TiME OUT: Time-specific machine-learning evaluation to optimize ultramassive transfusion.

BACKGROUND: Ultramassive transfusion (UMT) is a resource-demanding intervention for trauma patients in hemorrhagic shock, and associated mortality rates remains high. Current research has been unable to identify a transfusion ceiling or point where UMT transitions from lifesaving to futility. Furthermore, little consideration has been given to how time-specific patient data points impact decisions with ongoing high-volume resuscitation. Therefore, this study sought to use time-specific machine learning modeling to predict mortality and identify parameters associated with survivability in trauma patients undergoing UMT. METHODS: A retrospective review was conducted at a Level I trauma (2018-2021) and included trauma patients meeting criteria for UMT, defined as ≥20 red blood cell products within 24 hours of admission. Cross-sectional data were obtained from the blood bank and trauma registries, and time-specific data were obtained from the electronic medical record. Time-specific decision-tree models predicating mortality were generated and evaluated using area under the curve. RESULTS: In the 180 patients included, mortality rate was 40.5% at 48 hours and 52.2% overall. The deceased received significantly more blood products with a median of 71.5 total units compared with 55.5 in the survivors ( p < 0.001) and significantly greater rates of packed red blood cells and fresh frozen plasma at each time interval. Time-specific decision-tree models predicted mortality with an accuracy as high as 81%. In the early time intervals, hemodynamic stability, undergoing an emergency department thoracotomy, and injury severity were most predictive of survival, while, in the later intervals, markers of adequate resuscitation such as arterial pH and lactate level became more prominent. CONCLUSION: This study supports that the decision of "when to stop" in UMT resuscitation is not based exclusively on the number of units transfused but rather the complex integration of patient and time-specific data. Machine learning is an effective tool to investigate this concept, and further research is needed to refine and validate these time-specific decision-tree models. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level IV.

The Role of Adverse Pregnancy Outcomes in Conventional Cardiovascular Risk Prediction.

OBJECTIVE: Approximately one-third of women in the U.S. experience an adverse pregnancy outcome (APO), which are recognized as sex-specific cardiovascular disease (CVD) risk factors. We examine if APOs confer additional CVD risk beyond that of traditional CVD risk factors. METHODS: Women, age 40-79, with a pregnancy history and no pre-existing CVD were identified in the electronic health record of one health system (n = 2306). APOs included any APO, hypertensive disease of pregnancy (HDP), and gestational diabetes (GDM). Hazard ratios of time to CVD event were estimated from survival models using Cox proportional hazard regression. Discrimination, calibration, and net reclassification of re-estimated CVD risk prediction models including APOs were examined. RESULTS: There was no significant association between any APO, HDP, or GDM and time to CVD outcome in survival models (95% confidence intervals all include 1). Including any APO, HDP, GDM in the CVD risk prediction model did not significantly improve discrimination and there were no clinically relevant changes in net reclassification of cases and non-cases. The strongest predictor of time to CVD event in the survival models was Black race, with hazard ratios ranging from 1.59 to 1.62, statistically significant for all three models. CONCLUSION: Women with APOs did not have an additional risk of CVD, controlling for traditional risk factors in the PCE and this sex-specific factor did not improve risk prediction. Black race was consistently a strong predictor of CVD even with data limitations. Further study of APOs can help determine how to best use this information for CVD prevention in women.

Rectal Contrast CT Scans of Limited Utility in Diagnosing Colonic Injuries in Penetrating Trauma: A Meta-Analysis.

OBJECTIVES: Using rectal contrast computed tomography (CT) to identify traumatic colorectal injuries has become commonplace; however, these injuries remain relatively infrequent findings on CTs obtained for penetrating back and flank trauma. We conducted a meta-analysis to ascertain the efficacy of rectal contrast CT in identifying such injuries in victims penetrating injuries. METHODS: PubMed and Embase were queried for relevant articles between 1974 and 2022. Review articles, case studies, and non-English manuscripts were excluded. Studies without descriptive CT and operative findings were excluded. Positive scans refer to rectal contrast extravasation. Sensitivity and specificity of rectal contrast CT scans were calculated with aggregated CT findings that were cross-referenced with laparotomy findings. RESULTS: Only 8 manuscripts representing 506 patients quantified colorectal injuries and specified patients with rectal contrast extravasation. Seven patients with true colorectal injuries had no contrast extravasation on CT. There was one true positive scan. Another scan identified contrast extravasation, but laparotomy revealed no colorectal injury. Rectal contrast had sensitivity of 12.5%, specificity 99.8%, positive predictive value (PPV) 50%, negative predictive value (NPV) 99%, and a false negative rate of 88% in identifying colonic injuries. DISCUSSION: The summation of 8 manuscripts suggest that the addition of rectal contrast in identifying colonic and rectal injuries may be of limited utility given its poor sensitivity and may be unnecessary. In its absence, subtle clues such as hematomas, extraluminal air, IV-dye extravasation, and trajectory may be additional indicators of injury. Further investigations are required to demonstrate a true benefit for the addition of rectal contrast.

Giant leiomyosarcoma of the transverse colon.

Leiomyosarcoma (LMS) of the colon accounts for <1% of all colorectal malignancies. Our patient was a 72-year-old man with a history of aortic valvular disorder and congestive heart failure, who presented with an abdominal mass and no constitutional symptoms. The CT scan finding suggested a large tumour with both solid and cystic components. Intraoperatively, a portion of the involved colon was resected along with the tumour. Microscopically, the tumour was found to invade the muscularis propria layer of the transverse colon. The final diagnosis was LMS, FNCLCC grade 2 of 3 based on the histology and immunochemistry.

Impact of gonadectomy on maturational changes in brain volume in adolescent macaques.

Adolescence is a transitional period between childhood and adulthood characterized by significant changes in global and regional brain tissue volumes. It is also a period of increasing vulnerability to psychiatric illness. The relationship between these patterns and increased levels of circulating sex steroids during adolescence remains unclear. The objective of the current study was to determine whether gonadectomy, prior to puberty, alters adolescent brain development in male rhesus macaques. Ninety-six structural MRI scans were acquired from 12 male rhesus macaques (8 time points per animal over a two-year period). Six animals underwent gonadectomy and 6 animals underwent a sham operation at 29 months of age. Mixed-effects models were used to determine whether gonadectomy altered developmental trajectories of global and regional brain tissue volumes. We observed a significant effect of gonadectomy on the developmental trajectory of prefrontal gray matter (GM), with intact males showing peak volumes around 3.5 years of age with a subsequent decline. In contrast, prefrontal GM volumes continued to increase in gonadectomized males until the end of the study. We did not observe a significant effect of gonadectomy on prefrontal white matter or on any other global or regional brain tissue volumes, though we cannot rule out that effects might be detected in a larger sample. Results suggest that the prefrontal cortex is more vulnerable to gonadectomy than other brain regions.

Multi-stage adaptive enrichment trial design with subgroup estimation.

We consider the problem of estimating the best subgroup and testing for treatment effect in a clinical trial. We define the best subgroup as the subgroup that maximizes a utility function that reflects the trade-off between the subgroup size and the treatment effect. For moderate effect sizes and sample sizes, simpler methods for subgroup estimation worked better than more complex tree-based regression approaches. We propose a three-stage design with a weighted inverse normal combination test to test the hypothesis of no treatment effect across the three stages.

Characterizing the weight-glycemia phenotypes of type 1 diabetes in youth and young adulthood.

INTRODUCTION: Individuals with type 1 diabetes (T1D) present with diverse body weight status and degrees of glycemic control, which may warrant different treatment approaches. We sought to identify subgroups sharing phenotypes based on both weight and glycemia and compare characteristics across subgroups. RESEARCH DESIGN AND METHODS: Participants with T1D in the SEARCH study cohort (n=1817, 6.0-30.4 years) were seen at a follow-up visit >5 years after diagnosis. Hierarchical agglomerative clustering was used to group participants based on five measures summarizing the joint distribution of body mass index z-score (BMIz) and hemoglobin A1c (HbA1c) which were estimated by reinforcement learning tree predictions from 28 covariates. Interpretation of cluster weight status and glycemic control was based on mean BMIz and HbA1c, respectively. RESULTS: The sample was 49.5% female and 55.5% non-Hispanic white (NHW); mean±SD age=17.6±4.5 years, T1D duration=7.8±1.9 years, BMIz=0.61±0.94, and HbA1c=76±21 mmol/mol (9.1±1.9)%. Six weight-glycemia clusters were identified, including four normal weight, one overweight, and one subgroup with obesity. No cluster had a mean HbA1c <58 mmol/mol (7.5%). Cluster 1 (34.0%) was normal weight with the lowest HbA1c and comprised 85% NHW participants with the highest socioeconomic position, insulin pump use, dietary quality, and physical activity. Subgroups with very poor glycemic control (ie, ≥108 mmol/mol (≥12.0%); cluster 4, 4.4%, and cluster 5, 7.5%) and obesity (cluster 6, 15.4%) had a lower proportion of NHW youth, lower socioeconomic position, and reported decreased pump use and poorer health behaviors (overall p<0.01). The overweight subgroup with very poor glycemic control (cluster 5) showed the highest lipids and blood pressure (p<0.01). CONCLUSIONS: There are distinct subgroups of youth and young adults with T1D that share weight-glycemia phenotypes. Subgroups may benefit from tailored interventions addressing differences in clinical care, health behaviors, and underlying health inequity.

Estimating individualized treatment regimes from crossover designs.

The field of precision medicine aims to tailor treatment based on patient-specific factors in a reproducible way. To this end, estimating an optimal individualized treatment regime (ITR) that recommends treatment decisions based on patient characteristics to maximize the mean of a prespecified outcome is of particular interest. Several methods have been proposed for estimating an optimal ITR from clinical trial data in the parallel group setting where each subject is randomized to a single intervention. However, little work has been done in the area of estimating the optimal ITR from crossover study designs. Such designs naturally lend themselves to precision medicine since they allow for observing the response to multiple treatments for each patient. In this paper, we introduce a method for estimating the optimal ITR using data from a 2 × 2 crossover study with or without carryover effects. The proposed method is similar to policy search methods such as outcome weighted learning; however, we take advantage of the crossover design by using the difference in responses under each treatment as the observed reward. We establish Fisher and global consistency, present numerical experiments, and analyze data from a feeding trial to demonstrate the improved performance of the proposed method compared to standard methods for a parallel study design.

A molecular reconstruction approach to site-based 3D-RISM and comparison to GIST hydration thermodynamic maps in an enzyme active site.

Computed, high-resolution, spatial distributions of solvation energy and entropy can provide detailed information about the role of water in molecular recognition. While grid inhomogeneous solvation theory (GIST) provides rigorous, detailed thermodynamic information from explicit solvent molecular dynamics simulations, recent developments in the 3D reference interaction site model (3D-RISM) theory allow many of the same quantities to be calculated in a fraction of the time. However, 3D-RISM produces atomic-site, rather than molecular, density distributions, which are difficult to extract physical meaning from. To overcome this difficulty, we introduce a method to reconstruct molecular density distributions from atomic-site density distributions. Furthermore, we assess the quality of the resulting solvation thermodynamics density distributions by analyzing the binding site of coagulation Factor Xa with both GIST and 3D-RISM. We find good qualitative agreement between the methods for oxygen and hydrogen densities as well as direct solute-solvent energetic interactions. However, 3D-RISM predicts lower energetic and entropic penalties for moving water from the bulk to the binding site.

Longitudinal Phenotypes of Type 1 Diabetes in Youth Based on Weight and Glycemia and Their Association With Complications.

CONTEXT: Subclinical and clinical complications emerge early in type 1 diabetes (T1D) and may be associated with obesity and hyperglycemia. OBJECTIVE: Test how longitudinal "weight-glycemia" phenotypes increase susceptibility to different patterns of early/subclinical complications among youth with T1D. DESIGN: SEARCH for Diabetes in Youth observational study. SETTING: Population-based cohort. PARTICIPANTS: Youth with T1D (n = 570) diagnosed 2002 to 2006 or 2008. MAIN OUTCOME MEASURES: Participants were clustered based on longitudinal body mass index z score and HbA1c from a baseline visit and 5+ year follow-up visit (mean diabetes duration: 1.4 ± 0.4 years and 8.2 ± 1.9 years, respectively). Logistic regression modeling tested cluster associations with seven early/subclinical diabetes complications at follow-up, adjusting for sex, race/ethnicity, age, and duration. RESULTS: Four longitudinal weight-glycemia clusters were identified: The Referent Cluster (n = 195, 34.3%), the Hyperglycemia Only Cluster (n = 53, 9.3%), the Elevated Weight Only Cluster (n = 206, 36.1%), and the Elevated Weight With Increasing Hyperglycemia (EWH) Cluster (n = 115, 20.2%). Compared with the Referent Cluster, the Hyperglycemia Only Cluster had elevated odds of dyslipidemia [adjusted odds ratio (aOR) 2.22, 95% CI: 1.15 to 4.29], retinopathy (aOR 9.98, 95% CI: 2.49 to 40.0), and diabetic kidney disease (DKD) (aOR 4.16, 95% CI: 1.37 to 12.62). The EWH Cluster had elevated odds of hypertension (aOR 2.18, 95% CI: 1.19 to 4.00), dyslipidemia (aOR 2.36, 95% CI: 1.41 to 3.95), arterial stiffness (aOR 2.46, 95% CI: 1.09 to 5.53), retinopathy (aOR 5.11, 95% CI: 1.34 to 19.46), and DKD (aOR 3.43, 95% CI: 1.29 to 9.11). CONCLUSIONS: Weight-glycemia phenotypes show different patterns of complications, particularly markers of subclinical macrovascular disease, even in the first decade of T1D.

Identification of clinically relevant dysglycemia phenotypes based on continuous glucose monitoring data from youth with type 1 diabetes and elevated hemoglobin A1c.

BACKGROUND/OBJECTIVE: To identify and characterize subgroups of adolescents with type 1 diabetes (T1D) and elevated hemoglobin A1c (HbA1c) who share patterns in their continuous glucose monitoring (CGM) data as "dysglycemia phenotypes." METHODS: Data were analyzed from the Flexible Lifestyles Empowering Change randomized trial. Adolescents with T1D (13-16 years, duration >1 year) and HbA1c 8% to 13% (64-119 mmol/mol) wore blinded CGM at baseline for 7 days. Participants were clustered based on eight CGM metrics measuring hypoglycemia, hyperglycemia, and glycemic variability. Clusters were characterized by their baseline features and 18 months changes in HbA1c using adjusted mixed effects models. For comparison, participants were stratified by baseline HbA1c (≤/>9.0% [75 mmol/mol]). RESULTS: The study sample included 234 adolescents (49.8% female, baseline age 14.8 ± 1.1 years, baseline T1D duration 6.4 ± 3.7 years, baseline HbA1c 9.6% ± 1.2%, [81 ± 13 mmol/mol]). Three Dysglycemia Clusters were identified with significant differences across all CGM metrics (P < .001). Dysglycemia Cluster 3 (n = 40, 17.1%) showed severe hypoglycemia and glycemic variability with moderate hyperglycemia and had a lower baseline HbA1c than Clusters 1 and 2 (P < .001). This cluster showed increases in HbA1c over 18 months (p-for-interaction = 0.006). No other baseline characteristics were associated with Dysglycemia Clusters. High HbA1c was associated with lower pump use, greater insulin doses, more frequent blood glucose monitoring, lower motivation, and lower adherence to diabetes self-management (all P < .05). CONCLUSIONS: There are subgroups of adolescents with T1D for which glycemic control is challenged by different aspects of dysglycemia. Enhanced understanding of demographic, behavioral, and clinical characteristics that contribute to CGM-derived dysglycemia phenotypes may reveal strategies to improve treatment.

Testing inhomogeneous solvation theory in structure-based ligand discovery.

Binding-site water is often displaced upon ligand recognition, but is commonly neglected in structure-based ligand discovery. Inhomogeneous solvation theory (IST) has become popular for treating this effect, but it has not been tested in controlled experiments at atomic resolution. To do so, we turned to a grid-based version of this method, GIST, readily implemented in molecular docking. Whereas the term only improves docking modestly in retrospective ligand enrichment, it could be added without disrupting performance. We thus turned to prospective docking of large libraries to investigate GIST's impact on ligand discovery, geometry, and water structure in a model cavity site well-suited to exploring these terms. Although top-ranked docked molecules with and without the GIST term often overlapped, many ligands were meaningfully prioritized or deprioritized; some of these were selected for testing. Experimentally, 13/14 molecules prioritized by GIST did bind, whereas none of the molecules that it deprioritized were observed to bind. Nine crystal complexes were determined. In six, the ligand geometry corresponded to that predicted by GIST, for one of these the pose without the GIST term was wrong, and three crystallographic poses differed from both predictions. Notably, in one structure, an ordered water molecule with a high GIST displacement penalty was observed to stay in place. Inclusion of this water-displacement term can substantially improve the hit rates and ligand geometries from docking screens, although the magnitude of its effects can be small and its impact in drug binding sites merits further controlled studies.

Solvation thermodynamic mapping of molecular surfaces in AmberTools: GIST.

The expulsion of water from surfaces upon molecular recognition and nonspecific association makes a major contribution to the free energy changes of these processes. In order to facilitate the characterization of water structure and thermodynamics on surfaces, we have incorporated Grid Inhomogeneous Solvation Theory (GIST) into the CPPTRAJ toolset of AmberTools. GIST is a grid-based implementation of Inhomogeneous Fluid Solvation Theory, which analyzes the output from molecular dynamics simulations to map out solvation thermodynamic and structural properties on a high-resolution, three-dimensional grid. The CPPTRAJ implementation, called GIST-cpptraj, has a simple, easy-to-use command line interface, and is open source and freely distributed. We have also developed a set of open-source tools, called GISTPP, which facilitate the analysis of GIST output grids. Tutorials for both GIST-cpptraj and GISTPP can be found at ambermd.org. © 2016 Wiley Periodicals, Inc.

Spatial Decomposition of Translational Water-Water Correlation Entropy in Binding Pockets.

A number of computational tools available today compute the thermodynamic properties of water at surfaces and in binding pockets by using inhomogeneous solvation theory (IST) to analyze explicit-solvent simulations. Such methods enable qualitative spatial mappings of both energy and entropy around a solute of interest and can also be applied quantitatively. However, the entropy estimates of existing methods have, to date, been almost entirely limited to the first-order terms in the IST's entropy expansion. These first-order terms account for localization and orientation of water molecules in the field of the solute but not for the modification of water-water correlations by the solute. Here, we present an extension of the Grid Inhomogeneous Solvation Theory (GIST) approach which accounts for water-water translational correlations. The method involves rewriting the two-point density of water in terms of a conditional density and utilizes the efficient nearest-neighbor entropy estimation approach. Spatial maps of this second order term, for water in and around the synthetic host cucurbit[7]uril and in the binding pocket of the enzyme Factor Xa, reveal mainly negative contributions, indicating solute-induced water-water correlations relative to bulk water; particularly strong signals are obtained for sites at the entrances of cavities or pockets. This second-order term thus enters with the same, negative, sign as the first order translational and orientational terms. Numerical and convergence properties of the methodology are examined.

Parameterization of an effective potential for protein-ligand binding from host-guest affinity data.

Force field accuracy is still one of the "stalemates" in biomolecular modeling. Model systems with high quality experimental data are valuable instruments for the validation and improvement of effective potentials. With respect to protein-ligand binding, organic host-guest complexes have long served as models for both experimental and computational studies because of the abundance of binding affinity data available for such systems. Binding affinity data collected for cyclodextrin (CD) inclusion complexes, a popular model for molecular recognition, is potentially a more reliable resource for tuning energy parameters than hydration free energy measurements. Convergence of binding free energy calculations on CD host-guest systems can also be obtained rapidly, thus offering the opportunity to assess the robustness of these parameters. In this work, we demonstrate how implicit solvent parameters can be developed using binding affinity experimental data and the binding energy distribution analysis method (BEDAM) and validated using the Grid Inhomogeneous Solvation Theory analysis. These new solvation parameters were used to study protein-ligand binding in two drug targets against the HIV-1 virus and improved the agreement between the calculated and the experimental binding affinities. This work illustrates how benchmark sets of high quality experimental binding affinity data and physics-based binding free energy models can be used to evaluate and optimize force fields for protein-ligand systems. Copyright © 2015 John Wiley & Sons, Ltd.

Thermodynamics of Water in an Enzyme Active Site: Grid-Based Hydration Analysis of Coagulation Factor Xa.

Water molecules in the active site of an enzyme occupy a complex, heterogeneous environment, and the thermodynamic properties of active-site water are functions of position. As a consequence, it is thought that an enzyme inhibitor can gain affinity by extending into a region occupied by unfavorable water or lose affinity by displacing water from a region where it was relatively stable. Recent advances in the characterization of binding-site water, based on the analysis of molecular simulations with explicit water molecules, have focused largely on simplified representations of water as occupying well-defined hydration sites. Our grid-based treatment of hydration, GIST, offers a more complete picture of the complex distributions of water properties, but it has not yet been applied to proteins. This first application of GIST to protein-ligand modeling, for the case of Coagulation Factor Xa, shows that ligand scoring functions based on GIST perform at least as well as scoring functions based on a hydration-site approach (HSA), when applied to exactly the same simulation data. Interestingly, the displacement of energetically unfavorable water emerges as the dominant factor in the fitted scoring functions, for both GIST and HSA methods, while water entropy plays a secondary role, at least in the present context.

Grid inhomogeneous solvation theory: hydration structure and thermodynamics of the miniature receptor cucurbit[7]uril.

The displacement of perturbed water upon binding is believed to play a critical role in the thermodynamics of biomolecular recognition, but it is nontrivial to unambiguously define and answer questions about this process. We address this issue by introducing grid inhomogeneous solvation theory (GIST), which discretizes the equations of inhomogeneous solvation theory (IST) onto a three-dimensional grid situated in the region of interest around a solute molecule or complex. Snapshots from explicit solvent simulations are used to estimate localized solvation entropies, energies, and free energies associated with the grid boxes, or voxels, and properly summing these thermodynamic quantities over voxels yields information about hydration thermodynamics. GIST thus provides a smoothly varying representation of water properties as a function of position, rather than focusing on hydration sites where solvent is present at high density. It therefore accounts for full or partial displacement of water from sites that are highly occupied by water, as well as for partly occupied and water-depleted regions around the solute. GIST can also provide a well-defined estimate of the solvation free energy and therefore enables a rigorous end-states analysis of binding. For example, one may not only use a first GIST calculation to project the thermodynamic consequences of displacing water from the surface of a receptor by a ligand, but also account, in a second GIST calculation, for the thermodynamics of subsequent solvent reorganization around the bound complex. In the present study, a first GIST analysis of the molecular host cucurbit[7]uril is found to yield a rich picture of hydration structure and thermodynamics in and around this miniature receptor. One of the most striking results is the observation of a toroidal region of high water density at the center of the host's nonpolar cavity. Despite its high density, the water in this toroidal region is disfavored energetically and entropically, and hence may contribute to the known ability of this small receptor to bind guest molecules with unusually high affinities. Interestingly, the toroidal region of high water density persists even when all partial charges of the receptor are set to zero. Thus, localized regions of high solvent density can be generated in a binding site without strong, attractive solute-solvent interactions.

How dominant is the most efficient pathway through the potential energy landscape of a slowly diffusing disordered system?

It has been suggested that the most-efficient pathway taken by a slowly diffusing many-body system is its geodesic path through the parts of the potential energy landscape lying below a prescribed value of the potential energy. From this perspective, slow diffusion occurs just because these optimal paths become particularly long and convoluted. We test this idea here by applying it to diffusion in two kinds of well-studied low-dimensional percolation problems: the 2d overlapping Lorentz model, and square and simple-cubic bond-dilute lattices. Although the most efficient path should be at its most dominant with the high-dimensional landscapes associated with many-body problems, it is useful to examine simpler, low-dimensional, constant-potential-energy problems such as these ones, both because the simpler models lend themselves to more accurate geodesic-path-finding approaches, and because they offer a significant contrast to many of the models used in the traditional energy-landscape literature. Neither the continuum nor the lattice percolation examples are adequately described by our geodesic-path formalism in the weakly disordered (relatively-fast-diffusion) limit, but in both cases the formalism successfully predicts the existence of the percolation transition and (to a certain extent) the slow diffusion characteristic of near-percolation behavior. The numerical results for these models are not nearly accurate enough near their transitions to describe critical exponents, but the models do showcase the qualitative validity of the geodesic perspective in that they allow us to see explicitly how tortuous and sparse the optimal pathways become as the diffusion constants begin to vanish.

Preferential solvation dynamics in liquids: how geodesic pathways through the potential energy landscape reveal mechanistic details about solute relaxation in liquids.

It is not obvious that many-body phenomena as collective as solute energy relaxation in liquid solution should ever have identifiable molecular mechanisms, at least not in the sense of the well-defined sequence of molecular events one often attributes to chemical reactions. What can define such mechanisms, though, are the most efficient relaxation paths that solutions take through their potential energy landscapes. When liquid dynamics is dominated by slow diffusive processes, there are mathematically precise and computationally accessible routes to searching for such paths. We apply this observation to the dynamics of preferential solvation, the relaxation around a newly excited solute by a solvent composed of different components with different solvating abilities. The slow solvation seen experimentally in these mixtures stems from the dual needs to compress the solvent and to do solvent-solvent exchanges near the solute. By studying the geodesic (most efficient) paths for this combined process in a simple atomic liquid mixture, we show that the mechanism for preferential solvation features a reasonably sharp onset for slow diffusion, and that this diffusion involves a sequential, rather than concerted, series of solvent exchanges.

Microelectrical sensors as emerging platforms for protein biomarker detection in point-of-care diagnostics.

Current methods used to measure protein expression on microarrays, such as labeled fluorescent imaging, are not well suited for real-time, diagnostic measurements at the point of care. Studies have shown that microelectrical sensors utilizing silica nanowire, impedimetric, surface acoustic wave, magnetic nanoparticle and microantenna technologies have the potential to impact disease diagnosis by offering sensing characteristics that rival conventional sensing techniques. Their ability to transduce protein binding events into electrical signals may prove essential for the development of next-generation point-of-care devices for molecular diagnostics, where they could be easily integrated with microarray, microfluidic and telemetry technologies. However, common limitations associated with the microelectrical sensors, including problems with sensor fabrication and sensitivity, must first be resolved. This review describes governing technical concepts and provides examples demonstrating the use of various microelectrical sensors in the diagnosis of disease via protein biomarkers.