Moussab/ORKG-training-evaluation-set huggingface dataset:
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 — has research problem: Breast cancer
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 — has research problem: Breast cancer
Research field: Medicinal Chemistry and Pharmaceutics
Title: A Novel Nanoparticle Formulation for Sustained Paclitaxel Delivery
Abstract: PurposeTo develop a novel nanoparticle drug delivery system consisting of chitosan and glyceryl monooleate (GMO) for the delivery of a wide variety of therapeutics including paclitaxel.MethodsChitosan/GMO nanoparticles were prepared by multiple emulsion (o/w/o) solvent evaporation methods. Particle size and surface charge were determined. The morphological characteristics and cellular adhesion were evaluated with surface or transmission electron microscopy methods. The drug loading, encapsulation efficiency, in vitro release and cellular uptake were determined using HPLC methods. The safety and efficacy were evaluated by MTT cytotoxicity assay in human breast cancer cells (MDA-MB-231).ResultsThese studies provide conceptual proof that chitosan/GMO can form polycationic nano-sized particles (400 to 700 nm). The formulation demonstrates high yields (98 to 100%) and similar entrapment efficiencies. The lyophilized powder can be stored and easily be resuspended in an aqueous matrix. The nanoparticles have a hydrophobic inner-core with a hydrophilic coating that exhibits a significant positive charge and sustained release characteristics. This novel nanoparticle formulation shows evidence of mucoadhesive properties; a fourfold increased cellular uptake and a 1000-fold reduction in the IC50 of PTX.ConclusionThese advantages allow lower doses of PTX to achieve a therapeutic effect, thus presumably minimizing the adverse side effects.
Extracted statement — has research problem: Breast cancer
Research field: Microbial Physiology
Title: The Impact of Pyroglutamate: Sulfolobus acidocaldarius Has a Growth Advantage over Saccharolobus solfataricus in Glutamate-Containing Media
Abstract: Microorganisms are well adapted to their habitat but are partially sensitive to toxic metabolites or abiotic compounds secreted by other organisms or chemically formed under the respective environmental conditions. Thermoacidophiles are challenged by pyroglutamate, a lactam that is spontaneously formed by cyclization of glutamate under aerobic thermoacidophilic conditions. It is known that growth of the thermoacidophilic crenarchaeon Saccharolobus solfataricus (formerly Sulfolobus solfataricus) is completely inhibited by pyroglutamate. In the present study, we investigated the effect of pyroglutamate on the growth of S. solfataricus and the closely related crenarchaeon Sulfolobus acidocaldarius. In contrast to S. solfataricus, S. acidocaldarius was successfully cultivated with pyroglutamate as a sole carbon source. Bioinformatical analyses showed that both members of the Sulfolobaceae have at least one candidate for a 5-oxoprolinase, which catalyses the ATP-dependent conversion of pyroglutamate to glutamate. In S. solfataricus, we observed the intracellular accumulation of pyroglutamate and crude cell extract assays showed a less effective degradation of pyroglutamate. Apparently, S. acidocaldarius seems to be less versatile regarding carbohydrates and prefers peptidolytic growth compared to S. solfataricus. Concludingly, S. acidocaldarius exhibits a more efficient utilization of pyroglutamate and is not inhibited by this compound, making it a better candidate for applications with glutamate-containing media at high temperatures.
Extracted statement — has research problem: Utilization of pyroglutamate
Research field: Natural Language Processing
Title: SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge
Abstract: We present the results of the Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge, aiming to bring together researchers in educational NLP technology and textual entailment. The task of giving feedback on student answers requires semantic inference and therefore is related to recognizing textual entailment. Thus, we offered to the community a 5-way student response labeling task, as well as 3-way and 2way RTE-style tasks on educational data. In addition, a partial entailment task was piloted. We present and compare results from 9 participating teams, and discuss future directions.
Extracted statement — has research problem:  Joint Student Response Analysis
Research field: Natural Language Processing
Title: NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task
Abstract: We present the findings and results of theSecond Nuanced Arabic Dialect IdentificationShared Task (NADI 2021). This Shared Taskincludes four subtasks: country-level ModernStandard Arabic (MSA) identification (Subtask1.1), country-level dialect identification (Subtask1.2), province-level MSA identification (Subtask2.1), and province-level sub-dialect identifica-tion (Subtask 2.2). The shared task dataset cov-ers a total of 100 provinces from 21 Arab coun-tries, collected from the Twitter domain. A totalof 53 teams from 23 countries registered to par-ticipate in the tasks, thus reflecting the interestof the community in this area. We received 16submissions for Subtask 1.1 from five teams, 27submissions for Subtask 1.2 from eight teams,12 submissions for Subtask 2.1 from four teams,and 13 Submissions for subtask 2.2 from fourteams.
Extracted statement — has research problem: Arabic Dialect Identification
Research field: Natural Language Processing
Title: Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction
Abstract: While the fast-paced inception of novel tasks and new datasets helps foster active research in a community towards interesting directions, keeping track of the abundance of research activity in different areas on different datasets is likely to become increasingly difficult. The community could greatly benefit from an automatic system able to summarize scientific results, e.g., in the form of a leaderboard. In this paper we build two datasets and develop a framework (TDMS-IE) aimed at automatically extracting task, dataset, metric and score from NLP papers, towards the automatic construction of leaderboards. Experiments show that our model outperforms several baselines by a large margin. Our model is a first step towards automatic leaderboard construction, e.g., in the NLP domain.
Extracted statement — has research problem: Automatic leaderboard construction
Research field: Natural Language Processing
Title: BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language
Abstract: Automatic extraction of biological network information is one of the most desired and most complex tasks in biological and medical text mining. Track 4 at BioCreative V attempts to approach this complexity using fragments of large-scale manually curated biological networks, represented in Biological Expression Language (BEL), as training and test data. BEL is an advanced knowledge representation format which has been designed to be both human readable and machine processable. The specific goal of track 4 was to evaluate text mining systems capable of automatically constructing BEL statements from given evidence text, and of retrieving evidence text for given BEL statements. Given the complexity of the task, we designed an evaluation methodology which gives credit to partially correct statements. We identified various levels of information expressed by BEL statements, such as entities, functions, relations, and introduced an evaluation framework which rewards systems capable of delivering useful BEL fragments at each of these levels. The aim of this evaluation method is to help identify the characteristics of the systems which, if combined, would be most useful for achieving the overall goal of automatically constructing causal biological networks from text.
Extracted statement — has research problem: automatically constructing causal biological networks from text
Research field: Natural Language Processing
Title: Overview of the Cancer Genetics (CG) task of BioNLP Shared Task 2013
Abstract: We present the design, preparation, results and analysis of the Cancer Genetics (CG) event extraction task, a main task of the BioNLP Shared Task (ST) 2013. The CG task is an information extraction task targeting the recognition of events in text, represented as structured n-ary associations of given physical entities. In addition to addressing the cancer domain, the CG task is differentiated from previous event extraction tasks in the BioNLP ST series in addressing a wide range of pathological processes and multiple levels of biological organization, ranging from the molecular through the cellular and organ levels up to whole organisms. Final test set submissions were accepted from six teams. The highest-performing system achieved an Fscore of 55.4%. This level of performance is broadly comparable with the state of the art for established molecular-level extraction tasks, demonstrating that event extraction resources and methods generalize well to higher levels of biological organization and are applicable to the analysis of scientific texts on cancer. The CG task continues as an open challenge to all interested parties, with tools and resources available from http://2013. bionlp-st.org/.
Extracted statement — has research problem: Cancer genetics (CG) event extraction
Research field: Natural Language Processing
Title: Overview of the NLM-Chem BioCreative VII track: Full-text Chemical Identification and Indexing in PubMed articles
Abstract: The BioCreative NLM-Chem track calls for a community effort to fine-tune automated recognition of chemical names in biomedical literature. Chemical names are one of the most searched biomedical entities in PubMed and – as highlighted during the COVID-19 pandemic – their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We organized the BioCreative NLM-Chem track to call for a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: 1) Chemical Identification task, and 2) Chemical Indexing prediction task. For the Chemical Identification task, participants were expected to predict with high accuracy all chemicals mentioned in recently published full-text articles, both span (i.e., named entity recognition) and normalization (i.e., entity linking) using MeSH. For the Chemical Indexing task, participants identified which chemicals should be indexed as topics for the article's topic terms in the NLM article and indexing, i.e., appear in the listing of MeSH terms for the document. This manuscript summarizes the BioCreative NLM-Chem track. We received a total of 88 submissions in total from 17 teams worldwide. The highest performance achieved for the Chemical Identification task was 0.8672 f-score (0.8759 precision, 0.8587 recall) for strict NER performance and 0.8136 f-score (0.8621 precision, 0.7702 recall) for strict normalization performance. The highest performance achieved for the Chemical Indexing task was 0.4825 f-score (0.4397 precision, 0.5344 recall). The NLM-Chem track dataset and other challenge materials are publicly available at https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/. This community challenge demonstrated 1) the current substantial achievements in deep learning technologies can be utilized to further improve automated prediction accuracy, and 2) the Chemical Indexing task is substantially more challenging. We look forward to further development of biomedical text mining methods to respond to the rapid growth of biomedical literature. Keywords— biomedical text mining; natural language processing; artificial intelligence; machine learning; deep learning; text mining; chemical entity recognition; chemical indexing
Extracted statement — has research problem: chemical indexing
Research field: Natural Language Processing
Title: Overview of the CHEMDNER patents task
Abstract: A considerable effort has been made to extract biological and chemical entities, as well as their relationships, from the scientific literature, either manually through traditional literature curation or by using information extraction and text mining technologies. Medicinal chemistry patents contain a wealth of information, for instance to uncover potential biomarkers that might play a role in cancer treatment and prognosis. However, current biomedical annotation databases do not cover such information, partly due to limitations of publicly available biomedical patent mining software. As part of the BioCreative V CHEMDNER patents track, we present the results of the first named entity recognition (NER) assignment carried out to detect mentions of chemical compounds and genes/proteins in running patent text. More specifically, this task aimed to evaluate the performance of automatic name recognition strategies capable of isolating chemical names and gene and gene product mentions from surrounding text within patent titles and abstracts. A total of 22 unique teams submitted results for at least one of the three CHEMDNER subtasks. The first subtask, called the CEMP (chemical entity mention in patents) task, focused on the detection of chemical named entity mentions in patents, requesting teams to return the start and end indices corresponding to all the chemical entities found in a given record. A total of 21 teams submitted 93 runs, for this subtask. The top performing team reached an f-measure of 0.89 with a precision of 0.87 and a recall of 0.91. The CPD (chemical passage detection) task required the classification of patent titles and abstracts whether they do or do not contain chemical compound mentions. Nine teams returned predictions for this task (40 runs). The top run in terms of Matthew’s correlation coefficient (MCC) had a score of 0.88, the highest sensitivity ? Corresponding author
Extracted statement — has research problem: Chemical passage detection
Research field: Natural Language Processing
Title: Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task
Abstract: Manually curating chemicals, diseases and their relationships is significantly important to biomedical research, but it is plagued by its high cost and the rapid growth of the biomedical literature. In recent years, there has been a growing interest in developing computational approaches for automatic chemical-disease relation (CDR) extraction. Despite these attempts, the lack of a comprehensive benchmarking dataset has limited the comparison of different techniques in order to assess and advance the current state-of-the-art. To this end, we organized a challenge task through BioCreative V to automatically extract CDRs from the literature. We designed two challenge tasks: disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction. To assist system development and assessment, we created a large annotated text corpus that consisted of human annotations of chemicals, diseases and their interactions from 1500 PubMed articles. 34 teams worldwide participated in the CDR task: 16 (DNER) and 18 (CID). The best systems achieved an F-score of 86.46% for the DNER task—a result that approaches the human inter-annotator agreement (0.8875)—and an F-score of 57.03% for the CID task, the highest results ever reported for such tasks. When combining team results via machine learning, the ensemble system was able to further improve over the best team results by achieving 88.89% and 62.80% in F-score for the DNER and CID task, respectively. Additionally, another novel aspect of our evaluation is to test each participating system’s ability to return real-time results: the average response time for each team’s DNER and CID web service systems were 5.6 and 9.3 s, respectively. Most teams used hybrid systems for their submissions based on machining learning. Given the level of participation and results, we found our task to be successful in engaging the text-mining research community, producing a large annotated corpus and improving the results of automatic disease recognition and CDR extraction. Database URL: http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/
Extracted statement — has research problem: chemical-disease relation (CDR) extraction
Research field: Natural Language Processing
Title: Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task
Abstract: Manually curating chemicals, diseases and their relationships is significantly important to biomedical research, but it is plagued by its high cost and the rapid growth of the biomedical literature. In recent years, there has been a growing interest in developing computational approaches for automatic chemical-disease relation (CDR) extraction. Despite these attempts, the lack of a comprehensive benchmarking dataset has limited the comparison of different techniques in order to assess and advance the current state-of-the-art. To this end, we organized a challenge task through BioCreative V to automatically extract CDRs from the literature. We designed two challenge tasks: disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction. To assist system development and assessment, we created a large annotated text corpus that consisted of human annotations of chemicals, diseases and their interactions from 1500 PubMed articles. 34 teams worldwide participated in the CDR task: 16 (DNER) and 18 (CID). The best systems achieved an F-score of 86.46% for the DNER task—a result that approaches the human inter-annotator agreement (0.8875)—and an F-score of 57.03% for the CID task, the highest results ever reported for such tasks. When combining team results via machine learning, the ensemble system was able to further improve over the best team results by achieving 88.89% and 62.80% in F-score for the DNER and CID task, respectively. Additionally, another novel aspect of our evaluation is to test each participating system’s ability to return real-time results: the average response time for each team’s DNER and CID web service systems were 5.6 and 9.3 s, respectively. Most teams used hybrid systems for their submissions based on machining learning. Given the level of participation and results, we found our task to be successful in engaging the text-mining research community, producing a large annotated corpus and improving the results of automatic disease recognition and CDR extraction. Database URL: http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/
Extracted statement — has research problem: chemical-induced disease (CID) relation extraction