Analysis of change in gait in the ovine stifle: normal, injured, and anterior cruciate ligament reconstructed.

BACKGROUND: Many patients who undergo anterior cruciate ligament (ACL) reconstructive surgery develop post-traumatic osteoarthritis (PTOA). ACL reconstructive surgery may not fully restore pre-injury joint biomechanics, thereby resulting in further joint damage and contributing to the development of PTOA. In an ovine model of idealized ACL reconstruction (ACL-R), it has been shown that signs of PTOA develop within surgical joints by 20 weeks post-surgery. The aim of the present study was to investigate whether altered kinematics contribute to early PTOA development within ACL-R joints of the ovine injury model by comparing the gait of these surgical animals to the gait of a stable normal control group, and an unstable injury group in which the ACL and medial collateral ligament (MCL) had been transected. METHODS: Fifteen skeletally mature female sheep were allocated evenly into 3 treatment groups: normal control, ACL-R, and ACL/MCL Tx (each group n = 5). Each animal's gait was recorded at baseline, 4 weeks post injury, and 20 weeks post injury. Principal component analysis (PCA) was used to identify the kinematic patterns that may be discriminant between treatment groups. Results from previous studies were referenced to present the amount of gross PTOA-like changes that occurred in the joints. RESULTS: ACL-R and ACL/MCL transected (Tx) animals developed a similar amount of early PTOA-like changes within the surgical joints, but differed significantly in the amount of kinematic change present at 20 weeks post-surgery. We showed that the stifle joint kinematics of ACL/MCL Tx differed significantly from those of CTRL and the majority of ACL-R animals, while no significant differences in joint kinematic changes were found between ACL-R and CTRL animals. CONCLUSIONS: These results suggest that the early PTOA-like changes reported in the ACL-R model cannot be attributed exclusively to post-surgical kinematic changes, and therefore biologic components in the post-injury environment must be contributing significantly to PTOA development.

Cartilage boundary lubrication of ovine synovial fluid following anterior cruciate ligament transection: a longitudinal study.

OBJECTIVE: To assess ovine synovial fluid (oSF) from different post-injury time points for (1) proteoglycan-4 (PRG4) and hyaluronan (HA) concentration, (2) HA molecular weight (MW) distribution, (3) cartilage boundary lubrication function, and (4) lubricant composition-function relationships. The association between cartilage boundary lubrication and gross cartilage changes after injury was also examined. METHODS: oSF was collected 2, 4, 10, and 20 weeks post anterior cruciate ligament (ACL) transection in five skeletally mature sheep. PRG4 and HA concentrations were measured using sandwich enzyme-linked immunosorbent assay, and HA MW distribution by agarose gel electrophoresis. Cartilage boundary lubrication of oSF was assessed using a cartilage-cartilage friction test. Gross damage to articular cartilage was also quantified at 20 weeks using modified Drez scoring protocol. RESULTS: Early (2-4 weeks) after ACL injury, PRG4 concentrations were significantly higher (P = 0.045, P = 0.037), and HA concentrations were substantially lower (P = 0.005, P = 0.005) compared to 20 weeks. The HA MW distribution also shifted towards lower ranges in the early post-injury stage. The kinetic friction coefficients were significantly higher 2-4 weeks post injury (P = 0.008 and P = 0.049) compared to 20 weeks. Poor cartilage boundary lubricating ability early after injury was associated with cartilage damage at 20 weeks. CONCLUSION: Altered composition and diminished boundary lubrication of oSF early after ACL transection may pre-dispose the articular cartilage to degenerative changes and initiate osteoarthritis (OA). These observations also provide potential motivation for biotherapeutic interventions at earlier time points post injury.

Out-of-field beam characteristics of a 6 MV photon beam: results of a Monte Carlo study.

Detailed characteristics of particles in the periphery of a 6 MV photon beam resulting from the exposure of a water phantom were analyzed. The characteristics at the periphery were determined with respect to particles' origin and charge, using Monte Carlo simulations. Results showed that in the peripheral regions, the energy fluence and the mean energy distribution of particles are independent of depth, and the majority of charged particles originate in the irradiated volume. The results are used to examine out-of-field dosimetry factors.

A methodology for prediction of acute hypotensive episodes in ICU via a risk scoring model including analysis of ST-segment variations.

The aim of this study is to detect Acute Hypotensive Episodes (AHE) and Mean Arterial Pressure Dropping Regimes (MAPDRs) using ECG signal and Arterial Blood Pressure waveforms. To meet this end, the QRS complexes and end-systolic end-diastolic pulses are first extracted using two innovative Modified Hilbert Transform-Based algorithms namely as ECGMHT and BPMHT. A new smoothing algorithm is next developed based on piecewise polynomial fitting to smooth the fast fluctuations observed in RR-tachogram, systolic blood pressure (SBP) and diastolic blood pressure (DBP) trends. Afterwards, in order to consider the mutual influence of parameters on the evaluation of shock probability, a Sugeno Adaptive Network-based Fuzzy Inference System-ANFIS is trained using Hasdai et al. (J Am Coll Cardiol, 35: 136­143, 2000) parameters as input, with appropriate membership functions for each parameter. Using this network, it will be possible to incorporate the possible mutual influences between risk parameters such as heart rate, SBP, DBP, ST-segment episodes, age, gender, weight and some miscellaneous factors to the calculation of shock occurrence probability. In the next step, the proposed algorithm is applied to 15 subjects of the MIMIC II Database and AHE and MAPDRs (MAP ≤ 60 mmHg with a period of 30 min or more) are identified. As a result of this study, for a sequence of MAPDRs as long as 20 min or more, there will exist a consequent high peak with the duration of 3­4 min in the corresponding probability of cardiogenic shock diagram.

Parallel processing of ECG and blood pressure waveforms for detection of acute hypotensive episodes: a simulation study using a risk scoring model.

The aim of this study is to detect acute hypotensive episodes (AHE) and mean arterial pressure dropping regimes (MAPDRs) using electrocardiographic (ECG) signals and arterial blood pressure waveforms. To meet this end, the QRS complexes and end-systolic end-diastolic pulses are first extracted using two innovative modified Hilbert transform-based algorithms, namely ECGMHT and BPMHT. The resulting systolic and diastolic blood pressure pulses are then used to calculate the MAP trend. A new smoothing algorithm is developed, next based on piecewise polynomial fitting (PPF) to smooth the fast fluctuations observed in RR-tachogram and MAP trends. PPF algorithm operates by sequentially fitting N number of polynomials to the original signal and calculating the corresponding coefficients using the best linear unbiased estimation approach. In the next step, the proposed algorithm is applied to 15 subjects of the MIMIC II Database and AHE and MAPDRs (MAP ≤ 60 mmHg with a period of 30 min or more) are identified. As a result of this study, MAPDR is realised as a specific marker of cardiogenic shock, in that for a sequence of MAPDRs as long as 20 min or more, there will exist a consequent high peak with a duration of 3-4 min in the corresponding probability of cardiogenic shock diagram.

A robust wavelet-based multi-lead Electrocardiogram delineation algorithm.

A robust multi-lead ECG wave detection-delineation algorithm is developed in this study on the basis of discrete wavelet transform (DWT). By applying a new simple approach to a selected scale obtained from DWT, this method is capable of detecting QRS complex, P-wave and T-wave as well as determining parameters such as start time, end time, and wave sign (upward or downward). First, a window with a specific length is slid sample to sample on the selected scale and the curve length in each window is multiplied by the area under the absolute value of the curve. In the next step, a variable thresholding criterion is designed for the resulted signal. The presented algorithm is applied to various databases including MIT-BIH arrhythmia database, European ST-T Database, QT Database, CinC Challenge 2008 Database as well as high resolution Holter data of DAY Hospital. As a result, the average values of sensitivity and positive predictivity Se=99.84% and P+=99.80% were obtained for the detection of QRS complexes, with the average maximum delineation error of 13.7ms, 11.3ms and 14.0ms for P-wave, QRS complex and T-wave, respectively. The presented algorithm has considerable capability in cases of low signal-to-noise ratio, high baseline wander, and abnormal morphologies. Especially, the high capability of the algorithm in the detection of the critical points of the ECG signal, i.e. the beginning and end of T-wave and the end of the QRS complex was validated by cardiologists in DAY hospital and the maximum values of 16.4ms and 15.9ms were achieved as absolute offset error of localization, respectively.