Novel Membrane Emulsification Method of Producing Highly Uniform Silica Particles Using Inexpensive Silica Sources

A membrane emulsification method for production of monodispersed silica-based ion exchange particles through water-in-oil emulsion route is developed. A hydrophobic microsieve membrane with 15 mu m pore size and 200 pm pore spacing was used to produce droplets, with a mean size between 65 and 240 pm containing acidified sodium silicate solution (with 4 and 6 wt% SiO2) in kerosene. After drying, the final silica particles had a mean size in the range between 30 and 70 mu m. Coefficient of variation for both the droplets and particles did not exceed 35%. The most uniform particles had a mean diameter of 40 mu m and coefficient of variation of 17%. The particles were functionalised with 3-aminopropyltrimethoxysilane and used for chemisorption of Cu(II) from an aqueous solution of CuSO4 in a continuous flow stirred cell with slotted pore microfiltration membrane. Functionalised silica particles showed a higher binding affinity toward Cu(II) than nontreated silica particles.UK Colloids 2011 - International Colloid and Surface Science Symposium, Jul 04-06, 2011, London, Englan

Synthetic Protocells Interact with Viral Nanomachinery and Inactivate Pathogenic Human Virus

We present a new antiviral strategy and research tool that could be applied to a wide range of enveloped viruses that infect human beings via membrane fusion. We test this strategy on two emerging zoonotic henipaviruses that cause fatal encephalitis in humans, Nipah (NiV) and Hendra (HeV) viruses. In the new approach, artificial cell-like particles (protocells) presenting membrane receptors in a biomimetic manner were developed and found to attract and inactivate henipavirus envelope glycoprotein pseudovirus particles, preventing infection. The protocells do not accumulate virus during the inactivation process. The use of protocells that interact with, but do not accumulate, viruses may provide significant advantages over current antiviral drugs, and this general approach may have wide potential for antiviral development

FRET Detection of Lymphocyte Function-Associated Antigen-1 Conformational Extension

Lymphocyte function-associated antigen 1 (LFA-1, CD11a/CD18, αLβ2-integrin) and its ligands are essential for adhesion between T-cells and antigen-presenting cells, formation of the immunological synapse, and other immune cell interactions. LFA-1 function is regulated through conformational changes that include the modulation of ligand binding affinity and molecular extension. However, the relationship between molecular conformation and function is unclear. Here fluorescence resonance energy transfer (FRET) with new LFA-1-specific fluorescent probes showed that triggering of the pathway used for T-cell activation induced rapid unquenching of the FRET signal consistent with extension of the molecule. Analysis of the FRET quenching at rest revealed an unexpected result that can be interpreted as a previously unknown LFA-1 conformation

Detecting the Hidden Properties of Immunological Data and Predicting the Mortality Risks of Infectious Syndromes

Background: To extract more information, the properties of infectious disease data, including hidden relationships, could be considered. Here, blood leukocyte data were explored to elucidate whether hidden information, if uncovered, could forecast mortality. Methods: Three sets of individuals (n = 132) were investigated, from whom blood leukocyte profiles and microbial tests were conducted (i) cross-sectional analyses performed at admission (before bacteriological tests were completed) from two groups of hospital patients, randomly selected at different time periods, who met septic criteria [confirmed infection and at least three systemic inflammatory response syndrome (SIRS) criteria] but lacked chronic conditions (study I, n = 36; and study II, n = 69); (ii) a similar group, tested over 3 days (n = 7); and (iii) non-infected, SIRS-negative individuals, tested once (n = 20). The data were analyzed by (i) a method that creates complex data combinations, which, based on graphic patterns, partitions the data into subsets and (ii) an approach that does not partition the data. Admission data from SIRS+/infection+ patients were related to 30-day, in-hospital mortality. Results: The non-partitioning approach was not informative: in both study I and study II, the leukocyte data intervals of non-survivors and survivors overlapped. In contrast, the combinatorial method distinguished two subsets that, later, showed twofold (or larger) differences in mortality. While the two subsets did not differ in gender, age, microbial species, or antimicrobial resistance, they revealed different immune profiles. Non-infected, SIRS-negative individuals did not express the high-mortality profile. Longitudinal data from septic patients displayed the pattern associated with the highest mortality within the first 24 h post-admission. Suggesting inflammation coexisted with immunosuppression, one high-mortality sub-subset displayed high neutrophil/lymphocyte ratio values and low lymphocyte percents. A second high-mortality subset showed monocyte-mediated deficiencies. Numerous within- and between-subset comparisons revealed statistically significantly different immune profiles. Conclusion: While the analysis of non-partitioned data can result in information loss, complex (combinatorial) data structures can uncover hidden patterns, which guide data partitioning into subsets that differ in mortality rates and immune profiles. Such information can facilitate diagnostics, monitoring of disease dynamics, and evaluation of subset-specific, patient-specific therapies

Detecting the hidden properties of immunological data and predicting the mortality risks of infectious syndromes

Background: To extract more information, the properties of infectious disease data, including hidden relationships, could be considered. Here, blood leukocyte data were explored to elucidate whether hidden information, if uncovered, could forecast mortality. Methods: Three sets of individuals (n = 132) were investigated, from whom blood leukocyte profiles and microbial tests were conducted (i) cross-sectional analyses performedat admission (before bacteriological tests were completed) from two groups of hospital patients, randomly selected at different time periods, who met septic criteria [confirmed infection and at least three systemic inflammatory response syndrome (SIRS) criteria] but lacked chronic conditions (study I, n = 36; and study II, n = 69); (ii) a similar group, tested over 3 days (n = 7); and (iii) non-infected, SIRS-negative individuals, tested once (n = 20). The data were analyzed by (i) a method that creates complex data combinations, which, based on graphic patterns, partitions the data into subsets and (ii) an approach that does not partition the data. Admission data from SIRS+/infection+ patients were related to 30-day, in-hospital mortality. Results: The non-partitioning approach was not informative: In both study I and study II, the leukocyte data intervals of non-survivors and survivors overlapped. In contrast, the combinatorial method distinguished two subsets that, later, showed twofold (or larger) differences in mortality. While the two subsets did not differ ingender, age, microbial species, or antimicrobial resistance, they revealed different immune profiles. Non-infected, SIRS-negative individuals did not express the high-mortality profile. Longitudinal data from septicpatients displayed the pattern associated with the highest mortality within the first 24 h post-admission. Suggesting inflammation coexisted with immunosuppression, one high-mortality sub-subset displayed high neutrophil/lymphocyte ratio values and low lymphocyte percents. A second high-mortality subset showed monocyte-mediated deficiencies. Numerous within- and between-subset comparisons revealed statistically significantly different immune profiles. Conclusion: While the analysis of non-partitioned data can result in information loss, complex (combinatorial) data structures can uncover hidden patterns, which guide data partitioning into subsets that differ in mortality rates and immune profiles. Such information can facilitate diagnostics, monitoring of disease dynamics, and evaluation of subset-specific, patient-specific therapies. © 2016 Chatzipanagiotou, Ioannidis, Trikka-Graphakos, Charalampaki, Sereti, Piccinini, Higgins, Buranda, Durvasula, Hoogesteijn, Tegos and Rivas