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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.

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.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.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%.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.Dynamic visual training over 6 weeks improves stereoscopic performance including stereo acuity, response time and correctness.HINTERGRUND UND FRAGESTELLUNG: Gutes Stereosehen scheint bei Hochleistungssportlern und im Alltag eine wichtige Rolle zu spielen. Wir untersuchten die Fragestellung, ob sich das Stereosehen bei Sportlern mit bereits guter Stereosehschärfe durch ein digitales Sehtraining noch verbessern kann.N = 31 männliche und weibliche Tennisspieler/innen absolvierten innerhalb 6 Wochen mindestens 6 Trainingseinheiten mit je 192 stereoskopischen Einzeltests mit variablem Schwierigkeitsgrad von 15 bis 300 Bogensekunden. Die Trainingsstimuli waren dynamischer, die Teststimuli statischer Natur. Zur Darstellung der Reaktionszeitverbesserung in Abhängigkeit des Schwierigkeitsgrades wurde der Parameter „ReSt“ (Reaktionszeitzuwachs pro Stereodisparitätsabnahme) bestimmt.Die Reaktionszeit bei Stimuli von 15 Bogensekunden verkürzte sich durch das Sehtraining im Mittel signifikant von 3,9 s auf 1,6 s (59 %). Die Korrektheit der Versuche bei 30 Bogensekunden steigerte sich im Mittel signifikant um 23 %.Die beobachtete Verbesserung der Reaktionszeit im Sehtraining führte nicht zur Abnahme der Korrektheit bei der Beantwortung der visuellen Fragen und stellt damit insgesamt eine Verbesserung der Stereosehfähigkeit dar.Durch das digitale Sehtraining verbesserte sich das stereoskopische Sehen signifikant hinsichtlich Stereogrenzwinkel, Reaktionszeit und Korrektheit.

IntroductionThe history of the use of telemedicine in maritime medicine dates back to the 1930s. In the early 2000s, the German Navy built up a telemedicine network which today is installed in all ships and provides a connection to the medical infrastructure ashore. The objective of this study was to optimize the implementation of telemedicine based on the experience gained in the German Navy. For this purpose, qualitative and quantitative methods were used to identify determinants which affect the use of telemedicine in order to determine whether there is any need for optimization.MethodsThe study was conducted using a mixed methods design. First, guideline-based interviews were conducted with Navy medical officers who had been recruited via various email distribution lists. The interviews were then transcribed and coded. In an analysis, deductive and inductive categories were derived from the codes. Hypotheses were deduced from the interviews, too, and used to develop a questionnaire.Besides the medical officers, other Navy medical personnel with experience in the field of telemedicine took part in the survey. The study was concluded by a descriptive analysis of the quantitative data.ResultsThe analysis of the interviews revealed that a regular use of telemedicine workstations increased the users’ confidence and, in their opinion, improved medical treatment. Technical and organizational problems posed obstacles, which increased the use of partly insecure alternatives. A proper technical support was regarded as a precondition for effectively using telemedicine.The results of the quantitative analysis showed that consultation was mainly sought for dermatological (46%), surgical (24%), and internal (22%) conditions.ConclusionThe study revealed determinants for the use of telemedicine in the German Navy. Factors improving the motivation of the users should be strengthened in order to optimize the use of telemedicine. Furthermore, it can be assumed that a successful implementation will be supported by reducing or eliminating obstacles. The findings on the main reasons for seeking medical advice could be taken into account in the further planning of specific training.

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 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.

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.

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.

We investigated the effect of stimulation intensity (in percent of maximal tolerated stimulation current, mTSC), frequency, and impulse width on muscle fatigue.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.FIS and FIN were significantly lower in high-frequency protocols. No effects on FIS and FIN were found for intensity and impulse width.Stimulation frequency, but not impulse width or intensity, affected fatigue kinetics.