Muscle Segments Research Articles

An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles.

Imaging studies about the relevance of muscles in spinal disorders, and sarcopenia in general, require the segmentation of the muscles in the images which is very labour-intensive if performed manually and poses a practical limit to the number of investigated subjects. This study aimed at developing a deep learning-based tool able to fully automatically perform an accurate segmentation of the lumbar muscles in axial MRI scans, and at validating the new tool on an external dataset. A set of 60 axial MRI images of the lumbar spine was retrospectively collected from a clinical database. Psoas major, quadratus lumborum, erector spinae, and multifidus were manually segmented in all available slices. The dataset was used to train and validate a deep neural network able to segment muscles automatically. Subsequently, the network was externally validated on images purposely acquired from 22 healthy volunteers. The median Jaccard index for the individual muscles calculated for the 22 subjects of the external validation set ranged between 0.862 and 0.935, demonstrating a generally excellent performance of the network, although occasional failures were noted. Cross-sectional area and fat fraction of the muscles were in agreement with published data. The externally validated deep neural network was able to perform the segmentation of the paravertebral muscles in an accurate and fully automated manner, although it is not without limitations. The model is therefore a suitable research tool to perform large-scale studies in the field of spinal disorders and sarcopenia, overcoming the limitations of non-automated methods.

A self-training teacher-student model with an automatic label grader for abdominal skeletal muscle segmentation

Deep learning on a limited number of labels/annotations is a challenging task for medical imaging analysis. In this paper, we propose a novel self-training segmentation pipeline (Self-Seg in short) for segmenting skeletal muscle in CT images. Self-Seg starts with a small set of annotated images and then iteratively learns from unlabeled datasets to gradually improve the segmentation performance. Self-Seg follows a semi-supervised teacher-student learning scheme and there are two contributions: 1) we construct a self-attention UNet to improve segmentation over the classical UNet model, and 2) we implement an automatic label grader to implicitly incorporate medical knowledge for quality assurance of pseudo labels, from which good quality pseudo labels are identified to enhance learning of the segmentation model. We perform extensive experiments on three CT image datasets and show promising results on five evaluation settings, and we also compared our method to several baseline and related methods and achieved superior performance.

Discussion

Discussion

Psoas Cross-Sectional Measurements Using Manual CT Segmentation before and after Endovascular Aortic Repair (EVAR).

Sarcopenia has been associated with an increased incidence of adverse outcomes, including higher mortality, after endovascular aortic repair (EVAR). We aim to use computed tomography (CT) to quantify changes in total psoas muscles area (PMA) and psoas muscle density (PMD) after EVAR, and to evaluate the reproducibility of both measurements. PMA and PMD were assessed via manual segmentation of the psoas muscle on pre- and post-operative CT scans belonging to consecutive patients who underwent EVAR. Wilcoxon test was used to compare PMA and PMD before and after EVAR, and inter- and intra-reader agreements of both methods were evaluated through Bland–Altman analysis. A total of 50 patients, 42 of them males (84%), were included in the study. PMA changes from 1243 mm2 (1006–1445 mm2) to 1102 mm2 (IQR 937–1331 mm2), after EVAR (p < 0.001). PMD did not vary between pre-EVAR (33 HU, IQR 26.5–38.7 HU) and post-EVAR (32 HU, IQR 26–37 HU, p = 0.630). At inter-reader Bland–Altman analysis, PMA showed a bias of 64.0 mm2 and a coefficient of repeatability (CoR) of 359.2 mm2, whereas PMD showed a bias of −2.43 HU and a CoR of 6.19 HU. At intra-reader Bland–Altman analysis, PMA showed a bias of −81.1 mm2 and a CoR of 394.6 mm2, whereas PMD showed a bias of 1.41 HU and a CoR of 6.36 HU. In conclusion, PMA decreases after EVAR. A good intra and inter-reader reproducibility was observed for both PMA and PMD. We thus propose to use PMA during the follow-up of patients who underwent EVAR to monitor muscle depletion after surgery.

Thermovision as a Tool for Athletes to Verify the Symmetry of Work of Individual Muscle Segments.

In the presented research, we characterised the temperature profiles and the degree of preparation for exercise of individual muscle groups of athletes We hypothesise that by means of thermal imaging studies, the effectiveness of the warm-up can be monitored to determine whether the effort of individual muscles is equal and symmetrical, which can help to avoid a potential injury. In the study, thermographic imaging was performed on a group of athletes exercising on a rowing ergometer involving almost 80% of the muscle parts of the human body for intense and symmetrical exercise. Thermovision studies have confirmed, based on the increased temperature of the muscle areas, that the rowing ergometer involves many muscle groups in training. Moreover, based on the shape of the temperature function obtained from individual body regions of interest, it was shown that conventional exercise on a rowing ergometer causes almost symmetrical work of the right and left sides of the body. Obtained temperature changes in most of the studied muscle areas showed minimum temperature reached the beginning of training—mostly phases 1 and 2. During the subsequent phases, the temperature increase was monitored, stopping at resting temperature. Significantly, temperature variations did not exceed 0.5 °C in all post-training phases. Statistical analyses did not show any significant differences in the symmetry of right and left muscle areas corresponding to the muscle location temperature. Thermal imaging may be an innovative wholly non-invasive and safe method, because checking induces adaptation processes, which may become indicators of an athlete’s efficiency. The imaging can be continuously repeated, and automatic comparison of average temperature or temperature difference may provide some clues that protect athletes from overtraining or serious injuries.

Improved fillet quality in harvest-size Atlantic salmon fed high n-3 canola oil as a DHA-source

New sources of the very long-chain n-3 polyunsaturated fatty acids (n-3 VLC-PUFA) are needed for sustainable growth of salmon farming. In recent research high n-3 VLC-PUFA canola oil has shown promise as a safe and effective source of docosahexaenoic acid (DHA) and other n-3 PUFA in small Atlantic salmon. To study its long-term effects on performance and fillet quality under realistic farming conditions, a 12-month feeding experiment was carried out with Atlantic salmon growing to harvest weight (4.7 kg) in triplicate sea water cages. The fish were fed three diets containing graded inclusion levels of high n-3 canola oil resulting in levels of dietary DHA (22:6 n-3) of 4.0% (Standard; STD), 6.6% (Medium; MED) and 8.3% of total fatty acids (HIGH) and total EPA + DHA of 6.4, 9.1 and 11.0% of total fatty acids, respectively. The STD diet was considered to represent current commercial practice. There were no significant differences between the dietary groups in overall fish growth and weight at harvest, and no differences were seen in feed conversion ratio. Tissue FA-profiles mirrored the diets, giving increased DHA, EPA (20:5 n-3) and ALA (18:3 n-3) with increasing inclusion level of high n-3 LC-PUFA canola oil. Dietary inclusion of n-3 canola oil had no effect on muscle astaxanthin concentration, but instrumentally measured redness, yellowness and chroma increased with n-3 canola oil inclusion, suggesting a positive effect on colour via effects on muscle structure or composition. The prevalence and severity of dark melanin spots in the fillet was significantly reduced by increased inclusion level of n-3 canola oil. The prevalence of severe spots (score ≥ 2), average score and number of affected muscle segments were all lower in MED and HIGH, compared to STD.In conclusion, high n-3 canola oil is an efficient source of DHA and other n-3 fatty acids for harvest-size Atlantic salmon. Increased dietary supply of DHA and other n-3 PUFA, above minimum requirement levels for DHA, improved fillet pigmentation and reduced fillet melanin spots, without compromising fish growth.

Semantic Segmentation of Extraocular Muscles on Computed Tomography Images Using Convolutional Neural Networks.

Computed tomography (CT) imaging of the orbit with measurement of extraocular muscle size can be useful for diagnosing and monitoring conditions that affect extraocular muscles. However, the manual measurement of extraocular muscle size can be time-consuming and tedious. The purpose of this study is to evaluate the effectiveness of deep learning algorithms in segmenting extraocular muscles and measuring muscle sizes from CT images. Consecutive CT scans of orbits from 210 patients between 1 January 2010 and 31 December 2019 were used. Extraocular muscles were manually annotated in the studies, which were then used to train the deep learning algorithms. The proposed U-net algorithm can segment extraocular muscles on coronal slices of 32 test samples with an average dice score of 0.92. The thickness and area measurements from predicted segmentations had a mean absolute error (MAE) of 0.35 mm and 3.87 mm2, respectively, with a corresponding mean absolute percentage error (MAPE) of 7 and 9%, respectively. On qualitative analysis of 32 test samples, 30 predicted segmentations from the U-net algorithm were accepted while 2 were rejected. Based on the results from quantitative and qualitative evaluation, this study demonstrates that CNN-based deep learning algorithms are effective at segmenting extraocular muscles and measuring muscles sizes.

Deep Learning-Based Muscle Segmentation and Quantification of Full-Leg Plain Radiograph for Sarcopenia Screening in Patients Undergoing Total Knee Arthroplasty.

Sarcopenia, an age-related loss of skeletal muscle mass and function, is correlated with adverse outcomes after some surgeries. Here, we present a deep-learning-based model for automatic muscle segmentation and quantification of full-leg plain radiographs. We illustrated the potential of the model to predict sarcopenia in patients undergoing total knee arthroplasty (TKA). A U-Net-based deep learning model for automatic muscle segmentation was developed, trained and validated on the plain radiographs of 227 healthy volunteers. The radiographs of 403 patients scheduled for primary TKA were reviewed to test the developed model and explore its potential to predict sarcopenia. The proposed deep learning model achieved mean IoU values of 0.959 (95% CI 0.959–0.960) and 0.926 (95% CI 0.920–0.931) in the training set and test set, respectively. The fivefold AUC value of the sarcopenia classification model was 0.988 (95% CI 0.986–0.989). Of seven key predictors included in the model, the predicted muscle volume (PMV) was the most important of these features in the decision process. In the preoperative clinical setting, wherein laboratory tests and radiographic imaging are available, the proposed deep-learning-based model can be used to screen for sarcopenia in patients with knee osteoarthritis undergoing TKA with high sarcopenia screening performance.

Effect of Vibratory Inhibition and Pelvic Stabilization Exercise on Tone, Balance and Gross Motor Function in Spastic Cerebral Palsy- A Comparative Study

Objectives: Cerebral palsy is one of the leading causes of movement and posture disorders. Recently, Vibration therapy as a treatment method in clinical practice has been used as a complementary approach. This study compared the effects of Segmental Muscle Vibration and Pelvic Stabilization Exercises on Muscle Tone, Balance, and Gross Motor Function in spastic CP children.&#x0D; Materials and Methods: A total of 20 children with Spastic Cerebral Palsy, of both sexes (age range of 4-6 years) were included in the study. The Children were assigned into two groups. Group A received Segmental Muscle Vibration along with conventional treatment i.e. Passive Stretching Exercise and Group B received Pelvic Stabilization Exercise along with conventional treatment for 40 minute, 3 times a week for 3 weeks. Outcome measures include Modified Ashworth Scale, Pediatric Balance Scale and Gross Motor Function Measure-88 for measuring Tone (spasticity), Balance and Gross Motor Function.&#x0D; Results: A significant improvement in all variables (p &lt; 0.05) was observed in each group, with greater improvement in tone, balance and gross function indices in group A.&#x0D; Conclusion: From the results we concluded that both the Segmental Vibration Inhibition Therapy and Pelvic Stabilization exercise yielded a clinically significant improvement in spastic CP; but Segmental Vibration Inhibition Therapy shows more improvement in reduction in tone, improvement in balance and gross motor function.

Segmentation of Pectoral Muscle in Mammograms Using Granular Computing

In this paper, pectoral muscle segmentation was performed to study the presence of malignancy in the pectoral muscle region in mammograms. A combined approach involving granular computing and layering was employed to locate the pectoral muscle in mammograms. In most cases, the pectoral muscle is found to be triangular in shape and hence, the ant colony optimization algorithm is employed to accurately estimate the pectoral muscle boundary. The proposed method works with the left mediolateral oblique (MLO) view of mammograms to avoid artifacts. For the right MLO view, the method automatically mirrors the image to the left MLO view. The performance of this method was evaluated using the standard mini MIAS dataset (mammographic image analysis society). The algorithm was tested on 322 images and the overall accuracy of the system was about 97.47 %. The method is robust with respect to the view, shape, size and reduces the processing time. The approach correctly identifies images when the pectoral muscle is completely absent.

Defining Normal Ranges of Skeletal Muscle Area and Skeletal Muscle Index in Children on CT Using an Automated Deep Learning Pipeline: Implications for Sarcopenia Diagnosis.

BACKGROUND. Skeletal muscle area (SMA), representing skeletal muscle cross-sectional area at the L3 vertebral level, and skeletal muscle index (SMI), representing height-normalized SMA, can serve as markers of sarcopenia. Normal SMA and SMI values have been reported primarily in adults. OBJECTIVE. The purpose of this study was to use an automated deep learning (DL) pipeline for muscle segmentation on abdominal CT to define normative age- and sex-based values for pediatric muscle cross-sectional area as a guide for diagnosis of sarcopenia in children. METHODS. This retrospective study reviewed records of patients who underwent abdominal CT at Cincinnati Children's Hospital Medical Center from January 1, 2009, to January 3, 2019. Patients were excluded on the basis of age outside of the eligible range (2.00-18.99 years), body mass index (BMI) outside of 5-95% age-based percentiles using CDC and WHO growth charts, known medical condition, medication use, support devices, surgery, or missing axial images at the L3 level. A previously validated automated DL pipeline was used to identify an axial slice at L3 and segment skeletal muscle to generate SMA and SMI. Pearson correlation coefficients were computed. Quantile regression analysis was used to plot SMA and SMI as functions of age and sex and to determine age- and sex-based percentile values. RESULTS. Of 8817 patients who underwent abdominal CT during the study period, 2168 (mean age, 12.3 ± 4.3 [SD] years; 1125 female patients, 1043 male patients) met inclusion criteria. Mean BMI-for-age percentile based on CDC and WHO growth charts was 64.8% ± 25.3% for female patients and 61.4% ± 25.8% for male patients. SMA showed strong correlation with weight, height, age, and BMI for male (0.79-0.94) and female (0.75-0.90) patients; SMI showed weak-to-moderate correlation with weight, height, age, and BMI for male (0.25-0.57) and female (0-0.43) patients. Normal SMA and SMI ranges for age and sex were expressed as curves and as a lookup table, identifying 54 male and 59 female patients with muscle measurements below the 5th percentile regression curve. CONCLUSION. By using an automated DL pipeline in a large sample of carefully selected children, normal ranges for SMA and SMI were calculated as functions of age and sex. CLINICAL IMPACT. The normative values should aid the diagnosis of sarcopenia in children.

Clinical evaluation of automated segmentation for body composition analysis on abdominal L3 CT slices in polytrauma patients

IntroductionSarcopenia is a muscle disease that involves loss of muscle strength and physical function and is associated with adverse health effects. Even though sarcopenia has attracted increasing attention in the literature, many research findings have not yet been translated into clinical practice. In this article, we aim to validate a deep learning neural network for automated segmentation of L3 CT slices and aim to explore the potential for clinical utilization of such a tool for clinical practice. Materials and methodsA deep learning neural network was trained on a multi-centre collection of 3413 abdominal cancer surgery subjects to automatically segment muscle, subcutaneous and visceral adipose tissue at the L3 lumbar vertebral level. 536 Polytrauma subjects were used as an independent test set to show generalizability. The Dice Similarity Coefficient was calculated to validate the geometric similarity. Quantitative agreement was quantified using Bland-Altman's Limits of Agreement interval and Lin's Concordance Correlation Coefficient. To determine the potential clinical usability, randomly selected segmentation images were presented to a panel of experienced clinicians to rate on a Likert scale. ResultsDeep learning results gave excellent agreement versus a human expert operator for all of the body composition indices, with Concordance Correlation Coefficient for skeletal muscle index of 0.92, Skeletal muscle radiation attenuation 0.94, Visceral Adipose Tissue index 0.99 and Subcutaneous Adipose Tissue Index 0.99. Triple-blinded visual assessment of segmentation by clinicians correlated only to the Dice coefficient, but had no association to quantitative body composition metrics which were accurate irrespective of clinicians’ visual rating. ConclusionA deep learning method for automatic segmentation of truncal muscle, visceral and subcutaneous adipose tissue on individual L3 CT slices has been independently validated against expert human-generated results for an enlarged polytrauma registry dataset. Time efficiency, consistency and high accuracy relative to human experts suggest that quantitative body composition analysis with deep learning should is a promising tool for clinical application in a hospital setting.

The electromyographic analysis of orbicularis oculi muscle in epiphora.

Purpose:Functional epiphora is a clinical condition that presents with the complaint of watery eyes, but without anatomical stenosis in the lacrimal drainage system. Although the mechanism is not clear, there are various possibilities involving the movement of the orbicularis oculi muscle, especially its deeper segment (Horner’s muscle). We aimed to evaluate the function of the orbicularis oculi muscle in patients with patent, but dysfunctional lacrimal drainage system using a quantitative motor unit potential (MUP) analysis.Methods:Twenty-eight patients with functional epiphora (mean age = 59 years) and a control group of 28 volunteers were included in the study. Inclusion criteria were persistent and symptomatic epiphora or wiping >10 times per day and diagnosis confirmation by lacrimal irrigation test. Electromyography (EMG) was performed on the deeper segment of the orbicularis oculi muscle (medial and lateral parts). MUP parameters (duration time, amplitude, number of phases, number of turns, area, rise time, and thickness) were evaluated in both groups. Any increase in amplitude, prolongation time (>14 ms), number of turns, and satellite potential was taken as characteristic of the neurogenic type of epiphora, whereas shortened motor unit duration time, increased phase number, and low amplitude are the features of myopathic type.Results:Upon MUP analysis of the medial and lateral orbicularis oculi muscle, the increase in duration and thickness values in the medial part and the increase in duration, amplitude, area, and thickness values of the lateral part were found to be statistically significant in the patient group compared to the control group (P < 0.001). In the evaluation of the patients’ medial and lateral orbicularis oculi muscle, the increase in phase values and decrease in amplitude, area, and rise time values were found to be statistically significant (P = 0.024, P < 0.001, P < 0.001, and P = 0.010, respectively).Conclusion:These data show that functional epiphora is due to neurogenic damage of the orbicularis oculi muscle and should be investigated in more detail.

Deep learning body-composition analysis of clinically acquired CT-scans estimates creatinine excretion with high accuracy in patients and healthy individuals

Assessment of daily creatinine production and excretion plays a crucial role in the estimation of renal function. Creatinine excretion is estimated by creatinine excretion equations and implicitly in eGFR equations like MDRD and CKD-EPI. These equations are however unreliable in patients with aberrant body composition. In this study we developed and validated equations estimating creatinine production using deep learning body-composition analysis of clinically acquired CT-scans. We retrospectively included patients in our center that received any CT-scan including the abdomen and had a 24-h urine collection within 2 weeks of the scan (n = 636). To validate the equations in healthy individuals, we included a kidney donor dataset (n = 287). We used a deep learning algorithm to segment muscle and fat at the 3rd lumbar vertebra, calculate surface areas and extract radiomics parameters. Two equations for CT-based estimate of RenAl FuncTion (CRAFT 1 including CT parameters, age, weight, and stature and CRAFT 2 excluding weight and stature) were developed and compared to the Cockcroft-Gault and the Ix equations. CRAFT1 and CRAFT 2 were both unbiased (MPE = 0.18 and 0.16 mmol/day, respectively) and accurate (RMSE = 2.68 and 2.78 mmol/day, respectively) in the patient dataset and were more accurate than the Ix (RMSE = 3.46 mmol/day) and Cockcroft-Gault equation (RMSE = 3.52 mmol/day). In healthy kidney donors, CRAFT 1 and CRAFT 2 remained unbiased (MPE = − 0.71 and − 0.73 mmol/day respectively) and accurate (RMSE = 1.86 and 1.97 mmol/day, respectively). Deep learning-based extraction of body-composition parameters from abdominal CT-scans can be used to reliably estimate creatinine production in both patients as well as healthy individuals. The presented algorithm can improve the estimation of renal function in patients who have recently had a CT scan. The proposed methods provide an improved estimation of renal function that is fully automatic and can be readily implemented in routine clinical practice.

Label efficient segmentation of single slice thigh CT with two-stage pseudo labels.

Purpose: Muscle, bone, and fat segmentation from thigh images is essential for quantifying body composition. Voxelwise image segmentation enables quantification of tissue properties including area, intensity, and texture. Deep learning approaches have had substantial success in medical image segmentation, but they typically require a significant amount of data. Due to the high cost of manual annotation, training deep learning models with limited human label data is desirable, but it is a challenging problem. Approach: Inspired by transfer learning, we proposed a two-stage deep learning pipeline to address the thigh and lower leg segmentation issue. We studied three datasets, 3022 thigh slices and 8939 lower leg slices from the BLSA dataset and 121 thigh slices from the GESTALT study. First, we generated pseudo labels for thigh based on approximate handcrafted approaches using CT intensity and anatomical morphology. Then, those pseudo labels were fed into deep neural networks to train models from scratch. Finally, the first stage model was loaded as the initialization and fine-tuned with a more limited set of expert human labels of the thigh. Results: We evaluated the performance of this framework on 73 thigh CT images and obtained an average Dice similarity coefficient (DSC) of 0.927 across muscle, internal bone, cortical bone, subcutaneous fat, and intermuscular fat. To test the generalizability of the proposed framework, we applied the model on lower leg images and obtained an average DSC of 0.823. Conclusions: Approximated handcrafted pseudo labels can build a good initialization for deep neural networks, which can help to reduce the need for, and make full use of, human expert labeled data.

Fiber Type Differences in SDHmax and Mitochondrial Volume Density between Diaphragm and Tibialis Anterior Muscles

The diaphragm (DIAm) and tibialis anterior (TA) muscles comprise both fatigue resistant (type I and IIa) and fatigable (type IIx/IIb) muscle fibers. Type I and IIa fibers in the DIAm are very frequently recruited for breathing, whereas in the TA, type I and IIa fibers are recruited for balance and locomotion. More fatigable type IIx/IIb fibers are only infrequently recruited for higher force behaviors in both the DIAm and TA. The repetitive activation of the DIAm during breathing is associated with a duty cycle of 40‐50%, making the DIAm one of, if not the most active muscle in the body ‐ much more active than the TA. These differences in activation history lead to differences in energetic demands between the DIAm and TA. We hypothesized that type I and IIa fibers in the DIAm exhibit a greater mitochondrial volume density and respiratory capacity (SDHmax) compared to type I and IIa fibers in the TA. Muscle segments from both DIAm and TA were dissected from adult female and male Sprague‐Dawley rats (~300 g), pinned to cork at optimal sarcomere length and rapidly frozen in melting isopentane. Subsequently, transverse serial sections of muscle fibers were cut at 10 µm thickness. In a series of transverse sections, muscle fiber types were determined by immunoreactivity to specific myosin heavy chain (MyHC) isoform antibodies. In an alternate series of sections of the same muscle fibers, mitochondria were labeled using MitoTracker Green. In a third series of alternate sections, the maximum velocity of the succinate dehydrogenase reaction (SDHmax) was measured using a quantitative histochemical assay in which accumulation of the reaction product, nitroblue tetrazolium diformazan (NBTdfz), was measured as a change in optical density (OD) in muscle fibers every 15 s for a 10‐min period. Muscle fiber sections were imaged in 3D using an Olympus Fluoview confocal microscope (40X oil immersion lens; NA 1.4) with an optical slice of 0.5 µm. In the mitoTracker labeled sections, each optical slice was deconvolved to increase spatial resolution (0.125 µm) and after thresholding, binarized images were reconstructed in 3D and analyzed for mitochondrial volume and mitochondrial complexity index (MCI= SA3/16µ2V2) using ImageJ and NIS elements. Mitochondrial volume density and morphometry were analyzed using ImageJ. The mitochondrial volume density and SDHmax were greater in type I and IIa fibers of the DIAm compared to the TA. On the other hand, mitochondrial volume density and SDHmax of type IIx/IIb fibers were comparable between the DIAm and TA. These results support mitochondrial structural and functional adaptations in type I and IIa DIAm fibers based on activation history and greater energetic demand.

Impact of Aging on Fiber Cross‐Sectional Areas and Contractile and Fatigue properties of Intrinsic and Extrinsic Tongue Muscles

The intrinsic and extrinsic tongue muscles manipulate the position and shape of the tongue and are activated during many oral and respiratory behaviors. In old age, we found that sarcopenia results in selective atrophy of type IIx/IIb diaphragm muscle (DIAm) fibers and a reduction in maximum specific force. We hypothesized that tongue muscles are vulnerable to sarcopenia depending on the relative contribution of type IIx/IIb fibers to total muscle mass. In 6 and 24‐month‐old Fischer 344 rats (n=8 per age), different intrinsic and extrinsic togue muscles were dissected, mounted in a tissue chamber at optimal length and stimulated directly using platinum plate electrodes. Maximum isometric force and fatigue properties were then determined. After mechanical measurements, muscle segments were frozen at optimal sarcomere length in melting isopentane. Transverse sections of muscle fibers were cut at 10 µm, and fiber type was determined based on immunoreactivity to specific myosin heavy chain (MyHC) isoform antibodies. Muscle fiber images were acquired using a confocal microscope (40x 1.3NA objective), and fiber cross‐sectional areas as well as interstitial space were measured. In superior and inferior longitudinal intrinsic muscles specific force was reduced by ~25% in older rats and type IIx/IIb fibers were ~20% smaller. In the genioglossus muscle, specific force was reduced by ~20% in older rats, and the cross‐sectional areas of type IIx/IIb fibers were ~20% smaller. There were no age‐related changes in specific force or fiber cross‐sectional areas in the transversalis muscle. The residual force following 2‐min of fatiguing contractions remained similar in young compared to old rats, consistent with the selective effect of sarcopenia on type IIx/b fibers. The results of this study support our hypothesis that tongue muscles are susceptible to age‐related sarcopenia. The presence of sarcopenia may contribute to the increased incidence of obstructive sleep apnea in older individuals.

Force and Shortening Velocity are Reduced in 24‐month Fischer 344 Rats

The diaphragm muscle (DIAm) shortens and produces force to generate negative intra‐thoracic pressure for airflow into the lungs and positive intra‐abdominal pressure for airway clearance and expulsive behaviors. Breathing is a low force repetitive behavior that requires recruitment of fatigue resistant type I and IIa DIAm fibers. In contrast, short duration airway clearance behaviors are infrequent and associated with greater DIAm force that require the additional recruitment of more fatigable type IIx/IIb fibers. Aging is characterized by selective atrophy of type IIx/IIb DIAm fibers and decreased specific force, i.e., sarcopenia. Older individuals are able to breathe without difficulty but have trouble with some higher force airway clearance behaviors of the DIAm such as coughing and sneezing. In 6‐ and 24‐month Fischer 344 rats (n=6 per group), DIAm strips were dissected from the mid‐costal region, mounted in a tissue chamber filled with Rees‐Simpson solution, and stimulated directly to measure maximum isometric specific force (Po) and maximum shortening velocity (Vmax) using a Cambridge dual‐mode length‐force servo controller (model 300B). In addition, power fatigue was assessed during repetitive isovelocity (30%Vmax) contractions evoked by 75 Hz in 400‐ms‐duration stimulus trains repeated for a 2‐min period. Additional muscle segments were rapidly frozen at optimal sarcomere length in melting isopentane, and transverse muscle fiber sections were cut at 10 µm thickness for determination of muscle fiber type (based on myosin heavy chain immunoreactivity) and fiber cross‐sectional areas. The relationship between force (load) and shortening velocity was determined using the Hill equation. Aging significantly reduced maximum isometric force, maximum shortening velocity, and maximum power output of the DIAm in older rats. The reduction in power during repetitive isovelocity shortening was reduced in older rats, but the residual power output after 2 min stimulation was comparable between young and old rats. These changes in mechanical and fatigue properties were consistent with the selective atrophy of type IIx/IIb DIAm fibers and reflected the sustained output of type I and IIa DIAm fibers, which are recruited for essential breathing behaviors.

Application of a Second‐Order Control‐Systems Model to Investigate Effects of Impaired Capillary Signalling in Skeletal Muscle on Regulation of Capillary Blood Flow Velocity and Tissue Oxygenation

Microcirculation is the main region of the vasculature where oxygen (O2) is transferred directly from blood to metabolically active tissue. Tissue O2 partial pressure (PO2) in the microcirculation has been historically infeasible to measure, but critical as an indicator of capillary and surrounding tissue health. Thus, theoretical modelling of capillary‐tissue O2 transport is crucial as a complement to in vivo experiments in exploring structure‐function relationships in microvascular networks (Ellis et al, Microcirc 2012, 19:5). To properly complement experiments which stimulate O2 flow‐dependent regulation systems, modelling of microcirculatory O2 transport requires incorporation of knowledge on regulatory mechanisms.Capillary networks modulate their blood velocity to recruit sufficient O2 delivery in many local O2 conditions; this has led to the conclusion that endothelium signals upstream to arterioles in an O2‐dependent manner (Ghonaim et al, Microcirc 2021, e12699). Recently, a second‐order control systems model was developed to investigate the interaction between capillary‐tissue O2 transport and O2‐dependent blood flow regulation under an externally applied O2 stimulus. This utilized elements from previous arteriolar diameter alteration models in response to skeletal muscle O2 gradients, as well as elements from a recently developed continuous‐capillary O2 transport model (Afas et al, Math Biosci 2021, 333:108535).The control systems model aimed to predict modulations in blood velocity through endothelial signalling which homogenized outlet capillary PO2 in skeletal muscle. The model demonstrated that variations in O2 transport parameters such as capillary network density and tissue O2 consumption rate had varying effects on the averaged blood velocity in a small skeletal muscle segment perfused by a capillary network. In addition, the externally supplied PO2 had an effect on both blood velocity and the tissue‐capillary O2 balance. While the tissue‐capillary PO2 balance and velocity adaptation were investigated, the endothelial signalling parameters were not reported (Afas &amp; Goldman, Vasc. Bio. 2021, conference).The present study investigates implications of capillary network metric variations on the endothelial signal communicated upstream to modulate capillary blood flow in response to various muscle surface PO2 levels. Constraints imposed by pathogenic impairment of endothelial signalling will be investigated and interpreted in the context of capillary flow regulation.

Functional Modularization in the Neck Muscles of the Gerenuk (Litocranius walleri)

The gerenuk (Litocranius walleri) is an African ruminant that feeds on high growing foliage by standing erect on its hindlimbs and extending its long neck. We hypothesize that extensive myological adaptation would be present in the muscles that facilitate head stability and movement, as well as muscles that anchor the neck. The vertebral musculature from the atlas to the thoracic vertebrae of a gerenuk specimen was dissected and compared to that of other ruminants. Several muscles were found to have narrowed or shifted caudally along the vertebral column, resulting in their cranial‐most attachment point being the fifth cervical vertebrae (C5). Similarly, two muscles were found to have shifted cranially, resulting in their caudal‐most attachment point also being C5. Caudally shifted muscles include m. serratus ventralis cervicis, the scalene muscles, m. rhomboideus cervicis, and the cervical part of the trapezius. Cranially shifted muscles include m. splenius cervicis and m. longissimus capitis. These shifts indicate a significant degree of modularization occurring between head movement muscles and neck stability muscles, with C5 being the boundary. Further adaptation that was not part of modularization around C5 was found in the segmental muscles attaching between vertebral processes. Segments of m. intertransversarii and m. multifidi have elongated and undergone specialization at the cranial attachments. The segments of m. spinalis cervicis were found to have a complex configuration that interdigitates with the multifidi. Additionally, m. levator claviculae and m. iliocostalis cervicis were present, neither of which have been previously described in ruminants. Previously published work reported that osteological adaptation in the gerenuk neck is especially pronounced in C1 – C4, which is consistent with our findings of myological modularization occurring with C5 as the boundary.