Investigation and Resolution of Interference in the LC-QTOF-MS Detection of 4-MePPP

2021 ◽  
Author(s):  
Darren R Allen ◽  
Christopher Warnholtz ◽  
Brett C McWhinney
Keyword(s):  
Reference Material ◽  
Spectral Data ◽  
False Positive ◽  
In Silico ◽  
Spectral Library ◽  
In Silico Fragmentation ◽  
False Positive Detection ◽  
Product Ions ◽  
Synthetic Cathinone ◽  
Recent Addition

Abstract An interference resulting in the false-positive detection of the synthetic cathinone 4-MePPP in urine was suspected following the recent addition of 4-MePPP spectral data to an LC-QTOF-MS drug library. Although positive detection criteria were achieved, it was noted that all urine samples suspected of containing 4-MePPP also concurrently contained high levels of tramadol and its associated metabolites. Using QTOF-MS software elucidation tools, candidate compounds for the suspected interference were proposed. To provide further confidence in the identity of the interference, in silico fragmentation tools were used to match product ions generated in the analysis with product ions predicted from the theoretical fragmentation of candidate compounds. The ability of the suspected interference to subsequently produce the required product ions for spectral library identification of 4-MePPP was also tested. This information was used to provide a high preliminary confidence in the compound identity prior to purchase and subsequent confirmation with certified reference material. A co-eluting isobaric interference was identified and confirmed as an in-source fragment of the tramadol metabolite, N,N-bisdesmethyltramadol. Proposed resolutions for this interference are also described and subsequently validated by retrospective interrogation of previous cases of suspected interference.

Making reliable negative predictions of human skin sensitisation using an in silico fragmentation approach

2018 ◽  
Vol 95 ◽  
pp. 227-235 ◽  
Author(s):  
Martyn L. Chilton ◽  
Donna S. Macmillan ◽  
Thomas Steger-Hartmann ◽  
Jedd Hillegass ◽  
Phillip Bellion ◽  
...  
Keyword(s):  
Human Skin ◽  
In Silico ◽  
Skin Sensitisation ◽  
In Silico Fragmentation

scholarly journals Potential Antigenic Cross-reactivity Between Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and Dengue Viruses

2020 ◽  
Author(s):  
Yaniv Lustig ◽  
Shlomit Keler ◽  
Rachel Kolodny ◽  
Nir Ben-Tal ◽  
Danit Atias-Varon ◽  
...  
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Envelope Protein ◽  
False Positive ◽  
In Silico ◽  
Rapid Test ◽  
Cross Reactivity ◽  
Lateral Flow ◽  
Immunoglobulin M ◽  
Enzyme Linked Immunosorbent Assay ◽  
Elisa Testing

Abstract Background Coronavirus disease 2019 (COVID-19) and dengue fever are difficult to distinguish given shared clinical and laboratory features. Failing to consider COVID-19 due to false-positive dengue serology can have serious implications. We aimed to assess this possible cross-reactivity. Methods We analyzed clinical data and serum samples from 55 individuals with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. To assess dengue serology status, we used dengue-specific antibodies by means of lateral-flow rapid test, as well as enzyme-linked immunosorbent assay (ELISA). Additionally, we tested SARS-CoV-2 serology status in patients with dengue and performed in-silico protein structural analysis to identify epitope similarities. Results Using the dengue lateral-flow rapid test we detected 12 positive cases out of the 55 (21.8%) COVID-19 patients versus zero positive cases in a control group of 70 healthy individuals (P = 2.5E−5). This includes 9 cases of positive immunoglobulin M (IgM), 2 cases of positive immunoglobulin G (IgG), and 1 case of positive IgM as well as IgG antibodies. ELISA testing for dengue was positive in 2 additional subjects using envelope protein directed antibodies. Out of 95 samples obtained from patients diagnosed with dengue before September 2019, SARS-CoV-2 serology targeting the S protein was positive/equivocal in 21 (22%) (16 IgA, 5 IgG) versus 4 positives/equivocal in 102 controls (4%) (P = 1.6E−4). Subsequent in-silico analysis revealed possible similarities between SARS-CoV-2 epitopes in the HR2 domain of the spike protein and the dengue envelope protein. Conclusions Our findings support possible cross-reactivity between dengue virus and SARS-CoV-2, which can lead to false-positive dengue serology among COVID-19 patients and vice versa. This can have serious consequences for both patient care and public health.

scholarly journals False Positive Decremented Research for Fire and Smoke Detection in Surveillance Camera using Spatial and Temporal Features Based on Deep Learning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1167 ◽  
Author(s):  
Yeunghak Lee ◽  
Jaechang Shim
Keyword(s):  
Deep Learning ◽  
False Positive ◽  
Learning Algorithm ◽  
Detection Performance ◽  
Economic Losses ◽  
Smoke Detection ◽  
Temporal Features ◽  
False Positive Detection ◽  
Surveillance Cameras ◽  
Fire Flame

Fire must be extinguished early, as it leads to economic losses and losses of precious lives. Vision-based methods have many difficulties in algorithm research due to the atypical nature fire flame and smoke. In this study, we introduce a novel smoke detection algorithm that reduces false positive detection using spatial and temporal features based on deep learning from factory installed surveillance cameras. First, we calculated the global frame similarity and mean square error (MSE) to detect the moving of fire flame and smoke from input surveillance cameras. Second, we extracted the fire flame and smoke candidate area using the deep learning algorithm (Faster Region-based Convolutional Network (R-CNN)). Third, the final fire flame and smoke area was decided by local spatial and temporal information: frame difference, color, similarity, wavelet transform, coefficient of variation, and MSE. This research proposed a new algorithm using global and local frame features, which is well presented object information to reduce false positive based on the deep learning method. Experimental results show that the false positive detection of the proposed algorithm was reduced to about 99.9% in maintaining the smoke and fire detection performance. It was confirmed that the proposed method has excellent false detection performance.

scholarly journals Multiple Compounds Recognition from The Tandem Mass Spectral Data Using Convolutional Neural Network

Molecules ◽  
2019 ◽  
Vol 24 (24) ◽  
pp. 4590
Author(s):  
Jiali Lv ◽  
Jian Wei ◽  
Zhenyu Wang ◽  
Jin Cao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Spectral Data ◽  
Test Data ◽  
False Positive ◽  
Tandem Mass ◽  
True Positive ◽  
Mass Spectral Data ◽  
True Negative ◽  
Mass Spectral

Mixtures analysis can provide more information than individual components. It is important to detect the different compounds in the real complex samples. However, mixtures are often disturbed by impurities and noise to influence the accuracy. Purification and denoising will cost a lot of algorithm time. In this paper, we propose a model based on convolutional neural network (CNN) which can analyze the chemical peak information in the tandem mass spectrometry (MS/MS) data. Compared with traditional analyzing methods, CNN can reduce steps in data preprocessing. This model can extract features of different compounds and classify multi-label mass spectral data. When dealing with MS data of mixtures based on the Human Metabolome Database (HMDB), the accuracy can reach at 98%. In 600 MS test data, 451 MS data were fully detected (true positive), 142 MS data were partially found (false positive), and 7 MS data were falsely predicted (true negative). In comparison, the number of true positive test data for support vector machine (SVM) with principal component analysis (PCA), deep neural network (DNN), long short-term memory (LSTM), and XGBoost respectively are 282, 293, 270, and 402; the number of false positive test data for four models are 318, 284, 198, and 168; the number of true negative test data for four models are 0, 23, 7, 132, and 30. Compared with the model proposed in other literature, the accuracy and model performance of CNN improved considerably by separating the different compounds independent MS/MS data through three-channel architecture input. By inputting MS data from different instruments, adding more offset MS data will make CNN models have stronger universality in the future.

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