Emerging two-dimensional ferromagnetic semiconductors.

Two-dimensional (2D) semiconductors have attracted considerable attention for their potential in extending Moore's law and advancing next-generation electronic devices. Notably, the discovery and development of 2D ferromagnetic semiconductors (FMSs) open exciting opportunities in manipulating both charge and spin, enabling the exploration of exotic properties and the design of innovative spintronic devices. In this review, we aim to offer a comprehensive summary of emerging 2D FMSs, covering their atomic structures, physical properties, preparation methods, growth mechanisms, magnetism modulation techniques, and potential applications. We begin with a brief introduction of the atomic structures and magnetic properties of novel 2D FMSs. Next, we delve into the latest advancements in the exotic physical properties of 2D FMSs. Following that, we summarize the growth methods, associated growth mechanisms, magnetism modulation techniques and spintronic applications of 2D FMSs. Finally, we offer insights into the challenges and potential applications of 2D FMSs, which may inspire further research in developing high-density, non-volatile storage devices based on 2D FMSs.

Fusion-based underwater image enhancement with category-specific color correction and dehazing.

Underwater imaging is usually affected by water scattering and absorption, resulting in image blur and color distortion. In order to achieve color correction and dehazing for different underwater scenes, in this paper we report a fusion-based underwater image enhancement technique. First, statistics of the hue channel of underwater images are used to divide the underwater images into two categories: color-distorted images and non-distorted images. Then, category-specific combinations of color compensation and color constancy algorithms are used to remove the color shift. Second, a ground-dehazing algorithm using haze-line prior is employed to remove the haze in the underwater image. Finally, a channel-wise fusion method based on the CIE L* a* b* color space is used to fuse the color-corrected image and dehazed image. For experimental validation, we built a setup to acquire underwater images. The experimental results validate that the category-specific color correction strategy is robust to different categories of underwater images and the fusion strategy simultaneously removes haze and corrects color casts. The quantitative metrics on the UIEBD and EUVP datasets validate its state-of-the-art performance.

Weighted sampling-adaptive single-pixel sensing.

The novel single-pixel sensing technique that uses an end-to-end neural network for joint optimization achieves high-level semantic sensing, which is effective but computation-consuming for varied sampling rates. In this Letter, we report a weighted optimization technique for sampling-adaptive single-pixel sensing, which only needs to train the network once for any dynamic sampling rate. Specifically, we innovatively introduce a weighting scheme in the encoding process to characterize different patterns' modulation efficiencies, in which the modulation patterns and their corresponding weights are updated iteratively. The optimal pattern series with the highest weights is employed for light modulation in the experimental implementation, thus achieving highly efficient sensing. Experiments validated that once the network is trained with a sampling rate of 1, the single-target classification accuracy reaches up to 95.00% at a sampling rate of 0.03 on the MNIST dataset and 90.20% at a sampling rate of 0.07 on the CCPD dataset for multi-target sensing.

Image-free multi-character recognition.

The recently developed image-free sensing technique decouples semantic information directly from compressed measurements without image reconstruction, which maintains the advantages of both the light hardware and software. However, the existing attempts have failed to classify multi-semantic information with multiple targets in the practical fieldof-view. In this Letter, we report a novel image-free sensing technique to tackle the multi-target recognition challenge for the first time, to the best of our knowledge. Different from the convolutional layer stack of image-free single-pixel networks, the reported convolutional recurrent neural network (CRNN) uses the bidirectional LSTM architecture to predict the distribution of multiple characters simultaneously. The framework enables capture of the long-range dependencies, providing a high recognition accuracy of multiple characters. We demonstrate the technique's effectiveness in license plate detection, which achieves a recognition accuracy of 87.60% at a sampling rate of 5% with a refresh rate higher than 100 FPS.

Ultrahigh-security single-pixel semantic encryption.

Single-pixel encryption is a recently developed encryption technique enabling the ciphertext amount to be decreased. It adopts modulation patterns as secret keys and uses reconstruction algorithms for image recovery in the decryption process, which are time-consuming and can easily be illegally deciphered if the patterns are exposed. We report an image-free single-pixel semantic encryption technique that significantly enhances security. The technique extracts semantic information directly from the ciphertext without image reconstruction, which significantly reduces computing resources for end-to-end real-time decoding. Moreover, we introduce a stochastic mismatch between keys and ciphertext, with random measurement shift and dropout, which effectively enhances the difficulty of illegal deciphering. Experiments on the MNIST dataset validate that 78 coupling measurements (0.1 sampling rate) with stochastic shift and random dropout achieved 97.43% semantic decryption accuracy. In the worst situation, when all the keys are illegally obtained by unauthorized attackers, only 10.80% accuracy can be achieved (39.47% in an ergodic manner).

Recovery preparedness of global air transport influenced by COVID-19 pandemic: Policy intervention analysis.

The outbreak of COVID-19 constitutes an unprecedented disruption globally, in which risk management framework is on top priority in many countries. Travel restriction and home/office quarantine are some frequently utilized non-pharmaceutical interventions, which bring the worst crisis of airline industry compared with other transport modes. Therefore, the post-recovery of global air transport is extremely important, which is full of uncertainty but rare to be studied. The explicit/implicit interacted factors generate difficulties in drawing insights into the complicated relationship and policy intervention assessment. In this paper, a Causal Bayesian Network (CBN) is utilized for the modelling of the post-recovery behaviour, in which parameters are synthesized from expert knowledge, open-source information and interviews from travellers. The tendency of public policy in reaction to COVID-19 is analyzed, whilst sensitivity analysis and forward/backward belief propagation analysis are conducted. Results show the feasibility and scalability of this model. On condition that no effective health intervention method (vaccine, medicine) will be available soon, it is predicted that nearly 120 days from May 22, 2020, would be spent for the number of commercial flights to recover back to 58.52%-60.39% on different interventions. This intervention analysis framework is of high potential in the decision making of recovery preparedness and risk management for building the new normal of global air transport.

Protective effect of Aster tataricus extract on NLRP3-mediated pyroptosis of bladder urothelial cells.

Aster tataricus L.f. is a traditional Eastern Asian herbal medicine used for the relief of uroschesis-related illnesses and has been demonstrated clinically to exert satisfied effects. However, the mechanism of its therapeutic action remains unclear. The present study aimed to evaluate the protective mechanism of Aster tataricus extract (ATE) on CYP or LPS + ATP-induced interstitial cystitis (IC), we successfully constructed the induced IC Sprague-Dawley (SD) rat model and IC human urothelium cell (SV-HUC-1) model. The main compounds of ATE were determined by LC-MS. After intervention, the changes on the bladder wall morphology and inflammation were observed in each group. SV-HUC1 cell viability was measured by MTT and double stained with Hoechst 33342 and propidium iodide (PI). The expression levels of NLRP3, Pro-caspase-1, Caspsae-1 p20, GSDMD, GSDMD-N and Cleave-IL-1ß in vivo and in vitro in different groups were detected by Western blotting. ATE significantly alleviated oedema and haemorrhage and reduced the inflammation index and histopathological score in SD rat bladder. The results of cell revealed that ATE could improve cell viability and decrease pyroptosis ratio. The expression of NLRP3 and other pyroptosis-related protein was remarkably decreased by ATE both in vivo and in vitro. ATE may be used as an inhibitor of NLRP3 in treating IC. The discovery of NLRP3/Caspase-1/GSDMD-N as a new protective pathway provides a new direction for protecting cell against IC.

Dual-Aptamer-Conjugated Molecular Modulator for Detecting Bioactive Metal Ions and Inhibiting Metal-Mediated Protein Aggregation.

Bioactive metal ions play important roles in both physiological and pathological processes. Developing biosensing probes for bioactive metal ion detection can contribute to fields including disease diagnosis and therapy and studying the mechanisms of biological activities. In this work, we designed a dual-aptamer-conjugated molecular modulator that can detect Zn2+ and further inhibit Zn2+-induced amyloid ß (Aß) aggregation. The molecular modulator is able to selectively target Aß species and block Zn2+ due to the specific recognition capability of aptamers. With the binding of Zn2+, the fluorescence signal of this molecular modulator is restored, thus allowing for Zn2+ detection. More importantly, this molecular modulator can inhibit the generation of Zn2+-triggered Aß aggregates due to the trapping of Zn2+ around Aß species. Circular dichroism measurements reveal that the dual-aptamer-conjugated molecular modulator prevents the conformational transition of the Aß monomer from a random coil to a ß-sheet. Furthermore, after treating with the molecular modulator, no Aß aggregate is observed in the Aß solution with added Zn2+, demonstrating that Aß aggregation is successfully inhibited by this molecular modulator. Our approach provides a promising tool for detecting bioactive metal ions and studying the molecular mechanisms behind life activities.

Sustained Release Bilayer Tablet of Ibuprofen and Phenylephrine Hydrochloride: Preparation and Pharmacokinetics in Beagle Dogs.

Cold is a global common infectious disease accompanied by symptoms such as headache and stuffy nose. Ibuprofen (IBU) and phenylephrine hydrochloride (PE) were commonly used for common cold due to their different effects in relieving fever and the main symptoms such as nasal congestion and high sinus pressure. However, the commercial tablets of IBU and PE have to be administered 2 to 3 times per day due to their short half-life, with inconvenience for patient and fluctuations of plasma concentration. Bilayer tablet technology was utilized to design the IBU-PE sustained release tablets because of the significantly different solubility of IBU and PE in release media. The formulations of IBU layer and PE layer contain different viscosity grades of hydroxypropyl methylcellulose (HPMC) as sustained-release matrix, hydrophilic diluent, and traditional glidant and lubricant. The sustained release bilayer tablet exhibited satisfying sustained release performance with the mechanisms of diffusion and matrix erosion. Compared with the conventional tablets, the IBU-PE sustained release bilayer tablet expressed significantly sustained-release behavior with decreased Cmax and prolonged Tmax in fasted conditions for IBU and PE. Though IBU of IBU-PE sustained release bilayer tablet was bioequivalent to the commercial IBU tablet, the relative bioavailability of PE from the bilayer tablets was 87.49 ± 20.00% (90% confidence interval was 72.3 to 102.5%), indicating bioinequivalence probably due to the "first pass" effect.

Solution-Processable Two-Dimensional In2 Se3 Nanosheets as Efficient Photothermal Agents for Elimination of Bacteria.

Two-dimensional (2D) nanoflakes represent an appealing class of materials for optoelectronics applications due to their unique layered structures and excellent electronic properties. However, the lack of easy-to-manipulate and effective methods for large-scale production of these 2D materials limits their potential for applications. Also, few efforts have been made to explore their applications in biological fields. This work reports the preparation of large quantities of 2D In2 Se3 nanosheets through a solvent exfoliation technique. Transmission electron microscopy and atomic force microscopy results show that the In2 Se3 nanosheets are obtained with lateral sizes of tens of nanometers to hundreds of nanometers and thickness of 2-17 layers. Raman features coupled with the X-ray diffractometry results unequivocally confirm the as-prepared In2 Se3 nanosheets to be α phase. Moreover, these α-In2 Se3 nanosheets exhibit an excellent near-infrared (NIR) photothermal performance under an 808 nm laser irradiation. NIR photo-excitation of the α-In2 Se3 nanosheets in the presence of bacteria leads to a significant antibacterial effect, suggesting that these nanosheets have great potential to be photothermal antibacterial agents. Our work on α-In2 Se3 nanosheets presents an available method for exfoliating 2D layered materials, and highlights the potential application in chemical and biological fields of α-In2 Se3 nanosheets.

The Value of Older Donors' Klotho Level in Predicting Recipients' Short-Term Renal Function.

BACKGROUND The present organ shortage has led to increased use of kidneys from expanded-criteria donors, but the prognosis is disappointing due to poor graft quality. As a promising kidney protector, the Klotho gene's role in predicting short-term prognosis has not been assessed. MATERIAL AND METHODS We retrospectively analyzed data from 41 recipients and 25 donors. Multiple clinical variables were compared between different subgroups of donors or their corresponding recipients. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the distinguishing ability. Dynamic changes in serum Klotho, FGF-23, and urinary NGAL were assessed. RESULTS Serum Klotho level was significantly lower in donors age ≥50 years (p=0.017), and there was a moderate negative correlation between serum Klotho expression and age (r=-0.464, p=0.019). Moreover, detection of Klotho mRNA and immunohistochemical analysis in kidneys revealed the same trend as in serum. Furthermore, for older donors (age ≥50 years), serum Klotho level had a strong negative correlation with recipient eGFR 1 month post-transplant (r=-0.686, p=0.007), which was proved to be a good predictor for estimating graft function by ROC analysis. Additionally, during the post-transplant follow-up, serum Klotho levels increased slightly after a temporary decline, while serum FGF-23 and urinary NGAL decreased significantly and then stayed low thereafter. CONCLUSIONS Klotho level, which decreases with age, may be a potential predictor of short-term renal function, especially for grafts from older donors.

Prediction of drug-disease associations based on reinforcement symmetric metric learning and graph convolution network.

Accurately identifying novel indications for drugs is crucial in drug research and discovery. Traditional drug discovery is costly and time-consuming. Computational drug repositioning can provide an effective strategy for discovering potential drug-disease associations. However, the known experimentally verified drug-disease associations is relatively sparse, which may affect the prediction performance of the computational drug repositioning methods. Moreover, while the existing drug-disease prediction method based on metric learning algorithm has achieved better performance, it simply learns features of drugs and diseases only from the drug-centered perspective, and cannot comprehensively model the latent features of drugs and diseases. In this study, we propose a novel drug repositioning method named RSML-GCN, which applies graph convolutional network and reinforcement symmetric metric learning to predict potential drug-disease associations. RSML-GCN first constructs a drug-disease heterogeneous network by integrating the association and feature information of drugs and diseases. Then, the graph convolutional network (GCN) is applied to complement the drug-disease association information. Finally, reinforcement symmetric metric learning with adaptive margin is designed to learn the latent vector representation of drugs and diseases. Based on the learned latent vector representation, the novel drug-disease associations can be identified by the metric function. Comprehensive experiments on benchmark datasets demonstrated the superior prediction performance of RSML-GCN for drug repositioning.

Impact on sulfadiazine bio-accessibility in soils through organic diffusive gradients in thin films (o-DGT): Differentiation based on microplastic polymers, aging, and soil properties.

Due to the similar sources of swage irrigation, organic fertilizer, and sludge application, microplastics (MPs) and antibiotics coexist inevitably in the agriculture soils. However, the impacts of MPs with different polymer types and aging status on the bio-accessibility of co-existing antibiotics in soils remained unclear. Therefore, we using the diffusive gradients films for organic compounds devices (o-DGT) to evaluated the distribution of sulfadiazine (SDZ) in both paddy soil and saline soil amended with 0.5 % (w/w) MPs. Four polymer types (polyethylene: PE, polypropylene: PP, polyamide: PA, and polyethylene terephthalate: PET) and two aging statuses (aged PE and aged PP) of MPs were used in this study. Results showed that soil properties significantly influence the partition of SDZ in soil and soil solution, and SDZ gained a lower degradation rate but higher mobility in saline soil. MPs pose different impacts on partition of SDZ between paddy soil and saline soil. Notably, PP reduced the labile solid phase-solution phase partition coefficient (Kdl) by 17.7 % in paddy soil, while PE, PP, and aPE increased the Kdl value by 2.00, 1.62, and 2.81 times in saline soil. Besides, in saline soil, all the MPs reduced the SDZ concentration in the soil solution, while significantly increased the SDZ in o-DGT phase. Conversely, MPs did not impact the SDZ's o-DGT concentration in paddy soil. Additionally, MPs increased the R value of SDZ in two soils, especially in saline soil. It suggested that MPs could potentially enhance the resupply of SDZ from soil to plants, particularly under saline conditions. Furthermore, aged MPs had a more pronounced effect on these indicators compared to virgin MPs in saline soil. Therefore, MPs in soil poses a potential risk for biota's uptake of SDZ, particularly in fragile environment. Moreover, the risk intensifies with aged MPs.

U-Shape Transformer for Underwater Image Enhancement.

The light absorption and scattering of underwater impurities lead to poor underwater imaging quality. The existing data-driven based underwater image enhancement (UIE) techniques suffer from the lack of a large-scale dataset containing various underwater scenes and high-fidelity reference images. Besides, the inconsistent attenuation in different color channels and space areas is not fully considered for boosted enhancement. In this work, we built a large scale underwater image (LSUI) dataset, which covers more abundant underwater scenes and better visual quality reference images than existing underwater datasets. The dataset contains 4279 real-world underwater image groups, in which each raw image's clear reference images, semantic segmentation map and medium transmission map are paired correspondingly. We also reported an U-shape Transformer network where the transformer model is for the first time introduced to the UIE task. The U-shape Transformer is integrated with a channel-wise multi-scale feature fusion transformer (CMSFFT) module and a spatial-wise global feature modeling transformer (SGFMT) module specially designed for UIE task, which reinforce the network's attention to the color channels and space areas with more serious attenuation. Meanwhile, in order to further improve the contrast and saturation, a novel loss function combining RGB, LAB and LCH color spaces is designed following the human vision principle. The extensive experiments on available datasets validate the state-of-the-art performance of the reported technique with more than 2dB superiority. The dataset and demo code are available at https://bianlab.github.io/.

Resonant-Type Piezoelectric Pump Driven by Piezoelectric Stacks and a Rhombic Micro Displacement Amplifier.

To obtain a high flow rate, a resonant-type piezoelectric pump is designed, fabricated, and studied in this paper. The pump consists of four parts: a piezoelectric vibrator, a pump chamber, a check valve and a compressible space. The designed piezoelectric vibrator is composed of a rhombic micro displacement amplifier, counterweight blocks and two piezoelectric stacks with low-voltage drive and a large output displacement. ANSYS software (Workbench 19.0) simulation results show that at the natural frequency of 946 Hz, the designed piezoelectric vibrator will produce the maximum output displacement. The bilateral deformation is symmetrical, and the phase difference is zero. Frequency, voltage, and backpressure characteristics of the piezoelectric pump are investigated. The experimental results show that at a certain operating frequency, the flow rate and the backpressure of the piezoelectric pump both increase with the increase in voltage. When the applied voltage is 150 Vpp, the flow rate reaches a peak of 367.48 mL/min at 720 Hz for one diaphragm pump, and reaches a peak of 700.15 mL/min at 716 Hz for two diaphragm pumps.

The promoting effect of modified Dioscorea pills on vascular remodeling in chronic cerebral hypoperfusion via the Ang/Tie signaling pathway.

Objective: The objective of this study was to investigate the effect of modified Dioscorea pills (MDP) on microcirculatory remodeling in the hippocampus of rats with chronic cerebral hypoperfusion (CCH) through the angiopoietin (Ang)/tyrosine kinase receptor tyrosine kinase with immunoglobulin-like and EGF-like domains (Ang receptor) 2 (Tie-2) signaling pathways, which may underlie the cognitive improvement observed in CCH rats. Methods: Forty male Sprague-Dawley rats raised under specific pathogen-free conditions were randomly divided into three groups: control group (10 rats), model group (15 rats), and MDP group (15 rats). The rats in the model group and MDP group underwent bilateral common carotid artery occlusion using the 2-vessel occlusion (2-VO) method to induce CCH. Rats in the control group underwent the same surgical procedures as those in the model group, except for ligation and occlusion of the carotid arteries. After 1 week of 2-VO, rats in the MDP group were administered MDP condensed decoction intragastrically at a dose of 1 ml/100 g body weight (prepared by the Preparation Room of Hubei Provincial Hospital of Traditional Chinese Medicine) for 45 days, while rats in the other two groups received normal saline intragastrically with the same dose and duration as the MDP group. After the intervention, all rats were euthanized, and brain perfusion was performed to obtain the hippocampal tissue for analysis. Immunohistochemical staining for CD43 was performed to assess microvessel density (MVD); western blot and the reverse transcription-polymerase chain reaction (RT-PCR) were used to analyze the expression of proteins and genes in angiopoietin-1 (Ang-1), angiopoietin-2 (Ang-2), Tie-2, and vascular endothelial growth factor (VEGF) proteins and genes in the hippocampal tissue and compute the Ang-1/Ang-2 ratio. Results: MDP treatment reduced neuronal loss and promoted restoration of the damaged hippocampal structure in CCH rats. The model group showed significantly higher MVD (14.93 ± 1.92) compared to the control group (5.78 ± 1.65) (P < 0.01), whereas MDP treatment further increased MVD (21.19 ± 2.62). Western blot and RT-PCR analysis revealed that CCH significantly increased the expression of Ang-1, Ang-2, Tie-2, and VEGF proteins and genes, while MDP treatment further significantly upregulated the expression of these proteins and genes. In addition, MDP significantly elevated the gene and protein expression of the Ang-1/Ang-2 ratio compared to the control group (P = 0.041, P = 0.029). Conclusion: CCH induces microvascular neogenesis in the hippocampus, and MDP promotes angiogenesis and microcirculation remodeling in CCH rats via the Ang/Tie signaling pathway, which may be an important mechanism for its restorative effects on hippocampal perfusion and improvement of cognitive function in CCH rats.

High-resolution single-photon imaging with physics-informed deep learning.

High-resolution single-photon imaging remains a big challenge due to the complex hardware manufacturing craft and noise disturbances. Here, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging with enhancement of bit depth and imaging quality. We first studied the complex photon flow model of SPAD electronics to accurately characterize multiple physical noise sources, and collected a real SPAD image dataset (64 × 32 pixels, 90 scenes, 10 different bit depths, 3 different illumination flux, 2790 images in total) to calibrate noise model parameters. With this physical noise model, we synthesized a large-scale realistic single-photon image dataset (image pairs of 5 different resolutions with maximum megapixels, 17250 scenes, 10 different bit depths, 3 different illumination flux, 2.6 million images in total) for subsequent network training. To tackle the severe super-resolution challenge of SPAD inputs with low bit depth, low resolution, and heavy noise, we further built a deep transformer network with a content-adaptive self-attention mechanism and gated fusion modules, which can dig global contextual features to remove multi-source noise and extract full-frequency details. We applied the technique in a series of experiments including microfluidic inspection, Fourier ptychography, and high-speed imaging. The experiments validate the technique's state-of-the-art super-resolution SPAD imaging performance.

[A Family with Congenital Dysfibrinogenemia and Blood Transfusion].

OBJECTIVE: To investigate a family with congenital dysfibrinogenemia, and analyze the risk of hemorrhage and thrombosis and blood transfusion strategies. METHODS: Prothrombin time (PT), activated partial thromboplastin time (APTT) and thrombin time (TT) of the proband and her family members were detected by automatic coagulometer, fibrinogen (Fg) activity and antigen were detected by Clauss method and PT algorithm respectively. Meanwhile, thromboelastometry was analyzed for proband and her family members. Then, peripheral blood samples of the proband and her family members were collected, and all exons of FGA, FGB and FGG and their flanks were amplified by PCR and sequenced to search for gene mutations. RESULTS: The proband had normal APTT and PT, slightly prolonged TT, reduced level of Fg activity (Clauss method). The Fg of the proband's aunt, son and daughter all decreased to varying degrees. The results of thromboelastogram indicated that Fg function of the proband and her family members (except her son) was basically normal. Gene analysis showed that there were 6233 G/A (p.AαArg35His) heterozygous mutations in exon 2 of FGA gene in the proband, her children and aunt. In addition, 2 polymorphic loci were found in the family, they were FGA gene g.9308A/G (p.AαThr331Ala) and FGB gene g.12628G/A (p.BßArg478Iys) polymorphism, respectively. The proband was injected with 10 units of cryoprecipitate 2 hours before delivery to prevent bleeding, and no obvious bleeding occurred during and after delivery. CONCLUSION: Heterozygous mutation of 6233G/A (p.AαArg35His) of FGA gene is the biogenetic basis of the disease in this family with congenital dysfibrinogenemia.

In vitro and in vivo studies on a novel solid dispersion of repaglinide using polyvinylpyrrolidone as the carrier.

In order to improve the dissolution and absorption of the water insoluble drug repaglinide, a solid dispersion was developed by solvent method using polyvinylpyrrolidone K30 (PVP K30) as the hydrophilic carrier for the first time. Studies indicated that both solubility and the dissolution rate of repaglinide were significantly increased in the solid dispersion system compared with that of repaglinide raw material or physical mixtures. The repaglinide solid dispersions with PVP K30 solid state was characterized by polarizing microscopy, differential scanning calorimetry (DSC), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FT-IR). DSC and XRD studies indicated that repaglinide existed in an amorphous form in the solid dispersion. FT-IR analysis demonstrated the presence of intermolecular hydrogen bonding between repaglinide and PVP K30 in the solid dispersion. In the in situ gastrointestinal perfusion experiment, solid dispersion was shown to remarkably enhance the absorption of repaglinide in stomach and all segments of intestine. In vivo pharmacokinetic study in rats showed that immediate and complete release of repaglinide from the solid dispersion resulted in rapid absorption that significantly increased the bioavailability and the maximum plasma concentration over repaglinide raw material. These results demonstrated PVP K30 was an appropriate carrier for solid dispersion of repaglinide, with increased dissolution and oral absorption.