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E-Learning Three-Dimensional Anatomy of the Brainstem: Impact of Different Microscopy Techniques and Spatial Ability.

ABSTRACT: Polarized light imaging (PLI) is a new method which quantifies and visualizes nerve fiber direction. In this study, the educational value of PLI sections of the human brainstem were compared to histological sections stained with Luxol fast blue (LFB) using e-learning modules. Mental Rotations Test (MRT) was used to assess the spatial ability. Pre-intervention, post-intervention, and long-term (1 week) anatomical tests were provided to assess the baseline knowledge and retention. One-on-one electronic interviews after the last test were carried out to understand the students' perceptions of the intervention. Thirty-eight medical students, (19 female and 19 males, mean age 21.5 ± SD 2.4; median age: 21.0 years) participated with a mean MRT score of 13.2 ± 5.2 points and a mean pre-intervention knowledge test score of 49.9 ± 11.8%. A significant improvement in both, post-intervention and long-term test scores occurred after learning with either PLI or LFB e-learning module on brainstem anatomy (both P < 0.001). No difference was observed between groups in post-intervention test scores and long-term test scores (P = 0.913 and P = 0.403, respectively). A higher MRT-score was significantly correlated with a higher post-intervention test score (r_k = 0.321; P < 0.05, respectively), but there was not a significant association between the MRT- and the long-term scores (r_k = -0.078; P = 0.509). Interviews (n = 10) revealed three major topics: Learning (brainstem) anatomy by use of e-learning modules; The "need" of technological background information when studying brainstem sections; and Mnemonics when studying brainstem anatomy. Future studies should assess the cognitive burden of cross-sectional learning methods with PLI and/or LFB sections and their effects on knowledge retention.

SUBMITTER: Cappellen van Walsum AV

PROVIDER: S-EPMC9292761 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Many biological processes involve the collective generation and transmission of mechanical stresses across cell monolayers. In these processes, the monolayer undergoes lateral deformation and bending because of the tangential and normal components of the cell-generated stresses. Monolayer stress microscopy (MSM) methods have been developed to measure the intracellular stress distribution in cell monolayers. However, current methods assume plane monolayer geometry and neglect the contribution of bending to the intracellular stresses. This work introduces a three-dimensional (3D) MSM method that calculates monolayer stress from measurements of the 3D traction stresses exerted by the cells on a flexible substrate. The calculation is carried out by imposing equilibrium of forces and moments in the monolayer, subject to external loads given by the 3D traction stresses. The equilibrium equations are solved numerically, and the algorithm is validated for synthetic loads with known analytical solutions. We present 3D-MSM measurements of monolayer stress in micropatterned islands of endothelial cells of different sizes and shapes. These data indicate that intracellular stresses caused by lateral deformation emerge collectively over long distances; they increase with the distance from the island edge until they reach a constant value that is independent of island size. On the other hand, bending-induced intracellular stresses are more concentrated spatially and remain confined to within one to two cell lengths of bending sites. The magnitude of these bending stresses is highest at the edges of the cell islands, where they can exceed the intracellular stresses caused by lateral deformations. Our data from nonpatterned monolayers suggests that biomechanical perturbations far away from monolayer edges also cause significant localized alterations in bending tension. The localized effect of bending-induced stresses may be important in processes like cellular extravasation, which are accompanied by significant normal deflections of a cell monolayer (i.e., the endothelium) and require localized changes in monolayer permeability.

Project description:Objective: Facial changes are common among nearly all acromegalic patients. As they develop slowly, patients often fail to notice such changes before they become obvious. Consequently, diagnosis and treatment are often delayed. So far, convenient and accurate early detection of this disease is still unavailable. This study is designed to combine the use of 3D imaging and machine learning techniques in facial feature analysis and identification of acromegalic patients, in an effort to ascertain how both techniques performed in terms of applicability and value in the early detection of the disease. Methods: One hundred and twenty-four participants including 62 patients with acromegaly and 62 matched controls were enrolled. Using three-dimensional imaging techniques, 58 facial parameters were measured on each face. A two-way analysis of variance (ANOVA) and a post-hoc t-tests were conducted to examine the variations of these parameters with disease status and gender. Using linear discriminant analysis (LDA), we further distinguished patients from controls, characterized what combinations of the parameters could best predict disease state and their relative contributions. Results: Patients are significantly different from normal subjects in many variables, and facial changes of male patients are more significant than female ones. Both male and female patients present following major changes: the increase of facial length and breadth, the widening and elevation of the nose, the thickening of vermilion and the enlargement of the mandible. Facial variables which strongly related to the pathological states can be used to predict the morbid state with high accuracy (prediction accuracies 92.86% in females, p < 0.0001 and 75% in males, p < 0.001). We have further testified that only a few variables play a vital role in disease prediction and the vital combination of variables vary with gender. Conclusions: Three-dimensional imaging enables comprehensive and accurate quantification of facial characteristics, which makes it a promising technique to investigate facial features of acromegalic patients. In combination with machine learning technique, patients can be accurately identified and predicted by their facial variables. This approach might be beneficial for the early detection of acromegalic patients and timely consultation to improve their outcomes.

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E-Learning Three-Dimensional Anatomy of the Brainstem: Impact of Different Microscopy Techniques and Spatial Ability.

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