Abstract
Objectives
The addition of amphetamine to a sedating medication may reduce sedation but does it augment reaction time and sustained attention for workers? The purpose of this exploratory study was gain insight into between group differences that would assist hypothesis formation for a subsequent hypothesis testing study.
Methods
This study examined psychomotor vigilance task (PVT) performance for a group taking potentially sedating medications (opiates, benzodiazepines, anticholinergics, barbiturates or polypharmacy) while taking amphetamine to a group not taking amphetamine. Data was assessed using two-way between groups multivariate analysis of variance.
Results
Multivariate testing found a (p = .05; η2 = .044) difference in combined PVT measures between the amphetamine use groups. Tests of between-subjects effects established (p = .006; η2 = .042) a difference in the number of minor lapses between the groups. Estimated marginal means of minor lapses revealed that the group taking amphetamine had 2.8 times the mean number of minor lapses than the group not taking amphetamine. A non-statistically significant trend was noted for the estimated marginal means of each sedating medication class and the use or nonuse of amphetamines that appears to correspond with the sedating medication’s effect upon the cholinergic component of the attention system.
Conclusion
Using PVT data, this exploratory study has provided information useful for generating the hypothesis that co-administration of an amphetamine with a sedating medication will result in arousal with a deficit of sustained attention related to the sedating medication’s level of effect upon cholinergic activity.
Keywords: amphetamine, opioid, benzodiazepine, anticholinergic, psychomotor vigilance task, reaction time, sustained attention, workplace safety
Introduction
Health care providers wrote 259 million prescriptions for opioids and opiates in 2012 and roughly 75 million benzodiazepine prescriptions in 2008.1,2 The increasing reliance of Americans upon prescription medications for pain, anxiety and other maladies suggests that many are reporting to work with potentially sedating agents in their systems and these substances may be associated with work related accidents.3-6 Stimulants are often co-administered with narcotics to mitigate sedation. Most research supporting this practice has been done in the palliative care setting, investigating pharmacologic treatment of overt sedation, but these studies fail to address cognitive function.7-9 Additionally, none of the studies specifically referenced a working population.
Amphetamines have been used for cognitive and physical performance enhancement for nearly eight decades and are currently prescribed for several diagnoses, including narcolepsy, shift-work sleep disorder, appetite suppression and attention deficit hyperactivity disorder.10-12 Functional magnetic resonance imaging and positron emission tomography have identified the prefrontal and anterior cingulate cortices as principal sites of actions for these agents.13 It is in these areas that amphetamine increases extracellular norepinephrine and dopamine levels inducing arousal.14,15
Improvements in overall performance have been demonstrated after intake of amphetamines with sustain attention or tonic-alertness tasks appearing to be the most sensitive measures of the drug’s effects.16,17 Another study suggested that acute doses of amphetamine may also decrease several forms of impulsive behavior.18 However, it was demonstrated shortly after the discovery of amphetamine sulfate that a dose of 10 mg by mouth is incapable of significantly improving performance of a monotonous skilled task unless this performance has been reduced by previously existing fatigue.19 Users may perceive the drug as enhancing their cognition. However, objective measures indicate amphetamine has no more than small effects on cognition in healthy young adults and the effect may be related to their baseline dopaminergic tone.20,21 While amphetamine provides a low-level enhancement of function, it also causes less conservative movement estimation, incorrect signaling, failing to stop at a red traffic lights, reduced reaction times and increased risk taking behavior that might be responsible for amphetamine-related road fatalities.22-24
The complexity of this topic makes hypothesis formation difficult. The addition of amphetamine may reduce sedation but does co-administration of amphetamine augment reaction time and sustained attention for workers taking potentially impairing prescription medications? The answer to this question is very important in this era of over prescribing. Hypothesis generation is the first step towards pursuing a line of research that will provide clinically significant information that will ultimately be used to enhance workplace safety. The purpose of this exploratory study was to perform an uncontrolled retrospective assessment to gain insight into between group differences to assist hypothesis formation. A secondary aim was to ascertain any testing issues to be addressed in the design of a subsequent hypothesis testing study.
Methods
This study examined psychomotor vigilance task (PVT) performance for a group taking potentially sedating medications while taking prescribed dextroamphetamine, amphetamine or lisdexamfetamine to a group not taking an amphetamine. Optimal PVT performance depends on activation of the sustained attention system and the motor system to provide an objective measure of reaction time and sustained attention.25 Sustained attention is the specific class of attention most closely related to the alertness systems, more precisely the tonic-alertness system.17 Reaction time is the elapsed time between the presentation of a sensory stimulus and the subsequent behavioral response and it is dependent upon arrival of the stimulus at the sensory organ, conversion to a neural signal, neural transmission and processing, and muscular activation.26 Both of these measures are fundamental to the performance of safety sensitive duties. Powell and colleges, using PVT testing, found that a group with a BrAC of .02 gm/210 L had an average mean reaction time of 263 ms and an average of .84 minor lapses. When the group’s BrAC reached .08 gm/210 L the average mean reaction time increased to 276 ms and the number of minor lapses increased to 1.26.27
The population examined was males and females employed by a variety of industries from the confluence of Southern Indiana, Eastern Illinois and Western Kentucky that presented for pre-placement physical examinations or annual fitness-for-duty examinations. PVT testing was performed as an objective measure of sustained attention and reaction time per clinic protocol for any individual in a safety sensitive position taking a potentially impairing medication. Every physical examination completed from January 1, 2015 through December 31, 2015 that included PVT testing for potentially impairing medication use was chosen for this study. The electronic medical record (Systoc® 7.41) was searched and used for data collection. The age, gender, body mass index (BMI) and medications of each examinee were recorded along with the corresponding PVT results. The indication for amphetamine use varied and was not recorded. The Institutional Review Board of St. Vincent Evansville Medical Center approved the study design and granted an inform consent waiver.
Psychomotor vigilance task testing was performed using PC-PVT© software.28 Our system used a Fujitsu Lifebook T732™ with an Intel core™ i5 processor and 8 gigabytes of RAM running Windows 7 professional™ with a Razor™ Taipai gaming mouse. The PVT uses a 10 minute protocol, during which a millisecond counter is presented on the computer screen as the visual stimulus. The response is an immediate mouse button click. Each stimulus was delayed for a random period of between 2 and 10 seconds. A response during the delay was reported as a “false start”. No response within 65 seconds of stimulus presentation was described as “no-response”. A response between 500 ms and 1000 ms was described as a minor lapse and a response between 1000 ms and 65 seconds was described as a major lapse. The number of major lapses, minor lapses, and false starts, as well as the minimum reaction time, maximum reaction time, mean reaction time and median reaction time were reported at the end of each test session. Studies have suggested that physical fatigue is associated with an increased number of minor lapses (>500 msec) after controlling for age, BMI, depression and sleep apnea.29 According to video data, lapses greater than 2669 ms were 95% likely to be eyes closed episodes (micro-sleep), while those 500–549 ms were 95% likely to be eyes open episodes (inattention) and reaction times of 1217 ms had an equal probability of being eyes open or closed episodes.30
Data analysis began with generation and exploration of descriptive statistics. Comparisons between amphetamine use and no amphetamine use group characteristics were performed using χ2 tests for dichotomous variables and Mann-Whitney U test for continuous variables. Natural logarithmic transformation was performed on continuous dependent variables to mitigate the effects of non-normal distribution prior to further analysis. Statistical outliers were identified by calculation of Mahalanobis distances. Identified outliers were excluded from further analysis.31 Two-way between groups multivariate analysis of variance (MANOVA) was accomplished with the independent variables being the type of impairing medication used by each subject and the status of amphetamine use. The type of impairing medication was categorized as prescribed use of opiates, benzodiazepines, anticholinergics, barbiturates or polypharmacy. The dependent variables were the natural logarithms of the seven PVT measures. Analysis suggested that three measures fit the model. These measures were the number of major lapses, the number of minor lapses, and the mean reaction time. Missing data was handled by pairwise exclusion.
Results
The study began with 199 cases. However, two cases were excluded for illicit drug use identified by urine drug testing, and 10 (5.1%) cases were excluded for being statistical outliers, leaving 187 valid cases. Concomitant amphetamine use was noted for 25 (13.4%) of the cases. Demographic, anthropometric and PVT measures stratified by stimulant use category are summarized in Tables 1 and 2. Of the 187 cases, 83 were men (44.4%) and 104 were women (55.6%). There appeared to be no significant difference in the distribution of the sexes between the amphetamine use groups. The mean age of subjects was 41.43 years with 24.1% being greater than 30 years, 48.1% between 30 years and 50 years and 27.8% greater than 50 years. There was a significant difference in the age make-up of the amphetamine use groups with the group using amphetamine being younger. The mean BMI was 31.21 kg/m2; 20.3% having a BMI less than or equal to 25 kg/m2, 25.7% being between 26 and 30 kg/m2 and the remainder having a BMI greater than 30 kg/m2. The group not using amphetamine had a significantly higher mean BMI than the group using an amphetamine. Most of the PVT assessments were performed because of polysubstance use (39.0%) or opiate use (32.1%), while the rest were performed for benzodiazepine (21.4%) or anticholinergic (7.5%) use. There were no individuals prescribed barbiturates. There appeared to be no difference in the sedating medication use patterns between the amphetamine use groups.
Table 1. Frequency Table for Demographic and Anthropometric Characteristics.
| USING AMPHETAMINE N = 25 | NOT USING AMPHETAMINE N = 161 | ||||||
|---|---|---|---|---|---|---|---|
| VARIABLE | GROUP | FREQUENCY | VALID PERCENT | FREQUENCY | VALID PERCENT | P VALUE (2-SIDED) | |
| Sex | Male | 8 | 32 | 75 | 46.6 | *.251 | |
| Female | 17 | 68 | 86 | 53.4 | |||
| Impairing | Opiate | 6 | 24 | 54 | 33.5 | .586 | |
| Medication | Benzodiazepine | 7 | 28 | 32 | 19.9 | ||
| Anticholinergic | 1 | 4 | 13 | 8.1 | |||
| Polypharmacy | 11 | 44 | 62 | 38.5 | |||
| Age (years) | <30 years | 11 | 44 | 34 | 21.1 | .028 | |
| 30–50 years | 7 | 28 | 83 | 51.6 | |||
| >50 years | 7 | 28 | 44 | 27.3 | |||
| Body Mass | ≤25 | 10 | 40 | 28 | 17.5 | .030 | |
| Index (kg/m2) | 26–30 | 6 | 24 | 42 | 26.3 | ||
| >30 | 9 | 36 | 90 | 56.3 | |||
Notes: Chi-square test for independence (α = .05). *Yates’ correction for continuity.
Table 2. Descriptive Statistics for Continuous Variables Stratified by Amphetamine Use.
| VARIABLE | Group | N | MEAN (STANDARD DEVIATION) | SKEWNESS (STANDARD ERROR) | KURTOSIS (STANDARD ERROR) | *P VALUE (2-SIDED) |
|---|---|---|---|---|---|---|
| Age (years) | Taking Amphetamine | 25 | 37.20 (14.33) | .13 (.46) | ‒1.57 (.90) | .097 |
| Not Taking Amphetamine | 161 | 42.00 (11.66) | .06 (.19) | ‒.81 (.38) | ||
| BMI (kg/m2) | Taking Amphetamine | 25 | 28.98 (7.98) | 1.50 (.46) | 2.99 (.90) | .043 |
| Not Taking Amphetamine | 160 | 31.56 (7.02) | .51 (.19) | .22 (.38) | ||
| Major lapses | Taking Amphetamine | 25 | .16 (.37) | 1.97 (.46) | 2.06 (.90) | .058 |
| (>1000 ms) | Not Taking Amphetamine | 161 | .06 (.23) | 3.90 (.19) | 13.40 (.38) | |
| Major lapses | Taking Amphetamine | 25 | 1.16 (1.49) | 1.26 (.46) | .73 (.90) | .315 |
| (500–1000 ms) | Not Taking Amphetamine | 160 | .76 (1.06) | 2.28 (.19) | 8.37 (.38) | |
| False starts | Taking Amphetamine | 25 | 3.40 (2.60) | .70 (.46) | ‒.11 (.90) | .027 |
| Not Taking Amphetamine | 161 | 2.48 (3.15) | 3.03 (.19) | 12.33 (.38) | ||
| Minimum reaction time (ms) | Taking Amphetamine | 25 | 184.53 (18.04) | .29 (.46) | ‒.88 (.90) | .414 |
| Not Taking Amphetamine | 161 | 188.88 (23.79) | .55 (.19) | .48 (.38) | ||
| Maximum reaction time (ms) | Taking Amphetamine | 25 | 723.08 (569.45) | 2.33 (.46) | 5.22 (.90) | .786 |
| Not Taking Amphetamine | 160 | 606.42 (348.75) | 3.58 (.19) | 16.03 (.38) | ||
| Mean reaction time (ms) | Taking Amphetamine | 25 | 268.32 (39.76) | .65 (.46) | ‒.62 (.90) | .941 |
| Not Taking Amphetamine | 161 | 263.76 (30.03) | .67 (.19) | .56 (.38) | ||
| Median reaction time (ms) | Taking Amphetamine | 25 | 253.88 (35.26) | .93 (.46) | .72 (.90) | .933 |
| Not Taking Amphetamine | 161 | 252.24 (30.47) | .73 (.19) | .69 (.38) |
Note: *Mann-Whitney U test (α = .05).
PVT measures were evaluated and violated the assumption of normal distribution (Table 2). These measures were transformed logarithmically for subsequent MANOVA testing. The data met the assumption of linearity and homogeneity of variance-covariance matrices. Multivariate testing (Table 3) found a 95% probability (p = .05) of a difference in combined PVT measures between the amphetamine groups with 4.4% (η2 = .044) of the variance between the PVT measures explained by the amphetamine use of the subjects. No other statistically significant differences were found.
Table 3. Two-way Between Groups Multiple Analysis of Variance.
| MULTIVARIATE TESTS OF SIGNIFICANT DIFFERENCES AMONG THE GROUPS ON A LINEAR COMBINATION OF PVT MEASURES | ||||||
|---|---|---|---|---|---|---|
| EFFECT | PILLAI’S TRACE | F-VALUE | HYPOTHESIS DF | ERROR DF | SIG.a | PARTIAL ETA2 |
| Impairing Medication (A) | .069 | 1.385 | 9.0 | 531.0 | .191 | .023 |
| Stimulant use (B) | .044 | 2.655 | 3.0 | 175.0 | .050 | .044b |
| A * B | .056 | 1.131 | 9.0 | 531.0 | .339 | .019 |
| TESTS OF BETWEEN SUBJECTS EFFECTS | ||||||
|---|---|---|---|---|---|---|
| MAJOR LAPSESc | MINOR LAPSESd | MEAN REACTION TIMEc | ||||
| SOURCE OF VARIATION | F | SIG. | F | SIG. | F | SIG. |
| Impairing Medication (A) | .468 | .705 | 1.822 | .145 | 2.860 | .038 |
| Amphetamine use (B) | 1.039 | .309 | 7.695 | .006e | 2.585 | .110 |
| A * B | .088 | .967 | 2.817 | .041 | 1.164 | .325 |
Notes: aα = .05.
bConsistent with a small to moderate effect-size.
cα = .008 after mitigating for violation of the assumption of equality of variance and subsequent Bonferroni adjustment.
dα = .017 after Bonferroni adjustment.
ePartial Eta Squared = .042, small to moderate effect-size.
Levene’s test was used to test the assumption of equality of variances. The assumption was not met for major lapses and mean reaction time. This finding was mitigated by using an alpha level of .025 for these variables during testing of between-subjects effects. The alpha level for minor lapses remained .05.
Tests of between-subjects effects examined how the independent variables affected each of the three PVT measures (Table 3). Bonferroni adjustment was performed on the alpha levels of each of the dependent variables to reduce the chance of type 1 error. The adjusted alpha level for major lapses and mean reaction time was .008 and the adjusted alpha level for minor lapses was .017. Comparison of the individual PVT measures with the amphetamine use groups established, with greater than 99% probability (p = .006), a difference in the number of minor lapses between the groups. 4.2% (η2 = .042) of the variance in minor lapses between the groups is explained by amphetamine use. This was the only statistically significant difference discovered.
Inspection of estimated marginal means of minor lapses for the amphetamine use groups revealed that the group taking amphetamines had 2.8 times the mean number of minor lapses than the group not taking amphetamines (Table 4). A non-statistically significant trend was noted upon examination of the estimated marginal means for each impairing medication class and the use or nonuse of amphetamines. It appears that the group taking amphetamines had more minor lapses if they were taking anticholinergic medications (factor of 8.13), benzodiazepines (factor of 2.16) and opiate/opioids (factor of 1.32) than the corresponding groups not taking amphetamines (Table 4).
Table 4. Estimated Marginal Means for Between Subjects Effects Tests.
| 95% CONFIDENCE INTERVAL | ||||||
|---|---|---|---|---|---|---|
| DEPENDENT VARIABLE | IMPAIRING MEDICATION | STIMULANT USE GROUP | MEAN | STANDARD ERROR | LOWER BOUND | UPPER BOUND |
| Major Lapses (>1000 ms) | Aggregate | Taking Amphetamine | .123 | .076 | ‒.027 | 2.73 |
| Not Taking Amphetamine | .042 | .024 | ‒.006 | .090 | ||
| Opiates/Opioids | Taking Amphetamine | .167 | .105 | ‒.040 | .373 | |
| Not Taking Amphetamine | .037 | .035 | ‒.031 | .106 | ||
| Benzodiazepines | Taking Amphetamine | .143 | .097 | ‒.048 | .334 | |
| Not Taking Amphetamine | .031 | .045 | ‒.058 | .121 | ||
| Anticholinergics | Taking Amphetamine | 0 | .256 | ‒.506 | .506 | |
| Not Taking Amphetamine | 0 | .071 | ‒.140 | .140 | ||
| Polypharmacy | Taking Amphetamine | .182 | .077 | .029 | .334 | |
| Not Taking Amphetamine | .098 | .033 | .034 | .163 | ||
| Minor Lapses (500–1000 ms) | Aggregate | Taking Amphetamine | 2.007 | .324 | 1.368 | 2.646 |
| Not Taking Amphetamine | .708 | .103 | .504 | .912 | ||
| Opiates/Opioids | Taking Amphetamine | .933 | .447 | ‒.048 | 1.715 | |
| Not Taking Amphetamine | .704 | .149 | .410 | .997 | ||
| Benzodiazepines | Taking Amphetamine | 1.286 | .413 | .470 | 2.102 | |
| Not Taking Amphetamine | .594 | .193 | .212 | .997 | ||
| Anticholinergics | Taking Amphetamine | 5.000 | 1.094 | 2.841 | 7.159 | |
| Not Taking Amphetamine | .615 | .303 | .017 | 1.214 | ||
| Polypharmacy | Taking Amphetamine | .909 | .330 | .258 | 1.560 | |
| Not Taking Amphetamine | .918 | .140 | .642 | 1.194 | ||
| Mean Reaction Time (ms) | Aggregate | Taking Amphetamine | 281.756 | 8.900 | 264.192 | 299.321 |
| Not Taking Amphetamine | 265.038 | 2.845 | 259.424 | 270.652 | ||
| Opiates/Opioids | Taking Amphetamine | 255.000 | 12.282 | 230.763 | 279.237 | |
| Not Taking Amphetamine | 252.889 | 4.094 | 244.810 | 260.968 | ||
| Benzodiazepines | Taking Amphetamine | 273.571 | 11.371 | 251.132 | 296.011 | |
| Not Taking Amphetamine | 270.812 | 5.318 | 260.317 | 281.308 | ||
| Anticholinergics | Taking Amphetamine | 332.000 | 30.084 | 272.631 | 391.369 | |
| Not Taking Amphetamine | 269.385 | 8.344 | 252.919 | 285.851 | ||
| Polypharmacy | Taking Amphetamine | 266.455 | 9.071 | 248.554 | 284.355 | |
| Not Taking Amphetamine | 267.066 | 3.852 | 259.464 | 274.667 | ||
Discussion
PVT performance is known to be sensitive to differences in sex, medication use, age and BMI17,25. The sex distribution and sedating medication use patterns were not meaningfully different between the studied groups and do not appear to be a source of bias in this investigation. There was a significant difference in age distribution between the amphetamine use and not-use groups, with the amphetamine use group being much younger than the not-use group. The BMI makeup of the two groups was also considerably dissimilar with the amphetamine use group having a lower mean BMI. The disparity of both of these variables should have biased the results towards better performance of the amphetamine use group yet the group underperformed. This suggests that the actual difference between the amphetamine use and not-use groups may have been greater than reported.
The results suggest that individuals from the group taking amphetamine had significantly greater number of minor lapses consistent with impaired sustained attention. The estimated marginal means for between subjects’ effects revealed a tendency toward more minor lapses for the anticholinergic medications, benzodiazepines and opiate/opioids groups if they were taking amphetamine. There is a plausible physiologic explanation for these findings (Figure 1). Attention processing of stimulating cues is largely mediated by norepinephrine release from neurons originating in the locus coeruleus and terminating in the basal forebrain prompting acetylcholine release.32 Activation of the basal forebrain cholinergic projections is required for sustained attention performance. These projections stimulate the anterior cingulate, dorsolateral prefrontal and parietal cortices which modulate the function of the sensory regions by enhancing and biasing sensory input processing.17 Norepinephrine release from neurons originating in the locus coeruleus terminating in the amygdala initiate stress or fear responses and neurons terminating in the thalamus result in arousal.33 Different classes of medication upset this system by varying degrees with anticholinergic medications having the largest impact followed by benzodiazepines then opiates. Anticholinergic medications antagonize acetylcholine receptors in the attention and sensory areas, inhibiting acetylcholine dependent processes critical to sustained attention. Benzodiazepines produce GABA mediated attenuation of acetylcholine release disrupting acetylcholine dependent regulation of basal forebrain activity and sensory input processing. Opiates disrupt this system by suppressing norepinephrine release and subsequent acetylcholine release in the face of presenting stimuli. Amphetamines act by stimulating release and inhibiting reuptake of norepinephrine and dopamine augmenting arousal and contributing to stress and fear while lending little to bolster cholinergic dependent processes; the degree of which being dependent upon the mechanism inhibiting the cholinergic system.34,35 Essentially, the addition of a stimulant to a potentially impairing medication may leave the individual aroused and stressed, with deficient sustained attention.
Figure 1.
A Schematic Representation of The Sustained Attention System and its Relationship to Impairing Substances
Based on the findings suggested by this study I hypothesize that the addition of amphetamine to a medication regiment that includes a sedating medication may result in arousal but will adversely affect sustained attention and possibly reaction time. The degree of the attention deficit will depend on the mechanism and degree of cholinergic impairment imparted by the sedating medication.
This study has several sources of systematic and random error. There is inherent selection bias in the design where subjects prescribed amphetamines for subjective complaints and objective deficits are likely to have poor PVT performance relative to a group not prescribed amphetamines. The PVT is sensitive to variations in sleep deprivation which may have also introduced error.25 Random error may have been introduced based on the examinee’s medication compliance, degree of motivation and level of stress.25,17 These issues have little effect upon hypothesis formation, but will need to be controlled for in the design of a subsequent hypothesis testing study.
Summary
PVT performance has ecological validity in that it can reflect real-world risks. Deficits in sustained attention and timely reactions adversely affect many safety sensitive duties, especially those in which work-pace or timely reactions are essential.36 The findings suggest that many employed people have levels of sustained attention equal to or worse than people with BrAC concentrations of .08 gm/210 L. Using PVT data, this exploratory study has provided information useful for generating the hypothesis that co-administration of an amphetamine with a sedating medication will result in arousal, but with a sustained attention deficit which is dependent upon the mechanism of cholinergic impairment of the sedating medication.
There were several sources of systematic and random error which need to be addressed in the design of a subsequent hypothesis testing study. The design will require controlling for sleep deprivation, age, BMI, motivation and level of stress. Only stimulant naive subjects taking a stable dose of potentially sedating medication ought to be enrolled into the study. A third group that is not taking any potentially sedation or stimulating medications may also be added. We shall also consider adding a brief PVT practice session to assure understanding of proper test taking technique by examinees to mitigate random error presenting as statistical outliers.
This study was a first step toward providing clinically relevant data to be used for the improvement of workplace safety. As medical professionals we must be cognizant that each of our patients is part of a social system including the workforce. When therapies have the potential for causing cognitive impairment they not only put the patient at risk, but also put non-consenting third parties at risk. This study has provided useful information for hypothesis formation but it is clear that much work is to be done to test this hypothesis and provide actionable clinical recommendations.
Acknowledgments
None
Footnotes
Conflicts of Interest
There are no conflicts of interest or financial disclosures relevant to the topic of the submitted manuscript.
Disclosures
There are no outside sources of support to be identified. There have been no prior presentations of this manuscript.
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