1. Introduction 20642. Synthesis of Magnetic Nanoparticles 20662.1. Classical Synthesis by Coprecipitation 20662.2. Reactions in Constrained Environments 20682.3. Hydrothermal and High-TemperatureReactions20692.4. Sol-Gel Reactions 20702.5. Polyol Methods 20712.6. Flow Injection Syntheses 20712.7. Electrochemical Methods 20712.8. Aerosol/Vapor Methods 20712.9. Sonolysis 20723. Stabilization of Magnetic Particles 20723.1. Monomeric Stabilizers 20723.1.1. Carboxylates 20733.1.2. Phosphates 20733.2. Inorganic Materials 20733.2.1. Silica 20733.2.2. Gold 20743.3. Polymer Stabilizers 20743.3.1. Dextran 20743.3.2. Polyethylene Glycol (PEG) 20753.3.3. Polyvinyl Alcohol (PVA) 20753.3.4. Alginate 20753.3.5. Chitosan 20753.3.6. Other Polymers 20753.4. Other Strategies for Stabilization 20764. Methods of Vectorization of the Particles 20765. Structural and Physicochemical Characterization 20785.1. Size, Polydispersity, Shape, and SurfaceCharacterization20795.2. Structure of Ferro- or FerrimagneticNanoparticles20805.2.1. Ferro- and Ferrimagnetic Nanoparticles 20805.3. Use of Nanoparticles as Contrast Agents forMRI20825.3.1. High Anisotropy Model 20845.3.2. Small Crystal and Low Anisotropy EnergyLimit20855.3.3. Practical Interests of Magnetic NuclearRelaxation for the Characterization ofSuperparamagnetic Colloid20855.3.4. Relaxation of Agglomerated Systems 20856. Applications 20866.1. MRI: Cellular Labeling, Molecular Imaging(Inﬂammation, Apoptose, etc.)20866.2.

Magnetic Iron Oxide Nanoparticles: Synthesis, Stabilization, Vectorization, Physicochemical Characterizations, and Biological Applications

This Perspective explores and explains the fundamental dogma of nanoparticle delivery to tumours and answers two central questions: ‘how many nanoparticles accumulate in a tumour?’ and ‘how does this number affect the clinical translation of nanomedicines?’

/pdf/analysis-of-nanoparticle-delivery-to-tumours-kovi4y80u2.pdf

Analysis of nanoparticle delivery to tumours

Human Protein Reference Database (HPRD--http://www.hprd.org/), initially described in 2003, is a database of curated proteomic information pertaining to human proteins. We have recently added a number of new features in HPRD. These include PhosphoMotif Finder, which allows users to find the presence of over 320 experimentally verified phosphorylation motifs in proteins of interest. Another new feature is a protein distributed annotation system--Human Proteinpedia (http://www.humanproteinpedia.org/)--through which laboratories can submit their data, which is mapped onto protein entries in HPRD. Over 75 laboratories involved in proteomics research have already participated in this effort by submitting data for over 15,000 human proteins. The submitted data includes mass spectrometry and protein microarray-derived data, among other data types. Finally, HPRD is also linked to a compendium of human signaling pathways developed by our group, NetPath (http://www.netpath.org/), which currently contains annotations for several cancer and immune signaling pathways. Since the last update, more than 5500 new protein sequences have been added, making HPRD a comprehensive resource for studying the human proteome.

/pdf/human-protein-reference-database-2009-update-2hsuvdywil.pdf

Human Protein Reference Database—2009 update

Magnetic Nanoparticles in MR Imaging and Drug Delivery

https://dash.harvard.edu/bitstream/handle/1/29293165/Cancer%20Nanotechnology.pdf?sequence=1

Cancer Nanotechnology: The impact of passive and active targeting in the era of modern cancer biology

Nanomaterials with precise biological functions have considerable potential for use in biomedical applications. Here we investigate whether multivalent attachment of small molecules can increase specific binding affinity and reveal new biological properties of such nanomaterials. We describe the parallel synthesis of a library comprising 146 nanoparticles decorated with different synthetic small molecules. Using fluorescent magnetic nanoparticles, we rapidly screened the library against different cell lines and discovered a series of nanoparticles with high specificity for endothelial cells, activated human macrophages or pancreatic cancer cells. Hits from the last-mentioned screen were shown to target pancreatic cancer in vivo. The method and described materials could facilitate development of functional nanomaterials for applications such as differentiating cell lines, detecting distinct cellular states and targeting specific cell types.

https://www.chem.tamu.edu/rgroup/wooley/chem689/Articles/110927%20Weissleder%20et%20al_Nature%20Biotechnology_Cell%20targeting.pdf

Cell-specific targeting of nanoparticles by multivalent attachment of small molecules.

Background—Arterial calcification is associated with cardiovascular events; however, mechanisms of calcification in atherosclerosis remain obscure. Methods and Results—We tested the hypothesis that inflammation promotes osteogenesis in atherosclerotic plaques using in vivo molecular imaging in apolipoprotein E / mice (20 to 30 weeks old, n35). A bisphosphonate-derivatized near-infrared fluorescent imaging agent (excitation 750 nm) visualized osteogenic activity that was otherwise undetectable by x-ray computed tomography. Flow cytometry validated the target specifically in osteoblast-like cells. A spectrally distinct near-infrared fluorescent nanoparticle (excitation 680 nm) was coinjected to simultaneously image macrophages. Fluorescence reflectance mapping demonstrated an association between osteogenic activity and macrophages in aortas of apolipoprotein E / mice (R 2 0.93). Intravital dual-channel fluorescence microscopy was used to further monitor osteogenic changes in inflamed carotid arteries at 20 and 30 weeks of age and revealed that macrophage burden and osteogenesis concomitantly increased during plaque progression (P0.01 and P0.001, respectively) and decreased after statin treatment (P0.0001 and P0.05, respectively). Fluorescence microscopy on cryosections colocalized near-infrared fluorescent osteogenic signals with alkaline phosphatase activity, bone-regulating protein expression, and hydroxyapatite nanocrystals as detected by electron microscopy, whereas von Kossa and alizarin red stains showed no evidence of calcification. Real-time reverse-transcription polymerase chain reaction revealed that macrophage-conditioned media increased alkaline phosphatase mRNA expression in vascular smooth muscle cells. Conclusions—This serial in vivo study demonstrates the real-time association of macrophage burden with osteogenic activity in early-stage atherosclerosis and offers a cellular-resolution tool to identify preclinical microcalcifications. (Circulation. 2007;116:2841-2850.)

Osteogenesis Associates With Inflammation in Early-Stage Atherosclerosis Evaluated by Molecular Imaging In Vivo

Background
Peptides are important molecules with diverse biological functions and biomedical uses. To date, there does not exist a single, searchable archive for peptide sequences or associated biological data. Rather, peptide sequences still have to be mined from abstracts and full-length articles, and/or obtained from the fragmented public sources.

/pdf/pepbank-a-database-of-peptides-based-on-sequence-text-mining-4hv7r05o2q.pdf

PepBank - a database of peptides based on sequence text mining and public peptide data sources

The breadth of biological databases and their information content continues to increase exponentially. Unfortunately, our ability to query such sources is still often suboptimal. Here, we introduce and apply community voting, database-driven text classification, and visual aids as a means to incorporate distributed expert knowledge, to automatically classify database entries and to efficiently retrieve them. Using a previously developed peptide database as an example, we compared several machine learning algorithms in their ability to classify abstracts of published literature results into categories relevant to peptide research, such as related or not related to cancer, angiogenesis, molecular imaging, etc. Ensembles of bagged decision trees met the requirements of our application best. No other algorithm consistently performed better in comparative testing. Moreover, we show that the algorithm produces meaningful class probability estimates, which can be used to visualize the confidence of automatic classification during the retrieval process. To allow viewing long lists of search results enriched by automatic classifications, we added a dynamic heat map to the web interface. We take advantage of community knowledge by enabling users to cast votes in Web 2.0 style in order to correct automated classification errors, which triggers reclassification of all entries. We used a novel framework in which the database "drives" the entire vote aggregation and reclassification process to increase speed while conserving computational resources and keeping the method scalable. In our experiments, we simulate community voting by adding various levels of noise to nearly perfectly labelled instances, and show that, under such conditions, classification can be improved significantly. Using PepBank as a model database, we show how to build a classification-aided retrieval system that gathers training data from the community, is completely controlled by the database, scales well with concurrent change events, and can be adapted to add text classification capability to other biomedical databases. The system can be accessed at http://pepbank.mgh.harvard.edu
 .

/pdf/enhancing-navigation-in-biomedical-databases-by-community-4hnp6oejzh.pdf

Timur Shtatland

Papers

Cell-specific targeting of nanoparticles by multivalent attachment of small molecules.

Osteogenesis Associates With Inflammation in Early-Stage Atherosclerosis Evaluated by Molecular Imaging In Vivo

PepBank - a database of peptides based on sequence text mining and public peptide data sources

Enhancing navigation in biomedical databases by community voting and database-driven text classification