User acceptance of a touchless sterile system to control virtual orthodontic study models

Introduction In this article, we present an evaluation of user acceptance of our innovative hand-gesture-based touchless sterile system for interaction with and control of a set of 3-dimensional digitized orthodontic study models using the Kinect motion-capture sensor (Microsoft, Redmond, Wash). Methods The system was tested on a cohort of 201 participants. Using our validated questionnaire, the participants evaluated 7 hand-gesture-based commands that allowed the user to adjust the model in size, position, and aspect and to switch the image on the screen to view the maxillary arch, the mandibular arch, or models in occlusion. Participants' responses were assessed using Rasch analysis so that their perceptions of the usefulness of the hand gestures for the commands could be directly referenced against their acceptance of the gestures. Their perceptions of the potential value of this system for cross-infection control were also evaluated. Results Most participants endorsed these commands as accurate. Our designated hand gestures for these commands were generally accepted. We also found a positive and significant correlation between our participants' level of awareness of cross infection and their endorsement to use this system in clinical practice. Conclusions This study supports the adoption of this promising development for a sterile touch-free patient record-management system

Reaction lethality predictions.

<p>Trivial lethal—atleast one of the reactions in the double deletion pair is lethal in a single reaction deletion</p><p>Non-trivial lethal, single—reaction involved in the single deletion is lethal</p><p>Non-trivial lethal, double—reaction pair involved in the double deletion is lethal</p><p>NA—Not applicable.</p><p>Reaction lethality predictions.</p

Dissecting <i>Leishmania infantum</i> Energy Metabolism - A Systems Perspective

<div><p><i>Leishmania infantum</i>, causative agent of visceral leishmaniasis in humans, illustrates a complex lifecycle pertaining to two extreme environments, namely, the gut of the sandfly vector and human macrophages. <i>Leishmania</i> is capable of dynamically adapting and tactically switching between these critically hostile situations. The possible metabolic routes ventured by the parasite to achieve this exceptional adaptation to its varying environments are still poorly understood. In this study, we present an extensively reconstructed energy metabolism network of <i>Leishmania infantum</i> as an attempt to identify certain strategic metabolic routes preferred by the parasite to optimize its survival in such dynamic environments. The reconstructed network consists of 142 genes encoding for enzymes performing 237 reactions distributed across five distinct model compartments. We annotated the subcellular locations of different enzymes and their reactions on the basis of strong literature evidence and sequence-based detection of cellular localization signal within a protein sequence. To explore the diverse features of parasite metabolism the metabolic network was implemented and analyzed as a constraint-based model. Using a systems-based approach, we also put forth an extensive set of lethal reaction knockouts; some of which were validated using published data on <i>Leishmania</i> species. Performing a robustness analysis, the model was rigorously validated and tested for the secretion of overflow metabolites specific to <i>Leishmania</i> under varying extracellular oxygen uptake rate. Further, the fate of important non-essential amino acids in <i>L</i>. <i>infantum</i> metabolism was investigated. Stage-specific scenarios of <i>L</i>. <i>infantum</i> energy metabolism were incorporated in the model and key metabolic differences were outlined. Analysis of the model revealed the essentiality of glucose uptake, succinate fermentation, glutamate biosynthesis and an active TCA cycle as driving forces for parasite energy metabolism and its optimal growth. Finally, through our <i>in silico</i> knockout analysis, we could identify possible therapeutic targets that provide experimentally testable hypotheses.</p></div

The map of the iAS142 metabolic network—The iAS142 network comprises of 237 reactions that occur in 5 major model compartments: 4 cellular compartments—the glycosome, cytoplasm, the mitochondrion, the mitochondrial inter-membrane space and 1 extracellular compartment as shown in the Fig.

<p>The mitochondrial inter-membrane space though included in the model, is not explicitly shown in the Fig. The reactions of oxidative phosphorylation occur from the mitochondrial compartment (m) to the mitochondrial inter-membrane space (mm) and vice versa. The reactions occurring along the borders of the compartments are transport reactions. The metabolite exchanges have been shown at the bottom of the Fig.</p

Comparison of the <i>L</i>. <i>infantum</i> iAS142 network with other Trypanosomatid reconstructions.

<p>A.) Venn diagram showing the comparison between intracellular reactions (exchanges excluded) considered in the <i>L</i>. <i>infantum</i> iAS142, <i>L</i>. <i>major</i> iAC560 and <i>T</i>. <i>cruzi</i> iSR215 reconstructions. B.) Venn diagram showing the comparison between the metabolites considered in the <i>L</i>. <i>infantum</i> iAS142, <i>L</i>. <i>major</i> iAC560 models, and <i>T</i>. <i>cruzi</i> iSR215 reconstructions [the brackets ‘{}’ represent the set of elements constituting the particular area in the Venn Diagram]. C) Scatter plot showing comparison of the <i>L</i>. <i>infantum</i> iAS142 model with energy metabolism subset of <i>L</i>. <i>major</i> iAC560 model. D) Comparison of secretion of overflow metabolites between the <i>L</i>. <i>infantum</i> iAS142 and the energy metabolism subset of <i>L</i>. <i>major</i> iAC560 model—bar graph representing exchange fluxes from both the models.</p

Robustness analysis with respect to oxygen uptake to simulate overflow metabolite secretion.

<p>A) Secretion of different overflow metabolites and glucose uptake with variation of oxygen uptake B) Secretion of different overflow metabolites and glucose uptake with variation of oxygen uptake by fixing lower bound of glucose uptake to a flux value of 200 C) Secretion of different overflow metabolites with variation of oxygen uptake by fixing upper and lower bounds of glucose uptake to a flux value of 280. The constraints for glucose uptake were changed to its default values at oxygen uptake rate < = 40 as the simulation gave no solution. D) Secretion of different overflow metabolites with variation of oxygen uptake and fixing lower bound of CO₂ to a value of -1000 and upper bound to 0.</p

Comparison of the iAS142 biomass reaction with the iSR215 biomass reaction.

<p>A) and E) represent situations where glucose is the sole substrate with respect to the two biomasses respectively. In all the other cases an amino acid supplements glucose uptake, which can be identified by their respective colors. B), C), and D) depict the effect of variation in glucose uptake on fluxes through glutamate, aspartate and proline uptake respectively, using the iAS142 biomass reaction. Effect on biomass growth rate is also recorded in each case. F), G), and H) demonstrate the same, but when iSR215 biomass reaction is used.</p

Comparison of flux distributions between the promastigote and amastigote scenarios.

<p>A) Scatter plot showing the variation of fluxes from the promastigote to amastigote forms B) Bar plot showing differences between overflow metabolism in the two developmental stages of <i>L</i>. <i>infantum</i> C) Bar plot showing the differences in promastigote and amastigote growth as predicted from the model.</p

Dissecting <i>Leishmania infantum - Fig 2 </i> Energy Metabolism - A Systems Perspective

<p>A) Pie chart showing the pathways that comprise the iAS142 model B) Pie chart showing the percentage of model reactions belonging to different subcellular locations.</p