7D High-Dynamic Spin-Multiplexing.

Multiplexing technology creates several orthogonal data channels and dimensions for high-density information encoding and is irreplaceable in large-capacity information storage, and communication, etc. The multiplexing dimensions are constructed by light attributes and spatial dimensions. However, limited by the degree of freedom of interaction between light and material structure parameters, the multiplexing dimension exploitation method is still confused. Herein, a 7D Spin-multiplexing technique is proposed. Spin structures with four independent attributes (color center type, spin axis, spatial distribution, and dipole direction) are constructed as coding basic units. Based on the four independent spin physical effects, the corresponding photoluminescence wavelength, magnetic field, microwave, and polarization are created into four orthogonal multiplexing dimensions. Combined with the 3D of space, a 7D multiplexing method is established, which possesses the highest dimension number compared with 6 dimensions in the previous study. The basic spin unit is prepared by a self-developed laser-induced manufacturing process. The free state information of spin is read out by four physical quantities. Based on the multiple dimensions, the information is highly dynamically multiplexed to enhance information storage efficiency. Moreover, the high-dynamic in situ image encryption/marking is demonstrated. It implies a new paradigm for ultra-high-capacity storage and real-time encryption.

Spectrometer-Less Remote Sensing Image Classification Based on Gate-Tunable van der Waals Heterostructures.

Remote sensing technology, which conventionally employs spectrometers to capture hyperspectral images, allowing for the classification and unmixing based on the reflectance spectrum, has been extensively applied in diverse fields, including environmental monitoring, land resource management, and agriculture. However, miniaturization of remote sensing systems remains a challenge due to the complicated and dispersive optical components of spectrometers. Here, m-phase GaTe0.5Se0.5 with wide-spectral photoresponses (250-1064 nm) and stack it with WSe2 are utilizes to construct a two-dimensional van der Waals heterojunction (2D-vdWH), enabling the design of a gate-tunable wide-spectral photodetector. By utilizing the multi-photoresponses under varying gate voltages, high accuracy recognition can be achieved aided by deep learning algorithms without the original hyperspectral reflectance data. The proof-of-concept device, featuring dozens of tunable gate voltages, achieves an average classification accuracy of 87.00% on 6 prevalent hyperspectral datasets, which is competitive with the accuracy of 250-1000 nm hyperspectral data (88.72%) and far superior to the accuracy of non-tunable photoresponse (71.17%). Artificially designed gate-tunable wide-spectral 2D-vdWHs GaTe0.5Se0.5/WSe2-based photodetector present a promising pathway for the development of miniaturized and cost-effective remote sensing classification technology.

Reliability-based anti-disturbance control for systems with parametric stochastic uncertainty: A probabilistic LMI approach.

We propose a reliability-based anti-disturbance control (RADC) method for systems with parametric stochastic uncertainty based on the linear matrix inequality (LMI) and the limit state function. Differing from the existing anti-disturbance control, the parametric stochastic uncertainty is considered in both the concerned system and the exogenous disturbance system. With this consideration, the condition for system stability and performance robustness is described by a stochastic LMI which holds with a certain probability (reliability). Through the limit state function method, the stochastic LMI is subtly transformed into two probabilistic LMIs for two different cases. The proposed probabilistic LMIs contain two probabilistic parameters of reliability indexes that quantify the effect of parametric stochastic uncertainty. At different prescribed reliability indexes, controllers with different reliability can be flexibly and reliably designed. Two illustrative examples with Monte-Carlo verification are presented to demonstrate the feasibility and effectiveness of the proposed RADC method.

Diagnosis of Alzheimer's Disease Based on Accelerated Mirror Descent Optimization and a Three-Dimensional Aggregated Residual Network.

Alzheimer's disease (AD), a neuropsychiatric disorder, continually arises in the elderly. To date, no targeted medications have been developed for AD. Early and fast diagnosis of AD plays a pivotal role in identifying potential AD patients, enabling timely medical interventions, and mitigating disease progression. Computer-aided diagnosis (CAD) becomes possible with the burgeoning of deep learning. However, the existing CAD models for processing 3D Alzheimer's disease images usually have the problems of slow convergence, disappearance of gradient, and falling into local optimum. This makes the training of 3D diagnosis models need a lot of time, and the accuracy is often poor. In this paper, a novel 3D aggregated residual network with accelerated mirror descent optimization is proposed for diagnosing AD. First, a novel unbiased subgradient accelerated mirror descent (SAMD) optimization algorithm is proposed to speed up diagnosis network training. By optimizing the nonlinear projection process, our proposed algorithm can avoid the occurrence of the local optimum in the non-Euclidean distance metric. The most notable aspect is that, to the best of our knowledge, this is the pioneering attempt to optimize the AD diagnosis training process by improving the optimization algorithm. Then, we provide a rigorous proof of the SAMD's convergence, and the convergence of SAMD is better than any existing gradient descent algorithms. Finally, we use our proposed SAMD algorithm to train our proposed 3D aggregated residual network architecture (ARCNN). We employed the ADNI dataset to train ARCNN diagnostic models separately for the AD vs. NC task and the sMCI vs. pMCI task, followed by testing to evaluate the disease diagnostic outcomes. The results reveal that the accuracy can be improved in diagnosing AD, and the training speed can be accelerated. Our proposed method achieves 95.4% accuracy in AD diagnosis and 79.9% accuracy in MCI diagnosis; the best results contrasted with several state-of-the-art diagnosis methods. In addition, our proposed SAMD algorithm can save about 19% of the convergence time on average in the AD diagnosis model compared with the gradient descent algorithms, which is very momentous in clinic.

Adaptive Anti-Disturbance Control for a Class of Uncertain Nonlinear Systems With Composite Disturbances.

High-precision and safety control in face of disturbances and uncertainties is a challenging issue of both theoretical and practical importance. In this article, new adaptive anti-disturbance control schemes are proposed for a class of uncertain nonlinear systems with composite disturbances, including additive disturbances, multiplicative actuator faults, and implicit disturbances deeply coupled with system states. Both the cases with known and unknown control/fault directions are investigated. By properly fusing the techniques of disturbance observers and adaptive compensation, it is shown that all closed-loop signals are globally uniformly bounded and the tracking error converges to zero asymptotically, no matter the control/fault directions are known or not. In the case of known directions, the proposed control scheme, for the first time, guarantees asymptotic tracking and L ∞ tracking performance simultaneously in face of disturbances and actuator faults. Moreover, novel Nussbaum functions and a contradiction argument are introduced, which allow the system to have multiple unknown nonidentical control directions and unknown time-varying fault direction. Simulation results illustrate the effectiveness of the proposed control schemes.

Multiplicative fault concept and fault observer design for reliability study of degrading reaction wheel.

The reaction wheel is a typical actuator and a crucial weak link in the spacecraft attitude control system, and much attention has been paid to its reliability problem. Due to limited samples and high cost for reaction wheel life tests, a simulation method by introducing attitude coupling dynamics and multiplicative fault concept is developed to analyze the logic of electric current as a performance indicator and verify its accuracy for reliability modeling. Furthermore, a new and intrinsic performance indicator of multiplicative fault is proposed for more application scenarios of reliability modeling and an adaptive sliding mode observer is designed for fault estimation. An illustrative example shows that the performance indicator of multiplicative fault can be used for various mission scenarios but requires certain persistent excitation, while electric current is the opposite.

An Improved Convolutional Neural Network-Based Scene Image Recognition Method.

To solve the problems existing in the research of scene recognition, this paper studies a new convolutional neural network target detection model to achieve a better balance between the accuracy and speed of high-speed scene image recognition. First, aiming at the problem that the image is easy to be disturbed by impurities and poor quality in fine-grained image recognition, a preprocessing method based on the Canny edge detection is designed and the Canny operator is introduced to process the gray image. Second, the L2 regularization algorithm is used to optimize the basic network framework of the convolutional neural network, enhance the stability of the model in a complex environment, improve the generalization ability of the model, and improve the recognition accuracy of the algorithm to a certain extent. Finally, by collecting the campus environment datasets under different environmental conditions, the location recognition experiment and heat map visualization experiment are carried out. Experiments show that compared with the basic convolution neural network algorithm, the algorithm has better recognition performance and good generalization ability. The research of this study realizes the effective combination of multiframe convolution neural network and batch normalization algorithm and has a good practical effect on scene image recognition.

Alzheimer's disease diagnosis via multimodal feature fusion.

Alzheimer's disease (AD) is the most common neurodegenerative disorder in the elderly. Early diagnosis of AD plays a vital role in slowing down the progress of AD because there is no effective drug to treat the disease. Some deep learning models have recently been presented for AD diagnosis and have more satisfactory performance than classic machine learning methods. Nevertheless, most of the existing computer-aided diagnostic models used neuroimaging features for diagnosis, ignoring patients' clinical and biological information. This makes the AD diagnosis inaccurate. In this study, we propose a novel multimodal feature transformation and fusion model for AD diagnosis. The feature transformation aims to avoid the difference in feature dimensions between different modal data and further mine the significant features for AD diagnosis. A geometric algebra-based feature extension method is proposed to obtain different levels of high-dimensional features from patients' clinical and personal biological data. Then, an influence degree-based feature filtration algorithm is proposed to filtrate those features that have no apparent guiding significance for AD diagnosis. Finally, an ANN (Artificial Neural Network)-based framework is designed to fuse transformed features with neuroimaging features extracted by CNN (Convolutional Neural Network) for AD diagnosis. The more in-depth feature mining of patients' clinical information and biological information can significantly improve the performance of computer-aided AD diagnosis. The experiments are obtained on the ADNI dataset. Our proposed model can converge faster and achieves 96.2% accuracy in AD diagnostic task and 87.4% accuracy in MCI (Mild Cognitive Impairment) diagnostic task. Compared with other methods, our proposed approach has an excellent performance in AD diagnosis and surpasses SOTA (state-of-the-art) methods. Therefore, our model can provide more reasonable suggestions for clinicians to diagnose and treat disease.

Comparison between Amplatzer and Watchman Left Atrial Appendage Closure Devices for Stroke Prevention in Atrial Fibrillation: A Systematic Review and Meta-Analysis.

INTRODUCTION: The Amplatzer and Watchman left atrial appendage closure (LAAC) devices are the two most frequently used devices for LAAC devices worldwide. This meta-analysis aimed to compare the safety and efficacy of the two devices. METHODS: We searched the PubMed, EMBASE, and the Cochrane Library for studies up to February 6, 2022 that compared the safety and efficacy of the Amplatzer and Watchman devices. RESULTS: Fifteen studies including 2,150 patients in randomized controlled trials and 2,526 patients in observational studies were included in the meta-analysis. Amplatzer device was associated with higher rates of major procedure-related complications (odds ratio [OR]: 1.99, 95% confidence interval [CI]: 1.45-2.74, p < 0.0001) and device embolization (OR: 1.99, 95% CI: 1.09-3.64, p = 0.03). However, Amplatzer device had lower rates of total peridevice leak (PDL) (OR: 0.48, 95% CI: 0.27-0.83, p = 0.009), significant PDL (OR: 0.27, 95% CI: 0.12-0.57, p = 0.0007) and device-related thrombus (DRT) (OR: 0.67, 95% CI: 0.48-0.95, p = 0.02). No statistical differences were observed between the two devices in other safety and efficacy endpoints, such as pericardial effusion, cardiac tamponade, air embolism, vascular complications, ischemic stroke/transient ischemic attack (TIA), hemorrhagic stroke, all-cause death, cardiovascular death, and bleeding. CONCLUSIONS: Amplatzer LAAC device was associated with higher rates of major procedure-related complications, especially in device embolization. Watchman LAAC device was associated with higher rates of PDL and DRT. There were no significant differences between two devices in ischemic stroke/TIA, hemorrhagic stroke, all-cause death, cardiovascular death, and bleeding.

Estimating Quasiperiodic Disturbance With Unknown Frequency via Expectation-Maximization.

This article is concerned with a quasiperiodic disturbance estimation problem for dynamic control systems without prior knowledge on frequency. As a major challenge of our work, the quasiperiodic disturbance to be treated is always submerged by untargeted waves, leading to complicated coupling between disturbance separation and frequency identification. Existing approaches on quasiperiodic disturbance rejection have circumvented, rather than overcome, this challenge by assuming either a known frequency or a measurable disturbance signal. In this work, an expectation-maximization (EM) framework is proposed where disturbance signal separation and frequency identification are carried out in an iterative manner. In the E-step, the expected log-likelihood function is evaluated via reconstruction of the quasiperiodic signal based on the latest frequency estimate; and in the M-step, the frequency estimate is updated by maximizing the log-likelihood function obtained in the E-step. To facilitate recursive frequency estimation, an online EM algorithm is also developed based on the forward-only smoothing techniques. Furthermore, we show that the proposed method can be easily extended to deal with nonlinear system models and time-varying frequencies.

Diagnosis of Alzheimer's Disease with Ensemble Learning Classifier and 3D Convolutional Neural Network.

Alzheimer's disease (AD), the most common type of dementia, is a progressive disease beginning with mild memory loss, possibly leading to loss of the ability to carry on a conversation and respond to environments. It can seriously affect a person's ability to carry out daily activities. Therefore, early diagnosis of AD is conducive to better treatment and avoiding further deterioration of the disease. Magnetic resonance imaging (MRI) has become the main tool for humans to study brain tissues. It can clearly reflect the internal structure of a brain and plays an important role in the diagnosis of Alzheimer's disease. MRI data is widely used for disease diagnosis. In this paper, based on MRI data, a method combining a 3D convolutional neural network and ensemble learning is proposed to improve the diagnosis accuracy. Then, a data denoising module is proposed to reduce boundary noise. The experimental results on ADNI dataset demonstrate that the model proposed in this paper improves the training speed of the neural network and achieves 95.2% accuracy in AD vs. NC (normal control) task and 77.8% accuracy in sMCI (stable mild cognitive impairment) vs. pMCI (progressive mild cognitive impairment) task in the diagnosis of Alzheimer's disease.

Temporal change of retinal nerve fiber layer reflectance speckle in normal and hypertensive retinas.

This study investigated temporal change of retinal nerve fiber layer (RNFL) reflectance speckle in retinas with ocular hypertensive (OHT) damage and in control retinas from untreated eyes. Experimental OHT damage to rat retinas was induced by laser photocoagulation of the trabecular meshwork. A series of 660 nm reflectance images was collected from isolated retinas at 10-sec intervals. Areas containing speckled texture were selected on nerve fiber bundles. Correlation coefficients between images with different imaging delays were calculated and plotted as a function of delay. To evaluate the temporal change of speckles, decay of correlation coefficients with time was fitted with an exponential function characterized by a time constant τ. Reflectance per unit thickness (σ) of the areas was also measured and low σ was used as a surrogate of OHT damage. Speckle phenomena occurred in the control RNFL and the RNFL with reduced σ. In the control retinas, τ and σ were nearly constant along bundles but differed significantly among bundles in the same retinas. Among the control retinas, σ was similar, whereas τ varied significantly. In the retinas with OHT damage (low σ) τ could be within, greater or lower than the range in controls. The parameters τ and σ provide independent assessment of the RNFL with OHT damage. Measurements of temporal change of RNFL reflectance speckle may offer a method for detecting functional abnormality of the RNFL.

Inflammasome Activation Induces Pyroptosis in the Retina Exposed to Ocular Hypertension Injury.

Mechanical stress and hypoxia during episodes of ocular hypertension (OHT) trigger glial activation and neuroinflammation in the retina. Glial activation and release of pro-inflammatory cytokines TNFα and IL-1ß, complement, and other danger factors was shown to facilitate injury and loss of retinal ganglion cells (RGCs) that send visual information to the brain. However, cellular events linking neuroinflammation and neurotoxicity remain poorly characterized. Several pro-inflammatory and danger signaling pathways, including P2X7 receptors and Pannexin1 (Panx1) channels, are known to activate inflammasome caspases that proteolytically activate gasdermin D channel-formation to export IL-1 cytokines and/or induce pyroptosis. In this work, we used molecular and genetic approaches to map and characterize inflammasome complexes and detect pyroptosis in the OHT-injured retina. Acute activation of distinct inflammasome complexes containing NLRP1, NLRP3 and Aim2 sensor proteins was detected in RGCs, retinal astrocytes and Muller glia of the OHT-challenged retina. Inflammasome-mediated activation of caspases-1 and release of mature IL-1ß were detected within 6 h and peaked at 12-24 h after OHT injury. These coincided with the induction of pyroptotic pore protein gasdermin D in neurons and glia in the ganglion cell layer (GCL) and inner nuclear layer (INL). The OHT-induced release of cytokines and RGC death were significantly decreased in the retinas of Casp1-/-Casp4(11)del, Panx1-/- and in Wild-type (WT) mice treated with the Panx1 inhibitor probenecid. Our results showed a complex spatio-temporal pattern of innate immune responses in the retina. Furthermore, they indicate an active contribution of neuronal NLRP1/NLRP3 inflammasomes and the pro-pyroptotic gasdermin D pathway to pathophysiology of the OHT injury. These results support the feasibility of inflammasome modulation for neuroprotection in OHT-injured retinas.

Response of the Retinal Nerve Fiber Layer Reflectance and Thickness to Optic Nerve Crush.

Purpose: To study the effects of acute optic nerve damage on the reflectance of the retinal nerve fiber layer (RNFL) and to compare the time courses of changes of RNFL reflectance and thickness. Methods: A rat model of optic nerve crush (ONC) was compared with previously studied normal retinas. The reflectance and thickness of the RNFL were studied at 1 to 5 weeks after ONC. Reflectance spectra from 400 to 830 nm were measured for eyes with ONC, their contralateral untreated eyes, and eyes with sham surgery. Directional reflectance was studied by varying the angle of light incidence. RNFL thickness was measured by confocal microscopy. Results: After ONC, the RNFL reflectance remained directional. At 1 week, RNFL reflectance decreased significantly at all wavelengths (P < 0.001), whereas there was no significant change in RNFL thickness (P = 0.739). At 2 weeks, both RNFL reflectance and thickness decreased significantly, and by 5 weeks they declined to approximately 40% and 30%, respectively, of the normal values. Although RNFL reflectance decreased at all wavelengths, there was a greater reduction at short wavelengths. Spectral shape at long wavelengths was similar to the normal. Some of these changes were also found in the contralateral untreated eyes, but none of these changes were found in eyes with sham surgery. Conclusions: Decrease of RNFL reflectance after ONC occurs prior to thinning of the RNFL and the decrease is more prominent at short wavelengths. Direct measurement of RNFL reflectance, especially at short wavelengths, may provide early detection of axonal damage.

Reflectance Spectrum and Birefringence of the Retinal Nerve Fiber Layer With Hypertensive Damage of Axonal Cytoskeleton.

Purpose: Glaucoma damages the retinal nerve fiber layer (RNFL). This study used precise multimodal image registration to investigate the changes of the RNFL reflectance spectrum and birefringence in nerve fiber bundles with different degrees of axonal damage. Methods: The reflectance spectrum of individual nerve fiber bundles in normal rats and rats with experimental glaucoma was measured from 400 to 830 nm and their birefringence was measured at 500 nm. Optical measurements of the same bundles were made at different distances from the optic nerve head (ONH). After the optical measurements, the axonal cytoskeleton of the RNFL was evaluated by confocal microscopy to assess the severity of cytoskeletal change. Results: For normal bundles, the shape of the RNFL reflectance spectrum and the value of RNFL birefringence did not change along bundles. In treated retinas, damage to the cytoskeleton varied within and across retinas. The damage in retinal sectors was subjectively graded from normal-looking to severe. Change of spectral shape occurred near the ONH in all sectors studied. This change became more prominent and occurred farther from the ONH with increased damage severity. In contrast, RNFL birefringence did not show change in normal-looking sectors, but decreased in sectors with mild and moderate damage. The birefringence of severely damaged sectors was either within or below the normal range. Conclusions: Varying degrees of cytoskeletal damage affect the RNFL reflectance spectrum and birefringence differently, supporting differences in the ultrastructural basis for the two optical properties. Both properties, however, may provide a means to detect disease and to estimate ultrastructural damage of the RNFL in glaucoma.

Deferred Versus Immediate Stenting in Patients With ST-Segment Elevation Myocardial Infarction: A Systematic Review and Meta-Analysis.

BACKGROUND: A number of studies have evaluated the efficacy of deferred stenting vs immediate stenting in patients with ST-segment elevation myocardial infarction, but the findings were not consistent across these studies. This meta-analysis aims to assess optimal treatment strategies in patient with ST-segment elevation myocardial infarction. METHODS AND RESULTS: We searched the PubMed, EMBASE, and the Cochrane Library for studies that assessed deferred vs immediate stenting in patients with ST-segment elevation myocardial infarction. Nine studies including 1456 patients in randomized controlled trials and 719 patients in observational studies were included in the meta-analysis. No significant differences were observed in the incidence of no- or slow-reflow between deferred stenting and immediate stenting in randomized controlled trials (odds ratio [OR] 0.51, 95%CI 0.17-1.53, P=0.23, I2=70%) but not in observational studies (OR 0.13, 95%CI 0.06-0.31, P<0.0001, I2=0%). Deferred stenting was associated with an increase in long-term left ventricular ejection fraction (weighted mean difference 1.90%, 95%CI 0.77-3.03, P=0.001, I2=0%). No significant differences were observed in the rates of major adverse cardiovascular events (OR 0.53, 95%CI 0.27-1.01, P=0.06 [randomized OR 0.98, 95%CI 0.73-1.30, P=0.87, I2=0%; nonrandomized OR 0.30, 95%CI 0.15-0.58, P=0.0004, I2=0%]), major bleeding (OR=0.1.61, 95%CI 0.70-3.69, P=0.26, I2=0%), death (OR=0.78, 95%CI 0.53-1.15, P=0.22, I2=0%), MI (OR=0.97, 95%CI 0.34-2.78, P=0.96, I2=35%) and target vessel revascularization (OR 0.97, 95%CI 0.40-2.37, P=0.95, I2=24%), between deferred and immediate stenting. CONCLUSIONS: Compared with immediate stenting, a deferred-stenting strategy did not reduce the occurrence of no- or slow-reflow, death, myocardial infarction, or repeat revascularization compared with immediate stenting in patients with ST-segment elevation myocardial infarction, but showed an improved left ventricular function in the long term.

Comparative Efficacy and Safety of Everolimus-Eluting Bioresorbable Scaffold Versus Everolimus-Eluting Metallic Stents: A Systematic Review and Meta-analysis.

BACKGROUND: Theoretically, the everolimus-eluting bioresorbable vascular scaffold (BVS) could eliminate stent thrombosis and improve outcomes in patients having percutaneous coronary intervention. PURPOSE: To estimate the incidence of stent thrombosis after BVS implantation and to compare the efficacy and safety of BVSs versus everolimus-eluting metallic stents (EESs) in adults having percutaneous coronary intervention. DATA SOURCES: PubMed, EMBASE, Cochrane Central Register of Controlled Trials, conference proceedings, and relevant Web sites from inception through 20 January 2016. STUDY SELECTION: 6 randomized, controlled trials and 38 observational studies, each involving at least 40 patients with BVS implantation. DATA EXTRACTION: Two reviewers independently extracted study data and evaluated study risk of bias. DATA SYNTHESIS: The pooled incidence of definite or probable stent thrombosis after BVS implantation was 1.5 events per 100 patient-years (PYs) (95% CI, 1.2 to 2.0 events per 100 PYs) (126 events during 8508 PYs). Six randomized trials that directly compared BVSs with EESs showed a non-statistically significant increased risk for stent thrombosis (odds ratio [OR], 2.05 [CI, 0.95 to 4.43]; P = 0.067) and myocardial infarction (OR, 1.38 [CI, 0.98 to 1.95]; P = 0.064) with BVSs. The 6 observational studies that compared BVSs with EESs showed increased risk for stent thrombosis (OR, 2.32 [CI, 1.06 to 5.07]; P = 0.035) and myocardial infarction (OR, 2.09 [CI, 1.23 to 3.55]; P = 0.007) with BVSs. The relative rates of all-cause and cardiac death, revascularization, and target lesion failure were similar for BVSs and EESs. LIMITATION: Scarce comparative data, no published data from large trials with long-term follow-up, and limited quality and incomplete reporting of observational studies. CONCLUSION: Compared with EESs, BVSs do not eliminate and might increase risks for stent thrombosis and myocardial infarction in adults having percutaneous coronary intervention. Results of large trials with long-term follow-up are critically needed to establish the safety or at least the noninferiority of BVSs compared with EESs. PRIMARY FUNDING SOURCE: None.

Retinal nerve fiber layer reflectometry must consider directional reflectance.

Recent studies reveal that measurements of retinal nerve fiber layer (RNFL) reflectance provide more sensitive detection of glaucomatous damage than RNFL thickness, but most do not consider directional reflectance of the RNFL, an important source of variability. This study quantitatively compared RNFL directional reflectance, represented by an angular spread function (ASF), measured at different scattering angles, different wavelengths and different distances from the optic nerve head (ONH) and for bundles with different thicknesses (T). An ASF was characterized by its amplitude (A) and width (W). Internal reflectance of a bundle was expressed as A/T. The study found that A varied significantly with scattering angle and wavelength and that A/T was different among bundles but constant along the same bundle, indicating that the internal structure of axons may vary among bundles but does not change with distance. This study also found that W was larger near the ONH and at longer wavelengths, but did not depend on scattering angle or T. Because a 4.3° change in incident angle can change reflected intensity by a factor of 2.7, accounting for directional reflectance should improve the accuracy and reproducibility of RNFL reflectance measurements.

Comparison between Fondaparinux and Low-Molecular-Weight Heparin in Patients with Acute Coronary Syndrome: A Meta-Analysis.

OBJECTIVE: A number of studies have evaluated the efficacy and safety of fondaparinux versus low-molecular-weight heparin (LMWH) in patients with acute coronary syndrome (ACS), but the findings were not consistent across these studies. METHODS: Electronic databases and article references were searched for studies that assessed fondaparinux versus LMWH in ACS patients. RESULTS: Six studies met the inclusion criteria. There was a lower risk of major adverse cardiac events (MACE) with fondaparinux-based regimens both in randomized controlled trials (RCT; risk ratio, RR: 0.91, p = 0.04) and observational studies (RR: 0.85, p < 0.0001). Mortality decreased in fondaparinux-treated patients in RCT (RR: 0.84, p = 0.02), but not in observational studies (RR: 1.44, p = 0.64). For the analysis of myocardial infarction (MI), recurrent ischemia and stroke, none of the studies showed significant results. In addition, fondaparinux lowered the risk of major bleeding in RCT (RR: 0.62, p < 0.0001) and observational studies (RR: 0.65, p < 0.0001). The net clinical outcome also favored fondaparinux over LMWH in RCT (RR: 0.82, p < 0.0001) and observational studies (RR: 0.84, p < 0.0001). CONCLUSIONS: Among ACS patients, a fondaparinux-based regimen presented advantages regarding MACE and major bleeding, and a net clinical benefit compared with LMWH, although the benefit is minimal regarding MACE. For death, MI, recurrent ischemia and stroke, fondaparinux has not shown significant benefits.

[Determination of ilaprazole in beagle plasma and its pharmaeokineties by high performance liquid chromatography-mass spectrometry].

A sensitive, simple and specific high performance liquid chromatography-electro spray ionization mass spectrometry method was developed for the determination of ilaprazole in the plasma of beagles administered via i.v. bolus doses of ilaprazole. The procedure employed buspirone as the internal standard and a simple protein precipitation step. The separation was achieved using a C18 column (100 mm x 2.1 mm, 5 microm) with a mobile phase consisting of water-methanol-acetonitrile (69:8:23, v/v/v) containing 0.1% (v/v) formic acid at a flow rate of 0.2 mL/min. The detection was accomplished by a mass spectrometer using selected ion monitoring (SIM) in positive mode. The linearity was from 5 microg/L to 10,000 microg/L with a sensitivity of 5 microg/L as the lower limit of quantification. The inter- and intra- day precisions were within 9.00%. The mean recoveries at three spiked levels were about 106% and the matrix effects were less than 142.0%. The method described above was successfully applied to analyze the beagle plasma samples of ilaprazole in a pharmacokinetic study. The area under the plasma concentration-time curve (AUC(0-infinity) of ilaprazole after i.v. doses of 0.2, 0.8 and 3.2 mg/kg were (2.4 x 10(4) +/- 3 x 10(3)), (8.8 x 10(4) +/- 1.6 x 10(4)) and (5.4 x 10(5) +/- 8 x 10(4)) microg/L x min, respectively. On the basis of AUC, the pharmacokinetic property of ilaprazole was proposed to be linear dynamics.