Background Endothelial healing after deployment of cardiovascular devices is particularly important in the context of clinical outcome. It is therefore of great interest to develop tools for a precise prediction of endothelial growth after injury in the process of implant deployment. For experimental investigation of re-endothelialization in vitro cell migration assays are routinely used. However, semi-automatic analyses of live cell images are often based on gray value distributions and are as such limited by image quality and user dependence. The rise of deep learning algorithms offers promising opportunities for application in medical image analysis. Here, we present an intelligent cell detection (iCD) approach for comprehensive assay analysis to obtain essential characteristics on cell and population scale. Results In an in vitro wound healing assay, we compared conventional analysis methods with our iCD approach. Therefore we determined cell density and cell velocity on cell scale and the movement of the cell layer as well as the gap closure between two cell monolayers on population scale. Our data demonstrate that cell density analysis based on deep learning algorithms is superior to an adaptive threshold method regarding robustness against image distortion. In addition, results on cell scale obtained with iCD are in agreement with manually velocity detection, while conventional methods, such as Cell Image Velocimetry (CIV), underestimate cell velocity by a factor of 0.5. Further, we found that iCD analysis of the monolayer movement gave results just as well as manual freehand detection, while conventional methods again shows more frayed leading edge detection compared to manual detection. Analysis of monolayer edge protrusion by ICD also produced results, which are close to manual estimation with an relative error of 11.7%. In comparison, the conventional Canny method gave a relative error of 76.4%. Conclusion The results of our experiments indicate that deep learning algorithms such as our iCD have the ability to outperform conventional methods in the field of wound healing analysis. The combined analysis on cell and population scale using iCD is very well suited for timesaving and high quality wound healing analysis enabling the research community to gain detailed understanding of endothelial movement.
IntroductionStatins and exercise training (ET) improve endothelial function. The mechanisms by which statins in combination with ET alter vascular function are largely unknown. We investigated how statins influence the effects of ET on vascular function in obese rats.MethodsEight‐week‐old male Wistar rats (n = 46) received a high‐fat diet for 20 weeks. The rats were randomized after into four groups after eight weeks: sedentary (SED; n =11), exercise (EX; n=11), statin (STAT; n=13) and exercise with statin (EX/STAT; n=11). Simvastatin (10mg/d/kg) was administered in drinking water. The rats exercised for 12 weeks, 5 days/week for 1 h/day at 18 m/min. Endothelium‐dependent (acetylcholine [Ach]) and – independent (sodium‐nitroprusside [SNP]) vascular function was assessed in the abdominal aorta using in vitro myography. Gene expression and protein abundance of nitric‐oxide soluble guanylate cyclase (NO‐sGC) signaling was assessed using q‐PCR and Western blot.ResultsStatin treatment significantly reduced cholesterol levels and did not influence exercise duration or intensity. ET independent of statins improved endothelium dependent vasodilation in EX and EX/STAT compared to SED and STAT, respectively. In untreated rats ET did not alter SNP vasodilation. However, in statin treated animals, ET improved SNP vasodilation. ET and statins increased NO protein abundance by 40% and 80%, respectively, compared to SED. However, in treated rats ET reduced NO to 40% above SED. Protein phosphatase 1 regulatory subunit 14A (CPI17), an inhibitor protein of smooth muscle myosin phosphatase showed a significant statin and exercise interaction at gene expression level (p < .0001). In untreated rats, ET upregulated CPI17 gene expression by 50% (p < .05). In treated rats a downregulation of 35% (p < .05) was observed. The results were similar on the protein level. The myosin phosphatase target subunit 1 (MYPT1) which drives the cGMP independent smooth muscle myosin phosphatase calcium desensitization was increased due to statin treatment (40%) and not affected by ET. Statin reduced sGCα protein content by more than 80%. Protein kinase cGMP‐dependent 1 (PRKG1), which phosphorylates multiple targets implicated in modulating cellular calcium was significantly increased (50%; p < .05) in non‐treated but decreased in treated rats. Protein abundance of the sarco/endoplasmic reticulum Ca2+‐ATPase (SERCA), which drives the uptake of calcium into the sarcoplasmic reticulum was reduced by statins (50%) and not rescued by ET.ConclusionET independent of statin treatment improved endothelium‐dependent vasodilation. Interestingly, in untreated animals these improvements were most likely driven by NO, while in animals receiving statins, ET altered vascular smooth muscle calcium handling and desensitization. Overall our results suggests that individuals receiving statins should exercise to maintain vascular health.Support or Funding InformationDZHK (German Centre for Cardiovascular Research)This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.
Background Exercise training (ET) and statin treatment both alter skeletal muscle function. Purpose We investigated the effects of a combined exercise and statin use on skeletal muscle mitochondrial oxidative phosphorylation (OxPhos) and metabolic alterations in obese rats. Methods Eight-week-old male Wistar rats were used. A total of 14 animals received standard chow, while 46 rats were fed a high-fat diet (HFD) for 20 weeks. After 8 weeks, the rats were randomized into 6 groups: sedentary (n=8), ET (n=6), sedentary with HFD (n=11), ET with HFD (n=11), statin with HFD (n=13) and ET with HFD and statins (n=11). Simvastatin (10mg/d/kg) was added to the drinking water. ET was performed for 12 weeks, 5 days/week for 1 h/day at 18 m/min in a motorized running wheel. OxPhos was assessed by complex-specific antibodies and targeted metabolomics using the Biocrates p180 kit. All experiments were done on frozen samples of the M. gastrocnemicus. An ANOVA with fixed effects for diet, exercise, statin treatment and statin-exercise interaction was used to identify significantly different metabolites. Results Statin use was associated with significantly lower cholesterol levels, but did not affect exercise duration and intensity compared to none-use. In sedentary animals, HFD increased OxPhos complex II (succinate dehydrogenase), complex IV (cytochrome-c-oxidase) and V (ATP synthase) while statin treatment diminished this increase in all complexes. HFD increased complex IV independent of statin treatment but had no effect on complex II and V in ET rats. Complex IV was increased due to ET only in HFD fed rats compared to rats on normal chow but decreased in contrast to sedentary animals on a HFD. With regards to metabolomics, we found 57 metabolites which were influenced by HFD while no metabolites were identified with a significant effect for ET. A significant statin-exercise interaction was found for three lysophosphatidylcholines (lysoPC a C26.0, lysoPC a C26.1, lysoPC a C24.0), one phosphatidylcholine (PC aa C42.6) and one sphingomyelin (SM C16.1). HFD decreased the concentration of all mentioned metabolites compared to standard chow fed animals. Likewise, ET increased the concentration of metabolites compared to sedentary animals on HFD. Statin treatment led to an increase, while statin in combination with ET did not rescue this effect. Conclusion HFD induced severely impaired skeletal muscle OxPhos independent of ET and statin treatment. Our findings suggest a limiting rate of NADH production in the tricarboxylic acid cycle as a potential mechanism. However, ET prevented the increase in cytochrome-c-oxidation while statins blocked the HFD induced increase in ATP synthase. Our metabolomics results imply that future research should consider the lipotoxic effects of a HFD when assessing skeletal muscle alterations due to ET or statins. Of particular interest could be the 5 metabolites that have been shown to be impacted by a statin-exercise interaction.
BackgroundEndothelial healing after deployment of cardiovascular devices is particularly important in the context of clinical outcome. It is therefore of great interest to develop tools for a precise prediction of endothelial growth after injury in the process of implant deployment. For experimental investigation of re-endothelialization in vitro cell migration assays are routinely used. However, automatic analyses of live cell images are often based on gray value distributions and are as such limited by image quality and user dependence. The rise of deep learning algorithms offers promising opportunities for application in medical image analysis. Here, we present an intelligent cell detection (iCD) approach for comprehensive assay analysis to obtain essential characteristics on cell and population scale.ResultsIn an in vitro wound healing assay, we compared conventional analysis methods with our iCD approach. Therefore we determined cell density and cell velocity on cell scale and the movement of the cell layer as well as the gap closure between two cell monolayers on population scale. Our data demonstrate that cell density analysis based on deep learning algorithms is superior to an adaptive threshold method regarding robustness against image distortion. In addition, results on cell scale obtained with iCD are in agreement with manually velocity detection, while conventional methods, such as Cell Image Velocimetry (CIV), underestimate cell velocity by a factor of 0.5. Further, we found that iCD analysis of the monolayer movement gave results just as well as manual freehand detection, while conventional methods again shows more frayed leading edge detection compared to manual detection. Analysis of monolayer edge protrusion by ICD also produced results, which are close to manual estimation with an relative error of 13.2 %. In comparison, the conventional Canny method gave a relative error of 61 %. ConclusionThe results of our experiments indicate that deep learning algorithms such as our iCD have the ability to outperform conventional methods in the field of wound healing analysis. The combined analysis on cell and population scale using iCD is very well suited for timesaving and high quality wound healing analysis enabling the research community to gain detailed understanding of endothelial movement.
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