Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Machine Learning
Title: Autoformer: Searching transformers for visual recognition
Abstract: Recently, pure transformer-based models have shown great potentials for vision tasks such as image classification and detection. However, the design of transformer networks is challenging. It has been observed that the depth, embedding dimension, and number of heads can largely affect the performance of vision transformers. Previous models configure these dimensions based upon manual crafting. In this work, we propose a new one-shot architecture search framework, namely AutoFormer, dedicated to vision transformer search. AutoFormer entangles the weights of different blocks in the same layers during supernet training. Benefiting from the strategy, the trained supernet allows thousands of subnets to be very well-trained. Specifically, the performance of these subnets with weights inherited from the supernet is comparable to those retrained from scratch. Besides, the searched models, which we refer to AutoFormers, surpass the recent state-of-the-arts such as ViT and DeiT. In particular, AutoFormer-tiny/small/base achieve 74.7%/81.7%/82.4% top-1 accuracy on ImageNet with 5.7M/22.9M/53.7M parameters, respectively. Lastly, we verify the transferability of AutoFormer by providing the performance on downstream benchmarks and distillation experiments. Code and models are available at https://github.com/microsoft/Cream.
Extracted statement — keywords: Vision Transformer
Research field: Machine Learning
Title: Autoformer: Searching transformers for visual recognition
Abstract: Recently, pure transformer-based models have shown great potentials for vision tasks such as image classification and detection. However, the design of transformer networks is challenging. It has been observed that the depth, embedding dimension, and number of heads can largely affect the performance of vision transformers. Previous models configure these dimensions based upon manual crafting. In this work, we propose a new one-shot architecture search framework, namely AutoFormer, dedicated to vision transformer search. AutoFormer entangles the weights of different blocks in the same layers during supernet training. Benefiting from the strategy, the trained supernet allows thousands of subnets to be very well-trained. Specifically, the performance of these subnets with weights inherited from the supernet is comparable to those retrained from scratch. Besides, the searched models, which we refer to AutoFormers, surpass the recent state-of-the-arts such as ViT and DeiT. In particular, AutoFormer-tiny/small/base achieve 74.7%/81.7%/82.4% top-1 accuracy on ImageNet with 5.7M/22.9M/53.7M parameters, respectively. Lastly, we verify the transferability of AutoFormer by providing the performance on downstream benchmarks and distillation experiments. Code and models are available at https://github.com/microsoft/Cream.
Extracted statement — keywords: One-Shot
Research field: Machine Learning
Title: DeepWalk: online learning of social representations
Abstract: We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide F1 scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.
Extracted statement — Has method : Random walk
Research field: Materials Chemistry
Title: Mapping Intracellular Temperature Using Green Fluorescent Protein
Abstract: Heat is of fundamental importance in many cellular processes such as cell metabolism, cell division and gene expression. (1-3) Accurate and noninvasive monitoring of temperature changes in individual cells could thus help clarify intricate cellular processes and develop new applications in biology and medicine. Here we report the use of green fluorescent proteins (GFP) as thermal nanoprobes suited for intracellular temperature mapping. Temperature probing is achieved by monitoring the fluorescence polarization anisotropy of GFP. The method is tested on GFP-transfected HeLa and U-87 MG cancer cell lines where we monitored the heat delivery by photothermal heating of gold nanorods surrounding the cells. A spatial resolution of 300 nm and a temperature accuracy of about 0.4 °C are achieved. Benefiting from its full compatibility with widely used GFP-transfected cells, this approach provides a noninvasive tool for fundamental and applied research in areas ranging from molecular biology to therapeutic and diagnostic studies.
Extracted statement — Readout: Polarization anisotropy
Research field: Materials Chemistry
Title: CdSe Quantum Dots for Two-Photon Fluorescence Thermal Imaging
Abstract: The technological development of quantum dots has ushered in a new era in fluorescence bioimaging, which was propelled with the advent of novel multiphoton fluorescence microscopes. Here, the potential use of CdSe quantum dots has been evaluated as fluorescent nanothermometers for two-photon fluorescence microscopy. In addition to the enhancement in spatial resolution inherent to any multiphoton excitation processes, two-photon (near-infrared) excitation leads to a temperature sensitivity of the emission intensity much higher than that achieved under one-photon (visible) excitation. The peak emission wavelength is also temperature sensitive, providing an additional approach for thermal imaging, which is particularly interesting for systems where nanoparticles are not homogeneously dispersed. On the basis of these superior thermal sensitivity properties of the two-photon excited fluorescence, we have demonstrated the ability of CdSe quantum dots to image a temperature gradient artificially created in a biocompatible fluid (phosphate-buffered saline) and also their ability to measure an intracellular temperature increase externally induced in a single living cell.
Extracted statement — Material: Quantum dots
Research field: Medical Informatics and Medical Bioinformatics
Title: 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text
Abstract: The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records presented three tasks: a concept extraction task focused on the extraction of medical concepts from patient reports; an assertion classification task focused on assigning assertion types for medical problem concepts; and a relation classification task focused on assigning relation types that hold between medical problems, tests, and treatments. i2b2 and the VA provided an annotated reference standard corpus for the three tasks. Using this reference standard, 22 systems were developed for concept extraction, 21 for assertion classification, and 16 for relation classification. These systems showed that machine learning approaches could be augmented with rule-based systems to determine concepts, assertions, and relations. Depending on the task, the rule-based systems can either provide input for machine learning or post-process the output of machine learning. Ensembles of classifiers, information from unlabeled data, and external knowledge sources can help when the training data are inadequate.
Extracted statement — Data coverage: Clinical records
Research field: Medicinal Chemistry and Pharmaceutics
Title: Functionalization of Silver Nanoparticles Loaded with Paclitaxel-induced A549 Cells Apoptosis Through ROS-Mediated Signaling Pathways
Abstract: Background: Paclitaxel (PTX) is one of the most important and effective anticancer drugs for the treatment of human cancer. However, its low solubility and severe adverse effects limited clinical use. To overcome this limitation, nanotechnology has been used to overcome tumors due to its excellent antimicrobial activity. Objective: This study was to demonstrate the anticancer properties of functionalization silver nanoparticles loaded with paclitaxel (Ag@PTX) induced A549 cells apoptosis through ROS-mediated signaling pathways. Methods: The Ag@PTX nanoparticles were charged with a zeta potential of about -17 mv and characterized around 2 nm with a narrow size distribution. Results: Ag@PTX significantly decreased the viability of A549 cells and possessed selectivity between cancer and normal cells. Ag@PTX induced A549 cells apoptosis was confirmed by nuclear condensation, DNA fragmentation, and activation of caspase-3. Furthermore, Ag@PTX enhanced the anti-cancer activity of A549 cells through ROS-mediated p53 and AKT signalling pathways. Finally, in a xenograft nude mice model, Ag@PTX suppressed the growth of tumors. Conclusion: Our findings suggest that Ag@PTX may be a candidate as a chemopreventive agent and could be a highly efficient way to achieve anticancer synergism for human cancers.
Extracted statement — has cell line: A549 cells
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 — keywords: anti-cancer drugs
Research field: Medicinal Chemistry and Pharmaceutics
Title: Targeted Delivery of Insoluble Cargo (Paclitaxel) by PEGylated Chitosan Nanoparticles Grafted with Arg-Gly-Asp (RGD)
Abstract: Poor delivery of insoluble anticancer drugs has so far precluded their clinical application. In this study, we developed a tumor-targeting delivery system for insoluble drug (paclitaxel, PTX) by PEGylated O-carboxymethyl-chitosan (CMC) nanoparticles grafted with cyclic Arg-Gly-Asp (RGD) peptide. To improve the loading efficiency (LE), we combined O/W/O double emulsion method with temperature-programmed solidification technique and controlled PTX within the matrix network as in situ nanocrystallite form. Furthermore, these CMC nanoparticles were PEGylated, which could reduce recognition by the reticuloendothelial system (RES) and prolong the circulation time in blood. In addition, further graft of cyclic RGD peptide at the terminal of PEG chain endowed these nanoparticles with higher affinity to in vitro Lewis lung carcinoma (LLC) cells and in vivo tumor tissue. These outstanding properties enabled as-designed nanodevice to exhibit a greater tumor growth inhibition effect and much lower side effects over the commercial formulation Taxol.
Extracted statement — keywords: Arg-Gly-Asp (RGD)
Research field: Medicinal Chemistry and Pharmaceutics
Title: Targeted Delivery of Insoluble Cargo (Paclitaxel) by PEGylated Chitosan Nanoparticles Grafted with Arg-Gly-Asp (RGD)
Abstract: Poor delivery of insoluble anticancer drugs has so far precluded their clinical application. In this study, we developed a tumor-targeting delivery system for insoluble drug (paclitaxel, PTX) by PEGylated O-carboxymethyl-chitosan (CMC) nanoparticles grafted with cyclic Arg-Gly-Asp (RGD) peptide. To improve the loading efficiency (LE), we combined O/W/O double emulsion method with temperature-programmed solidification technique and controlled PTX within the matrix network as in situ nanocrystallite form. Furthermore, these CMC nanoparticles were PEGylated, which could reduce recognition by the reticuloendothelial system (RES) and prolong the circulation time in blood. In addition, further graft of cyclic RGD peptide at the terminal of PEG chain endowed these nanoparticles with higher affinity to in vitro Lewis lung carcinoma (LLC) cells and in vivo tumor tissue. These outstanding properties enabled as-designed nanodevice to exhibit a greater tumor growth inhibition effect and much lower side effects over the commercial formulation Taxol.
Extracted statement — Polymer: Arg-Gly-Asp (RGD)
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 — keywords: 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 — keywords: Breast cancer
Research field: Medicinal Chemistry and Pharmaceutics
Title: Paclitaxel-loaded poly(D,L-lactide-co-glycolide) nanoparticles for radiotherapy in hypoxic human tumor cells in vitro
Abstract: Radioresistant hypoxic cells may contribute to the failure of radiation therapy in controlling certain tumors. Some studies have suggested the radiosensitizing effect of paclitaxel. The poly(D,L-lactide-co-glycolide)(PLGA) nanoparticles containing paclitaxel were prepared by o/w emulsification-solvent evaporation method. The physicochemical characteristics of the nanoparticles (i.e. encapsulation efficiency, particle size distribution, morphology, in vitro release) were studied. The morphology of the two human tumor cell lines: a carcinoma cervicis (HeLa) and a hepatoma (HepG2), treated with paclitaxel-loaded nanoparticles was photomicrographed. Flow cytometry was used to quantify the number of the tumor cells held in the G2/M phase of the cell cycle. The cellular uptake of nanoparticles was evaluated by transmission electronic microscopy. Cell viability was determined by the ability of single cell to form colonies in vitro. The prepared nanoparticles were spherical in shape with size between 200nm and 800nm. The encapsulation efficiency was 85.5％. The release behaviour of paclitaxel from the nanoparticles exhibited a biphasic pattern characterised by a fast initial release during the first 24 h, followed by a slower and continuous release. Co-culture of the two tumor cell lines with paclitaxel-loaded nanoparticles demonstrated that the cell morphology was changed and the released paclitaxel retained its bioactivity to block cells in the G2/M phase. The cellular uptake of nanoparticles was observed. The free paclitaxel and paclitaxel-loaded nanoparticles effectively sensitized hypoxic HeLa and HepG2 cells to radiation. Under this experimental condition, the radiosensitization of paclitaxel-loaded nanoparticles was more significant than that of free paclitaxel.Keywords: Paclitaxel；Drug delivery；Nanoparticle；Radiotherapy；Hypoxia；Human tumor cells；cellular uptake
Extracted statement — keywords: Cellular uptake
Research field: Medicinal Chemistry and Pharmaceutics
Title: Ferumoxytol for treatment of iron deficiency anemia in patients with chronic kidney disease
Abstract: Background: Iron deficiency anemia (IDA) is a common problem in patients with chronic kidney disease (CKD). Use of intravenous (i.v.) iron effectively treats the resultant anemia, but available iron products have side effects or dosing regimens that limit safety and convenience. Objective: Ferumoxytol (Feraheme™) is a new i.v. iron product recently approved for use in treatment of IDA in CKD patients. This article reviews the structure, pharmacokinetics, and clinical trial results on ferumoxytol. The author also offers his opinions on the role of this product in clinical practice. Methods: This review encompasses important information contained in clinical and preclinical studies of ferumoxytol and is supplemented with information from the US Food and Drug Administration. Results/conclusion: Ferumoxytol offers substantial safety and superior efficacy compared with oral iron therapy. As ferumoxytol can be administered as 510 mg in < 1 min, it is substantially more convenient than other iron products in nondialysis patients. Although further experience with this product is needed in patients at higher risk of drug reactions, ferumoxytol is likely to be highly useful in the hospital and outpatient settings for treatment of IDA.
Extracted statement — Indication: Chronic kidney disease
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 — Mechanism of Antiviral Action: color change