Antibody responses to galectin-8, TARP and TRAP1 in prostate cancer patients treated with a GM-CSF-secreting cellular immunotherapy

A critical factor in clinical development of cancer immunotherapies is the identification of tumor-associated antigens that may be related to immunotherapy potency. In this study, protein microarrays containing >8,000 human proteins were screened with serum from prostate cancer patients (N = 13) before and after treatment with a granulocyte–macrophage colony-stimulating factor (GM-CSF)-secreting whole cell immunotherapy. Thirty-three proteins were identified that displayed significantly elevated (P ≤ 0.05) signals in post-treatment samples, including three proteins that have previously been associated with prostate carcinogenesis, galectin-8, T-cell alternative reading frame protein (TARP) and TNF-receptor-associated protein 1 (TRAP1). Expanded analysis of antibody induction in metastatic, castration-resistant prostate cancer (mCRPC) patients (N = 92) from two phase 1/2 trials of prostate cancer immunotherapy, G-9803 and G-0010, indicated a significant (P = 0.03) association of TARP antibody induction and median survival time (MST). Antibody induction to TARP was also significantly correlated (P = 0.036) with an increase in prostate-specific antigen doubling time (PSADT) in patients with a biochemical (PSA) recurrence following prostatectomy or radiation therapy (N = 19) from in a previous phase 1/2 trial of prostate cancer immunotherapy, G-9802. RNA and protein encoding TARP and TRAP1 was up-regulated in prostate cancer tissue compared to matched normal controls. These preliminary findings suggest that antibody induction to TARP may represent a possible biomarker for treatment response to GM-CSF secreting cellular immunotherapy in prostate cancer patients and demonstrates the utility of using protein microarrays for the high-throughput screening of patient-derived antibody responses.     

Characterization of a substance P-Gly12 amidating enzyme in human cerebrospinal fluid

Enzyme activity capable of converting the glycine-extended substance P precursor, substance P-Gly12, into substance P was purified from human cerebrospinal fluid. The conversion reaction was monitored by radioimmunoassay measurement of substance P formation. The chemical identity of the product was verified by reversed-phase HPLC. The enzyme reaction was stimulated by Cu(II) ion and ascorbic acid and inhibited by the presence of diethyldithiocarbamate. By HPLC molecular sieving, the major enzyme activity appeared as a protein of 26,000 molecular weight.     

Trace elements in rock salt and their bioavailability estimated from solubility in acid

In this study, 18 partly commercially available samples of rock salt from Austria, Germany, Pakistan, Poland, Switzerland, and Ukraine were investigated with respect to their content of trace elements using instrumental neutron activation analysis. Elements detected were Al, Ba, Br, Ca, Ce, Cl, Co, Cr, Cs, Eu, Fe, Hf, La, Mn, Na, Rb, Sb, Sc, Sm, Sr, Ta, Tb, Th, and Zn, some of them only in individual cases. An estimation of the bioavailability of these trace elements was performed by dissolving an equivalent of the sodium chloride samples in diluted hydrochloric acid (simulating stomach acid), filtering off the insoluble components, and analyzing the evaporated filtrate. It could be shown that in most cases bioactive trace elements like Fe can be found in rock salt in the form of almost insoluble compounds and are therefore not significantly bioavailable, whereas thorium, for example, was partly bioavailable in two cases. A significant contribution to the recommended daily intake of metal trace elements by using rock salt for nutrition can be excluded.     

Gross intestinal morphometry and allometry in primates

Although it is generally assumed that among mammals and within mammal groups, those species that rely on diets consisting of greater amounts of plant fiber have larger gastrointestinal tracts (GIT), statistical evidence for this simple claim is largely lacking. We compiled a dataset on the length of the small intestine, caecum, and colon in 42 strepsirrhine, platyrrhine, and catarrhine primate species, using specimens with known body mass (BM). We tested the scaling of intestine length with BM, and whether dietary proxies (percentage of leaves and a diet quality index) were significant covariates in these scaling relationships, using two sets of models: one that did not account for the phylogenetic structure of the data, and one that did. Intestine length mainly scaled geometrically at exponents that included 0.33 in the confidence interval; Strepsirrhini exhibited particularly long caeca, while those of Catarrhini were comparatively short. Diet proxies were only significant for the colon and the total large intestine (but not for the small intestine or the caecum), and only in conventional statistics (but not when accounting for phylogeny), indicating the pattern occurred across but not within clades. Compared to terrestrial Carnivora, primates have similar small intestine lengths, but longer large intestines. The data on intestine lengths presented here corroborate recent results on GIT complexity, suggesting that diet, as currently described, does not exhaustively explain GIT anatomy within primate clades.     

A Mathematical Framework for Combining Decisions of Multiple Experts toward Accurate and Remote Diagnosis of Malaria Using Tele-Microscopy

We propose a methodology for digitally fusing diagnostic decisions made by multiple medical experts in order to improve accuracy of diagnosis. Toward this goal, we report an experimental study involving nine experts, where each one was given more than 8,000 digital microscopic images of individual human red blood cells and asked to identify malaria infected cells. The results of this experiment reveal that even highly trained medical experts are not always self-consistent in their diagnostic decisions and that there exists a fair level of disagreement among experts, even for binary decisions (i.e., infected vs. uninfected). To tackle this general medical diagnosis problem, we propose a probabilistic algorithm to fuse the decisions made by trained medical experts to robustly achieve higher levels of accuracy when compared to individual experts making such decisions. By modelling the decisions of experts as a three component mixture model and solving for the underlying parameters using the Expectation Maximisation algorithm, we demonstrate the efficacy of our approach which significantly improves the overall diagnostic accuracy of malaria infected cells. Additionally, we present a mathematical framework for performing ‘slide-level’ diagnosis by using individual ‘cell-level’ diagnosis data, shedding more light on the statistical rules that should govern the routine practice in examination of e.g., thin blood smear samples. This framework could be generalized for various other tele-pathology needs, and can be used by trained experts within an efficient tele-medicine platform.