Interactions of La2+ with phosphatidylserine vesicles: binding, phase transition, leakage, 31P-NMR and fusion

The interaction of La2+ with phosphatidylserine vesicles is studied by differential scanning calorimetry, 140La binding, 31P-NMR chemical shifts and relaxation rates, carboxyfluorescein and [14C]sucrose release, X-ray diffraction and freeze-fracture electron microscopy. In the presence of La3+ concentrations above 1 mM and an incubation temperature of 38 degrees C, i.e., at the phase transition temperature of the complex La/phosphatidylserine, the binding ratio of La/lipid exceeds a 1/3 ratio, reaching saturation at a 1/2 ratio. Analysis, employing a modified Gouy-Chapman equation, indicates a significant increase in the intrinsic binding constant of La/phosphatidylserine when the La3+ concentrations exceeds the threshold concentration for leakage. The analysis illustrates that at the molecular level the binding of La3+ can be comparable to or even weaker than that of Ca2+, but that even when present at smaller concentrations La3+ competes with and partially displaces Ca2+ from membranes or other negatively charged surfaces. The results suggest that the sequence La3+ greater than Ca2+ greater than Mg2+ reflects both the binding strength of these cations to phosphatidylserine as well as their ability to induce leakage, enhancement of 31P spin-lattice relaxation rates, fusion and other structural changes. The leakage, fusion, and other structural changes are more pronounced at the phase transition temperature of the La/lipid complex.     

Automated thermal imaging monitors the local response to cervical cancer brachytherapy

Malignant tumors have high metabolic and perfusion rates, which result in a unique temperature distribution as compared to healthy tissues. Here, we sought to characterize the thermal response of the cervix following brachytherapy in women with advanced cervical carcinoma. Six patients underwent imaging with a thermal camera before a brachytherapy treatment session and after a 7-day follow-up period. A designated algorithm was used to calculate and store the texture parameters of the examined tissues across all time points. We used supervised machine learning classification methods (K Nearest Neighbors and Support Vector Machine) and unsupervised machine learning classification (K-means). Our algorithms demonstrated a 100% detection rate for physiological changes in cervical tumors before and after brachytherapy. Thus, we showed that thermal imaging combined with advanced feature extraction could potentially be used to detect tissue-specific changes in the cervix in response to local brachytherapy for cervical cancer.