Background: Both tinnitus and hearing loss are multidimensional. The purpose of this study was to identify and determine the degree of mental fatigue in patients with hearing loss and/or tinnitus participating in audiological rehabilitation, and to examine the self-reported mental fatigue scale (MFS) in this patient group. Methods: Patients undergoing audiological rehabilitation at the Department of Audiology and Neurotology, Karolinska University Hospital, Sweden, between 2011 and 2017 who completed a self-reported MFS questionnaire were investigated. Data on 76 patients were analysed in this pilot study. Patients were also assessed using the Tinnitus Handicap Inventory (THI). Results: The study population had an age range of 38–65 years, and most had normal hearing (37%) or mild to moderate hearing loss (46%). Only 17% had severe to profound hearing loss. A total of 56.5% had tinnitus, of whom 39.5% scored ≥57 on the THI, indicating severe tinnitus, whereas 43.5% reported no tinnitus. The MFS scores, ranging from 13 to 42.5 points, were divided into three severity levels for analysis: 10.5–15, 15.5–20, and ≥20.5. In total, 67% of the patients had MFS scores ≥ 20.5. Importantly, most of the participants (90%) with a THI score ≥ 57 belonged to that group. A significant positive correlation between a high MFS score and severe tinnitus was found. Conclusions: The study reveals that severe mental fatigue is more common in patients with severe tinnitus than sole hearing loss. Globally, hearing loss was the third leading cause of years lived with disability in 2019 and the second leading cause of impairment ,4, and iTinnitus is a troublesome condition that exerts negative effects on cognitive function, e.g., working memory and concentration . TinnituFatigue or mental fatigue is a complex, subjective phenomenon to define. Fatigue is often described as “early exhaustion after physical or mental activity” related, e.g., to lack of energy, sleep deprivation, and fatigability . FatigueThe validated self-reported mental fatigue scale (MFS) developed by Johansson et al. in 2010  and adapPatients with severe hearing loss and/or severe tinnitus should participate in an extended audiological rehabilitation program involving interdisciplinary rehabilitation with at least three hearing care professionals  and/or participate in group rehabilitation to experience the maximum benefits of the treatments available . In SwedSince 2011, the self-reported MFS questionnaire has been used during the audiological rehabilitation of patients with hearing loss and/or patients with severe tinnitus at Karolinska University Hospital, Stockholm, Sweden. The present study is the first to report on the use of the MFS questionnaire in patients with hearing loss and tinnitus in Sweden.The overall aim of the present pilot study was to use the MFS to identify and investigate the degree of mental fatigue caused by hearing loss and eventual tinnitus in patients undergoing audiological rehabilitation. Furthermore, the purpose was to examine the usefulness of a self-reported MFS as a diagnostic instrument for mental fatigue in these patient groups.The main conclusions indicate a relationship between severe tinnitus and severe mental fatigue in patients with normal hearing or mild to moderate hearing loss. The study found a relationship between tinnitus and severe mental fatigue with consequences that affect negatively patients’ daily lives. It is important to create guidelines for audiological interventions and treatments for patients with mental fatigue in combination with hearing loss and tinnitus.Patients participating in extended audiological rehabilitation from 2011 to 2017 at the Department of Audiology and Neurotology, Karolinska University Hospital in Sweden who completed the self-reported MFS were included in the study. A mental fatigue score of 10.5 or more was the inclusion criterion. A score of 10.5 or more  in the sThe present pilot study is based on data from 76 consecutive patient medical records. Of these, 23 (30.5%) were men and 53 (69.5%) were women. Information on the study and a consent form were sent to all 76 patients, all of whom signed consent forms. The known diagnoses for hearing impairment in the study group varied, such as hearing impairment in childhood diagnosed in 15 (19.5%) patients, genetic causes in 3 (4%), Ménière’s disease in 4 (5.5%), otosclerosis in 10 (13%), chronic otitis in 7 (9%), meningitis in 3 (4%), rubella in 2 (2.5%), and ototoxic causes in 2 (2.5%). There were other diagnoses such as in two cases a radical mastoidectomy, two patients with vestibular schwannoma, two with labyrinthitis, three with sudden deafness, and in three cases facial nerve paralysis. Some of the patients had other problems in addition to hearing loss or tinnitus, such as 9% having previous head trauma, whiplash injury, or other problems with the neck or back; 15.5% having psychiatric symptoms or diagnosed depression; 5% having dizziness or balance difficulties; and 4% having fibromyalgia.Several patients suffered from severe tinnitus when they visited our clinic , which sThe validated MFS contains 15 questions evaluating affective, cognitive, and sensory symptoms. Each question is responded to by selecting one of four levels (0–3): no (0), mild (1), moderate (2), and severe (3) problems . The value of the MFS questionnaire as a diagnostic instrument was evaluated. The MFS data, hearing data from medical records, sick leave information, data on the various types of audiological rehabilitation received, and factors such as sex and age were analysed. Continuous data were analysed using unpaired t-tests, and categorical data were analysed using chi-square tests. The means, medians, standard deviations, and percentages were calculated. The study divides the MFS scores into three levels based on questions 1–14: 10.5–15, 15.5–20, and ≥20.5. The data do not have to be normally allocated in linear regression if the number of subjects is above 30 [Statistical analyses were performed using IBMR) analysis was performed on the MFS score, PTA, and THI. A on-parametric Kruskal–Wallis test was performed to check the differences between various independent groups  and a dependent MFS score.The study used statistical analyses such as correlation analysis and the Kruskal–Wallis test to better understand the interactions between variables. Next, a principal component analysis (PCA) was performed with the MFS score as the dependent variable to identify the relationship between the MFS and THI score. Pearson’s correlation coefficient (n = 18) of the patients, 6 men (33.3%) and 12 women (66.7%), lived alone.The demographic data of the 76 patients in the study population, stratified by gender, are shown in n = 28, 37%) had PTAs of normal hearing or moderate hearing loss at 41–70 dB HL  in the better ear. There were 13 patients (17%) with severe to profound hearing loss (71–90 dB HL and 91–130 dB HL), and another 13 patients (17%) had mild hearing loss.Most of the participants (There were 43 participants (56.5%) that reported problems with tinnitus, of whom 30 patients (39.5%), 8 (35%) men and 22 (41.5%) women, had severe tinnitus, based on a THI score of 57 or more . There wThere were three sub-groups in the study population: patients with normal hearing or mild to moderate hearing loss without amplification; patients with hearing loss and hearing aids; and patients with hearing loss and cochlear implants (CI). See Of the total 76 patients, 57 (75%) had bilateral hearing aids, and 2 (2.6%) had bilateral CIs. A total of seven (9.2%) patients had a unilateral hearing aid, and four (5.3%) had hearing aid in combination with a CI on the opposite ear. Finally, six (7.9%) patients did not have any kind of aid at all. These six patients had tested hearing aids or stimulators for tinnitus but returned them due to no use of them.n = 51, 67%) had an MFS score of 20.5 or more. In total, 11 (14.5%) scores were ≥30, and all of them had a high THI score of 78–100 points. The distribution of the 76 patients across the three MFS scores  is shown in The mean MFS score was 23.4 (SD 5.7). The scores ranged from 13 to 42.5 points, with a median of 23.0 points. Most patients  with normal hearing (0–25 dB HL) and MFS scores greater than 20.5, 9 patients (45%) had MFS scores between 15.5 and 20, and only 2 patients (40%) had MFS scores between 10.5 and 15. A similar distribution was observed in the patients with moderate hearing loss (41–70 dB HL). The six patients (8%) with profound hearing loss (91–130 dB HL) were distributed equally between the MFS 15.5–20 and ≥20.5 groups.n = 68, 89.5%) were working, although 44 (58%) of them were on sick leave when they arrived at the clinic. During ongoing rehabilitation at the clinic, 73 of them (96%) were on sick leave. According to the medical records, a total of 30% of the study population worked in a school or preschool, 29% worked in administration or IT, and 12% worked in healthcare.The distributions of occupation and sick leave across the three MFS score groups are shown in n = 27), 53% were in the MFS score ≥ 20.5 group; the difference was significant . However, seven patients (13.5%) in the THI < 57 group were also in the MFS score ≥ 20 group. Patients with no tinnitus but with hearing impairment mostly had MFS scores greater than 20.5 .The severity of tinnitus, indicated by a THI ≥ 57, THI < 57, or no tinnitus, was also investigated across the three MFS score groups . Of the The mean hearing thresholds with pure-tone air conduction averages (PTAs) at four frequencies  were 44.1 dB HL in the better ear (SD 29.23) (median: 38 dB HL. Range: 3–130 dB HL) and 64.8 dB HL in the worse ear (median: 64 dB Hl. Range: 4–130 dB HL).r = 0.677 **. The MFS score levels were separated  and correlated significantly for moderate MFS (15.5–20) and severe MFS (≥20.5) scores with the PTA4 for the better ear and tinnitus scores. No significant correlation was detected for mild MFS (10.5–15). r = 0.027) of the total variance in the MFS score. For the THI scores, a mild r = 0.45 explained about 45.9% of the total variance in MFS score.The correlation analysis in ρ = 0.002 **  and dependent MFS scores, a Kruskal–Wallis test was performed. The Kruskal–Wallis test confirms the relationship between MFS scores and a THI score ≥ 57, corresponding to an increasing trend in the MFS score when tinnitus increases with a positive correlation 0.002 ** . Furthertinnitus .p < 0.0001) with a correlation (Pearson’s R2) of 0.45   confirmeTo our knowledge, this is one of the first attempts to use the Swedish version of the MFS self-assessment questionnaire to assess patients with hearing loss and/or tinnitus. Severe mental fatigue was found among patients with normal hearing, as well as with mild to moderate hearing loss. Interestingly, a strong relationship between severe MFS scores and tinnitus were confirmed. Patients with severe tinnitus (≥57) seemed to have better hearing thresholds (PTAs) than patients with less tinnitus (<57) or no tinnitus in their better ear, as shown in Three sub-groups were identified: patients without amplification, patients with hearing aids, and patients with CIs. The amplification type was dependent on whether patients had normal hearing with tinnitus, mild/moderate hearing loss, or severe hearing loss. The mental fatigue reported by patients in each of these groups could have been influenced by various sources, for example, their hearing situation, their amplification type or lack thereof, or the impact of tinnitus, if present.Notably, our findings indicated that 51 of 76 patients (67%) had a severe MFS score of 20.5 or more. Strikingly, most patients with the highest MFS scores had severe tinnitus. Apparently, severe tinnitus is associated with serious effects on mental fatigue. It is also noteworthy that of these, 11 patients had MFS scores above 30. All 11 patients had severe tinnitus with THI scores between 78–100, and only 2 of them had normal hearing. Similar to the present study, an earlier study showed no relationship between the degree of hearing loss and fatigue, but it did not study tinnitus .To date, the MFS instrument has not been validated for use in assessing mental fatigue in patients with hearing loss with or without tinnitus. A recent publication by the team at Vanderbilt outlines the development and validation of a new measure of listening-related fatigue . A recenHornsby et al.  concludeMental fatigue is commonly associated with brain injuries. The MFS scale used in the present study was developed and validated by Johansson et al.  for use The study population in the present pilot underwent extended audiological rehabilitation and consisted of 76 patients with varying degrees of mental fatigue. Most patients in the present study were between 41 and 60 years of age at the time they were referred to receive hearing healthcare at the clinic. Most of the patients included in the study with mental fatigue were women (69.5%). One can speculate on the tendency for gender differences in mental fatigue. Women may be more willing to seek help for their audiological disturbances than men. An earlier study found women received extended audiological rehabilitation significantly more often compared to men . It was Taking individual questions into account regarding mental fatigue/fatigability, mental recovery, concentration, slowness of thinking, stress sensitivity, sensitivity to noise, and reduced sleep, severe tinnitus appears to have serious consequences on patients’ daily lives. A previous study pointed out that severe tinnitus and vertigo strongly and negatively affected QoL . SimilarSome limitations to the study could be addressed. First, the present pilot study is based on retrospective data previously collected during routine clinical appointments. Second, it was not possible to sort the patients into separate groups representing either tinnitus or hearing loss because many patients had both tinnitus and hearing loss. Only seven patients had normal hearing, but they had a THI score ≥ 57, while some of the patients had no hearing loss in the better ear, but they had hearing loss in the worse ear. An attempt to differentiate between these groups was one of the purposes of the final analysis of the overall patient dataset in terms of the MFS scores. This could not be realized in the present study. Third, patients were not separated in the analysis based on whether they had amplification (hearing aids or CI) or not. This was because only two patients had bilateral CIs and four patients had a CI in combination with a hearing aid. A follow-up study is planned with a larger sample and this limitation can then be mitigated. Fourth, one of the aims of the study was to determine the usefulness of the MFS as a diagnostic instrument. A potential limitation of questionnaires such as the MFS is that it is based on self-reporting and cannot distinguish between the impact of severe tinnitus and severe anxiety, which might manifest similarly in terms of mental fatigue. Finally, this pilot study did not have a control group. The study did not include patients with low mental fatigue scores (<10.5), or people in general with hearing loss and tinnitus, and does not utilize any other self-reporting measure previously used in hearing loss populations as a comparison. The authors plan to include a wider population, including a control group, in a follow-up study.Applying knowledge from earlier studies using the MFS instrument, a score of 10.5 or more implicates problems with mental fatigue that are likely to affect a patient’s daily life, including work and/or social activities . Mental Most of the study population had serious problems with mental fatigue. Several had normal hearing, while others had mild, moderate, and severe to profound hearing loss. More than half of the study group had tinnitus. Of those with tinnitus, 4 of 10 had severe tinnitus, and 90% had MFS scores indicating that they had severe mental fatigue. The results of the present study indicate a strong relationship between mental fatigue and severe tinnitus.Obviously, this is an area that has so far been often overlooked. The results of the present study indicate that the self-reported MFS seems to have multidimensional questions and could be used as a diagnostic instrument for this patient group, but further investigation is needed to find methods of hearing and/or tinnitus rehabilitation that can reduce or shorten the duration of mental fatigue. This is especially important in terms of the negative effect mental fatigue has on patients’ daily lives. The study indicates a benefit of the use of the MFS for both patients and clinics, but further studies are needed to validate the questionnaire.Finally, our results lead to the conclusion that there is a need for specific guidelines on the treatment of patients with hearing loss and/or tinnitus who have mental fatigue.


N = 99) were collected prospectively through the Netherlands Brain Bank from donors with Parkinson’s disease (and dementia), dementia with Lewy bodies, multiple system atrophy, Alzheimer’s disease, other neurodegenerative diseases and non-neurological controls. Multiple retinal and optic nerve cross-sections were immunostained with anti-α-synuclein antibodies  and assessed for aggregates and inclusions. α-Synuclein was observed as Lewy neurites in the retina and oligodendroglial cytoplasmic inclusions in the optic nerve and was highly associated with Lewy body disease (P < 0.001) and multiple system atrophy (P = 0.001). In all multiple system atrophy cases, the optic nerve showed oligodendroglial cytoplasmic inclusions, while retinal Lewy neurites were absent, despite coincidental brain Lewy pathology. With high specificity (97%) and sensitivity (82%), retinal/optic nerve α-synuclein differentiates primary α-synucleinopathies from other cases and controls. α-Synuclein pathology occurs specifically in the retina and optic nerve of primary α-synucleinopathies as opposed to other neurodegenerative diseases—with and without α-synuclein co-pathology—and controls. The absence of retinal Lewy neurites in multiple system atrophy could contribute to the development of an in vivo retinal biomarker that discriminates between Lewy body disease and multiple system atrophy.There is increasing interest in studying retinal biomarkers for various neurodegenerative diseases. Specific protein aggregates associated with neurodegenerative diseases are present in the retina and could be visualised in a non-invasive way. This study aims to assess the specificity and sensitivity of retinal α-synuclein aggregates in neuropathologically characterised α-synucleinopathies, other neurodegenerative diseases and non-neurological controls. Post-mortem eyes ( Therefore, the retina is considered a potential source of biomarkers for neurodegenerative brain diseases. The optic nerve consists of glial cells and the axons of retinal ganglion cells, which extend as the optic tract to the lateral geniculate nucleus and superior colliculus, forming a direct connection with the brain. The retina contains ganglion cells displaying similarities with neurons of the central nervous system, interneurons, and different types of glial cells2. Existing data suggests that the retina mirrors ongoing pathology associated with neurodegenerative diseases in the brain10, supporting the potential of in-vivo retinal imaging as a non-invasive diagnostic approach.The retina and optic nerve are embryonically derived from the central nervous system and show similar structural and functional characteristics to the brain11. α-Synucleinopathies—of which Parkinson’s disease is the most common form—can be subdivided into two major classes: Lewy body disease and multiple system atrophy13. Lewy body disease encompasses the clinically defined entities Parkinson’s disease, Parkinson’s disease dementia and dementia with Lewy bodies. It is neuropathologically characterised by neuronal αSyn aggregation forming Lewy bodies and Lewy neurites11. Multiple system atrophy is characterised by oligodendroglial cytoplasmic inclusions (GCIs). These neuropathological aggregates are associated with neuronal death and demyelination14. Pathological formation of αSyn aggregates are thought to be caused by the binding of cytoplasmic αSyn with lipid membranes disrupting their role in regulating neurotransmission and synaptic function16. Lewy bodies and GCIs are primarily composed of αSyn phosphorylated at serine 129 (pSer129), which is why this phosphorylated αSyn epitope is considered a marker for pathology and suggested as a potential diagnostic and progression biomarker in Parkinson’s disease18. Despite their shared presence of αSyn aggregates, α-synucleinopathies show significant clinical and pathological heterogeneity.Several neurodegenerative diseases are characterised by the aggregation of α-synuclein (αSyn) and therefore termed α-synucleinopathies20. In Parkinson’s disease, native αSyn was found as neuronal, globular inclusions in the inner nuclear layer and ganglion cell layer8. Since αSyn pSer129 has gained interest as a key marker for pathology, different studies on the retina have reported the presence of αSyn pSer129 specifically in Parkinson’s disease9 and dementia with Lewy bodies7 as opposed to control cases. Ortuño-Lizarán and colleagues9, studying whole-mount retinas of Parkinson’s disease cases, reported αSyn pSer129 deposits resembling brain Lewy bodies. αSyn pSer129 was observed in ganglion cell perikarya, axonal fibres and dendrites. Additional αSyn pSer129 immunostaining on cross-sections showed inclusions in the inner nuclear and ganglion cell layers resembling characteristic Lewy bodies and neurites. Despite these significant findings, one study could not confirm the presence of αSyn pathology in the retinas of Parkinson’s disease as opposed to controls21.Under normal conditions, αSyn is endogenously present in all retinal layers in cell bodies and processes9. In-vivo examination of αSyn in the retina may provide insight into the pathogenesis of α-synucleinopathies and the potential efficacy of therapeutic interventions. While previous studies have already established the presence of Lewy pathology in the retina in Parkinson’s disease9, dementia with Lewy bodies7 and incidental Lewy body disease9, the ophthalmologic occurrence of αSyn in multiple system atrophy has not been reported before. Moreover, it remains unclear whether αSyn is present in the retina of cases with other neurodegenerative diseases and with αSyn co-pathology in the brain, for instance, in the presence of incidental, amygdala-only αSyn pathology22. This study evaluated the presence of ophthalmologic αSyn pathology in a large cohort with different neurodegenerative diseases, including primary α-synucleinopathies and other neurodegenerative disorders with and without αSyn co-pathology, and non-neurological controls.It has been reported that the level of αSyn pathology in the retina correlated positively with the severity of Parkinson’s disease and may therefore serve as a biomarker for the diseaseP = 0.16), sex (P = 0.08) or post-mortem interval (P = 0.06). Parkinson’s disease (dementia) had a significantly higher disease duration than all other groups (P < 0.001). A significant difference was found between groups for visual hallucinations and dementia (P < 0.001). α-Synucleinopathies had a significantly higher Braak LB stage (P < 0.001) and LPC stage (P < 0.001).Individual case details are reported in Supplementary Tables n = 22). Moreover, although all multiple system atrophy cases had additional Lewy body brain pathology, only two of them  also showed neurites in the optic nerve head at the level of the lamina cribrosa  showed αSyn pathology in the retina or optic nerve, indicating a high specificity.Further exploring the relation between αSyn pathology in the retina and optic nerve versus the presence of αSyn pathology in the brain, the optic nerve was found to be more accurately associated with the presence of αSyn in the brain than the retina , as is shown in Fig. P < 0.001)  Table . NotablyP = 0.02), whereas dementia and disease duration were not.In addition, the relation between clinical variables and the presence of αSyn pathology in the retina/optic nerve was studied Table . CorrectDeveloping retinal biomarkers for various neurodegenerative diseases requires understanding of the retina and optic nerve pathology and the connection with neuropathology in the brain. In this post-mortem study, the prevalence of αSyn pathology in the retina and optic nerve was assessed in a well-characterised cohort with different neurodegenerative diseases and non-neurological controls. αSyn pathology was mainly observed in the retina and optic nerve of primary α-synucleinopathies as opposed to other neurodegenerative disorders and controls without αSyn in the brain. There is a high specificity (97%) and sensitivity (82%) for the presence of αSyn in the retina and optic nerve in relation to brain αSyn pathology as part of a primary α-synucleinopathy. Results from this study suggest that αSyn pathology in the retina and optic nerve is associated with the distribution of αSyn pathology according to the Braak LB stage and thus mirrors the pathological presence of αSyn in the brain.9. In the present study, αSyn pathology was observed as Lewy neurites in the retina and optic nerve and as GCIs in the optic nerve. Sporadically and only in primary synucleinopathy cases, cytoplasmic inclusions were observed in the ganglion cell layer, presumably in ganglion cells. These structures resemble what was earlier described as Lewy body-like inclusions9. However, retinal cytoplasmic inclusions observed in the present study did not show similarity with the classic halo-morphology seen in the brain, nor were they similar in size. The absence of classic Lewy bodies in this study suggests that the retina differs from the brain in response mechanisms to αSyn-associated pathology and may hold essential qualities such as a highly metabolic environment aimed at preventing the formation of protein aggregation until the stage of classic inclusion bodies23.The observation of αSyn pathology in the retina and optic nerve tissue of cases with α-synucleinopathies is in concordance with previous studies10,24, reported on the presence and characterisation of αSyn pathology in the retina in α-synucleinopathies, αSyn pathology in multiple system atrophy had not yet been described. Multiple system atrophy cases showed αSyn pathology in the form of cytoplasmic inclusion bodies resembling GCIs in the optic nerve, which were not observed in other cases with optic nerve tissue available. In-vivo optical coherence tomography data support optic nerve pathology in multiple system atrophy. In their review, Mendoza-Santiesteban and colleagues25 outlined data on patients with multiple system atrophy that show a specific pattern of damage of the post-laminar myelinated optic nerve axons. They describe damaging of the M-cell axons arising from the peripheral retina and the sparing of the P-cell axons originating from the macular area. This leads to retinal nerve fibre layer thinning around the optic nerve head in the superior, nasal and inferior quadrant, but not in the temporal quadrant, as opposed to the more general retinal nerve fibre layer thinning seen in Parkinson’s disease cases. The high specificity and sensitivity found in the present study for GCI pathology in the optic nerve with relation to the presence of multiple system atrophy-associated brain pathology, and the absence of Lewy pathology in the retinas of these cases, makes the eye an exciting candidate in the search for biomarkers that can discriminate between α-synucleinopathies.Although previous studies26, evidence was outlined for multiple routes of disease progression that are different from Braak’s proposed caudorostral pathway27, and that pathological progression may vary between subtypes. The pathological variability of ophthalmologic αSyn seen in this study could be explained by this heterogeneity among α-synucleinopathies. The significant association with the Braak LB staging system suggests that αSyn pathology in the retina/optic nerve does depend on localisation and/or load of αSyn pathology in the brain. In particular, the strong association with stage VI and the high proportion of cases with αSyn pathology in the retina/optic nerve within the neocortical LPC stage suggests that αSyn pathology in the retina/optic nerve may predict neocortical αSyn involvement.We found a clear relation between Lewy pathology in the retina/optic nerve and αSyn pathology in the brain. Nonetheless, in 18% of the primary α-synucleinopathy cases, no αSyn pathology in the retina or optic nerve was observed. One explanation for the absence of αSyn pathology could be that pathology was missed due to a Type II error in assessing cross-sections. On the other hand, the lack of αSyn pathology may suggest that the neuronal tissue of the eye is differently involved within the hierarchical spreading of αSyn pathology in the brain. In a recent review by Jellinger28. The authors state that amygdala-predominant Lewy bodies are Alzheimer’s disease interacting pathologies as opposed to brainstem and limbic Lewy bodies. This would explain the absence of αSyn pathology in the retina and optic nerve of cases with amygdala-only αSyn pathology and suggests that the eye has a disease-specific involvement in the spread of αSyn pathology of the brain.Ophthalmologic αSyn was also present in one Alzheimer’s disease and one frontotemporal lobar degeneration-tau case, showing substantial αSyn pathology in the brain. More interestingly, all Alzheimer’s disease cases with amygdala-predominant αSyn presence did not show αSyn pathology in the retina or optic nerve. A recent post-mortem study demonstrates distinct distribution patterns of Lewy body pathology in the presence of Alzheimer’s disease29. To understand the association between complex visual hallucinations as seen in Lewy body disease and αSyn pathology in the retina and optic nerve, future studies should focus on the possibility of hierarchical spreading of αSyn pathology along the retino-geniculo-cortical pathway31.Interestingly, a significant association was observed between αSyn pathology in the retina/optic nerve and visual hallucinations. Given the phenomenologically complex nature of the hallucinations of subjects in this cohort, it is likely that the underlying cause lies in the presence of cortico-cerebral αSyn pathology rather than ophthalmologic αSyn pathologyA strength of this study is that multiple antibodies detecting different epitopes were used to asses ophthalmologic αSyn pathology. Additionally, besides the retina, also optic nerve tissue was assessed. Data from this study indicate that αSyn pathology is more prominent in the optic nerve and optic nerve head than in the retina. Therefore, future studies should focus on exploring the optic nerve head, including the lamina cribrosa in addition to the retina, to develop methods for detecting αSyn pathology using in-vivo scanning modalities. A limitation of this study is that only cross-sections and not whole-mount retinas were assessed, and pathology could have been missed, potentially causing a Type II error.In conclusion, this study on a mixed cohort of neurodegenerative diseases and non-neurological controls demonstrates that the detection of αSyn pathology in the retina and optic nerve shows a high specificity (97%) and sensitivity (82%) regarding the presence of primary α-synucleinopathy of the brain. αSyn pathology in the retina and optic nerve correlates with Lewy body disease and multiple system atrophy as opposed to Alzheimer’s disease and other neurodegenerative diseases and is associated with higher Braak LB stages. Here, we report that the hallmark GCI pathology observed in the brain in cases of multiple system atrophy is also detected in the optic nerve. The absence of Lewy pathology in the retina in multiple system atrophy could contribute to developing an in-vivo biomarker discriminating between multiple system atrophy and other α-synucleinopathies. The results of this study support the development of retinal biomarkers for neurodegenerative diseases such as α-synucleinopathies.32 . The VUmc medical ethics committee approved the Netherlands Brain Bank donor programme (reference# 2009/148). All donors consented in writing to use their tissue and clinical records for research purposes in compliance with ethical standards. Brain autopsies were performed according to the Code of Conduct of Brain Net Europe33.Post-mortem eyes and brain tissue of 99 donors were collected from 2009 until 2022 by the Netherlands Brain Bank34, including assessment of Braak-stage for Lewy bodies (Braak LB stage)27 and Alzheimer’s disease neuropathological changes35, including Thal-phase for Aβ37, Braak-stage for NFTs38 and CERAD score for neuritic plaque pathology39. Cases were grouped according to clinicopathological diagnosis in Parkinson’s disease (and dementia)41 (n = 21), dementia with Lewy bodies (n = 5)42, Multiple system atrophy (n = 7), Alzheimer’s disease37 (n = 19), other neurodegenerative diseases45 (n = 22) and non-neurological controls (n = 25). During screening, 12 cases were found to have a medical history of ophthalmologic disease, including glaucoma (n = 5), age-related macular degeneration (n = 3), optic neuritis (n = 1), retinal detachment (n = 2) and macular pucker (n = 1). This was, however, not an exclusion criterium for this study. Cohort characteristics are shown in Supplementary Table Brain autopsy was performed within 12 h post-mortem, after which brain tissue was formalin-fixed  and embedded in paraffin. Neuropathological diagnosis was performed (AR) according to the guidelines of the BrainNet Europe Consortium5.Post-mortem enucleated eyes were directly collected in 4% paraformaldehyde (PFA) and stored for 48 h before further processing  was assessed in the brainstem  and limbic system . Clinicopathological suspected α-synucleinopathies were additionally assessed in the neocortex . Sections were air-dried overnight at 37 °C before staining. Sections were deparaffinised and rehydrated using sequential incubations in xylene, alcohol and water. Endogenous peroxidase activity was suppressed by incubating the sections with 0.3% H2O2 in phosphate buffer saline  for 30 min. Antigen retrieval was performed in 10 mM/L citrate buffer (pH 6.0) and heated using an autoclave (20 min at 121 °C). Retina sections immunostained with KM51 (dilution 1:100) and LB509  were additionally pre-treated with 100% formic acid (10 min at RT) and washed with PBS. Sections were incubated overnight at RT with primary antibodies LB509, KM51 and anti-αSyn pSer12946  diluted in antibody diluent . Omission of the primary antibody was used as a negative control, and mesencephalon tissue from a Parkinson’s disease case was used as a positive control. After primary antibody incubation, the sections were washed with PBS, incubated with anti-mouse/rabbit HRP Envision  (30 min) and subsequently washed using PBS. 3,3’-Diaminobenzine  was used as a chromogen for colour development (5 min at RT). Sections were counterstained with haematoxylin, dehydrated using alcohol and xylene and mounted using Quick-D .Tissue sections (10 μm thickness) from formalin-fixed, paraffin-embedded retina of the superior and inferior axis were sequentially mounted on TOMO glass slides . Brain tissue sections (7 μm thickness) were mounted on Superfrost plus glass slides . In all cases, according to the protocol of the BrainNet Europe ConsortiumTissue sections were stained for the αSyn markers  as described above. After DAB treatment, antibody complexes were removed by heating the sections in citrate buffer using a microwave . The tissue sections were incubated with either anti-SOX10  or anti-NeuN  for 1 h at room temperature. An alkaline phosphatase (AP)-conjugated goat anti-rabbit Ig  was incubated for 1 h, and colour development was performed using Liquid Permanent Red (DAKO). Tissue sections were mounted using Aquatex aqueous mounting medium .2 per section. Staging according to the new proposed Lewy pathology consensus criteria (LPC)47 was performed by scoring the brain tissue in a dichotomous manner, resulting in “amygdala-predominant” also encompassing cases with “amygdala-only” presence of αSyn49, “brainstem-predominant”, “limbic” and “neocortical” Lewy pathology. Assessment of αSyn pathology in the brain and the retina/optic nerve was performed separately and masked for diagnosis. All figures were composed with GraphPad Prism  and Adobe Photoshop .The presence and localisation of αSyn pathology were assessed in 99 donors. Per case, a total of four sections were immunostained: three sections of the superior retina immunostained with LB509, pSer129 and KM51, and one section of the inferior retina using LB509  version 28. A Supplementary material


We present an uncommon case of a pediatric patient with sudden-onset sensorineural hearing loss (SSNHL), a medical condition in which a person experiences a rapid loss of 30 or more decibels within a matter of hours or days. The patient is a nine-year-old female who, two years prior, suddenly lost hearing in her left ear after a 24-hour episode of nausea, vomiting, and left ear pain. She presented to our clinic two years after the episode, long after the window for evidence-based treatment for acute SSNHL, such as corticosteroid therapy or antivirals, had passed. However, she remembered the moment of her hearing loss vividly, an uncommon occurrence in pediatric patients. CT, MRI, family history, and physical exam were unremarkable. The patient had a brief hearing aid trial where she described being able to hear the sound but did not have any clarity in understanding the sound. The patient was ultimately treated with a unilateral cochlear implant and showed excellent subjective and audiogram responses. Continued research on the management of SSNHL in pediatric patients who present outside of the acute therapeutic window is needed. Sudden onset sensorineural hearing loss (SSNHL) is a measurable unilateral or bilateral hearing impairment lasting less than 72 hours . The incA nine-year-old female presented to the clinic accompanied by her father with complaints of chronic left-sided hearing loss. The patient reported that two years prior, she had been swimming and experienced a sudden episode of otalgia with associated nausea and vomiting, which lasted for about 24 hours and resolved spontaneously but was followed by a subjective sense of left hearing loss. Her father reported that the patient had been less “attentive” over the past two years, which finally prompted them to be evaluated. Upon evaluation via a hearing screen and audiogram, she was found to have a profound hearing loss in the left ear with normal hearing on the right Figure .The patient is an only child with no known family history of hearing loss. She was born healthy at full term, and she passed her newborn hearing screen. A physical exam revealed a well-developed nine-year-old female with unremarkable findings, including an intact tympanic membrane and surrounding structures. MRI Figure  and CT  were obtPhysical exams, imaging findings, and treatment options were discussed with the patient and medical decision-makers. The possible treatment options offered were continued observation with no intervention, using a contralateral routing of signals (CROS) hearing aid, a bone conduction hearing implant (Baha), and a cochlear implant. Ultimately, the patient and family elected a cochlear implant after the shared decision-making process and an unsatisfactory hearing aid trial. High-resolution computed tomography (HRCT) was referenced for surgical planning. A commercially available, FDA-approved implant was selected, and surgical placement was successful and without incident. Recovery followed the typical course, and since the time of the implant, the patient follow-up visits have shown excellent response to the implant, and the patient is doing well and is satisfied with the outcomes. Audiogram shows significant improvement from pre-operative measurements, with hearing measuring within normal levels Figure .Symptoms of SSNHL include a sudden loss of hearing, tinnitus, and vertigo. The cause of SSNHL is most often not well understood and is only identified in 10% of all cases . Among kMost current literature suggests the management of SSNHL with steroids such as prednisone, which may be prescribed to reduce inflammation and swelling in the inner ear to improve or stabilize hearing. While these can be systemically administered, some data suggests that intratympanic steroid therapy can be more effective than oral steroids, especially when administered at higher doses . TranstySSNHL is a complex, multifactorial, and poorly understood condition facing patients and providers. The literature offers little recourse for providers treating late management of SSNHL in pediatric patients where the window for the mainstay of treatment, namely transtympanic corticosteroid therapy, has passed. To this end, we present a pediatric patient who suffered profound unilateral SSNHL two years prior to presentation and demonstrated an excellent response to cochlear implantation.


The H9N2 avian influenza virus has spread to the whole world and become one of the dominant subtype influenza viruses in chickens in China. H9N2 virus could not only result in great economic losses by reducing egg production but also serve as a gene vector to provide its gene segments to other emerging severe influenza A viruses to cause higher mortality and more serious consequences. Thus, developing models to predict H9N2 status should be given priority. Our main aim was to use the machine learning method  and regular production data  to predict the occurrence and development of H9N2 in laying hen houses and evaluate their performance in disease prediction. Additionally, we assessed the working ability of the framework with different time frames to predict H9N2 status in advance within a 3-day period. We found that this framework could work well in prediction with high accuracy and sensitivity, and with more information introduced into the model, more “don’t care values” would be added into datasets to affect model performance by forming attribute noise. Besides, our study recommended efficient frameworks and models for H9N2 status prediction and provided practical potential uses in the livestock and poultry industry.The H9N2 avian influenza virus has become one of the dominant subtypes of avian influenza virus in poultry and has been significantly harmful to chickens in China, with great economic losses in terms of reduced egg production or high mortality by co-infection with other pathogens. A prediction of H9N2 status based on easily available production data with high accuracy would be important and essential to prevent and control H9N2 outbreaks in advance. This study developed a machine learning framework based on the XGBoost classification algorithm using 3 months’ laying rates and mortalities collected from three H9N2-infected laying hen houses with complete onset cycles. A framework was developed to automatically predict the H9N2 status of individual house for future 3 days  with five time frames . It had been proven that a high accuracy rate > 90%, a recall rate > 90%, a precision rate of >80%, and an area under the curve of the receiver operator characteristic ≥ 0.85 could be achieved with the prediction models. Models with day + 0 and day − 1 were highly recommended to predict H9N2 status + 0 and H9N2 status + 1 for the direct or auxiliary monitoring of its occurrence and development. Such a framework could provide new insights into predicting H9N2 outbreaks, and other practical potential applications to assist in disease monitor were also considerable. In recent years, various infectious diseases have posed risks to the livestock and poultry industry , such asFor infectious disease management, the most appropriate strategy is accurate and rapid detection and control . ResearcOrnithobacterium rhinotracheale (ORT) + H9N2 avian influenza virus, ORT and H9N2 virus alone, respectively [Different infectious diseases do not only show different symptoms in pathological changes but also show different effects on production performance indicators due to differences in virulence, adaptability and replication; even different strains of the same disease can have different clinical manifestations . For exaectively . These iGenerally, effective detection and monitoring are necessary for infectious disease management . This stAvian Influenza caused by H9N2 avian influenza virus (“H9N2 Avian Influenza” referred to as “H9N2” below) can pose a serious threat to both the poultry industry and public health safety, and an effective and rapid diagnostic method for H9N2 occurrence and development is urgently needed for prompt prevention and control.This study collected information on H9N2 occurrence and development and correlated production data from a large-scale concentrated laying hen farm located in Beijing. Trained and experienced veterinarians, who routinely conduct disease identification and abnormity diagnosing, helped to confirm the H9N2 cases (including the start and end of this epidemic) with their experiences and the results of the avian influenza virus antigen detection card based on the colloidal gold method.immu), and the treatment for H9N2 infection were confirmed, recorded, and provided by a veterinarian. Veterinarians treated the laying hens with drugs from the day that the first H9N2-infected bird was detected until a non-H9N2-infected bird was confirmed.Data were collected from 3 infected laying hen houses every day from the period of October to December 2012, covering the whole onset cycle of each outbreak. The numbers of days of having H9N2 and non-H9N2 were 37 and 88, with a case-to-control ratio of approximately 1:2, fitting the typically recommended ratio of observational studies of disease incidence . VariablXGBoost is 1 of the latest tree-boosting algorithms based on a novel sparsity-aware algorithm and weighted quantile sketch to develop a “strong” learner using all prediction results of “weak” learners ,28 with Hyperparameters, such as the regularization parameters in XGBoost, can greatly affect the performance of a model . In thisAll of the XGBoost classification algorithm running, hyperparameters tuning, and statistical analysis were conducted using Python software 3.6 and the scikit-learn toolkit. The examples of Python codes and collated datasets were provided as The overall workflow for the prediction task is given in Laying hens inevitably suffer different diseases or breeding equipment failures in the normal breeding process, which will affect the normal laying rate and mortality changing regulars. In order to avoid the impact of outliers in long-term production data on the classification performance of the model and to reduce the data requirement for small sample events such as infectious disease outbreaks, this study tried to investigate the suitable time frame using for H9N2 status confirming. Given that H9N2 can cause sudden changes in production performance, choosing long-term data is not obviously helpful for the prediction of disease outbreaks; this study set 5 time frames rolling windows of today (day + 0), the previous 1 day (day − 1), the previous 2 days (day − 2), the previous 3 days (day − 3), and the previous 4 days (day − 4) to study the suitable time frame for H9N2 status confirming. In each time frame case, 3 XGBoost models were built to predict coupled future 3 days’ values, of which model #1 would predict today’s status , model #2 would predict the next day’s status , and model #3 would predict the future 2nd day’s status . The H9N2 status was defined into 2 categories, “no” (marked as 0) and “yes” (marked as 1), of which “no” meant no H9N2 case, and “yes” meant having an H9N2 case. Due to each prediction being targeted on a 3-day period, a time series 3-day rolling window was applied to create input and output variables in train or test datasets, as shown in TP), true negatives (TN), false positives (FP), and false negatives (FN), of which the sum of TP, TN, FP and FN was the total number of observed days in 3rd laying hen house (N). Accuracy rate, recall rate, precision rate, and the area under the curve (AUC) of the ROC were used to assess the performances of the XGBoost models [(1)Accuracy rate (ACC)There were 4 generally used criteria calculated to evaluate and compare the prediction capacity of developed models with different time frames. This study defined the 0 status (no H9N2 case) as negative and the 1 status (having H9N2 case) as positive. The agreement of the results between the actual and predicted status was described by the number of true positives (t models :(1)Accur(2)Recall rate (RR)(3)Precision rate (PR)AUC of ROCAUC is an indicator of the discriminative or predictive ability of prediction models. The prediction ability could be generally judged based on the AUC values: AUC = 0.5, no discrimination; 0.5 < AUC < 0.6, poor discrimination; 0.6 ≤ AUC < 0.7, fair discrimination; 0.7 ≤ AUC < 0.8, acceptable discrimination; 0.8 ≤ AUC < 0.9, excellent discrimination; and AUC ≥ 0.9, outstanding discrimination . A highemax values from 3 to 24 °C and Tmin values from −10 to 9 °C; the temperature of house 3 was significantly lower than the other 2 houses (p < 0.05). The average RH range of 3 houses was from 35 to 65%. With the QRimmu above 80%, the extreme values of laying rate, mortality, and Nab-hens were 84.1%, 0.20%, and 265 hens, respectively, after the hens were infected with H9N2.The descriptive statistics associated with the variables about the information of H9N2 occurrence and development and correlated production data are shown in The data from house 1 was taken as an example to reflect the basic changing of environment and production variables in A higher AUC value shows a better prediction performance of the evaluated model. Analyzing Due to the best performances of models to predict H9N2 status + 0, In the present study, the XGBoost classification algorithm was applied to H9N2-related data to develop models designed to predict the H9N2 status among laying hen houses using temporal variables and production variables as predictors. The predictive performances of XGBoost models were evaluated and compared.Studies proved low temperatures would help avian influenza viruses survive in the environment to facilitate viruses’ introduction and outbreaks . Meanwhip < 0.01). Besides, H9N2 would cause high mortality associated with co-infection with other pathogens [p < 0.01).Since the hens were infected, H9N2 would spread to the whole house, and H9N2 had risen to be the most prevalent subtype of avian influenza virus in China, causing great economic losses because of the reduced egg production ,36. Cleaathogens , such asathogens , H6N1 [3athogens , H7N7 [3athogens , H7N9 [4athogens , and H10athogens . H9N2 coathogens . In our Furthermore, 1 or 2 days before the H9N2 had been detected, the laying rate and mortality changed abnormally compared with the data from the days before. In this stage, H9N2 was not detected, which might be because of its low concentration, or because it caused such little impact on production to fail to raise the alarm among farmers and veterinarians. For epidemics, a delay of 1 or 2 days can lead to a wider impact on animal health and performance, especially the H9N2 can serve as vehicles by donating their gene segments to other influenza A viruses  to causeSeveral studies have reported computational fluid dynamics modeling, air dispersion modeling, and transmission network modeling on HPAI using past outbreaks of infectious viral animal diseases to determine the contribution of airborne transmission towards HPAI spread  during iDifferent from analytical models used in finding the relationship between different variables , the epiOnce the animal suffers from a disease, the production performance usually changes suddenly. Unlike time series cross-validation models, this study chose five time frames with a fixed number of days rather than the accumulated days to predict H9N2 status. Compared with ACC, RR, PR, and AUC of different models, the models of time frames with more days generally showed worse and fluctuant performances in predicting H9N2 status + 0 and +1, which might be due to more additional and useless information introduced into the model training. For the prediction of a binary outcome, imbalanced classification often occurs when many outputs belong to one of two classes . As showFurthermore, in XGBoost classification models, the laying rate had higher importance than mortality features; this was consistent with the conclusions of the impact of H9N2 on poultry production , where tThis study provided an acceptable and reliable framework to predict H9N2 status from +0th to +2nd days with a high accuracy rate, recall rate and AUC within the cross-validation. Different from the prediction generated by traditional production curves ,52, thisAt present, Python software used in this study is free and open access, which has the ability to handle different types of data, from small to large datasets. Moreover, many open-source machine learning platforms are available and easy to operate only if the users have basic computer or statistical programming knowledge .This study developed a framework using XGBoost classification models and demonstrated that their abilities with different time frames were capable of predicting H9N2 status in individual laying hen houses in advance within a 3-day period. H9N2 status prediction models worked well with accuracy and sensitivity ranging from excellent to outstanding, of which an accuracy rate > 90%, a recall rate > 90%, a precision rate > 80%, and an AUC ≥ 0.85 could be achieved. Among them, the models of day + 0 and day − 1 were highly recommended to predict H9N2 status + 0 and H9N2 status + 1 for direct or auxiliary monitoring of the occurrence and development of H9N2. Authorities and veterinarians may consider applying the XGBoost classification algorithm to the currently used computer-based animal epidemic surveillance system or farm management system to enhance its early warning capability. Furthermore, the framework utilized in this study may also be considered for application to data from other infectious diseases, which have similar impacts on production performances.


Maintaining the structural integrity of the cochlea and preserving residual hearing is crucial for patients, especially for those for whom electric acoustic stimulation is intended. Impedances could reflect trauma due to electrode array insertion and therefore could serve as a biomarker for residual hearing. The aim of this study is to evaluate the association between residual hearing and estimated impedance subcomponents in a known collective from an exploratory study.A total of 42 patients with lateral wall electrode arrays from the same manufacturer were included in the study. For each patient, we used data from audiological measurements to compute residual hearing, impedance telemetry recordings to estimate near and far-field impedances using an approximation model, and computed tomography scans to extract anatomical information about the cochlea. We assessed the association between residual hearing and impedance subcomponent data using linear mixed-effects models.p < 0.001). No significant effect of far-field impedance was found.The progression of impedance subcomponents showed that far-field impedance was stable over time compared to near-field impedance. Low-frequency residual hearing demonstrated the progressive nature of hearing loss, with 48% of patients showing full or partial hearing preservation after 6 months of follow-up. Analysis revealed a statistically significant negative effect of near-field impedance on residual hearing (−3.81 dB HL per kΩ; Our findings suggest that near-field impedance offers higher specificity for residual hearing monitoring, while far-field impedance was not significantly associated with residual hearing. These results highlight the potential of impedance subcomponents as objective biomarkers for outcome monitoring in cochlear implantation. With more than 1 million implanted devices worldwide, the cochlear implant (CI) is the most successful treatment for patients suffering from partial to complete deafness –4. HowevReliable biomarkers are needed for continuous monitoring and adaptation to changes in residual hearing, especially for electric acoustic CI recipients . ImpedanIn an exploratory study, we demonstrated the association between clinical impedances and residual hearing . The terWe performed a refined retrospective analysis of residual hearing and impedance subcomponent data from the same cases as previously reported in Wimmer et al. . The stuAccording to the standard clinical procedure, we used a clinical audiometer along with insert earphones or a headphone to measure pure tone air conduction hearing thresholds in dB hearing level (HL) at seven frequencies . All audiological measurements were performed in an acoustic chamber. We calculated residual hearing as the absolute difference between the maximum detectable hearing levels by the audiometer  and the measured hearing thresholds at the corresponding frequency. Low-frequency Pure tone average (PTA) was computed as the mean of residual hearing at frequencies between 0.125 and 1 kHz.Impedance telemetry data were recorded using the standard clinical protocol of the manufacturer's telemetry software  and were recorded in the same sessions as pure tone audiometry. In addition, to analyze the by-case progression of impedances over time, we retrieved all available impedance telemetry data from the day of implantation up to a maximum follow-up time of 100 months. The clinical electrode impedance can be subdivided into two components: near-field and far-field impedance. The near-field impedance is associated with the local cochlear microenvironment around the stimulating electrode. The far-field impedance provides information about the electrical return path through biological tissue from the stimulating electrode to the ground electrode on the implant body . We estiWe used the open-source software 3D slicer  to measuAOC and BOC are the cochlear base length and width subtracted by 1 mm, respectively.where We used a linear mixed-effects model to assess the relationship between near and far-field impedances and residual hearing. For each case, repeated measurements were taken on different days after cochlear implantation. To account for the dependence of observations, we used a case-level random intercepts model, which allows the intercept to vary per case, i.e., for each case a unique effect is added to the overall intercept. Random slopes of impedance subcomponents allowed different by-case slopes for the independent variables.(near-field impedance + far-field impedance ∣ case-level)]. We compared the model depending on impedance subcomponents to a second model depending on the total clinical impedance and the same fixed effects as in the first model.First, we created a model with residual hearing (in dB HL) as the dependent variable and impedance subcomponents (in kΩ) as the independent variables. In addition, the follow-up time (in months), the implanted side (left vs. right), the gender , the electrode array type , the cochlear duct length (in mm), and age (in years) were included as fixed effects. We added interaction terms between the impedance subcomponents and follow-up time, as impedance changes are associated with follow-up time . We assuR2), and the amount of explained variance due to grouping [using the Intraclass correlation coefficient (ICC)]. The statistical analysis was performed in the RStudio environment (The models were compared in terms of the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), the amount of explained variance of the dependent variable by the independent variables . Of the 24 female and 18 male patients, there were 21 left and right-sided implantations, respectively. With 26 cases, the majority were implanted with a FLEX28 electrode array. Partially inserted electrode arrays were present in six cases . After activation for 6 months, the median word recognition score was 100%  for the German Freiburg numbers test and 58%  for the German Freiburg monosyllabic word test see .We analyzed impedance subcomponents progression based on 635 recordings with a mean follow-up time of 24 months (ranging from 21 to 99 months). Mean far-field impedances were more stable over time compared to mean near-field impedances . In general, mean levels of far-field impedances were higher at the apical than at the basal electrodes. Eight cases showed patterns of impedance deviation that were not seen in the other cases . We obseThe progression of residual hearing and impedances  over time shows, for most of the 42 cases, a negative correlation between the two variables see –4. Preopp < 0.001; p < 0.001). The FLEX28, FLEXSoft, and Standard electrode arrays were associated with significantly lower preoperative residual hearing than the FLEX24 electrode array . The interaction of time with both near-field and far-field impedances showed an association with residual hearing . No significant effects of far-field impedance, side, gender, age at implantation, or cochlear duct length were found.We included a total of 152 audiological measurements and the same number of concurrent telemetry recordings from the 42 cases in the linear mixed-effects models for residual hearing. Near-field impedance had a statistically significant negative effect on residual hearing . The comparison of the two linear mixed-effects models resulted in lower AIC and BIC indices of the near and far-field model compared to the clinical impedance model (see R2 of the near and far-field model was 2% higher (R2 = 87%), while the marginal R2  was the same for both models. The ICC of the near and far-field model was 3% higher compared to the clinical impedance model (ICC = 76%).The linear mixed-effects model depending on clinical impedance resulted in the same significant effects on residual hearing as the near and far-field model see . Clinicaodel see . In addiNear-field impedances increased strongly until the first month after implantation , which wWe propose to assign the unusual patterns of near-field impedances  to three groups . The lonSlopes of residual hearing progression over time were negative in all cases except for case 18 see . SeventyRa in cases with loss of residual hearing, while the polarization impedance Zp remained stable. In our study, near-field impedance consists of the polarization impedance Zp and the bulk resistance Rb (Rb is part of the access resistance Ra). This contrasts with Leblans et al. . Because the improvement in explaining variance is small (2%), the subcomponent model does not provide substantially better performance on group level. However, the model can explicitly demonstrate the isolated contributions to long-term variation of the near- and far-field components and serve as a basis for further investigation. In this context, we consider near-field impedance to be most clinically relevant. In addition, individual long-term variations in far-field impedances can be isolated that could otherwise have affected the accuracy of the model.Near-field impedance showed a strong association with residual hearing  as an additional fixed effect . The model resulted in the same significant effects on residual hearing, except for the FLEXSoft electrode array see . The effIn contrast to Wimmer et al. , we did During model checks, we observed collinearities between independent variables. High Variance inflation factors (VIFs) were present for the near-field impedance  and the interaction between follow-up time and far-field impedance . As VIF might be inflated in the presence of interaction terms , we checWe could not measure near and far-field impedances directly but estimated them from the recorded impedance matrix using an approximation model . EstimatThis study did not investigate the influence of electrode insertion depth on residual hearing. As electrode location is a potential biomarker for intracochlear trauma and hearing preservation , this coIn this study, we showed that near-field impedance is strongly associated with postoperative residual hearing. This supports our hypothesis that impedance subcomponents provide a more specific analysis of the cochlear microenvironment and hearing performance compared with conventional impedance telemetry. As far-field impedance was not associated with changes in residual hearing, we concluded that residual hearing is primarily influenced by the local cochlear microenvironment around the electrode contacts. To further improve modeling accuracy for residual hearing, future studies will need to use advanced measurement techniques for direct measurement of impedance subcomponents.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Kantonale Ethikkommission Bern Universität Bern (ID 2019-01578). Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.SS: methodology, software, formal analysis, visualization, and writing—original draft. PA: methodology and writing—review and editing. SW: data curation and writing—review and editing. MC: resources and writing—review and editing. WW: conceptualization, methodology, formal analysis, writing—review and editing, project administration, and funding acquisition. All authors contributed to the article and approved the submitted version.


Autoimmune inner ear disease typically presents with bilateral hearing loss that progresses over weeks or months though its mechanisms are unknown. Corticosteroids are the first-line treatment, but their responses are variable and relapses are frequent. Thus, many experts have sought to replace corticosteroids with immunosuppressive agents.A 35-year-old woman experienced a progressive hearing impairment, initially on the left side and later becoming bilateral. Her response to corticosteroid monotherapy was temporary, and there have been two relapse episodes over several months.Autoimmune inner ear disease was considered due to evidence of autoimmunity combined with a clinical course of bilateral and recurrent sensorineural hearing loss and a partial response to corticosteroid therapy.The patient received a 3-day mini-pulse of methylprednisolone at 250 mg/d, followed by 12 mg/d maintenance, and concurrently began an azathioprine regimen gradually increasing to 100 mg/day as a corticosteroid-sparing agent.Three weeks after immunosuppressive therapy, hearing and pure-tone audiometry improved, and after 7 weeks, methylprednisolone was tapered to 8 mg/d. The dosage was further reduced by adding methotrexate at 7.5 mg/week, resulting in a reduction to 4 mg/d as maintenance therapy after 4 weeks.For patients who are unresponsive to corticosteroids or experience difficulty tolerating them, a combination therapy of methotrexate and azathioprine is recommended as a viable alternative as this regimen is well-tolerated and yields positive outcomes. Autoimmune inner ear disease (AIED) typically presents with bilateral hearing loss that progresses over weeks or months, but the mechanisms underlying AIED remain unknown. Corticosteroids  are considered the first-line treatment; however, treatment responses are variable. Besides, many patients experience frequent relapses when using corticosteroid monotherapy. Due to the above reasons, many experts tried to use immunosuppressive agents to replace the use of corticosteroids. This article reports a woman with AIED, whose response to corticosteroid monotherapy was transient, accompanied by 2 relapse episodes over months. We prescribed methotrexate and azathioprine as combination therapy, which led to a significant improvement in her pure-tone average. We recorded all pure-tone audiometry (PTA) data to evaluate the response to treatment. In addition, we reviewed the literature to assess the effects of current immunosuppressant therapy options for improving AIED.A 35-year-old woman experienced progressive left-sided hearing impairment, which started in October 2014. At the time of initial symptom presentation, the patient was pregnant at 16 weeks of gestation and refused to accept medical treatment after seeking medical attention due to fears that the medication posed a pregnancy risk. Her left hearing function deteriorated gradually until she reported profound left-sided hearing loss at 31 weeks of gestation. She delivered a healthy full-term baby in April 2015. Due to her concern about the potential adverse effects of drug treatment on breastfeeding infants and her preservation of normal hearing in her right ear at that time, she did not seek any treatment postpartum until progressive right-side hearing impairment developed starting in March 2018 , cochlear Meniere disease (sudden hearing loss with fluctuation), or cochlear (otosclerotic) deafness (progresses in years). Corticosteroids are the first-line treatment for AIE.,3 Although about 70% of patients may initially respond to corticosteroid therapy, the treatment effect often decreases over time. Finally, only 14% of patients had real therapeutic responses to corticosteroids.–8AIED is an uncommon disease that typically presents with bilateral hearing loss but is sometimes asymmetrical. The clinical features of AIED are variable and can include fluctuating or progressive hearing loss (over a period of weeks or months), sometimes accompanied by functional vestibular impairment. AIED can present as an isolated inner ear disease or as secondary to systemic autoimmune disorders.,9,10 An AIED diagnosis depends on the clinical presentation and responsiveness to corticosteroid treatment, with no currently available serologic marker  and biological agents  are potential options for refractory AIED.Corticosteroids remain the mainstay treatment for AIED.,29–32Cyclophosphamide is no longer frequently used to treat AIED due to its side effect profile and poor results in hearing improvement.,33 While methotrexate appears effective for treating vestibular symptoms, the hearing response to methotrexate can vary, with 0 to 70% of cases experiencing hearing improvements.–37Methotrexate has been used as an alternative treatment for refractory AIED due to better long-term tolerability than cyclophosphamide and fewer side effects than corticosteroids. Noguchi et al suggested that azathioprine combined with oral corticosteroids could be used for hearing losses that preferentially compromise low frequencies and gradual deterioration, such as in patients with hearing loss and Bechet disease. A longitudinal and observational descriptive study showed that the relapse rate of patients treated with azathioprine was 0.52 relapses/year, with 50% relapsing by 9.70 months. In contrast, the relapse rate of patients treated with corticosteroid therapy was 2.01 relapses/year, with patients relapsing at 5.25 months on average . The response was defined in line with the recent consensus on hearing improvement as a threshold shift of ≥15 dB at 1 frequency, ≥10 dB at 2 or more consecutive frequencies, or a 12% change in discrimination score within 3 months of commencing the therapy.There are some limitations in this case. First, the diagnosis of AIED is very challenging due to the lack of clear serological tests and biomarkers. We diagnosed AIED based on its typical clinical presentation, disease course, and the exclusion of other possible diseases. Second, although the patient had a good response under methotrexate and azathioprine combination therapy, we can’t well illustrate the special prognostic factors in this patient. Third, we didn’t try other immunosuppressive agents as steroid-sparing agents due to the awareness of possible side effects.AIED is an uncommon disease. Although corticosteroids are often prescribed as the first-line treatment, treatment responses are variable, and many patients experience a relapse when using corticosteroid monotherapy. We recommend using combination therapy with methotrexate and azathioprine as steroid-sparing agents for patients who are refractory to corticosteroids alone in treatment or have poor tolerability of corticosteroids, as this regimen is well-tolerated with good treatment response.Conceptualization: Po-Chang Wu.Data curation: Kuan-Hsuan Huang.Formal analysis: Kuan-Hsuan Huang, Hsiao-Ching Lin.Validation: Chia-Der Lin.Writing – original draft: Hsiao-Ching Lin.Writing – review & editing: Chia-Der Lin, Po-Chang Wu.


We can hear low and high notes and both quiet and loud sounds. We are also very good at telling the difference between sounds that are similar, like the speech sounds “argh” and “ah,” and picking apart sounds that are mixed together, like when an orchestra is playing. But how do human hearing abilities compare to those of other animals? In this article, we discover how the inner ear determines hearing abilities. Many other mammals can hear very high notes that we cannot, and some can hear quiet sounds that we cannot. However, humans may be better than any other species at distinguishing similar sounds. We know this because, milliseconds after the sounds around us go into our ears, other sounds come  Our ears allow us to communicate with one another and to explore the world around us. They enable us to understand speech, hear music, and keep ourselves safe. They help other animals hunt, eat, and avoid being eaten. Most of the work of hearing is done inside our heads, in the inner ear, which contains the most intricate and rapidly moving parts in the body. Sound moves through the air as vibrations and is caught by the part of the ear we can see—the outer ear. The vibrations then move down the ear canal, through the middle ear, and into the inner ear. Here, the vibrations are converted into electrical signals that travel along nerves to the brain. The brain then works out what is making the sound, where the sound is coming from, and whether we need to do anything about it. Hearing these vibrations tells us that something out there is making a sound, but it also tells us a lot about the sound: is it an adult asking us if we have done our homework, or a friend asking whether we want a cookie? Most animals can hear some sounds that humans cannot hear, but it turns out that human hearing is remarkable in a different way.frequency of the sound. We measure frequency in Hertz (abbreviated Hz), which is the number of vibrations per second. We are remarkably sensitive to the frequency of vibration, and we perceive different frequencies as different musical notes, or “pitches.” How do we do that?The sounds that travel as vibrations to the inner ear vary a lot—that is why they sound different! One important way that vibrations vary is in how fast the air is vibrating . ImagineThe rate of vibration of sound waves in the air. The number of times per second that air molecules complete a cycle of being squashed together, expand out, and back.cochlea, is a spiral tube, shaped like a snail shell. Along the spiral is a sound frequency map, laid out like a piano keyboard. Incredibly, our ears sort out sounds from low to high, much like a piano keyboard! This important discovery was made almost 100 years ago by the Hungarian scientist Georg von Bekesy. It won him a Nobel Prize in 1961. The inner ear, or The spiral shaped structure of the inner ear, where sounds are detected and organized according to frequency.Responses to sound are physically organized along the length of the cochlea, or across the surface of the brain, with orderly increasing sound frequency, like a graph see .detect sounds, not to make sounds like a piano. Instead of piano strings that make sounds of varying frequencies, the cochlea has locations along its spiral that detect vibrations of specific frequencies. Each location is most sensitive to a particular frequency and is connected to specific nerve fibers that send signals to the brain. The brain then works out the frequencies of the sounds we hear by determining which set of nerves is sending the signal.Of course, the job of the cochlea is to The cochlea sends a constant chatter of electrical signals to the brain, telling us about the frequencies of sounds in the air as they change over time. The sound from an orchestra, or from human speech, is a complex combination of vibrations—lots of frequencies all at once! Somehow, we make sense of these complex sounds most of the time.Comparing ourselves to other animals can help us better understand how hearing works. There are a few ways to measure how “good” our hearing is. One way is to look at the range of low to high sounds we can hear. Although humans cannot hear all frequencies, we can hear sounds both higher and lower in frequency than the notes on the piano. In fact, a keyboard covering the entire frequency range that a young human ear can hear  would require about 120 keys instead of the 88 found on a grand piano (32 more—you would need long arms!). However, some animals can hear much higher frequencies than people can. Cats and dogs can hear frequencies twice as high as humans . Mice hear in the ultrasonic range  but cannot really hear frequencies below 1,000 Hz, which are the frequencies important for human speech and music  cannot hear. Not only can we not hear what mice are saying, but mice cannot hear much of what we are saying, either.something is out there, but we need to know more. Is it a bus that might run you over, or a friend asking you to play? Imagine your friend is talking with the television on in the background. Can you separate the words coming from the TV from the words your friend is speaking? How selective your hearing is to sounds of similar frequencies affects how easy or di cult it is to tell sounds apart. This frequency selectivity also affects how well you can distinguish between different sounds, like the words “go” and “slow.”Being able to detect sounds tells us that Different parts of the cochlea respond to different frequencies. In a more selective cochlea, each part of the cochlea responds to a smaller range of frequencies.Until recently, scientists thought that all mammals had similar frequency selectivity. It now seems that human hearing has better frequency selectivity than the hearing of most other species. Humans quite possibly hold the world record in this respect ! Figure When testing hearing, it is difficult to measure humans and animals the same way. In humans, we can easily ask, “Is sound A different from sound B?” By varying the frequency of sounds, we can measure how accurately humans can distinguish various frequencies. It is not so easy to ask a mouse or a dog such questions! On the other hand, in animals we can directly monitor how the nerve fibers and brain cells respond to different sounds. This usually involves complex brain surgery, which makes it unsuitable for use in people.real differences in hearing or just a result of using different measurement techniques. It is possible to train animals to perform hearing tests similar to those used on humans, but it is very difficult and timing consuming. Your dog might sit down on command but imagine the difficulty of training it to sit or give you a paw when it hears two adjacent notes on the piano! Recording from the nerves of the inner ear may one day be possible in humans, but it is very difficult and has not yet been achieved.Because the measurement methods for selectivity are different for humans and animals, it can be challenging to compare results across species. When we see differences, we must ask whether they are into the ear, they come out as well! When a sound enters the inner ear, the sensory cells of the cochlea pick up the vibrations and then add more vibrations of their own, which bounce back out of the ear like an echo. These are called otoacoustic emissions and they are often used to test the hearing of newborn babies. Otoacoustic emissions can also be used to probe the frequency selectivity of the cochlea, although it involves some rather complicated mathematics. In a nutshell, the longer it takes a sound to come back out of the ear, the more frequency selective the cochlea is, and therefore the more different are the patterns of nerve firings in response to different frequencies.A solution to this dilemma came from a rather surprising technique. In 1978, a scientist named David Kemp discovered that sounds do not just go Sounds that are produced by the inner ear vibrating. These can be spontaneous or produced in response to sound. Vibrations in the inner ear result in sound at the outer ear.more frequency selective than the ears of other animals. More recently, we carefully explored whether measuring otoacoustic emissions is accurate, by painstakingly making all the different types of measurements we have talked about here  in a single species: the ferret . The. The3]. e ferret . Ferretswhy human hearing is so selective.Although many species can hear sounds that we cannot, it certainly looks like humans are better at separating sounds and telling similar sounds apart than other species are. Perhaps this ability is related to our amazing skills in communicating. Outstanding frequency selectivity may well have played a role in human evolution and the development of language and communication [see this Frontiers for Young Minds article , and thiTo sum up, we have known for a long time that in terms of range of frequencies and sensitivity to quiet sounds, a human would not win the top prize in a “spot-the-sound” competition against other animals. There would likely be another animal that could spot a quiet sound more easily than us! However, we now know that human hearing is more frequency selective than other animals. So perhaps we could win the inter-species “spot-the-difference-between-sounds” competition!