Pipelined biomedical event extraction rivaling joint learning.

Biomedical event extraction is an information extraction task to obtain events from biomedical text, whose targets include the type, the trigger, and the respective arguments involved in an event. Traditional biomedical event extraction usually adopts a pipelined approach, which contains trigger identification, argument role recognition, and finally event construction either using specific rules or by machine learning. In this paper, we propose an n-ary relation extraction method based on the BERT pre-training model to construct Binding events, in order to capture the semantic information about an event's context and its participants. The experimental results show that our method achieves promising results on the GE11 and GE13 corpora of the BioNLP shared task with F1 scores of 63.14% and 59.40%, respectively. It demonstrates that by significantly improving the performance of Binding events, the overall performance of the pipelined event extraction approach or even exceeds those of current joint learning methods.

Joint learning-based causal relation extraction from biomedical literature.

Causal relation extraction of biomedical entities is one of the most complex tasks in biomedical text mining, which involves two kinds of information: entity relations and entity functions. One feasible approach is to take relation extraction and function detection as two independent sub-tasks. However, this separate learning method ignores the intrinsic correlation between them and leads to unsatisfactory performance. In this paper, we propose a joint learning model, which combines entity relation extraction and entity function detection to exploit their commonality and capture their inter-relationship, so as to improve the performance of biomedical causal relation extraction. Experimental results on the BioCreative-V Track 4 corpus show that our joint learning model outperforms the separate models in BEL statement extraction, achieving the F1 scores of 57.0% and 37.3% on the test set in Stage 2 and Stage 1 evaluations, respectively. This demonstrates that our joint learning system reaches the state-of-the-art performance in Stage 2 compared with other systems.

LitCovid ensemble learning for COVID-19 multi-label classification.

The Coronavirus Disease 2019 (COVID-19) pandemic has shifted the focus of research worldwide, and more than 10 000 new articles per month have concentrated on COVID-19-related topics. Considering this rapidly growing literature, the efficient and precise extraction of the main topics of COVID-19-relevant articles is of great importance. The manual curation of this information for biomedical literature is labor-intensive and time-consuming, and as such the procedure is insufficient and difficult to maintain. In response to these complications, the BioCreative VII community has proposed a challenging task, LitCovid Track, calling for a global effort to automatically extract semantic topics for COVID-19 literature. This article describes our work on the BioCreative VII LitCovid Track. We proposed the LitCovid Ensemble Learning (LCEL) method for the tasks and integrated multiple biomedical pretrained models to address the COVID-19 multi-label classification problem. Specifically, seven different transformer-based pretrained models were ensembled for the initialization and fine-tuning processes independently. To enhance the representation abilities of the deep neural models, diverse additional biomedical knowledge was utilized to facilitate the fruitfulness of the semantic expressions. Simple yet effective data augmentation was also leveraged to address the learning deficiency during the training phase. In addition, given the imbalanced label distribution of the challenging task, a novel asymmetric loss function was applied to the LCEL model, which explicitly adjusted the negative-positive importance by assigning different exponential decay factors and helped the model focus on the positive samples. After the training phase, an ensemble bagging strategy was adopted to merge the outputs from each model for final predictions. The experimental results show the effectiveness of our proposed approach, as LCEL obtains the state-of-the-art performance on the LitCovid dataset. Database URL: https://github.com/JHnlp/LCEL.

Multi-probe attention neural network for COVID-19 semantic indexing.

BACKGROUND: The COVID-19 pandemic has increasingly accelerated the publication pace of scientific literature. How to efficiently curate and index this large amount of biomedical literature under the current crisis is of great importance. Previous literature indexing is mainly performed by human experts using Medical Subject Headings (MeSH), which is labor-intensive and time-consuming. Therefore, to alleviate the expensive time consumption and monetary cost, there is an urgent need for automatic semantic indexing technologies for the emerging COVID-19 domain. RESULTS: In this research, to investigate the semantic indexing problem for COVID-19, we first construct the new COVID-19 Semantic Indexing dataset, which consists of more than 80 thousand biomedical articles. We then propose a novel semantic indexing framework based on the multi-probe attention neural network (MPANN) to address the COVID-19 semantic indexing problem. Specifically, we employ a k-nearest neighbour based MeSH masking approach to generate candidate topic terms for each input article. We encode and feed the selected candidate terms as well as other contextual information as probes into the downstream attention-based neural network. Each semantic probe carries specific aspects of biomedical knowledge and provides informatively discriminative features for the input article. After extracting the semantic features at both term-level and document-level through the attention-based neural network, MPANN adopts a linear multi-view classifier to conduct the final topic prediction for COVID-19 semantic indexing. CONCLUSION: The experimental results suggest that MPANN promises to represent the semantic features of biomedical texts and is effective in predicting semantic topics for COVID-19 related biomedical articles.

Extraction of causal relations based on SBEL and BERT model.

Extraction of causal relations between biomedical entities in the form of Biological Expression Language (BEL) poses a new challenge to the community of biomedical text mining due to the complexity of BEL statements. We propose a simplified form of BEL statements [Simplified Biological Expression Language (SBEL)] to facilitate BEL extraction and employ BERT (Bidirectional Encoder Representation from Transformers) to improve the performance of causal relation extraction (RE). On the one hand, BEL statement extraction is transformed into the extraction of an intermediate form-SBEL statement, which is then further decomposed into two subtasks: entity RE and entity function detection. On the other hand, we use a powerful pretrained BERT model to both extract entity relations and detect entity functions, aiming to improve the performance of two subtasks. Entity relations and functions are then combined into SBEL statements and finally merged into BEL statements. Experimental results on the BioCreative-V Track 4 corpus demonstrate that our method achieves the state-of-the-art performance in BEL statement extraction with F1 scores of 54.8% in Stage 2 evaluation and of 30.1% in Stage 1 evaluation, respectively. Database URL: https://github.com/grapeff/SBEL_datasets.

GPR17 plays a role in ischemia-induced endogenous repair of immature neonatal cerebral White matter.

Whether GPR17 has the same distribution and repair mechanism in immature white matter with periventricular leukomalacia (PVL) as in the adult brain remains to be determined. This study tried to explore the expression phase and site of GPR17, and to investigate the effect of silencing GPR17 on endogenous repair mechanism of immature white matter with PVL. Ischemic PVL in vivo results showed that GPR17 gene and protein expression increased more in the PVL than in the sham group at 12 h-24 h and 72h to 7 days after PVL. NG2+/GPR17+progenitor cells at 48 h-96 h and O4+/GPR17+precursor cells at 72h to 7d were also significantly increased in the PVL compared to the sham groups. Results in vitro showed that oxygen-glucose deprivation (OGD) also induced more GPR17 gene and protein expression than control at 48 h-72 h. There were more NG2+/GPR17+progenitor cells at 24 h-48 h and O4+/GPR17+precursor cells at 48 h-72 h in the OGD groups, as well. The functional role of GPR17 in the intrinsic repair response to ischemia was tested using GPR17 gene silencing. The progenitor cells and OL precursors in the OGD+GPR17 silencing group were both significantly less than those in the control, OGD and OGD+gene silencing control groups. The apoptotic percentage of cells in OGD+GPR17 silencing group was also much higher. In summary, ischemia-induced GPR17 expression was shown to contribute to glial-derived progenitor cell proliferation and differentiation into OL precursors, which may provide a therapeutic target for immature neonatal white matter injury after ischemia.

The extraction of complex relationships and their conversion to biological expression language (BEL) overview of the BioCreative VI (2017) BEL track.

Knowledge of the molecular interactions of biological and chemical entities and their involvement in biological processes or clinical phenotypes is important for data interpretation. Unfortunately, this knowledge is mostly embedded in the literature in such a way that it is unavailable for automated data analysis procedures. Biological expression language (BEL) is a syntax representation allowing for the structured representation of a broad range of biological relationships. It is used in various situations to extract such knowledge and transform it into BEL networks. To support the tedious and time-intensive extraction work of curators with automated methods, we developed the BEL track within the framework of BioCreative Challenges. Within the BEL track, we provide training data and an evaluation environment to encourage the text mining community to tackle the automatic extraction of complex BEL relationships. In 2017 BioCreative VI, the 2015 BEL track was repeated with new test data. Although only minor improvements in text snippet retrieval for given statements were achieved during this second BEL task iteration, a significant increase of BEL statement extraction performance from provided sentences could be seen. The best performing system reached a 32% F-score for the extraction of complete BEL statements and with the given named entities this increased to 49%. This time, besides rule-based systems, new methods involving hierarchical sequence labeling and neural networks were applied for BEL statement extraction.

Chemical-induced disease relation extraction via attention-based distant supervision.

BACKGROUND: Automatically understanding chemical-disease relations (CDRs) is crucial in various areas of biomedical research and health care. Supervised machine learning provides a feasible solution to automatically extract relations between biomedical entities from scientific literature, its success, however, heavily depends on large-scale biomedical corpora manually annotated with intensive labor and tremendous investment. RESULTS: We present an attention-based distant supervision paradigm for the BioCreative-V CDR extraction task. Training examples at both intra- and inter-sentence levels are generated automatically from the Comparative Toxicogenomics Database (CTD) without any human intervention. An attention-based neural network and a stacked auto-encoder network are applied respectively to induce learning models and extract relations at both levels. After merging the results of both levels, the document-level CDRs can be finally extracted. It achieves the precision/recall/F1-score of 60.3%/73.8%/66.4%, outperforming the state-of-the-art supervised learning systems without using any annotated corpus. CONCLUSION: Our experiments demonstrate that distant supervision is promising for extracting chemical disease relations from biomedical literature, and capturing both local and global attention features simultaneously is effective in attention-based distantly supervised learning.

Hierarchical sequence labeling for extracting BEL statements from biomedical literature.

BACKGROUND: Extracting relations between bio-entities from biomedical literature is often a challenging task and also an essential step towards biomedical knowledge expansion. The BioCreative community has organized a shared task to evaluate the robustness of the causal relationship extraction algorithms in Biological Expression Language (BEL) from biomedical literature. METHOD: We first map the sentence-level BEL statements in the BC-V training corpus to the corresponding text segments, thus generating hierarchically tagged training instances. A hierarchical sequence labeling model was afterwards induced from these training instances and applied to the test sentences in order to construct the BEL statements. RESULTS: The experimental results on extracting BEL statements from BioCreative V Track 4 test corpus show that our method achieves promising performance with an overall F-measure of 31.6%. Furthermore, it has the potential to be enhanced by adopting more advanced machine learning approaches. CONCLUSION: We propose a framework for hierarchical relation extraction using hierarchical sequence labeling on the instance-level training corpus derived from the original sentence-level corpus via word alignment. Its main advantage is that we can make full use of the original training corpus to induce the sequence labelers and then apply them to the test corpus.

Combining relation extraction with function detection for BEL statement extraction.

The BioCreative-V community proposed a challenging task of automatic extraction of causal relation network in Biological Expression Language (BEL) from the biomedical literature. Previous studies on this task largely used models induced from other related tasks and then transformed intermediate structures to BEL statements, which left the given training corpus unexplored. To make full use of the BEL training corpus, in this work, we propose a deep learning-based approach to extract BEL statements. Specifically, we decompose the problem into two subtasks: entity relation extraction and entity function detection. First, two attention-based bidirectional long short-term memory networks models are used to extract entity relation and entity function, respectively. Then entity relation and their functions are combined into a BEL statement. In order to boost the overall performance, a strategy of threshold filtering is applied to improve the precision of identified entity functions. We evaluate our approach on the BioCreative-V Track 4 corpus with or without gold entities. The experimental results show that our method achieves the state-of-the-art performance with an overall F1-measure of 46.9% in stage 2 and 21.3% in stage 1, respectively.

Chemical-induced disease relation extraction via convolutional neural network.

This article describes our work on the BioCreative-V chemical-disease relation (CDR) extraction task, which employed a maximum entropy (ME) model and a convolutional neural network model for relation extraction at inter- and intra-sentence level, respectively. In our work, relation extraction between entity concepts in documents was simplified to relation extraction between entity mentions. We first constructed pairs of chemical and disease mentions as relation instances for training and testing stages, then we trained and applied the ME model and the convolutional neural network model for inter- and intra-sentence level, respectively. Finally, we merged the classification results from mention level to document level to acquire the final relations between chemical and disease concepts. The evaluation on the BioCreative-V CDR corpus shows the effectiveness of our proposed approach. Database URL: http://www.biocreative.org/resources/corpora/biocreative-v-cdr-corpus/.

Chemical-induced disease relation extraction with various linguistic features.

Understanding the relations between chemicals and diseases is crucial in various biomedical tasks such as new drug discoveries and new therapy developments. While manually mining these relations from the biomedical literature is costly and time-consuming, such a procedure is often difficult to keep up-to-date. To address these issues, the BioCreative-V community proposed a challenging task of automatic extraction of chemical-induced disease (CID) relations in order to benefit biocuration. This article describes our work on the CID relation extraction task on the BioCreative-V tasks. We built a machine learning based system that utilized simple yet effective linguistic features to extract relations with maximum entropy models. In addition to leveraging various features, the hypernym relations between entity concepts derived from the Medical Subject Headings (MeSH)-controlled vocabulary were also employed during both training and testing stages to obtain more accurate classification models and better extraction performance, respectively. We demoted relation extraction between entities in documents to relation extraction between entity mentions. In our system, pairs of chemical and disease mentions at both intra- and inter-sentence levels were first constructed as relation instances for training and testing, then two classification models at both levels were trained from the training examples and applied to the testing examples. Finally, we merged the classification results from mention level to document level to acquire final relations between chemicals and diseases. Our system achieved promisingF-scores of 60.4% on the development dataset and 58.3% on the test dataset using gold-standard entity annotations, respectively. Database URL:https://github.com/JHnlp/BC5CIDTask.

White matter and SVZ serve as endogenous sources of glial progenitor cells for self-repair in neonatal rats with ischemic PVL.

Mounting evidence suggests that endogenous progenitor cells may initiate cerebral WM repair. This study was designed to determine whether endogenous glial progenitor cells derived from either the subventricular zone (SVZ) or the white matter (WM) contribute to WM repair in a neonatal rat model of ischemic periventricular leukomalacia (PVL). Additionally, the role of G protein-coupled receptor 17 (GPR17), recently shown to act as a sensor for WM damage, was explored to assess its potential recruitment and activation of endogenous glial progenitor cells for such WM self-repair. Our in vivo and in vitro models consisted of five-day-old neonatal rats or cultured glial progenitor cells derived from both the SVZ and WM of these rats, randomly divided into sham/control and induced ischemic PVL/oxygen-glucose deprivation (OGD) groups. The WM of all PVL rats showed either mild or severe histopathological changes, with significantly increased in vivo apoptosis and poor myelination compared to those of the sham group. Significantly more apoptotic and necrotic cells were also detected in the OGD glial progenitor cell cultures derived from the SVZ and WM at all time intervals. The glial progenitor cells were significantly increased in both the SVZ (NG2⁺/GPR17⁻/BrdU⁺) and WM (NG2⁺/GPR17⁺/BrdU⁺) within 72 h after PVL; preOLs were also increased significantly in both the SVZ (O4⁺/GPR17⁻/BrdU⁺) and WM (O4⁺/GPR17⁺/BrdU⁺) within 7d after PVL in vivo or OGD in vitro. However, the more differentiated CNPase⁺/GPR17⁻/BrdU⁺ and MBP⁺/GPR17⁻/BrdU⁺ OLs in the SVZ and WM remained significantly less than those in the sham groups up to 14d or 21d after OGD or PVL, respectively. Hence, both the WM and SVZ were found to be potential endogenous sources of glial progenitor cells for WM repair in PVL rats. However their endogenous self-repair capacity appeared to be limited, since the more mature OLs did not completely recover from experimental ischemia, even after 14-21d.

Diphenyleneiodonium protects preoligodendrocytes against endotoxin-activated microglial NADPH oxidase-generated peroxynitrite in a neonatal rat model of periventricular leukomalacia.

The contribution of microglial activation to preoligodendroglial (preOL) damage in the central nervous system (CNS) is considered to be one of the principal causes of periventricular leukomalacia (PVL) pathogenesis. The present study explores the effect of diphenyleneiodonium (DPI), a NADPH oxidase (NOX) inhibitor, on protection of preOLs from bacterial lipopolysaccharide (LPS)-induced microglial toxicity in vivo and in vitro. In vitro, preOLs co-cultured with microglia exhibited increased preOL apoptosis, accompanied by overproduction of superoxide anion (O(2)(-)) and the formation of peroxynitrite (ONOO(-)) after LPS exposure. LPS also significantly up-regulated accumulation of activated microglial NOX subunits p67-phox and gp91-phox in the plasma membrane. Diphenyleneiodonium (DPI) (10µm) was found to significantly attenuate up-regulation of this NOX activity. In vivo, DPI was administered (1mg/kg/day) by subcutaneous injection for 3 days to two-day-old neonatal Sprague-Dawley rats subjected to intracerebral injection of LPS. Treatment with DPI within 24h of LPS injection significantly ameliorated white matter injury, decreasing preOL loss, O(2)(-) generation, and ONOO(-) formation, and inhibiting p67-phox, gp91-phox synthesis and p67phox membrane translocation in microglia. These results indicated that LPS-induced preOL apoptosis may have been mediated by microglia-derived ONOO(-). DPI prevented this LPS-induced brain injury, most likely by inhibiting ONOO(-) formation via NOX, thereby preventing preOL loss and immature white matter injury.

[Effect of single or combined application of UDP-glucose, GDNF and memantine on improvement of white matter injury in neonatal rats assessed with light and electron microscopy pathologically].

OBJECTIVE: To evaluate pathologically the effect of the single or combined application of UDP-glucose, GDNF and memantine on the improvement of white matter injury in neonatal rats with periventricular leukomalacia (PVL) under light and electron microscopy. METHODS: A five-day-old neonatal rat model for PVL was established by ligation of the lateral common carotid artery following 120-minute hypoxia. Rats were randomly divided into six groups (30 rats in each group): sham-operated, PVL, UDP-glucose (UDP-glucose 2000 mg/kg intraperitoneally after PVL), GDNF (GDNF 100 µg/kg intracerebrally after PVL), tmemantine (memantine 20 mg/kg intraperitoneally after PVL), and a combination administration of three drugs (UDP-glucose, GDNF and memantine). The rats were sacrificed 7 or 21 days after PVL for assessment of pathological changes in the white matter under both light and electron microscopy. The number and thickness of the myelin sheath in the white matter were measured under electron microscopy, and both of pathological grading and scoring were undertaken under light microscopy. RESULTS: There was rare and sparse myelinogenesis with a loose arrangement of nerve fibers in the white matter under electron microscopy in the PVL group at 7 and 21 days after PVL. The number and thickness of the myelin sheath in the PVL group were significantly less than in the sham-operated, UDP-glucose, GDNF, memantine and combination administration groups (P<0.01). The results of pathological grading of white matter under light microscopy showed that all rats in the PVL group manifested either mild injury (38%-50%) or severe injury (50%-62%) at 7 and 21 days after PVL. The majority of rats (50%-88%) in the four drug administration groups had normal white matter at 7 and 21 days after PVL. The pathological scores at 7 and 21 days after PVL in the PVL group were the highest, and they were significantly higher than in the other five groups (P<0.05). CONCLUSIONS: The single or combined application of UDP-glucose, GDNF and memantine may significantly improve pathological changes in the white matter of rats with PVL. The favorable effect is inferred to be closely correlated with the improvement of brain microenvironment and the enhancement of nerve regeneration promoted by the three drugs.

Periventricular leukomalacia long-term prognosis may be improved by treatment with UDP-glucose, GDNF, and memantine in neonatal rats.

The therapeutic effects of UDP-glucose (UDPG), an endogenous agonist of GPR17 that may promote the self-repair of white matter, glial cell line-derived neurotrophic factor (GDNF), a neurotrophic factor correlated with the growth and survival of nerve cells, and memantine, an antagonist of NMDA receptors, were evaluated for functional improvement of neonatal rats with experimental periventricular leukomalacia (PVL). Five day-old neonatal rat pups were subjected to an ischemia-induced model of PVL. The pups were then randomly divided into sham, PVL, PVL plus UDPG, PVL plus GDNF, and PVL plus memantine groups. All pups were weighed and the age at first eye opening recorded. Pathological changes and myelin sheath formation in the white matter were assessed under both light and electron microscopy on day 7 and 21 after induction of PVL. Values of escape latency (EL) and swimming distance (SD) in Morris water maze test, and the modified inclined plane scores in Rivlin inclined plane test were recorded for rats on day 26. Pups in the PVL group were found to be significantly lower in weight (p<0.05), delayed in age at first eye opening (p<0.01), and impaired in their inclined plane (p<0.01) and Morris water maze (p<0.01) performance compared with those in the sham, UDPG, GDNF and memantine groups. Histopathological grading of the white matter classified all pups in the PVL group with significantly more severe injury (p<0.01), and the number and thickness of their myelin sheaths were significantly less (p<0.01), compared to the UDPG, GDNF, memantine, or sham groups. These results indicate that treatment with UDPG, GDNF, and memantine may significantly improve long-term prognosis in neonatal rats with cerebral white matter injury, characteristic of PVL.

[Endogenous self-repair in immature white matter induced by ischemia in neonatal rats].

OBJECTIVE: To study in vivo the endogenous self-repair mechanism in immature white matter induced by ischemia in neonatal rats with periventricular leukomalacia (PVL). METHODS: Five-day-old neonatal Sprague-Dawley (SD) rats were randomly divided into sham and PVL groups. Rat model of PVL was prepared by ligation of the right common carotid artery following 2 hours of exposure to 8% oxygen. Pathological changes and myelination in the white matter were assessed under light and electron microscopy at 7 and 21 days after PVL. O4-positive OL precursor cells in the white matter were determined with immunofluorescence staining. Activation, proliferation, migration and differentiation of glial progenitor cells in SVZ were observed using immunofluorescent double labeling of either NG2 (marker of progenitor cells) and 5-bromodeoxyuridine (BrdU), or O4 (marker of OL precursor cells) and BrdU. RESULTS: All rats in the PVL group manifested either mild or severe white matter injury under light microscopy, and had higher pathological scores of white matter compared with the sham group at 7 and 21 days after PVL (P<0.05). Electron microscopy showed that the number and thickness of myelin sheath in the PVL group were significantly reduced compared with the sham group (P<0.01). O4-positive OL precursor cells in the white matter observed under fluorescence microscopy were significantly reduced in the PVL group compared with the sham group (P<0.05). BrdU/NG2-positive cells in the SVZ increased significantly in the PVL group 48 hours after PVL and migrated into the periventricular area, reaching a peak on day 7 after PVL. BrdU/O4-positive newborn cells began to appear in the periventricular area 72 hours after PVL, and the number of BrdU/O4-positive cells in the PVL group was statistically more than in the sham group on day 21 after PVL (P<0.05). CONCLUSIONS: Ischemia may induce brain self-repair in neonatal rats, resulting in activation and proliferation of NG2 glial progenitor cells in the SVZ migration and differentiation into OL precursor cells in periventricular white matter.

Tree kernel-based protein-protein interaction extraction from biomedical literature.

There is a surge of research interest in protein-protein interaction (PPI) extraction from biomedical literature. While most of the state-of-the-art PPI extraction systems focus on dependency-based structured information, the rich structured information inherent in constituent parse trees has not been extensively explored for PPI extraction. In this paper, we propose a novel approach to tree kernel-based PPI extraction, where the tree representation generated from a constituent syntactic parser is further refined using the shortest dependency path between two proteins derived from a dependency parser. Specifically, all the constituent tree nodes associated with the nodes on the shortest dependency path are kept intact, while other nodes are removed safely to make the constituent tree concise and precise for PPI extraction. Compared with previously used constituent tree setups, our dependency-motivated constituent tree setup achieves the best results across five commonly used PPI corpora. Moreover, our tree kernel-based method outperforms other single kernel-based ones and performs comparably with some multiple kernel ones on the most commonly tested AIMed corpus.

[Effects of glial cell line-derived neurotrophic factor and memantine on long-term prognosis in neonatal rats with periventricular leukomalacia].

OBJECTIVE: To evaluate the effects of glial cell line-derived neurotrophic factor (GDNF) and memantine on the long-term prognosis in neonatal rats with ischemia-induced periventricular leukomalacia (PVL). METHODS: Thirty-two 5-day-old neonatal rats were randomly divided into 4 groups: sham-operated, PVL, GDNF-treated and memantine-treated. PVL was induced by right carotid artery ligation and hypoxia in the PVL, GDNF-treated and memantine-treated groups. GDNF (100 µg/kg) or memantine (20 mg/kg) was injected in the two treatment groups immediately after PVL inducement. The weight of the rats was measured immediately before and after hypoxia ischemia (HI). Both of Morris water maze test and Rivlin inclined plane test were performed at 26 days old (21 days after HI). The values of the escape latency (EL) and swimming distance, and the maximum inclined plane degree which the rats could stand at least 5 seconds were compared among the four groups. RESULTS: The lower weight, the prolonged mean values of EL and swimming distance and the reduced maximum inclined plane degree were observed in the PVL group compared to those in the sham-operated, GDNF-treated and memantine-treated groups. There were no significant differences in the weight, the values of EI and swimming distance and the maximum inclined plane degree between the two treatment groups and the sham-operated group. CONCLUSIONS: The administration of either GDNF or memantine can markedly increase the abilities of spatial discrimination,learning and memory, and motor coordination, promote weight gain, and improve long-term prognosis in rats with PVL.

[1400W blocks death pathway of LPS-induced activated-microglia to preOLs].

OBJECTIVE: To explore the efficacy of inductible nitric oxide synthase (iNOS) inhibitor 1400W in vivo in blocking the death pathway of lipopolysaccharide (LPS)-induced activated-microglia to preoligodendrocytes (preOLs) in neonatal rats with infective-type periventricular leukomalacia (PVL) induced by LPS. METHODS: Two-day-old neonatal rats were randomly divided into: a sham-operated group, an untreated PVL group, and four 1400W-treated PVL groups that were subcutaneously administrated with 20 mg/kg of 1400W at 0 h, 8 hrs, 16 hrs, and 24 hrs after LPS induction, respectively. The brain specimens were obtained 5 days after LPS induction. The pathological assessment of cerebral white matter was performed under a light microscope. Concentrations of nitric oxide (NO) were measured by nitric acid-deoxidize colorimetry. Synthesis of iNOS was determined by Western blot analysis. Peroxynitrite (ONOO(-)) level and the amount of preOLs were determined by immunocytochemistry. RETHODS: The obvious injuries of periventricular white matter, massive loss of positive O4-labelled preOLs, and increased levels of NO, ONOO(-) and iNOS were observed in neonatal rats with PVL. Compared to the untreated PVL group, the use of 1400W at 0 h, 8 hrs and 16 hrs after LPS induction significantly improved white matter injuries, reduced the levels of NO, ONOO(-) and iNOS, and increased the amount of O4-labelled preOLs. However, the use of 1400W at 24 hrs after LPS induction did not result in the improvements. CONCLUSIONS: iNOS inhibitor 1400W can effectively block the toxicity of LPS-activated microglia to preOLs and protect cerebral white matter through inhibiting iNOS and reducing the production of NO and ONOO(-). The use of 1400W within 16 hrs after LPS induction may provide cerebral protections in neonatal rats with PVL.