Determination of phosphodiesterase 5 (PDE5)inhibitors in instant coffee premixes using liquid chromatography-high-resolution mass spectrometry (LC-HRMS)

© 2019 Elsevier B.V. As a widely consumed beverage, coffee tends to be a target for intentional adulteration. This study describes the application of modified quick, easy, cheap, effective, rugged, and safe (QuEChERS)coupled to liquid chromatography-high-resolution mass spectrometry (LC-HRMS)for simultaneous screening, identification, and quantification of undeclared phosphodiesterase 5 (PDE5)inhibitors in instant coffee premixes (ICPs). The mass spectrometer was operated in auto MS/MS acquisition for simultaneous MS and MS/MS experiments. Qualitative establishments from the suspected-target screening and targeted identification processes led to an unambiguous analyte assignment from the protonated molecule ([M+H]+)precursor ion which is subsequently used for quantification of 23 targeted PDE5 inhibitors. The analytical method validation covered specificity, linearity, range, accuracy, limit of detection (LOD), limit of quantification (LOQ), precisions, matrix effect (ME), and extraction recovery (RE). The specificity was established using the optimised chromatographic separation as well as the distinguishable [M+H]+ precursor ion. The linearity of each target analyte was demonstrated with a coefficient of determination (r2)of >0.9960 over the expected range of sample concentrations. The accuracy ranged from 88.1%–119.3% with LOD and LOQ of <70 ng/mL and 80 ng/mL, respectively. Excellent precisions were established within 0.4%–9.1% of the relative standard deviation. An insignificant ME within −5.2% to +8.7% was achieved using three different strategies of chromatography, sample extraction, and sample dilution. The RE was good for all target analytes within 84.7%–123.5% except for N-desethylacetildenafil at low (53.8%)and medium (65.1%)quality control levels. The method was successfully applied to 25 samples of ICPs where 17 of them were found to be adulterated with PDE5 inhibitors and their analogues. Further quantification revealed the total amount of these adulterants ranged from 2.77 to 121.64 mg per sachet

Data on the optimisation and validation of a liquid chromatography-high-resolution mass spectrometry (LC-HRMS) to establish the presence of phosphodiesterase 5 (PDE5) inhibitors in instant coffee premixes

© 2019 The Authors This paper presents the data on the optimisation and validation of a liquid chromatography-high-resolution mass spectrometry (LC-HRMS) to establish the presence of phosphodiesterase 5 (PDE5) inhibitors and their analogues as adulterants in instant coffee premixes. The method development data covered chromatographic optimisation for better analyte separation and isomeric resolution, mass spectrometry optimisation for high sensitivity and sample preparation optimisation for high extraction recovery (RE) and low matrix effect (ME). The validation data covered specificity, linearity, range, accuracy, limit of detection, limit of quantification, precisions, ME, and RE. The optimisation and validation data presented here is related to the article: “Determination of phosphodiesterase 5 (PDE5) inhibitors in instant coffee premixes using liquid chromatography-high-resolution mass spectrometry (LC-HRMS)” Mohd Yusop et al., 2019

Isolation and identification of an isomeric sildenafil analogue as an adulterant in an instant coffee premix

Abstract The proliferation of adulterated health foods and beverages in the market demands a comprehensive analytical strategy to identify the adulterants, particularly those of isomeric phosphodiesterase 5 (PDE5) inhibitors. An instant coffee premix (ICP) purchased from an online retailer was flagged for suspected adulteration through PDE5 inhibition assay. The ICP was then analysed using suspected-target and non-targeted screenings of a liquid chromatography-quadrupole time-of-flight mass spectrometry. Based on these findings, a PDE5 inhibitor initially assigned as compound X was isolated from the ICP by employing a liquid chromatography-diode array detection before its structural elucidation with liquid chromatography-ultraviolet (LC-UV) spectroscopy and nuclear magnetic resonance (NMR) spectroscopy. The suspected-target screening matched the protonated molecule ([M?+?H]+) precursor ion of compound X at m/z 499.2310 with two suspected analytes that are structural isomers of one another. The fragmentation patterns of compound X were comparable to those analogues in the dithiocarbodenafil group through the non-targeted screening. These findings, complemented by the LC-UV and NMR spectroscopy data, together with the chromatographic separation of related structural isomers, conclude the identity of compound X. To our best knowledge, this is the first study to report the presence of 3,5-dimethylpiperazinyl-dithiodesmethylcarbodenafil in an ICP sample. Key points The herbal-based male sexual performance products? lucrative market has instigated their rampant adulteration, particularly with PDE5 inhibitors. The adulterated products may also contain analogues of the approved PDE5 inhibitors, which usually passed into the market undetected as they are not included in the routine targeted screening procedure. The present study detected, isolated, and identified an isomeric sildenafil analogue from an instant coffee premix sample using rapid qualitative assay and comprehensive analytical analysis. This paper highlighted the applicability of the established strategies for routine casework, particularly in a forensic drug testing laboratory

The Use of International Classification of Diseases Codes to Identify Patients with Pancreatitis: A Systematic Review and Meta-analysis of Diagnostic Accuracy Studies.

BACKGROUND: Hospital discharge codes are increasingly used in gastroenterology research, but their accuracy in the setting of acute pancreatitis (AP) and chronic pancreatitis (CP), one of the most frequent digestive diseases, has never been assessed systematically. The aim was to conduct a systematic literature review and determine accuracy of diagnostic codes for AP and CP, as well as the effect of covariates. METHODS: Three databases (Pubmed, EMBASE and Scopus) were searched by two independent reviewers for relevant studies that used International Classification of Disease (ICD) codes. Summary estimates of sensitivity, specificity and positive predictive value were obtained from bivariate random-effects regression models. Sensitivity and subgroup analyses according to recurrence of AP and age of the study population were performed. RESULTS: A total of 24 cohorts encompassing 18,106 patients were included. The pooled estimates of sensitivity and specificity of ICD codes for AP were 0.85 and 0.96, respectively. The pooled estimates of sensitivity and specificity of ICD codes for CP were 0.75 and 0.94, respectively. The positive predictive value of ICD codes was 0.71 for either AP or CP. It increased to 0.78 when applied to incident episode of AP only. The positive predictive value decreased to 0.68 when the ICD codes were applied to paediatric patients. CONCLUSION: Nearly three out of ten patients are misidentified as having either AP or CP with the indiscriminate use of ICD codes. Limiting the use of ICD codes to adult patients with incident episode of AP may improve identification of patients with pancreatitis in administrative databases.COSMOS (Clinical and epidemiOlogical inveStigations in Metabolism, nutritiOn, and pancreatic diseaseS (COSMOS) program) is supported in part by the Royal Society of New Zealand (Rutherford Discovery Fellowship to Associate Professor Petrov

Forecasting future Humphrey Visual Fields using deep learning

Purpose To determine if deep learning networks could be trained to forecast future 24–2 Humphrey Visual Fields (HVFs). Methods All data points from consecutive 24–2 HVFs from 1998 to 2018 were extracted from a university database. Ten-fold cross validation with a held out test set was used to develop the three main phases of model development: model architecture selection, dataset combination selection, and time-interval model training with transfer learning, to train a deep learning artificial neural network capable of generating a point-wise visual field prediction. The pointwise mean absolute error (PMAE) and difference in Mean Deviation (MD) between predicted and actual future HVF were calculated. Results More than 1.7 million perimetry points were extracted to the hundredth decibel from 32,443 24–2 HVFs. The best performing model with 20 million trainable parameters, CascadeNet- 5, was selected. The overall point-wise PMAE for the test set was 2.47 dB (95% CI: 2.45 dB to 2.48 dB), and deep learning showed a statistically significant improvement over linear models. The 100 fully trained models successfully predicted future HVFs in glaucomatous eyes up to 5.5 years in the future with a correlation of 0.92 between the MD of predicted and actual future HVF and an average difference of 0.41 dB. Conclusions Using unfiltered real-world datasets, deep learning networks show the ability to not only learn spatio-temporal HVF changes but also to generate predictions for future HVFs up to 5.5 years, given only a single HVF

Gene expression relationship between prostate cancer cells of Gleason 3, 4 and normal epithelial cells as revealed by cell type-specific transcriptomes

Background: Prostate cancer cells in primary tumors have been typed CD10(-)/CD13(-)/CD24(hi)/CD26(+)/CD38(lo)/CD44(-)/CD104(-). This CD phenotype suggests a lineage relationship between cancer cells and luminal cells. The Gleason grade of tumors is a descriptive of tumor glandular differentiation. Higher Gleason scores are associated with treatment failure. Methods: CD26(+) cancer cells were isolated from Gleason 3+3 (G3) and Gleason 4+4 (G4) tumors by cell sorting, and their gene expression or transcriptome was determined by Affymetrix DNA array analysis. Dataset analysis was used to determine gene expression similarities and differences between G3 and G4 as well as to prostate cancer cell lines and histologically normal prostate luminal cells. Results: The G3 and G4 transcriptomes were compared to those of prostatic cell types of non-cancer, which included luminal, basal, stromal fibromuscular, and endothelial. A principal components analysis of the various transcriptome datasets indicated a closer relationship between luminal and G3 than luminal and G4. Dataset comparison also showed that the cancer transcriptomes differed substantially from those of prostate cancer cell lines. Conclusions: Genes differentially expressed in cancer are potential biomarkers for cancer detection, and those differentially expressed between G3 and G4 are potential biomarkers for disease stratification given that G4 cancer is associated with poor outcomes. Differentially expressed genes likely contribute to the prostate cancer phenotype and constitute the signatures of these particular cancer cell types.National Institutes of Health (NIH)[CA111244]National Institutes of Health (NIH)[CA98699]National Institutes of Health (NIH)[CA85859]National Institutes of Health (NIH)[DK63630][P50-GMO-76547

Estimating Retinal Sensitivity Using Optical Coherence Tomography With Deep-Learning Algorithms in Macular Telangiectasia Type 2

IMPORTANCE: As currently used, microperimetry is a burdensome clinical testing modality for testing retinal sensitivity requiring long testing times and trained technicians. OBJECTIVE: To create a deep-learning network that could directly estimate function from structure de novo to provide an en face high-resolution map of estimated retinal sensitivity. DESIGN, SETTING, AND PARTICIPANTS: A cross-sectional imaging study using data collected between January 1, 2016, and November 30, 2017, from the Natural History Observation and Registry of macular telangiectasia type 2 (MacTel) evaluated 38 participants with confirmed MacTel from 2 centers. MAIN OUTCOMES AND MEASURES: Mean absolute error of estimated compared with observed retinal sensitivity. Observed retinal sensitivity was obtained with fundus-controlled perimetry (microperimetry). Estimates of retinal sensitivity were made with deep-learning models that learned on superpositions of high-resolution optical coherence tomography (OCT) scans and microperimetry results. Those predictions were used to create high-density en face sensitivity maps of the macula. Training, validation, and test sets were segregated at the patient level. RESULTS: A total of 2499 microperimetry sensitivities were mapped onto 1708 OCT B-scans from 63 eyes of 38 patients (mean [SD] age, 74.3 [9.7] years; 15 men [39.5%]). The numbers of examples for our algorithm were 67 899 (103 053 after data augmentation) for training, 1695 for validation, and 1212 for testing. Mean absolute error results were 4.51 dB (95% CI, 4.36-4.65 dB) when using linear regression and 3.66 dB (95% CI, 3.53-3.78 dB) when using the LeNet model. Using a 49.9 million–variable deep-learning model, a mean absolute error of 3.36 dB (95% CI, 3.25-3.48 dB) of retinal sensitivity for validation and test was achieved. Correlation showed a high degree of agreement (Pearson correlation r = 0.78). By paired Wilcoxon rank sum test, our model significantly outperformed these 2 baseline models (P < .001). CONCLUSIONS AND RELEVANCE: High-resolution en face maps of estimated retinal sensitivities were created in eyes with MacTel. The maps were of unequalled resolution compared with microperimetry and were able to correctly delineate functionally healthy and impaired retina. This model may be useful to monitor structural and functional disease progression and has potential as an objective surrogate outcome measure in investigational trials

Integration of Genome Scale Metabolic Networks and gene regulation of metabolic enzymes with Physiologically Based Pharmacokinetics

The scope of Physiologically Based Pharmacokinetic (PBPK) modelling can be expanded by assimilation of the mechanistic models of intracellular processes from Systems Biology field. Genome Scale Metabolic Networks (GSMNs) represent a whole set of metabolic enzymes expressed in human tissues. Dynamic models of the gene regulation of key drug metabolism enzymes are available. Here, we introduce GSMNs and review ongoing work on integration of PBPK, GSMNs and metabolic gene regulation. We demonstrate example models

Genomic analysis of the function of the transcription factor gata3 during development of the Mammalian inner ear

We have studied the function of the zinc finger transcription factor gata3 in auditory system development by analysing temporal profiles of gene expression during differentiation of conditionally immortal cell lines derived to model specific auditory cell types and developmental stages. We tested and applied a novel probabilistic method called the gamma Model for Oligonucleotide Signals to analyse hybridization signals from Affymetrix oligonucleotide arrays. Expression levels estimated by this method correlated closely (p<0.0001) across a 10-fold range with those measured by quantitative RT-PCR for a sample of 61 different genes. In an unbiased list of 26 genes whose temporal profiles clustered most closely with that of gata3 in all cell lines, 10 were linked to Insulin-like Growth Factor signalling, including the serine/threonine kinase Akt/PKB. Knock-down of gata3 in vitro was associated with a decrease in expression of genes linked to IGF-signalling, including IGF1, IGF2 and several IGF-binding proteins. It also led to a small decrease in protein levels of the serine-threonine kinase Akt2/PKB beta, a dramatic increase in Akt1/PKB alpha protein and relocation of Akt1/PKB alpha from the nucleus to the cytoplasm. The cyclin-dependent kinase inhibitor p27(kip1), a known target of PKB/Akt, simultaneously decreased. In heterozygous gata3 null mice the expression of gata3 correlated with high levels of activated Akt/PKB. This functional relationship could explain the diverse function of gata3 during development, the hearing loss associated with gata3 heterozygous null mice and the broader symptoms of human patients with Hearing-Deafness-Renal anomaly syndrome