Altered gray matter volume and functional connectivity in lung cancer patients with bone metastasis pain.

Bone metastasis pain (BMP) is a severe chronic pain condition. Our previous studies on BMP revealed functional brain abnormalities. However, the potential effect of BMP on brain structure and function, especially gray matter volume (GMV) and related functional networks, have not yet been clearly illustrated. Voxel-based morphometry and functional connectivity (FC) analysis methods were used to investigate GMV and intrinsic FC differences in 45 right-handed lung cancer patients with BMP(+), 37 lung cancer patients without BMP(-), and 45 healthy controls (HCs). Correlation analysis was performed thereafter with all clinical variables by Pearson correlation. Compared to HCs, BMP(+) group exhibited decreased GMV in medial frontal gyrus (MFG) and right middle temporal gyrus (MTG). Compared with BMP(-) group, BMP(+) group exhibited reduced GMV in cerebelum_6_L and left lingual gyrus. However, no regions with significant GMV differences were found between BMP(-) and HCs groups. Receiver operating characteristic analysis indicated the potential classification power of these aberrant regions. Correlation analysis revealed that GMV in the right MTG was positively associated with anxiety in BMP(+) group. Further FC analysis demonstrated enhanced interactions between MFG/right MTG and cerebellum in BMP(+) patients compared with HCs. These results showed that BMP was closely associated with cerebral alterations, which may induce the impairment of pain moderation circuit, deficits in cognitive function, dysfunction of emotional control, and sensorimotor processing. These findings may provide a fresh perspective and further neuroimaging evidence for the possible mechanisms of BMP. Furthermore, the role of the cerebellum in pain processing needs to be further investigated.

Ionic Reactivity of 2-Isocyanoaryl Thioethers: Access to 2-Halo and 2-Aminobenzothia/Selenazoles.

An ionic cascade insertion/cyclization reaction of thia-/selena-functionalized arylisocyanides has been successfully developed for the efficient and practical synthesis of 2-halobenzothiazole/benzoselenazole derivatives. This synthetic protocol, incorporating a halogen atom when forming the five-membered ring of benzothia/selenazoles, is different from the existing ones, where halogenation of the preformed benzothia/selenazole precursors happens. Additionally, a facile access to 2-aminobenzothiazoles is also achieved by the one-pot cascade reaction of 2-isocyanoaryl thioethers, iodine, and amines.

Case report: A cystic capillary hemangioma located at the conus medullaris mimicking hemangioblastoma.

Capillary hemangiomas, usually found in skin and mucosal tissues, are rarely encountered within the spinal cord, presenting a significant diagnostic challenge. We report a rare case of intradural extramedullary capillary hemangioma at the conus medullaris in a 66-year-old female patient. Our initial diagnosis leaned towards a cystic hemangioblastoma based on MRI findings due to the presence of cystic formation with an enhanced mural nodule. However, surgical exploration and subsequent pathological examination revealed the lesion as a capillary hemangioma. To the authors' knowledge, this case may represent the first documented instance of a spinal capillary hemangioma that mimics a cystic hemangioblastoma.

[Early efficacies of total knee arthroplasty for knees with severe lateral instability].

OBJECTIVE: To evaluate the outcomes of primary total knee arthroplasty (TKA) in the treatment of knee with severe lateral instability and summarize the essential points of operation and rehabilitation. METHODS: From February 2005 to August 2010, primary TKA was performed in 27 severe lateral unstable knees (25 cases), including 3 males (3 knees) and 22 females (24 knees). Their mean age was 57.8 (37-71) years. And their primary diseases included rheumatoid arthritis (22 knees in 21 cases) and osteoarthritis (5 knees in 4 cases). Thirteen lateral unstable knees were accompanied with 18.08° ± 5.96°(15-35°) varus deformity; in the rest 14 knees, there was medial instability with 20.71° ± 7.03° (15-35°) valgus deformity. Blood loss volume, operative duration and complications were recorded. During the follow-up period, HSS score, knee stability and varus/valgus status were recorded preoperatively, 1, 3, 6, 12 months and then annually postoperatively. RESULTS: AORI type I bone defect was found at the proximal tibia in 18 knees and distal lateral femoral condyle in 10 knees. All defects were reconstructed with cement or autograft. AORI type II bone defects at proximal tibia in 3 knees were reconstructed with metal augmentation. Blood loss during the first 24 hours were (438.9 ± 109.5) (400-700) ml and operative duration (91.1 ± 11.6) (70-110) min. The mean follow-up period was (41.6 ± 10.9) (27-60) months. At the final follow-up, the HSS score increased from (45.8 ± 5.4) to (85.4 ± 4.5) (t = 30.15, P < 0.01) .Five knees in 5 cases had mild postoperative instability. All cases were allowed to walk with knee orthosis for 4-6 weeks. At the end of follow-up, mild lateral instability of 2 knees persisted. One augmented knee had osteolysis beneath metal block. CONCLUSION: TKA for knees with severe lateral instability requires a deep understanding of causes and a rational treatment. Proper handling of bone defects and careful release of lateral soft tissue are two critical points for postoperative knee stability. Wearing knee orthosis during the early postoperative stage may be helpful or residual mild instability.

Adaptively Weighted k-Tuple Metric Network for Kinship Verification.

Facial image-based kinship verification is a rapidly growing field in computer vision and biometrics. The key to determining whether a pair of facial images has a kin relation is to train a model that can enlarge the margin between the faces that have no kin relation while reducing the distance between faces that have a kin relation. Most existing approaches primarily exploit duplet (i.e., two input samples without cross pair) or triplet (i.e., single negative pair for each positive pair with low-order cross pair) information, omitting discriminative features from multiple negative pairs. These approaches suffer from weak generalizability, resulting in unsatisfactory performance. Inspired by human visual systems that incorporate both low-order and high-order cross-pair information from local and global perspectives, we propose to leverage high-order cross-pair features and develop a novel end-to-end deep learning model called the adaptively weighted k -tuple metric network (AW k -TMN). Our main contributions are three-fold. First, a novel cross-pair metric learning loss based on k -tuplet loss is introduced. It naturally captures both the low-order and high-order discriminative features from multiple negative pairs. Second, an adaptively weighted scheme is formulated to better highlight hard negative examples among multiple negative pairs, leading to enhanced performance. Third, the model utilizes multiple levels of convolutional features and jointly optimizes feature and metric learning to further exploit the low-order and high-order representational power. Extensive experimental results on three popular kinship verification datasets demonstrate the effectiveness of our proposed AW k -TMN approach compared with several state-of-the-art approaches. The source codes and models are released.1.

[Effects of Tuina and Chinese patent drug Shuxuetong injection on deep venous thrombosis and functional rehabilitation after total knee arthroplasty].

OBJECTIVE: To evaluate the efficacy of Tuina and Chinese patent drug Shuxuetong injection in preventing patients undergoing total knee arthroplasty from deep venous thrombosis and in functional rehabilitation. METHODS: A total of 120 patients with diagnosed rheumatoid arthritis in the Department of Orthopaedic Surgery, Shanghai Guanghua Hospital of Integrated Traditional Chinese and Western Medicine in China were enrolled for this study. The patients underwent total knee arthroplasty and were divided into treatment group (n=60) and control group (n=60) after surgery. Patients in the control group received conventional rehabilitation training, including using a continuous passive motion machine and training of muscle contractions of the lower limb. Patients in the treatment group were administered Shuxuetong injection and Tuina based on the conventional rehabilitation training. The course of treatment lasted for 2 weeks. Hospital for Special Surgery (HSS) knee score, rate of deep venous thrombosis and range of motion of the knee joint were evaluated before and after treatment. RESULTS: There was no significant difference in HSS knee score and range of motion as compared before and after treatment in two group (P>0.05). The rate of deep venous thrombosis of the treatment group was 13.33%, which was lower than 20% of the control group (P<0.05). CONCLUSION: Tuina combined with Shuxuetong injection treatment can prevent deep venous thrombosis in patients with rheumatoid arthritis after total knee arthroplasty.

The value of diffusion tensor tractography delineating corticospinal tract in glioma in rat: validation via correlation histology.

BACKGROUND: An assessment of the degree of white matter tract injury is important in neurosurgical planning for patients with gliomas. The main objective of this study was to assess the injury grade of the corticospinal tract (CST) in rats with glioma using diffusion tensor imaging (DTI). METHODS: A total 17 rats underwent 7.0T MRI on day 10 after tumor implantation. The apparent diffusion coefficient (ADC) and fractional anisotropy (FA) were acquired in the tumor, peritumoral and contralateral areas, and the ADC ratio (ipsilateral ADC/contralateral ADC) and rFA (relative FA = ipsilateral FA/contralateral FA) in the peritumoral areas were measured. The CST injury was divided into three grades and delineated by diffusion tensor tractography reconstruction imaging. The fiber density index (FDi) of the ipsilateral and contralateral CST and rFDi (relative FDi = ipsilateral FDi/contralateral FDi) in the peritumoral areas were measured. After the mice were sacrificed, the invasion of glioma cells and fraction of proliferating cells were observed by hematoxylin-eosin and Ki67 staining in the tumor and peritumoral areas. The correlations among the pathology results, CST injury grade and DTI parameter values were calculated using a Spearman correlation analysis. One-way analysis of variance was performed to compare the different CST injury grade by the rFA, rFDi and ADC ratio values. RESULTS: The tumor cells and proliferation index were positively correlated with the CST injury grade (r = 0.8857, 0.9233, P < 0.001). A negative correlation was demonstrated between the tumor cells and the rFA and rFDi values in the peritumoral areas (r = -0.8571, -0.5588), and the proliferation index was negatively correlated with the rFA and rFDi values (r = -0.8571, -0.5588), while the ADC ratio was not correlated with the tumor cells or proliferation index. The rFA values between the CST injury grades (1 and 3, 2 and 3) and the rFDi values in grades 1 and 3 significantly differed (P < 0.05). CONCLUSIONS: Diffusion tensor imaging may be used to quantify the injury degrees of CST involving brain glioma in rats. Our data suggest that these quantitative parameters may be used to enhance the efficiency of delineating the relationship between fiber tracts and malignant tumor.

Sharable and Individual Multi-View Metric Learning.

This paper presents a sharable and individual multi-view metric learning (MvML) approach for visual recognition. Unlike conventional metric leaning methods which learn a distance metric on either a single type of feature representation or a concatenated representation of multiple types of features, the proposed MvML jointly learns an optimal combination of multiple distance metrics on multi-view representations, where not only it learns an individual distance metric for each view to retain its specific property but also a shared representation for different views in a unified latent subspace to preserve the common properties. The objective function of the MvML is formulated in the large margin learning framework via pairwise constraints, under which the distance of each similar pair is smaller than that of each dissimilar pair by a margin. Moreover, to exploit the nonlinear structure of data points, we extend MvML to a sharable and individual multi-view deep metric learning (MvDML) method by utilizing the neural network architecture to seek multiple nonlinear transformations. Experimental results on face verification, kinship verification, and person re-identification show the effectiveness of the proposed sharable and individual multi-view metric learning methods.

Discriminative Deep Metric Learning for Face and Kinship Verification.

This paper presents a new discriminative deep metric learning (DDML) method for face and kinship verification in wild conditions. While metric learning has achieved reasonably good performance in face and kinship verification, most existing metric learning methods aim to learn a single Mahalanobis distance metric to maximize the inter-class variations and minimize the intra-class variations, which cannot capture the nonlinear manifold where face images usually lie on. To address this, we propose a DDML method to train a deep neural network to learn a set of hierarchical nonlinear transformations to project face pairs into the same latent feature space, under which the distance of each positive pair is reduced and that of each negative pair is enlarged. To better use the commonality of multiple feature descriptors to make all the features more robust for face and kinship verification, we develop a discriminative deep multi-metric learning method to jointly learn multiple neural networks, under which the correlation of different features of each sample is maximized, and the distance of each positive pair is reduced and that of each negative pair is enlarged. Extensive experimental results show that our proposed methods achieve the acceptable results in both face and kinship verification.

[Experimental study on effect of rhizoma drynariae flavone on bone destruction of collagen inducted arthritis rat].

OBJECTIVE: Through establishing the rat model of CIA to evaluate the effect and mechanism of Rhizoma Drynariae Flavone on bone destruction of CIA rat. METHODS: Subcutaneous injection of bovine type II collagen was used to induce Wistar rats to fall ill, and then established the rat model of CIA. The rats whose inflammation scores reached to two points or above were randomly divided into four groups, and were treated accordingly. The effect of Rhizoma Drynariae Flavone on bone destruction was evaluated. RESULTS: At 12 weeks after treatment, bone trabecular area percentage and bone trabecular number in Rhizoma Drynariae Flavone group, Rhizoma Drynariae Flavone-1/2 Etanercept group, Etanercept group was obviously higher than that of sterilization water group (P < 0.05); and the trabecular resolving power of these groups was obviously less than that of sterilization water group (P < 0.05). CONCLUSION: Rhizoma Drynariae Flavone can obviously inhibit inflammation of joint bone destruction of CIA rats,the effect may be related with bone trabecular number reduction and trabecular resolving power increasing.