Metformin alleviates spinal cord injury by inhibiting nerve cell ferroptosis through upregulation of heme oxygenase-1 expression

Previous studies have reported upregulation of heme oxygenase-1 in different central nervous system injury models.Heme oxygenase-1 plays a critical anti-inflammatory role and is essential for regulating cellular redox homeostasis.Metformin is a classic drug used to treat type 2 diabetes that can inhibit ferroptosis.Previous studies have shown that,when used to treat cardiovascular and digestive system diseases,metformin can also upregulate heme oxygenase-1 expression.Therefore,we hypothesized that heme oxygenase-1 plays a significant role in mediating the beneficial effects of metformin on neuronal ferroptosis after spinal cord injury.To test this,we first performed a bioinformatics analysis based on the GEO database and found that heme oxygenase-1 was upregulated in the lesion of rats with spinal cord injury.Next,we confirmed this finding in a rat model of T9 spinal cord compression injury that exhibited spinal cord nerve cell ferroptosis.Continuous intraperitoneal injection of metformin for 14 days was found to both upregulate heme oxygenase-1 expression and reduce neuronal ferroptosis in rats with spinal cord injury.Subsequently,we used a lentivirus vector to knock down heme oxygenase-1 expression in the spinal cord,and found that this significantly reduced the effect of metformin on ferroptosis after spinal cord injury.Taken together,these findings suggest that metformin inhibits neuronal ferroptosis after spinal cord injury,and that this effect is partially dependent on upregulation of heme oxygenase-1.

Early identification of scientific breakthroughs through outlier analysis based on research entities

Purpose:To address the“anomalies”that occur when scientific breakthroughs emerge,this study focuses on identifying early signs and nascent stages of breakthrough innovations from the perspective of outliers,aiming to achieve early identification of scientific breakthroughs in papers.Design/methodology/approach:This study utilizes semantic technology to extract research entities from the titles and abstracts of papers to represent each paper’s research content.Outlier detection methods are then employed to measure and analyze the anomalies in breakthrough papers during their early stages.The development and evolution process are traced using literature time tags.Finally,a case study is conducted using the key publications of the 2021 Nobel Prize laureates in Physiology or Medicine.Findings:Through manual analysis of all identified outlier papers,the effectiveness of the proposed method for early identifying potential scientific breakthroughs is verified.Research limitations:The study’s applicability has only been empirically tested in the biomedical field.More data from various fields are needed to validate the robustness and generalizability of the method.Practical implications:This study provides a valuable supplement to current methods for early identification of scientific breakthroughs,effectively supporting technological intelligence decision-making and services.Originality/value:The study introduces a novel approach to early identification of scientific breakthroughs by leveraging outlier analysis of research entities,offering a more sensitive,precise,and fine-grained alternative method compared to traditional citation-based evaluations,which enhances the ability to identify nascent breakthrough innovations.

Masked Sentence Model Based on BERT for Move Recognition in Medical Scientific Abstracts

Purpose:Mo ve recognition in scientific abstracts is an NLP task of classifying sentences of the abstracts into different types of language units.To improve the performance of move recognition in scientific abstracts,a novel model of move recognition is proposed that outperforms the BERT-based method.Design/methodology/approach:Prevalent models based on BERT for sentence classification often classify sentences without considering the context of the sentences.In this paper,inspired by the BERT masked language model(MLM),we propose a novel model called the masked sentence model that integrates the content and contextual information of the sentences in move recognition.Experiments are conducted on the benchmark dataset PubMed 20K RCT in three steps.Then,we compare our model with HSLN-RNN,BERT-based and SciBERT using the same dataset.Findings:Compared with the BERT-based and SciBERT models,the F1 score of our model outperforms them by 4.96%and 4.34%,respectively,which shows the feasibility and effectiveness of the novel model and the result of our model comes closest to the state-of-theart results of HSLN-RNN at present.Research limitations:The sequential features of move labels are not considered,which might be one of the reasons why HSLN-RNN has better performance.Our model is restricted to dealing with biomedical English literature because we use a dataset from PubMed,which is a typical biomedical database,to fine-tune our model.Practical implications:The proposed model is better and simpler in identifying move structures in scientific abstracts and is worthy of text classification experiments for capturing contextual features of sentences.Originality/value:T he study proposes a masked sentence model based on BERT that considers the contextual features of the sentences in abstracts in a new way.The performance of this classification model is significantly improved by rebuilding the input layer without changing the structure of neural networks.

Automatic Keyphrase Extraction from Scientific Chinese Medical Abstracts Based on Character-Level Sequence Labeling

Purpose:Automatic keyphrase extraction(AKE)is an important task for grasping the main points of the text.In this paper,we aim to combine the benefits of sequence labeling formulation and pretrained language model to propose an automatic keyphrase extraction model for Chinese scientific research.Design/methodology/approach:We regard AKE from Chinese text as a character-level sequence labeling task to avoid segmentation errors of Chinese tokenizer and initialize our model with pretrained language model BERT,which was released by Google in 2018.We collect data from Chinese Science Citation Database and construct a large-scale dataset from medical domain,which contains 100,000 abstracts as training set,6,000 abstracts as development set and 3,094 abstracts as test set.We use unsupervised keyphrase extraction methods including term frequency(TF),TF-IDF,TextRank and supervised machine learning methods including Conditional Random Field(CRF),Bidirectional Long Short Term Memory Network(BiLSTM),and BiLSTM-CRF as baselines.Experiments are designed to compare word-level and character-level sequence labeling approaches on supervised machine learning models and BERT-based models.Findings:Compared with character-level BiLSTM-CRF,the best baseline model with F1 score of 50.16%,our character-level sequence labeling model based on BERT obtains F1 score of 59.80%,getting 9.64%absolute improvement.Research limitations:We just consider automatic keyphrase extraction task rather than keyphrase generation task,so only keyphrases that are occurred in the given text can be extracted.In addition,our proposed dataset is not suitable for dealing with nested keyphrases.Practical implications:We make our character-level IOB format dataset of Chinese Automatic Keyphrase Extraction from scientific Chinese medical abstracts(CAKE)publicly available for the benefits of research community,which is available at:https://github.com/possible1402/Dataset-For-Chinese-Medical-Keyphrase-Extraction.Originality/value:By designing comparative experiments,our study demonstrates that character-level formulation is more suitable for Chinese automatic keyphrase extraction task under the general trend of pretrained language models.And our proposed dataset provides a unified method for model evaluation and can promote the development of Chinese automatic keyphrase extraction to some extent.

Chemokine Ligand 13 Expression is Abundant in the Tumor Microenvironment and Indicates Poor Prognosis of Kidney Clear Cell Carcinoma

The chemokine ligand 13-chemokine receptor 5(CXCL13-CXCR5)axis has been characterized as a critical tumor-promoting signaling pathway in the tumor microenvironment(TME)in multiple types of solid tumors.In this study,we analyzed the expression profile of CXCL13 in kidney clear cell carcinoma(KIRC)and its correlation with tumor-infiltrating immune cells(TIICs).A monoclonal antibody against CXCL13 with high affinity and purity was generated in our lab for western blot and immunohistochemistry(IHC).Bioinformatic analysis was performed based on bulk-seq data from the Cancer Genome Atlas(TCGA)-KIRC and single-cell RNA-seq data from scRNASeqDB and PanglaoDB.Results showed that high CXCL13 expression in TME was associated with shorter progression-free survival(PFS),disease-specific survival(DSS),and overall survival(OS).KIRC cell lines,as well as several other cancer cell lines,had negative CXCL13 expression.IHC staining from the Human Protein Atlas(HPA)and our tissue array indicated that CXCL13 might be mainly expressed by TIICs,but not KIRC tumor cells.CXCL13 expression was strongly and positively correlated withγδT cell abundance in TME.Besides,γδT cell infiltration was associated with poor survival of KIRC.Methylation 450k array data showed that CXCL13 promoter hypomethylation was common in TIICs.The methylation level of cg16361705 within the CXCL13 promoter might play an important role in modulating CXCL13 transcription.In conclusion,our study revealed that CXCL13 expression andγδT cell infiltration in TME is associated with unfavorable survival of KIRC.TIICs,most possiblyγδT cells,are the dominant source of CXCL13 in KIRC TME.

The aromatic peptide protected gold nanoclusters with significant Stokes shift:Ligand-mediated two-step FRET

During the last decade,a great variety of ligand protected gold nanoclusters(AuNCs)have been synthesized,and their broad applications have been intensively reported.Although the spectroscopic properties of AuNCs have been comprehensively explored,the mechanism of the significant Stokes shift(>200 nm)and the specific role played by surface ligands have not been clearly explained yet.In this study,a series of fluorescent AuNCs with huge Stokes shift(up to 530 nm)were successfully prepared by employing the rationally designed tri-peptides as the protecting ligands,and their spectroscopic properties were systematically investigated.The detailed measurements on the example product,YCY-AuNCs(Tyr-Cys-Tyr liganded AuNCs),showed that the energy absorbed by the tyrosine(~250 nm)can be effectively transferred through the ligand-mediated two-step Förster resonance energy transfer(FRET)process and released as fluorescence emission in the near-infrared fluorescence(NIR)range(~780 nm),which resulted in the significant apparent Stokes shift.The YCY ligands play a critical role by offering the tyrosine groups(donor of the first FRET pair),generating the dityrosine-like structure on the AuNCs surface(acceptor of the first FRET pair and donor of the second FRET pair),and protecting the cores(acceptor of the second acceptor).The additional ligand exchange experiments and the investigation on the other AuNCs further demonstrated that the sufficient high density of the aromatic groups is also essential to mediate the two-step FRET and achieve the remarkable Stokes shift.We believe that the aromatic ligand-mediated FRET mechanism not only offers a new theoretical explanation for the huge Stokes shift exhibited in AuNCs,but also provides a general strategy for the construction of new materials with large Stokes shift.

Peptidomimetic-liganded gold nanoclusters for controlled iron delivery and synergistic suppression of tumor growth

A novel peptidomimetic-liganded gold nanocluster(CDp-AuNC)is proposed for the synergistic suppression of tumor growth.Taking advantages of the multi-capabilities offered by the surface ligands,including iron chelation,glutathione peroxidases-1(GPx-1)binding,and tumor cells recognition,CDp-AuNCs are able to function as the nanocarriers to deliver iron in a controlled manner for the ferroptosis therapy and as the inhibitors for GPx-1 to induce the apoptosis of tumor cells.The Fe2+@CDp-AuNC nanocomplexes are fabricated through a facile self-assembly method.The experimental data verify that the nanocomplexes are internalized specifically by tumor cells with high efficiency.The acidic microenvironment in endosomes triggers the collapse of the nanocomplexes and thereby releases Fe2+to induce ferroptosis and CDp-AuNCs to inhibit the enzyme activity of GPx-1.Benefiting from the H_(2)O_(2)-depleted pathway inhibition and ferroptosis acceleration,the intracellular reactive oxygen species(ROS)level could be enhanced significantly.As a consequence,the apoptosis/ferroptosis of 4T1 cells as well as the tumor elimination in vivo are observed after treatment with the Fe2+@CDp-AuNC nanocomplexes at a relatively low dose.The facile iron loading method,simple construction procedure,and outstanding tumor suppression performance,provide CDp-AuNCs great application promise.More importantly,the strategy of peptidomimetic ligands design provides a transferable approach to building multifunctional nanomaterials.

RCMR 280k:Refined Corpus for Move Recognition Based on PubMed Abstracts

Existing datasets for move recognition,such as PubMed 20ok RCT,exhibit several problems that significantly impact recognition performance,especially for Background and Objective labels.In order to improve the move recognition performance,we introduce a method and construct a refined corpus based on PubMed,named RCMR 280k.This corpus comprises approximately 280,000 structured abstracts,totaling 3,386,008 sentences,each sentence is labeled with one of five categories:Background,Objective,Method,Result,or Conclusion.We also construct a subset of RCMR,named RCMR_RCT,corresponding to medical subdomain of RCTs.We conduct comparison experiments using our RCMR,RCMR_RCT with PubMed 380k and PubMed 200k RCT,respectively.The best results,obtained using the MSMBERT model,show that:(1)our RCMR outperforms PubMed 380k by 0.82%,while our RCMR_RCT outperforms PubMed 200k RCT by 9.35%;(2)compared with PubMed 380k,our corpus achieve better improvement on the Results and Conclusions categories,with average F1 performance improves 1%and 0.82%,respectively;(3)compared with PubMed 200k RCT,our corpus significantly improves the performance in the Background and Objective categories,with average F1 scores improves 28.31%and 37.22%,respectively.To the best of our knowledge,our RCMR is among the rarely high-quality,resource-rich refined PubMed corpora available.Our work in this paper has been applied in the SciAlEngine,which is openly accessible for researchers to conduct move recognition task.

Enhanced wettability and thermal stability of nano-SiO2/poly（vinyl alcohol）-coated polypropylene composite separators for lithium-ion batteries

To improve the electrolyte wettability and thermal stability of polypropylene （PP） separators, nano- SiO2/poly（vinyl alcohol）-coated PP composite separators were prepared using a simple but efficient sol-gel and dip-coating method. The effects of the tetraethoxysilane （TEOS） dosage on the morphology, wettability, and thermal stability of the composite separators were investigated using Fourier-transform infrared spectroscopy, scanning electron microscopy, and contact-angle measurements. All the composite separators gave a smaller contact angle, higher electrolyte uptake, and lower thermal shrinkage compared with the PP separator, indicating enhanced wettability and thermal stability. Unlike the case for a traditional physical mixture, Si-O-C covalent bonds were formed in the coating layer. The composite separator with a TEOS dosage of 7.5 wt% had a unique porous structure combining hierarchical pores with interstitial voids, and gave the best wettability and thermal stability. The ionic conductivity of the composite separator containing 7.5 wt% TEOS was 1.26 mS/cm, which is much higher than that of the PP separator （0.74 mS/cm）. The C-rate and cycling performances of batteries assembled with the composite separator containing 7.5 wt% TEOS were better than those of batteries containing PP separators.

White Light Emission Characteristics of Tb3+/Sm3+Co-Doped Glass Ceramics Containing YPO4 Nanocrystals

The Tb3＋/Sm3＋ single-doped and co-doped glasses and glass ceramics containing YPO4 nanocrystals have been synthesized by melt quenching method. The structural and luminescent properties of these glass specimens were investigated. Under 375 nm wavelength excitation, the emission spectra combined with blue, green and red bands were observed, which achieved the white light emission. Moreover, the energy transfer between Tb3＋ and Sm3＋ ions was validated by decay lifetime measurement and energy level diagram. The color coordinates （x = 0.333, y = 0.333）, correlated color temperature （5595 K） and the color render- ing index （Ra = 80.5） indicated that the glass ceramics were considered to be good lighting source. Hence, the YPO4-based Tb3＋/Sm3＋ co-doped glass ceramics can act as potential matrix materials for white light- emitting diodes under ultraviolet excitation.

The ideas and methods of structural monitoring of the scientific and technical information resources on the Web

Due to its openness and timeliness,the S&T Web information has become one of the most important resources for strategic intelligence monitoring.However,since S&T Web information is unstructured and lack of semantic description,it is a challenge to transfer the unstructured Web information into structured semantic knowledge.To solve this problem,the authors propose a method for structural monitoring of the S&T Web information resources.By using the knowledge extraction technologies,the authors firstly extract the knowledge objects as well as the relationship between objects from the Web resources and convert the free text into calculable structured knowledge units.Based on those extracted structured information,the authors build various kinds of monitoring models to realize research profiling for specific research fields.Based on those ideas,the authors implement the automated Web information monitoring system suitable for research field monitoring.A research profiling experiment also is carried out based on the semantic resources which are converted from the monitored Web data.

Methods of text theme identification based on graph mining

With the development of the internet,electronic text is booming.These text resources,especially scientific journal papers,contain rich semantic and linked information.How to demonstrate the core topics quickly and accurately to assist researchers and improve research efficiency has been an urgent issue in text mining.Nodes and edges of graph can represent terms and their relations of texts,so many researchers tried to combine graph mining with natural language

A method of semantic representation and organization of the historic knowledge on contemporary China

China has a huge volume of historical resources on its contemporary history. However, the organization of these historical resources is not satisfactory. On the basis of related studies, this paper proposes a method, which is called 'Mining down, Organizing up', to represent and organize the historical knowledge on contemporary China. Based on a contemporary Chinese historical ontology, this method extracts knowledge objects and facts from unstructured historical text items, forms a historical knowledge network on contemporary China, and realizes multidimensional knowledge organization at a higher level based on the relations such as time, subclass, hierarchy, and statistics. Based on this method, we represented and organized the historical knowledge on contemporary China from text resources, and developed a system to implement historical knowledge visualization, reorganization and other new applications including knowledge maps, relevance analysis, and national historical fact reconstruction etc. This study shows that the 'Mining down, Organizing up' method can realize the fine-grained representation of the historical knowledge on contemporary China and innovative application of knowledge organization based on historical knowledge objects. It can be used as a kind of new knowledge representation and organization methods applicable in other fields.