Cryo‐Immunoelectron Microscopy of Adherent Cells Improved by the Use of Electrospun Cell Culture Substrates

Electrospun nanofibres are an excellent cell culture substrate, enabling the fast and non‐disruptive harvest and transfer of adherent cells for microscopical and biochemical analyses. Metabolic activity and cellular structures are maintained during the only half a minute‐long harvest and transfer process. We show here that such samples can be optimally processed by means of cryofixation combined either with freeze‐substitution, sample rehydration and cryosection‐immunolabelling or with freeze‐fracture replica‐immunolabelling. Moreover, electrospun fibre substrates are equally suitable for complementary approaches, such as biochemistry, fluorescence microscopy and cytochemistry.     

Health Care Utilization and Symptom Severity in Ghanaian Children – a Cross-Sectional Study

The aim of this study was to identify factors influencing health care utilization behavior for children with mild or severe disease symptoms in rural Ghana. Between March and September 2008 a cross-sectional health care utilization survey was conducted and 8,715 caregivers were interviewed regarding their intended behavior in case their children had mild or severe fever or diarrhea. To show associations between hospital attendance and further independent factors (e.g. travel distance or socio-economic status) prevalence ratios were calculated for the four disease symptoms. A Poisson regression model was used to control for potential confounding. Frequency of hospital attendance decreased constantly with increasing distance to the health facility. Being enrolled in the national health insurance scheme increased the intention to attend a hospital. The effect of the other factors diminished in the Poisson regression if modeled together with travel distance. The observed associations weakened with increasing severity of symptoms, which indicates that barriers to visit a hospital are less important if children experience a more serious illness. As shown in other studies, travel distance to a health care provider had the strongest effect on health care utilization. Studies to identify local barriers to access health care services are important to inform health policy making as they identify deprived populations with low access to health services and to early treatment.     

Epitope Predictions Indicate the Presence of Two Distinct Types of Epitope-Antibody-Reactivities Determined by Epitope Profiling of Intravenous Immunoglobulins

Epitope-antibody-reactivities (EAR) of intravenous immunoglobulins (IVIGs) determined for 75,534 peptides by microarray analysis demonstrate that roughly 9% of peptides derived from 870 different human protein sequences react with antibodies present in IVIG. Computational prediction of linear B cell epitopes was conducted using machine learning with an ensemble of classifiers in combination with position weight matrix (PWM) analysis. Machine learning slightly outperformed PWM with area under the curve (AUC) of 0.884 vs. 0.849. Two different types of epitope-antibody recognition-modes (Type I EAR and Type II EAR) were found. Peptides of Type I EAR are high in tyrosine, tryptophan and phenylalanine, and low in asparagine, glutamine and glutamic acid residues, whereas for peptides of Type II EAR it is the other way around. Representative crystal structures present in the Protein Data Bank (PDB) of Type I EAR are PDB 1TZI and PDB 2DD8, while PDB 2FD6 and 2J4W are typical for Type II EAR. Type I EAR peptides share predicted propensities for being presented by MHC class I and class II complexes. The latter interaction possibly favors T cell-dependent antibody responses including IgG class switching. Peptides of Type II EAR are predicted not to be preferentially presented by MHC complexes, thus implying the involvement of T cell-independent IgG class switch mechanisms. The high extent of IgG immunoglobulin reactivity with human peptides implies that circulating IgG molecules are prone to bind to human protein/peptide structures under non-pathological, non-inflammatory conditions. A webserver for predicting EAR of peptide sequences is available at www.sysmed-immun.eu/EAR.     

Knee Adduction Moment and Medial Contact Force – Facts about Their Correlation during Gait

The external knee adduction moment is considered a surrogate measure for the medial tibiofemoral contact force and is commonly used to quantify the load reducing effect of orthopedic interventions. However, only limited and controversial data exist about the correlation between adduction moment and medial force. The objective of this study was to examine whether the adduction moment is indeed a strong predictor for the medial force by determining their correlation during gait. Instrumented knee implants with telemetric data transmission were used to measure tibiofemoral contact forces in nine subjects. Gait analyses were performed simultaneously to the joint load measurements. Skeletal kinematics, as well as the ground reaction forces and inertial parameters, were used as inputs in an inverse dynamics approach to calculate the external knee adduction moment. Linear regression analysis was used to analyze the correlation between adduction moment and medial force for the whole stance phase and separately for the early and late stance phase. Whereas only moderate correlations between adduction moment and medial force were observed throughout the whole stance phase (R² = 0.56) and during the late stance phase (R² = 0.51), a high correlation was observed at the early stance phase (R² = 0.76). Furthermore, the adduction moment was highly correlated to the medial force ratio throughout the whole stance phase (R² = 0.75). These results suggest that the adduction moment is a surrogate measure, well-suited to predicting the medial force ratio throughout the whole stance phase or medial force during the early stance phase. However, particularly during the late stance phase, moderate correlations and high inter-individual variations revealed that the predictive value of the adduction moment is limited. Further analyses are necessary to examine whether a combination of other kinematic, kinetic or neuromuscular factors may lead to a more reliable prediction of the force magnitude.     

Analytical FcRn affinity chromatography for functional characterization of monoclonal antibodies

The neonatal Fc receptor (FcRn) is important for the metabolic fate of IgG antibodies in vivo. Analysis of the interaction between FcRn and IgG in vitro might provide insight into the structural and functional integrity of therapeutic IgG that may affect pharmacokinetics (PK) in vivo. We developed a standardized pH gradient FcRn affinity liquid chromatography method with conditions closely resembling the physiological mechanism of interaction between IgG and FcRn. This method allows the separation of molecular IgG isoforms, degradation products and engineered molecules based on their affinity to FcRn. Human FcRn was immobilized on the column and a linear pH gradient from pH 5.5 to 8.8 was applied. FcRn chromatography was used in comparison to surface plasmon resonance to characterize different monoclonal IgG preparations, e.g., oxidized or aggregated species. Wild-type and engineered IgGs were compared in vitro by FcRn chromatography and in vivo by PK studies in huFcRn transgenic mice. Analytical FcRn chromatography allows differentiation of IgG samples and variants by peak pattern and retention time profile. The method can distinguish: 1) IgGs with different Fabs, 2) oxidized from native IgG, 3) aggregates from monomer and 4) antibodies with mutations in the Fc part from wild-type IgGs. Changes in the FcRn chromatographic behavior of mutant IgGs relative to the wild-type IgG correlate to changes in the PK profile in the FcRn transgenic mice. These results demonstrate that FcRn affinity chromatography is a useful new method for the assessment of IgG integrity.     

Slow Oscillation Amplitudes and Up-State Lengths Relate to Memory Improvement

There is growing evidence of the active involvement of sleep in memory consolidation. Besides hippocampal sharp wave-ripple complexes and sleep spindles, slow oscillations appear to play a key role in the process of sleep-associated memory consolidation. Furthermore, slow oscillation amplitude and spectral power increase during the night after learning declarative and procedural memory tasks. However, it is unresolved whether learning-induced changes specifically alter characteristics of individual slow oscillations, such as the slow oscillation up-state length and amplitude, which are believed to be important for neuronal replay. 24 subjects (12 men) aged between 20 and 30 years participated in a randomized, within-subject, multicenter study. Subjects slept on three occasions for a whole night in the sleep laboratory with full polysomnography. Whereas the first night only served for adaptation purposes, the two remaining nights were preceded by a declarative word-pair task or by a non-learning control task. Slow oscillations were detected in non-rapid eye movement sleep over electrode Fz. Results indicate positive correlations between the length of the up-state as well as the amplitude of both slow oscillation phases and changes in memory performance from pre to post sleep. We speculate that the prolonged slow oscillation up-state length might extend the timeframe for the transfer of initial hippocampal to long-term cortical memory representations, whereas the increase in slow oscillation amplitudes possibly reflects changes in the net synaptic strength of cortical networks.     

Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma

Metabolomics as one of the most rapidly growing technologies in the “-omics” field denotes the comprehensive analysis of low molecular-weight compounds and their pathways. Cancer-specific alterations of the metabolome can be detected by high-throughput mass-spectrometric metabolite profiling and serve as a considerable source of new markers for the early differentiation of malignant diseases as well as their distinction from benign states. However, a comprehensive framework for the statistical evaluation of marker panels in a multi-class setting has not yet been established. We collected serum samples of 40 pancreatic carcinoma patients, 40 controls, and 23 pancreatitis patients according to standard protocols and generated amino acid profiles by routine mass-spectrometry. In an intrinsic three-class bioinformatic approach we compared these profiles, evaluated their selectivity and computed multi-marker panels combined with the conventional tumor marker CA 19-9. Additionally, we tested for non-inferiority and superiority to determine the diagnostic surplus value of our multi-metabolite marker panels. Compared to CA 19-9 alone, the combined amino acid-based metabolite panel had a superior selectivity for the discrimination of healthy controls, pancreatitis, and pancreatic carcinoma patients $ [ {\text{volume under ROC surface}}\;\left( {\text{VUS}} \right) = 0. 8 9 1 { }\left( { 9 5\,\% {\text{ CI }}0. 7 9 4- 0. 9 6 8} \right)]. $ We combined highly standardized samples, a three-class study design, a high-throughput mass-spectrometric technique, and a comprehensive bioinformatic framework to identify metabolite panels selective for all three groups in a single approach. Our results suggest that metabolomic profiling necessitates appropriate evaluation strategies and—despite all its current limitations—can deliver marker panels with high selectivity even in multi-class settings.