Descriptive statistics for measures of sphericity.

<p>Mean values, SEM, intra- and inter-individual standard deviation (SD) and r ratio in IgG2b isotype control samples (representative for all IgG isotype controls) of CONT.</p

Erythrocyte sphericity and buffer osmolarity.

<p>Kurtosis of FSC signal of erythrocytes of 10 subjects of first study part incubated in buffer mediums with varying osmolarity. With increasing osmolarity of buffer medium, sphericity of erythrocytes decreases as indicated by decreasing kurtosis, *indicates significant difference (p<0.05) to kurtosis/sphericity values at 259 mosmol/kg and ^†indicates significant difference (p<0.05) to kurtosis/sphericity values at 285 mosmol/kg (Friedman-test with adjusted pairwise comparisons).</p

Fighting High Molecular Weight in Bioactive Molecules with Sub-Pharmacophore-Based Virtual Screening

A new subpharmacophore-based virtual screening method is introduced. Subpharmacophores are derived from large active molecules to detect small bioactive molecules as seeds for starting points in medicinal chemistry programs. A large data set was assembled from the ChEMBL database to check the validity of this approach. Molecules for 133 targets with molecular weights between 450 and 850 were selected as queries. For the query molecules, the pharmacophore descriptors were calculated. Up to 56 000 subpharmacophore descriptors with five to seven pharmacophore points were derived from the query pharmacophores. The subpharmacophore descriptors were used as queries to screen 1079 test data sets, containing decoys and spike molecules. A maximum upper molecular weight limit of 400 Da was set for the test molecules. Three different chemical fingerprint descriptors were used for comparison purposes. The subpharmacophore approach detected active molecules for 85 out of 133 targets and outperformed the chemical fingerprints. This ligand-based virtual screening experiment was triggered by the needs of medicinal chemistry. Applying the subpharmacophore method in a medicinal chemistry program, where a lead molecule with a molecular weight of 800 Da was available, resulted in a new series of molecules with molecular weights below 400

Study design.

<p>Samples were taken at different time points after blood reinfusion. For the control group, no samples were collected at 3 (3 h) and 6 hours (6 h).</p

Selected FSC histograms illustrating bimodal and monomodal distributions.

<p>Selected FSC histograms illustrating bimodal and monomodal distributions. x-axis displays the channels used for analysis, y-axis gives the number of events for each channel. Dashed lines indicate channel 101 chosen for calculation of SphI. The median value for the respective part of the distribution is marked by short lines. For subject 11 (Full, IgG2b, d7), kurtosis = −1.21, PCD = −0.15, SphI = 2.47 (median 1 = 148, median 2 = 60). For subject 16 (Full, IgG2b, d3), kurtosis = −0.36, PCD = −0.27, SphI = 1.70 (median 1 = 129, median 2 = 76).</p

Fighting High Molecular Weight in Bioactive Molecules with Sub-Pharmacophore-Based Virtual Screening

A new subpharmacophore-based virtual screening method is introduced. Subpharmacophores are derived from large active molecules to detect small bioactive molecules as seeds for starting points in medicinal chemistry programs. A large data set was assembled from the ChEMBL database to check the validity of this approach. Molecules for 133 targets with molecular weights between 450 and 850 were selected as queries. For the query molecules, the pharmacophore descriptors were calculated. Up to 56 000 subpharmacophore descriptors with five to seven pharmacophore points were derived from the query pharmacophores. The subpharmacophore descriptors were used as queries to screen 1079 test data sets, containing decoys and spike molecules. A maximum upper molecular weight limit of 400 Da was set for the test molecules. Three different chemical fingerprint descriptors were used for comparison purposes. The subpharmacophore approach detected active molecules for 85 out of 133 targets and outperformed the chemical fingerprints. This ligand-based virtual screening experiment was triggered by the needs of medicinal chemistry. Applying the subpharmacophore method in a medicinal chemistry program, where a lead molecule with a molecular weight of 800 Da was available, resulted in a new series of molecules with molecular weights below 400

Longitudinal presentation of erythrocyte sphericity.

<p>Kurtosis of FSC signal of IgG2b isotype control samples of CONT subjects at all time points. Values of the respective subjects are connected by dashed lines. Solid bold line connects mean values of distribution of kurtosis at the respective time point.</p

Results of correlation analysis between measures of sphericity.

<p>Spearman ρ correlation coefficients for the respective sphericity indicators. Values in brackets describe correlation for native erythrocytes (isotype controls).</p>*<p>Indicates significant correlation (p<0.05).</p

Correlations between respective indicators of sphericity.

<p>Scatterplots illustrating the relation between the respective indicators of sphericity in all samples of second study part (A,B) and isotype control samples (C,D). Relation between kurtosis and SphI appears inverse proportional.</p

Fighting High Molecular Weight in Bioactive Molecules with Sub-Pharmacophore-Based Virtual Screening

A new subpharmacophore-based virtual screening method is introduced. Subpharmacophores are derived from large active molecules to detect small bioactive molecules as seeds for starting points in medicinal chemistry programs. A large data set was assembled from the ChEMBL database to check the validity of this approach. Molecules for 133 targets with molecular weights between 450 and 850 were selected as queries. For the query molecules, the pharmacophore descriptors were calculated. Up to 56 000 subpharmacophore descriptors with five to seven pharmacophore points were derived from the query pharmacophores. The subpharmacophore descriptors were used as queries to screen 1079 test data sets, containing decoys and spike molecules. A maximum upper molecular weight limit of 400 Da was set for the test molecules. Three different chemical fingerprint descriptors were used for comparison purposes. The subpharmacophore approach detected active molecules for 85 out of 133 targets and outperformed the chemical fingerprints. This ligand-based virtual screening experiment was triggered by the needs of medicinal chemistry. Applying the subpharmacophore method in a medicinal chemistry program, where a lead molecule with a molecular weight of 800 Da was available, resulted in a new series of molecules with molecular weights below 400