Anti-F10 polyclonal antibody [Biotin]

Tissue factor (TF), a blood coagulation protein, plays an important role in tumor growth, invasion, and metastasis. Ixolaris, a tick-derived non-immunogenic molecule that binds to TF, has demonstrated in vivo inhibitory effect on murine models of melanoma, including primary growth and metastasis. This work aimed to: I) develop an efficient and stable labeling technique of ixolaris with Iodine-131(I-131); II) compare the biodistribution of I-131 and I-131-ixolaris in tumor-free and melanoma-bearing mice; III) evaluate whether I-131-ixolaris could serve as an antimetastatic agent. Ixolaris radioiodination was performed using iodogen, followed by liquid paper chromatography. Labeling stability and anticoagulant activity were measured. Imaging studies were performed after intravenous administration of free I-131 or I-131-ixolaris in a murine melanoma model employing the B16-F10 cell line. Animals were divided in three experimental groups: the first experimental group, D0, received a single-dose of 9.25 MBq of I-131-ixolaris at the same day the animals were inoculated with melanoma cells. In the second group, D15, a single-dose of 9.25 MBq of I-131-ixolaris or free I-131 was applied into mice on the fifteenth day after the tumor induction. The third group, D1-D15, received two therapeutic doses of 9.25 MBq of I-131-ixolaris or I-131. In vitro studies demonstrated that I-131-ixolaris is stable for up to 24 h and retains its inhibitory activity on blood coagulation. Biodistribution analysis and metastasis assays showed that all treatment regimens with I-131-ixolaris were effective, being the double-treatment (D1/D15) the most effective one. Remarkably, treatment with free I-131 showed no anti-metastatic effect. I-131-ixolaris is a promising theranostic agent for metastatic melanoma.

The first regional total electron content (TEC) model over the entire African region (known as AfriTEC model) using empirical observations is developed and presented. Artificial neural networks were used to train TEC observations obtained from Global Positioning System receivers, both on ground and onboard the Constellation Observing System for Meteorology, Ionosphere, and Climate satellites for the African region from years 2000 to 2017. The neural network training was implemented using inputs that enabled the networks to learn diurnal variations, seasonal variations, spatial variations, and variations that are connected with the level of solar activity, for quiet geomagnetic conditions (-20 nT <= Dst <= 20 nT). The effectiveness of three solar activity indices (sunspot number, solar radio flux at 10.7-cm wavelength [F10.7], and solar ultraviolet [UV] flux at 1 AU) for the neural network trainings was tested. The F10.7 and UV were more effective, and the F10.7 was used as it gave the least errors on the validation data set used. Equatorial anomaly simulations show a reduced occurrence during the June solstice season. The distance of separation between the anomaly crests is typically in the range from about 11.5 +/- 1.0 degrees to 16.0 +/- 1.0 degrees. The separation is observed to widen as solar activity levels increase. During the December solstice, the anomaly region shifts southwards of the equinox locations; in year 2012, the trough shifted by about 1.5 degrees and the southern crest shifted by over 2.5 degrees.