No abstract

Spectral analysis of heart rate variability (HRV) might provide an index of relative sympathetic (SNS) and parasympathetic nervous system (PNS) activity during exercise. Eight subjects completed six 17-min submaximal exercise tests and one resting measurement in the upright sitting position. During submaximal tests, work rate (WR) was increased for the initial 3 min in a ramp fashion until it reached constant WRs of 20 W, or 30, 60, 90, 100, and 110% of the predetermined ventilatory threshold (Tvent). Ventilatory profile and alveolar gas exchange were monitored breath by breath, and beat-to-beat HRV was measured as R-R intervals of an electrocardiogram. Spectral analysis was applied to the HRV from 7 to 17 min. Low-frequency (0-0.15 Hz) and high-frequency (0.15-1.0 Hz) areas under power spectra (LO and HI, respectively) were calculated. The indicator of PNS activity (HI) decreased dramatically (P less than 0.05) when the subjects exercised compared with rest and continued to decrease until the intensity reached 60% Tvent. The indicator of SNS activity (LO/HI) remained unchanged up to 100% Tvent, whereas it increased abruptly (P less than 0.05) at 110% Tvent. The results suggested that (cardiac) PNS activity decreased progressively from rest to a WR equivalent to 60% Tvent, and SNS activity increased only when exercise intensity exceeded Tvent.

ReviewThe aims of the European Academy of Otology and Neurootology/Japan Otological Society (EAONO/JOS) Joint Consensus Statements on Definition, Classification and Staging are as follows:1. The definitions provide terminologies in the description of cholesteatoma. 2. The classification categorized cholesteatoma into distinct categories to facilitate the comparison of surgical outcomes across reports. 3. The staging system reflects the severity of the cholesteatoma, the difficulty to achieve complete removal, and the subsequent restoration of normal function.The authors wish to present the final consensus first, followed by an explanation of the methodology on how the EAONO/JOS consensus was reached by the steering group.The clinical classification of middle ear mucosa is summarized in Figure 1. 1 EAONO/JOS Joint Consensus Statements on the Definitions, Classification and Staging of Middle Ear CholesteatomaThe European Academy of Otology and Neurotology (EAONO) has previously published a consensus document on the definitions and classification of cholesteatoma. It was based on the Delphi consensus methodology involving the broad EAONO membership. At the same time, the Japanese Otological Society (JOS) had been working independently on the "Classification and Staging of Cholesteatoma." EAONO and JOS then decided to collaborate and produce a joint consensus document. The EAONO/JOS joint consensus on "Definitions, Classification and Staging of Middle Ear Cholesteatoma" was formally presented at the 10th International Conference on Cholesteatoma and Ear Surgery in Edinburgh, June 5-8, 2016. The international otology community who attended the consensus session was given the chance to debate and give their support or disapproval. The statements on the "Definitions of Cholesteatoma" received 89% approval. The "Classification of Cholesteatoma" received almost universal approval (98%). The "EAONO/JOS Staging System on Middle Ear Cholesteatoma" had a majority of approval (75%). Some international otologists wanted to see more prognostic factors being incorporated in the staging system. In response to this, the EAONO/JOS steering group plans to set up an "International Otology Outcome Working Group" to work on a minimum common otology data set that the international otology community can use to evaluate their surgical outcome. This will generate a large database and help identify relevant prognostic factors that can be incorporated into the staging system in future revisions. KEYWORDS:2 J Int Adv Otol 2017; 13(1): 1-8 Definitions and Statements on Cholesteatoma1. Cholesteatoma is a mass formed by the keratinizing squamous epithelium in the tympanic cavity and/or mastoid and subepithelial connective tissue and by the progressive accumulation of keratin debris with/without a surrounding inflammatory reaction.2. Cholesteatoma consists of matrix (keratinizing squamous epithelium), perimatrix (varying thickness of the subepithelial connective tissue), and keratin debris.3. The pathophysiology of cholesteatoma is not completel...

In the present study, we reinvestigated the question of whether human heart rate variability (HRV) is fractal in nature. Ten healthy volunteers participated in either of two studies conducted while beat-by-beat long-term HRV (8,500 heartbeats) was recorded for 2-3 h in the quiet, awake state in the supine position. In the first study, five subjects were tested four times each to evaluate the basic fractal nature of human HRV. The other five subjects were examined for the effects of oral propranolol (2 x 80 mg/day) on the fractal property of HRV in the second study. HRV data were analyzed by coarse-graining spectral analysis to break down their total power into harmonic and nonharmonic (fractal) components. The harmonic component was further divided into low (0.0-0.15 Hz; LF)- and high (> 0.15 Hz; HF)-frequency components. From these spectral components, %Fractal, %LF, and %HF as functions of total power were calculated. The fractal component was used to calculate the spectral exponent, beta. The %Fractal of human resting HRV was 85.5 +/- 4.4% (mean +/- SD, n = 20). The beta for the fractal HRV was 1.08 +/- 0.18 (n = 20). With propranolol, these basic properties of fractal HRV dynamics remained unchanged despite an increase in the mean RR interval (placebo, 912 +/- 111 ms; propranolol, 1,134 +/- 133 ms, P < 0.05) and a change in the harmonic spectral shape evaluated by LF/HF (placebo, 2.76 +/- 1.57; propranolol, 1.82 +/- 0.81, P < 0.05). For short-term data, less power was extracted as fractal because of the absence of the very low frequency component, yet the beta and LF/HF were unchanged from long-term data. These findings indicate that 1) the observed inversely proportional frequency (1/f) spectrum in human resting HRV is due to underlying random fractal dynamics and 2) the sympathetic nervous system seemed to play a minor role in modulating the fractal HRV dynamics.

The objectives of the present study were to investigate autonomic nervous system influence on heart rate during physical exercise and to examine the relationship between the fractal component in heart rate variability (HRV) and the system's response. Ten subjects performed incremental exercise on a cycle ergometer, consisting of a 5-min warm-up period followed by a ramp protocol, with work rate increasing at a rate of 2.0 W/min until exhaustion. During exercise, alveolar gas exchange, plasma norepinephrine (NE) and epinephrine (E) responses, and beat-to-beat HRV were monitored. HRV data were analyzed by "coarse-graining spectral analysis" (Y. Yamamoto and R. L. Hughson. J. Appl. Physiol. 71: 1143-1150, 1991) to break down their total power (Pt) into harmonic and nonharmonic (fractal) components. The harmonic component was further divided into low-frequency (0.0-0.15 Hz) and high-frequency (0.15-0.8 Hz) components, from which low-frequency and high-frequency power (Pl and Ph, respectively) were calculated. Parasympathetic (PNS) and sympathetic (SNS) nervous system activity indicators were evaluated by Ph/Pt and Pl/Ph, respectively. From the fractal component, the fractal dimension (DF) and the spectral exponent (beta) were calculated. The PNS indicator decreased significantly (P < 0.05) when exercise intensity exceeded 50% of peak oxygen uptake (VO2 peak). Conversely, the SNS indicator initially increased at 50-60% VO2peak (P < 0.05) and further increased significantly (P < 0.05) at > 60% VO2peak when there were also more pronounced increases in NE and E.(ABSTRACT TRUNCATED AT 250 WORDS)

We tested the hypothesis that the spontaneous beat-by-beat interactions of systolic blood pressure (SBP) and R-R interval reflected true baroreflex events rather than chance interactions. Original data sets of 1,024 heartbeats obtained in seated rest from six healthy subjects [R-R interval = 953 +/- 94 (+/- SE) ms] were compared with isospectral [generated by a windowed (inverse) Fourier transform with phase randomization] and isodistribution (data points randomly shuffled) surrogate data sets. The isospectral data set was used to test for random phase relationships, and the isodistribution data set was used for effects of white noise between SBP and R-R interval. Spontaneous baroreflex sequences were defined as three or more beats in which SBP and the R-R interval of the same (lag 0), next (lag 1), or next following (lag 2) beat changed in the same direction. The total number of baroreflex sequences in the original data was significantly greater than the surrogates (P < 0.001). In the original data, there were significantly (P < 0.001) more lag 0 than lag 1 or lag 2 baroreflex sequences. Therefore, these results indicated that spontaneous baroreflex sequences represented physiological rather than chance interactions and that baroreflex responses can occur within the same beat.

Beat-by-beat variations in blood pressure and RR-interval are interrelated by the actions of baroreflex and non-baroreflex responses. This study had two purposes: (1) to examine the spontaneous relationships between RR-interval and systolic blood pressure to determine the relative occurrence of baroreflex and non-baroreflex responses in humans, and (2) to compare the beat-sequence method with a cross spectral estimate of the baroreflex response slope. Eight healthy men were studied during 10 h of quiet, seated rest, and six men and three women were studied during rest, rest plus fixed pace breathing, and a cold pressor test. RR-interval and continuous, non-invasive arterial blood pressure were measured with a computerized system. A baroreflex sequence was defined by a series of at least three consecutive heart beats in which systolic pressure and the following RR-interval either both increased or both decreased. A non-baroreflex relationship was defined by sequences of at least three beats by opposite directional changes of RR-interval and systolic pressure of that beat. The results showed that there were approximately 30% as many non-baroreflex compared to baroreflex slopes. Individual subject mean baroreflex and non-baroreflex slopes were highly correlated (r = 0.72, P < 0.001). Absolute slope values were not different, and they were unaffected by time, fixed pace breathing, or cold pressor test. The data showed the relatively simple beat-by-beat sequence method to yield spontaneous baroreflex response slopes that were quantitatively similar to, and highly correlated with (r = 0.85-0.94), baroreflex response slopes calculated by spectral analysis methods.

No abstract