with the emergence production of new synthetic ATS drugs with sophisticated design compounds. In the present study, we proposed a one-dimensional convolutional neural network (1DCNN) model to perform ATS drug classification as an alternative method. We investigate as well as explore the classification behavior of 1DCNN with the utilization of the existing novel 3D molecular descriptors as ATS drugs representation to become the model input. The proposed 1DCNN model is composed of one convolutional layer to reduce the model complexity. Besides, pooling operation that is a standard part of traditional CNN is not applied in this architecture to have more features in the classification phase. The dropout regularization technique is employed to improve model generalization. Experiments were conducted to find the optimal values for three dominant hyper-parameters of the 1DCNN model which are the filter size, transfer function, and batch size. Our findings found that kernel size 11, exponential linear unit (ELU) transfer function and batch size 32 are optimal for the 1DCNN model. A comparison with several machine learning classifiers has shown that our proposed 1DCNN has achieved comparable performance with the Random Forest classifier and competitive performance with the others.
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Affiliation(s)
- Norfadzlia Mohd Yusof
- Fakulti Teknologi Kejuruteraan Elektrik dan Elektronik, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, 76100, Melaka, Malaysia
| | - Azah Kamilah Muda
- Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, 76100, Melaka, Malaysia.
| | - Satrya Fajri Pratama
- Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, 76100, Melaka, Malaysia
| | - Ramon Carbo-Dorca
- Institut de Qu´ımica Computacional i Cata`lisi, Universitat de Girona, 17071, Girona, Catalonia, Spain
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Mouazer A, Sedki K, Tsopra R, Lamy JB. Visual Comparison of Guidelines: Method and Application to Potentially Inappropriate Medication Lists. Stud Health Technol Inform 2021; 281:248-52. [PMID: 34042743 DOI: 10.3233/SHTI210158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Therapeutic guidelines developed by experts are essential tools for improving therapy and drug prescription. Several guidelines often exist that target the same patient, from different organizations and countries. The case of lists for the detection of potentially inappropriate medications (PIMs) is an example which illustrates how these guidelines can be varied and multiple. In order to have an overview to the divergences and similarities between different lists of PIMs, we propose a visual method to compare PIMs lists, based on set visualization, and we apply it to 5 guidelines.
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Amini Pouya M, Afshani SM, Maghsoudi AS, Hassani S, Mirnia K. Classification of the present pharmaceutical agents based on the possible effective mechanism on the COVID-19 infection. Daru 2020; 28:745-764. [PMID: 32734518 PMCID: PMC7391927 DOI: 10.1007/s40199-020-00359-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 07/14/2020] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVES There are several types of research on the COVID-19 disease which have been conducting. It seems that prevailing over the pandemic would be achieved only by mastering over the virus pathophysiology. We tried to categorize the massive amount of available information for useful interpretation. EVIDENCE ACQUISITION We searched databases with different keywords and search strategies that focus on virulence and pathophysiology of COVID-19. The present review has aimed to gather and categorize all implemented drugs based on the susceptible virulence mechanisms, and the pathophysiological events in the host cells, discussing and suggesting treatments. RESULTS As a result, the COVID-19 lifecycle were categorized as following steps: "Host Cell Attachment" which is mainly conducted with ACE2 receptors and TMPRSS2 from the host cell and Spike (S) protein, "Endocytosis Pathway" which is performed mainly by clathrin-mediated endocytosis, and "Viral Replication" which contains translation and replication of RNA viral genome. The virus pathogenicity is continued by "Inflammatory Reactions" which mainly caused moderate to severe COVID-19 disease. Besides, the possible effective therapeutics' mechanism and the pharmaceutical agents that had at least one experience as a preclinical or clinical study on COVID-19 were clearly defined. CONCLUSION The treatment protocol would be occasional based on the stage of the infection and the patient situation. The cocktail of medicines, which could affect almost all mentioned stages of COVID-19 disease, might be vital for patients with severe phenomena. The classification of the possible mechanism of medicines based on COVID-19 pathogenicity.
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Affiliation(s)
- Maryam Amini Pouya
- Department of Pharmaceutics, School of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyyedeh Maryam Afshani
- Department of Pharmacoeconomics, School of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
| | - Armin Salek Maghsoudi
- Department of Toxicology and Pharmacology, School of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
| | - Shokoufeh Hassani
- Department of Toxicology and Pharmacology, School of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran.
- Toxicology and Diseases Group (TDG), Pharmaceutical Sciences Research Center (PSRC), the Institute of Pharmaceutical Sciences (TIPS), Tehran University of Medical Sciences, Tehran, Iran.
| | - Kayvan Mirnia
- Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran.
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Abstract
BACKGROUND The history of discovery of analgesic drugs has followed a trajectory from original serendipitous discovery of plant-derived substances to laboratory creation of customized molecules that are intentionally designed to interact with specific receptors of neurotransmitters involved in either the transmission of the pain signal or the attenuation of such a signal. The drugs most recently developed have been designed to provide incremental greater separation between pain relief and adverse effects. The result has been drugs that have individualized pharmacodynamic and pharmacokinetic characteristics that represent specific advances in basic science and translate into unique clinical profiles. Several of the drugs include non-opioid components. They retain some of the features of opioids, but have distinct clinical characteristics that differentiate them from traditional opioids. Thus they defy simple classification as opioids. SCOPE A summary is provided of the development of the modern view of multi-mechanistic pain and its treatment using analgesics that have multi-mechanisms of action (consisting of both opioid and non-opioid components). Descriptions of examples of such current analgesics and of those that have pharmacokinetic characteristics that result in atypical opioid clinical profiles are given. FINDINGS By serendipity or design, several current strong analgesics have opioid components of action, but have an additional non-opioid mechanism of action or some pharmacokinetic feature that gives them an atypical opioid clinical profile and renders them not easily classified as classical opioids. CONCLUSION An appreciation that there are now opioid analgesics that differentiate from classical opioids in ways that defy their simplistic classification as opioids suggests that recognition of subclasses of opioid analgesics would be more accurate scientifically and would be more informative for healthcare providers and regulators. This would likely lead to positive outcomes for the clinical use and regulatory control of the current drugs, and provide direction/strategy for the discovery of new drugs.
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Affiliation(s)
- Robert B Raffa
- Temple University School of Pharmacy , Philadelphia, PA , USA
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Abstract
This paper considers drug classifications and terms widely used in US survey research, and compares these to classifications and terms used by drug users. We begin with a critical review of drug classification systems, including those oriented to public policy and health services as well as survey research. We then consider the results of a pile sort exercise we conducted with 76 respondents within a mixed method study of Southeast Asian American adolescent and young adult drug users in urban Northern California, USA. We included the pile sort to clarify how respondents handled specific terms which we understood to be related to Ecstasy and methamphetamines. Results of the pile sort were analyzed using graphic layout algorithms as well as content analysis of pile labels. Similar to the national surveys, our respondents consistently differentiated Ecstasy terms from methamphetamine terms. We found high agreement between some specific local terms (thizz, crystal) and popular drug terms, while other terms thought to be mainstream (crank, speed) were reported as unknown by many respondents. In labeling piles, respondents created taxonomies based on consumption method (in particular, pill) as well as the social contexts of use. We conclude by proposing that divergences between drug terms utilized in survey research and those used by drug users may reflect two opposing tendencies: the tendency of survey researchers to utilize standardized language that constructs persons and experiences as relatively homogeneous, varying only within measurable degrees, and the tendency of drug users to utilize specialized language (argot) that reflects their understandings of their experiences as hybrid and diverse. The findings problematize the validity of drug terms and categories used in survey research.
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Affiliation(s)
- Juliet P. Lee
- Prevention Research Center, Pacific Institute for Research and Evaluation
| | - Tamar M.J. Antin
- Prevention Research Center, Pacific Institute for Research and Evaluation
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