Loss of salmeterol bronchoprotection against exercise in relation to ADRB2 Arg16Gly polymorphism and exhaled nitric oxide.

RATIONALE: ß2-Agonists are the treatment of choice for exercise-induced bronchoconstriction (EIB) and act through specific receptors (ADRB2). Arg16Gly polymorphisms have been shown to affect responses to regular use of ß2-agonists. OBJECTIVES: To evaluate the influence of the Arg16Gly receptor polymorphism on salmeterol bronchoprotection in EIB and assess predictors of bronchoprotection. METHODS: A prospective, genotype-blinded, randomized trial was performed in 26 subjects (12 Arg16Arg and 14 Gly16Gly) with EIB who were not on controller therapy. Subjects were administered salmeterol, 50 µg twice a day for 2 weeks, and underwent an exercise challenge 9 hours after the first and last drug dose. In addition to genotype, FEV1, response to salmeterol, degree of EIB, and exhaled nitric oxide (FE(NO)) at baseline were examined for their association with loss of bronchoprotection (LOB). MEASUREMENTS AND MAIN RESULTS: The maximum exercise-induced FEV1 fall was 27.9 ± 1.4% during the run-in period, 8.1 ± 1.2% (70.3 ± 4.1% bronchoprotection) after the first salmeterol dose, and 22.8 ± 3.2% (18.9 ± 11.5% bronchoprotection) after 2 weeks of salmeterol (P = 0.0001). The Arg16Gly polymorphisms were not associated with the LOB in response to salmeterol. FeNO values at baseline were significantly related to the LOB (r = 0.47; P = 0.01). Mean change was a 74 ± 13% LOB in subjects with FE(NO) levels greater than 50 ppb and a 7 ± 16% gain in bronchoprotection in those with FE(NO) levels less than 25 ppb (P = 0.01). CONCLUSIONS: The LOB that occurs with chronic long-acting ß2-agonists use is not affected by ADRB2 Arg16Gly polymorphisms. High FE(NO) was associated with marked LOB. Use of long-acting ß2-agonists before achieving a reduction in FeNO may need to be avoided. Clinical trial registered with www.clinicaltrials.gov (NCT 00595361).

Classification of hand and wrist movements via surface electromyogram using the random convolutional kernels transform.

Prosthetic devices are vital for enhancing personal autonomy and the quality of life for amputees. However, the rejection rate for electric upper-limb prostheses remains high at around 30%, often due to issues like functionality, control, reliability, and cost. Thus, developing reliable, robust, and cost-effective human-machine interfaces is crucial for user acceptance. Machine learning algorithms using Surface Electromyography (sEMG) signal classification hold promise for natural prosthetic control. This study aims to enhance hand and wrist movement classification using sEMG signals, treated as time series data. A novel approach is employed, combining a variation of the Random Convolutional Kernel Transform (ROCKET) for feature extraction with a cross-validation ridge classifier. Traditionally, achieving high accuracy in time series classification required complex, computationally intensive methods. However, recent advances show that simple linear classifiers combined with ROCKET can achieve state-of-the-art accuracy with reduced computational complexity. The algorithm was tested on the UCI sEMG hand movement dataset, as well as on the Ninapro DB5 and DB7 datasets. We demonstrate how the proposed approach delivers high discrimination accuracy with minimal parameter tuning requirements, offering a promising solution to improve prosthetic control and user satisfaction.