Poincaré analysis detects pathological limb loading rate variability in post-anterior cruciate ligament reconstruction individuals

BACKGROUND

Post-ACLR individuals can experience repeated exposure to variable limb loading, which contributes to development of knee osteoarthritis. Variable limb loading can present as loading rate variability (LRV) and is magnified during tasks like fast walking when the system is stressed. Nonlinear measures that evaluate temporal variability have successfully detected changes in gait variability associated with altered motor control, however, appropriately describing and uncovering the nature of gait variability has been challenging. Here, Poincaré analysis, a nonlinear method unique in its ability to capture different aspects of variability, served to uncover and quantify changes in limb LRV. It was hypothesized that post-ACLR individuals' overloaded limbs would quantitatively and graphically demonstrate greater short-term stride-to-stride and long-term limb LRV during fast walking compared to the underloaded and healthy control limbs.

METHODS

Fourteen post-ACLR individuals and fourteen healthy controls completed a walking protocol on an instrumented treadmill where they walked at 1.0 m/s and 1.5 m/s for 5-minutes each. A Welch's test was performed to compare differences in short-term and long-term LRV metrics for the post-ACLR individuals' overloaded and underloaded limbs and the healthy controls' right limbs.

RESULTS

Analyses revealed that the post-ACLR individuals' overloaded limb exhibited significantly greater short-term and long-term values compared to the underloaded and healthy control limbs at 1.5 m/s (p<0.05). Additionally, the loading rate data was widely scattered across the plots for the overloaded limb, indicating greater LRV.

SIGNIFICANCE

Poincaré analysis successfully identified that post-ACLR overloaded limbs exhibited impaired motor control during fast walking based on quantitative and graphical changes in variability. This highlights the clinical applications of Poincaré analysis, with the plots potentially serving as an easy-to-interpret diagnostic tool for pathological limb LRV.

Elevated Electron Temperature Coincident with Observed Fusion Reactions in a Sheared-Flow-Stabilized Z Pinch

The sheared-flow-stabilized Z pinch concept has been studied extensively and is able to produce fusion-relevant plasma parameters along with neutron production over several microseconds. We present here elevated electron temperature results spatially and temporally coincident with the plasma neutron source. An optical Thomson scattering apparatus designed for the FuZE device measures temperatures in the range of 1-3 keV on the axis of the device, 20 cm downstream of the nose cone. The 17-fiber system measures the radial profiles of the electron temperature. Scanning the laser time with respect to the neutron pulse time over a series of discharges allows the reconstruction of the T_{e} temporal response, confirming that the electron temperature peaks simultaneously with the neutron output, as well as the pinch current and inductive voltage generated within the plasma. Comparison to spectroscopic ion temperature measurements suggests a plasma in thermal equilibrium. The elevated T_{e} confirms the presence of a plasma assembled on axis, and indicates limited radiative losses, demonstrating a basis for scaling this device toward net gain fusion conditions.

Time series modeling characterizes stride time variability to identify individuals with neurodegenerative disorders

The progressive death and dysfunction of neurons causes altered stride-to-stride variability in individuals with Amyotrophic Lateral Sclerosis (ALS) and Huntington's Disease (HD). Yet these altered gait dynamics can manifest differently in these populations based on how and where these neurodegenerative disorders attack the central nervous system. Time series analyses can quantify differences in stride time variability which can help contribute to the detection and identification of these disorders. Here, autoregressive modeling time series analysis was utilized to quantify differences in stride time variability amongst the Controls, the individuals with ALS, and the individuals with HD. For this study, fifteen Controls, 12 individuals with ALS and 15 individuals with HD walked up and down a hallway continuously for 5-min. Participants wore force sensitive resistors in their shoes to collect stride time data. A second order autoregressive (AR) model was fit to the time series created from the stride time data. The mean stride time and two AR model coefficients served as metrics to identify differences in stride time variability amongst the three groups. The individuals with HD walked with significantly greater stride time variability indicating a more chaotic gait while the individuals with ALS adopted more ordered, less variable stride time dynamics (p < 0.001). A plot of the stride time metrics illustrated how each group exhibited significantly different stride time dynamics. The stride time metrics successfully quantified differences in stride time variability amongst individuals with neurodegenerative disorders. This work provided valuable insight about how these neuromuscular disorders disrupt motor coordination leading to the adoption of new gait dynamics.

Effect of Purposely Induced Asymmetric Walking Perturbations on Limb Loading After Anterior Cruciate Ligament Reconstruction

Background

Patients often sustain prolonged neuromuscular dysfunction after anterior cruciate ligament reconstruction (ACLR). This dysfunction can present as interlimb loading rate asymmetries linked to reinjury and knee osteoarthritis progression.

Purpose/Hypothesis

To evaluate how asymmetric walking protocols can reduce interlimb loading rate asymmetry in patients after ACLR. It was hypothesized that asymmetric walking perturbations would (1) produce a short-term adaptation of interlimb gait symmetry and (2) induce the temporary storage of these new gait patterns after the perturbations were removed.

Study Design

Descriptive laboratory study.

Methods

Fifteen patients who had undergone ACLR were asked to perform an asymmetric walking protocol during the study period (2022-2023). First, to classify each limb as overloaded or underloaded based on the vertical ground-reaction force loading rate for each limb, participants were asked to perform baseline symmetric walking trials. Participants then performed an asymmetric walking trial for 10 minutes, where one limb was moving 0.5 m/s faster than the other limb (1 vs 1.5 m/s), followed by a 2-minute 1 m/s symmetric deadaptation walking trial. This process was repeated with the limb speeds switched for a second asymmetric trial.

Results

Participants adopted a new, symmetric interlimb loading rate gait pattern over time in response to the asymmetric trial, where the overloaded limb was set at 1 m/s. A linear mixed-effects model detected a significant change in gait dynamics (P < .001). The participants exhibited negative aftereffects after this asymmetric perturbation, indicating the temporary storage of the new gait pattern. No positive short-term gait adaptation or storage was observed when the overloaded limb was set to a faster speed.

Conclusion

Asymmetric walking successfully produced the short-term adaptation of interlimb loading rate symmetry in patients after ACLR and induced the temporary storage of these gait patterns in the initial period when the perturbation was removed.

Clinical Relevance

These findings are promising, as they suggest that asymmetric walking could serve as an effective gait retraining protocol that has the potential to improve long-term outcomes in patients after ACLR.

Classification Model for Differentiating Post-ACLR Individuals Using Loading Rate Variation

In post-ACLR individuals, gait variability often represents the presence of altered motor control. Quantifying variable limb loading is challenging, yet nonlinear analyses have been successful in detecting changes in gait variability due to altered motor control. Here, nonlinear metrics were derived and used to train multiple machine learning models to classify between healthy controls and post-ACLR individuals. The metrics were extracted from individuals' vertical ground reaction force data during a fast-walking trial as variable limb loading is exacerbated when the system is stressed and being challenged. It was hypothesized that effective differentiation between healthy control and post-ACLR individuals would be achieved using machine learning models derived from limb loading rate variability measures. Seventeen healthy control and fourteen post-ACLR participants with measured between-limb loading rate asymmetries completed the walking protocol. Ground reaction force data was collected on an instrumented treadmill where they performed walking trials at 1.5 m/s. Nonlinear limb loading rate measures extracted from the healthy controls and post-ACLR participants' data served as inputs to the models in order to train them to distinguish between the two states. A Decision Tree Classifier that utilized a bagging strategy was the best model for distinguishing between healthy control and post-ACLR participants. The model was successful in classifying participants, reporting an accuracy score of 73%, precision score of 100%, and an AUC score of 0.77, despite the smaller dataset. The ability to detect and classify post-ACLR loading rate variation has significant clinical implications, as these methods could be implemented in clinical settings to diagnose pathological limb loading dynamics and/or altered motor control.Clinical Relevance- This classification model can be easily integrated into the clinic to help diagnose pathological limb loading based solely on vertical ground reaction forces and can aid clinicians in providing data-driven metrics to help inform rehabilitation decisions.

A new metric for characterizing limb loading dynamics in post anterior cruciate ligament reconstruction individuals

BACKGROUND

Unresolved neuromuscular deficits often persist in post-anterior cruciate ligament reconstruction (ACLR) individuals manifesting as altered impact and active peak force production during running that can contribute to detrimental limb loading. Elevated impact and active peaks are common in pathological populations indicating a stiffer limb loading strategy. Although impact and active peaks are sensitive to changes in limb loading, to our knowledge, there are no established, standardized measures or cutoff criteria to differentiate between healthy and pathological limb loading. However, prior studies have demonstrated that the ratio between traditional biomechanical measures can be used to successfully establish quantifiable and graphical ranges to delineate between healthy and pathological movement.

RESEARCH QUESTION

Therefore, this study sought to exploit the impact-to-active peak ratio to generate a new, standardized metric to quantify and characterize limb loading dynamics in healthy controls and post-ACLR individuals during running.

METHODS

Twenty-eight post-ACLR individuals and 18 healthy controls performed a running protocol. Impact peak and active peak data were extracted from their strides as they ran at a self-selected speed. A linear regression model was fit to the healthy control data and the models 95 % prediction intervals were used to define a boundary region of healthy limb loading dynamics.

RESULTS

The post-ACLR individuals produced a higher impact-to-active peak ratio than the healthy controls indicating that they adopted a stiffer limb loading strategy. The boundary regions derived from the impact and active peak model successfully classified the healthy controls and post-ACLR individual's limb loading dynamics with an accuracy, sensitivity, and specificity of 89 %, 100 %, and 75 %, respectively.

SIGNIFICANCE

The ability to effectively evaluate limb loading dynamics using impact and active peaks can provide clinicians with a new, non-invasive metric to quantify and characterize healthy and pathological movement in a clinical setting.

Differing hypertrophy patterns from open and closed kinetic chain training affect quadriceps femoris center of mass and moment of inertia

Purpose: To determine whether kinetic chain pattern during knee extensor strength training influences quadriceps femoris center of mass and moment of inertia about the hip in a predictable manner as such changes can affect running economy.

Methods: Twelve participants completed 8 weeks of both unilateral open (OKC) and closed (CKC) kinetic chain resistance training on opposing legs. Changes in quadriceps femoris muscle volume (VOLQF), center of mass location (CoMQF), and moment of inertia (IQF) about the hip were determined from magnetic resonance images scans. Regional hemodynamics of the vastus lateralis taken at 30% and 70% of muscle length during OKC and CKC bouts early in the training program were measured using near-infrared spectroscopy (NIRS) and used post hoc to predict changes in CoMQF.

Results: While increases in VOLQF were similar between OKC (Δ79.5 ± 87.9 cm3) and CKC (Δ60.2 ± 110.5 cm3, p = 0.29), the patterns of hypertrophy differed; a distal shift in CoMQF (Δ2.4 ± 0.4 cm, p &lt; 0.001) and increase in IQF (Δ0.017 ± 0.014 kg m2, p &lt; 0.001) occurred in OKC but not in CKC (CoMQF: Δ-2.2 ± 2.0 cm, IQF: Δ-0.022 ± 0.020 kg m2, p &gt; 0.05). Regional hemodynamics assessed by NIRS during a single training session displayed similar exercise and regional differences and predicted 39.6% of observed changes in CoMQF.

Conclusions: Exercise selection influences muscle shape sufficiently to affect CoMQF and IQF, and these changes may be predicted in part from NIRS measurements during a single workout. Given IQF is inversely related to running economy and since CKC exercise provides a more proximal pattern of hypertrophy than OKC, it may be more preferential for running. The results from the present study also highlight the potential of NIRS as a tool for predicting patterns of hypertrophy between different exercises and exercise conditions.

A multi-chord, two-color interferometer using Hilbert transform phase detection for measuring electron density in spheromak plasmas

A new, four-chord, CO₂/He-Ne heterodyne interferometer has been designed and built for measuring line-averaged plasma density in the HIT-SI3 and subsequent HIT-SIU sustained spheromak devices. The two-color system successfully eliminates vibration-induced errors caused by mirrors that are secured to the vacuum chamber and is able to resolve electron densities n_e in the full operating range of 10¹⁸-10²⁰ m^-3 in both experiments with an integrated error of 4.7 × 10¹⁷ m^-2. Data are presented from high toroidal current plasma discharges, showing the time evolution of electron densities n_e and j_ϕ/n_e along multiple chords.

Classification Model for Discriminating Trunk Fatigue During Running

PURPOSE

Fatigue is often associated with increased injury risk. Many studies have focused on fatigue in the lower extremity muscles brought on by running, yet few have examined the relationship between fatigue of the core musculature and associated changes in running gait. To investigate the relationship between trunk fatigue and running dynamics, this study had two goals: (1) to use machine learning to determine which gait parameters are most associated with trunk fatigue; and (2) to develop a machine learning algorithm that uses those parameters to classify individuals with trunk fatigue. We hypothesized that we could effectively differentiate between the non-fatigued and fatigued states using machine learning models derived from running gait parameters.

METHODS

Seventy-two individuals performed a trunk fatigue protocol. Lower extremity running biomechanics were collected pre- and post- the trunk fatigue protocol using an instrumented treadmill and 10-camera motion capture system.The fatiguing protocol involved executing a series of trunk fatiguing exercises until established fatigue criteria were reached. Gait variables extracted from the non-fatigued and fatigued states served as model inputs to aid in the development of the machine learning model that would distinguish between non-fatigued and fatigued running.

RESULTS

The machine learning protocol determined three variables - stance time, maximum propulsive GRF and maximum braking GRF - to be the best discriminators between non-fatigued and fatigued running. The SVM with Bagging was the best performing model that discriminated between non-fatigued and fatigued running with an accuracy of 82%, precision of 77%, recall of 90%, and area under the receiver operating curve of 0.91.

CONCLUSION

The machine learning model was effective in classifying between non-fatigued and fatigued running using three gait parameters extracted from GRF waveforms. The ability to classify fatigue using these easy to measure GRF derived parameters enhances the ability for the model to be integrated into wearable technology and the clinical setting to aid in the detection of fatigue and potentially injury, as fatigue is often a precursor to injury.Clinical Relevance- This model has the potential to be implemented in a clinical setting to determine the onset of trunk fatigue through basic gait analysis, involving only the ground reaction forces. This model would be aimed toward injury prevention since fatigue is linked to increased risk of injury.

A Metric for Identifying Stress Fractures in Runners

PURPOSE

Stress fractures are common overuse running injuries. Individuals with stress fractures exhibit running biomechanics characterized by elevated impact peak and loading rate. While elevated impact peak and loading rate are associated with stress fractures, there are few established metrics used to identify the presence of stress fractures in individuals. Here this study aims to exploit the linear relationship between the impact peak and loading rate to establish a metric to help identify individuals with stress fractures. We hypothesize that the ratio between the impact peak and loading rate will serve as a metric to delineate between healthy controls and those with stress fractures.

METHODS

Fifteen healthy controls and 11 individuals with stress fractures performed a running protocol. A linear regression model fit to the stress fracture impact peak and loading rate data produced a lower 95% confidence limit boundary that served as the demarcation line between the two groups.

RESULTS

Individuals with stress fractures tended to reside above the line with the line accurately classifying 82% of the individuals with stress fractures.

CONCLUSION

The analysis supported the hypothesis and demonstrated how the relationship between impact peak and loading rate can help identify the presence of stress fractures in individuals.Clinical Relevance- The relationship between impact peak and loading rate has the potential to serve as clinically useful metric to identify stress fractures during running.

High-speed feedback control of an oscillating magnetic helicity injector using a graphics processing unit

A real-time control system has been developed to control the amplitude, phase, and offset of bulk plasma parameters inside an oscillating magnetic helicity injector. Control software running entirely on an Nvidia Tesla P40 graphical processing unit is able to receive digitizer inputs and send response patterns to a Pulse Width Modulation (PWM) controller with a minimum control loop period of 12.8 µs. With an input digitization rate of 10 MS/s, a three-parameter proportional integral differential controller is shown to be sufficient to inform the PWM controller to drive the desired oscillating plasma waveform with a frequency of 16.6 kHz that is located near the resonance of a coupled RLC circuit. In particular, the temporal phase of the injector waveform is held within 10° of the target value. Control is demonstrated over the toroidal modal structure of the imposed magnetic perturbations of the helicity injection system, allowing a new class of discharges to be studied.

Force and Rate Metrics Provide Return-to-Sport Criterion after ACL Reconstruction

PURPOSE

Peak vertical ground reaction force and linear loading rate can be valuable metrics for return-to-sport assessment because they represent limb loading dynamics; yet, there is no defined cutoff criterion to differentiate between healthy and altered limb loading. Studies have shown that healthy individuals exhibit strong first-order relationships between gait variables whereas individuals with pathological conditions did not. Thus, this study sought to explore and exploit this first-order relationship to define a region of healthy limb dynamics, which individuals with pathological conditions would reside outside of, to rapidly assess individuals with altered limb loading dynamics for return to sport. We hypothesized that there would be a strong first-order linear relationship between vertical ground reaction force peak force and linear loading rate in healthy controls' limbs, which could be exploited to identify abnormal limb loading dynamics in post-anterior cruciate ligament reconstruction (ACLR) individuals.

METHODS

Thirty-one post-ACLR individuals and 31 healthy controls performed a running protocol. A first-order regression analysis modeled the relationship between peak vertical ground reaction forces and linear vertical ground reaction force loading rate in the healthy control limbs to define a region of healthy dynamics to evaluate post-ACLR reconstructed limb dynamics.

RESULTS

A first-order regression model aided in the determination of cutoff criteria to define a region of healthy limb dynamics. Ninety percent of the post-ACLR reconstructed limbs exhibited abnormal limb dynamics based on their location outside of the region of healthy dynamics.

CONCLUSION

This approach successfully delineated between healthy and abnormal limb loadings dynamics in controls and post-ACLR individuals. The findings demonstrate how force and loading rate-dependent metrics can help develop criteria for individualized post-ACLR return-to-sport assessment.

Characterizing gait pattern dynamics during symmetric and asymmetric walking using autoregressive modeling

Gait asymmetry is often observed in populations with varying degrees of neuromuscular control. While changes in vertical ground reaction force (vGRF) peak magnitude are associated with altered limb loading that can be observed during asymmetric gait, the challenge is identifying techniques with the sensitivity to detect these altered movement patterns. Autoregressive (AR) modeling has successfully delineated between healthy and pathological gait during running; but has been little explored in walking. Thus, AR modeling was implemented to assess differences in vGRF pattern dynamics during symmetric and asymmetric walking. We hypothesized that the AR model coefficients would better detect differences amongst the symmetric and asymmetric walking conditions than the vGRF peak magnitude mean. Seventeen healthy individuals performed a protocol that involved walking on a split-belt instrumented treadmill at different symmetric (0.75m/s, 1.0 m/s, 1.5 m/s) and asymmetric (Side 1: 0.75m/s-Side 2:1.0 m/s; Side 1:1.0m/s-Side 2:1.5 m/s) gait conditions. Vertical ground reaction force peaks extracted during the weight-acceptance and propulsive phase of each step were used to construct a vGRF peak time series. Then, a second order AR model was fit to the vGRF peak waveform data to determine the AR model coefficients. The resulting AR coefficients were plotted on a stationarity triangle and their distance from the triangle centroid was computed. Significant differences in vGRF patterns were detected amongst the symmetric and asymmetric conditions using the AR modeling coefficients (p = 0.01); however, no differences were found when comparing vGRF peak magnitude means. These findings suggest that AR modeling has the sensitivity to identify differences in gait asymmetry that could aid in monitoring rehabilitation progression.

Classification of Post-Anterior Cruciate Ligament Reconstruction Running Dynamics using Non-Traditional Features

Despite extensive rehabilitation, nearly half of all post-anterior cruciate ligament reconstruction (ACLR) individuals are unable to perform dynamic tasks at the level they did prior to their injury. This inability can be attributed to unresolved neuromuscular deficits that manifest as altered limb dynamics. While traditional discrete metrics; such as peak vertical ground reaction force (vGRF) and peak knee flexion angle, have been used to successfully differentiate between healthy and pathological running dynamics, recent studies have shown that non-traditional metrics derived from autoregressive (AR) modeling and Smoothed Pseudo Wigner-Ville (SPWV) analysis techniques can also successfully delineate between healthy and pathological populations and could potentially possess greater sensitivity than the traditional metrics. Thus, the objective of this study was to compare the performance of classification models generated from traditional and nontraditional metrics collected from healthy controls and post-ACLR individuals during a running protocol. We hypothesized that the non-traditional metric-based classification model would outperform the traditional metric based model. Thirty-one controls and 31 post-ACLR individuals performed a running protocol from which the traditional metrics - peak vGRF, linear vGRF loading rate and peak knee flexion angle - and nontraditional metrics - dynamic vGRF ratio, AR model coefficients, and a SPWV derived low frequency-high frequency ratio - were extracted from vGRF and knee flexion running waveforms. The results indicated that a fine Gaussian SVM classification model derived from the non-traditional metrics had an accuracy of 87%, specificity of 83% and sensitivity of 61% and it outperformed the classification model derived from traditional metrics. These findings indicate that additional, valuable information can be ascertained from non-traditional metrics that evaluate waveform dynamics. Additionally, it suggests that this or similar models can be used to track the restoration of healthy running dynamics in post-ACLR individuals during rehabilitation.

Using Time-Frequency Analysis to Characterize Altered Knee Dynamics in Post ACL Reconstruction Individuals

More than 250,000 individuals suffer an anterior cruciate ligament (ACL) injury in the United States each year requiring surgery and rehabilitation. However, despite exhaustive rehabilitation individuals are often plagued by neuromuscular deficits that lead to detrimental knee loading and knee osteoarthritis. Traditionally, time domain-based metrics like peak sagittal plane knee angle are used to quantify differences in knee mechanics; however, additional information can potentially be elucidated from time-frequency analyses. Here Smoothed Pseudo Wigner-Ville (SPWV), a time-frequency analysis technique, was used to investigate differences in knee loading dynamics between healthy controls and post ACL reconstruction individuals during running. The results indicated that post ACL reconstruction individuals adopt significantly different loading strategies in their injured limb than their non-injured limb. Individuals adopt a stiffer, more restrictive movement strategy delineated by a stronger low frequency to high frequency (LF/HF) ratio while the non-injured limb exhibit a more oscillatory motion (p<; 0.001). The time domain metrics were unable to identify differences between the ACL injured and non-injured limbs. The ability of SPWV to provide both quantitative and visual means to detect these differences supports its use as a clinical tool to track and monitor joint health.

Autoregressive modeling to assess stride time pattern stability in individuals with Huntington's disease

Background

Huntington’s disease (HD) is a progressive, neurological disorder that results in both cognitive and physical impairments. These impairments affect an individual’s gait and, as the disease progresses, it significantly alters one’s stability. Previous research found that changes in stride time patterns can help delineate between healthy and pathological gait. Autoregressive (AR) modeling is a statistical technique that models the underlying temporal patterns in data. Here the AR models assessed differences in gait stride time pattern stability between the controls and individuals with HD. Differences in stride time pattern stability were determined based on the AR model coefficients and their placement on a stationarity triangle that provides a visual representation of how the patterns mean, variance and autocorrelation change with time. Thus, individuals who exhibit similar stride time pattern stability will reside in the same region of the stationarity triangle. It was hypothesized that individuals with HD would exhibit a more altered stride time pattern stability than the controls based on the AR model coefficients and their location in the stationarity triangle.

Methods

Sixteen control and twenty individuals with HD performed a five-minute walking protocol. Time series’ were constructed from consecutive stride times extracted during the protocol and a second order AR model was fit to the stride time series data. A two-sample t-test was performed on the stride time pattern data to identify differences between the control and HD groups.

Results

The individuals with HD exhibited significantly altered stride time pattern stability than the controls based on their AR model coefficients (AR1 p < 0.001; AR2 p < 0.001).

Conclusions

The AR coefficients successfully delineated between the controls and individuals with HD. Individuals with HD resided closer to and within the oscillatory region of the stationarity triangle, which could be reflective of the oscillatory neuronal activity commonly observed in this population. The ability to quantitatively and visually detect differences in stride time behavior highlights the potential of this approach for identifying gait impairment in individuals with HD.

Altered movement strategy of chronic ankle instability individuals with postural instability classified based on Nyquist and Bode analyses

BACKGROUND

The aim of the current study was to assess movement strategies during a single leg balance in chronic ankle instability individuals with unstable postural control strategy identified by Nyquist and Bode analyses in conjunction with sample entropy.

METHODS

Thirty-three participants with self-reported chronic ankle instability and 22 healthy controls performed single-leg eyes closed static balance trials. The sagittal and frontal plane kinematics in the lower extremity and trunk as well as center of pressure trajectories were recorded during three, 20-second trials. The Nyquist and Bode stability analyses, which classify center of pressure waveforms as stable based on the resulting gain and phase margins, were performed to identify the presence of postural control deficits. Sample entropy was implemented to analyze movement strategies during the task.

FINDINGS

Based on the Nyquist and Bode stability analyses, we included 19 out of 33 chronic ankle instability participants with unstable postural control strategy and 16 out of 22 controls with stable postural control strategy in the final analyses. Chronic ankle instability participants demonstrated a significantly lower sample entropy value in sagittal and frontal plane trunk kinematics and sagittal plane hip kinematics compared to the controls. No between-group differences existed in other kinematic measures.

INTERPRETATION

The lower sample entropy values in participants with chronic ankle instability indicates that those with postural control deficits may increase reliance on the trunk and hip joint contributions to the maintenance of postural control, reflecting changes in the sensorimotor constraints on movement patterns during the task.

Identification of knee gait waveform pattern alterations in individuals with patellofemoral pain using fast Fourier transform

Patellofemoral pain (PFP) is one of the most common overuse injuries of the knee. Previous research has found that individuals with PFP exhibit differences in peak hip kinematics; however, differences in peak knee kinematics, where the pain originates, are difficult to elucidate. To better understand the mechanism behind PFP, we sought to characterize differences in knee gait kinematic waveform patterns in individuals with PFP compared to healthy individuals using fast Fourier transform (FFT). Sixteen control and sixteen individuals with PFP participated in a fast walk protocol. FFT was used to decompose the sagittal, frontal and transverse plane knee gait waveforms into sinusoidal signals. A two-way ANOVA and Bonferroni post hoc analysis compared group, limb and interaction effects on sagittal, frontal and transverse amplitude, frequency and phase components between control and PFP individuals gait waveforms. Differences in frequency and phase values were found in the sagittal and frontal plane knee waveforms between the control and PFP groups. The signal-to-noise ratio also reported significant differences between the PFP and control limbs in the sagittal (p<0.01) and frontal planes (p = 0.04). The findings indicate that differences in gait patterns in the individuals with PFP were not the result of amplitude differences, but differences attributed to temporal changes in gait patterns detected by the frequency and phase metrics. These changes suggest that individuals with PFP adopted a more deliberate, stiffer gait and exhibit altered joint coordination. And the FFT technique could serve as a fast, quantifiable tool for clinicians to detect PFP.

Empirical Based Modeling for the Assessment of Dynamic Knee Stability: Implications for Anterior Cruciate Ligament Injury Risk

Anterior cruciate ligament (ACL) injuries are common sports injuries, costing the U.S. roughly $1 billion annually. To better understand the underlying injury mechanism, Nyquist and Bode stability criteria were applied to assess frontal plane dynamic knee stability among male Australian Football players during the weight-acceptance phase of single-leg jump landing. Out of 30 landings, 19 were classified as stable and 11 as unstable. Medial and lateral vasti, hamstring and gastrocnemii muscle activation waveforms were analyzed in parallel to determine if individuals with stable and unstable frontal plane joint biomechanics possessed different lower limb neuromuscular strategies. The total quadriceps muscle activation during the stable landings were significantly higher (p=0.02) than during the unstable landings. Additionally, the vasti exhibited a medial dominance during the stable landings compared to the unstable (p=0.06). These results suggest that individuals with unstable frontal plane knee landing mechanics may have reduced recruitment of the muscles crossing the knee; specifically, the medial muscles, which could limit their ability to compress and support the joint. The stability criteria were able to classify stable and unstable knee mechanics. And the differences in muscle activation during these stable and unstable landings provided new insights towards the ACL injury mechanism and possible injury prevention countermeasures.

Bilateral hippocampal increase following first-episode psychosis is associated with good clinical, functional and cognitive outcomes

BACKGROUND

Hippocampal pathology has been proposed to underlie clinical, functional and cognitive impairments in schizophrenia. The hippocampus is a highly plastic brain region; examining change in volume, or change bilaterally, over time, can advance understanding of the substrate of recovery in psychosis.

METHOD

Magnetic resonance imaging and outcome data were collected at baseline and 6-year follow-up in 42 first-episode psychosis subjects and 32 matched controls, to investigate whether poorer outcomes are associated with loss of global matter and hippocampal volumes. Bilateral hippocampal increase (BHI) over time, as a marker of hippocampal plasticity was hypothesized to be associated with better outcomes. Regression analyses were performed on: (i) clinical and functional outcomes with grey matter volume change and BHI as predictor variables; and (ii) cognitive outcome with BHI as predictor.

RESULTS

BHI was present in 29% of psychosis participants. There was no significant grey matter loss over time in either patient or control groups. Less severe illness course and lesser symptom severity were associated with BHI, but not with grey matter change. Employment and global function were associated with BHI and with less grey matter loss. Superior delayed verbal recall was also associated with BHI.

CONCLUSIONS

BHI occurs in a minority of patients following their first psychotic episode and is associated with good outcome across clinical, functional and cognitive domains.

The differential effect of illicit drug use on cognitive function in first-episode psychosis and healthy controls

OBJECTIVE

Illicit drug use can result in impairment in cognitive function in healthy individuals. Individuals with a psychotic disorder also show a deficit in cognitive function. Drug use may simply contribute to the characteristic cognitive deficit found in psychosis or alternatively result in a 'double deficit'. This study aims to investigate the association between drug use and cognitive function at the first-episode of psychosis and in community-matched controls.

METHOD

One hundred and seventy-seven patients at the first episode of psychosis completed a battery of neuropsychological tests. Those that had used drugs in the previous year (n = 80) were compared with those who had not used drugs in the previous year (n = 97). A subsample of the first-episode psychosis patients were compared with community-matched controls (n = 110) according to drug-use status.

RESULTS

Patients with a first episode of psychosis who had used drugs performed equally to those who had not used drugs on neuropsychological tests. In contrast, healthy controls who had used drugs in the previous year performed worse on tests of executive function and working memory compared with those controls that had not used drugs.

CONCLUSION

There are differential associations of illicit drug misuse with cognitive function for first-episode psychosis patients and healthy controls.

Differing patterns of brain structural abnormalities between black and white patients with their first episode of psychosis

BACKGROUND

African-Caribbean and black African people living in the UK are reported to have a higher incidence of diagnosed psychosis compared with white British people. It has been argued that this may be a consequence of misdiagnosis. If this is true they might be less likely to show the patterns of structural brain abnormalities reported in white British patients. The aim of this study therefore was to investigate whether there are differences in the prevalence of structural brain abnormalities in white and black first-episode psychosis patients.

METHOD

We obtained dual-echo (proton density/T2-weighted) images from a sample of 75 first-episode psychosis patients and 68 healthy controls. We used high resolution magnetic resonance imaging and voxel-based methods of image analysis. Two separate analyses were conducted: (1) 34 white British patients were compared with 33 white British controls; (2) 41 African-Caribbean and black African patients were compared with 35 African-Caribbean and black African controls.

RESULTS

White British patients and African-Caribbean/black African patients had ventricular enlargement and increased lenticular nucleus volume compared with their respective ethnic controls. The African-Caribbean/black African patients also showed reduced global grey matter and increased lingual gyrus grey-matter volume. The white British patients had no regional or global grey-matter loss compared with their normal ethnic counterparts but showed increased grey matter in the left superior temporal lobe and right parahippocampal gyrus.

CONCLUSIONS

We found no evidence in support of our hypothesis. Indeed, the finding of reduced global grey-matter volume in the African-Caribbean/black African patients but not in the white British patients was contrary to our prediction.

Minor physical anomalies in patients with first-episode psychosis: their frequency and diagnostic specificity

BACKGROUND

An increased prevalence of minor physical anomalies (MPAs) has been extensively documented in schizophrenia but their specificity for the disorder remains unclear. We investigated the prevalence and the predictive power of MPAs in a large sample of first-episode psychotic patients across a range of diagnoses.

METHOD

MPAs were examined in 242 subjects with first-episode psychosis (50% schizophrenia, 45% affective psychosis and 5% substance-induced psychosis) and 158 healthy controls. Categorical principal components analysis and analysis of variance were undertaken, and individual items with the highest loading were tested using the chi2 test.

RESULTS

Overall facial asymmetry, assymetry of the orbital landmarks, and frankfurt horizontal significantly differentiated patients with schizophrenia and affective psychosis from controls, as did a 'V-shaped' palate, reduced palatal ridges, abnormality of the left ear surface and the shape of the left and right ears. Patients with affective psychosis had significantly lowered eye fissures compared with control subjects.

CONCLUSIONS

MPAs are not specific to schizophrenia, suggesting a common developmental pathway for non-affective and affective psychoses. The topographical distribution of MPAs in this study is suggestive of an insult occurring during organogenesis in the first trimester of pregnancy.

Cathepsin D activity in human peripheral blood mononuclear leukocytes

To investigate the cellular origins of cathepsin D (CD) in inflammatory lesions, the CD content of lymphocyte subsets, monocytes, and macrophages were compared. Human monocytes, B lymphocytes, CD4+ T lymphocytes, and CD8+ T lymphocytes were separated from peripheral blood of normal donors. CD content was 0.13 +/- .01 micrograms equivalents of CD per million cells and significant differences between different cell types were not found. To determine the CD content of macrophages, differentiation of peripheral blood monocytes was induced by either in vitro culture or treatment with 4 beta-phorbol-12-myristate-13-acetate (PMA). Macrophages induced by five-day culture contained four times more CD than unstimulated monocytes, and macrophages induced by 18-h treatment with 20 mg/ml 4 beta-PMA contained nine times more CD than monocytes treated with 4 alpha-PMA, an inactive stereoisomer of 4 beta-PMA. These results suggest that macrophages are one of the enriched sources of CD in inflammatory lesions.

Activation of astrocytic lysosomal proteinases by factors released by mononuclear leukocytes

Lysosomal proteinases are increased in the tissue lesions of experimental allergic encephalomyelitis and have been implicated in the degradation of myelin proteins. The cellular origins of the increased proteinases are not known but reactive astrocytes found in areas of increased activity are candidate cells. To evaluate the potential of astrocytes as the source of these proteinases, cathepsin B (CB) and cathepsin D (CD) levels were measured in lysates of cultured astrocytes from neonatal rats. Because astrocytes are activated by inflammatory mediators in demyelinating lesions the effect of activation on proteinase levels was examined. Culture supernatants from mononuclear leukocytes stimulated with either concanavalin A or phytohemagglutinin (PHA) induced significant increases in the astrocytic proteinases. Neither PHA alone, interleukin-1, interleukin-2, nor gamma-interferon induced significant increases. Fractions of the supernatant from PHA stimulated mononuclear leukocytes were tested and activity was found in fractions corresponding to a molecular weight of 45-50,000. These studies demonstrate that astrocytes contain significant amounts of CB and CD activity which can be increased by a factor or factors released by activated mononuclear leukocytes.

A dopamine receptor model and its application in the design of a new class of rigid pyrrolo[2,3-g]isoquinoline antipsychotics

A hypothetical model of the interaction of antipsychotic drugs with the dopamine receptor is described. This three-dimensional molecular model has been developed on the basis of plausible intermolecular interactions between pharmacophoric groups of diverse types of antipsychotic drugs and postulated amino acid side chain substituents of the receptor protein. Three essential binding sites (one possibly required for antagonism) and one lipophilic auxiliary binding site are identified. The geometry is defined via the three-dimensional structures of drugs exhibiting receptor activity, including (R)-apomorphine, (+)-dexclamol, and molindone (whose crystal structure has been determined). A new conformationally rigid pyrrolo[2,3-g]isoquinoline derivative has been designed to conform to the receptor model. The compound (+/-)-1 (2,6-dimethyl-3-ethyl-4,4a,5,6,7,8,8a,9-octahydro-4a,8a-trans-1H-pyrrolo[2,3-g] isoquinolin-4-one; Ro 22-1319) exhibits potent antipsychotic-like activity. The activity is stereospecific, residing in the (-) enantiomer, predicted and confirmed by X-ray crystal structure analysis of (-)-1.HCl to have the 4aR,8aR absolute configuration.

Cost of a cardiac surgical and a general thoracic surgical patient to the National Health Service in a London teaching hospital

The cost of the inpatient stay for a typical aortic valve replacement and for an oesophagectomy were determined by recording and costing every aspect of the patients' care from admission until discharge. This method of cost calculation was found to be satisfactory and could be used by other centres to allow comparisons between hospitals or countries. At St Thomas's Hospital in 1977 the cost of a cardiac operation was 2755 pounds, an oesophagectomy 1870 pounds, and a general surgical operation 564 pounds.