Influence of kinematic analysis methods on detecting ankle and subtalar joint instability.

Patients with subtalar joint instability may be misdiagnosed with ankle instability, which may lead to chronic instability at the subtalar joint. Therefore, it is important to understand the difference in kinematics after ligament sectioning and differentiate the changes in kinematics between ankle and subtalar instability. Three methods may be used to determine the joint kinematics; the Euler angles, the Joint Coordinate System (JCS) and the helical axis (HA). The purpose of this study was to investigate the influence of using either method to detect subtalar and ankle joints instability. 3D kinematics at the ankle and subtalar joint were analyzed on 8 cadaveric specimens while the foot was intact and after sequentially sectioning the anterior talofibular ligament (ATFL), the calcaneofibular ligament (CFL), the cervical ligament and the interosseous talocalcaneal ligament (ITCL). Comparison in kinematics calculated from sensor and anatomical landmarks was conducted as well as the influence of Euler angles and JCS rotation sequence (between ISB recommendation and previous research) on the subtalar joint. All data showed a significant increase in inversion when the ITCL was sectioned. There were differences in the data calculated using sensors coordinate systems vs. anatomic coordinate systems. Anatomic coordinate systems were recommended for these calculations. The Euler angle and JCS gave similar results. Differences in Euler angles and JCS sequence lead to the same conclusion in detecting instability at the ankle and subtalar joint. As expected, the HA detected instability in plantarflexion at the ankle joint and in inversion at the subtalar joint.

Effect of training with and without a load on military fitness tests and marksmanship.

The purpose of this study was to determine whether military-style training performed while carrying a weighted vest and backpack (Load condition) resulted in superior training adaptations (specifically, changes in military fitness and marksmanship) than did more conventional training (No-Load condition). A total of 33 college-aged men and women (16 Load, 17 No-Load) completed all testing and 9 weeks of training (1 h·d, 4 d·wk). No-Load training consisted of military calisthenics, sprints, agility drills, and running. Load training was similar except that running was replaced with stair climbing, and Load increased across the 9 weeks to 20 kg for women and 30 kg for men. Pretraining and posttraining, all subjects performed an uphill treadmill test with full load to determine peak oxygen consumption (VO(2)peak), the marine physical fitness test (PFT) and combat fitness test (CFT) without load, other fitness tests, and an indoor marksmanship test using a laser-fitted carbine. The marksmanship test was performed with full load and done before and immediately after a 200-m shuttle run performed in 60 seconds. Both groups significantly improved their VO(2)peak, PFT, and CFT scores by similar amounts. Pretraining, shooting score decreased significantly after the 200-m run and then rapidly recovered, with no difference between groups. A similar, but nonsignificant, pattern in shooting scores was seen in both groups posttraining. In conclusion, loaded training did not produce measurable advantages compared with unloaded training in this population. A strenuous anaerobic challenge caused a temporary reduction in marksmanship.

Effects of training on physical performance wearing personal protective equipment.

We evaluated the effects of wearing a weighted vest during 6 weeks of military-style training. Forty-three subjects were randomly assigned to a control group or a vest group (carrying 4-5 kg for 2 weeks, and 8-10 kg for 4 weeks), with 37 completing the study (17 vest, 20 control). Both groups performed stair climbing in addition to standard Marine Corps training for 1 hour, four times per week. Pre- and post-tests were performed while wearing military personal protective equipment, with the exception of the Marine Physical Readiness Test (PRT). Both groups significantly improved PRT scores (8.4% 3-mile run, 28-38% calisthenics) and an agility drill (4.4%). Significant improvements in uphill treadmill performance (6.8% vest, 3.0% control) and maximal oxygen consumption (10.7% vest, 6.8% control) were approximately twice as much in the vest versus control group, although these differences did not reach significance (p = 0.16 and 0.13, respectively).

Studying the effects of correlation on protein selection in proteomics data.

Recently, Efron (2007) provided methods for assessing the effect of correlation on false discovery rate (FDR) in large-scale testing problems in the context of microarray data. Although FDR procedure does not require independence of the tests, existence of correlation grossly under- or overestimates the number of critical genes. Here, we briefly review Efron's method and apply it to a relatively smaller spectrometry proteomics data. We show that even here the correlation can affect the FDR values and the number of proteins declared as critical.

Effects of ultrasonic scaling and hand-activated scaling on tactile sensitivity in dental hygiene students.

PURPOSE: This study was conducted to determine if tactile sensitivity varies in dental hygiene students who use the ultrasonic scaler, as compared to those who scale with hand-activated instruments. METHODS: A two-group, randomized subjects, pretest-posttest design was carried out mid-semester for five weeks on 40 first-year dental hygiene students who met the inclusion criteria of this study and who agreed to participate. A convenience sample of 40 consenting, first-year dental hygiene students were randomly assigned to one of two groups (experimental or control). After establishing a baseline tactile sensitivity score with the Vibratory Sensory Analyzer (VSA), experimental group subjects used the ultrasonic scaler to remove 4 cc's of artificial calculus from a typodont in a controlled, simulated clinical setting for 45 minutes, while each control subject manually scaled 4 cc's of artificial calculus on a typodont in a controlled, simulated situation for 45 minutes. Immediately following exposure to either the ultrasonic scaler or hand-activated scaling instruments, tactile sensitivity scores were obtained using the VSA. Analysis of variance with one repeated measures factor was used to determine between group and within group differences on the pretest and posttest tactile sensitivity scores. RESULTS: Results revealed that tactile sensitivity increased after a 45-minute scaling session with the ultrasonic scaler. Pretest to posttest changes in tactile sensitivity for the ultrasonic scaling group exhibited a much larger threshold as compared to those in the hand-activated scaling group, supporting a gain in students' level of sensitivity with stimulus (vibration). Tactile sensitivity decreased in those who used hand-activated scaling instruments. The thumb, index, and middle fingers of students in both groups showed similarities in tactile sensitivity, with the index finger being the most sensitive. CONCLUSION: Tactile sensitivity decreases with hand-activated scaling and increases with ultrasonic scaling over a 45-minute period. Short-term vibration exposure from the ultrasonic scaler is insufficient to negatively affect tactile sensitivity.

Computational protein biomarker prediction: a case study for prostate cancer.

BACKGROUND: Recent technological advances in mass spectrometry pose challenges in computational mathematics and statistics to process the mass spectral data into predictive models with clinical and biological significance. We discuss several classification-based approaches to finding protein biomarker candidates using protein profiles obtained via mass spectrometry, and we assess their statistical significance. Our overall goal is to implicate peaks that have a high likelihood of being biologically linked to a given disease state, and thus to narrow the search for biomarker candidates. RESULTS: Thorough cross-validation studies and randomization tests are performed on a prostate cancer dataset with over 300 patients, obtained at the Eastern Virginia Medical School using SELDI-TOF mass spectrometry. We obtain average classification accuracies of 87% on a four-group classification problem using a two-stage linear SVM-based procedure and just 13 peaks, with other methods performing comparably. CONCLUSIONS: Modern feature selection and classification methods are powerful techniques for both the identification of biomarker candidates and the related problem of building predictive models from protein mass spectrometric profiles. Cross-validation and randomization are essential tools that must be performed carefully in order not to bias the results unfairly. However, only a biological validation and identification of the underlying proteins will ultimately confirm the actual value and power of any computational predictions.

Protocols for disease classification from mass spectrometry data.

We report our results in classifying protein matrix-assisted laser desorption/ionization-time of flight mass spectra obtained from serum samples into diseased and healthy groups. We discuss in detail five of the steps in preprocessing the mass spectral data for biomarker discovery, as well as our criterion for choosing a small set of peaks for classifying the samples. Cross-validation studies with four selected proteins yielded misclassification rates in the 10-15% range for all the classification methods. Three of these proteins or protein fragments are down-regulated and one up-regulated in lung cancer, the disease under consideration in this data set. When cross-validation studies are performed, care must be taken to ensure that the test set does not influence the choice of the peaks used in the classification. Misclassification rates are lower when both the training and test sets are used to select the peaks used in classification versus when only the training set is used. This expectation was validated for various statistical discrimination methods when thirteen peaks were used in cross-validation studies. One particular classification method, a linear support vector machine, exhibited especially robust performance when the number of peaks was varied from four to thirteen, and when the peaks were selected from the training set alone. Experiments with the samples randomly assigned to the two classes confirmed that misclassification rates were significantly higher in such cases than those observed with the true data. This indicates that our findings are indeed significant. We found closely matching masses in a database for protein expression in lung cancer for three of the four proteins we used to classify lung cancer. Data from additional samples, increased experience with the performance of various preprocessing techniques, and affirmation of the biological roles of the proteins that help in classification, will strengthen our conclusions in the future.