Development of an Endotracheal Intubation Formative Assessment Tool

BACKGROUND

Valid methods for providing detailed formative feedback on direct laryngoscopy and endotracheal intubation (ETI) performance do not exist. We are developing an observation-based assessment tool for measuring performance and providing feedback during ETI.

METHODS

Based on the literature and interviews of experts, we proposed an initial ETI metric with 22 items. Six anesthesiology experts used it to assess the quality of ETI performance in videotaped intubations. Following metric revisions, 2 expert groups assessed 2 collections of videos (27 total) using the revised metric. Two reference standards for comparison with metric scores were created with a third and fourth group of experts; (1) an average global rating (1-100) of each ETI performance and (2) average rank-ordered performance from best to worst. Rater agreement and correlations between the 2 methods were calculated. Regression analysis determined items that optimally discriminated quality. When calculating a score based on all clinically important terms, multiple weightings were evaluated.

RESULTS

Metric items had high average rater agreement (80%) with intraclass correlation coefficients averaging 0.83. Correlations of the reference rank and score were high for both video collections (-0.96, P < .05, and -0.95, P < .05). Regression coefficients for different item weighting methods indicated strong relationships with global ratings (averaging r = 0.89, P < .05) and rankings averaging -0.85, P < .05). Prediction of global ratings using regression achieved high accuracy (R ² = 0.8218).

CONCLUSIONS

High observer agreement and strong correlations between metric and rank data support the validity of using this metric to assess ETI performance. Different weighting models yielded scores that correlated strongly with the ratings and ranks from global assessment. When using the metric to predict competency, a 3-item regression model is most accurate in predicting a global score.

A deep learning-based hybrid approach for the solution of multiphysics problems in electrosurgery

Multiphysics modeling of evolving topology in the electrosurgical dissection of soft hydrated tissues is a challenging problem, requiring heavy computational resources. In this paper, we propose a hybrid approach that leverages the regressive capabilities of deep convolutional neural networks (CNN) with the precision of conventional solvers to accelerate Multiphysics computations. The electro-thermal problem is solved using a finite element method (FEM) with a Krylov subspace-based iterative solver and a deflation-based block preconditioner. The mechanical deformation induced by evaporation of intra- and extracellular water is obtained using a CNN model. The CNN is trained using a supervised learning framework that maps the nonlinear relationship between the micropore pressure and deformation field for a given tissue topology. The simulation results show that the hybrid approach is significantly more computationally efficient than a FEM-based solution approach using a block-preconditioned Krylov subspace solver and a parametric solution approach using a proper generalized decomposition (PGD) based reduced order model. The accuracy of the hybrid approach is comparable to the ground truth obtained using a standard multiphysics solver. The hydrid approach overcomes the limitations of end-to-end learning including the need for massive datasets for training the network.

Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences

Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this regard. However, machine learning alone ignores the fundamental laws of physics and can result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large datasets from different sources and different levels of resolution. Here we demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. We review the current literature, highlight applications and opportunities, address open questions, and discuss potential challenges and limitations in four overarching topical areas: ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches. Towards these goals, we leverage expertise in applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, and medicine. Our multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision making for the benefit of human health.

A machine learning approach to predict surgical learning curves

BACKGROUND

Contemporary surgical training programs rely on the repetition of selected surgical motor tasks. Such methodology is inherently open ended with no control on the time taken to attain a set level of proficiency, given the trainees' intrinsic differences in initial skill levels and learning abilities. Hence, an efficient training program should aim at tailoring the surgical training protocols to each trainee. In this regard, a predictive model using information from the initial learning stage to predict learning curve characteristics should facilitate the whole surgical training process.

METHODS

This paper analyzes learning curve data to train a multivariate supervised machine learning model. One factor is extracted to define the trainees' learning ability. An unsupervised machine learning model is also utilized for trainee classification. When established, the model can predict robustly the learning curve characteristics based on the first few trials.

RESULTS

We show that the information present in the first 10 trials of surgical tasks can be utilized to predict the number of trials required to achieve proficiency (R²=0.72) and the final performance level (R²=0.89). Furthermore, only a single factor, learning index, is required to describe the learning process and to classify learners with unique learning characteristics.

CONCLUSION

Using machine learning models, we show, for the first time, that the first few trials contain sufficient information to predict learning curve characteristics and that a single factor can capture the complex learning behavior. Using such models holds the potential for personalization of training regimens, leading to greater efficiency and lower costs.

Measurement of ϒ(1S) Elliptic Flow at Forward Rapidity in Pb-Pb Collisions at sqrt[s_{NN}]=5.02 TeV

The first measurement of the ϒ(1S) elliptic flow coefficient (v_{2}) is performed at forward rapidity (2.5<y<4) in Pb-Pb collisions at sqrt[s_{NN}]=5.02  TeV with the ALICE detector at the LHC. The results are obtained with the scalar product method and are reported as a function of transverse momentum (p_{T}) up to 15  GeV/c in the 5%-60% centrality interval. The measured ϒ(1S)v_{2} is consistent with 0 and with the small positive values predicted by transport models within uncertainties. The v_{2} coefficient in 2<p_{T}<15  GeV/c is lower than that of inclusive J/ψ mesons in the same p_{T} interval by 2.6 standard deviations. These results, combined with earlier suppression measurements, are in agreement with a scenario in which the ϒ(1S) production in Pb-Pb collisions at LHC energies is dominated by dissociation limited to the early stage of the collision, whereas in the J/ψ case there is substantial experimental evidence of an additional regeneration component.

Investigations of Anisotropic Flow Using Multiparticle Azimuthal Correlations in pp, p-Pb, Xe-Xe, and Pb-Pb Collisions at the LHC

Measurements of anisotropic flow coefficients (v_{n}) and their cross-correlations using two- and multiparticle cumulant methods are reported in collisions of pp at sqrt[s]=13  TeV, p-Pb at a center-of-mass energy per nucleon pair sqrt[s_{NN}]=5.02  TeV, Xe-Xe at sqrt[s_{NN}]=5.44  TeV, and Pb-Pb at sqrt[s_{NN}]=5.02  TeV recorded with the ALICE detector. The multiplicity dependence of v_{n} is studied in a very wide range from 20 to 3000 particles produced in the midrapidity region |η|<0.8 for the transverse momentum range 0.2<p_{T}<3.0  GeV/c. An ordering of the coefficients v_{2}>v_{3}>v_{4} is found in pp and p-Pb collisions, similar to that seen in large collision systems, while a weak v_{2} multiplicity dependence is observed relative to nucleus-nucleus collisions in the same multiplicity range. Using a novel subevent method, v_{2} measured with four-particle cumulants is found to be compatible with that from six-particle cumulants in pp and p-Pb collisions. The magnitude of the correlation between v_{n}^{2} and v_{m}^{2}, evaluated with the symmetric cumulants SC(m,n) is observed to be positive at all multiplicities for v_{2} and v_{4}, while for v_{2} and v_{3} it is negative and changes sign for multiplicities below 100, which may indicate a different v_{n} fluctuation pattern in this multiplicity range. The observed long-range multiparticle azimuthal correlations in high multiplicity pp and p-Pb collisions can neither be described by pythia 8 nor by impact-parameter-Glasma, music, and ultrarelativistic quantum molecular dynamics model calculations, and hence, provide new insights into the understanding of collective effects in small collision systems.

First Observation of an Attractive Interaction between a Proton and a Cascade Baryon

This Letter presents the first experimental observation of the attractive strong interaction between a proton and a multistrange baryon (hyperon) Ξ^{-}. The result is extracted from two-particle correlations of combined p-Ξ^{-}⊕p[over ¯]-Ξ[over ¯]^{+} pairs measured in p-Pb collisions at sqrt[s_{NN}]=5.02  TeV at the LHC with ALICE. The measured correlation function is compared with the prediction obtained assuming only an attractive Coulomb interaction and a standard deviation in the range [3.6, 5.3] is found. Since the measured p-Ξ^{-}⊕p[over ¯]-Ξ[over ¯]^{+} correlation is significantly enhanced with respect to the Coulomb prediction, the presence of an additional, strong, attractive interaction is evident. The data are compatible with recent lattice calculations by the HAL-QCD Collaboration, with a standard deviation in the range [1.8, 3.7]. The lattice potential predicts a shallow repulsive Ξ^{-} interaction within pure neutron matter and this implies stiffer equations of state for neutron-rich matter including hyperons. Implications of the strong interaction for the modeling of neutron stars are discussed.

Objective assessment of surgical skill transfer using non-invasive brain imaging

BACKGROUND

Physical and virtual surgical simulators are increasingly being used in training technical surgical skills. However, metrics such as completion time or subjective performance checklists often show poor correlation to transfer of skills into clinical settings. We hypothesize that non-invasive brain imaging can objectively differentiate and classify surgical skill transfer, with higher accuracy than established metrics, for subjects based on motor skill levels.

STUDY DESIGN

18 medical students at University at Buffalo were randomly assigned into control, physical surgical trainer, or virtual trainer groups. Training groups practiced a surgical technical task on respective simulators for 12 consecutive days. To measure skill transfer post-training, all subjects performed the technical task in an ex-vivo environment. Cortical activation was measured using functional near-infrared spectroscopy (fNIRS) in the prefrontal cortex, primary motor cortex, and supplementary motor area, due to their direct impact on motor skill learning.

RESULTS

Classification between simulator trained and untrained subjects based on traditional metrics is poor, where misclassification errors range from 20 to 41%. Conversely, fNIRS metrics can successfully classify physical or virtual trained subjects from untrained subjects with misclassification errors of 2.2% and 8.9%, respectively. More importantly, untrained subjects are successfully classified from physical or virtual simulator trained subjects with misclassification errors of 2.7% and 9.1%, respectively.

CONCLUSION

fNIRS metrics are significantly more accurate than current established metrics in classifying different levels of surgical motor skill transfer. Our approach brings robustness, objectivity, and accuracy in validating the effectiveness of future surgical trainers in translating surgical skills to clinically relevant environments.

Atomic flux distribution from a low-divergent dark wall oven

Nearly collimated atomic beam is of interest for a variety of experiments. This article reports a simple way of modifying the atomic beam distribution using a dark wall oven and describes detailed study of outcoming atoms' spatial distribution. A simple design is obtained by employing the fact that inhomogeneous thermal distribution along a capillary results due to its partial resistive heating. Based on this phenomenon, we have designed a dark wall oven consisting of a reservoir, collimator, and cold absorber at the exit end of atoms, where all three are fabricated out of a single stainless steel capillary. The nearly collimated spatial distribution of the atoms resulting due to the absorber eliminating the atoms diverging above a certain angle is modeled and experimentally verified. A divergence as minimum as 1.2(1)° corresponding to a half angle θ_1/2 = 0.9(1)° is measured at an oven temperature of 250 °C that produces an atomic flux of about 8 × 10⁹ atoms s^-1. Total flux as estimated using our measured spatial distribution of atoms matches well with the numerically simulated values of it for the dark wall oven.

In vivo Layer-specific Mechanical Characterization of Porcine Stomach Tissue using Ultrasound Elastography

This paper presents in vivo mechanical characterization of the muscularis, submucosa and mucosa of the porcine stomach wall under large deformation loading. This is important for the development of gastrointestinal pathology-specific surgical intervention techniques. The study is based on testing the cardiac and fundic glandular stomach regions using a custom-developed compression elastography setup. Particular attention has been paid to elucidate the heterogeneity and anisotropy of tissue response. A Fung hyperelastic material model has been used to model the mechanical response of each tissue layer. A univariate analysis comparing the initial shear moduli of the three layers indicates that the muscularis (5.69±4.06 kPa) is the stiffest followed by the submucosa (3.04±3.32 kPa) and the mucosa (0.56±0.28 kPa). The muscularis is found to be strongly distinguishable from the mucosa tissue in the cardiac and fundic region based on a multivariate discriminant analysis. The cardiac muscularis is observed to be stiffer than the fundic muscularis tissue (shear moduli of 7.96±3.82 kPa vs. 3.42±2.96 kPa), more anisotropic (anisotropic parameter of 2.21±0.77 vs. 1.41±0.38), and strongly distinguishable from its fundic counterpart. Finally, a univariate comparison of the in vivo and ex vivo initial shear moduli for each layer shows that the muscularis and submucosa tissues are softer while in vivo, but the mucosa tissue is stiffer while in vivo. The mechanical properties highlight the inhomogeneity and anisotropy of multilayer stomach tissue.

Effect of microchannel structure and fluid properties on non-inertial particle migration

In this work, we investigate the influence of channel structure and fluid rheology on non-inertial migration of non-Brownian polystyrene beads. Particle migration in this regime can be found in biomedical, chemical, environmental and geological applications. However, the effect of fluid rheology on particle migration in porous media remains to be clearly understood. Here, we isolate the effects of elasticity and shear thinning by comparing a Newtonian fluid, a purely elastic (Boger) fluid, and a shear-thinning elastic fluid. To mimic the complexity of geometries in real-world application, a random porous structure is created through a disordered arrangement of cylindrical pillars in the microchannel. Experiments are repeated in an empty channel and in channels with an ordered arrangement of pillars, and the similarities and differences in the observed particle focusing are analyzed. It is found that elasticity drives the particles away from the channel walls in an empty microchannel. Notably, particle focusing is unaffected by curved streamlines in an ordered porous microchannel and particles stay away from pillars in elastic fluids. Shear-thinning is found to reduce the effect of focusing and a broader region of particle concentration is observed. It is also noteworthy that the rheological characteristics of the fluid are not important for the particle distribution in a randomly arranged pillared microchannel and particles have a uniform distribution for all suspending fluids. Moreover, discussion on the current discrepancy in the literature about the equilibrium positions of the particles in a channel is extended by analyzing the results obtained in the current experiments.

Azimuthal Anisotropy of Heavy-Flavor Decay Electrons in p-Pb Collisions at sqrt[s_{NN}]=5.02 TeV

Angular correlations between heavy-flavor decay electrons and charged particles at midrapidity (|η|<0.8) are measured in p-Pb collisions at sqrt[s_{NN}]=5.02  TeV. The analysis is carried out for the 0%-20% (high) and 60%-100% (low) multiplicity ranges. The jet contribution in the correlation distribution from high-multiplicity events is removed by subtracting the distribution from low-multiplicity events. An azimuthal modulation remains after removing the jet contribution, similar to previous observations in two-particle angular correlation measurements for light-flavor hadrons. A Fourier decomposition of the modulation results in a positive second-order coefficient (v_{2}) for heavy-flavor decay electrons in the transverse momentum interval 1.5<p_{T}<4  GeV/c in high-multiplicity events, with a significance larger than 5σ. The results are compared with those of charged particles at midrapidity and those of inclusive muons at forward rapidity. The v_{2} measurement of open heavy-flavor particles at midrapidity in small collision systems could provide crucial information to help interpret the anisotropies observed in such systems.

Waveform Dependent Electrosurgical Effects on Soft Hydrated Tissues

Electrosurgical procedures are ubiquitously used in surgery. The commonly used power modes, including the coagulation and blend modes, utilize non-sinusoidal or modulated current waveforms. For the same power setting, the coagulation, blend and pure cutting modes have different heating and thermal damage outcomes due to the frequency dependence of electrical conductivity of soft hydrated tissues. In this paper, we propose a multi-physics model of soft tissues to account for the effects of multi-frequency electrosurgical power modes within the framework of a continuum thermomechanical model based on mixture theory. Electrical and frequency spectrum results from different power modes at low and high power settings are presented. Model predictions are compared with in vivo electrosurgical heating experiments on porcine liver tissue. The accuracy of the model in predicting experimentally observed temperature profiles is found to be overall greater when frequency-dependence is included. An Arrhenius type model indicates that more tissue damage is correlated with larger duty cycles in multi-frequency modes.

A task and performance analysis of endoscopic submucosal dissection (ESD) surgery

BACKGROUND

ESD is an endoscopic technique for en bloc resection of gastrointestinal lesions. ESD is a widely-used in Japan and throughout Asia, but not as prevalent in Europe or the US. The procedure is technically challenging and has higher adverse events (bleeding, perforation) compared to endoscopic mucosal resection. Inadequate training platforms and lack of established training curricula have restricted its wide acceptance in the US. Thus, we aim to develop a Virtual Endoluminal Surgery Simulator (VESS) for objective ESD training and assessment. In this work, we performed task and performance analysis of ESD surgeries.

METHODS

We performed a detailed colorectal ESD task analysis and identified the critical ESD steps for lesion identification, marking, injection, circumferential cutting, dissection, intraprocedural complication management, and post-procedure examination. We constructed a hierarchical task tree that elaborates the order of tasks in these steps. Furthermore, we developed quantitative ESD performance metrics. We measured task times and scores of 16 ESD surgeries performed by four different endoscopic surgeons.

RESULTS

The average time of the marking, injection, and circumferential cutting phases are 203.4 (σ: 205.46), 83.5 (σ: 49.92), 908.4 s. (σ: 584.53), respectively. Cutting the submucosal layer takes most of the time of overall ESD procedure time with an average of 1394.7 s (σ: 908.43). We also performed correlation analysis (Pearson's test) among the performance scores of the tasks. There is a moderate positive correlation (R = 0.528, p = 0.0355) between marking scores and total scores, a strong positive correlation (R = 0.7879, p = 0.0003) between circumferential cutting and submucosal dissection and total scores. Similarly, we noted a strong positive correlation (R = 0.7095, p = 0.0021) between circumferential cutting and submucosal dissection and marking scores.

CONCLUSIONS

We elaborated ESD tasks and developed quantitative performance metrics used in analysis of actual surgery performance. These ESD metrics will be used in future validation studies of our VESS simulator.

Microfluidics-enabled orientation and microstructure control of macroscopic graphene fibres

Macroscopic graphene structures such as graphene papers and fibres can be manufactured from individual two-dimensional graphene oxide sheets by a fluidics-enabled assembling process. However, achieving high thermal-mechanical and electrical properties is still challenging due to non-optimized microstructures and morphology. Here, we report graphene structures with tunable graphene sheet alignment and orientation, obtained via microfluidic design, enabling strong size and geometry confinements and control over flow patterns. Thin flat channels can be used to fabricate macroscopic graphene structures with perfectly stacked sheets that exhibit superior thermal and electrical conductivities and improved mechanical strength. We attribute the observed shape and size confinements to the flat distribution of shear stress from the anisotropic microchannel walls and the enhanced shear thinning degree of large graphene oxide sheets in solution. Elongational and step expansion flows are created to produce large-scale graphene tubes and rods with horizontally and perpendicularly aligned graphene sheets by tuning the elongational and extensional shear rates, respectively.

Correction to: Validation of a virtual intracorporeal suturing simulator

The surname of Sreekanth Arikatla incorrectly appeared as Sreekanth Artikala.

A continuum thermomechanical model of in vivo electrosurgical heating of hydrated soft biological tissues

Radio-frequency (RF) heating of soft biological tissues during electrosurgical procedures is a fast process that involves phase change through evaporation and transport of intra- and extra-cellular water, and where variations in physical properties with temperature and water content play significant role. Accurately predicting and capturing these effects would improve the modeling of temperature change in the tissue allowing the development of improved instrument design and better understanding of tissue damage and necrosis. Previous models based on the Pennes' bioheat model neglect both evaporation and transport or consider evaporation through numerical correlations, however, do not account for changes in physical properties due to mass transport or phase change, nor capture the pressure increase due to evaporation within the tissue. While a porous media approach can capture the effects of evaporation, transport, pressure and changes in physical properties, the model assumes free diffusion of liquid and gas without a careful examination of assumptions on transport parameters in intact tissue resulting in significant under prediction of temperature. These different approaches have therefore been associated with errors in temperature prediction exceeding 20% when compared to experiments due to inaccuracies in capturing the effects of evaporation losses and transport. Here, we present a model of RF heating of hydrated soft tissue based on mixture theory where the multiphase nature of tissue is captured within a continuum thermomechanics framework, simultaneously considering the transport, deformation and phase change losses due to evaporation that occur during electrosurgical heating. The model predictions are validated against data obtained for in vivo ablation of porcine liver tissue at various power settings of the electrosurgical unit. The model is able to match the mean experimental temperature data with sharp gradients in the vicinity of the electrode during rapid low and high power ablation procedures with errors less than 7.9%. Additionally, the model is able to capture fast vaporization losses and the corresponding increase in pressure due to vapor buildup which have a significant effect on temperature prediction beyond 100 °C.

Development and face validation of a virtual camera navigation task trainer

BACKGROUND

The fundamentals of laparoscopic surgery (FLS) trainer box, which is now established as a standard for evaluating minimally invasive surgical skills, consists of five tasks: peg transfer, pattern cutting, ligation, intra- and extracorporeal suturing. Virtual simulators of these tasks have been developed and validated as part of the Virtual Basic Laparoscopic Skill Trainer (VBLaST) (Arikatla et al. in Int J Med Robot Comput Assist Surg 10:344-355, 2014; Zhang et al. in Surg Endosc 27(10):3603-3615, 2013; Sankaranarayanan et al. in J Laparoendosc Adv Surg Tech 20(2):153-157, 2010; Qi et al. J Biomed Inform 75:48-62, 2017). The virtual task trainers have many advantages including automatic real-time objective scoring, reduced costs, and eliminating human proctors. In this paper, we extend VBLaST by adding two camera navigation system tasks: (a) pattern matching and (b) path tracing.

METHODS

A comprehensive camera navigation simulator with two virtual tasks, simplified and cheaper hardware interface (compared to the prior version of VBLaST), graphical user interface, and automated metrics has been designed and developed. Face validity of the system is tested with medical students and residents from the University at Buffalo's medical school.

RESULTS

The subjects rated the simulator highly in all aspects including its usefulness in training to center the target and to teach sizing skills. The quality and usefulness of the force feedback scored the lowest at 2.62.

Families of Children Newly Diagnosed With Cancer Incur Significant Out-of-Pocket Expenditure for Treatment: Report of a Multi-Site Prospective Longitudinal Study From India (INPOG-ACC-16-01)

Background: Diagnosis of cancer in a child places considerable economic burden on families. The health expenditures are more catastrophic in resource limited countries like India where GDP spend on health is just over 1% and financing of treatment is usually out-of-pocket (OOP). Consequently parents may abandon their child's cancer treatment to ensure financial sustainability of the family. Research in this area is mostly from resource rich countries and OOP expenditure burden remains unknown in India. Aim: The objective of this study is to describe the OOP expenditure incurred by families of children (< 18 years age) with cancer being treated in India prior to and during cancer directed treatment. Methods: A prospective cost of illness study from a family household perspective was conducted in 14 centers (5 public, 5 private and 4 charitable trust sector) in 4 cities in India from 2016-2018. Baseline family demographic and socioeconomic data were collected followed by OOP expenditure incurred prior to start of treatment. For the duration of the child's treatment, a social worker contacted parents at regular intervals to record their expenditure on cancer directed treatment. Data collection was stopped when one of these happened - completion of treatment or death or progression/relapse or abandonment or transfer. Data were described descriptively and a univariate/multivariate analysis using logistic regression was done to detect factors associated with OOP expenditure. Results: 394 children (63% male, median age 5 years) with cancer (64% leukemia/lymphoma, 33% solid tumors, 3% CNS tumors) were enrolled from public (45%), charitable trust (28%) and private (27%) sector hospitals. They were symptomatic for a median duration of 6 weeks (range 0 to 104 weeks). 88% had no insurance and 73% were from families with monthly income of ≤ 10,000 rupees (≤ 159 US$). Mean OOP expenditure was Rs 209,500 (3325 US$) which is 195% of per capita income (1706 US$) of India. OOP expenditure from onset of symptoms to start of treatment was Rs 53,104 (843 US$) of which 77% was medical (15% laboratory tests, 11% medicines, 9% hospital bed costs) and 23% nonmedical (12% travel, 6% food, 3% lodging). OOP expenditure on cancer directed treatment was Rs 156,396 (2482 US$) of which 64% was medical (9% hospital bed costs, 9% supportive care drugs, 8% laboratory tests) and 36% nonmedical (19% food, 9% travel, 6% lodging). On univariate analysis age, gender, city, type of treatment facility, insurance, type of cancer, driving time and distance were significantly associated with OOP expenditure but only insurance and type of treatment facility were found significant on multivariate analysis. Conclusion: Families of children with cancer incur significant OOP expenditure prior to and during cancer directed treatment, which includes a significant portion on nonmedical expenses. Expenditure varied significantly by insurance and type of treatment facility.

Assessing bimanual motor skills with optical neuroimaging

Measuring motor skill proficiency is critical for the certification of highly skilled individuals in numerous fields. However, conventional measures use subjective metrics that often cannot distinguish between expertise levels. We present an advanced optical neuroimaging methodology that can objectively and successfully classify subjects with different expertise levels associated with bimanual motor dexterity. The methodology was tested by assessing laparoscopic surgery skills within the framework of the fundamentals of a laparoscopic surgery program, which is a prerequisite for certification in general surgery. We demonstrate that optical-based metrics outperformed current metrics for surgical certification in classifying subjects with varying surgical expertise. Moreover, we report that optical neuroimaging allows for the successful classification of subjects during the acquisition of these skills.

Ultrasound elastography reliably identifies altered mechanical properties of burned soft tissues

Although burn injury to the skin and subcutaneous tissues is common in both civilian and military scenarios, a significant knowledge gap exists in quantifying changes in tissue properties as a result of burns. In this study, we present a noninvasive technique based on ultrasound elastography which can reliably assess altered nonlinear mechanical properties of a burned tissue. In particular, ex vivo porcine skin tissues have been exposed to four different burn conditions: (i) 200°F for 10s, (ii) 200°F for 30s, (iii) 450°F for 10s, and (iv) 450°F for 30s. A custom-developed instrument including a robotically controlled ultrasound probe and force sensors has been used to compress the tissue samples to compute two parameters (C₁₀ and C₂₀) of a reduced second-order polynomial hyperelastic material model. The results indicate that while the linear model parameter (C₁₀) does not show a statistically significant difference between the test conditions, the nonlinear model parameter (C₂₀) reliably identifies three (ii-iv) of the four cases (p<0.05) when comparing burned with unburned tissues with a classification accuracy of 60-87%. Additionally, softening of the tissue is observed because of the change in structure of the collagen fibers. The ultrasound elastography-based technique has potential for application under in vivo conditions, which is left for future work.

Search for Tensor, Vector, and Scalar Polarizations in the Stochastic Gravitational-Wave Background

The detection of gravitational waves with Advanced LIGO and Advanced Virgo has enabled novel tests of general relativity, including direct study of the polarization of gravitational waves. While general relativity allows for only two tensor gravitational-wave polarizations, general metric theories can additionally predict two vector and two scalar polarizations. The polarization of gravitational waves is encoded in the spectral shape of the stochastic gravitational-wave background, formed by the superposition of cosmological and individually unresolved astrophysical sources. Using data recorded by Advanced LIGO during its first observing run, we search for a stochastic background of generically polarized gravitational waves. We find no evidence for a background of any polarization, and place the first direct bounds on the contributions of vector and scalar polarizations to the stochastic background. Under log-uniform priors for the energy in each polarization, we limit the energy densities of tensor, vector, and scalar modes at 95% credibility to Ω_{0}^{T}<5.58×10^{-8}, Ω_{0}^{V}<6.35×10^{-8}, and Ω_{0}^{S}<1.08×10^{-7} at a reference frequency f_{0}=25  Hz.

D-Meson Azimuthal Anisotropy in Midcentral Pb-Pb Collisions at sqrt[s]_{NN}=5.02 TeV

The azimuthal anisotropy coefficient v_{2} of prompt D^{0}, D^{+}, D^{*+}, and D_{s}^{+} mesons was measured in midcentral (30%-50% centrality class) Pb-Pb collisions at a center-of-mass energy per nucleon pair sqrt[s_{NN}]=5.02  TeV, with the ALICE detector at the LHC. The D mesons were reconstructed via their hadronic decays at midrapidity, |y|<0.8, in the transverse momentum interval 1<p_{T}<24  GeV/c. The measured D-meson v_{2} has similar values as that of charged pions. The D_{s}^{+} v_{2}, measured for the first time, is found to be compatible with that of nonstrange D mesons. The measurements are compared with theoretical calculations of charm-quark transport in a hydrodynamically expanding medium and have the potential to constrain medium parameters.