Anthrahydroquinone­2,6­disulfonate attenuates PQ­induced acute lung injury through decreasing pulmonary microvascular permeability via inhibition of the PI3K/AKT/eNOS pathway.

In paraquat (PQ)­induced acute lung injury (ALI)/ acute respiratory distress syndrome, PQ disrupts endothelial cell function and vascular integrity, which leads to increased pulmonary leakage. Anthrahydroquinone­2,6­disulfonate (AH2QDS) is a reducing agent that attenuates the extent of renal injury and improves survival in PQ­intoxicated Sprague­Dawley (SD) rats. The present study aimed to explore the beneficial role of AH2QDS in PQ­induced ALI and its related mechanisms. A PQ­intoxicated ALI model was established using PQ gavage in SD rats. Human pulmonary microvascular endothelial cells (HPMECs) were challenged with PQ. Superoxide dismutase, malondialdehyde, reactive oxygen species and nitric oxide (NO) fluorescence were examined to detect the level of oxidative stress in HPMECs. The levels of TNF­α, IL­1ß and IL­6 were assessed using an ELISA. Transwell and Cell Counting Kit­8 assays were performed to detect the migration and proliferation of the cells. The pathological changes in lung tissues and blood vessels were examined by haematoxylin and eosin staining. Evans blue staining was used to detect pulmonary microvascular permeability. Western blotting was performed to detect target protein levels. Immunofluorescence and immunohistochemical staining were used to detect the expression levels of target proteins in HPMECs and lung tissues. AH2QDS inhibited inflammatory responses in lung tissues and HPMECs, and promoted the proliferation and migration of HPMECs. In addition, AH2QDS reduced pulmonary microvascular permeability by upregulating the levels of vascular endothelial­cadherin, zonula occludens­1 and CD31, thereby attenuating pathological changes in the lungs in rats. Finally, these effects may be related to the suppression of the phosphatidylinositol­3­kinase (PI3K)/protein kinase B (AKT)/endothelial­type NO synthase (eNOS) signalling pathway in endothelial cells. In conclusion, AH2QDS ameliorated PQ­induced ALI by improving alveolar endothelial barrier disruption via modulation of the PI3K/AKT/eNOS signalling pathway, which may be an effective candidate for the treatment of PQ­induced ALI.

Colonoscopy-assisted removal of an impaction foreign body at the rectosigmoid junction: A case report.

BACKGROUND: When an anorectal foreign body is found, its composition and shape should be evaluated, and a timely and effective treatment plan should be developed based on the patient's symptoms to avoid serious complications such as intestinal perforation caused by displacement of the foreign body. CASE SUMMARY: A 54-year-old male was admitted to our outpatient clinic on June 3, 2023, due to a rectal foreign body that had been embedded for more than 24 h. The patient reported using a glass electrode tube to assist in the recovery of prolapsed hemorrhoids, however, the electrode tube was inadvertently inserted into the anus and could not be removed by the patient. During hospitalization, the patient underwent surgery, and the foreign body was dragged into the rectum with the aid of colonoscopy. The anus was dilated with a comb-type pulling hook and an anal fistula pulling hook to widen the anus and remove the foreign body, and the local anal symptoms were then relieved with topical drugs. The patient was allowed to eat and drink, and an entire abdominal Computed tomography (CT) and colonoscopy were reviewed 3 d after surgery. CT revealed no foreign body residue and colonoscopy showed no metal or other residues in the colon and rectum, and no apparent intestinal tract damage. CONCLUSION: The timeliness and rationality of the surgical and therapeutic options for this patient were based on a literature review of the clinical signs and conceivable conditions in such cases. The type, material and the potential risks of rectal foreign bodies should be considered.

Multi-task reconstruction network for synthetic diffusion kurtosis imaging: Predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.

PURPOSE: To assess the feasibility and clinical value of synthetic diffusion kurtosis imaging (DKI) generated from diffusion weighted imaging (DWI) through multi-task reconstruction network (MTR-Net) for tumor response prediction in patients with locally advanced rectal cancer (LARC). METHODS: In this retrospective study, 120 eligible patients with LARC were enrolled and randomly divided into training and testing datasets with a 7:3 ratio. The MTR-Net was developed for reconstructing Dapp and Kapp images from apparent diffusion coefficient (ADC) images. Tumor regions were manually segmented on both true and synthetic DKI images. The synthetic image quality and manual segmentation agreement were quantitatively assessed. The support vector machine (SVM) classifier was used to construct radiomics models based on the true and synthetic DKI images for pathological complete response (pCR) prediction. The prediction performance for the models was evaluated by the receiver operating characteristic (ROC) curve analysis. RESULTS: The mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) for tumor regions were 0.212, 24.278, and 0.853, respectively, for the synthetic Dapp images and 0.516, 24.883, and 0.804, respectively, for the synthetic Kapp images. The Dice similarity coefficient (DSC), positive predictive value (PPV), sensitivity (SEN), and Hausdorff distance (HD) for the manually segmented tumor regions were 0.786, 0.844, 0.755, and 0.582, respectively. For predicting pCR, the true and synthetic DKI-based radiomics models achieved area under the curve (AUC) values of 0.825 and 0.807 in the testing datasets, respectively. CONCLUSIONS: Generating synthetic DKI images from DWI images using MTR-Net is feasible, and the efficiency of synthetic DKI images in predicting pCR is comparable to that of true DKI images.

Modality-Specific Information Disentanglement From Multi-Parametric MRI for Breast Tumor Segmentation and Computer-Aided Diagnosis.

Breast cancer is becoming a significant global health challenge, with millions of fatalities annually. Magnetic Resonance Imaging (MRI) can provide various sequences for characterizing tumor morphology and internal patterns, and becomes an effective tool for detection and diagnosis of breast tumors. However, previous deep-learning based tumor segmentation methods from multi-parametric MRI still have limitations in exploring inter-modality information and focusing task-informative modality/modalities. To address these shortcomings, we propose a Modality-Specific Information Disentanglement (MoSID) framework to extract both inter- and intra-modality attention maps as prior knowledge for guiding tumor segmentation. Specifically, by disentangling modality-specific information, the MoSID framework provides complementary clues for the segmentation task, by generating modality-specific attention maps to guide modality selection and inter-modality evaluation. Our experiments on two 3D breast datasets and one 2D prostate dataset demonstrate that the MoSID framework outperforms other state-of-the-art multi-modality segmentation methods, even in the cases of missing modalities. Based on the segmented lesions, we further train a classifier to predict the patients' response to radiotherapy. The prediction accuracy is comparable to the case of using manually-segmented tumors for treatment outcome prediction, indicating the robustness and effectiveness of the proposed segmentation method. The code is available at https://github.com/Qianqian-Chen/MoSID.

Beam-wise dose composition learning for head and neck cancer dose prediction in radiotherapy.

Automatic and accurate dose distribution prediction plays an important role in radiotherapy plan. Although previous methods can provide promising performance, most methods did not consider beam-shaped radiation of treatment delivery in clinical practice. This leads to inaccurate prediction, especially on beam paths. To solve this problem, we propose a beam-wise dose composition learning (BDCL) method for dose prediction in the context of head and neck (H&N) radiotherapy plan. Specifically, a global dose network is first utilized to predict coarse dose values in the whole-image space. Then, we propose to generate individual beam masks to decompose the coarse dose distribution into multiple field doses, called beam voters, which are further refined by a subsequent beam dose network and reassembled to form the final dose distribution. In particular, we design an overlap consistency module to keep the similarity of high-level features in overlapping regions between different beam voters. To make the predicted dose distribution more consistent with the real radiotherapy plan, we also propose a dose-volume histogram (DVH) calibration process to facilitate feature learning in some clinically concerned regions. We further apply an edge enhancement procedure to enhance the learning of the extracted feature from the dose falloff regions. Experimental results on a public H&N cancer dataset from the AAPM OpenKBP challenge show that our method achieves superior performance over other state-of-the-art approaches by significant margins. Source code is released at https://github.com/TL9792/BDCLDosePrediction.

Uncertainty-aware refinement framework for ovarian tumor segmentation in CECT volume.

BACKGROUND: Ovarian cancer is a highly lethal gynecological disease. Accurate and automated segmentation of ovarian tumors in contrast-enhanced computed tomography (CECT) images is crucial in the radiotherapy treatment of ovarian cancer, enabling radiologists to evaluate cancer progression and develop timely therapeutic plans. However, automatic ovarian tumor segmentation is challenging due to factors such as inhomogeneous background, ambiguous tumor boundaries, and imbalanced foreground-background, all of which contribute to high predictive uncertainty for a segmentation model. PURPOSE: To tackle these challenges, we propose an uncertainty-aware refinement framework that aims to estimate and refine regions with high predictive uncertainty for accurate ovarian tumor segmentation in CECT images. METHODS: To this end, we first employ an approximate Bayesian network to detect coarse regions of interest (ROIs) of both ovarian tumors and uncertain regions. These ROIs allow a subsequent segmentation network to narrow down the search area for tumors and prioritize uncertain regions, resulting in precise segmentation of ovarian tumors. Meanwhile, the framework integrates two guidance modules that learn two implicit functions capable of mapping query features sampled according to their uncertainty to organ or boundary manifolds, guiding the segmentation network to facilitate information encoding of uncertain regions. RESULTS: Firstly, 367 CECT images are collected from the same hospital for experiments. Dice score, Jaccard, Recall, Positive predictive value (PPV), 95% Hausdorff distance (HD95) and Average symmetric surface distance (ASSD) for the testing group of 77 cases are 86.31%, 73.93%, 83.95%, 86.03%, 15.17  mm and 2.57  mm, all of which are significantly better than that of the other state-of-the-art models. And results of visual comparison shows that the compared methods have more mis-segmentation than our method. Furthermore, our method achieves a Dice score that is at least 20% higher than the Dice scores of other compared methods when tumor volumes are less than 20 cm 3 $^3$ , indicating better recognition ability to small regions by our method. And then, 38 CECT images are collected from another hospital to form an external testing group. Our approach consistently outperform the compared methods significantly, with the external testing group exhibiting substantial improvements across key evaluation metrics: Dice score (83.74%), Jaccard (69.55%), Recall (82.12%), PPV (81.61%), HD95 (12.31 mm), and ASSD (2.32 mm), robustly establishing its superior performance. CONCLUSIONS: Experimental results demonstrate that the framework significantly outperforms the compared state-of-the-art methods, with decreased under- or over-segmentation and better small tumor identification. It has the potential for clinical application.

Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches.

Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.

Breast Fibroglandular Tissue Segmentation for Automated BPE Quantification With Iterative Cycle-Consistent Semi-Supervised Learning.

Background Parenchymal Enhancement (BPE) quantification in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) plays a pivotal role in clinical breast cancer diagnosis and prognosis. However, the emerging deep learning-based breast fibroglandular tissue segmentation, a crucial step in automated BPE quantification, often suffers from limited training samples with accurate annotations. To address this challenge, we propose a novel iterative cycle-consistent semi-supervised framework to leverage segmentation performance by using a large amount of paired pre-/post-contrast images without annotations. Specifically, we design the reconstruction network, cascaded with the segmentation network, to learn a mapping from the pre-contrast images and segmentation predictions to the post-contrast images. Thus, we can implicitly use the reconstruction task to explore the inter-relationship between these two-phase images, which in return guides the segmentation task. Moreover, the reconstructed post-contrast images across multiple auto-context modeling-based iterations can be viewed as new augmentations, facilitating cycle-consistent constraints across each segmentation output. Extensive experiments on two datasets with various data distributions show great segmentation and BPE quantification accuracy compared with other state-of-the-art semi-supervised methods. Importantly, our method achieves 11.80 times of quantification accuracy improvement along with 10 times faster, compared with clinical physicians, demonstrating its potential for automated BPE quantification. The code is available at https://github.com/ZhangJD-ong/Iterative-Cycle-consistent-Semi-supervised-Learning-for-fibroglandular-tissue-segmentation.

A Hierarchical Graph V-Net With Semi-Supervised Pre-Training for Histological Image Based Breast Cancer Classification.

Numerous patch-based methods have recently been proposed for histological image based breast cancer classification. However, their performance could be highly affected by ignoring spatial contextual information in the whole slide image (WSI). To address this issue, we propose a novel hierarchical Graph V-Net by integrating 1) patch-level pre-training and 2) context-based fine-tuning, with a hierarchical graph network. Specifically, a semi-supervised framework based on knowledge distillation is first developed to pre-train a patch encoder for extracting disease-relevant features. Then, a hierarchical Graph V-Net is designed to construct a hierarchical graph representation from neighboring/similar individual patches for coarse-to-fine classification, where each graph node (corresponding to one patch) is attached with extracted disease-relevant features and its target label during training is the average label of all pixels in the corresponding patch. To evaluate the performance of our proposed hierarchical Graph V-Net, we collect a large WSI dataset of 560 WSIs, with 30 labeled WSIs from the BACH dataset (through our further refinement), 30 labeled WSIs and 500 unlabeled WSIs from Yunnan Cancer Hospital. Those 500 unlabeled WSIs are employed for patch-level pre-training to improve feature representation, while 60 labeled WSIs are used to train and test our proposed hierarchical Graph V-Net. Both comparative assessment and ablation studies demonstrate the superiority of our proposed hierarchical Graph V-Net over state-of-the-art methods in classifying breast cancer from WSIs. The source code and our annotations for the BACH dataset have been released at https://github.com/lyhkevin/Graph-V-Net.

A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows screening, follow up, and diagnosis for breast tumor with high sensitivity. Accurate tumor segmentation from DCE-MRI can provide crucial information of tumor location and shape, which significantly influences the downstream clinical decisions. In this paper, we aim to develop an artificial intelligence (AI) assistant to automatically segment breast tumors by capturing dynamic changes in multi-phase DCE-MRI with a spatial-temporal framework. The main advantages of our AI assistant include (1) robustness, i.e., our model can handle MR data with different phase numbers and imaging intervals, as demonstrated on a large-scale dataset from seven medical centers, and (2) efficiency, i.e., our AI assistant significantly reduces the time required for manual annotation by a factor of 20, while maintaining accuracy comparable to that of physicians. More importantly, as the fundamental step to build an AI-assisted breast cancer diagnosis system, our AI assistant will promote the application of AI in more clinical diagnostic practices regarding breast cancer.

A novel cosmetic approach for partial matricectomy in treating ingrown toenails.

BACKGROUND: Toenails play a great part in protecting toes and peripheral soft tissues, simultaneously playing a cosmetic role. The ideal treatment should result in a functional and aesthetic outcome. OBJECTIVE: To describe a novel, aesthetic and minimally invasive method to treat ingrown toenail. METHODS: We retrospectively analyzed 436 lesions of 395 ingrown toes in 353 patients with a mean age of 26.0 ± 13.4 (range 10-55) from June 2014 to March 2020 in our department. A novel cosmetic approach for partial matricectomy in treating ingrown toenails was undergone. The average follow-up time was 27.5 ± 2.8 months. The average period prior to work resumption, recurrence rate, and infection rate were measured. Mean pain Visual Analogue Scale (VAS) and Mean satisfaction VAS were used to evaluate the foot appearance. RESULTS: The average period prior work resumption was 2.2 ± 2.1 days (range, 0-7 days). The recurrence rate was 1.6% (7 lesions in 6 patients) at more than 2 years of follow-up. There was no critical complication except infection (0.46%). Mean pain VAS reduced from a preoperative score of 7.7 ± 1.5 points (range, 6-10 points) to a postoperative 3-day score of 2.2 ± 1.0 points (range, 1-4 points; p < 0.001) while Mean satisfaction VAS improved from 1.5 ± 1.3 points (range, 0-3 points) to 9.2 ± 0.6 points (range, 8-10 points; p < 0.001). CONCLUSION: Our proposed approach is minimally invasive relative to conventional methods, which can achieve comparable efficacy to treat ingrown toenails with granulation tissue. Therefore, it can serve as another option to treat this specific type of ingrown toenails.

Antimicrobial Substances and Mechanisms of Lactobacillus rhamnosus against Gardnerella vaginalis.

Bacterial vaginosis (BV) is a common vaginal disease associated with abnormal changes in the vaginal microbiome. Our previous study found that Lactobacillus rhamnosus has a good therapeutic effect on bacterial vaginosis by inhibiting the most prominent bacterium associated with BV, Gardnerella vaginalis. In this study, we show that acetic acid and lactic acid are the main substances in the cell-free supernatant (CFS) of L. rhamnosus that inhibit the growth of G. vaginalis. Further study on the mechanism showed that acetic acid and lactic acid alter the morphology of the G. vaginalis cells, eventually causing the cells to shrink or burst, resulting in exudation of their intracellular contents. In addition, these two organic acids also dissipate the membrane potential of bacterial cells, affecting their synthesis of ATP. A reduced activity of the Na+/K+-ATPase leads to abnormal ATP metabolism, and ultimately inhibits the growth and reproduction of G. vaginalis. Our study provides valuable information for the widespread application of L. rhamnosus in the treatment of bacterial vaginosis.

Enhanced organic-inorganic heterojunction of polypyrrole@Bi2WO6: Fabrication and application for sensitive photoelectrochemical immunoassay of creatine kinase-MB.

In this report, enhanced organic-inorganic heterojunction of polypyrrole@Bi2WO6 was fabricated and applied for sensitive photoelectrochemical (PEC) immunoassay of creatine kinase-MB (CK-MB). Specifically, heterostructured polypyrrole@Bi2WO6 photoelectrode was prepared and sandwich immunorecognition were integrated for the CK-MB immunoassay. In the detection, with the aid of alkaline phosphate (ALP)-induced biocatalytic precipitation (BCP), the precipitation-dependent suppression of the photocurrent can be recorded due to the impediment of the interfacial mass and electron transfer. On the basis of target-controlled BCP formation, a novel PEC immunoassay could be developed for the sensitive and specific CK-MB detection. This work manifested the great potential of polypyrrole@Bi2WO6 heterojunction as a novel platform for PEC bioanalysis development and also a PEC method for CK-MB detection. This work is expected to stimulate more interest in the design and implementation of numerous other organic-inorganic heterojunction for advanced PEC bioanalysis development.

MiR-218-5p targets LHFPL3 to regulate proliferation, migration, and epithelial-mesenchymal transitions of human glioma cells.

Glioblastoma (GBM) is a main subtype of high-grade gliomas with features in progressive brain tumor. Lipoma HMGIC fusion partner-like 3 (LHFPL3) is reported to be highly expressed in malignant glioma, but the relationship and mechanism between LHFPL3 and tumor is inexplicit. The present study aimed to screen the miRNAs targeting LHFPL3 and verify the pathogenesis and development of gliomas. Bioinformatics software predicted that miR-218-5p and miR-138-5p can specifically bind to LHFPL3 mRNA. And the expression of miR-218-5p and miR-138-5p was down-regulated in glioma cell lines and glioma tissues from the patients compared with the normal cells. While dual luciferase activity experiment confirmed, only miR-218-5p can directly bind to LHFPL3. After miR-218-5p transfection of U251 and U87 cells, cytological examinations found a reduction in cell activity, proliferation and invasive ability. Further study showed that miR-218-5p transfection could inhibit epithelial-mesenchymal transitions (EMT). Therefore, miR-218-5p targeting LHFPL3 mRNA plays significant roles in preventing the invasiveness of glioma cells. The present study also revealed a novel mechanism for miRNA-LHFPL3 interaction in glioma cells, which may be potential targets for developing therapies in treating glioma.

Functional Changes of Dendritic Cells in C6 Glioma-Bearing Rats That Underwent Combined Argon-Helium Cryotherapy and IL-12 Treatment.

OBJECTIVE: The aim of this study was to explore changes in tumor tissues of glioma-bearing rats that underwent argon-helium cryoablation as well as changes in antitumor immunity before and after combined interleukin 12 treatment. METHODS: Two hundred sixty Wistar rats were randomly divided into a blank control group, intravenous injection interleukin-12 group, cryotherapy group, and cryotherapy + intravenous injection group. C6 glioma cells proliferated in vitro were implanted subcutaneously on the backs of rats to establish C6 glioma-bearing animal models. Each group underwent the corresponding treatments, and morphological changes in tumor tissues were examined using hematoxylin-eosin staining. CD11c staining was examined using immunohistochemistry, and differences in dendritic cells and T-cell subsets before and after treatment were analyzed using flow cytometry. RESULTS: The control group showed no statistical changes in terms of tumor tissue morphology and cellular immunity, cryotherapy group, and cryotherapy + intravenous injection group, among which the count for the cryotherapy + intravenous injection group was significantly higher than those of all other groups. In the argon-helium cryotherapy group, tumor cells were damaged and dendritic cell markers were positive. The number of CD11c+ and CD86+ cells increased significantly after the operation as did the cytokine interferon-γ level (P < .01), suggesting a shift toward Th1-type immunity. CONCLUSION: Combined treatment of argon-helium cryoablation and interleukin 12 for gliomas not only effectively injured tumor tissues but also boosted immune function and increased antitumor ability. Therefore, this approach is a promising treatment measure for brain gliomas.

Integration of DNA bio-gates and duplex-specific nuclease signal amplification: towards electrochemiluminescence detection of survivin mRNA.

Using the DNA bio-gate and duplex-specific nuclease assisted target recycling, a facile electrochemiluminescence assay was developed for the sensitive detection of survivin mRNA.

Treatment of Dutch rat models of glioma using EphrinA1-PE38/GM-CSF chitosan nanoparticles by in situ activation of dendritic cells.

Although dendritic cells (DCs) used in DC-based immunotherapy are loaded with tumor-associated antigens, the antitumor immune response is effectively stimulated in cytotoxic specific T lymphocytes (CTLs), thereby achieving targeted killing of tumor cells without harming surrounding normal cells. This makes it a highly promising new form of therapy. In this study, we successfully created chitosan-coated EphrinA1-PE38/GM-CSF nanoparticles and transplanted them into tumor-bearing rats, resulting in the effective killing of glioma tissue and a prolonged life span. Further immunohistochemistry and flow cytometry studies revealed that the treatment was effective in increasing the number of dendritic cell activations under an immunomodulatory response. These results suggest that chitosan-coated EphrinA1-PE38/GM-CSF nanoparticles may be effective in inducing in situ activation of DCs in glioma-bearing rats, thereby generating DC vaccines in vivo.

A novel recombinant protein of ephrinA1-PE38/GM-CSF activate dendritic cells vaccine in rats with glioma.

Dendritic cells loaded with tumor-associated antigens can effectively stimulate the antitumor immune response of cytotoxic T lymphocytes in the body, which facilitates the development of novel and effective treatments for cancer. In this study, the adenovirus-mediated ephrinA1-PE38/GM-CSF was successfully constructed using the overlap extension method, and verified with sequencing analysis. HEK293 cells were infected with the adenovirus and the cellular expression of ephrinA1-PE38/GM-CSF was measured with an enzyme-linked immunosorbent assay. The recombinant adenovirus was then delivered into the tumor-bearing rats and the results showed that such treatment significantly reduced the volumes of gliomas and improved the survival of the transplanted rats. The results from immunohistochemistry and flow cytometry suggested that this immunomodulatory agent cause activation of dendritic cells. The findings that ephrinA1-PE38/GM-CSF had a high efficacy in the activation of the dendritic cells would facilitate the development of in vivo dendritic-cell vaccines for the treatment of gliomas in rats. Our new method of DC vaccine production induces not only a specific local antitumor immune response but also a systemic immunotherapeutic effect. In addition, this method completely circumvents the risk of contamination related to the in vitro culture of DCs, thus greatly improving the safety and feasibility of clinical application of the DC vaccines in glioma.

Bone marrow stromal cells combined with oxiracetam influences the expression of B-cell lymphoma 2 in rats with ischemic stroke.

This study aimed to investigate the combination effects of bone marrow stromal cells (BMSCs) and oxiracetam for ischemic stroke. Forty Sprague Dawley female rats (220 ± 20 g) were subjected to a 2-hour ischemic middle cerebral artery occlusion (MCAO)-24 hours reperfusion model. The rats were randomly divided into 4 groups. Rats from BMSCs group, oxiracetam group, and BMSCs + oxiracetam group accepted injection of BMSCs (3 × 10(6) cells), oxiracetam (800 mg/kg), and BMSCs + oxiracetam, respectively. Rats from control group did not receive any interventions after ischemia reperfusion. The neurologic function was examined by modified neurological severity scores (mNSS). B-cell lymphoma 2 (Bcl-2) expression and apoptosis were detected by immunohistochemistry and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) staining. The mNSS was decreased in all treatment groups and that in BMSCs + oxiracetam group was lower than BMSCs group and oxiracetam group (P < .05). The expression of Bcl-2 was unregulated in all treatment groups (P < .05), and similarly, the expression of Bcl-2 in BMSCs + oxiracetam group was higher than BMSCs group and oxiracetam group (P < .05). Control group displayed more TUNEL-positive cells than the treatment groups, and BMSCs + oxiracetam group displayed less apoptotic cells than BMSCs group or oxiracetam group (P < .05). Transplantation of BMSCs can promote the recovery of neurologic function in MCAO rats, and the effect of BMSCs combined with oxiracetam was better than the either one. Upregulation of Bcl-2 resulting in a decrease of apoptosis may be one of the mechanisms of BMSCs treatment for cerebral ischemic stroke.