KM04416 suppressed lung adenocarcinoma progression by promoting immune infiltration.

OBJECTIVES: Lung adenocarcinoma (LUAD) is a malignant tumor originating from the bronchial mucosa or glands of the lung, with the fastest increasing morbidity and mortality. Therefore, the prognosis of lung cancer remains poor. Glycerol-3-phosphate dehydrogenase 2 (GPD2) is a widely existing protein pattern sequence in biology and is closely related to tumor progression. The therapy values of GPD2 inhibitor in LUAD were unclear. Therefore, we aimed to analyze the therapy values of GPD2 inhibitor in LUAD. MATERIALS AND METHODS: The Cancer Genome Atlas (TCGA)-LUAD database was used to analyze the expression levels of GPD2 in LUAD tissues. The relationship between GPD2 expression and LUAD patient survival was analyzed by Kaplan-Meier method. Moreover, KM04416 as a target inhibitor of GPD2 was used to further investigate the therapy value of GPD2 inhibitor in LUAD cells lines (A549 cell and H1299 cell). The TISIDB website was used to investigate the associations between GPD2 expression and immune cell infiltration in LUAD. RESULTS: The results showed that GPD2 is overexpressed in LUAD tissues and significantly associated with poor survival. KM04416 can suppress the progression of LUAD cells by targeting GPD2. Low expression of GPD2 is related to high infiltration of immune cells. CONCLUSIONS: In summary, our present study found that targeting inhibition of GPD2 by KM04416 can suppress LUAD progression via adjusting immune cell infiltration.

TdDS-UNet: top-down deeply supervised U-Net for the delineation of 3D colorectal cancer.

Automatically delineating colorectal cancers with fuzzy boundaries from 3D images is a challenging task, but the problem of fuzzy boundary delineation in existing deep learning-based methods have not been investigated in depth. Here, an encoder-decoder-based U-shaped network (U-Net) based on top-down deep supervision (TdDS) was designed to accurately and automatically delineate the fuzzy boundaries of colorectal cancer. TdDS refines the semantic targets of the upper and lower stages by mapping ground truths that are more consistent with the stage properties than upsampling deep supervision. This stage-specific approach can guide the model to learn a coarse-to-fine delineation process and improve the delineation accuracy of fuzzy boundaries by gradually shrinking the boundaries. Experimental results showed that TdDS is more customizable and plays a role similar to the attentional mechanism, and it can further improve the capability of the model to delineate colorectal cancer contours. A total of 103, 12, and 29 3D pelvic magnetic resonance imaging volumes were used for training, validation, and testing, respectively. The comparative results indicate that the proposed method exhibits the best comprehensive performance, with a dice similarity coefficient (DSC) of 0.805 ± 0.053 and a hausdorff distance (HD) of 9.28 ± 5.14 voxels. In the delineation performance analysis section also showed that 44.49% of the delineation results are satisfactory and do not require revisions. This study can provide new technical support for the delineation of 3D colorectal cancer. Our method is open source, and the code is available athttps://github.com/odindis/TdDS/tree/main.

LD-UNet: A long-distance perceptual model for segmentation of blurred boundaries in medical images.

The blurriness of boundaries in medical image target regions hinders further improvement in automatic segmentation accuracy and is a challenging problem. To address this issue, we propose a model called long-distance perceptual UNet (LD-UNet), which has a powerful long-|distance perception ability and can effectively perceive the semantic context of an entire image. Specifically, LD-UNet utilizes global and local long-distance induction modules, which endow the model with contextual semantic induction capabilities for long-distance feature dependencies. The modules perform long-distance semantic perception at the high and low stages of LD-UNet, respectively, effectively improving the accuracy of local blurred information assessment. We also propose a top-down deep supervision method to enhance the ability of the model to fit data. Then, extensive experiments on four types of tumor data with blurred boundaries are conducted. The dataset includes nasopharyngeal carcinoma, esophageal carcinoma, pancreatic carcinoma, and colorectal carcinoma. The dice similarity coefficient scores obtained by LD-UNet on the four datasets are 73.35%, 85.93%, 70.04%, and 82.71%. Experimental results demonstrate that LD-UNet is more effective in improving the segmentation accuracy of blurred boundary regions than other methods with long-distance perception, such as transformers. Among all models, LD-UNet achieves the best performance. By visualizing the feature dependency field of the models, we further explore the advantages of LD-UNet in segmenting blurred boundaries.

Luteolin induces ferroptosis in prostate cancer cells by promoting TFEB nuclear translocation and increasing ferritinophagy.

BACKGROUND: As the second most common cancer in men and the leading cause of cancer-related death, prostate cancer (PCa) could potentially be treated by inducing ferroptosis. In this study, we aimed to investigate whether luteolin could induce ferroptosis in PCa cells through the transcription Factor EB (TFEB). METHODS: Different concentrations of luteolin were applied to treat normal prostate epithelial cells RWPE-1 and PCa cell lines DU145, PC-3, VCaP, and LNcaP. Ferrostatin-1 (Fer-1), Necrostain-1 (Nec-1), 3-methyladenine (3-MA), chloroquine (CQ), and the apoptosis inhibitor benzyloxycarbonyl-Val-Ala-Asp(OMe)-fluoromethylketone (Z-VAD-FMK) were added to treat DU145 and PC-3 cells. Additionally, we knocked down TFEB and performed in vitro cell experiments. Finally, tumor-forming experiments in nude mice were conducted to verify luteolin mechanism in PCa after knocking down TFEB. RESULTS: There was no significant difference in RWPE-1 at 12, 24, and 48 h after treatment with 60 µM luteolin. However, a significant difference was observed between DU145 and PC-3 cells. Luteolin exhibited a promoting effect on PCa cell death. After treatment with luteolin, cell viability, and Ki67 expression were decreased, and AnV-PI-positive dead cells were increased. Fer-1, Nec-1, 3-MA, and Z-VAD-FMK reversed luteolin effects on DU145 and PC-3 cell viability, proliferation, and AnV-PI-positive dead cells. Among them, Fer-1 and 3-MA were more effective. Luteolin-induced increased autophagy and ferroptosis in DU145 and PC-3 cells. Moreover, luteolin promoted ferroptosis by inducing increased autophagy in DU145 and PC-3 cells. However, knockdown of TFEB reversed the ability of luteolin to induce lysosome degradation of ferritin. In addition, luteolin promoted PCa ferroptosis by inducing ferritinophagy in vivo. CONCLUSIONS: Luteolin-induced ferroptosis in PCa cells by promoting TFEB nuclear translocation and increasing ferritinophagy.

Semi-automatic fine delineation scheme for pancreatic cancer.

BACKGROUND: Pancreatic cancer fine delineation in medical images by physicians is a major challenge due to the vast volume of medical images and the variability of patients. PURPOSE: A semi-automatic fine delineation scheme was designed to assist doctors in accurately and quickly delineating the cancer target region to improve the delineation accuracy of pancreatic cancer in computed tomography (CT) images and effectively reduce the workload of doctors. METHODS: A target delineation scheme in image blocks was also designed to provide more information for the deep learning delineation model. The start and end slices of the image block were manually delineated by physicians, and the cancer in the middle slices were accurately segmented using a three-dimensional Res U-Net model. Specifically, the input of the network is the CT image of the image block and the delineation of the cancer in the start and end slices, while the output of the network is the cancer area in the middle slices of the image block. Meanwhile, the model performance of pancreatic cancer delineation and the workload of doctors in different image block sizes were studied. RESULTS: We used 37 3D CT volumes for training, 11 volumes for validating and 11 volumes for testing. The influence of different image block sizes on doctors' workload was compared quantitatively. Experimental results showed that the physician's workload was minimal when the image block size was 5, and all cancer could be accurately delineated. The Dice similarity coefficient was 0.894 ± 0.029, the 95% Hausdorff distance was 3.465 ± 0.710 mm, the normalized surface Dice was 0.969 ± 0.019. By completing the accurate delineation of all the CT images, the speed of the new method is 2.16 times faster than that of manual sketching. CONCLUSION: Our proposed 3D semi-automatic delineative method based on the idea of block prediction could accurately delineate CT images of pancreatic cancer and effectively deal with the challenges of class imbalance, background distractions, and non-rigid geometrical features. This study had a significant advantage in reducing doctors' workload, and was expected to help doctors improve their work efficiency in clinical application.

Conv-TabNet: an efficient adaptive color correction network for smartphone-based urine component analysis.

The camera function of a smartphone can be used to quantitatively detect urine parameters anytime, anywhere. However, the color captured by different cameras in different environments is different. A method for color correction is proposed for a urine test strip image collected using a smartphone. In this method, the color correction model is based on the color information of the urine test strip, as well as the ambient light and camera parameters. Conv-TabNet, which can focus on each feature parameter, was designed to correct the color of the color blocks of the urine test strip. The color correction experiment was carried out in eight light sources on four mobile phones. The experimental results show that the mean absolute error of the new method is as low as 2.8±1.8, and the CIEDE2000 color difference is 1.5±1.5. The corrected color is almost consistent with the standard color by visual evaluation. This method can provide a technology for the quantitative detection of urine test strips anytime and anywhere.

Ridge operator-assisted delineation of capsulorhexis border for cataract surgery.

Background: With the continuous development of machine vision and imaging technology and its application in computer-aided diagnosis, it is clinically important to use computer technology to assist physicians in accurate cataract surgery. The capsulorhexis directly affects the outcome of cataract surgery, therefore, we design a method to automatically determine the virtual boundary of capsulorhexis for cataract surgery planning and tracking in-vivo to help surgeons achieve a more ideal capsulotomy geometry. Methods: In this study, an effective method was proposed to detect and display the location of capsulorhexis in cataract videos in-vivo. The initial step was locating the entire eye area by analyzing the connected components of the mirror reflective points in the image in the cataract surgery video. Then, an operator was designed for ridge edge variation and used to extract pupil edge features. Lastly, circular Hough transform was used to detect the pupillary margin and calculate the boundary between the scleral limbus and the virtual capsulorhexis border in accordance with the pupillary margin and finally displayed it in-vivo during cataract surgery. Results: The method was tested on eight videos of cataract surgery and the results showed that 98.52% accuracy was achieved in the localization of the specular reflection point. We compared the proposed operator with the Sobel, Scharr, Laplace and Canny operators and the results showed that our operator achieved the smallest mean square error with the greatest structural similarity. Conclusions: The analysis demonstrated that the proposed operator outperformed other operators in detection and achieved satisfactory results in the videos of actual cataract surgeries.

Leukocyte subtype classification with multi-model fusion.

Accurate classification of leukocytes is crucial for the diagnosis of hematologic malignancies, particularly leukemia. However, traditional leukocyte classification methods are time-consuming and subject to subjective interpretation by examiners. To address this issue, we aimed to develop a leukocyte classification system capable of accurately classifying 11 leukocyte classes, which would aid radiologists in diagnosing leukemia. Our proposed two-stage classification scheme involved a multi-model fusion based on ResNet for rough leukocyte classification, which focused on shape features, followed by fine-grained leukocyte classification using support vector machine for lymphocytes based on texture features. Our dataset consisted of 11,102 microscopic leukocyte images of 11 classes. Our proposed method achieved accurate leukocyte subtype classification with high levels of accuracy, sensitivity, specificity, and precision of 97.03 ± 0.05, 96.76 ± 0.05, 99.65 ± 0.05, and 96.54 ± 0.05, respectively, in the test set. The experimental results demonstrate that the leukocyte classification model based on multi-model fusion can effectively classify 11 leukocyte classes, providing valuable technical support for enhancing the performance of hematology analyzers.

Multiscale feature fusion network for 3D head MRI image registration.

BACKGROUND: Image registration technology has become an important medical image preprocessing step with the wide application of computer-aided diagnosis technology in various medical image analysis tasks. PURPOSE: We propose a multiscale feature fusion registration based on deep learning to achieve the accurate registration and fusion of head magnetic resonance imaging (MRI) and solve the problem that general registration methods cannot handle the complex spatial information and position information of head MRI. METHODS: Our proposed multiscale feature fusion registration network consists of three sequentially trained modules. The first is an affine registration module that implements affine transformation; the second is to realize non-rigid transformation, a deformable registration module composed of top-down and bottom-up feature fusion subnetworks in parallel; and the third is a deformable registration module that also realizes non-rigid transformation and is composed of two feature fusion subnetworks in series. The network decomposes the deformation field of large displacement into multiple deformation fields of small displacement by multiscale registration and registration, which reduces the difficulty of registration. Moreover, multiscale information in head MRI is learned in a targeted manner, which improves the registration accuracy, by connecting the two feature fusion subnetworks. RESULTS: We used 29 3D head MRIs for training and seven volumes for testing and calculated the values of the registration evaluation metrics for the new algorithm to register anterior and posterior lateral pterygoid muscles. The Dice similarity coefficient was 0.745 ± 0.021, the Hausdorff distance was 3.441 ± 0.935 mm, the Average surface distance was 0.738 ± 0.098 mm, and the Standard deviation of the Jacobian matrix was 0.425 ± 0.043. Our new algorithm achieved a higher registration accuracy compared with state-of-the-art registration methods. CONCLUSIONS: Our proposed multiscale feature fusion registration network can realize end-to-end deformable registration of 3D head MRI, which can effectively cope with the characteristics of large deformation displacement and the rich details of head images and provide reliable technical support for the diagnosis and analysis of head diseases.

Prognostic potential of a voxelwise invasion risk map of nasopharyngeal carcinoma based on a coordinate system of the nasopharynx.

Background: Tumor invasion risk (TIR) is an important prognostic factor in nasopharyngeal carcinoma (NPC). We propose a novel prognostic analytic method for NPC based on a voxelwise analysis of TIR in a coordinate system of the nasopharynx. Methods: A stable nasopharynx coordinate system was constructed based on anatomical landmarks to obtain an accurate TIR profile for NPC. The coordinate system was validated by image registration of the lateral pterygoid muscle (LPM). The tumors were registered to the coordinate system through shift, scale, and rotation transformations. The voxelwise TIR map for NPC was obtained by superposition of all registered and mirrored tumor regions of interest. The minimum risk (MinR) point of the tumor region was used as an independent prognostic factor for NPC. The cutoff value was calculated with density plot and validated with restricted cubic splines (RCSs), and then the patients were divided into 2 groups for overall survival (OS) analysis. Results: The first voxelwise TIR map of NPC was obtained based on 778 patients. The OS of patients with a low TIR was 76.8% and was 92.6% for patients with a high TIR [P<0.001; hazard ratio (HR) =1/0.45; 95% CI: 0.27-0.77; adjusted P=0.004]. Thus, patients with a low TIR had a poor prognosis, whereas patients with a high TIR had a good prognosis. The MinR may be better at grading the prognosis of patients compared to the American Joint Committee on Cancer (AJCC) staging or tumor/node (T/N) classification systems. Conclusions: The voxelwise TIR map provides a new method for the prognostic analysis of NPC. Potential clinical applications of voxelwise TIR mapping are clinical target volume (CTV) delineation and dose-painting for NPC.

Quercetin in Tonglong Qibi decoction ameliorates testosterone-induced benign prostatic hyperplasia in rats by regulating Nrf2 signalling pathways and oxidative stress.

Benign prostatic hyperplasia (BPH) is a common urological disease in older males. Existing pharmacotherapy shows several side effects, and the exploration of new therapeutic strategies is of high significance. Tonglong Qibi (TQ) decoction was proved to ameliorate BPH, while the underlying mechanisms are still unclear. In the current study, we explored the anti-BPH effects of TQ in vivo and identified its main therapeutic component and the underlying mechanisms in vitro. We demonstrated that TQ mitigated BPH in rats and showed no toxicity to the liver and reproductive system. Network pharmacology identified quercetin as the main component in TQ treating BPH. Quercetin reduced proliferation, oxidative stress, and increased Nrf2 expression in hyperplastic prostate epithelial cells. These findings indicate that quercetin in TQ alleviates BPH via inhibiting oxidative stress and activating the Nrf2 signalling pathway.

Automatic location scheme of anatomical landmarks in 3D head MRI based on the scale attention hourglass network.

BACKGROUND AND OBJECTIVE: An anatomical landmark is biologically meaningful point in medical images and often used for medical image registration. The purpose of this study is to automatically locate anatomical landmarks from 3D medical images. METHODS: A two-step automatic location scheme of anatomical landmarks in 3D medical image was designed in this study. In the first step, the full convolutional neural network was used for slice detection from a 3D medical image. In the second step, the scale attention hourglass network was used for landmark location in the detected slice and could overcome the difficulty of similar anatomical structures and different image parameters. This method was implemented and tested on four stable anatomical landmarks in 3D head MRI. RESULTS: A total of 500 and 300 3D head volumes were used for training and testing, respectively. Results showed that the slice detection accuracy reached 85.7% and that the maximum location error was less than one slice. The average accuracy of the four anatomical landmarks in the detected slice reached 87.2%, and the spatial distance was 2.4 ± 2.4, which obtained better performance compared with hourglass network and feature pyramid networks. CONCLUSIONS: This method can be useful for locating anatomical landmarks in 3D head MRI and provides technical support for medical image registration and big data analysis.

BSMM-Net: Multi-modal neural network based on bilateral symmetry for nasopharyngeal carcinoma segmentation.

Introduction: Automatically and accurately delineating the primary nasopharyngeal carcinoma (NPC) tumors in head magnetic resonance imaging (MRI) images is crucial for patient staging and radiotherapy. Inspired by the bilateral symmetry of head and complementary information of different modalities, a multi-modal neural network named BSMM-Net is proposed for NPC segmentation. Methods: First, a bilaterally symmetrical patch block (BSP) is used to crop the image and the bilaterally flipped image into patches. BSP can improve the precision of locating NPC lesions and is a simulation of radiologist locating the tumors with the bilateral difference of head in clinical practice. Second, modality-specific and multi-modal fusion features (MSMFFs) are extracted by the proposed MSMFF encoder to fully utilize the complementary information of T1- and T2-weighted MRI. The MSMFFs are then fed into the base decoder to aggregate representative features and precisely delineate the NPC. MSMFF is the output of MSMFF encoder blocks, which consist of six modality-specific networks and one multi-modal fusion network. Except T1 and T2, the other four modalities are generated from T1 and T2 by the BSP and DT modal generate block. Third, the MSMFF decoder with similar structure to the MSMFF encoder is deployed to supervise the encoder during training and assure the validity of the MSMFF from the encoder. Finally, experiments are conducted on the dataset of 7633 samples collected from 745 patients. Results and discussion: The global DICE, precision, recall and IoU of the testing set are 0.82, 0.82, 0.86, and 0.72, respectively. The results show that the proposed model is better than the other state-of-the-art methods for NPC segmentation. In clinical diagnosis, the BSMM-Net can give precise delineation of NPC, which can be used to schedule the radiotherapy.

Metabolite changes in the ipsilateral and contralateral cerebral hemispheres in rats with middle cerebral artery occlusion.

Cerebral ischemia not only causes pathological changes in the ischemic areas but also induces a series of secondary changes in more distal brain regions (such as the contralateral cerebral hemisphere). The impact of supratentorial lesions, which are the most common type of lesion, on the contralateral cerebellum has been studied in patients by positron emission tomography, single photon emission computed tomography, magnetic resonance imaging and diffusion tensor imaging. In the present study, we investigated metabolite changes in the contralateral cerebral hemisphere after supratentorial unilateral ischemia using nuclear magnetic resonance spectroscopy-based metabonomics. The permanent middle cerebral artery occlusion model of ischemic stroke was established in rats. Rats were randomly divided into the middle cerebral artery occlusion 1-, 3-, 9- and 24-hour groups and the sham group. 1H nuclear magnetic resonance spectroscopy was used to detect metabolites in the left and right cerebral hemispheres. Compared with the sham group, the concentrations of lactate, alanine, γ-aminobutyric acid, choline and glycine in the ischemic cerebral hemisphere were increased in the acute stage, while the concentrations of N-acetyl aspartate, creatinine, glutamate and aspartate were decreased. This demonstrates that there is an upregulation of anaerobic glycolysis (shown by the increase in lactate), a perturbation of choline metabolism (suggested by the increase in choline), neuronal cell damage (shown by the decrease in N-acetyl aspartate) and neurotransmitter imbalance (evidenced by the increase in γ-aminobutyric acid and glycine and by the decrease in glutamate and aspartate) in the acute stage of cerebral ischemia. In the contralateral hemisphere, the concentrations of lactate, alanine, glycine, choline and aspartate were increased, while the concentrations of γ-aminobutyric acid, glutamate and creatinine were decreased. This suggests that there is a difference in the metabolite changes induced by ischemic injury in the contralateral and ipsilateral cerebral hemispheres. Our findings demonstrate the presence of characteristic changes in metabolites in the contralateral hemisphere and suggest that they are most likely caused by metabolic changes in the ischemic hemisphere.

Recombinant adenovirus-mediated overexpression of 3ß-hydroxysteroid-Δ24 reductase.

3ß-Hydroxysteroid-Δ24 reductase (DHCR24) is a multifunctional enzyme that localizes to the endoplasmic reticulum and has neuroprotective and cholesterol-synthesizing activities. DHCR24 overexpression confers neuroprotection against apoptosis caused by amyloid ß deposition. The present study aimed to construct two recombinant adenoviruses driving DHCR24 expression specifically in neurons. Two SYN1 promoter DNA fragments were obtained from human (h) and rat (r). Recombinant Ad-r(h)SYN1-DHCR24 was transfected into AD-293, N2A (mouse neuroblastoma), and MIN6 (mouse pancreatic carcinoma) cells. Western blot analysis showed DHCR24 was specially expressed in 293 and N2A cells, but no specific band was found in MIN6 cells. This demonstrates that the recombinant adenoviruses successfully express DHCR24, and no expression is observed in non-neuronal cells. TUNEL assay results showed apoptosis was inhibited in adenovirus-transfected neurons. Detecting reactive oxygen species by immunofluorescence, we found that adenovirus transfection inhibits apoptosis through scavenging excess reactive oxygen species. Our findings show that the recombinant DHCR24 adenoviruses induce neuron-specific DHCR24 expression, and thereby lay the foundation for further studies on DHCR24 gene therapy for Alzheimer's disease.

3 ß-hydroxysteroid-Δ 24 reductase (DHCR24) protects neuronal cells from apoptotic cell death induced by endoplasmic reticulum (ER) stress.

3ß-Hydroxysteroid-Δ24 reductase (DHCR24) is an endoplasmic reticulum (ER)-localized multifunctional enzyme that possesses anti-apoptotic and cholesterol-synthesizing activities. Accumulating evidence suggests that ER stress is involved in the pathogenesis of neurodegenerative disease. In this study, we investigated whether DHCR24 may function as a neuroprotective protein under ER stress. Neuroblastoma N2A cells were infected with adenovirus expressing myc-tagged DHCR24 (Ad-DHCR24) or lacZ (Ad-lacZ, serving as a control) and subjected to ER-stress, induced with Tunicamycin (TM). Cells infected with Ad-DHCR24-myc were resistant to TM-induced apoptosis, and showed weaker level of caspase-12 activity. These cells also exhibited lower levels of Bip and CHOP proteins than Ad-LacZ-infected cells. Moreover, a stronger and rapid activation of PERK, and a prolonged activation of JNK and p38 were observed in Ad-LacZ-infected cells. The generation of intracellular reactive oxygen species from ER stress was also diminished by the overexpression of DHCR24. Additionally, intracellular cholesterol level was also elevated in the Ad-DHCR24-infected cells, accompanied by a well-organized formation of caveolae (cholesterol-rich microdomain) on the plasma membrane, and improved colocalization of caveolin-1 and insulin-like growth factor 1 receptor. These results demonstrated for the first time that DHCR24 could protect neuronal cells from apoptosis induced by ER stress.

Effect of salvia miltiorrhiza bunge on cerebral infarct in ischemia-reperfusion injured rats.

According to the theory of traditional Chinese medicine, cerebral infarction results from blood stasis, and the method of quickening the blood and dispelling stasis is used to treat cerebral infarct. salvia miltorrhiza bunge (SM) is a Chinese herb, which is considered to have an action of quickening the blood and dispelling stasis, and is frequently used to treat related disorders of blood stasis such as cerebrovascular accident and ischemic heart disease. The aim of the present study was to investigate the effect of SM on cerebral infarct in ischemia-reperfusion injured rats. A total of 30 Sprague-Dawley (SD) rats were studied. A model of focal cerebral infarct was developed by occluding both common carotid arteries and the right middle cerebral artery for 90 minutes. After 24 hours reperfusion, the rats were killed and the brain tissue was stained with 2, 3, 5-triphenyl-tetrazolium chloride (TTC). The areas of cerebral infarct were calculated, and lumino-chemiluminesence (CL) counts and lucigenin-CL counts of peripheral blood taken at this time were measured. The changes in the area of cerebral infarct were used as an index to evaluate the effect of SM on cerebral infarct. The results indicated that pretreatment with intraperitoneal injection of 30 mg/kg and 15 mg/kg SM reduced the area of cerebral infarct and also reduced the luminol-CL counts of peripheral blood in ischemia-reperfusion injured rats. This study has demonstrated that SM can reduce the area of cerebral infarct in ischemia-reperfusion injured rats, suggesting it may be useful in the treatment of cerebral infarct in humans. The therapeutic effect of SM may be partly due to its free radical scavenging activities.