Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Medicinal Chemistry and Pharmaceutics
Title: Multiplex Paper-Based Colorimetric DNA Sensor Using Pyrrolidinyl Peptide Nucleic Acid-Induced AgNPs Aggregation for Detecting MERS-CoV, MTB, and HPV Oligonucleotides
Abstract: The development of simple fluorescent and colorimetric assays that enable point-of-care DNA and RNA detection has been a topic of significant research because of the utility of such assays in resource limited settings. The most common motifs utilize hybridization to a complementary detection strand coupled with a sensitive reporter molecule. Here, a paper-based colorimetric assay for DNA detection based on pyrrolidinyl peptide nucleic acid (acpcPNA)-induced nanoparticle aggregation is reported as an alternative to traditional colorimetric approaches. PNA probes are an attractive alternative to DNA and RNA probes because they are chemically and biologically stable, easily synthesized, and hybridize efficiently with the complementary DNA strands. The acpcPNA probe contains a single positive charge from the lysine at C-terminus and causes aggregation of citrate anion-stabilized silver nanoparticles (AgNPs) in the absence of complementary DNA. In the presence of target DNA, formation of the anionic DNA-acpcPNA duplex results in dispersion of the AgNPs as a result of electrostatic repulsion, giving rise to a detectable color change. Factors affecting the sensitivity and selectivity of this assay were investigated, including ionic strength, AgNP concentration, PNA concentration, and DNA strand mismatches. The method was used for screening of synthetic Middle East respiratory syndrome coronavirus (MERS-CoV), Mycobacterium tuberculosis (MTB), and human papillomavirus (HPV) DNA based on a colorimetric paper-based analytical device developed using the aforementioned principle. The oligonucleotide targets were detected by measuring the color change of AgNPs, giving detection limits of 1.53 (MERS-CoV), 1.27 (MTB), and 1.03 nM (HPV). The acpcPNA probe exhibited high selectivity for the complementary oligonucleotides over single-base-mismatch, two-base-mismatch, and noncomplementary DNA targets. The proposed paper-based colorimetric DNA sensor has potential to be an alternative approach for simple, rapid, sensitive, and selective DNA detection.
Extracted statement — has outcome: paper-based colorimetric assay for DNA detection
Research field: Medicinal Chemistry and Pharmaceutics
Title: Novel coronavirus-like particles targeting cells lining the respiratory tract
Abstract: Virus like particles (VLPs) produced by the expression of viral structural proteins can serve as versatile nanovectors or potential vaccine candidates. In this study we describe for the first time the generation of HCoV-NL63 VLPs using baculovirus system. Major structural proteins of HCoV-NL63 have been expressed in tagged or native form, and their assembly to form VLPs was evaluated. Additionally, a novel procedure for chromatography purification of HCoV-NL63 VLPs was developed. Interestingly, we show that these nanoparticles may deliver cargo and selectively transduce cells expressing the ACE2 protein such as ciliated cells of the respiratory tract. Production of a specific delivery vector is a major challenge for research concerning targeting molecules. The obtained results show that HCoV-NL63 VLPs may be efficiently produced, purified, modified and serve as a delivery platform. This study constitutes an important basis for further development of a promising viral vector displaying narrow tissue tropism.
Extracted statement — Mechanism of Antiviral Action: Selectively transduce cells expressing the ACE2 protein
Research field: Medicinal Chemistry and Pharmaceutics
Title: Oral delivery of anti-TNF antibody shielded by natural polyphenol-mediated supramolecular assembly for inflammatory bowel disease therapy
Abstract: Rationale: Anti-tumor necrosis factor (TNF) therapy is a very effective way to treat inflammatory bowel disease. However, systemic exposure to anti-TNF-α antibodies through current clinical systemic administration can cause serious adverse effects in many patients. Here, we report a facile prepared self-assembled supramolecular nanoparticle based on natural polyphenol tannic acid and poly(ethylene glycol) containing polymer for oral antibody delivery. Method: This supramolecular nanoparticle was fabricated within minutes in aqueous solution and easily scaled up to gram level due to their pH-dependent reversible assembly. DSS-induced colitis model was prepared to evaluate the ability of inflammatory colon targeting ability and therapeutic efficacy of this antibody-loaded nanoparticles. Results: This polyphenol-based nanoparticle can be aqueous assembly without organic solvent and thus scaled up easily. The oral administration of antibody loaded nanoparticle achieved high accumulation in the inflamed colon and low systemic exposure. The novel formulation of anti-TNF-α antibodies administrated orally achieved high efficacy in the treatment of colitis mice compared with free antibodies administered orally. The average weight, colon length, and inflammatory factors in colon and serum of colitis mice after the treatment of novel formulation of anti-TNF-α antibodies even reached the similar level to healthy controls. Conclusion: This polyphenol-based supramolecular nanoparticle is a promising platform for oral delivery of antibodies for the treatment of inflammatory bowel diseases, which may have promising clinical translation prospects.
Extracted statement — Highlights: The average weight, colon length, and inflammatory factors in colon and serum of colitis mice after the treatment of novel formulation of anti-TNF-α antibodies even reached the similar level to healthy controls
Research field: Medicinal Chemistry and Pharmaceutics
Title: Comparison, synthesis and evaluation of anticancer drug-loaded polymeric nanoparticles on breast cancer cell lines
Abstract: Breast cancer is a major form of cancer, with a high mortality rate in women. It is crucial to achieve more efficient and safe anticancer drugs. Recent developments in medical nanotechnology have resulted in novel advances in cancer drug delivery. Cisplatin, doxorubicin, and 5-fluorouracil are three important anti-cancer drugs which have poor water-solubility. In this study, we used cisplatin, doxorubicin, and 5-fluorouracil-loaded polycaprolactone-polyethylene glycol (PCL-PEG) nanoparticles to improve the stability and solubility of molecules in drug delivery systems. The nanoparticles were prepared by a double emulsion method and characterized with Fourier Transform Infrared (FTIR) spectroscopy and Hydrogen-1 nuclear magnetic resonance (1HNMR). Cells were treated with equal concentrations of cisplatin, doxorubicin and 5-fluorouracil-loaded PCL-PEG nanoparticles, and free cisplatin, doxorubicin and 5-fluorouracil. The 3-[4,5-dimethylthiazol-2yl]-2,5-diphenyl tetrazolium bromide (MTT) assay confirmed that cisplatin, doxorubicin, and 5-fluorouracil-loaded PCL-PEG nanoparticles enhanced cytotoxicity and drug delivery in T47D and MCF7 breast cancer cells. However, the IC50 value of doxorubicin was lower than the IC50 values of both cisplatin and 5-fluorouracil, where the difference was statistically considered significant (p˂0.05). However, the IC50 value of all drugs on T47D were lower than those on MCF7.
Extracted statement — Highlights: The IC50 value of all drugs on T47D were lower than those on MCF7.
Research field: Medicinal Chemistry and Pharmaceutics
Title: Resveratrol loaded polymeric micelles for theranostic targeting of breast cancer cells
Abstract: Treatment of breast cancer underwent extensive progress in recent years with molecularly targeted therapies. However, non-specific pharmaceutical approaches (chemotherapy) persist, inducing severe side-effects. Phytochemicals provide a promising alternative for breast cancer prevention and treatment. Specifically, resveratrol (res) is a plant-derived polyphenolic phytoalexin with potent biological activity but displays poor water solubility, limiting its clinical use. Here we have developed a strategy for delivering res using a newly synthesized nano-carrier with the potential for both diagnosis and treatment. Methods: Res-loaded nanoparticles were synthesized by the emulsion method using Pluronic F127 block copolymer and Vitamin E-TPGS. Nanoparticle characterization was performed by SEM and tunable resistive pulse sensing. Encapsulation Efficiency (EE%) and Drug Loading (DL%) content were determined by analysis of the supernatant during synthesis. Nanoparticle uptake kinetics in breast cancer cell lines MCF-7 and MDA-MB-231 as well as in MCF-10A breast epithelial cells were evaluated by flow cytometry and the effects of res on cell viability via MTT assay. Results: Res-loaded nanoparticles with spherical shape and a dominant size of 179±22 nm were produced. Res was loaded with high EE of 73±0.9% and DL content of 6.2±0.1%. Flow cytometry revealed higher uptake efficiency in breast cancer cells compared to the control. An MTT assay showed that res-loaded nanoparticles reduced the viability of breast cancer cells with no effect on the control cells. Conclusions: These results demonstrate that the newly synthesized nanoparticle is a good model for the encapsulation of hydrophobic drugs. Additionally, the nanoparticle delivers a natural compound and is highly effective and selective against breast cancer cells rendering this type of nanoparticle an excellent candidate for diagnosis and therapy of difficult to treat mammary malignancies.
Extracted statement — Highlights: the nanoparticle delivers a natural compound and is highly effective and selective against breast cancer cells rendering this type of nanoparticle an excellent candidate for diagnosis and therapy of difficult to treat mammary malignancies.
Research field: Medicinal Chemistry and Pharmaceutics
Title: Resveratrol loaded polymeric micelles for theranostic targeting of breast cancer cells
Abstract: Treatment of breast cancer underwent extensive progress in recent years with molecularly targeted therapies. However, non-specific pharmaceutical approaches (chemotherapy) persist, inducing severe side-effects. Phytochemicals provide a promising alternative for breast cancer prevention and treatment. Specifically, resveratrol (res) is a plant-derived polyphenolic phytoalexin with potent biological activity but displays poor water solubility, limiting its clinical use. Here we have developed a strategy for delivering res using a newly synthesized nano-carrier with the potential for both diagnosis and treatment. Methods: Res-loaded nanoparticles were synthesized by the emulsion method using Pluronic F127 block copolymer and Vitamin E-TPGS. Nanoparticle characterization was performed by SEM and tunable resistive pulse sensing. Encapsulation Efficiency (EE%) and Drug Loading (DL%) content were determined by analysis of the supernatant during synthesis. Nanoparticle uptake kinetics in breast cancer cell lines MCF-7 and MDA-MB-231 as well as in MCF-10A breast epithelial cells were evaluated by flow cytometry and the effects of res on cell viability via MTT assay. Results: Res-loaded nanoparticles with spherical shape and a dominant size of 179±22 nm were produced. Res was loaded with high EE of 73±0.9% and DL content of 6.2±0.1%. Flow cytometry revealed higher uptake efficiency in breast cancer cells compared to the control. An MTT assay showed that res-loaded nanoparticles reduced the viability of breast cancer cells with no effect on the control cells. Conclusions: These results demonstrate that the newly synthesized nanoparticle is a good model for the encapsulation of hydrophobic drugs. Additionally, the nanoparticle delivers a natural compound and is highly effective and selective against breast cancer cells rendering this type of nanoparticle an excellent candidate for diagnosis and therapy of difficult to treat mammary malignancies.
Extracted statement — Highlights: the newly synthesized nanoparticle is a good model for the encapsulation of hydrophobic drugs.
Research field: Medicinal Chemistry and Pharmaceutics
Title: Oral delivery of anti-TNF antibody shielded by natural polyphenol-mediated supramolecular assembly for inflammatory bowel disease therapy
Abstract: Rationale: Anti-tumor necrosis factor (TNF) therapy is a very effective way to treat inflammatory bowel disease. However, systemic exposure to anti-TNF-α antibodies through current clinical systemic administration can cause serious adverse effects in many patients. Here, we report a facile prepared self-assembled supramolecular nanoparticle based on natural polyphenol tannic acid and poly(ethylene glycol) containing polymer for oral antibody delivery. Method: This supramolecular nanoparticle was fabricated within minutes in aqueous solution and easily scaled up to gram level due to their pH-dependent reversible assembly. DSS-induced colitis model was prepared to evaluate the ability of inflammatory colon targeting ability and therapeutic efficacy of this antibody-loaded nanoparticles. Results: This polyphenol-based nanoparticle can be aqueous assembly without organic solvent and thus scaled up easily. The oral administration of antibody loaded nanoparticle achieved high accumulation in the inflamed colon and low systemic exposure. The novel formulation of anti-TNF-α antibodies administrated orally achieved high efficacy in the treatment of colitis mice compared with free antibodies administered orally. The average weight, colon length, and inflammatory factors in colon and serum of colitis mice after the treatment of novel formulation of anti-TNF-α antibodies even reached the similar level to healthy controls. Conclusion: This polyphenol-based supramolecular nanoparticle is a promising platform for oral delivery of antibodies for the treatment of inflammatory bowel diseases, which may have promising clinical translation prospects.
Extracted statement — Highlights: The novel formulation of anti-TNF-α antibodies administrated orally achieved high efficacy in the treatment of colitis mice compared with free antibodies administered orally
Research field: Medicinal Chemistry and Pharmaceutics
Title: A self-adjuvanted nanoparticle based vaccine against infectious bronchitis virus
Abstract: Infectious bronchitis virus (IBV) affects poultry respiratory, renal and reproductive systems. Currently the efficacy of available live attenuated or killed vaccines against IBV has been challenged. We designed a novel IBV vaccine alternative using a highly innovative platform called Self-Assembling Protein Nanoparticle (SAPN). In this vaccine, B cell epitopes derived from the second heptad repeat (HR2) region of IBV spike proteins were repetitively presented in its native trimeric conformation. In addition, flagellin was co-displayed in the SAPN to achieve a self-adjuvanted effect. Three groups of chickens were immunized at four weeks of age with the vaccine prototype, IBV-Flagellin-SAPN, a negative-control construct Flagellin-SAPN or a buffer control. The immunized chickens were challenged with 5x104.7 EID50 IBV M41 strain. High antibody responses were detected in chickens immunized with IBV-Flagellin-SAPN. In ex vivo proliferation tests, peripheral mononuclear cells (PBMCs) derived from IBV-Flagellin-SAPN immunized chickens had a significantly higher stimulation index than that of PBMCs from chickens receiving Flagellin-SAPN. Chickens immunized with IBV-Flagellin-SAPN had a significant reduction of tracheal virus shedding and lesser tracheal lesion scores than did negative control chickens. The data demonstrated that the IBV-Flagellin-SAPN holds promise as a vaccine for IBV.
Extracted statement — Mechanism of Antiviral Action: The second heptad repeat (HR2) region of IBV spike proteins
Research field: Natural Language Processing
Title: SciREX: A Challenge Dataset for Document-Level Information Extraction
Abstract: Extracting information from full documents is an important problem in many domains, but most previous work focus on identifying relationships within a sentence or a paragraph. It is challenging to create a large-scale information extraction (IE) dataset at the document level since it requires an understanding of the whole document to annotate entities and their document-level relationships that usually span beyond sentences or even sections. In this paper, we introduce SciREX, a document level IE dataset that encompasses multiple IE tasks, including salient entity identification and document level N-ary relation identification from scientific articles. We annotate our dataset by integrating automatic and human annotations, leveraging existing scientific knowledge resources. We develop a neural model as a strong baseline that extends previous state-of-the-art IE models to document-level IE. Analyzing the model performance shows a significant gap between human performance and current baselines, inviting the community to use our dataset as a challenge to develop document-level IE models. Our data and code are publicly available at https://github.com/allenai/SciREX .
Extracted statement — description: a document level IE dataset that encompasses multiple IE tasks, including salient entity identification and document level N-ary relation identification from scientific articles.
Research field: Natural Language Processing
Title: SciREX: A Challenge Dataset for Document-Level Information Extraction
Abstract: Extracting information from full documents is an important problem in many domains, but most previous work focus on identifying relationships within a sentence or a paragraph. It is challenging to create a large-scale information extraction (IE) dataset at the document level since it requires an understanding of the whole document to annotate entities and their document-level relationships that usually span beyond sentences or even sections. In this paper, we introduce SciREX, a document level IE dataset that encompasses multiple IE tasks, including salient entity identification and document level N-ary relation identification from scientific articles. We annotate our dataset by integrating automatic and human annotations, leveraging existing scientific knowledge resources. We develop a neural model as a strong baseline that extends previous state-of-the-art IE models to document-level IE. Analyzing the model performance shows a significant gap between human performance and current baselines, inviting the community to use our dataset as a challenge to develop document-level IE models. Our data and code are publicly available at https://github.com/allenai/SciREX .
Extracted statement — description: develop a neural model as a strong baseline that extends previous state-of-the-art IE models to document-level IE.
Research field: Natural Language Processing
Title: Overview of the gene ontology task at BioCreative IV
Abstract: Gene Ontology (GO) annotation is a common task among model organism databases (MODs) for capturing gene function data from journal articles. It is a time-consuming and labor-intensive task, and is thus often considered as one of the bottlenecks in literature curation. There is a growing need for semiautomated or fully automated GO curation techniques that will help database curators to rapidly and accurately identify gene function information in full-length articles. Despite multiple attempts in the past, few studies have proven to be useful with regard to assisting real-world GO curation. The shortage of sentence-level training data and opportunities for interaction between text-mining developers and GO curators has limited the advances in algorithm development and corresponding use in practical circumstances. To this end, we organized a text-mining challenge task for literature-based GO annotation in BioCreative IV. More specifically, we developed two subtasks: (i) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and (ii) to automatically identify relevant GO terms for the genes in a given article (a concept-recognition task). With the support from five MODs, we provided teams with >4000 unique text passages that served as the basis for each GO annotation in our task data. Such evidence text information has long been recognized as critical for text-mining algorithm development but was never made available because of the high cost of curation. In total, seven teams participated in the challenge task. From the team results, we conclude that the state of the art in automatically mining GO terms from literature has improved over the past decade while much progress is still needed for computer-assisted GO curation. Future work should focus on addressing remaining technical challenges for improved performance of automatic GO concept recognition and incorporating practical benefits of text-mining tools into real-world GO annotation. Database URL: http://www.biocreative.org/tasks/biocreative-iv/track-4-GO/.
Extracted statement — description: developed two subtasks: (i) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and (ii) to automatically identify relevant GO terms for the genes in a given article (a concept-recognition task).
Research field: Natural Language Processing
Title: GATE: an architecture for development of robust HLT applications
Abstract: In this paper we present GATE, a framework and graphical development environment which enables users to develop and deploy language engineering components and resources in a robust fashion. The GATE architecture has enabled us not only to develop a number of successful applications for various language processing tasks (such as Information Extraction), but also to build and annotate corpora and carry out evaluations on the applications generated. The framework can be used to develop applications and resources in multiple languages, based on its thorough Unicode support.
Extracted statement — description: framework and graphical development environment which enables users to develop and deploy language engineering components and resources in a robust fashion. The GATE architecture has enabled us not only to develop a number of successful applications for various language processing tasks (such as Information Extraction), but also to build and annotate corpora and carry out evaluations on the applications generated.
Research field: Natural Language Processing
Title: HeidelTime: High Quality Rule-Based Extraction and Normalization of Temporal Expressions
Abstract: In this paper, we describe HeidelTime, a system for the extraction and normalization of temporal expressions. HeidelTime is a rule-based system mainly using regular expression patterns for the extraction of temporal expressions and knowledge resources as well as linguistic clues for their normalization. In the TempEval-2 challenge, HeidelTime achieved the highest F-Score (86%) for the extraction and the best results in assigning the correct value attribute, i.e., in understanding the semantics of the temporal expressions.
Extracted statement — description: HeidelTime is a rule-based system mainly using regular expression patterns for the extraction of temporal expressions and knowledge resources as well as linguistic clues for their normalization.