How these transients are generated and regulate brain function in vivo remains unclear. Here we show that Drosophila astrocytes exhibit spontaneous, activity-independent microdomain Ca2+ transients in their fine processes. Astrocyte microdomain Ca2+ transients are mediated by the TRP channel TrpML, stimulated by reactive oxygen species (ROS), and can be enhanced in frequency by the neurotransmitter tyramine via the TyrRII receptor. Interestingly, many astrocyte microdomain Ca2+ transients are closely associated with tracheal elements, which dynamically extend filopodia throughout the central nervous system (CNS) to deliver O2 and regulate gas exchange. Many astrocyte microdomain Ca2+ transients are spatio-temporally correlated with the initiation of tracheal filopodial retraction. Loss of TrpML leads to increased tracheal filopodial numbers, growth, and increased CNS ROS. We propose that local ROS production can activate astrocyte microdomain Ca2+ transients through TrpML, and that a subset of these microdomain transients promotes tracheal filopodial retraction and in turn modulate CNS gas exchange.Astrocytes exhibit spatially-restricted near-membrane microdomain Ca Octopamine and tyramine stimulate cell-wide astrocyte Ca2+ increase through the dual-specificity Octopamine-Tyramine Receptor (Oct-TyrR) and the TRP channel Water witch (Wtrw). This astrocyte-mediated signaling event downstream of octopamine and tyramine is critical for in vivo neuromodulation: astrocyte-specific elimination of Oct-TyrR or Wtrw in larvae blocks the ability of octopamine and tyramine to silence downstream dopaminergic neurons, and alters both simple chemosensory behavior and a touch-induced startle response  in astrocytes using the astrocyte-specific alrm-Gal4 driver. We acutely dissected 3rd instar larval CNS and live-imaged myr-GCaMP5a signals in the ventral nerve cord (VNC)  seconds  in the astrocytes of intact L1 (1st instar) larvae   . We collrd (VNC) . We founquencies . The ave seconds . Microdog window , and Ca2) larvae , suggest (LaCl3) , suggest2+ transients spanned astrocyte-astrocyte cell boundaries, or if they appeared only within the domain of single cells. We used a flippable construct expressing either QF or Gal4 under the control of the alrm promoter, along with two genetically encoded Ca2+ indicators: QUAS::myr-GCaMP5a and UAS::myr-R-GECO1  or octopamine (nM18Tβh) did not significantly alter the frequency of astrocyte microdomain Ca2+ transients, nor did mutations in Oct-TyrR, which we previously showed was essential for activation of whole-cell Ca2+ transients in astrocytes  in astrocytes rescued decreased microdomains in trpml mutants  , and astby TrpA1 . The molr larvae  and acutr larvae . Astrocy by ~60% , and exp mutants , arguing reduced , suggest2+ events in astrocyte cell bodies, and found that tyramine induced comparable Ca2+ rise in astrocytes in control and 1trpml mutants  led to a TrpML-dependent increase in astrocyte microdomain Ca2+ events, while, reciprocally, addition of the antioxidant N-acetyl cysteine completely abolished them  may serve as factors to coordinate their activity.Previous work has shown that reactive oxygen species (ROS) can activate TrpML , and aste to ROS . We therhed them  and 3. TDrosophila infiltrate the CNS neuropil where they associate with neural processes, synapses, and tracheal elements , retraction (20.7%), extension and retraction (75.3%), or stationary (2.3%) . The vas2+ transients. Interestingly, 52% of tracheal filopodial tips overlapped, at some point, with an astrocyte microdomain Ca2+ transient  and more broadly distributed  within an circular area beginning 7.5 μm away from filapodial tips and extending outward , we examined tracheal structure in larval CNS. We first counted the total number of protrusions from a pair of most posterior ganglion trachea (mpgTr) that innervate a few segments from A5 to A8/9 in the ventral nerve cord. We found that mpgTr in the ventral nerve cord in 1trpml mutants exhibited increased total length compared to those in control animals . In live preparations of 1trpml mutants we found a ~ 3 fold increase in oxidized DHE+ puncta, likely in mitochondria  demonstrated individual astrocyte microdomain Ca2+ transients can span astrocyte-astrocyte boundaries, which might argue for an extrinsic mechanism regulating their production. Alternatively, it might indicate that astrocyte-astrocyte coupling of Ca2+ signaling events is strong during their production. Astrocyte microdomain Ca2+ transients are not suppressed by blockade of action potentials with tetrodotoxin, suggesting they are not activity dependent, although we also cannot formally rule out a role for spontaneous release of neurotransmitters at synapses.Astrocyte microdomain Ca mammals . They arTdc2 mutants, which lack tyramine and octopamine in CNS, we conclude that spontaneous astrocyte microdomain Ca2+ transients do not require the production of tyramine or octopamine in vivo. Nevertheless, and similar to application of norepinephrine to mouse preparations  to indirectly activate Ca2+ entry through other cation channels at the cell surface. Alternatively, TrpML might function at the cell surface to directly drive Ca2+ entry, or stimulate entry from the extracellular space by other channels, or release from intracellular stores. It is also important to note that while we have assayed Ca2+ entry, TrpML is also capable of passing several other cations . We previously showed either of these can activate the Oct-TyrR receptor and the TRP channel Waterwitch on astrocytes, and in turn through the adenosine receptor AdoR, mediate many of the physiological and behavioral changes exerted by tyramine and octopamine  and they are sensitive to ROS.Astrocyte microdomain Caof event . In mamme to ROS . Similartopamine . In cont2+ transients are associated with mitochondria, which have been proposed to serve as a source of ROS, potentially through transient opening of the mPTP  could explain why although only half of all observed astrocyte microdomain Ca2+ transients are associated with tracheal filopodia, all appear to be potently regulated by ROS. Perhaps the remaining half of astrocyte microdomain Ca2+ transients not associated with trachea are responding to mitochondria-based metabolic needs through ROS signaling. Finally, approximately half of tracheal filopodia we observed were not visibly associated with astrocyte microdomain Ca2+ transients, yet they were able to extend and retract. This argues that other mechanisms exist to modulate tracheal dynamics in the CNS and astrocyte microdomain Ca2+ transients only represent one regulatory mechanism.In mouse, a subset of microdomain Cathe mPTP . These echondria . Multipl2-consuming metabolism is thought to fluctuate vigorously in response to neural activity. The close association of astrocyte microdomain Ca2+ transients with trachea, the larval breathing apparatus, and with tracheal retraction in particular, suggests a role in modulating CNS gas exchange. Increase in O2 delivery to tissues can lead to hyperoxia and elevated production of ROS. TrpML has recently been found to be a ROS sensor , which argues that astrocyte-tracheal signaling through TrpML can regulate long-term structural changes in the tracheal morphology. Based on these findings, we propose that an important role for a subset of astrocyte microdomain Ca2+ transients in the larval CNS is to coordinate gas exchange through regulation of tracheal dynamics, thereby balancing O2 delivery/CO2 removal according to local metabolic needs.Maintaining a healthy, spatiotemporally regulated normoxic environment to prevent either hypoxia or hyperoxia in CNS is an enormous and ongoing challenge, as the OS sensor , which w a model  where a Drosophila strains used include: Bloomington stock center 1trpml (28992), 2trpml (42230), JF01239trpml (31294), JF01878tyrR (25857), JF02749tyrRII (27670), 1trpA1 (36342), 2trpm (35527), 3nompC (42258), 1trp (5692), 302trpl (31433), 1pkd2 (24495), JF01244trpγ (31299), JF01242pyx (31297), btl-Gal4 (8807), UAS-Lifeact-GFP (57326), UASp-αTub-GFP (7373), 10XUAS-IVS-myr::tdTomato (32222), UAS-trpml-myc (57372), UAS-GFP-Lamp1 (42714). honoOct-TyrR  to generate constructs pUAST-myr::GCaMP5a, pQUAST-myr::GCaMP5a, pUAST-myr::R-GECO1, pJFRC19-13XLexAop2-IVS-myr::GCaMP6s for injection. The transgenic flies were injected and recovered by Rainbow Transgenic Flies, Inc .The full-length ORFs of GCaMP5a, R-GECO1, GCaMP6s with an in-frame DNA fragment encoding the myristoylation signal peptide at 5’-end were cloned into vectors pUAST, pQUAST, pJFRC19  was sandwiched in 30 µl halocarbon oil 27  between a slide and a 22 × 22 mm coverslip , then a 3 min time-lapse video was taken immediately on a spinning disk confocal microscope equipped with a 40X oil immersion objective.Card instar larvae expressing myr-GCaMP5a in astrocytes  was performed in the imaging buffer (pH7.2) containing 110 mM NaCl, 5.4 mM KCl, 0.3 mM CaCl2, 0.8 mM MgCl2, 10 mM D-glucose, 10 mM HEPES, the CNS was immediately transferred to a silicone coated petri dish, immersed in 100 µl imaging buffer (1.2 mM CaCl2), and immobilized gently by sticking the attached nerves onto the silicone surface with forceps. The petri dish then was placed on the stage of a spinning disk confocal microscope equipped with a 40X water dipping objective. The focal plane was fixed around where most of the dorsal lateral astrocytes start to appear in the field of view. After 4 min acclimation and stabilization, a 6 min movie  was taken for analysis. The 488 nm and 516 nm channels were alternated for imaging both the microdomain Ca2+ transients in astrocytes and the dynamics of tracheal filopodia. To keep the tracheal filopodia in focus during the course of extension and retraction, images spanning 5 µM in z depth were taken.CNS dissection from early 32+ transients per minute) of microdomain Ca2+ transients in each preparation was initially quantified as follows: a 100 µm X 100 µm window was cropped from each movie and resulted in nine smaller, side-to-side 100pixel X 100pixel windows (line drawing over movies) in which the number of microdomain Ca2+ transients was counted manually. The total number of microdomain Ca2+ transients in each preparation (100 µm X 100 µm) was acquired by adding up all the numbers counted in these 9 100pixel X 100pixel windows. The cutoff for defining a Ca2+ transient is 5% change in delta F/F0, which is evaluated by post hoc calculations after manually selecting active events. After the automatic Ca2+ signal analysis software Astrocyte Quantitative Analysis (AQuA), and we didn’t find a significant difference . The intensity of microdomain Ca2+ transients was measured with software Volocity , and the amplitude of microdomain Ca2+ transients was defined by (Ft-F0)/F0  as percentage. The full width at half maximum (FWHM) of astrocyte microdomains was acquired using software Igor Pro .The frequency (the number of microdomain Cais (AQuA) became p2+) containing drugs  was directly applied onto the preparations, then imaging continued for another 3 min. The chemicals used for bath application experiments include: Tocris, tetrodotoxin . Sigma-Aldrich, lanthanum chloride , acetylcholine , γ-aminobutyric acid , glutamate , tyramine , octopamine , N-acetyl cysteine , hydrogen peroxide .For bath application of compounds, halfway through the 6 min imaging window (~3 min), 100 µl imaging buffer  was immediately transferred in 4% formaldehyde for fixation for 20 min . Washing in PBS for 3 × 10 min. Permeabilization in PBS + 0.3% Triton X-100 for 2 hr. Primary antibody  incubation at 4 ℃ for ~72 hr. Secondary antibody incubation at room temperature for ~2 hr. Tracheal branches in ventral nerve cord were illuminated with 408 nm laser light and emitting autofluorescence was imaged. Each individual branch was then traced manually with Simple Neurite Tracer (Fiji).The CNS dissected in PBS from 3rd instar larvae  were made exactly in the same way for Ca2+ imaging. Incubation in 100 µl imaging buffer (1.2 mM Ca2+) containing 30 µM DHE for 8 min before imaging . The puncta were automatically counted by the segmentation tool in Slidebook.The CNS preparations from 3post hoc tests, or unpaired t-test. Comparison within groups was tested by paired t-test. p<0.05 was considered statistically significant. *p<0.05, **p<0.01.All statistics were performed in Graphpad. No data were excluded for analyses. 2–3 replications were successfully performed for each experiment. Comparison between groups was tested by one-way ANOVA with Tukey’s Source data for  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:trpml in regulating astrocyte microdomain calcium oscillations in Drosophila. They link this biology to regulation of local tracheal filopodial dynamics and ROS activity, suggesting one role for astrocyte calcium dynamics is to control local gas exchange in Drosophila larvae. This study represents a significant advance in understanding how microdomain calcium transients are generated and regulate CNS physiology and function.The manuscript by Ma and Freeman describes a role for the TRP channel Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Ronald Calabrese as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Brian A MacVicar (Reviewer #2).Thank you for submitting your article "TrpML-mediated astrocyte microdomain Ca 2+ transients regulate astrocyte-tracheal interactions" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife but that some conclusions require a modest amount of additional new data, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, when editors judge that a submitted work as a whole belongs in eLife, either of which would be linked to the original paper.Our expectation is that the authors will eventually carry out the additional experiments and report on how they affect the relevant conclusions either in a preprint on bioRxiv or medRxiv, or if appropriate, as a Research Advance in Summary:2+ transients in the Drosophila nervous system and their relationship to the physiology and behavior of CNS trachea. Using genetic Ca2+ reporters, they first show that astrocyte processes exhibit transient changes in Ca2+ concentration within microdomains. They present results suggesting that extracellular Ca2+ is required for Ca2+ changes within microdomains, and they identify the TrpML ion channel, in a targeted genetic screen, as a critical component of the mechanism regulating Ca2+ within microdomains. Importantly, Ca2+ transients are almost eliminated with astrocyte-specific knockdown of 1trpml. The authors further show that tyramine, acting through a fly tyramine receptor (TyrRII), can enhance Ca2+ transients but that it is not required for such transients. They demonstrate that the Ca2+ microdomains are in close proximity to filopodia of tracheal tubules, and go on to show that transient changes in Ca2+ within such microdomains can influence the behavior of tracheal filopodia.Ma and Freeman present results from studies of astrocyte Ca2+ within such microdomains is strongly correlated with filapodial retraction events. Additional findings show that extension of tracheal filopodia is enhanced in the 1trpml mutant, whereas retraction events are normal, suggesting that the TrpML channel is important for inhibition of extension events. Finally, the authors show that there is overgrowth of trachea and higher levels of reactive oxygen species in the 1trpml mutant, suggesting a role of the TrpML channel and Ca2+ in tracheal growth and gas exchange.Their results suggest that increased Ca2+ regulation in astrocytes. The results are important as similar astrocyte Ca2+ microdomains regulate CNS physiology in mammals, and Drosophila provides an important genetic model for the analysis of astrocyte Ca2+ signaling. However, there are a few issues and suggestions for improvement, as well as points that need clarification prior to publication.The manuscript is clearly written and represents a significant advance in understanding CaEssential revisions:trpml directly mediates calcium microdomain influx or tone down conclusion and add more to the Discussion that discusses alternative models.1) Based on the data presented, it is not clear whether the TRP channel is the primary source of calcium influx for microdomain oscillations, or secondarily coupled to the process to control the frequency of the events only. Please clarify this or provide more convincing data that trpml localized in Drosophila astrocytes? All reviewers agree that it is important to know whether TrpML is acting in astrocyte lysosomes or at the cell membrane to regulate CNS and tracheal functions. Localizing it would help, as would measuring the amount of calcium influx signal in the absence of the channel --- the frequency goes down, but they still appear to be present. See reviewer comments for specific comments and suggestions.2) Where is 3) Please address whether astrocytes are regulating trachea in the mature larval CNS, as detailed in comments by reviewer 3. Alternatively, if the alteration of tracheal function is a consequence of abnormal astrocyte development.Drosophila and mouse astrocytes need to be clarified in some cases, and the authors should be more careful in discussing the different types of mouse and rat astrocyte calcium signals (see specific comments by reviewer 2)4) The comparisons made between the results in Reviewer #1:2+ regulation in astrocytes. The results are important as similar astrocyte Ca2+ microdomains regulate CNS physiology in mammals, and Drosophila provides an important genetic model for the analysis of astrocyte Ca2+ signaling.The manuscript of Ma and Freeman is clearly written and represents a significant advance in understanding CaThe major issues detailed below should be addressed prior to publication:2+ transients within astrocyte microdomains. The presentation of these results leads the reader to think that TrpML acts at the astrocyte cellular membrane to regulate Ca2+ entry. While this might be true, The TrpML channel has been described as a lysosomal membrane rather than a cellular membrane channel, and it is clearly required for cell migration and phagocytosis via regulation of actomyosin contractility . Is there any evidence that TrpML is even present at the cellular membrane? Have the authors determined localization of the channel in astrocytes? If not, the presentation will lead most readers to think its site of action is the cellular membrane when this has not been demonstrated and is likely not to be the case. A more detailed model for TrpML, taking its localization into account, is needed.1) The authors show that elimination of extracellular and the TrpML channel inhibit Ca2+ transients is to modulate CNS gas exchange through TrpML and ROS signaling." This suggests that TrpML might have a modulatory role in the mature nervous system. However, it seems that all of the authors' results about TrpML could be interpreted as a requirement for the channel in astrocytes during development with a consequent effect on physiology of the tracheal system. Indeed, the authors show that tracheal overgrowth occurs in the channel mutant. I wonder if there is any evidence for a role of TrpML in the mature nervous system. Did the authors attempt a conditional knockdown of TrpML in the mature larval CNS? What about inhibition of TrpML in mature CNS astrocytes using an antagonist that blocks Ca2+ release from lysosomes? Or, activation of TrpML with an agonist in the mature CNS? Or, transient blockade of Ca2+ entry in the mature larval CNS? Is it known whether such manipulations alter tracheal function? It would seem important to determine whether astrocytes actually modulate trachea and/or gas exchange in the mature CNS since this is the implication of the authors' model.2) The authors propose "that one important physiological role for astrocyte microdomain Ca2+ permeable, it is also permeable to other cations. It is described in FlyBase as a cation channel involved in regulation of endosomal trafficking, autophagy, lateral inhibition and TOR signaling in hemocytes and perhaps other cell types. Is regulation of astrocytes and tracheal function as simple as modulation of Ca2+ entry or is it more complicated than that? I wonder if Ca2+ microdomains are affected indirectly because of an effect on endosomal trafficking. Some discussion of this is needed.3) Although TrpML is known to be Ca1trpml mutant or 1trpml knockdown flies. Does the CNS appear to be completely normal with absence of TrpML?4) There is no mention of CNS abnormalities in the Reviewer #2:Drosophila astrocytes occur, are triggered by reactive oxygen species (ROS), are enhanced in frequency by the neurotransmitter tyramine via the TyrRII receptor and that they decrease in frequency by ~70% to 80% in trpml loss of function mutants. The authors also show that tracheal growth is modified by these calcium signals in astrocytes. These data provide intriguing counterparts to observations in mammalian astrocytes with respect to calcium signaling. However, there are comparisons that the authors make between the results in Drosophila and mouse astrocytes that need to be clarified in some cases and the authors should be more careful in discussing the different types of mouse and rat astrocyte calcium signals. Finally, the TRPML1 channel is expressed on lysosomes according to the report they cite from Zhang et al., 2016. The results presented here indicate expression on the plasma membrane based on the block by lanthanum and effects of removing external calcium. This puzzle and apparent discrepancy should be addressed by the authors.This is very interesting paper that provides convincing data that calcium microdomains in 2+ events decrease by ~70% to 80% in trpml loss of function mutants, in both intact 1st instar larvae  and acute CNS preparations from 3rd instar larvae  "While knockout of 11 of these TRP channels had no effect, we found that microdomain CaDrosophila as a model system.This is a key finding of this study and this careful analysis shows the strength and power of working with 2+ transients in Drosophila share many features with those observed in mammals."2) "Astrocyte microdomain CaThe fly astrocyte microdomains have overlap with the observations more recently in mammalian astrocytes but the authors should be careful to differentiate between the older observations of IP3-mediated calcium release from ER  and the observations of calcium entry dependent microdomains. I don't think the microdomains were observed using the older dye loading techniques. For example, the Nett et al. paper showed almost complete block of the astrocyte calcium signals by the IP3-R antagonist heparin. In my opinion these fly microdomains that are described here are most similar to the microdomains first reported by Srinivasan et al., 2015. Rungta et al., 2016, supports Srinivasan et al. and their data also point to a calcium entry pathway independent of IP3R mediated release and not altered in TRPA1-/- mice. Of course, there appear to be multiple sources of calcium that generate microdomains and Agarwal et al., 2017, point to a mechanism of calcium release from mitochondria. The differences between these various mechanisms generating calcium transients should be clearly stated because different types of calcium signals generated in different regions of astrocytes may potentially have different functions (as would be expected in neurons).3) This observation of ROS sensitivity in this study is consistent with the report by Zhang et al., 2016, describing the ROS sensitivity of TRPMl1 channels. However, there is a significant difference in that Zhang et al. describe the calcium signals as arising from the ROS triggered release of calcium from lysosomes into the cytoplasm not as a plasma membrane channel leading to entry from the extracellular space. The authors should reconcile this important difference and describe which signals are lysosomal release dependent.4) "We note that those that exhibited only retraction did so very early in the imaging window, which could indicate that we began our imaging after extension had been initiated."This paragraph is not critical but is confusing. It's clear that 6 min is sufficient to observe a cycle of extension -retraction but what is surprising is that retractions alone occur at a higher rate than extensions alone. Wouldn't these be the same if the probability was similar? Alternatively could imaging the tissue may make retractions much more likely perhaps by generating ROS itself from the tissue illumination in the spinning disk confocal?5) "Together, these observations indicate that a large fraction of astrocyte2+ transients are spatiotemporally correlated with the retraction ofmicrodomain Caadjacent tracheal filopodia."This conclusion seems reasonable based on the imaging data.Reviewer #3:trpml in regulating astrocyte microdomain calcium oscillations in Drosophila. They link this biology to regulation of local tracheal filopodial dynamics and ROS activity, suggesting one role for astrocyte calcium dynamics is to control local gas exchange in Drosophila larvae. The authors' data set is quite interesting and relevant to the field. I have a few suggestions for improvement and some general clarification points as well.The manuscript by Ma and Freeman describes a role for the TRP channel trpml channel directly mediates microdomain calcium influx in astrocytes, but I don't think their data separates a role for the channel being the source of calcium influx or secondarily coupled to the process to control the frequency of the events only. As such, the authors need to state this or provide more convincing data that trpml directly mediates calcium microdomain influx.1) The authors imply that the trpml knockdowns and mutants as they do for wildtype in Figure 1B. Is trpml required only for the frequency of these events, or also the amplitude. The authors must have this data already in hand. If the amplitude of the residual events in the same, it seems more likely that trpml is controlling when these events occur, rather than being the direct channel for gating the calcium events themselves.2) Related to the point above, the authors should document and show the amplitude of calcium transients in trpml localized in Drosophila astrocytes – is it on the plasma membrane at sites of calcium influx? If it is located on lysosomes or internal compartments, I have a hard time understanding how it could be directly mediating calcium influx from the extracellular space.3) Similarly, where is trpml RNAi line from Figure 2D and Figure 4B for the retraction/extension assays in Figure 4A. In 4A, only the trpml mutant is used to show defects in tracheal filopodia. If this biology requires trpml in astrocytes, knocking it down only in these cells should trigger this phenotype. Alternatively, this biology could be coming from an independent role of trpml in tracheal cells. This experiment is important, especially given how small some of the reported changes appear to be. Since the authors used trpml RNAi knockdown in astrocytes for all there other manipulations, it raises a question as to why not here in one of the most important experiments.4) It would be helpful to use the 5) The most difficult part of the paper for me is Figure 3D. I have a hard time finding compelling images here to support the model. From the bottom astrocyte GCaMP traces (red) and tracheal filopodial length (red) traces, only pair 2 looks compelling – red goes up, green goes down. The rest are bouncing around all over the place and it is hard to see a compelling correlation. Would it be possible to add a supplementary figure showing more of just these traces for all (or a larger representative sample) of the data included in Figure 3E. That figure shows a perfect correlation, but the raw data provided do not look convincing. Seeing more of those raw data would be very helpful for convincing the readers of this correlation. Essential revisions:1) Based on the data presented, it is not clear whether the TRP channel is the primary source of calcium influx for microdomain oscillations, or secondarily coupled to the process to control the frequency of the events only. Please clarify this or provide more convincing data that trpml directly mediates calcium microdomain influx or tone down conclusion and add more to the Discussion that discusses alternative models.We agree that TrpML may be the channel, or coupled to the channel that drive the majority of calcium influx. Given that we cannot test this directly, we have toned down these conclusions and added a discussion of these alternative models to the Discussion.2) Where is trpml localized in Drosophila astrocytes? All reviewers agree that it is important to know whether TrpML is acting in astrocyte lysosomes or at the cell membrane to regulate CNS and tracheal functions. Localizing it would help, as would measuring the amount of calcium influx signal in the absence of the channel --- the frequency goes down, but they still appear to be present. See reviewer comments for specific comments and suggestions.This is an interesting question that we have addressed by assaying localization of TrpML in astrocytes. In new data  we show that TrpML-MYC is localized (1) near the membrane of astrocyte cell bodies, and appears to be sub-cortical based on co-staining with plasma membrane-tethered GFP; and (2) in a punctate pattern throughout astrocytes, which largely overlaps with the endo-lysosomal marker GFP-Lamp1.3) Please address whether astrocytes are regulating trachea in the mature larval CNS, as detailed in comments by reviewer 3. Alternatively, if the alteration of tracheal function is a consequence of abnormal astrocyte development.rd larval instar. For instance, tracheal morphologies were quantified in Figure 4—figure supplement 1B in 3rd instar larvae.Astrocyte development based on the analysis of a number of markers and cellular morphology appears normal in TrpML mutants . We have no evidence that loss of TrpML (in mutants or RNAi lines) results in developmental defects in astrocytes. Astrocyte modulation of trachea occurs at all larval stages tested, including the 34) The comparisons made between the results in Drosophila and mouse astrocytes need to be clarified in some cases, and the authors should be more careful in discussing the different types of mouse and rat astrocyte calcium signals (see specific comments by reviewer 2)We have clarified this point throughout the manuscript according to reviewer #2’s request.Reviewer #1:2+ regulation in astrocytes. The results are important as similar astrocyte Ca2+ microdomains regulate CNS physiology in mammals, and Drosophila provides an important genetic model for the analysis of astrocyte Ca2+ signaling.The manuscript of Ma and Freeman is clearly written and represents a significant advance in understanding CaThe major issues detailed below should be addressed prior to publication:2+ transients within astrocyte microdomains. The presentation of these results leads the reader to think that TrpML acts at the astrocyte cellular membrane to regulate Ca2+ entry. While this might be true, The TrpML channel has been described as a lysosomal membrane rather than a cellular membrane channel, and it is clearly required for cell migration and phagocytosis via regulation of actomyosin contractility . Is there any evidence that TrpML is even present at the cellular membrane? Have the authors determined localization of the channel in astrocytes? If not, the presentation will lead most readers to think its site of action is the cellular membrane when this has not been demonstrated and is likely not to be the case. A more detailed model for TrpML, taking its localization into account, is needed.1) The authors show that elimination of extracellular and the TrpML channel inhibit CaIn the revised manuscript we have added data to address this directly by assaying localization of TrpML in astrocytes. In new data  we show that TrpML-MYC is localized (1) near the membrane of astrocyte cell bodies, and appears to be sub-cortical based on co-staining with membrane-tethered GFP; and (2) in a punctate pattern throughout astrocytes, which largely overlaps with the endo-lysosomal marker GFP-Lamp1. As such, both possibilities exist—that TrpML is functioning near/at the membrane or at endo-lysosomes. We have included this possibility in our model for proposed TrpML function.2+ transients is to modulate CNS gas exchange through TrpML and ROS signaling." This suggests that TrpML might have a modulatory role in the mature nervous system. However, it seems that all of the authors' results about TrpML could be interpreted as a requirement for the channel in astrocytes during development with a consequent effect on physiology of the tracheal system. Indeed, the authors show that tracheal overgrowth occurs in the channel mutant. I wonder if there is any evidence for a role of TrpML in the mature nervous system. Did the authors attempt a conditional knockdown of TrpML in the mature larval CNS? What about inhibition of TrpML in mature CNS astrocytes using an antagonist that blocks Ca2+ release from lysosomes? Or, activation of TrpML with an agonist in the mature CNS? Or, transient blockade of Ca2+ entry in the mature larval CNS? Is it known whether such manipulations alter tracheal function? It would seem important to determine whether astrocytes actually modulate trachea and/or gas exchange in the mature CNS since this is the implication of the authors' model.2) The authors propose "that one important physiological role for astrocyte microdomain Catrpml mutants or when trpml is depleted from astrocytes . Our best evidence for active regulation of tracheal elements by astrocytes in the mature (3rd instar) larval nervous system is that bath application of tyramine, which activates microdomain transients, drives filopodial retraction in live preparations . We included our new data in the revised manuscript that showed trpml knockdown specifically at the 3rd instar larval stage did result in faster tracheal filopodial growth. Interestingly, such knockdown in trachea also make trachea filopodia grow more rapidly. This suggests that trpml function both in astrocytes and trachea controlling tracheal filopodial dynamics through increasing growth rate. Finally, All of the approaches suggested above to manipulate calcium would likely work in live preparations  for blockade of different aspects of calcium signaling in astrocytes, but it would not spare trachea from influence, and more importantly not be technically feasible for us to do this chronically, allow animals to grow, and then observe how this alters tracheal morphology. Nevertheless, in response to this concern we will temper our conclusions regarding active regulation of gas exchange and leave open alternative possibilities for the phenotypes we observe.Our assumption is that by modulating tracheal growth, astrocytes can thereby regulate gas exchange by increasing or decreasing tracheal filopodial coverage of the CNS, since trachea is the primary route of gas exchange in the brain. Our argument is based on the increased ROS in 2+ permeable, it is also permeable to other cations. It is described in FlyBase as a cation channel involved in regulation of endosomal trafficking, autophagy, lateral inhibition and TOR signaling in hemocytes and perhaps other cell types. Is regulation of astrocytes and tracheal function as simple as modulation of Ca2+ entry or is it more complicated than that? I wonder if Ca2+ microdomains are affected indirectly because of an effect on endosomal trafficking. Some discussion of this is needed.3) Although TrpML is known to be Catrpml mutants, it is the frequency that changes ; (2) tyramine stimulates normal somatic calcium signal in trpml mutant astrocytes. ; (3) astrocyte development even in trpml mutants appears normal, and one might expect developmental defects if endosomal trafficking was significantly perturbed.The reviewer is correct, TrpML may exert its effects through other ions, or by moving multiple ions. It may also be more complicated than the model we propose, and we have discussed these points in the revised manuscript. We do not think there is a general disruption of calcium signaling based on the following evidence: (1) in new data we show that the amplitude of calcium transients in astrocytes in not altered in 1 mutant or trpml1 knockdown flies. Does the CNS appear to be completely normal with absence of TrpML?4) There is no mention of CNS abnormalities in the trpmltrpml do mimic mucolipidosis type IV, in that we can visualize autofluorescent material in the CNS. We also note  that the lethality of trpml mutants is a result of defective astrocyte function—we are able to rescue ~100% of trpml mutants to adulthood by astrocyte-specific expression of TrpML .Overall the CNS appears grossly normal based on size and the morphology of glia, and major tracheal branches (minor overgrowth phenotypes). However, we note that some phenotypes in Reviewer #2:This is very interesting paper that provides convincing data that calcium microdomains in Drosophila astrocytes occur, are triggered by reactive oxygen species (ROS), are enhanced in frequency by the neurotransmitter tyramine via the TyrRII receptor and that they decrease in frequency by ~70% to 80% in trpml loss of function mutants. The authors also show that tracheal growth is modified by these calcium signals in astrocytes. These data provide intriguing counterparts to observations in mammalian astrocytes with respect to calcium signaling. However, there are comparisons that the authors make between the results in Drosophila and mouse astrocytes that need to be clarified in some cases and the authors should be more careful in discussing the different types of mouse and rat astrocyte calcium signals.Thank you for making this point. We have tried to clarify this comparison in the revised manuscript to be accurate and fair. We welcome any additional feedback from the reviewer on clarification if appropriate..Finally, the TRPML1 channel is expressed on lysosomes according to the report they cite from Zhang et al., 2016. The results presented here indicate expression on the plasma membrane based on the block by lanthanum and effects of removing external calcium. This puzzle and apparent discrepancy should be addressed by the authors.In the revised manuscript we have added data to address this directly by assaying localization of TrpML in astrocytes. In new data  we show that TrpML-MYC is localized (1) near the membrane of astrocyte cell bodies, and appears to be sub-cortical based on co-staining with membrane-tethered GFP; and (2) in a punctate pattern throughout astrocytes, which largely overlaps with the endo-lysosomal marker GFP-Lamp1. As such, both possibilities exist—that TrpML is functioning near/at the membrane or at endo-lysosomes. We have included this possibility in our model for proposed TrpML function.2+ events decrease by ~70% to 80% in trpml loss of function mutants, in both intact 1st instar larvae  and acute CNS preparations from 3rd instar larvae  "While knockout of 11 of these TRP channels had no effect, we found that microdomain CaThis is a key finding of this study and this careful analysis shows the strength and power of working with Drosophila as a model system.We thank the reviewer for the enthusiasm and support for the model.2+ transients in Drosophila share many features with those observed in mammals "Astrocyte microdomain Ca2015)."The fly astrocyte microdomains have overlap with the observations more recently in mammalian astrocytes but the authors should be careful to differentiate between the older observations of IP3-mediated calcium release from ER  and the observations of calcium entry dependent microdomains. I don't think the microdomains were observed using the older dye loading techniques. For example, the Nett et al. paper showed almost complete block of the astrocyte calcium signals by the IP3-R antagonist heparin. In my opinion these fly microdomains that are described here are most similar to the microdomains first reported by Srinivasan et al., 2015. Rungta et al., 2016, supports Srinivasan et al. and their data also point to a calcium entry pathway independent of IP3R mediated release and not altered in TRPA1-/- mice. Of course, there appear to be multiple sources of calcium that generate microdomains and Agarwal et al., 2017, point to a mechanism of calcium release from mitochondria. The differences between these various mechanisms generating calcium transients should be clearly stated because different types of calcium signals generated in different regions of astrocytes may potentially have different functions (as would be expected in neurons).This is an excellent point raised by the reviewer. In the revised manuscript we have more carefully discussed the diversity of types of calcium signals, and focused primarily on comparison to Agarwal .3) This observation of ROS sensitivity in this study is consistent with the report by Zhang et al., 2016, describing the ROS sensitivity of TRPMl1 channels. However, there is a significant difference in that Zhang et al. describe the calcium signals as arising from the ROS triggered release of calcium from lysosomes into the cytoplasm not as a plasma membrane channel leading to entry from the extracellular space. The authors should reconcile this important difference and describe which signals are lysosomal release dependent.Based on the comments of several reviewers we have now included localization studies for TrpML (see above). Based on its localization we believe it could be working by lysosomal release, or by release from the sub-cortical region of the plasma membrane. At the moment we cannot resolve which compartment is the crucial one for astrocyte signaling, so we have left open both possibilities in our model.2+ transients using trpml-GCaMP5G  and myr-R-GECO1 (which should be limited to the plasma membrane)  . In many4) "We note that those that exhibited only retraction did so very early in the imaging window, which could indicate that we began our imaging after extension had been initiated."This paragraph is not critical but is confusing. It's clear that 6 min is sufficient to observe a cycle of extension -retraction but what is surprising is that retractions alone occur at a higher rate than extensions alone. Wouldn't these be the same if the probability was similar? Alternatively could imaging the tissue may make retractions much more likely perhaps by generating ROS itself from the tissue illumination in the spinning disk confocal?The reviewer is correct in their assumption. We did indeed observe a statistically equal number of extension and retraction events  throughout our 6-min imaging window. We apologize for our unclear description and have tried to clarify this. The point we were trying to make is that there are very few filopodia that only retract, and usually they appear at the very beginning of imaging.5) "Together, these observations indicate that a large fraction of astrocyte2+ transients are spatiotemporally correlated with the retraction ofmicrodomain Caadjacent tracheal filopodia."This conclusion seems reasonable based on the imaging data.Reviewer #3:The manuscript by Ma and Freeman describes a role for the TRP channel trpml in regulating astrocyte microdomain calcium oscillations in Drosophila. They link this biology to regulation of local tracheal filopodial dynamics and ROS activity, suggesting one role for astrocyte calcium dynamics is to control local gas exchange in Drosophila larvae. The authors' data set is quite interesting and relevant to the field. I have a few suggestions for improvement and some general clarification points as well.1) The authors imply that the trpml channel directly mediates microdomain calcium influx in astrocytes, but I don't think their data separates a role for the channel being the source of calcium influx or secondarily coupled to the process to control the frequency of the events only. As such, the authors need to state this or provide more convincing data that trpml directly mediates calcium microdomain influx.We agree with this point, which was also raised by another reviewer, and have clarified this in our revised manuscript.2) Related to the point above, the authors should document and show the amplitude of calcium transients in trpml knockdowns and mutants as they do for wildtype in Figure 1B. Is trpml required only for the frequency of these events, or also the amplitude. The authors must have this data already in hand. If the amplitude of the residual events in the same, it seems more likely that trpml is controlling when these events occur, rather than being the direct channel for gating the calcium events themselves.trpml mutants are not significantly different .We thank the reviewer for pointing this out. In the revised manuscript we show that the amplitude of calcium transients in control and 3) Similarly, where is trpml localized in Drosophila astrocytes – is it on the plasma membrane at sites of calcium influx? If it is located on lysosomes or internal compartments, I have a hard time understanding how it could be directly mediating calcium influx from the extracellular space.In the revised manuscript we have added data to address this directly by assaying localization of TrpML in astrocytes. In new data  we show that TrpML-MYC is localized (1) near the membrane of astrocyte cell bodies, and appears to be sub-cortical based on co-staining with membrane-tethered GFP; and (2) in a punctate pattern throughout astrocytes, which largely overlaps with the endo-lysosomal marker GFP-Lamp1. As such, both possibilities exist—that TrpML is functioning near/at the membrane or at endo-lysosomes. We have included this possibility in our model for proposed TrpML function.4) It would be helpful to use the trpml RNAi line from Figure 2D and Figure 4B for the retraction/extension assays in Figure 4A. In 4A, only the trpml mutant is used to show defects in tracheal filopodia. If this biology requires trpml in astrocytes, knocking it down only in these cells should trigger this phenotype. Alternatively, this biology could be coming from an independent role of trpml in tracheal cells. This experiment is important, especially given how small some of the reported changes appear to be. Since the authors used trpml RNAi knockdown in astrocytes for all there other manipulations, it raises a question as to why not here in one of the most important experiments.trpml mutants. These data indicate that astrocyte TrpML can indeed regulate tracheal growth. It seems that tracheal TrpML can also influence growth to some extent. The full effect in the trpml mutant is therefore likely due to additivity between astrocytic and tracheal TrpML.This is an excellent point. We have done these experiments and the data are now included in Figure 4—figure supplement 1A. We found that astrocyte knockdown increased extension rate similar to the 5) The most difficult part of the paper for me is Figure 3D. I have a hard time finding compelling images here to support the model. From the bottom astrocyte GCaMP traces (red) and tracheal filopodial length (red) traces, only pair 2 looks compelling – red goes up, green goes down. The rest are bouncing around all over the place and it is hard to see a compelling correlation. Would it be possible to add a supplementary figure showing more of just these traces for all (or a larger representative sample) of the data included in Figure 3E. That figure shows a perfect correlation, but the raw data provided do not look convincing. Seeing more of those raw data would be very helpful for convincing the readers of this correlation.We have included an additional set of traces and video frames.


Circular RNAs (circRNAs) are covalently closed circular structures without 5′ caps and 3′ tails, which are mainly formed from precursor mRNAs (pre-mRNAs) via back-splicing of exons. With the development of RNA sequencing and bioinformatic analysis, circRNAs were recently rediscovered and found to be widely expressed in the tree of life. Cerebellar degeneration-related protein 1 antisense RNA (CDR1as) is recognized as one of the most well-identified circRNAs. It contains over 70 miR-7 binding sites and can regulate gene activity by sponging miR-7. Increasing numbers of studies have recently demonstrated that CDR1as is abnormally expressed in many types of tumors, such as colorectal cancer, cholangiocarcinoma and osteosarcoma, and plays a vital role in the development of cancer. However, there are few reviews focusing on CDR1as and cancer. Hence, it is important to review and discuss the role of CDR1as in cancer. Here, we first review the main biological features of CDR1as. We then focus on the expression and roles of CDR1as in cancer. Finally, we summarize what is known on the role of CDR1as in cancer and discuss future prospects in this area of research. Circular RNAs (circRNAs) are covalently closed circular structures without 5′ caps and 3′ tails, which are mainly formed from precursor mRNAs (pre-mRNAs) via the back-splicing of exons . The eleCircRNAs exert their function through several mechanisms . FirstlyCerebellar degeneration-related protein 1 antisense RNA (CDR1as), also known as circular RNA sponge for miR-7 (ciRS-7) , is recoCDR1as is a closed circular RNA formed from the antisense transcript of the cerebellum degeneration-related antigen 1 (CDR1) gene, whose length is 1,485 bp . CDR1as There is increasing evidence that CDR1as can act as an miRNA sponge by absorbing several miRNAs . Among tMany studies have demonstrated that CDR1as is expressed abnormally in many cancer types . The expNumerous studies have demonstrated that CDR1as is involved in the regulation of tumor growth by sponging several miRNAs and regulating multiple signaling pathways. For example, miR-7 acts as a tumor suppressor that can regulate cellular proliferation and various biological process by triggering the signal transduction of the growth factors . CDR1as Cancer metastasis is the critical step of cancer development and progression and many studies reported that CDR1as could influence cancer metastasis. As described above, CDR1as can promote cancer metastasis via multiple signaling pathways . In addiChemoresistance is the main obstacle to cancer therapy and remains a great challenge for improving the clinical outcomes of cancer patients . A numbeCircRNAs were previously considered to be the products of faulty RNA splicing . HoweverRS, WW, and JL collected the related manuscript. CJ and XZ drafted and revised the manuscript. RW designed the review. JT and LL participated in the design of the review and helped to draft and revise the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Single cardio-metabolic risk factors are each known modifiable risk factors for adverse health and quality of life outcomes. Yet, evidence on the clustered effect of these parameters and the metabolic syndrome (MetS) on health-related quality of life (HRQoL) is still limited and mostly cross-sectional. The objectives of this study were to identify clusters of cardio-metabolic physiological functioning, to assess their associations with HRQoL in comparison with the MetS, to elucidate the modifying role of physical activity, and to assess differences in health service utilization.. The 36-item Short-Form Health Survey (SF-36) was used to assess HRQoL. Quantile regressions were performed with and without adjustment for physical activity, to detect independent associations of the latent classes, MetS and physical activity with HRQoL. To assess the modifying role of physical activity, we additionally grouped participants based on the combination of physical activity and latent classes or MetS, respectively. Logistic regressions were used to investigate health service utilization as outcome.This study is based on longitudinal data from two time points (2010/11 & 2017/18) of the Swiss Study on Air Pollution and Lung and Heart Diseases (SAPALDIA). Latent class analysis (LCA) grouped participants based on a priori selected cardio-metabolic and MetS related physiological functioning variables The LCA resulted in three classes labeled “Healthy” (30% of participants in 2017/18), “At risk” and “Unhealthy” (29%). The Unhealthy class scored lowest in all physical component scores of HRQoL. Compared to healthy and active participants, inactive participants in the “Unhealthy” class showed lower scores in the physical functioning domain both cross-sectionally ) and longitudinally. This group had an odds ratio of 2.69  for being hospitalized in the previous 12 months.These results point to subjects with adverse cardio-metabolic physiological functioning and low activity levels as an important target group for health promotion and maintenance of well-being. The promotion of physical activity at the early stages of aging seems pivotal to mitigate the impact of the MetS on HRQoL at higher age. In the Western world, the proportion of people aged 80 years and above is estimated to double by 2080 compared to 2014 . At the Previous studies have commonly investigated effects of lifestyle and physiological functioning parameters on HRQoL domains one factor at a time. Yet, the simultaneous examination of these risk factors, such as combinations of cardio-metabolic physiological functioning and physical activity, provides valuable insights for possible improvements in population health and HRQoL , 8. WithA clustering approach is frequently used to examine the possible synergy of several factors and to give insights on important underlying patterns, e.g. related to physiological functioning. Several clustering methods (model and non-model-based) are available for this purpose. Latent Class Analysis (LCA) is a model-based approach that attempts to detect homogeneous groups within a heterogeneous population. It is a sophisticated tool to capture the complexity of interrelated risk factors . Three rFurthermore, the metabolic syndrome (MetS) is a good example for the importance of investigating effects of multiple predictors with one health outcome. In particular, components of the metabolic syndrome, i.e. central obesity, hyperglycemia, dyslipidemia, and hypertension, and thus lifestyle factors related to the MetS are prevalent early warning signs in aging populations and increase the risk of a range of common chronic cerebrovascular, cardiovascular, and neurological disorders known to be associated with severe disability . As showWhether physical activity can rescue persons with MetS from poor quality of life remains poorly understood with some limited suggestive evidence –17. But However, none of the mentioned studies looked at the association of the identified clusters in combination with physical activity status with HRQoL domains in a longitudinal population-based setting with nation-wide coverage. Consequently, there is a research gap in assessing the linked patterns of cardio-metabolic physiological factors and physical activity – being the most prevalent lifestyle variable - with associations of HRQoL.To enhance the understanding of joint cross-sectional and longitudinal associations of physiological functioning and physical activity with HRQoL in the general population, the objectives of this study were (1) to identify common latent classes of physiological functioning related to cardio-metabolic health in the Swiss Study on Air Pollution and Lung and Heart Diseases (SAPALDIA), (2) to assess associations of the identified latent classes with HRQoL and compare them to MetS-HRQoL associations, (3) to identify possible protective influences of physical activity in these associations and finally (4) to elucidate differences in health service utilization between the latent classes and persons with and without MetS.SAPALDIA is a population-based cohort with associated biobank initiated in 1991. In SAPALDIA1, 9′651 adults (18–62 years) were randomly recruited from eight study areas in Switzerland representing the country’s geographic and cultural diversity . In the n = 1′746 participants. While cross-sectional analyses were restricted to SAPALDIA4 participants 55+, we used data of the same subjects from the prior follow-up (SAPALDIA3) to assess longitudinal associations.The current research is based on the subsample of participants of the third follow-up , who were aged 55 years and older at the time. Unlike previous SAPALDIA surveys, data collection in SAPALDIA4 was primarily questionnaire based. Only SAPALDIA participants who were aged 55 years and older and who had answered all SAPALDIA4 questionnaires were subsequently invited for a health assessment (SAPALDIA55+) to one of the eight local study centers . The SAPALDIA55+ health visit focused on the collection of healthy aging related determinants and preclinical aging endpoints. SAPALDIA4 involved a total of 5′149 participants answering multiple self-administered questionnaires. The subgroup of participants aged 55 years and older who underwent a health examination and answered additional age-related questions consisted of The present analysis ultimately involved 1′124 of the SAPALDIA4 55+ participants who provided complete information on all relevant variables. For 902 of these subjects complete information was also available from their SAPALDIA3 assessment for longitudinal analyses  as well as the covariates: sex, age, and education level (Low = Primary School (≤ 9 years), Middle = Secondary school, middle school or apprenticeship (≤12 years), High = Technical College or University (≥12 years)). We chose for this population, education levels as a proxy for socio-economic status, as this measure has proven in previous studies to well represent the socio-economic status .We further used information on health service utilization as secondary endpoint – next to HRQoL -, which was also assessed in the SAPALDIA4 questionnaire. We defined three health care utilization variables: Visit to physician(s); to hospital(s); combination of visits to physician(s) and hospital(s). The information were reported for the 12 months preceding the survey (yes vs. no for each). We treated these response variables as binary because nearly all participants reported 0 or 1 visit in the specified period.Furthermore, we used information on doctor’s diagnosis reported in the SAPALDIA4 questionnaires for sensitivity analysis. Specifically, we defined a variable capturing the following cardiovascular diseases: Myocardial Infarction, Angina Pectoris, Heart Insufficiency, Claudicatio Intermittens and Stroke.Questionnaires at SAPALDIA3 and SAPALDIA4 also included the 36-Item Short-Form Health Survey (SF-36), a widely used HRQoL assessment tool that was validated in large population-based surveys as well as in clinical settings , 26. TheThe questionnaire is designed to provide a summary of physical and mental health scores, based on eight domains. The physical component comprises physical functioning (PF), bodily pain (BP), role-physical (RP) and general health perception (GH). The mental component reflects the vitality (VT), social role functioning (SF), role emotional (RE) and mental health perception (MH). Scores for each subscale range from 0 to 100, and higher scores indicate better HRQoL . Three dThe health examination consisted of measurements of weight , height , hip and waist circumference , bio-impedance analysis (BIA) using two different devices , blood pressure and heart rate measurements ; non-fasting blood glucose (only measured at SAPALDIA3); diagnostics for HbA1c and triglycerides  , 31.Blood pressure measurements at SAPALDIA3&4 were taken after the participant was seated for at least 10 min. Two measurements were taken, with a break of 3 min between measurements. The blood measurements for glycemia and triglycerides were taken in a non-fasting state. From the anthropometric measurements, body mass index (BMI) (SAPALDIA3&4) and waist to hip ratio (SAPALDIA3 & 4) were derived.Five categorical variables at SAPALDIA3&4, which reflect cardio-metabolic physiological functioning were considered for identifying latent classes. At both time points (SAPALDIA3 & 4) variables with evidence-based thresholds or recommendations were categorized accordingly, namely systolic blood pressure  diastol.Table 1The metabolic syndrome at SAPALDIA3&4 was defined based on the Joint Interim Statement (JIS) . The JISIn a first step  LCA was In order to detect the appropriate number of classes and maximize model fit, we started with a one-class model and increased the number of latent classes up to six. The final model was selected by examining the Bayesian information criterion (BIC) and the Akaike Information Criterion (AIC) in the first place. These indices have shown to be useful for determining the appropriate number of classes for LCA . We checTo assess cross-sectional and longitudinal associations of the latent classes with HRQoL domains and to compare them to according MetS-HRQoL associations, we performed quantile regression models with adjustment for individual predictor variables  as well as for HRQoL at baseline in the case of longitudinal associations. Quantile regression models were chosen to address the problem of left-skewed distribution of HRQoL measures.In order to assess confounding and effect modification by physical activity in the above associations, we first ran the models with and without adjustment for physical activity and second created composite variables classifying participants based on a combination of latent classes or MetS with physical activity status.Finally, we used logistic regression models to assess cross-sectional associations of the composite variables with health service utilization.For sensitivity analyses we reran analyses by excluding subjects with self-reported diagnoses of a cardiovascular disease at SAPALDIA4 .We performed all analyses using Stata 15 .The descriptive characteristics of the study population can be seen in Table 1 = 340 N2 = 455, N3 = 329) within the study sample, which increases statistical power, while the SAPALDIA3 sample showed fewer subjects in the third class .At both time points SAPALDIA3 & 4) the main values of the BIC and AIC reached its minimum in the model consisting of three classes. The adjusted BIC as well as the CAIC supported these results , while at SAPALDIA3 this class consisted of 29% of the participants.Class 3, labeled “Unhealthy” had an overrepresentation of individuals that were obese, in contrast to the other classes that contained no obese participants. Accordingly, participants in class 3 were more likely to have a high percentage of body fat, to be hypertensive, to have high glycemia and triglyceride values. In the SAPALDIA4 sample 29% was assigned to this class, while in the SAPADALDIA3 sample only 15%.In regards to the socio-demographic distribution of the latent classes at SAPALDIA4 Table  we obserThe results of the quantile regression analyses Table A showed Participants with MetS showed associations in the same direction with HRQoL domains as the Unhealthy group Table B. NeitheFigure The association patterns for the composite MetS/physical activity variables with HRQoL Fig. B were coThe exact coefficients for Fig. The longitudinal results are displayed in Table In Table Table The risk of hospitalization was also strongest for the group MetS & inactive ) and No MetS & inactive group ), compared to the reference group . This longer-term clustering of physiological functioning is likely the result of temporal tracking of lifestyle and behavioral habits. The class termed “Unhealthy” characterized with a high percentage of participants with obesity and a high percentage of body fat in the presence of cardio-metabolic pathophysiology was associated with considerably lower HRQoL scores in all physical component scores of HRQoL. The largest difference to the “Healthy” reference class in terms of median scores was found for the PF and BP domains, which were also lower in the “At risk” class. Differences in HRQoL between classes were larger for physical compared to mental health components. Yet, the VT subdomain, belonging to the mental health component also showed considerable differences for the Unhealthy class compared to the Healthy class. Even though the coefficients were smaller and in part statistically not significant in the longitudinal analysis, we found similar results as in the cross-sectional analyses. These consistent results give an indication on prospective pathways leading from the latent classes to decreased HRQoL. As we adjusted for HRQoL at baseline (SAPALDIA3), we may also argue that reverse causality has been partly excluded, even though much more evidence is needed to prove this hypothesis. Of interest from a primary prevention perspective is the fact that the observed cross-sectional and longitudinal associations were not driven by the sub-group of persons who had already progressed to a clinical diagnosis of cardiovascular disease. Our results thus underline the relevance and also opportunity to improve HRQoL at older age through interventions that promote a healthy lifestyle in the “middle-aged” group.The risk factors being more prevalent in members of the “Unhealthy Class” agreed well with the cluster of risk factors defining the MetS . AlthougIn this study, we were specifically interested in assessing whether the promotion of physical activity in aging populations can attenuate the short- and longer-term adverse consequences of poor physiological functioning and of the MetS on HRQoL. As previously reported, physical activity levels were high in this population (73% of participants meeting WHO guidelines ), but alWe observed in a remarkably consistent way that physical inactivity was associated with lower HRQoL scores in many domains, both in all latent classes and in persons with and without MetS. For the physical functioning domains, HRQoL scores were mostly lowest in physically inactive persons assigned to the Unhealthy class as well as combined with the presence of the MetS. These results are in line with the few randomized controlled trials demonstrating the benefit of physical activity for HRQoL in subjects with MetS or cardiovascular diseases –50.The direct associations of physical activity with HRQoL and the moderating influence of physical activity on the association of the “Unhealthy” latent class as well as of MetS with HRQoL underline the pivotal role of physical activity in preventing and mitigating diseases . PhysicaResults from previous research on the association of single components making up the “Unhealthy” class with HRQoL domains are mostly consistent with our findings , 12. EveIn this study, we observed a clear socio-economic gradient across the three latent classes, with participants at the lowest educational level being over-represented in the class with the lowest HRQoL scores. Socio-economic gradients have previously been identified for obesity and associated lifestyles –62, for The information on socio-economic status and physical activity were self-reported by the participants, which could lead to measurement errors. The fact that participants at SAPALDIA3 were interviewed, whereas at SAPALDIA4 participants self-administered questionnaires, could have introduced differential misclassification of physical activity. But it is unlikely that this would have altered the main messages of our study, given the consistency of longitudinal and cross-sectional results. All other variables included in the analysis were objectively assessed. Triglyceride and glycemia tests and the type of blood as test source (venous versus capillary blood) differed between SAPALDIA3 and SAPALDIA4. While this could again be the source of differential misclassification over time, the consistency of the cross-sectional and longitudinal results suggests this bias to be minimal. The variables reflecting cardio-metabolic physiological functioning were chosen based on the evidence reported in the literature. Our current LCA may furthermore be sensitive to the chosen categories for the predictor variables. Yet, the consistency in results between latent classes and MetS points to the fact that the chosen cutoffs for categorization are clinically relevant. As in any cohort, we cannot exclude bias due to loss-to-follow-up. Several of the factors leading to or included in the categorization of participant’s physiological functioning influenced participation at follow-up. Persons with healthy habits were more likely to participate in SAPALDIA4. Older persons were less likely to participate at follow-up, in part because they had died. It is therefore likely that the effect of poor health habits including obesity and physiological functioning on HRQoL was underestimated. Given this loss-to-follow-up, the SAPALDIA4 participants are most likely no longer representative of the current general population in Switzerland. Yet, with regard to physical activity the observed distribution in our study sample is highly comparable to the distribution recently observed in the population-based Swiss Health Survey. The generalizability of our findings to other study settings needs testing, particularly in the light of the very high physical activity level of the Swiss population in general .Despite these limitations, the findings of this study strongly support that it is possible to reduce the number of physiological functioning variables using LCA. The latent classes corresponded well with the cluster of the MetS, which underlines the significant interplay of these variables.The LCA was facilitated by access to data from the SAPALDIA participants, who are deeply characterized on various physiological domains. The population-based design of the study is an important prerequisite for the generalizability of the findings, at least within the Swiss setting, although participation and survivor bias always pose a threat to validity and generalizability of any long-running cohort. Finally, the longitudinal design of this study adds knowledge to possible pathways in the investigated associations.This study, investigating associations of HRQoL with cardio-metabolic physiological functioning parameters combined with physical activity status in a general population sample, emphasizes the relevance of promoting primary and secondary prevention to maintain well-being in aging populations. In a country like Switzerland with sufficient economic resources, the lack of investments into primary and secondary prevention  seems toFurthermore, the underrepresentation of primary prevention and screening programs in disease control leads to the clustering of factors among the lower social classes and therefore to inequality in HRQoL despite the strong economy of Switzerland. In the light of increased promotion of personalized medicine as opposed to public health and prevention programs, as well as imbalanced investments into medical treatments, our results may help to understand and guide how to avoid further widening of medical inequalities. This study primarily underlines the importance of physical activity promotion on health and quality of life outcomes of the factors making up the metabolic syndrome.Additional file 1: Additional Table 1. Baseline characteristics at SAPALDIA 1 of participants who reached the age of 55+ at the time of the SAPALDIA4 55+ health assessments, stratified by participation status in the current study. Additional Table 2. Summary of model fit indices for 1 to 6 latent classes at SAPALDIA4. Additional Table 3. Summary of model fit indices for 1 to 6 latent classes at SAPALDIA3. Additional Table 4. Proportions and class-specific probabilities for the 3 latent classes at SAPALDIA4. Additional Table 5. Proportions and class-specific probabilities for the 3 latent classes at SAPALDIA3. Additional Table 6. Descriptive differences in sex, age and education between the three latent classes, SAPALDIA4. Additional Table 6. Descriptive differences in sex, age and education between the three latent classes, SAPALDIA4. Additional Fig. 1. Descriptive differences of median HRQoL scores of the three latent classes  at SAPALDIA4. Additional Table 7. Cross-sectional adjusted HQRoL associations of categories combining physical activity with latent classes (A7A) as well as metabolic syndrome (A7B) (SAPALDIA4). Additional Table 8. Cross-sectional associations of composite variable categories defined by latent classes (A8A) and metabolic syndrome (A8B), respectively, and physical activity status, with median levels of HRQoL, subjects not reporting any cardiovascular disease (SAPALDIA4). Additional Table 9. Prospective associations of composite variable categories defined by latent classes (A9A) and metabolic syndrome (A9B), respectively, and physical activity status at SAPALDIA3, with median levels of HRQoL at SAPALDIA4, adjusted for respective HRQoL domain at SAPALDIA3, subjects not reporting any cardiovascular at SAPALDIA4. Additional Table 10. Cross-sectional associations of composite variable categories defined by latent classes (A10A) and metabolic syndrome (A10B), respectively, and physical activity status with health service utilization in the last 12 months, subjects not reporting any cardiovascular (SAPALDIA4).


Hip fractures constitute a major health problem in elderly people and are often fall-related. Several factors can contribute to a fall episode leading to hip fracture, including fall-risk-increasing drugs (FRIDs), which are often used by elderly people.We aimed to investigate the prevalence of medication-related falls and to assess the role of FRIDs and potentially inappropriate medications (PIMs) in a population of elderly patients hospitalized for a hip fracture.We reviewed the patient records of 200 consecutive patients, aged ≥65 years, who were admitted for a hip fracture and evaluated whether medications were likely to have contributed to the fall episode. PIMs were identified using the Screening Tool of Older Persons’ Prescriptions version 2 (STOPP) and by evaluating indications, contra-indications and interactions of the prescribed medications for each patient.FRIDs were used by 175 patients (87.5%). Medications were considered a likely contributor to the fall in 82 patients (41%). These were most often psychotropic medications alone or in combination with antihypertensives and/or diuretics. The 82 patients with suspected medication-related falls used more medications, FRIDs and PIMs than the rest of the patients, and in 74 (90%) of the 82 patients, at least one medication considered to be a contributor to the fall was also a PIM.The prevalence of suspected medication-related falls was 41%. It seems likely that a medication review could have reduced, though not eliminated, the risk of falling in this group of patients. A hip fracture is associated with considerable socio-economic costs  and consGiven the high prevalence of FRIDs and PIMs among elderly patients, it may be hypothesized that medication reviews and interventions to reduce FRIDs and PIMs in this group would effectively reduce the risk of falling. However, two recent randomized studies investigating the effect of FRID-withdrawal among more than 600 elderly people experiencing a fall , and theEven though the prevalence of FRID users among patients with a hip fracture is high, it is not known how often FRIDs actually contribute to the fall. This knowledge is essential to understanding how much the incidence of hip fracture could potentially be reduced by stopping the use of these medications. Thus, the aims of our study were to estimate the prevalence of medication-related falls leading to a hip fracture in a population of elderly patients admitted to a joint orthopaedic and geriatric ward and to assess the role of FRIDs and PIMs.This study was a retrospective cross-sectional study. Two hundred consecutive patients with hip fracture, aged 65 years or older, admitted to a Danish University Hospital during a period of 24 weeks in 2017 were identified by a search in the hospital’s database using the ICD-10 codes for fracture of the femur (DS72-DS729).A consultant in Clinical Pharmacology (CUA) reviewed all patient records, focusing on 1) the description of the fall episode, including fall-related symptoms and the conclusions regarding the causes of the fall, made by the attending geriatrician during admission; 2) comorbidities, demographic data and medication list at the time of admission; 3) blood pressure, respiratory frequency, peripheral saturation, body temperature and heart rate at the time of and during admission; 4) laboratory data including c-reactive protein, leucocyte count, electrolytes, haemoglobin, liver and renal parameters, and blood glucose; 5) the results of other diagnostic evaluations performed during admission; and 6) medication withdrawals or changes during admission.FRIDs were defined in accordance with previous work on fall-risk-related medications , 17, 18:The clinical pharmacologist excluded a suspected medication-related fall if a patient had not experienced a fall, was not using any medications or FRIDs, or if a non-medication-related fall cause was described in the patient record. Otherwise, the clinical pharmacologist and a consultant in Geriatrics  discussed the case in order to obtain a consensus about whether one or more medications were likely contributors to the fall. Medications were generally considered likely contributors to the fall if their effects, adverse effects or interactions could have caused or aggravated symptoms or clinical findings related to the fall episode, for example, orthostatic hypotension. If we concluded that medications were likely contributors to the fall, we defined the patient as having had a suspected medication-related fall. If the fall was more likely explained by the consequences of acute or chronic disease, tripping or extrinsic factors, we did not consider medications likely contributors. Patients who were not attended by an on-site geriatrician during admission were evaluated retrospectively following the same procedure as that used for the rest of the patients.Study data were collected and managed using REDCap  electronic data capture tools hosted at Aalborg University. REDCap is a secure, web-based application designed to support data capture for research studies . Data wen = 21 (10.5%)), followed by the presence of low blood pressure or symptoms of orthostatic hypotension in patients who were using medications able to decrease blood pressure (n = 17 (8.5%)). Medications were suspected to contribute to a fall by impairing the functional level in 12 patients (6%) and to contribute to accidental happenings leading to a fall by influencing cognition or balance in 11 patients (5.5%). In both instances, psychotropic drugs were the most commonly involved. Diuretics were thought to play a role by inducing hyponatremia in four patients (2%). Finally, the circumstances of the fall episode were not well described in 17 patients (8.5%), but the presence of FRIDs and no other obvious causes of the fall led us to define these as having had a suspected medication-related fall.A fall preceded the hip fracture in 197 (98.5%) patients, and the fall was considered a low energy trauma, indicating an osteoporotic fracture, in 175 (87.5%) patients. Three patients (1.5%) (group 1) did not experience a fall, and eight (4%) (group 2) did not take any medication. In 59 patients (29.5%) (group 3), the fall episode seemed more likely to have been caused by an extrinsic factor, such as a push by a large animal or another person, or tripping. Consequences of a chronic or acute disease, such as influenza, urinary tract infection or uncontrolled atrial fibrillation, were considered the main cause of the fall in 48 patients (24%) (group 4). Finally, we considered 82 (41%) patients as having had a suspected medication-related fall (group 5)  of the patients in the study population during admission.A summary of the populations’ demographic and clinical characteristics is shown in Table One or more comorbidities were present in 195 (97.5%) patients, and the most common comorbidities were hypertension, osteoporosis and atrial fibrillation Table .Table 2The median number of prescribed medications used at admission was seven, ranging from 0 to 27. At least 1 FRID was prescribed to 175 (87.5%) patients, and the median number of used FRIDs was three, ranging from zero to seven. A detailed list of medications used at the time of admission is available in Table The group of patients with a suspected medication-related fall was markedly older than the group in which an extrinsic factor or tripping was considered the most likely reason group 3). Otherwise, only the number of prescribed medications and FRIDs at the time of admission and the proportion of PIMs were significantly higher in patients with a suspected medication-related fall than in patients in the other groups  patients, and more than one medication was considered likely to a role in the fall episode in 66 (80.5%) of the 82 patients with a suspected medication-related fall. All medications suspected to contribute to falls were FRIDs. Psychotropic medications, including antidepressants, benzodiazepines, benzodiazepine-like medications, antipsychotics, antiepileptics and opioids, were suspected of contributing in 68 (83%) patients, whereas antihypertensive medications and diuretics likely played a role in 30 (36.5%) and 29 (35%) of the 82 patients with a suspected medication-related fall, respectively. The medication subclasses likely to have contributed to the fall episodes in more than five patients are shown in Fig. p < 0.001). In 74 (90%) of the 82 patients with a suspected medication-related fall , PIMs were suspected to have contributed directly to the fall leading to admission. A single medication was considered both a PIM and a contributor to the fall in 36 (44%) patients, and more than one medication was considered a PIM and contributor to the fall in 38 (46%) of the 82 patients with a suspected medication-related fall. In 42 (51%) of the patients with a suspected medication-related fall, the number of medications found likely to be involved in the fall episode was higher than the number of identified PIMs.PIMs were identified in 141 patients (70.5%). Among patients with a suspected medication-related fall, PIMs were identified in 79 (96%) patients compared to 62 (52%) of the 118 patients without a suspected medication-related fall  of the total population. Still, intervention studies are warranted and the present data may support planning of such studies.Additional file 1: Table S1. Medications used at the time of admission


Semantic textual similarity is a common task in the general English domain to assess the degree to which the underlying semantics of 2 text segments are equivalent to each other. Clinical Semantic Textual Similarity  is the semantic textual similarity task in the clinical domain that attempts to measure the degree of semantic equivalence between 2 snippets of clinical text. Due to the frequent use of templates in the Electronic Health Record system, a large amount of redundant text exists in clinical notes, making ClinicalSTS crucial for the secondary use of clinical text in downstream clinical natural language processing applications, such as clinical text summarization, clinical semantics extraction, and clinical information retrieval.Our objective was to release ClinicalSTS data sets and to motivate natural language processing and biomedical informatics communities to tackle semantic text similarity tasks in the clinical domain.We organized the first BioCreative/OHNLP ClinicalSTS shared task in 2018 by making available a real-world ClinicalSTS data set. We continued the shared task in 2019 in collaboration with National NLP Clinical Challenges (n2c2) and the Open Health Natural Language Processing (OHNLP) consortium and organized the 2019 n2c2/OHNLP ClinicalSTS track. We released a larger ClinicalSTS data set comprising 1642 clinical sentence pairs, including 1068 pairs from the 2018 shared task and 1006 new pairs from 2 electronic health record systems, GE and Epic. We released 80% (1642/2054) of the data to participating teams to develop and fine-tune the semantic textual similarity systems and used the remaining 20% (412/2054) as blind testing to evaluate their systems. The workshop was held in conjunction with the American Medical Informatics Association 2019 Annual Symposium.r=.9010, r=.8967, and r=.8864, respectively. Most top-performing systems used state-of-the-art neural language models, such as BERT and XLNet, and state-of-the-art training schemas in deep learning, such as pretraining and fine-tuning schema, and multitask learning. Overall, the participating systems performed better on the Epic sentence pairs than on the GE sentence pairs, despite a much larger portion of the training data being GE sentence pairs.Of the 78 international teams that signed on to the n2c2/OHNLP ClinicalSTS shared task, 33 produced a total of 87 valid system submissions. The top 3 systems were generated by IBM Research, the National Center for Biotechnology Information, and the University of Florida, with Pearson correlations of The 2019 n2c2/OHNLP ClinicalSTS shared task focused on computing semantic similarity for clinical text sentences generated from clinical notes in the real world. It attracted a large number of international teams. The ClinicalSTS shared task could continue to serve as a venue for researchers in natural language processing and medical informatics communities to develop and improve semantic textual similarity techniques for clinical text. Semantic textual similarity (STS) is a common task in the general English domain to assess the degree to which the underlying semantics of 2 segments of text are equivalent to each other. Equivalency is usually assessed using ordinal scaled output ranging from complete semantic equivalence to complete semantic dissimilarity. Applications of STS include machine translation, summarization, text generation, question answering, short answer grading, semantic search, and dialogue and conversational systems.Clinical Semantic Textual Similarity  is the application of STS techniques in the clinical domain that attempts to measure the degree of semantic equivalence between 2 snippets of clinical text. Due to the wide adoption of electronic health record (EHR) systems, a vast volume of free-text EHR data has been generated , such asStudies that evaluated and measured redundancy in clinical text  showed tThe STS shared task has been held annually since 2012 to encourage and support research in this area -10. HoweIn 2019, we continued the shared task as a collaboration with National NLP Clinical Challenges (n2c2) and the Open Health Natural Language Processing (OHNLP) consortium under the name n2c2/OHNLP track on ClinicalSTS . Our aimClinicalSTS provides paired clinical text snippets for each participant. The clinical text snippets are mostly sentences extracted from clinical notes. The participating systems are asked to return a numerical score indicating the degree of semantic similarity between the 2 sentences. Performance is measured by the Pearson correlation coefficient between the predicted similarity scores and human judgments. The ClinicalSTS scores fall on an ordinal scale, ranging from 0 to 5, where 0 means that the 2 clinical text snippets are completely dissimilar  and 5 means that the 2 snippets have complete semantic equivalence. Our previous publications ,13 showeWe collected the data set for the 2019 ClinicalSTS shared task from EHRs at the Mayo Clinic’s clinical data warehouse. Both the study and a waiver of informed consent were approved by the Mayo Clinic Institutional Review Board in accordance with 45 CFR 46.116 . Since the Mayo Clinic had completed a systemwide EHR transition across all care sites from GE Healthcare to Epic Systems Corporation, the data set in the 2019 shared task combined the data set from the 2018 shared task, which was an annotated subset of the MedSTS data set , and a nParticipating teams were required to sign a Data Use Agreement to get access to the challenge data set. Each team could submit up to 3 runs for the testing data, with every run having 1 line for each sentence pair that provided the similarity score assigned by the system as a floating-point number.Similar to the general STS shared tasks, ClinicalSTS used the Pearson correlation coefficient between the predicted scores and the reference standard on the testing set to evaluate the submitted systems. We released a public script computing the Pearson correlation coefficient to the participating teams.Our previous publication  providesr=.3709, followed by Levenshtein distance similarity with r=.2816 and Ratcliff/Obershelp with r=.2480.In addition, we used 3 surface lexical similarity methods as the baseline method to calculate the similarity scores in the testing data set, namely the Ratcliff/Obershelp pattern-matching algorithm, cosine similarity, and Levenshtein distance similarity. For more details about these baselines, please refer to our previous publication . The cosr=.9010, an 8.2% increase from the previous year’s best system. Overall, the median correlation score for the testing data set was r=.8291, a 3.4% increase from the previous year. We also compared the best run with other top systems using the Wilcoxon signed rank t test (P<.001).k t test . We founWe also compared the performance of valid systems for sentence pairs from GE and Epic EHR systems in the testing data set . OverallWe have given an overview of the 2019 n2c2/OHNLP ClinicalSTS shared task that aimed to measure the degree of semantic equivalence between 2 snippets of clinical text. We described how we prepared the data set in this year’s shared task differently from that in the previous year, the participating teams and their systems, and the results. We witnessed an increasing research interest in the ClinicalSTS task among the NLP and medical informatics communities and increased system performance for the task. We also observed several limitations during the data preparation. There were limitations in the reference standard data creation, particularly for annotating the medication-related sentence pairs in the data set. Concerns were raised by participating teams regarding the judgement for those pairs. We also found that some sentence pairs seemed to be semantically equivalent but were assigned low similarity scores. For example, sentence 1 is “Thank you for choosing the Name, APRN, C.N.P., M.S. care team for your health care needs!” and sentence 2 is “Thank you for choosing the Name, M.D. care team for your health care needs!” The reason for the score (4.0) is that the degree of the provider is different. The provider in the first sentence is a nurse, whereas that in the second sentence is a physician. Thus, these 2 sentences are not equivalent. Another example is sentence 1: “Thank you for choosing the Name M.D. care team for your health care needs!” and sentence 2: “Thank you for allowing us to assist in the care of your patient.” The reason for the score (2.0) is that the first sentence contains more details about the provider, whereas the second has fewer details.Although there was a record number of 87 valid systems participating in the shared task, this is still not large enough to be able to extrapolate statistical analysis results to draw a convincing conclusion. The performance difference of these participating systems in the sentence pairs from different EHR systems may be attributable to bias in the system and the sampling data set.In our future work, we might subcategorize the sentence pairs into different topics, such as medication or clinical workflow. We could provide tailored annotation guidelines according to the topic and invite subdomain experts with specific background  to review sentences pairs in different topics .r=.9010, an 8.2% increase from the previous year’s best system. We also compared the performance for sentences from both GE and Epic EHR systems and found better performance on the Epic sentence pairs than on the GE sentence pairs. The ClinicalSTS task remains challenging given the complexity of clinical texts. The ClinicalSTS shared task could continue to serve as a venue for researchers in NLP and medical informatics communities to develop and improve STS techniques for clinical text.ClinicalSTS is an important technique in many downstream clinical applications, such as clinical text summarization, clinical semantic information retrieval, and clinical decision support systems. In this paper, we provided an overview of the 2019 n2c2/OHNLP ClinicalSTS shared task that focused on computing semantic similarity for clinical text sentences generated from clinical notes in the real world. For this shared task, 33 international teams submitted a total of 87 valid systems. The top performing systems applied state-of-the-art NLP neural language models, such as BERT and XLNet, and state-of-the-art training schemas in deep learning, such as pretraining and fine-tuning schema. The best system used multitask learning and achieved a Pearson correlation coefficient of