Label Information Research Articles

Abstract PO5-25-07: A research impact assessment method to evaluate Susan G. Komen’s research funding impact on drugs approved for breast cancer treatment

Abstract Susan G. Komen (Komen) is the largest private funder of breast cancer research, having invested nearly $1.1 billion in cutting edge breast cancer research with a focus on conquering deadly metastatic and aggressive breast cancers, advancing precision medicine, and addressing disparities in breast cancer care and outcomes. There is a growing demand on Komen and other research funders from patients, donors, and other constituents to understand how investments in biomedical research translate into clinical impact that is meaningful to patients, the public, and others beyond academic publications and other bibliometrics. Komen uses a variety of approaches to evaluate the impact of our research investments, including a recently developed method to determine how Komen’s funding has impacted discovery and development of breast cancer drugs approved by the FDA from 2012-2022. Utilizing bibliometric analysis derived from the Dimensions for Funders platform, we systematically identified links between Komen-funded research, Komen-funded researchers, and FDA approved drugs. First, we identified fundamental clinical trials that led to initial and expanded FDA approval of each drug from prescribing information labels and press releases. Using API technology, we then performed a bibliometric linkage analysis and analyzed citation results from fundamental clinical trial studies, working backwards to capture fundamental pre-clinical and basic research studies that identified each drug’s target and its functions in the context of breast cancer biology. After collecting and curating publications in an unbiased manner, we evaluated each published study and contributing authors for Komen support. We identified specific “touchpoints” for fundamental studies including direct Komen funding for the published study and/or Komen support of key personnel. Early results revealed direct and indirect Komen research touchpoints across the research pipeline for several drugs, including direct touchpoints for basic, pre-clinical, and clinical trial studies that led to FDA approval of the PI3K inhibitor alpelisib, basic research and pre-clinical studies that led to FDA approval of CDK4/6 inhibitors, and basic research studies that led to FDA approval of PARP inhibitors olaparib and talazoparib. By adopting this approach, we can demonstrate the impact of Komen’s research investment across the full development timeline for each drug. This will allow us to measure and communicate how Komen’s investment in innovative research from discovery to clinical development is driving advances in breast cancer clinical care that improves outcomes. Citation Format: Dana Brantley-Sieders, Lauren Leslie, Amy Dworkin, Kimberly Sabelko, Kari Wojtanik. A research impact assessment method to evaluate Susan G. Komen’s research funding impact on drugs approved for breast cancer treatment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-25-07.

Assessment of Some of the Feeding Practices and Local Infant Flours' Consumption by 6 to 23 Months Children in the City of Ouagadougou, Burkina Faso

Background: From birth to 6 months, children must be exclusively breastfed, and from 6 months, food diversification starts to supplement breast milk. This cross-sectional study aims to evaluate some of the feeding practices regarding children aged 6 to 23 months in the city of Ouagadougou. Methods: First, data were food consumption data as well as recalling meals over the past 24 hours were collected. Then, technological and labeling data regarding infant flours produced locally and sold in Ouagadougou were obtained. For analysis, data were entered into Sphinx V5 and IBM SPSS Statistics 20 software, and means were compared using XLSTAT 2016 software. Results: In total, 33 local infant flours were evaluated, and all the formulations contained cereals. Fruits and legumes (30.30%), milk (30.30%), and eggs (0,00%) were the least used materials. Instant type flours were rarely encountered (9.09%), and packaging in plastic bags was consumed the most (57.57%). Regarding labeling information, batch number (28%) and the method of preservation (32%) were the least identified. With respect to food consumption, 94.84% of the children were breastfed and 98.33% aged 6 to 8 months consumed solid foods. At least 85.28% of women considered the age of 6 months for introduction of solid foods. Concerning consumption of infant porridges, imported infant porridges were preferred the most (37.44%). Conclusion: Concerning local infant flour, some of them did not meet WHO guidelines. With regard to food practice indicators in Ouagadougou, some of them are relatively satisfactory; however, there is still a need to raise awareness regarding feeding practices.

Quantification of phenolic compounds in dietary supplements and antioxidant activities

Dietary supplements (DS) are products that are recommended for the treatment of various health issues, especially during the global pandemic, and the accuracy of the labeling information of these supplements plays a significant role in human health. In this study, a novel high performance liquid chromatography-diode array detector (HPLC-DAD) method was developed and validated to determine the accuracy of the labeling information of commercial dietary supplements containing phenolic compounds (rutin, quercetin, and resveratrol). The mobile phase, flow rate, and column temperature were optimized. Validation studies were carried out to prove the validity of the developed method. The limit of detection (LOD) and limit of quantification (LOQ) values ​​of rutin, quercetin, and resveratrol were found in the range of 4.83-6.62 ng/mL and 16.09-22.07 ng/mL, respectively. Recoveries% were calculated in the range of 99.52-100.19% and 99.78-100.34% in the intra-day and inter-day analysis, respectively. The IC50 values of the dietary supplement extracts obtained by the 2,2-diphenyl-1-picrylhydrazyl (DPPH) method ranged from 0.08 to 0.76 mg/mL. Total Antioxidant Capacity (TAC) values of DS1, DS2, DS3, DS4, DS5, measured employing the Cupric Reducing Antioxidant Capacity (CUPRAC) method, were found to be 0.09, 0.03, 0.10, 0.10 and 0.40 mmol trolox per gram of extract, respectively. KEY WORDS: Dietary supplement, HPLC-DAD, Phenolic compound, Antioxidant activity Bull. Chem. Soc. Ethiop. 2024, 38(4), 853-862.                                                              DOI: https://dx.doi.org/10.4314/bcse.v38i4.3

Semi-supervised Multi-view Clustering based on NMF with Fusion Regularization

Multi-view clustering has attracted significant attention and application. Nonnegative matrix factorization is one popular feature of learning technology in pattern recognition. In recent years, many semi-supervised nonnegative matrix factorization algorithms were proposed by considering label information, which has achieved outstanding performance for multi-view clustering. However, most of these existing methods have either failed to consider discriminative information effectively or included too much hyper-parameters. Addressing these issues, a semi-supervised multi-view nonnegative matrix factorization with a novel fusion regularization (FRSMNMF) is developed in this article. In this work, we uniformly constrain alignment of multiple views and discriminative information among clusters with designed fusion regularization. Meanwhile, to align the multiple views effectively, two kinds of compensating matrices are used to normalize the feature scales of different views. Additionally, we preserve the geometry structure information of labeled and unlabeled samples by introducing the graph regularization simultaneously. Due to the proposed methods, two effective optimization strategies based on multiplicative update rules are designed. Experiments implemented on six real-world datasets have demonstrated the effectiveness of our FRSMNMF comparing with several state-of-the-art unsupervised and semi-supervised approaches.

The occurrence of aflatoxins and labelling compliance of locally produced za’atar mix products

Za’atar mix products are mainly composed of the dried and ground leaves and/or blossoms of wild and cultivated plant species (Origanum, Thymbra, Thymus, and Satureja) with the addition of condiments. The aim of this study was to evaluate the occurrence of aflatoxins, chemical composition (carbohydrates, fibre, fat, protein, moisture, ash, and acid contents), mineral content (Na, Ca, and K), and colour traits (L*a*b*) in relation to food label and food standards compliance. Measured and labelled fat content did not agree for approximately 91% of the samples. There was also no agreement between the measured and labelled fibre contents. The total content of aflatoxins in the tested samples ranged from 2 to 63.7 ng g−1. Eleven (69%) of the 16 analysed products had total aflatoxins higher than the maximum permitted limit of the European Commission. The KAS and LAZ products had significantly lighter colour (the highest L* values), while the ALAQ product had the darkest colour (lowest L* value). The range of sodium content in the tested products was 105.1–1425.3 mg/100 g. In conclusion, za’atar mix products that are available in local markets do not have accurate nutritional labelling information, and the occurrence of aflatoxins was very high. Further studies are needed to evaluate the reasons for these quality defects.

Domain-invariant feature learning with label information integration for cross-domain classification

Domain-invariant feature learning with label information integration for cross-domain classification

Cognitive influence on the evaluation of wine: The impact and assessment of price

Price is one of the most important product-extrinsic factors influencing the consumers’ response to, and presumably experience of, wine. This is ironic inasmuch as the research tends to highlight either no, or else even a slightly negative relationship between price and liking in typical consumers when they taste wines blind. Nevertheless, providing price information, especially when it is high leads to enhanced taste ratings, especially for low to mid-priced wines. Similarly, bottle and label information (that makes a wine look cheaper or more expensive) has also been shown to influence the evaluation of wine by regular consumers (i.e., non-experts). Indeed, product-extrinsic information often appears to outweigh the product-intrinsic sensory attributes of wine in people’s hedonic (in not necessarily in their sensory-discriminative) ratings. Such findings therefore highlight the importance of cognitive as compared to direct sensory cues in the evaluation of wine. This narrative historical review critically reviews and evaluates the published experimental literature that has examined the impact of price on wine ratings.

LogMS: a multi-stage log anomaly detection method based on multi-source information fusion and probability label estimation

Introduction: Log anomaly detection is essential for monitoring and maintaining the normal operation of systems. With the rapid development and maturation of deep learning technologies, deep learning-based log anomaly detection has become a prominent research area. However, existing methods primarily concentrate on directly detecting log data in a single stage using specific anomaly information, such as log sequential information or log semantic information. This leads to a limited understanding of log data, resulting in low detection accuracy and poor model robustness.Methods: To tackle this challenge, we propose LogMS, a multi-stage log anomaly detection method based on multi-source information fusion and probability label estimation. Before anomaly detection, the logs undergo parsing and vectorization to capture semantic information. Subsequently, we propose a multi-source information fusion-based long short-term memory (MSIF-LSTM) network for the initial stage of anomaly log detection. By fusing semantic information, sequential information, and quantitative information, MSIF-LSTM enhances the anomaly detection capability. Furthermore, we introduce a probability label estimation-based gate recurrent unit (PLE-GRU) network, which leverages easily obtainable normal log labels to construct pseudo-labeled data and train a GRU for further detection. PLE-GRU enhances the detection capability from the perspective of label information. To ensure the overall efficiency of the LogMS, the second-stage will only be activated when anomalies are not detected in the first stage.Results and Discussion: Experimental results demonstrate that LogMS outperforms baseline models across various log anomaly detection datasets, exhibiting superior performance in robustness testing.

Real-World Performance Benchmarks in the Reading Ability of Prescription Drug Labels by Community-Dwelling Older Adults.

Despite known recommendations regarding standards for print size and the intuitive importance of vision in reading prescription labels, the predictive nature of vision and prescription label readability remains largely undefined. Furthermore, while the importance of vision is recognized, various demographic factors associated with the ability to read prescription labels have not been fully elucidated. Describe relationships between visual acuity, point size, and readability of prescription labels and provide insight into demographic factors associated with prescription label readability. Cross-sectional examination of prescription label readability by older, community-dwelling adults. Subjects were evaluated as to demographics, visual acuity, and ability to read test instruments consisting of unaltered prescription label features of five medications dispensed by community pharmacies and two drug samples. Descriptive statistics in conjunction with a logit predictive model were employed for data analysis. Instructions for medication use were most recognizable, identified and correctly read by 95.60% of the study cohort while directions for the use of drug samples were lowest (34.91%). Among prescription label features, auxiliary labels consistently demonstrated poor readability. Level of visual acuity was statistically related to the ability to read prescription labels while identifying prescription label components increased proportionally with point size. Race, gender, and history of a recent eye examination were statistically significant predictors of prescription label reading ability. Visual acuity alone was found to explain approximately 26% of the variablity in ability to read Rx labels. Visual acuity is predictive of the ability to access Rx label information and should be considered a modifiable variable for improving prescription label reading ability amenable by appropriate eye care and spectacle correction.

TO-UGDA: target-oriented unsupervised graph domain adaptation

Graph domain adaptation (GDA) aims to address the challenge of limited label data in the target graph domain. Existing methods such as UDAGCN, GRADE, DEAL, and COCO for different-level (node-level, graph-level) adaptation tasks exhibit variations in domain feature extraction, and most of them solely rely on representation alignment to transfer label information from a labeled source domain to an unlabeled target domain. However, this approach can be influenced by irrelevant information and usually ignores the conditional shift of the downstream predictor. To effectively address this issue, we introduce a target-oriented unsupervised graph domain adaptive framework for graph adaptation called TO-UGDA. Particularly, domain-invariant feature representations are extracted using graph information bottleneck. The discrepancy between two domains is minimized using an adversarial alignment strategy to obtain a unified feature distribution. Additionally, the meta pseudo-label is introduced to enhance downstream adaptation and improve the model’s generalizability. Through extensive experimentation on real-world graph datasets, it is proved that the proposed framework achieves excellent performance across various node-level and graph-level adaptation tasks.

Assessment of nutrition facts label utilization and macro-nutrient intake among patients with chronic diseases in Saudi Arabia

ObjectivesTo investigate and compare nutritional label use, influencing factors, and macro-nutrient intake among individuals with chronic diseases and other healthy individuals in the Kingdom of Saudi Arabia (KSA). DesignA cross-sectional study in KSA. ParticipantsN = 380 males/females aged 18–50 years, healthy and others with chronic diseases. Variables measuredData were collected using a self-reported questionnaire containing chronic disease factors, label use behavior factors and three 24-h dietary recalls. AnalysisSeveral statistical tests were used, including the Chi-square (ᵪ2), the Independent Sample T-test, the ANOVA test, and Pearson's correlation with p-value ≤0.05. ResultsMore chronic disease patients (56.1 %), especially hypercholesterolemia patients, than healthy individuals (43.9 %) reported that they checked and used the information on the nutritional fact labels. All the chronic disease patients consumed less than the recommended nutrient intake (RNI) for carbohydrates and fiber. However, the protein and total fat intake were more than the RNI. The highest intake was among diabetes/at risk of diabetes patients, with an average of 1710.9 kcal. ConclusionsThe present study concluded that chronic disease patients tend to demonstrate superior behaviour in reading food labels compared to those who are healthy. Thus, healthy individuals would benefit from reading the label information, possibly reducing their risk of developing chronic disease.

Distance metric learning-based multi-granularity neighborhood rough sets for attribute reduction

Attribute reduction is a hot research topic in data mining, in which rough set theory-based attribute reduction methods have been widely focused. The neighborhood rough set (NRS) model has good generalization performance and practicality in uncertainty reasoning, so it is often used for attribute reduction in recent years. Calculating the distance between samples in different attribute spaces is a key step in the NRS-based attribute reduction methods, which directly affects the performance of the reduction algorithm. However, the NRS model uses a fixed computational paradigm in calculating the distance between samples and does not consider the influence of labels in the attribute spaces on the distance calculation, which is not conducive to improving the performance of the reduction algorithm. Distance metric learning takes full account of the labels information in the multi-dimensional attribute space, and it can learn the distance between samples by taking into account the integrated principle that samples with the same label are closer and samples with different labels are further away, which helps to reduce the classification uncertainty. Inspired by this, this paper firstly incorporates distance metric learning into the NRS model from the perspective of multi-dimensional attribute space and proposes a distance metric learning-based multi-granularity neighborhood rough set (DmlMNRS) model. The related properties of the DmlMNRS model are also introduced and proved. Then, the DmlMNRS-based attribute reduction criterion and the significance of the attributes are defined. A DmlMNRS-based heuristic attribute reduction (DMNHAR) algorithm is designed based on this. Finally, experiments are performed on fifteen publicly datasets, and the experimental results show that the proposed algorithm has better robustness and classification performance.

GAN-based self-supervised message passing graph representation learning

Graph representation learning aims to learn embedding representations of nodes in graph-structured data through supervised or unsupervised methods. However, for unlabeled or sparsely labeled graph datasets, conventional supervised approaches fail to perform adequately, and existing unsupervised methods have difficulty achieving high accuracy on multiple downstream tasks such as node classification and link prediction at lower embedding dimensions. To address this issue, we propose a novel self-supervised graph representation learning approach called GAN-based Message Passing Graph Representation Learning. This approach significantly improves the accuracy of node embeddings without relying on label information. Specifically, GMP-GL has developed a generator based on neighbor-weighted random walk to obtain positive and negative node sample pairs, as well as a discriminator that distinguishes whether a real connection exists between node pairs. Through adversarial learning, GMP-GL automatically learns node embeddings. Furthermore, GMP-GL optimizes the training outcomes of the generative adversarial model through self-supervised embedding aggregation learning, thus introducing rich structural information from the graph for the subsequent rounds of generative adversarial training. In addition, GMP-GL employs multiple autoencoders to generate and filter original embeddings, ensuring that the pre-embeddings preserve a significant amount of original feature information for formal learning. The study selects six real-world datasets and conducts comparative experiments with ten SOTA baseline methods. The experimental results demonstrate that the proposed model not only surpasses previous unsupervised methods but also matches or even exceeds the performance of supervised methods. This indicates the great potential of the proposed approach for representation learning tasks on unlabeled graph datasets and its effective applicability to real-world problems.

Zero-shot discrete hashing with adaptive class correlation for cross-modal retrieval

Zero-shot retrieval aims to transfer knowledge from seen classes to unseen classes by embedding semantic information on class attributes, thus solving the unseen class retrieval problem. However, existing works have focused mainly on unimodal zero-shot retrieval tasks. In this paper, we introduce an efficient method, termed zero-shot discrete hashing with adaptive class correlation (ZSDH-ACC), to speed up cross-modal retrieval. Specifically, this proposed method combines label information with class attribute information to construct a semantic enhancement embedding, in which the problem of class attribute correspondence of multilabel instances can be solved. Furthermore, we learn semantic enhancement embedding to merge more semantic information for feature representation, and its goal is to learn more discriminative hash codes and hash functions. In addition, our proposed method adaptively learns the correlation between class attributes and then embeds more class attribute information into hash codes. Finally, pairwise similarity is used to constrain the learning of hash codes, and thus more discriminative hash codes can be generated. Extensive experimental results on four benchmark multimodal datasets demonstrate that the proposed ZSDH-ACC method can achieve encouraging performance in cross-modal retrieval tasks. The source code of this paper can be obtained from https://github.com/szq0816/ZSDH_ACC.

Improving Graph Convolutional Network with Learnable Edge Weights and Edge-Node Co-Embedding for Graph Anomaly Detection.

The era of Industry 4.0 is gradually transforming our society into a data-driven one, which can help us uncover valuable information from accumulated data, thereby improving the level of social governance. The detection of anomalies, is crucial for maintaining societal trust and fairness, yet it poses significant challenges due to the ubiquity of anomalies and the difficulty in identifying them accurately. This paper aims to enhance the performance of the current Graph Convolutional Network (GCN)-based Graph Anomaly Detection (GAD) algorithm on datasets with extremely low proportions of anomalous labels. This goal is achieved through modifying the GCN network structure and conducting feature extraction, thus fully utilizing three types of information in the graph: node label information, node feature information, and edge information. Firstly, we theoretically demonstrate the relationship between label propagation and feature convolution, indicating that the Label Propagation Algorithm (LPA) can serve as a regularization penalty term for GCN, aiding in training and enabling learnable edge weights, providing a basis for incorporating node label information into GCN networks. Secondly, we introduce a method to aggregate node and edge features, thereby incorporating edge information into GCN networks. Finally, we design different GCN trainable weights for node features and co-embedding features. This design allows different features to be projected into different spaces, greatly enhancing model expressiveness. Experimental results on the DGraph dataset demonstrate superior AUC performance compared to baseline models, highlighting the feasibility and efficacy of the proposed approach in addressing GAD tasks in the scene with extremely low proportions of anomalous data.

Supervised contrastive learning for graph representation enhancement

Graph Neural Networks (GNNs) have exhibited significant success in various applications, but they face challenges when labeled nodes are limited. A novel self-supervised learning paradigm has emerged, enabling GNN training without labeled nodes and even surpassing GNNs with limited labeled data. However, self-supervised methods lack class-discriminative node representations due to the absence of labeled information during training. In this paper, we exploit a supervised graph contrastive learning approach (SGCL) framework to tackle the issue of limited labeled nodes, ensuring coherent grouping of nodes within the same class. We propose augmentation techniques based on a novel centrality function to highlight important topological structures. Additionally, we introduce a supervised contrastive learning method that removes the necessity for negative samples and simplifies complex elements effortlessly. Our approach combines supervised contrastive loss and node similarity regularization while achieving consistent grouping of unlabeled nodes with labeled ones. Furthermore, we utilize the pseudo-labeling technique to propagate label information to distant nodes and address the underfitting problem, especially with low-degree nodes. Experimental results on real-world graphs demonstrate that SGCL outperforms both semi-supervised and self-supervised methods in node classification.

Allocating municipal services to individuals with complex rehabilitation needs – a discourse analysis of individual administrative decision letters

BackgroundRehabilitation is considered paramount for enhancing quality of life and reducing healthcare costs. As a result of healthcare reforms, Norwegian municipalities have been given greater responsibility for allocating rehabilitation services following discharge from hospital. Individual decision letters serve as the basis for implementing services and they have been described as information labels on the services provided by the municipality. They play an important role in planning and implementing the services in collaboration with the individual applicants. Research indicates that the implementation of policies may lead to unintended consequences, as individuals receiving municipal services perceive them as fragmented. This perception is characterised by limited user involvement and a high focus on body functions. The aim of this study was to examine how municipal decision letters about service allocation incorporate the recommendations made in the official national guideline and reflect a holistic approach to rehabilitation, coordination and user involvement for individuals with comprehensive needs.MethodsThe decision letters of ten individuals with moderate to severe brain injury allocating rehabilitation services in two municipalities were examined. It was assessed whether the content was in accordance with the authorities’ recommendations, and a discourse analysis was conducted using four tools adapted from an established integrated approach.ResultsThe letters primarily contained standard texts concerning legal and administrative regulations. They were predominantly in line with the official guideline to municipal service allocation. From a rehabilitation perspective, the focus was mainly on medically oriented care, scarcely referring to psychosocial needs, activity, and participation. The intended user involvement seemed to vary between active and passive status, while the coordination of services was given limited attention.ConclusionsThe written decision letters did fulfil legal and administrative recommendations for service allocation. However, they did not fulfil their potential to serve as a means of conveying rehabilitation issues, such as specification of the allocated services, a holistic approach to health, coordination, or the involvement of users in decision processes. These elements must be incorporated throughout the allocation process if the policies are to be implemented as intended. Findings can have international relevance for discussions between clinicians and policy makers.

Towards Few-Label Vertical Federated Learning

Federated Learning (FL) provided a novel paradigm for privacy-preserving machine learning, enabling multiple clients to collaborate on model training without sharing private data. To handle multi-source heterogeneous data, vertical federated learning (VFL) has been extensively investigated. However, in the context of VFL, the label information tends to be kept in one authoritative client and is very limited. This poses two challenges for model training in the VFL scenario: On the one hand, a small number of labels cannot guarantee to train a well VFL model with informative network parameters, resulting in unclear boundaries for classification decisions; On the other hand, the large amount of unlabeled data is dominant and should not be discounted, and it’s worthwhile to focus on how to leverage them to improve representation modeling capabilities. In order to address the above two challenges, Firstly, we introduce supervised contrastive loss to enhance the intra-class aggregation and inter-class estrangement, which is to deeply explore label information and improve the effectiveness of downstream classification tasks. Secondly, for unlabeled data, we introduce a pseudo-label-guided consistency mechanism to induce the classification results coherent across clients, which allows the representations learned by local networks to absorb the knowledge from other clients, and alleviates the disagreement between different clients for classification tasks. We conduct sufficient experiments on four commonly used datasets, and the experimental results demonstrate that our method is superior to the state-of-the-art methods, especially in the low-label rate scenario, and the improvement becomes more significant.

Single- and Cross-Lingual Speech Emotion Recognition Based on WavLM Domain Emotion Embedding

Unlike previous approaches in speech emotion recognition (SER), which typically extract emotion embeddings from a trained classifier consisting of fully connected layers and training data without considering contextual information, this research introduces a novel approach. It integrates contextual information into the feature extraction process. The proposed approach is based on the WavLM representation and incorporates a contextual transform, along with fully connected layers, training data, and corresponding label information, to extract single-lingual WavLM domain emotion embeddings (SL-WDEEs) and cross-lingual WavLM domain emotion embeddings (CL-WDEEs) for single-lingual and cross-lingual SER, respectively. To extract CL-WDEEs, multi-task learning is employed to remove language information, marking it as the first work to extract emotion embeddings for cross-lingual SER. Experimental results on the IEMOCAP database demonstrate that the proposed SL-WDEE outperforms some commonly used features and known systems, while results on the ESD database indicate that the proposed CL-WDEE effectively recognizes cross-lingual emotions and outperforms many commonly used features.

Detection of Acheta domesticus by real-time PCR in food and feed

Abstract Edible insects are rich in protein and can serve as a significant source of vitamins and minerals. The house cricket (Acheta domesticus) stands out as one of the most nutritious edible insects. In various parts of the world, crickets are consumed roasted, baked, fried, boiled and in the form of cricket flour, a powder of dried and ground crickets. In Europe, processed animal proteins derived from eight insect species (including A. domesticus) have received authorization for use in fish, pig and poultry feed. Therefore, the development of a method for detecting house crickets is essential to verify product compliance and provide accurate labelling information to the end user. In this study, we have established a real-time PCR assay for the specific detection of house crickets. This method is based on the amplification of a mitochondrial fragment that codes for cytochrome b. We assessed its specificity by testing it against 39 other insect species, 7 plant species and 31 other animal species. Furthermore, we successfully evaluated the amplification efficiency, sensitivity, robustness, applicability on commercial samples and transferability to a second laboratory.