The Molecular Basis of Resistance Antiretroviral Markers and Polymorphisms of the Human Immunodeficiency Virus-1 Subtype Crf01-ae Protease Gene in Naïve and Treatment Failure Patients in Bali

Application of antiretrovirals (ARVs) in patients with Human Immunodeficiency Virus (HIV) infection has proven to reduce mortality rates and prolong life expectancy. On the other hand, the use of antiretroviral drugs has incited the emergence of HIVDR. The resistance is due to mutation at genes associated with drug resistance. Nowadays, the determination of resistance markers mutations are based on HIV-1 subtype B. However, the majority of HIV in Indonesia, particularly in Bali are of subtype CRF01_AE. Genetic variation between HIV viruses has led to variations in subtypes; therefore, resistance markers of subtype B could be polymorphisms of non-B subtypes. This study aims to determine the number and types of the resistance markers mutations and polymorphisms that occur on the PR gene of HIV-1 subtype CRF01_AE of naïve and treatment failure patients in Bali. This is an observational cross-sectional analytical study, conducted at two VCT clinics in Denpasar, during the period of April 2010 until October 2011. Samples consist of 18 HIV patients with treatment failure and 30 naïve HIV patients. Mutations were evaluated using PCR, sequenced and aligned were carried out using MEGA4. Interpretations of the mutations were made based on the Stanford HIV database. Hypothesis tests used were Mann-Whitney because of abnormal distribution of data. Hypothesis was accepted if the significant level p<0.05. This study found that of the demographic data, only the predisposing factors of the two groups were significantly different (p<0.05). Two patients with treatment failure and 5 naïve patients were found to have L10LV/I mutations. Only one patient with treatment failure had the I54FI mutation. No major mutations were found among the two study groups. The number and types of minor mutations were not significantly different (p>0.05) between the naïve group and treatment failure group. M36I and H69K polymorphisms of the PR gene were found in all the study samples. In conclusion of this study, two types of major mutations were found, L10LV/I and I54FI. The number and types of the resistance markers mutations towards the protease inhibitor (PI) group were not significantly different between the two study groups. M36I, H69K mutations of the PR gene are markers of polymorphisms of HIV-1 subtype CRF01_AE

Complexity analysis of surface electromyography for assessing the myoelectric manifestation of muscle fatigue: A review

The surface electromyography (sEMG) records the electrical activity of muscle fibers during contraction: one of its uses is to assess changes taking place within muscles in the course of a fatiguing contraction to provide insights into our understanding of muscle fatigue in training protocols and rehabilitation medicine. Until recently, these myoelectric manifestations of muscle fatigue (MMF) have been assessed essentially by linear sEMG analyses. However, sEMG shows a complex behavior, due to many concurrent factors. Therefore, in the last years, complexity-based methods have been tentatively applied to the sEMG signal to better individuate the MMF onset during sustained contractions. In this review, after describing concisely the traditional linear methods employed to assess MMF we present the complexity methods used for sEMG analysis based on an extensive literature search. We show that some of these indices, like those derived from recurrence plots, from entropy or fractal analysis, can detect MMF efficiently. However, we also show that more work remains to be done to compare the complexity indices in terms of reliability and sensibility; to optimize the choice of embedding dimension, time delay and threshold distance in reconstructing the phase space; and to elucidate the relationship between complexity estimators and the physiologic phenomena underlying the onset of MMF in exercising muscles

The Association of Fatigue With Decreasing Regularity of Locomotion During an Incremental Test in Trained and Untrained Healthy Adults

Fatigue is a key factor that affects human motion and modulates physiology, biochemistry, and performance. Prolonged cyclic human movements (locomotion primarily) are characterized by a regular pattern, and this extended activity can induce fatigue. However, the relationship between fatigue and regularity has not yet been extensively studied. Wearable sensor methodologies can be used to monitor regularity during standardized treadmill tests (e.g., the widely used Bruce test) and to verify the effects of fatigue on locomotion regularity. Our study on 50 healthy adults [27 males and 23 females; <40 years; five dropouts; and 22 trained (T) and 23 untrained (U) subjects] showed how locomotion regularity follows a parabolic profile during the incremental test, without exception. At the beginning of the trial, increased walking speed in the absence of fatigue is associated with increased regularity (regularity index, RI, a. u., null/unity value for aperiodic/periodic patterns) up until a peak value (RI = 0.909 after 13.8 min for T and RI = 0.915 after 13.4 min for U subjects; median values, n. s.) and which is then generally followed (after 2.8 and 2.5 min, respectively, for T/U, n. s.) by the walk-to-run transition (at 12.1 min for both T and U, n. s.). Regularity then decreases with increased speed/slope/fatigue. The effect of being trained was associated with significantly higher initial regularity [0.845 (T) vs 0.810 (U), p < 0.05 corrected], longer test endurance [23.0 min (T) vs 18.6 min (U)], and prolonged decay of locomotor regularity [8.6 min (T) vs 6.5 min (U)]. In conclusion, the monitoring of locomotion regularity can be applied to the Bruce test, resulting in a consistent time profile. There is evidence of a progressive decrease in regularity following the walk-to-run transition, and these features unveil significant differences among healthy trained and untrained adult subjects

A 45-Second Self-Test for Cardiorespiratory Fitness:Heart Rate-Based Estimation in Healthy Individuals

Cardio-respiratory fitness (CRF) is a widespread essential indicator in Sports Science as well as in Sports Medicine. This study aimed to develop and validate a prediction model for CRF based on a 45 second self-test, which can be conducted anywhere. Criterion validity, test re-test study was set up to accomplish our objectives. Data from 81 healthy volunteers (age: 29 ± 8 years, BMI: 24.0 ± 2.9), 18 of whom females, were used to validate this test against gold standard. Nineteen volunteers repeated this test twice in order to evaluate its repeatability. CRF estimation models were developed using heart rate (HR) features extracted from the resting, exercise, and the recovery phase. The most predictive HR feature was the intercept of the linear equation fitting the HR values during the recovery phase normalized for the height2 (r2 = 0.30). The Ruffier-Dickson Index (RDI), which was originally developed for this squat test, showed a negative significant correlation with CRF (r = -0.40), but explained only 15% of the variability in CRF. A multivariate model based on RDI and sex, age and height increased the explained variability up to 53% with a cross validation (CV) error of 0.532 L ∙ min-1 and substantial repeatability (ICC = 0.91). The best predictive multivariate model made use of the linear intercept of HR at the beginning of the recovery normalized for height2 and age2; this had an adjusted r2 = 0. 59, a CV error of 0.495 L·min-1 and substantial repeatability (ICC = 0.93). It also had a higher agreement in classifying CRF levels (κ = 0.42) than RDI-based model (κ = 0.29). In conclusion, this simple 45 s self-test can be used to estimate and classify CRF in healthy individuals with moderate accuracy and large repeatability when HR recovery features are included