phyloBARCODER: A Web Tool for Phylogenetic Classification of Eukaryote Metabarcodes Using Custom Reference Databases.

We developed phyloBARCODER (https://github.com/jun-inoue/phyloBARCODER), a new web tool that can identify short DNA sequences to the species level using metabarcoding. phyloBARCODER estimates phylogenetic trees based on the uploaded anonymous DNA sequences and reference sequences from databases. Without such phylogenetic contexts, alternative, similarity-based methods independently identify species names and anonymous sequences of the same group by pairwise comparisons between queries and database sequences, with the caveat that they must match exactly or very closely. By putting metabarcoding sequences into a phylogenetic context, phyloBARCODER accurately identifies (i) species or classification of query sequences and (ii) anonymous sequences associated with the same species or even with populations of query sequences, with clear and accurate explanations. Version 1 of phyloBARCODER stores a database comprising all eukaryotic mitochondrial gene sequences. Moreover, by uploading their own databases, phyloBARCODER users can conduct species identification specialized for sequences obtained from a local geographic region or those of nonmitochondrial genes, e.g. ITS or rbcL.

COL1A2 Barcoding: Bone Species Identification via Shotgun Proteomics.

Species identification of fragmentary bones remains a challenging task in archeology and forensics. A species identification method for such fragmentary bones that has recently attracted interest is the use of bone collagen proteins. Here, we describe a method similar to DNA barcoding that reads collagen protein sequences in bone and automatically determines the species by performing sequence database searches. The method is almost identical to conventional shotgun proteomics analysis of bone samples, except that the database used by the SEQUEST search engine consisted only of entries for collagen type 1 alpha 2 (COL1A2) proteins from various vertebrates. Accordingly, the COL1A2 peptides that differ in sequence among species act as species marker peptides. In SEQUEST-based shotgun proteomics, the protein entries that contain more marker peptide sequences are assigned higher scores; therefore, the highest-scoring protein entry will be the COL1A2 entry for the species from which the analyzed bone was derived. We tested our method using bone samples from 30 vertebrate species and found that all species were correctly identified. In conclusion, COL1A2 can be used as a bone protein barcode and can be read through shotgun proteomics, allowing for automatic bone species identification. Data are available via ProteomeXchange with the identifier PXD045402.

Comparative analysis of medicinal plants Scutellaria baicalensis and common adulterants based on chloroplast genome sequencing.

BACKGROUND: Scutellaria baicalensis Georgi has been extensively used as a medicinal herb in China for over 2000 years. They may be intentionally or inadvertently substituted or blended with comparable species in the local market, threatening clinical medication safety. Molecular markers are effective tools to prevent misidentification and eliminate doping and falsification among Scutellaria plants. This study screened four highly variable regions to identify Scutellaria and its adulterants. In addition, a phylogenetic analysis was performed using the complete cp genome combined with published Scutellaria species samples. Moreover, a comparative analysis of the cp genomes was conducted to investigate the cp genome evolution of S. baicalensis. RESULTS: The complete cp genome of five species of Scutellaria was sequenced for the first time, and four previously published Scutellaria species were re-sequenced. They all exhibited a conserved quadripartite structure in their cp genomes, including two distinct regions, namely a small and large single copy region, respectively, and two inverted repeats encompassing the majority of ribosomal RNA genes. Furthermore, the nine species exhibited high conservation from aspects of the genome structure, codon usage, repeat sequences, and gene content. Four highly variable regions (matK-rps16, ndhC-trnV-UAC, psbE-petL, and rps16-trnQ-UUG) may function as potential molecular markers for differentiating S. baicalensis from its adulterants. Additionally, the monophyly of Scutellaria was ascertained and could be reclassified into two subgenera, subgenus Anaspis and subgenus Scutellaria, as evidenced by the phylogenetic analyses on sequences of cp genome and shared protein-coding sequences. According to the molecular clock analysis, it has been inferred that the divergence of Scutellaria occurred at approximately 4.0 Mya during the Pliocene Epoch. CONCLUSION: Our study provides an invaluable theoretical basis for further Scutellaria species identification, phylogenetics, and evolution analysis.

GenoMycAnalyzer: a web-based tool for species and drug resistance prediction for Mycobacterium genomes.

BACKGROUND: Drug-resistant tuberculosis (TB) is a major threat to global public health. Whole-genome sequencing (WGS) is a useful tool for species identification and drug resistance prediction, and many clinical laboratories are transitioning to WGS as a routine diagnostic tool. However, user-friendly and high-confidence automated bioinformatics tools are needed to rapidly identify M. tuberculosis complex (MTBC) and non-tuberculous mycobacteria (NTM), detect drug resistance, and further guide treatment options. RESULTS: We developed GenoMycAnalyzer, a web-based software that integrates functions for identifying MTBC and NTM species, lineage and spoligotype prediction, variant calling, annotation, drug-resistance determination, and data visualization. The accuracy of GenoMycAnalyzer for genotypic drug susceptibility testing (gDST) was evaluated using 5,473 MTBC isolates that underwent phenotypic DST (pDST). The GenoMycAnalyzer database was built to predict the gDST for 15 antituberculosis drugs using the World Health Organization mutational catalogue. Compared to pDST, the sensitivity of drug susceptibilities by the GenoMycAnalyzer for first-line drugs ranged from 95.9% for rifampicin (95% CI 94.8-96.7%) to 79.6% for pyrazinamide (95% CI 76.9-82.2%), whereas those for second-line drugs ranged from 98.2% for levofloxacin (95% CI 90.1-100.0%) to 74.9% for capreomycin (95% CI 69.3-80.0%). Notably, the integration of large deletions of the four resistance-conferring genes increased gDST sensitivity. The specificity of drug susceptibilities by the GenoMycAnalyzer ranged from 98.7% for amikacin (95% CI 97.8-99.3%) to 79.5% for ethionamide (95% CI 76.4-82.3%). The incorporated Kraken2 software identified 1,284 mycobacterial species with an accuracy of 98.8%. GenoMycAnalyzer also perfectly predicted lineages for 1,935 MTBC and spoligotypes for 54 MTBC. CONCLUSIONS: GenoMycAnalyzer offers both web-based and graphical user interfaces, which can help biologists with limited access to high-performance computing systems or limited bioinformatics skills. By streamlining the interpretation of WGS data, the GenoMycAnalyzer has the potential to significantly impact TB management and contribute to global efforts to combat this infectious disease. GenoMycAnalyzer is available at http://www.mycochase.org .

BioEncoder: A metric learning toolkit for comparative organismal biology.

In the realm of biological image analysis, deep learning (DL) has become a core toolkit, for example for segmentation and classification. However, conventional DL methods are challenged by large biodiversity datasets characterized by unbalanced classes and hard-to-distinguish phenotypic differences between them. Here we present BioEncoder, a user-friendly toolkit for metric learning, which overcomes these challenges by focussing on learning relationships between individual data points rather than on the separability of classes. BioEncoder is released as a Python package, created for ease of use and flexibility across diverse datasets. It features taxon-agnostic data loaders, custom augmentation options, and simple hyperparameter adjustments through text-based configuration files. The toolkit's significance lies in its potential to unlock new research avenues in biological image analysis while democratizing access to advanced deep metric learning techniques. BioEncoder focuses on the urgent need for toolkits bridging the gap between complex DL pipelines and practical applications in biological research.

Loop-Mediated Isothermal Amplification Assay to Detect Invasive Malaria Vector Anopheles stephensi Mosquitoes.

Spread of the Anopheles stephensi mosquito, an invasive malaria vector, threatens to put an additional 126 million persons per year in Africa at risk for malaria. To accelerate the early detection and rapid response to this mosquito species, confirming its presence and geographic extent is critical. However, existing molecular species assays require specialized laboratory equipment, interpretation, and sequencing confirmation. We developed and optimized a colorimetric rapid loop-mediated isothermal amplification assay for molecular An. stephensi species identification. The assay requires only a heat source and reagents and can be used with or without DNA extraction, resulting in positive color change in 30-35 minutes. We validated the assay against existing PCR techniques and found 100% specificity and analytical sensitivity down to 0.0003 ng of genomic DNA. The assay can successfully amplify single mosquito legs. Initial testing on samples from Marsabit, Kenya, illustrate its potential as an early vector detection and malaria mitigation tool.

Chloroplast genomes of Caragana tibetica and Caragana turkestanica: structures and comparative analysis.

BACKGROUND: The genus Caragana encompasses multiple plant species that possess medicinal and ecological value. However, some species of Caragana are quite similar in morphology, so identifying species in this genus based on their morphological characteristics is considerably complex. In our research, illumina paired-end sequencing was employed to investigate the genetic organization and structure of Caragana tibetica and Caragana turkestanica, including the previously published chloroplast genome sequence of 7 Caragana plants. RESULTS: The lengths of C. tibetica and C. turkestanica chloroplast genomes were 128,433 bp and 129,453 bp, respectively. The absence of inverted repeat sequences in these two species categorizes them under the inverted repeat loss clade (IRLC). They encode 110 and 111 genes (4 /4 rRNA genes, 30 /31tRNA genes, and 76 /76 protein-coding genes), respectively. Comparison of the chloroplast genomes of C. tibetica and C. turkestanica with 7 other Caragana species revealed a high overall sequence similarity. However, some divergence was observed between certain intergenic regions (matK-rbcL, psbD-psbM, atpA-psbI, and etc.). Nucleotide diversity (π) analysis revealed the detection of five highly likely variable regions, namely rps2-atpI, accD-psaI-ycf4, cemA-petA, psbN-psbH and rpoA-rps11. Phylogenetic analysis revealed that C. tibetica's sister species is Caragana jubata, whereas C. turkestanica's closest relative is Caragana arborescens. CONCLUSIONS: The present study provides worthwhile information about the chloroplast genomes of C. tibetica and C. turkestanica, which aids in the identification and classification of Caragana species.

Comprehensive comparative analysis and development of molecular markers for Lasianthus species based on complete chloroplast genome sequences.

BACKGROUND: Lasianthus species are widely used in traditional Chinese folk medicine with high medicinal value. However, source materials and herbarium specimens are often misidentified due to morphological characteristics and commonly used DNA barcode fragments are not sufficient for accurately identifying Lasianthus species. To improve the molecular methods for distinguishing among Lasianthus species, we report the complete chloroplast (CP) genomes of Lasianthus attenuatus, Lasianthus henryi, Lasianthus hookeri, Lasianthus sikkimensis, obtained via high-throughput Illumina sequencing. RESULTS: These showed CP genomes size of 160164-160246 bp and a typical quadripartite structure, including a large single-copy region (86675-86848 bp), a small single-copy region (17177-17326 bp), and a pair of inverted repeats (28089-28135 bp). As a whole, the gene order, GC content and IR/SC boundary structure were remarkably similar among of the four Lasianthus CP genomes, the partial gene length and IR, LSC and SSC regions length are still different. The average GC content of the CP genomes was 36.71-36.75%, and a total of 129 genes were detected, including 83 different protein-coding genes, 8 different rRNA genes and 38 different tRNA genes. Furthermore, we compared our 4 complete CP genomes data with publicly available CP genome data from six other Lasianthus species, and we initially screened eleven highly variable region fragments were initially screened. We then evaluated the identification efficiency of eleven highly variable region fragments and 5 regular barcode fragments. Ultimately, we found that the optimal combination fragment' ITS2 + psaI-ycf4' could authenticated the Lasianthus species well. Additionally, the results of genome comparison of Rubiaceae species showed that the coding region is more conservative than the non-coding region, and the ycf1 gene shows the most significant variation. Finally, 49 species of CP genome sequences belonging to 16 genera of the Rubiaceae family were used to construct phylogenetic trees. CONCLUSIONS: Our research is the first to analyze the chloroplast genomes of four species of Lasianthus in detail and we ultimately determined that the combination fragment' ITS2 + psaI-ycf4' is the optimal barcode combination for identifying the genus of Lasianthus. Meanwhile, we gathered the available CP genome sequences from the Rubiaceae and used them to construct the most comprehensive phylogenetic tree for the Rubiaceae family. These investigations provide an important reference point for further studies in the species identification, genetic diversity, and phylogenetic analyses of Rubiaceae species.

Fine-grained image classification on bats using VGG16-CBAM: a practical example with 7 horseshoe bats taxa (CHIROPTERA: Rhinolophidae: Rhinolophus) from Southern China.

BACKGROUND: Rapid identification and classification of bats are critical for practical applications. However, species identification of bats is a typically detrimental and time-consuming manual task that depends on taxonomists and well-trained experts. Deep Convolutional Neural Networks (DCNNs) provide a practical approach for the extraction of the visual features and classification of objects, with potential application for bat classification. RESULTS: In this study, we investigated the capability of deep learning models to classify 7 horseshoe bat taxa (CHIROPTERA: Rhinolophus) from Southern China. We constructed an image dataset of 879 front, oblique, and lateral targeted facial images of live individuals collected during surveys between 2012 and 2021. All images were taken using a standard photograph protocol and setting aimed at enhancing the effectiveness of the DCNNs classification. The results demonstrated that our customized VGG16-CBAM model achieved up to 92.15% classification accuracy with better performance than other mainstream models. Furthermore, the Grad-CAM visualization reveals that the model pays more attention to the taxonomic key regions in the decision-making process, and these regions are often preferred by bat taxonomists for the classification of horseshoe bats, corroborating the validity of our methods. CONCLUSION: Our finding will inspire further research on image-based automatic classification of chiropteran species for early detection and potential application in taxonomy.

The complete chloroplast genome of Aristolochia fangchi provided insights into the phylogeny and species identification of Aristolochia.

Aristolochia fangchi is an important species within the family Aristolochiaceae, most of which contain nephrotoxic aristolochic acid. The inadvertent use of Aristolochiaceae plants as raw ingredients in the manufacturing of patent medicine poses a significant risk warranting considerable attention. In this study, we assembled and analyzed the complete chloroplast genome of Aristolochia fangchi, which is a 159 867 bp long circular molecule. Functional annotation of the A. fangchi plastome unveiled a total of 113 genes, including 79 protein-coding genes, 30 tRNA genes, and 4 rRNA genes. Subsequently, a series of genome structure and characteristic evaluations were conducted against the A. fangchi plastome. Further phylogenetic analysis suggested that a plausible phylogenetic relationship among Aristolochiaceae derived from the concatenated sequences of shared conserved genes rather than from the entire chloroplast genome with one IR copy. Finally, a DNA polymorphism assessment against a dozen Aristolochia plastomes yielded multiple potential regions for biomarker designation. Six pairs of primers were generated and underwent both in silico and actual PCR validations. In conclusion, this study identified the unique characteristics of the A. fangchi plastome, providing invaluable insights for further investigations on species identification and the phylogeny evolution between A. fangchi and its related species.

A gentle introduction to computer vision-based specimen classification in ecological datasets.

Classifying specimens is a critical component of ecological research, biodiversity monitoring and conservation. However, manual classification can be prohibitively time-consuming and expensive, limiting how much data a project can afford to process. Computer vision, a form of machine learning, can help overcome these problems by rapidly, automatically and accurately classifying images of specimens. Given the diversity of animal species and contexts in which images are captured, there is no universal classifier for all species and use cases. As such, ecologists often need to train their own models. While numerous software programs exist to support this process, ecologists need a fundamental understanding of how computer vision works to select appropriate model workflows based on their specific use case, data types, computing resources and desired performance capabilities. Ecologists may also face characteristic quirks of ecological datasets, such as long-tail distributions, 'unknown' species, similarity between species and polymorphism within species, which impact the efficacy of computer vision. Despite growing interest in computer vision for ecology, there are few resources available to help ecologists face the challenges they are likely to encounter. Here, we present a gentle introduction for species classification using computer vision. In this manuscript and associated GitHub repository, we demonstrate how to prepare training data, basic model training procedures, and methods for model evaluation and selection. Throughout, we explore specific considerations ecologists should make when training classification models, such as data domains, feature extractors and class imbalances. With these basics, ecologists can adjust their workflows to achieve research goals and/or account for uncertainty in downstream analysis. Our goal is to provide guidance for ecologists for getting started in or improving their use of machine learning for visual classification tasks.

Simple and field-adapted species identification of biological specimens combining multiplex multienzyme isothermal rapid amplification, lateral flow dipsticks, and universal primers for initial rapid screening without standard PCR laboratory.

Species identification of biological specimens can provide the valuable clues and accelerate the speed of prosecution material processing for forensic investigation, especially when the case scene is inaccessible and the physical evidence is cumbersome. Thus, establishing a rapid, simple, and field-adapted species identification method is crucial for forensic scientists, particularly as first-line technology at the crime scene for initial rapid screening. In this study, we established a new field-adapted species identification method by combining multiplex multienzyme isothermal rapid amplification (MIRA), lateral flow dipstick (LFD) system, and universal primers. Universal primers targeting COX I and COX II genes were used in multiplex MIRA-LFD system for seven species identification, and a dedicated MIRA-LFD system primer targeting CYT B gene was used to detect the human material. DNA extraction was performed by collecting DNA directly from the centrifuged supernatant. Our study found that the entire amplification process took only 15 min at 37 °C and the results of LFDs could be visually observed after 10 min. The detection sensitivity of human material could reach 10 pg, which is equivalent to the detection of single cell. Different common animal samples mixed at the ratio of 1 ng:1 ng, 10 ng:1 ng, and 1 ng:10 ng could be detected successfully. Furthermore, the damaged and degraded samples could also be detected. Therefore, the convenient, feasible, and rapid approach for species identification is suitable for popularization as first-line technology at the crime scene for initial rapid screening and provides a great convenient for forensic application.

Forensic species identification: practical guide for animal and plant DNA analysis.

The importance of non-human DNA in the forensic field has increased greatly in recent years, together with the type of applications. The molecular species identification of animal and botanical material may be crucial both for wildlife trafficking and crime scene investigation. However, especially for forensic botany, several challenges slow down the implementation of the discipline in the routine.Although the importance of molecular analysis of animal origin samples is widely recognized and the same value is acknowledged to the botanical counterpart, the latter does not find the same degree of application.The availability of molecular methods, especially useful in cases where the material is fragmented, scarce or spoiled preventing the morphological identification, is not well known. This work is intended to reaffirm the relevance of non-human forensic genetics (NHFG), highlighting differences, benefits and pitfalls of the current most common molecular analysis workflow for animal and botanical samples, giving a practical guide. A flowchart describing the analysis paths, divided in three major working areas (inspection and sampling, molecular analysis, data processing and interpretation), is provided. More real casework examples of the utility of non-human evidence in forensic investigations should be shared by the scientific community, especially for plants. Moreover, concrete efforts to encourage initiatives in order to promote quality and standardization in the NHFG field are also needed.

Development and validation of a universal primer pair for the taxonomic and phylogenetic studies of vertebrates.

BACKGROUND: Recent studies in the field of molecular identification have described 16S rRNA gene as a highly informative fragment of mitochondrial DNA for species discrimination. This study presents a newly developed universal primer pair yielding an approximately 350 bp fragment of mitochondrial 16S rRNA, variable enough to encompass and identify all vertebrate classes. METHODS AND RESULTS: The primers were designed by aligning and analyzing over 1500 16S rRNA sequences downloaded from the NCBI nucleotide database. A total of 93 vertebrate species, spanning 27 orders and 55 families, were PCR-amplified to validate the primers. All the target species were successfully amplified and identified when aligned with reference sequences from the NCBI nucleotide database. Using the Kimura 2-parameter model, low intra-species genetic divergence of the target region was observed - from 0 to 4.63%, whereas relatively higher inter-species genetic divergence was observed, ranging from 4.88% to 69.81%. Moreover, the newly developed primers were successfully applied to a direct PCR protocol, making the workflow very cost-effective, time-saving and less laborious in comparison to conventional PCR. CONCLUSIONS: The short length, high variability and conserved priming sites of the target fragment across all vertebrate species make it a highly desirable marker for species identification and discrimination.

Sustainable Lignin Extraction Using a Mild Acidic γ-Valerolactone Process for the Identification of White and Red Oaks.

Current wood identification struggles to differentiate white and red oak (Quercus alba and Quercus rubra) due to highly similar microstructures, as demonstrated by morphological microscope analysis. The present research explores lignin composition as a potential discriminating factor. Here, a rapid and sustainable method for extracting high-quality lignin from oak samples using acidic γ-valerolactone (GVL) under mild conditions is described. As-extracted lignin is thoroughly characterized using various analytical methods, and results reveal a distinct structural difference between the lignin from the two species. White oak lignin possesses a unique "Hibbert ketone" unit detectable by nuclear magnetic resonance spectroscopy (NMR), which is absent in red oak lignin. In addition, infrared spectroscopy differentiates the species based on specific carbonyl groups present in their lignin. These findings suggest that identifying the presence of the Hibbert ketone unit in lignin may offer a highly efficient and reliable method for differentiating white and red oak, opening new avenues for wood identification.

Evaluation of phenotypic and genotypic methods for the identification and characterization of bacterial isolates recovered from catheter-associated urinary tract infections.

AIMS: Urinary tract infections are the most common hospital-acquired infection, 80% of which are associated with catheterization. Diagnostic methods may influence the reported identities of these pathogens, and phenotypic testing under laboratory conditions may not reflect infection phenotypes. This study aimed to evaluate the efficacy of diagnostic methods and whether medium composition alters phenotypes by characterizing catheter-associated urinary tract infection isolates from a UK hospital. METHODS AND RESULTS: We compared five bacterial identification methods, including biochemical testing, matrix-assisted laser desorption/ionization biotyping, and genome sequencing, finding differences in genus- or species-level identifications. Antibiotic susceptibility comparisons between phenotypic assays and genomic predictions showed high agreement only in multidrug-resistant strains. To determine whether growth rate and biofilm formation were affected by medium composition, strains were grown in both planktonic and biofilm states. Low planktonic growth and significant biofilm formation were observed in artificial urine compared to rich laboratory media, underscoring the importance of assay design. CONCLUSIONS: This study highlights the risks of relying on a single diagnostic method for species identification, advocating for whole-genome sequencing for accuracy. It emphasizes the continued importance of phenotypic methods in understanding antibiotic resistance in clinical settings and the need for characterization conditions that mirror those encountered by pathogens in the body.

Using a DNA mini-barcode within the ITS region to identify toxic Amanita in mushroom poisoning cases.

Mushroom poisoning contributes significantly to global foodborne diseases and related fatalities. Amanita mushrooms frequently cause such poisonings; however, identifying these toxic species is challenging due to the unavailability of fresh and intact samples. It is often necessary to analyze residues, vomitus, or stomach extracts to obtain DNA sequences for the identification of species responsible for causing food poisoning. This usually proves challenging to obtain usable DNA sequences that can be analyzed using conventional molecular biology techniques. Therefore, this study aimed to develop a DNA mini-barcoding method for the identification of Amanita species. Following the evaluation and optimization of universal primers for DNA mini-barcoding in Amanita mushrooms, we found that the internal transcribed spacer (ITS) gene sequence primer ITS-a was the most suitable DNA barcode primer for identifying Amanita species. Forty-three Amanita samples were subsequently amplified and sequenced. The sequences obtained were analyzed for intra- and inter-species genetic distances, and a phylogenetic tree was constructed. The findings indicated that the designed primers had strong universality among the Amanita samples and could accurately identify the target gene fragment with a length of 290 bp. Notably, the DNA mini-barcode accurately identified the 43 Amanita samples, demonstrating high consistency with the conventional DNA barcode. Furthermore, it effectively identified DNA from digested samples. In summary, this DNA mini-barcode is a promising tool for detecting accidental ingestion of toxic Amanita mushrooms. It may be used as an optimal barcode for species identification and traceability in events of Amanita-induced mushroom poisoning. KEY POINTS: • Development of a DNA mini-barcoding method for Amanita species identification without fresh samples. • The ITS-a primer set was optimized for robust universality in Amanita samples. • The mini-barcode is suitable for screening toxic mushroom species in mushroom poisoning cases.

Rapid Direct Identification of Microbial Pathogens and Antimicrobial Resistance Genes in Positive Blood Cultures Using a Fully Automated Multiplex PCR Assay.

This study assessed the performance of the BioFire Blood Culture Identification 2 (BCID2) panel in identifying microorganisms and antimicrobial resistance (AMR) profiles in positive blood cultures (BCs) and its influence on turnaround time (TAT) compared with conventional culture methods. We obtained 117 positive BCs, of these, 102 (87.2%) were correctly identified using BCID2. The discordance was due to off-panel pathogens detected by culture (n = 13), and additional pathogens identified by BCID2 (n = 2). On-panel pathogen concordance between the conventional culture and BCID2 methods was 98.1% (102/104). The conventional method detected 19 carbapenemase-producing organisms, 14 extended-spectrum beta-lactamase-producing Enterobacterales, 18 methicillin-resistant Staphylococcus spp., and four vancomycin-resistant Enterococcus faecium. BCID2 correctly predicted 53 (96.4%) of 55 phenotypic resistance patterns by detecting AMR genes. The TAT for BCID2 was significantly lower than that for the conventional method. BCID2 rapidly identifies pathogens and AMR genes in positive BCs.

Rapid identification of mosquito species and age by mass spectrometric analysis.

BACKGROUND: A rapid, accurate method to identify and to age-grade mosquito populations would be a major advance in predicting the risk of pathogen transmission and evaluating the public health impact of vector control interventions. Whilst other spectrometric or transcriptomic methods show promise, current approaches rely on challenging morphological techniques or simple binary classifications that cannot identify the subset of the population old enough to be infectious. In this study, the ability of rapid evaporative ionisation mass spectrometry (REIMS) to identify the species and age of mosquitoes reared in the laboratory and derived from the wild was investigated. RESULTS: The accuracy of REIMS in identifying morphologically identical species of the Anopheles gambiae complex exceeded 97% using principal component/linear discriminant analysis (PC-LDA) and 84% based on random forest analysis. Age separation into 3 different age categories (1 day, 5-6 days, 14-15 days) was achieved with 99% (PC-LDA) and 91% (random forest) accuracy. When tested on wild mosquitoes from the UK, REIMS data could determine the species and age of the specimens with accuracies of 91 and 90% respectively. CONCLUSIONS: The accuracy of REIMS to resolve the species and age of Anopheles mosquitoes is comparable to that achieved by infrared spectroscopy approaches. The processing time and ease of use represent significant advantages over current, dissection-based methods. Importantly, the accuracy was maintained when using wild mosquitoes reared under differing environmental conditions, and when mosquitoes were stored frozen or desiccated. This high throughput approach thus has potential to conduct rapid, real-time monitoring of vector populations, providing entomological evidence of the impact of alternative interventions.

DNA barcoding of herbal supplements on the US commercial market associated with the purported treatment of COVID-19.

INTRODUCTION: The COVID-19 pandemic was associated with an increased global use of traditional medicines, including Ayurvedic herbal preparations. Due to their growing demand, their processed nature, and the complexity of the global supply chain, there is an increased risk of adulteration in these products. OBJECTIVES: The objective of this study was to assess the use of DNA barcoding for species identification in herbal supplements on the US market associated with the Ayurvedic treatment of respiratory symptoms. METHODS: A total of 54 commercial products containing Ayurvedic herbs were tested with four DNA barcoding regions (i.e., rbcL, matK, ITS2, and mini-ITS2) using two composite samples per product. Nine categories of herbs were targeted: amla, ashwagandha, cinnamon, ginger, guduchi, tribulus, tulsi, turmeric, and vacha. RESULTS: At least one species was identified in 64.8% of products and the expected species was detected in 38.9% of products. Undeclared plant species, including other Ayurvedic herbs, rice, and pepper, were detected in 19 products, and fungal species were identified in 12 products. The presence of undeclared plant species may be a result of intentional substitution or contamination during harvest or processing, while fungal DNA was likely associated with the plant material or the growing environment. The greatest sequencing success (42.6-46.3%) was obtained with the matK and rbcL primers. CONCLUSION: The results of this study indicate that a combination of genetic loci should be used for DNA barcoding of herbal supplements. Due to the limitations of DNA barcoding in identification of these products, future research should incorporate chemical characterization techniques.