This 11-cis-retinol is oxidized selectively in cones to the 11-cis-retinal used for pigment regeneration. However, the enzyme responsible for the oxidation of 11-cis-retinol remains unknown. Here, we sought to determine whether retinol dehydrogenase 10 (RDH10), upregulated in rod/cone hybrid retinas and expressed abundantly in Müller cells, is the enzyme that drives this reaction. We created mice lacking RDH10 either in cone photoreceptors, Müller cells, or the entire retina. In vivo electroretinography and transretinal recordings revealed normal cone photoresponses in all RDH10-deficient mouse lines. Notably, their cone-driven dark adaptation both in vivo and in isolated retina was unaffected, indicating that RDH10 is not required for the function of the retina visual cycle. We also generated transgenic mice expressing RDH10 ectopically in rod cells. However, rod dark adaptation was unaffected by the expression of RDH10 and transgenic rods were unable to use cis-retinol for pigment regeneration. We conclude that RDH10 is not the dominant retina 11-cis-RDH, leaving its primary function in the retina unknown.Pigment regeneration is critical for the function of cone photoreceptors in bright and rapidly-changing light conditions. This process is facilitated by the recently-characterized retina visual cycle, in which Müller cells recycle spent all- This isomerization activates the phototransduction enzymatic cascade and leads to the generation of a light response  followed by transfer of the recycled 11-cis-retinal to both rod and cone photoreceptors in the adjacent retina , is believed to participate in the esterification of 11-cis-retinol, facilitating its storage in Müller cells19. Finally, cellular retinaldehyde-binding protein (CRALBP) expressed in Müller cells was found to play an important role in regulating the delivery of chromophore from Müller cells to cones20. However, the enzyme of the retina visual cycle responsible for the oxidation of 11-cis-retinol in cone photoreceptors remains unknown. As only cones, not rods, can oxidize cis-retinol to the cis-retinal needed for pigment regeneration21, this unidentified cone-specific 11-cis-RDH is likely to be important in regulating access to the retina visual cycle.Despite the clear functional significance of the retina visual cycle, some of its key molecular components remain unknown. A recent study suggested that dihydroceramide desaturase-1 (DES1) is the Müller cell isomerase responsible for converting all-rd7 mouse retina can employ the typically cone-specific retina visual cycle5. Notably, these hybrid rods also can use 11-cis-retinol for pigment regeneration and dark adaptation, suggesting that rd7 rods might express the unidentified cone-specific 11-cis-RDH. At the start of the current study, differential expression analysis of rd7 and wild-type retinas by RNA-seq revealed upregulation of retinol dehydrogenase 10 (Rdh10) in the rd7 retina. RDH10 is a 38 kDa short-chain dehydrogenase/reductase (SDR) family member, previously reported to be expressed in the RPE and Müller cells24. Notably, RDH10 is predisposed towards oxidation and can use cis-retinoids as substrate25, both attributes fitting the profile of the unknown enzyme that oxidizes 11-cis-retinol in cone cells. Recently, the selective deletion of RDH10 in the RPE was found to delay both regeneration of rod visual pigment and rod dark adaptation26, indicating a role for this enzyme in the canonical RPE visual cycle. However, the possible involvement of RDH in the retina visual cycle and its function in the retina have not been investigated.Unlike wild-type rods, hybrid rod photoreceptors in the In this study, we sought to determine whether RDH10 is necessary and sufficient for enabling photoreceptor cells to utilize the retina visual cycle. We carried out loss-of-function experiments with mice lacking RDH10 in their cone cells, Müller cells, or the entire retina, and gain-of-function experiments with mice expressing transgenic RDH10 in their rod cells, to examine the potential role of RDH10 in the intraretinal visual cycle and cone function.rd7 mouse, which lacks the rod developmental transcription factor Nr2e327, can use the retina visual cycle5. Equally important, this study demonstrated that unlike wild-type mouse rod cells, rd7 rods can employ the presumptive product of the intraretinal visual cycle, 11-cis-retinol, for pigment regeneration. Because the form of retinoid used for pigment regeneration is 11-cis-retinal, these results suggest that hybrid rd7 rods have gained the normally cone-specific ability to oxidize 11-cis-retinol to 11-cis-retinal. Thus, rd7 rods represent a unique model to potentially identify the RDH enzyme that drives the oxidation of 11-cis-retinol in cones.A recent study showed that hybrid rods in the rd7 mice27 which failed to reveal a likely RDH candidate gene for the oxidation of 11-cis-retinol. We next performed RNA-seq analysis of wild-type and rd7 retinas and identified 416 genes significantly upregulated in rd7 retinas 28. By crossing Rdh10flox/flox mice with the cone-specific Hrgp-Cre mouse line29, we generated cone-specific RDH10 knockout mice. Similarly, the Pdgfra-Cre line30 and the Six3-Cre line31 were used to knock out RDH10 in Müller cells and the entire retina, respectively. Finally, to investigate if RDH10 is sufficient for photoreceptors to use the retina visual cycle, we also generated transgenic mice that express RDH10 in rods (Rdh10+ mice).To facilitate the expression analyses and subsequent functional characterization of the role of RDH10 in cone pigment regeneration, we generated several mouse lines with altered RDH10 expression. To determine if RDH10 is necessary or sufficient for enabling photoreceptors to oxidize 11-Rdh10 mRNA by RT-PCR. Rdh10 mRNA levels were substantially reduced in retinas obtained from Pdgfra-Cre Rdh10flox/flox mice , an ~37 kDa protein was detected in lysates from wild-type retinas but was absent from tes Fig. , left. Anas Fig. , right. ion Fig. , left. Iion Fig. , left anion Fig. , right. Rdh10+ retina, it was not downregulated in Six3-Cre Rdh10flox/flox retinas, suggesting that it corresponds to a cross-reaction of the RDH10 antibody. Accordingly, this band was later identified as Kinectin1 by mass spectrometry (data not shown). Although the reason for the upregulation of this protein in Rdh10+ retinas remains unclear, this lack of antibody specificity is likely the reason for our inability to unambiguously visualize the expression of endogenous RDH10 in cones from retinal cross-sections (not shown).Our RDH10 immunoblot analysis also revealed an immunoreactive band with an apparent molecular weight of 150 kDa Fig. . Even thcis-retinol as part of the retina visual cycle, its deletion in cone cells should prevent access to this pathway, potentially causing chromophore deficiency and the degeneration of cone cells. Here, we recorded and compared cone responses from Hrgp-Cre control mice and Hrgp-Cre Rdh10flox/flox mice, in which the expression of Rdh10 should be abrogated in cone cells. To isolate the cone component of the response, all recordings for this and subsequent cone experiments were performed with mice lacking the rod transducin α subunit (GNAT1). Removal of Gnat1 in mice (Gnat1−/−) prevents rods from generating a light response, leaving only the cone-driven response, while preserving the morphology of the retina32.We began our physiological analysis by determining if RDH10 is required by cone photoreceptors for their normal function. If RDH10 is the only enzyme that oxidizes 11-in vivo revealed comparable cone b-wave maximal response amplitudes between control mice  recordings ice Fig.  and miceice Fig. . This reice Fig. . Togetheex vivo transretinal recordings. To isolate the cone-driven a-wave component of the transretinal signal, synaptic transmission was blocked by adding the mGluR6 agonist DL-AP4 to the perfusion solution34. The so obtained cone responses from control Hrgp-Cre  and in mice lacking RDH10 in the entire retina display normal dark-adapted response waveforms  in Müller cells as the first functionally confirmed molecular component of the retina visual cycle20. However, the rest of this molecular pathway remains uncharacterized. RDH10 expressed in RPE and Müller cells has been proposed to support the visual cycle24. A recent report demonstrated that the deletion of RDH10 selectively in RPE cells indeed compromised rod function26. Here, we examined the possible role of RDH10 as the cone-specific 11-cis-RDH. Surprisingly, considering the robust expression of RDH10 in the retina, its targeted deletion in cones, Müller cells, or the entire retina did not affect the overall viability and visual function of cone cells, and, critically, did not suppress their dark adaptation. These findings suggest that RDH10 is not involved in the recycling of chromophore in the retina and does not play a role in cone pigment regeneration. Alternatively, it is possible that redundancy in retina RDHs, similar to the case in rod photoreceptors35 and RPE cells26 prevented us from observing a cone phenotype in the retinas of RDH10-deficient mice. Indeed, RDH10 and another RDH, retSDR1, have been reported to activate each other in a reciprocal manner to maintain retinoid homeostasis40.It has long been known that cone cells dark adapt faster than rod cellscis-RDH(s) in mammalian cone cells and to determine the function of RDH10 in the retina visual cycle. Because the deletion of RDH10 in the entire retina, including Müller cells and cones, in the current study did not affect cone function and dark adaptation, it is unlikely that RDH10 enables Müller cells to produce 11-cis-retinal for use by cones or enables cones to oxidize 11-cis-retinol produced by Müller cells. While the function of RDH10 in the adult retina remains unclear, it has been suggested that it might support retinal development by producing the retinaldehyde substrate for retinoic acid biosynthesis41.Our study presents the first attempt to identify the 11-Rdh10+ rod cells did not demonstrate an acceleration in their dark adaptation, and rod-driven in vivo ERG responses remained normal, suggesting that unlike rd7 rods, these transgenic rods were unable to use 11-cis-retinol produced by the retina visual cycle. To rule out restrictions in the flow of retinoids between Rdh10+ rods and the processes of Müller cells36, we also directly tested the ability of rod cells expressing RDH10 to utilize cis-retinol for pigment regeneration. However, we observed no evidence of pigment regeneration. These findings clearly demonstrate that RDH10 is insufficient for cis-retinol oxidation and access to the retina visual cycle.Transgenic cis-retinol oxidation enzyme in cone cells. Are there other candidates for the 11-cis-RDH? A recent study demonstrated that in the salamander, oxidation of cis-retinol occurs in the outer segments of cones42. Therefore, the cone-specific cis retinol RDH would be expected to be localized in the outer segments of cones. As reviewed by Parker and Crouch, both retSDR1 and RDH8 are found in cone outer segments, whereas RDH12 is in cone inner segments43. A more recent review of RDHs44 includes information on RDH13, expressed in human, monkey, and mouse rod and cone inner segments45 and RDH14, localized in the outer segments of bovine rods and cones, as well as in Müller cells46. The cell specificity of RDH14 in mouse retina and its subcellular distribution have not been determined. Considering that RDH8, RDH12, and RDH13 are also expressed in rod cells, they are unlikely to be the cone-specific RDH that mediates access to the retina visual cycle, leaving retSDR1 and RDH14 as potential candidates. Based on a recent biochemical study of carp cones, RDH13L also has been proposed to be the essential 11-cis-RDH47, suggesting a similar role for its mammalian homolog, RDH1448. The physiological role of RDH14 has not been examined to date and therefore, future studies should focus on evaluating RDH13L in fish and RDH14 in mammals as likely cis-retinol oxidation enzymes of the cone-specific retina visual cycle. The functional role of retSDR1, which reciprocally modulates RDH10, also remains to be established in cone photoreceptors.Our extensive results demonstrating that RDH10 is not required for the function of the retina visual cycle leave open the question of the identity of the Hrgp-Cre29, the Müller cell-specific Cre mice, Pdgfra-Cre30, and the retina-specific Cre mice, Six3-Cre31 were obtained from Jackson Laboratory . Rdh10flox/flox mice28 were crossed with Hrgp-Cre mice to delete RDH10 selectively in cone cells. Müller cell-specific RDH10 knockout mice were generated by crossing Rdh10flox/flox mice with Pdgfra-Cre mice. Finally, Rdh10flox/flox mice were crossed with Six3-Cre mice to delete RDH10 in the entire retina. The cone-specific Cre and retina-specific Cre experiments were performed with mice lacking the rod transducin α subunit , which greatly facilitated the isolation of the cone component of the light response without compromising retinal morphology32.All experimental protocols were in accordance with the Guide for the Care and Use of Laboratory Animals and were approved by the Washington University Animal Studies Committee and the Case Western Reserve University Animal Care Committee. Unless otherwise specified, experiments were performed with 2-4 month-old mice of both sexes using their littermates or age-matched animals as controls. The cone-specific Cre mice, Rdh10 mice (Rdh10+) were generated by the Transgenic Core in the Department of Ophthalmology and Visual Sciences at Washington University. The transgene of the mouse Rdh10 cDNA was introduced by the human rhodopsin kinase promoter49 in order to drive RDH10 expression ectopically in rod photoreceptors. The mouse Rdh10 sequence was described in a previous study22.Transgenic g. Thirty microliters of each supernatant were used per well for immunoblotting analyses.The RDH10 antibody was obtained from ProteinTech, Rosemont, IL (#14644). Both retinas from each mouse were collected in 200 µl 2X Laemmli buffer (2% (w/v) SDS, 62.5 mM Tris-HCl pH 6.8, 25% (w/v) glycerol, 0.04% (w/v) bromophenol blue) supplemented with 5% (v/v) β-mercaptoethanol and mechanically disrupted with zirconium beads  for 3 min at speed 6. Homogenates were then boiled and centrifuged for 10 min at 17,000 In vivo electroretinography (ERG) and ex vivo transretinal recordings were performed to measure photoreceptor function and dark adaptation as previously described50. Prior to these tests, mice were transferred to a light-proof cabinet with water and food and dark-adapted for at least 18 h. For in vivo ERG experiments, dark-adapted mice were anesthetized by intraperitoneal injection of 100/20 mg/kg ketamine/xylazine cocktail and placed on a heating pad with thermal feedback to avoid hypothermia. Eyes were treated with 1% (w/v) atropine sulfate and 2.5% (w/v) hypromellose solution  to dilate the pupils and ensure proper contact with the electrodes. Contact-lens electrodes were then carefully attached to the cornea for recording the transretinal voltage signals relative to a needle electrode inserted under the scalp. After stabilizing the baseline in darkness for 15 min, ERG light responses were triggered and recorded by using a UTAS Visual Diagnostic System with BigShotTM Ganzfeld . Flash intensities of the Ganzfeld LED were calibrated with an S471 optometer . To maximize the photoactivation of mouse rhodopsin (502 nm) and M/L cone visual pigment (508 nm)51, 530 nm wavelength Ganzfeld flashes were used for light stimuli below 25 cds/m2 and white xenon flashes were used for flash intensities higher than 25 cds/m2. Multiple sweeps per flash intensity were averaged and analyzed with a custom-programmed Excel document.2 followed by cervical dislocation. Eyes were immediately enucleated and the corneas and lenses removed. Retinas were dissected from the rest of the eye and placed in a Petri dish filled with Locke’s solution, mounted into a custom-made recording chamber52, and perfused with 33 °C Locke’s solution, containing 0.1% minimum essential media (MEM) vitamins and 0.1% MEM amino acids  and bubbled with O2/CO2 (95%/5%). Retinas were perfused for 15 min before the recordings to allow signal stabilization. Test flashes of 512 nm wavelength to selectively excite M/L cone visual pigment were generated with a custom-built LED system. Retinal flash responses were amplified by a differential amplifier , low-pass filtered at 30 Hz by an 8-pole Bessel filter , digitized at 1 kHz , and then recorded and analyzed with Axon pCLAMP software (Molecular Devices). The results of all electrophysiological experiments were graphed with Origin .For transretinal recordings, dark-adapted mice were euthanized with COin vivo ERGs and 3 seconds of 512 nm for ex vivo transretinal recordings to achieve ~90% photobleach), immediately followed by a series of timed test flashes to estimate the sensitivity (Sf) and/or maximal response amplitude (Rmax) of photoreceptors as previously described50. The sensitivity and maximal response amplitude values were then normalized to their corresponding pre-bleached dark-adapted levels (SfDA and RmaxDA). To explore the efficiency of the visual cycle of RDH10-null retinas during and after a prolonged photobleach, Six3-Cre Rdh10flox/flox mice were also exposed to 15 min of 300 cd/m2 530 nm light before recording their recovery as previously described37.For dark adaptation experiments, eyes or dissected retinas were briefly exposed to bright green LED light  for 2 min to photobleach 50% of the rhodopsin. Bleached retinas were immediately transferred to another dish containing Locke’s solution supplemented with 100 µM 9-cis-retinol  or 9-cis-retinal (MilliporeSigma), 0.1% (v/v) ethanol and 1% (w/v) lipid-free bovine serum albumin (to promote the delivery of hydrophobic retinoids to rods) and incubated in an oxygenated dark box at room temperature for 3 h. Finally, single-cell suction recordings were obtained from rods as previously described5.Sensitivity recovery measurements from rod photoreceptors were performed as follows. First, retinas obtained from dark-adapted mice were transferred to a Petri dish filled with Locke’s solution supplemented with 0.1% (w/v) lipid-free bovine serum albumin  to promote the release of toxic all-RNA-seq. Retinas were harvested from 3-week-old C57BL/6J and rd7 mice. For each strain, two biological replicates, each consisting of 8 retinas from two males and two females, were collected and processed as previously described53. The library was subjected to one lane of sequencing on the Illumina HiSeq 2000 (single 50 base pair reads) at the Genome Technology Access Center at Washington University School of Medicine. Reads were mapped to mm9 using TopHat v1.4.1 and analyzed with Cufflinks v1.3.0 for transcript assembly and differential expression55. A total of 1.9 × 108 reads were generated, of which 1.7 × 108 were mapped, and biological replicates showed a high degree of correlation (r2 > 0.97). Genes were identified as significantly differentially expressed at a false discovery rate (FDR) of 5%.qRT-PCR. Retinas were dissected from mice and total RNA was isolated with the RNeasy Mini kit . Total cDNA was synthesized with a quantitative cDNA Kit (Qiagen). qRT-PCR was performed with SYBR Green Master . Gene expression was normalized with Gapdh.Raw and processed RNA-seq data have been deposited in Gene Expression Omnibus .Supplementary InformationSupplemental Table 1


Tinnitus describes the subjective perception of a sound despite the absence of external stimulation. Being a sensory symptom the majority of studies focusses on the auditory pathway. In the recent years, a series of studies suggested a crucial involvement of the limbic system in the manifestation and development of chronic tinnitus. Regarding cognitive symptoms, several reviews addressed the presence of cognitive impairments in tinnitus as well and concluded that attention and memory processes are affected. Despite the importance for social communication and the reliance on a highly functional auditory system, speech comprehension remains a largely neglected field in tinnitus research. This is why we review here the existing literature on speech and language functions in tinnitus patients. Reviewed studies suggest that speech comprehension is impaired in patients with tinnitus, especially in the presence of competing noise. This is even the case in tinnitus patients with normal hearing thresholds. Additionally, speech comprehension measures seem independent of other measures such as tinnitus severity and perceived tinnitus loudness. According to the majority of authors, the speech comprehension difficulties arise as a result of central processes or dysfunctional neuroplasticity. Tinnitus describes the perception of a sound in the absence of physical stimulation and is also called a phantom percept. As tinnitus is characterized as a sensory and auditory phenomenon, most studies addressed dysfunctions of the auditory pathways. Several studies suggested that not only peripheral, but also central auditory systems and dysfunctional neuroplastic processes as a consequence of deafferentation contribute to the development of the impairment Møller, . ConsequDue to neuroplasticity and the strong linkage of auditory pathways, the limbic system and central processes, it is not surprising that a range of cognitive processes are impaired in tinnitus patients. To date, there are three reviews with each having a slightly different focus  model  AND (“speech perception” OR “speech recognition”)) NOT (implant* OR transplant*) NOT review NOT Schwannoma NOT cancer NOT sudden NOT Menière) was undertaken in the PubMed and PsycINFO databases as well as the Cochrane Library in September 2016. Additional articles were obtained through the references of studies identified during the initial search . Even though peripheral hearing loss was similar in both groups, the THL group performed worse than the HL group in speech-in-noise tasks, particularly for difficult items. There was no correlation between speech perception abilities and perceived handicap. The authors suggested that tinnitus should be considered as a symptom which may also have central causes rather than only peripheral damage. Hearing loss does not even have to be present for the occurrence of impairment in speech perception as reported by Soalheiro et al. , thus arguing for no peripheral impairment, performed worse than normal hearing controls, both when sentences were displayed in silence and with competing noise, i.e., there was no influence of hearing condition. Similar findings were reported on 15 Dutch speakers wearing cochlear implants suffering from one-sided tinnitus . The only study that did not find evidence for speech comprehension impairments is by Tugumia et al.  to simpler sentences, the interaction with working memory can be tested. The use of text passages requiring frequent or rare shifts of attention could evidence the interplay of speech processing with attentional requirements. Eye-tracking proved as a useful technique to measure shifts and the locus of attention and could therefore be applied in tinnitus-research to investigate the interplay of speech processing and attentional focus (e.g., Andersson et al., DI, BM and CD: literature search, conception of review; wrote the manuscript. OG-L, GFV and GS: conception of review; wrote 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.


Telocytes (TCs), a recently discovered novel type of interstitial cells, were also found in a wide variety of human and mammalian reproductive organs/tissues, including uterus, oviduct and placenta. Previously, we demonstrated that TCs-conditioned media was capable of activating peritoneal macrophages (pMACs) through paracrine effects. This study investigates the hypothesis that direct interaction of TCs with pMACs will also play a significant role in immunoregulation of pMACs.TCs and pMACs were derived from the uterus and intraperitoneal cavity of female BALB/c mice, respectively. TCs were identified by immunofluorescence and then co-cultured directly with pMACs for 24 h without added cytokines, to observe the in vitro biological behavior of pMACs. We used histochemical staining to study morphology and mitochondrial metabolism of pMACs, scanning electron microscopy to study heterocellular junctions, flow cytometry to investigate mitochondrial membrane potential (ΔΨm) and apoptosis, and transwell chambers to study invasion ability. Student-t test was used accordingly.P < 0.05). Meanwhile, direct physical cell-to-cell interaction promoted the development of heterocellular junctions between TCs and pMACs. Furthermore, TCs treatment markedly reduced the depletion of ΔΨm in co-cultured pMACs , and inhibited their apoptosis (P < 0.05). Functionally, pMACs co-cultured with TCs showed enhanced invasion ability (P < 0.05).Presently, TCs with typical structure and immunophenotype of double CD-34-positive/vimentin-positive were successfully isolated. pMACs co-cultured with TCs showed obviously morphological activation, with enhanced energy metabolism , led to the enhanced treatment efficacy of EMs in a simple and clinically feasible fashion. Telocytes (TCs), a special type of interstitial cells, firstly identified by Popescu’s group, were also found in female reproductive organs, including the uterus, fallopian tubes, placenta –11. TCs In our previous animal studies, with the aid of transmission electron microscopy, TCs was found to have specific ultrastructural features and was essential for the maintenance of the structural and functional integrity of oviduct tissue. Meanwhile, by their Tps, TCs connected to various activated immunocytes  via heterocellular junctions both in normal and disease-affected oviduct tissues , 7. FurtOn the other hand, macrophages are the most important and widely distributed immunocytes, and are also a key component of innate immunity located in the peritoneal/pelvic cavity. They can remove various benign and malignant cell components from the female reproductive tract, to prevent immune-related gynecologic diseases, such as endometriosis (EMs), infertility, pre-cancerous and cancerous diseases. Defective immunosurveillance of female pelvis, especially the immune dysfunction of pMACs, such as pathological accumulation and/or functional alterations of pMACs, probably changes the survival, growth and spread of autologous endometrial cell debris contained in the retrograded menstrual blood, thus contributing to the pathogenesis of EMs, and immune-mediated reproductive problems –50.To further reveal unique immunological properties of TCs, this study investigates the hypothesis that direct interaction of TCs with pMACs will also play a significant role in immunoregulation of pMACs. Currently, pMACs were directly co-cultured with TCs and further investigated their biological behavior in vitro, including morphology study by histochemical staining, heterocellular junctions by scanning electron microscopy (SEM), mitochondrial membrane potential (ΔΨm) and apoptosis assay by flow cytometry, invasion ability by transwell chambers assay. Such knowledge will be helpful to add more information on local immunoregulation/immunosurveillance roles of TCs and potential functional consequence on immune-related EMs, encourage the development of novel diagnosis and treatments for diseases with proved immune etiology.Female BALB/c mice (20–25 g) aged 8–10 weeks, were obtained from the Animal Research Center of Soochow University, Suzhou, China. Mice were housed in a specific pathogen-free environment with full access to water and food before experiments. All experimental protocols were approved by The Institutional Animal Care and Use Committee at Soochow University.3 fragments and incubated in DMEM/F12  supplemented with 0.1% collagenase type II (Sigma-Aldrich) for enzymatic dissociation at 37 °C, combined with mechanical dissociation with mild agitation through a Pasteur pipette every 15 min. After 90 min, 10% FBS (Gibco) was added gradually to quench the enzymatic reaction. Collected cells were then centrifuged (302g for 10 min) and re-suspended in DMEM/F12 with 10% FBS, seeded in plastic 25-cm2 culture flasks  and incubated in a humidified CO2 incubator (5% CO2 with 95% air) at 37 °C for 2 h to prepare for TCs monolayer attachment. Then the culture medium was replaced with a complete medium which was changed every 48 h thereafter. After 3–4 days of primary cell culture, uterine TCs were transferred to 6-well culture plates containing 2.5 ml of serum-free DMEM/F12 to reach a final concentration of 5 × 104 dissociated cells/well.Uterine tissues were harvested from the mice killed with excessive sodium pentobarbital  under sterile conditions. Then tissues blocks were washed three times with phosphate buffered saline (PBS) , cut into 1 mmFresh TCs was harvested and smeared on slides, then fixed in 4% paraformaldehyde for 20 min, washed with PBS three times and permeabilization with 0.5% Triton X-100 for 15 min, then blocked in 3% bovine serum albumin (BSA) for 1 h. After that, cells were incubated with primary antibodies: rat anti-vimentin  and rabbit anti-CD34  overnight at 4 °C. After washed three times with PBS, cells were exposed for 1 h at 37 °C to goat anti-rat lgG-FITC  and goat anti-rabbit IgG-CY3 , then counterstained with DAPI . Finally, cells slides were mounted with antifade medium  and photographed under a fluorescent inverted microscope .5 dissociated cells/well) was inoculated into 6-well plates to directly co-cultured with TCs (5 × 104 dissociated cells/well) at a ratio of about 10:1. The co-cultured TCs and pMACs were examined for morphologic alterations at the time points of 0, 12 and 24 h, respectively and imaged under light microscope .Briefly, BALB/c mice received an intraperitoneal injection of 2 ml of 3% thioglycollate (Sigma-Aldrich) for pMACs enrollment. Then 3 days later, mice were killed with an overdose of sodium pentobarbital via a subcutaneous injection, then pMACs were harvest from the intraperitoneal cavity and isolated, counted microscopically using trypan blue dye. Then pMACs  at room temperature for 10 min, then washed with PBS again and imaged under light microscope.2 at 37 °C). Then, the fluorescence intensity of the co-cultured system was detected three times in each group under fluorescence microscopy . Meanwhile, same amount of pMACs without TCs served as control conditions. Mean fluorescence intensity (MFI) was semi-quantitatively analyzed by Image-Pro Plus 6.0 software , for 0 and 24 h, respectively [Mitochondria labelling was used to detect energy metabolism alterations of pMACs and TCs in the direct co-cultured system. At the time point of 0 and 24 h, the co-cultured cells (same cell ratio as above-mentioned) were incubated in phenol red-free DMEM, with 100 nmol/l MitoTracker Green  for 30 min, in a humidified atmosphere (5% COectively .5 and TCs 5 × 104) were harvested, smeared on slides, washed three times with PBS to remove cell debris (5 min for each change), then fixed as the following: 3% buffered glutaraldehyde (pH 7.2) for 30 min at room temperature followed by PBS washing; dehydrated through graded ethanol  (5 min for each change); then pre-processed with vacuum deposition of gold plating. TCs and pMACs were observed and imaged under HITACHI S-450 scanning electron microscope .At the time point of 24 h, the freshly direct co-cultured samples , serum-free DMEM/F12 with 80 μg/ml dexamethasone for positive control . After 24 h of apoptotic induction, pMACs in both groups were added with 5 μl of FITC-conjugated Annexin V (Annexin V-FITC) and 5 μl of propidium iodide (PI)  and incubated for 10 min at room temperature in the dark according to the manufacturer’s protocol, then 400 μl binding buffer was added to each tube. Annexin V-FITC/PI-stained cells were measured by flow cytometry within 1 h  and the acquired data were analyzed by CellQuest software. The population of Annexin +/PI + cells was defined as late apoptosis/secondary necrosis, while annexin +/PI − cells was defined as early apoptosis .6 cells/well) were inoculated into TCs (1 × 105 cells/well) in 6-well plates, with the same number of pMACs cultured alone in 6-well plates for control conditions. The non-adherent cells were removed after incubation at 37 °C for 4 h to allow pMACs to adhere to the surface of the plastic culture plates in both groups. Then, pMACs in both groups were treated with above-mentioned two stimuli (UV and DXM) and control conditions. After 24 h of apoptotic induction, pMACs in both groups were collected by centrifugation at 302g for 10 min.The ΔΨm was measured by using mitochondria-specific cationic dye  and Rhodamine-123 dye , respectively. Briefly, pMACs (1 × 106 cells/well) were incubated in DMEM/F12 with 2.5 µg/ml JC-1 in the dark at 37 °C for 30 min. Then determined by flow cytometry  immediately. JC-1 enters, aggregates in mitochondria of non-apoptotic cells and turns red, but gathered in the cytosol of apoptotic cells and turns green. The ratio of green to red fluorescence intensities reflects tendency of negative changes in ∆Ψm.First, JC-1 was employed to analysis mitochondrial depolarization in pMACs. After induction of apoptosis, pMACs in both samples (1 × 106 cells/well) were incubated with 1 mM Rh-123 at 37 °C for 0.5 h. Then washed with cold PBS three times and immediately analysis by FL1 channel of flow cytometry for the detection of fluorescence intensity, which showed a positive tendency to ΔΨm.Second, Rhodamine fluorescence was employed to analyze mitochondrial membrane polarization of pMACs. Similarly, after induction of apoptosis, pMACs in both samples . The average number of pMACs invasion on the lower surface of membrane was counted in five individual random high-powered fields (400×) for each membrane and images were captured under phase-contrast microscope. Assays were performed in triplicate for each treatment group.In order to observe invasion ability of pMACs in response to TCs exposure, Transwell chambers assay based on Matrigel invasion was performed . The upper chambers were filled with serum-free DMEM/F12 and mixed with Matrigel  (1:7 dilution) to solidify at room temperature for 3 h. pMACs was seeded onto the top chamber in 400 µl serum-free medium to reach a concentration of 5 × 10t test was applied for two independent samples. A difference with P < 0.05 was considered statistically significant.The data were expressed as mean ± standard deviation (SD), and statistical analysis was performed by using SPSS software . Student’s Typical uterine TCs can be identified by characteristic structures and immunophenotype after 3 or 4 days of primary cell culture. TCs demonstrated with positive vimentin fluorescence (green)/CD34 fluorescence (red) in its cellular body and the whole length of Tps, which was characterized with alternations of thin (podomer) and thick segments (podom) Fig. a, b. AndIn a co-cultured system, under phase contrast microscopy, TCs showed typical slender piriform-shaped cellular body, and one or more extremely long, thin Tps, with characteristic podomer and podom Fig. . NeverthOn the other hand, pMACs changed gradually and indicated significant activation during the whole process Fig. a–c. FirsP < 0.05). Meanwhile, in Fig. P > 0.1).Energy metabolism of pMACs and TCs was detected by mitochondrial labelling and semi-quantitative analysis of MFI for each group at 0 and 24 h, respectively. As indicated in Fig. Based on the observed phenomenon that: (a), no special morphologic changes occurred for TCs, while obvious activation of pMACs at 24 h; (b), the ratio of pMACs and TCs was 10:1 in the co-cultured system; (c), more importantly, no significant difference of the background MFI for cultured-alone pMACs in two time points, therefore, here we can approximately take the total MFI changes as the value of pMACs. Thus, the above results indicated active energy metabolism of pMACs at 24 h in the co-cultured system.After co-cultured for 24 h, under SEM observation, TCs can be identified with a small cellular body and characteristic moniliform branched TP. The activated pMACs with irregular shape can be observed with an abundant fluff-like structure on the surface of cell membrane. Most importantly, SEM reveals direct heterocellular junctions between TP and activated pMACs . When exposure to UV, the co-cultured pMACs (32.43 ± 6.452%) was significantly lower than cultured-alone pMACs (43.7 ± 5.048%) (P < 0.05); and in DXM group, the co-cultured pMACs (48.3 ± 7.163%) was significantly lower than cultured-alone pMACs (61.07 ± 2.684%) (P < 0.05). Thus, under three conditions, the considerably low percentage of apoptotic pMACs were detected in the co-cultured system compared with those of cultured-alone pMACs.Apoptosis of pMACs in both groups, which were treated with above-mentioned two stimuli (UV and DXM) and control conditions, were examined by Annexin V-FITC/PI double staining and flow cytometry. The apoptotic proportion was defined as the percentage of cells with the fluorescence of Annexin +/PI − and Annexin +/PI + . As shown in Fig. In order to investigate potential pathway of apoptosis of pMACs, which were treated with above-mentioned two stimuli (UV and DXM) and control conditions, ∆Ψm was further studied by JC-1 and Rhodamine fluorescence, respectively. As indicated in Fig. P < 0.05). Such results showed that loss of ∆Ψm in pMACs was markedly decreased by TCs.First, for JC-1 analysis, as shown in Fig. P < 0.05). Such results showed that ∆Ψm in pMACs was markedly increased by TCs.On the other hand, similar results were observed in Rhodamine fluorescence assay. As shown in Fig. P < 0.05). Moreover, the pMACs were obviously activated with polyhedron shapes and typical pseudopodia  increased significantly than in the cultured-alone groups (2.286 ± 3.592) Fig. a–c  are increasingly recruited to the microenvironment of EMs site. This initial phases of pMACs are considered the major effectors cells of host defense system, playing an indispensable role to recognize, remove or clearance of endogenous ectopic endometrial cellular debris. While in late stage of EMs, the recruited pMACs infiltrates into the endometriotic lesions, where they undergo alternative activation (i.e. M2) , 63. ThiThe obtained results were consistent with our previous in vitro evidence, in which pMACs can be activated by TCM . Both ofAs we know, current therapeutic strategies for EMs are mainly surgery and hormone drugs, with many unavoidable side-effects. On the other hand, TCs transplantation (or together with stem cells) have been shown to be potentially efficacious for treatment of acute myocardial infarction , 36. AmoTaken together, these data demonstrate that, direct physical cell-to-cell interaction facilitates the formation of heterocellular junctions between TCs and pMACs, presumably responsible for the observed activation of pMACs via mitochondrial signaling pathway. However, a possible involvement of mitochondrial K+ channels, the role of oxidative stress in the signaling pathway of apoptosis in pMACs, and the complex cross-talks among these apoptotic pathways, are still to be elucidated.Nevertheless, this in vitro study in turn opens an interesting field of TCs biological behavior. TCs-educated pMACs are important mediators in the local immune microenvironment and will be a key factor in the pathological process of EMs. Our findings not only propose the possibility of a new efficient way of pMACs activation, but also provide a promising application of TCs-educated pMACs for treatment of EMs in a simple and clinically feasible fashion.


Behavioral resistance protects insects from microbial infection. However, signals inducing insect hygiene behavior are still relatively unexplored. Our previous study demonstrated that olfactory signals from microbes enhance insect hygiene behavior, and gustatory signals even induce the behavior. In this paper, we postulated a cross-talk between behavioral resistance and innate immunity. To examine this hypothesis, we employed a previously validated behavioral test to examine the function of taste signals in inducing a grooming reflex in decapitated flies. Microbes, which activate different pattern recognition systems upstream of immune pathways, were applied to see if there was any correlation between microbial perception and grooming reflex. To narrow down candidate elicitors, the grooming induction tests were conducted with highly purified bacterial components. Lastly, the role of DAP-type peptidoglycan in grooming induction was confirmed. Our results demonstrate that cleaning behavior can be triggered through recognition of DAP-type PGN by its receptor PGRP-LC. Drosophila. Drosophila clean themselves vigorously (i.e. grooming) when touched with bacterial extracts, or with bitter chemicals (like quinine), but not with sugar or water , thi, thiE. ctration) . In contre test) . Groominre test) . TCT is Taken together, these results suggest that peptidoglycan from Gram-negative bacteria plays a role in inducing the grooming reflex. In all following experiments, we kept using the standard LPS from Sigma because it is a cheap and convenient source of peptidoglycan.Drosophila senses Gram-negative bacterial peptidoglycan via two PGRPs, membrane-bound PGRP-LC and intracellular PGRP-LE -PGRP-LC;PGRPLCE12 rescue clearly recovered the response in comparison with w;;PGRP-LC mutants  . There ware test). We alsoare test). The resRNAi against PGRP-LC did not show the response  and white w1118 flies were used as wild-type controls. As for immunity mutants, w;;PGRP-LCE12 (in w1118 background or with Cantonized 2nd chromosome) y, w, PGRP-LE112 and w;;PGRP-LB△ were used. PGRP-LCE12, PGRP-LBΔand PGRP-LE112 lines have been described previously [eviously , 41–43. y, w;act5-Gal4 driver line (DGRC# 107727) was crossed to w;;UAS-PGRP-LCx or to w;;UAS- PGRP-LCRNAi. In Gal4-UAStests, balanced siblings were employed as controls.The Gal4-UAS system  was usedEscherichia coli and Acetobacter aceti, the Gram-positive bacteria Lactobacillus bulgaricus, Listeria monocytogenes and Mycoplasma fermentans, and the entomopathogenic fungus Beauveria bassiana.We tested the Gram-negative bacteria E. coli strain of TOP 10 was grown in liquid LB medium at 37°C. For heat-killed preparations, E. coli was washed with distilled water and heated at 95°C for 5 minutes. A. aceti ATCC 53264 was grown in liquid SH medium at 28°C. A. aceti was washed by distilled water and heated at 95°C for 5 minutes. 7.9 x 109 /ml bacterial suspension was diluted 100, 102, 104 and 106 fold. L. bulgaricus was grown in liquid MRS medium at 40°C, then it was washed by distilled water and heated at 95°C for 5 minutes.The Listeria monocytogenes and Mycoplasma fermentans were purchased from InvivoGen,  and adjusted to 1.0 x 109 / ml. The suspensions were diluted 100, 102, 104 and 106 fold. B. bassiana was grown in liquid LB medium for fungi. B. bassiana was washed by distilled water and heated at 95°C for 5 minutes. All microbial suspensions were adjusted to highest concentration, and then was diluted 100, 102, 104 and 106 fold. They have a pH of about 7.0.Heat-killed E. coli was purchased from Sigma (L2630)[E. coli was purchased from InvivoGen (tlrl-eblps). Peptidoglycan from the Gram-positive bacterium Bacillus subtilis and the soluble ß-glucan laminarin were purchased from InvivoGen . Peptidoglycan from E. coli and TCT were gifts from Dominique Mengin-Lecreulx . All chemical suspensions have a pH of about 7.0.Standard LPS from a (L2630), and higBriefly, 10 flies were beheaded by a single cut made at the neck with micro-scissors. Micro-scissors were washed and wiped by 70% ethanol before and after use. Beheaded flies were placed in an upright position on a clean paper sheet and allowed to recover. To stimulate them, the wings, front legs, or hind legs were gently touched with a sharpened toothpick that was previously soaked in a test solution. To avoid contamination, the paper sheet was changed between each test, and a new toothpick was sharpened before each test. Grooming behavior after touching by toothpick was observed, and its intensity scored as 0, 1, 2, or 3. A score of 0 indicated no grooming, and a score of 1 or greater indicated that grooming occurred. Because grooming duration has been shown varied widely in previous studies, the strength of induction was scored as follows: 1, grooming that stopped within 10 seconds; 2, grooming that lasted more than 10 seconds and less than 20 seconds; and 3, grooming that lasted over 20 seconds. Four-day-old flies were used in testing. Each substance was tested on 20 females and 20 males (n = 40).To examine concentration-dependent increases in grooming behavior in headless flies with respect to sex, chemical, and fly strain, Chi-square test  was applied. Additionally, Dunnett’s test  was conducted to examine behavioral induction at each concentration.S1 FigE. coli is provided as a reference. This data belongs to the previous publication in Front. Behav. Neurosci. [A significant increase in response from that of the control (water) is indicated by asterisks: * indicates p < 0.05, ** indicates p < 0.01, and *** indicates p < 0.001 (Dunnett’s test). Please note that the data of CS flies to (TIF)Click here for additional data file.S2 FigStandard LPS was used as a stimulus. n = 40 (n = 20 for each sex). Data represents mean +/- SE, analyzed as in (TIF)Click here for additional data file.S3 FigPGRP-LCE12 mutants in orange circle with orange line: CS background and white circle with blue line: w1118 background. (b) Grooming behavior induced by quinine in PGRP-LCE12 mutants of orange circle with orange line: CS background and white circle with blue line: w1118 background flies. Standard LPS was used as a stimulus. n = 40 (n = 20 for each sex). Data represents mean +/- SE, analysed as in (a) Grooming behavior induced in 10-day old (TIF)Click here for additional data file.S4 FigRNAi flies black circle with black line: actGal4x-PGRP-LCRNAi and white circle with red line control (siblings). (b) Grooming behavior induced by LPS contact on front/hind legs in PGRP-LCx overexpressing flies by Gal4-UAS system. Concentration dependent 40 repetitions were conducted by two concentration range: 10−5–10−2 mg/ml range of standard LPS and 10−1–10 mg/ml range of standard LPS. (c) and (d) illustrated the grooming induction in control flies (siblings). Standard LPS was used as a stimulus. n = 40 (n = 20 for each sex). Data represents mean +/- SE, analysed as in (a) Grooming behavior induced in offspring of act-Gal4 x PGRP-LC(TIF)Click here for additional data file.S1 TableMicrobes, which induced significant concentration-dependent behavioral increase were in white zone, and no-significance in gray zone. White zone in a table shows significant difference (p < 0.05), and grey zone indicates no-significance (p > 0.05).(TIF)Click here for additional data file.S2 TableMutants, which showed significant concentration-dependent behavioral increase were in white zone, and no-significance in gray zone. White zone in a table shows significant difference (p < 0.05), and grey zone indicates no-significance (p > 0.05).(TIF)Click here for additional data file.S3 TableMutants, which showed significant concentration-dependent behavioral increase were in white zone, and no-significance in gray zone. White zone in a table shows significant difference (p < 0.05), and grey zone indicates no-significance (p > 0.05).(TIF)Click here for additional data file.S4 TableMutants, which showed significant concentration-dependent behavioral increase were in white zone, and no-significance in gray zone. White zone in a table shows significant difference (p < 0.05), and grey zone indicates no-significance (p > 0.05).(TIF)Click here for additional data file.


Specific knowledge of urinary incontinence (UI) and its interrelation with physical and cognitive health is essential to working towards prevention of UI and to improving quality of treatment and care. The purpose of this study was to determine the association between UI and the activities of daily living (ADL) hierarchy scale, the cognitive performance scale (CPS) and comorbid conditions.n ≥65 years were included  at the time of admission to a nursing home. Statistical analysis was done by means of descriptive statistics and logistic regression analysis.The cross-sectional retrospective analysis of 357 nursing homes in Switzerland was based on data of the Minimum Data Set of the Resident Assessment Instrument 2.0 (RAI-MDS). The analysis examined the effect of ADL hierarchy scale, CPS, joint motion and comorbidities on UI. WomeThe prevalence of UI was 54.7%, the mean ADL hierarchy scale (± SD) 2.42 ± 3.26 (range = 0–6), the mean CPS 1.95 ± 1.67 (range = 0–6). There was a gradual increase in the odds ratio (OR) for UI depending on the ADL hierarchy scale, from the hierarchy scales of “supervised” to “total dependence” of 1.43 – 30.25. For CPS, the OR for UI from “borderline intact” to “very severe impairment” was 1.35 – 5.99. Considering the interaction between ADL and CPS, all ADL hierarchies remained significantly associated with UI, however for CPS this was the case only in the lower hierarchies. Of the 11 examined comorbid conditions, only diabetes mellitus (OR 1.19), dementia (OR 1.01) and arthrosis/arthritis (OR 1.53) were significantly associated with UI.The study indicated that impairment in ADL performance is strongly associated with UI, more than CPS performance and comorbidities. Physical more than cognitive training in order to improve or at least stabilize ADL performance could be a way to prevent or reduce the process of developing UI. It is associated with a significantly reduced health-related quality of life  and deprng homes .UI is often multifactorial in etiology; next to physiological age-related changes in lower urinary tract function, risk factors outside the lower urinary tract, such as co-existing disabilities and comorbidities, are causing or contributing to UI –12. SpecHence, the purpose of this study was to identify whether dependency on activities of daily living, cognitive performance and comorbid conditions are associated and interrelated with UI in women at the time of nursing home admission.This is a cross-sectional secondary analysis of the healthcare data from the Minimum Data Set (MDS) of the Swiss Version of the Resident Assessment Instrument (RAI) 2.0 , 14. TheThe RAI-MDS is a widely used and standardized instrument for the assessment of the health status and care needs of old people living in a nursing home . In SwitThe RAI-MDS item on bladder incontinence was used to identify female nursing home residents with UI at their age of admission. A five-point coding scale is used to describe continence patterns depending on frequency of leakage . For our analysis, the definition of UI recommended by the International Continence Society (ICS) was utilized, which considers any involuntary leakage of urine . TherefoIn the RAI-MDS 2.0, bladder control is recorded with appliances (for example bladder catheter), thus nursing home residents presenting with a bladder catheter or ostomy were excluded from analysis see Fig. , becauseThe ADL Hierarchy Scale derived from the RAI-MDS was used to assess functional physical health. The ADL Hierarchy Scale has been shown to be a valid measure for ADL in nursing home residents. This scale is based on four items  and early-loss ADL  are assigned lower scores than late-loss ADL (for example eating). It has seven levels ranging from total independence (level 0) to total dependence (level 6) .Cognitive function was measured using the Cognitive performance scale (CPS). The CPS combines five selected items of the RAI-MDS within a hierarchical seven-category rating scale, ranging from no cognitive impairment (level 0) to very severe impairment (level 6). The five MDS items used to construct the CPS include two cognitive measures , one communication measure (making self-understood), one ADL measure (eating), and comatose status . The CPSIn the RAI-MDS, the presence of limitation in range of joint motion and loss of voluntary movement is recorded by each body part . In this work, we focused on arm, hand, leg and foot in order to detect whether a difference in the prevalence of UI exists relative to the functional impairment of upper or lower extremities.In the RAI-MDS, diseases or infections are coded that have a relationship to the resident’s current ADL status, cognitive status, mood or behaviour status, medical treatments, nursing monitoring or risk of death. The noted disease conditions require a physician-documented diagnosis in the clinical record. In this study, we focused on the following comorbidities: diabetes mellitus, arteriosclerosis, heart disease, cardiac dysrhythmias, congestive heart failure, hypertension, arthrose/arthritis, osteoporosis, dementia, cerebrovascular accident, anxiety disorder, depression, asthma, emphysema/COPD, chronic urinary tract infection and renal failure.Descriptive statistics were calculated for all measures. Logistic regression was performed to assess the impact of residents’ age at admission, ADL hierarchy, CPS, limitation in range of motion, and comorbidities on UI at the first assessment after admission to a nursing home. In a first step, a model with all selected variables included was calculated. Thereafter, the effect of every single predictor variable on the model was tested to exclude all predictor variables that do not contribute to the model and to get a more parsimonious model. As a result, the predictors of the final model were reduced to age at admission, ADL hierarchy, CPS, diabetes, arthrosis and dementia. However, as the Hosmer-Lemeshow test indicated poor model fit due to potential misspecification of the model, interaction between ADL hierarchy and CPS was additionally considered. All statistical analyses were calculated with SPSS 21  for Macintosh, using a significance level of α = .001.Among the 44’811 eligible women  of the variance in UI, and 71.3% of all cases were correctly classified.The variables of “joint motion of upper and lower extremities” were excluded in the process of model fitting. In a first model, where all selected variables were included, the analysis of the limitation in range of motion (reference: limitation on both sides of the body) showed a significant reduction in the OR in “limitation on one side of the body” related to leg  or foot . Apart from that, no more significant OR by upper as well as by lower extremities was found.A further model, including the relevant variables only, revealed an age-related difference  in UI. The odds to be incontinent were 20% higher for a female resident with an age between 75 and 84 years and 75% higher for an over 94 years old female resident compared with somebody with an age between 65 and 74 years. Of all comorbid conditions only diabetes mellitus, dementia and arthrosis/arthritis were relevant , excluded from the main analysis, were younger (mean age of 83.2 ± 7.3 years), more comorbid (comorbidities of 3.29 ± 1.92) and more impaired according to the ADL (mean 4.35 ± 1.10) and CPS scale (2.59 ± 1.92) compared to the main collective .When adjusted by the interaction between the ADL hierarchy scale und the CPS, higher levels of ADL dependence were still related to a higher OR. The odds to be UI for a female resident who was limited or dependent in ADL was more than 2 times respectively more than 4 times higher than for an ADL independent female resident. As there were fewer cases in the highest groups  with some non-existing combinations , leading to large standard errors for those estimates especially in the model with interaction . In a cohort of community-dwelling persons a positive linear association between the number of comorbid conditions and the prevalence of UI was found . The preA strength of our study is its large sample size, which is much larger than in previous reported studies , 27, 28,UI is a highly prevalent health issue, affecting more than half of the women at the time of nursing home admission. UI is a multifactorial syndrome resulting from or exacerbated by several risk factors. Management of UI should focus on modification of risk factors among nursing home residents. Our study confirms for female nursing home residents the association between ADL , 21, 22,


The main analysis was designed to identify clinically relevant auditory and non-auditory measures for speech-in-noise prediction using auditory  and cognitive tests , as well as questionnaires assessing various self-reported measures . Using stepwise linear regression analysis, 62% of the variance in unaided speech-in-noise performance was explained, with measures Pure-tone average (PTA), Age, and Verbal intelligence emerging as the three most important predictors. In the complementary analysis, those individuals with the same hearing loss profile were separated into hearing aid users (HAU) and non-users (NU), and were then compared regarding potential differences in the test measures and in explaining unaided speech-in-noise recognition. The groupwise comparisons revealed significant differences in auditory measures and self-reported subjective hearing problems, while no differences in the cognitive domain were found. Furthermore, groupwise regression analyses revealed that Verbal intelligence had a predictive value in both groups, whereas Age and PTA only emerged significant in the group of hearing aid NU.Differences in understanding speech in noise among hearing-impaired individuals cannot be explained entirely by hearing thresholds alone, suggesting the contribution of other factors beyond standard auditory ones as derived from the audiogram. This paper reports two analyses addressing individual differences in the explanation of unaided speech-in-noise performance among  Among elderly listeners, hearing impairment is typically associated with increased difficulties in understanding speech, especially in adverse listening conditions such as the presence of noise . Speech A number of studies have focused on the contribution of cognitive abilities, confirming a link between certain cognitive abilities and speech recognition in noise in older hearing-impaired listeners e.g., . When acBesides hearing thresholds and cognitive factors, further auditory measures have been linked to speech intelligibility such as measures of spectral and temporal resolution e.g., , 2007, tDespite the technological advances in assistive hearing devices, studies from high-income countries show that hearing impairment remains undertreated , particuSumming up, a growing body of evidence suggests that factors beyond standard audiometric measures account for inter-individual differences in speech recognition in noise, although results are equivocal and unexplained variance hints at the potential contribution of further factors or possibly, measurement errors. Many studies used rather time-consuming experimental methods or examined a small number of subjects. Here, we addressed these issues by investigating speech-in-noise recognition of a large sample of hearing-impaired elderly listeners, tested with a battery of different auditory and non-auditory measures. Furthermore, given that up to date a comparison between HAU and NU is missing, we differentiated between older hearing-impaired HAU and NU with the same audiogram profile for groupwise analyses. It is to point out that the test battery was originally designed to extensively test and characterize normal-hearing and hearing-impaired individuals with and without hearing aids out of a cohort, and not primarily with the aim to predict unaided speech-in-noise performance. Thus, measures were selected based on suitability for a clinical setting where measurement time is limited and thus requesting short, easily implementable, yet reliable tests.Here, two successive analyses are reported, each addressing one aim. (1) The first aim was to identify clinically relevant auditory and non-auditory measures for predicting unaided speech-in-noise recognition for a large sample of older hearing-impaired individuals, and to determine the respective  predictive value for the identified auditory -, cognitive-, self-reported-, and health measures. To that end, a full-data regression was applied on the whole sample of participants using a stepwise regression algorithm . (2) Secondly, we compared HAU and NU with the same hearing loss profile regarding potential differences in the test measures and regarding unaided speech recognition to identify whether regressors differ as a function of hearing loss profile and hearing aid use .mean = 71.1 ± 5.8 years). The included participants showed a hearing impairment range from very mild to severe .We analyzed data from 438 hearing-impaired individuals  with a sensorineural hearing loss aged 60–85 years  of the audiogram’s air conduction measurement across the frequencies 0.5, 1, 2, and 4 kHz. In case of equal PTAs for both sides, the right ear measures were selected. The only exception constituted the Adaptive Categorical Loudness Scaling measures where the average across both ears was used.Speech recognition. Speech reception thresholds in noise were measured with the Goettingen Sentence Test (GOESA) by The GOESA applied an adaptive SRT measurement  in the c50%-SRT, that is, the signal-to-noise ratio that corresponds to 50% intelligibility, served as the outcome measure for the regression analyses (see Supplementary Table 1.The Audiogram. Pure-tone audiometry was conducted via a Unity II Audiometer with HAD200 headphones: (1) air-conduction hearing thresholds at the frequencies 0.125, 0.25, 0.5, 0.75, 1, 1.5, 2, 4, 6, and 8 kHz, (2) bone-conduction thresholds at the frequencies 0.5, 0.75, 1, 1.5, 2, and 4 kHz, and (3) uncomfortable loudness level at the frequencies 0.5, 1, 2, and 4 kHz. For measuring the uncomfortable loudness level, participants were presented with 1-s pure tones starting at a dB level above threshold and increasing in steps of 5 dB. They are instructed to press a button when their uncomfortable loudness level is reached. The total audiometric testing time was 13 min.PTA across the frequencies 0.5, 1, 2, and 4 kHz was submitted as potential predictor variable. Moreover, the Uncomfortable loudness level (audiogram) at the single frequencies 0.5, 1, 2, and 4 kHz entered the analyses , Medium loudness level for 1.5 and 4 kHz (at 25 CU), and Uncomfortable loudness level for 1.5 and 4 kHz (at 50 CU). Additionally, the slope of the lower part of the fitted loudness function, which is associated with loudness recruitment, entered as potential predictor  in steps of five, from which levels for particular loudness categories can be derived. A monotonically increasing loudness function was fitted to the responses for each of the ACALOS measurements using the BTUX fitting method . This reVerbal intelligence. Cognitive performance was assessed with the German vocabulary test  was used as an exclusion criterion in case of suspected dementia (0–8 points). For regression analyses, the participants’ raw scores of each subtest were used instead of the age-normed scores, as we wanted to relate the inter-individual age-related changes in different cognitive domains to the speech-recognition performance.The self-report measures were obtained from a questionnaire being completed at home by the participants and a face-to-face anamnesis interview at the Hörzentrum. The 11-page home questionnaire contained numerous questions regarding the main topics “hearing anamnesis” , “health status” , “technology commitment” (see below), and “demographic questions” . The variables entering the analyses are given in the Supplementary Table At the beginning of the test session at the Hörzentrum, trained diagnostic staff furthermore performed an anamnesis which encompassed more detailed questions regarding the current hearing aid use as well as questions relating to education, occupation and income with reference to the socio-economic status. Checking the home questionnaire and completing the face-to-face anamnesis took about 13 min.Health status: SF-12 and multimorbidity. The home questionnaire included the German version of the SF-12 Health Survey that constitutes a multipurpose self-reported generic measure of health status and health-related quality of life  and Mental sum score (SF-12)  and demographic (2) measures, self-reports (17), as well as cognitive (6), health (3), and economic-technical (2) measures. Just like the selection of measures, group formation and labeling was done in a knowledge-driven approach with the subject areas not being necessarily mutually exclusive. The potential predictors entering the analyses as well as the outcome variable are shown in the Supplementary Table In a first step, we aimed to identify auditory and non-auditory measures for predicting speech perception in noise in older hearing-impaired listeners with different degrees of hearing impairment using all 438 participants. We applied a stepwise linear regression algorithm and used the very mild to severe, and further differentiating between flat/moderately sloping and steep sloping hearing loss, resulting in seven audiograms for flat/moderately sloping hearing loss  and three standard audiograms for steep sloping hearing loss , as shown in Figure 2. Applying an algorithm by Grouping variable (Bisgaard), see Supplementary Table For taking into account the different severities of hearing loss, we further categorized the participants with respect to their hearing loss profiles according to 2.5% of the data of the potential predictor variables were missing values. The number of missing values per variable is shown in the Supplementary Table Linear regression models are commonly compared by means of the Bayesian information criterion (BIC) measuring the trade-off between model fit and model complexity . Using t50%-SRT in noise. In a preliminary descriptive step, we calculated two-sided Pearson’s correlation tests (against zero) of the 50%-SRT outcome variable and the potential continuous predictor variables. As shown in Supplementary Table p < 0.01) except for the three health factors . Highest positive correlations were obtained between the 50%-SRT and the PTA (Pearson correlation coefficient r = 0.74) as well as the Hearing threshold level for 1.5 kHz of the loudness scaling (r = 0.62).The aim of this study was to identify measures beyond audiological standard measures that influence the 50%-SRT and the 43 potential predictor variables into the stepwise algorithm stepBIC. As the distribution of the 50%-SRT variable was right-skewed, the outcome variable y was transformed using the function log(y + 10), with -10 < min(y) < 0, to approximately comply with the normality assumption.Subsequently, we performed a full-data regression approach entering the In addition to the pre-processing steps mentioned above, we transformed part of our potential predictors for the data-driven full regression approach: To remove skewness for six of the continuous covariates, a logarithmic, square root or reciprocal transformation was applied . The deck-fold cross-validation. This technique uses a subset of the analysis dataset to fit a model and the remaining dataset to estimate the model performance. Here, the analysis dataset consisted of the log-transformed outcome variable, the 43 pre-processed potential predictor variables, and the Grouping variable (Bisgaard) of n = 438 individuals. Following k = 10 subsets with balanced sample sizes. First, the stepwise regression algorithm was applied to part of the analysis dataset comprising all the subsets except the first one . The resulting model and the held-out subset  were used to estimate the percentage of variance explained and the root-mean-square error. Then, this procedure was reapplied with the second subset as test dataset, and so on. To increase the precision of the estimates, we repeated this 10-fold cross-validation procedure five times, as recommended in In order to improve the predictive performance of the full-data regression approach, we applied a resampling technique called To strengthen the validity of this analysis, we additionally applied a LASSO regression approach using the glmnet-function of the R-package glmnet . We thenAll analyses were conducted using the statistical SAS software version 9.4  was 0.17.In this main analysis, we considered the whole sample of 438 individuals irrespective of degree of hearing impairment and hearing aid use. To characterize these participants regarding their speech-recognition performance in noise, we entered the log-transformed Figure 3. In all of the 50 regression models, the three variables PTA, Age, and Verbal intelligence were selected as explanatory variables for the log-transformed 50%-SRT, followed by the subjectively reported occurrence of Familial hearing loss (48 out of 50 iterations).Thirteen different variables were selected as predictor variables by the stepwise selection algorithm, as shown in Table 1. We therefore ordered the predictors thematically according to their subject areas and considered, in a first and second step, the change in the amount of R2 by adding the variables PTA and Age to the regression model, as these variables are known as measures explaining most of the SRT variance , and finally, the Grouping variable (Bisgaard). Given that the amount of the explained variance depends on the order of the entered predictors described above, the major proportion of the 50%-SRT variance was explained by the PTA (58.48%), as presented in Table 1. The Grouping variable (Bisgaard) added another 2.55%, followed by Verbal intelligence with 1.51%. The six self-reports together explained 6.58%, and the variable Age 0.96%. The least variance was explained by the further auditory measures (0.52%) and the health measure (0.14%).To examine the predictive performance of the selected variables, we calculated the variance accounted for as the respective variables were added successively to the regression model, as shown in variance . Then, fAge might cover the contribution of other variables correlated with it, we performed the same analysis without the variable Age as potential predictor. The mean variance explained across the 50 iterations was 60.04% and the mean test error was 0.18. As shown in Table 2, the resulting model is similar to the previous one with PTA and Verbal intelligence again emerging as the most frequently selected predictors (50 out of 50 iterations). In contrast, the auditory measure Uncomfortable loudness level for 2 kHz (audiogram) and the cognitive measure Wordlist (DemTect) are now selected 15 and one time, correspondingly, while the variables Ear noise and Physical sum score (SF-12) are no longer among the predictors. While the variance explained by PTA remained constant, the predictive value of the other auditory measures increased slightly to 1.19%. The selection of the variable Wordlist (DemTect) did not add to the amount of variance accounted for by the cognitive measures compared to the previous model.To test whether the inclusion of the variable Age alone, a third analysis was conducted, now only including Age as predictor. The proportion of explained variance was 5.66%.Finally, to test the predictive value of When comparing results of the linear regression approach to LASSO regression, we found that the ten most frequently selected predictors by stepBIC were consistent with the resulting predictors after applying LASSO regression to the analysis dataset.The main aim of this analysis was to identify clinically relevant auditory and non-auditory measures for predicting unaided speech-in-noise recognition for a large sample of older hearing-impaired individuals, and to determine the respective  predictive value for the identified measures. As the test battery was originally designed to characterize normal-hearing and hearing-impaired individuals while establishing a cohort, clinically relevant refers to the short duration and easy implementation of the corresponding tests in a clinical setting.50%-SRT). This result is in agreement with previous studies typically reporting around 70% explained variance , accounting together for 59% of variance when added successively to a model. This suggests that hearing thresholds contribute substantially to the variance in unaided speech-in-noise recognition for a large sample of older individuals with varying degrees of hearing impairment. Partial correlations between 50%-SRT and PTA revealed that the correlation remained almost unchanged when accounting for Age . Moreover, in a second model excluding the variable Age, PTA was again consistently selected (50 out of 50). This confirms prior findings identifying hearing loss repeatedly as the primary and best predictor for unaided speech-in noise performance in elderly hearing-impaired listeners , is an indicator of crystallized intellectual abilities and loads highly on a factor of g-factor . Thus, the contribution of Verbal intelligence to differences in speech-recognition performance may not be attributed primarily to age-related differences in crystallized intellectual ability. Instead, the present findings suggest that hearing-impaired individuals with higher verbal intellectual abilities perform better in understanding speech in noise, independent of Age. When conducting the analysis without the variable Age, the variable Wordlist (DemTect) was selected as predictor, although in only one out of 50 iterations. This immediate recall of a wordlist probes short-term verbal memory. As discussed earlier, measures of memory capacity have been recurrently linked to speech-in-noise performance , the Verbal fluency subtest, and the overall sum score in middle-aged and elderly hearing-impaired individuals in an aided condition. This association, however, failed to yield significance in the unaided condition. In our study, the selection of the variable Wordlist (DemTect) did not add to the overall variance accounted for by the cognitive measures compared to the model with Age.Compared to other cognitive domains important for understanding speech in noise, such as working memory which declines with age , Verbal lifespan . Accordiance cf. . In thisLoudness recruitment for 4 kHz  emerged as frequently selected predictor (33 of 50). Recruitment describes a phenomenon where the rate of perceived loudness with increasing level grows more rapidly than normal  in this study was marginal (<1%).Among the other auditory measures, the variable n normal . It is pn normal , 2002. Irocedure . The prerocedure . LikewisFamilial hearing loss (48 of 50), Sudden hearing loss (30 of 50), Subjective hearing problems in quiet (24 of 50), Subjective hearing problems in noise (20 of 50), Ear noise (3 of 50), as well as Hearing loss detected (2 of 50)  and the occurrence of a sudden hearing loss in the past, are related to inter-individual differences in speech-recognition performance. Furthermore, self-reported hearing problems seem relevant. Overall, findings on the relationship between subjective self-reports and the performance in “objective” speech-recognition tests vary across studies, ranging from no correlation to consistent correlations. Various factors have been investigated and discussed as possible mediators for hearing-impaired individuals, such as the use of hearing aids, the methods applied to obtain the signal-to-noise ratio, cognitive factors, and the equivalence of the specific listening situation addressed by both measures (cf. Subjective hearing problems in quiet and noise), contribute to variability in speech-in-noise performance, and might be of predictive value in addition to objective measures of hearing sensitivity as assessed by pure-tone audiometry.Moreover, several variables of the self-reports were recurrently selected as predictors, namely ures cf. . For oldures cf. . Our resThese findings are also of interest in the light of the review by Physical sum score (SF-12) (1 of 50) was selected, reflecting the self-reported physical health status of the individuals. It should be pointed out that the measure was the least frequently selected variable, and that its additional predictive value was marginal (<0.5%). This seems to relate to prior findings showing that speech perception measures correlate less with generic health questionnaires than with hearing-specific questionnaires  was repeatedly selected as relevant predictor (27 of 50) suggesting that inter-individual differences in speech performance are linked to different audiogram profiles. PTA typically using the average across the four frequencies 0.5, 1, 2, and 4 kHz, these audiograms consider the profile across 10 frequencies ranging from 0.25 to 8 kHz. These profiles allow a categorization of hearing loss based on the degree (very mild to severe) and additionally, based on the slope of the profile   cf. . The preIn this complementary analysis, we were interested in the question whether hearing-aid users (HAU) differ from those with the same hearing loss profile who have not been provided with a hearing aid yet, the so-called NU, regarding the test measures and the regressor variables for speech recognition in noise.Hearing aid use (current status) as categorization criterion for HAU and NU. 93% of the HAU indicated that they used their hearing aids every day during the last 14 days, with an average daily wearing period of 6 h, supporting that most of HAU indeed use their hearing aid(s) and are experienced. All HAU were included, irrespective of one- or two-sided hearing aids, the duration of hearing aid supply, and the daily wearing period. To characterize HAU and NU, the Sex and Age, as well as the PTA and 50%-SRT distributions are shown in Table 3.As the aim of the complementary analysis was to compare HAU to NU with the same hearing loss profile, we first categorized the 438 participants from the main analysis based on their current status of hearing aid use. Current hearing aid supply  and use  was assessed in the questionnaire and verified in the anamnesis. We aimed at categorizing the individuals regarding their status of hearing aid use and experience, and correspondingly, categorized individuals who own a hearing aid but never use it as NU. Thus, we applied the variable Figure 2, main analysis. This two-step categorization allowed for a groupwise comparison between HAU and NU showing the same type of hearing impairment in terms of their audiogram profile. This resulted in 13 subsets , see Figure 4. Due to the small sample sizes in some subgroups and unbalanced group sizes between HAU and NU, we only considered the individuals in subgroup N2, i.e., those with a mild and flat/moderately sloping hearing loss, for this analysis. Thus, we compared HAU-N2 (n = 45) to NU-N2 (n = 61) regarding the test measures and the regressor variables for speech recognition in noise.To account for differences in the degree of hearing impairment between the two groups and motivated by the results of the main analysis, we further classified the individuals with respect to their audiogram profiles according to U-Tests on the test measure variables. As this involved multiple pairwise comparisons, the Bonferroni correction was used to maintain the Type I error rate at 0.05 and the adjusted p-values are reported.In a first step, we investigated potential group differences regarding the test measures between HAU and NU with the audiogram profile N2. To that end, pairwise comparisons were conducted using independent samples Mann–Whitney-50%-SRT was more symmetric within the two groups, we did not transform the outcome variable for the regression analyses at group level. Furthermore, none of the potential regressor variables was transformed to avoid different transformations for one regressor across the groups, thus, yielding comparable results. Finally, in contrast to the main analysis, the cross-validation was omitted in the group analyses as the sample size of the analyzed groups was considered too small for performing a k-fold cross-validation. According to k-fold cross-validation generally has potential issues with high variance and bias that become negligible only for large training sets.Secondly, the stepwise linear regression algorithm was applied for the two subgroups in order to characterize HAU and NU comprising the same hearing loss profile in terms of explaining measures for speech recognition. As the distribution of the Grouping variable (Bisgaard), Hearing aid use (current status)), and the variable Duration of hearing aid supply as this variable was only meaningful for HAU.As potential regressor variables, the same variables as described in the main analysis were taken into account except for the two variables that were used for categorization . Thus, the unaided speech-recognition performance of HAUs was on par with the performance of NU with the same mild hearing loss. Likewise, groups did not differ significantly in the variable Age, as shown in Figure 6.The distributions of PTA (padj < 0.001) , Hearing threshold level for 4 kHz  (padj < 0.001), and Hearing threshold level for 1.5 kHz  (padj < 0.001). More precisely, HAU-N2 showed higher values than NU-N2 in all these variables. Furthermore, HAU-N2 and NU-N2 differed significantly in the self-reported measure Subjective hearing problems in quiet (padj < 0.001), such that HAU-N2 reported more subjective hearing problems without hearing aids than NU-N2. The other auditory, cognitive and self-reported measures did not reveal significant differences.Pairwise comparisons revealed significant group differences for the auditory variables Table 4. For HAU-N2, 35% of the variance in the 50%-SRT was explained by the model. Verbal intelligence emerged as the most important regressor as the stepBIC algorithm selected this variable first (p < 0.001) and explained 15% variance. Secondly, the health measure Multimorbidity sum score was selected (p < 0.01) explaining 14%, and lastly, the subjectively reported measure Hearing loss detected (p < 0.05) with 6% accounted variance. In the group NU-N2, 58% of the variability was accounted for by six regressors. Age emerged as first predictor (p < 0.001) explaining 27% variance. Among the remaining regressors were two subjectively reported auditory measures , two auditory ones  and the cognitive measure Verbal intelligence (p < 0.01).The results of the two linear regression models are shown in In this complementary analysis, we compared HAU and NU with the same mild hearing loss profile regarding group differences in the test measures and regarding the regressor variables for speech recognition in noise. To allow a comparison between HAU and NU, we first grouped the 438 individuals of the main analysis based on their current status of hearing aid use into HAU and NU. Secondly, to avoid the confound of stronger hearing impairment in one group and motivated by the results of the main analysis, we matched those groups based on their audiogram profile and only compared HAU and NU with the same audiogram profile (N2).First, we probed whether there are any significant differences with respect to the test measures between HAU and NU having the same hearing loss profile.mild, flat/moderately sloping hearing loss (N2) revealed significant differences for the PTA, Hearing threshold level for 1.5 and 4 kHz , as well as Subjective hearing problems in quiet. Hence, HAUs with a mild hearing loss differed from NU mainly with respect to auditory-related measures, a result that is particularly interesting given the fact that the groups were matched based on the audiogram profile, thus showing the same degree and pattern of hearing loss. The results indicate that although having similar SRTs, these groups still differ in their hearing thresholds as derived from the categorical loudness scaling. There is evidence for adaptive functional plasticity in the top-down regulation and perception of loudness in HAUs  (p < 0.05), as well as for the variables Medium loudness level for 1 and 4 kHz  (p < 0.05), such that HAU showed a trend for higher values. When applying Bonferroni correction, however, these differences were no longer significant. Likewise, no significant differences were found for the Uncomfortable loudness level. Nevertheless, elevated hearing thresholds in HAU might point toward plasticity in the auditory system.Group comparisons for individuals with a  in HAUs , indicat in HAUs  and that in HAUs  followin50%-SRTs, HAU with a mild hearing loss also showed higher self-reported Subjective hearing problems in quiet (without hearing aids). While the results of the main analysis indicated that subjectively reported hearing difficulties are linked to speech-in-noise performance, this relationship could be mediated by the underlying degree of hearing loss. The present group analysis prompts that groups matched regarding their degree of hearing loss still display differences in these subjectively reported measures. This relates to previous studies showing that individual perception of hearing problems influenced, among others, help seeking as well as hearing aid uptake and use  might relate to missing differences in cognitive abilities. Another possibility that should be outlined is the focus of this analysis on individuals with a mild hearing loss only to be able to compare HAU to NU with a matched hearing loss profile. It is possible that more severe hearing impairments might yield a different picture.As the present study is only a cross-sectional snapshot, we cannot draw conclusions on whether hearing aid use is independently associated with cognitive decline or not. The results merely indicate that the investigated groups of HAU and NU with a mild hearing loss did not differ Socio-economic status sum score. This is compliant to prior findings indicating hearing aid acquisition/uptake is mostly unrelated to the socio-economic position  and tertiary (NU-N2) regressor, Verbal intelligence showed a predictive value of 15 and 8%, respectively. The subjectively reported measure Hearing loss detected was likewise among the selected regressors in both groups, contributing in each with 6% to the overall variance. This variable might be thought of as an additional indicator of the severity of (subjective) hearing loss.In conformity with the results of the main analysis, the cognitive measure Multimorbidity sum score contributed to the SRT prediction in the sense that individual with more self-reported medical conditions showed lower speech-in-noise performance. In contrast to the main analysis, the health measure had a substantial predictive value for HAU. With 14% it explained almost as much as the cognitive measure.In the HAU-N2 group, the health measure Age constituted the most important regressor explaining the most variance (27%). While this is compliant to the results by SRT and Age after controlling for the degree of hearing loss in terms of PTA, it cannot explain why Age is only selected in the NU-N2 but not in the HAU-N2 group. Among the remaining selected regressors in the NU-N2 group was another subjectively reported measure (Sudden hearing loss) accounting for 9%, and two auditory measures , together explaining 8%. While it might appear surprising that the PTA entered as significant regressor only in the NU group, it is to point out that the groups were controlled for degree of hearing loss such that the individuals in one group displayed the same degree of hearing loss according to the aforementioned Bisgaard categorization. Apparently, PTA emerged as significant regressor for the NU-N2 group as it showed a higher variability in PTA with a number of individuals having PTA values below 26 despite this classification . Thus, a classification based on the PTA according to WHO guidelines might label those individuals as normal-hearing . Moreover, the fact that no single cognitive component always showed a significant association to speech in noise across prior studies has been discussed with respect to the variability in complexity of the cognitive tasks as well as to the variations regarding the speech-in-noise task and specific listening situations (Verbal intelligence and the missing effects of memory-related measures might lie in the unaided measurements. As aided measurements have revealed stronger associations between memory and SRT than unaided ones (Furthermore, cognitive abilities were equally selected as primary predictor, although explaining marginal additional variance. Unlike previous studies, we found a consistent association between speech-in-noise performance and pan test , we usedtuations . Anotherded ones , it is psteep sloping hearing loss which might be concealed when using the PTA for classification.The present results further suggest that auditory measures beyond pure-tone audiometry such as loudness recruitment and auditory-related self-report measures have an additional predictive value for unaided speech-in-noise prediction. Moreover, classifying the hearing-impaired individuals with respect to their specific audiogram profiles seems reasonable. The applied categorization takes into account different slopes of the audiogram such as the Despite the wide range of included measures, the overall prediction left 38% variance unexplained suggesting that further relevant measures might have been missed. This can be attributed to the fact that the test battery was originally designed for a characterization of individuals from a cohort and not primarily for unaided speech-in-noise prediction. Other factors that have been linked to speech recognition before and were not included in the battery comprise, among others, measures of spectral and temporal processing , and in When contrasting HAUs with NU in the context of the complementary analysis, the groupwise comparisons regarding the regressor variables revealed significant differences between HAU and NU with a mild hearing loss with respect to auditory measures and self-reports. These differences should be taken into account in the context of hearing aid prescription and hearing aid fittings. Hearing-impaired individuals who are not yet supplied with a hearing aid subjectively perceive less hearing problems, nevertheless they might already benefit from a hearing aid. This suggests that the awareness of one’s own hearing problems is of particular relevance for further help seeking and hearing aid acquisition. Thus, measures of subjective hearing problems should take a more prominent role in the future.50%-SRT measurement was unaided, the results may be different for aided measurements. Furthermore, groupwise analyses only focused on groups with a mild hearing impairment and can thus not be generalized to more severe forms.Some limitations should be underlined such as the observational character of these analyses. Initially, data were collected in order to characterize normal-hearing and hearing-impaired individuals, which explains the selection of the corresponding auditory and non-auditory measures. Besides the facts that secondary data were used for the analyses and that this is not a prospective study, HAU were largely overrepresented in the sample and it cannot be ruled out that the sample differed from the population in a systematic way in other aspects as well, as self-motivated hearing-aid uptake might be associated with individual characteristics that also affect speech-in-noise recognition cf. . This shThe strength of this work, on the other hand, lies in the large overall sample size which allows for a generalizability of the results regarding the overall regression analysis of the main analysis. Moreover, we included a wide range of auditory, cognitive and self-report measures simultaneously, thus being able to specifically identify relevant measures in terms of additional predictive value. At the same time, these measures are suitable for the clinical setting with a total measurement time of approximately 1 h. Furthermore, the present classification based on audiogram profiles gives a more comprehensive picture compared to the commonly used PTA by considering a wider range of frequencies and different slopes.AG and MT conceptualized the research question, analyzed the data, interpreted the results and wrote the manuscript. Data acquisition was conducted by the Hörzentrum Oldenburg GmbH under the responsibility of KW. CT and HC supervised the work. CT, KW, MM, and HC critically reviewed and significantly contributed to the manuscript. All authors approved the final version of the manuscript for publication. All authors agree to be accountable for all aspects of the work and in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.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.