Rabeeh Majidi, PhD

@rabeehm.bsky.social

203 Followers 152 Following

President and CEO @Orthokintrack, ex-Lead R&D Eng @Sensata ,ex-Research fellow @Harvard ,Alumni @UofR @WPI Social Media Committee @orssociety.bsky.social #ORSSMC, femvocate, she/her

Methodology for computing stress-time exposure from the discrete element analysis (DEA) computed contact stresses. For each increment of the gait cycle, contact stresses (top row) were multiplied by the length of time spent in that loading configuration to obtain contact stress-time data (middle row). These contact stress-time data were summed across the gait cycle to obtain a per-step value, which was then scaled by both an assumed 2 million steps per year and either (1) patient age at the time of surgery for preoperative models or (2) time from periacetabular osteotomy (PAO) until most recent outcome measures for postoperative models (bottom row). This summation and scaling process provided a single measure of contact stress-time exposure.

A flowchart showing the allocation of mice in this study. All mice were allowed weight-bearing with immobilization during their cage activities.

In vivo plantar flexor contractile function. (A) Peak isometric-torque normalized to body mass was lower in VML-0.6 g mice compared with VML-NoTx mice (p = 0.030). (B) Peak isometric-torque normalized to gastrocnemius muscle mass. (C) Isokinetic power varied with contractile velocity, independent of group. (D) Passive stiffness about the ankle joint at 20° dorsiflexion was greater in VML-0.6 g mice compared with both VML-NoTx and VML-1.0 g mice (p = 0.008). Individual points represent each mouse; data in (A), (B), and (D) analyzed by one-way ANOVA with Tukey's HSD and data in (C) analyzed by repeated measures ANOVA. Mean ± SD, p < 0.05 significant difference noted by * (different than VML-No-Tx) and # (different than both VML-NoTx and VML-1.0 g).