Laparoscopic Sleeve Gastrectomy: 2 cm versus 6 cm Distance from the Pylorus

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Fluid Structure Interaction (FSI) simulation of the human eye under the air puff tonometry using Computational Fluid Dynamics (CFD)

The air puff test is a non-contact method used in different areas to investigate the material behaviour or the biomechanical properties of biological tissues such as skin, cornea, and soft tissue tumours and also to study fruit firmness or meat tenderness. For the human eye, having a valid and fully coupled numerical simulation of the air puff test is very helpful and can greatly benefit to reduce a lot of time and cost of experimental testing. The gab in research in this area is considering the fluid structure interaction effect between the cornea, the air puff and the eye internal fluid. The simulation of the air puff test on the human eye is a Multi-physics problem which means; coupling between different numerical models and solvers with different governing equations and exchanging the data between them during the solution. A Computational Fluid Dynamics (CFD) model has been generated for an impinging air jet of maximum velocity of 168 m/s over a time span of 30ms and a coupling between the CFD model and the Finite Element (FE) model of the human eye has been successfully achieved for accurate simulation of the Fluid Structure Interaction (FSI) effect on the human eye cornea deformation. The air puff test is a non-contact method used in different areas to investigate the material behaviour or the biomechanical properties of biological tissues such as skin, cornea, and soft tissue tumours and also to study fruit firmness or meat tenderness. For the human eye, having a valid and fully coupled numerical simulation of the air puff test is very helpful and can greatly benefit to reduce a lot of time and cost of experimental testing. The gab in research in this area is considering the fluid structure interaction effect between the cornea, the air puff and the eye internal fluid. The simulation of the air puff test on the human eye is a Multi-physics problem which means; coupling between different numerical models and solvers with different governing equations and exchanging the data between them during the solution. A Computational Fluid Dynamics (CFD) model has been generated for an impinging air jet of maximum velocity of 168 m/s over a time span of 30ms and a coupling between the CFD model and the Finite Element (FE) model of the human eye has been successfully achieved for accurate simulation of the Fluid Structure Interaction (FSI) effect on the human eye cornea deformation

Fluid Structure Interaction (FSI) Simulation of the human eye under the air puff tonometry using Computational Fluid Dynamics (CFD)

The air puss test is a non-contact method used in different areas to investigate the material behaviour or the biomechanical properties of biological tissues such as skin, cornea, and soft tissue tumours and also to study fruit rmness or meat tenderness. For the human eye, having a valid and fully coupled numerical simulation of the air puff test is very helpful and can greatly bene t to reduce a lot of time and cost of experimental testing. The gab in research in this area is considering the uid structure interaction effect between the cornea, the air puff and the eye internal uid. The simulation of the air puff test on the human eye is a Multi-physics problem which means; coupling between different numerical models and solvers with different governing equations and exchanging the data between them during the solution. A Computational Fluid Dynamics (CFD) model has been generated for an impinging air jet of maximum velocity of 168 m/s over a time span of 30ms and a coupling between the CFD model and the Finite Element (FE) model of the human eye has been successfully achieved for accurate simulation of the Fluid Structure Interaction (FSI) effect on the human eye cornea deformation

Role of impinging jets in the biomechanical correction of the intraocular pressure ( IOP ) measurement

Glaucoma is one of the ocular diseases which develops when the eye internal fluid cannot drain properly and intraocular pressure builds up. This can result in damage to the optic nerve and the nerve fibers from the retina and early diagnosis is very important as any damage to the eyes cannot be reversed [1]–[3]. Non-contact IOP measurement techniques like corneal response analyzers including CorVis-ST are very popular. The technique depends on impingement of an air puff to the cornea and recording the corneal response to the impact force from the puff using high speed Scheimpflug imaging. The aim of this study is to improve the accuracy of the IOP measurements by considering the fluid structure interaction effect between the cornea, the air puff and the eye internal fluid through a parametric study of numerical models and their comparisons with the clinical data

Patient-specific air puff-induced loading using machine learning

Introduction: The air puff test is a contactless tonometry test used to measure the intraocular pressure and the cornea’s biomechanical properties. Limitations that most challenge the accuracy of the estimation of the corneal material and the intraocular pressure are the strong intercorrelation between the intraocular pressure and the corneal parameters, either the material properties that can change from one person to another because of age or the geometry parameters like central corneal thickness. This influence produces inaccuracies in the corneal deformation parameters while extracting the IOP parametric equation, which can be reduced through the consideration of the patient-specific air puff pressure distribution taking into account the changes in corneal parameters. This air puff pressure loading distribution can be determined precisely from the fluid-structure interaction (FSI) coupling between the air puff and the eye model. However, the computational fluid dynamics simulation of the air puff in the coupling algorithm is a time-consuming model that is impractical to use in clinical practice and large parametric studies. Methods: By using a supervised machine learning algorithm, we predict the time-dependent air puff pressure distribution for different corneal parameters via a parametric study of the corneal deformations and the gradient boosting algorithm. Results: The results confirmed that the algorithm gives the time-dependent air puff pressure distribution with an MAE of 0.0258, an RMSE of 0.0673, and an execution time of 93 s, which is then applied to the finite element model of the eye generating the corresponding corneal deformations taking into account the FSI influence. Using corneal deformations, the response parameters can be extracted and used to produce more accurate algorithms of the intraocular pressure and corneal material stress-strain index (SSI). Discussion: Estimating the distribution of air pressure on the cornea is essential to increase the accuracy of intraocular pressure (IOP) measurements, which serve as valuable indicator of corneal disease. We find that the air puff pressure loading is largely influenced by complex changes in corneal parameters unique to each patient case. With our innovative algorithm, we can preserve the same accuracy developed by the CFD-based FSI model, while reducing the computational time from approximately 101000 s (28 h) to 720 s (12 min), which is about 99.2% reduction in time. This huge improvement in computational cost will lead to significant improvement in the parametric equations for IOP and the Stress-Strain Index (SSI)

Long-Distance Three-Color Neuronal Tracing in Fixed Tissue Using NeuroVue Dyes

Dissecting development of neuronal connections is critical for understanding neuronal function in both normal and diseased states. Charting the development of the multitude of connections is a monumental task, since a given neuron typically receives hundreds of convergent inputs from other neurons and provides divergent outputs for hundreds of other neurons. Although progress is being made utilizing various mutants and/or genetic constructs expressing fluorescent proteins like GFP, substantial work remains before a database documenting the development and final location of the neuronal pathways in an adult animal is completed. The vast majority of developing neurons cannot be specifically labeled with antibodies and making specific GFP-expressing constructs to tag each of them is an overwhelming task. Fortunately, fluorescent lipophilic dyes have emerged as very useful tools to systematically compare changes in neuronal networks between wild-type and mutant mice. These dyes diffuse laterally along nerve cell membranes in fixed preparations, allowing tracing of the position of a given neuron within the neuronal network in murine mutants fixed at various stages of development. Until recently, however, most evaluations have been limited to one, or at most, two color analyses. We have previously reported three color neuronal profiling using the novel lipophilic dyes NeuroVue (NV) Green, Red and Maroon (Fritzsch et al., Brain. Res. Bull. 66:249–258, 2005). Unfortunately such three color experiments have been limited by the fact that NV Green and its brighter successor, NV Emerald, both exhibit substantially decreased signal intensities when times greater than 48 hours at 37°C are required to achieve neuronal profile filling (unpublished observations). Here we describe a standardized test system developed to allow comparison of candidate dyes and its use to evaluate a series of 488 nm-excited green-emitting lipophilic dyes. The best of these, NV Jade, has spectral properties well matched to NV Red and NV Maroon, better solubility in DMF than DiO or DiA, improved thermostability compared with NV Emerald, and the ability to fill neuronal profiles at rates of 1 mm per day for periods of at least 5 days. Use of NV Jade in combination with NV Red and NV Maroon substantially improves the efficiency of connectional analysis in complex mutants and transgenic models where limited numbers of specimens are available

Anticonvulsant Potential of Certain New (2 E

Anticonvulsant potential and neurotoxicity of certain new imidazole-containing arylsemicarbazones 6a–p are reported. The test compounds 6a–p exhibited anticonvulsant activity mainly in the scPTZ screen. Compound 6p emerged as the most active surrogate displaying 100% protection at a dose level of 636 μmol/kg in the scPTZ screen without any neurotoxicity. The assigned (E)-configuration of the title compounds 6a–p was confirmed via single crystal X-ray structure of compound 6g