Automated detection of complex animal behaviors remains a challenging problem in neuroscience, particularly for behaviors that consist of disparate sequential motions. Grooming is a prototypical stereotyped behavior that is often used as an endophenotype in psychiatric genetics. Here, we used mouse grooming behavior as an example and developed a general purpose neural network architecture capable of dynamic action detection at human observer-level performance and operating across dozens of mouse strains with high visual diversity. We provide insights into the amount of human annotated training data that are needed to achieve such performance. We surveyed grooming behavior in the open field in 2457 mice across 62 strains, determined its heritable components, conducted GWAS to outline its genetic architecture, and performed PheWAS to link human psychiatric traits through shared underlying genetics. Our general machine learning solution that automatically classifies complex behaviors in large datasets will facilitate systematic studies of behavioral mechanisms. Behavior is one of the ultimate and most complex outputs of the body’s central nervous system, which controls movement, emotion and mood. It is also influenced by a person’s genetics. Scientists studying the link between behavior and genetics often conduct experiments using animals, whose actions can be more easily characterized than humans. However, this involves recording hours of video footage, typically of mice or flies. Researchers must then add labels to this footage, identifying certain behaviors before further analysis.This task of annotating video clips – similar to image captioning – is very time-consuming for investigators. But it could be automated by applying machine learning algorithms, trained with sufficient data. Some computer programs are already in use to detect patterns of behavior, however, there are some limitations. These programs could detect animal behavior (of flies and mice) in trimmed video clips, but not raw footage, and could not always accommodate different lighting conditions or experimental setups. Here, Geuther et al. set out to improve on these previous efforts to automate video annotation.To do so, they used over 1,250 video clips annotated by experienced researchers to develop a general-purpose neural network for detecting mouse behaviors. After sufficient training, the computer model could detect mouse grooming behaviors in raw, untrimmed video clips just as well as human observers could. It also worked with mice of different coat colors, body shapes and sizes in open field animal tests.Using the new computer model, Geuther et al. also studied the genetics underpinning behavior – far more thoroughly than previously possible – to explain why mice display different grooming behaviors. The algorithm analyzed 2,250 hours of video featuring over 60 kinds of mice and thousands of other animals. It found that mice bred in the laboratory groom less than mice recently collected from the wild do. Further analyses also identified genes linked to grooming traits in mice and found related genes in humans associated with behavioral disorders.Automating video annotation using machine learning models could alleviate the costs of running lengthy behavioral experiments and enhance the reproducibility of study results. The latter is vital for translating behavioral research findings in mice to humans. This study has also provided insights into the amount of human-annotated training data needed to develop high-performing computer models, along with new understandings of how genetics shapes behavior. Behavior, the primary output of the nervous system, is complex, hierarchical, dynamic, and high dimensional . PreciseBehavioral action recognition falls under multiple types of computer vision problems, including action classification, event detection, and temporal action localization. Action classification, a task closely related to image captioning, trains a classifier to apply action labels to manually pre-trimmed video clips. This problem has already been largely solved, with the exceptional performance for networks competing in data sets such as Kinetics-400, Moments in Time, Youtube-8M, and many other available benchmark data sets . HoweverDrosophila  and to identify the genetic architecture that regulates heritable variation in grooming and open-field behaviors in the laboratory mouse. Combined we propose a generalizable solution to complex action detection and apply it toward grooming behavior.Behavior varies widely on both time and space scales, from fine spatial movements such as whisking, blinking, or tremors to large spatial movements such as turning or walking, and temporally from milliseconds to minutes. We sought to develop a classifier that could observe and predict complex behaviors produced by the mouse. Grooming consists of syntaxes that are small or micro-motions (paw lick) to mid-size movements  and large movements (flank licking) Our approach to annotating grooming classified each frame in a video as the mouse being in one of two states: grooming or not grooming. We specified that a frame should be annotated as grooming when the mouse is performing any of the syntaxes of grooming, whether or not the mouse is performing a stereotyped syntactic chain of grooming. This included a wide variety of postures and action durations which contribute to a diverse visual appearance. This also explicitly included individual paw licks as grooming, despite isolated paw licks not constituting a bout of grooming. Scratching was excluded from being classified as grooming.We investigated the variability in manual grooming annotations by humans by tasking five trained annotators with labeling the same six 5 min videos  predictions were real, there were some false positives in which the mouse was resting in a grooming-like posture. To mitigate these rare false positives, we implemented a heuristic to adjust predictions. We experimentally identified that grooming motion typically causes ellipse-fit shape changes (W/L) to have a standard deviation greater than This approach handled varying mouse postures and physical appearance well, e.g. coat color and body weight. We observed good performance over a wide variety of postures and coat colors –9. Even We designed a variety of grooming behavioral metrics that describe both grooming quantity and grooming pattern. Following prior work , we defiOnce we had the number of bouts and total duration, we calculated the average bout duration by dividing the two. For measurement purposes, we calculated the 5 min, 20 min, and 55 min summaries of these measurements. We included 5 and 20 min because these are typical open field assay durations.Using 1 min binned data, we calculated a variety of grooming pattern metrics . We fittWe compared a variety of open field measurements including both grooming behavior and classical open field measurements . We sepaWith this trained classifier, we sought to determine whether sex and environment affected grooming behavior in an open field, specifically grooming duration. We used data collected over 29 months for two strains, C57BL/6J and C57BL/6NJ to carry out this analysis. These two strains are substrains that were identical in 1951 and are two of the most widely used strains in mouse studies . C57BL/6We found an effect of strain  and afternoon (noon to 4pm). We observed a clear effect of time of day (We compared the grooming levels of mice that were shipped from production rooms in a nearby building at our institution to our testing room with mice bred and raised in a room adjacent to the testing room (B2B). These production rooms contain a variety of possible confounding variables such as microbiome, noise, and technician-related stress. We specifically note that these room differences should not be due to genetic drift because of JAX’s Genetic Stability Program , which periodically re-derives the strain from frozen embryos . Six proWe tested two light levels, 350–450 lux and 500–600 lux white light (5600K). We observed significant effects of light levels on grooming behavior  mouse production facility. These were tested over a 31-month period and in most cases consisted of a single mouse shipment of mice from JAX production. Other than C57BL/6J and C57BL6/NJ, on average we tested eight males and eight females of an average age of 11 weeks for each strain. Each mouse was tested for 55 min in the open field as previously described . This daWe observed large continuous variance in total grooming time, average length of grooming bouts, and the number of grooming bouts in the 55 min open field assay . Total gWe investigated the pattern of grooming over time by constructing a rate of change in 5 min bins for each strain . There aMus musculus domesticus(M.m domesticus) 95% and Mus musculus musculus 5% . ASD is a complex neurodevelopmental disorder leading to repetitive behaviors and deficits in communication and social interaction . CompareWe found that total grooming time in BTBR is high compared with C57BL/6J but is not exceptionally high compared to all strains , or evenNext we wished to understand the underlying genetic architecture of complex mouse grooming behavior and open field behaviors, and to relate these to human traits. We used the data from the 51 inbred strains and 11 F1 hybrid strains to carry out a genome wide association study (GWAS). We did not include the eight wild derived strains because they are highly divergent and can skew mouse GWAS analysis. We analyzed the 24 phenotypes categorized into four categories – open field activity, anxiety, grooming pattern, and quantity . We usedWe analyzed each phenotype using GEMMA, and considered the resulting Wald test p-value. In order to correct for the multiple SNPs we tested , and to account for the correlations between SNPs genotypes, we obtained an empirical threshold for the p-values by shuffling the values of one normally distributed phenotype (OFDistTraveled20m) and taking the minimal p-value of each permutation. This process resulted in a p-value threshold of  of 0.05 . We defi) blocks , resulti) blocks .GWAS analysis of each phenotype resulted in 2 to 22 QTL . OverallSox5 has been extensively linked to neuronal differentiation, patterning, and stem cell maintenance  . We carr2 genes) . This anntenance . Its dysntenance . A totallanguage . Ctnnb1,Finally, we wanted to link genes that are associated with open field and grooming phenotypes in the mouse with human phenotypes. We hypothesized that common underlying genetic and neuronal architectures exist between mouse and human, however, they can give rise to disparate phenotypes in each organism. For example, the misregulation of a pathway in the mouse may lead to over-grooming phenotype but in humans the same pathway perturbation may manifest itself as neuroticism or obsessive compulsive disorder. These relationships between phenotypes can be revealed through identification of common underlying genetic architectures. In order to link human and mouse phenotypes, we carried out PheWAS analysis with the 860 mouse grooming and open field genes with at least 6 degrees of pleiotropy identified in the mouse GWAS. We identified 509 human orthologs out of 860 mouse genes and downloaded PheWAS summary statistics from gwasATLAS. The gwasATLAS (Release 2: v20190117) contains 4756 GWAS from 473 unique studies across 3302 unique traits which are classified into 28 general domains and 192 subchapters obtained from either ICD10 or ICF10 . For eacSurprisingly, we found orthologs that show high levels of pleiotropy in mouse GWAS and the resulting human PheWAS. FOXP1 is the most pleiotropic gene with 35 human phenotypic associations, while SOX5 is second with 33 associations. In order to prioritize candidate modules for further research, first, we produced a ranked list of modules by calculating modularity score, which measures the strength of division of a network into modules. The high-ranking modules represent most promising candidates for further research . ModularGrooming is an ethologically conserved, neurobiologically important behavior that is often used as an endophenotype for psychiatric illnesses. It is a prototypical stereotyped, patterned behavior with highly variable posture and temporal length. Tools to automatically quantify behaviors such as grooming are needed by the behavioral research community and can be leveraged to gain insight into other complex behaviors . We presWhile the machine learning community has implemented a wide variety of solutions for human action detection, few applications have been applied to animal behavior. This may be in part due to the wide availability of human action data sets and the stringent performance requirements for human bio-behavioral research. We observed that the cost of achieving this stringent performance is very high, requiring a large quantity of annotations. More often than not, experimental paradigms are limited by cost to be short or small enough to cost less to allow manual annotation of the data.Machine learning approaches have been previously applied to automated annotation of behavioral data. We observed that our 3D convolutional neural network outperforms a JAABA classifier when trained on the same training data set. Our neural network achieved 91.9% true positive rate with a 5% false positive rate while thee JAABA classifier achieved 64.2% true positive rate at a 5% false positive rate. This improvement makes the neural network solution usable for application to biological problems. This improvement is not uniform over all samples but is instead localized to certain types of grooming bouts. This suggests that although the JAABA classifier is powerful, it may be most useful for smaller and more uniform data sets. Experimental paradigms and behaviors with diverse expression will require a more powerful machine learning approach.With the grooming classifier, we determined the genetic and environmental factors that regulate this behavior. In a large data set collected over an 18-month period using two reference strains, C57BL/6J and C67BL/6N, we assessed effects on grooming of several fixed and dynamic factors including, sex, strain, age, time of day, season, tester, room origin, white noise, and body weight. All mice were housed in identical conditions for at least a week prior to testing. As expected, we observe strong effects of sex, time of day, and season. In many but not all studies, and not in the present study, tester effects have been observed in the open field in both mice and rats . A recenTo our surprise, we found that room of origin had a strong effect on grooming behavior of C57BL/6J and C57BL/6N. We did not see a clear directionality of effect between shipping mice and those bred in an adjacent room, and in some cases the effect size was high (z > 1). We hypothesize that this may be due to room-specific stress which has previously been demonstrated to alter grooming . PresumaWe carried out a large strain survey to characterize and account for genetic diversity in grooming behavior in the laboratory mouse. We found three types of grooming patterns under our test conditions. Type 1 consists of mice that escalate and deescalate grooming within the 55 min open field test. Strains in this group are often sleeping by the end of the assay, indicating a low arousal state toward the end of the assay. We hypothesize that these strains use grooming as a form of successful de-arousal, a behavior that has been previously noted in rats, birds, and apes . SimilarM.m domesticus, the wild derived inbred lines we tested represent M.m musculus, M.m castaneous, and M.m molossinus subspecies. Even though there are dozens of classical inbred strains, there are approximately 5 million SNPs between any two classical inbred laboratory strains such as C57BL/6J and DBA2J . Each convolution layer that we applied is zero-padded to maintain the same height and width dimension. Additionally, each convolution layer is followed by batch normalization and ReLU activation. First, we applied two sequential 3D convolution layers with a kernel size of 3 × 3 × 3 and number of filters of 4. Second, we applied a max pooling layer of shape 2 × 2 × 2 to result in a new tensor shape of 8 × 64 × 64 × 4. We repeated this two 3D convolution and max pool, doubling the filter depth each time, an additional three more times which results in a 1 × 8 × 8 × 32 tensor shape. We applied two final 3D convolutions with a 1 × 3 × 3 kernel size and 64 filter depth, resulting in a 1 × 8 × 8 × 64 tensor shape. Here, we flattened the network to produce a 64 × 64 tensor. After flattening we applied two fully connected layers, each with 128 filter depth, batch normalization, and ReLU activations. Finally, we added one more fully connected layer with only two filter depth and a softmax activation. This final layer was used as the output probabilities for not grooming and grooming predictions for the last of the 16 frames.We trained four individual neural networks using the same training set and four independent initializations. During training, we randomly sample video chunks from the data set where the final frame contains an annotation where the annotators agree. Since we sample a 16 frame duration, this refers to the 16th frame’s annotation. If a frame selected does not have 15 frames of video earlier, the tensor is padded with 0-initialized frames. We apply random rotations and reflections of the data, achieving an 8x increase in effective data set size. The loss function we use in our network is a categorical cross entropy loss, comparing the softmax prediction from the network to a one-hot vector with the correct classification. We use the Adam optimizer with an initial learning rate of We trained JAABA classifiers using two different approaches. Our first approach was using the guidelines provided by the software developers (JAABA Interactive Training). This involves interactively and iteratively training classifiers. The data selection approach is to annotate some data, then prioritize new annotations where the algorithm is unsure or incorrectly making predictions. We continued this interactive training until the algorithm no longer made improvements in a k-fold cross validation.Our second approach was to subset our large annotated data set to fit into JAABA and train on the agreeing annotations. Initially, we attempted to utilize the entire training data set, but our machine did not have enough RAM to handle the entire training data set. The workstation we used contained 96 GB of available RAM. We wrote a custom script to convert our annotation format to populate annotations in a JAABA classifier file. To confirm our data was input correctly, we examined the annotations from within the JAABA interface. Once we created this file, we could simply train the JAABA classification using JAABA’s interface. After training, we applied the model to the validation data set to compare with our neural network models. We repeated this with various sizes of training data sets.Here, we describe a variety of grooming behavioral metrics that we use in following analyses. Following the approach that has previously been proposed, we define a single grooming bout as a duration of continuous time spent grooming without interruption that exceeds 3 s . We alloFrom here, we sum the total duration of grooming calls in all grooming bouts to calculate the total duration of grooming. Note that this excludes un-joined grooming segments less than 3 s duration as they are not considered a bout. Additionally, we count the total number of bouts.Once we have the number of bouts and total duration, we calculate the average bout duration by dividing the two. Finally, we bin the data into one minute time segments and fit a linear line to the data. Positive slopes for total grooming duration infer that the individual mouse is increasing its time spent grooming the longer it remains in the open field test. Negative slopes for total grooming duration infer that the mouse spends more time grooming at the start of the open field test than at the end. This is typically due to the mouse choosing to spend more time doing another activity over grooming, such as sleeping. Positive slopes for number of bouts infer that the mouse is initiating more grooming bouts the longer it remains in the open field test.For k-means clustering of the behavior across strains, we visually inspected the data and decided three clusters was optimal. We z-score transformed grooming features as inputs to the k-means algorithm to determine cluster membership. Finally, we projected the clusters discovered by the k-means to a 2D space formed by principal components (PC).Q1 is the first quartile, Q3 is the third quartile, and Protocols for data collection were previously described in https://github.com/KumarLabJax and https://www.kumarlab.org/data/).All behavioral data will be available in the Mouse Phenome Database (MPD), and code and models will be available in Kumar Lab Github account (https://phenome.jax.org/genotypes). The Mouse Diversity Array (MDA) genotypes were used, di-allele genomes were deduced from parent genomes. SNPs with at least 10% MAF and at most 5% missing data were used, resulting with 222,967 SNPs out of 470,818 SNPs genotyped in the MDA array. LMM method from the GEMMA software package  . First, we identified human orthologs of the 860 mouse grooming and open field genes with at least 6 degrees of pleiotropy. For each human ortholog, we downloaded PheWAS summary statistics from gwasATLAS (0190117) . We focu0190117) , using b0190117) . All the0190117) . In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:We congratulate you on your development of a neural network that automatically identifies grooming in mice. Your work to characterize grooming behavior across strains, sex and conditions for C57 mice , to identify loci and genes linked to grooming and open field behavior and to identify human homologs for genes linked to grooming in mice will have sustained influence on the field.Decision letter after peer review:eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Michael Taffe as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Ioana Carcea (Reviewer #2); Nancy Padilla (Reviewer #3).Thank you for submitting your article "Action detection using a neural network elucidates the genetics of mouse grooming behavior" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as Summary:In summary, Geuther et al. demonstrate the use of machine learning to identify and classify grooming behaviors in an impressively varied cohort of mice species. Using a 3D convolutional neural network that automatically identifies grooming in mice, they conducted a genome-wide association study (GWAS) to identify loci and genes linked to grooming and open-field behavior. Finally, the authors performed analyses to identify human homologs for genes linked to grooming in mice, with a focus on loci associated with psychiatric illness in humans. Novel findings include the identification of gene-phenotype modules that identify genes linked to both human and mouse phenotypes.Essential revisions:1) It is unclear how robust the grooming algorithm is to new videos/new animals. Were the training video clips all from a small subset of videos? The authors say 2M frames were annotated from 1,253 video segments, but it is unclear how many strains/mice/videos (not video clips or segments) they came from in total. In the Materials and methods, they mention that the training data comes from 1,253 video clips but there are no additional details. How many animals were represented in the training dataset and in the testing dataset? How robust is the algorithm to differences in animal size (which can be affected by camera distance) and to video frame rate? Where all videos used with this network taken at identical camera distances, video frame rates, and backgrounds (e.g. home cage vs. white background)? Since the training dataset needed for good performance is so large (2M frames) understanding the flexibility of the network is crucial for the community to adopt it successfully. When using and implementing the network, the devil is on the details.2) The authors train JAABA with 20% of their dataset and show in Figure 3A and supplementary figure that it performs worse than the new network with the same 20%, but AUCs are relatively similar. But to be sure that this is not a fluke of the specific subset of the data used and to have statistical power to claim the superiority the authors should subsample the training data and repeat the comparison more times. This way they can determine if there the network with 20% of the data outperforms JAABA in every case or with X probability.3) The authors use k-means clustering for different analyses, but there are no details on how the different clustering procedures were done.4) Reviewers have concerns related to the robustness of the 3D convolutional neural network. We believe that in order for the scientific community to take advantage of this tool the authors need to provide more information. Essential revisions:1) It is unclear how robust the grooming algorithm is to new videos/new animals. Were the training video clips all from a small subset of videos? The authors say 2M frames were annotated from 1,253 video segments, but it is unclear how many strains/mice/videos (not video clips or segments) they came from in total. In the Materials and methods, they mention that the training data comes from 1,253 video clips but there are no additional details. How many animals were represented in the training dataset and in the testing dataset? How robust is the algorithm to differences in animal size (which can be affected by camera distance) and to video frame rate? Where all videos used with this network taken at identical camera distances, video frame rates, and backgrounds (e.g. home cage vs. white background)? Since the training dataset needed for good performance is so large (2M frames) understanding the flexibility of the network is crucial for the community to adopt it successfully. When using and implementing the network, the devil is on the details.We thank the reviewers for pointing these out and we agree that it is important to provide detailed information about the training data. The annotated video data were sampled from our published strain survey dataset . All videos in this dataset were imaged using nearly identical hardware and as such frame rate, camera distance, and a white open field arena background are held constant. We expect that it is necessary to utilize techniques such as transfer learning to allow this network to generalize well outside these environmental conditions.As per the reviewers’ request, we have expanded the details on the training dataset. In the new supplementary figure for Figure 2 , we provide visual descriptions for training dataset metadata. Additionally, we now include a table containing metadata for the entire annotated dataset (TrainingDatasetMeta_SupTable.zip). The 1253 video clips were sampled from 157 videos each which contained a different mouse. These 157 mice represent 60 different strains and a wide range of body weights (11g – 44g). While all validation clips also had training clips sampled from the same video, they were non-overlapping and separated in time.We have added details of the video data collection and new supplementary data to the text of the paper.2) The authors train JAABA with 20% of their dataset and show in Figure 3A and supplementary figure that it performs worse than the new network with the same 20%, but AUCs are relatively similar. But to be sure that this is not a fluke of the specific subset of the data used and to have statistical power to claim the superiority the authors should subsample the training data and repeat the comparison more times. This way they can determine if there the network with 20% of the data outperforms JAABA in every case or with X probability.We have added an additional supplementary figure panel to address this concern. Figure 3—figure supplement 1 now contains a second panel to show how the performance of JAABA scales with different training dataset sizes. Additionally, we also now report AUC values for all ROC plots. We observe that performance saturates for JAABA when using 10% of our annotated data, by only achieving an increase of 0.005 AUC (from 0.925 to 0.93) while doubling the size of training data. Interestingly, JAABA using 5% outperforms our neural network using 10%. This data shows that although JAABA may perform better using limited small datasets, both a neural network approach and a larger training dataset is necessary for generalizing on larger and more varied data.Related edits are located in the subsection “Proposed Neural Network Solution” and the new panel B for Figure 3—figure supplement 1 in the figure legend. We have also added indicator to Figure 3B, to indicate the True Positive at 5% False Positive for JAABA and Neural Network solution. The improvement seen in the neural network solution is large .3) The authors use k-means clustering for different analyses, but there are no details on how the different clustering procedures were done.We have added text in the Materials and methods to describe methods surrounding Figure 8’s clustering. Briefly, we visually inspected the data and decided there should be 3 clusters. Then, we used zscore transformed features as inputs to the k-means algorithm to determine cluster membership. We projected the clusters discovered by the k-means to a 2D space formed by principal components (PC).We have adjusted the Materials and methods text of Figure 9D to better describe the clustering of grouped SNPs. Briefly, we ran the analysis for a variety of clusters and visually selected the results which looked to cluster the best visually.4) Reviewers have concerns related to the robustness of the 3D convolutional neural network. We believe that in order for the scientific community to take advantage of this tool the authors need to provide more information.We interpret “robustness” and “need to provide more information” at two levels. As in critique 1, robustness implies how well does the network work on visual diversity . The network we train is one of the most robust in the field since it recognizes behavior across high visual diversity of the mouse. The network we have trained provides accurate classification across a diversity of mouse visual appearance . In fact we are not aware of another work that attempts to operate across all diverse mouse strains for action detection. Furthermore, the network can always be improved for new strains or environments by adding examples to the training data.The comment could also refer to whether the architecture of the network has been optimized. This could be done by adding more layers or modifying the structure of the architecture. If we added more layers or made the network larger or smaller would we still obtain the same level of accuracy? We already achieve human observer level performance and don’t expect to exceed that performance, a limitation set by the human generated training data. We believe that identifying a more efficient network is important, but also outside the scope of this paper. The field of machine learning network optimization which seeks to maintain accuracy while minimizing compute is a subfield of machine learning and will require further research. These are important future work.


Borrelia burgdorferi is the causative agent of Lyme borreliosis, which is the most common tick-borne human disease in Europe and North America. Currently, the diagnosis of Lyme borreliosis is based on serological tests allowing indirect detection of anti-Borrelia antibodies produced by patients. Their main drawback is a lack of sensitivity in the early phase of disease and an incapacity to prove an active infection. Direct diagnostic tests are clearly needed. The objectives of this study were to produce tools allowing sensitive detection of potential circulating Borrelia antigens and to evaluate them in a mouse model. We focused on two potential early bacterial makers, the highly variable OspC protein and the conserved protein FlaB. High-affinity monoclonal antibodies were produced and used to establish various immunoassays and western blot detection. A very good limit of detection for OspC as low as 17 pg/mL of sample was achieved with SPIE-IA. In infected mice, we were able to measure OspC in plasma with a mean value of 10 ng/mL at 7 days post-inoculation. This result suggests that OspC could be a good blood marker for diagnosis of Lyme borreliosis and that the tools developed during this study could be very useful. This infection is caused by spirochetes of Borrelia burgdorferi sensu lato (s.l.) of which at least five species  are pathogenic to humans3. The clinical manifestations are varied and polymorphic 4. The early infection phase is characterized by typical erythema migrans (EM) in 70–80% of cases5, often associated with nonspecific symptoms such as fever, myalgia, fatigue and joint pain6. If patients are not treated, the disease may progress to an early disseminated stage by hematogenous dissemination of the bacteria toward different organs and skin7. During this phase multiple cutaneous signs and cardiac, neurological and joint symptoms can occur. Late manifestations may occur several months to years after the onset of the infection and include mainly arthritis, acrodermatitis chronica atrophicans and late neuroborreliosis8.Lyme borreliosis is the most common reported tick-borne infectious disease in Europe9 and insufficient performances of the related laboratory assays. If there is a suspected bite and clinical signs suggestive of Lyme disease, serological tests based on detection of the host antibody response against Borrelia can be done10. The recommended approach for laboratory diagnosis of Lyme borreliosis is the two-tiered testing strategy, which includes an indirect enzyme-linked immunosorbent assay (ELISA) followed by a more specific western blot in order to rule out false positives11. These tests do not prove the existence of an active infection and are not recommended in the early localized phase of the disease. A recent meta-analysis showed that serological tests have heterogeneous sensitivity depending on the stage of the disease with only 50% during early manifestations (3–30 days post-infection)13. At this stage, the low sensitivity of serological testing is due to a weak or absent antibody response. Another pitfall of serological testing is the lack of standardization of the assay, which uses various recombinant proteins or whole-cell heterogeneous Borrelia to capture specific antibodies, thus producing undesirable variations in the test results. Direct detection of Borrelia in patients is difficult. Culture is not routinely used due to low sensitivity and a long incubation period of up to 12 weeks for bacterial growth14. Polymerase chain reaction (PCR) is generally very efficient in pathogen detection15, but in the case of Lyme disease the sensitivity is insufficient in blood due to the very low level of circulating bacteria14. PCR in synovial biopsy ranges in sensitivity from 40 to 90% and can be useful for diagnosis of Lyme arthritis. However, Borrelia DNA may remain in tissues after treatment and so is not a proof of active infection17.In the absence of typical EM, the diagnosis of Lyme disease is difficult to establish due to the complexity of the clinical diagnosisBorrelia protein in patients. In the past, assays based on capture and detection of Lyme antigen in urine or blood using anti-Borrelia polyclonal antibodies were developed18 but were later invalidated for measurement in patients due to low accuracy, sensitivity and specificity19. More recently, a new method based on the concentration of outer surface protein A (OspA) in a large volume of urine followed by detection with western blot was reported to be efficient in the early detection of active infection20, but robust validation is still needed.Reliable, accurate, sensitive and rapid tests for active infection are thus needed for better care management of patients as soon as possible before they develop disseminated Lyme disease. Few studies have reported the direct detection of Borrelia antigens and, second, the development of sensitive immunological methods that could be useful for direct diagnosis of Lyme disease. We focused on two Borrelia proteins, flagellin B (FlaB), a major component of the periplasmic flagellar filament crucial for bacterial mobility, and OspC, a lipoprotein needed for the establishment of early localized infection. Both proteins are described as early immunodominant antigens after infection21 and were also shown to be detectable in skin biopsy from humans with EM lesions using mass spectrometry analysis22. Two strategies were pursued for immunization. For FlaB, which is a highly conserved protein among Borrelia species, we used a recombinant protein as immunogen. For OspC, considering its high variability between B. burgdorferi species and strains of the same species, an approach using a highly conserved OspC peptide motif as immunogen was investigated. After characterization and selection of the best mAbs, we developed two sensitive detection methods. One is based on antigen immunocapture followed by western blot detection, while the second uses solid phase immobilized epitope immunoassays (SPIE-IA)23. These assays were evaluated for their capacity to detect FlaB and OspC in the tissues and plasma of experimentally infected mice. This study shows that OspC could be a good blood marker, for direct early diagnosis of Lyme disease.The aim of the present work was to develop specific and sensitive immunoassays based on the direct detection of bacterial antigens. This first involves the production of a well-characterized and high-affinity monoclonal antibodies (mAbs) against potential circulating Borrelia strains and species, we raised antibodies against a recombinant FlaB expressed in E. coli. Throughout the screening and cloning process, secreted hybridoma antibodies were assayed using two methods. The first measures antibody binding with biotinylated recombinant FlaB and the second measures binding with whole-cell Borrelia burgdorferi extract (strain BO23) coated on a solid phase. A total of 21 hybridomas were selected for their high reactivity for both sources of FlaB and were successfully cloned and stabilized. Purified mAbs were further characterized by sandwich immunoassays, western blots and affinity determination by biolayer interferometry.Given that FlaB protein has a high sequence homology between different 5Borrelia. The best sandwich immunoassays, including either FlaB-15 or FlaB-18 as capture mAb and FlaB-20 as tracer, were further evaluated in serial dilutions using an extract of Borrelia (data not shown). The limit of detection (LoD) was around 1 × 104Borrelia per mL.In an attempt to develop a sensitive ELISA for FlaB detection, we performed an mAb combinatorial analysis as described in materials and methods. Among the 441 combinations resulting from the 21 anti-FlaB mAbs, 361 pairs of antibodies provided good detection for 10Borrelia by western blot. A single band around 41 kDa, corresponding to the molecular mass of FlaB, was observed with all 21 mAbs, with recombinant FlaB and Borrelia cells. Three highly efficient mAbs, FlaB-15, FlaB-18 and FlaB-20, were further evaluated on a range of bacteria from 10 to 1000 spirochetes and recombinant FlaB. As shown in Fig. Borrelia burgdorferi  with 10–100 spirochetes detected according to the strain and 50 pg of recombinant FlaB.The 21 anti-FlaB mAbs were tested for their ability to recognize recombinant FlaB and whole-cell D between 0.23 and 2.10 nM . Of 189 hybridomas multiplying after fusion, a total of 7 hybridomas satisfying all screening assays were finally cloned and stabilized.Mice were immunized with BSA coupled to C10 peptide through a cysteine added to its N-terminal extremity. After fusion of spleen cells with myeloma cells, hybridomas were screened using three different assays based on the binding of antibodies to the C10 peptide, to full-length recombinant OspC and to whole-cell D of 0.46 nM, due essentially to its very low dissociation constant.Kinetic affinity parameters of the anti-C10 peptide mAbs are reported in Table Borrelia burgdorferi s.l., competition experiments were performed with various peptide sequences corresponding to the C-terminal OspC decapeptide of Borrelia burgdorferi s.l. in the NCBI database. From 89 sequences of OspC, we identified ten different decapeptides presenting one or two amino acid substitutions with regard to the C10 consensus sequence (see Table app) of the C10-11 mAb for each peptide variant was determined as the concentration of peptide inducing 50% inhibition of the binding of labeled C10 peptide. For four peptides , no inhibition was observed, while for three of them there is only one amino acid substitution. The same result was observed with all other anti-C10 peptide mAbs produced. As shown in Fig. app ranging from 60 to 179 nM, i.e. 2–5 times greater than the Kapp of the reference C10 peptide (33 nM). These results suggest that the OspC variants corresponding to these peptides should be recognized by the C10-11 mAb, but with a lower affinity resulting in a lower sensitivity of detection. For recombinant OspC, a Kapp of 1 nM was determined, which is consistent with the data obtained by biolayer interferometry. The discrepancy in affinity between the C10 peptide and the OspC protein may possibly reflect a conformational difference between the free C10 peptide and the same peptide engaged in a peptide bond with the rest of OspC protein, as was the immunogen. According to the frequency of each variant reported in the NCBI database, the mAb C10-11 could thus detect OspC protein of more than 90% of Borrelia burgdorferi strains.In order to evaluate the capacity of the C10-11 mAb to bind OspC protein from all potential pathogenic 26. This method is based on the use of a single epitope, such as the C10 peptide, recognized successively by a capture antibody and a tracer antibody (see “Methods”). To identify the best pair of antibodies for OspC detection by SPIE-IA, antibody combinatorial analysis was performed as described in materials and methods. The best combination, involving C10-11 as well as capture and tracer antibody, was selected according to the strongest signal-to-noise ratio observed with OspC protein. A standard curve established with OspC ranging from 50 pg/mL to 12 ng/mL and fitted using a nonlinear regression model is presented in Fig. SPIE-IA technology, developed several years ago in our laboratory, is the most appropriate method for measurement of small molecules bearing a single epitope in excess reagent formatB. garinii, which strongly expresses OspC in vitro as checked by SDS-PAGE. All mAbs recognized the recombinant OspC and the native OspC of Borrelia garinii with an apparent molecular weight around 23 kDa. The more efficient antibody, mAb C10-11, was further evaluated on four different Borrelia strains that express OspC in culture , blood, ears, skin near the site of inoculation, heart, bladder and joints were collected and analyzed by western blot in combination with immuno-concentration. The presence of Borrelia in tissues was also checked by qPCR. The quantity of Borrelia per µg of DNA was estimated using a qPCR standard curve of Borrelia DNA .Figure 4, although they present some serious limitations. Firstly, there is a time lag (up to 3 weeks) before detectable amounts of specific antibodies are produced by the host. Secondly, serological tests can demonstrate exposure to bacteria, but cannot prove an active infection or a reinfection14. Therefore, sensitive tests allowing direct detection of Borrelia or of components of Borrelia are needed. Until now, attempts using antigen capture to detect Borrelia antigens in blood and urine collected by non-invasive or minimally invasive sampling have not proven accurate enough and in some cases were even questionable14. The major difficulty in the detection of Borrelia in patient samples is the extremely low level of circulating bacteria and their high antigenic inter- and intra-species variability. Moreover, the antigenic repertoire of Borrelia changes during the course of infection, which makes the situation still more complicated.Currently, serological tests are recommended for the diagnosis of Lyme borreliosisBorrelia markers: FlaB and OspC. FlaB is a highly conserved structural protein of the flagella and the outer surface protein C (OspC) is a lipoprotein with high sequence variability within and between genospecies28 and is upregulated during the early phase of infection29. Since the capacity of an immunoassay to detect a target antigen with high sensitivity and specificity depends largely on the characteristics of the antibodies used, we decided to produce our own mAbs directed against either the FlaB protein or a highly conserved 10-amino-acid C-terminal peptide of OspC. This peptide was reported as a major immunodominant and probably surface-exposed epitope30, which makes it a good candidate as immunogen for production of anti-OspC antibodies. Throughout antibody screening, we paid particular attention to selection of specific and high-affinity antibodies. Of 20 anti-FlaB mAbs produced, two were selected as having the best affinity and optimal capacity to detect FlaB or different Borrelia strains by ELISA or immunoblot. Western blot detection was much more sensitive than ELISA in detecting Borrelia, with as few as ten bacteria detected per assay. For OspC, seven anti-peptide mAbs were produced, one of which, C10-11, displayed a remarkably high affinity for the recombinant OspC in the subnanomolar range.With the aim of developing a sensitive direct diagnostic immunoassay for early Lyme disease, we focused on the detection of two potential early circulating Borrelia burgdorferi s.l. is challenging. A least twenty five OspC major groups have been reported31 within B. burgdorferi s.s32 with up to 30% divergence in amino acid sequence between OspC groups33 and 58 OspC groups within the three species B. burgdorferi, B. garinii and B. afzelii34. It was important to consider the variability in the C-terminal OspC epitope between Borrelia strains and to have an overview of their impact on the binding of the C10-11 mAb. This was accomplished by performing competitive binding experiments with various peptides corresponding to the C-terminal epitope of all known full-length OspC sequences of Borrelia burgdorferi s.l. available in the NCBI database. Comparison of the different 10-amino-acid C-terminals of OspC shows a difference of no more than two amino acids. We showed that the C10-11 mAb was able to bind seven different peptides representing more than 90% of Borrelia burgdorferi strains reported in the NCBI database. This suggests that less than 10% of Borrelia burgdorferi s.l. could not be detected with the C10-11 mAb. To ensure their detection, production of new antibodies directed against the different C10 peptide variants are ongoing in our laboratory.Due to the high variability of OspC, the production of mAbs able to recognize OspC produced by all 22.Usually, in two-site immunometric assays the antigen is taken in a sandwich between two different antibodies recognizing two distinct epitopes. In the case of OspC, to ensure the detection of a maximum number of OspC groups, we developed a SPIE-IA targeting the C-terminal epitope of OspC. In buffer or in complex mouse tissue matrices, this assay has been shown to specifically measure OspC with a LoD ranging between 17 and 100 pg/mL (i.e. around 0.1 and 0.5 fmol per assay) according to the type of sample. This is the first time that an immunoassay has been described for OspC detection. As far as we know, only one study has reported direct detection and quantification of OspC in mouse or human skin matrices, with an estimated LoD of 1 fmol of OspC peptide per assay by liquid chromatography-mass spectrometryOne of the main problems with biomarkers present in extremely small quantities in tissuesis that it is often necessary to enrich the target to make it detectable and to avoid matrix effects that can interfere with the assay. In this study, we set up an immuno-enrichment method with antibodies coupled to magnetic beads followed by western blot detection. The advantage of this method is that it is possible to analyze a larger amount of tissue than with other methods, such as immunoassays in microtiter plates, and to multiplex the detection of specific biomarkers while limiting the nonspecific signals. By combining the immuno-enrichment with anti-FlaB and anti-OspC mAbs, we were able to detect simultaneously as low as 10 bacteria (or 50 pg of recombinant FlaB protein) and 300 pg of recombinant OspC in one mL after western blot analysis in buffer or complex tissue matrices.Borrelia infection, we used a mouse model of Borrelia infection after intradermal inoculation. At 7 days post-inoculation corresponding to the peak of replication at the injection site, we detected a high quantity of OspC between 7.2 ng/mg and 1.4 µg/mg of skin. These levels are consistent with our Borrelia DNA quantification in skin and the high level of OspC production reported in host-adapted spirochetes35. This is also consistent with the results of quantification of OspC by liquid chromatography-mass spectrometry in the skin of experimentally infected mice36, with an estimated 300 fmol of OspC  in 1 mg of skin biopsy. Taking into account the analytical sensitivity of western blot detection for OspC and FlaB protein, we clearly observed a higher level of OspC than FlaB in skin, ears and heart. Interestingly, in the disseminated phase we observed, in addition to monomeric and dimeric OspC, a lower molecular mass form of OspC in different tissues not found in in vitro cultivated Borrelia. Given that this form is recognized by the anti-C-terminal OspC mAb, it can be concluded that it corresponds to an N-terminal truncated form of OspC. There has been only one study reporting the western blot analysis of OspC protein expression in different tissues of infected mice37, with the detection of a unique OspC band by western blot. It is not surprising that the authors did not find the smaller form of OspC because the analysis was restricted to hydrophobic detergent-phase protein and thus to lipidated N-terminal forms of OspC.To evaluate the capacity of our immunological methods to detect OspC and FlaB in tissues and to investigate these two proteins as potential serum biomarkers for early human Borrelia antigen in plasma appears to culminate on day 7 p.i as compared to day 14 p.i, with an average level of OspC around 10 ng/mL of plasma. This early detection of Borrelia antigen in plasma is consistent with Borrelia detection by culture from infected mouse blood38, showing a peak of positive animals between 10 and 15 days p.i. This is also consistent with the PCR detection of Borrelia burgdorferi DNA in blood and positive blood culture for Borrelia in human patients in the early phase39 or early disseminated phase40. As well as Borrelia DNA detection in blood by PCR, the detection of OspC protein in blood could be of use in diagnosis of early Lyme disease at the onset of clinical symptoms when serology testing has a poor diagnostic value.More interestingly, we showed that it is possible to detect OspC and FlaB in the blood of infected mice. As in tissues, OspC was found to be much more predominant than FlaB in plasma. Furthermore, OspC is mainly represented by its lower molecular mass form with an apparent molecular weight of 20 kDa. At this stage of the study, we do not know if all or only a part of OspC detected in the plasma is physically bound to the bacterium. However, since OspC is anchored in the bacterial cell membrane via an N-terminal lipid, the smaller form of OspC could corresponds to circulating OspC released by bacteria. The production of Borrelia antigen in infected animal plasma. The very sensitive immunodiagnostic tools described in this study may be of great interest for early direct diagnosis of Lyme disease using blood samples. The SPIE-IA OspC is a simple and rapid test that can be easily implemented in any clinical laboratory. It could be a useful complement to serological testing, particularly in the early phase of the disease when anti-Borrelia antibodies are missing. The next step would be to test a large cohort of patients with clinical symptoms to check the detectability of OspC in plasma and confirm the value of OspC as an early biomarker in blood. Furthermore, the antibodies and immune-affinity enrichment methods described in this work could be very usefully associated with methods of detection other than immunoblotting, such as mass spectrometry, to enhance their analytical performance.In conclusion, this work presents the first direct detection of Borrelia burgdorferi sensu lato strains , Borrelia burgdorferi strain 297(ATCC 53899), Borrelia afzelii BO23 (ATCC 51992), and Borrelia burgdorferi B31 (ATCC 35210) were grown in BSK-II medium  at 34 °C. The spirochetes were counted and motility was checked in a Neubauer chamber by dark-field microscopy.Low passage (fewer than five passages) All animal experiments were performed in accordance with the European Directive 210/63/ECC on the protection of animals used for scientific purposes. Animals were housed in facilities authorized by the Veterinary Inspection Department of Essonne (France) . Animal care was supervised by a dedicated veterinarian and animal technicians. Experimental procedures were conform to the European Directive 210/63/EU and were approved by the Ethics Committee of the Commissariat à l’Energie Atomique  and by the French Ministry of Higher Education and Research under registration numbers APAFIS#11171-20177090615235646 v3 (for infection experiments) and APAFIS#3085-2015120909154560 v1 (for mouse immunization). All animal experiments were also performed in compliance with the ARRIVE guidelines.NdeI and XhoI restriction sites of the IPTG inducible pET22b vector, allowing the expression of OspC with an N-terminal histidine tag. Escherichia coli BL21 (DE3) transformed with OspC-pET22b was grown in Luria broth with 100 µg/mL ampicillin at 220 rpm and 37 °C until the mid-log phase was reached (OD at 600 nm = 0.6) and induced with the addition of 1 mM isopropyl-β-d-thiogalactoside (IPTG) for 4 h at 37 °C. The culture was centrifuged for 20 min at 2500×g at 4 °C and cells were resuspended in binding buffer A  supplemented with 1 mM 4-(2-aminoethyl) benzene sulfonyl fluoride hydrochloride. The cells were disrupted by sonication (1 min at 14 W) and then centrifuged at 14,000×g for 20 min at 4 °C. The final supernatant was purified on pre-equilibrated nickel chelating Sepharose Fast flow . After washing with binding buffer, elution of recombinant OspC protein was performed with binding buffer A supplemented with 0.5 M imidazole. The eluted fractions were pooled and dialyzed against 50 mM potassium phosphate buffer, pH 7.4, 150 mM NaCl. Purified OspC recombinant protein was quantified using the Pierce BCA protein assay kit  and the purity was assayed by electrophoresis using the Protein 230 kit and the Agilent 2100 Bioanalyzer system. OspC was stored at − 20 °C until use. Recombinant FlaB (synthetic gene based on the sequence published in GenBank: GU826786) was produced as described above for OspC, except that the binding buffer A was replaced by a denaturing buffer containing 100 mM potassium phosphate buffer, 10 mM Tris, 150 mM NaCl, 8 M urea, pH 8.The OspC gene was synthesized  based on the published sequence in GenBank: ABA42057.1. The synthetic gene was inserted between the Biozzi mice  were immunized by four intraperitoneal injections at 3-week intervals of 50 µg of FlaB protein mixed with alum adjuvant or 50 µg of C10 peptide: H-CPVVAESPKKP-O conjugated to bovine serum albumin (BSA)-succinimidyl-4- mixed with alum adjuvant. The two mice presenting the highest antibody titer (see “ELISA method” below) were selected and received three intravenous injections of 50 µg of FlaB or C10-BSA. Two days after the last boost, hybridomas were produced by fusing spleen cells with NS1 myeloma cells and were subsequently cloned by limiting dilution as previously described41. Throughout the screening process and cloning steps, hybridoma culture supernatants were tested for the presence of anti-FlaB antibodies by ELISA. mAbs were produced in culture supernatant and purified by protein G affinity chromatography  using an AKTAxpress system .Four 12-week-old female 42. Briefly, 2 nmol of C10 peptide-SH was reacted with 0.1 nmol of AChE-SMCC  in sodium phosphate buffer pH 6, 5 mM EDTA overnight at 4 °C. The tracer C10 peptide-AChE was then purified by gel filtration on a Zeba spin desalting column 7 K MWCO .mAbs and recombinant protein were labeled with biotin for use as conjugates. Briefly, 0.67 nmol of antibody dissolved in 400 µL of 0.1 M borate buffer pH 9 was reacted with 13.3 nmoles of biotin-N-hydroxysuccinimide ester  dissolved in water-free DMF. After a 1-h reaction at room temperature (RT), 100 μL of 1 M Tris–HCl pH 8.0 was added for 1 h at RT. Finally, 500 μL of enzyme immunoassay (EIA) buffer (see “Composition” below) was added and this preparation was stored at − 20 °C until use. OspC10-peptide was conjugated to the tetrameric AChE enzyme (G4) as previously described43. After three washes, AChE activity was revealed by addition of Ellman's reagent44. For evaluation of the recognition of Borrelia by the antibodies, we used a direct ELISA. For this purpose, 96-well microplates  were coated with 100 µL per well of sonicated Borrelia afzelii BO23 at 105 sp/mL and then blocked with EIA buffer , 0.15 M NaCl and 0.01% sodium azide) for at least 4 h at RT. Then 50 µL of serum dilution or hybridoma culture supernatant was loaded per well and reacted for 2 h at RT. After three washes, plates were reacted for 2 h at RT with 100 μL per well of 1 EU/mL of AChE-labeled goat anti-mouse antibodies. After three washes, AChE activity was revealed by addition of Ellman's reagent.To titrate anti-FlaB and anti-C10 peptide antibodies in mouse sera and in hybridoma culture supernatants, microtiter plates  coated with goat anti-mouse antibodies  at 5 µg/mL and blocked with EIA buffer  were prepared. Before use, plates were washed with washing buffer (10 mM potassium phosphate buffer pH 7.4 and 0.05% Tween 20) and then 50 µL of serum dilution or hybridoma culture supernatant was loaded per well and reacted for 2 h. After three washes, plates were reacted with 50 μL per well of 100 ng/mL of biotinylated FlaB or 1 Ellman unit (EU)/mL of AChE-labeled C10 peptide. For FlaB, plates were washed again and reacted with AChE-labeled streptavidin for 30 minBorrelia strains or recombinant OspC and FlaB were suspended in Laemmli buffer containing 2% SDS and 2-mercaptoethnol, denatured for 5 min at 95 °C, and run on SDS PAGE (10 or 13.4% resolving). Proteins were transferred onto a polyvinylidene difluoride membrane using the Trans-Blot turbo Transfer System . Briefly, the membrane was blocked with 5% BSA in phosphate buffer saline containing 0.1% Tween 20 (PBST). Each mAb labeled with biotin was diluted to 0.5 µg/mL in 1% BSA in PBST and reacted for 1 h at RT with the proteins transferred to the membrane. The membrane was subjected to three washes in PBST and incubated for 20 min at RT with 1:40,000 Pierce Streptavidin Poly-Horseradish Peroxidase  in 3% BSA in PBST. After four washes in PBST, the proteins were detected by chemiluminescence , using the ChemiDoc Imaging System . Western blots were analyzed using the ChemiDoc Touch Imaging System. Images are processed with Image Lab Touch Software version 2.2.0.08. All exposures used correspond to the optimal exposure of the system with optimal settings regarding resolution wait time.For western blotting, bacterial suspensions of different Borrelia burgdorferi as a target. Immobilization of the capture mAb in microtiter plates was performed by distributing 100 µL/well of the antibody at a concentration of 10 µg/mL in 0.05 M potassium phosphate buffer pH 7.4 and incubating the plates overnight at RT. The plates were then saturated with EIA buffer for 4 h at RT, and stored at 4 °C until use. Plates were then washed three times and 100 µL of a suspension containing 106 spirochetes/mL of sonicated Borrelia afzelii or 100 µL of a recombinant FlaB (10 ng/mL and 1 ng/mL) was loaded per well. After overnight incubation at 4 °C and three washing cycles, 100 µL of biotin-labeled tracer mAb (100 ng/mL) was added and reacted at RT for 4 h. Plates were washed, and after addition of 100 µL/well of AChE-labeled streptavidin conjugate (2 EU/mL) were reacted at RT for 1 h. After three washes, AChE activity was revealed by Ellman's colorimetric method. A similar combinatorial analysis was performed for the selection of the best anti-C10 peptide antibody pairs using recombinant OspC (from 100 pg to 2 ng/mL) and the SPIE-IA method.To evaluate the best mAb pairs to be used in a two-site immunometric test for FlaB detection, a combinatorial analysis was carried out using each mAb either as capture or conjugate antibody using FlaB or a suspension of sonicated 23 allows the measurement of small molecules in a “two-site immunometric” format. This method is based on the use of a single epitope recognized successively by the capture antibody and the tracer antibody, and involves covalent cross-linking of the analyte to the solid phase.The SPIE-IA method developed several years ago in our laboratoryBriefly, OspC samples were reacted with solid phase  coated with anti-C10-peptide mAbs for 2 h at RT. After a first washing step, the solid phase-bound OspC was cross-linked with the capture mAb using glutaraldehyde (0.1% for 5 min at RT) before a second washing step. Residual glutaraldehyde reactivity was neutralized and solid phase-bound proteins were denatured with borate trimethylamine (10 mg/mL) in methanol/HCL (v/v). After a third washing step, biotinylated C10-11 mAbs were added at 100 ng/mL and reacted for 2 h at RT. After a new washing step, solid phase-bound OspC was reacted with AChE-labeled streptavidin conjugate  and then revealed with Ellman’s reagent.D determination, each mAb was loaded onto the sensor at 10 µg/mL, following by a baseline step in EIA 0.02% Tween20 buffer. The sensors were exposed to different Flab or OspC concentrations (from 200 to 5 nM) and association was measured for 300 s, followed by 10 min of dissociation in buffer. The sensor surfaces were regenerated by dipping in 10 mM glycine buffer at pH 1.3 for 20 s. Data analysis was performed using the Octet Data analysis software, version 10.0 . After reference sensor subtraction (sensor dipped in 0 nM of antigen), experimental data were fitted with the binding equations describing a 1:1 binding model. The association rate constant (ka), dissociation rate constant (kdis), and equilibrium dissociation constant (KD) were obtained. The KD values were calculated as the ratio kdis/ka.Antibody binding kinetics were measured using biolayer interferometry on an Octet Red96e from fortéBio  according to the manufacturer’s instructions. Briefly, anti-FlaB or anti-C10 peptide mAbs were loaded onto anti-mouse IgG Fc-capture (AMC) biosensors from fortéBio  previously hydrated by immersion in an assay buffer (EIA 0.02% Tween20) for at least 10 min. Assays were performed with shaking at 1000 rpm and at 25 °C. For KThe apparent affinity of anti-C10 peptide mAbs for different variants of C10 peptides and recombinant OspC was measured by competitive ELISA, using goat anti-mouse antibodies  coated on the wells of a microtiter plate. A mix of anti-C10 peptide mAb (1 ng/mL), AChE-labeled C10 peptide (0.5 EU/mL) and different concentrations of competitor peptide or recombinant OspC were reacted overnight in the wells at 4 °C. After three washes, AChE-bound activity was revealed with Ellman’s reagent for 1 h. The peptides synthesized by Genecust were > 85% pure: PVVAESPKKP, PVVAEAPKKP, PVVAESPKKT, PVVAESPKNP, PVVAETPKKP, PVVAENPKKP, PVVVESPKKP, PVMAESPKKP, PVVAKNPKKP, PVVAKSPKKP, PVVAKTPKKP, PIVAESPKNP, PIVAESPKK.5Borrelia afzelii BO23 strain in BSK medium. In parallel, a negative control group (n = 5) was injected with BSK medium. At 7 or 14 days post-inoculation mice (five mice per group at different times after infection) were sacrificed and the blood, heart, bladder, joints, skin at the inoculation site and ears were collected for DNA amplification and immunoassays. Two more mice were included in the study for plasma analysis at 14 days post-inoculation.Four-week-old female CB17scid mice from Janvier were subcutaneously infected with 1045 and 2 µL of DNA samples. The PCR cycles were carried out with an initial denaturation for 3 min at 95 °C; 40 cycles of denaturation for 10 s at 95 °C, annealing for 30 s at 63.4 °C. After amplification, a melting curve was acquired by heating the product from 65 to 95 °C for 5 min. Melting curves were used to determine the specificity of the PCR. To determine the copy number of the target genes in the samples, a standard curve was established with known amounts of purified Borrelia DNA. The matrix effect of mouse DNA on the amplification of Borrelia DNA was checked and had no impact on the Cq values.Mouse DNA extraction was performed using the DNeasy Blood and Tissue Kit . The purified DNA was eluted with AE buffer  in 200 μL for each mouse organ and tissue. PCR was performed on each sample to amplify the 5S-23S intergenic spacer region. Amplification was performed in a CFX96 Touch Real-Time PCR Detection System  with a 20 μL final volume containing 10 µL Fast EvaGreen master mix, 400 µg of BSA/μL, 500 nM of each primer RRC (5′-CTGCGAGTTCGCGGGAGAG-3′) and RRB (5′-AAGCTCCTAGGCATTCACCATA-3′)g, supernatants were collected and protein concentrations were determined using the Pierce BCA protein assay kit .Mouse tissues were collected in a clean tube with aseptic instruments and stored at − 20 °C until use. Tissue samples (15 mg) were placed in homogenizer tubes with two ceramic matrix CK28  and 300 µL of lysis buffer  supplemented with protease inhibitor cocktails . Samples were processed with a Precellys 24 tissue homogenizer  using four cycles of 20 s at 6500 rpm. Then, samples were sonicated twice for 1 min. Samples were centrifuged for 10 min at 15,000×To improve the detection of both FlaB and OspC antigen in tissues and serum samples, we developed a concentration method based on immunoaffinity enrichment on magnetic beads  followed by western blot detection. First, we optimized tissue extraction by testing different extraction buffers and mechanical cell disruption conditions. The efficiency of extraction was checked by protein quantification (see “Methods”).The effectiveness of immunoconcentration experiments is an important factor for detection of low-abundance proteins, especially in complex matrices. To determine the optimal conditions for immunocapture of OspC and FlaB, we investigated different parameters, including (1) selection of antibodies bound to beads, (2) sample dilution, (3) time of immunocapture, and (4) washing step. The efficiency of immunocapture using the best conditions i.e. immunocapture of 0.5% tissue extract (W/V) or tenfold diluted plasma with FlaB15 and C10-11 mAbs for 1 h, three washes and elution in Laemmli buffer yielded over 90% of FlaB and OspC from 5 mg of tissues or 50 µL of plasma.Finally, Immunoprecipitation was performed as follows: mAbs (200 µg) were coupled to 10 mg of magnetic beads and saturated with BSA as recommended by the manufacturer. Samples (100 µL) were diluted tenfold with EIA buffer and incubated with 20 µL. of beads coupled to antibodies for 1 h at RT. After three washes with PBS-Tween, the beads were resuspended in 15 µL of Laemmli buffer (without 2-mercaptoethanol). Incubation and washing steps were performed on a King Fisher Duo Prime robot  and bound protein was analyzed by immunoblot.® software v.5.04  with a nonlinear regression model using a two-site specific binding curve fit. LoD is defined as the lowest protein concentration giving a signal greater than the nonspecific binding calculated as the mean of 12 measurements of the buffer or unspiked matrix + 3 standard deviations. LoQ is defined as the lowest concentration of protein that can be quantitatively determined and is calculated as the mean of 12 measurements of the buffer or unspiked matrix + 10 standard deviations.For all immunoassay formats, LoD and LoQ were calculated using PRISMSupplementary Figures.


Mycoplasma hyopneumoniae and PCV2. Pigs were inoculated intratracheally with M. hyopneumoniae at 4 weeks of age followed by another intranasal inoculation at 6 weeks of age with one of three PCV2 genotypes. Dual infection with two pathogens produced moderate and severe dyspnea, lethargy, and reduced weight gain in pigs regardless of the PCV2 genotype evaluated compared with pigs only inoculated with PCV2. The overall levels of PCV2d viremia and severity of lymphoid lesions, and PCV2-antigen within lymphoid lesions were significantly higher in pigs dually inoculated with M. hyopneumoniae/PCV2d when compared with all other dually inoculated groups. The level of PCV2 viremia and the production of PCV2-associated lymphoid lesions did not differ significantly among PCV2a, PCV2b, and PCV2d single-inoculated pig groups. The results of this study demonstrated that M. hyopneumoniae potentiated the replication of PCV2d more than it did with the other PCV2 genotypes as measured by lymphoid lesion severity.The objective of this study was to compare the virulence of three different porcine circovirus type 2 (PCV2) genotypes  in pigs infected with either one of these three PCV2 genotypes versus pigs dually inoculated with  Mycoplasma hyopneumoniae are two of the major porcine pathogens and are responsible for enormous economic losses to pig producers [M. hyopneumoniae is the primary causative agent of enzootic pneumonia. Enzootic pneumonia, a chronic respiratory disease, is widespread and characterized by reduced average daily weight gain and reduced feed conversion efficiency, while it simultaneously increases the predisposed animals to secondary infections [Porcine circovirus type 2 (PCV2) and roducers ,2. PCV2 roducers . It is troducers . M. hyopfections .M. hyopneumoniae is frequently found as a co-infectious agent in PCVAD [M. hyopneumoniae [M. hyopneumoniae.PCVAD is considered a multifactorial disease whose full expression of clinical disease is reached by a variety of infectious agents as cofactors . PCV2 inin PCVAD  and has in PCVAD . PCV2a heumoniae . PCV2a, eumoniae . TherefoM. hyopneumoniae/PCV2a, M. hyopneumoniae/PCV2b, or M. hyopneumoniae/PCV2d. Regardless of the PCV2 genotype, pigs dually inoculated with M. hyopneumoniae/PCV2 developed severe respiratory distress by 7 days post-inoculation (dpi) that continued through 21 dpi. The respiratory sign scores at 7 to 21 dpi in pigs dually inoculated with M. hyopneumoniae/PCV2a, M. hyopneumoniae/PCV2b, and M. hyopneumoniae/PCV2d were significantly (p < 0.05) higher compared with those of pigs singularly inoculated with PCV2a, PCV2b, or PCV2d only and those of negative control pigs  lower average body weights compared to pigs inoculated with either PCV2a, PCV2b or PCV2d alone, as well as negative controls at 63 days old (21 dpi). Pigs dually inoculated with M. hyopneumoniae/PCV2a, M. hyopneumoniae/PCV2b, and M. hyopneumoniae/PCV2d had a significantly lower (p < 0.05) average daily weight gain  between 28 and 63 days of age compared to pigs singularly inoculated with either PCV2a, PCV2b, PCV2d, or the negative controls  in terms of average body weight among the seven groups. Pigs dually inoculated with controls .M. hyopneumoniae/PCV2a, M. hyopneumoniae/PCV2b, and M. hyopneumoniae/PCV2d and pigs singularly inoculated with PCV2a, PCV2b, and PCV2d had a significantly (p < 0.05) higher S/P ratio at 14 and 21 dpi compared to negative control pigs  higher number of PCV2 genomic copies compared to pigs singularly inoculated with either PCV2a, PCV2b or PCV2d at 7, 14, and 21 dpi. Pigs dually inoculated with M. hyopneumoniae/PCV2d had a significantly (p < 0.05) higher number of PCV2 genomic copies compared to pigs dually inoculated with M. hyopneumoniae/PCV2a, and M. hyopneumoniae/PCV2b at 21 dpi  microscopic lymphoid lesion scores compared to pigs singularly inoculated with PCV2a, PCV2b, and PCV2d alone at 21 dpi. Pigs dually inoculated with M. hyopneumoniae/PCV2d had significantly higher (p < 0.05) microscopic lymphoid lesion scores compared to pigs dually inoculated with M. hyopneumoniae/PCV2a and M. hyopneumoniae/PCV2b at 21 dpi  higher number of PCV2-positive cells per unit of tissue in their lymph nodes than pigs singularly inoculated with PCV2a, PCV2b, or PCV2d. Pigs dually inoculated with M. hyopneumoniae/PCV2d had a significantly (p < 0.05) higher number of PCV2-positive cells per unit of tissue in their lymph nodes than pigs dually inoculated with M. hyopneumoniae/PCV2a and M. hyopneumoniae/PCV2b  between PCV2 viral blood load and the severity of lymphoid lesions.Positive and significant correlations were found in pigs dually inoculated with PCV2 and M. hyopneumoniae/PCV2 resulted in the reproduction of PCVAD across all three PCV2 genotypes. Pigs dually infected with M. hyopneumoniae/PCV2 met the criteria of PCVAD classification such as typical clinical signs including ADWG, pathological lesions and presence of PCV2 antigen within lymphoid lesions. The present results are consistent with previous studies, where pigs dually infected with PCV2a and M. hyopneumoniae induced the full clinical manifestation of PCVAD [M. hyopneumoniae occurs.Unlike the pigs infected with a single genotype of PCV2, a dual infection with of PCVAD . This stM. hyopneumoniae clearly potentiates PCV2-associated disease in dually infected pigs. Nevertheless, the specific mechanisms by which M. hyopneumoniae enhances PCV2-associated lesions have not been determined. M. hyopneumoniae is traditionally considered a strict extracellular pathogen and the site of infection remains limited mainly at the lung and cilia of bronchial and bronchiolar lining epithelial cells [M. hyopneumoniae [M. hyopneumoniae, may be a pivotal mechanism in providing the PCV2 organism with the ability to invade and survive in alveolar macrophages for further dissemination to the liver, spleen, kidneys, and lymph node [M. hyopneumoniae can interact with each other in the same or neighboring infected macrophage, promoting high levels of replication of PCV2 in lymphoid tissues.In the present study, al cells ,10. It heumoniae ,12. The mph node ,12,13. IM. hyopneumoniae/PCV2d produced significantly higher levels of PCV2 loads in blood and lymph nodes with more severe lymphoid lesions at 21 dpi only when compared with pigs dually infected with either M. hyopneumoniae/PCV2a or M. hyopneumoniae/PCV2b. Nonetheless, there are no significant differences in serology, levels of PCV2 loads in blood at other points, and ADWG among PCV2 genotypes in pigs dually infected with M. hyopneumoniae and PCV2. It currently cannot be explained with complete certainty as to why M. hyopneumoniae potentiates the replication of PCV2d more than other PCV2 genotypes, leading to a greater severe lymphoid lesion severity. This dual infection is based on the sequential model, which is M. hyopneumoniae infection followed 14 days later by PCV2 infection [M. hyopneumoniae-infected pigs, cells positive for interferon-γ (IFN-γ) were detected at 7 dpi, with their numbers increasing at 14 and 21 dpi, and slightly decreasing thereafter [M. hyopneumoniae infection. Additional studies are necessary to elucidate the replication preference of PCV2d by M. hyopneumoniae.Pigs dually infected with nfection . In M. hereafter . PCV2d hereafter . Moreoveereafter . It is sM. hyopneumoniae used merely as co-factor to induce full manifestation of PCVAD. In addition, there were no significant differences in the levels of M. hyopneumoniae laryngeal loads in the pigs dually infected with M. hyopneumoniae/PCV2a, M. hyopneumoniae/PCV2b, and M. hyopneumoniae/PCV2d. Since the effect of PCV2 on M. hyopneumoniae was not determined in this study, it is not necessary to include the pigs singularly infected with M. hyopneumoniae, in order to avoid unnecessary usage of pigs. Further studies are needed to determine the effect of PCV2 on M. hyopneumoniae including M. hyopneumoniae-infected group.This experiment is to compare the virulence among the three PCV2 genotypes. Therefore, M. hyopneumoniae. The results of this study are clinically relevant as PCV2d is the predominant genotype in Asian and North American swine populations [This is the first pathogenicity and virulence comparative study of PCV2a, PCV2b and PCV2d genotypes experimentally infected into pigs both independently and as a co-infection with ulations ,20,21,22ulations . In contulations ,24. ThisM. hyopneumoniae-free based on serological testing, long-term clinical and slaughter history. The farm was seropositive for PCV2 but did not show PCVAD. Pigs were seronegative for PRRSV , PCV2 , and M. hyopneumoniae . Piglets were confirmed negative for PCV2  and PRRSV viremia, and M. hyopneumoniae laryngeal shedding by real-time polymerase chain reaction (PCR) testing upon arrival [Forty-two clinically healthy, colostrum-fed conventional pigs from sows that had not been previously vaccinated against PCV2 were purchased at 25 days of age from a commercial farm that was free of PRRSV. The farm was also  arrival ,27,28.http://www.r.project.org, accessed on 25 July 2021). A 0.05 significance level, 0.4 effect size, and 70% power were used to calculate the minimum number of piglets needed per group. This value was determined as 5.67, therefore, at least six pigs were designated per group.For the study, pigs were allocated into 7 groups (6 pigs per group) using the random number generator function from Excel  . A minimM. hyopneumoniae/PCV2 groups were intramuscularly anesthetized with a mixture of 2.2 mg/kg xylazine hydrochloride , 2.2 mg/kg tiletamine hydrochloride, and 2.2 mg/kg zolazepam hydrochloride . Pigs were then inoculated intratracheally with 7 mL of M. hyopneumoniae (strain SNU98703) culture medium containing 107 color changing units (CCU)/mL. Experimental inoculation of pigs with M. hyopneumoniae (strain SNU98703) induced mycoplasmal pneumonia in the lungs [Pigs in each group were randomly assigned into seven separate rooms. At −14 dpi (28 days of age), pigs in the three he lungs .M. hyopneumoniae/PCV2 groups were each inoculated intranasally with either 3 mL of PCV2a , PCV2b , or PCV2d  inoculum containing 1.2 × 105 50% tissue culture infective dose (TCID50/mL), based on their groups. Pigs in the PCV2a, PCV2b, and PCV2d groups were each inoculated intranasally with 3 mL of PCV2a, PCV2b, and PCV2d, respectively, as mentioned above. Pigs in the negative control group were inoculated intranasally with 3 mL of phosphate-buffered saline .At 0 dpi (42 days of age), pigs in the three Blood samples were collected from each pig by jugular venipuncture at −14, 0, 7, 14, and 21 dpi. Pigs were sedated by an intravenous injection of sodium pentobarbital and then euthanized by electrocution at 21 dpi as previously described . TissuesPigs were monitored daily for clinical signs and scored weekly using a score ranking system that ranged from 0  to 6  . All obsThe live weight of each pig was measured at 28 (−14 dpi) and 63 (21 dpi) days of age. ADWG was analyzed over the time period between 28 and 63 days of age. ADWG during the different production stages was calculated as the difference between the starting and final weight divided by the duration of the stage.Serum samples from all experimental groups were collected at −14, 0, 7, 14, and 21 dpi. A commercial kit  was used to extract DNA from serum samples for PCV2. Genomic DNA copy numbers for PCV2a, PCV2b, and PCV2d were quantified by real-time PCR ,26.M. hyopneumoniae. The number of genomic DNA copies for M. hyopneumoniae was then quantified by real-time PCR [Laryngeal swab samples from all experimental groups were collected at −14, 0, 7, 14, and 21 dpi. A commercial kit  was used to extract DNA from laryngeal swabs for time PCR .Serum samples were also tested for antibodies against PCV2 . Serum samples were considered positive for PCV2 antibodies if the optical density (OD) was greater than 0.3 according to the manufacturer’s instructions.For the morphometric analysis of histopathological changes in superficial inguinal lymph nodes, three sections of that lymph node were examined “blindly” . Lymph nhttp://imagej.nih.gov/ij/download.html, accessed on 25 July 2021). For each slide of lymph node tissue, 10 fields were randomly selected, and the number of positive cells per unit area (0.25 mm2) was counted. The mean values were also calculated [Immunohistochemistry (IHC) and morphometric analysis of IHC were carried out as previously described . Positivlculated .p < 0.05 was considered to be significant.Prior to statistical analysis, real-time PCR data were log-transformed to reduce variance and positive skewness. Data were tested for normal distribution using the Shapiro-Wilk test. A one-way analysis of variance (ANOVA) was used to examine whether significant statistical differences existed among the seven groups for each time point. When a one-way ANOVA test result showed statistical significance, a post-hoc test was conducted for a pairwise comparison with Tukey’s adjustment. If the normality assumption was not met, the Kruskal-Wallis test was performed. When the result from the Kruskal Wallis test showed statistical significance, a Mann-Whitney test with Tukey’s adjustment was performed to compare the differences among the groups. Spearman’s  correlations were applied to determine the relationship between the levels of PCV2 viremia and severity of lymphoid lesions. A value of


Hearing loss limits communication and social activity, and hearing aids (HAs) are an efficient rehabilitative option for improving oral communication and speech comprehension, as well as the psychosocial comfort of people with hearing loss. To overcome this problem, over-the-counter amplification devices including personal sound amplification products and wearable augmented reality devices (WARDs) have been introduced.This study aimed to evaluate the clinical effectiveness of WARDs for patients with mild to moderate hearing loss.A total of 40 patients (18 men and 22 women) with mild to moderate hearing loss were enrolled prospectively in this study. All participants were instructed to wear a WARD, Galaxy Buds Pro (Samsung Electronics), at least 4 hours a day for 2 weeks, for amplifying ambient sounds. Questionnaires including the Korean version of the abbreviated profile of hearing aid benefit (K-APHAB) and the Korean adaptation of the international outcome inventory for hearing aids (K-IOI-HA) were used to assess personal satisfaction in all participants. Audiologic tests, including sound field audiometry, sound field word recognition score (WRS), and the Korean version of hearing in noise test (K-HINT), were administered to 14 of 40 patients. The tests were performed under two conditions: unaided and aided with WARDs.P<.001). However, scores regarding aversiveness were worse under the aided condition (P<.001). K-IOI-HA findings indicated high user satisfaction after the 2-week field trial. On audiologic evaluation, the K-HINT did not show significant differences between unaided and aided conditions (P=.97). However, the hearing threshold on sound field audiometry (P=.001) and the WRS (P=.002) showed significant improvements under the aided condition.The mean age of the participants was 55.4 (SD 10.7) years. After 2 weeks of the field trial, participants demonstrated a benefit of WARDs on the K-APHAB. Scores of 3 subscales of ease of communication, reverberation, and background noise were improved significantly (WARDs can be beneficial for patients with mild to moderate hearing loss as a cost-effective alternative to conventional hearing aids. Hearing loss limits communication and social activity, leading to disorders in language and cognitive impairment . AccordiHearing aids (HAs) are an efficient rehabilitative option for improving oral communication and speech comprehension as well as the psychosocial comfort of people with hearing loss . AlthougTo overcome this problem, over-the-counter (OTC) amplification devices including personal sound amplification products (PSAPs) have been introduced. PSAPs are defined by the US Food and Drug Administration as wearable electronic products for customers with hearing loss to amplify sounds in certain environments. In general, PSAPs are less expensive and simpler sound amplification devices with fewer features and less functionality than digital HAs. However, some studies have suggested some kinds of PSAPs as alternative devices for those with mild to moderate hearing loss . In addiAlthough most previous studies evaluated clinical effectiveness of PSAPs compared to conventional HAs, there are insufficient data on the WARD’s ability to help people with hearing loss. To the best of our knowledge, no clinical field trial assessing the effectiveness of WARDs in the daily lives of hearing-impaired people has been conducted. Thus, the aims of this study were to investigate the hearing outcomes in patients with mild to moderate hearing loss aided with WARDs and to quantify the patient’s subjective outcomes using the Korean version of the abbreviated profile of hearing aid benefit (K-APHAB) and the Korean adaptation of the international outcome inventory for hearing aids (K-IOI-HA) questionnaires in 2-week field trials. Furthermore, we attempted to assess the correlation between personal satisfaction and audiologic performance with WARDs.The sample size was determined on the basis of previous research determining the effect of a web-based intervention program on positive changes in hearing aid use . The resThis study was approved by the institutional review board of Samsung Medical Center in Seoul, South Korea , and conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants.Galaxy Buds Pro  was used for hearing rehabilitation in this study. Galaxy Buds Pro has its own smartphone app, Galaxy Wearable. Users can use the Ambient Sound feature with the app for sound amplification. Similar to PSAPs or HAs, users can control the level of sound in their surroundings using Galaxy Buds Pro. The Galaxy wearable device consists of 4 levels, of which only level 4 provided sound amplification. Therefore, the level was set at 4 in this study.Each participant was provided with a pair of Galaxy Buds Pro for this field trial and taught how to use the device. Participants were required to use the device when having difficulties in communication or listening and for more than 4 hours a day for 2 weeks. They wore the device during daily activity such as conversation, TV watching, or driving.We also recommended to stop wearing the devices when if the participants feel any pain or have any other troubles in their ears. Before the beginning of the field trial, participants filled out the K-APHAB questionnaire as a baseline .After 2 weeks, participants returned the device and filled out the K-APHAB and the K-IOI-HA questionnaires to assess the benefit of using the device . The APHThree audiologic test batteries were administered in this study: (1) sound field audiometry, (2) sound-field word recognition score (WRS), and (3) the Korean version of hearing in noise test (K-HINT). The associated measurements were conducted with 14 subjects who agreed to participate in accordance with institutional review board approval.Unaided and aided thresholds were obtained in sound field audiometry. Warble tones of 0.25, 0.5, 1, 2, 3, 4, and 6 kHz were presented through a loudspeaker located 1 m from the participant. The participant wore the WARD in both ears to mimic how the device would be used in his/her daily life. Speech perception with and without the WARD was evaluated through sound-field WRS. In total, 25 monosyllabic words from the Korean standard monosyllabic word list (KS-MWL-A) were presented at 50 dB HL through a loudspeaker located 1 m from the participant . The part test was conducted to compare the scores of questionnaires before and after intervention. The paired t test also was used to compare the variables of audiological measurements between unaided and aided conditions. In addition, Pearson correlation coefficients were calculated to further investigate relationships between scores on the questionnaires and laboratory assessments. A significance level of P=.05 was applied to determine statistical significance.All statistical analyses were completed using SPSS . The paired 2-tailed A total of 40 participants  with mild to moderate hearing loss were enrolled. The mean hearing threshold in pure-tone average was 40.75 (SD 6.63) dB on the right side and 41.16 (SD 7.93) dB on the left side .The demographic characteristics of the enrolled patients are summarized in P<.001). The RV subscale before using the WARD (Pre RV) was 51.58 points (SD 20.49). Under the aided condition, the RV subscale (Post RV) was 27.35 (SD 13.97) points, which was significantly improved (P<.001). There was a significant difference between the BN subscale under unaided (Pre BN) and aided conditions (Post BN) (P<.001). Pre BN score was 48.28 (SD 18.75) points, while the Post BN score was 35.17 (SD 18.18) points. Additionally, the global score  showed significant improvement under the aided condition compared with the unaided condition (P<.001). In contrast, the AV subscale score in the aided condition (Post AV) was worse than that in the unaided condition (Pre AV). There was a significant difference between Pre AV points and Post AV points (P<.001).The K-APHAB results for all 40 subjects under unaided and aided conditions are shown in P<.001; The resulting scores on the K-IOI-HA for the 40 subjects were 3.3 (SD 0.5) points for daily USE, 3.0 (SD 0.9) points for BEN, 3.6 (SD 0.9) points for RAL, 3.0 (SD 1.0) points for SAT with devices and services, 3.7 (SD 0.9) points for RPR, 4.2 (SD 0.9) points for IO, and 3.0 (SD 0.7) points for QOL. In addition, the mean K-IOI-HA Factor 2 score was significantly higher than the Factor 1 score P<.001; .P=.001). Sound field WRS significantly improved in the aided condition from 55.43%  to 67.71%  (P=.002). However, there was no significant differences in the K-HINT score between the unaided and aided conditions  dB HL; this was significantly decreased under the aided condition to 40.48 (SD 6.99) dB HL (nditions .P<.05). BN benefit also showed a significant correlation with WRS improvement (P<.05). However, EC benefit did not show a significant correlation with WRS improvement (P=.99), and BN benefit did not show a significant correlation with dB SNR improvement on the K-HINT (P=.20). No additional correlations were found between the parameters.Correlation analysis between scores from questionnaire and audiologic evaluation was performed . The EC This study investigated personal satisfaction with the WARD among patients with mild to moderate hearing loss. Furthermore, the performance of the WARD was evaluated through audiologic tests including sound field audiometry, WRS, and the K-HINT. The results revealed significantly increased subjective satisfaction with the WARD. Furthermore, sound field audiometry and WRS also showed significant improvement under the aided condition.In this study, significant improvements were shown in EC, RV, and BN subscales under aid with a WARD. These results indicated that if patients use a WARD, their difficulties in listening were improved in various situations including quiet or easy conversation, loud sounds or sound at a distance, and in the presence of ambient noise. However, the AV subscale was significantly increased under the aided condition: loud and noisy sounds were amplified with a WARD with a resulting increase in discomfort.Worse scores on the AV subscale occur commonly, even in HA users. Johnson et al  reportedIn this study, the average of all question scores was 3.0 points or higher. In particular, the IO score was higher than 4 points, indicating decreased inconvenience to others owing to hearing difficulty when using the WARD. Compared with previously published IOI-HA norms, the distribution characteristics of the mean scores were consistent with the normative data . The comThe average threshold of sound field audiometry was significantly improved with WARDs in this study. Sound field WRS was increased while wearing a WARD. Thus, hearing-impaired people can receive listening benefit from WARDs under quiet conditions. However, speech recognition in a noisy environment was worse under the aided condition. Reed at al  reportedP<.05). We expected the WRS improvement and EC benefit to have a significant correlation because both were derived in a quiet environment. We also expected that dB SNR improvement and BN benefit would have a significant correlation because both results were derived in a noisy environment. However, some results in this study did not agree with our expectations. The group with high satisfaction in a quiet environment showed dB SNR improvement, and the group with high satisfaction in a noisy environment had WRS improvement.Interpreting the correlation between objective and subjective results requires caution. We analyzed the correlation between benefits from K-APHAB scores and parameters from audiologic measurements in this study. These were not significantly correlated, but there were some weak correlations between personal satisfaction and audiologic performance. We confirmed that the improvement in dB SNR on the K-APHAB and the EC benefit on the K-APHAB and that between the BN benefit and the WRS on the K-APHAB had significant correlations (The reason for this correlation analysis can be considered to be due to the difference in the test conditions. First, as mentioned in previous studies, the laboratory-based evaluation method is a static, limited, and 1-way communication method, whereas the evaluation method experienced by patients in everyday life is a dynamic and expanded interactive communication method . Second,Overall satisfaction with a WARD in daily life was high in this study, and we speculate that this high satisfaction might be influenced by the current COVID-19 pandemic. All Koreans must wear face masks when outdoors. Personal protective equipment such as the facial mask creates difficulty in understanding and communication in hearing impaired patients; these patients cannot read the speaker’s lips, and sound clarity is decreased . In thisThis study has some limitations. The first limitation is the lack of generalizability to the WARDs market. Only one kind of WARD, Galaxy Buds Pro, was used in this study. Even though availability of hearing devices is limited, WARDs vary considerably in style, quality, and technology. Therefore, further research using a variety of hearable devices is needed to generalize the feasibility of WARDs. Second, we did not take into consideration certain subject demographics including the duration of hearing loss, education, income, and perceived social support, which play an important role in determining personal satisfaction. Future large-scale research should consider the contribution of these factors to audiologic outcomes and personal satisfaction related to WARD benefit. Finally, we did not take into account the shape and size of the ear canal. Since the shape and size of human ear canal are very diverse, it might be possible that there were some participants who had WARDs that were either too large or too small . Even thOur results indicate that WARDs could be helpful for individuals with mild to moderate hearing loss, especially under quiet conditions. Owing to high price and poor accessibility of HAs, OTC hearing devices such as WARDs could be an alternative partial solution for hearing loss. In the near future, WARDs will have greater potential as technology develops and government regulation changes. Further large-scale comparative research regarding the clinical effectiveness of WARDs is necessary.