Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude-only fMRI data.

Brain networks extracted by independent component analysis (ICA) from magnitude-only fMRI data are usually denoised using various amplitude-based thresholds. By contrast, spatial source phase (SSP) or the phase information of ICA brain networks extracted from complex-valued fMRI data, has provided a simple yet effective way to perform the denoising using a fixed phase change. In this work, we extend the approach to magnitude-only fMRI data to avoid testing various amplitude thresholds for denoising magnitude maps extracted by ICA, as most studies do not save the complex-valued data. The main idea is to generate a mathematical SSP map for a magnitude map using a mapping framework, and the mapping framework is built using complex-valued fMRI data with a known SSP map. Here we leverage the fact that the phase map derived from phase fMRI data has similar phase information to the SSP map. After verifying the use of the magnitude data of complex-valued fMRI, this framework is generalized to work with magnitude-only data, allowing use of our approach even without the availability of the corresponding phase fMRI datasets. We test the proposed method using both simulated and experimental fMRI data including complex-valued data from University of New Mexico and magnitude-only data from Human Connectome Project. The results provide evidence that the mathematical SSP denoising with a fixed phase change is effective for denoising spatial maps from magnitude-only fMRI data in terms of retaining more BOLD-related activity and fewer unwanted voxels, compared with amplitude-based thresholding. The proposed method provides a unified and efficient SSP approach to denoise ICA brain networks in fMRI data.

Constrained CPD of Complex-Valued Multi-Subject fMRI Data via Alternating Rank-R and Rank-1 Least Squares.

Complex-valued shift-invariant canonical polyadic decomposition (CPD) under a spatial phase sparsity constraint (pcsCPD) shows excellent separation performance when applied to band-pass filtered complex-valued multi-subject fMRI data. However, some useful information may also be eliminated when using a band-pass filter to suppress unwanted noise. As such, we propose an alternating rank- R and rank-1 least squares optimization to relax the CPD model. Based upon this optimization method, we present a novel constrained CPD algorithm with temporal shift-invariance and spatial sparsity and orthonormality constraints. More specifically, four steps are conducted until convergence for each iteration of the proposed algorithm: 1) use rank- R least-squares fit under spatial phase sparsity constraint to update shared spatial maps after phase de-ambiguity; 2) use orthonormality constraint to minimize the cross-talk between shared spatial maps; 3) update the aggregating mixing matrix using rank- R least-squares fit; 4) utilize shift-invariant rank-1 least-squares on a series of rank-1 matrices reconstructed by each column of the aggregating mixing matrix to update shared time courses, and subject-specific time delays and intensities. The experimental results of simulated and actual complex-valued fMRI data show that the proposed algorithm improves the estimates for task-related sensorimotor and auditory networks, compared to pcsCPD and tensorial spatial ICA. The proposed alternating rank- R and rank-1 least squares optimization is also flexible to improve CPD-related algorithm using alternating least squares.

Modified Chevron Osteotomy with Distal Soft Tissue Release for Treating Moderate to Severe Hallux Valgus Deformity: A Minimal Clinical Important Difference Values Study.

OBJECTIVE: To explore whether modified Chevron osteotomy together with distal soft tissue release would correct moderate to severe HV deformity and what is the minimal clinical important difference (MCID) for objective and subjective evaluating parameters. METHODS: From March 2018 to January 2019, 40 hallux valgus patients (including moderate to severe) were enrolled in this retrospective study. The cohort included four males and 36 females. The average age at surgery was 50.95 (range 22-75) years. All patients underwent modified Chevron osteotomy together with distal soft tissue release and completed at least one follow-up at clinic. The American Orthopaedic Foot and Ankle forefoot score (AOFAS, forefoot), Visual Analog Scale (VAS), and Foot Function Index (FFI) were all collected before and after surgery. Besides, the hallux valgus angle (HVA), 1st-2nd intermetatarsal angle (IMA) and distal metatarsal articular angle (DMAA) were measured both before surgery and at last follow-up. All MCID values were calculated by employing distribution-based method. RESULTS: Thirty-seven patients (92.5%) showed satisfied result at a mean 14.3-month follow-up (range 13-22 month). Two patients complained about residual pain at the bunion, and overcorrection (hallux varus) occurred in one patient. Meanwhile, no patient observed nonunion. Being female, age more than 60, residual HVA deformity (>15°), and post IMA more than 9° showed no statistical relationship with the post-operation residual pain (P > 0.05). However, high VAS score before surgery (more than 7) showed strong correlation with residual pain (P < 0.01). The subjective MCID value was 9.50 for AOFAS, 18.92 for FFI, and 1.27 for VAS, respectively. CONCLUSION: The modified Chevron osteotomy together with distal soft tissue release could achieve a satisfied result for moderate to severe HV deformity at early follow-up. The residual pain was associated with severe pain before surgery (VAS more than 7).

Low-Rank Tucker-2 Model for Multi-Subject fMRI Data Decomposition With Spatial Sparsity Constraint.

Tucker decomposition can provide an intuitive summary to understand brain function by decomposing multi-subject fMRI data into a core tensor and multiple factor matrices, and was mostly used to extract functional connectivity patterns across time/subjects using orthogonality constraints. However, these algorithms are unsuitable for extracting common spatial and temporal patterns across subjects due to distinct characteristics such as high-level noise. Motivated by a successful application of Tucker decomposition to image denoising and the intrinsic sparsity of spatial activations in fMRI, we propose a low-rank Tucker-2 model with spatial sparsity constraint to analyze multi-subject fMRI data. More precisely, we propose to impose a sparsity constraint on spatial maps by using an lp norm ( ), in addition to adding low-rank constraints on factor matrices via the Frobenius norm. We solve the constrained Tucker-2 model using alternating direction method of multipliers, and propose to update both sparsity and low-rank constrained spatial maps using half quadratic splitting. Moreover, we extract new spatial and temporal features in addition to subject-specific intensities from the core tensor, and use these features to classify multiple subjects. The results from both simulated and experimental fMRI data verify the improvement of the proposed method, compared with four related algorithms including robust Kronecker component analysis, Tucker decomposition with orthogonality constraints, canonical polyadic decomposition, and block term decomposition in extracting common spatial and temporal components across subjects. The spatial and temporal features extracted from the core tensor show promise for characterizing subjects within the same group of patients or healthy controls as well.

Sparse representation of complex-valued fMRI data based on spatiotemporal concatenation of real and imaginary parts.

BACKGROUND: Spatial sparsity has been found to be in line with the intrinsic characteristic of brain activation. However, identifying a sparse representation of complex-valued fMRI data is challenging due to high noise within the phase data. NEW METHODS: We propose to reduce the noise by combining real and imaginary parts of complex-valued fMRI data along spatial and temporal dimensions to form a real-valued spatiotemporal concatenation model. This model not only enables flexible usage of existing real-valued sparse representation algorithms but also allows for the reconstruction of complex-valued spatial and temporal components from their real and imaginary estimates. We propose to select components from both real and imaginary estimates to reconstruct the complex-valued component, using phase denoising to recover weak brain activity from high-amplitude noise. RESULTS: The K-SVD algorithm was used to obtain a sparse representation within the spatiotemporal concatenation model. The results from simulated and experimental complex-valued fMRI datasets validated the efficacy of our method. COMPARISON WITH EXISTING METHODS: Compared to a magnitude-only approach, the proposed method detected additional voxels manifest within several specific regions expected to be involved but likely missing from the magnitude-only data, e.g., in the anterior cingulate cortex region. Simulation results showed that the additional voxels were accurate and unique information from the phase data. Compared to a complex-valued dictionary learning algorithm, our method exhibited lower noise for both magnitude and phase maps. CONCLUSIONS: The proposed method is robust to noise and effective for identifying a sparse representation of the natively complex-valued fMRI data.

Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data With a Phase Sparsity Constraint.

Canonical polyadic decomposition (CPD) of multi-subject complex-valued fMRI data can be used to provide spatially and temporally shared components among groups with both magnitude and phase information. However, the CPD model is not well formulated due to the large subject variability in the spatial and temporal modalities, as well as the high noise level in complex-valued fMRI data. Considering that the shift-invariant CPD can model temporal variability across subjects, we propose to further impose a phase sparsity constraint on the shared spatial maps to denoise the complex-valued components and to model the inter-subject spatial variability as well. More precisely, subject-specific time delays are first estimated for the complex-valued shared time courses in the framework of real-valued shift-invariant CPD. Source phase sparsity is then imposed on the complex-valued shared spatial maps. A smoothed l0 norm is specifically used to reduce voxels with large phase values after phase de-ambiguity based on the small phase characteristic of BOLD-related voxels. The results from both the simulated and experimental fMRI data demonstrate improvements of the proposed method over three complex-valued algorithms, namely, tensor-based spatial ICA, shift-invariant CPD and CPD without spatiotemporal constraints. When comparing with a real-valued algorithm combining shift-invariant CPD and ICA, the proposed method detects 178.7% more contiguous task-related activations.

Spatial source phase: A new feature for identifying spatial differences based on complex-valued resting-state fMRI data.

Spatial source phase, the phase information of spatial maps extracted from functional magnetic resonance imaging (fMRI) data by data-driven methods such as independent component analysis (ICA), has rarely been studied. While the observed phase has been shown to convey unique brain information, the role of spatial source phase in representing the intrinsic activity of the brain is yet not clear. This study explores the spatial source phase for identifying spatial differences between patients with schizophrenia (SZs) and healthy controls (HCs) using complex-valued resting-state fMRI data from 82 individuals. ICA is first applied to preprocess fMRI data, and post-ICA phase de-ambiguity and denoising are then performed. The ability of spatial source phase to characterize spatial differences is examined by the homogeneity of variance test (voxel-wise F-test) with false discovery rate correction. Resampling techniques are performed to ensure that the observations are significant and reliable. We focus on two components of interest widely used in analyzing SZs, including the default mode network (DMN) and auditory cortex. Results show that the spatial source phase exhibits more significant variance changes and higher sensitivity to the spatial differences between SZs and HCs in the anterior areas of DMN and the left auditory cortex, compared to the magnitude of spatial activations. Our findings show that the spatial source phase can potentially serve as a new brain imaging biomarker and provide a novel perspective on differences in SZs compared to HCs, consistent with but extending previous work showing increased variability in patient data.

Model order effects on ICA of resting-state complex-valued fMRI data: Application to schizophrenia.

BACKGROUND: Component splitting at higher model orders is a widely accepted finding for independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. However, our recent study found that intact components occurred with subcomponents at higher model orders. NEW METHOD: This study investigated model order effects on ICA of resting-state complex-valued fMRI data from 82 subjects, which included 40 healthy controls (HCs) and 42 schizophrenia patients. In addition, we explored underlying causes for distinct component splitting between complex-valued data and magnitude-only data by examining model order effects on ICA of phase fMRI data. A best run selection method was proposed to combine subject averaging and a one-sample t-test. We selected the default mode network (DMN)-, visual-, and sensorimotor-related components from the best run of ICA at varying model orders from 10 to 140. RESULTS: Results show that component integration occurred in complex-valued and phase analyses, whereas component splitting emerged in magnitude-only analysis with increasing model order. Incorporation of phase data appears to play a complementary role in preserving integrity of brain networks. COMPARISON WITH EXISTING METHOD(S): When compared with magnitude-only analysis, the intact DMN component obtained in complex-valued analysis at higher model orders exhibited highly significant subject-level differences between HCs and patients with schizophrenia. We detected significantly higher activity and variation in anterior areas for HCs and in posterior areas for patients with schizophrenia. CONCLUSIONS: These results demonstrate the potential of complex-valued fMRI data to contribute generally and specifically to brain network analysis in identification of schizophrenia-related changes.

Adaptive independent vector analysis for multi-subject complex-valued fMRI data.

BACKGROUND: Complex-valued fMRI data can provide additional insights beyond magnitude-only data. However, independent vector analysis (IVA), which has exhibited great potential for group analysis of magnitude-only fMRI data, has rarely been applied to complex-valued fMRI data. The main challenges in this application include the extremely noisy nature and large variability of the source component vector (SCV) distribution. NEW METHOD: To address these challenges, we propose an adaptive fixed-point IVA algorithm for analyzing multiple-subject complex-valued fMRI data. We exploited a multivariate generalized Gaussian distribution (MGGD)- based nonlinear function to match varying SCV distributions in which the MGGD shape parameter was estimated using maximum likelihood estimation. To achieve our de-noising goal, we updated the MGGD-based nonlinearity in the dominant SCV subspace, and employed a post-IVA de-noising strategy based on phase information in the IVA estimates. We also incorporated the pseudo-covariance matrix of fMRI data into the algorithm to emphasize the noncircularity of complex-valued fMRI sources. RESULTS: Results from simulated and experimental fMRI data demonstrated the efficacy of our method. COMPARISON WITH EXISTING METHOD(S): Our approach exhibited significant improvements over typical complex-valued IVA algorithms, especially during higher noise levels and larger spatial and temporal changes. As expected, the proposed complex-valued IVA algorithm detected more contiguous and reasonable activations than the magnitude-only method for task-related (393%) and default mode (301%) spatial maps. CONCLUSIONS: The proposed approach is suitable for decomposing multi-subject complex-valued fMRI data, and has great potential for capturing additional subject variability.

Multi-subject fMRI analysis via combined independent component analysis and shift-invariant canonical polyadic decomposition.

BACKGROUND: Canonical polyadic decomposition (CPD) may face a local optimal problem when analyzing multi-subject fMRI data with inter-subject variability. Beckmann and Smith proposed a tensor PICA approach that incorporated an independence constraint to the spatial modality by combining CPD with ICA, and alleviated the problem of inter-subject spatial map (SM) variability. NEW METHOD: This study extends tensor PICA to incorporate additional inter-subject time course (TC) variability and to connect CPD and ICA in a new way. Assuming multiple subjects share common TCs but with different time delays, we accommodate subject-dependent TC delays into the CP model based on the idea of shift-invariant CP (SCP). We use ICA as an initialization step to provide the aggregating mixing matrix for shift-invariant CPD to estimate shared TCs with subject-dependent delays and intensities. We then estimate shared SMs using a least-squares fit post shift-invariant CPD. RESULTS: Using simulated fMRI data as well as actual fMRI data we demonstrate that the proposed approach improves the estimates of the shared SMs and TCs, and the subject-dependent TC delays and intensities. The default mode component illustrates larger TC delays than the task-related component. COMPARISON WITH EXISTING METHOD(S): The proposed approach shows improvements over tensor PICA in particular when TC delays are large, and also outperforms SCP with SM orthogonality constraint and SCP with ICA-based SM initialization. CONCLUSIONS: TCs with subject-dependent delays conform to the true situation of multi-subject fMRI data. The proposed approach is suitable for decomposing multi-subject fMRI data with large inter-subject temporal and spatial variability.

ICA of full complex-valued fMRI data using phase information of spatial maps.

BACKGROUND: ICA of complex-valued fMRI data is challenging because of the ambiguous and noisy nature of the phase. A typical solution is to remove noisy regions from fMRI data prior to ICA. However, it may be more optimal to carry out ICA of full complex-valued fMRI data, since any filtering or voxel-based processing may disrupt information that can be useful to ICA. NEW METHOD: We enable ICA of the full complex-valued fMRI data by utilizing phase information of estimated spatial maps (SMs). The SM phases are first adjusted to properly represent spatial phase changes of all voxels based on estimated time courses (TCs), and then these are used to segment the voxels into BOLD-related and unwanted voxels based on a criterion of TC real-part power maximization. Single-subject and group phase masks are finally constructed to remove the unwanted voxels from the individual and group SM estimates. RESULTS: Our method efficiently estimated not only the task-related component but also the non-task-related component DMN. COMPARISON WITH EXISTING METHOD(S): Our method extracted 139-331% more contiguous and reasonable activations than magnitude-only infomax for the task-related component and DMN at |Z|>2.5, and detected more BOLD-related voxels, but eliminated more unwanted voxels than ICA of complex-valued fMRI data with pre-ICA de-noising. Our TC-based phase de-ambiguity exhibited higher accuracy and robustness than the SM-based method. CONCLUSIONS: The TC-based phase de-ambiguity is essential to prepare the SM phases. The SM phases provide a new post-ICA index for reliably identifying and suppressing the unwanted voxels.

Tensor decomposition of EEG signals: a brief review.

Electroencephalography (EEG) is one fundamental tool for functional brain imaging. EEG signals tend to be represented by a vector or a matrix to facilitate data processing and analysis with generally understood methodologies like time-series analysis, spectral analysis and matrix decomposition. Indeed, EEG signals are often naturally born with more than two modes of time and space, and they can be denoted by a multi-way array called as tensor. This review summarizes the current progress of tensor decomposition of EEG signals with three aspects. The first is about the existing modes and tensors of EEG signals. Second, two fundamental tensor decomposition models, canonical polyadic decomposition (CPD, it is also called parallel factor analysis-PARAFAC) and Tucker decomposition, are introduced and compared. Moreover, the applications of the two models for EEG signals are addressed. Particularly, the determination of the number of components for each mode is discussed. Finally, the N-way partial least square and higher-order partial least square are described for a potential trend to process and analyze brain signals of two modalities simultaneously.

[Simulation of long-range transport potential of POPs in Poyang Lake].

The long-range transport potential (LRTP) and overall persistence (Pov) of 5 typical persisitent organic pollutants (POPs) through air and water in Poyang Lake were estimated by the TaPL3 model. The characteristic travel distance (CTD) and Pov of different POPs were compared. In addition, the key parameters were examined by the sensitivity analysis method using p, p'-DDT as an example. The results showed that the CTD(Air) of p, p'-DDT, gamma-HCH, HCB, PCP and 2, 3, 7, 8-TCDD ranged from 432 km (2, 3, 7, 8-TCDD) to 86 479 km (HCB), and the value of Pov(Air) ranged from 85.6 d (PCP) to 2 231 d (HCB), when POPs were emitted to the atmosphere. Soil phase was the main fate of these typical POPs, and it was about 72.0% percent of the total phase. Meanwhile, the CTD(Water) was from 4 207 km (PCP) to 1.19 x 10(5) km (gamma-HCH), and Pov(Water) was from 103 d (PCP) to 2 890 d (HCB), when POPs were emitted to the water. Sediment phase was the main fate of these typical POPs, and it was about 52.5% percent of the total phase. Half-life in the environment and octanol-water partition coefficient logarithm of POPs were the two main physical-chemical parameters that affected CTD and Pov. When compared with other similar studies in China, the CTD(Air) of related POPs in Poyang Lake is in the middle level. While the CTD(Water) was a little higher than other areas, which was due to the higher water depth and water flow velocity of Poyang Lake. Therefore, the higher water depth and water flow velocity were two significantly-affected parameters of CTD(Water). The results could provide a scientific basis to studies of environmental process and risks of POPs in Poyang Lake.

[Posterolateral approaches for treatment of pilon fractures].

OBJECTIVE: To evaluate the effect and complication of surgical treatment for Pilon fracture using the posterolateral approach. METHODS: From August 2009 to March 2011, 15 patients with Pilon fractures (2 in B3,13 in C) and with a separate displaced posterior malleolar fragment was treated in two-stage: the first stage management was on stabiliztion of the soft tissue envelope with temporary external fixator of spanning arthritis, and the second stage management was open reduction and internal fixation with posterolateral approach and anteromedial or anteralateral approach. RESULTS: All patients were followed-up for 12 to 17 months (14.2 months in average). Thirteen of the 15 fractures healed, but 2 fractures needed autologous bone graft procedure duo to nonuion. There was no wound complication related to poterolateral incion. Fourteen fractures had less than 2 mm of incongruity of distal tibia joint. According to Baired-Jackson criteria, the results were excellent in 2 cases, good in 7, fair in 4, and poor in 2. CONCLUSION: The posterolateral approach offers direct visualization for the reduction and fixation of the fibula and posterior distal fragment of the tibia Pilon fractures, faciliate the management of this difficult fracture pattern.

[Preliminary analysis of management for hospitalized elders with hip fractures].

OBJECTIVE: To analyze various clinical parameters of elderly hip fractures so as to improve the management of elder hip fractures in China. METHODS: The data of elder patients with hip fracture (primary diagnosis was fracture of femoral neck or intertrochanter) admitted into our department between 2002 to 2010 were collected. And the relevant clinical parameters included case number, age, ratio of concurrent chronic disease and duration and cost of hospitalization. The software of SAS was used for statistical analysis. RESULTS: A total of 1626 patients (M/F = 547/1079) ≥ 65 yr old with femoral neck fracture were admitted. Average age was 74.7 ± 6.4 yr (65 - 99) and annual average increasing rate 0.5% (-0.1% - 1.8%). The ratio of concurrent chronic disease was 53.3%. Average duration of hospitalization was 18.3 ± 10.9 days (1 - 114) and annual average increasing rate was -6.3% (-19.2% - 8.4%). Average cost of hospitalization was 38 758.04 ± 24 558.15 yuan (76.8 - 339 987.49) and annual average increasing rate 6.4% (-8.7% - 40.0%). A total of 892 patients (M/F = 362/530) ≥ 65 yr with femoral intertrochanteric fracture were admitted. Average age was 76.7 ± 6.8 yr (65 - 105) and annual average increasing rate 1.3% (-1.8% - 4.3%). The ratio of concurrent chronic disease was 55.8%. Average duration of hospitalization was 15.7 ± 8.7 days (1 - 78) and annual average increasing rate -4.5% (-22.1% - 8.0%). Average cost of hospitalization was 35 183.45 ± 21 427.47 yuan (75.3 - 148 150.41) and annual average increasing rate 18.3% (-3.7% - 79.9%). CONCLUSION: The number, age and therapeutic cost of elder patients with hip fracture are increasing continuously. Elderly hip fracture is becoming a serious problem of public health.

Simultaneous source localization and polarization estimation via non-orthogonal joint diagonalization with vector-sensors.

Joint estimation of direction-of-arrival (DOA) and polarization with electromagnetic vector-sensors (EMVS) is considered in the framework of complex-valued non-orthogonal joint diagonalization (CNJD). Two new CNJD algorithms are presented, which propose to tackle the high dimensional optimization problem in CNJD via a sequence of simple sub-optimization problems, by using LU or LQ decompositions of the target matrices as well as the Jacobi-type scheme. Furthermore, based on the above CNJD algorithms we present a novel strategy to exploit the multi-dimensional structure present in the second-order statistics of EMVS outputs for simultaneous DOA and polarization estimation. Simulations are provided to compare the proposed strategy with existing tensorial or joint diagonalization based methods.

[Primary clinical application of traction reductor for lower limb fracture].

OBJECTIVE: To develop a traction reductor for the reduction of lower limb fractures during the minimally invasive surgery and explore its safety and efficacy. METHODS: From February 2007 to March 2009, closed or limited open reduction plus percutaneous plate and screw internal-fixation were conducted in 34 patients with fracture of distal femur and tibia metaphysic, among which there were 3 distal femoral fractures (2 33-B, 1 33-C), 14 proximal tibial fractures (9 41-A, 3 41-B, 2 41-C) and 17 distal tibial fractures (9 43-A, 5 43-B, 3 43-C, 2 Gustilo I a), according to the Association for Osteosynthesis-Orthopaedic Trauma Association (AO-OTA) classification. Besides, closed reduction plus interlocking intramedullary nailing on tibial shaft fracture were applied in 36 patients (7 42-A, 21 42-B, 8 42-C, 2 Gustilo I a). All the 70 patients, with an average age of 37.6 years (range: 17 to 63 years) and average time before surgery of 4.7 d (range: 0.7 to 12.0 d), underwent reduction by self-designed traction reductor for lower limb fracture in the surgery. The reduction duration and C-arm fluoroscopy time were recorded. Recovery of the force line of affected limbs after surgery was determined by whether the line from anterior superior iliac spine to the interdigit between the first and second toe-web passed the patella center. And the distance from bilateral anterior superior iliac spine to medial malleolus tip as well as the difference between lower limbs were recorded to determine the recovery of length after surgery. Meanwhile, the varus-valgus and anteroposterior angulations after reduction were measured by AP and lateral X-ray. RESULTS: The reduction duration was 12.7 min (range: 7 to 31 min); X-ray fluoroscopy time, 1.3 min (range: 0.4 to 3.0 min); length difference between both lower limbs (6.5 ± 1.1) mm; and axial alignment difference (7.0 ± 1.8) mm. The X-ray result showed that varus-valgus angle was (2.75 ± 0.16)°; and anteroposterior angulation (5.13 ± 0.51)°. CONCLUSION: The traction reductor for lower limb fracture could achieve satisfying fracture reduction in the minimally invasive surgery of distal femur, tibia metaphysic and tibial shaft fracture.

[Treatment of malunited calcaneus fracture with subtalar distraction bone block fusion].

OBJECTIVE: To report and evaluate the results of subtalar distraction bone block fusion in the treatment of malunited calcaneus fracture. METHODS: From September 2004 to January 2008, 32 cases of malunited calcaneus fracture were treated, among which 28 cases were classified type II and 4 cases type III by Stephens-Sander's classification. Preoperative X-ray and CT examination demonstrated a talocalcaneal angle of 18.1 degrees ± 2.3 degrees , and an AOFAS score of 36.3 ± 4.1. Subtalar distraction bone block fusion was performed in all cases in this series. Regular follow-up was done with talocalcaneal angle measurement and AOFAS scoring. RESULTS: All the 32 patients had been followed-up of 34 months, ranging from 24 to 65 months, only to reveal a primary wound healing without infection in all but one, in which superficial skin necrosis occurred postoperatively and healed after dressing-changes. Bone healing at the fusion site was seen 3 months after operation in all cases. At the final follow-up, the talocalcaneal angle was 22.9° ± 1.9° and the AOFAS score 77.5 ± 4.1, both demonstrating a significant difference (P < 0.05), when compared with those before operation. CONCLUSION: Subtalar distraction bone block fusion, together with the lateral wall decompression, can correct the main deformity and reduce major symptoms induced by the malunion of calcaneus fractures, being a convenient and practical option for the treatment of malunited calcaneus fracture.

Quality control and original discrimination of Ganoderma lucidum based on high-performance liquid chromatographic fingerprints and combined chemometrics methods.

In this paper, the feasibility and advantages of employing high-performance liquid chromatographic (HPLC) fingerprints combined with chemometrics methods for quality control of the cultured fruiting bodies of Ganoderma lucidum were investigated and demonstrated for the first time. In order to compare the HPLC fingerprints chromatograms between G. lucidum from different origins, the similarities of all the 60 samples and relative peak areas of 19 characteristic compounds were firstly calculated respectively. Then different pattern recognition procedures, including hierarchical cluster analysis (HCA), principal component analysis (PCA), partial least squares-discrimination analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) were applied to classify the G. lucidum samples according to their cultivated origins. Consistent results were obtained to show that G. lucidum samples could be successfully grouped in accordance with the province of origin. Furthermore, four marker constituents were screened out to be the most discriminant variables, which could be applied to accurate discrimination and quality control of G. lucidum by quantitative analysis. Finally, the chemical properties of those samples were also investigated to find out the differences of quality between them. Ranked in decreasing order, the quality of the G. lucidum can be arranged as Jinzhai/Huangshan, Shandong followed by Zhejiang samples. Our results revealed that the developed method has potential perspective for the original discrimination and quality control of G. lucidum.

Discrimination of Ganoderma lucidum according to geographical origin with near infrared diffuse reflectance spectroscopy and pattern recognition techniques.

A rapid and nondestructive near infrared (NIR) method combined with chemometrics was used to discriminate Ganoderma lucidum according to cultivation area. Raw, first, and second derivative NIR spectra were compared to develop a robust classification rule. The chemical properties of G. lucidum samples were also investigated to find out the difference between samples from six varied origins. It could be found that the amount of polysaccharides and triterpenoid saponins in G. lucidum samples was considerably different based on cultivation area. These differences make NIR spectroscopic method viable. Principal component analysis (PCA), discriminant partial least-squares (DPLS) and discriminant analysis (DA) were applied to classify the geographical origins of those samples. The results showed that excellent classification could be obtained after optimizing spectral pre-treatment. For the discriminating of samples from three different provinces, DPLS provided 100% correct classifications. Moreover, for samples from six different locations, the correct classifications of the calibration as well as the validation data set were 96.6% using the DA method after the SNV first derivative spectral pre-treatment. Overall, NIR diffuse reflectance spectroscopy using pattern recognition was shown to have significant potential as a rapid and accurate method for the identification of herbal medicines.