Acetylcholine (ACh) spillover from motor endplates occurs after neuronal firing bursts being potentiated by cholinesterase inhibitors (e.g., neostigmine). Nicotinic α7 receptors (α7nAChR) on perisynaptic Schwann cells (PSCs) can control ACh spillover by unknown mechanisms. We hypothesized that adenosine might be the gliotransmitter underlying PSCs‐nerve terminal communication. Rat isolated hemidiaphragm preparations were used to measure (1) the outflow of [3H]ACh, (2) real‐time transmitter exocytosis by video‐microscopy with the FM4‐64 fluorescent dye, and (3) skeletal muscle contractions during high‐frequency (50 Hz) nerve stimulation bursts in the presence of a selective α7nAChR agonist, PNU 282987, or upon inhibition of cholinesterase activity with neostigmine. To confirm our prediction that α7nAChR‐mediated effects require direct activation of PSCs, we used fluorescence video‐microscopy in the real‐time mode to measure PNU 282987‐induced [Ca2+]i transients from Fluo‐4 NW loaded PSCs in non‐stimulated preparations. The α7nAChR agonist, PNU 282987, decreased nerve‐evoked diaphragm tetanic contractions. PNU 282987‐induced inhibition was mimicked by neostigmine and results from the reduction of ACh exocytosis measured as decreases in [3H]ACh release and FM4‐64 fluorescent dye unloading. Methyllycaconitine blockage of α7nAChR and the fluoroacetate gliotoxin both prevented inhibition of nerve‐evoked ACh release and PSCs [Ca2+]i transients triggered by PNU 282987 and neostigmine. Adenosine deamination, inhibition of the ENT1 nucleoside outflow, and blockage of A1 receptors prevented PNU 282987‐induced inhibition of transmitter release. Data suggest that α7nAChR controls tetanic‐induced ACh spillover from the neuromuscular synapse by promoting adenosine outflow from PSCs via ENT1 transporters and retrograde activation of presynaptic A1 inhibitory receptors. image

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Social media platforms have become powerful tools for startups, helping them find customers and raise funding. In this study, we applied a social media intelligence-based methodology to analyze startups’ content and to understand how their communication strategies may differ during their scaling process. To understand if a startup’s social media content reflects its current business maturation position, we first defined an adequate life cycle model for startups based on funding rounds and product maturity. Using Twitter as the source of information and selecting a sample of known Portuguese IT startups at different phases of their life cycle, we analyzed their Twitter data. After preprocessing the data, using latent Dirichlet allocation, topic modeling techniques enabled the categorization of the data according to the topics arising in the published contents of the startups, making it possible to discover that contents can be grouped into five specific topics: “Fintech and ML,” “IT,” “Business Operations,” “Product/Service R&D,” and “Bank and Funding.” By comparing those profiles against the startup’s life cycle, we were able to understand how contents change over time. This provided a diachronic profile for each company, showing that while certain topics remain prevalent in the startup’s scaling, others depend on a particular phase of the startup’s cycle. Our analysis revealed that startups’ social media content differs along their life cycle, highlighting the importance of understanding how startups use social media at different stages of their development.

Background/Aims: In this study, we evaluated the functional impact of facilitatory presynaptic adenosine A_2A and muscarinic M₁ receptors in the recovery of neuromuscular tetanic depression caused by the blockage of high-affinity choline transporter (HChT) by hemicholinium-3 (HC-3), a condition that mimics a myasthenia-like condition. Methods: Rat diaphragm preparations were indirectly stimulated via the phrenic nerve trunk with 50-Hz frequency trains, each consisting of 500–750 supramaximal intensity pulses. The tension at the beginning (A) and at the end (B) of the tetanus was recorded and the ratio (R) B/A calculated. Results: Activation of A_2A and M₁ receptors with CGS21680 (CGS; 2 nmol/L) and McN-A-343c (McN; 3 μmol/L) increased R values. Similar facilitatory effects were obtained with forskolin (FSK; 3 μmol/L) and phorbol 12-myristate 13-acetate (PMA; 10 μmol/L), which activate adenylate cyclase and protein kinase C respectively. HC-3 (4 μmol/L) decreased transmitter exocytosis measured by real-time videomicroscopy with the FM4-64 fluorescent dye and prevented the facilitation of neuromuscular transmission caused by CGS, McN, and FSK, with a minor effect on PMA. The acetylcholinesterase inhibitor, neostigmine (NEO; 0.5 μmol/L), also decreased transmitter exocytosis. The paradoxical neuromuscular tetanic fade caused by NEO (0.5 μmol/L) was also prevented by HC-3 (4 μmol/L) and might result from the rundown of the positive feedback mechanism operated by neuronal nicotinic receptors (blocked by hexamethonium, 120 μmol/L). Conclusion: Data suggest that the recovery of tetanic neuromuscular facilitation by adenosine A_2A and M₁ receptors is highly dependent on HChT activity and may be weakened in myasthenic patients when HChT is inoperative.

Social media platforms have become powerful tools for startups, helping them find customers and raise funding. Analysing the contents posted through social media would help them make the best use of this communication and scale their business. To understand if a startup’s social media content reflects its position in its business maturation, we start by defining an adequate life cycle model for startups based on two dimensions: funding rounds and product maturity. Using Twitter as social media source of information for known Portuguese IT startups, each at their life cycle’s different phases, their tweets’ data has been analyzed. Topic modeling techniques have enabled the categorization of the data according to the topics arising in the published contents, making it possible to discover that contents can be grouped into five specific topics: “Fintech and ML”, “IT”, “Business Operations”, “Product/Service R&D”, and “Bank and Funding”. Comparing those profiles against the startup’s life cycle to understand how contents change over time provides a diachronic profile for each company. We discovered that while some topics are prevalent in the startup’s scaling, others depend on the startup’s particular phase of the cycle, revealing that startups’ Twitter social media content differs along their life cycle.