Accommodative Intraocular Lenses (AIOLs) have long been thought to be a promising solution for restoring visual accommodation after cataract surgery, but their performance critically depends on both the geometric design and the selected materials. The present study aims to compare how haptic design and material combination affect the simulated paraxial power response and mechanical behavior of selected dual-optic AIOL configurations under prescribed radial compression. Twelve dual-optic AIOL finite element models were constructed by combining three different haptic designs (non-folded, mono-folded, and multi-folded) with four different combinations of materials. A prescribed radial inward displacement was applied to each model to analyze the deformation, reaction force, and stress distributions. The resulting deformations were then used for modeling optical ray tracing to evaluate optical power changes. Comparative analyses were conducted to assess the influence of haptic design and material parameters on the paraxial power response and mechanical performance of the AIOLs. Both the haptic design and optic materials affect the optomechanical behavior of the AIOL. At a common radial displacement, multi-folded and mono-folded haptics yield greater power change than non-folded haptics. The multi-folded design yields greater power change per unit force than the mono-folded design. Material combinations also influence refractive power changes. High stresses were concentrated at the peripheral region of the anterior optic for mono- and multi-folded designs and at the junction between posterior optic and haptic for the non-folded design. Under the same applied radial displacement, the multi-folded haptic design provides greater power change per unit force than the mono-folded and non-folded designs. The simulated paraxial power response and biomechanical behavior of the dual-optic AIOL are jointly determined by haptic design and material selection, which should be considered in future AIOL development.
Women represent more than half the U.S. workforce, yet women’s health research and education remain systematically underprioritized, hindering technological innovation in this domain. Recent policy initiatives have elevated the profile of women’s health and created urgency for engineering education to align with this shift. Despite growing recognition that advanced engineering approaches are essential to address sex-specific diseases and physiological differences, most biomedical engineering students graduate without exposure to women’s health as a thematic focus or to the clinical problems and existing solutions in this space. To address this gap, we present a best practices guide based on six independently designed women’s health engineering courses. Drawing on pedagogical literature and collective teaching experience, we document core elements and provide practical frameworks for both standalone women’s health courses and integration into existing biomedical engineering curricula. This work is designed to lower the barrier for action, such that institutions can implement women’s health engineering education scaled to their capacity and constraints. By training engineers to recognize and address systematic gaps in women’s health engineering, we prepare them to build the life-changing technologies that women have long been waiting for.
Primary mitral regurgitation (MR) resulting from chordal rupture leads to acute volume overload and elevated left atrial pressure. Transcatheter edge-to-edge mitral valve repair (TMVr) devices can effectively reduce MR, but their impact on global cardiac mechanics and diastolic filling remains difficult to predict. This study develops a whole-heart FSI framework for TMVr by extending a previously developed whole-heart model to represent chordal rupture and edge-to-edge repair and to quantify their structural and hemodynamic consequences. A three-dimensional FSI model of the heart derived from cardiac CT imaging was used to simulate three conditions: healthy baseline, chordal rupture-induced MR, and post-TMVr. The immersed finite element–difference method enables fully coupled simulation of blood flow, valve dynamics, and myocardial contraction. Hemodynamic metrics, including regurgitant fraction (RF), left atrial pressure (LAP), stroke volume (SV), stroke work (SW), and pressure–volume relationships, were compared with clinical reference data. Chordal rupture produced moderate-to-severe MR (RF = 38
Accurate preoperative assessment of lymph node metastasis (LNM) in early gastric cancer (EGC) is critical for choosing endoscopic or surgical treatment. Most computational pathology studies rely on resection specimens obtained after curative gastrectomy, which inherently limits their preoperative relevance. Our study developed a multimodal artificial intelligence (AI) framework to estimate LNM risk using preoperative whole slide images (WSIs) of endoscopic biopsies, aiming to assist clinicians in optimizing treatment strategies before the surgery and reducing unnecessary surgical risks for patients. We developed a multimodal transformer-based model with a clinical-guided co-attention mechanism to fuse pathological features and clinical variables. To overcome the information scarcity inherent in fragmented biopsy specimens, we extracted mucosa and submucosa regions from WSIs of surgical tissues to serve as pseudo-biopsies. A curriculum learning (CL) paradigm was further employed to transition the model from idealized pseudo-biopsies to real-world biopsy samples. Through the analysis of a dataset comprising 1,099 WSIs of surgical tissues and 189 WSIs of endoscopic biopsies, our framework achieved an area under the curve (AUC) of 0.759 on WSIs of surgical tissues and an AUC of 0.727 on preoperative WSIs of endoscopic biopsies. Our findings demonstrate the feasibility of a multimodal AI framework tailored for endoscopic biopsy WSIs in preoperatively assessing LNM risk in T1 EGC. As an investigational clinical decision-support tool, this framework holds promising potential to assist clinicians in preoperative risk stratification, serving as a preliminary step toward optimizing prospective surgical management.
Impedance pumping offers a valveless flow generation mechanism with potential applications spanning microfluidic biomedical systems such as lab-on-chip and organ-on-chip devices, where membrane actuation is commonly used to manipulate fluid flow. Previous studies have proposed various mechanisms underlying impedance pumping, such as resonance, even in linear systems. However, the relative contributions of nonlinearity, wave dynamics, and resonance to net flow generation remain poorly understood, limiting optimal design of these systems. A comprehensive set of fluid-structure interaction numerical simulations was performed on a mini-sized rectangular, membrane-actuated pump model. Each simulation employed three-dimensional, time-dependent, laminar, incompressible Navier-Stokes equations coupled with nonlinear, large-deformation membrane equations. The small-scale model resulted in low-Reynolds number and high-Womersley-number conditions, a previously under-explored regime relevant to such microfluidic biomedical applications. While previous studies have examined these mechanisms separately, none have established an explicit link between them. Three key findings emerge: (1) nonlinearity via steady streaming directly causes net flow rate (NFR) generation, (2) resonance and wave dynamics amplify velocity oscillations and enhance nonlinear effects but do not directly generate NFR, and (3) optimal pincher placement must align with standing-wave antinodes, an outcome that has not been previously recognized. These findings reconcile apparently contradictory wave-based and nonlinearity-based explanations in the literature. This study provides novel mechanistic insight into the interaction between nonlinearity, resonance, and wave dynamics in valveless pumping. The antinode placement criterion and clarification of how nonlinearity, resonance, and wave dynamics interaction provide qualitative design guidelines for impedance pump design in microfluidic biomedical devices.
