Impaired cardiomyocyte contraction rate is detrimental to cardiac function and often lethal. Despite advancements in the field, there is a paucity of information regarding the coordination of molecules implicated in regulating the heart rate. Striatin (STRN) is a dynamic protein with binding domains to calmodulin (CaM) and caveolin (Cav), both of which are regulators of myocardial function. However, its role in cardiomyocyte contraction is not yet determined. Herein, we show that STRN is expressed in cardiomyocytes and is more abundant in atrial myocardium than in ventricles. Cardiac expression of STRN (protein and mRNA) was developmentally regulated with the highest expression being at neonatal stage (day one) and the lowest in adult rats (13 weeks). CaM pulldown assay indicated that the interaction of cardiac STRN with CaM and caveolin-3 (Cav-3) was calcium sensitive. Interestingly, the overexpression of STRN induced an increase (∼2-fold) in the rate of the spontaneous contraction of cultured cardiomyocytes, while the knockdown of STRN reduced their contraction rate (∼40%). The expression level of STRN was inversely proportional to the interaction of Cav-3 with the CaM/STRN complex. Collectively, our data delineate a novel role for STRN in regulating cardiomyocyte spontaneous contraction rate and the dynamics of the STRN/Cav-3/CaM complex.

Structural dilation of cardiomyocytes (CMs) imposes a decline in cardiac performance that precipitates cardiac failure and sudden death. Since membrane proteins are implicated in dilated cardiomyopathy and heart failure, we evaluated the expression of the sarcolemmal membrane-associated protein (SLMAP) in dilated cardiomyopathy and its effect on CM contraction. We found that all 3 SLMAP isoforms (SLMAP-1, -2, and -3) are expressed in CMs and are downregulated in human dilated ventricles. Knockdown of SLMAPs in cultured CMs transduced with recombinant adeno-associated viral particles releasing SLMAP-shRNA precipitated reduced spontaneous contractile rate that was not fully recovered in SLMAP-depleted CMs challenged with isoproterenol (ISO), thus phenotypically mimicking heart failure performance. Interestingly, the overexpression of the SLMAP-3 full-length isoform induced a positive chronotropic effect in CMs that was more pronounced in response to ISO insult (vs. ISO-treated naïve CMs). Confocal live imaging showed that H9c2 cardiac myoblasts overexpressing SLMAP-3 exhibit a higher intracellular calcium transient peak when treated with ISO (vs. ISO-treated cells carrying a control adeno-associated viral particle). Proteomics revealed that SLMAP-3 interacts with the regulator of CM contraction, striatin. Collectively, our data demonstrate that SLMAP-3 is a novel regulator of CM contraction rate and their response to adrenergic stimuli. Loss of SLMAPs phenotypically mimics cardiac failure and crystallizes SLMAPs as predictive of dilated cardiomyopathy and heart failure.

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The increasing use of three-dimensional scans in dentistry motivates the use of artificial intelligence systems in several clinical scopes. One of these scopes is implant dentistry, which is a complex process with multiple criteria of success. The dentist analyzes cone-beam computed tomography (CBCT) images and formulates a detailed implant treatment plan by specifying the length, diameter, position, and angulation of implants while considering the prosthodontic treatment plan, bone morphology, and position of adjacent vital anatomical structures. A supervised deep learning model was built to determine the optimum implant position, angulation, diameter, and length as dictated by prosthetic and anatomical requirements. The main goal of building this model was to facilitate the planning procedure and reduce the planning time of implant treatment, especially in multiple implant cases. This study had two phases; the first was detecting and placing a bounding box around the fiducial markers and adjacent bone within the CBCT images by training YOLOv3 model. The second phase trained six deep regression models to extract the apical and occlusal coordinates (X, Y, Z) of the implants; each model predicted one target value. In addition, two deep classification models were developed for predicting the implants’ intra-osseous length and intra-osseous diameter. After testing these models, YOLOv3 in the first phase showed 46.78\% average precision; in the regression model, the mean absolute error ranged from 11.931 to 15.954, and the classification of the intra-osseous diameter showed 76\% accuracy, and the intra-osseous length showed 59\% accuracy. A subjective assessment by an expert prosthodontist found the results to be promising regarding selection of implant size, and mesiodistal placement of the implants. However, angulation and vertical and bucco-lingual position of the implants was unacceptable. Thus, further investigations and model training are required to improve the accuracy of the output.

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