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Find video protocols related to scientific articles indexed in Pubmed.
Correlations between milk and plasma levels of amino and carboxylic acids in dairy cows.
J. Proteome Res.
PUBLISHED: 08-23-2013
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The objective of this study was to investigate the relationship between the concentrations of 19 amino acids, glucose, and seven carboxylic acids in the blood and milk of dairy cows and their correlations with established markers of ketosis. To that end, blood plasma and milk specimens were collected throughout lactation in two breeds of dairy cows of different milk yield. Plasma concentrations of glucose, pyruvate, lactate, ?-aminobutyrate, ?-hydroxybutyrate (BHBA), and most amino acids, except for glutamate and aspartate, were on average 9.9-fold higher than their respective milk levels. In contrast, glutamate, aspartate, and the Krebs cycle intermediates succinate, fumarate, malate, and citrate were on average 9.1-fold higher in milk than in plasma. For most metabolites, with the exception of BHBA and threonine, no significant correlations were observed between their levels in plasma and milk. Additionally, milk levels of acetone showed significant direct relationships with the glycine-to-alanine ratio and the BHBA concentration in plasma. The marked decline in plasma concentrations of glucose, pyruvate, lactate, and alanine in cows with plasma BHBA levels above the diagnostic cutoff point for subclinical ketosis suggests that these animals fail to meet their glucose demand and, as a consequence, rely increasingly on ketone bodies as a source of energy. The concomitant increase in plasma glycine may reflect not only the excessive depletion of protein reserves but also a potential deficiency of vitamin B6.
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Distinct metabolic differences between various human cancer and primary cells.
Electrophoresis
PUBLISHED: 06-14-2013
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Recent years have seen resurging interest in cancer cell metabolism and the role of secreted cancer metabolites in modulating the tumor stroma. Using a combination of nontargeted and targeted LC and GC-MS methods, the exometabolomes of three leukemia, two melanoma, three renal cell carcinoma, two colorectal adenocarcinoma, four hepatocellular carcinoma, three breast cancer, two bladder carcinoma, and one glioblastoma cell line, as well as five primary cultures of human melanocytes, hepatocytes, monocytes, CD4 and CD8 lymphocytes, that had been all cultivated under identical conditions, were investigated. Unsupervised affinity propagation clustering of the metabolic footprints yielded five distinct clusters that grouped the investigated cell cultures mainly according to the tissue of origin. A common expected feature of all neoplastic cells was high lactate production. Extracellular arginine and nicotinamide were major discriminants between normal and neoplastic hepatocytes. Further, significant differences in the assimilation of di- and tripeptides were observed. This finding appears to underscore the importance of peptides for meeting the increased bioenergetic and biosynthetic demands of many cancers.
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MetaboQuant: a tool combining individual peak calibration and outlier detection for accurate metabolite quantification in 1D (1)H and (1)H-(13)C HSQC NMR spectra.
BioTechniques
PUBLISHED: 04-09-2013
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Solution nuclear magnetic resonance (NMR) spectroscopy is widely used to analyze complex mixtures of organic compounds such as biological fluids and tissue extracts. Targeted profiling approaches with reliable compound quantitifcation are hampered, however, by signal overlap and other interferences. Here, we present a tool named MetaboQuant for automated compound quantification from pre-processed 1D and 2D heteronuclear single quantum coherence (HSQC) NMR spectral data and concomitant validation of results. Performance of MetaboQuant was tested on a urinary spike-in data set and compared with other quantification strategies. The use of individual calibration factors in combination with the validation algorithms of MetaboQuant raises the reliability of the quantification results. MetaboQuant can be downloaded at http://genomics.uni-regensburg.de/site/institute/software/metaboquant/ as stand-alone software for Windows or run on other operating systems from within Matlab. Separate software for peak fitting and integration is necessary in order to use MetaboQuant.
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Changes in the hepatic mitochondrial and membrane proteome in mice fed a non-alcoholic steatohepatitis inducing diet.
J Proteomics
PUBLISHED: 01-09-2013
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Non-alcoholic steatohepatitis (NASH) accounts for a large proportion of cryptic cirrhosis in the Western societies. Nevertheless, we lack a deeper understanding of the underlying pathomolecular processes, particularly those preceding hepatic inflammation and fibrosis. In order to gain novel insights into early NASH-development from the first appearance of proteomic alterations to the onset of hepatic inflammation and fibrosis, we conducted a time-course analysis of proteomic changes in liver mitochondria and membrane-enriched fractions of female C57Bl/6N mice fed either a mere steatosis or NASH inducing diet. This data was complemented by quantitative measurements of hepatic glycerol-containing lipids, cholesterol and intermediates of the methionine cycle. Aside from energy metabolism and stress response proteins, enzymes of the urea cycle and methionine metabolism were found regulated. Alterations in the methionine cycle occur early in disease progression preceding molecular signs of inflammation. Proteins that hold particular promise in the early distinction between benign steatosis and NASH are methyl-transferase Mettl7b, the glycoprotein basigin and the microsomal glutathione-transferase.
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New aspects of an old drug--diclofenac targets MYC and glucose metabolism in tumor cells.
PLoS ONE
PUBLISHED: 01-01-2013
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Non-steroidal anti-inflammatory drugs such as diclofenac exhibit potent anticancer effects. Up to now these effects were mainly attributed to its classical role as COX-inhibitor. Here we show novel COX-independent effects of diclofenac. Diclofenac significantly diminished MYC expression and modulated glucose metabolism resulting in impaired melanoma, leukemia, and carcinoma cell line proliferation in vitro and reduced melanoma growth in vivo. In contrast, the non-selective COX inhibitor aspirin and the COX-2 specific inhibitor NS-398 had no effect on MYC expression and glucose metabolism. Diclofenac significantly decreased glucose transporter 1 (GLUT1), lactate dehydrogenase A (LDHA), and monocarboxylate transporter 1 (MCT1) gene expression in line with a decrease in glucose uptake and lactate secretion. A significant intracellular accumulation of lactate by diclofenac preceded the observed effect on gene expression, suggesting a direct inhibitory effect of diclofenac on lactate efflux. While intracellular lactate accumulation impairs cellular proliferation and gene expression, it does not inhibit MYC expression as evidenced by the lack of MYC regulation by the MCT inhibitor ?-cyano-4-hydroxycinnamic acid. Finally, in a cell line with a tetracycline-regulated c-MYC gene, diclofenac decreased proliferation both in the presence and absence of c-MYC. Thus, diclofenac targets tumor cell proliferation via two mechanisms, that is inhibition of MYC and lactate transport. Based on these results, diclofenac holds potential as a clinically applicable MYC and glycolysis inhibitor supporting established tumor therapies.
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NMR metabolomic analysis of dairy cows reveals milk glycerophosphocholine to phosphocholine ratio as prognostic biomarker for risk of ketosis.
J. Proteome Res.
PUBLISHED: 12-09-2011
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Ketosis is a common metabolic disease in dairy cows. Diagnostic markers for ketosis such as acetone and beta-hydroxybutyric acid (BHBA) are known, but disease prediction remains an unsolved challenge. Milk is a steadily available biofluid and routinely collected on a daily basis. This high availability makes milk superior to blood or urine samples for diagnostic purposes. In this contribution, we show that high milk glycerophosphocholine (GPC) levels and high ratios of GPC to phosphocholine (PC) allow for the reliable selection of healthy and metabolically stable cows for breeding purposes. Throughout lactation, high GPC values are connected with a low ketosis incidence. During the first month of lactation, molar GPC/PC ratios equal or greater than 2.5 indicate a very low risk for developing ketosis. This threshold was validated for different breeds (Holstein-Friesian, Brown Swiss, and Simmental Fleckvieh) and for animals in different lactations, with observed odds ratios between 1.5 and 2.38. In contrast to acetone and BHBA, these measures are independent of the acute disease status. A possible explanation for the predictive effect is that GPC and PC are measures for the ability to break down phospholipids as a fatty acid source to meet the enhanced energy requirements of early lactation.
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Estimating classification probabilities in high-dimensional diagnostic studies.
Bioinformatics
PUBLISHED: 07-22-2011
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Classification algorithms for high-dimensional biological data like gene expression profiles or metabolomic fingerprints are typically evaluated by the number of misclassifications across a test dataset. However, to judge the classification of a single case in the context of clinical diagnosis, we need to assess the uncertainties associated with that individual case rather than the average accuracy across many cases. Reliability of individual classifications can be expressed in terms of class probabilities. While classification algorithms are a well-developed area of research, the estimation of class probabilities is considerably less progressed in biology, with only a few classification algorithms that provide estimated class probabilities.
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PROCOS: computational analysis of protein-protein complexes.
J Comput Chem
PUBLISHED: 04-15-2011
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One of the main challenges in protein-protein docking is a meaningful evaluation of the many putative solutions. Here we present a program (PROCOS) that calculates a probability-like measure to be native for a given complex. In contrast to scores often used for analyzing complex structures, the calculated probabilities offer the advantage of providing a fixed range of expected values. This will allow, in principle, the comparison of models corresponding to different targets that were solved with the same algorithm. Judgments are based on distributions of properties derived from a large database of native and false complexes. For complex analysis PROCOS uses these property distributions of native and false complexes together with a support vector machine (SVM). PROCOS was compared to the established scoring schemes of ZRANK and DFIRE. Employing a set of experimentally solved native complexes, high probability values above 50% were obtained for 90% of these structures. Next, the performance of PROCOS was tested on the 40 binary targets of the Dockground decoy set, on 14 targets of the RosettaDock decoy set and on 9 targets that participated in the CAPRI scoring evaluation. Again the advantage of using a probability-based scoring system becomes apparent and a reasonable number of near native complexes was found within the top ranked complexes. In conclusion, a novel fully automated method is presented that allows the reliable evaluation of protein-protein complexes.
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State-of-the art data normalization methods improve NMR-based metabolomic analysis.
Metabolomics
PUBLISHED: 04-05-2011
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Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metabolites by variable scaling and variance stabilization methods. The impact of all methods tested on sample classification was evaluated on urinary NMR fingerprints obtained from healthy volunteers and patients suffering from autosomal polycystic kidney disease (ADPKD). Performance in terms of screening for differentially produced metabolites was investigated on a dataset following a Latin-square design, where varied amounts of 8 different metabolites were spiked into a human urine matrix while keeping the total spike-in amount constant. In addition, specific tests were conducted to systematically investigate the influence of the different preprocessing methods on the structure of the analyzed data. In conclusion, preprocessing methods originally developed for DNA microarray analysis, in particular, Quantile and Cubic-Spline Normalization, performed best in reducing bias, accurately detecting fold changes, and classifying samples. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0350-z) contains supplementary material, which is available to authorized users.
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Detection of autosomal dominant polycystic kidney disease by NMR spectroscopic fingerprinting of urine.
Kidney Int.
PUBLISHED: 03-09-2011
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Autosomal dominant polycystic kidney disease (ADPKD) is a frequent cause of kidney failure; however, urinary biomarkers for the disease are lacking. In a step towards identifying such markers, we used multidimensional-multinuclear nuclear magnetic resonance (NMR) spectroscopy with support vector machine-based classification and analyzed urine specimens of 54 patients with ADPKD and slightly reduced estimated glomerular filtration rates. Within this cohort, 35 received medication for arterial hypertension and 19 did not. The results were compared with NMR profiles of 46 healthy volunteers, 10 ADPKD patients on hemodialysis with residual renal function, 16 kidney transplant patients, and 52 type 2 diabetic patients with chronic kidney disease. Based on the average of 51 out of 701 NMR features, we could reliably discriminate ADPKD patients with moderately advanced disease from ADPKD patients with end-stage renal disease, patients with chronic kidney disease of other etiologies, and healthy probands with an accuracy of >80%. Of the 35 patients with ADPKD receiving medication for hypertension, most showed increased excretion of proteins and also methanol. In contrast, elevated urinary methanol was not found in any of the control and other patient groups. Thus, we found that NMR fingerprinting of urine differentiates ADPKD from several other kidney diseases and individuals with normal kidney function. The diagnostic and prognostic potential of these profiles requires further evaluation.
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Automated protein NMR structure determination in solution.
Methods Mol. Biol.
PUBLISHED: 09-14-2010
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The main drawback of protein NMR spectroscopy today is still the extensive amount of time required for solving a single structure. The main bottleneck in this respect is the manual evaluation of the experimental spectra. A clear solution to this challenge is the development of automated methods for this purpose. At the current stage of development, this goal has been almost or in a few cases fully reached for favorable cases such as well-behaved, stably folding smaller proteins below the 25 kDa range. For larger and/or more difficult molecules, the input of a human expert is still required. However, even here, automated routines will substantially speed up the structure determination process. In this report, we will summarize recent developments in this field and especially emphasize practical aspects important for a successful automated protein structure determination in solution. An important aspect closely related to structure determination is structure validation. Therefore, we devote a section to automated approaches for this topic.
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Comparison of serum versus plasma collection in gas chromatography--mass spectrometry-based metabolomics.
Electrophoresis
PUBLISHED: 06-22-2010
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Bovine serum, EDTA-plasma and EDTA-plasma fortified with acetylsalicylic acid (ASA) as antioxidant were compared with regard to their suitability for metabolomic studies. Metabolic fingerprints were generated from GC-TOF-MS data using the Leco ChromaTOF software in combination with the in-house retention time correction and data alignment tool INCA. A total of 6, 9 and 21 significant features with a false discovery rate of <0.05 were identified by INCA upon comparing EDTA- versus EDTA-ASA-plasma, EDTA-plasma versus serum and EDTA-ASA-plasma versus serum, respectively. To confirm that the observed signal intensities in the GC-TOF-MS fingerprints reflected true metabolite abundances, 19 amino acids, glucose and 6 organic acids were quantified by means of GC-MS using stable-isotope-labeled internal standards. As observed with the fingerprints, only the concentrations of lactate and citrate were found to be significantly lower in EDTA-plasma and serum, respectively, whereas the concentrations of the other metabolites were similar among the three sample types investigated.
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Chemical shift optimization in multidimensional NMR spectra by AUREMOL-SHIFTOPT.
J. Biomol. NMR
PUBLISHED: 02-21-2009
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A problem often encountered in multidimensional NMR-spectroscopy is that an existing chemical shift list of a protein has to be used to assign an experimental spectrum but does not fit sufficiently well for a safe assignment. A similar problem occurs when temperature or pressure series of n-dimensional spectra are to be evaluated automatically. We have developed two different algorithms, AUREMOL-SHIFTOPT1 and AUREMOL-SHIFTOPT2 that fulfill this task. In the present contribution their performance is analyzed employing a set of simulated and experimental two-dimensional and three-dimensional spectra obtained from three different proteins. A new z-score based on atom and amino acid specific chemical shift distributions is introduced to weight the chemical shift contributions in different dimensions properly.
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Advances in amino acid analysis.
Anal Bioanal Chem
PUBLISHED: 02-12-2009
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Amino acids are important targets for metabolic profiling. For decades, amino acid analysis has been accomplished by either cation-exchange or reversed-phase liquid chromatography coupled to UV absorbance or fluorescence detection of pre-column or post-column-derivatized amino acids. Recent years have seen great progress in the development of direct-infusion or hyphenated mass spectrometry in the analysis of free amino acids in physiological fluids, because mass spectrometry not only matches optical detection in sensitivity, but also offers superior selectivity. The advent of cryo-probes has also brought NMR spectroscopy within the detection limits required for the analysis of free amino acids. But there is still room for further improvement, including expansion of the analyte spectrum, reduction of sample preparation and analysis time, automation, and synthesis of affordable isotope standards.
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Residual dipolar couplings in short peptidic foldamers: combined analyses of backbone and side-chain conformations and evaluation of structure coordinates of rigid unnatural amino acids.
Chembiochem
PUBLISHED: 01-22-2009
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A flexible tool for rigid systems. Residual dipolar couplings (RDCs) have proven to be valuable NMR structural parameters that provide insights into the backbone conformations of short linear peptidic foldamers, as illustrated here. This study demonstrates that RDCs at natural abundance can provide essential structural information even in the case of short linear peptides with unnatural amino acids. In addition, they allow for the detection of proline side-chain conformations and are used as a quality check for the parameterizations of rigid unnatural amino acids.
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Performance evaluation of algorithms for the classification of metabolic 1H NMR fingerprints.
J. Proteome Res.
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Nontargeted metabolite fingerprinting is increasingly applied to biomedical classification. The choice of classification algorithm may have a considerable impact on outcome. In this study, employing nested cross-validation for assessing predictive performance, six binary classification algorithms in combination with different strategies for data-driven feature selection were systematically compared on five data sets of urine, serum, plasma, and milk one-dimensional fingerprints obtained by proton nuclear magnetic resonance (NMR) spectroscopy. Support Vector Machines and Random Forests combined with t-score-based feature filtering performed well on most data sets, whereas the performance of the other tested methods varied between data sets.
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Diclofenac inhibits lactate formation and efficiently counteracts local immune suppression in a murine glioma model.
Int. J. Cancer
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Lactate formation in highly proliferative tumors such as malignant gliomas is associated with poor survival and contributes to the suppression of local immunity. Here, we report that diclofenac used at nontoxic concentrations significantly decreased lactate production in murine glioma cells and inhibited the expression of lactate dehydrogenase-A in vitro. Lactate reduction was accompanied by a dose-dependent inhibition of cell growth and a cell cycle arrest at the G2/M checkpoint. In the presence of diclofenac, murine bone marrow-derived dendritic cells (DCs) showed enhanced IL-12, but decreased IL-10 secretion on Toll-like receptor stimulation with R848 that correlated with reduced lactate levels in the glioma cell coculture and a blockade of signal transducers and activators of transcription 3 phosphorylation. In vivo, diclofenac treatment diminished intratumoral lactate levels and resulted in a significant delay of glioma growth. Ex vivo analyses revealed that tumor-infiltrating DCs regained their capacity to produce IL-12 on R848 stimulation. Moreover, diclofenac reduced the number of tumor-infiltrating regulatory T cells and impaired the upregulation of the Treg activation marker CD25. Nevertheless, a single intratumoral injection of R848 combined with diclofenac failed to induce an additional survival advantage in glioma-bearing mice. Further analyses illustrated that the presence of diclofenac during T-cell activation compromised INF-? production and T-cell proliferation, indicating that immunotherapeutic approaches have to be carefully timed when combined with diclofenac. In summary, diclofenac appears as an attractive agent for targeting lactate production and counteracting local immune suppression in malignant gliomas.
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Targeting melanoma metastasis and immunosuppression with a new mode of melanoma inhibitory activity (MIA) protein inhibition.
PLoS ONE
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Melanoma is the most aggressive form of skin cancer, with fast progression and early dissemination mediated by the melanoma inhibitory activity (MIA) protein. Here, we discovered that dimerization of MIA is required for functional activity through mutagenesis of MIA which showed the correlation between dimerization and functional activity. We subsequently identified the dodecapeptide AR71, which prevents MIA dimerization and thereby acts as a MIA inhibitor. Two-dimensional nuclear magnetic resonance (NMR) spectroscopy demonstrated the binding of AR71 to the MIA dimerization domain, in agreement with in vitro and in vivo data revealing reduced cell migration, reduced formation of metastases and increased immune response after AR71 treatment. We believe AR71 is a lead structure for MIA inhibitors. More generally, inhibiting MIA dimerization is a novel therapeutic concept in melanoma therapy.
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Early changes in the liver-soluble proteome from mice fed a nonalcoholic steatohepatitis inducing diet.
Proteomics
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Despite the increasing incidence of nonalcoholic steatohepatitis (NASH) with the rise in lifestyle-related diseases such as the metabolic syndrome, little is known about the changes in the liver proteome that precede the onset of inflammation and fibrosis. Here, we investigated early changes in the liver-soluble proteome of female C57BL/6N mice fed an NASH-inducing diet by 2D-DIGE and nano-HPLC-MS/MS. In parallel, histology and measurements of hepatic content of triglycerides, cholesterol and intermediates of the methionine cycle were performed. Hepatic steatosis manifested itself after 2 days of feeding, albeit significant changes in the liver-soluble proteome were not evident before day 10 in the absence of inflammatory or fibrotic signs. Proteomic alterations affected mainly energy and amino acid metabolism, detoxification processes, urea cycle, and the one-carbon/S-adenosylmethionine pathways. Additionally, intermediates of relevant affected pathways were quantified from liver tissue, confirming the findings from the proteomic analysis.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

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We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.