Diagnostic blood testing is the most prevalent medical procedure performed in the world and forms the cornerstone of modern health care delivery. Yet blood tests are still predominantly carried out in centralized labs using large-volume samples acquired by manual venipuncture, and no end-to-end solution from blood draw to sample analysis exists today. Our group is developing a platform device that merges robotic phlebotomy with automated diagnostics to rapidly deliver patient information at the site of the blood draw. The system couples an image-guided venipuncture robot, designed to address the challenges of routine venous access, with a centrifuge-based blood analyzer to obtain quantitative measurements of hematology. In this paper, we first present the system design and architecture of the integrated device. We then perform a series of in vitro experiments to evaluate the cannulation accuracy of the system on blood vessel phantoms. Next, we assess the effects of vessel diameter, needle gauge, flow rate, and viscosity on the rate of sample collection. Finally, we demonstrate proof-of-concept of a white cell assay on the blood analyzer using in vitro human samples spiked with fluorescently labeled microbeads.

This paper presents a portable imaging device designed to detect peripheral blood vessels for cannula insertion that are otherwise difficult to visualize beneath the skin. The device combines near infrared stereo vision, ultrasound, and real-time image analysis to map the 3D structure of subcutaneous vessels. We show that the device can identify adult forearm vessels and be used to guide manual insertions in tissue phantoms with increased first-stick accuracy compared to unassisted cannulation. We also demonstrate that the system may be coupled with a robotic manipulator to perform automated, image-guided venipuncture. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Venipuncture is the most common invasive medical procedure performed in the United States and the number one cause of hospital injury. Failure rates are particularly high in pediatric and elderly patients, whose veins tend to deform, move, or roll as the needle is introduced. To improve venipuncture accuracy in challenging patient populations, we have developed a portable device that autonomously servos a needle into a suitable vein under image guidance. The device operates in real time, combining near-infrared and ultrasound imaging, computer vision software, and a 9 degrees-of-freedom robot that servos the needle. In this paper, we present the kinematic and mechanical design of the latest generation robot. We then investigate in silico and in vitro the mechanics of vessel rolling and deformation in response to needle insertions performed by the robot. Finally, we demonstrate how the robot can make real-time adjustments under ultrasound image guidance to compensate for subtle vessel motions during venipuncture.

Accessing the venous bloodstream to deliver fluids or obtain a blood sample is the most common clinical routine practiced in the U.S. Practitioners continue to rely on manual venipuncture techniques, but success rates are heavily dependent on clinician skill and patient physiology. In the U.S., failure rates can be as high as 50% in difficult patients, making venipuncture the leading cause of medical injury. To improve the rate of first-stick success, we have developed a portable autonomous venipuncture device that robotically servos a needle into a suitable vein under image guidance. The device operates in real time, combining near-infrared and ultrasound imaging, image analysis, and a 7-degree-of-freedom (DOF) robotic system to perform the venipuncture. The robot consists of a 3-DOF gantry to image the patient's peripheral forearm veins and a miniaturized 4-DOF serial arm to guide the cannula into the selected vein under closed-loop control. In this paper, we present the system architecture of the robot and evaluate the accuracy and precision through tracking, free-space positioning, and in vitro phantom cannulation experiments. The results demonstrate submillimeter accuracy throughout the operating workspace of the manipulator and a high rate of success when cannulating phantom veins in a skin-mimicking tissue model.

Controlling electroporation and determination of its design parameters govern the performance in many electroporation applications in the fields of medicine, biology, and food industry. This work provides an enhanced high throughput signal processing method to enable non-expert, objective, automatic and fast analysis of large datasets of microscopic images of high throughput electroporation experiments. This can enable optimal design of vital electroporation parameters including the optimal thresholds for the electric field strength, and the optimal number of pulses. In this work the non-expert visualization platform and the methods are described, and implemented on normal dermal human fibroblasts cell culture.

MOTIVATION: Primary purpose of modeling gene regulatory networks for developmental process is to reveal pathways governing the cellular differentiation to specific phenotypes. Knowledge of differentiation network will enable generation of desired cell fates by careful alteration of the governing network by adequate manipulation of cellular environment. RESULTS: We have developed a novel integer programming-based approach to reconstruct the underlying regulatory architecture of differentiating embryonic stem cells from discrete temporal gene expression data. The network reconstruction problem is formulated using inherent features of biological networks: (i) that of cascade architecture which enables treatment of the entire complex network as a set of interconnected modules and (ii) that of sparsity of interconnection between the transcription factors. The developed framework is applied to the system of embryonic stem cells differentiating towards pancreatic lineage. Experimentally determined expression profile dynamics of relevant transcription factors serve as the input to the network identification algorithm. The developed formulation accurately captures many of the known regulatory modes involved in pancreatic differentiation. The predictive capacity of the model is tested by simulating an in silico potential pathway of subsequent differentiation. The predicted pathway is experimentally verified by concurrent differentiation experiments. Experimental results agree well with model predictions, thereby illustrating the predictive accuracy of the proposed algorithm. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The design hypothesis, architectures, and computational modeling of a novel peptide based nanoGripper are presented in this paper. We engineered the alpha-helical coiled coil portion of the yeast transcriptional activator peptide called GCN4 to obtain an environmentally-responsive nanoGripper. The dimeric coiled-coil peptide consists of two identical ~4.5nm long and ~3nm wide polypeptide chains. The actuation mechanism depends on the modification of electrostatic charges along the peptide by varying the pH of the solution resulting in the reversible movement of helices and therefore, creating the motion of a gripper. Using molecular dynamics simulations we showed that pH changes led to a reversible opening of up-to 1.5 nm which is approximately 150 % of the initial separation of the nanoGripper. We also investigated the forces generated by the nanoGripper upon pH actuation. Using a new method based on a modified steered molecular dynamics technique we were able to show that the force output of the nanoGripper is comparable to that generated by ATP-based molecular motors such as myosin and kinesin even though our molecular tweezer is smaller in size to these molecular motors.

MOTIVATION: The living cell array quantifies the contribution of activated transcription factors upon the expression levels of their target genes. The direct manipulation of the regulatory mechanisms offers enormous possibilities for deciphering the machinery that activates and controls gene expression. We propose a novel bi-clustering algorithm for generating non-overlapping clusters of reporter genes and conditions and demonstrate how this information can be interpreted in order to assist in the construction of transcription factor interaction networks.

BACKGROUND: Thermal injury is among the most severe forms of trauma and its effects are both local and systemic. Response to thermal injury includes cellular protection mechanisms, inflammation, hypermetabolism, prolonged catabolism, organ dysfunction and immuno-suppression. It has been hypothesized that gene expression patterns in the liver will change with severe burns, thus reflecting the role the liver plays in the response to burn injury. Characterizing the molecular fingerprint (i.e., expression profile) of the inflammatory response resulting from burns may help elucidate the activated mechanisms and suggest new therapeutic intervention. In this paper we propose a novel integrated framework for analyzing time-series transcriptional data, with emphasis on the burn-induced response within the context of the rat animal model. Our analysis robustly identifies critical expression motifs, indicative of the dynamic evolution of the inflammatory response and we further propose a putative reconstruction of the associated transcription factor activities. RESULTS: Implementation of our algorithm on data obtained from an animal (rat) burn injury study identified 281 genes corresponding to 4 unique profiles. Enrichment evaluation upon both gene ontologies and transcription factors, verifies the inflammation-specific character of the selections and the rationalization of the burn-induced inflammatory response. Conducting the transcription network reconstruction and analysis, we have identified transcription factors, including AHR, Octamer Binding Proteins, Kruppel-like Factors, and cell cycle regulators as being highly important to an organism's response to burn response. These transcription factors are notable due to their roles in pathways that play a part in the gross physiological response to burn such as changes in the immune response and inflammation. CONCLUSION: Our results indicate that our novel selection/classification algorithm has been successful in selecting out genes with play an important role in thermal injury. Additionally, we have demonstrated the value of an integrative approach in identifying possible points of intervention, namely the activation of certain transcription factors that govern the organism's response.

Dynamic and kinematic analysis is performed to predict and verify the performance of a new nanoscale biomolecular motor: The Viral Protein Linear (VPL) Motor. The motor is based on a conformational change observed in a family of viral envelope proteins when subjected to a changing pH environment. The conformational change produces a motion of about 10 nm, making the VPL a basic linear actuator which can be further interfaced with other organic/inorganic nanoscale components such as DNA actuators and carbon nanotubes. The proteins used in the motor are subjected to Molecular Dynamics simulation using the software called CHARMm (Chemistry at Harvard Molecular Mechanics). The results of dynamics are further verified by performing a set of kinematic simulations using direct and inverse kinematics methods.

MOTIVATIONS: Classification of biological samples for diagnostic purposes is a difficult task because of the many decisions involved on the number, type and functional manipulations of the input variables. This study presents a generally applicable strategy for systematic formulation of optimal diagnostic indexes. To this end, we develop a novel set of computational tools by integrating regression optimization, stepwise variable selection and cross-validation algorithms. RESULTS: The proposed discrimination methodology was applied to plasma and tissue (liver) metabolic profiling data describing the time progression of liver dysfunction in a rat model of acute hepatic failure generated by d-galactosamine (GalN) injection. From the plasma data, our methodology identified seven (out of a total of 23) metabolites, and the corresponding transform functions, as the best inputs to the optimal diagnostic index. This index showed better time resolution and increased noise robustness compared with an existing metabolic index, Fischer's BCAA/AAA molar ratio, as well as indexes generated using other commonly used discriminant analysis tools. Comparison of plasma and liver indexes found two consensus metabolites, lactate and glucose, which implicate glycolysis and/or gluconeogenesis in mediating the metabolic effects of GalN.