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Objective: We retrospectively screened 350,116 electronic health records (EHRs) to identify suspected patients for Pompe disease. Using these suspected patients, we then describe their phenotypical characteristics and estimate the prevalence in the respective population covered by the EHRs. Methods: We applied Symptoma's Artificial Intelligence-based approach for identifying rare disease patients to retrospective anonymized EHRs provided by the "University Hospital Salzburg" clinic group. Within 1 month, the AI screened 350,116 EHRs reaching back 15 years from five hospitals, and 104 patients were flagged as probable for Pompe disease. Flagged patients were manually reviewed and assessed by generalist and specialist physicians for their likelihood for Pompe disease, from which the performance of the algorithms was evaluated. Results: Of the 104 patients flagged by the algorithms, generalist physicians found five "diagnosed," 10 "suspected," and seven patients with "reduced suspicion." After feedback from Pompe disease specialist physicians, 19 patients remained clinically plausible for Pompe disease, resulting in a specificity of 18.27% for the AI. Estimating from the remaining plausible patients, the prevalence of Pompe disease for the greater Salzburg region [incl. Bavaria (Germany), Styria (Austria), and Upper Austria (Austria)] was one in every 18,427 people. Phenotypes for patient cohorts with an approximated onset of symptoms above or below 1 year of age were established, which correspond to infantile-onset Pompe disease (IOPD) and late-onset Pompe disease (LOPD), respectively. Conclusion: Our study shows the feasibility of Symptoma's AI-based approach for identifying rare disease patients using retrospective EHRs. Via the algorithm's screening of an entire EHR population, a physician had only to manually review 5.47 patients on average to find one suspected candidate. This efficiency is crucial as Pompe disease, while rare, is a progressively debilitating but treatable neuromuscular disease. As such, we demonstrated both the efficiency of the approach and the potential of a scalable solution to the systematic identification of rare disease patients. Thus, similar implementation of this methodology should be encouraged to improve care for all rare disease patients. [ABSTRACT FROM AUTHOR]

ObjectiveWe retrospectively screened 350,116 electronic health records (EHRs) to identify suspected patients for Pompe disease. Using these suspected patients, we then describe their phenotypical characteristics and estimate the prevalence in the respective population covered by the EHRs.MethodsWe applied Symptoma's Artificial Intelligence-based approach for identifying rare disease patients to retrospective anonymized EHRs provided by the “University Hospital Salzburg” clinic group. Within 1 month, the AI screened 350,116 EHRs reaching back 15 years from five hospitals, and 104 patients were flagged as probable for Pompe disease. Flagged patients were manually reviewed and assessed by generalist and specialist physicians for their likelihood for Pompe disease, from which the performance of the algorithms was evaluated.ResultsOf the 104 patients flagged by the algorithms, generalist physicians found five “diagnosed,” 10 “suspected,” and seven patients with “reduced suspicion.” After feedback from Pompe disease specialist physicians, 19 patients remained clinically plausible for Pompe disease, resulting in a specificity of 18.27% for the AI. Estimating from the remaining plausible patients, the prevalence of Pompe disease for the greater Salzburg region [incl. Bavaria (Germany), Styria (Austria), and Upper Austria (Austria)] was one in every 18,427 people. Phenotypes for patient cohorts with an approximated onset of symptoms above or below 1 year of age were established, which correspond to infantile-onset Pompe disease (IOPD) and late-onset Pompe disease (LOPD), respectively.ConclusionOur study shows the feasibility of Symptoma's AI-based approach for identifying rare disease patients using retrospective EHRs. Via the algorithm's screening of an entire EHR population, a physician had only to manually review 5.47 patients on average to find one suspected candidate. This efficiency is crucial as Pompe disease, while rare, is a progressively debilitating but treatable neuromuscular disease. As such, we demonstrated both the efficiency of the approach and the potential of a scalable solution to the systematic identification of rare disease patients. Thus, similar implementation of this methodology should be encouraged to improve care for all rare disease patients.

The expanding and dynamic market of new psychoactive substances (NPSs) poses challenges for laboratories worldwide. The retrospective data analysis (RDA) of previously analyzed samples for new targets can be used to investigate analytes missed in the first data analysis. However, RDA has historically been unsuitable for routine evaluation because reprocessing and reevaluating large numbers of forensic samples are highly work- and time-consuming. In this project, we developed an efficient and scalable retrospective data analysis workflow that can easily be tailored and optimized for groups of NPSs. The objectives of the study were to establish a retrospective data analysis workflow for benzodiazepines in whole blood samples and apply it on previously analyzed driving-under-the-influence-of-drugs (DUID) cases. The RDA workflow was based on a training set of hits in ultrahigh-performance liquid chromatography–quadrupole time-of-flight–mass spectrometry (UHPLC-QTOF-MS) data files, corresponding to common benzodiazepines that also had been analyzed with a complementary UHPLC–tandem mass spectrometry (MS/MS) method. Quantitative results in the training set were used as the true condition to evaluate whether a hit in the UHPLC-QTOF-MS data file was true or false positive. The training set was used to evaluate and set filters. The RDA was used to extract information from 47 DBZDs in 13,514 UHPLC-QTOF-MS data files from DUID cases analyzed from 2014 to 2020, with filters on the retention time window, count level, and mass error. Sixteen designer and uncommon benzodiazepines (DBZDs) were detected, where 47 identifications had been confirmed by using complementary methods when the case was open (confirmed positive finding), and 43 targets were not reported when the case was open (tentative positive finding). The most common tentative and confirmed findings were etizolam (n = 26), phenazepam (n = 13), lorazepam (n = 9), and flualprazolam (n = 8). This method efficiently found DBZDs in previously acquired UHPLC-QTOF-MS data files, with only nine false-positive hits. When the standard of an emerging DBZD becomes available, all previously acquired DUID data files can be screened in less than 1 min. Being able to perform a fast and accurate retrospective data analysis across previously acquired data files is a major technological advancement in monitoring NPS abuse.

Systematic retrospective processing of previously analysed biological samples has been proven to be a valuable tool in the search for new drugs (e.g. new psychoactive substances (NPS)) and for quality assessment in clinical and forensic toxicology. In a previous study, we developed a strategy for retrospective data-analysis using a personalized library of synthetic cannabinoids, designer benzodiazepines and synthetic opioids obtained from the crowdsourced database HighResNPS (https://highresnps.com). In this study, the same strategy was employed for the compounds within the groups of NPS that were not previously included such as synthetic cathinones, phenethylamines, aminoindanes, arylalkylamines, piperazine derivates, piperidines, pyrrolidines, indolalkylamines and arylcyclohexylamines. Synthetic opioids and designer benzodiazepines, which were not part of the previous study, were also included. To enhance the effectiveness of the retrospective analysis, a predicted retention time was included for all entries. Data files from the analysis of 2186 forensic post mortem samples with an Agilent Technologies 6540 ultra-high pressure liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) performed in the laboratory from January 2014 to December 2021 were retrospectively processed with the up-to-date library. Tentative findings were classified in two groups: The findings where MS/MS data was acquired for library match (category 1) and the less certain findings where such data lacked (category 2). Five compounds of category 1 (three synthetic cathinones and two indolalkylamines) were identified in 12 samples. Only one of the findings, 4-MEAPP (4-methyl-α-ethylaminopentiophenone), was deemed plausible after reviewing case information. As many as 501 presumably positive category 2 findings were detected. Using the predicted retention time as an additional criterion the number was significantly reduced but still too high for a manual review. This work has demonstrated that the strategy developed in the previous study can be applied to other NPS groups. However, it is important to note the limitations such a method may have in detecting compounds at very low concentrations. • Using an established strategy to detect NPS retrospectively in post mortem samples. • Integrating the crowdsourced database HighResNPS consisting of HR-MS data of NPS. • Predicted retention times was used in the evaluation of presumable identifications. • 4-methyl-α-ethylaminopentiophenone was detected in a reprocessed old sample. [ABSTRACT FROM AUTHOR]

Environmental risk assessment associated with aquatic and terrestrial contamination is mostly based on predicted or measured environmental concentrations of a limited list of chemicals in a restricted number of environmental compartments. High resolution mass spectrometry (HRMS) can provide a more comprehensive picture of exposure to harmful chemicals, particularly through the retrospective analysis of digitally stored HRMS data. Using this methodology, our study characterized the contamination of various environmental compartments including 154 surface water, 46 urban effluent, 67 sediment, 15 soil, 34 groundwater, 24 biofilm, 41 gammarid and 49 fish samples at 95 sites widely distributed over the Swiss Plateau. As a proof-of-concept, we focused our investigation on antifungal azoles, a class of chemicals of emerging concern due to their endocrine disrupting effects on aquatic organisms and humans. Our results demonstrated the occurrence of antifungal azoles and some of their (bio)transformation products in all the analyzed compartments (0.1–100 ng/L or ng/g d.w.). Comparison of actual and predicted concentrations showed the partial suitability of level 1 fugacity modelling in predicting the exposure to azoles. Risk quotient calculations additionally revealed risk of exposure especially if some of the investigated rivers and streams are used for drinking water production. The case study clearly shows that the retrospective analysis of HRMS/MS data can improve the current knowledge on exposure and the related risks to chemicals of emerging concern and can be effectively employed in the future for such purposes.

In this work, a retrospective screening based on ultra-high-performance liquid chromatography (UHPLC) coupled with high-resolution mass spectrometry (HRMS) based on Orbitrap-Q-Exactive Focus™ was used to check the occurrence of regulated and emerging mycotoxins in bulk milk samples. Milk samples were collected from dairy farms in which corn silage was the main ingredient of the feeding system. The 45 bulk milk samples were previously analyzed for a detailed untargeted metabolomic profiling and classified into five clusters according to the corn silage contamination profile, namely: (1) low levels of Aspergillus- and Penicillium-mycotoxins; (2) low levels of fumonisins and other Fusarium-mycotoxins; (3) high levels of Aspergillus-mycotoxins; (4) high levels of non-regulated Fusarium-mycotoxins; (5) high levels of fumonisins and their metabolites. Multivariate statistics based on both unsupervised and supervised analyses were used to evaluate the significant fold-change variations of the main groups of mycotoxins detected when comparing milk samples from clusters 3, 4, and 5 (high contamination levels of the corn silages) with cluster 1 and 2 (low contamination levels of the corn silages). Overall, 14 compounds showed a significant prediction ability, with antibiotic Y (VIP score = 2.579), bikaverin (VIP score = 1.975) and fumonisin B2 (VIP score = 1.846) being the best markers. The k-means clustering combined with supervised statistics showed two discriminant groups of milk samples, thus revealing a hierarchically higher impact of the whole feeding system (rather than the only corn silages) together with other factors of variability on the final mycotoxin contamination profile. Among the discriminant metabolites we found some Fusarium mycotoxins, together with the tetrapeptide tentoxin (an Alternaria toxin), the α-zearalenol (a catabolite of zearalenone), mycophenolic acid and apicidin. These preliminary findings provide new insights into the potential role of UHPLC-HRMS to evaluate the contamination profile and the safety of raw milk to produce hard cheese.

The information acquired by high resolution quadrupole-time of flight mass spectrometry (QTOF-MS) allows target analysis as well as retrospective screening for the presence of suspect or unknown emerging pollutants which were not included in the target analysis. Targeted quantification of eight azole antifungal drugs in wastewater effluent as well as new and relatively simple retrospective suspect and non-target screening strategy for emerging pollutants using UHPLC-QTOF-MS is described in this work. More than 300 (parent compounds and transformation products) and 150 accurate masses were included in the retrospective suspect and non-target screening, respectively. Tentative identification of suspects and unknowns was based on accurate masses, peak intensity, blank subtraction, isotopic pattern (mSigma value), compound annotation using data bases such as KEGG and CHEBI, and fragmentation pattern interpretation. In the targeted analysis, clotrimazole, fluconazole, itraconazole, ketoconazole and posaconazole were detected in the effluent wastewater sample, fluconazole being with highest average concentration (302.38 ng L−1). The retrospective screening resulted in the detection of 27 compounds that had not been included in the target analysis. The suspect compounds tentatively identified included atazanavir, citalopram, climbazole, bezafibrate estradiol, desmethylvenlafaxine, losartan carboxylic acid and cetirizine, of which citalopram, estradiol and cetirizine were confirmed using a standard. Carbamazepine, atrazine, efavirenz, lopinavir, fexofenadine and 5-methylbenzotriazole were among the compounds detected following the non-targeted screening approach, of which carbamazepine was confirmed using a standard. Given the detection of the target antifungals in the effluent, the findings are a call for a wide assessment of their occurrence in aquatic environments and their role in ecotoxicology as well as in selection of drug resistant fungi. The findings of this work further highlights the practical benefits obtained for the identification of a broader range of emerging pollutants in the environment when retrospective screening is applied to high resolution and high accuracy mass spectrometric data. Image 1 • LC-MS target azole antifungal quantification and retrospective screening for untargeted compounds were applied to wastewater. • Target analysis revealed the occurrence of fluconazole at relatively higher concentration than the other azoles. • Retrospective suspect and non-target screening enabled further detection of 27 emerging pollutants. • Estradiol, atrazine and carbamazepine were among the compounds detected using the retrospective screening. The work provides practical evidence for the potential of retrospective screening on detecting wider range of emerging pollutants in the environment. [ABSTRACT FROM AUTHOR]

A comprehensive strategy combining a quantitative method for 28 mycotoxins and a post-target screening for other 245 fungal and bacterial metabolites in dry pet food samples were developed using an acetonitrile-based extraction and an ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS) method. The proposed method showed satisfactory validation results according to Commission Decision 2002/657/EC. Average recoveries from 72 to 108% were obtained for all studied mycotoxins, and the intra-/inter-day precision were below 9 and 14%, respectively. Results showed mycotoxin contamination in 99% of pet food samples (n = 89) at concentrations of up to hundreds µg/kg, with emerging Fusarium mycotoxins being the most commonly detected mycotoxins. All positive samples showed co-occurrence of mycotoxins with the simultaneous presence of up to 16 analytes per sample. In the retrospective screening, up to 54 fungal metabolites were tentatively identified being cyclopiazonic acid, paspalitrem A, fusaric acid, and macrosporin, the most commonly detected analytes.

• This work provides a sensitive screening means for carbamate toxicant exposure. • Carbamyl-nonapeptides generated from exposed plasma were selected as biomarkers. • This approach provides a tool for the carbamate toxicants' toxicokinetics. Carbamate pesticides are extensively used in agriculture for their inhibition to acetylcholinesterase and damages to the insects' neural systems. Because of their toxicity, human poisoning incidents caused by carbamate pesticide exposure have occurred from time to time. What's more, some lethally toxic carbamate toxicants known as carbamate nerve agents (CMNAs) have been supplemented in Schedule 1 of the Annex on Chemicals in the Chemical Weapons Convention (CWC) by Organisation of the Prohibition of Chemical Weapons (OPCW) from 2020. And some other carbamates, like physostigmine, have been used in clinical treatment as anticholinergic drugs and their misuse may also cause damages to the body. Similar to organophosphorus toxicants, carbamate toxicants would react with butyrylcholinesterase (BChE) in plasma when entering the human body, resulting in the BChE adducts, based on which the exposure of carbamate toxicants could be detected retrospectively. In this study, methylcarbamyl nonapeptide and dimethylcarbamyl nonapeptide from pepsin digestion of BChE adducts were identified with ultra-high performance liquid chromatography–tandem mass spectrometry (UHPLC–MS/MS) in product ion scan mode. Carbofuran was chosen as the target to establish the detection method of carbamate toxicant exposure based on methylcarbamyl nonapeptide digested from methylcarbamyl BChE. Procainamide-gel affinity purification, pepsin digestion and UHPLC–MS/MS analysis in multiple reaction monitoring (MRM) mode were applied. Under the optimized conditions of sample preparation and UHPLC–MS/MS MRM analysis, the limits of detection (LODs) reached 10.0 ng/mL of plasma exposed to carbofuran with satisfactory specificity. The quantitation approach was established with d 3 -carbofuran-exposed plasma as the internal standard (IS) and the linearity range was 30.0–1.00 × 103 nmol/L (R2 >0.998) with the accuracy of 95.6%–107% and precision of ≤9% relative standard deviation (RSD). The applicability was also evaluated by N,N-dimethyl-carbamates with the LODs of 30.0 nmol/L for pirimicarb-exposed plasma based on dimethylcarbamyl nonapeptide. Because most of carbamate toxicants has methylcarbamyl or dimethylcarbamyl groups, this approach could be applied on the retrospective screening of carbamate toxicant exposure including CMNAs, carbamate pesticides or carbamate drugs. This study could provide an effective means in the fields of CWC verification, toxicological mechanism investigation and down-selection of potential treatment options. [ABSTRACT FROM AUTHOR]

Non-target screening (NTS) methods are rapidly gaining in popularity, empowering researchers to search for an ever-increasing number of chemicals. Given this possibility, communicating the confidence of identification in an automated, concise and unambiguous manner is becoming increasingly important. In this study, we compiled several pieces of evidence necessary for communicating NTS identification confidence and developed a machine learning approach for classification of the identifications as reliable and unreliable. The machine learning approach was trained using data generated by four laboratories equipped with different instrumentation. The model discarded substances with insufficient identification evidence efficiently, while revealing the relevance of different parameters for identification. Based on these results, a harmonized IP-based system is proposed. This new NTS-oriented system is compatible with the currently widely used five level system. It increases the precision in reporting and the reproducibility of current approaches via the inclusion of evidence scores, while being suitable for automation. [Display omitted] • A model was developed to classify identifications as reliable and unreliable. • Machine learning provided insight for the weights of the most informative parameters. • Identification confidence was influenced mostly by fragmentation and isotopic fit. • An identification point (IP) system scaled from 0 to 1 was proposed and applied. • The IP system was connected with the widely used identification confidence levels. [ABSTRACT FROM AUTHOR]

In this work, a retrospective screening based on ultra-high-performance liquid chromatography (UHPLC) coupled with high-resolution mass spectrometry (HRMS) based on Orbitrap-Q-Exactive Focus™ was used to check the occurrence of regulated and emerging mycotoxins in bulk milk samples. Milk samples were collected from dairy farms in which corn silage was the main ingredient of the feeding system. The 45 bulk milk samples were previously analyzed for a detailed untargeted metabolomic profiling and classified into five clusters according to the corn silage contamination profile, namely: (1) low levels of Aspergillus- and Penicillium-mycotoxins; (2) low levels of fumonisins and other Fusarium-mycotoxins; (3) high levels of Aspergillus-mycotoxins; (4) high levels of non-regulated Fusarium-mycotoxins; (5) high levels of fumonisins and their metabolites. Multivariate statistics based on both unsupervised and supervised analyses were used to evaluate the significant fold-change variations of the main groups of mycotoxins detected when comparing milk samples from clusters 3, 4, and 5 (high contamination levels of the corn silages) with cluster 1 and 2 (low contamination levels of the corn silages). Overall, 14 compounds showed a significant prediction ability, with antibiotic Y (VIP score = 2.579), bikaverin (VIP score = 1.975) and fumonisin B2 (VIP score = 1.846) being the best markers. The k-means clustering combined with supervised statistics showed two discriminant groups of milk samples, thus revealing a hierarchically higher impact of the whole feeding system (rather than the only corn silages) together with other factors of variability on the final mycotoxin contamination profile. Among the discriminant metabolites we found some Fusarium mycotoxins, together with the tetrapeptide tentoxin (an Alternaria toxin), the α-zearalenol (a catabolite of zearalenone), mycophenolic acid and apicidin. These preliminary findings provide new insights into the potential role of UHPLC-HRMS to evaluate the contamination profile and the safety of raw milk to produce hard cheese. [ABSTRACT FROM AUTHOR]

A comprehensive strategy combining a quantitative method for 28 mycotoxins and a post-target screening for other 245 fungal and bacterial metabolites in dry pet food samples were developed using an acetonitrile-based extraction and an ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS) method. The proposed method showed satisfactory validation results according to Commission Decision 2002/657/EC. Average recoveries from 72 to 108% were obtained for all studied mycotoxins, and the intra-/inter-day precision were below 9 and 14%, respectively. Results showed mycotoxin contamination in 99% of pet food samples (n = 89) at concentrations of up to hundreds µg/kg, with emerging Fusarium mycotoxins being the most commonly detected mycotoxins. All positive samples showed co-occurrence of mycotoxins with the simultaneous presence of up to 16 analytes per sample. In the retrospective screening, up to 54 fungal metabolites were tentatively identified being cyclopiazonic acid, paspalitrem A, fusaric acid, and macrosporin, the most commonly detected analytes. [ABSTRACT FROM AUTHOR]