Enhanced CH₄ Recovery Induced via Structural Transformation in the CH₄/CO₂ Replacement That Occurs in sH Hydrates

The CH₄/CO₂ replacement that occurs in sH hydrates is investigated, with a primary focus on the enhanced CH₄ recovery induced via structural transformation with a CO₂ injection. In this study, neohexane (NH) is used as a liquid hydrocarbon guest in the sH hydrates. Direct thermodynamic measurements and spectroscopic identification are investigated to reveal the replacement process for recovering CH₄ and simultaneously sequestering CO₂ in the sH (CH₄ + NH) hydrate. The hydrate phase behavior and the ¹³C NMR and Raman spectroscopy results of the CH₄ + CO₂ + NH systems demonstrate that CO₂ functions as a coguest of sH hydrates in CH₄-rich conditions, and that the structural transition of sH to sI hydrates occurs in CO₂-rich conditions. CO₂ molecules are found to preferentially occupy the medium 4³5⁶6³ cages of sH hydrates or the large 5¹²6² cages of sI hydrates during the replacement. Due to the favorable structural transition and resulting re-establishment of guest distributions, approximately 88% of the CH₄ is recoverable from sH (CH₄ + NH) hydrates with a CO₂ injection. The hydrate dissociation and subsequent reformation caused by the structural transformation of sH to sI is also confirmed using a high-pressure microdifferential scanning calorimeter through the detection of the significant heat flows generated during the replacement

Static and Dynamic Permeability Assay for Hydrophilic Small Molecules Using a Planar Droplet Interface Bilayer

Because numerous drugs are administered through an oral route and primarily absorbed at the intestine, the prediction of drug permeability across an intestinal epithelial cell membrane has been a crucial issue in drug discovery. Thus, various <i>in vitro</i> permeability assays have been developed such as the Caco-2 assay, the parallel artificial membrane permeability assay (PAMPA), the phospholipid vesicle-based permeation assays (PVPA) and Permeapad. However, because of the time-consuming and quite expensive process for culturing cells in the Caco-2 assay and the unknown microscopic membrane structures of the other assays, a simpler yet more accurate and versatile technique is still required. Accordingly, we developed a new platform to measure the permeability of small molecules across a planar freestanding lipid bilayer with a well-defined area and structure. The lipid bilayer was constructed within a conventional UV spectrometer cell, and the transport of drug molecules across the bilayer was recorded by UV absorbance over time. We then computed the permeability from the time-dependent diffusion equation. We tested this assay for five exemplary hydrophilic drugs and compared their values with previously reported ones. We found that our assay has a much higher permeability compared to the other techniques, and this higher permeability is related to the thickness of the lipid bilayer. Also we were able to measure the dynamic permeability upon the addition of a membrane-disrupting surfactant demonstrating that our assay has the capability to detect real-time changes in permeability across the lipid bilayer

Structural Determinants of Chirally Selective Transport of Amino Acids through the α‑Hemolysin Protein Nanopores of Free-Standing Planar Lipid Membranes

Despite the importance of the enantioselective transport of amino acids through transmembrane protein nanopores from fundamental and practical perspectives, little has been explored to date. Here, we study the transport of amino acids through α-hemolysin (αHL) protein pores incorporated into a free-standing lipid membrane. By measuring the transport of 13 different amino acids through the αHL pores, we discover that the molecular size of the amino acids and their capability to form hydrogen bonds with the pore surface determine the chiral selectivity. Molecular dynamics simulations corroborate our findings by revealing the enantioselective molecular-level interactions between the amino acid enantiomers and the αHL pore. Our work is the first to present the determinants for chiral selectivity using αHL protein as a molecular filter

Natural Killer T Cells in Advanced Melanoma Patients Treated with Tremelimumab

<div><p>A significant barrier to effective immune clearance of cancer is loss of antitumor cytotoxic T cell activity. Antibodies to block pro-apoptotic/downmodulatory signals to T cells are currently being tested. Because invariant natural killer T cells (iNKT) can regulate the balance of Th1/Th2 cellular immune responses, we characterized the frequencies of circulating iNKT cell subsets in 21 patients with melanoma who received the anti-CTLA4 monoclonal antibody tremelimumab alone and 8 patients who received the antibody in combination with MART-1_26–35 peptide-pulsed dendritic cells (MART-1/DC). Blood T cell phenotypes and functionality were characterized by flow cytometry before and after treatment. iNKT cells exhibited the central memory phenotype and showed polyfunctional cytokine production. In the combination treatment group, high frequencies of pro-inflammatory Th1 iNKT CD8⁺ cells correlated with positive clinical responses. These results indicate that iNKT cells play a critical role in regulating effective antitumor T cell activity.</p></div

Phenotype analysis of iNKT cells over time after tremelimumab plus MART-1/DC.

<p>Analysis of early activated CD25−HLA−DR+PD1− (A, B, C) and CD25+HLA−DR+PD1− (D, E, F), late activated CD25−HLA−DR+PD1+ (G, H, I), central memory CCR5+CD62L+CD137− (J, K, L), effector CCR5+CD62L−CD137− (M, N, O), and terminal differentiated CD45RA+CD56+ (P, R, S) iNKT cells. The percentages of iNKT CD4+ (A, D, G, J, M, P), iNKT DN (B, E, H, K, N, R) and iNKT CD8+ (C, F, I, L, O, S) cells were quantified at different times during the double treatment with tremelimumab plus MART-1/DC. Results expressed as means plus/minus sd and statistical significance. Squares represent non-responders and circles responders to the treatment.</p

CD3 and iNKT cells and subsets before and after tremelimumab single agent treatment.

<p>After gating by morphology, live cells were gated on CD3⁺ T cells; CD3⁺ T cells (A), CD4⁺ and CD8⁺ (B); iNKT cells (double positive for TCR-Vα24/Vβ11; C); iNKT CD4⁺, iNKT CD8⁺ and iNKT DN (D) subsets were analyzed before (open boxes in B and D panels) and after treatment (greyed boxes in B and D panels). In panels b and d, results are presented as average of the percentage and standard deviation (sd) for both clinical trials (n = 21 for tremelimumab alone and n = 8 for tremelimumab plus Mart1/DC).</p

Phenotyping of iNKT subsets.

<p>Frequencies of iNKT CD4 (A, D, G), iNKT CD8 (B, E, H), and DN (C, F, I) subsets. Upper panels (A, B, C), tremelimumab alone clinical trial; middle and lower panels (D, E, F), tremelimumab plus Mart-1/DC trial. First and second row represented all iNKT subsets analyzed for CCR7 and CD45RA expression before (B) and after (P) treatment for both clinical trials. The last row represented iNKT subsets analyzed for CCR7 and CD45RA expression for responders (R) and non-responders (N) only for tremelimumab plus Mart-1/DC trial. EMRA = effector memory RA; CM = central memory, and EM = effector memory.</p

Phenotyping characteristic of Tremelimumab plus MART1/DC patients segregated by responders (R) and non-responders (NR) independent of the time points.

<p>Early activated (CD25⁻HLA−DR⁺PD1⁻ & CD25⁺HLA−DR⁺PD1⁻), n = 40 (NR) to 48 (R) time points; late activated (CD25⁻HLA−DR⁺PD1⁺), n = 20 (NR) to 31 (R); effectors (CCR5⁺CD62L⁺CD137⁻), n = 20 (NR) to 31 (R); and effectors terminally differentiated (CD45⁺CD56⁺), n = 20 (NR) to 31 (R).</p

Description of the two tremelimumab clinical trials.

<p>(A) CONSORT flowchart. Tremelimumab alone (GA). (B) and tremelimumab plus Mart1/DC (NRA). (C). Baseline is day 0, and numbers represent days after dosing that blood was drawn. NA-Not Aplicable.</p

Percentage of iNKT cells and its subsets in samples taken from patients in the tremelimumab plus MART-1/DC study.

<p>Evolution of iNKT cells and iNKT cells subsets during the treatment in the combined study in responders (circles) and non-responders (squares); (A) number of iNKT per million T cells; (B) number of iNKT CD4⁺ per million T-cells; (C) number of iNKT CD8⁺ per million T-cells and; (D) number of iNKT DN per million T-cells. In the Figure, regression lines for each data set are shown; error bars represent one sd from the mean for each point.</p