Search

Abstract The antiarrhythmic and cardiac electrophysiological effects of SZV-2649 that contains a 2,6-diiodophenoxy moiety but lacks the benzofuran ring system present in amiodarone, were studied in mammalian cell line, rat and dog cardiac preparations. SZV-2649 exerted antiarrhythmic effects against coronary artery occlusion/reperfusion induced ventricular arrhythmias in rats and in acetylcholine- and burst stimulation induced atrial fibrillation in dogs. SZV-2649 inhibited hERG and GIRK currents in HEK cells (IC50: 342 and 529 nM, respectively). In canine ventricular myocytes, SZV-2649 (10 µM) decreased the densities of IKr, and Ito outward and INaL and ICaL inward currents. The compound (2.5–10 µM) elicited Class IB type Vmax reducing and Class III type action potential duration prolonging effects in dog right ventricular muscle preparations. In canine atrial muscle, SZV-2629 (2.5–10 µM) moderately prolonged action potential duration and this effect was greatly augmented in preparations pretreated with 1 µM carbachol. In conclusion, SZV-2649, has antiarrhythmic effects based on its multiple ion channel blocking properties. Since its chemical structure substantially differs from that of amiodarone, it is expected that SZV-2649 would exhibit fewer adverse effects than the currently used most effective multichannel inhibitor drug amiodarone and may be a promising molecule for further development.

Abstract The estrus cycle in female rodents has been shown to affect a variety of physiological functions. However, little is known about its presumably thorough effect on auditory processing during the sleep–wake cycle and sleep deprivation. Vertex auditory evoked potentials (vAEPs) were evoked by single click tone stimulation and recorded during different stages of the estrus cycle and sleep deprivation performed in metestrus and proestrus in female rats. vAEPs showed a strong sleep-dependency, with the largest amplitudes present during slow wave sleep while the smallest ones during wakefulness. Higher amplitudes and longer latencies were seen in the light phase during all vigilance stages. The largest amplitudes were found during proestrus (light phase) while the shortest latencies were seen during estrus (dark phase) compared to the 2nd day diestrus baseline. High-amplitude responses without latency changes were also seen during metestrus with increased homeostatic sleep drive. More intense and faster processing of auditory information during proestrus and estrus suggesting a more effective perception of relevant environmental cues presumably in preparation for sexual receptivity. A 4-h sleep deprivation resulted in more pronounced sleep recovery in metestrus compared to proestrus without difference in delta power replacement suggesting a better tolerance of sleep deprivation in proestrus. Sleep deprivation decreased neuronal excitability and responsiveness in a similar manner both during metestrus and proestrus, suggesting that the negative consequences of sleep deprivation on auditory processing may have a limited correlation with the estrus cycle stage.

Abstract In this work we aim to provide a quantitative method allowing the probing of the physiological status of honeybee colonies by providing them with a gentle, short, external artificial vibrational shockwave, and recording their response. The knock is provided by an external electromagnetic shaker attached to the outer wall of a hive, driven by a computer with a 0.1 s long, monochromatic vibration at 340Hz set to an amplitude that occasionally yields a mild response from the bees, recorded by an accelerometer placed in the middle of the central frame of the colony. To avoid habituation, the stimulus is supplied at randomised times, approximately every hour. The method is pioneered with a pilot study on a single colony hosted indoors, then extended onto eight outdoors colonies. The results show that we can quantitatively sense the colony’s overall mobility, independently from another physiological aspect, which is phenomenologically explored. Using this, a colony that is queenless is easily discriminated from the others.

Abstract Little is known about mite gait, but it has been suggested that there could be greater variation in locomotory styles for arachnids than insects. The Varroa destructor mite is a devastating ectoparasite of the honeybee. We aim to automatically detect Varroa-specific signals in long-term vibrational recordings of honeybee hives and additionally provide the first quantification and characterisation of Varroa gait through the analysis of its unique vibrational trace. These vibrations are used as part of a novel approach to achieve remote, non-invasive Varroa monitoring in honeybee colonies, requiring discrimination between mite and honeybee signals. We measure the vibrations occurring in samples of freshly collected capped brood-comb, and through combined critical listening and video recordings we build a training database for discrimination and classification purposes. In searching for a suitable vibrational feature, we demonstrate the outstanding value of two-dimensional-Fourier-transforms in invertebrate vibration analysis. Discrimination was less reliable when testing datasets comprising of Varroa within capped brood-cells, where Varroa induced signals are weaker than those produced on the cell surface. We here advance knowledge of Varroa vibration and locomotion, whilst expanding upon the remote detection strategies available for its control.

Abstract Transorbital sonography (TOS) could be a swift and convenient method to detect the atrophy of the optic nerve, possibly providing a marker that might reflect other quantitative structural markers of multiple sclerosis (MS). Here we evaluate the utility of TOS as a complementary tool for assessing optic nerve atrophy, and investigate how TOS-derived measures correspond to volumetric brain markers in MS. We recruited 25 healthy controls (HC) and 45 patients with relapsing–remitting MS and performed B-mode ultrasonographic examination of the optic nerve. Patients additionally underwent MRI scans to obtain T1-weighted, FLAIR and STIR images. Optic nerve diameters (OND) were compared between HC, MS patients with and without history of optic neuritis (non-ON) using a mixed-effects ANOVA model. The relationship between within-subject-average OND and global and regional brain volumetric measures was investigated using FSL SIENAX, voxel-based morphometry and FSL FIRST. OND was significantly different between HC-MS (HC = 3.2 ± 0.4 mm, MS = 3 ± 0.4 mm; p

Abstract In this work, we disclose a non-invasive method for the monitoring and predicting of the swarming process within honeybee colonies, using vibro-acoustic information. Two machine learning algorithms are presented for the prediction of swarming, based on vibration data recorded using accelerometers placed in the heart of honeybee hives. Both algorithms successfully discriminate between colonies intending and not intending to swarm with a high degree of accuracy, over 90% for each method, with successful swarming prediction up to 30 days prior to the event. We show that instantaneous vibrational spectra predict the swarming within the swarming season only, and that this limitation can be lifted provided that the history of the evolution of the spectra is accounted for. We also disclose queen toots and quacks, showing statistics of the occurrence of queen pipes over the entire swarming season. From this we were able to determine that (1) tooting always precedes quacking, (2) under natural conditions there is a 4 to 7 day period without queen tooting following the exit of the primary swarm, and (3) human intervention, such as queen clipping and the opening of a hive, causes strong interferences with important mechanisms for the prevention of simultaneous rival queen emergence.

Abstract One of the most interesting and everyday natural phenomenon is the formation of different patterns after the evaporation of liquid droplets on a solid surface. The analysis of dried patterns from blood droplets has recently gained a lot of attention, experimentally and theoretically, due to its potential application in diagnostic medicine and forensic science. This paper presents evidence that images of dried blood droplets have a signature revealing the exhaustion level of the person, and discloses an entirely novel approach to studying human dried blood droplet patterns. We took blood samples from 30 healthy young male volunteers before and after exhaustive exercise, which is well known to cause large changes to blood chemistry. We objectively and quantitatively analysed 1800 images of dried blood droplets, developing sophisticated image processing analysis routines and optimising a multivariate statistical machine learning algorithm. We looked for statistically relevant correlations between the patterns in the dried blood droplets and exercise-induced changes in blood chemistry. An analysis of the various measured physiological parameters was also investigated. We found that when our machine learning algorithm, which optimises a statistical model combining Principal Component Analysis (PCA) as an unsupervised learning method and Linear Discriminant Analysis (LDA) as a supervised learning method, is applied on the logarithmic power spectrum of the images, it can provide up to 95% prediction accuracy, in discriminating the physiological conditions, i.e., before or after physical exercise. This correlation is strongest when all ten images taken per volunteer per condition are averaged, rather than treated individually. Having demonstrated proof-of-principle, this method can be applied to identify diseases.

The transcriptional regulator JDP2 (Jun dimerization protein 2) has been identified as a prognostic marker for patients to develop heart failure after myocardial infarction. We now performed in vivo studies on JDP2-overexpressing mice, to clarify the impact of JDP2 on heart failure progression. Therefore, during birth up to the age of 4 weeks cardiac-specific JDP2 overexpression was prevented by doxycycline feeding in transgenic mice. Then, JDP2 overexpression was started. Already after 1 week, cardiac function, determined by echocardiography, decreased which was also resembled on the cardiomyocyte level. After 5 weeks blood pressure declined, ejection fraction and cardiac output was reduced and left ventricular dilatation developed. Heart weight/body weight, and mRNA expression of ANP, inflammatory marker genes, collagen and fibronectin increased. Collagen 1 protein expression increased, and fibrosis developed. As an additional sign of elevated extracellular matrix remodeling, matrix metalloproteinase 2 activity increased in JDP2 mice. Thus, JDP2 overexpression is deleterious to heart function in vivo. It can be concluded that JDP2 overexpression provokes cardiac dysfunction in adult mice that is accompanied by hypertrophy and fibrosis. Thus, induction of JDP2 is a maladaptive response contributing to heart failure development.