Gender-specific Indian Paediatric and Adult Reference Data for Muscle Function Parameters Assessed Using Jumping Mechanography.

OBJECTIVE: To establish age and gender-specific paediatric and adult reference data for muscle function parameters assessed using Jumping Mechanography in the Indian population. METHODS: 2056 healthy individuals (1068 males), aged 5 to 60 years, performed 2 tests on a force platform (Leonardo Mechanograph, Novotec). Maximum power (Pmax) was assessed by single two legged jump and maximum force (Fmax) by multiple one legged hopping. LMS method was used to generate age and gender-specific reference curves for 5 - 20y group and mean ± SD and median ± IQR are presented for 21 - 60y group. RESULTS: In 5 - 20y group, Pmax and Fmax increased with age while in 21 - 60y group, the parameters declined with age. Females had lower Pmax values than males, consistently through all age groups. In children <15y, there were no intergender differences in Fmax, however, in further age groups, females had lower Fmax (p<0.001). Our participants showed lower Pmax and Fmax when compared with machine reference data based on German population (p<0.001). CONCLUSION: We present ethnicity-specific reference values for muscle function by Jumping Mechanography. These values are intended to help in clinical assessment of muscle function of Indian population and to identify those at risk of poor muscle function.

Machine learning-enhanced electrical impedance myography to diagnose and track spinal muscular atrophy progression.

Objective.To evaluate electrical impedance myography (EIM) in conjunction with machine learning (ML) to detect infantile spinal muscular atrophy (SMA) and disease progression.Approach. Twenty-six infants with SMA and twenty-seven healthy infants had been enrolled and assessed with EIM as part of the NeuroNEXT SMA biomarker study. We applied a variety of modern, supervised ML approaches to this data, first seeking to differentiate healthy from SMA muscle, and then, using the best method, to track SMA progression.Main Results.Several of the ML algorithms worked well, but linear discriminant analysis (LDA) achieved 88.6% accuracy on subject muscles studied. This contrasts with a maximum of 60% accuracy that could be achieved using the single or multifrequency assessment approaches available at the time. LDA scores were also able to track progression effectively, although a multifrequency reactance-based measure also performed very well in this context.Significance.EIM enhanced with ML promises to be effective for providing effective diagnosis and tracking children and adults with SMA treated with currently available therapies. The normative trends identified here may also inform future applications of the technology in very young children. The basic analyses applied here could also likely be applied to other neuromuscular disorders characterized by muscle atrophy.

Mechanomyography-Based Metric Scale for Spasticity: A Pilot Descriptive Observational Study.

(1) Background: The Modified Ashworth Scale (MAS) is commonly used clinically to evaluate spasticity, but its qualitative nature introduces subjectivity. We propose a novel metric scale to quantitatively measure spasticity using mechanomyography (MMG) to mitigate these subjective effects. (2) Methods: The flexor and extensor muscles of knee and elbow joints were assessed with the Modified Ashworth Scale (MAS) during the acquisition of mechanomyography (MMG) data. The median absolute amplitude of the MMG signals was utilized as a key descriptor. An algorithm was developed to normalize the MMG signals to a universal gravitational (G) acceleration scale, aligning them with the limits and range of MAS. (3) Results: We evaluated 34 lower and upper limbs from 22 volunteers (average age 39.91 ± 13.77 years) of both genders. Polynomial regression provided the best fit (R2 = 0.987), with negligible differences (mean of 0.001 G) between the MAS and MMG. We established three numerical sets for the median, minimum, and maximum MMG(G) values corresponding to each MAS range, ensuring consistent alignment of the Modified Ashworth levels with our proposed scale. (4) Conclusions: Muscle spasticity can now be quantitatively and semi-automatically evaluated using our algorithm and instrumentation, enhancing the objectivity and reliability of spasticity assessments.

A wrapper framework for feature selection and ELM weights optimization for FMG-based sign recognition.

Force myography (FMG) is increasingly gaining importance in gesture recognition because of it's ability to achieve high classification accuracy without having a direct contact with the skin. In this study, we investigate the performance of a bracelet with only six commercial force sensitive resistors (FSR) sensors for classifying many hand gestures representing all letters and numbers from 0 to 10 in the American sign language. For this, we introduce an optimized feature selection in combination with the Extreme Learning Machine (ELM) as a classifier by investigating three swarm intelligence algorithms, which are the binary grey wolf optimizer (BGWO), binary grasshopper optimizer (BGOA), and binary hybrid grey wolf particle swarm optimizer (BGWOPSO), which is used as an optimization method for ELM for the first time in this study. The findings reveal that the BGWOPSO, in which PSO supports the GWO optimizer by controlling its exploration and exploitation using inertia constant to improve the convergence speed to reach the best global optima, outperformed the other investigated algorithms. In addition, the results show that optimizing ELM with BGWOPSO for feature selection can efficiently improve the performance of ELM to enhance the classification accuracy from 32% to 69.84% for classifying 37 gestures collected from multiple volunteers and using only a band with 6 FSR sensors.

Non invasive techniques for direct muscle quality assessment after exercise intervention in older adults: a systematic review.

BACKGROUND: The aging process induces neural and morphological changes in the human musculoskeletal system, leading to a decline in muscle mass, strength and quality. These alterations, coupled with shifts in muscle metabolism, underscore the essential role of physical exercise in maintaining and improving muscle quality in older adults. Muscle quality's morphological domain encompasses direct assessments of muscle microscopic and macroscopic aspects of muscle architecture and composition. Various tools exist to estimate muscle quality, each with specific technical requirements. However, due to the heterogeneity in both the studied population and study methodologies, there is a gap in the establishment of reference standards to determine which are the non-invasive and direct tools to assess muscle quality after exercise interventions. Therefore, the purpose of this review is to obtain an overview of the non-invasive tools used to measure muscle quality directly after exercise interventions in healthy older adults, as well as to assess the effects of exercise on muscle quality. MAIN TEXT: To address the imperative of understanding and optimizing muscle quality in aging individuals, this review provides an overview of non-invasive tools employed to measure muscle quality directly after exercise interventions in healthy older adults, along with an assessment of the effects of exercise on muscle quality. RESULTS: Thirty four studies were included. Several methods of direct muscle quality assessment were identified. Notably, 2 studies harnessed CT, 20 utilized US, 9 employed MRI, 2 opted for TMG, 2 adopted myotonometry, and 1 incorporated BIA, with several studies employing multiple tests. Exploring interventions, 26 studies focus on resistance exercise, 4 on aerobic training, and 5 on concurrent training. CONCLUSIONS: There is significant diversity in the methods of direct assessment of muscle quality, mainly using ultrasound and magnetic resonance imaging; and a consistent positive trend in exercise interventions, indicating their efficacy in improving or preserving muscle quality. However, the lack of standardized assessment criteria poses a challenge given the diversity within the studied population and variations in methodologies.. These data emphasize the need to standardize assessment criteria and underscore the potential benefits of exercise interventions aimed at optimizing muscle quality.

Neuromuscular disease auxiliary diagnosis using a portable magnetomyographic system.

Objective. The measurement of electromyography (EMG) signals with needle electrodes is widely used in clinical settings for diagnosing neuromuscular diseases. Patients experience pain during needle EMG testing. It is significant to develop alternative diagnostic modalities.Approach. This paper proposes a portable magnetomyography (MMG) measurement system for neuromuscular disease auxiliary diagnosis. Firstly, the design and operating principle of the system are introduced. The feasibility of using the system for auxiliary diagnosis of neuromuscular diseases is then studied. The magnetic signals and needle EMG signals of thirty subjects were collected and compared.Main results. It is found that the amplitude of muscle magnetic field signal increases during mild muscle contraction, and the signal magnitudes of the patients are smaller than those of normal subjects. The diseased muscles tested in the experiment can be distinguished from the normal muscles based on the signal amplitude, using a threshold value of 6 pT. The MMG diagnosis results align well with the needle EMG diagnosis. In addition, the MMG measurement indicates that there is a persistence of spontaneous activity in the diseased muscle.Significance.The experimental results demonstrate that it is feasible to auxiliary diagnose neuromuscular diseases using the portable MMG system, which offers the advantages of non-contact and painless measurements. After more in-depth, systematic, and quantitative research, the portable MMG could potentially be used for auxiliary diagnosis of neuromuscular diseases. The clinical trial registration number is ChiCTR2200067116.

Evaluation of mechanomyogram efficacy as a tool for assessing paired-pulse inhibition of blink reflex early R1 component.

INTRODUCTION/AIMS: Paired-pulse stimulation provides clinically useful information regarding sensory inhibition. When supraorbital nerve stimulation is repeated within a short interval, the response to the second stimulation is reduced to varying degrees. This magnitude of change in stimulation response can be monitored by electromyogram (EMG) or by mechanomyogram (MMG) as in this report. MMG has some advantages such as being less time consuming and lacking stimulus artifact. We compared the use of MMG and EMG to validate MMG as an effective method of assessing blink reflex paired-pulse inhibition. METHODS: Eight volunteers participated. Participants received electrical stimulation to the supraorbital nerve of each side. A paired-pulse paradigm was employed, varying the conditioning-test interval between 5 and 800 ms. The R1 component of the induced blink reflex was simultaneously recorded by EMG using a pair of electrodes placed on the lower eyelid and by MMG using an accelerometer placed between the electrodes. RESULTS: The correlation coefficient of the R1 amplitude between MMG and EMG of the grand-averaged waveforms was 0.99. The average participant r value was .91 (range .76-.99). Similar analyses were performed for the amplitude variation of the second response relative to the first response. Results correlated well, yielding r values of .97 and .86 for the grand-averaged waveform and the average for each subject. DISCUSSION: The present results demonstrate that MMG could be an alternative to EMG in assessing paired-pulse inhibition of the electrical blink reflex R1 component.

Accuracy and Precision of Three Acceleromyographs, Three Electromyographs, and a Mechanomyograph Measuring the Train-of-four Ratio in the Absence of Neuromuscular Blocking Drugs.

BACKGROUND: The accuracy and precision of currently available, widely used acceleromyograph and electromyograph neuromuscular blockade monitors have not been well studied. In addition, the normalization of the train-of-four ratio from acceleromyography (train-of-four ratio [T4/T1] divided by the baseline train-of-four ratio) has not been validated in comparison to mechanomyography. METHODS: Enrolled patients had surgery under general anesthesia with a supraglottic airway and without any neuromuscular blocking drugs. Three acceleromyograph monitors, three electromyograph monitors, and a mechanomyograph built in the authors' laboratory were tested. Most patients had an electromyograph and the mechanomyograph on one arm and a third monitor on the contralateral arm. Train-of-four ratios were collected every 12 to 20 s for the duration of the anesthetic. At least 1,000 train-of-four ratios were recorded for each device. Gauge repeatability and reproducibility analysis was performed. RESULTS: Twenty-eight patients were enrolled. In total, 9,498 train-of-four ratio measurements were collected. Since no neuromuscular blocking drugs were used, the expected train-of-four ratio was 1.0. All of the acceleromyograph monitors produced overshoot in the train-of-four ratio (estimated means, 1.10 to 1.13) and substantial variability (gauge SDs, 0.07 to 0.18). Normalization of the train-of-four ratio measured by acceleromyography improved the estimated mean for each device (0.97 to 1.0), but the variability was not improved (gauge SDs, 0.06 to 0.17). The electromyograph and the mechanomyograph monitors produced minimal overshoot (estimated means, 0.99 to 1.01) and substantially less variation (gauge SDs, 0.01 to 0.02). For electromyography and mechanomyography, 0.3% of all train-of-four ratios were outside of the range 0.9 to 1.1. For acceleromyography, 27 to 51% of normalized train-of-four ratios were outside the range of 0.9 to 1.1. CONCLUSIONS: Three currently available acceleromyograph monitors produced overshoot and substantial variability that could be clinically significant. Normalization corrected the overshoot in the average results but did not reduce the wide variability. Three electromyograph monitors measured the train-of-four ratio with minimal overshoot and variability, similar to a mechanomyograph.

Wearable Surface Deformation Myography (sDMG) System for Recognition of Locomotion Modes.

This study designs a wearable sensing system for locomotion mode recognition using lower-limb skin surface curvature deformation caused by the morphological changes of musculotendinous complexes and soft tissues. Flexible bending sensors are embedded into stretch pants, enabling curvature deformations of specific skin segments above lower-limb muscle groups to be captured in a noncontact manner. To evaluate the performance of this system, we conducted experiments on eight able-bodied subjects completing seven common locomotive activities, including walking, running, ramp ascending/descending, stair ascending/descending, and standing. The system measured seven channels of deformation signals from two cross-sections on the shank and the thigh. The collected signals were distinguishable across different locomotion modes and exhibited consistency when monitoring steps. Using selected time-domain features and a linear discriminant analysis (LDA) classifier enabled the proposed system to continuously recognize locomotion modes with an average accuracy of 96.5%. Furthermore, the system maintains recognition performance with 95.7% accuracy even after removing and reapplying the sensors. Finally, we conducted comparison experiments to analyze how window length, feature selection, and the number of channels affect recognition performance, providing insights for optimization. We believe that this novel signal platform holds great potential as a valuable supplementary tool in wearable human motion detection, enriching the information diversity for motion analysis, and enabling new possibilities for further advancements and applications in fields including biomedical engineering, textiles, and computer graphics.

BiLSTM-Based Joint Torque Prediction From Mechanomyogram During Isometric Contractions: A Proof of Concept Study.

Quantifying muscle strength is an important measure in clinical settings; however, there is a lack of practical tools that can be deployed for routine assessment. The purpose of this study is to propose a deep learning model for ankle plantar flexion torque prediction from time-series mechanomyogram (MMG) signals recorded during isometric contractions (i.e., a similar form to manual muscle testing procedure in clinical practice) and to evaluate its performance. Four different deep learning models in terms of model architecture (based on a stacked bidirectional long short-term memory and dense layers) were designed with different combinations of the number of units (from 32 to 512) and dropout ratio (from 0.0 to 0.8), and then evaluated for prediction performance by conducting the leave-one-subject-out cross-validation method from the 10-subject dataset. As a result, the models explained more variance in the untrained test dataset as the error metrics (e.g., root-mean-square error) decreased and as the slope of the relationship between the measured and predicted joint torques became closer to 1.0. Although the slope estimates appear to be sensitive to an individual dataset, >70% of the variance in nine out of 10 datasets was explained by the optimal model. These results demonstrated the feasibility of the proposed model as a potential tool to quantify average joint torque during a sustained isometric contraction.

Channel Selection for Gesture Recognition Using Force Myography: A Universal Model for Gesture Measurement Points.

Gesture recognition has emerged as a significant research domain in computer vision and human-computer interaction. One of the key challenges in gesture recognition is how to select the most useful channels that can effectively represent gesture movements. In this study, we have developed a channel selection algorithm that determines the number and placement of sensors that are critical to gesture classification. To validate this algorithm, we constructed a Force Myography (FMG)-based signal acquisition system. The algorithm considers each sensor as a distinct channel, with the most effective channel combinations and recognition accuracy determined through assessing the correlation between each channel and the target gesture, as well as the redundant correlation between different channels. The database was created by collecting experimental data from 10 healthy individuals who wore 16 sensors to perform 13 unique hand gestures. The results indicate that the average number of channels across the 10 participants was 3, corresponding to an 75% decrease in the initial channel count, with an average recognition accuracy of 94.46%. This outperforms four widely adopted feature selection algorithms, including Relief-F, mRMR, CFS, and ILFS. Moreover, we have established a universal model for the position of gesture measurement points and verified it with an additional five participants, resulting in an average recognition accuracy of 96.3%. This study provides a sound basis for identifying the optimal and minimum number and location of channels on the forearm and designing specialized arm rings with unique shapes.

Implementation of surface mechanomyography as a novel approach for objective evaluation of phasic muscle stretch reflexes in people with type 2 diabetes.

INTRODUCTION: Diabetic peripheral neuropathy is the most common complication of diabetes producing metabolic disruptions in the peripheral nervous system. Alteration in the predictable nature of tendon reflexes is the most common indicator suggesting the possibility of diabetic neuropathy. Evaluation of tendon reflexes is a part of various clinical scoring systems that assess neuropathy. The conventional reflex grading scales are subjective, lack temporal data, and have high inter-rater variability. Hence, an indigenous quantification tool was developed to evaluate the tendon reflexes in order to assess diabetic peripheral neuropathy. MATERIALS AND METHODS: A cross-sectional study was carried out in 140 healthy volunteers and 140 patients with type 2 diabetes. The mean age of controls and diabetics (49.1 ± 8.9, 50.7 ± 7.5) years, weight (66.9 ± 9.4, 69.8 ± 11.5) kilograms and BMI (24.5 ± 3.8, 26.1 ± 4.7), respectively. All of them are subjected to evaluation of tendon reflexes using the reflex quantification tool comprised of surface mechanomyography and electrogoniometry that can provide various static and dynamic variables of tendon reflex. RESULTS: The dynamic variables such as reflex amplitude, muscle velocity and angular velocity were significantly low in diabetic patients (p: <0.001) whereas latency and duration (p: <0.001) were prolonged. Furthermore, no significant difference was observed in the application of tendon striking force (p: 0.934) among the participants. CONCLUSION: The current study demonstrates that the proposed reflex quantification tool provides several dynamic variables of patellar tendon reflex, which are significantly affected and altered in diabetic patients suggesting the involvement of peripheral neurons.

Assessing paraspinal muscle atrophy with electrical impedance myography: Limitations and insights.

Paraspinal muscle atrophy is gaining attention in spine surgery due to its link to back pain, spinal degeneration and worse postoperative outcomes. Electrical impedance myography (EIM) is a noninvasive diagnostic tool for muscle quality assessment, primarily utilized for patients with neuromuscular diseases. However, EIM's accuracy for paraspinal muscle assessment remains understudied. In this study, we investigated the correlation between EIM readings and MRI-derived muscle parameters, as well as the influence of dermal and subcutaneous parameters on these readings. We retrospectively analyzed patients with lumbar spinal degeneration who underwent paraspinal EIM assessment between May 2023 to July 2023. Paraspinal muscle fatty infiltration (FI) and functional cross-sectional area (fCSA), as well as the subcutaneous thickness were assessed on MRI scans. Skin ultrasound imaging was assessed for dermal thickness and the echogenicities of the dermal and subcutaneous layers. All measurements were performed on the bilaterally. The correlation between EIM readings were compared with ultrasound and MRI parameters using Spearman's correlation analyses. A total of 20 patients (65.0% female) with a median age of 69.5 years (IQR, 61.3-73.8) were analyzed. The fCSA and FI did not significantly correlate with the EIM readings, regardless of frequency. All EIM readings across frequencies correlated with subcutaneous thickness, echogenicity, or dermal thickness. With the current methodology, paraspinal EIM is not a valid alternative to MRI assessment of muscle quality, as it is strongly influenced by the dermal and subcutaneous layers. Further studies are required for refining the methodology and confirming our results.

Optical Myography-Based Sensing Methodology of Application of Random Loads to Muscles during Hand-Gripping Training.

Hand-gripping training is important for improving the fundamental functions of human physical activity. Bernstein's idea of "repetition without repetition" suggests that motor control function should be trained under changing states. The randomness level of load should be visualized for self-administered screening when repeating various training tasks under changing states. This study aims to develop a sensing methodology of random loads applied to both the agonist and antagonist skeletal muscles when performing physical tasks. We assumed that the time-variability and periodicity of the applied load appear in the time-series feature of muscle deformation data. In the experiment, 14 participants conducted the gripping tasks with a gripper, ball, balloon, Palm clenching, and paper. Crumpling pieces of paper (paper exercise) involves randomness because the resistance force of the paper changes depending on the shape and layers of the paper. Optical myography during gripping tasks was measured, and time-series features were analyzed. As a result, our system could detect the random movement of muscles during training.

An Approach to Using Electrical Impedance Myography Signal Sensors to Assess Morphofunctional Changes in Tissue during Muscle Contraction.

This present work is aimed at conducting fundamental and exploratory studies of the mechanisms of electrical impedance signal formation. This paper also considers morphofunctional changes in forearm tissues during the performance of basic hand actions. For this purpose, the existing research benches were modernized to conduct experiments of mapping forearm muscle activity by electrode systems on the basis of complexing the electrical impedance signals and electromyography signals and recording electrode systems' pressing force using force transducers. Studies were carried out with the involvement of healthy volunteers in the implementation of vertical movement of the electrode system and ultrasound transducer when the subject's upper limb was positioned in the bed of the stand while performing basic hand actions in order to identify the relationship between the morphofunctional activity of the upper limb muscles and the recorded parameters of the electro-impedance myography signal. On the basis of the results of the studies, including complex measurements of neuromuscular activity on healthy volunteers such as the signals of electro-impedance myography and pressing force, analyses of the morphofunctional changes in tissues during action performance on the basis of ultrasound and MRI studies and the factors influencing the recorded signals of electro-impedance myography are described. The results are of fundamental importance and will enable reproducible electro-impedance myography signals, which, in turn, allow improved anthropomorphic control.

Needle electromyography does not meaningfully impact findings in MR-neurography/-myography.

INTRODUCTION: Magnetic resonance neurography (MRN) and myography (MRM) are emerging imaging methods for detecting diseases of the peripheral nerve system (PNS). Most patients with PNS diseases also undergo needle electromyography (EMG). This study examined whether EMG led to lesions that were detectable using MRN/MRM and whether these lesions could impair image interpretation. METHODS: Ten patients who underwent clinically indicated EMG were recruited. MRN/MRM was performed before and 2-6 h after EMG, and if achievable, 2-3 days later. T2 signal intensity (SI) of the tibialis anterior muscle (TA) was quantified, and sizes and SI of the new lesions were measured. Visual rating was performed independently by three neuroradiologists. RESULTS: T2 lesions at the site of needle insertion, defined as focal edema, were detectable in 9/10 patients. The mean edema size was 31.72 mm2 (SD = 14.42 mm2 ) at the first follow-up. Susceptibility-weighted imaging lesions, defined as (micro) hematomas were detected in 5/10 patients (mean size, 23.85 mm2 [SD = 12.59 mm2 ]). General muscle SI of the TA did not differ between pre- and post-EMG examinations. Lesions size was relatively small, and the readers described image interpretation as not impaired by these lesions. DISCUSSION: This study showed that focal edema and hematomas frequently occurred after needle EMG and could be observed using MRN/MRM. As general muscle SI was not affected and image interpretation was not impaired, we concluded that needle EMG did not interfere with MRN/MRM.

A survey on the state of the art of force myography technique (FMG): analysis and assessment.

Precise feedback assures precise control commands especially for assistive or rehabilitation devices. Biofeedback systems integrated with assistive or rehabilitative robotic exoskeletons tend to increase its performance and effectiveness. Therefore, there has been plenty of research in the field of biofeedback covering different aspects such as signal acquisition, conditioning, feature extraction and integration with the control system. Among several types of biofeedback systems, Force myography (FMG) technique is a promising one in terms of affordability, high classification accuracies, ease to use, and low computational cost. Compared to traditional biofeedback systems such as electromyography (EMG) which offers some invasive techniques, FMG offers a completely non-invasive solution with much less effort for preprocessing with high accuracies. This work covers the whole aspects of FMG technique in terms of signal acquisition, feature extraction, signal processing, developing the machine learning model, evaluating tools for the performance of the model. Stating the difference between real-time and offline assessment, also highlighting the main uncovered points for further study, and thus enhancing the development of this technique.

AI-Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction.

Motion recognition (MR)-based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human-computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non-intrusive muscle-sensing wearable device, that in conjunction with machine learning, enables motion-control-based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16-channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower-limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle-sensing-based somatosensory interaction, using the proposed wearable device, for enabling the real-time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography-based methods for achieving accurate human motion capture, which can further broaden the applications of motion-interactive wearable devices for the coming metaverse age.

Optimization of resting tension for wire myography in male rat pulmonary arteries.

Wire myography to test vasomotor functions of blood vessels ex-vivo are well-established for the systemic circulation, however, there is no consensus on protocols for pulmonary arteries. We created a standardized wire myography protocol for healthy rat PAs and validated this in a pulmonary hypertension (PH) model. Vessels stretched to higher initial tensions (5.0, 7.5 and 10.0 mN) exhibited a uniform response to phenylephrine, a larger dynamic range, and lower EC50 values. The endothelium-mediated relaxation showed that moderate tensions (7.5 and 10.0 mN) produced robust responses with higher maximum relaxation and lower EC50 values. For endothelium independent responses, the higher initial tension groups had lower and more consistent EC50 values than the lower initial tension groups. Pulmonary arteries from rats with PH were more responsive to vasoactive drugs when subjected to a higher initial tension. Notably, vessels in the PH group subjected to 15.0 mN exhibited high dynamic ranges in contractile and relaxation responses without tearing. Lastly, we observed attenuated cholinergic responses in these vessels-consistent with endothelial dysfunction in PH. Therefore, a moderate initial tension of 7.5-10.0 mN is optimal for healthy rat pulmonary arteries and a higher initial tension of 15.0 mN is optimal for pulmonary arteries from animals with PH.