Self-grooming is an innate, naturalistic behavior found in a wide variety of organisms. The control of rodent grooming has been shown to be mediated by the dorsolateral striatum through lesion studies and in-vivo extracellular recordings. Yet, it is unclear how populations of neurons in the striatum encode grooming. We recorded single-unit extracellular activity from populations of neurons in freely moving mice and developed a semi-automated approach to detect self-grooming events from 117 hours of simultaneous multi-camera video recordings of mouse behavior. We first characterized the grooming transition-aligned response profiles of striatal projection neuron and fast spiking interneuron single units. We identified striatal ensembles whose units were more strongly correlated during grooming than during the entire session. These ensembles display varied grooming responses, including transient changes around grooming transitions or sustained changes in activity throughout the duration of grooming. Neural trajectories computed from the identified ensembles retain the grooming related dynamics present in trajectories computed from all units in the session. These results elaborate striatal function in rodent self-grooming and demonstrate that striatal grooming-related activity is organized within functional ensembles, improving our understanding of how the striatum guides action selection in a naturalistic behavior. In mammals, grooming serves to care for the outer body surface, for de-arousal12, thermoregulation15, and water economy16. Rodent grooming is an innate behavior20, with mice showing primitive forms of facial grooming as early as postnatal day 1 (p1)17. From p4-8, the kinematics of grooming movements become more tightly coordinated. After p9, mice start to perform movements similar to those performed by adults. Rat pups begin to exhibit functional face grooming at p6-89, but do not perform long sequences of grooming until p14–2120, the time of development when many complex motor programs begin to emerge22.Self-grooming is an evolutionarily conserved, ethologically relevant, and innate behavior. Self-grooming is found in arthropods29. The striatum has been shown to encode action space30 and to flexibly combine behavioral motifs into actions25. Cells within the striatum, predominantly consisting of striatal spiny projection neurons33, are organized into functional clusters of co-active units; this clustering is considered to be important for striatal network dynamics35 and behavioral control40. The striatum has been implicated in the production of self-grooming behavior, and lesions of the striatum disrupt grooming bouts41. Within the striatum, neurons of the dorsolateral striatum43 and nearby central striatal regions44 display grooming-related activity. Yet, how populations or ensembles of neurons in the striatum—defined here as sets of neurons that are more likely than chance to be co-active—encode self-grooming remains unclear. Given the likely importance of ensemble activity in the striatum, elaborating whether grooming-associated neural activity maps onto striatal ensembles is significant, and it remains to be elucidated.The selection and initiation of actions is controlled by the striatumHere we recorded simultaneous activity of populations of neurons in the dorsolateral striatum of freely moving mice during spontaneous grooming using extracellular probes. Because grooming can be a relatively rare behavior, many hours of data must be acquired to capture an adequate sample of its neural correlates. To overcome this obstacle, we developed a semi-automated approach to detect grooming bouts from behavioral videos using 3D pose estimation and postural heuristics. We found striatal projection neurons (SPN) and fast spiking interneurons (FSI) with temporally diverse grooming-related activity. Furthermore, we identified striatal ensembles that encode core parameters of grooming bouts, including the transitions in and out of individual bouts, as well as bout duration.Mice were placed in a transparent, triangular arena with video recorded from each side to capture spontaneous behavior , B. We i47. Units that did not fit the criteria for SPNs or FSIs were labelled as ‘other’ and excluded from further analyses.  from each group reflects units that undergo a step-like increase or decrease in their activity at the grooming transition CA48 on tThe event-triggered average responses of striatal neurons reveal several patterns of firing rate modulation aligned with the start or end of grooming. However, because this analysis relies on trial-averaging, it cannot be used to determine whether the striatum contains ensembles of neurons that are consistently co-active. To address this question, we looked for functional clusters, or ‘ensembles’ of co-active striatal neurons with grooming related activity. To achieve this, we first constructed a matrix containing the grooming activity of all units in a given session, where the activity of each unit plus 5 seconds before and after grooming was concatenated. This matrix is shown for an example session in 50, where we identify dimensions of the neural covariance matrix that capture more variance than expected by chance and found each session to have three to ten ensembles  and 70% of recorded FSIs (41 units) were clustered into ensembles of two or more units . The maj cluster . The dis2 = 0.239, p = 0.004). The distribution of the number of units within each cluster is shown in 2 = 0.01, p = 0.582). The distribution of the percent of clustered units in each session is shown in The distribution of the number of ensembles identified in each session is shown in Having demonstrated the existence of ensemble activity during grooming, we next asked whether ensemble activity was enriched during specific timepoints during grooming. A heatmap displaying the average grooming activity for all significant ensembles is shown in To contrast the encoding of grooming-related activity in ensembles vs unclustered units, we used nonnegative matrix factorization to perform dimensionality reduction on the neural activity during grooming within a single session. The trajectories traced out by the population activity of all units during all grooming bouts in an example session are shown in 52 and learned behaviors54. Our single unit data elaborate the changes in striatal unit activity around the transitions of a naturalistic behavior. Single unit responses were heterogenous and included units with changes in activity at the start or end of grooming, as well as units that were active or silenced throughout the duration of grooming. We identified striatal ensembles with units that were more correlated during grooming than during the entire session. These striatal ensembles encoded grooming start time, end time, and bout duration. Single session trajectories computed from ensemble activity retain most of the dynamics present in trajectories computed from all units in the session.Here, we developed a semi-automated approach to identify grooming events in mouse behavioral videos. We recorded population activity in the dorsolateral striatum of freely moving mice and found SPNs and FSIs with activity that encodes the start and end of spontaneous grooming bouts. Previous studies reported changes in striatal single unit activity around the start and end of naturalistic39 although our identified ensembles are less spatially compact than those previously reported. Notably, however, striatal 2-photon imaging approaches and microendoscopic imaging of genetically encoded calcium indicators yield a recording area that extends along the medial-lateral and anterior posterior axes , whereas our four-shank electrophysiological recordings yield a recording field that extends along the anterior-posterior and dorsal-ventral axes , potentially accounting for this difference in findings. Together, the ensemble distances recorded via both methods suggest the possibility that striatal ensembles are isotropically organized, which is well-aligned with studies of cortical and thalamic projection patterns to the striatum57.The distribution of pairwise distances between units within an ensemble is shifted towards smaller distances, compared to the distribution of distances for units that do not belong to the same ensemble. This is consistent with striatal ensembles detected using imaging techniques39.While the ensembles we characterized were identified during grooming behavior, it is possible that they are not grooming-specific. One possibility for the functional organization of striatal ensembles is that ensemble membership is stable across different behaviors. Then, ensemble activation encodes some common movement motif or state that is present across multiple behavioral settings. However, the units within our identified striatal ensembles were more correlated during grooming than during the entire recording session. Our data therefore support an alternative to the stable membership hypothesis, wherein individual units can be members of multiple ensembles, such that when a given unit is active, the behavior the animal is performing can only be determined by looking at the population level. For example, a set of units might be members of a single ensemble that encodes the start of grooming, but each unit could also be part of different ensembles that encode aspects of other natural behaviors, such as eating or walking. This ‘mixed selectivity’ is broadly consistent with previously reported data on striatal activity patterns and cluster memberships that are not conserved across divergent behavioral motifs59. FSIs receive direct cortical input, form inhibitory synapses onto SPNs61, and are interconnected amongst themselves via gap junctions on their dendrites62. Previous studies found uncoordinated, idiosyncratic task-related changes in FSI activity63, as well as changes in FSI activity that correlated with movement features like velocity64, with one study showing grooming-related activity in a small number of recorded putative FSIs43. We recorded units that match spike waveform features and firing rates of FSIs59 and found them to have grooming related activity. These units displayed decreased or increased activity during grooming, as well as transient activity changes at the transitions into and out of grooming. Consistent with strong connectivity present between FSIs and SPNs65, a majority of FSIs (70%) were part of identified ensembles and almost all FSIs were part of ensembles together with SPNs.Striatal FSIs are parvalbumin-containing GABAergic interneurons that make up approximately 1% of striatal neurons68. Previous recordings of direct and indirect pathway striatal ensemble activity have found that d- and iSPNs exhibit a similar time course of behavior-related changes in their activity38 and have similar ensemble organization30, suggesting mixed pathway membership within ensembles. Indeed, simultaneous recording of direct and indirect pathway SPNs showed that striatal ensembles are composed of d- and iSPNs in equal proportions39. These prior data suggest that striatal ensembles encoding self-grooming might be composed of both d- and iSPNs, in similar proportions and with similar encoding of grooming, although this remains to be confirmed.Striatal control of movement is mediated via two parallel pathways: the direct and indirect pathway, composed of dSPNs and iSPNs70. This sequence is comprised of four distinctive phases and found in a variety of species10. A large body of work has established a function for the striatum in the sequential ordering of syntactic grooming phases72. On a longer time scale, grooming is one of many behaviors an animal can perform at a given time. Since self-grooming, although broadly important, is not often an immediate concern, grooming is considered a low priority behavior73. Thus, the control of grooming has been characterized by a disinhibition model, in which grooming takes place in the time left over by other higher priority behaviors when grooming would be inhibited74. Previous studies have demonstrated a striatal role in behavioral sequencing on this longer time scale as well30. Thus, self-grooming represents an ethologically meaningful behavioral paradigm providing a path to study neural control of an innate, conserved behavior at multiple spatiotemporal scales.Within bouts of rodent self-grooming, animals perform highly stereotyped grooming sequences called syntactic groomingAnimals were handled according to protocols approved by the Northwestern University Animal Care and Use Committee (protocol number: IS00009022). Adult male and female C57BL/6J mice (p57–105) were used in this study . All mice were group-housed in a humidity-controlled, ambient temperature facility, with standard feeding, 12 hr. light-dark cycle, and enrichment procedures.Mice were anesthetized with isoflurane  and placed on a small animal stereotax frame . A 4-shank 64-channel silicon electrode  was implanted into the dorsolateral striatum  and secured with Vetbond  followed by dental cement . The electrode shanks were aligned with the brain’s anterior-posterior axis. A skull screw was connected to the electrode ground wire and fastened to the skull above the ipsilateral cerebellum. The screw was secured with Vetbond followed by dental cement. Mice were monitored following the surgery to ensure a full recovery and were administered post-operative analgesics. Mice recovered for at least 5 days after implantation.2O, Westbrook, ME) for food and hydration. One mouse was in the arena at a time. The arena was cleaned with 70% ethanol after each session. The arena was enclosed by a dark curtain and illuminated by infrared LEDs. Experiments were conducted during the animal’s active phase.On the first day of experimentation, mice were acclimated to the experimental arena for 30 minutes. All subsequent experimental sessions were 120 minutes long. The experimental arena was an equilateral triangular arena constructed from transparent acrylic . To facilitate alignment of videos and neural data, the video and neural recordings were simultaneously initiated by pressing a button connected to the microcontroller.A 64-channel headstage  was connected to the implanted electrode. In the arena, the headstage was connected to a 12-channel commutator supported by a balance arm . Neural recordings were acquired at 30 kHz with an Open Ephys acquisition board77 in MATLAB . We only considered units labeled as ‘good’ by KiloSort. Additionally, we manually inspected the waveforms of all units labeled as ‘good’ and excluded units with waveform shapes that did not resemble an action potential. Recordings were performed from each animal until the number of ‘good units, as identified by KiloSort, stayed below 15 for 2 consecutive days (27.3 ± 4.3 days since implant).Neural recordings were spike sorted offline using KiloSort347. Units were classified as putative SPNs (88.2 %) if they had peak width > 150 μs, peak-valley interval > 500 μs, and mean firing rate ≤ 10 Hz. Units were classified as FSIs (3.2 %) if they had peak width ≤ 150 μs, peak-valley interval ≤ 500 μs, and mean firing rate ≥ 0.1 Hz. Units that were not identified as either putative SPNs or FSIs were labeled ‘other’ (8.6 %) and excluded from all analyses.All recorded units were classified as either putative striatal projection neurons (SPN), fast-spiking interneurons (FSI), or ‘other’ based on their spike waveform and mean firing rate across the entire session following established criteriaAfter the recordings, coronal brain sections were obtained from all mice to determine electrode placement. Mice were deeply anesthetized with isoflurane and transcardially perfused with 4% paraformaldehyde (PFA) in 0.1 M phosphate buffered saline (PBS). Brains were post-fixed for 1–2 days and washed in PBS. Brains were then sectioned on a vibratome  at 60 μm from frontal cortex to posterior striatum and then dried and cover slipped under glycerol:TBS (9:1) with Hoechst 33342 . Sections were imaged on an Olympus VS120 slide scanning microscope  with DAPI for localization of cell nuclei and FITC for background fluorescence to enable electrode localization.78 in R79. Histological slices were analyzed and registered to the Allen Mouse Brain Common Coordinate Framework (CCFv3)80 as described previously81. Striatal sections with electrode tracts were analyzed at 60 μm intervals and frontal lobe cortical sections, which rarely contained electrode tracts, were analyzed at 100–200 μm intervals. A total of 6–18 section images were analyzed per mouse brain. Electrode placement for each brain section was denoted at the most ventral location that the electrode tract was observed. Medial-lateral and dorsal-ventral coordinates registered to the CCFv3 were obtained for each section and plotted on a representative coronal section using the WholeBrain package.Coronal slices containing electrode tracts were processed using WholeBrain software packageWe identified mouse grooming bouts in behavioral videos using a semi-automated 4-step procedure . As deta83 to detect 15 keypoint positions  on the frames from each camera. We labeled the 15 keypoints in 1,627 frames from 14 videos, 3 animals, and all 3 cameras. We trained a ResNet-50-based neural network85 with default parameters on 90% of the labeled frames for 300,000 iterations. We validated with 1 shuffle and got a train error of 3.88 pixels and test error of 6.64 pixels (image size: 1440 × 608 pixels). We used a p-cutoff of 0.6 to condition the x and y coordinates for future analyses. With the p-cutoff, the train error was 3.27 pixels and test error 5.06 pixels. This network was then used to analyze all videos from the same experimental setting.For 2D pose estimation, we used DeepLabCut (version 2.2.0.5)86. We calibrated our 3 cameras with a 3-minute video of a ChArUco board throughout the camera fields of view (125 FPS. Image size 1440 × 608 pixels). The ChArUco board was printed on paper and taped onto a stiff plastic board. The ChArUco pattern had 7×10 squares containing 4-bit markers and a dictionary size of 50 markers. Before triangulation, we applied a Viterbi filter to the 2D poses with F = 12 frames. The triangulation was performed via an optimization constrained on the distance between the two eyes and the distance between each eye and the snout. We chose scale_smooth = 5, scale_length = 2, and score_threshold = 0.3.For 3D triangulation of the 2D poses we relied on Anipose87.To isolate times when the mouse was likely grooming, we identified frames where the mouse was rearing, and its paws were near its head. To identify frames where the mouse was rearing, we set thresholds on the snout height and the distance between the midpoint of the eyes and the midpoint of the hindlimbs. To identify frames where the mouse’s paws were near its head, we set thresholds on the distance between the midpoint of the paws and the snout, the distance between the midpoint of the paws and the midpoint of the eyes, and height of the midpoint of the paws. Per-frame labels within 1.2 seconds were merged to form predictions over a window. Predicted grooming bouts less than 2 seconds long were discarded. Parameter values were evaluated by generating predictions for a set of videos and viewing the predictions aligned to the video in BENTO88 using the Time v3.289 and Speed controller90 extensions. To assess inter-annotator reliability, all annotators were also given the same 2-hour session to annotate. The average pairwise Jaccard index (or Intersection over Union) computed on the annotations was 0.76 ± 0.04.Grooming bout start and stop times were manually refined by 4 trained annotators, with each annotator labeling a different subset of videos. Annotators used the predictions from the previous thresholding step to navigate the videos and score grooming behaviors. The videos were annotated in VLC media playerTo isolate units with grooming related activity, we first down sampled activity to 2 Hz, by summing spike counts within non-overlapping 500 ms bins. We defined units as having grooming-related activity if their average activity at the start or end of grooming was 2 standard deviations above their average activity during a 3 second-long, grooming-free baseline period. For activity aligned to groom start, we used the activity from 5 to 2 seconds before grooming start as the baseline and compared that to the activity from 1 second before until 1 second after the grooming start time. For activity aligned to groom end, the activity from 2 to 5 seconds after grooming was chosen as the baseline and was compared to the average activity from 1 second before until 1 second after groom end. These selection criteria were only used to identify neurons to serve as examples in We characterized the grooming responses exhibited by the recorded units by computing each unit’s event-triggered average response in a ± 5-second window relative to the start and (separately) end of grooming. Responses were down sampled to 4 Hz, by summing spike counts within non-overlapping 250 ms bins. To simplify interpretation, we restricted averaging to grooming bouts that were ‘well-isolated.’ Specifically, because we included the 5 seconds before and after each bout in this analysis, grooming bouts that started within 10 seconds of the end of the previous bout were not included in this analysis. An exception to this is instances with less than 3 seconds between the end of one grooming bout and the start of the next, in which case we merged these two annotations into a single bout.48 on the set of event-triggered averages. Specifically, we performed PCA separately on SPN activity aligned to groom start, SPN activity aligned to groom end, FSI activity aligned to groom start, and FSI activity aligned to groom end. For each cell type and condition combination, we concatenated the associated units to form a units-by-time matrix, relative to groom start/stop. The response of each unit in this matrix was then Z-scored, after which PCA was used to extract the first two principal components.To better visualize and interpret grooming-associated activity we next performed principal component analysis (PCA)To group units into ‘response types’ for visualization, we examined the weight (or ‘score’) for each unit’s contribution to the largest and second-largest principal component. Units for which the absolute value of this weight was greater for the first principal component than the second formed the “PC1” response group, while those with greater-magnitude weights for the second principal component formed the “PC2” response group. Finally, we sorted the units within each group according to their contribution to the first (PC1) or second (PC2) principal component of the dataset (weight * activity).To identify grooming-associated striatal ensembles within a given session, we took the neurons-by-time matrix of recorded spiking from the full two-hour recording session and excluded all frames that did not occur during grooming or within a 5-second window before or after grooming. Due to the low firing rate of SPNs, it was rare to find units that were co-active at high sampling rates. Therefore, to instead identify units that were frequently active at around the same time, we binned spike counts at a sampling rate of 0.667 Hz (bin size of 1.5 seconds). The spike counts for each unit were then normalized to between zero and one, and further smoothed by convolving with a Gaussian filter with a standard deviation of 3 seconds.k-means51 to identify sets of cells that were often co-active. We chose to use meta-k-means for clustering, because it does not force all units in a session to be assigned to a cluster, and it allows for the number of ensembles to be greater or less than the initial choice of k. Briefly, meta-k-means employs repeated runs of standard k-means clustering to identify groups of units that are consistently clustered together. As with standard k-means, meta-k-means requires the user specify an initial number of clusters k, which we set to be the square root of the number of units in the given session, as in Barbera et al38. We then ran 1,000 repeats of the k-means clustering algorithm on the matrix of filtered grooming-related activity described above, with units as variables and time bins as observations. Cluster centroids for each repeat were initialized using the greedy k-means++ algorithm91. As in prior work92, units that were assigned to the same cluster in >80% of k-means runs were considered to be part of the same meta-k-means cluster.Using this smoothed and coarsened estimate of cell activity, we next applied meta-k-means algorithm, we used the silhouette score93 to evaluate the outcome of a potential cluster merge. To ensure adequate sample size for downstream analyses, ensemble identification was restricted to sessions with at least 30 SPNs and FSIs.When merging clusters as part of the meta-50. Briefly, if two neurons have correlated firing, then we expect to observe common fluctuations in their spiking activity during recording. This can be visualized by plotting the firing rates of the two neurons against each other, in the form of a direction in neural activity space along which observations tend to be distributed. Conversely, without any correlated firing, the variance in the dataset will be roughly equally distributed in all directions, and one would not expect to see structure when plotting the activity of two cells against each other. These relationships hold for any n-dimensional set of neurons, where structure emerges from correlated activity. The eigenvectors of the covariance matrix of a dataset represent the directions of maximally shared variance, thereby capturing the correlations present in the data. Each eigenvalue quantifies the variance of the data along the axis defined by its corresponding eigenvector. Thus, in a matrix of neurons-by-time, the eigenvalues of its covariance matrix capture the correlations among neurons  and comparing the eigenvalues to those computed from the covariance matrix of a shuffled version of the data provides a measure of the number of ensembles present.To compute a statistical estimate on the number of ensembles present in each session we used an eigenvalue based statistical method from Peyrache at alth percentile of this null distribution.To apply this method, we first binned spike counts at the same sampling rate as for clustering  and Z-scored the activity of each neuron. Next, we shuffled the time bins for each neuron independently, and computed the maximal eigenvalue of the covariance matrix of the shuffled data. We repeated this 5,000 times to form a null distribution and counted the number of eigenvalues from the covariance matrix of the actual data that were above the 99To compare ensemble average activity during grooming across grooming bouts of varying duration, we linearly temporally rescaled the activity of each unit during each grooming bout to a single fixed length. We chose the length for the interpolated values from the distribution of grooming bout durations from all clustered sessions. To avoid ‘blurring’ activity associated with the start and end of grooming bouts, the activity during the 5 seconds before and after each bout was not interpolated.th, and 97.5th percentiles of the distribution of randomly sampled ensemble activity. To generate this distribution, we computed the population-average ensemble activity during 1,000 random duration windows of time sampled throughout the 2-hour recording session. Start times were sampled uniformly from 5 seconds into the recording session until 5 seconds before the end of the session. Window durations were sampled from the distribution of observed grooming bout durations across all sessions.To test for periods during grooming when the grooming ensemble average activity was significantly higher or lower than chance, we computed the mean, 2.594. For the neural trajectories, we decomposed a matrix containing each unit’s activity during all grooming bouts within that session. For each unit, we concatenated its activity during all grooming bouts including the five seconds before and after each bout. For the ensemble trajectories, we averaged the activity of all units within each ensemble and concatenated each ensemble’s activity during all grooming bouts including 5 seconds before and after each bout. We initialized using Nonnegative Double Singular Value Decomposition with zeros filled with the average neural activity and minimized the Frobenius norm of the loss.To visualize neural trajectories, neural activity during grooming was decomposed onto 3 components using non-negative matrix factorizationAll statistical tests were two-sided. Statistical significance was set to p = 0.05. Summary data in all figures are reported as mean ± SEM.95 unless stated otherwise. In addition to those mentioned elsewhere, we used the following Python packages: numpy96, scipy97, matplotlib98, scikit-learn99, and pandas101.All custom software was written in Python


The use of hearing aids can provide plasticity to the hearing system as well as improve speech recognition as time goes by.To compare the influence of the length of hearing aid use on the benefit obtained with the hearing aids in adults and the elderly, new hearing aids users.Prospective study with 40 individuals with mild to moderate-severe sensorineural hearing loss, gathered in 2 groups: Adults Group – 13 people aged between 28 and 59 years old; and Elderly Group – 27 people aged between 61 and 78 years old. These people were assessed 14 and 90 days after hearing aid fitting. Sentence recognition threshold in silence and under noise as well as the percentage indexes of sentences recognition in silence and under noise were obtained.The comparison between values obtained after 14 and 90 days of hearing aid use did not show statistically significant differences. When comparing values between the groups, no statistically significant difference was observed either.We did not find influences of the length of hearing aid use and the benefit obtained from using them; the results achieved by adults and the elderly were similar. Peripheral hearing loss affects hearing as a whole. Not only is there a quantitative decrease in sound, but also loss of speech recognition, which causes a negative social and emotional impact that affects the quality of life.Adaptation of hearing aids is the therapy of choice when hearing loss can no longer be corrected by medication or surgery. Hearing aids are important not only for communication and spatial orientation but also because hearing reaffirms an individual's existence as a human being.Hearing aids raise the intensity of ambient sounds, thereby increasing auditory stimulation. Such stimulation may foster plasticity in the auditory system and in time improve speech recognition.The time period related to improved performance in speech recognition tests for adaptation of a hearing aid, as subjects learn to use new available speech cues with amplification, is named perceptual acclimatization.The effect of perceptual acclimatization is to systematically change hearing performance with time; this is unrelated to changes in available acoustic information for hearing aid users.In hearing aid performance tests, using words and sentences is an approximate representation of daily speech conditions. One of the most important features to be measured in human hearing is the ability to understand speech, which makes it possible to assess receptive communicative function and yields data about how a subject operates in daily hearing situations.The Portuguese Sentence List (PSL) testA period of use of hearing aids is needed to reestablish speech abilities and for the benefits of hearing aids to be evaluated; it is also essential to carry out studies aiming at verifying the effects of perceptual acclimatization to monitor the development of hearing in a new hearing aid user.Based on this background, the purpose of this study was to assess young and elderly subjects with mild to moderately severe sensorineural hearing loss, new to hearing aids, by applying the PSL test,•Age equal to or over 18 years;•An audiological diagnosis of mild to moderately severe sensorineural hearing loss,•A speech recognition threshold equal to or below 65 dB SL in the best ear.•Having been referred for use of binaural hearing aids;•Not having started using hearing aids;•No factor that might interfere with the test, such as neurological conditions and/or altered verbal fluency;This study was carried out at the Hearing Aid Laboratory (Laboratório de Próteses Auditivas) of the institution of origin. It was registered at the Project Office under the number 019731 and approved by the institutional review board (certificate number 0138.0.243.000-06). All participants signed a free informed consent form after receiving information about the purpose of the study and its method. The inclusion criteria were:From January to October 2008, subjects that visited the Hearing Aid Laboratory and met the inclusion criteria were preselected to start hearing aid selection and adaptation procedures. Of 210 patients, 47 were preselected. Among these patients, those with other health conditions or any other impediment to returning for a second evaluation were excluded. Thus, of 47 preselected subjects, 40 were fully evaluated and comprised the sample.1Group A (Adults [18 to 59 years]): 13 new users of hearing aids aged from 28 to 59 years (mean age = 48.77 years), of which four were male and nine were female.2Group I (Elderly [over 60 years]): 27 new users of hearing aids aged from 61 to 78 years (mean age = 68.85 years), of which 12 were male and 15 were female.The 40 subjects were grouped according to age, as follows:•SRTS - sentence recognition threshold in silence;•SRTN - sentence recognition threshold in noise;•SRPRS - sentence recognition percentage rate in silence;•SRPRN - sentence recognition percentage rate in noise.Assessments and reassessments were done from January 2008 to January 2009. All subjects undertook investigation of:Free field tests were carried out in two evaluation sessions: 1st evaluation 14 days after hearing aid adaptation; 2nd evaluation 90 days after hearing aid adaptation.The same evaluation period was applied for each patient.Subjects used hearing aids with the setting applied by the technical team in charge of patients at the Hearing Aid Laboratory in both evaluation sessions; there were no changes between each.A clinical history based on a closed question questionnaire was taken prior to the first evaluation for information about personal data, hearing complaints, the otological history, daily life habits, and education level. The results of pure tone audiometries, speech recognition threshold tests, and speech recognition percentage rates were recorded; these had served as proof of hearing loss and were the baseline for hearing aid programming.SRTS, SRTN, SRPRS and SRPRN were tested with the PSL test,•Presentation of sentences 1 to 10 of list 1A without competing noise to familiarize subjects with the test.•Presentation of list 5B without competing noise to measure the SRTS.•Presentation of list 6B without competing noise and with speech at 65 dB A to measure the SRPRS.•Presentation of sentences 11 to 20 of list 1A with competing noise at 65 dB A to familiarize subjects with the test.•Presentation of list 1B, with competing noise at 65 dB A to measure the SRTN.•Presentation of list 2B, with fixed competing noise and speech at 65 dB A resulting in a S/N ratio equal to zero to measure the SRPRN.The test was applied in an acoustic booth in free field; subjects were facing the source of sound one meter away, at 0°° – 0°° azimuth. The application sequence in the 1st and 2nd evaluations was as follows:The choice of lists was made because two tests with different aims were being conducted concomitantly. Selecting lists 1B, 2B, 5B and 6B resulted in no test list being repeated under similar conditions in any test.The sentence presentation technique was based on a sequential, adaptive or ascending-descending strategyThe procedure for measuring thresholds consisted of presenting a stimulus under a certain condition with or without competing noise. If a subject was able to correctly recognize the speech stimulus, its intensity was decreased at preset intervals. Otherwise its intensity was increased. This procedure was repeated until the end of the list.Based on the literature, 4 dB intervals were used until the first change in the type of response; additional stimulus presentation intervals differed by 2 dB until the end of the list.For the thresholds, the mean values were calculated from the presentation intensity of sentences at which the first response change occurred.For percentage rates, the intensity was fixed throughout the sentence list; all correctly answered sentences were added up, which corresponded to 10 percentage points per sentence of the list.Measurements were taken in free field after calibrating the equipment according to the characteristics of the test signal and the ambient acoustic conditions. The sound pressure level at which subjects perceived speech and noise were set during calibration. A digital Radio Shack sound pressure-measuring device was used for this purpose; it was placed one meter from the loudspeaker at a midpoint for both ears. An A6 measuring scale was used, which is adequate for measuring continuous noise and extreme intermittent noise values.The sentence presentation intensity was gauged based on a recorded pure tone on the CD channel in which the sentences were recorded. This pure tone was a continuous reference tone, and was applied to maintain constant presentation conditions. This is because speech signals are complex sounds with a 30 dB variation between most and least intense sounds, oscillating 12 dB over and 18 dB below the mean;Before starting the evaluations, each CD output channels were gauged using the audiometer VU-meter. For this purpose a 1,000 Hz tone present in one channel, and a marking noise present in the other channel, were set at zero. Previous studies have noted that sentences had been recorded on the CD at a mean intensity of 7 dB below a pure tone intensity.Measurements were taken in an acoustic booth; a Damplex DA65 model dual-channel digital audiometer and a TA 1010 model amplification system for free field audiometry were used. Sentences were presented using a Britania B5279 model CD player with the lineout option coupled to the audiometer.Data were analyzed descriptively and statistically treated by considering the behavior of variables, comparing the results of evaluations at two different times for each group. Wilcoxon's test was applied, as it does not require data to have a normal distribution; it also tested whether the differences of two related values were statistically significant.Differences between groups were verified for each evaluation. The Mann-Whitney test was applied for this purpose; it tested whether two independent variables not distributed normally had statistically significant differences. The significance level was 5% for both tests.According to the literature, speech recognition improvements in perceptual acclimatization are not evaluated immediately in new hearing aid users.A study of the perceptual acclimatization phenomenon in adult new hearing aid usersAnother study aimed to verify possible evidences of functional plasticity in the auditory system;The PSLOur study generally showed no significant differences in the benefits gained with hearing aids; at an individual level, however, we found that 53.85% of adults and 48.15% of elderly patients had some sign of improvement in SRTS and SRTN when comparing the evaluation and reevaluation tests. Among adult subjects that improved, mean changes were 2.80 dB for the SRTS and 1.19 dB for the SRTN. In elderly subjects, mean improvements were 4.29 dB (SRTS) and 1.86 dB (SRTN). With such differences, we agreed with the influence of individual and environmental peculiarities,16 and that such factors partly explain why amplification induced functional plasticity in some of our subjects, albeit poorly perceived, but not in others. Some of our subjects mentioned that they were using hearing aids continuously and were pleased with its benefits; they may have done so because the whole process involves undertaking specialized tests, hearing aid selection adaptation, and clinical visits free of charge. Thus, our patients may have involuntarily taken on a satisfied attitude when in reality they may not have assumed their right to make complaints. Statements along these lines in subjective assessments have been made, where patients reveal attitudes of humbleness and gratitude for having been given hearing aids free of charge, and do not feel it right to complain.,Auditory perceptual acclimatization is a systematic change in the auditory system with time.Based on these statements, it is possible to question the effective use of hearing aids during our study period in adult and elderly subjects that showed no change in speech recognition with time. If about 50% of subjects showed improved SRTS and SRTN tests, then why did the other half show not changes or even worse results? Again we respect individualities, but raised the hypothesis that most of the patients in this study used hearing aids only during short periods during the first months after adaptation, even after receiving proper guidance and having free access to speech therapists for consultation. We also conjectured whether a hearing training program for hearing aids to specifically encourage speech recognition and intelligibility abilities would have yielded superior results.We emphasize that the sensitivity of the PSL test6 (used in this study) for proving the effects of perceptual acclimatization has been demonstrated in another study; we believe that the method for measuring the SRTS and/ or SRTN was not a determining factor to not generally demonstrate the effects of perceptual acclimatization.Analysis of the SRPRS  and the We chose to fix the intensity of speech at 65 dB for measuring the SRPRS. As such, the method allowed most subjects to attain maximum scores at the first evaluation, thereby not permitting any to reach superior scores in the reevaluation. We felt that this procedure was not valid for verifying the influence of time of amplification use on the benefits gained from hearing aids. We therefore suggest that the SRTS be used as a basis for choosing at which level the intensity should be fixed for measuring the SRPRS.Concerning the SRPRN, subjects were evaluated under fixed noise and speech stimuli at 65 dB A, a S/N ratio equal to zero. Our analysis showed that no improvements in speech recognition occurred in adult or elderly subjects from days 14 to 90 after amplification use.As with SRTS and SRTN results, SRPRN values differences were not statistically significant in our overall evaluation. At an individual level, however, 46.16% of adults and 37/04% of elderly subjects had, respectively, intermediate changes of 20% and 21% in their SRPRN results. The same comments made above apply here: individuality, environmental influences, and limited use of hearing aids.A study that aimed to show SRPRN changes as the S/N ratio was alteredWe found no papers in the literature associating sentence recognition percentage rates gathered by PSL testing with the effects of perceptual acclimatization.We also investigated any differences between Group A and Group I results for all evaluations and reevaluations. Although subjects in both groups had similar hearing losses in degree and configuration, we expected Group I to yield worse results than Group A, especially because of the aging process. Our results, however, indicated that adult and elderly subjects performed similarly.Although this study did not yield statistically significant results, the effects of hearing performance with time should never be underrated when adapting hearing aids. We also emphasize the importance of hearing training, regardless of age, as an ally for improved speech recognition.•No time of use of amplification effect on benefits of hearing aids was found in adult and elderly subjects, based on the PSL test.•Adult and elderly subjects had similar SRTS, SRTN, SRPRS and SRPRN results, after 14 and 90 days of hearing aid use.A critical analysis of results showed that:


In perennial plants such as pecan, once reproductive maturity is attained, there are genetic switches that are regulated and required for flower development year after year. Pecan trees are heterodichogamous with both pistillate and staminate flowers produced on the same tree. Therefore, defining genes exclusively responsible for pistillate inflorescence and staminate inflorescence (catkin) initiation is challenging at best. To understand these genetic switches and their timing, this study analyzed catkin bloom and gene expression of lateral buds collected from a protogynous (Wichita) and a protandrous (Western) pecan cultivar in summer, autumn and spring. Our data showed that pistillate flowers in the current season on the same shoot negatively impacted catkin production on the protogynous ‘Wichita’ cultivar. Whereas fruit production the previous year on ‘Wichita’ had a positive effect on catkin production on the same shoot the following year. However, fruiting the previous year nor current year pistillate flower production had no significant effect on catkin production on ‘Western’ (protandrous cultivar) cultivar. The RNA-Seq results present more significant differences between the fruiting and non-fruiting shoots of the ‘Wichita’ cultivar compared to the ‘Western’ cultivar, revealing the genetic signals likely responsible for catkin production. Our data presented here, indicates the genes showing expression for the initiation of both types of flowers the season before bloom. Carya illinoinensis) trees are monoecious, forming staminate and pistillate flowers on separate inflorescences. Pollination in pecan is anemophilous (wind pollinated) and trees manifest dichogamy, meaning that staminate and pistillate flowers of an individual genotype are functional at different times [Pecan  were selected for staminate bloom data collection. The genotypes of the trees were confirmed through genotyping using the primers developed by Grauke et al., 2003 . In the To see the morphological differences in formation of catkin primordia in bud tissues, 15 dormant buds from ‘Western’ and 15 dormant buds from ‘Wichita’ cultivars collected in February were excised. Samples were fixed in 2.5% glutaraldehyde solution buffered with 0.1M imidazole-HCl and stored at 4°C. Vertical dissection of individual dormant buds was performed using a clean, stainless steel razor blade into two halves, samples were placed on sands under water and were first examined using a model M165FC stereofluorescence microscope  using the GFP1 and Violet filter sets to evaluate favorable planes through the presumptive tissues. Then, for Laser scanning confocal microscopy, the cut surfaces of the selected buds were mounted on the coverslip surface of glass-bottom microwell dishes  and examined with a model TCS SP5II confocal microscope system  using the 488 nm Argon laser line for fluorescence excitation and a 20x long working distance objective lens. Fluorescence emission was collected in three channels, green from 500–570 nm, yellow-orange from 580–620 and red from 650–720 nm, as stacks of optical sections approximately 30–40 micrometers deep. Final images of comparable buds were displayed as graphic overlays of the three channels.Bud tissue samples from two pecan cultivars, ‘Wichita’ and ‘Western’, were collected in June 13 June 21, 2017, and September 16, 2016, and March 5, and March 22, 2018. Samples were collected from three individual trees for each cultivar from the same location as samples collected for microscopy. On each tree, four fruiting shoots and four non-fruiting shoots were randomly selected for sample collection. The lateral buds were removed and frozen directly into liquid nitrogen. For each tree, bud samples from fruiting shoots were bulked together and samples from non-fruiting shoots of each tree were bulked together. Samples from different trees kept separated and used as replicates. In March, there was not yet any active growth or pistillate flowers formed, so the fruiting/non-fruiting samples were labeled based on the shoot fruiting status from the previous year  and those that produced no fruit (non-fruiting) the previous season). Samples were homogenized separately with mortar and pestle using liquid nitrogen. Approximately 100 mg of frozen tissue was utilized for RNA extraction. Total RNA was extracted using plant/fungi total RNA purification kit  according to manufacturer`s instruction. All samples were DNase treated using the Qiagen DNAse kit to remove any DNA contamination. Sequencing libraries were constructed using an Illumina TruSeq stranded mRNA library kit (20020595) and TruSeq RNA UD Indexes (20022371) using standard protocols. Libraries were sequenced on a NovaSeq 6000 using paired ends and a read length of 150 base pairs.https://digitalinsights.qiagen.com). All transcriptomes were analyzed for a selection of 1229 genes due to their known function in flowering in other species  in addition to highly expressed genes in pecan ‘bud and catkin’ and ‘bud and pistillate’ tissue specific analyses. All the analyses were based on a log2 fold change of higher than 1.5 and adjusted p-value of less than 0.05. The RNA-Seq data reads are available in NCBI BioProject PRJNA782058.All reads were trimmed for adapter, quality, and length. Reads were trimmed using quality scores with 0.05 limit through automatic and/or trim adapter list to remove the read-through adapters. Then all the reads were trimmed to remove a maximum of 2 ambiguous bases at either 3`or 5`end of the reads and reads shorter than 5 nucleotides length were discarded. Trimmed reads were then mapped to the pecan reference genome of ‘87MX 3.2–11’ V.1.1 (Phytozome). Read alignment performed with a mismatch cost of 2, insertion and deletion costs of 3, length and similarity fraction of 0.8 and a maximum number of 10 hits for a read. Differential gene expression analyses were performed using CLC Genomics workbench 12.0.2 . The reaction tube was incubated at room temperature for 15 min, then 1 μl of 25 mM EDTA solution was added to each reaction followed by the second incubation at 65°C for 10 min. The DNase-treated RNA was quantified using a NanoDrop 1000 spectrophotometer  and was stored at -80°C freezer. 20 ng of DNase-treated RNA was utilized for synthesizing the complementary DNA (cDNA) using a SuperScript® IV Reverse Transcriptase kit . For the initial reaction 1 μl of 50 μM oligo dT primer, and 1.0 μl of 10 mM dNTP mix was added to 20 ng DNAse-treated RNA. Sterile water was added to bring the volume to a total of 14 μl. Reaction was incubated at 65°C for 5 min followed by a one-minute incubation on ice and vortexed for 3 seconds. Then, 4 μl of 5X SuperScript™ IV buffer, 1 μl of SuperScript™ IV Reverse Transcriptase and 1 μl of 100 mM DTT were added to the reaction tube based on the manufacturer protocol . The reaction tubes  were then incubated at 52.5°C for 10 minutes followed by another 10-minute incubation at 80°C. cDNA was stored at -20°C for qPCR assays. Gene specific primers and probes were designed for nd CAL-A  and syntTo ascertain the impact of fruiting/non-fruiting on the production of catkin flowers the next year, we counted catkins from ‘Western’ and ‘Wichita’ trees. The previous year fruiting status, pistillate flower formation on the same year and the catkin counts were recorded and taken into consideration for catkin bloom analyses. The data from this study indicated that in 2019 there were an average of 5.0 individual catkins on ‘Wichita’ shoots that were non-fruiting in 2018 compared to the 8.2 individual catkins on ‘Wichita’ shoots that were fruiting in 2018 . HoweverCatkins on protandrous cultivars such as ‘Western’, bloom and shed pollen generally earlier than protogynous cultivars such as ‘Wichita’. The microscopy images of the buds collected in February 2019, before the beginning of the growing season, indicate the differences in catkin primordia formation/differentiation between ‘Western’ and ‘Wichita’ that occurred the previous season. In Three genotyped ‘Western’ and three genotyped ‘Wichita’ pecan trees were used for collection of buds at different time points during the summer 2017 season. For each sample, four individual buds were collected and pulled. For the ‘Fruiting’ samples, buds were collected from shoots that were bearing fruit and for non-fruiting samples, buds were collected from shoots that were not bearing fruit. There was a total of 12 samples for each timepoint . For the 48 libraries represented in this study there were a total of 446,893,776 reads of which 96.4% were mapped to the 87Mx3-2.11 pecan genome [The principal component analysis (PCA) plot  and the MALE STERILITY 2 (MS2) and a non-characterized gene  non-fruiting and fruiting samples . These gJMJ30, MS2, GASA14, AIL1, ARR5, and GRF7  which was upregulated in non-fruiting sample and the second gene was MADS27 with higher expression in the ‘Western’ fruiting samples. Previous studies have reported higher expression of MADS27 in the spur buds with a high flowering rate compared to other bud types in Apple [In the season prior to flowering, several genes were differentially expressed between our samples that may have role in the initiation of male and/or female flowers. After the end of the growing season and the subsequent harvest, pecan trees are in a dormancy state during late fall and winter. Microscopy images from dormant buds collected in February 2019, showed that catkin primordia were fully developed in ‘Western’ buds as opposed to the ‘Wichita’ buds . To furtca Borkh).th (2018)  . Five ofh (2018) . SPL7, F samples .nd was 18s rRNA. There were 4 upregulated genes in fruiting samples including 2 uncharacterized genes, MYB21/24 and an auxin-induced protein 15A-like.Bud samples collected on March 22, 2018, had 5 DEGs between the ‘Wichita’ non-fruiting samples and the ‘Wichita’ fruiting samples . The onlnd had 10 DEGs between ‘Western’ non-fruiting and ‘Western’ fruiting samples  formation challenging at best. In previous studies, it was found that pecan tree shoots that had previously produced fruit were less likely to produce pistillate flowers the following season as opposed to those branches (shoots) that did not produce fruit [In perennial plants such as ce fruit , 8. Our RLP12, MYB108, ERF113, NFD4, WRKY75) that were differentially expressed between ‘Wichita’ and ‘Western’ samples (fruiting and non-fruiting) might have roles in catkin development (anther development) and could provide some preliminary clues for dichogamy in pecan. MYB genes have been shown to function in the formation of male flowers in other plant species. In other plant species, the MYB genes are involved in a transcriptional cascade that mediates stamen and pollen maturation [myb108 mutant exhibited reduced male fertility and this phenotype was associated with reduced pollen viability and delayed anther dehiscence [MYB24 and MYB108 have overlapping functions that facilitates stamen and pollen maturation in response to jasmonic acid (JA) [MYB21 and MYB24 specifically regulate male fertility and the MYB21/24 double mutant exhibited male sterility due to defects in anther dehiscence, pollen maturation and filament elongation [MYB21 and MYB24 along with another member of MYB family, MYB57, are GA-dependent stamen-enriched genes and the triple mutants in Arabidopsis conferred short stamen and lead to male sterility [MYB21 and MYB24 promote petal and stamen development and have a role in gynoecium growth [ARG7 gene family. The ARG7 gene family can regulate plant growth hormones and affect pistil development [ARG7 played a role in pecan floral organ development [RNA-Seq analyses on the selected flowering genes from the bud samples collected from ‘Wichita’ and ‘Western’ cultivars revealed that the differences between the fruiting and non-fruiting shoots of ‘Wichita’ were more distinct than the fruiting and non-fruiting shoots of ‘Western’ in the season before bloom (June and September). Based on previous studies and microscopy images , we knowturation –29. The hiscence . Previoucid (JA) . In Arabcid (JA) . Previouongation . MYB21 aterility . Other sm growth . The auxelopment . PreviouMYB108, DCN1, RLP12, COL11, BHLH137, MIK2, RFK1, SRS1) in ‘Wichita’ samples compared to ‘Western’ samples in March were putative candidate genes for anther and pollen development in pecan. Comparison of ‘Wichita’ fruiting and non-fruiting samples also suggested SPL7 and MS2 as two other possible catkin specific genes. The SPL7 gene (SQUAMOSA PROMOTER BINDING-LIKE7) is one of the miR156-targeted SPL genes that is involved in the control of phase transition and flowering by regulating AP1 and FUL in Arabidopsis [SPL gene, control the aging pathway of flowering [SPL7 in switchgrass promoted flowering whereas downregulation of this gene moderately delayed flowering [MALE STERILITY 2 (MS2) gene encodes a protein that is involved in male gametogenesis and is required for proper pollen development [The catkin bloom patterns revealed that pistillate formation on ‘Wichita’ cultivar shoots negatively impacted catkin production on the same shoot in the same year. As ‘Western’ does not follow the same pattern, it is likely that the differentially expressed genes that were upregulated in the ‘Western’ samples are most likely related to catkin initiation/formation. Some DEGs with higher expression in the ‘Western’ fruiting shoots collected in June were most likely related to catkin initiation and genes highly expressed in the ‘Western’ fruiting samples from September could be putative genes for anther development. As it is expected for ‘Wichita’ catkin primordia to fully develop right before bloom, it is compelling that some higher expressed genes  and BHLH137  in ‘Wichita’ non-fruiting samples were observed on March 22, 2018. Higher expression of these genes could potentially repress catkin development and bloom in ‘Wichita’ trees. Unfortunately, due to the destructive nature of sample collection for RNA-Seq, we lack the information regarding the ultimate fate of these buds.Shoots that did not bear fruit in 2017 for both ‘Wichita’ and ‘Western’ were most likely to bear pistillate flowers in May 2018. Our data showed a considerable number of DEGs between non-fruiting shoots of ‘Wichita’ and ‘Western’ in March 2018. Higher expression of some flower repressor genes (less likely to produce fruit the following year), defining the pistillate specific genes were especially challenging. The current study specifically looked at the differentially expressed genes. Our previous analyses presented the expression of hundreds of known flowering genes, among which, several were known to be female specific genes in other species [To the best of our knowledge, no significant differences in the number of catkins between ‘Western’ and ‘Wichita’ have previously been reported. Here we show that there is a negative impact on staminate bloom on shoots that currently have pistillate flowers, suggesting that pistillate flower production in ‘Wichita’ may occur at the expense of catkin production but no differences were observed for ‘Western’ catkins. This study needs to be performed on additional protandrous and protogynous pecan cultivars to determine if there is indeed a direct relationship between pistillate flower and catkin production on protogynous pecan cultivars. Based on the previous studies, it was hypothesized that in pecan, flower initiation for both staminate and pistillate flowers occur one season before bloom. Our data presented gene expression evidence for the initiation of both flower types in the year before bloom, however, since both ‘Wichita’ and ‘Western’ non-fruiting shoots were expected to bear fruit the following year, and the caveat of alternate bearing is proportional Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S6 Fig(TIF)Click here for additional data file.S7 Fig(TIF)Click here for additional data file.S8 Fig(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.


This article has been corrected.1In the Original Investigation titled “Prevalence of Hearing Loss and Hearing Aid Use Among Adults in France in the CONSTANCES Study,”


The authors evaluated pure tone audiometry with and without specific ear protectors.The purpose of this case control study was to measure the level of sound attenuation by earplugs.The evaluation included sixty ears of 30 subjects of both sexes, aged between 20 and 58 years, of various professional activities, with normal hearing thresholds, and following ten hours of auditory rest. The statistical results of pure tone audiometry at 500 to 4000 Hertz with and without specific ear protectors were analyzed.These results were compared with those provided by the ear protector manufacturer.The results show that the rate of sound reduction was similar to the manufacturer's specifications. Humankind has knows about the harmful effects of noise for 2500 years; there are reports of deafness in workers living close to the river Nile cataracts, in Ancient Egypt.The first studies relating certain professional activities with specific diseases were made 300 years ago. In 1700, the work “De Morbis Artificum Diatriba,” by the physician Bernardino Ramazzini, was published in Italy. It contained reports about diseases occurring in 50 professions that developed during the transformation of a predominantly agricultural society into an industrial one. Between 1760 and 1830, England hosted a movement that would forever change humankind; it was the Industrial Revolution, one of its consequences being severe health problems for the workers of that time. This situation led the British parliament to pass the Health and Morals of Apprentices Act in 1802, which is considered the first law for protecting workers.It is general knowledge that in the past few centuries humankind has generated an ever noisier society, which has made sound intensity into the most diffused form of pollution in the modern world and the most frequent of harmful agents against health within the work environment.In Brazil, the Ministry of Labor controls the safety and health conditions of workers through Regulating Acts that define harmful physical, chemical and biological agents against worker's health (NR-9). Unhealthy activities and operations are regulated by the NR-15; its annex 1 defines impact noise tolerance limits. These law, however, do not foresee periodic audiograms for workers exposed to chemical products, as decree 3080 of the Ministry of Social Security only recognizes the causal nexus for solvents.In 1994, the Comite Nacional de Ruido e Conservacao Auditiva do Brasil  defined the expression noise-induced hearing loss (NIHL) as the gradual loss of auditory acuity due to continuous exposure to elevated noise levels, always sensorineural, always irreversible, bilateral, rarely resulting in profound hearing loss, initiating at 3000, 4000 and 6000 Hz, and eventually affecting other frequencies. The victim of this cochlear condition present tinnitus and intolerance to loud noise.NIHL is the second most common form of sensorial hearing loss, after presbyacusis (age-related hearing loss).According to Seligmam, an analysis of hearing loss inducing agents should include a clinical history, the occupational history, a physical examination, and an audiological evaluation. Pure tone audiometry, which is generally important for assessing hearing loss, provides few data about the communicating abilities of individuals with NIHL. This is relevant, since the difficulty in recognizing sentences is significant in these patients.14Certain agents may induce and accelerate hearing loss, particularly when associated with noise. These include: chemical products , antibiotics , diuretics  and other substances .Two types of industrial noise that are harmful for hearing have been identified: impulse noise  and continuous noise, which is the commonest, and which causes more auditory damage, particularly at 4000 Hz.There are many studies about NIHL in various professions, such as symphonic orchestra musicians, as well as comparisons between Brazilian civil and military aviation professionals. Other studies have assessed household appliances as inducers of NIHL.19It is known that hearing aids (sound amplification devices) may also injure auditory organs as an undesirable side effect.a)losses over 25db at 4khz - 6khz - 8khz;b)loss of 3khz;c)loss of 2khz;d)loss of 1khz;e)loss of 500hz.For purposes of assessing hearing loss, audiograms may be classified according to the five categories proposed by Freitas et al.:Although these tests were done in normal hearing individuals, these parameters are useful for classifying hearing losses in the general population.The diagnosis and treatment of NIHL is a major concern for otorhinolaryngologists, for public health reasons, for research and in daily medical office practice. The need to characterize hearing losses, particularly in industry workers, has shown that all of the conclusions and reports have been based on noise attenuation levels provided by IPE manufacturers that usually accompany their products. Some of these equipments do not state their attenuation levels, which required evaluating whether the available data were in fact compatible with the clinical evidence.Two questions should be answered when assessing auditory injury due to professional or leisure activities: was the individual using ear protectors? If so, why was this equipment not effective in preserving hearing? A careful clinical history and supporting exams reveal important data about the progression of hearing loss, both of which are essential for the diagnosis and for medical reports. There are various ear protectors available in the market, such as earmuffs and earplugs (in-ear type). We decided to study noise attenuation levels of a specific earplug, thus encouraging our colleagues to investigate NIHL.The purpose of this study was to compare noise attenuation data provided by IPE manufacturers, specifically about earplugs, with data obtained in our tests, thus assessing the actual noise attenuation for users in professional activities.Audiometric testing (pure tone audiometry) was used in selecting 60 ears for this study - following 10 hours of auditory rest - between February and July 2003. The Research Ethics Committee approved this study (number 055/03).Normal hearing adult individuals evaluated previously, with no race restrictions, having rested at least 10 hours before testing, and that authorized their participation in this study.Individuals with any hearing loss of metabolic disease associated with deafness.aStarkey mod. WRC audiometer aMadsen Zodiac 901 immitance meter anOtoSonic acoustic boothcircunauralearphoneearplugprotectorFeatures: Pomp Plus C.A. 5745 - NRR 21dB- ANSI standard S 12.61984NRRSF 17dB- ANSI standard S 12.6-19973030 individuals aged between 20 and 58 years .Subjects underwent pure tone audiometry at all frequencies to exclude those with any hearing loss. Those that were selected were required to rest their hearing with ear protectors during 10 hours, after which a new audiometry was done. Results were collected at 500-1000-2000-4000 Hz and compared to the results obtained before ear protection. A statistical analysis was done at the end of testing.Audiograms were classified according to OSHA standards to assess hearing loss.Two statistical analyses were done; the first (method A) was done based on the ANSI standards S 12.6 1984 (attenuation NR 21 dBNA) and the second analysis (method B) was done based on the ANSI standards S 12.6 1997 (attenuation NRsf 17 dBNA). These values are closer to those found in actual working environments.Four z-tests were made (one for each frequency) to find whether ear protectors actually attenuated sound at the same intensity as the values indicated by manufacturers. This test was chosen because, as shown in Attenuation NRR* 21 dBNA: ANSI standard S12.6 - 1984Attenuation NRRsf 17 dBNA: ANSI standards S12.6 -1997Noise attenuation data obtained by method B  results in noise attenuation values closer to those observed in actual use within the working environment.We used z-tests, one for each frequency; a complete statistical analysis was done in method A (NR 21 dBNA ANSI standard S 12.6 1984) and in method B (NRsf 17 dBNA ANSI standard S 12.6 1997). Noise attenuation data collected by method B are subjective, a real ear method in which listeners place the earmuffs themselves; in this cases, noise attenuation values are closer to those observed in actual working environments. The noise attenuation value in this method is the NRsf, which provides an estimate of noise in the protected ear after environmental noise is subtracted. NIOSH corrections (70% for plugs) should not be applied in this method, as attenuation values are close to actual values.In method A , we obtained lower mean attenuation levels in 60 ears compared to those informed by manufacturers. At all frequencies, the manufacturer's variability was higher than observed results. A 35 dBNA attenuation was achieved only in one ear at 500 dBNA.In method B, we obtained mean noise attenuation in 60 ears close to that informed by manufacturers, as shown on We undertook the t-test to confirm our results, and found that its result was different from that obtained in the z-test only at 500Hz; in this case, the standard deviation informed by the manufacturers is higher than that observed in the samples.Noise may be defined as any undesirable auditory signal. Human ears respond differently from person to person to a given sound. It has been demonstrated that people subjected to noise over 85 dBNA may present hearing problems, annoyance, lack of concentration, insomnia and stress. Noise induced pathophysiological alterations in the ear appear to result from decreased intracellular oxygen levels in Corti organ cells, edema of the sensory epithelium and eventual loss of stereocilia.25It has also been demonstrated that noise may cause permanent hearing loss or temporary auditory fatigue; in both cases, audiograms show the acoustic notch effect, the so-called micro noise trauma.Only continuous audiometries provide information about the side, location and shape of the acoustic notch, which results from the influence of noise on auditory organs. After a day of work in noisy environments, workers present temporary hearing loss, from which they recover after a period of rest. This is an important sign for healthcare professionals, requiring them to increase the subject's awareness about the harmful effects of noise.28No ear achieved the expected attenuation, except at 500Hz. Maximum values at the other four frequencies were lower than the attenuation informed by manufacturers. The results shows that the earplug type protectors that we used in this study are effective in attenuating sound pressure levels in noisy environments. The information provided by manufacturers about noise attenuation levels were statistically comparable to those found in our study. The importance of ear protectors for public health reasons and in certain leisure activities was confirmed in this study. Medical reports may be based on these data, and companies should always provide protectors from manufacturers that provide noise attenuation data and its standard deviation.Method A and B results  allow us to state that noise attenuation levels informed by manufacturers of the earplugs we used in this study were similar to the results. It is thus possible to use these data for calculating individual protection and as references in medical forensic reports.


Trace metals have side-effect on human health. The association between trace metals exposure and hearing loss remains unclear.A total of 8,128 participants were exacted for analysis of association between trace metals and hearing loss from the database of the National Health and Nutrition Examination Survey (NHANES) (2013–2018). Multivariable logistic regression and restricted cubic spline models were used to examine the association between trace metals and hearing loss.p < 0.05). After adjusting for confounders, compared with the reference of the lowest quartile, the ORs with 95%CIs for hearing loss across quartiles were 1.14 , 1.49 , 1.32  for cobalt, and 1.35 , 1.58 , 1.75  for tin. Individuals with the level of cobalt at third quartile had 49% higher risks of hearing loss than those at lowest quartile. And participants with highest quartile of tin had 1.75-folds risks of hearing loss than those with lowest quartile of tin. There were increasing trends in risks of hearing loss with a raised level of thallium (p for trend <0.05). Restricted cubic spline regression analysis indicated that there was a nonlinear association between hearing loss and the levels of tin (p for nonlinearity = 0.021). Subgroup analysis showed that individuals of female, without hypertension and diabetes, and with a higher level of low-density lipoprotein cholesterol had modified effects on the associations between hearing loss and exposure to tin.Participants with hearing loss had a higher level of lead, cadmium, molybdenum, tin, thallium, and tungsten (all Our study indicated that exposure to cobalt and tin were significantly associated with hearing loss. It is widely known that metals perform a critical function in the biochemical process. Essential trace elements  play a crucial role in enzyme synthesis, which regulate cellular homeostasis and energy production . Other eHearing loss is the fourth leading contributor to years lived with a disability worldwide . Varioushttps://www.cdc.gov/nchs/nhanes/index.htm). Potential participants were exacted from the dataset of 2013–2018 . Excluding those with unclear hearing conditions , and missing data on urine trace metals . A total of 8,128 participants were enrolled for further analysis. The survey protocol was approved by the NCHS research ethics review board, and all participants provided written informed consent. This study complied with the Declaration of Helsinki.The NHANES survey is a cross-sectional program recruiting a representative sample of participants from the United States population based on a complex, multistage probability design. The program collected data from demographic, clinical, and laboratory tests for surveyed individuals. The NHANES database can be acquired publicly from the Centers for Disease Control and Prevention National Center for Health Statistics .Urine samples were measured for trace level element analysis by inductively coupled plasma mass spectrometry (ICP-MS) coupled with dynamic reaction cell technology (DRC). Liquid samples were introduced into the ICP through a nebulizer and spray chamber carried by a flowing argon stream, which ionizes the atoms. The ions are finally counted in rapid sequence at the detector allowing individual isotopes of an element to be determined. More detailed descriptions can be available at Hearing loss in this study was defined as severe difficulty hearing. A household questionnaire on disability was provided to the surveyed participants by trained interviewers. Hearing loss was recorded if the person answered “yes” to the following question: are you deaf or do you have serious difficulty hearing.Demographic data including age, gender, and race/ethnicity  were collected from self-reports. As atherogenic risk factors are involved in the pathogenesis of hearing loss, covariates related to atherosclerosis were selected and controlled. Self-reported diabetes history, hypertension history, or hyperlipidemia history (yes/no), smoker (smoked at least 100 cigarettes in life), and alcohol user  were documented from questionnaire data. Noise exposure was defined as “ever exposed to very loud noise at work.” The level of triglyceride, total cholesterol, low-density lipoprotein cholesterol, and urine trace metals were obtained from laboratory data.t-test. Nonnormal distributed continuous data were reported as median with interquartile range (IQR) and compared by nonparametric Mann–Whitney U-test. Categorical variables were calculated with counts and percentages and were assessed by the Chi-square test. Log2-transform of trace metals was performed to facilitate the interpretation due to its skewed distribution.Data distribution was determined by the Kolmogorov-Smirnov test. Continuous data with normal distribution were presented as mean ± deviation and were analyzed with independent sample student's Multivariate logistic regression analysis was used to assess the association of trace metals quartiles with hearing loss. Model 1 was adjusted for age and gender. Model 2 was adjusted for age, gender, race, hypertension, diabetes, hyperlipidemia, and noise exposure. And model 3 was adjusted for age, gender, race, hypertension, diabetes, hyperlipidemia, noise exposure, triglyceride, total cholesterol, and low-density lipoprotein cholesterol. In order to explore the effect of multiple-metal on hearing loss, we conducted a collinearity diagnostic to determine whether there are collinearities among these metals. Variance inflation factors (VIF) and tolerance are used to identify a collinearity (Tolerance<0.1 or VIF>10 is considered to be collinear). We include ten trace metals into covariates when they are non-collinear, as well as the covariates in the model 3 to build a multiple-metal based model. The odds ratio (OR) with 95% confidence intervals (CI) was calculated.p < 0.05 was considered statistically significant.A restricted cubic spline regression model was applied to explore any nonlinear relationship between trace metals and hearing loss . Subgrop < 0.05). As for the concentration of urine trace metals, the hearing loss group had a higher level of lead, cadmium, molybdenum, tin, thallium, and tungsten (All p < 0.05). The general presentation of baseline characteristics was shown in A total of 8,128 participants were included in this study, with a prevalence of hearing loss of approximately 5.5%. There were significant differences between the hearing group and non-hearing group in age, gender, race/ethnicity, hypertension, diabetes, hyperlipidemia, noise exposure, levels of triglyceride, total cholesterol, and low-density lipoprotein cholesterol (All p < 0.05). Individuals with the level of cobalt at third quartile had 49% higher risks of hearing loss than those at lowest quartile. And participants with highest quartile of tin had 1.75-folds risks of hearing loss than those with lowest quartile of tin. Restricted cubic spline indicated that there was a nonlinear association between hearing loss and the levels of tin  in the prevalence of hearing loss. Secondly, tin was firstly found to be significantly associated with hearing loss in a large sample cross-sectional study. Lastly, the trends of association between hearing loss and the level of tin were nonlinear.Hearing loss affects individuals worldwide with nearly 6%-8% of the world population . In our A series of case reports showed that systemic cobalt toxicity was closed related to peripheral neuropathy, vision loss, sensorineural hearing loss, and cognitive decline , 7, 12. Tin is a naturally existing element, which is found in inorganic and organic forms in the environment. Exposure to tin and tin compounds in the U.S. population is almost ubiquitous . Tin levHeavy metals are particular chemical class of elements and present in the environment. Most of them are harmful even at low concentrations. Several potential mechanisms are implicated in metals toxicity including production of oxygen reactive species (ROS), interaction with thiol groups of proteins, incorrect protein folding and mimicry of the essential elements for intracellular transport, and depletion of antioxidants enzymes . CadmiumThere are several limitations of our study. Firstly, a cross-sectional study design can only infer the correlation, but causality. Secondly, despite the attempts to adjust hearing-related confounders, unmeasured or unknown covariates may influence the effectiveness of the conclusion. Additionally, the mechanism of the association between hearing loss and trace metals has not been fully addressed. Finally, the data of this study was derived from the United state, the generalizability of the conclusion to other regions or populations should be very cautious.Our study indicated that exposure to cobalt and tin were significantly associated with hearing loss.https://www.cdc.gov/nchs/nhanes/index.htm.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The studies involving human participants were reviewed and the survey protocol was approved by the NCHS research ethics review board. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.PZ: conceptualization, methodology, and writing-original draft. ML: data curation, software, formal analysis, and writing-review and editing. WC: writing-original draft. JJ, FX, and LX: investigation. YC: validation. All authors contributed to the article and approved the submitted version.This work was supported by the Strengthening Health through Science and Education Program of Nanjing Commission of Health (SZDZK202002). The funding body played no role in the design of the study and collection, analysis, and interpretation of data, and in writing the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This study aimed to explore the association between hearing loss severity, hearing aid use, and subjective memory complaints in a large cross-sectional study in Norway.Data were drawn from the fourth wave of the Trøndelag Health Study . The hearing threshold was defined as the pure-tone average of 0.5, 1, 2, and 4 kHz in the better ear. The participants were divided into five groups: normal hearing or slight/mild/moderate/severe hearing loss. Subjective self-reported short-term and long-term memory complaints were measured by the nine-item Meta-Memory Questionnaire (MMQ). The sample included 20,092 individuals  who completed both hearing and MMQ tasks. A multivariate analysis of variance  was used to evaluate the association between hearing status and hearing aid use (in the hearing-impaired groups) and long-term and short-term subjective memory complaints.A multivariate analysis of variance, followed by univariate ANOVA and pairwise comparisons, showed that hearing loss was associated only with more long-term subjective memory complaints and not with short-term subjective memory complaints. In the hearing-impaired groups, the univariate main effect of hearing aid use was only observed for subjective long-term memory complaints and not for subjective short-term memory complaints. Similarly, the univariate interaction of hearing aid use and hearing status was significant for subjective long-term memory complaints and not for subjective short-term memory complaints. Pairwise comparisons, however, revealed no significant differences between hearing loss groups with respect to subjective long-term complaints.This cross-sectional study indicates an association between hearing loss and subjective long-term memory complaints but not with subjective short-term memory complaints. In addition, an interaction between hearing status and hearing aid use for subjective long-term memory complaints was observed in hearing-impaired groups, which calls for future research to examine the effects of hearing aid use on different memory systems. Hearing loss is one of the most common sensory disabilities in older adults. It adversely affects language understanding. Hearing loss is also a risk factor for cognitive decline  and demThe extent to which hearing loss affects the human memory system has been less extensively investigated. The human memory system consists of multiple theoretical systems, including short-term memory  and long-term memory. Long-term memory is typically categorized into declarative memory  and non-declarative memory  , 8.n = 34), those with subjective memory complaints (n = 61) performed poorly only on a sentence-identification-in-noise test. No significant differences were observed between groups in terms of 4PTA and quick speech-in-noise test results. The association between speech-in-noise testing and memory complaints in Jayakody et al. ].Using a larger sample than prior studies, the present study aimed to investigate the association between hearing loss, hearing aid use, and subjective memory complaints, after controlling for confounders including age, sex, education, and health variables, using data from the HUNT4 Hearing in Norway. No study has yet examined the extent to which hearing aid use affects subjective memory complaints in people with hearing loss.The HUNT4 Hearing, which was a part of HUNT4, was conducted in Nord-Trøndelag County in Norway . However, subsequent univariate ANOVAs revealed a main effect of hearing status for the subjective long-term memory complaints  and not for subjective short-term memory complaints . Bonferroni-adjusted pairwise comparisons in subjective long-term memory showed that the normal hearing group (M = 6.96) reported significantly fewer complaints than the slight hearing loss group only .F = 1.46, p = 0.19]. However, the main effect for hearing aid use was marginally significant . The interaction between hearing status and hearing aid use was not significant . A subsequent univariate ANOVA analysis showed that the main effect of hearing status was not significant for neither subjective long-term memory complaints  nor subjective short-term memory complaints . The main effect of hearing aid use was significant for subjective long-term memory  but not for subjective short-term memory . The interaction between hearing status and hearing aid use was only significant for subjective long-term memory complaints  and not for subjective short-term memory complaints . All pairwise comparisons showed no significant differences between hearing loss groups with respect to subjective long-term memory.Using data from a large cross-sectional study, we found that hearing loss is associated with increased subjective long-term memory complaints and not with subjective short-term memory complaints. Our findings add to the literature by showing that the negative effect of hearing loss on subjective memory complaints depends, to some extent, on the type of memory system. These findings are in line with the findings of Rönnberg et al.  who repoCurhan et al. , 14 showto combine speech cues that are distributed across time and frequency to finally infer meaning from the incoming speech signal. Thus, working memory is an active memory system in language understanding in people with hearing loss which subsequently results in less or no deterioration of working memory due to hearing loss.Several possible mechanisms have been hypothesized to explain the link between hearing loss and memory function. Short-term memory has a limited capacity to carry out operations for encoding, storage, rehearsal, and subsequent recall of information. Rönnberg et al. , in theiRegarding long-term memory, the ELU model  assumes The strength of the present study lies in the large sample of participants in a population-based study that provides sufficient power to detect small associations between various types of hearing status and types of subjective memory complaints. In addition, the present study was the only one that used objective hearing measures to assess the hearing status of participants to evaluate the association between hearing status and subjective memory complaints. Controlling cofounders that likely biased the results was the other strength. One important limitation of the present study is that the duration of hearing aid use was not collected in HUNT4. We assume that the duration of hearing aid use plays a critical role in the association between hearing loss severity and subjective memory complaints in people with hearing loss. We encourage future studies to include the duration of hearing aid use on the association between hearing loss and subjective or objective memory functioning in people with hearing loss. The duration of hearing loss is another possible factor in the association between hearing loss and subjective memory complaints. Unfortunately, the data regarding the duration of hearing loss were not available in the HUNT 4. The present study, however, is a cross-sectional study that limits the inference of causality. Future longitudinal studies are needed to determine the direction of causality between hearing loss and subjective memory complaints and also the extent to which hearing aid use affects subjective memory complaints in people with hearing loss. Furthermore, as noted in the Method section, the participation rate was quite low at 43% which may limit the generalizability of the results. In addition, there may be potential confounders (like genetic factors) that were not included in our study. Another limitation of the current study is the small effect sizes found for the main effects of hearing loss on subjective long-term memory complaints. This may suggest that other factors associated with aging are contributing to a decrease in subjective memory complaints in people with hearing loss.This study indicates an association between hearing loss and subjective long-term memory complaints and not for subjective short-term memory complaints. An interaction between hearing status and hearing aid use on subjective long-term memory was observed in hearing-impaired groups, which demands future research attention.kontakt@hunt.ntnu.no.The data analyzed in this study was obtained from the Trøndelag Health Study (the HUNT Study), the following licenses/restrictions apply: Access to the datasets is subject to approval by the Principal Investigators of the HUNT Study. Requests to access these datasets should be directed to the HUNT Study, The studies involving human participants were reviewed and the study was approved by the regional committee for medical and health research ethics and The Norwegian Data Protection Authority. The patients/participants provided their written informed consent to participate in this study.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.


The inner ear has previously been thought of as an immune-privileged organ owing to the existence of its tight junction-based blood-labyrinth barrier. Evidence has revealed that macrophage-like cells exist in the cochlea, and that innate immune responses may cause inner ear inflammation, resulting in sensorineural hearing loss. This Research Topic gathers articles that improve our understanding of tissue-resident macrophages in the cochlea, the association of the innate immune response with sensorineural hearing loss, and prognostic markers for sensorineural hearing loss.Miwa and Okano introduces tissue-resident macrophages in the inner ear. The authors described the origin and diversification of tissue-resident macrophages in the inner ear, as well as in other organ tissues, including the brain, skin, kidney, and spleen. They then discuss the function of tissue-resident macrophages in several systemic autoimmune diseases, such as multiple sclerosis, systemic lupus erythematosus, autoimmune uveitis, Muckle Wells syndrome, and inflammatory bowel diseases. Based on these findings, they speculated on the function of tissue-resident macrophages in hearing loss caused by autoimmune inner ear disease or autoinflammatory diseases.The first article on this topic by Liu et al. presented the distribution of immune cells, including macrophages, in human cochlear tissues using immunohistochemistry, confocal microscopy, and multichannel super-resolution structured-illumination microscopy. Multitude of IBA1 positive macrophages scatter in human cochlear tissues, including the spiral ligament, stria vascularis, tympanic covering layer, Reissner's membrane, spiral limbus, and osseous spiral lamina. Cochlear macrophages exhibit different morphological and immunostaining characteristics, depending on their localization. They speculated on the function of macrophages based on their morphological and immunostaining characteristics, such that amoeboid macrophages surrounding spiral ganglion neurons have rich expression of fractalkine genes, suggesting their essential role in nerve protection.Ito et al.. A unique subset of tissue-resident macrophages is primarily located adjacent to the blood vessels in the intermediate cell layer of the stria vascularis. Perivascular macrophages form the blood-labyrinth barrier and regulate blood vessel permeability in the stria vascularis, which has been shown to be important for the formation of endocochlear potential. Perivascular macrophages have phagocytic features. They are activated in a mouse model of Slc26a4 associated hearing loss, resulting in an increase of pigment granules in the cytoplasm and an elevation of hearing thresholds.The unique characteristics of tissue-resident macrophages in the stria vascularis have been demonstrated by NLRP3 cause genetic diseases, including cryopyrin-associated periodic syndrome (CAPS) and non-syndromic sensorineural hearing loss (DFNA34). Patients with both CAPS and DFNB34 present with sensorineural hearing loss characterized by cochlear inflammation caused by NLRP3 variants. An article by Nakanishi et al. summarizes the current understanding of the mechanisms of the NLRP3 inflammasome, characteristics of patients with CAPS and DFNA34, and the possible pathogenesis of hearing loss.Inflammasomes are innate immune sensors expressed in immune cells, including monocytes and macrophages. Of the inflammasomes, the NLRP3 inflammasome is the most well characterized. Recent studies revealed that variants of Nlrp3 variants, since they could not survive to an age when hearing evaluation was possible. Kim et al. reported the first mouse model that manifested quantifiable hearing loss caused by an Nlrp3 variant. To express the Nlrp3 variant (p.D301N) in the inner ear, Nlrp3D301NneoR mice were crossed with Gfi1Cre knock-in mice to establish conditional Nlrp3 variant expression in cochlear and hematopoietic cells. Nlrp3D301NneoR/+; Gfi1Cre/+ mice exhibited severe to profound hearing loss, and mutant NLRP3 expression was found in the cochlear tissues, including the spiral prominence, outer sulcus, organ of Corti, inner sulcus, and spiral ganglion neurons.Previously, no mouse model could be used to investigate the pathogenesis of hearing loss caused by Zheng et al. presents an association between liver function and SSNHL. They found that lower hearing impairment at onset and higher serum albumin levels were strongly associated with better treatment outcomes.Sudden sensorineural hearing loss (SSNHL) is one of the most common emergent diseases in otolaryngology clinics and has a significant impact on the quality of life. Although the treatment for SSNHL is controversial, the systemic administration of corticosteroids is the most commonly used initial therapy. After treatment, complete recovery was observed in approximately 40% of the patients with SSNHL. Thus, the treatment of SSNHL remains challenging, and its pathogenesis and prognostic factors are still under investigation. Nelson et al. aimed to characterize the mechanisms by which corticosteroids exert their effects, and the cell types in which they exert their effects in the inner ear. Using single-cell and single-nucleus transcriptome datasets, they revealed that steroid-responsive genes are localized to specific cell types and various regions in the cochlea, including the stria vascularis, the organ of Corti, and spiral ganglion neurons. The analysis demonstrated that steroid-responsive differentially expressed genes in spiral ganglion neurons were associated with angiogenesis, apoptosis, and cytokine-mediated anti-inflammatory pathways.There is heterogeneity in the response of patients with hearing instability to corticosteroid therapy. We hope that the readers will find that this Research Topic improves our understanding of the role of cochlear inflammation and immunity, and provides a better foundation for the development of potential therapeutic treatments for sensorineural hearing loss.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.