Estimated Brain Tissue Response Following Impacts Associated With and Without Diagnosed Concussion

Kinematic measurements of head impacts are sensitive to sports concussion, but not highly specific. One potential reason is these measures reflect input conditions only and may have varying degrees of correlation to regional brain tissue deformation. In this study, previously reported head impact data recorded in the field from high school and collegiate football players were analyzed using two finite element head models (FEHM). Forty-five impacts associated with immediately diagnosed concussion were simulated along with 532 control impacts without identified concussion obtained from the same players. For each simulation, intracranial response measures (max principal strain, strain rate, von Mises stress, and pressure) were obtained for the whole brain and within four regions of interest (ROI; cerebrum, cerebellum, brain stem, corpus callosum). All response measures were sensitive to diagnosed concussion; however, large inter-athlete variability was observed and sensitivity strength depended on measure, ROI, and FEHM. Interestingly, peak linear acceleration was more sensitive to diagnosed concussion than all intracranial response measures except pressure. These findings suggest FEHM may provide unique and potentially important information on brain injury mechanisms, but estimations of concussion risk based on individual intracranial response measures evaluated in this study did not improve upon those derived from input kinematics alone.     

Consciousness Indexing and Outcome Prediction with Resting-State EEG in Severe Disorders of Consciousness

We applied the following methods to resting-state EEG data from patients with disorders of consciousness (DOC) for consciousness indexing and outcome prediction: microstates, entropy (i.e. approximate, permutation), power in alpha and delta frequency bands, and connectivity (i.e. weighted symbolic mutual information, symbolic transfer entropy, complex network analysis). Patients with unresponsive wakefulness syndrome (UWS) and patients in a minimally conscious state (MCS) were classified into these two categories by fitting and testing a generalised linear model. We aimed subsequently to develop an automated system for outcome prediction in severe DOC by selecting an optimal subset of features using sequential floating forward selection (SFFS). The two outcome categories were defined as UWS or dead, and MCS or emerged from MCS. Percentage of time spent in microstate D in the alpha frequency band performed best at distinguishing MCS from UWS patients. The average clustering coefficient obtained from thresholding beta coherence performed best at predicting outcome. The optimal subset of features selected with SFFS consisted of the frequency of microstate A in the 2–20 Hz frequency band, path length obtained from thresholding alpha coherence, and average path length obtained from thresholding alpha coherence. Combining these features seemed to afford high prediction power. Python and MATLAB toolboxes for the above calculations are freely available under the GNU public license for non-commercial use (https://qeeg.wordpress.com)     

Detection of MRSA in nasal swabs—marked reduction of time to report for negative reports by substituting classical manual workflow with total lab automation

In 2016, the workflow for MRSA detection in nasal swabs was changed from a classic-manual workflow to an automated workflow using total lab automation (TLA; BD Kiestra). This change entailed a reduction of the incubation time from 2 days to 20 h and reading of plates on weekdays and weekends instead of weekdays only. The workflow alteration did not include the introduction of 24/7. We wanted to follow up on the consequences for the times to report (TTR). We compared the TTR of all nasal swabs, which were sent for MRSA detection from June until August in 2015 (workflow—classic-manual) and in 2016 (workflow—automated). We calculated median TTR and interquartile ranges for the three possible reporting outcomes (negative, MRSA-known, MRSA-new) per day and workflow. A multivariable linear regression modeled the exposure variables workflow, day, and reporting outcome on TTR including interaction variables. The quantity and reasons for a TTR longer than 3 days were analyzed. During both 3-month periods, a total of 16,111 reports were issued (2015:7620; 2016:8491). The median TTR for negative reports was 48:28 (hh:mm) in 2015 and 23:58 in 2016. In the linear regression, all exposure variables had a strong and highly significant (p?<?0.001) influence on the TTR. The number of reports with a TTR longer than 3 days shrank from 2418 (2015) to 60 (2016). The workflow alteration halved the median TTR for negative reports and the number of reports with a TTR longer than 3 days was reduced by 97.5%.     

Reinterventions after dynamic intraligamentary stabilization in primary anterior cruciate ligament repair

Background

The goal of this study was to perform an in-depth analysis of the frequency and cause of secondary interventions subsequent to primary anterior cruciate ligament (ACL) repair with dynamic intraligamentary stabilization (DIS).

Ultrasound point shear wave elastography of the pancreas: comparison of patients with type 1 diabetes and healthy volunteers – results from a pilot study

Background

The aims of this study were to establish shear wave elastography of the pancreas by comparing measurements in patients with type 1 diabetes (T1D) and healthy volunteers and to consider whether this method could contribute to the screening or prevention of T1D.

Methods

This pilot study included 15 patients with T1D (10 men, 5 women) and 15 healthy volunteers (10 men, 5 women) as controls. Measurements were performed with a Siemens Acuson S3000 (Siemens Healthcare, Erlangen, Germany) using a 6C1 convex transducer and the Virtual Touch? tissue quantification (VTQ) method.

Results

The mean shear wave velocity of the head of the pancreas was 1.0?±?0.2?m/s (median: 1.1?m/s) for the study group and likewise 1.0?±?0.2?m/s (median: 0.9?m/s) for the control group. Velocities of 1.2?±?0.2?m/s (median: 1.2?m/s) were measured in the body of the pancreas in both groups. There was a significant difference between the values obtained in the tail of the pancreas: patients 1.1?±?0.1?m/s (median: 1.0?m/s) versus controls 0.9?±?0.1?m/s (median: 0.8?m/s) (p?=?0.0474). The mean value in the whole pancreas of the study group was not significantly above that of the control group: 1.1?±?0.1?m/s (median: 1.0?m/s) versus 1.0?±?0.1?m/s (median: 1.0?m/s) (p?=?0.2453).

Conclusions

Sonoelastography of the pancreas revealed no overall difference between patients with T1D and healthy volunteers. Patients with T1D showed higher values only in the tail segment. Future studies need to determine whether specific regional differences can be found in a larger study population.

     

Differential sensing for the regio- and stereoselective identification and quantitation of glycerides

Glycerides are of interest to the areas of food science and medicine because they are the main component of fat. From a chemical sensing perspective, glycerides are challenging analytes because they are structurally similar to one another and lack diversity in terms of functional groups. Furthermore, because animal and plant fat consists of a number of stereo- and regioisomeric acylglycerols, their components remain challenging analytes for chromatographic and mass spectrometric determination, particularly the quantitation of species in mixtures. In this study, we demonstrated the use of an array of cross-reactive serum albumins and fluorescent indicators with chemometric analysis to differentiate a panel of mono-, di-, and triglycerides. Due to the difficulties in identifying the regio- and stereochemistry of the unsaturated glycerides, a sample pretreatment consisting of olefin cross-metathesis with an allyl fluorescein species was used before array analysis. Using this simple assay, we successfully discriminated 20 glycerides via principal component analysis and linear discriminant analysis (PCA and LDA, respectively), including stereo- and regioisomeric pairs. The resulting chemometric patterns were used as a training space for which the structural characteristics of unknown glycerides were identified. In addition, by using our array to perform a standard addition analysis on a mixture of triglycerides and using a method introduced herein, we demonstrated the ability to quantitate glyceride components in a mixture.Glycerides are the primary component of animal fats and vegetable oils (1). They consist of one, two, or three fatty acids esterified on glycerol, and hence are referred to as mono-, di-, and triglycerides, respectively. The structural diversity of glycerides derives in part from their fatty acid alkyl groups, which can differ in carbon number (i.e., chain length), the degree of unsaturation, the position of olefins, and the configuration of the olefins (i.e., cis/trans). Furthermore, these fatty acid alkyl groups can be connected to the sn-1, -2, or -3 carbons on glycerol. Hence, a variety of regio- and stereoisomers can exist for glycerides, posing a challenge for mass spectrometry (2). Further, because the differences in chain length primarily result from the presence of greater or fewer methylene groups, NMR spectroscopy can be ambiguous (3).The analysis of glycerides is primarily important to the food and nutrition industries for tasks such as authenticating edible oils (4), designing foods with certain physical properties (5), and studying how fats are digested and absorbed (6). In particular, classifying all of the various kinds of regio- and stereoisomers of glycerides is biologically important because lipases, enzymes that catalyze the hydrolysis of glycerides into fatty acids and glycerol, exhibit selectivity based on these features of the glyceride substrates. As examples, the position and configuration of olefins, the identity of fatty acid alkyl groups, as well as their position on glycerol (i.e., sn-1,3 versus sn-2), all contribute to differing biological activity (7, 8). Studying the selectivity of these lipases has applications in understanding diseases, including fat malabsorption disorders, hypercholesterolemia, atherosclerosis, and diabetes (9, 10). Research on metabolic disorders has shown that fatty acid accumulation can exert a toxic or a protective effect on a tissue, depending on the specific tissue type (e.g., liver, cardiac, or skeletal muscle) (11, 12) and health state (e.g., diabetic) (13, 14) as well as on the fatty acids (e.g., saturated or unsaturated) (15). Sequestration of fatty acids by esterification to glycerides is one pathway by which these effects are regulated (16). Thus, a deeper understanding of the distinct roles of the cellular storage of structurally different glycerides in normal and disease states is a desirable avenue of research (17). However, currently only limited information is available about the composition of glycerides in adipose and nonadipose tissue.The most common method of glyceride identification is mass spectrometry (MS) (2, 18). However, as alluded to above, this approach has drawbacks. Because glycerides are neutral molecules, they must be ionized to be analyzed by MS. Saponification can be used to obtain the fatty acids, which are both volatile and charged, thereby facilitating MS analysis, but information about the glyceride structure is lost in this process (18). Electrospray ionization and atmospheric pressure chemical ionization are used to ionize glycerides directly; however, the ion yields are low compared with preionized lipids (19, 20). Furthermore, the ability of a glyceride to be ionized using these methods often varies. For example, ion abundance generally increases with increasing number of double bonds in the fatty acid alkyl chain and can also depend on fatty acid alkyl chain length (21). These significant variations in ion abundance mean that ionization methodologies must be developed and tailored to a specific application to satisfactorily detect each glyceride of interest (19). Finally, these variations render the quantification of glycerides, particularly in a complex mixture, quite challenging when using MS (22).Regio- and stereoisomers further confound the discrimination of glycerides by MS, because isomers share the same mass. Other techniques such as chemical derivitization of the glycerides, ion fragmentation, and specialized HPLC must be coupled with MS to effect differentiation of isomeric species. For example, ozonolysis has been used to cleave the double bonds in unsaturated glycerides before ionization to deduce the positions of double bonds (23). Nonaqueous reverse-phase (NARP)-HPLC can resolve cis/trans isomers of triacylglycerols and double-bond positional isomers after treatment of the olefins with bromine (24). Silver ion chromatography has been used to separate triacylglycerol positional isomers under specifically developed solvent and column temperature conditions (25). Silver cationization as a postcolumn treatment in conjunction with NARP-HPLC and ion fragmentation has also been used for triglyceride positional isomer determination (26, 27) Thus, although these current approaches to glyceride isomer analysis have been successful, they are complicated, labor intensive, time-consuming, and at times inconsistent in their results (26).Because glycerides are structurally very similar to one another, we believed that a differential sensing array-based approach would be most suitable for their classification. Our hypothesis was that if a cross-reactive array could be created that was responsive to the subtle structural differences inherent in glycerides, it could be used to pattern individual glycerides, identify structural features of unknown glycerides, and potentially quantitate glycerides in a mixture. Cross-reactive arrays have been successfully used in a number of sensing applications (28–33). Differential sensing mimics the mammalian senses of olfaction and gustation by detecting the pattern of response of an analyte to a collection of semiselective receptors (34, 35). In mammals, the characteristic pattern for a scent or taste is interpreted and stored by the brain (36). In the laboratory, chemometric routines such as principal component analysis (PCA) and linear discriminant analysis (LDA) are used to extract the relevant information from the array. Both PCA and LDA are multivariate methods that reduce the dimensionality of a data set. PCA does so by finding unbiased orthogonal axes that describe decreasing extents of variance in the data derived from different samples (classes) and repetitions of the samples (37). Any grouping of like samples represents intrinsic similarities between the sample datasets whereas separate classification represents differences in that variable space. LDA classifies samples by calculating discriminant functions that maximize the separation between predetermined classes and minimizes the separation within these classes (38, 39). Thus, LDA is a supervised method, meaning that the classes are provided as inputs into the algorithm. For this reason, a validation method called a leave-one-out cross-validation is used to test the predictive value of the model. Further, LDA can be used to predict the identity of unknowns by identifying which classes in the training set the unknowns most resemble.Therefore, the goal of this project was to develop an array of cross-reactive receptors that could discriminate glycerides. The glycerides selected are shown in Fig. 1. The panel includes commercially available mono-, di-, and triacylglycerols with fatty acid alkyl groups that are relevant to mammalian biology (40). Moreover, the panel consists of examples of each of the following stereo- and regioisomers: (i) cis/trans olefins (D1 and D2; T2 and T3), (ii) differing position of the olefin (T3 and T4), and (iii) differing position of the fatty acid alkyl groups on the glycerol (D5 and D6). Clearly, it would be extremely challenging to create highly selective receptors for each individual glyceride, and thus a differential sensing method seems the only reasonable approach to creating an optical sensing routine to identify and classify these structures.Open in a separate windowFig. 1.Glyceride panel with structures and names.Because glycerides are extremely hydrophobic analytes, we postulated that serum albumins (SAs) would be suitable cross-reactive receptors with which to test our hypothesis. SA is a common plasma protein that binds hydrophobic molecules to transport them through the hydrophilic environment of blood plasma (41). The protein binds a number of endogenous compounds: long-chain fatty acids (K_a = 10⁶–10⁷ M⁻¹) (42), bile acids (K_a = 10³–10⁵ M⁻¹) (43), and steroids (K_a = 10³–10⁵ M⁻¹) (44–46), as well as many drugs, toxins, and fluorophores (41). Despite being composed of fatty acid alkyl groups, glycerides bind less tightly to SAs and in a different location than their fatty acid counterparts (47). The primary sequence of SAs differs between species, which thus exhibit differences in ligand binding (41). Previously, we have used arrays of SAs for the differentiation of other hydrophobic analytes including fatty acids (48), terpenes (49), and plasticizers (50). However, none of these previous studies involved differences between the analyte structures as subtle as glycerides do, nor had we challenged our methods to identify structural aspects of an unknown. Furthermore, we had never implemented a quantitation assay in a complex mixture. Because the binding of ligands to SAs is known to depend on subtle differences in their structure (41), we anticipated that success could be achieved but would be highly dependent upon the signaling modality and potentially analyte prederivitization.Thus, herein we describe a method using SAs to fingerprint glycerides that classifies them as mono-, di-, or triglycerides. The glycerides were further classified based on fatty acid chain length, ester positions on glycerol, and olefin regio- and stereochemistry. For the unsaturated glycerides in the panel, differentiation based on olefin position and stereochemistry was achieved by the use of a pretreatment olefin metathesis. Using the protocols described herein, structural features of unknown glycerides could be identified. Furthermore, the quantitation of trilinolein in a mixture of triglycerides was achieved by application of the standard addition method using a net analyte signal technique (SANAS) presented herein.