BackgroundHerbal products available to consumers in the marketplace may be contaminated or substituted with alternative plant species and fillers that are not listed on the labels. According to the World Health Organization, the adulteration of herbal products is a threat to consumer safety. Our research aimed to investigate herbal product integrity and authenticity with the goal of protecting consumers from health risks associated with product substitution and contamination.MethodsWe used DNA barcoding to conduct a blind test of the authenticity for (i) 44 herbal products representing 12 companies and 30 different species of herbs, and (ii) 50 leaf samples collected from 42 herbal species. Our laboratory also assembled the first standard reference material (SRM) herbal barcode library from 100 herbal species of known provenance that were used to identify the unknown herbal products and leaf samples.ResultsWe recovered DNA barcodes from most herbal products (91%) and all leaf samples (100%), with 95% species resolution using a tiered approach (rbcL + ITS2). Most (59%) of the products tested contained DNA barcodes from plant species not listed on the labels. Although we were able to authenticate almost half (48%) of the products, one-third of these also contained contaminants and or fillers not listed on the label. Product substitution occurred in 30/44 of the products tested and only 2/12 companies had products without any substitution, contamination or fillers. Some of the contaminants we found pose serious health risks to consumers.ConclusionsMost of the herbal products tested were of poor quality, including considerable product substitution, contamination and use of fillers. These activities dilute the effectiveness of otherwise useful remedies, lowering the perceived value of all related products because of a lack of consumer confidence in them. We suggest that the herbal industry should embrace DNA barcoding for authenticating herbal products through testing of raw materials used in manufacturing products. The use of an SRM DNA herbal barcode library for testing bulk materials could provide a method for 'best practices? in the manufacturing of herbal products. This would provide consumers with safe, high quality herbal products.

Backgroud: Probiotics have been shown to benefit human health through several mechanisms, including their role in improving the health of our gastrointestinal tracts. The health benefits of probiotics are strain specific, and therefore it is critical to include the correct strains in probiotic products when claiming specific health benefits. Several studies have reported issues concerning the accuracy of labeling of commercial probiotic products, including inaccurate taxonomy, missing species, or undeclared species. Consequently, there is a growing need to develop and validate assays to reliably verify strain identity in commercial probiotic products. PCR-based methods are the most commonly used methods for food species ingredient diagnostics because they are simple, fast, sensitive, and can be validated. Objective: The aim of this paper is to set the guidelines for validating targeted qualitative real-time PCR assays to verify the presence of specific strains in a probiotic supplement. Methods and Results: Qualitative real-time PCR assays are validated to evaluate the assay performance in terms of specificity, sensitivity, repeatability, and reproducibility in detecting target strains. Conclusions and Highlights: Setting these guidelines will facilitate and streamline the validation process for qualitative real-time PCR-based assays for probiotic identity authentication in support of quality assurance systems.

Background In the medical imaging domain, deep learning‐based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between estimated brain age and biological age is an established biomarker of brain health and such models may benefit from increased cross‐site generalizability. Purpose To develop and evaluate a deep learning‐based image harmonization method to improve cross‐site generalizability of deep learning age prediction. Study Type Retrospective. Population Eight thousand eight hundred and seventy‐six subjects from six sites. Harmonization models were trained using all subjects. Age prediction models were trained using 2739 subjects from a single site and tested using the remaining 6137 subjects from various other sites. Field Strength/Sequence Brain imaging with magnetization prepared rapid acquisition with gradient echo or spoiled gradient echo sequences at 1.5 T and 3 T. Assessment StarGAN v2, was used to perform a canonical mapping from diverse datasets to a reference domain to reduce site‐based variation while preserving semantic information. Generalization performance of deep learning age prediction was evaluated using harmonized, histogram matched, and unharmonized data. Statistical Tests Mean absolute error (MAE) and Pearson correlation between estimated age and biological age quantified the performance of the age prediction model. Results Our results indicated a substantial improvement in age prediction in out‐of‐sample data, with the overall MAE improving from 15.81 (±0.21) years to 11.86 (±0.11) with histogram matching to 7.21 (±0.22) years with generative adversarial network (GAN)‐based harmonization. In the multisite case, across the 5 out‐of‐sample sites, MAE improved from 9.78 (±6.69) years to 7.74 (±3.03) years with histogram normalization to 5.32 (±4.07) years with GAN‐based harmonization. Data Conclusion While further research is needed, GAN‐based medical image harmonization appears to be a promising tool for improving cross‐site deep learning generalization. Level of Evidence 4 Technical Efficacy Stage 1

Background: PCR methods are the most commonly used DNA-based identity tool in the commercial food, beverage, and natural health product markets. These methods are routinely used to identify foodborne pathogens and allergens in food. Proper validation methods for some sectors have been established, while there are none in other markets, such as botanicals. Results: A survey of the literature indicates that some validation criteria are not addressed when developing PCR tests for botanicals. Objective: We provide recommendations for qualitative real-time PCR methods for validating identity tests for botanical ingredients. Methods: These include common criteria that underpin the development and validation of rigorous tests, including (1) the aim of the validation test, (2) the applicability of different matrix variants, (3) specificity in identifying the target species ingredient, (4) sensitivity in detecting the smallest amount of the target material, (5) repeatability of methods, (6) reproducibility in detecting the target species in both raw and processed materials, (7) practicability of the test in a commercial laboratory, and (8) comparison with alternative methods. In addition, we recommend additional criteria, according to which the practicability of the test method is evaluated by transferring the method to a second laboratory and by comparison with alternative methods. Conclusions and Highlights: We hope that these recommendations encourage further publication on the validation of PCR methods for many botanical ingredients. These properly validated PCR methods can be developed on small, real-time biotechnology that can be placed directly into the supply chain ledger in support of highly transparent data systems that support QC from the farm to the fork of the consumer.