Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Materials Chemistry
Title: Up-scalable and controllable electrolytic production of photo-responsive nanostructured silicon
Abstract: The electrochemical reduction of solid silica has been investigated in molten CaCl2 at 900 °C for the one-step, up-scalable, controllable and affordable production of nanostructured silicon with promising photo-responsive properties. Cyclic voltammetry of the metallic cavity electrode loaded with fine silica powder was performed to elaborate the electrochemical reduction mechanism. Potentiostatic electrolysis of porous and dense silica pellets was carried out at different potentials, focusing on the influences of the electrolysis potential and the microstructure of the precursory silica on the product purity and microstructure. The findings suggest a potential range between −0.60 and −0.95 V (vs. Ag/AgCl) for the production of nanostructured silicon with high purity (>99 wt%). According to the elucidated mechanism on the electro-growth of the silicon nanostructures, optimal process parameters for the controllable preparation of high-purity silicon nanoparticles and nanowires were identified. Scaling-up the optimal electrolysis was successful at the gram-scale for the preparation of high-purity silicon nanowires which exhibited promising photo-responsive properties.
Extracted statement — temperature: 900 °C
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 — Paricle size of nanoparicles: 179±22 nm
Research field: Medicinal Chemistry and Pharmaceutics
Title: New Method for Delivering a Hydrophobic Drug for Photodynamic Therapy Using Pure Nanocrystal Form of the Drug
Abstract: A carrier-free method for delivery of a hydrophobic drug in its pure form, using nanocrystals (nanosized crystals), is proposed. To demonstrate this technique, nanocrystals of a hydrophobic photosensitizing anticancer drug, 2-devinyl-2-(1-hexyloxyethyl)pyropheophorbide (HPPH), have been synthesized using the reprecipitation method. The resulting drug nanocrystals were monodispersed and stable in aqueous dispersion, without the necessity of an additional stabilizer (surfactant). As shown by confocal microscopy, these pure drug nanocrystals were taken up by the cancer cells with high avidity. Though the fluorescence and photodynamic activity of the drug were substantially quenched in the form of nanocrystals in aqueous suspension, both these characteristics were recovered under in vitro and in vivo conditions. This recovery of drug activity and fluorescence is possibly due to the interaction of nanocrystals with serum albumin, resulting in conversion of the drug nanocrystals into the molecular form. This was confirmed by demonstrating similar recovery in presence of fetal bovine serum (FBS) or bovine serum albumin (BSA). Under similar treatment conditions, the HPPH in nanocrystal form or in 1% Tween-80/water formulation showed comparable in vitro and in vivo efficacy.
Extracted statement — Uses drug: 2-devinyl-2-(1-hexyloxyethyl)pyropheophorbide (HPPH)
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 — Drug loading: 6.2±0.1%
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 — Drug entrapment efficiency: 73±0.9%
Research field: Medicinal Chemistry and Pharmaceutics
Title: Influence of Process and Formulation Parameters on Dissolution and Stability Characteristics of Kollidon® VA 64 Hot-Melt Extrudates
Abstract: The objective of the present study was to investigate the effects of processing variables and formulation factors on the characteristics of hot-melt extrudates containing a copolymer (Kollidon® VA 64). Nifedipine was used as a model drug in all of the extrudates. Differential scanning calorimetry (DSC) was utilized on the physical mixtures and melts of varying drug–polymer concentrations to study their miscibility. The drug–polymer binary mixtures were studied for powder flow, drug release, and physical and chemical stabilities. The effects of moisture absorption on the content uniformity of the extrudates were also studied. Processing the materials at lower barrel temperatures (115–135°C) and higher screw speeds (50–100 rpm) exhibited higher post-processing drug content (~99–100%). DSC and X-ray diffraction studies confirmed that melt extrusion of drug–polymer mixtures led to the formation of solid dispersions. Interestingly, the extrusion process also enhanced the powder flow characteristics, which occurred irrespective of the drug load (up to 40% w/w). Moreover, the content uniformity of the extrudates, unlike the physical mixtures, was not sensitive to the amount of moisture absorbed. The extrusion conditions did not influence drug release from the extrudates; however, release was greatly affected by the drug loading. Additionally, the drug release from the physical mixture of nifedipine–Kollidon® VA 64 was significantly different when compared to the corresponding extrudates (f2 = 36.70). The extrudates exhibited both physical and chemical stabilities throughout the period of study. Overall, hot-melt extrusion technology in combination with Kollidon® VA 64 produced extrudates capable of higher drug loading, with enhanced flow characteristics, and excellent stability.
Extracted statement — Carrier for hot melt extrusion: Kollidon® VA 64
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 — Protein Target: 5-oxoprolinase
Research field: Natural Language Processing
Title: Results of the WNUT16 Named Entity Recognition Shared Task
Abstract: This paper presents the results of the Twitter Named Entity Recognition shared task associated with W-NUT 2016: a named entity tagging task with 10 teams participating. We outline the shared task, annotation process and dataset statistics, and provide a high-level overview of the participating systems for each shared task.
Extracted statement — Total teams: 10 teams
Research field: Natural Language Processing
Title: SQuAD: 100,000+ Questions for Machine Comprehension of Text
Abstract: We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. The dataset is freely available at this https URL
Extracted statement — Amount of Questions: 100,000+
Research field: Natural Language Processing
Title: BioCreAtIvE Task 1A: gene mention finding evaluation
Abstract: Abstract Background The biological research literature is a major repository of knowledge. As the amount of literature increases, it will get harder to find the information of interest on a particular topic. There has been an increasing amount of work on text mining this literature, but comparing this work is hard because of a lack of standards for making comparisons. To address this, we worked with colleagues at the Protein Design Group, CNB-CSIC, Madrid to develop BioCreAtIvE (Critical Assessment for Information Extraction in Biology), an open common evaluation of systems on a number of biological text mining tasks. We report here on task 1A, which deals with finding mentions of genes and related entities in text. "Finding mentions" is a basic task, which can be used as a building block for other text mining tasks. The task makes use of data and evaluation software provided by the (US) National Center for Biotechnology Information (NCBI). Results 15 teams took part in task 1A. A number of teams achieved scores over 80% F-measure (balanced precision and recall). The teams that tried to use their task 1A systems to help on other BioCreAtIvE tasks reported mixed results. Conclusion The 80% plus F-measure results are good, but still somewhat lag the best scores achieved in some other domains such as newswire, due in part to the complexity and length of gene names, compared to person or organization names in newswire.
Extracted statement — Total teams: 15 teams
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 — number of papers: 1500 PubMed articles
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 — Total teams: 17 teams
Research field: Natural Language Processing
Title: Overview of the BioCreative VII LitCovid Track: multi-label topic classification for COVID-19 literature annotation
Abstract: The BioCreative LitCovid track calls for a community effort to tackle automated topic annotation for COVID-19 literature. The number of COVID-19-related articles in the literature is growing by about 10,000 articles per month, significantly challenging curation efforts and downstream interpretation. LitCovid is a literature database of COVID-19related articles in PubMed, which has accumulated more than 180,000 articles with millions of accesses each month by users worldwide. The rapid literature growth significantly increases the burden of LitCovid curation, especially for topic annotations. Topic annotation in LitCovid assigns one or more (up to eight) labels to articles. The annotated topics have been widely used both directly in LitCovid (e.g., accounting for ~20% of total uses) and downstream studies such as knowledge network generation and citation analysis. It is, therefore, important to develop innovative text mining methods to tackle the challenge. We organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. This article summarizes the BioCreative LitCovid track in terms of data collection and team participation. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/. It consists of over 30K PubMed articles, one of the largest multilabel classification datasets on biomedical literature. There were 80 submissions in total from 19 teams worldwide. The highestperforming submissions achieved 0.8875, 0.9181, and 0.9394 for macro F1-score, micro F1-score, and instance-based F1-score, respectively. We look forward to further participation in developing biomedical text mining methods in response to the rapid growth of the COVID-19 literature. Keywords—biomedical text mining; natural language processing; artificial intelligence; machine learning; deep learning; multi-label classification; COVID-19; LitCovid;
Extracted statement — Total teams: 19 teams