Cardiac MRI segmentation using shifted-window multilayer perceptron mixer networks.

Objectives. In this work, we proposed a deep-learning segmentation algorithm for cardiac magnetic resonance imaging to aid in contouring of the left ventricle, right ventricle, and Myocardium (Myo).Approach.We proposed a shifted window multilayer perceptron (Swin-MLP) mixer network which is built upon a 3D U-shaped symmetric encoder-decoder structure. We evaluated our proposed network using public data from 100 individuals. The network performance was quantitatively evaluated using 3D volume similarity between the ground truth contours and the predictions using Dice score coefficient, sensitivity, and precision as well as 2D surface similarity using Hausdorff distance (HD), mean surface distance (MSD) and residual mean square distance (RMSD). We benchmarked the performance against two other current leading edge networks known as Dynamic UNet and Swin-UNetr on the same public dataset.Results.The proposed network achieved the following volume similarity metrics when averaged over three cardiac segments: Dice = 0.952 ± 0.017, precision = 0.948 ± 0.016, sensitivity = 0.956 ± 0.022. The average surface similarities were HD = 1.521 ± 0.121 mm, MSD = 0.266 ± 0.075 mm, and RMSD = 0.668 ± 0.288 mm. The network shows statistically significant improvement in comparison to the Dynamic UNet and Swin-UNetr algorithms for most volumetric and surface metrics withp-value less than 0.05. Overall, the proposed Swin-MLP mixer network demonstrates better or comparable performance than competing methods.Significance.The proposed Swin-MLP mixer network demonstrates more accurate segmentation performance compared to current leading edge methods. This robust method demonstrates the potential to streamline clinical workflows for multiple applications.

Real-time tunable notched waveguide based on voltage controllable ferroelectric resonator.

In the present study, we have devised and conducted an investigation into a real-time tunable notched waveguide, employing a voltage-controllable plasmonic resonator. This plasmonic resonator is meticulously engineered from a ferroelectric substrate featuring a compound multilayer structure, thereby conferring it with the remarkable capability of flexible permittivity control. Furthermore, we have implemented two non-intersecting Archimedean spiral electrodes on the surface of the ferroelectric substrate, dedicated to applying the bias field onto the controllable plasmonic ferroelectric resonator (CPFR). Notably, our system affords the capability to finely tune both the magnetic and electric modes, achieving precise adjustments of 8.7% and 11%, respectively. The performance is complemented by minimal insertion loss, rapid response times, and a broad range of potential applications, positioning it as a candidate for a diverse array of notched waveguide scenarios.

Advancing medical imaging with language models: featuring a spotlight on ChatGPT.

This review paper aims to serve as a comprehensive guide and instructional resource for researchers seeking to effectively implement language models in medical imaging research. First, we presented the fundamental principles and evolution of language models, dedicating particular attention to large language models. We then reviewed the current literature on how language models are being used to improve medical imaging, emphasizing a range of applications such as image captioning, report generation, report classification, findings extraction, visual question response systems, interpretable diagnosis and so on. Notably, the capabilities of ChatGPT were spotlighted for researchers to explore its further applications. Furthermore, we covered the advantageous impacts of accurate and efficient language models in medical imaging analysis, such as the enhancement of clinical workflow efficiency, reduction of diagnostic errors, and assistance of clinicians in providing timely and accurate diagnoses. Overall, our goal is to have better integration of language models with medical imaging, thereby inspiring new ideas and innovations. It is our aspiration that this review can serve as a useful resource for researchers in this field, stimulating continued investigative and innovative pursuits of the application of language models in medical imaging.

MGMT promoter methylation prediction based on multiparametric MRI via vision graph neural network.

Purpose: Glioblastoma (GBM) is aggressive and malignant. The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in GBM tissue is considered an important biomarker for developing the most effective treatment plan. Although the standard method for assessing the MGMT promoter methylation status is via bisulfite modification and deoxyribonucleic acid (DNA) sequencing of biopsy or surgical specimens, a secondary automated method based on medical imaging may improve the efficiency and accuracy of those tests. Approach: We propose a deep vision graph neural network (ViG) using multiparametric magnetic resonance imaging (MRI) to predict the MGMT promoter methylation status noninvasively. Our model was compared to the RSNA radiogenomic classification winners. The dataset includes 583 usable patient cases. Combinations of MRI sequences were compared. Our multi-sequence fusion strategy was compared with those using single MR sequences. Results: Our best model [Fluid Attenuated Inversion Recovery (FLAIR), T1-weighted pre-contrast (T1w), T2-weighted (T2)] outperformed the winning models with a test area under the curve (AUC) of 0.628, an accuracy of 0.632, a precision of 0.646, a recall of 0.677, a specificity of 0.581, and an F1 score of 0.661. Compared to the winning models with single MR sequences, our ViG utilizing fused-MRI showed a significant improvement statistically in AUC scores, which are FLAIR (p=0.042), T1w (p=0.017), T1wCE (p=0.001), and T2 (p=0.018). Conclusions: Our model is superior to challenge champions. A graph representation of the medical images enabled good handling of complexity and irregularity. Our work provides an automatic secondary check pipeline to ensure the correctness of MGMT methylation status prediction.

Curcumin alleviates orofacial allodynia and improves cognitive impairment via regulating hippocampal synaptic plasticity in a mouse model of trigeminal neuralgia.

OBJECTIVE: Cognitive impairment, one of the most prevalent complications of trigeminal neuralgia, is troubling for patients and clinicians due to limited therapeutic options. Curcumin shows antinociception and neuroprotection pharmacologically, suggesting that it may have therapeutic effect on this complication. This study aimed to investigate whether curcumin alleviates orofacial allodynia and improves cognitive impairment by regulating hippocampal CA1 region synaptic plasticity in trigeminal neuralgia. METHODS: A mouse model of trigeminal neuralgia was established by partially transecting the infraorbital nerve (pT-ION). Curcumin was administered by gavage twice daily for 14 days. Nociceptive thresholds were measured using the von Frey and acetone test, and the cognitive functions were evaluated using the Morris water maze test. Dendritic spines and synaptic ultrastructures in the hippocampal CA1 area were observed by Golgi staining and transmission electron microscopy. RESULTS: Curcumin intervention increased the mechanical and cold pain thresholds of models. It decreased the escape latency and distance to the platform and increased the number of platform crossings and dwell time in the target quadrant of models, and improved spatial learning and memory deficits. Furthermore, it partially restored the disorder of the density and proportion of dendritic spines and the abnormal density and structure of synapses in the hippocampal CA1 region of models. CONCLUSION: Curcumin alleviates abnormal orofacial pain and cognitive impairment in pT-ION mice by a mechanism that may be related to the synaptic plasticity of hippocampal CA1, suggesting that curcumin is a potential strategy for repairing cognitive dysfunction under long-term neuropathic pain conditions.

Ring Finger Protein 146-mediated Long-chain Fatty-acid-Coenzyme a Ligase 4 Ubiquitination Regulates Ferroptosis-induced Neuronal Damage in Ischemic Stroke.

Ischemic stroke (IS) is one of the leading causes of disability and death worldwide. Long-chain fatty-acid-coenzyme A ligase 4 (ACSL4) is a critical isozyme for ferroptosis that participates in the progression of IS. RING finger protein 146 (RNF146) is an E3 ligase predicted to interact with ACSL4 and regulated by activating transcription factor 3 (ATF3). The molecular mechanism of the RNF146/ACSL4 axis in IS is still unclear. Oxygen-glucose deprivation/reperfusion (OGD/R) treatment was used as the in vitro model, and middle cerebral artery occlusion (MCAO) mice were established for the in vivo model for IS. The protein level of ACSL4 was monitored by Western blot during ischemic injury. RNF146 was overexpressed in vitro and in vivo. The interaction of RNF146 and ACSL4 was determined by co-immunoprecipitation (Co-IP) assay. Chromatin immunoprecipitation (ChIP) assay and luciferase assay were utilized to determine the regulation of ATF3 on RNF146. Ferroptosis was evaluated by the levels of lactate dehydrogenase (LDH), malondialdehyde (MDA), Fe2+, and protein levels of related genes including ACSL4, SLC7A11, and GPX4. ACSL4 was downregulated upon OGD treatment and then increased by re-oxygenation. RNF146 was responsible for the ubiquitination and degradation of ACSL4 protein. RNF146 overexpression could prevent the stimulation of OGD/R-induced LDH, MDA, and Fe2+ levels and ferroptosis-related gene expression. ATF3 could activate the transcription and expression of RNF146, leading to the inhibition of OGD/R-induced neuron ferroptosis. The ATF3-mediated RNF146 could alleviate neuronal damage in IS by regulating ACSL4 ubiquitination and ferroptosis, providing a novel theoretical basis for exploring therapeutic targets and strategies.

Astragaloside IV alleviates neuronal ferroptosis in ischemic stroke by regulating fat mass and obesity-associated-N6-methyladenosine-acyl-CoA synthetase long-chain family member 4 axis.

Ischemic stroke (IS) is a detrimental neurological disease with limited treatment options. Astragaloside IV (As-IV) was a promising bioactive constituent in the treatment of IS. However, the functional mechanism remains unclear. Here, IS cell and mouse models were established by oxygen glucose deprivation/re-oxygenation (OGD/R) and middle cerebral artery occlusion (MCAO). Quantitative reverse transcription PCR (RT-qPCR), Western blotting, or Immunofluorescence staining measured related gene and protein expression of cells or mice brain tissues, and the results revealed altered expression of acyl-CoA synthetase long-chain family member 4 (Acsl4), fat mass and obesity-associated (Fto), and activation transcription factor 3 (Atf3) after treatment with As-IV. Then, increased N6 -methyladenosine (m6 A) levels caused OGD/R or MCAO were reduced by As-IV according to the data from methylated RNA immunoprecipitation (MeRIP)-qPCR and dot blot assays. Moreover, through a series of functional experiments such as observing mitochondrial changes under transmission electron microscopy (TEM), evaluating cell viability by cell counting kit-8 (CCK-8), analyzing infract area of brain tissues by 2,3,5-triphenyltetrazolium chloride (TTC) staining, measuring levels of malondialdehyde (MDA), lactate dehydrogenase (LDH), Fe2+ , solute carrier family 7 member 11 (Slc7a11) and glutathione peroxidase 4 (Gpx4) and concentration of glutathione (GSH), we found that Fto knockdown, Acsl4 overexpression or Atf3 knockdown promoted the viability of OGD/R cells, inhibited cell ferroptosis, reduced infract size, while As-IV treatment or Fto overexpression reversed these changes. In mechanism, the interplays of YTH N6 -methyladenosine RNA-binding protein 3 (Ythdf3)/Acsl4 and Atf3/Fto were analyzed by RNA-pull down, RNA immunoprecipitation (RIP), chromatin immunoprecipitation (ChIP) and dual-luciferase reporter assay. Fto regulated the m6 A levels of Acsl4. Ythdf3 bound to Acsl4, and modulated its levels through m6 A modification. Atf3 bound to Fto and positively regulated its levels. Overall, As-IV promoted the transcription of Fto by upregulating Atf3, resulting in decreased m6 A levels of Acsl4, thus, improving neuronal injury in IS by inhibiting ferroptosis.

Reinforcement learning in medical image analysis: Concepts, applications, challenges, and future directions.

MOTIVATION: Medical image analysis involves a series of tasks used to assist physicians in qualitative and quantitative analyses of lesions or anatomical structures which can significantly improve the accuracy and reliability of medical diagnoses and prognoses. Traditionally, these tedious tasks were finished by experienced physicians or medical physicists and were marred with two major problems, low efficiency and bias. In the past decade, many machine learning methods have been applied to accelerate and automate the image analysis process. Compared to the enormous deployments of supervised and unsupervised learning models, attempts to use reinforcement learning in medical image analysis are still scarce. We hope that this review article could serve as the stepping stone for related research in the future. SIGNIFICANCE: We found that although reinforcement learning has gradually gained momentum in recent years, many researchers in the medical analysis field still find it hard to understand and deploy in clinical settings. One possible cause is a lack of well-organized review articles intended for readers without professional computer science backgrounds. Rather than to provide a comprehensive list of all reinforcement learning models applied in medical image analysis, the aim of this review is to help the readers formulate and solve their medical image analysis research through the lens of reinforcement learning. APPROACH & RESULTS: We selected published articles from Google Scholar and PubMed. Considering the scarcity of related articles, we also included some outstanding newest preprints. The papers were carefully reviewed and categorized according to the type of image analysis task. In this article, we first reviewed the basic concepts and popular models of reinforcement learning. Then, we explored the applications of reinforcement learning models in medical image analysis. Finally, we concluded the article by discussing the reviewed reinforcement learning approaches' limitations and possible future improvements.

Abdomen CT multi-organ segmentation using token-based MLP-Mixer.

BACKGROUND: Manual contouring is very labor-intensive, time-consuming, and subject to intra- and inter-observer variability. An automated deep learning approach to fast and accurate contouring and segmentation is desirable during radiotherapy treatment planning. PURPOSE: This work investigates an efficient deep-learning-based segmentation algorithm in abdomen computed tomography (CT) to facilitate radiation treatment planning. METHODS: In this work, we propose a novel deep-learning model utilizing U-shaped multi-layer perceptron mixer (MLP-Mixer) and convolutional neural network (CNN) for multi-organ segmentation in abdomen CT images. The proposed model has a similar structure to V-net, while a proposed MLP-Convolutional block replaces each convolutional block. The MLP-Convolutional block consists of three components: an early convolutional block for local features extraction and feature resampling, a token-based MLP-Mixer layer for capturing global features with high efficiency, and a token projector for pixel-level detail recovery. We evaluate our proposed network using: (1) an institutional dataset with 60 patient cases and (2) a public dataset (BCTV) with 30 patient cases. The network performance was quantitatively evaluated in three domains: (1) volume similarity between the ground truth contours and the network predictions using the Dice score coefficient (DSC), sensitivity, and precision; (2) surface similarity using Hausdorff distance (HD), mean surface distance (MSD) and residual mean square distance (RMS); and (3) the computational complexity reported by the number of network parameters, training time, and inference time. The performance of the proposed network is compared with other state-of-the-art networks. RESULTS: In the institutional dataset, the proposed network achieved the following volume similarity measures when averaged over all organs: DSC = 0.912, sensitivity = 0.917, precision = 0.917, average surface similarities were HD = 11.95 mm, MSD = 1.90 mm, RMS = 3.86 mm. The proposed network achieved DSC = 0.786 and HD = 9.04 mm on the public dataset. The network also shows statistically significant improvement, which is evaluated by a two-tailed Wilcoxon Mann-Whitney U test, on right lung (MSD where the maximum p-value is 0.001), spinal cord (sensitivity, precision, HD, RMSD where p-value ranges from 0.001 to 0.039), and stomach (DSC where the maximum p-value is 0.01) over all other competing networks. On the public dataset, the network report statistically significant improvement, which is shown by the Wilcoxon Mann-Whitney test, on pancreas (HD where the maximum p-value is 0.006), left (HD where the maximum p-value is 0.022) and right adrenal glands (DSC where the maximum p-value is 0.026). In both datasets, the proposed method can generate contours in less than 5 s. Overall, the proposed MLP-Vnet demonstrates comparable or better performance than competing methods with much lower memory complexity and higher speed. CONCLUSIONS: The proposed MLP-Vnet demonstrates superior segmentation performance, in terms of accuracy and efficiency, relative to state-of-the-art methods. This reliable and efficient method demonstrates potential to streamline clinical workflows in abdominal radiotherapy, which may be especially important for online adaptive treatments.

End-to-end artificial intelligence platform for the management of large vessel occlusions: A preliminary study.

OBJECTIVES: In this study, we developed a deep learning pipeline that detects large vessel occlusion (LVO) and predicts functional outcome based on computed tomography angiography (CTA) images to improve the management of the LVO patients. METHODS: A series identifier picked out 8650 LVO-protocoled studies from 2015 to 2019 at Rhode Island Hospital with an identified thin axial series that served as the data pool. Data were annotated into 2 classes: 1021 LVOs and 7629 normal. The Inception-V1 I3D architecture was applied for LVO detection. For outcome prediction, 323 patients undergoing thrombectomy were selected. A 3D convolution neural network (CNN) was used for outcome prediction (30-day mRS) with CTA volumes and embedded pre-treatment variables as inputs. RESULT: For LVO-detection model, CTAs from 8,650 patients (median age 68 years, interquartile range (IQR): 58-81; 3934 females) were analyzed. The cross-validated AUC for LVO vs. not was 0.74 (95% CI: 0.72-0.75). For the mRS classification model, CTAs from 323 patients (median age 75 years, IQR: 63-84; 164 females) were analyzed. The algorithm achieved a test AUC of 0.82 (95% CI: 0.79-0.84), sensitivity of 89%, and specificity 66%. The two models were then integrated with hospital infrastructure where CTA was collected in real-time and processed by the model. If LVO was detected, interventionists were notified and provided with predicted clinical outcome information. CONCLUSION: 3D CNNs based on CTA were effective in selecting LVO and predicting LVO mechanical thrombectomy short-term prognosis. End-to-end AI platform allows users to receive immediate prognosis prediction and facilitates clinical workflow.

Construction of an efficient Claviceps paspali cell factory for lysergic acid production.

Lysergic acid (LA) is the key precursor of ergot alkaloids, and its derivatives have been used extensively for the treatment of neurological disorders. However, the poor fermentation efficiency limited its industrial application. At the same time, the hardship of genetic manipulation has hindered the metabolic engineering of Claviceps strains to improve the LA titer further. In this study, an efficient genetic manipulation system based on the protoplast-mediated transformation was established in the industrial strain Claviceps paspali. On this basis, the gene lpsB located in the ergot alkaloids biosynthetic gene cluster was deleted to construct the LA-producing cell factory. Plackett-Burman and Box-Behnken designs were used in shaking flasks, achieving an optimal fermentation medium composition. The final titer of LA and iso-lysergic acid (ILA) reached 3.7 g·L-1, which was 4.6 times higher than that in the initial medium. Our work provides an efficient strategy for the biosynthesis of LA and ILA and lays the groundwork for its industrial production.

Active control of terahertz waves based on p-Si hybrid PIT metasurface device under avalanche breakdown.

Active control of terahertz waves is a critical application for terahertz devices. Silicon is widely used in large-scale integrated circuit and optoelectronic devices, and also shows great potential in the terahertz field. In this paper, a p-Si hybrid metasurface device is proposed and its terahertz characteristics under avalanche breakdown effect is investigated. In the study, a plasmon-induced transparency (PIT) effect caused by the near-field coupling of the bright mode and the dark mode is observed in the transmission spectrum. Due to avalanche breakdown effect, the resonance of the PIT metamaterial disappears as the current increased. Carriers existed in the interface between the metasurface and substrate result to a dipole resonance suppression. When the current continues increasing, the maximal modulation depth can reach up to 99.9%, caused by the avalanche effect of p-Si. Experimental results demonstrate that the avalanche breakdown p-Si can achieve a performance modulation depth, bringing much more possibilities for terahertz devices.

Saikosaponin A improved depression-like behavior and inhibited hippocampal neuronal apoptosis after cerebral ischemia through p-CREB/BDNF pathway.

Post-stroke depression(PSD) is a common complication and associates with poor physical recovery, low quality of life and high mortality after cerebral infarction. However, the pathogenesis of PSD have not been elucidated thoroughly now, and there is a lack of effective therapy in clinic. It reported that Saikosaponin A, one of the main constituents from Chinese herb Bupleurum chinense, has pharmacological activity in anti-depression. Thus, this study aimed to elucidate the potential effects and mechanisms of Saikosaponin A on the depression-like behavior after cerebral ischemic injury in rats. The rat model of PSD was induced by middle cerebral artery occlusion(MCAO) combined with chronic unpredictable mild stress(CUMS) and isolation. Behavior tests including open field test, beam-walking test, sucrose preference and forced swimming tests were performed. Western blot and immunohistochemistry were adopted to evaluate expression of phosphorylated cAMP response element binding protein(p-CREB), brain derived neurotrophic factor(BDNF) and apoptosis-related molecules in the dentate gyrus region of rat hippocampus. The TUNEL assay was used to determine neuronal apoptosis. We found that the rats subjected to MCAO combined with CUMS and isolation experienced significant depressive-like behavior. Administration of Saikosaponin A significantly ameliorated depressive-like behavior, and inhibited neuronal apoptosis, enhanced the level of p-CREB, BDNF and Bcl-2, reduced the level of Bax, Caspase-3 in the hippocampus of PSD rats. These results revealed that Saikosaponin A improved depression-like behavior and inhibited hippocampal neuronal apoptosis after cerebral ischemia, presumably through increasing the expression of BDNF, p-CREB and Bcl-2, as well as decreasing the level of Bax, Caspase-3.

Optical-Relayed Entanglement Distribution Using Drones as Mobile Nodes.

Entanglement distribution has been accomplished using a flying drone, and this mobile platform can be generalized for multiple mobile nodes with optical relay among them. Here we develop the first optical relay to reshape the wave front of photons for their low diffraction loss in free-space transmission. Using two drones, where one distributes the entangled photons and the other serves as relay node, we achieve entanglement distribution with Clauser-Horne-Shimony-Holt S parameter of 2.59±0.11 at 1 km distance. Key components for entangled source, tracking, and relay are developed with high performance and are lightweight, constructing a scalable airborne system for multinode connectio and toward mobile quantum networks.

Wireless phone use and adult meningioma risk: a systematic review and Meta-analysis.

Introduction: Several studies explored the effects of exposure to radiofrequency-electromagnetic field (RF-EMF) and extremely low frequency (ELF) EMF emitted from mobile phones on meningioma among adults. However, the results could not reach an agreement. This meta-analysis was conducted to confirm the relationship between adult meningioma risk and the use of a wireless phone. Methods: Pertinent studies were identified by searching PubMed and Embase up to August 2018. The random- or fixed-effects model was used to combine the results depending on the heterogeneity of the analysis. The publication bias was evaluated using Egger's regression asymmetry test. The subgroup analysis was performed by time since the first use of wireless phone and laterality (ipsilateral/contralateral). Results: Eight studies were enrolled in this meta-analysis. The pooled results suggested that the ever use of wireless phone led to a borderline decreased adult meningioma risk [odds ratio (OR) 0.90; 95% confidence interval (CI) 0.83-0.99] with no heterogeneity (I2 = 5.3%; p = 0.391). A decreased risk of meningioma was seen in short-term (OR = 0.85; 95% CI = 0.77-0.94) users. Neither decreased nor increased risk of meningioma was observed in mid-term (OR = 0.93, 95% CI = 0.75-1.16) and long-term (OR = 1.05, 95% CI = 0.93-1.19) users. Neither ipsilateral (OR = 1.05, 95% CI = 0.90-1.22) nor contralateral (OR = 0.86, 95% CI = 0.62-1.18) wireless phone use was associated with the risk of meningioma. Conclusions: This meta-analysis suggested a relationship between decreased meningioma risk and wireless phone use. However, the findings need further validation.

Predictive value on diffusion weighted imaging scores for basilar artery occlusion after endovascular treatment.

PURPOSE: To assess the predictive value of three scoring systems based on diffusion weighted imaging in basilar artery occlusion patients after endovascular treatment. METHODS: We analyzed clinical and radiological data of patients with basilar artery occlusion from January 2010 to June 2019, with modified Rankin Scale of 0-2 and 3-6 defined as favorable outcome and unfavorable outcome at three months. Diffusion weighted imaging posterior circulation ASPECTS Score (DWI pc-ASPECT Score), Renard diffusion weighted imaging Score, and diffusion weighted imaging Brainstem Score were used to evaluate the early ischemic changes. RESULTS: There were a total of 88 basilar artery occlusion patients enrolled in the study after endovascular treatment, with 33 of them getting a favorable outcome. According to the analysis, the time from onset to puncture within 12 h (odds ratio: 4.34; 95% confidence interval: 1.55-12.16; P = 0.01), presence of collateral flow via PCoA (odds ratio: 0.31; 95%CI: 0.12-0.79; P = 0.01) or between PICA and SCA (odds ratio: 0.18; 95%CI: 0.07-0.47; P = 0.00), equal or less than 15 points on baseline NIHSS (area under the curve 0.79, 95% CI 0.69-0.89; sensitivity = 69.1%, specificity = 81.8%; P = 0.00), and equal or less than 1.5 points on diffusion weighted imaging Renard score (area under the curve 0.63, 95% CI 0.51-0.75; sensitivity = 83.6%, specificity = 39.4%; P = 0.046) were independently associated with favorable outcome. CONCLUSIONS: Renard diffusion weighted imaging score may be an independent predictor of functional outcome in basilar artery occlusion patients after endovascular treatment.

Effect and Safety of Hydroxysafflor Yellow A for Injection in Patients with Acute Ischemic Stroke of Blood Stasis Syndrome: A Phase II, Multicenter, Randomized, Double-Blind, Multiple-Dose, Active-Controlled Clinical Trial.

OBJECTIVE: To assess the effect and safety of Hydroxysafflor Yellow A for Injection (HSYAI) in treating patients with acute ischemic stroke (AIS) and blood stasis syndrome (BSS). METHODS: A multicenter, randomized, double-blind, multiple-dose, active-controlled phase II trial was conducted at 9 centers in China from July 2013 to September 2015. Patients with moderate or severe AIS and BSS were randomly assigned to low-, medium-, high-dose HSYAI groups (25, 50 and 70 mg/d HSYAI by intravenous infusion, respectively), and a control group (Dengzhan Xixin Injection (, DZXXI) 30 mL/d by intravenous infusion), for 14 consecutive days. The primary outcome was the Modified Rankin Scale (mRS) score ⩽1 at days 90 after treatment. The secondary outcomes included the National Institute of Health Stroke Scale (NIHSS) score ⩽1, Barthel Index (BI) score ⩾95, and BSS score reduced ⩾30% from baseline at days 14, 30, 60, and 90 after treatment. The safety outcomes included any adverse events during 90 days after treatment. RESULTS: Of the 266 patients included in the effectiveness analysis, 66, 67, 65 and 68 cases were in the low-, medium-, and high-dose HSYAI and control groups, respectively. The proportions of patients in the medium- and high-dose HSYAI groups with mRS score ⩽1 at days 90 after treatment were significantly larger than the control group (P<0.05). The incidences of favorable outcomes of NIHSS and BI at days 90 after treatment as well as satisfactory improvement of BSS at days 30 and 60 after treatment in the medium- and high-dose HSYAI groups were all significantly higher than the control group (P<0.05). No significant difference was reported among the 4 groups in any specific adverse events (P>0.05). CONCLUSIONS: HSYAI was safe and well-tolerated at all doses for treating AIS patients with BSS. The medium (50 mg/d) or high dose (75 mg/d) might be the optimal dose for a phase III trial. (Registration No. ChiCTR-2000029608).

Optical radiation manipulation of Si-Ge2Sb2Te5 hybrid metasurfaces.

Active optical metadevices have attracted growing interest for the use in nanophotonics owing to their flexible control of optics. In this work, by introducing the phase-changing material Ge2Sb2Te5 (GST), which exhibits remarkably different optical properties in different crystalline states, we investigate the active optical radiation manipulation of a resonant silicon metasurface. A designed double-nanodisk array supports a strong toroidal dipole excitation and an obvious electric dipole response. When GST is added, the toroidal response is suppressed, and the toroidal and electric dipoles exhibit pronounced destructive interference owing to the similarity of their far-field radiation patterns. When the crystallization ratio of GST is varied, the optical radiation strength and spectral position of the scattering minimum can be dynamically controlled. Our work provides a route to flexible optical radiation modulation using metasurfaces.