Microbubbles are detected prior to larger bubbles following decompression.

Using dual-frequency ultrasound (DFU), microbubbles (<10 µm diameter) have been detected in tissue following decompression. It is not known if these microbubbles are the precursors for B-mode ultrasound-detectable venous gas emboli (bmdVGE). The purpose of this study was to determine if microbubbles could be detected intravascularly postdecompression and to investigate the temporal relationship between microbubbles and larger bmdVGE. Anesthetized swine (n = 15) were exposed to 4.0-4.5 ATA for 2 h, followed by decompression to 0.98 ATA. Microbubble presence and VGE grade were measured using DFU and B-mode ultrasound, respectively, before and for 1 h postdecompression, approximately every 4-5 min. Microbubbles appeared in the bloodstream postdecompression, both in the presence and absence of bmdVGE. In swine without bmdVGE, microbubbles remained elevated for the entire 60-min postdecompression period. In swine with bmdVGE, microbubble signals were detected initially but then returned to baseline. Microbubbles were not detected with the sham dive. Mean bmdVGE grade increased over the length of the postdecompression data collection period. Comparison of the two response curves revealed significant differences at 5 and 10 min postdecompression, indicating that microbubbles preceded bmdVGE. These findings indicate that decompression-induced microbubbles can 1) be detected intravascularly at multiple sites, 2) appear in the presence and absence of bmdVGE, and 3) occur before bmdVGE. This supports the hypothesis that microbubbles precede larger VGE bubbles. Microbubble presence may be an early marker of decompression stress. Since DFU is a low-power ultrasonic method, it may be useful for operational diving applications.

Microbubble detection following hyperbaric chamber dives using dual-frequency ultrasound.

Venous gas emboli (VGE) can be readily detected in the bloodstream using existing ultrasound methods. No method currently exists to detect decompression-induced microbubbles in tissue. We hypothesized that dual-frequency ultrasound (DFU) could detect these microbubbles. With DFU, microbubbles are driven with two frequencies: a lower "pump" (set to the resonant frequency of the desired bubble size) and a higher "image" frequency. A bubble of the resonant size emits the sum and difference of the two transmitted frequencies. For this study we used a pump frequency of 2.25 MHz and an image frequency of 5.0 MHz, which detects bubbles of roughly 1-10 µm in diameter in a water tank. Four anesthetized swine were pressurized at 4.5 ATA for 2 h and decompressed over 5 min, inducing moderate to very severe VGE scores. Four sites on the thigh of each swine were monitored with DFU before and after the dives. A single mock dive was also performed. The number of sites returning signals consistent with microbubbles increased dramatically after the chamber dive (P < 0.01), but did not change with the mock dive. The increase in DFU signal after the chamber dive was sustained and present at multiple sites in multiple swine. This research shows for the first time that decompression-induced tissue microbubbles can be detected using DFU and that DFU could be used to monitor decompression-induced microbubbles at multiple sites on the body. Additionally, DFU could be used to track the time course of microbubble formation and growth during decompression stress.

Qualitative analysis of verbal fluency output: review and comparison of several scoring methods.

We compared three approaches to scoring qualitative aspects of verbal fluency performance in 88 healthy young adults. Phonemic and semantic fluency output was scored for word clustering and switching between clusters. Convergent validity analyses using other tests presumed to tap into strategy use (California Verbal Learning Test, Ruff Figural Fluency Test) support scoring of phonemic and semantic clusters on both fluency tasks. Task-discrepant clustering (e.g., semantic clustering on phonemic fluency) may index intentional strategy use on both fluency tasks, whereas task consistent clustering (e.g., phonemic clustering on phonemic fluency) appears strategic only on semantic fluency. Switching can be decomposed into subtypes that appear to reflect different cognitive processes on phonemic versus semantic fluency. Principal components analyses suggest that earlier scoring methods do not fully capture the "process" aspects of verbal fluency performance.

Physiological and anthropometric determinants of sport climbing performance.

OBJECTIVE: To identify the physiological and anthropometric determinants of sport climbing performance. METHODS: Forty four climbers (24 men, 20 women) of various skill levels (self reported rating 5.6-5.13c on the Yosemite decimal scale) and years of experience (0.10-44 years) served as subjects. They climbed two routes on separate days to assess climbing performance. The routes (11 and 30 m in distance) were set on two artificial climbing walls and were designed to become progressively more difficult from start to finish. Performance was scored according to the system used in sport climbing competitions where each successive handhold increases by one in point value. Results from each route were combined for a total climbing performance score. Measured variables for each subject included anthropometric (height, weight, leg length, arm span, % body fat), demographic (self reported climbing rating, years of climbing experience, weekly hours of training), and physiological (knee and shoulder extension, knee flexion, grip, and finger pincer strength, bent arm hang, grip endurance, hip and shoulder flexibility, and upper and lower body anaerobic power). These variables were combined into components using a principal components analysis procedure. These components were then used in a simultaneous multiple regression procedure to determine which components best explain the variance in sport rock climbing performance. RESULTS: The principal components analysis procedure extracted three components. These were labelled training, anthropometric, and flexibility on the basis of the measured variables that were the most influential in forming each component. The results of the multiple regression procedure indicated that the training component uniquely explained 58.9% of the total variance in climbing performance. The anthropometric and flexibility components explained 0.3% and 1.8% of the total variance in climbing performance respectively. CONCLUSIONS: The variance in climbing performance can be explained by a component consisting of trainable variables. More importantly, the findings do not support the belief that a climber must necessarily possess specific anthropometric characteristics to excel in sport rock climbing.