Communication difficulties are one of the core criteria in diagnosing autism spectrum disorder (ASD), and are often characterized by speech reception difficulties, whose biological underpinnings are not yet identified. This deficit could denote atypical neuronal ensemble activity, as reflected by neural oscillations. Atypical cross-frequency oscillation coupling, in particular, could disrupt the joint tracking and prediction of dynamic acoustic stimuli, a dual process that is essential for speech comprehension. Whether such oscillatory anomalies already exist in very young children with ASD, and with what specificity they relate to individual language reception capacity is unknown. We collected neural activity data using electroencephalography (EEG) in 64 very young children with and without ASD  while they were exposed to naturalistic-continuous speech. EEG power of frequency bands typically associated with phrase-level chunking , phonemic encoding , and top-down control  were markedly reduced in ASD relative to typically developing (TD) children. Speech neural tracking by δ and θ (4–8 Hz) oscillations was also weaker in ASD compared with TD children. After controlling gaze-pattern differences, we found that the classical θ/γ coupling was replaced by an atypical β/γ coupling in children with ASD. This anomaly was the single most specific predictor of individual speech reception difficulties in ASD children. These findings suggest that early interventions  targeting the disruption of β/γ coupling and the upregulation of θ/γ coupling could improve speech processing coordination in young children with ASD and help them engage in oral interactions.SIGNIFICANCE STATEMENT Very young children already present marked alterations of neural oscillatory activity in response to natural speech at the time of autism spectrum disorder (ASD) diagnosis. Hierarchical processing of phonemic-range and syllabic-range information (θ/γ coupling) is disrupted in ASD children. Abnormal bottom-up (low-γ) and top-down (low-β) coordination specifically predicts speech reception deficits in very young ASD children, and no other cognitive deficit. Autism spectrum disorder (ASD) primarily refers to disorders of social interactions; however, individuals with ASD are seldom spared from language difficulties. Many people with ASD have severe language impairment, and even high-functioning ASD individuals with excellent language skills have difficulty understanding speech in noisy environments and/or when exposed to multiple speakers . IndividA large stream of recent studies shows that neural oscillations, i.e., the synchronous activity of neuronal populations, play a critical role in speech reception, mainly in parsing the speech flow into meaningful linguistic units: phonemes, syllables, words, phrases, etc. . While tIn typically developing (TD) children, the ability to track the speech temporal structure develops very early . The infOscillation anomalies in response to speech have previously been reported in ASD, such as decreased γ responses to rapid spectrotemporal transitions associated with diphones , increasIn the present study, we used high-density electroencephalography (EEG) to compare oscillatory neural processing of age-appropriate naturalistic speech in 64 very young children (1.31–5.56 years old) with and without ASD. Given the major developmental changes occurring in large-scale brain networks around this age in ASD , exploriSixty-four children  with and without ASD drawn from the Geneva Autism Cohort  underwenp = 0.19; see For all ASD children, the clinical diagnosis was formally corroborated using the Autism Diagnostic Observation Schedule-Generic ADOS-G;  or the A10.1523/JNEUROSCI.0112-22.2023.t2-1Extended Data Table 2-1Table 2-1, XLSX file.Psychometric data of every participant. The Table was organized for easily download. Download Developmental functioning was assessed using the Mullen Scales of Early Learning MSEL; , which cThe communication skills assessment, i.e., receptive language and expressive language, was completed by the Vineland Adaptive Behavior Scales, second edition VABS-II; , testingThree symptom severity levels, i.e., low, moderate, and high, were based on ADOS-G  or the AA). An envelope spectral decomposition was performed using the fast Fourier transform . We found dominant frequencies between 1 and 7 Hz, with peaks at 1.17, 3.32, and 4.69 Hz, overlapping with the syllable rate range  as determined by averaging peaks within the 150-ms minimum-peak-distance that were associated with the averaged French-syllable duration .We explored cortical speech processing during a passive, naturalistic task with a relatively low cognitive demand suitable for young children. Participants watched four French Trotro ,b,c,d caFig. 1duration . The spe10.1523/JNEUROSCI.0112-22.2023.f1-1Extended Data Figure 1-1Figure 1-1, TIF file.Stimulus properties and schematic of the processing pipeline. There were four main steps of the processing: stimulus presentation and EEG and eye-tracking data recording (step 1), preprocessing of stimuli , data processing based on purpose ; and last step, step 4, between-group statistical analysis. For detailed information on each step, please see Materials and Methods. Download EEG data were acquired using a Hydrocel Geodesic Sensor Net  EEG system with 129 scalp electrodes at a 1000-Hz sampling rate. The recording reference electrode was located on the vertex (Cz), and a real-time bandpass filter at 0–100 Hz was applied to the incoming signal. The first two cartoon videos were presented in the first block, and the last two cartoon videos in a second block, with a ∼5 min “dynamic image” task  in betwehttps://sites.google.com/site/cartoolcommunity/). First, the dataset underwent a dimensionality reduction to 110 channels by excluding cheek and neck electrodes that are often contaminated by muscle artifacts. Then, a zero-phase fourth-order Butterworth bandpass filter between 0.1 and 70 Hz was applied to the EEG signals, as well as a notch filter at 50 Hz to remove power line interference. Each participant’s dataset was then visually inspected to exclude periods contaminated by movement artifacts. An independent component analysis (ICA) was computed on the dataset to identify and remove components with eye blinks, saccades, electrical line noise, and heartbeat artifacts. The dimensionality of the components was equivalent to the number of electrodes kept after bad channel detection. ICA was performed in MATLAB and Cartool. The first step was the detection of bad channels, i.e., with too large signal amplitude because of defective electrodes. The remaining data were decomposed into independent components using the Runica algorithm (EEGLAB function). Based on the ICA components’ time course and topographies, noisy components were visually determined and excluded.The EEG signal preprocessing was conducted using the EEGLAB v2019 toolbox within the MATLAB environment  and CartSubsequently, a spherical spline interpolation was used to interpolate the channels contaminated by noise using the ICA-corrected data. Finally, the Cartool spatial filter  was applt test comparison t(62) = −2.54, p = 0.014). There were 35.12 ± 1.88  trials in the TD group, against 32.97 ± 4.48  in the ASD group. The mean trial duration was similar across groups (unpaired t(62) = 0.98, p = 0.33). The mean trial duration was 2454.4 ± 92.15 ms (mean ± SD) in the ASD group and 2431.8 ± 93.02 ms (mean ± SD) in the TD group.A trial was defined based on the beginning and end of each speech chunk in the cartoon, leading to a total of 50 trials with an average duration of 1.69 s. The detected artifact periods were replaced by NaNs (not a number). After excluding artifact time segments, fewer trials remained in the ASD group than in the TD group (unpaired https://www.tobiipro.com) with a 300-Hz sampling rate. The cartoon frames allowed for a visual angle of 26°47′ × 45°53′ (height × width). A five-point calibration procedure consisting of child-friendly animations was performed using an inbuilt program in the Tobii system. The calibration procedure was repeated if the eye-tracking device failed to detect the participant’s gaze position accurately. The lighting conditions in the testing room were constant for all acquisitions. Younger participants sat on their parent’s lap to make them feel comfortable and minimize head and body movements. All participants watched all four cartoon videos in the same order. To extract fixations, we used the Tobii IV-T Fixation filter , we collected gaze data using a Tobii TX300 eye-tracking system . Next, we used the earth mover’s distance , a measure of the distance between the two gaze distributions. We repeated this process 200 times to obtain an empirical null distribution for the EMD. It is important to note that two distinct types of distributions are being referred to here: the distributions of gaze for the ASD and TD groups, and the empirical null distribution of EMDs between these two distributions. To compute the one-tail probability, we ranked the observed EMD within the empirical null distribution. A rank >195 (top 5%) within the 200-element null distribution was considered significant. The approach was applied frame-by-frame to obtain single frame gaze-distribution difference (B), and a cumulative gaze-distribution over frames in the whole speech excerpt  and the cumulative gaze-distribution difference . Although gaze distribution did not significantly differ between groups within each frame (B), a significant effect was found on the cumulative gaze distribution (C); the observed EMD  is distinct from the 95% of the null distribution . We then assessed individual gaze divergence in ASD by calculating the distance between every ASD gaze and the TD gaze norm on each frame. For comparison and interpretation, the distance was normalized to 0–1 and named Proximity Index . Figure 2D indicates that for every child with ASD, the gaze pattern diverges (PI) from the TD group to a different extent. The PI values were included as covariates to reduce the gaze pattern bias in EEG signals.To assess potential group differences in visual exploration, we first followed timation A. Next, nce EMD; , known ifference B, and a  excerpt C, top anfference C, bottomch frame B, a signribution C; the obndex PI; . A high To identify the oscillatory responses to speech, a Morlet wavelet transformation was performed from 5 s before to 5 s after each speech chunk (making sure the epoch would not include any artifacts) onset between 0.1 and 50 Hz with five cycles for Gaussian taper at each EEG electrode. Then time course between 1 s preonset to 1 s postonset was selected (based on those speech chunks with the shortest duration) on a trial-by-trial basis, and the power was averaged across trials and normalized by decibel conversion (dB) over a −1000- to 0-ms baseline period (for which an audio stimulus was presented but no speech), allowing for between-group comparisons. EEG power in the different frequency bands of interest was defined as the mean power value across 0–1000 ms postspeech stimulus onset. We compared ASD and TD groups in four frequency bands that are relevant for speech processing, i.e., δ (1–3 Hz), θ (4–8 Hz), β (12–20 Hz), and low-γ (25–35 Hz).z-transformed correlations in each fold and then taking the inverse Fisher's transformation of the resulting mean z-score. The model was trained separately for each individual subject.To probe differences between ASD and TD in oscillatory speech tracking, a multiple linear regression (MLR) model with a distributed lag between −300 and 300 ms with 50-ms steps  was usedWe deployed the approach separately in the two frequency bands implicated in speech tracking, i.e., θ and δ, and computed the amplitude of specific bands to obtain the oscillation tracking index. The amplitude was the absolute value of the Hilbert transform in band-specific filtered EEG, i.e., 4–8 Hz for θ and 1–3 Hz for δ.fp) and high-frequency amplitude (fa). Considering the filters for extracting fa must be wide enough to capture the center frequency ± the modulating fp to detect PAC (fp bandwidth was kept narrow (center frequency ± 1Hz) to extract sinusoidal waveforms. Furthermore, changes in the modulation power spectrum between speech and baseline periods were visually inspected in each participant to confirm that oscillation peaks/troughs were present at each modulating frequency-band of interest fp. For instance, if the interested modulating frequency band was 4–8 Hz, we confirmed that participants presented a real peak in the power spectrum at 4–8 Hz. Next, the coupling between fp and fa was quantified using the Kullback–Leibler modulation index  and a uniform amplitude-phase distribution (Q).The time-courses from electrode clusters selected from the EEG power and neural tracking analyses were examined for phase-amplitude coupling changes (PAC). The first step was applying the Hilbert transform to each bandpass Butterworth filtered trial  to obtaitect PAC , we decion index . The KL-D) was computed using the following formula Using KL-MI-Tort, we calculated PAC between phases 2–15 Hz (in 1-Hz steps) and amplitudes 16–50 Hz (in 2-Hz steps) for the time period 0–1000 ms following speech onset and a 1000 ms prestimulus baseline period. MI values were calculated separately for each trial and averaged to obtain a single MI value per amplitude and phase. To normalize MI values, this was repeated using surrogate data, created by shuffling trial and phase-carrying information (200 surrogates).In order to show the relationship between the brain activity and the neurophysiological variables, we tested whether autism severity was predicted by using only band-specific power per electrode , or only neural tracking values per electrode, or only phase-amplitude coupling matrices per cluster . For this, we trained a Linear Discriminant Analysis (LDA) classifier using a 10-fold nested cross-validation procedure, which separates the data into test and training sets. The training set was further separated using a 5-fold cross-validation approach for parameter search (Grid search). The LDA classifier was trained using a diagonal shared covariance matrix. The cross-validation process ensured that the training and testing datasets were not overlapping, avoiding misleading results because of overfitting. The input to the classifier was the band-specific EEG power, neural-tracking, and PAC for each participant, and the label to be predicted was symptom severity . All empirical thresholds were obtained through cumulative binomial distribution .R2 values, which indicate the prediction power of a given feature, i.e., higher R2s signal higher prediction accuracy.Then, we probed the relationship between the neural and cognitive skills using a regularized linear model per frequency band , per neural tracking per electrode, and per PAC matrix  to determine which critical oscillation (or combination of oscillations) was the best predictor of cognitive skills within group, e.g., the higher the tracking value, the higher the speech reception. The linear model was based on Lasso regression which requires finding the best hyper-parameters within high dimensional data . We alsohttp://fieldtriptoolbox.org) with gaze divergence as a covariate to remove any possible bias because of gaze differences between groups. In detail, between-group differences (ASD vs TD) were assessed with unpaired t-statistics. First, clusters of significant group differences were obtained by considering at least two adjacent electrodes whose t value exceeded a 5% significance threshold . The maximum t value within each cluster was carried forward. Next, a null distribution was obtained by randomizing the gaze divergence label 1000 times and calculating the largest cluster-level t value for each permutation. The maximum t value within each original cluster was then compared against this null distribution, with values exceeding a threshold of p < 0.05 deemed significant.Between-group statistical comparisons of band-specific EEG power and neural tracking were done using cluster-based nonparametric permutation tests with Monte Carlo randomization  using tht test for all electrodes collectively analyzed. For group comparison statistics, only one value of θ-/δ-neural tracking per participant was included in the unpaired t test comparing ASD and TD groups.In addition, between-group differences of neural tracking were tested by means of unpaired t test was performed (speech vs baseline), and then MI values exceeding a 5% significance threshold of null distribution were grouped into clusters.To assess changes in the comodulograms of PAC between the speech and baseline periods, we processed the nonparametric cluster-based statistics . First aA), we computed the EEG power spectrum of speech compared with baseline in several frequency bands of interest, i.e., δ (1–3 Hz), θ (4–8 Hz), low-γ (25–35 Hz), and β (12–20 Hz). A between-group statistical comparison . Download Figure 4-1, TIF file.Comparison of α frequency power in 31 children with ASD and 33 TD peers. Children with ASD had comparable α power relative to their TD peers  in the ASD group, and 85,653 ± 6712.7 ms (mean ± SD) in the TD group. The model was trained by a 10-fold cross-validation approach, meaning that the duration of each fold was around 8093.5 ms for ASD and 8565.3 ms for TD. The duration in both groups was enough to get a valid value. Please note that the speech content is not expected to affect low-frequency neural tracking. Therefore, although the balance across groups was not perfect, the result remains reliable.t(62) = 2.19, p = 0.03, η2 = 0.07; Fig. 5A). This θ effect was most prominent in a specific cluster of 12 posterior-occipital electrodes . The δ-band signal from all electrodes permitted the reconstruction of the stimulus envelopes equally well in ASD and TD participants (unpaired t(62) = 0.18, p = 0.86, η2 = 0.0005; Fig. 5A). However, we found reduced speech envelope reconstruction accuracy from the δ-band signal in ASD participants relative to TD participants in a specific cluster of seven parieto-occipital electrodes . These results denote that although the overall θ power was unchanged in children with ASD . Before computing PAC and assessing PAC changes, we checked that there were clear power spectrum peaks and troughs at each modulating frequency band of interest  across modulating (<15 Hz) and modulated (16–50 Hz) frequencies using the KL-MI-Tort. This approach was applied to those clusters showing significant between-group differences in the EEG power . While TD showed a unique and strong δ-θ/low-γ PAC on mid-central (B) and posterior-occipital electrodes (D), very young children with ASD exhibited wholly different patterns. The most significant difference was the extra presence of a consistent low-β/low-γ PAC in both mid-central and posterior-occipital electrodes (D). In summary, compared with the TD group, children with ASD show atypical δ/θ-γ coordination, and a robust low-β/low-γ coupling anomaly.Although results from mid-central electrodes showed significant PAC clusters between the 3- to 8-Hz (phase) and the 22- to 38-Hz (amplitude) frequency ranges in both groups, there was only minimal overlap between ASD and TD B. While -central B and posectrodes D, very yectrodes D. In sumAmong all the oscillatory features that were found atypical in children with ASD, not all of them have the potential to specifically account for individual traits and, in particular, language abilities. We, therefore, assessed whether the group differences observed at the neurophysiological level could first predict the ASD severity, and, more importantly, whether they could predict the speech reception scores obtained by the children in the MSEL  and VABS-II (parent-reported measure of functioning in everyday life). Conversely, we also sought to find out whether some of the oscillatory anomalies detected in power, tracking, and PAC analyses, could be more generally involved in several other cognitive skills .A).We first used nested LDA classification to determine which neurophysiological feature(s), i.e., EEG power, speech-tracking values, and PAC, best predicted the ASD symptom severity level, i.e., low, moderate, and high. Classification using all scalp electrodes showed that θ power (accuracy: 55.8%), low-γ power (accuracy: 51.7%), δ tracking (accuracy: 48.3%) were good predictors of autism severity , empirical chance level: 46.387%]. These results demonstrate that θ power, γ power, δ tracking, β/γ coupling contain critical information about autism severity.In addition, low-β/low-γ PAC was a good predictor of autism severity in both clusters [accuracy 49.2% in the mid-central cluster and 51.7% in the posterior-occipital cluster B, empiriA). Conversely, neural tracking, which was also markedly different across groups, was predictive of three cognitive subscales of MSEL in ASD (receptive and expressive language and fine motor skills) in the δ range, and of all but receptive language in the θ range . In the TD group, neural tracking accounted for none of the MSEL scores, despite a trend for θ tracking to predict both language reception and expression. These results were also found for the language components as assessed by VABS-II (B). In TD, the R2 values were very low for all cognitive aspects in the δ range, and for motor and visual in the θ range .We then used Lasso, with a nested cross-validation approach, to determine whether individual language skills could be predicted from oscillatory features and which features accounted most selectively for individual language development status assessed by MSEL  and VABSFig. 8 VABS-II B. In TD,C, 9C, Fig. 8C, 9C). The post hoc analysis of the dependency of language reception on β/γ PAC indicated that the stronger the anomaly, the worse the speech reception was . For display purposes, after confirming the performance of the algorithm, we used whole data to run the hyperparameter optimization, and with the best estimator we then retrained the algorithm y = f(x). We, therefore, obtained a fitted model y = 17.45·fp + 0.43·fa-5.71·MI-142.19 (for MSEL) and y = 0·fp + 0·fa-0.62·MI + 12.26 (for VABS-II), in which y represents language reception, fp and fa represent the frequency of phase and amplitude respectively, and MI refers to PAC value. Overall, PAC was the most specific predictor of language reception in ASD and TD children: the presence of θ/γ PAC predicts good speech reception in TD children, whereas the added presence of atypical β/γ PAC signals poor reception in ASD. Importantly, PAC features were much more sensitive than power and neural tracking to predict individual language reception scores.The most relevant feature for predicting language reception development was phase-amplitude coupling. As expected from studies in typical adults  and fromFigs. 8The goal of this study was to determine whether speech-related oscillatory anomalies in ASD are already present in early childhood, around the time of ASD diagnosis. Given the established computational role of neural oscillations in chunking the syllable stream, encoding phonemic information, and predicting speech timing and linguistic content , we alsoOur results showed decreased low-γ activity among ASD relative to TD children, notably on mid-central electrodes, a scalp location that strongly captures auditory cortex activity . PreviouAlthough globally we found a similar level of θ activity in both groups, θ-range tracking was deficient among children with ASD, meaning that θ activity, although present, did not typically follow the speech temporal structure. An equivalent level of global θ power in TD and ASD is consistent with previous studies . HoweverOur results also show reduced δ power and δ-range speech tracking among children with ASD. δ-range activity signals phrase-level chunks, which do not necessarily have a physical/acoustic counterpart in the speech stimulus . δ ActivDuring continuous speech perception, top-down predictive mechanisms are also important, particularly to make sense of acoustic signals that might be unclear or ambiguous, or simply to follow the speaker’s speech rate. Typically, predictive mechanisms are signaled by the low-β band , which wThe key finding of this report is the atypical low-β/low-γ PAC found among children with ASD in conjunction with weaker β power. A PAC anomaly involving the low-β phase might follow from the reduced global β power in ASD, possibly allowing low-γ and γ bursts to occur more strongly within β troughs. Reduced β gating could result in letting past neural activity that is unrelated to the predicted speech structure, possibly leading to a feeling of being overwhelmed by unformatted acoustic inputs to which no linguistic value or meaning can be attributed. The presence of an abnormal low-β/low-γ coupling in ASD was not influenced by atypical visual scene exploration in ASD, as possible gaze differences were controlled for in the statistical analysis. This interpretation aligns well with the clinically described auditory avoidance in ASD children . The othAs cortical oscillations arise from excitatory-inhibitory interactions within and across specific cortical laminae , auditorDisruptions of synchronous neural activity in the cortex  and otheIn conclusion, our results confirmed the relevance of θ/low-γ coupling in speech reception in very young TD children and showed that cross-frequency patterns were markedly disrupted in children diagnosed with ASD. While θ/γ coupling closely predicted individual language reception abilities in TD children, the speech reception deficit in ASD was predicted by the unusual presence of β/γ coupling in auditory-sensitive brain regions. Cross-frequency coupling features appear as a promising language development endophenotype, bridging the gap from genetics to behavior while offering a precise entry point for interventions targeting the normalization of cross-frequency oscillatory functions. Although our study alone cannot establish causality, demonstrating the presence of oscillatory anomalies at the age of diagnosis is an important step toward reaching a causal explanation for speech reception difficulties in ASD.


The degree of endolymphatic hydrops in Meniere's disease may be related to hearing loss. However, the results of prior studies have been inconsistent. We aimed to investigate the relationship between endolymphatic hydrops and hearing loss characteristics in Meniere’s disease.This study included 54 patients (62 ears) with Meniere’s disease. Patients underwent three-dimensional real inversion recovery sequences for magnetic resonance imaging and pure-tone audiometry. Endolymphatic hydrops were assessed according to Gurkov’s criteria (2011). Correlations between different degrees of endolymphatic hydrops and pure-tone audiometry, as well as staging, were analysed.p < 0.05). There was no significant correlation between vestibular endolymphatic hydrops and pure-tonal audiometry or staging . The degree of hydrops in the middle turn was correlated with the mid-frequency hearing threshold (p < 0.05). The degree of cochlear hydrops was correlated with the audiometry, low-frequency hearing thresholds, mid-frequency hearing thresholds and staging (p < 0.01).Pure tone audiometry and staging were higher for vestibular endolymphatic hydrops complicated by cochlear ones than isolated cochlear or vestibular hydrops (both The types and sites of endolymphatic hydrops based on a 3D-real inversion recovery sequence can be used to indicate the degree of hearing loss in patients with Meniere’s disease.Level III. Meniere's Disease (MD) is an inner ear disorder of unknown origin, with Endolymphatic Hydrops (EH) as the main pathological feature.The diagnosis of MD has traditionally relied on medical history, specialist examinations, and audiological findings.The degree of EH as revealed by inner ear MRI after the administration of Gd correlates with the severity of hearing loss. However, the results of studies have been inconsistent due to the varied techniques of inner ear MRI and the assessment criteria used to determine the degree of EH, as well as inter-subject variation. Three-dimensional Fluid-Attenuated Inversion Recovery (3D-FLAIR) sequences are mostly used for inner ear MRI after the administration of Gd contrast agents, but the use of Three-Dimensional real Inversion Recovery  sequences has gradually increased. An advantage of 3D-real IR over 3D-FLAIR is the high signal-to-noise ratio and contrast-to-noise ratio,This study used 3D-real IR sequences for Gd-enhanced posterior inner ear MRI, and the assessment criteria proposed by Gürkov et al.A total of 54 patients who attended a university hospital in China between April 2019 and May 2023 and were diagnosed with MD based on the 2015 guidelines and had not received previous treatment were included.All patients underwent Pure-Tone Audiometry (PTA) within 1 week of MRI. PTA was performed in a standard sound-isolation room with background noise of <30 dB(A). This test was conducted using the Otometrics audiometer (Astera), which measured the conventional frequency thresholds of 250–8000 Hz for air conduction and 500–4000 Hz for bone conduction. According to the PTA grading criteria of the 1997 World Health Group, the average thresholds of binaural air conduction at 500, 1000, 2000, and 4000 Hz were calculated as the average hearing threshold (PTA 500–4000 Hz). The average thresholds at 1000 and 2000 Hz were used as the mid-frequency hearing threshold (PTA 1000–2000 Hz) and those at 4000 and 8000 Hz were used as the high-frequency hearing threshold (PTA 4000–8000 Hz). MD staging was obtained by calculating the average hearing thresholds at 500, 1000, 2000, and 3000 Hz2, matrix 384 × 384, slice thickness 0.8 mm, PAT2, and scan time 6 min 38 s.The patient was placed in the prone position with the head tilted to the side, and the external auditory canal was locally disinfected. The tympanic membrane surface was subsequently anesthetized using tetracaine hydrochloride and punctured at the anterior lower quadrant; 0.5 mL of a Gd contrast agent  was then injected into the tympanic chamber. This procedure was repeated on the other ear. After the injection, the patient was instructed to lie in the supine position with the affected ear facing upward for approximately 30 min, with minimal talking and swallowing. MRI was performed 24 h later. The specific parameters of the 3D-real IR sequence were as follows: echo time 187 ms, repetition time 6000 ms, Inversion Time (TI) 1730 ms, bandwidth 501 Hz/Px, echo train length 124, flip angle 120°, field-of-view 200 × 200 mmThe MRI results were classified according to the degree of EH using the criteria described by Gürkov et al.t-test was used to compare the mean hearing thresholds at different hydrops sites. The Mann–Whitney U test was used to compare the clinical staging of different sites. The correlations among the degree of EH, PTA, and the clinical stage were analyzed using the Spearman rank correlation test; p < 0.05 was considered to indicate statistical significance. All data were analyzed using SPSS 25.0.Measurement data and count data are reported as the mean ± standard deviation and rate (%), respectively. Fisher's exact test was used to analyze EH in different locations within the cochlea. The The PTA 500–4000, 250–500, 1000–2000, and 4000–8000 Hz values for the 62 affected ears were 58.97 ± 18.15, 59.48 ± 18.27, 57.34 ± 20.21, and 64.84 ± 18.95 dBHL, respectively. The staging results for the 62 affected ears were as follows: 5 ears in stage 1, 6 ears in stage 2, 36 ears in stage 3, and 15 ears in stage 4.t = 2.150, p < 0.05) but did not significantly differ from that of the no hydrops group . Staging in the isolated cochlear or vestibular EH group was longer than that in the cochlear and vestibular EH group  but did not significantly differ from that in the no hydrops group .There were 2 ears with no hydrops, 8 ears with isolated cochlear EH, 3 ears with isolated vestibular EH, and 49 ears with cochlear and vestibular EH. The PTA 500–4000 Hz value was 61.25 ± 17.79 dBHL for the cochlear and vestibular EH group, 48.52 ± 17.73 dBHL for the isolated cochlear or vestibular EH group, and 54.38 ± 2.65 dBHL for the no hydrops group. The PTA of the cochlear and vestibular EH group was higher than that of the isolated cochlear or vestibular EH group (p > 0.05). There was also no significant correlation between the vestibular EH of the affected ear and the PTA 250–500, 1000–2000, and PTA 4000–8000 Hz values .In terms of the degree of vestibular EH in the 62 affected ears, 10 ears were grade 0, 29 ears were grade 1, and 23 ears were grade 2 . There wr = 0.417, p = 0.001; r-values of 0.479 and 0.426, respectively; both p < 0.01). There was no significant correlation between the degree of cochlear EH in the affected ear and the PTA 4000–8000 Hz value (p > 0.05). However, the degree of cochlear EH in the affected ear was significantly correlated with staging , while the PTA 4000–8000 Hz value in the affected ear was not significantly correlated with the degree of basal turn EH (p > 0.05) , the middle turn EH rate was 91.94% (57/62), and the basal turn EH rate was 90.32% (56/62); the differences among these three rates were not significant ( > 0.05) .Table 2EMRI after administration of Gd contrast agents is a commonly employed clinical tool to assess inner ear EH. While Gd contrast agents penetrate the perilymphatic space of the inner ear, they cannot penetrate the endolymphatic space due to the blood-labyrinth barrier. Perilymphatic spaces with injected Gd contrast agents appear hyperintense, while hypointensity is observed in endolymphatic spaces without Gd contrast agents; this allows for differentiation of the endolymphatic and perilymphatic spaces, and thus the objective assessment of EH. The administration of Gd contrast agents to the inner ear involves delivery via the middle ear using intravenous injection. After Gd contrast agents are injected via a tympanic membrane puncture, they pass through the round window membrane into the perilymphatic space. Tympanic membrane puncture contrast is superior to intravenous administration.The TI of 3D-FLAIR is set to the endolymph over the zero point, at which the endolymph and cerebrospinal fluid are suppressed and appear as a hypointense area. In contrast, the perilymph is observed as a hyperintense region on the image. Since the surrounding bone is also hypointense, it is difficult to completely distinguish it from the boundary of the endolymph. This leads to some difficulties in the determination of EH. In this study, a 3D-real IR sequence was used for real-part reconstruction, and the TI of the inversion recovery sequence was at the middle of the point at which the endolymph and perilymph crossed zero. The perilymph, surrounding bone tissue, and endolymph appeared hyperintense, isointense, and hypointense, respectively, on the image. This greatly facilitated a more accurate distinction and identification of the site and degree of EH.7The various criteria used for the identification of EH differ slightly among researchers, and an international standard is lacking. In 2009, Nakashima et al.MD is one of the most common causes of peripheral vertigo and is often associated with recurrent fluctuating hearing loss. Hearing loss often differs in the distinct regions of the inner ear in which EH occurs. The PTA of patients with vestibular and cochlear EH was previously found to be higher than that of patients with isolated cochlear or vestibular EHThe association between the degree of EH and the hearing threshold has also been reported previously. Yang et al.Some studies have focused on the association between hearing thresholds in different frequency bands and the degree of effusion. For example, Zhang et al.The cochlear structure produces a specific frequency-location map due to its frequency specificity for sound encoding. Therefore, different locations of cochlear EH may cause hearing loss with different frequency characteristics. Our evaluation of hearing thresholds for different EH sites in the cochlea revealed that cochlear middle turn EH was significantly correlated with the mid-frequency hearing threshold, while no such association was found between basal turn EH and the high-frequency threshold. This may be attributed to the possibility that MD causes some alteration of the original cochlear frequency-position map after EH.This study has a few limitations. Our results showed that PTA in patients with both vestibular and cochlear EH was higher than that of patients with isolated cochlear or vestibular EH. However, as there were too few ears with isolated vestibular EH, we combined isolated vestibular EH and isolated cochlear EH into one group for analysis. There is a need for further studies with larger sample sizes, particularly for cases with no hydrops. Furthermore, due to the difficulty and subjectivity in determining the degree of apical turn EH, this study only evaluated the presence or absence of hydrops and did not grade cochlear parietal rotation EH.In summary, the greater the degree of EH in patients with MD, the greater the hearing loss. This association is particularly evident in cases of cochlear EH. A significant correlation is reported between EH in different cochlear sites and hearing loss in different frequency bands, mainly between cochlear middle turn EH and the mid-frequency PTA hearing threshold.The authors declare no conflicts of interest.


Theoretically, we present a mixed quantum-classicalformalism of the sample response to enable the simultaneous quantumdescription of high-frequency oscillators and a classical descriptionof the bath. We derive the strength and nature of interaction andfind different coupling between CH3 stretch and low-frequencymodes. This general approach enables quantitative and mode-specificanalysis of coupled quantum and classical dynamics in complex chemicalsystems.The interaction of quantum-mechanical systems with afluctuatingthermal environment (bath) is fundamental to molecular mechanics andenergy transport/dissipation. Its complete picture requires mode-specificmeasurements of this interaction and an understanding of its nature.Here, we present a combined experimental and theoretical study providingdetailed insights into the coupling between a high-frequency vibrationaltwo-level system and thermally excited terahertz modes. Experimentally,two-dimensional terahertz-infrared-visible spectroscopy reports directlyon the coupling between quantum oscillators represented by CH Coupling to a bath is central to the evolutionof quantum states giving rise to their energy fluctuations, dephasing,and thermalization. When there is also direct interaction betweenquantum systems, excitation transfer within quantum moiety is affectedby bath dynamics, which causes fluctuations in their interaction strength.Open quantum systems, two-levelsystems continuously exchanging energy with their environment, areubiquitous in chemistry, ranging from molecules in solution to moleculesat surfaces and at confinement. In these systems, high-frequency electronicexcitations or molecular vibrations with energy quanta well-abovethe thermal energy 1 in which the thermal bath is usually representedby its spectral density determined either phenomenologically4 or from numerical calculations.6 These considerationsare typically based on significant approximations. Their benchmarkingwith detailed experimental data for real-world systems can help improvetheories to the point that these could address cases relevant to chemistry.Numerous theoretical approaches have been developed,15 While insightful, such experiments are inherently limited concerningthe details of the bath modes that shape the system dynamics, sincethere is intrinsically no mode specificity in the bath modes’thermal excitation.Yet, experimental access to molecular open quantum systems is decidedlymore challenging due to the limitations of techniques. Experimentalaccess to the coupling is typically obtained indirectly using nonlinearspectroscopy, particularly transient absorption and two-dimensionalelectronic (2D ES) and infrared (2D IR) spectroscopies. These techniquescan provide population relaxation and transfer rates, spatial reorientation,dephasing, and frequency fluctuations of a high-frequency quantumsystem.17 This technique allowsfor quantifying system-bath coupling strength in a mode-specific mannerfor both the LFM and HFM responses. However, deducing the characteristicsof HFM-LFM coupling from 2D TIRV spectra requires rigorous simulations.Up to now, three different approaches have been reported in the literaturefor such simulations: using classical molecular dynamics (MD),4 ring-polymer MD (RPMD),19 and multimode Brownian oscillator (BO)20 models. MDhas the advantage of explicitly considering intermolecular forces;thus, LFMs in this method have a clear physical origin. However, whileclassical approximation for low-frequency coordinates in MD calculationsis reasonable, classical treatment of the quantum HFMs dynamics isa considerable simplification. RPMD accounts for quantum effects  in the ground state of HFMs; however, its considerationof HFMs excitation remains essentially classical. In contrast, theBO model allows a quantum description of HFMs, but simplifies thebath degrees of freedom using a harmonic ansatz and postulating theirstatic, homogeneous spectral density. An alternative approach, basedon MD consideration of LFMs and quantum consideration of HFMs, canprovide an understandable physical origin of their dynamics, allowingone to explicitly account for their microscopic inhomogeneity andcorrelations and thus improve accuracy and insights of the calculations.Detailed insights into the coupling betweenhigh-frequency quantummodes (HFMs) and low-frequency bath modes (LFMs) of molecular vibrationsare enabled by the recently developed two-dimensional terahertz-infrared-visible(2D TIRV) spectroscopy.23 In such a mixed quantum-classicalformalism, the Hamiltonian of the quantum moiety depends parametricallyon classical LFMs coordinates. The evolution of the latter is producedby (independently parametrized) MD simulation, which explicitly accountsfor microscopic structural and dynamic inhomogeneity of the LFMs coordinates.Following these ideas, here we derive a quantum-classical sample responsefunction measured with 2D TIRV spectroscopy. The obtained result isan analogue of the fully classical equilibrium-nonequilibrium formalismpreviously developed by Hasegawa and Tanimura.24A similar idea has been successfully used in computational absorption,Raman and 2DIR spectroscopies.3 stretch quantum oscillators in liquid dimethyl sulfoxide (DMSO).Studying coupling of a C–H stretch with bath degrees of freedomusing conventional vibrational spectroscopy, e.g., spectral diffusionin 2D IR, is impeded by their typically close-to-homogeneous lineshapes.Symmetric and asymmetric CH3 stretching vibrations of DMSOmolecules have frequencies of 2913 and 2996 cm–1, respectively (∼370 meV) and a weak shoulder at 308 cm–1, which are assignedto intramolecular CH3–CH3 twist (v23), wagging (v11) and C–S–C bending (v12) vibrations, respectively.27 These modes are labeled by numbers1, 2, and 3  . We labe2, and 3 1c. We di1-frequency)is obtained by Fourier transform of this interference along the τ-axis.Spectral resolution along the infrared axis (ω2-frequency)is obtained by correcting the spectrometer frequency for the knownVIS frequency. Time delay δ between signal and LO is used indata processing to separate signals produced by VIS+IR+THz and VIS+IR–THzexcitation pathways.17Details of the 2D TIRV spectroscopy and treatmentof the signalscan be found in ref  see als.In brieDMSO | of neat liquidDMSO at 295 K. In the first quadrant, the signal originates from thesum-frequency mixing of the THz and IR fields. It contains pronouncedpeaks parallel to the ω1 axis at ω2 = 2913 and 2996 cm–1—frequencies of theCH3 stretching vibrations. This signal is broad over theω1-frequency axis but contains sharp features aroundthe ω1 frequencies of twist and wagging intramolecularmotions. The shape of ΓDMSO is affected by the spectralintensities and phases of the employed laser pulses. To eliminatethis and extract the sample response function SDMSO(3) , we use the 2D TIRV response of an ∼1 μmthick SiNx membrane as a reference, as described elsewhere.28SDMSO(3)  in the first quadrant. This signal is generatedby first and second interactions with THz and IR pulses, respectively.28 For each of the C–H stretching vibrationalmodes, the SDMSO(3) response contains three peaks at ω1 ≈ 300, 326, and 376 cm–1. They reflectcoupling with the intramolecular LFMs, which are pronounced in theabsorption spectrum  with conventional dipolemoment operator μ. The energy-level spacing of the terahertzmodes is only ≈1.5–2 times larger than the thermal energyat room temperature (∼207 cm–1). Therefore,with reasonable accuracy, their dynamics can be considered classically.33 To this end, we use a partial Wigner representationof N low-frequency degrees of freedom are represented by classical coordinateQ and momentum P . The total K high-frequency degreesof freedom are represented by quantum operators of coordinate q̂ and momentum p̂ . We use a common approximation and neglect the influenceof (excited) quantum degrees of freedom on the motion of classicaldegrees of freedom.35 The obtained mixed quantum-classical representation of the responsefunction is given byObtaining quantitative insight into the interactionof CHkB, ℏ, and T are Boltzmann and reduced Planck constants and absolutetemperature,respectively. The state of the classical subsystem is described bythe probability density function f, with f being an equilibriumdistribution. The state of the quantum subsystem after photoexcitationby an IR field is described by the quantum density operator |f,g⟩⟩, where |g⟩ and |f⟩ are quantum ground and excitedstates, respectively. μq0c (Q) is the classical dipole moment associated with low-frequencyclassical degrees of freedom Q and calculated atequilibrium position q0 of quantum degreesof freedom. In q0c takenat time –t1 –t2, when classical coordinates are Q(−t1 –t2). Excitationof quantum modes by an IR pulse is described by matrix element μfgq of quantum dipole moment operator μq  associated withthe quantum subsystem. This quantum operator parametrically dependson the classical coordinates Q and P. Matrix element of μq  is calculated for states |f⟩ and |g⟩ at time –t2. Following photoexcitation, the evolutionof the quantum subsystem for a time period t2 is given by the quantum superoperator Uq  actingon state |f,g⟩⟩. Operator Uq  depends explicitly on time t2 and parametricallyon classical coordinates Q and P. Πq is the polarizability operatorassociated with the quantum subsystem, which depends parametricallyon the classical coordinates Q and P.Here, f,g⟩⟩ during thetime period t2 and is embedded in theevolution superoperator Uq. Quantum evolution superoperator Uq  is a solution to the quantumLiouville equation of motion.In Lq is the Liouvillesuperoperator of only quantum degrees of freedom. The Liouville superoperator LqQ  representsthe interaction between quantum and classical coordinates—itacts on the state of the quantum subsystem and parametrically dependson classical coordinates. For the CH3 stretching vibrationsof DMSO, we use the approximation of isolated resonances; i.e., weneglect intra- and intermolecular excitation transfer between them.In these limits, HFMs evolution reduces to the form37ωfg is the instantaneous frequency ofmode |f⟩, and this frequency depends on classicalcoordinates Q. Response function can, thus, be writtenin the following form.Here ωfg of the high-frequencycoherence is affected by the interactionbetween the quantum system and the bath degrees of freedom. The latteris represented by two distinct types of modes. Bright modes with nonzerotransition dipole moment interact with THz pulse and, thus, appearin the 2D TIRV spectrum explicitly at corresponding ω1-frequencies. Dark modes with zero transition dipole moments arenot excited by the THz pulse and do not produce a signal. Still, theyinfluence the spectrum by perturbing frequencies of HFMs and thuscausing a broadening of spectral lines along the ω2-frequency axis.Instantaneous frequency mn according to m = s,a; n = 1,2,3,4). In this approximation, the LFM andHFM dynamics of all eight resonances are uncorrelated. Assuming smalldeviations Qn of brightLFMs from their equilibrium positions, we consider the Taylor seriesof the frequency ωmn (Q) of each CH3 mode up to the first-orderterm. In this approximation, ωmn is linearly proportional to the LFM coordinate.Using Cmn arethe coupling strengths for each HFM-LFM pair . The frequency fluctuation term δωmn (t) represents theinteraction of CH3 stretching vibrations with dark bathmodes. Therefore, in our model, we explicitly account for the interactionbetween the quantum system and both bright and dark modes of environment.Coefficients 2-frequencies of the peaks match the frequencies of the CH3 stretching vibrations. Such signals are produced by a two-quantumtransition upon interaction with the infrared laser pulse.17 In this case, the interaction with the visiblepulse promotes a single-quantum transition and does not require anyanharmonicity of the polarizability—i.e., we assume constantpolarizability of CH3 stretching vibrations that equalsits ensemble average value. We derive relative polarizabilities ofsymmetric and asymmetric modes from the Raman spectrum of DMSO (q (Q)|s,g⟩⟩ = 1 a.u. and ⟨⟨Πq (Q)|a,g⟩⟩= 0.72 a.u. . The two-quantumtransition upon infrared interaction requires mechanical anharmonicityand/or anharmonicity of the CH3 stretching transition dipolemoment. We consider the response function S(3) for these two extreme cases separately.In the experimental 2D TIRV spectrum of DMSO, the ωmnC =0. In the approximationof small deviation, the transition dipole moment μmn between the ground and first excitedstate of the mth CH3 stretch oscillator depends linearly on the low-frequencycoordinates (Taylor series truncated at the first order term), sothatm0 is the transition dipolemoment at the equilibrium position of Q, and thecoefficients Bmn representthe strengths of HFM-LFM anharmonicity for each pair of vibrationalmodes. We derive μ0 of symmetric and asymmetric CH3 stretches from their integral intensities in the absorptionspectrum. Assuming the transition dipole moment of symmetric stretchμs0 = 1 a.u., we obtain the relative transition dipole momentof asymmetric stretch μa0=1.48 a.u.  of each mode.We use a harmonic model of the LFMs of DMSO because of their narrowline width and Lorentzian lineshapes. However, we emphasize that Thus, the time-dependent dipole momentof LFMs has the formZnQn0 is the transition dipole moment of a modeand can be obtained from the absorption spectrum (ZnQn0)2. Assuming Z1Q10 = 1 a.u., relative transition dipolesare 3.69 and 3 a.u. for n = 2 and 3, respectively and ωmn(t) generates a trajectory. To perform averaging over an ensemble ofclassical coordinates (∬dQdP...), we calculate the expression below the integral in t1 = 0···3000 fs and t2 = 0···1500 fs in time steps of Δt = 15 fs for 2 × 106 trajectories. To properlysample CH3 stretch oscillations, we decreased their frequencyby 2912 cm–1. This offset is then added to the ω2-frequency produced in the calculation. At every time step,we assume a uniform probability distribution for frequency fluctuationsδΩmn (t)and δωmn(t) and a phase change given by δΩmn (t)Δt andδωmn (t)Δt, respectively. Averaging the responses generated by thetrajectories produces time-domain response function S(3) . The 2D spectrum is obtained by two-dimensional Fouriertransformation of the calculated S(3) .Realization of Bmn, Ωn, ωmn, and the range of frequency fluctuations δΩmn and δωmn listed in 1 = 315–405 cm–1, ω2 = 2915–2947 cm–1 the slopes of contours are significantly different in both realand imaginary parts of the spectra  of the CH3 stretch frequencies. Besides that, mechanicalcoupling with bright LFMs produces additional perturbation of CH3 frequencies—i.e., in Cmn ≠ 0.. Polarizabilities and transition dipole momentsof CH3 modes are constant with the relative values discussedabove (in Bmn = 0).In the second limit, we consider only the mechanicalanharmonicityof the CH3 stretching vibrations and bright LFMs are not correlated.In this case, a single resonance in the 2D TIRV spectrum has lineshape symmetric with respect to its center  is Hamiltonian of a classical subsystem.Thus, in an exact approach, Q(t)and P(t) can be obtained from equilibriumclassical MD simulations. For a given trajectory of classical coordinates,one can use numerical integration of the Schrodinger equation (NISE)39 to calculate the evolution of the quantum subsystem during a timeperiod t2 |f,g⟩⟩ in QdP...f. Such exact consideration ofcoupled quantum and classical dynamics can be particularly criticalfor systems with interacting quantum oscillators, like the O–Hstretch in water and the C=O stretch in peptides—wherethe dependence of interaction strength on classical coordinates needsto be accounted for explicitly.Our resultsdemonstrate that for isolated vibrational quantum modes,such as the C–H stretch, their coupling to the bath can bederived from experimental data using 3 stretching vibrations of liquid DMSO,we demonstrate that seemingly simple homogeneous energy broadeningof a quantum oscillator can involve unequal interaction strengthswith different thermally excited classical bath modes. The high qualityof the experimental data and their excellent agreement with the theoreticalmodel allow for determining a predominantly mechanical mechanism ofHFM-LFM interaction; i.e., fluctuations of bath modes affect the potentialof C–H covalent bonds. This coupling in the DMSO molecule caninfluence the reaction coordinate and facilitate intramolecular energyredistribution in several chemical reactions that involve breakingthe C–H bonds. The developed quantum-classical descriptionof sample response enables simultaneous explicit quantum considerationof high-frequency and classical consideration of thermally excitedlow-frequency vibrational dynamics. This formalism may especiallybenefit the investigation of excitonically delocalized quantum vibrationsin molecular solids, liquids, and biomolecules, where inhomogeneityand correlations of quantum and classical subsystems can be critical.In summary, in this work, weuse 2D TIRV spectroscopy experimentstogether with a novel quantum-classical description of sample responseto directly measure and quantify the mode-specific coupling betweenquantum vibrational states and their classical environment. Usingthe example of CH40 IR pulse is produced using commercial TOPAS  coupled to NDFG (noncollineardifference frequency generation) unit. The TOPAS is pumped by a laserbeam with ≈1 mJ/pulse energy. The VIS pulse (fwhm ≈10 cm–1) is produced using a 4f pulse shaper froma laser beam with ≈0.8 mJ/pulse energy. The local oscillator(LO) pulse is generated in a displaced Sagnac interferometer by thesum frequency mixing of IR and VIS fields in a type 1 BBO crystal. All of the pulsesare focused and overlapped at the sample to generate the third-ordersignal. After the sample, the signal and LO are collimated and directedto a spectrometer  equippedwith an electron-multiplying charge-coupled device (EMCCD) detector. We use parallel polarization of THz, IR, VIS,and signal fields. Energies of IR and VIS pulses at the sample areabout 1.5 and 4.5 μJ/pulse, respectively. More details on theexperimental setup, methods of data acquisition, and processing areprovided elsewhere.17We use a femtosecond amplifiedTi:sapphire laser system  with a repetition rate of 1 kHz, central wavelength of 800nm, and full width at half-maximum (fwhm) of ∼60 nm. The outputof the laser is split into three beams to generate broad-band terahertz(THz), broad-band infrared (IR), and narrow-band visible (VIS) pulses.THz pulse is produced from a laser beam with an energy of about 1.1mJ/pulse by two-color mixing in air plasma.x are 7.33 and1.33 ps, respectively, with the same start position. It takes around25 (5) min to acquire one spectrum of DMSO (SiNx).Time domaindata are acquired by varying the time delay betweenTHz and the IR/VIS pulse pair in steps of 10 fs using a motorizeddelay stage ). The total time intervalsof scans for DMSO and SiNx membrane  with 0.5 mm × 0.5mm aperture .The back window is 2 mm thick CaF2 window. Front and backwindows are separated by a 1 mm thick Viton O-ring.The front window of the DMSO sample cell is a 50 nm thick low-stressSiN


Virtual Services in the Health Sciences Library: A Handbook, those services had been done in the li-brary; for others, the library had con-sidered adding virtual services. For those libraries that have not made the jump, this text presents strategies to which all health sciences libraries would find a useful reference.Health sciences libraries serving universities and medical facilities have long used technology to provide library services, but COVID-19 presented libraries with unique challenges. Libraries shut their doors and adapted to conducting reference, instruction, and outreach, remotely. For some libraries described in The text presents readers with various perspectives on how medical librarians pivoted existing services to virtual services in the wake of COVID-19. Themes running through the book include relevance and engagement with users, and the challenges of instruction, technology, staff training and scheduling. The book offers librarians perspectives on their response to the shutdown, including innovations in reference services, instruction, and out-reach. Some chapter authors reflect on how the experience allowed them to gather data to inform efforts and improve efficiency and effectiveness during the COVID shutdown and beyond. The text shows that some libraries found the switch to virtual services to be productive and chose to continue services developed during the shutdown.Various authors, including the following, emphasized their efforts at increasing the library's relevance to users. In chapter 2, Solomon, et al., describe the Countway Library's yearlong brand-building project which occurred prior to COVID-19 (p. 19-20). The brand-building process “define[s] the brand's positioning through an emotional message that reveals what the brand stands for, how it benefits its users, and why they should care” (p. 20). Solomon, et al., adds that brand-positioning helped in communicating information during the COVID-19 shutdown (p. 22-28). However, in chapter 6, Hickner et al., used the COVID-19 shutdown as an opportunity to digitize archival materials and put them online as virtual outreach (p. 75-76). They adapted an annual lecture series to Zoom; because of the online availability, attendance increased 400% (p. 77).Beyer, et al. describe their library's phased evolution towards virtual reference. Phase one was virtual reference via email; later, they implemented an FAQ (p. 57). In 2013, the library began using video conferencing (p. 57). The library wanted to implement online chat, but could not until staffing made it feasible (p. 61). Online chat launch was planned for Fall 2020, but COVID-19 moved it to March (p. 58). Implementing it “in stages … helped to ensure it was sustainable” (p. 59).In Chapter 7, Trow relates to readers the challenges of under-funded and under-utilized rural hospital libraries (p. 82). She adds that hospitals “’lack … reserves, … and … must rely on revenues from pricey elective procedures” (p. 81). Trow notes that hospitals' “goal is to … break even, and [ideally] … make money” (p. 82). Hospital libraries do not make money, so are vulnerable to being cut (p. 82). Despite the profit motive, Trow cites the need for “ongoing clinical support for medical decision-making”; she adds that underfunded hospital libraries rely on virtual service, self-service, or clerks and “struggle to demonstrate value and … relevance” (p. 82). In 2019, Trow's library came under scrutiny and changes were needed (p. 83). After surveying users, online resources were acquired to support hospital needs (p. 87-89). The library implemented chat software to track data, thus improving library efficiency and relevance (p. 90-93).Chapter 4 Harding and Scull looked at textbook affordability. Some pricey health sciences textbooks require costly codes to access content (p. 40). During the COVID-19 shutdown, publishers allowed free access to resources for a limited time, which the authors note was useful, but later, users wanted to “get it back” (p. 41). They add that library purchased ebooks are often unaffordable for libraries (p. 45) “… and that health sciences librarians [must find] … their own solutions” (p. 42). Open educational resources (OER) are viable options to textbooks; despite faculty concerns for the “quality of OER,” studies have shown that students perform well with either option (p. 42-43). They add that “programs [exist] … to incentivize faculty to adopt, adapt, or create OER” (p. 44).Several authors address challenges such as technology, staff scheduling, virtual reference, and instruction. Scull details how Dartmouth College Biomedical Libraries addressed staffing, particularly information services (reference). In 2019, the libraries finished an information services efficiency and effectiveness improvement project (p. 31-33). Previously, librarians had been “on call” for reference, but other needs had increased, so changes were needed (p. 31-32). Previously, staff used multiple shared email accounts for reference, causing difficulties in collecting data for analysis (p. 32). The staff at Dartmouth Library was trained in LibAnswers and LibChat in 2019 and was able to implement the service during the shutdown (p. 32). All shared email was automatically forwarded to LibAnswers, allowing for capturing data for analysis (p. 32). Reference questions were triaged, and routine questions were answered by information services; patrons needing research assistance were referred to a librarian (p. 32).Even though library staff had a system in place to “accommodate the change” (p. 35), they scrambled to adapt, which presented technology, supplies, and staff schedule challenges (p. 33). Information services used Springshare as the virtual reference desk (p. 33). The library later assessed virtual reference and found that nearly half of users found “virtual reference to be … convenient” and others “found [it] … productive” (p. 35). Scull added that “[s]taff proved themselves to be highly competent and adaptable …”, but she emphasizes the need to keep in touch with them about “development and training” (p. 37).Several authors discuss creating or adapting library instruction and issues with library instruction during COVID-19. Evener and Chase (chapter 1) note that their library designed workshops “with the goal of filling [academic skills] gaps students or faculty may have” (p. 6). The “modules are primarily asynchronous,” though some synchronous attendance is required (p. 6). Hickner, et al. adapted instruction to Zoom and coordinated with IT services for technical help (72). They found it helpful to coordinate with others who helped improve their skills “in educational technologies” (p. 73). (Chapter 9) Monnin and Nardella emphasize the differing needs of online and in-person learners (p. 113). They add that “emergency remote teaching” differs from “traditional online … or face-to-face teaching” and that COVID-19 has “unique … challenges, including fewer resources, increased technical difficulties, and less … support” (p. 114).Virtual Services in the Health Sciences Library: A Handbook, libraries successfully delivered library services and resources by pivoting to virtual services during the COVID-19 shutdown, and as a result improved the efficiency and sustainability of services through innovation, strategy, and planning. Efforts to compare this text to similar titles have yielded a few recent texts, including Implementing Virtual Ref-erence Services: A LITA Guide (2013), Reaching Diverse Audiences with Virtual Reference and Instruction: A Practical Guide for Librarians (2019), and Medical Librarian 2.0: Use of Web 2.0 Technologies in Reference Services (2018). All three titles concern virtual library services, although the first two are not focused on health sciences libraries. Given its practical strategies and engaging text, this reviewer finds Virtual Services in the Health Sciences Library: A Handbook to be an inspiring and a highly recommended reference for health sciences libraries.In the text,


This study demonstrates that sterigmatocystin (STC) interacts non-covalently with various cyclodextrins (CDs), showing the highest binding affinity for sugammadex (a γ-CD derivative) and γ-CD, and an almost order of magnitude lower affinity for β-CD. This difference in affinity was studied using molecular modelling and fluorescence spectroscopy, which demonstrated a better insertion of STC into larger CDs. In parallel, we showed that STC binds to human serum albumin (HSA)  with an almost two order of magnitude lower affinity compared to sugammadex and γ-CD. Competitive fluorescence experiments clearly demonstrated an efficient displacement of STC from the STC–HSA complex by cyclodextrins. These results are a proof-of-concept that CDs can be used to complex STC and related mycotoxins. Similarly, as sugammadex extracts neuromuscular relaxants  from blood and blocks their bioactivity, it could also be used as first aid upon acute intoxication to encapsulate a larger part of the STC mycotoxin from serum albumin. In this research, we focused on sterigmatocystin (STC), a less thoroughly investigated derivative of AFB1, since people may be more exposed to STC as one of the most common indoor mycotoxins [7-guanine adduct [1 [1 share the bis-furane structural moiety  signal close to the visible range (ICD band at 350 nm) [1 upon ingestion, inhalation, or through the skin requires transport of these mycotoxins by blood, commonly involving serum albumin, the most abundant blood protein and a well-known transporter of small molecules [1, HSA may form covalent adducts with up to 2.3% of absorbed AFB1 [SVK 3.98 ± 0.06 M−1, obtained by fluorescence spectroscopy) and, based on molecular modelling, the heme binding site was suggested as the most probable STC binding site [Human serum albumin (HSA) is the most abundant serum protein. Along with its numerous physiological functions, which include the regulation of oncotic pressure and transport of fatty acids, hormones, bile salts, amino acids, metals, and toxic metabolites, it also participates in the transport of drugs and xenobiotics to their target organs or tissue. The interactions of drugs and xenobiotics with HSA are important to monitor because of their role in distribution, metabolism, and elimination of drugs and xenobiotics, as well as in the assessment of their desired and undesired effects . Similarrcinogen , is the cotoxins ,5,6. Rese adduct . Howeverdduct [1 . Althougl moiety a,b, STC  350 nm) . We used 350 nm) . Howeverolecules . Followibed AFB1 . A recening site . ConsequKa = 1.03 × 107 M−1) [1 mostly forms an inclusion complex in cyclodextrin’s hydrophobic interior, resulting in enhanced AFB1 fluorescence [1 group into β-CD is supported by hydrogen bonds formed between the carbonyl AFB1 group and secondary hydroxyl β-CD groups [Cyclodextrins are particularly suitable for eliminating toxic substances in vivo because their lipophilic cavity enables formation of complexes with lipophilic small molecules, while at the same time they are perfectly soluble in physiological fluids due to their polyhydroxy hydrophilic coat. This has been adopted in the pharmaceutical industry and cyclodextrins are widely used as solubilizing agents as well as antidotes. Sugammadex, a registered drug prescribed for the reversal of block anaesthesia is a great example of an antidote . Sugamma107 M−1) , and is 107 M−1) ,17. For rescence . MoleculD groups . RegardiPrevious research has shown that cyclodextrins are useful additives in improving the fluorescent properties of mycotoxins , and becKs from experimental data obtained by UV/Vis and CD spectroscopic methods, as well as isothermal titration calorimetry (ITC). Furthermore, STC displacement from the STC-HSA complex was examined by fluorometric titration of the pre-formed STC-HSA complex with sugammadex, β-CD, and γ-CD.In this paper, the non-covalent interactions between STC and β-CD, γ-CD, and sugammadex were explored and compared to interactions between STC and HSA. Competitive fluorescence experiments were conducted to demonstrate displacement of STC from the STC-HSA complex by cyclodextrins, which was also confirmed by molecular dynamics simulations. The affinity was calculated as logWe have previously shown that in aqueous solutions, STC forms very stable non-covalent aggregates, dissociating only at submicromolar concentrations . Thus, STitration of STC aggregates with γ-CD, β-CD, and sugammadex resulted in a strong decrease in the ICD signal intensity at 350 nm a–c, wherThe results of CD experiments strongly support binding of STC to cyclodextrins and HSA; however, they should be verified by an independent method. It could be expected that a switch from an STC aggregate to an STC–host complex would have an impact on the STC UV/Vis spectrum. For that reason, we performed UV/Vis titrations, which showed a strong hypochromic effect upon addition of any CD , and fitSo far, we have studied interactions of STC aggregated in an aqueous solution with various hosts, thus monitoring STC deaggregation as a consequence of binding to a host. Now, with the aim to directly monitor a single STC molecule binding to a host, we presumed that upon addition of a small aliquot of STC stock solution in acetonitrile (in which STC is not aggregated ) to a aqλexc = 280 nm) coincides with negligible absorbance in the STC UV spectrum, thus avoiding the inner filter effect [HSA is intrinsically fluorescent and binding of a small molecule to any of its binding sites causes a change in HSA fluorescence emission . Convenir effect . AdditioKs = 4.5–5.0) with respect to the binding constant obtained from fluorometric titration (logKs = 6.2) can be attributed to the STC deaggregation process (present only in CD and UV titrations), which considerably competed with STC binding to HSA. Thus, addition of the STC acetonitrile solution to HSA allowed dominant binding of a single STC molecule to HSA, with no significant interference of STC aggregates. This also corroborates with a previously reported binding constant for the STC/HSA complex [SVK 3.98 ± 0.06 M−1). Much lower values of the apparent binding constants obtained for the STC–HSA complex from CD and UV titrations  but likely involves a portion of the free STC in solution, whereby these free STC molecules are prone to forming STC aggregates. Although such a complex multicomponent system with several intertwined thermodynamic equilibria of different kinetics is not an ideal system for competitive experiments, the obtained results clearly demonstrate that addition of any cyclodextrin to the STC–HSA complex caused an increase in HSA fluorescence up to the maximum emission intensity of free HSA .The spectrophotometric experiments shown above revealed that STC can form strong non-covalent interactions with both HSA and cyclodextrins. To study these interactions by an independent method and to reveal more information on the thermodynamic profile, we decided to perform isothermal titration microcalorimetry (ITC) studies. However, STC has a low solubility in water and forms highly stable aggregates in aqueous solutions ; thus, eG°), which could be correlated to a combination of hydrophobic interactions and H-bonding interactions likely to happen within the HSA binding site. A negative value of ΔG° points to the spontaneity of the binding process.Indeed, titration of the STC aggregate with HSA A revealeG° points to the spontaneity of the binding process. Since the STC deaggregation process would likely be the same as in the HSA titration, the cumulative endothermic binding energetics in In contrast to HSA, addition of sugammadex to the STC aggregate resulted in an endothermically driven binding interaction B. Again,TΔS° = 35 kJ/mol, and an unfavourable enthalpy, ΔH° = 7.89 kJ/mol. The positive change in entropy is an indication of hydrophobic interactions and brings an additional contribution to the negative Gibbs free energy (ΔG° = –27.1 kJ/mol), while a small positive enthalpy change indicates few electrostatic interactions occurring. Considering that |ΔH°| < |TΔS°|, the binding reaction occurring between the STC–HSA complex and sugammadex is driven mainly by changes in entropy. It should be stressed that titration of HSA alone with sugammadex did yield heat changes of approximately 4 kJ/mol, although complete HSA saturation was not achieved  were subjected to 150 ns MD simulations to study their stability and binding constants. In the studied time frame, both complexes were stable ; the sli®, Merck Sharp & Dohme Limited, Hertfordshire, UK) was kindly provided by Medika d.d., Zagreb, Croatia; cyclodextrin-β hydrate was obtained from Alfa Aesar, Hengelo, the Netherlands; and sodium cacodylate buffer was prepared by mixing 0.2 M solution of sodium cacodylate  with 0.2 M hydrochloric acid  at 25 °C until an I of 50 mM and a pH of 7.0 ± 0.02 were reached.STC, CD-γ, and HSA were purchased from Sigma Aldrich, Darmstad, Germany; sugammadex (Bridion−4 M) were prepared in a cacodylate buffer, while in the case of sugammadex, the clinically applied solution (0.05 M) was used. All working solutions for spectrophotometric titrations were prepared in a cacodylate buffer. The STC stock solution (8.9 × 10−3 M) was prepared in acetonitrile. For certain experiments, the STC aggregate solution was prepared by dissolving STC (acetonitrile stock solution) in a cacodylate buffer (2.2 × 10−5 M) and incubating for 30 min at room temperature. The aggregate formation was confirmed by the unique CD spectrum band at 350 nm according to Jaksic et al., 2019 [Stock solutions of β-CD (0.005 M), γ-CD (0.01 M), and HSA 1.73 × 10−4 M wereI = 1.5 mM, pH 7.40 ± 0.02). For microcalorimetric (ITC) titrations, the original sugammadex solution was diluted by bidistilled water, the STC stock solution was prepared in DMSO (0.015 M), while the STC and HSA working stock solutions were prepared ex tempore in a freshly prepared NaCl solution  in bidistilled water (l = 1 cm) by addition of γ-CD (0.001 M), sugammadex (4.6 × 10−4 M), β-CD (0.005 M), and HSA (1.74 × 10−4 M) to the preformed STC aggregate in a cacodylate buffer (2 × 10−5 M). The UV/Vis spectra were recorded on a Varian Cary 100 Bio spectrophotometer . UV/Vis titrations were performed at room temperature in a quartz cuvette (l = 1 cm) by addition of γ-CD (0.001 M), sugammadex (4.6 × 10−4 M), β-CD (0.005 M), and HSA (1.74 × 10−4 M) to the preformed STC aggregate in a cacodylate buffer (2 × 10−6 M). The instrument was set to a scanning speed of 200 nm/min, and an average of 3 accumulations of the spectra in the range of 220–550 nm were recorded on a JASCO J-815 spectropolarimeter .CD titrations were performed at room temperature in a quartz cuvette (l = 1 cm) upon addition of STC (0.5–1 μL) dissolved in acetonitrile (8.9 × 10−3 M) to a HSA solution in a cacodylate buffer (2 × 10−6 M). Competitive fluorescence experiments were performed in the same cuvette by addition of γ-CD (0.01 M) and β-CD (0.005 M) prepared in a cacodylate buffer and the original sugammadex solution (0.05 M). The excitation wavelength was set to 280 nm, and the emission spectrum was recorded in the 300–500 nm range. The slits were set to 10/10.Fluorescent spectra were measured on a Cary Eclipse fluorescence spectrophotometer (Agilent Technologies) in quartz cuvettes  and Origin Lab 9.0 .−5 M) or sugammadex solution (5 × 10−4 M) into the calorimeter sample cell containing the pre-formed STC aggregate (10−5 M) in a solvent composed of NaCl in bidistilled water , which was continuously stirred at 500 rpm. The maximum DMSO concentration in the solvent was 0.067%. Bidistilled water was placed in the reference cell. Differential power (DP) was arbitrarily applied to each titration. The interval between successive aliquots was 100 s. To correct for the thermal effect, control experiments were performed in which HSA or sugammadex solutions were added to the solvent, as well as a control experiment in which the solvent was added to the STC solution; all of which were performed under the same conditions as in the original titrations. Thereafter, the heat resulting from dilution was subtracted from the STC–HSA and STC–sugammadex titration data. In competitive titration experiments, all titrations were carried out in the same way as described above, where after the end of the STC (10−5 M) titration with HSA (2.5 × 10−5 M), the burette was cleaned and loaded with sugammadex (5 ×10−4 M), which was then added in small aliquots to the pre-formed STC–HSA complex. The reverse competition experiment was performed similarly; HSA (2.5 × 10−5 M) was added to the pre-formed STC–sugammadex complex at the end of the STC (10−5 M) titration with sugammadex (5 ×10−4 M).The thermodynamic parameters for STC interactions with HSA and sugammadex were performed at 25 °C on a MicroCal PEAQ-ITC calorimeter . The STC stock solution in DMSO (0.015 M) was used in these experiments so that a stable STC aggregate could be prepared in an aqueous solvent with the lowest possible amount of organic solvent (0.067%). Solutions were thoroughly degassed prior to titration under vacuum for 10 min to avoid any bubble formation in the sample cell. The sample cell volume was 200 µL and the burette volume was 40 µL. Titrations were performed by adding aliquots of 1 µL of HSA solution  . Conform+ ions. After this, the complex was submitted to geometry optimization in the AMBER16 program [−1). The SHAKE algorithm [MD simulations were conducted at 300 K for two different complexes in the presence of water: the STC–sugammadex complex  and the STC–HSA complex (obtained by docking). A GAFF force field was used to model the cyclodextrin and STC molecules, while an AMBER ff14SB force field was used to model HSA, after which the protein molecule was solvated in a truncated octahedral box of TIP3P water molecules spanning a 12 Å thick buffer and was neutralized by Na program , employilgorithm  was usedlgorithm  was emplGbind, of the simulated complexes was calculated using the MM/GBSA  protocol [Gbind from snapshots of the MD trajectory [Gbind was calculated in the following manner:Gbind. This protocol also identifies the nature of the energy change, i.e., the type of interaction, the solvation energy, and the entropic contribution [The binding energy, ∆protocol ,36, avaiprotocol . MM/GBSAajectory , with anajectory . ∆Gbind ribution ,39. In tResults obtained from three spectrophotometric methods, as well as from a spectrophotometry-independent microcalorimetric method, support the formation of STC–cyclodextrin inclusion complexes. However, STC’s unique ability to strongly aggregate at biologically relevant concentrations in aqueous solutions  was a siThree methods, CD, UV/Vis spectrophotometry, and ITC microcalorimetry, were performed under conditions in which STC aggregates. The results showed high apparent binding constants between sugammadex and γ-CD, and an almost order of magnitude lower affinity towards β-CD . In parallel, CD, UV/Vis, and ITC data showed that STC binds to HSA  with almost a two order of magnitude lower affinity compared to the sugammadex and γ-CD complexes. Only the fluorometric experiments, in which intrinsically fluorescent HSA was titrated by addition of an STC acetonitrile solution (in which STC is not aggregated), was able to demonstrate the interaction of HSA and a single STC molecule, revealing the STC micromolar binding constant. This approach, which allowed performing competitive experiments, demonstrated that all cyclodextrins can extract STC from HSA, as proven by recovery of the free HSA fluorescence. These results were confirmed by an ITC competition experiment, also demonstrating the efficient extraction of STC from HSA by sugammadex. Thus, the observed competition mechanism resembles dissociation of the rocuronium–HSA complex caused by rocuronium encapsulation by sugammadex ,20.Gbind for STC–cyclodextrin and STC–HSA complexes, supporting the idea that cyclodextrins could be used for displacement of STC from HSA. This is due to STC’s aromatic and hydrophobic structure which nicely fits inside the hydrophobic sugammadex cavity, which at the same time has six oxygen atoms which can form hydrogen bonds with the hydroxy groups of sugammadex, further stabilizing the STC molecule inside the sugammadex ring. This results in a very stable complex which can even be formed when STC is already bound to HSA.Molecular modelling studies confirmed a good structural fit of STC inside the sugammadex molecule, as well as to the HSA molecule. The binding constants obtained through molecular dynamics simulations showed similar ΔTo conclude, the obtained results can be considered as a proof-of-concept on a molecular level that sugammadex could be used as first aid after an acute intoxication to encapsulate a larger part of STC mycotoxin from serum albumin in blood. These findings strongly support further pharmaco(toxico)kinetic studies with STC and analogous mycotoxins by protocols developed for clinically used rocuronium/sugammadex systems.


Correction: BMC Cancer 20, 806 (2020)10.1186/s12885-020-07264-8.Following publication of the original article , the autwhen the 18th patient in each arm has undergone first radiographic assessment .”In the line of the study procedures section “In total, 44 patients will be enrolled per group. After n = 18 patients have been enrolled to the HYPO- or CON-treatment arm, respectively, an interim efficacy analysis for the respective arm will be conducted based on the objective response rate (ORR) at 12 weeks after first durvalumab administration”, the sentence was refined to “[…] the objective response rate (ORR), In the study endpoint section, in the line “The ORR evaluated 12 weeks after first durvalumab administration (according to RECIST 1.1) is set as the primary efficacy endpoint.”, the sentence changed to “The ORR according to RECIST 1.1 is set as the primary efficacy endpoint.”