T Cells Bearing a Chimeric Antigen Receptor against Prostate-Specific Membrane Antigen Mediate Vascular Disruption and Result in Tumor Regression.

Aberrant blood vessels enable tumor growth, provide a barrier to immune infiltration, and serve as a source of protumorigenic signals. Targeting tumor blood vessels for destruction, or tumor vascular disruption therapy, can therefore provide significant therapeutic benefit. Here, we describe the ability of chimeric antigen receptor (CAR)-bearing T cells to recognize human prostate-specific membrane antigen (hPSMA) on endothelial targets in vitro as well as in vivo. CAR T cells were generated using the anti-PSMA scFv, J591, and the intracellular signaling domains: CD3ζ, CD28, and/or CD137/4-1BB. We found that all anti-hPSMA CAR T cells recognized and eliminated PSMA(+) endothelial targets in vitro, regardless of the signaling domain. T cells bearing the third-generation anti-hPSMA CAR, P28BBζ, were able to recognize and kill primary human endothelial cells isolated from gynecologic cancers. In addition, the P28BBζ CAR T cells mediated regression of hPSMA-expressing vascular neoplasms in mice. Finally, in murine models of ovarian cancers populated by murine vessels expressing hPSMA, the P28BBζ CAR T cells were able to ablate PSMA(+) vessels, cause secondary depletion of tumor cells, and reduce tumor burden. Taken together, these results provide a strong rationale for the use of CAR T cells as agents of tumor vascular disruption, specifically those targeting PSMA. Cancer Immunol Res; 3(1); 68-84. ©2014 AACR.

Performance of matrix-assisted laser desorption ionization-time of flight mass spectrometry for identification of bacterial strains routinely isolated in a clinical microbiology laboratory.

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has recently been introduced in diagnostic microbiology laboratories for the identification of bacterial and yeast strains isolated from clinical samples. In the present study, we prospectively compared MALDI-TOF MS to the conventional phenotypic method for the identification of routine isolates. Colonies were analyzed by MALDI-TOF MS either by direct deposition on the target plate or after a formic acid-acetonitrile extraction step if no valid result was initially obtained. Among 1,371 isolates identified by conventional methods, 1,278 (93.2%) were putatively identified to the species level by MALDI-TOF MS and 73 (5.3%) were identified to the genus level, but no reliable identification was obtained for 20 (1.5%). Among the 1,278 isolates identified to the species level by MALDI-TOF MS, 63 (4.9%) discordant results were initially identified. Most discordant results (42/63) were due to systematic database-related taxonomical differences, 14 were explained by poor discrimination of the MALDI-TOF MS spectra obtained, and 7 were due to errors in the initial conventional identification. An extraction step was required to obtain a valid MALDI-TOF MS identification for 25.6% of the 1,278 valid isolates. In conclusion, our results show that MALDI-TOF MS is a fast and reliable technique which has the potential to replace conventional phenotypic identification for most bacterial strains routinely isolated in clinical microbiology laboratories.

Hydrogeophysical data integration at larger scales: Application of Bayesian sequential simulation for the characterization of heterogeneous alluvial aquifers

Knowledge of the spatial distribution of hydraulic conductivity (K) within an aquifer is critical for reliable predictions of solute transport and the development of effective groundwater management and/or remediation strategies. While core analyses and hydraulic logging can provide highly detailed information, such information is inherently localized around boreholes that tend to be sparsely distributed throughout the aquifer volume. Conversely, larger-scale hydraulic experiments like pumping and tracer tests provide relatively low-resolution estimates of K in the investigated subsurface region. As a result, traditional hydrogeological measurement techniques contain a gap in terms of spatial resolution and coverage, and they are often alone inadequate for characterizing heterogeneous aquifers. Geophysical methods have the potential to bridge this gap. The recent increased interest in the application of geophysical methods to hydrogeological problems is clearly evidenced by the formation and rapid growth of the domain of hydrogeophysics over the past decade (e.g., Rubin and Hubbard, 2005).

How reliable is the monitoring of permanent vegetation plots? A test with multiple observers

Questions: A multiple plot design was developed for permanent vegetation plots. How reliable are the different methods used in this design and which changes can we measure? Location: Alpine meadows (2430 m a.s.l.) in the Swiss Alps. Methods: Four inventories were obtained from 40 m(2) plots: four subplots (0.4 m(2)) with a list of species, two 10m transects with the point method (50 points on each), one subplot (4 m2) with a list of species and visual cover estimates as a percentage and the complete plot (40 m(2)) with a list of species and visual estimates in classes. This design was tested by five to seven experienced botanists in three plots. Results: Whatever the sampling size, only 45-63% of the species were seen by all the observers. However, the majority of the overlooked species had cover < 0.1%. Pairs of observers overlooked 10-20% less species than single observers. The point method was the best method for cover estimate, but it took much longer than visual cover estimates, and 100 points allowed for the monitoring of only a very limited number of species. The visual estimate as a percentage was more precise than classes. Working in pairs did not improve the estimates, but one botanist repeating the survey is more reliable than a succession of different observers. Conclusion: Lists of species are insufficient for monitoring. It is necessary to add cover estimates to allow for subsequent interpretations in spite of the overlooked species. The choice of the method depends on the available resources: the point method is time consuming but gives precise data for a limited number of species, while visual estimates are quick but allow for recording only large changes in cover. Constant pairs of observers improve the reliability of the records.

Trabecular bone score in type 1 diabetes--a cross-sectional study.

UNLABELLED: Trabecular bone score (TBS) seems to provide additive value on BMD to identify individuals with prevalent fractures in T1D. TBS did not significantly differ between T1D patients and healthy controls, but TBS and HbA1c were independently associated with prevalent fractures in T1D. A TBS cutoff <1.42 reflected prevalent fractures with 91.7 % sensitivity and 43.2 % specificity. INTRODUCTION: Type 1 diabetes (T1D) increases the risk of osteoporotic fractures. TBS was recently proposed as an indirect measure of bone microarchitecture. This study aimed at investigating the TBS in T1D patients and healthy controls. Associations with prevalent fractures were tested. METHODS: One hundred nineteen T1D patients (59 males, 60 premenopausal females; mean age 43.4 ± 8.9 years) and 68 healthy controls matched for gender, age, and body mass index (BMI) were analyzed. The TBS was calculated in the lumbar region, based on two-dimensional (2D) projections of DXA assessments. RESULTS: TBS was 1.357 ± 0.129 in T1D patients and 1.389 ± 0.085 in controls (p = 0.075). T1D patients with prevalent fractures (n = 24) had a significantly lower TBS than T1D patients without fractures (1.309 ± 0.125 versus 1.370 ± 0.127, p = 0.04). The presence of fractures in T1D was associated with lower TBS (odds ratio = 0.024, 95 % confidence interval (CI) = 0.001-0.875; p = 0.042) but not with age or BMI. TBS and HbA1c were independently associated with fractures. The area-under-the curve (AUC) of TBS was similar to that of total hip BMD in discriminating T1D patients with or without prevalent fractures. In this set-up, a TBS cutoff <1.42 discriminated the presence of fractures with a sensitivity of 91.7 % and a specificity of 43.2 %. CONCLUSIONS: TBS values are lower in T1D patients with prevalent fractures, suggesting an alteration of bone strength in this subgroup of patients. Reliable TBS cutoffs for the prediction of fracture risk in T1D need to be determined in larger prospective studies.