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Latency of auditory evoked potential monitoring the effects of general anesthetics on nerve fibers and synapses.

Huang B, Liang F, Zhong L, Lin M, Yang J, Yan L, Xiao J, Xiao Z - Sci Rep (2015)

Bottom Line: Auditory evoked potential (AEP) is an effective index for the effects of general anesthetics.However, it's unknown if AEP can differentiate the effects of general anesthetics on nerve fibers and synapses.Therefore, we conclude that, AEP latency is superior to amplitude for the effects of general anesthetics, ∆L monitors the effect of hypothermia on nerve fibers, and ∆I monitors a combined effect of anesthesia and hypothermia on synapses.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Physiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, PR China [2] Department of Anesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, PR China.

ABSTRACT
Auditory evoked potential (AEP) is an effective index for the effects of general anesthetics. However, it's unknown if AEP can differentiate the effects of general anesthetics on nerve fibers and synapses. Presently, we investigated AEP latency and amplitude changes to different acoustic intensities during pentobarbital anesthesia. Latency more regularly changed than amplitude during anesthesia. AEP Latency monotonically decreased with acoustic intensity increase (i.e., latency-intensity curve) and could be fitted to an exponential decay equation, which showed two components, the theoretical minimum latency and stimulus-dependent delay. From the latency-intensity curves, the changes of these two components (∆L and ∆I) were extracted during anesthesia. ∆L and ∆I monitored the effect of pentobarbital on nerve fibers and synapses. Pentobarbital can induce anesthesia, and two side effects, hypoxemia and hypothermia. The hypoxemia was not related with ∆L and ∆I. However, ∆L was changed by the hypothermia, whereas ∆I was changed by the hypothermia and anesthesia. Therefore, we conclude that, AEP latency is superior to amplitude for the effects of general anesthetics, ∆L monitors the effect of hypothermia on nerve fibers, and ∆I monitors a combined effect of anesthesia and hypothermia on synapses. When eliminating the temperature factor, ∆I monitors the anesthesia effect on synapses.

No MeSH data available.


Related in: MedlinePlus

Comparison among changes in SPO2, temperature, L80, ∆L and ∆I.(a,b) SPO2 and temperature changes with anesthesia time were hereafter labeled as SPO2- and temperature-time curves. (c) The normalized temperature-time curves of mice (n = 11) and the fitting curves to a polynomial regression equation (polynomial order = 4) (magenta curve (R2 = 0.854, intercept = −0.144, B1 = 3.575, B2 = −2.122, B3 = −1.029, B4 = −0.273). The normalized and fitting methods were similar to those in Fig. 2e. (d) Comparison among temperature, L80, ∆L and ∆I fitting curves (peak time: 0.547 ± 0.115 (∆I curve) vs. 0.642 ± 0.059 (T curve), **P < 0.01; peak time: 0.547 ± 0.115 (∆I curve) vs. 0.643 ± 0.114 (L80 curve), **P < 0.01; peak time: 0.547 ± 0.115 (∆I curve) vs. 0.688 ± 0.103 (∆L curve), **P < 0.01; one-way ANOVA and multiple comparison LSD’s test). (e) Comparison among four groups of absolute values of residuals obtained by fitting normalized temperature-, latency-, ∆L- and ∆I-time curves (P25 = 0.026, P50 = 0.058, P75 = 0.136, mean = 0.091 and SD = 0.093 for temperature; P25 = 0.029, P50 = 0.064, P75 = 0.117, mean = 0.089 and SD = 0.083 for latency; P25 = 0.038, P50 = 0.074, P75 = 0.122. mean = 0.096 and SD = 0.087 for ∆L; P25 = 0.058, P50 = 0.118, P75 = 0.202, mean = 0.141 and SD = 0.112 for ∆I; *P < 0.008, K Independent Samples Tests and multiple comparison 2 Independent Samples Tests). (f) Curves after one fitting curve (L80, ∆L or ∆I) in Fig. 6C subtracted another fitting curve (∆L or T) in Fig. 6C.
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f6: Comparison among changes in SPO2, temperature, L80, ∆L and ∆I.(a,b) SPO2 and temperature changes with anesthesia time were hereafter labeled as SPO2- and temperature-time curves. (c) The normalized temperature-time curves of mice (n = 11) and the fitting curves to a polynomial regression equation (polynomial order = 4) (magenta curve (R2 = 0.854, intercept = −0.144, B1 = 3.575, B2 = −2.122, B3 = −1.029, B4 = −0.273). The normalized and fitting methods were similar to those in Fig. 2e. (d) Comparison among temperature, L80, ∆L and ∆I fitting curves (peak time: 0.547 ± 0.115 (∆I curve) vs. 0.642 ± 0.059 (T curve), **P < 0.01; peak time: 0.547 ± 0.115 (∆I curve) vs. 0.643 ± 0.114 (L80 curve), **P < 0.01; peak time: 0.547 ± 0.115 (∆I curve) vs. 0.688 ± 0.103 (∆L curve), **P < 0.01; one-way ANOVA and multiple comparison LSD’s test). (e) Comparison among four groups of absolute values of residuals obtained by fitting normalized temperature-, latency-, ∆L- and ∆I-time curves (P25 = 0.026, P50 = 0.058, P75 = 0.136, mean = 0.091 and SD = 0.093 for temperature; P25 = 0.029, P50 = 0.064, P75 = 0.117, mean = 0.089 and SD = 0.083 for latency; P25 = 0.038, P50 = 0.074, P75 = 0.122. mean = 0.096 and SD = 0.087 for ∆L; P25 = 0.058, P50 = 0.118, P75 = 0.202, mean = 0.141 and SD = 0.112 for ∆I; *P < 0.008, K Independent Samples Tests and multiple comparison 2 Independent Samples Tests). (f) Curves after one fitting curve (L80, ∆L or ∆I) in Fig. 6C subtracted another fitting curve (∆L or T) in Fig. 6C.

Mentions: Pentobarbital can induce hypoxemia34 and hypothermia35. Therefore, the SPO2 and rectal temperature (T) were measured every 10 min after pentobarbital anesthesia. The SPO2 always decreased at initial 20 min or 30 min, then increased and kept relatively steady during anesthesia (Fig. 6a). The SPO2 were not lower than 90%. The SPO2 changes were not similar to the changes of L80 (Fig. 2c), ∆L (Fig. 5c) and ∆I (Fig. 5d) in shape. However, temperature changes, normalized by subtracting the temperature of the first recording time point from the original temperatures of all recording time points, were similar in shape for all mice (Fig. 6b), which seemed inversely to the changes of the L80 (Fig. 2c), ∆L (Fig. 5c) and ∆I (Fig. 5d). The normalized temperature-time curves (Fig. 6c) with the same methods as those in Fig. 2e overlapped well and could be fit with a polynomial regression equation (polynomial order = 4) (Fig. 6c, the magenta curve). Plotting the fitting curves for the T-, L80-, ∆L- and ∆I-time curves together (Fig. 6d), shows that the three curves for the T, L80 and ∆L were almost superimposed with no significant difference in the peak time between any two curves (one-way ANOVA, F (3, 67) = 6.454, P = 0.001; multiple comparison LSD’s test, P = 0.976 for T and L80, P = 0.240 for T and ∆L, P = 0.174 for L80 and ∆L), but that a clear difference between the ∆I fitting curve and the T, L80 or ∆L fitting curve (one-way ANOVA, F (3, 67) = 6.454, P = 0.001; multiple comparison LSD’s test, P = 0.018 for ∆I and T, P = 0.005 for ∆I and L80, P = 5.979 × 10−5 for ∆I and ∆L). In addition, the absolute values of residuals obtained by fitting normalized ∆I-time curves were larger than any one of the other three residuals for T-, L80- or ∆L-time curves (Fig. 6e, K Independent Samples Tests, x2 = 58.333, P = 3.130 × 10−7, multiple comparison 2 Independent Samples Tests, P = 3.134 × 10−7 for ∆I and T, P = 2.110 × 10−11 for ∆I and L80, P = 1.174 × 10−8 for ∆I and ∆L), but there was no significant difference between any two of the latter three residuals (Fig. 6e, K Independent Samples Tests, x2 = 58.333, P = 3.130 × 10−7, multiple comparison 2 Independent Samples Tests, P = 0.593 for T and L80, P = 0.120 for T and ∆L, P = 0.169 for L80 and ∆L). Furtherly, the L80 fitting curve subtracted the ∆L fitting curve showed almost a line fluctuated around zero (Fig. 6f, the blue curve). It also happened to the ∆L fitting curve subtracted the T fitting curve (Fig. 6f, the red curve). However, the ∆I fitting curve subtracted the T fitting curve or the ∆L fitting curve presented a similar gradual decrease tendency with anesthesia time (Fig. 6f, the green or cyan curve).


Latency of auditory evoked potential monitoring the effects of general anesthetics on nerve fibers and synapses.

Huang B, Liang F, Zhong L, Lin M, Yang J, Yan L, Xiao J, Xiao Z - Sci Rep (2015)

Comparison among changes in SPO2, temperature, L80, ∆L and ∆I.(a,b) SPO2 and temperature changes with anesthesia time were hereafter labeled as SPO2- and temperature-time curves. (c) The normalized temperature-time curves of mice (n = 11) and the fitting curves to a polynomial regression equation (polynomial order = 4) (magenta curve (R2 = 0.854, intercept = −0.144, B1 = 3.575, B2 = −2.122, B3 = −1.029, B4 = −0.273). The normalized and fitting methods were similar to those in Fig. 2e. (d) Comparison among temperature, L80, ∆L and ∆I fitting curves (peak time: 0.547 ± 0.115 (∆I curve) vs. 0.642 ± 0.059 (T curve), **P < 0.01; peak time: 0.547 ± 0.115 (∆I curve) vs. 0.643 ± 0.114 (L80 curve), **P < 0.01; peak time: 0.547 ± 0.115 (∆I curve) vs. 0.688 ± 0.103 (∆L curve), **P < 0.01; one-way ANOVA and multiple comparison LSD’s test). (e) Comparison among four groups of absolute values of residuals obtained by fitting normalized temperature-, latency-, ∆L- and ∆I-time curves (P25 = 0.026, P50 = 0.058, P75 = 0.136, mean = 0.091 and SD = 0.093 for temperature; P25 = 0.029, P50 = 0.064, P75 = 0.117, mean = 0.089 and SD = 0.083 for latency; P25 = 0.038, P50 = 0.074, P75 = 0.122. mean = 0.096 and SD = 0.087 for ∆L; P25 = 0.058, P50 = 0.118, P75 = 0.202, mean = 0.141 and SD = 0.112 for ∆I; *P < 0.008, K Independent Samples Tests and multiple comparison 2 Independent Samples Tests). (f) Curves after one fitting curve (L80, ∆L or ∆I) in Fig. 6C subtracted another fitting curve (∆L or T) in Fig. 6C.
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f6: Comparison among changes in SPO2, temperature, L80, ∆L and ∆I.(a,b) SPO2 and temperature changes with anesthesia time were hereafter labeled as SPO2- and temperature-time curves. (c) The normalized temperature-time curves of mice (n = 11) and the fitting curves to a polynomial regression equation (polynomial order = 4) (magenta curve (R2 = 0.854, intercept = −0.144, B1 = 3.575, B2 = −2.122, B3 = −1.029, B4 = −0.273). The normalized and fitting methods were similar to those in Fig. 2e. (d) Comparison among temperature, L80, ∆L and ∆I fitting curves (peak time: 0.547 ± 0.115 (∆I curve) vs. 0.642 ± 0.059 (T curve), **P < 0.01; peak time: 0.547 ± 0.115 (∆I curve) vs. 0.643 ± 0.114 (L80 curve), **P < 0.01; peak time: 0.547 ± 0.115 (∆I curve) vs. 0.688 ± 0.103 (∆L curve), **P < 0.01; one-way ANOVA and multiple comparison LSD’s test). (e) Comparison among four groups of absolute values of residuals obtained by fitting normalized temperature-, latency-, ∆L- and ∆I-time curves (P25 = 0.026, P50 = 0.058, P75 = 0.136, mean = 0.091 and SD = 0.093 for temperature; P25 = 0.029, P50 = 0.064, P75 = 0.117, mean = 0.089 and SD = 0.083 for latency; P25 = 0.038, P50 = 0.074, P75 = 0.122. mean = 0.096 and SD = 0.087 for ∆L; P25 = 0.058, P50 = 0.118, P75 = 0.202, mean = 0.141 and SD = 0.112 for ∆I; *P < 0.008, K Independent Samples Tests and multiple comparison 2 Independent Samples Tests). (f) Curves after one fitting curve (L80, ∆L or ∆I) in Fig. 6C subtracted another fitting curve (∆L or T) in Fig. 6C.
Mentions: Pentobarbital can induce hypoxemia34 and hypothermia35. Therefore, the SPO2 and rectal temperature (T) were measured every 10 min after pentobarbital anesthesia. The SPO2 always decreased at initial 20 min or 30 min, then increased and kept relatively steady during anesthesia (Fig. 6a). The SPO2 were not lower than 90%. The SPO2 changes were not similar to the changes of L80 (Fig. 2c), ∆L (Fig. 5c) and ∆I (Fig. 5d) in shape. However, temperature changes, normalized by subtracting the temperature of the first recording time point from the original temperatures of all recording time points, were similar in shape for all mice (Fig. 6b), which seemed inversely to the changes of the L80 (Fig. 2c), ∆L (Fig. 5c) and ∆I (Fig. 5d). The normalized temperature-time curves (Fig. 6c) with the same methods as those in Fig. 2e overlapped well and could be fit with a polynomial regression equation (polynomial order = 4) (Fig. 6c, the magenta curve). Plotting the fitting curves for the T-, L80-, ∆L- and ∆I-time curves together (Fig. 6d), shows that the three curves for the T, L80 and ∆L were almost superimposed with no significant difference in the peak time between any two curves (one-way ANOVA, F (3, 67) = 6.454, P = 0.001; multiple comparison LSD’s test, P = 0.976 for T and L80, P = 0.240 for T and ∆L, P = 0.174 for L80 and ∆L), but that a clear difference between the ∆I fitting curve and the T, L80 or ∆L fitting curve (one-way ANOVA, F (3, 67) = 6.454, P = 0.001; multiple comparison LSD’s test, P = 0.018 for ∆I and T, P = 0.005 for ∆I and L80, P = 5.979 × 10−5 for ∆I and ∆L). In addition, the absolute values of residuals obtained by fitting normalized ∆I-time curves were larger than any one of the other three residuals for T-, L80- or ∆L-time curves (Fig. 6e, K Independent Samples Tests, x2 = 58.333, P = 3.130 × 10−7, multiple comparison 2 Independent Samples Tests, P = 3.134 × 10−7 for ∆I and T, P = 2.110 × 10−11 for ∆I and L80, P = 1.174 × 10−8 for ∆I and ∆L), but there was no significant difference between any two of the latter three residuals (Fig. 6e, K Independent Samples Tests, x2 = 58.333, P = 3.130 × 10−7, multiple comparison 2 Independent Samples Tests, P = 0.593 for T and L80, P = 0.120 for T and ∆L, P = 0.169 for L80 and ∆L). Furtherly, the L80 fitting curve subtracted the ∆L fitting curve showed almost a line fluctuated around zero (Fig. 6f, the blue curve). It also happened to the ∆L fitting curve subtracted the T fitting curve (Fig. 6f, the red curve). However, the ∆I fitting curve subtracted the T fitting curve or the ∆L fitting curve presented a similar gradual decrease tendency with anesthesia time (Fig. 6f, the green or cyan curve).

Bottom Line: Auditory evoked potential (AEP) is an effective index for the effects of general anesthetics.However, it's unknown if AEP can differentiate the effects of general anesthetics on nerve fibers and synapses.Therefore, we conclude that, AEP latency is superior to amplitude for the effects of general anesthetics, ∆L monitors the effect of hypothermia on nerve fibers, and ∆I monitors a combined effect of anesthesia and hypothermia on synapses.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Physiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, PR China [2] Department of Anesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, PR China.

ABSTRACT
Auditory evoked potential (AEP) is an effective index for the effects of general anesthetics. However, it's unknown if AEP can differentiate the effects of general anesthetics on nerve fibers and synapses. Presently, we investigated AEP latency and amplitude changes to different acoustic intensities during pentobarbital anesthesia. Latency more regularly changed than amplitude during anesthesia. AEP Latency monotonically decreased with acoustic intensity increase (i.e., latency-intensity curve) and could be fitted to an exponential decay equation, which showed two components, the theoretical minimum latency and stimulus-dependent delay. From the latency-intensity curves, the changes of these two components (∆L and ∆I) were extracted during anesthesia. ∆L and ∆I monitored the effect of pentobarbital on nerve fibers and synapses. Pentobarbital can induce anesthesia, and two side effects, hypoxemia and hypothermia. The hypoxemia was not related with ∆L and ∆I. However, ∆L was changed by the hypothermia, whereas ∆I was changed by the hypothermia and anesthesia. Therefore, we conclude that, AEP latency is superior to amplitude for the effects of general anesthetics, ∆L monitors the effect of hypothermia on nerve fibers, and ∆I monitors a combined effect of anesthesia and hypothermia on synapses. When eliminating the temperature factor, ∆I monitors the anesthesia effect on synapses.

No MeSH data available.


Related in: MedlinePlus