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Abstract

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The index of consciousness (IoC), the permutation entropy (PE), and the approximate entropy are recent EEG-derived indices of anaesthetic depth. In this study, a rabbit model under fentanyl and isoflurane anaesthesia was used to compare the performance of these indices and also the classic median and spectral edge frequency 95%.

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EEG recordings were obtained from six rabbits. Animals received fentanyl for premedication, followed by induction with propofol and maintenance with isoflurane. Anaesthetic depth was evaluated according to a clinical scale from 1 (awake) to 4 (surgical anaesthesia). Animals were submitted to surgical implantation of a small device in the lumbar muscles. A correction factor for the EEG suppression ratio was applied to the spectral parameters and to the PE. The correlation of the indices with the clinical scale of anaesthesia was analysed using prediction probability. Repeated-measures analysis of variance or its non-parametric equivalent was used to analyse the indices values at the study times and to compare their variability.

The IoC showed the best mean prediction probability value [0.94 (0.01)] followed by burst suppression-corrected PE [0.91(0.03)]. Both parameters also showed less variability than the others.

The IoC and PE are promising indices for anaesthetic depth monitoring. The PE might benefit from the application of a burst suppression correction at deeper stages of anaesthesia. The rabbit is useful as a translational research animal model for the validation of clinical indices.

• A rabbit model was used to compare processed EEG indices of isoflurane/fentanyl anaesthetic depth.

• The recently introduced index of consciousness and permutation entropy were the most reliable and least variable indices for predicting anaesthetic depth compared with other parameters.

• This provides a useful translational model for evaluation and comparison of EEG monitors of anaesthesia.

Several parameters have been derived from the EEG to translate its complex information into a number for intraoperative monitoring of anaesthetic depth. The index of consciousness (IoC) is the most recently introduced commercial monitor for this purpose.^1,2 On the other hand, there are open-source parameters such as permutation entropy (PE) and approximate entropy (AE) that have also been studied.^3–6 Distinct from the IoC and entropy-based parameters, which are based on non-linear signals analysis methods, the classic median edge frequency (MEF) and spectral edge frequency 95% (SEF) are mathematically simpler.⁷

In order to find the best parameter for anaesthetic depth monitoring, it is essential to compare the performance of existing parameters. Animal models can give important insights at this level, as they provide controlled conditions with minimized variability and high-quality EEG recordings.^8,9 The rabbit has the advantage of having a thin muscular layer between the skin and the skull resulting in little electromyographic (EMG) artifacts in extracranial recordings. We have previously shown the potential of processed EEG indices to reflect different anaesthetic depths from intracranial recordings in laboratory conditions.¹⁰ However, it is essential to understand how they behave in conditions similar to the clinical setting, with extracranial recordings and during surgical procedures while preserving the high standardization of animal experimentation. In the present study, we used the rabbit under fentanyl–isoflurane anaesthesia as a potential translational research animal model for the validation of clinical indices due to its anatomical characteristics. The performance of different EEG-derived parameters: IoC, PE, AE, MEF, SEF, and the burst suppression-corrected MEF, SEF, and PE (BSMEF, BSSEF, and BSPE) was compared.

Methods

Animals

All procedures were carried out under personal and project licences approved by the national regulatory office (Direcção Geral de Veterinária—DGV). Six healthy male New Zealand White rabbits, average weight 3.03 (0.03) kg, were anaesthetized for this study.

EEG recording

The EEG recordings were performed using the IoC-View monitor (Aircraft Medical, Barcelona, Spain). Before induction of anaesthesia, animal heads were shaved and cleaned, and surface layers were removed with fine sandpaper and acetone. Animals were used to human handling before experiments and showed no visible signs of discomfort during the initial preparation procedure. Gel-coated silver–silver chloride electrodes (Swaromed, Innsbruck, Austria) were applied to record the EEG. Two electrodes (one for each eye) were placed 1 cm caudal to the lateral eye canthus; a central electrode was placed on the midline on the frontal bone 3 cm away from each previously applied electrode. This localization was based on previous works for the BIS monitor in rabbits⁸ and has been concluded to give the best quality EEG signal after testing different positions in pilot studies with the IoC-View monitor.

Impedance was automatically checked by the monitor and maintained below 15 000 Ohms at 1024 Hz. The electrodes were connected to the IoC-View monitor, which was connected by Bluetooth to a personal computer with the IoC-View graph software version 1.4 installed, a storage software provided by the manufacturer.

Anaesthesia and monitoring

After EEG baseline recording in the awake animals for 5 min, the fur on the ears was clipped, the skin was cleaned with alcohol, and a local analgesic cream was applied to the ears skin (EMLA, Nycomed US Inc., New York, NY, USA). Thirty minutes later, two 22 G catheters were placed, one in the marginal ear vein and another into the central ear artery for arterial pressure monitoring. Both auricular catheter systems were flushed with heparinized saline and fixed to the skin. All animals then received 10 µg kg⁻¹ of fentanyl (B Braun, Melsungen, Germany) i.v. The animals were then oxygenated with a facial mask at 5 litre min⁻¹ for 5 min.

Anaesthesia was induced with propofol (10 mg kg⁻¹) i.v. over 60 s. After blind orotracheal intubation with a tracheal tube (2.5 mm in internal diameter), isoflurane was administered through a calibrated vaporizer (Abbott, Amadora, Portugal) with a T-Ayres respiratory circuit connected to an isoflurane absorber.

The isoflurane end-tidal concentration was adjusted to ∼3% (1.5 MAC) at a fresh gas flow of 3 litre min⁻¹ in 100% oxygen for maintenance.

The rabbits were placed in ventral recumbence above a heating blanket and rectal temperature was continuously monitored and maintained at 37–38°C. Anaesthetic monitoring included cardiorespiratory monitoring provided by a Datex S/5 Anaesthetic station (Datex Ohmeda, Helsinki, Finland) which included: pulse-oximetry and heart rate monitored with the probe placed in the tongue or the ear, invasive mean arterial pressure (MAP), inspired and end-tidal concentrations of oxygen, carbon dioxide, and isoflurane (e′ISO). These data were stored using the RugloopII Vet software [developed by Tom DeSmet (Demed Engineering, Gent, Belgium)]. Animals were submitted to surgical implantation of a small device in the lumbar muscles. Skin incision was ∼3 cm long and after gentle dissection, a 1 cm long biomaterial implant was inserted in the lumbar muscles. Surgery was always performed by the same surgeon. At the end of the surgery, the vaporizer was switched off and the fresh gas flow rate was increased to 5 litre min⁻¹ of 100% oxygen. Once the rabbits regained swallowing reflexes, extubation was performed. Animals were considered recovered from anaesthesia when they exhibited an alert stance and had regained ambulation and limb coordination. Continuous infusion of physiological saline at a rate of 10 ml kg⁻¹ h⁻¹ was maintained during anaesthesia. Postoperative analgesia was provided by subcutaneous administration of 4 mg kg⁻¹ carprofen (Rymadil^®, Pfizer Saúde Animal, Carnaxide, Portugal).

A clinical evaluation of depth of anaesthesia was performed according to a subjective numerical scale of anaesthesia from 1 (awake) to 4 (surgical anaesthesia) (Table 1) based on the evaluation of muscular tone, eyelid reflex, corneal reflex, laryngeal reflex, ear pinch, and digital (pedal) reflexes. This evaluation was always performed by the same investigator who was blinded to the EEG.

Definition of anaesthetic states and attributed numerical scale

Data analysis

Derivation of indices

The IoC-View Graph 1.4 software stored the EEG data as binary files which were further converted to MATLAB format to be processed in offline using the MATLAB^® software (MathWorks, Natick, MA, USA).

The IoC and the EEG suppression ratio (ESR), equivalent to the burst suppression ratio (BS)⁷ and the EMG activity, were automatically derived by the IoC-View monitor. The monitor also displayed the signal quality index (SQI) every second. The indices were derived in epochs of 8 s and only epochs that showed an SQI value of 100 were considered. A band-pass filter was applied to the signal (0.5–32 Hz) before derivation of the open-source indices. These consisted in the PE, AE, MEF, and SEF. The spectral parameters were corrected for the presence of burst suppression patterns using the values of ESR recorded by the IoC-View monitor according to the correction factor proposed by Rampil.⁷ The same correction factor was applied to PE resulting in BSPE, calculated as follows: BSPE=PE×(1−ESR/100).

The AE and the PE were computed according to published algorithms.^3,5 Briefly, the calculation of AE depends on three factors: the embedding dimension (m), the number of samples considered for each calculation (n), and the noise threshold (r). In this study, n=2000, m=2, and r=0.2 were selected, based on previous studies.³ For the PE calculation, the length of subvectors (m) and the analysed signal interval (length N) are main factors. In this study, we used m=3 and N=2000, as in previous works.^6,10 A more detailed description of the AE and PE calculation is available.^5,6

Contrary to these parameters, for which algorithms are published, the IoC is a proprietary index and only the general aspects of its calculation are known. It was developed to match the anaesthetic depths observed in a database from patients anaesthetized with a variety of anaesthetics. For its calculation, a fast Fourier transform is carried out after applying a Hamming function to a 3 s window of the EEG. The energies in different frequency bands (1–6, 6–12, 10–20, 30–45 Hz) are used as inputs to a logistic exponential regression combined with fuzzy logic rules. Symbolic dynamics, which transforms a time series into a symbol sequence, provides a model for the orbits of the dynamical system via a space of sequences and is used to facilitate a non-linear analysis of the signal.¹ Similar to PE, calculating the attributes of the symbol sequence can reveal non-linear characteristics of the EEG. Finally, the ESR is calculated over a 30 s window and is also incorporated in the final index calculation. In humans, decreasing values of IoC correspond to gradual loss of consciousness and a deepening of the depth of anaesthesia. In a unitless scale from 99 to 0, a value of 99 indicates an awake patient and a value of 0 indicates a flat EEG.^1,2

Variability evaluation

To evaluate the variability of each parameter, the median absolute deviation from the median value (MAD) was calculated. It reflects how the indices fluctuate around the median. To allow a meaningful comparison between the different indices studied, the MAD was normalized by using the median of the index measurements as denominator. This method is similar to the performance error parameter used to evaluate the performance of infusion pumps in the previous work.¹¹ The product was then multiplied by 100 and resulted in the percentage of deviation from the median value, and was named as variability indicator. A similar application of the MAD to compare indices of anaesthetic depth has been reported.¹² In order to ensure that the resultant variability was inherent to the index and not to fluctuations in anaesthetic depth, only data from periods with stable anaesthetic depth were used. Thus, two study periods with steady anaesthetic depth were selected: the awake state (T0) and the anaesthetized state (T4).

Statistical analysis

The EEG processing indices were exported from MATLAB to Graphpad Prism (Version 5, GraphPad Software Inc., San Diego, CA, USA) for statistical analysis. The Kolmogorov–Smirnov normality test was used. All tests were detailed with a statistical significance defined as P<0.05.

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The capacity of the studied indices (IoC, PE, AE, MEF, SEF, BSPE, BSMEF, and BSSEF) to detect the different anaesthetic states reflected in the numerical scale of anaesthesia (Table 1) was evaluated using prediction probability (Pk) statistics by correlating the parameter values during 64 s (eight measurements) at each of the defined study periods with the numerical scale. Pk was calculated using a custom spreadsheet macro, PKMACRO.¹³ A Pk of 1 means that the parameter always decreases as the subject reaches deeper anaesthetic states. Alternatively, a Pk value of 0.5 means that the indicator is useless for predicting the depth of anaesthesia. To show the response for each individual, the Pk was calculated for each animal.

To assess the behaviour of each index at study times, statistical comparisons were performed between the previously defined study times (from T0 to T10) using analysis of variance (anova) for repeated measures with the Bonferroni correction for normal data, and the Friedman test with Dunn's post hoc test for non-parametric data. The EMG, MAP, and e′ISO were also analysed at these study times.

The variability indicator was calculated for the awake state and the anaesthetized state for each index of anaesthetic depth individually for each animal, and the variability between indices was analysed using anova for repeated measures with the Bonferroni correction. Mean and standard error of the mean or standard deviation of the six animals are presented in the Results section.

Results

Anaesthesia and surgery were uneventful for every animal. The total duration of anaesthesia was 37 (5.5) min and surgery lasted 17.9 (1.9) min. During anaesthetic maintenance, depth of anaesthesia as determined by clinical observation was similar in all rabbits (negative reaction to ear pinch, eyelid and limb withdrawal reflexes, and positive corneal reflex). None of the animals showed nystagmus or spontaneous eyelid reflex during surgery. Apnoea did not occur in any animal and the end-tidal carbon dioxide was maintained between 4.8 and 5.9 kPa. No complications were observed during recovery from anaesthesia.

A numerical scale of anaesthesia was previously defined based on clinical signs (Table 1). A correspondence was found between the scale of anaesthetic depth and the study periods as follows: 1 (awake) corresponded to the periods before administration of fentanyl (T0) and after regaining ambulation (T10); 2 (drowsy) was observed from the recovery of righting reflex to regaining ambulation (T9); 3 (sedated) was observed after fentanyl administration until induction (T1), and 4 (surgical anaesthesia) was the state observed from the moment when the ear pinch reflex was lost to the moment of discontinuation of isoflurane (T2–T8).

Regarding EEG recordings, all animals showed a similar pattern with a shift from high frequency and low-voltage waves to low frequency and high-amplitude waves after induction of anaesthesia. None of the animals showed BS patterns after induction with propofol, but four had BS patterns during the surgery period. PE, MEF, and SEF showed a tendency to increase when BS was present. This made it useful to apply a correction for BS in these indices.

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Prediction probabilities (Pk) were calculated between the subjective anaesthetic depth scale and the studied parameters. IoC showed the best mean Pk value [0.94 (0.007)] followed by BSPE [0.91 (0.03)] and PE [0.90 (0.04)]. For SEF, it increased from 0.82 (0.09) to 0.84 (0.09), with the introduction of the BS correction. For AE, MEF, and BSMEF, Pk values were 0.81 (0.09), 0.66 (0.13), and 0.71 (0.11) (Fig. 1).

Prediction probabilities (Pks) for the studied indices: IoC, index of consciousness; PE, permutation entropy; AE, approximate entropy; MEF, median edge frequency; SEF, spectral edge frequency 95%; BSPE, burst suppression corrected PE; BSMEF, BS corrected MEF; and BSSEF, BS corrected SEF. Each animal's Pk value is shown by a symbol (n=6).

Prediction probabilities (Pks) for the studied indices: IoC, index of consciousness; PE, permutation entropy; AE, approximate entropy; MEF, median edge frequency; SEF, spectral edge frequency 95%; BSPE, burst suppression corrected PE; BSMEF, BS corrected MEF; and BSSEF, BS corrected SEF. Each animal's Pk value is shown by a symbol (n=6).

The variability of each index was studied for the awake and the anaesthetized states (Table 2). MEF and BSMEF in the awake state showed significantly higher variability than all the other indices (P<0.001). In the anaesthetized state, IoC, PE, and BSPE had significantly lower variability values than MEF, BSMEF, SEF, and BSSEF (P<0.001). The introduction of a BS correction increased the variability of PE, but the difference was not statistically significant.

Variability indicators (%) for the studied indices: IoC, index of consciousness; PE, permutation entropy; AE, approximate entropy; MEF, median edge frequency; SEF, spectral edge frequency 95%; BSPE, burst suppression corrected PE; BSMEF, BS corrected MEF; and BSSEF, BS corrected SEF. The mean and standard deviation are presented (n=6). The variability indicators are presented separately for the awake and the anaesthetized states

Variability indicator (%)

Awake	Anaesthetized
IoC	0.8 (1.1)	1.8 (1.9)
PE	0.8 (0.3)	1.4 (0.6)
AE	4.7 (1.6)	6.0 (2.9)
MEF	22.5 (15.9)	10.7 (6.1)
SEF	3.7 (1.4)	9.5 (3.0)
BSPE	0.8 (0.3)	1.8 (0.4)
BSMEF	22.5 (15.9)	10.8 (5.9)
BSSEF	3.7 (1.4)	9.0 (3.0)

Variability indicator (%)

Awake	Anaesthetized
IoC	0.8 (1.1)	1.8 (1.9)
PE	0.8 (0.3)	1.4 (0.6)
AE	4.7 (1.6)	6.0 (2.9)
MEF	22.5 (15.9)	10.7 (6.1)
SEF	3.7 (1.4)	9.5 (3.0)
BSPE	0.8 (0.3)	1.8 (0.4)
BSMEF	22.5 (15.9)	10.8 (5.9)
BSSEF	3.7 (1.4)	9.0 (3.0)

Variability indicators (%) for the studied indices: IoC, index of consciousness; PE, permutation entropy; AE, approximate entropy; MEF, median edge frequency; SEF, spectral edge frequency 95%; BSPE, burst suppression corrected PE; BSMEF, BS corrected MEF; and BSSEF, BS corrected SEF. The mean and standard deviation are presented (n=6). The variability indicators are presented separately for the awake and the anaesthetized states

Variability indicator (%)

Awake	Anaesthetized
IoC	0.8 (1.1)	1.8 (1.9)
PE	0.8 (0.3)	1.4 (0.6)
AE	4.7 (1.6)	6.0 (2.9)
MEF	22.5 (15.9)	10.7 (6.1)
SEF	3.7 (1.4)	9.5 (3.0)
BSPE	0.8 (0.3)	1.8 (0.4)
BSMEF	22.5 (15.9)	10.8 (5.9)
BSSEF	3.7 (1.4)	9.0 (3.0)

Variability indicator (%)

Awake	Anaesthetized
IoC	0.8 (1.1)	1.8 (1.9)
PE	0.8 (0.3)	1.4 (0.6)
AE	4.7 (1.6)	6.0 (2.9)
MEF	22.5 (15.9)	10.7 (6.1)
SEF	3.7 (1.4)	9.5 (3.0)
BSPE	0.8 (0.3)	1.8 (0.4)
BSMEF	22.5 (15.9)	10.8 (5.9)
BSSEF	3.7 (1.4)	9.0 (3.0)

The values of the studied parameters at the main study times (from T0 to T10) are shown in Figure 2 as mean and standard error of the mean. The values of the EMG, MAP, and e′ISO were also analysed (Table 3). None of the studied parameters showed a significant decrease after fentanyl administration and no BS activity appeared (from T0 to T1; Fig. 2).

Mean arterial pressure (MAP), electromyographic activity (EMG) and end-tidal isoflurane concentration (e′ISO) (n=6) at the different study times: T0, baseline recording; T1, 1 min after fentanyl administration; T2, right before intubation; T3, when the ear pinch reflex was lost; T4, 1 min before incision; T5, 1 min after incision; T6, 5 min after incision; T7, 10 min after incision; T8, end of surgery; T9, at extubation; T10, regaining ambulation. The mean and standard deviation are presented. Statistically significant differences with the previous study time: ***P<0.001

Discussion

The performance of the most recent EEG-derived indices was compared using an alternative rabbit model subjected to routine surgery under fentanyl–isoflurane anaesthesia. The indices studied included the IoC, PE, AE, MEF, and SEF. PE, MEF, and SEF were also studied with a correction for BS incorporated and designated as BSPE, BSMEF, and BSSEF, respectively.

IoC showed the best performance in detecting different depths of anaesthesia, as reflected by its higher prediction probability, followed by BSPE and PE. AE, SEF, and BSSEF also had acceptable performances but showed much higher variability than IoC and PE.

The application of the BS correction slightly improved the performance of PE, SEF, and MEF. However, two animals did not show any BS patterns. This might explain the very similar prediction probability means obtained for the original and the corrected parameters. It is known that the spectral parameters, such as MEF and SEF, may suffer paradoxical increases when BS patterns appear due to its high-frequency components.^14–16 A similar paradoxical increase has been reported in PE in humans during sevoflurane anaesthesia⁵ and from intracranial recordings in isoflurane-anaesthetized rats.¹⁰ The latter study was a laboratory-based study with excellent signal quality recordings. On the basis of those findings, it would be expected that BSPE would have better performance than IoC, but this was not observed. This highlights the importance of pre-processing when performing extracranial recordings in the clinical setting, as the better performance of IoC in the present study might be related to the artifact detection system incorporated on the IoC-View monitor. This is a proprietary monitor and its exact calculation algorithm is not published, but it is known that, as in other commercial monitors, it has a BS ratio component,^1,2 which might also contribute to its good performance during volatile anaesthesia. Nevertheless, PE showed lower variability than IoC in the anaesthetized state, demonstrating its robustness, which has previously been suggested as an important benefit for practical use.^5,6 The spectral parameters are the most well-studied EEG-derived parameters, and are known to be highly sensitive to artifacts.^5,15 This could explain the higher variability and lower prediction probabilities observed in the present study with spectral parameters than with non-linear analysis methods.

Regarding the behaviour of the indices at different times of the study, although IoC, PE, and BSPE had good prediction probabilities with the anaesthetic depth scale, the effect of fentanyl could not be detected by any of the indices. This could be related to difficulties for EEG responses to reflect the analgesic component effects, as opioid administration during general anaesthesia has been associated with excitatory patterns on the EEG.^17–19 The administration of remifentanil during anaesthesia has been reported to affect the reliability of anaesthetic depth indices both by affecting the occurrence of clinical endpoints²⁰ and by changing the indices values, particularly in the case of AE.²¹ This could be a reason for its inferior performance compared with IoC and PE observed in the present study.

Similar prediction probability values were found for AE, SEF, and MEF in a study performed in children anaesthetized with isoflurane, in which the clinical scale used to evaluate depth of anaesthesia was comparable with the one used in the present study.²²

The usefulness of an index of depth of anaesthesia could be related to its capacity to differentiate between depths that are not easily accessed by clinical evaluation, such as heavy sedation and light anaesthesia.²³ The depths of anaesthesia achieved in the present study were very different and easily distinguished; however, some of the EEG parameters, such as AE and MEF, were not able to detect such differences. The lack of variation in isoflurane concentrations during maintenance of anaesthesia could be a limitation of the present study, as it was not possible to study the parameters’ capacity to detect small variations in depth of anaesthesia. Nevertheless, the objective was not to reproduce several depths of general anaesthesia, but to mimic in an animal model the conditions of the clinical setting where anaesthetic concentration is kept relatively constant during surgery.

During surgery, all the parameters were stable and none showed changes after incision. This stability was also observed in the haemodynamic parameters, which suggests that pain was adequately prevented with the anaesthetic protocol used. There are divergent data in the literature concerning the capacity of the EEG to reflect pain perception.^24,25 This could not be evaluated in this study because the analgesic component seemed adequate to prevent pain perception, as shown by the absence of clinical responses to the surgical stimulation. Studying the performance of anaesthetic depth indices during the co-administration of hypnotics and analgesics is thus essential, as in clinical practice anaesthesia is rarely performed without analgesic administration.

Low variability in results can be achieved by using animal models for anaesthesia research. However, most animal electrophysiological studies during anaesthesia are performed with intracranial electrodes. Although the quality of the recordings obtained is higher, there are several difficulties to translate that research into the clinical setting. Regarding translational research between animals and humans, the rabbit is useful because it has a very small muscle layer between the skull and the skin that allows the recording of good-quality extracranial signals with little EMG interference.

Differences in brain waves between mammals are not well described.²⁶ The EEG changes observed in this study are similar to previous findings in other species^17,27,28 including humans⁷ and rabbits²⁸ during isoflurane anaesthesia, with progressive slowing of EEG waves and the appearance of burst suppression at deeper stages. Although animal observations cannot be directly translated to humans, it could be interesting to develop a sufficiently robust parameter that is capable of detecting changes caused by anaesthetics on the EEG of different mammals. The BS correction seems to be an important component to add to such a parameter.

In conclusion, of the most recently introduced anaesthetic depth indices, the IoC and the PE were the least variable and most reliable in reflecting different anaesthetic depths during anaesthesia with isoflurane and fentanyl in a rabbit model. This model appears useful as a translational research animal model for the validation of clinical indices.

Conflict of interest

None declared.

Funding

Aura Silva was funded by Fundação para a Ciência e Tecnologia (Portugal) (project PTDC/EEA-ACR/69288/2006). Almir Souza was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Brazil).

Acknowledgement

The authors would like to acknowledge Dr Pedro Baratta from Universidade Fernando Pessoa (Porto, Portugal).

References

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