bluuebunny/crossref_metadata_2025_split huggingface dataset:
Protocols for disease classification from mass spectrometry data
Authors: Wagner Michael , Naik Dayanand , Pothen Alex 
Published: September 2003

AbstractWe report our results in classifying protein matrix‐assisted laser desorption/ionization‐time of flight mass spectra obtained from serum samples into diseased and healthy groups. We discuss in detail five of the steps in preprocessing the mass spectral data for biomarker discovery, as well as our criterion for choosing a small set of peaks for classifying the samples. Cross‐validation studies with four selected proteins yielded misclassification rates in the 10–15% range for all the classification methods. Three of these proteins or protein fragments are down‐regulated and one up‐regulated in lung cancer, the disease under consideration in this data set. When cross‐validation studies are performed, care must be taken to ensure that the test set does not influence the choice of the peaks used in the classification. Misclassification rates are lower when both the training and test sets are used to select the peaks used in classification versus when only the training set is used. This expectation was validated for various statistical discrimination methods when thirteen peaks were used in cross‐validation studies. One particular classification method, a linear support vector machine, exhibited especially robust performance when the number of peaks was varied from four to thirteen, and when the peaks were selected from the training set alone. Experiments with the samples randomly assigned to the two classes confirmed that misclassification rates were significantly higher in such cases than those observed with the true data. This indicates that our findings are indeed significant. We found closely matching masses in a database for protein expression in lung cancer for three of the four proteins we used to classify lung cancer. Data from additional samples, increased experience with the performance of various preprocessing techniques, and affirmation of the biological roles of the proteins that help in classification, will strengthen our conclusions in the future.
Tree‐based disease classification using protein data
Authors: Zhu Hongtu , Yu Chang‐Yung , Zhang Heping 
Published: September 2003

AbstractA reliable and precise classification of diseases is essential for successful diagnosis and treatment. Using mass spectrometry from clinical specimens, scientists may find the protein variations among disease and use this information to improve diagnosis. In this paper, we propose a novel procedure to classify disease status based on the protein data from mass spectrometry. Our new tree‐based algorithm consists of three steps: projection, selection and classification tree. The projection step aims to project all observations from specimens into the same bases so that the projected data have fixed coordinates. Thus, for each specimen, we obtain a large vector of ‘coefficients’ on the same basis. The purpose of the selection step is data reduction by condensing the large vector from the projection step into a much lower order of informative vector. Finally, using these reduced vectors, we apply recursive partitioning to construct an informative classification tree. This method has been successfully applied to protein data, provided by the Department of Radiology and Chemistry at Duke University.
Decision tree classification of proteins identified by mass spectrometry of blood serum samples from people with and without lung cancer
Authors: Markey Mia K. , Tourassi Georgia D. , Floyd Carey E. 
Published: September 2003

AbstractA classification and regression tree (CART) model was trained to classify 41 clinical specimens as disease/nondisease based on 26 variables computed from the mass‐to‐charge ratio (m/z) and peak heights of proteins identified by mass spectroscopy. The CART model built on all of the specimens (no cross‐validation) had an error rate of 4/41 = 10%. The CART model suggests that mass spectra peaks in the 8000–10 000, 20 000–30 000, 45 000–60 000, and >125 000 m/z ranges may be valuable in distinguishing between the disease/nondisease specimens. The area under the receiver operating characteristics curve was 0.80 ± 0.07 for leave‐one‐out cross‐validation.
A comprehensive approach to the analysis of matrix‐assisted laser desorption/ionization‐time of flight proteomics spectra from serum samples
Authors: Baggerly Keith A. , Morris Jeffrey S. , Wang Jing , Gold David , Xiao Lian‐Chun , Coombes Kevin R. 
Published: September 2003

AbstractFor our analysis of the data from the First Annual Proteomics Data Mining Conference, we attempted to discriminate between 24 disease spectra (group A) and 17 normal spectra (group B). First, we processed the raw spectra by (i) correcting for additive sinusoidal noise (periodic on the time scale) affecting most spectra, (ii) correcting for the overall baseline level, (iii) normalizing, (iv) recombining fractions, and (v) using variable‐width windows for data reduction. Also, we identified a set of polymeric peaks (at multiples of 180.6 Da) that is present in several normal spectra (B1–B8). After data processing, we found the intensities at the following mass to charge (m/z) values to be useful discriminators: 3077, 12 886 and 74 263. Using these values, we were able to achieve an overall classification accuracy of 38/41 (92.6%). Perfect classification could be achieved by adding two additional peaks, at 2476 and 6955. We identified these values by applying a genetic algorithm to a filtered list of m/z values using Mahalanobis distance between the group means as a fitness function.
Machine learning approaches to lung cancer prediction from mass spectra
Authors: Hilario Melanie , Kalousis Alexandros , Müller Markus , Pellegrini Christian 
Published: September 2003

AbstractWe addressed the problem of discriminating between 24 diseased and 17 healthy specimens on the basis of protein mass spectra. To prepare the data, we performed mass to charge ratio (m/z) normalization, baseline elimination, and conversion of absolute peak height measures to height ratios. After preprocessing, the major difficulty encountered was the extremely large number of variables (1676 m/z values) versus the number of examples (41). Dimensionality reduction was treated as an integral part of the classification process; variable selection was coupled with model construction in a single ten‐fold cross‐validation loop. We explored different experimental setups involving two peak height representations, two variable selection methods, and six induction algorithms, all on both the original 1676‐mass data set and on a prescreened 124‐mass data set. Highest predictive accuracies (1–2 off‐sample misclassifications) were achieved by a multilayer perceptron and Naïve Bayes, with the latter displaying more consistent performance (hence greater reliability) over varying experimental conditions. We attempted to identify the most discriminant peaks (proteins) on the basis of scores assigned by the two variable selection methods and by neural network based sensitivity analysis. These three scoring schemes consistently ranked four peaks as the most relevant discriminators: 11683, 1403, 17350 and 66107.
A prototype methodology combining surface‐enhanced laser desorption/ionization protein chip technology and artificial neural network algorithms to predict the chemoresponsiveness of breast cancer cell lines exposed to Paclitaxel and Doxorubicin under in vitro conditions
Authors: Mian Shahid , Ball Graham , Hornbuckle Jo , Holding Finn , Carmichael James , Ellis Ian , Ali Selman , Li Geng , McArdle Stephanie , Creaser Colin , Rees Robert 
Published: September 2003

AbstractAn ability to predict the likelihood of cellular response towards particular chemotherapeutic agents based upon protein expression patterns could facilitate the identification of biological molecules with previously undefined roles in the process of chemoresistance/chemosensitivity, and if robust enough these patterns might also be exploited towards the development of novel predictive assays. To ascertain whether proteomic based molecular profiling in conjunction with artificial neural network (ANN) algorithms could be applied towards the specific recognition of phenotypic patterns between either control or drug treated and chemosensitive or chemoresistant cellular populations, a combined approach involving MALDI‐TOF matrix‐assisted laser desorption/ionization‐time of flight mass spectrometry, Ciphergen protein chip technology and ANN algorithms have been applied to specifically identify proteomic ‘fingerprints’ indicative of treatment regimen for chemosensitive (MCF‐7, T47D) and chemoresistant (MCF‐7/ADR) breast cancer cell lines following exposure to Doxorubicin or Paclitaxel. The results indicate that proteomic patterns can be identified by ANN algorithms to correctly assign ‘class’ for treatment regimen (e.g. control/drug treated or chemosensitive/chemoresistant) with a high degree of accuracy using boot‐strap statistical validation techniques and that biomarker ion patterns indicative of response/non‐response phenotypes are associated with MCF‐7 and MCF‐7/ADR cells exposed to Doxorubicin. We have also examined the predictive capability of this approach towards MCF‐7 and T47D cells to ascertain whether prediction could be made based upon treatment regimen irrespective of cell lineage. Models were identified that could correctly assign class (control or Paclitaxel treatment) for 35/38 samples of an independent dataset. A similar level of predictive capability was also found (> 92%; n = 28) when proteomic patterns derived from the drug resistant cell line MCF‐7/ADR were compared against those derived from MCF‐7 and T47D as a model system of drug resistant and drug sensitive phenotypes. This approach might offer a potential methodology for predicting the biological behaviour of cancer cells towards particular chemotherapeutics and through protein isolation and sequence identification could result in the identification of biological molecules associated with chemosensitive/chemoresistance tumour phenotypes.
Proteomic analysis of acrylamide gel separated proteins immobilized on polyvinylidene difluoride membranes following proteolytic digestion in the presence of 80% acetonitrile
Authors: Bunai Keigo , Nozaki Manabu , Hamano Miyuki , Ogane Shinya , Inoue Taro , Nemoto Tadashi , Nakanishi Hiroshi , Yamane Kunio 
Published: September 2003

AbstractTwo‐dimensional polyacrylamide gel electrophoresis (2‐D PAGE) combined with mass spectrometry (MS) is a highly accurate and sensitive means of identifying proteins. We have developed a novel method for digesting proteins on polyvinylidene difluoride (PVDF) membranes for subsequent matrix‐assisted laser desorption/ionization‐time of flight (MALDI‐TOF) MS analysis. After Tricine sodium dodecyl sulfate (SDS)‐PAGE, separated proteins were electroblotted onto PVDF membranes in a semidry discontinuous buffer system, visualized by staining with Coomassie Blue, excised, digested with trypsin or lysC in 80% acetonitrile, and then analyzed by MALDI‐TOF MS. This method has several advantages over in‐gel digestion in terms of sample handling, sensitivity, and time. We identified 105 fmol of Bacillus subtilis SecA and 100∼500 fmol of standard proteins. We also analyzed the submembrane protein fraction solubilized by 1% n‐dodecyl‐β‐D‐maltoside from B. subtilis membranes after separation by 2‐D PAGE, and identified 116 protein spots. This method can detect proteins at the 10∼50 fmol level by pooling more than ten identical electroblotted protein spots.
Profiling of cytokine expression by biotin‐labeled‐based protein arrays
Authors: Lin Ying , Huang Ruochun , Chen Li‐Pai , Lisoukov Henry , Lu Zhen‐Hai , Li Shiyong , Wang Cheng C. , Huang Ruo‐Pan 
Published: September 2003

AbstractGlobal analysis of protein expression holds great promise in basic research and patient care. Previously we demonstrated that multiple cytokines could be detected simultaneously using an enzyme‐linked immunosorbent assay protein array system with high sensitivity and specificity. In this paper, we described a biotin‐labeled‐based protein array system to detect multiple cytokines simultaneously from biological samples. In this new approach, proteins from a variety of biological sources are labeled with biotin. The biotin‐labeled proteins are then incubated with antibody chips. Targeted proteins are captured by the array antibodies spotted on the antibody chips. The presence of targeted proteins is detected using Cy3‐ or Cy5‐conjugated streptavidin and signals are imaged by laser scanner. The system also can be easily adapted to a two‐color binding assay, allowing measurement of the levels of proteins in a test sample with respect to a reference sample at the same chip. To demonstrate its potential applications, we applied this technology to profile human cytokines, chemokines, growth factors, angiogenic factors and proteases in estrogen receptor (ER)+ and ER− cells. These results suggest that biotin‐labeled‐based antibody chip technology can provide a practical and powerful means of profiling hundreds or thousands of proteins for research and clinical purposes.
Application of sensitive fluorescent dyes in linkage of laser microdissection and two‐dimensional gel electrophoresis as a cancer proteomic study tool
Authors: Kondo Tadashi , Seike Masahiro , Mori Yasuharu , Fujii Kazuyasu , Yamada Tesshi , Hirohashi Setsuo 
Published: September 2003

AbstractThe combination of laser microdissection and two‐dimensional gel electrophoresis (2‐D PAGE) has been developed to perform proteomic analysis on specific populations of cells in cancer tissues. However, as conventional low sensitivity silver staining was used for spot detection, the microdissection required to obtain an adequate amount of protein for 2‐D PAGE is laborious and only a restricted number of protein spots could be visualized. As a consequence, this technology was impractical for direct clinical applications and had a limited impact on cancer studies. To solve these problems, we developed an application in which fluorescent dyes label the proteins extracted from microdissected tissues prior to 2‐D PAGE separation. In this application, a small amount of protein, less than 6.6 μg, was enough to generate a 2‐D profile with approximately 1500 protein spots. This technique was applied to compare the proteome of normal intestinal epithelium with that of adenoma in Min mice. Thirty‐seven protein spots reproducibly showed significant differences in intensities. Mass spectrometric analysis and Western blotting identified eight of them, including prohibitin, 14‐3‐3zeta, tropomyosin 3 and Hsp84. These results indicate that fluorescence labeling of proteins from microdissected tissues prior to 2‐D PAGE is a powerful cancer proteomic study tool.
Proteome of monocytes primed with lipopolysaccharide: Analysis of the abundant proteins
Authors: Gadgil Himanshu S. , Pabst Karen M. , Giorgianni Francesco , Umstot Edward S. , Desiderio Dominic M. , Beranova‐Giorgianni Sarka , Gerling Ivan C. , Pabst Michael J. 
Published: September 2003

AbstractWe performed a proteomic analysis of monocytes primed by lipopolysaccharide (LPS) in vitro, using two‐dimensional gels stained with Coomassie blue. We found 16 proteins of ∼500 detected that either increased or decreased in abundance as a result of priming by LPS (14 with P ≤ 0.05). The proteins were identified by comparing the masses of their tryptic peptides with those of all known proteins, using matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry and the SWISS‐PROT database. Identities were confirmed by matching the sequence of several tryptic peptides, using liquid chromatography electrospray‐ionization quadrupole ion trap mass spectrometry. There were increases in the protective enzymes superoxide dismutase and catalase, in four calgranulins, in the cytokine pre‐B cell enhancing factor, and in annexin 2, macrophage capping protein, transketolase, pyruvate kinase, and serine/threonine protein kinase 10. Proteins that decreased in abundance were integrin alpha‐IIB, protein disulfide isomerase, and platelet‐activating factor acetylhydrolase. Many of these altered proteins have interesting functions in inflammation.
Treatment of mice with a herbal preparation (Mentat) protects against radiation‐induced mortality
Authors: Jagetia Ganesh Chandra , Shrinath Baliga Manjeshwar 
Published: September 2003

AbstractThe effect of various doses (0, 5, 10, 20, 40, 80, 100, 120 and 160 mg/kg b. wt.) of 50% ethanolic extract of mentat (a herbal preparation) was studied on the survival of mice exposed to 10 Gy of γ‐radiation. Treatment of mice with different doses of mentat consecutively for ﬁve days before irradiation delayed the onset of mortality and reduced the symptoms of radiation sickness when compared with the non‐drug treated irradiated controls. Most of the doses of mentat provided protection against the gastrointestinal (GI) death, however, the highest protection against GI death was observed for 80 mg/kg mentat. This was also true for bone marrow deaths, where the highest number of survivors were observed at 30 days post‐irradiation in this group (i.e. 80 mg/kg) when compared with the other doses of mentat. The evaluation of acute toxicity showed that mentat was non‐toxic up to a dose of 1.5 g/kg b. wt., where no drug‐induced mortality was observed. The LD50 dose of mentat was found to be 1.75 g/kg b. wt. Our study demonstrates that mentat can provide good radioprotection at a dose of 80 mg/kg, which is far below its toxic dose. Copyright © 2003 John Wiley & Sons, Ltd.
Inhibitory phenolic amides on lipopolysaccharide‐induced nitric oxide production in RAW 264.7 cells from Beta vulgaris var. cicla seeds
Authors: Kim Youngleem , Han Mi Seon , Lee Ji Suk , Kim Jinwoong , Kim Young Choong 
Published: September 2003

AbstractFour phenolic compounds were isolated from Beta vulgaris L. var. cicla L. (Chenopodiaceae). These isolated compounds were identiﬁed as N‐cis‐feruloyl 3‐O‐methyldopamine (1), N‐cis‐feruloyl tyramine (2), N‐trans‐feruloyl 3‐O‐methyldopamine (3), N‐trans‐feruloyl tyramine (4), respectively, by spectroscopic analysis. The phenolic amides 1–4 exhibited modest inhibitory activity on LPS‐activated nitric oxide production dose‐dependently in RAW 264.7 cells. Copyright © 2003 John Wiley & Sons, Ltd.
The in vitro effect of aqueous extract of Nigella sativa seeds on nitric oxide production
Authors: Mahmood M. Saeed , Gilani A. H. , Khwaja Afsheen , Rashid Ayesha , Ashfaq M. K. 
Published: September 2003

AbstractThe in vitro effect of aqueous extract of Nigella sativa seeds on nitric oxide (NO) production by murine macrophages was studied. Murine peritoneal macrophages were pre‐incubated with the extract and then activated with Escherichia coli lipopolysaccharride. NO production was measured after 24 hours by spectrophotometry. The plant extract caused a dose‐dependent decrease in NO production. Dialyzed preparation of the extract did not affect NO production. However, the boiled fraction of the extract resulted in a dose‐dependent inhibition of NO apparently comparable to that of the whole extract. These results indicate that the aqueous extract of N. sativa seeds exhibits an inhibitory effect on nitric oxide production by murine macrophages and the active component(s) is/are non‐protein in nature. In view of the fact that nitric oxide is a pro‐inﬂammatory mediator, this study validates the traditional use of the Nigella sativa seeds for the treatment of rheumatism. Copyright © 2003 John Wiley & Sons, Ltd.
Antitussive activity of Abies webbiana Lindl. leaf extract against sulphur dioxide‐induced cough reﬂex in mice
Authors: Nayak S. S. , Ghosh A. K. , Srikanth K. , Debnath B. , Jha T. 
Published: September 2003

AbstractThe methanol extract of A. webbiana Lindl was evaluated for its effect on a cough model induced by sulphur dioxide gas in mice. When administered orally it exhibited signiﬁcant antitussive activity compared with the control in a dose dependent manner. The antitussive activity of the extract was compared with that of codeine phosphate, a prototype antitussive agent. The A. webbiana leaf extract (400 and 600 mg/kg) showed maximum inhibition of cough frequency by 71.69% and 78.67%, respectively, when compared with the control group and was comparable in effect to codeine phosphate. Copyright © 2003 John Wiley & Sons, Ltd.
Bioassay‐guided isolation and identiﬁcation of antifungal compounds from ginger
Authors: Ficker C. , Smith M. L. , Akpagana K. , Gbeassor M. , Zhang J. , Durst T. , Assabgui R. , Arnason J. T. 
Published: September 2003

AbstractA bioassay‐guided isolation of antifungal compounds from an African land race of ginger, Zingiber ofﬁcinale Roscoe, led to the identiﬁcation of [6], [8] and [10]‐gingerols and [6]‐gingerdiol as the main antifungal principles. The compounds were active against 13 human pathogens at concentrations of <1 mg/mL. The gingerol content of the African land race was at least 3 × higher than that of typical commercial cultivars of ginger. Therefore, ginger extracts standardized on the basis of the identiﬁed compounds, could be considered as antifungal agents for practical therapy. Copyright © 2003 John Wiley & Sons, Ltd.
Tourism dynamics: Cyprus' hotel value chain and proﬁtability
Authors: Georgantzas Nicholas C. 
Published: September 2003

AbstractIn response to the Cyprus Hotel Association's aim to test how Cyprus' year 2010 ofﬁcial strategy may affect tourist arrivals, hotel bed capacity and proﬁtability, this study looks at Cyprus' hotel value chain within the island's tourism customer–supplier value chain. A system dynamics simulation model helps compute multiple strategic scenarios of what might happen to Cyprus' tourism over the next 40 years. The faithful reproduction of historical tourism and bed capacity data through a 40‐year time horizon shows the model's usefulness. The computed scenarios assess Cyprus' hotel value‐chain sensitivity to policy parameters, tourism growth and tourism seasonality, along with their potential effects on the hotel value chain and proﬁtability. Copyright © 2003 John Wiley & Sons, Ltd.
XPS inelastic background optimization: algorithm and results
Authors: Jo Masatoshi 
Published: September 2003

AbstractThe x‐ray photoelectron spectrum of a solid is a superposition of the peaks and the inelastic background. Using the background optimization technique it is possible to make a precise separation of them. The separated peaks and background have shown very physically consistent results in relation to the peak intensity, peak shape, loss function, etc. The most remarkable point of the present formalism is that very little sample‐specific knowledge is needed for the analysis. In this paper, details of the method and the results obtained both previously and recently on the metals Cu, Al and Ag will be reviewed. Copyright © 2003 John Wiley & Sons, Ltd.