Clinical study on the optic nerve protection of calcium dobesilate in treating glaucoma

AIM:To observe the protective effect of calcium dobesilate on optic nerve of glaucoma patients whose intraocular pressure has been under control through operation.<p>METHODS: A total of 78 inpatients(89 eyes)with glaucoma in this hospital from January, 2011 to February, 2012 were enrolled. All patients were randomly divided into treatment group and control group with 6 months in a course. The visual acuity, visual field(mean sensibility, MS), and intraocular pressure were measured at the experiment onset and 6 months later to evaluate the clinical effect.<p>RESULTS: There were no statistically significant differences in the visual acuity, intraocular pressure between the two groups neither at the experiment onset nor after 6 months(<i>P</i>>0.05). The mean sensibility(MS)of the treatment group was obviously improved(14.56±5.03 <i>vs </i>18.9±5.77, <i>P</i><0.05)with statistically significant differences before and after the treatment, while the MS remained unchanged in the control group(14.75±5.17 <i>vs </i>13.48±4.69). There are statistically significant difference between the treatment group and the control group. Compared to the ratio of improvement, stability, and deterioration in the control group(10.3%, 48.7%, 41.0%), the indexes were changed obviously in the treatment group(64.1%, 28.2%, 7.7%,<i>P</i><0.05)with statistically significant difference between the two groups.<p>CONCLUSION:Calcium dobesilate can improve mean retinal sensitivity and protect the optic nerve in glaucoma patients whose intraocular pressure has been controlled

Investigating Neural Substrates of Individual Independence and Interdependence Orientations via Efficiency-based Dynamic Functional Connectivity : A Machine Learning Approach

Fundings: Beihang University and Capital Medical University Advanced Innovation Center for Big DataBased Precision Medicine Plan; 10.13039/501100001809-National Natural Science Foundation of China; 10.13039/501100000275-Leverhulme Trust;Peer reviewedPostprin

Two-stage sparse representation clustering for dynamic data streams

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Data streams are a potentially unbounded sequence of data objects, and the clustering of such data is an effective way of identifying their underlying patterns. Existing data stream clustering algorithms face two critical issues: 1) evaluating the relationship among data objects with individual landmark windows of fixed size and 2) passing useful knowledge from previous landmark windows to the current landmark window. Based on sparse representation techniques, this article proposes a two-stage sparse representation clustering (TSSRC) method. The novelty of the proposed TSSRC algorithm comes from evaluating the effective relationship among data objects in the landmark windows with an accurate number of clusters. First, the proposed algorithm evaluates the relationship among data objects using sparse representation techniques. The dictionary and sparse representations are iteratively updated by solving a convex optimization problem. Second, the proposed TSSRC algorithm presents a dictionary initialization strategy that seeks representative data objects by making full use of the sparse representation results. This efficiently passes previously learned knowledge to the current landmark window over time. Moreover, the convergence and sparse stability of TSSRC can be theoretically guaranteed in continuous landmark windows under certain conditions. Experimental results on benchmark datasets demonstrate the effectiveness and robustness of TSSRC

The divided brain : Functional brain asymmetry underlying self-construal

Acknowledgments This research is partly supported by the National Natural Science Foundation of China (62071049, 61801026) & Capital Medical University Advanced Innovation Center for Big Data-Based Precision Medicine Plan (BHME-201907), and the Leverhulme Trust (RPG-2019-010).Peer reviewedPublisher PD

The effects of metabolic indicators and immune biomarkers on pregnancy outcomes in women with recurrent spontaneous abortion: a retrospective study

BackgroundThe etiology of recurrent spontaneous abortion (RSA) remains elusive despite specific investigations affirming the association between RSA and thyroid autoimmunity (TAI). This study explores the immunological and metabolic profiles of RSA patients exhibiting positive thyroid antibodies and their connection with the rates of first-trimester miscarriage and live births. The aim is to provide further guidance for clinical interventions.MethodsA retrospective analysis included 478 women with RSA. Thyroid profile, thyroid peroxidase antibodies, and anti-thyroglobulin antibodies were measured in all participants. The clinical characteristics and pregnancy outcomes of RSA women were compared between thyroid autoimmunity (TAI)-positive and TAI-negative patients. Significant factors associated with adverse pregnancy outcomes and risk prediction models were explored in TAI-positive patients. Correlation analysis was used to identify specific metabolic or immune biomarkers associated with thyroid autoantibodies.ResultsThe prevalence of TAI was 18.6%. Compared with women without TAI, the thyroid-stimulating hormone (TSH) concentration of TAI-positive RSA was significantly higher (2.80 ± 2.98 vs 1.89 ± 1.17, p=0.006). After 28 weeks, the live birth rate of the TAI-positive group was lower than that of the TAI-negative group, with statistical significance (p&lt;0.05). The immune biomarkers that differed between RSA women with live births and those with first-trimester miscarriages were complement C4 and interleukin-6, respectively, in TAI-negative and TAI-positive women. Then, a risk prediction model for first-trimester miscarriage was constructed for TAI-positive women with an AUC of 0.81. Finally, some factors related to thyroid peroxidase antibody (TPO-Ab) levels were explored, and it was found that TPO-Ab levels were positively correlated with free thyroxine and negatively correlated with 25 hydroxyvitamin D, interleukin-4, and fasting blood glucose in RSA patients.ConclusionTAI-positive RSA patients have higher first-trimester miscarriage rates and a lower live birth rate, which may be related to metabolic immune shifts in TAI-positive RSA patients

Diaqua­bis­(benzoato-κO)bis­[4,4,5,5-tetra­methyl-2-(pyridin-4-yl-κN)imidazoline-1-oxyl 3-oxide]cobalt(II)

The title compound, [Co(C7H5O2)2(C12H16N3O2)2(H2O)2], was obtained from a conventional solvent evaporation method. The complex mol­ecule is centrosymmetric, so pairs of equivalent ligands lie trans to each other in a slightly distorted octa­hedral CoN2O4 geometry. The CoII ion is coordinated by the pyridine N atoms from NITpPy ligands [NITpPy is 4,4,5,5-tetra­methyl-2-(pyridin-4-yl)imidazoline-1-oxyl 3-oxide), water O atoms and two monodentate benzoate O atoms. The complex mol­ecules are connected by O—H⋯O hydrogen bonds between water mol­ecules and benzoate ligands, forming chains parallel to [100]. π–π stacking inter­actions between the benzoate ligands with centroid–centroid distances of 3.752 (2) Å connect the chains into layers parallel to (10-1)

Real-Time Human Motion Capture Driven by a Wireless Sensor Network

The motion of a real object model is reconstructed through measurements of the position, direction, and angle of moving objects in 3D space in a process called “motion capture.” With the development of inertial sensing technology, motion capture systems that are based on inertial sensing have become a research hot spot. However, the solution of motion attitude remains a challenge that restricts the rapid development of motion capture systems. In this study, a human motion capture system based on inertial sensors is developed, and the real-time movement of a human model controlled by real people’s movement is achieved. According to the features of the system of human motion capture and reappearance, a hierarchical modeling approach based on a 3D human body model is proposed. The method collects articular movement data on the basis of rigid body dynamics through a miniature sensor network, controls the human skeleton model, and reproduces human posture according to the features of human articular movement. Finally, the feasibility of the system is validated by testing of system properties via capture of continuous dynamic movement. Experiment results show that the scheme utilizes a real-time sensor network-driven human skeleton model to achieve the accurate reproduction of human motion state. The system also has good application value