For named entity recognition, incorporating dictionary feature into the deep neural model can obtain better performance. However, the behavior of deep models which incorporate dictionary features is ...not clear because analyzing this behavior is difficult. Besides, using the dictionary features which depend on a dictionary may train a poor model. In this work, we present an incorporating token-level dictionary feature method which use a labeled dataset rather than an external dictionary. This approach makes it easier to quantify the effect of dictionary features and decouples dictionary features from the external dictionary during the training stage. Additionally, we conduct several experiments which consist of three parts. First, we show the effectiveness of our proposed method. Second, we study the behavior of incorporating dictionary features from two perspectives: during the training and the testing stage. Third, we research the embedding of dictionary features.
A patient's response to immune checkpoint inhibitors (ICIs) is a complex quantitative trait, and determined by multiple intrinsic and extrinsic factors. Three currently FDA-approved predictive ...biomarkers (progra1mmed cell death ligand-1 (PD-L1); microsatellite instability (MSI); tumor mutational burden (TMB)) are routinely used for patient selection for ICI response in clinical practice. Although clinical utility of these biomarkers has been demonstrated in ample clinical trials, many variables involved in using these biomarkers have poised serious challenges in daily practice. Furthermore, the predicted responders by these three biomarkers only have a small percentage of overlap, suggesting that each biomarker captures different contributing factors to ICI response. Optimized use of currently FDA-approved biomarkers and development of a new generation of predictive biomarkers are urgently needed. In this review, we will first discuss three widely used FDA-approved predictive biomarkers and their optimal use. Secondly, we will review four novel gene signature biomarkers: T-cell inflamed gene expression profile (GEP), T-cell dysfunction and exclusion gene signature (TIDE), melanocytic plasticity signature (MPS) and B-cell focused gene signature. The GEP and TIDE have shown better predictive performance than PD-L1, and PD-L1 or TMB, respectively. The MPS is superior to PD-L1, TMB, and TIDE. The B-cell focused gene signature represents a previously unexplored predictive biomarker to ICI response. Thirdly, we will highlight two combined predictive biomarkers: TMB+GEP and MPS+TIDE. These integrated biomarkers showed improved predictive outcomes compared to a single predictor. Finally, we will present a potential nucleic acid biomarker signature, allowing DNA and RNA biomarkers to be analyzed in one assay. This comprehensive signature could represent a future direction of developing robust predictive biomarkers, particularly for the cold tumors, for ICI response.
RNA sequencing (RNAseq) is one of the most commonly used techniques in life sciences, and has been widely used in cancer research, drug development, and cancer diagnosis and prognosis. Driven by ...various biological and technical questions, the techniques of RNAseq have progressed rapidly from bulk RNAseq, laser-captured micro-dissected RNAseq, and single-cell RNAseq to digital spatial RNA profiling, spatial transcriptomics, and direct
sequencing. These different technologies have their unique strengths, weaknesses, and suitable applications in the field of clinical oncology. To guide cancer researchers to select the most appropriate RNAseq technique for their biological questions, we will discuss each of these technologies, technical features, and clinical applications in cancer. We will help cancer researchers to understand the key differences of these RNAseq technologies and their optimal applications.
Analysis of microRNAs (miRNAs) is important in cancer diagnostics and therapy. Conventional methods used to extract miRNA for analysis are generally time-consuming. A novel approach for rapid and ...sensitive extraction of miRNAs is urgently need for clinical applications. Herein, a novel strategy based on electrical potential-assisted DNA-RNA hybridization was designed for miRNA extraction. The entire extraction process was accomplished in approximately 3 min, which is much shorter than the commercial adsorption column method, at more than 60 min, or the TRIzol method, at more than 90 min. Additionally, the method offered the advantages of simplicity and specificity during the extraction process by electrical potential-assisted hybridization of single-stranded DNA and RNA. Taking let-7a as an example, satisfactory results were achieved for miRNA extraction in serum, demonstrating the applicability in miRNA nucleic acid amplification.
Graphical abstract
Aerobic glycolysis is one of the hallmarks of cancer. The metabolic phenotype of tumor cells is characterized by preferential dependence on glycolysis under aerobic conditions. Recent researchers ...have provided a piece of information on the effectiveness of targeting glycolysis. Thus, targeted glucose metabolism therapy is still a research hotspot. Interleukin 37 (IL-37) plays an important role in tumor development. Previous studies have found that IL-37 can inhibit the progression of lung adenocarcinoma in a variety of ways. For example, IL-37 can inhibit the migration and invasion of lung adenocarcinoma by inhibiting the interleukin 6(IL-6)/ Signal transducing activator of transcription 3(STAT3) pathway. IL-37 inhibits tumor growth by regulating RNA methylation at the M6A site of lung adenocarcinoma. It has been found that overexpression of IL-37 in macrophages can reverse the Warburg effect. The mechanism of IL-37 on glucose metabolism of tumor cells has not been studied. In research, glucose uptake and lactic acid production were inhibited in A549 cells with recombinant human IL-37(rhIL-37). Also, rhIL-37 inhibited the expression level of PFKFB3 in A549 cells. To verify whether the two aspects of rhIL-37's effects on A549 cells are related, we applied PFK15, a specific inhibitor of PFKFB3, to prove that rhIL-37 inhibits the glucose uptake and lactate production of A549 cells by inhibiting the expression of PFKFB3, and further inhibits the progression of lung adenocarcinoma.
Address parsing is a crucial task in natural language processing, particularly for Chinese addresses. The complex structure and semantic features of Chinese addresses present challenges due to their ...inherent ambiguity. Additionally, different task scenarios require varying levels of granularity in address components, further complicating the parsing process. To address these challenges and adapt to low-resource environments, we propose CapICL, a novel Chinese address parsing model based on the In-Context Learning (ICL) framework. CapICL leverages a sequence generator, regular expression matching, BERT semantic similarity computation, and Generative Pre-trained Transformer (GPT) modeling to enhance parsing accuracy by incorporating contextual information. We construct the sequence generator using a small annotated dataset, capturing distribution patterns and boundary features of address types to model address structure and semantics, which mitigates interference from unnecessary variations. We introduce the REB–KNN algorithm, which selects similar samples for ICL-based parsing using regular expression matching and BERT semantic similarity computation. The selected samples, raw text, and explanatory text are combined to form prompts and inputted into the GPT model for prediction and address parsing. Experimental results demonstrate significant achievements of CapICL in low-resource environments, reducing dependency on annotated data and computational resources. Our model’s effectiveness, adaptability, and broad application potential are validated, showcasing its positive impact in natural language processing and geographical information systems.
Purpose
Accumulated evidence has indicated that the gut microbiome affected the pharmacology of anti-diabetic agents, and their metabolic products induced by the agents transformed the structure of ...gastrointestinal microbiota in return. However, the studies around heredity, ethnicity, or living condition, referring to human microbiome were mostly represented by an occidental pattern partial and rare studies that focused on the effect of several first-line hypoglycemic agents on the gut flora in a single medical center. Therefore, we aimed to explore the interaction between gut microbiome and type 2 diabetes (T2D) or hypoglycemics in Chinese population.
Methods
A total of 130 T2D patients with a specific hypoglycemic treatment and 50 healthy volunteers were enrolled in this study. Gut microbiome compositons were analyzed by 16S ribosomal RNA gene-based sequencing protocol.
Results
Hypoglycemic agents contributed to the alteration of specific species in gut bacteria rather than its total diversity. Metformin increased the abundance of
Spirochaete
,
Turicibacter
, and
Fusobacterium
. Insulin also increased
Fusobacterium
, and α-glucosidase inhibitors (α-GIs) contributed to the plentitude of
Bifidobacterium
and
Lactobacillus
. Both metformin and insulin improved taurine and hypotaurine metabolism, and α-GI promoted several amino acid pathways. Although the community of gut microbiota with metformin and insulin showed similarity, significant differences were available in each diabetic group with hypoglycemia.
Conclusions
Gut microbiota is significantly associated with anti-diabetic agents. The gut microbiome and metabolism have shown respective characteristics in different T2D groups, which were also significantly different from the healthy group. This study provides some new insights for identification and exploration of the pathogenesis of T2D.
Rheumatoid arthritis (RA) is an ordinarily occurring autoimmune disease with systemic inflammatory. Targeted drug delivery systems have many successful applications in the treatment of rheumatoid ...arthritis. In order to develop nanoparticles for targeted delivery of Celastrol (Cel) to rheumatoid arthritis and specific drug release, the dextran sulfate (DS) was modified as the targeting molecular by binding to the scavenger receptor of macrophage. The dextran-sulfate-PVGLIG-celastrol (DS-PVGLIG-Cel), named DPC, amphiphilic polymeric prodrug was synthesized and characterized. The resulting DPC@Cel micelles had the average size of 189.9 nm. Moreover, the micelles had ultrahigh entrapment efficiency (about 44.04%) and zeta potential of −11.91 mV. In the in vitro release study, due to the excessive production of matrix metalloproteinase-2 (MMP-2) at the inflammatory joint, the MMP-2 reactive peptide was used to crack in the inflammatory microenvironment to accelerate the release of Cel. The results have shown that the nanoparticles can effectively deliver Cel to activated macrophages and significantly improve the bioavailability. In vivo experiments showed that DPC@Cel have better anti-rheumatoid arthritis effects and lower systemic toxicity than free Cel. This study provided a new therapeutic strategy for the treatment of RA.
Using the word as a basic unit may undermine Chinese event detection model's performance because of the inaccurate word boundaries generated by segmentation tools. Besides, word embeddings are ...contextual independent and cannot handle the polysemy of event triggers, which may prevent us from obtaining the desired performance. To address these issues, we propose a BiLSTM-CRF (Bidirectional Long Short-Term Memory Conditional Random Field) model using contextualized representations, which regards event detection task as a character-level sequence labeling problem and uses contextualized representations to disambiguate event triggers. Experiments show that our proposed method sets a new state-of-the-art, which proves Chinese characters could replace words for the Chinese event detection task. Besides, using contextualized representation reduces the false positive case, which verifies that this kind of representation could remedy the weakness of the word embedding technique. Based on the results, we believe that character-level models are worth exploring in the future.
Abstract
We present an interim analysis of a registered clinical study (NCT04800133) to establish immunobridging with various antibody and cellular immunity markers and to compare the immunogenicity ...and reactogenicity of 2-dose BNT162b2 and CoronaVac in healthy adolescents as primary objectives. One-dose BNT162b2, recommended in some localities for risk reduction of myocarditis, is also assessed. Antibodies and T cell immune responses are non-inferior or similar in adolescents receiving 2 doses of BNT162b2 (BB,
N
= 116) and CoronaVac (CC,
N
= 123) versus adults after 2 doses of the same vaccine (BB,
N
= 147; CC,
N
= 141) but not in adolescents after 1-dose BNT162b2 (B,
N
= 116). CC induces SARS-CoV-2 N and N C-terminal domain seropositivity in a higher proportion of adolescents than adults. Adverse reactions are mostly mild for both vaccines and more frequent for BNT162b2. We find higher S, neutralising, avidity and Fc receptor-binding antibody responses in adolescents receiving BB than CC, and a similar induction of strong S-specific T cells by the 2 vaccines, in addition to N- and M-specific T cells induced by CoronaVac but not BNT162b2, possibly implying differential durability and cross-variant protection by BNT162b2 and CoronaVac, the 2 most used SARS-CoV-2 vaccines worldwide. Our results support the use of both vaccines in adolescents.
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