Effects of stimulation frequency, amplitude, and impulse width on muscle fatigue.

INTRODUCTION: We investigated the effect of stimulation intensity (in percent of maximal tolerated stimulation current, mTSC), frequency, and impulse width on muscle fatigue. METHODS: Using a randomized crossover design, 6 parameter combinations (80% mTSC, 80 Hz, 400 µs; 60% mTSC, 80 Hz, 400 µs; 80% mTSC, 20 Hz, 400 µs; 60% mTSC, 20 Hz, 400 µs; 80% mTSC, 80 Hz, 150 µs; 60% mTSC, 80 Hz, 150 µs) were tested in both legs of 13 athletic men (age 26 ± 2.3). The slope of the linear regression line over all tetani (FIS) and the number of tetani whose force was above 50% of the initial tetanus (FIN) were used to quantify fatigue. RESULTS: FIS and FIN were significantly lower in high-frequency protocols. No effects on FIS and FIN were found for intensity and impulse width. CONCLUSIONS: Stimulation frequency, but not impulse width or intensity, affected fatigue kinetics.

[Stress fractures in the military context]. / Stressfrakturen im militärischen Kontext.

BACKGROUND: Soldiers, especially as recruits, are exposed to significantly elevated stress patterns of the foot due to occupation-related marching and excessive running. This can lead to military-specific stress fractures of the metatarsals, i.e., marching fractures. The treatment and prevention of stress fractures are of particular importance in the military context due to the impact on operational capability and treatment costs. A uniform classification of these fractures does not yet exist. OBJECTIVE: Review of stress fractures in the military setting with presentation of the incidence, risk factors, classification, treatment and prevention possibilities. MATERIAL AND METHODS: A PubMed®-based review of the current literature on stress fractures in the military context was conducted and the results were discussed with a focus on specific military medical treatment options. RESULTS: There are several possibilities to classify stress fractures, the most well-known being a 4-level magnetic resonance imaging (MRI)-based classification. Prevention and treatment possibilities are multifaceted but so far insufficiently validated. CONCLUSION: Military-specific stress fractures should be grouped according to a 4-level and MRI-based classification. The treatment options include both conservative and surgical measures and should be implemented taking the patient's individual requirements into account. Preventive measures play a key role in the military context. They include the adaptation of screening tools, training and equipment and require continuous evaluation and development.

Teleglaucoma Using a New Smartphone-Based Tool for Visual Field Assessment.

PRCIS: COVID-19 underlines the importance of telemedical diagnostics. The smartphone-based campimetry (Sb-C) is a newly developed digital application allowing visual field testing using a head-mounted device and a smartphone. It enables visual field screening remotely from a clinic. BACKGROUND: Sb-C is a newly developed tool for functional ophthalmic diagnosis. This study aimed to examine the comparability of the Sb-C and Octopus 900 to ensure ophthalmological care in times of social distancing. METHODS: Total 93 eyes were included in the study. After an ophthalmological examination, the visual field was tested by the Octopus program G1 and by the smartphone-based campimeter. The Sb-C was performed using VR glasses and an iPhone 6. The software Sb-C was downloaded and installed as SmartCampiTracker app and is examining the 30-degree visual field with 59 test positions corresponding to the G pattern of Octopus G1. Sensitivities were recorded and saved on the app. In addition, test-retest reliability was tested on 6 ophthalmologically healthy participants. RESULTS: The group comprised 48 women and 45 men (mean age: 62.52±12.2 y) including 19 controls, 17 patients with ocular hypertension, 11 preperimetric glaucomas, and 46 perimetric glaucomas. The mean sensitivity (MS) of all points of G1 perimetry was 23.13 dB (95% CI, 22.08-24.18). The MS of the Sb-C was 21.23 dB (95% CI, 20.37-22.08). The correlation between the mean MS measured by G1 perimetry and the Sb-C was strong ( r =0.815, P <0.05). The test-retest reliability showed a correlation of r =0.591 ( P <0.05) . CONCLUSIONS: With some technical adjustments, the Sb-C shows promise for screening glaucoma and monitoring disease progression remotely from an ophthalmologic clinic.

Virtual reality for assessing stereopsis performance and eye characteristics in Post-COVID.

In 2019, we faced a pandemic due to the coronavirus disease (COVID-19), with millions of confirmed cases and reported deaths. Even in recovered patients, symptoms can be persistent over weeks, termed Post-COVID. In addition to common symptoms of fatigue, muscle weakness, and cognitive impairments, visual impairments have been reported. Automatic classification of COVID and Post-COVID is researched based on blood samples and radiation-based procedures, among others. However, a symptom-oriented assessment for visual impairments is still missing. Thus, we propose a Virtual Reality environment in which stereoscopic stimuli are displayed to test the patient's stereopsis performance. While performing the visual tasks, the eyes' gaze and pupil diameter are recorded. We collected data from 15 controls and 20 Post-COVID patients in a study. Therefrom, we extracted features of three main data groups, stereopsis performance, pupil diameter, and gaze behavior, and trained various classifiers. The Random Forest classifier achieved the best result with 71% accuracy. The recorded data support the classification result showing worse stereopsis performance and eye movement alterations in Post-COVID. There are limitations in the study design, comprising a small sample size and the use of an eye tracking system.

[Binocular vision training for professional athletes]. / Binokulares Sehtraining bei Hochleistungssportlern.

BACKGROUND: Optimal visual abilities including stereo acuity seem to be an important issue in sports. There is increasing evidence that stereo acuity can be sustainably improved by digital vision training even for people with good stereo acuity. STUDY DESIGN AND TEST METHODS: In this study 31 male and female tennis players (professionals, young professionals, coaches and former professionals) completed at least 6 training units each with 192 dynamic stereoscopic tasks (N = 1152) within 6 weeks including a 4-option test with different levels of difficulty on a 3D screen at a distance of 5 m. The parameter reaction time and correctness at 15-300 arcseconds was determined. For a more precise representation of the reaction time improvement as a function of the difficulty level, the parameter reaction time increase per stereo disparity reduction (ReST) was defined. RESULTS: Reaction time to 15 arcsecond stimuli significantly decreased from 3.9 s to 1.6 s (59%) as a result of digital vision training. The correctness at 30 arcsecond stimuli significantly increased by 23%. DISCUSSION: The observed improvement in reaction time during vision training did not result in decreasing correctness when answering the visual questions. This represents an overall improvement in stereo vision. CONCLUSION: Dynamic visual training over 6 weeks improves stereoscopic performance including stereo acuity, response time and correctness.

Antagonizing microRNA-19a/b augments PTH anabolic action and restores bone mass in osteoporosis in mice.

Postmenopausal bone loss often leads to osteoporosis and fragility fractures. Bone mass can be increased by the first 34 amino acids of human parathyroid hormone (PTH), parathyroid hormone-related protein (PTHrP), or by a monoclonal antibody against sclerostin (Scl-Ab). Here, we show that PTH and Scl-Ab reduce the expression of microRNA-19a and microRNA-19b (miR-19a/b) in bone. In bones from patients with lower bone mass and from osteoporotic mice, miR-19a/b expression is elevated, suggesting an inhibitory function in bone remodeling. Indeed, antagonizing miR-19a/b in vivo increased bone mass without overt cytotoxic effects. We identified TG-interacting factor 1 (Tgif1) as the target of miR-19a/b in osteoblasts and essential for the increase in bone mass following miR-19a/b inhibition. Furthermore, antagonizing miR-19a/b augments the gain in bone mass by PTH and restores bone loss in mouse models of osteoporosis in a dual mode of action by supporting bone formation and decreasing receptor activator of NF-κB ligand (RANKL)-dependent bone resorption. Thus, this study identifies novel mechanisms regulating bone remodeling, which opens opportunities for new therapeutic concepts to treat bone fragility.

Predicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature design.

OBJECTIVE: Optimizing timing of defibrillation by evaluating the likelihood of a successful outcome could significantly enhance resuscitation. Previous studies employed conventional machine learning approaches and hand-crafted features to address this issue, but none have achieved superior performance to be widely accepted. This study proposes a novel approach in which predictive features are automatically learned. METHODS: A raw 4s VF episode immediately prior to first defibrillation shock was feed to a 3-stage CNN feature extractor. Each stage was composed of 4 components: convolution, rectified linear unit activation, dropout and max-pooling. At the end of feature extractor, the feature map was flattened and connected to a fully connected multi-layer perceptron for classification. For model evaluation, a 10 fold cross-validation was employed. To balance classes, SMOTE oversampling method has been applied to minority class. RESULTS: The obtained results show that the proposed model is highly accurate in predicting defibrillation outcome (Acc = 93.6 %). Since recommendations on classifiers suggest at least 50 % specificity and 95 % sensitivity as safe and useful predictors for defibrillation decision, the reported sensitivity of 98.8 % and specificity of 88.2 %, with the analysis speed of 3 ms/input signal, indicate that the proposed model possesses a good prospective to be implemented in automated external defibrillators. CONCLUSIONS: The learned features demonstrate superiority over hand-crafted ones when performed on the same dataset. This approach benefits from being fully automatic by fusing feature extraction, selection and classification into a single learning model. It provides a superior strategy that can be used as a tool to guide treatment of OHCA patients in bringing optimal decision of precedence treatment. Furthermore, for encouraging replicability, the dataset has been made publicly available to the research community.

Salivary Markers for Quantitative Dehydration Estimation During Physical Exercise.

Salivary markers have been proposed as noninvasive and easy-to-collect indicators of dehydrations during physical exercise. It has been demonstrated that threshold-based classifications can distinguish dehydrated from euhydrated subjects. However, considerable challenges were reported simultaneously, for example, high intersubject variabilities in these markers. Therefore, we propose a machine-learning approach to handle the intersubject variabilities and to advance from binary classifications to quantitative estimations of total body water (TBW) loss. For this purpose, salivary samples and reference values of TBW loss were collected from ten subjects during a 2-h running workout without fluid intake. The salivary samples were analyzed for previously investigated markers (osmolality, proteins) as well as additional unexplored markers (amylase, chloride, cortisol, cortisone, and potassium). Processing all these markers with a Gaussian process approach showed that quantitative TBW loss estimations are possible within an error of 0.34 l, roughly speaking, a glass of water. Furthermore, a data analysis illustrated that the salivary markers grow nonlinearly during progressive dehydration, which is in contrast to previously reported linear observations. This insight could help to develop more accurate physiological models for salivary markers and TBW loss. Such models, in turn, could facilitate even more precise TBW loss estimations in the future.

Effects of lymphatic drainage and local cryo exposition regeneration after high-intensive exercises.

BACKGROUND: Recovery from exercise and competition is important in sports medicine, particularly when rest periods are short. The objective is to determine the efficacy of cryo exposition (CRY) and manual lymphatic drainage (MLD) to hasten short term recovery of muscle performance after eccentric contractions. METHODS: In a randomized controlled trial, 30 healthy sport students (21 males, 9 females; age: 25.7±2.8 years) performed 4×20 eccentric contractions of knee extensors, followed by 30 min MLD, CRY, or rest (RST) under controlled laboratory environment. Maximal voluntary contractions (MVC), electrically induced muscle fatigue (FI), and electrically induced tetani (EIT) at low (T2: 20 Hz) and high frequencies were tested. RESULTS: Force decline and recovery kinetics regarding MVC, FI, and EIT did not differ significantly (p<0.05) between groups. That is, 24 h after the intervention, MVC (MLD: 80.9±5.5%; CRY: 81.1±8.5%; RST: 83.5±7.3%), FI (MLD: 83.2±23.7%; CRY: 81.2±38.8%; RST: 93.2±22.9%), and EIT (T1: MLD: 53.0±29.5%; CRY: 39.0±32.9%; RST: 46.3±26.1%; T2: MLD: 84.2±27.2%; CRY: 64.2±24.2%; RST: 66.6±22.3%) were similarly depressed irrespective of applied treatments. CONCLUSION: Neither CRY nor MLD hastened the recovery of muscle performance, when applied for 30 min. Identification number of the Primary Registry Network: DRKS00007608.

A Temperature-Based Bioimpedance Correction for Water Loss Estimation During Sports.

The amount of total body water (TBW) can be estimated based on bioimpedance measurements of the human body. In sports, TBW estimations are of importance because mild water losses can impair muscular strength and aerobic endurance. Severe water losses can even be life threatening. TBW estimations based on bioimpedance, however, fail during sports because the increased body temperature corrupts bioimpedance measurements. Therefore, this paper proposes a machine learning method that eliminates the effects of increased temperature on bioimpedance and, consequently, reveals the changes in bioimpedance that are due to TBW loss. This is facilitated by utilizing changes in skin and core temperature. The method was evaluated in a study in which bioimpedance, temperature, and TBW loss were recorded every 15 min during a 2-h running workout. The evaluation demonstrated that the proposed method is able to reduce the error of TBW loss estimation by up to 71%, compared to the state of art. In the future, the proposed method in combination with portable bioimpedance devices might facilitate the development of wearable systems for continuous and noninvasive TBW loss monitoring during sports.

Data mining in the U.S. National Toxicology Program (NTP) database reveals a potential bias regarding liver tumors in rodents irrespective of the test agent.

Long-term studies in rodents are the benchmark method to assess carcinogenicity of single substances, mixtures, and multi-compounds. In such a study, mice and rats are exposed to a test agent at different dose levels for a period of two years and the incidence of neoplastic lesions is observed. However, this two-year study is also expensive, time-consuming, and burdensome to the experimental animals. Consequently, various alternatives have been proposed in the literature to assess carcinogenicity on basis of short-term studies. In this paper, we investigated if effects on the rodents' liver weight in short-term studies can be exploited to predict the incidence of liver tumors in long-term studies. A set of 138 paired short- and long-term studies was compiled from the database of the U.S. National Toxicology Program (NTP), more precisely, from (long-term) two-year carcinogenicity studies and their preceding (short-term) dose finding studies. In this set, data mining methods revealed patterns that can predict the incidence of liver tumors with accuracies of over 80%. However, the results simultaneously indicated a potential bias regarding liver tumors in two-year NTP studies. The incidence of liver tumors does not only depend on the test agent but also on other confounding factors in the study design, e.g., species, sex, type of substance. We recommend considering this bias if the hazard or risk of a test agent is assessed on basis of a NTP carcinogenicity study.

On sweat analysis for quantitative estimation of dehydration during physical exercise.

Quantitative estimation of water loss during physical exercise is of importance because dehydration can impair both muscular strength and aerobic endurance. A physiological indicator for deficit of total body water (TBW) might be the concentration of electrolytes in sweat. It has been shown that concentrations differ after physical exercise depending on whether water loss was replaced by fluid intake or not. However, to the best of our knowledge, this fact has not been examined for its potential to quantitatively estimate TBW loss. Therefore, we conducted a study in which sweat samples were collected continuously during two hours of physical exercise without fluid intake. A statistical analysis of these sweat samples revealed significant correlations between chloride concentration in sweat and TBW loss (r = 0.41, p <; 0.01), and between sweat osmolality and TBW loss (r = 0.43, p <; 0.01). A quantitative estimation of TBW loss resulted in a mean absolute error of 0.49 l per estimation. Although the precision has to be improved for practical applications, the present results suggest that TBW loss estimation could be realizable using sweat samples.