Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling: a case study

The objective of this study is to compare the abilities of logistic, auto-logistic and artificial neural network (ANN) models for quantifying the relationships between land uses and their drivers. In addition, the application of the results obtained by the three techniques is tested in a dynamic land-use change model (CLUE-s) for the Paochiao watershed region in Taiwan. Relative operating characteristic curves (ROCs), kappa statistics, multiple resolution validation and landscape metrics were used to assess the ability of the three techniques in estimating the relationship between driving factors and land use and its subsequent application in land-use change models. The validation results illustrate that for this case study ANNs constitute a powerful alternative for the use of logistic regression in empirical modeling of spatial land-use change processes. ANNs provide in this case a better fit between driving factors and land-use pattern. In addition, auto-logistic regression performs better than logistic regression and nearly as well as ANNs. Auto-logistic regression and ANNs are considered especially useful when the performance of more conventional models is not satisfactory or the underlying data relationships are unknown. The results indicate that an evaluation of alternative techniques to specify relationships between driving factors and land use can improve the performance of land-use change models

Preparation and in vitro evaluation of 177Lu-iPSMA-RGD as a new heterobivalent radiopharmaceutical

This study aimed to synthesize a new 177Lu-iPSMA-RGD heterobivalent radiopharmaceutical, as well as to assess the in vitro radiopharmaceutical potential to target cancer cells overexpressing PSMA and a(v) b(3) integrins. The radiotracer prepared with a radiochemical purity of 98.8 ± 1.0% showed stability in human serum, specific recognition with suitable affinity to PSMA and a(v)b(3) integrins, and capability to inhibit cancer cell proliferation and VEGF signaling (antiangiogenic effect). Results warrant further preclinical studies to establish the 177Lu-iPSMA-RGD potential as a dual therapeutic radiopharmaceutical.CONACyT-CB-2016-01-28152

Normalisation to Blood Activity Is Required for the Accurate Quantification of Na/I Symporter Ectopic Expression by SPECT/CT in Individual Subjects

The utilisation of the Na/I symporter (NIS) and associated radiotracers as a reporter system for imaging gene expression is now reaching the clinical setting in cancer gene therapy applications. However, a formal assessment of the methodology in terms of normalisation of the data still remains to be performed, particularly in the context of the assessment of activities in individual subjects in longitudinal studies. In this context, we administered to mice a recombinant, replication-incompetent adenovirus encoding rat NIS, or a human colorectal carcinoma cell line (HT29) encoding mouse NIS. We used 99mTc pertechnetate as a radiotracer for SPECT/CT imaging to determine the pattern of ectopic NIS expression in longitudinal kinetic studies. Some animals of the cohort were culled and NIS expression was measured by quantitative RT-PCR and immunohistochemistry. The radioactive content of some liver biopsies was also measured ex vivo. Our results show that in longitudinal studies involving datasets taken from individual mice, the presentation of non-normalised data (activity expressed as %ID/g or %ID/cc) leads to ‘noisy’, and sometimes incoherent, results. This variability is due to the fact that the blood pertechnetate concentration can vary up to three-fold from day to day. Normalisation of these data with blood activities corrects for these inconsistencies. We advocate that, blood pertechnetate activity should be determined and used to normalise the activity measured in the organ/region of interest that expresses NIS ectopically. Considering that NIS imaging has already reached the clinical setting in the context of cancer gene therapy, this normalisation may be essential in order to obtain accurate and predictive information in future longitudinal clinical studies in biotherapy