Schilaty, Nathan D

Background: Anterior cruciate ligament (ACL) injury reduction training has focused on lower body strengthening and landing stabilization. In vitro studies have shown that quadriceps forces increase ACL strain, and hamstring forces decrease ACL strain. However, the magnitude of the effect of the quadriceps and hamstrings forces on ACL loading and its timing during in vivo landings remains unclear. Purpose: To investigate the effect and timing of knee muscle forces on ACL loading during landing. Study Design: Descriptive laboratory study. Methods: A total of 13 young female athletes performed drop vertical jump trials, and their movements were recorded with 3-dimensional motion capture. Lower limb joint motion and muscle forces were estimated with OpenSim and applied to a musculoskeletal finite element (FE) model to estimate ACL loading during landings. The FE simulations were performed with 5 different conditions that included/excluded kinematics, ground-reaction force (GRF), and muscle forces. Results: Simulation of landing kinematics without GRF or muscle forces yielded an estimated median ACL strain and force of 5.1% and 282.6 N. Addition of GRF to kinematic simulations increased ACL strain and force to 6.8% and 418.4 N ( P < .05). Addition of quadriceps force to kinematics + GRF simulations nonsignificantly increased ACL strain and force to 7.2% and 478.5 N. Addition of hamstrings force to kinematics + GRF simulations decreased ACL strain and force to 2.6% and 171.4 N ( P < .001). Addition of all muscles to kinematics + GRF simulations decreased ACL strain and force to 3.3% and 195.1 N ( P < .001). With hamstrings force, ACL loading decreased from initial contact (time of peak: 1-18 milliseconds) while ACL loading without hamstrings force peaked at 47 to 98 milliseconds after initial contact ( P = .024-.001). The knee flexion angle increased from 20.9° to 73.1° within 100 milliseconds after initial contact. Conclusion: Hamstrings activation had greater effect relative to GRF and quadriceps activation on ACL loading, which significantly decreased and regulated the magnitude and timing of ACL loading during in vivo landings. Clinical Relevance: Clinical training should focus on strategies that influence increased hamstrings activation during landing to reduce ACL loads.

Weaker hamstrings muscular forces and lower ratio of the hamstrings/quadriceps muscular forces in female athletes have been identified as modifiable risk factors for anterior cruciate ligament (ACL) injuries. However, sex differences in athletes’ ability to react to visual cues (Choice Visual‐Motor Reaction Time: VMRT) and to generate knee muscular forces (rate of force development: RFD) immediately following the visual cues were largely unknown. Therefore, the purpose of the study was to examine sex differences in Choice VMRT and RFD. A total of 50 high school basketball athletes (26F/24 M) participated in the study. Subjects sat in the knee dynamometer chair with their knee secured at 70° of knee flexion and performed knee extension or flexion maximum voluntary isometric contractions immediately after they saw the visual cue: “UP” or “DOWN” arrows, respectively. Choice VMRT was defined as the time between the visual cue and the initiation of muscular force development (>5Newtons). RFD was calculated by dividing the changes in forces over the changes in time at four time points (0–50/100/150/200 ms). Peak muscular forces and RFD were normalized to their body mass. Average of three trials in each direction (flexion and extension) in each leg was used for statistical analyses. Females had significantly slower Choice VMRT (p < 0.001–0.027) and lower knee extension RFD at 100 ms (p = 0.005). In addition, females had significantly higher knee flexion/extension ratio than males in late RFD (150 ms and 200 ms) (p < 0.004). The current study has provided additional sensorimotor characteristics of athletes and sexes in addition to their knee muscular characteristics.

Background: Frontal plane trunk lean with a side-to-side difference in lower extremity kinematics during landing increases unilateral knee abduction moment and consequently anterior cruciate ligament (ACL) injury risk. However, the biomechanical features of landing with higher ACL loading are still unknown. Validated musculoskeletal modeling offers the potential to quantify ACL strain and force during a landing task. Purpose: To investigate ACL loading during a landing and assess the association between ACL loading and biomechanical factors of individual landing strategies. Study Design: Descriptive laboratory study. Methods: Thirteen young female athletes performed drop vertical jump trials, and their movements were recorded with 3-dimensional motion capture. Electromyography-informed optimization was performed to estimate lower limb muscle forces with an OpenSim musculoskeletal model. A whole-body musculoskeletal finite element model was developed. The joint motion and muscle forces obtained from the OpenSim simulations were applied to the musculoskeletal finite element model to estimate ACL loading during participants’ simulated landings with physiologic knee mechanics. Kinematic, muscle force, and ground-reaction force waveforms associated with high ACL strain trials were reconstructed via principal component analysis and logistic regression analysis, which were used to predict trials with high ACL strain. Results: The median (interquartile range) values of peak ACL strain and force during the drop vertical jump were 3.3% (–1.9% to 5.1%) and 195.1 N (53.9 to 336.9 N), respectively. Four principal components significantly predicted high ACL strain trials, with 100% sensitivity, 78% specificity, and an area of 0.91 under the receiver operating characteristic curve ( P < .001). High ACL strain trials were associated with (1) knee motions that included larger knee abduction, internal tibial rotation, and anterior tibial translation and (2) motion that included greater vertical and lateral ground-reaction forces, lower gluteus medius force, larger lateral pelvic tilt, and increased hip adduction. Conclusion: ACL loads were higher with a pivot-shift mechanism during a simulated landing with asymmetry in the frontal plane. Specifically, knee abduction can create compression on the posterior slope of the lateral tibial plateau, which induces anterior tibial translation and internal tibial rotation. Clinical Relevance: Athletes are encouraged to perform interventional and preventive training to improve symmetry during landing.

Parkinson’s disease is associated with degeneration of neuromelanin (NM)-containing substantia nigra dopamine (DA) neurons and subsequent decreases in striatal DA transmission. Dopamine spontaneously forms a melanin through a process called melanogenesis. The present study examines conditions that promote/prevent DA melanogenesis. The kinetics, intermediates, and products of DA conversion to melanin in vitro, and DA melanogenesis under varying levels of Fe3+, pro-oxidants, and antioxidants were examined. The rate of melanogenesis for DA was substantially greater than related catecholamines norepinephrine and epinephrine and their precursor amino acids tyrosine and l-Dopa as measured by UV-IR spectrophotometry. Dopamine melanogenesis was concentration dependent on the pro-oxidant species and Fe3+. Melanogenesis was enhanced by the pro-oxidant hydrogen peroxide (EC50 = 500 μM) and decreased by the antioxidants ascorbate (IC50 = 10 μM) and glutathione (GSH; IC50 = 5 μM). Spectrophotometric results were corroborated by tuning a fast-scan cyclic voltammetry system to monitor DA melanogenesis. Evoked DA release in striatal brain slices resulted in NM formation that was prevented by GSH. These findings suggest that DA melanogenesis occurs spontaneously under physiologically-relevant conditions of oxidative stress and that NM may act as a marker of past exposure to oxidative stress.

Background: Knee abduction moment during landing has been associated with anterior cruciate ligament (ACL) injury. However, accurately capturing this measurement is expensive and technically rigorous. Less complex variables that lend themselves to easier clinical integration are desirable. Purpose: To corroborate in vitro cadaveric simulation and in vivo knee abduction angles from landing tasks to allow for estimation of ACL strain in live participants during a landing task. Study Design: Descriptive laboratory study. Methods: A total of 205 female high school athletes previously underwent prospective 3-dimensional motion analysis and subsequent injury tracking. Differences in knee abduction angle between those who went on to develop ACL injury and healthy controls were assessed using Student t tests and receiver operating characteristic analysis. A total of 11 cadaveric specimens underwent mechanical impact simulation while instrumented to record ACL strain and knee abduction angle. Pearson correlation coefficients were calculated between these variables. The resultant linear regression model was used to estimate ACL strain in the 205 high school athletes based on their knee abduction angles. Results: Knee abduction angle was greater for athletes who went on to develop injury than for healthy controls ( P < .01). Knee abduction angle at initial contact predicted ACL injury status with 78% sensitivity and 83% specificity, with a threshold of 4.6° of knee abduction. ACL strain was significantly correlated with knee abduction angle during cadaveric simulation ( P < .01). Subsequent estimates of peak ACL strain in the high school athletes were greater for those who went on to injury (7.7-8.1% ± 1.5%) than for healthy controls (4.1-4.5% ± 3.6%) ( P < .01). Conclusion: Knee abduction angle exhibited comparable reliability with knee abduction moment for ACL injury risk identification. Cadaveric simulation data can be extrapolated to estimate in vivo ACL strain. Athletes who went on to ACL injury exhibited greater knee abduction and greater ACL strain than did healthy controls during landing. Clinical Relevance: These important associations between the in vivo and cadaveric environments allow clinicians to estimate peak ACL strain from observed knee abduction angles. Neuromuscular control of knee abduction angle during dynamic tasks is imperative for knee joint health. The present associations are an important step toward the establishment of a minimal clinically important difference value for ACL strain during landing.

Background: The most commonly damaged structures of the knee are the anterior cruciate ligament (ACL), medial collateral ligament (MCL), and menisci. Given that these injuries present as either isolated or concomitant, it follows that these events are driven by specific mechanics versus coincidence. This study was designed to investigate the multiplanar mechanisms and determine the important biomechanical and demographic factors that contribute to classification of the injury outcome. Hypothesis: Linear discriminant analysis (LDA) would accurately classify each injury type generated by the mechanical impact simulator based on biomechanical input variables (ie, ligament strain and knee kinetics). Study Design: Controlled laboratory study. Methods: In vivo kinetics and kinematics of 42 healthy, athletic participants were measured to determine stratification of injury risk (ie, low, medium, and high) in 3 degrees of knee forces/moments (knee abduction moment, anterior tibial shear, and internal tibial rotation). These stratified kinetic values were input into a cadaveric impact simulator to assess ligamentous strain and knee kinetics during a simulated landing task. Uniaxial and multiaxial load cells and implanted strain sensors were used to collect mechanical data for analysis. LDA was used to determine the ability to classify injury outcome by demographic and biomechanical input variables. Results: From LDA, a 5-factor model (Entropy R 2 = 0.26) demonstrated an area under the receiver operating characteristic curve (AUC) for all 5 injury outcomes (ACL, MCL, ACL+MCL, ACL+MCL+meniscus, ACL+meniscus) of 0.74 or higher, with “good” prediction for 4 of 5 injury classifications. A 10-factor model (Entropy R 2 = 0.66) improved the AUC to 0.86 or higher, with “excellent” prediction for 5 injury classifications. The 15-factor model (Entropy R 2 = 0.85), produced 94.1% accuracy with the AUC 0.98 or higher for all 5 injury classifications. Conclusion: Use of LDA accurately predicted the outcome of knee injury from kinetic data from cadaveric simulations with the use of a mechanical impact simulator at 25° of knee flexion. Thus, with clinically relevant kinetics, it is possible to determine clinical risk of injury and also the likely presentation of singular or concomitant knee injury. Clinical Relevance: LDA demonstrates that injury outcomes are largely characterized by specific mechanics that can distinguish ACL, MCL, and medial meniscal injury. Furthermore, as the mechanics of injury are better understood, improved interventional prehabilitation can be designed to reduce these injuries.