Torsemide in the Management of Pulmonary Edema.

Pulmonary edema, either cardiogenic or noncardiogenic, is caused by fluid accumulation in the alveolar spaces. Cardiogenic pulmonary edema (CPE), one of the causes of congestive heart failure (CHF), is treated with loop diuretics. Torsemide and furosemide were found to be useful in the treatment of CHF-associated pulmonary edema due to their ability to lower pulmonary capillary pressure and left ventricular end-diastolic pressure, respectively. Pharmacological features of torsemide, such as greater bioavailability, higher absorption rate, and efficacy, make it a better alternative for treating pulmonary edema than the regularly used loop diuretic, furosemide. Torsemide administered intravenously was found to be both efficacious and well tolerated in CPE. However, more research is needed to determine its usefulness in non-CPE.

Intertrochanteric Hip Fractures: Pearls and Pitfalls in Managing Difficult Fractures.

Intertrochanteric hip fractures are among the most common osteoporotic fractures seen by orthopaedic surgeons. These fractures have a significant effect on a patient's mobility, independence, and mortality. In addition, they represent a substantial component of health care spending. Treatment is almost universally surgical, and surgeons must pay attention to patient optimization, fracture characteristics, and surgical planning. The goal of surgical intervention is to maximize the patient's ability to return to preinjury level of function by early postoperative mobilization. This can be achieved by obtaining and maintaining reduction to fracture healing.

Evolution of Negative Pressure Wound Therapy in Orthopaedic Trauma.

SUMMARY: Negative Pressure Wound Therapy (NPWT) has evolved from open wound management to now include closed incision management. It has been a major advance in the management of open wounds and closed incisional wounds especially in orthopaedic trauma surgery. Because of the success of NPWT in the late 1990s and early 2000s, surgeons began using NPWT with adjuncts on closed incisions as a way to help prevent surgical wound dehiscence especially in at-risk patients for wound problems. It has been well established that obesity, diabetes, and smoking in addition to other comorbidities increase the risk of wound dehiscence and surgical site infections in orthopaedic patients. It is widely used for open wound management, often associated with open fractures, and in the mitigation of risk of surgical site infections over closed incisions (incisional negative pressure wound therapy). Newer systems allow the use of various topical wound solutions to be instilled in conjunction with NPWT, termed NPWTi-d. This has shown promising results in difficult wounds that may be resistant to standard NPWT. This article reviews the evolution and use of NPWT in orthopaedic trauma.

Machine learning-driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins.

RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades.

Unveiling the Dynamics of KRAS4b on Lipid Model Membranes.

Small GTPase proteins are ubiquitous and responsible for regulating several processes related to cell growth and differentiation. Mutations that stabilize their active state can lead to uncontrolled cell proliferation and cancer. Although these proteins are well characterized at the cellular scale, the molecular mechanisms governing their functions are still poorly understood. In addition, there is limited information about the regulatory function of the cell membrane which supports their activity. Thus, we have studied the dynamics and conformations of the farnesylated KRAS4b in various membrane model systems, ranging from binary fluid mixtures to heterogeneous raft mimics. Our approach combines long time-scale coarse-grained (CG) simulations and Markov state models to dissect the membrane-supported dynamics of KRAS4b. Our simulations reveal that protein dynamics is mainly modulated by the presence of anionic lipids and to some extent by the nucleotide state (activation) of the protein. In addition, our results suggest that both the farnesyl and the polybasic hypervariable region (HVR) are responsible for its preferential partitioning within the liquid-disordered (Ld) domains in membranes, potentially enhancing the formation of membrane-driven signaling platforms.

Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media.

Fine-scale models that represent first-principles physics are challenging to represent at larger scales of interest in many application areas. In nanoporous media such as tight-shale formations, where the typical pore size is less than 50 nm, confinement effects play a significant role in how fluids behave. At these scales, fluids are under confinement, affecting key properties such as density, viscosity, adsorption, etc. Pore-scale Lattice Boltzmann Methods (LBM) can simulate flow in complex pore structures relevant to predicting hydrocarbon production, but must be corrected to account for confinement effects. Molecular dynamics (MD) can model confinement effects but is computationally expensive in comparison. The hurdle to bridging MD with LBM is the computational expense of MD simulations needed to perform this correction. Here, we build a Machine Learning (ML) surrogate model that captures adsorption effects across a wide range of parameter space and bridges the MD and LBM scales using a relatively small number of MD calculations. The model computes upscaled adsorption parameters across varying density, temperature, and pore width. The ML model is 7 orders of magnitude faster than brute force MD. This workflow is agnostic to the physical system and could be generalized to further scale-bridging applications.

Computing long time scale biomolecular dynamics using quasi-stationary distribution kinetic Monte Carlo (QSD-KMC).

It is a challenge to obtain an accurate model of the state-to-state dynamics of a complex biological system from molecular dynamics (MD) simulations. In recent years, Markov state models have gained immense popularity for computing state-to-state dynamics from a pool of short MD simulations. However, the assumption that the underlying dynamics on the reduced space is Markovian induces a systematic bias in the model, especially in biomolecular systems with complicated energy landscapes. To address this problem, we have devised a new approach we call quasistationary distribution kinetic Monte Carlo (QSD-KMC) that gives accurate long time state-to-state evolution while retaining the entire time resolution even when the dynamics is highly non-Markovian. The proposed method is a kinetic Monte Carlo approach that takes advantage of two concepts: (i) the quasistationary distribution, the distribution that results when a trajectory remains in one state for a long time (the dephasing time), such that the next escape is Markovian, and (ii) dynamical corrections theory, which properly accounts for the correlated events that occur as a trajectory passes from state to state before it settles again. In practice, this is achieved by specifying, for each escape, the intermediate states and the final state that has resulted from the escape. Implementation of QSD-KMC imposes stricter requirements on the lengths of the trajectories than in a Markov state model approach as the trajectories must be long enough to dephase. However, the QSD-KMC model produces state-to-state trajectories that are statistically indistinguishable from an MD trajectory mapped onto the discrete set of states for an arbitrary choice of state decomposition. Furthermore, the aforementioned concepts can be used to construct a Monte Carlo approach to optimize the state boundaries regardless of the initial choice of states. We demonstrate the QSD-KMC method on two one-dimensional model systems, one of which is a driven nonequilibrium system, and on two well-characterized biomolecular systems.

Management of Closed Incisions Using Negative-Pressure Wound Therapy in Orthopedic Surgery.

Negative-pressure wound therapy (NPWT) has been a major advance in the management of open wounds, especially in orthopedic trauma surgery. Due to the success of NPWT, surgeons began using NPWT on closed incisions as a way to help prevent surgical wound dehiscence, especially in at-risk patients for wound problems. It has been well established that obesity, diabetes mellitus, and smoking in addition to other comorbidities increase the risk of wound dehiscence and surgical site infections in orthopedic patients. This article reviews the current literature and provides an overview on the use of NPWT on closed incisions in orthopedic trauma procedures and joint arthroplasty.

Adaptive resolution molecular dynamics technique: Down to the essential.

We investigate the role of the thermodynamic (TD) force as an essential and sufficient technical ingredient for an efficient and accurate adaptive resolution algorithm. Such a force applied in the coupling region of an adaptive resolution molecular dynamics setup assures thermodynamic equilibrium between atomistically resolved and coarse-grained regions, allowing the proper exchange of molecules. We numerically prove that indeed for systems as relevant as liquid water and 1,3-dimethylimidazolium chloride ionic liquid, the combined action of the TD force and thermostat allows for computationally efficient and numerically accurate simulations, beyond the current capabilities of adaptive resolution setups, which employ switching functions in the coupling region.

Path integral-GC-AdResS simulation of a large hydrophobic solute in water: a tool to investigate the interplay between local microscopic structures and quantum delocalization of atoms in space.

We perform large scale quantum (path integral) molecular dynamics simulations of a C60-like molecule in water. The path integral (PI) technique allows for the description of the delocalization of atoms in space and of its consequences on the structure and dynamics of the hydrogen bonding network around the solute. We then employ the adaptive resolution method GC-AdResS, which unambiguously defines the essential (necessary) degrees of freedom required for a certain property, to analyze the locality of the water structure around the solute. We show that the feature of locality is independent of the use of a quantum or classical model of water. However the water structure around the solute obtained from classical simulations is more ordered and rigid than the structure found in quantum simulations. With this study we mainly intend to show that GC-AdResS, besides its computational efficiency, can be used as a powerful tool of multiscale analysis; this capability, in turn, can be used to speculate about processes at larger scales. We make an example, whose current validity is restricted to the molecular models specifically used, regarding the possible role of quantum effects in the aggregation of fullerene-like molecules in water.

Closed incision negative pressure therapy: international multidisciplinary consensus recommendations.

Surgical site occurrences (SSOs) affect up to or over 25% of patients undergoing operative procedures, with the subset of surgical site infections (SSIs) being the most common. Commercially available closed incision negative pressure therapy (ciNPT) may offer surgeons an additional option to manage clean, closed surgical incisions. We conducted an extensive literature search for studies describing ciNPT use and assembled a diverse panel of experts to create consensus recommendations for when using ciNPT may be appropriate. A literature search of MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials using key words 'prevention', 'negative pressure wound therapy (NPWT)', 'active incisional management', 'incisional vacuum therapy', 'incisional NPWT', 'incisional wound VAC', 'closed incisional NPWT', 'wound infection', and 'SSIs' identified peer-reviewed studies published from 2000 to 2015. During a multidisciplinary consensus meeting, the 12 experts reviewed the literature, presented their own ciNPT experiences, identified risk factors for SSOs and developed comprehensive consensus recommendations. A total of 100 publications satisfied the search requirements for ciNPT use. A majority presented data supporting ciNPT use. Numerous publications reported SSI risk factors, with the most common including obesity (body mass index ≥30 kg/m2 ); diabetes mellitus; tobacco use; or prolonged surgical time. We recommend that the surgeon assess the individual patient's risk factors and surgical risks. Surgeons should consider using ciNPT for patients at high risk for developing SSOs or who are undergoing a high-risk procedure or a procedure that would have highly morbid consequences if an SSI occurred.

Path integral molecular dynamics within the grand canonical-like adaptive resolution technique: Simulation of liquid water.

Quantum effects due to the spatial delocalization of light atoms are treated in molecular simulation via the path integral technique. Among several methods, Path Integral (PI) Molecular Dynamics (MD) is nowadays a powerful tool to investigate properties induced by spatial delocalization of atoms; however, computationally this technique is very demanding. The above mentioned limitation implies the restriction of PIMD applications to relatively small systems and short time scales. One of the possible solutions to overcome size and time limitation is to introduce PIMD algorithms into the Adaptive Resolution Simulation Scheme (AdResS). AdResS requires a relatively small region treated at path integral level and embeds it into a large molecular reservoir consisting of generic spherical coarse grained molecules. It was previously shown that the realization of the idea above, at a simple level, produced reasonable results for toy systems or simple/test systems like liquid parahydrogen. Encouraged by previous results, in this paper, we show the simulation of liquid water at room conditions where AdResS, in its latest and more accurate Grand-Canonical-like version (GC-AdResS), is merged with two of the most relevant PIMD techniques available in the literature. The comparison of our results with those reported in the literature and/or with those obtained from full PIMD simulations shows a highly satisfactory agreement.

What does scalar timing tell us about neural dynamics?

The "Scalar Timing Law," which is a temporal domain generalization of the well known Weber Law, states that the errors estimating temporal intervals scale linearly with the durations of the intervals. Linear scaling has been studied extensively in human and animal models and holds over several orders of magnitude, though to date there is no agreed upon explanation for its physiological basis. Starting from the assumption that behavioral variability stems from neural variability, this work shows how to derive firing rate functions that are consistent with scalar timing. We show that firing rate functions with a log-power form, and a set of parameters that depend on spike count statistics, can account for scalar timing. Our derivation depends on a linear approximation, but we use simulations to validate the theory and show that log-power firing rate functions result in scalar timing over a large range of times and parameters. Simulation results match the predictions of our model, though our initial formulation results in a slight bias toward overestimation that can be corrected using a simple iterative approach to learn a decision threshold.

Chemical potential of liquids and mixtures via adaptive resolution simulation.

We employ the adaptive resolution approach AdResS, in its recently developed Grand Canonical-like version (GC-AdResS) [H. Wang, C. Hartmann, C. Schütte, and L. Delle Site, Phys. Rev. X 3, 011018 (2013)], to calculate the excess chemical potential, µ(ex), of various liquids and mixtures. We compare our results with those obtained from full atomistic simulations using the technique of thermodynamic integration and show a satisfactory agreement. In GC-AdResS, the procedure to calculate µ(ex) corresponds to the process of standard initial equilibration of the system; this implies that, independently of the specific aim of the study, µ(ex), for each molecular species, is automatically calculated every time a GC-AdResS simulation is performed.

The role of the cation in the solvation of cellulose by imidazolium-based ionic liquids.

We present a systematic molecular dynamics study examining the roles of the individual ions of different alkylimidazolium-based ionic liquids in the solvation of cellulose. We examine combinations of chloride, acetate, and dimethylphosphate anions paired with cations of increasing tail length to elucidate the precise role of the cation in solvating cellulose. In all cases we find that the cation interacts with the nonpolar domains of cellulose through dispersion interactions, while interacting electrostatically with the anions bound at the polar domains of cellulose. Furthermore, the structure and dimensions of the imidazolium head facilitate the formation of large chains and networks of alternating cations and anions that form a patchwork, satisfying both the polar and nonpolar domains of cellulose. A subtle implication of increasing tail length is the dilution of the anion concentration in the bulk and at the cellulose surface. We show how this decreased concentration of anions in the bulk affects hydrogen bond formation with cellulose and how rearrangements from single hydrogen bonds to multiple shared hydrogen bonds can moderate the loss in overall hydrogen bond numbers. Additionally, for the tail lengths examined in this study we observe only a very minor effect of tail length on the solvation structure and overall interaction energies.

Electromagnetic navigation reduces surgical time and radiation exposure for proximal interlocking in retrograde femoral nailing.

OBJECTIVES: To compare the time required for proximal locking screw placement between a standard freehand technique and the navigated technique, and to quantify the reduction in ionizing radiation exposure. METHODS: A fresh frozen cadaver model was used for 48 proximal interlocking screw procedures. Each procedure consisted of insertion of 2 anteroposterior locking screws. Standard fluoroscopic technique was used for 24 procedures, and an electromagnetic navigation system was used for the remaining 24 procedures. Procedure duration was recorded using an electronic timer and radiation doses were documented. RESULTS: Mean total insertion time for both proximal interlocking screws was 405 ± 165.7 seconds with the freehand technique and 311 ± 78.3 seconds in the navigation group (P = 0.002). All procedures resulted in successful locking screw placement. Mean ionizing radiation exposure time for proximal locking was 29.5 ± 12.8 seconds. CONCLUSIONS: Proximal locking screw insertion using the navigation technique evaluated in this work was significantly faster than the standard fluoroscopic method. The navigated technique is effective and has the potential to prevent ionizing radiation exposure.

Scaling of perceptual errors can predict the shape of neural tuning curves.

Weber's law, first characterized in the 19th century, states that errors estimating the magnitude of perceptual stimuli scale linearly with stimulus intensity. This linear relationship is found in most sensory modalities, generalizes to temporal interval estimation, and even applies to some abstract variables. Despite its generality and long experimental history, the neural basis of Weber's law remains unknown. This work presents a simple theory explaining the conditions under which Weber's law can result from neural variability and predicts that the tuning curves of neural populations which adhere to Weber's law will have a log-power form with parameters that depend on spike-count statistics. The prevalence of Weber's law suggests that it might be optimal in some sense. We examine this possibility, using variational calculus, and show that Weber's law is optimal only when observed real-world variables exhibit power-law statistics with a specific exponent. Our theory explains how physiology gives rise to the behaviorally characterized Weber's law and may represent a general governing principle relating perception to neural activity.

Observed mechanism for the breakup of small bundles of cellulose Iα and Iß in ionic liquids from molecular dynamics simulations.

Explicit, all-atom molecular dynamics simulations are used to study the breakup of small bundles of cellulose Iα and Iß in the ionic liquids [BMIM]Cl, [EMIM]Ac, and [DMIM]DMP. In all cases, significant breakup of the bundles is observed with the initial breakup following a common underlying mechanism. Anions bind strongly to the hydroxyl groups of the exterior strands of the bundle, forming negatively charged complexes. Binding also weakens the intrastrand hydrogen bonds present in the cellulose strands, providing greater strand flexibility. Cations then intercalate between the individual strands, likely due to charge imbalances, providing the bulk to push the individual moieties apart and initiating the separation. The peeling of an individual strand from the main bundle is observed in [EMIM]Ac with an analysis of its hydrogen bonds with other strands showing that the chain detaches glucan by glucan from the main bundle in discrete, rapid events. Further analysis shows that the intrastrand hydrogen bonds of each glucan tend to break for a sustained period of time before the interstrand hydrogen bonds break and strand detachment occurs. Examination of similar nonpeeling strands shows that, without this intrastrand hydrogen bond breakage, the structural rigidity of the individual unit can hinder its peeling despite interstrand hydrogen bond breakage.

On the precision of quasi steady state assumptions in stochastic dynamics.

Many biochemical networks have complex multidimensional dynamics and there is a long history of methods that have been used for dimensionality reduction for such reaction networks. Usually a deterministic mass action approach is used; however, in small volumes, there are significant fluctuations from the mean which the mass action approach cannot capture. In such cases stochastic simulation methods should be used. In this paper, we evaluate the applicability of one such dimensionality reduction method, the quasi-steady state approximation (QSSA) [L. Menten and M. Michaelis, "Die kinetik der invertinwirkung," Biochem. Z 49, 333369 (1913)] for dimensionality reduction in case of stochastic dynamics. First, the applicability of QSSA approach is evaluated for a canonical system of enzyme reactions. Application of QSSA to such a reaction system in a deterministic setting leads to Michaelis-Menten reduced kinetics which can be used to derive the equilibrium concentrations of the reaction species. In the case of stochastic simulations, however, the steady state is characterized by fluctuations around the mean equilibrium concentration. Our analysis shows that a QSSA based approach for dimensionality reduction captures well the mean of the distribution as obtained from a full dimensional simulation but fails to accurately capture the distribution around that mean. Moreover, the QSSA approximation is not unique. We have then extended the analysis to a simple bistable biochemical network model proposed to account for the stability of synaptic efficacies; the substrate of learning and memory [J. E. Lisman, "A mechanism of memory storage insensitive to molecular turnover: A bistable autophosphorylating kinase," Proc. Natl. Acad. Sci. U.S.A. 82, 3055-3057 (1985)]. Our analysis shows that a QSSA based dimensionality reduction method results in errors as big as two orders of magnitude in predicting the residence times in the two stable states.