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.


The involved image filtering for removing noise was chosen rather aggressively, and also removed some effects of the bone remodelling. In this paper, we quantify these filtering settings using ex vivo reproducibility HR-pQCT images, and determine the least-detectable bone remodelling using in vivo reproducibility HR-pQCT images, as well as testing whether the approach is capable of capturing age-related bone remodelling by use of in vivo long-term HR-pQCT images. We found that a threshold value of 225 mg HA/cm3 for the filtering led to acceptable results with falsely determined bone remodelling of less than 0.5%, and that the least-detectable bone formation and bone resorption are 2.0 ± 1.0% and 2.2 ± 0.7% respectively. We also found that age-related local bone remodelling can be captured satisfactorily in postmenopausal women. The latter revealed new insights into the effect of ageing on bone remodelling, and showed that bone remodelling seems to take place through a few small formation packets and many large resorption volumes leading to a net bone loss. We conclude that local in vivo bone remodelling can be successfully assessed with time-lapse in vivo HR-pQCT capable of assessing age-related changes in bone remodelling.We previously developed an image analysis approach for the determination of local sites of bone remodelling using time-lapse  The cells responsible for this adaptation process are known as osteocytes , osteoblasts (the bone-forming cells), and osteoclasts (the bone-resorbing cells) . Biochemin vivo micro-computed tomography (micro-CT) in animal experimental studies [in vivo micro-CT images. It is based on the image registration of two subsequent scans of the same animal where image voxels present only in the segmented baseline scan are considered bone resorption and voxels present only in the segmented follow-up scan are considered bone formation. Using the same approach, Brouwers et al. [in vivo micro-CT in mice and showed that local bone formation is associated with high mechanical loading whereas local bone resorption is associated with low mechanical loading. Furthermore, the same group established an approach to directly extract bone formation and bone resorption parameters from time-lapse in vivo micro-CT images and therefore a protocol for dynamic bone remodelling parameters [Recently, the assessment of bone remodelling has received increased attention using non-invasive and non-destructive  studies –8 (for a studies ). Waarsi studies  have pros et al.  studied s et al.  used timrameters , 11.in vivo in patients at peripheral sites such as the distal radius and tibia with a nominal isotropic voxel size of 82 or 61 micrometres  has the potential to characterise the 3D bone microstructure d Müller ). Recentd Müller . Howeverex vivo reproducibility HR-pQCT images, scanning was performed on a total of 15 fresh frozen cadaveric forearms . All forearms were disarticulated at the elbow, thawed and scanned three times within 48 hours. The time lag between each scan was within the same day and scanning was performed by a single operator. None of the donors were from a vulnerable population and all donors or next of kin provided written informed consent that was freely given. The study was approved by an independent Ethics Committee (Comité de Protection des Personnes Sud-Est II).For the in vivo reproducibility HR-pQCT images, image data from a previous study [For the us study  were usein vivo HR-pQCT images, scans of 9 untreated postmenopausal women (50–80 years old) with no history of previous fracture or bone-related ailments were chosen from the OFELY Cohort [For the time-lapse Y Cohort , 15. AllImaging was performed in the present and also the previous studies –15 usingin vivo micro-CT images [Several image registration algorithms have been reported for the processing of time-lapse T images , 17. HerT images , 18, 19.3) is defined and only voxels for which the change in bone density exceeds the positive and negative value of the threshold are classified as sites of bone formation and bone resorption, respectively. The other voxels are classified as unchanged sites. In the second step, starting with the classified voxels from the first step, only those voxels forming consistent clusters of at least 5 voxels are filtered. This step removes most of the noise from the images. Since the choice of the threshold value largely determines the outcome of the approach, several threshold values were tested in the present study. Note that no Laplace-Hamming filtering is performed as recommended by the manufacturer for standard HR-pQCT image segmentation because this filter would not retain the voxel grey-level-density calibration. To visualise the results, bone formation and bone resorption voxels are overlapped with the segmented baseline image resulting in a binary image containing only information inside the LCV indicating unchanged bone (grey) and local bone formation (green) and resorption (magenta). Each image was segmented individually to match the bone volume to total volume as calculated by dividing the mean trabecular density by 1200 mg HA/cm3, which is assumed to represent fully mineralised bone [The density values of the baseline and follow-up images were subtracted voxel-by-voxel within the LCV. The resulting grey-level image therefore represents differences in local densities between follow-up images. These differences can indicate local bone remodelling, but can also be related to artefacts that would be revealed in reproducibility image data such as differences in calibration between scans, errors introduced by interpolating registered images, and noise in the images. To reduce the effects of artefacts on the results, a two-step filter approach was previously developed . In the sed bone . This seex vivo reproducibility HR-pQCT images were used. The threshold value for filtering was varied from 200 to 275 mg HA/cm3 in steps of 25 mg HA/cm3 and the number of voxels classified as bone formation and bone resorption were assessed for each case. Differences in voxel values in this study can relate to errors in the reproducibility of the images, calibration errors, and errors in the 3D registration algorithm. No bone remodelling sites should be detected in the case in the reproducibility image data. This test case, therefore, quantifies the real least-detectable bone remodelling with a single scanner at a single time point, which defines that no smaller effects can be detected with the present approach.To quantify the filter setting, the in vivo bone remodelling, the in vivo reproducibility HR-pQCT images were used. Because, in this case, bone remodelling could be falsely determined as a result of motion artefacts, baseline and follow-up images (per pair) were classified into three different groups according to their image quality .To determine the least-detectable in vivo HR-pQCT images were used.To test the sensitivity of the approach for capturing the effects of ageing on long-term bone remodelling in postmenopausal women when using the parameters determined in the earlier studies, the time-lapse 3) were isolated and expressed as a percentage of the baseline bone volume (BV). For the threshold values ranging from 200 to 275 mg HA/cm3, the ex vivo least-detectable bone remodelling  was determined by averaging the percentage of resorbed and formed bone over all cadaveric forearms. The in vivo least-detectable bone remodelling  was determined for all images and then for each image quality group individually. Finally, age-related bone remodelling  in postmenopausal women was determined independently from image quality and at 2, 4, and 6 years follow-up.From the three-coloured images generated by the present image analysis, volumes defined as resorbed and formed bone (mmin vivo reproducibility scans (45 radius and 45 tibia), the image quality ranged from grade 1 to 3 . In the time-lapse in vivo age-related bone remodelling image data, no scan was excluded and overall scanning had good image quality (grade 1).Image quality was good in the observed cadaveric forearms . Among the 90 A good visual and quantitative agreement (correlation coefficient r>0.99) was obtained for all 3D image registrations. After the registration, the average percentage of the LCV retained for analysis between baseline and follow-up scans was 90.4%, ranging from 77.1% to 95.8%.ex vivo local bone remodelling expressed as a percentage of the number of baseline bone voxels ranged from 0.6% to 0.03% for threshold values between 200 and 275 mg HA/cm3  and bone resorption (-6.9%) exceeded the detection limit by a factor of about 2 or more, suggesting that already at the 1-year follow-up reliable detection of bone formation and bone resorption is possible. Since the age-related local bone remodelling revealed in our study is higher than the reproducibility of bone morphometric parameters of in vivo HR-pQCT, our results furthermore indicate that age-related changes were found. A threshold value of 225 mg HA/cm3 was chosen as the filter setting because it leads to a small and acceptable total error of less than 0.5% which is in agreement with the reproducibility of bone density of in vivo HR-pQCT [in vivo bone remodelling (2% of the baseline volume) is small enough to capture even minor changes in the bone microstructure. However, this is only true for good quality images since the least-detectable local in vivo bone remodelling is increased to 4.7% if including lower quality images. Consistent with our findings, Waarsing et al. [in vivo micro-CT in animals. They reported that their registration algorithm might cause a shift of about one pixel between positions of bone surfaces when registered scans were overlapped. For in vivo HR-pQCT, such a 1-voxel offset would result in considerably larger changes in bone density than the few percent here.We found that the filtering approach developed here leads to acceptable results: the least-detectable e-centre , 25 and e-centre  in vivo  HR-pQCT , 25. Theg et al.  reportedThe analysis of age-related local bone remodelling revealed that the rate of bone resorption was much higher than that of bone formation resulting in an average net decrease of about 1.6% per year at the cortical and trabecular level. A comparison of directly extracted volumes between bone formation and bone resorption in each woman revealed that the amount of formed bone is considerably lower than the resorbed one. This implies that, with age, bone remodelling seems to take place by few small formation packets and many large resorption volumes.Furthermore, visualisation of the three-coloured images at the 2, 4 and 6 year follow-up shows that trabeculae become progressively thinner, perforated, and then removed from the trabecular compartment due to ageing. Dalzell et al.  found thin vivo HR-pQCT image acquisition, which could result in reproducibility errors and might also affect the image registration. From previous studies, it has been reported that increased subject motion introduces errors resulting in lower bone mineral density [It should be noted that our study has some limitations. We used transformed follow-up images, which are slightly less detailed due to the interpolation required for the transformation . Present density . This isin vivo bone remodelling can be successfully assessed with time-lapse in vivo HR-pQCT. The approach is capable of capturing age-related in vivo bone remodelling over a 2-year time period indicating that the effects of disorders might also be possible to capture as the related changes would be expected to be larger.In conclusion, we found that local


LARP4B and EIF5B. Our results unravel strong therapeutic opportunities by targeting tumour dependencies on NMD-elicit mutations.Nonsense-mediated decay (NMD) eliminates transcripts with premature termination codons. Although NMD-induced loss-of-function has been shown to contribute to the genesis of particular cancers, its global functional consequence in tumours has not been characterized. Here we develop an algorithm to predict NMD and apply it on somatic mutations reported in The Cancer Genome Atlas. We identify more than 73 K mutations that are predicted to elicit NMD (NMD-elicit). NMD-elicit mutations in tumour suppressor genes (TSGs) are associated with significant reduction in gene expression. We discover cancer-specific NMD-elicit signatures in TSGs and cancer-associated genes. Our analysis reveals a previously unrecognized dependence of hypermutated tumours on hypofunction of genes that are involved in chromatin remodelling and translation. Half of hypermutated stomach adenocarcinomas are associated with NMD-elicit mutations of the translation initiators  Nonsense-mediated decay (NMD) eliminates transcripts with premature stop codons and has been linked to cancer genesis. Here, the authors develop an algorithm to predict NMD and perform a pan-cancer analysis that finds that some hypermutated cancers are dependent on mutations that elicit NMD. Nonsense-mediated decay (NMD) is a physiological cellular surveillance system that degrades abnormal mRNAs with premature termination codons (PTCs)56In this work we used three recently validated rulesTo classify TCGA mutations to NMD-elicit, NMD-escape or others, we developed a prediction algorithm based on the three aforementioned rules of eliciting NMD . For eacP<2.2e−16, one-sided t-test and P=0.1, one-side t-test). Of note, stomach adenocarcinoma (STAD), kidney cancer (KIRP) and colon cancer (COAD) had a disproportionately higher number of NMD-elicit mutations, compared to other cancers with similar mutation frequency =0.54, TP53 (23 cancer types affected) and NF1 (22 cancer types affected). However, the frequency of NMD-elicit mutations appears to be lower than other LoF mutations. For example, NMD-elicit TP53 mutations occurred in 8.8% of all cancer samples , while missense non-synonymous mutations in the same gene occurred in 22.2% of samples. NMD-elicit mutations in these genes are associated with a significant reduction in gene expression  test and z-score, see Methods). We conducted a global analysis of all reported mutations in TCGA and observed that the NMD-elicit mutations that had the lowest z-scores (highest magnitude of reduction) and the most frequency tended to occur in known TSGs or the previously reported significantly mutated genes (SMGs) in cancerATRX gene frequently harbours NMD-elicit mutations in low grade glioma and sarcoma. APC is often affected by NMD-elicit mutations in colon cancer (COAD) and rectal cancer (READ). Of particular note is that BLCA is uniquely affected by KDM6A NMD-elicit mutations when compared with other tumour types.We observed that not all NMD-elicit mutations were associated with a similar magnitude of reduction in gene expression. In addition, NMD-elicit mutations seemed to target particular genes at higher frequency than others. For example, the two genes that were most widely affected by NMD-elicit mutations were the TSGs z-scores compared to NMD-elicit mutations in other genes  that were enriched in NMD-elicit mutations in the hypermutated samples when compared to other types of mutations such as silent mutation. This analysis identified genes that were involved in DNA repair such as MSH2, MSH6 and ASCC3 . There was also an enrichment in pathways involved in RNA binding and splicing. Most notably, there were frequent NMD-elicit mutations in genes such as LARP4B, YTHDC1 and EIF5B (FDR<5e−05 and LARP4B (22/54 compared) and EIF5B (12/54) in hypermutated stomach adenocarcinoma cases. Note that 25 out of 54 hypermutated cases had NMD-elicit mutations of at least one of these two genes .We identified six tumours that had frequent occurrence of hypermutation and that were known to be frequently associated with microsatellite instability (MSI). We identified NMD-sensitive genes . Similarly, 8 out of 12 NMD-elicit mutations in PTEN occurred because of a deletion of an A . NMD-elicit mutations in PTEN were associated with a profound and significant reduction in PTEN expression . To confirm these results, we analysed data from an independent study on STAD that conducted whole-genome sequencing of 100 STADsLARP4B and three of those mutations occurred at the same site mentioned in TCGA data . Two out of the ten MSI samples had frameshift mutations in EIF5B. For PTEN, there were three cancers that had NMD-elicit mutation out of the ten MSI cancers  across all cancer types, which is consistent with previous studies1516TP53, but that they are associated with significant LoF because of reduction in expression particularly in TSGs. The association between NMD-elicit mutations in TSGs and the very low expression level further supports the viewpoint that NMD is a promising target for cancer treatment. Recent work into the mechanisms of NMD regulation has provided feasible approaches41920222324We found that NMD-elicit mutations target certain TSGs  in inducing NMD has remained unclear. Whether or not mutations in members of the NMD complex may have an impact on eliciting NMD is difficult to study on a global scale because of the low number of mutations in this complex and the lack of clarity as to whether any particular mutation in this complex is associated with LoF. In addition, our results do not take into account the potential suppression of NMD by the tumour microenvironment as recently reported35In summary, we provide a comprehensive annotation of NMD in all mutations reported by TCGA. Our work unravels unrecognized dependencies of tumours on NMD-mediated LoF with potential therapeutic opportunities37383940https://xenabrowser.net)http://gdac.broadinstitute.org)The following four files, RNA-seq data , somatic mutations annotation file  for results of DNA sequencing, CNV data  and clinical data ’ cohort at the TGCA hub and the GA4GH-BD2K (TOIL) hub on USCS Xena (We retrieved the coding sequences (CDSs) and positional annotations of transcripts provided by UCSC  via the R interface. The canonical isoform was defined as the one with the longest CDS. For each gene, we extracted the positional information from the TxDb package and the longest CDS from the BSgenome package, based on the reference genome (hg19/NCBI Build 37). The positional information included the chromosomal start and stop positions of exons. We calculated the relative positions of exon–exon junctions and the relative position of mutations on the CDSs. Combining the mutation data from TCGA and the wild-type CDSs gave the mutated sequences. For this analysis, we have excluded 22 genes with PTCs in the wild-type sequence, mutations that occur on the splice sites or on the exon–intron junctions not annotated by TCGA, mutations where the reference allele does not match the nucleic acid on the reference genome, 32 genes with CDS that do not start with start codons, which may be incorrectly annotated, and 47 genes starting with start codon but without in-frame stop codon. The reason for the first criteria is that the mRNA may have strong second structure where the NMD fails. The reason for the second criteria is that the splice-site mutations may induce alternative splicing, which affects mRNA levels in a complex way. The third criterion is to exclude the possible incorrect mutation annotation.Our algorithm first detected all possible start codons (ATA and ATG) and stop codons  in their mutated sequences. Among all of the possible start codons, the most upstream one was denoted as the putative start position. The stop position was selected by the two criteria. The first one is that the stop codon is in the same frame as the start codon, because the CDS must be the multiples of three. The second one is that the stop position is the closest one to the start codon among all the candidates.On the basis of the predicted relative positions of the stop codon and the last exon–exon junction in the variant, the classification of NMD was conducted by three rules, that the gene has at least two exons, that the stop codon in the variant is more than 50 bp upstream from the last exon–exon junction and that the PTC is more than 200 bp downstream from the start codon. If the mutation satisfied the above rules, the mutation was classified into the NMD-elicit mutation; otherwise, into NMD-escape mutations that generate PTCs, or non-PTC mutations.In addition to the aforementioned filtering, the certain types of mutations were also excluded from analysis. The first type is the mutations of genes without Entrez gene IDs or with multiple corresponding Entrez gene IDs. The second type is the repeated annotations of the same mutations, but multiple mutations within one gene in one sample are retained to avoid underestimation of NMD effect. The third type includes the mutations with TCGA annotation as ‘Splice_Site’, ‘3′UTR’, ‘5′UTR’, ‘IGR’, ‘Intron’ and ‘RNA’.We detected NMD-elicit mutations from the missense mutations, NMs, silent mutations, nonstop mutations, mutations on the translation start site, in-frame indels and frameshift indels annotated by TCGA. We compared our prediction results (NMD-elicit mutations versus others) with the original TCGA annotations . We valiStatistical test. We compared the expression levels using the nonparametric MWW test (wilcox.test in R) or the t-test. The MWW tests and t-tests without the statement of one-sided are two-sided.Calculation of REV. To validate the impact of NMD-elicit mutations on the expression levels, we calculated the relative expression of variant to mask the gene-specific effect for each mutation, including NMD-elicit and other mutations. The relative expression was defined by the rank of the expression level of the variant (the gene with a mutation) in its background expression. The gene-and-cancer-specific background expression was defined as the expression levels of a gene from these samples from the same cancer, which have no somatic mutations or CNV and belong to the same NMF cluster (Broad firehose) or from the same cancer if the NMF clustering was absent. For example, in J total samples, gene i has NMD-elicit mutations in m samples (1∼m), and does not have NMD-elicit mutations in the other n samples (m+1∼m+n). Denote ei,j as the expression level of gene i in sample j, E=ei,m+n+1,ei,m+2+2, ei,j as the background expression. The REV of each mutation of gene i in sample m can be calculated by E) denotes the number of wild-type samples from the same cancer and the Ri,m denotes the ranking of ei,m in the background expression level. The lower the relative expressions of variant, the larger the downregulation of expression of the variant. We compared the relative expression of predicted NMD-elicit mutations with the NMD-escape or non-PTC mutations in the frameshift deletions, frameshift insertions and NMs, by the one-side MWW test (wilcox.test in R).http://cancer.sanger.ac.uk/census)TTN and MUC16  for each group separately. The z-score is the statistic U normalized by N1·N2, which ranges from 0 to 1.To mask the NMD-insensitive genes that do not show decrease in their expression even with NMD-elicit mutations, we defined and calculated the z-score means the more decreased expression of the genes that harbours NMD-elicit mutations. The z-scores no less than 0.5 means the magnitude of NMD-associated reduction in expression is negligible in this gene. On the basis of z-scores, we classified genes into NMD-sensitive genes (z-scores ≤0.3), intermediate (0.3<z-scores≤0.4) and NMD-insensitive ones (z-scores >0.4),The lower We defined hypermutated samples as the ones with over 1,000 filtered mutations. The non-hypermutated samples were the ones with less than 100 filtered mutations. BLCA, COAD, LUAD, SKCM, STAD and UCEC had more than 30 hypermutated samples and were used for further analysis. The MSI data were downloaded from previous pan-cancer analysisP value by Fisher exact test, adjusted by FDR. The ratio can measure the enrichment of NMD in hypermutation. It was normalized by the total number of each mutation type to mask the negative selection on the NMD-elicit mutations. To identify important pathways or molecular functions, we took the significantly enriched NMD-sensitive genes (FDR<0.05) as the input for the gene ontology enrichment analysis (top GO in R) by the classic and weighted algorithm. The validation data set was from an independent data setTo quantify the gene-specific enrichment in NMD-elicit mutations in hypermutated samples, we calculated the normalized ratio between NMD-elicit mutations and silent mutations  between the hypermutated and non-hypermutated samples. We calculated the https://github.com/ZYBunnyHu/NMD-paper) without restriction. The R package for predicting NMD-elicit mutations, masonmd (Make Sense of NMD), are available in GitHub with the identifier (DOI: 10.5281/zenodo.546698)The pan-cancer data and the computer codes that support the findings of this study are available in GitHub .Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Previously, we found women with positive anticentromere antibody showed impaired potential of oocyte maturation and embryo cleavage; the possible mechanism behind this phenomenon was still unknown. in vitro coculture with mouse embryos.  Thus, the present study aimed to preliminarily explore whether ACA could penetrate into the living embryos and impair their developmental potential via in vitro culture with polyclonal anticentromere protein A (CENP-A) antibody; then, immunofluorescence assay was performed to determine the penetration of antibody into embryos, and embryo development potential was observed.  Mouse embryos were collected and used for  All embryos cultured with anti-CENP-A antibody exhibited immunofluorescence on the nucleus, while none of the embryos from the control groups showed immunofluorescence. Additionally, embryos cultured with anti-CENP-A antibody experienced significant growth impairment compared with controls. in vitro prior to implantation. However, the precise mechanism needs further clarification. Mouse embryos may be a direct target for ACA  A recent study revealed disorders of oocyte maturation and early embryonic development in women with positive anticentromere antibody (ACA) in their peripheral blood . More reAs early as the 1990s, researchers attempted to microinject ACA into mouse eggs, which led to disorders of chromosomal movement and segregation . It is kCentromere is a DNA-protein complex, and its assembly is coregulated by centromeric chromatins and their associated protein complex , 17. Cenin vitro coculture with mouse embryos.It was speculated that ACA might be one of the ANAs most closely associated with abnormal oocyte maturation and embryo cleavage. Therefore, the aim of the present study was to explore the potential impact of ACA on early-stage embryos via Superovulation was induced in outbred ICR mice by stimulating with pregnant mare's serum gonadotrophin ) and human chorionic gonadotrophin (10 IU i.p. after 48 h) and mated with ICR males. The female mice were killed 24 h after mating. Early-stage embryos were collected by sharp dissection of the fallopian tubes and used in the experiments. The Ethics Committee of the First Affiliated Hospital of Sun Yat-Sen University approved this study.μg/mL . For the antibody group, rabbit polyclonal antibody to mouse CENP-A  was added to the medium. The antibody concentration in the medium was 35 terature ). For thμg /mL DAPI  for 15 min, again rinsed, and fixed in a dish for subsequent microscopic observation. In order to rule out false positive of the experimental group, embryos from antibody group were incubated with PBS instead of 488-labeled donkey antirabbit IgG (antibody group for control).On the second and third days of culture, three to five embryos were picked from each dish of the three groups for the immunofluorescence assay, to detect whether the signals of anti-CENP-A antibody were present in the embryos after coculture. The procedures for the immunofluorescence assay were as follows: The embryos were fixed in 4% polyoxymethylene and then permeated with 0.5% Triton X-100 , followed by sealing in 5% normal donkey serum . After that, the embryos were incubated for 1 h with 488-labeled donkey antirabbit IgG , rinsed, incubated with 1 The collection and culturing of the embryos for this embryotoxicity assay were the same as described earlier except that the embryos remained in the dishes for the entire 3-day period. The embryos in each group were examined to determine their stage of development on the third and fifth days (the first day referred to the day of oocyte collection). The following developmental stages were recorded: 6- to 8-cell stages on the third day, and blastocyst, morula, 2- to 8-cell, and atretic stages on the fifth day.P value less than 0.05 was considered statistically significant by chi-square test among the three groups, and a P value less than 0.0167 was used to indicate statistical significance in the partition of chi-square tests between groups.Statistical analysis was performed using SPSS 13 . A chi-square test and partition of chi-square tests were used to compare qualitative data. A All embryos cultured with anti-CENP-A antibody exhibited strong immunofluorescence in their nuclei, while none of the embryos from the PBS and blank control groups, as well as the antibody group for control, showed immunofluorescence .Compared with the PBS and blank control groups, the percentages of 6- to 8-cell stage embryos on the third day, as well as blastula and morula stage embryos on the fifth day, were significantly lower in the antibody group. The developmental potentials of embryos were comparable between the PBS and blank control groups .in vitro. However, the precise epitopes were not known because no nuclear antigens or phospholipids were found in the zona. In mouse 2-cell stage embryos, a set of nucleoproteins is transiently synthesized and changes in embryonic chromatin composition, suggesting that early embryos may possess epitopes for ANA [In 1999, researchers found that all mouse embryos cultured with purified antinuclear IgG exhibited strong immunofluorescence on the embryonic cells and experienced significant growth impairment or death compared with those cultured with control immunoglobulins . This in for ANA . Further for ANA .in vitro prior to implantation. In addition, for the majority of cocultured embryos, always only one or some of the blastomeres showed fluorescence. Perhaps, the density of structures in and around the centromere prevents anti-CENP-A antibody accessibility, or the blastomere with detectable fluorescence was inclined to apoptosis and displayed relatively loose structures that enabled anti-CENP-A antibody accessibility. However, the precise mechanism needs further clarification.In the present study, all embryos cultured with polyclonal anti-CENP-A antibody showed strong immunofluorescence of antibody against nuclear components (which were speculated to be anti-CENP-A antibody) and experienced apparent embryonic growth impairment, indicating that mouse embryos may be a direct target for some ACAs Although no definite concept exists for antibody entering the living cells, and the mechanism involved is unknown yet, these studies provided evidence for antibodies entering the living cells. For example, the anti-ribonucleoprotein-IgG could selectively enter the T-lymphocytes, while relatively less Fc receptors were present on the surface of T-lymphocytes. It suggested that some other mechanisms relevant to non-Fc regulation might be involved, which might be associated with antigen-like structures on the lymphocyte surface, implying that the ribonucleoprotein antibodies interacted with the ribonucleoprotein antigens on the cell surface to regulate their access into the living cells .in vitro [This study also found that the embryonic developmental potential was significantly impaired after culture with anti-CENP-A antibody, exhibiting significantly lower percentages of 6- to 8-cell stage embryos on the third day and blastula stage embryos on the fifth day. In fact, a previous study found that mouse embryos cultured with purified IgG from ANA-positive serum showed significantly impaired embryonic development potential . ANA couin vitro .In conclusion, embryos cultured with anti-CENP-A antibody experienced significant growth impairment or death. Thus, embryos could be a direct target for anti-CENP-A antibody.


A new contrast and stress-free approach for detecting myocardial ischemia has been recently demonstrated using Cardiac Phase-resolved Blood Oxygen-Level-Dependent (CP-BOLD) MRI. To detect the disease, CP-BOLD relies on the observation that myocardial signal intensity varies as a function of cardiac phase. These changes are not readily visible and post-processing is necessary; including myocardial segmentation and registration. Automated analysis approaches, which can obtain pixel-level determination of ischemia, are desirable since they may lead to improved accuracy in detection of disease. To achieve this, precise segmentation and non-linear registration of the myocardium among the frames (the cardiac phases) in the cine stack would be required. Unfortunately, at present due to BOLD contrast variations, classical approaches to segmentation and registration fail to reach desirable accuracy. Here we propose an algorithm that jointly finds suitable myocardial segmentation and elastically registers the heart.Flow- and motion-compensated 2D short-axis CP-BOLD was acquired along the mid ventricle in 10 canines at baseline and under severe LAD stenosis. Imaging studies were performed on a 1.5T Espree . TR/TE 6.2/3.1 ms; spatial resolution = 1.2 × 1.2 × 8 mm3, flip-angle = 70°, ~25 frames per cardiac cycle. The main principle of the proposed approach is to use an external initial database of pre-segmented images (and dictionaries for myocardium and background), to first come up with an initial segmentation (relying on classification when projecting on discriminatory dictionaries) of an input CP-BOLD patient stack. Then, images in the cardiac cycle (cine acquisition) are registered using the sparse coefficients of the projections to establish a new similarity metric. Afterwards, the obtained segmentation is refined; which gives the opportunity to refie the dictionaries (adapting them to the dataset under consideration). This refinement of segmentation and dictionaries is continued till convergence. The flowchart and detailed explanation of the algorithm is shown in figure As seen in table The experiments clearly underline the need for a new representation in image registration and segmentation. Using the information from the same subject to train the dictionaries improves the accuracy of myocardium segmentation both in the cases of health and disease. Although further experiments are necessary to validate this approach, CP-BOLD can open the road to repeatable, truly non-invasive diagnosis of ischemic heart disease.Dice overlap measures for the proposed method compared with segmentation without refinement and two state of the art methods for image registration DRAMMS  and MIND


The dynamic turnover of hippocampal neurons is implicated in the regulation of cognitive and affective behavior. Extending our previous demonstration that administration of dexamethasone (ND) to neonatal rats depletes the resident population of neural precursor cells (NPC) and restrains the size of the neurogenic regions, we now show that the adverse effects of ND persist into adulthood. Specifically, ND impairs repletion of the neurogenic pool and neurogenesis; ND also compromises cognitive performance, the ability to actively adapt to an acute stressor and, the efficacy of glucocorticoid (GC) negative feedback. Interestingly, although ND depletes the neurogenic pool, it does not permanently abolish the proliferative machinery of the residual NPC population; however, ND increases the susceptibility of hippocampal granule neurons to apoptosis. Although the antidepressant fluoxetine (FLX) reverses the latter phenomenon, it does not replenish the NPC pool. Treatment of ND-treated adult rats with FLX also improves GC negative feedback, albeit without rescuing the deleterious effects of ND on behavior. In summary, ND leads to protracted disruption of mental functions, some of which are resistant to antidepressant interventions. We conclude that manipulation of the NPC pool during early life may jeopardize the therapeutic potential of antidepressants in adulthood. The decision to pay particular attention to emotional and cognitive performance during adulthood was based on our earlier demonstration that ND lastingly depletes the neurogenic pool and retards volumetric growth of the rat dentate gyrus.30 Lastly, given that antidepressants increase hippocampal neurogenesis32 and concomitantly reverse some of the anomalous behaviors induced by stress during adulthood,33 we also examined the potential of fluoxetine (FLX), a commonly-used antidepressant, to ameliorate the undesired effects of ND exposure.The present study involved broad behavioral phenotyping of adult rats that had received DEX during early postnatal life , with a focus on emotional and stress-coping behavior, and hippocampus-dependent spatial memory. As hyperactivity of this axis is causally linked with impaired learning and memoryn=5 per cage). Procedures on animals were approved by the Regierung von Oberbayern and complied with European Union Directive 2010/63/EU. Throughout, animals were exposed to light from 0600 to 1800 (ZT0-ZT18), and all training and behavioral test sessions occurred between 0900 and 1200 (ZT3-ZT6). Group sizes for behavioral and morphological experiments were decided on the basis of pilot experiments on pup and adult rats (see figure captions for exact group sizes).Experiments were carried out on litters derived from 13 Wistar dams , culled to 10 pups per litter at birth. On weaning (21 days) animals from different litters were randomly assigned to new housing groups ; the neonatal vehicle- and DEX-treated groups are hereafter referred to as CON and ND, respectively. Animals that showed signs of weakness or discomfort were immediately culled in accordance with rules on animal welfare.Male pups received s.c. injections of vehicle  or DEX  on postnatal day (PND) 1–7  for 4 weeks , blood samples (~100 μl) were withdrawn at the daily nadir (ZT3) and peak (ZT18) of corticosterone (CORT) secretion; harvested serum was assayed for CORT by radioimmunoassay . Blood samples were obtained after venepuncture of the dorsal tail vein, which was then sealed by application of light pressure before returning the animals to their home cages; during this whole procedure (<2 min), rats were lightly restrained with a soft towel. Animals were handled, restrained and underwent sham-venepunctures on 2–3 occasions before collection of experimental test probes.34 was tested between ZT3 and ZT5. Tail blood samples, collected at 0 min and at 30 and 120 min thereafter, were subsequently assayed for CORT.The response of the HPA axis to an acute psychological stressor (air-puff)35 was used to examine the efficacy of negative feedback regulation of the HPA. A bolus of DEX was administered at ZT12 and tail-blood samples were collected for CORT measurements at ZT18.The dexamethasone suppression test (DST)36 anxiety (open field (OF) and elevated plus maze (EPM) tests),38 and capacity to adopt an active and adaptive behavior during acute stressor (forced-swim test)39 were tested in rats . Tests were performed between ZT3 and ZT6, with intervals of several days between each test to avoid carry-over effects; daily vehicle and FLX injections were administered after each behavioral test  after 6 days in vitro (DIV). Cells were then washed with phosphate-buffered saline and re-incubated in culture medium; in some cases, cultures were treated with DEX (10−6 M) for a further 48 h.Primary neuronal cultures from the hippocampi of 4-day-old (P4) Wistar rats were prepared as previously described.NV) in the SGZ (defined as a two-cell layer-thick zone on the inner side of the granule cell layer of the dentate gyrus) were estimated (by an investigator blind to the treatment).8 The total number of SGZ cells of a given chemophenotype was derived from the product of Nv and SGZ volume. Immunostaining of BrdU-incorporating NPC in vitro was performed and analyzed as described previously.30At the end of the experiment, rats (aged 4 months) were anaesthetized, perfused with paraformaldehyde (4%) and killed; brains carefully excised, cryo-preserved and stored at −80 °C until sectioning  . Sectionn=3 per group) were lysed and immunoblotted40 to detect Bax , Bcl-xl  and Bcl-2 , using an enhanced chemiluminescence detection kit . Blots were scanned, checked for linearity of signal and quantified  after subtraction of local background. Normalized data are expressed as a percentage of controls.Hippocampi, obtained from subgroups of animals at the end of the experiment . Numerical data (mean±s.e.) were subjected to 2-tailed Student t-tests or analysis of variance and appropriate post-hoc analysis . Two-way ANOVA, followed by appropriate post-hoc tests were used to determine the effects and interactions of the ND and FLX treatments.Appropriate sample sizes were determined on the basis of previous experiments in our laboratory , evidenced by the significant reduction in the number of Sox2+ cells within the GCL of ND-treated animals  cells P=0.02; , Ki67 , but not Sox2-positive NPCs , in the SGZ; specifically, FLX significantly increased the number of proliferating cells  NPCs in the GCL of FLX-treated vs untreated ND-exposed rats   . This, t43 exposure to DEX (24 h) reduced the number of BrdU-positive cells by ~40% . Following the withdrawal of DEX, the number of BrdU-incorporating cells increased to levels found in control cultures within 24 h . Predictably,43 As depicted in P>0.05, and p27-DEX vs p27-CON at 24 h, P>0.05). These in vitro findings provide a first mechanistic insight into how ND depletes the resident NPC pool and prevents replenishment of the granule cell population by agents such as FLX ), albeit without a contemporaneous increase in the number of activated caspase 3-positive cells . Although FLX did not significantly influence these ratios in control rats , the antidepressant reversed them in ND-treated animals   .5 and spatial recognition memory.46 In experiments to examine the impact of ND-induced depletion of NPC in the novel object-recognition test, ND and FLX did not significantly influence exploration time in either the sample , relocated object test  or novel object test , as measured in an OF arena  but not of FLX ; no significant interaction effects of ND and FLX were detected . As shown in P=0.007). No differences were found between FLX-treated and control rats in the spatial recognition test (control vs FLX: P=0.48). Further, FLX did not improve the deficit in spatial memory displayed by ND animals  and spent less time in the center of the OF arena  (P=0.03); moreover, FLX did not relieve ND-increased anxiety  . A simil: P=0.09 .P=0.001), and the number of entries into (P=0.05), the open arms of the maze  and failed to exert anxiolytic actions in the ND-treated group ; these observations are consistent with previous reports that FLX does not always produce anxiolytic effects in rodents.57ND-treated individuals showed signs of increased anxiety-like behavior in the EPM in terms of time spent in  and were immobile (floating) for a significantly longer time  and ND-exposed rats , ND-treated rats responded to acute stress with a sluggish and significantly blunted corticosterone response (P=0.001). Further, results of a DST showed that ND-treated animals are impaired in terms of GC negative feedback: whereas the daily nocturnal rise in corticosterone secretion was fully suppressed by DEX in control animals, DEX only partially suppressed corticosterone secretion in ND-treated animals  population of NPC in the dentate gyrus are vulnerable to GC-induced apoptosis,30 the present study was designed to examine the functional consequences (and reversibility) of ND exposure. This was considered important because DEX appears in the WHO Model List of Essential Medicines for children .Neuronal turnover in the hippocampus is a dynamic process which includes neurogenesis and apoptosis in the SGZ of the dentate gyrus.66 As the inhibitory effect of DEX on neuronal proliferation can be spontaneously reversed, the persistent impairment of the neurogenic pool is most likely due to the loss of neural progenitors during neonatal life. The finding that ND-exposed adult rats display reduced GCL volumes and deficits in spatial and object-recognition memory confirms earlier suggestions that neurogenesis is important for the manifestation of hippocampus-dependent behaviors.67The results obtained in the present study show that ND has a persistent negative impact on the neurogenic capacity of the hippocampus. In ND-treated animals, the reduction in the hippocampal pool of NPC (Sox2+ cells) and the number of actively dividing NPC (Ki67+/Sox2+ cells) in ND-treated animals is accompanied by shrinkage of the SGZ; however, it is important to note that some cells may escape the actions of DEX because they do not express GR.42 and of NPC within the SGZ.44 In the present study, adult ND-treated rats did not display increased hippocampal levels of cleaved caspase 3, a marker of active apoptosis. However, ND treatment was associated with higher expression ratios of pro-apoptotic (Bax) vs anti-apoptotic (Bcl-2 and Bcl-xl) proteins, indicating greater vulnerability of hippocampal cells to apoptosis.45 As ND treatment depletes the NPC pool through apoptosis,30 and astrocytes are resistant to DEX-induced apoptosis,40 we suggest that the cells displaying signs of vulnerability to apoptosis during adulthood are likely to be mature granule neurons.GC stimulate apoptosis of hippocampal neurons68 Failure to curtail GC secretion in a timely manner compromises stress-coping ability and may trigger the onset of depression and anxiety,69 cognitive dysfunction49 and possibly, Alzheimer disease pathology.70 The association between ND treatment and impaired GC negative feedback, together with the behavioral phenotype observed, reinforces the view that exposure to stress or GC during early life can lead to protracted/lifelong disruption of mental functions.71 Interestingly, we found that administration of the antidepressant FLX during adulthood reverses the ND-triggered inhibition of mitosis in the dentate gyrus and the disruptive effect of ND on GC negative feedback; however, FLX failed to rescue ND-induced impairments in memory and adaptive behavior. This observation, which is consistent with a previous report that adult neurogenesis is required for FLX to normalize HPA axis activity,72 also suggests that a mechanism, other than proliferation, contributes to the overall therapeutic effects of FLX, a possibility worthy of further investigation.The hippocampus occupies a key position in the central regulatory cascades that serve to restrain GC secretion following stress.74 FLX was found to stimulate mitosis within the dentate gyrus of ND-exposed animals, but not control animals. Strikingly, however, FLX did not restore the NPC pool in ND-treated animals, indicating that chronic FLX treatment in adulthood is not sufficient to rescue the effects of ND treatment. GC receptors, which mediate the apoptotic effects of DEX, are expressed by both quiescent neural progenitors and amplifying neural progenitors in the SGZ.30 Interestingly, FLX application during adulthood only promotes division of amplifying neural progenitors;75 this offers a plausible explanation for why FLX treatment stimulates neurogenesis in the absence of a concomitant increase in the NPC pool. In this context, it is important to recall that NPC have a limited capacity for self-renewal and that neurogenesis wanes with increasing age;77 accordingly, accelerated age-related deterioration of hippocampal functions that depend on neurogenesis would be a predictable outcome of ND treatment.Consistent with the results of studies with various antidepressants,9 The results of the present study show that both, ND and FLX induce hyperanxiety; they also show that FLX does not act as an anxiolytic when administered to ND-exposed animals. Interpretation of these findings is challenging because of the highly variable direction of effects of FLX on anxiety levels in animals: FLX has been variably described to have no effects,52 anxiolytic effects53 or anxiogenic effects57 in animals; these disparate reports most likely reflect differences in animal strain,54 age78 and/or experimental conditions such as handling.79Given that the hippocampus is reciprocally connected with the amygdala, bed nucleus of the stria terminalis, nucleus accumbens and medial prefrontal cortex, it is not surprising that neurogenesis in the hippocampus has an impact on mood and emotion.18 On the other hand, and notwithstanding the limitations of extrapolating results from one species to another, it deserves mentioning, (i) that neurogenesis is only transiently inhibited when DEX is administered to adult rats,64 and (ii) that prenatal DEX does not persistently impair the volume and proliferative and differentiation capacity of the non-human primate hippocampus.80 Therefore, exploration of the existence of developmental windows during which DEX therapy may be safely applied will be a worthwhile pursuit in future investigations.In summary, our results demonstrate that ND impairs a variety of hippocampus-dependent functions, ranging from neuroendocrine homeostatic mechanisms to the regulation of affective and cognitive behaviors. These impairments occur contemporaneously with the depletion of the hippocampal NPC pool, persistent inhibition of hippocampal neurogenesis and, increased vulnerability of hippocampal neurons to apoptosis. As FLX administration enhances the survival of hippocampal neurons, we suggest that timely application of antidepressants may help rescue at least some of the behavioral functions that are lost following ND-induced reductions in the neurogenic capacity of the hippocampus. Our findings that early postnatal life represents a phase during which NPC are particularly sensitive to DEX lend support to the American Academy of Pediatrics’ policy on the use of glucocorticoids in pediatric practice.