Role of branchiomotor neurons in controlling food intake of zebrafish larvae.

The physical act of eating or feeding involves the coordinated action of several organs like eyes and jaws, and associated neural networks. Moreover, the activity of the neural networks controlling jaw movements (branchiomotor circuits) is regulated by the visual, olfactory, gustatory and hypothalamic systems, which are largely well characterized at the physiological level. By contrast, the behavioral output of the branchiomotor circuits and the functional consequences of disruption of these circuits by abnormal neural development are poorly understood. To begin to address these questions, we sought to evaluate the feeding ability of zebrafish larvae, a direct output of the branchiomotor circuits, and developed a qualitative assay for measuring food intake in zebrafish larvae at 7 days post-fertilization. We validated the assay by examining the effects of ablating the branchiomotor neurons. Metronidazole-mediated ablation of nitroreductase-expressing branchiomotor neurons resulted in a predictable reduction in food intake without significantly affecting swimming ability, indicating that the assay is robust. Laser-mediated ablation of trigeminal motor neurons resulted in a significant decrease in food intake, indicating that the assay is sensitive. Importantly, in larvae of a genetic mutant with severe loss of branchiomotor neurons, food intake was abolished. These studies establish a foundation for dissecting the neural circuits driving a motor behavior essential for survival.

A Probabilistic Approach to Surgical Tasks and Skill Metrics.

Identifying and quantifying the activities that compose surgery is essential for effective interventions, computer-aided analyses and the advancement of surgical data science. For example, recent studies have shown that objective metrics (referred to as objective performance indicators, OPIs) computed during key surgical tasks correlate with surgeon skill and clinical outcomes. Unambiguous identification of these surgical tasks can be particularly challenging for both human annotators and algorithms. Each surgical procedure has multiple approaches, each surgeon has their own level of skill, and the initiation and termination of surgical tasks can be subject to interpretation. As such, human annotators and machine learning models face the same basic problem, accurately identifying the boundaries of surgical tasks despite variable and unstructured information. For use in surgeon feedback, OPIs should also be robust to the variability and diversity in this data. To mitigate this difficulty, we propose a probabilistic approach to surgical task identification and calculation of OPIs. Rather than relying on tasks that are identified by hard temporal boundaries, we demonstrate an approach that relies on distributions of start and stop times, for a probabilistic interpretation of when the task was performed. We first use hypothetical data to outline how this approach is superior to other conventional approaches. Then we present similar analyses on surgical data. We find that when surgical tasks are identified by their individual probabilities, the resulting OPIs are less sensitive to noise in the identification of the start and stop times. These results suggest that this probabilistic approach holds promise for the future of surgical data science.

How to Bring Surgery to the Next Level: Interpretable Skills Assessment in Robotic-Assisted Surgery.

INTRODUCTION: A surgeon's technical skills are an important factor in delivering optimal patient care. Most existing methods to estimate technical skills remain subjective and resource intensive. Robotic-assisted surgery (RAS) provides a unique opportunity to develop objective metrics using key elements of intraoperative surgeon behavior which can be captured unobtrusively, such as instrument positions and button presses. Recent studies have shown that objective metrics based on these data (referred to as objective performance indicators [OPIs]) correlate to select clinical outcomes during robotic-assisted radical prostatectomy. However, the current OPIs remain difficult to interpret directly and, therefore, to use within structured feedback to improve surgical efficiencies. METHODS: We analyzed kinematic and event data from da Vinci surgical systems (Intuitive Surgical, Inc., Sunnyvale, CA, USA) to calculate values that can summarize the use of robotic instruments, referred to as OPIs. These indicators were mapped to broader technical skill categories of established training protocols. A data-driven approach was then applied to further sub-select OPIs that distinguish skill for each technical skill category within each training task. This subset of OPIs was used to build a set of logistic regression classifiers that predict the probability of expertise in that skill to identify targeted improvement and practice. The final, proposed feedback using OPIs was based on the coefficients of the logistic regression model to highlight specific actions that can be taken to improve. RESULTS: We determine that for the majority of skills, only a small subset of OPIs (2-10) are required to achieve the highest model accuracies (80-95%) for estimating technical skills within clinical-like tasks on a porcine model. The majority of the skill models have similar accuracy as models predicting overall expertise for a task (80-98%). Skill models can divide a prediction into interpretable categories for simpler, targeted feedback. CONCLUSION: We define and validate a methodology to create interpretable metrics for key technical skills during clinical-like tasks when performing RAS. Using this framework for evaluating technical skills, we believe that surgical trainees can better understand both what can be improved and how to improve.

Nanoparticle mediated thermal ablation of breast cancer cells using a nanosecond pulsed electric field.

In the past, ablation of cancer cells using radiofrequency heating techniques has been demonstrated, but the current methodology has many flaws, including inconsistent tumor ablation and significant ablation of normal cells. Other researchers have begun to develop a treatment that is more selective for cancer cells using metallic nanoparticles and constant electric field exposure. In these studies, cell necrosis is induced by heating antibody functionalized metallic nanoparticles attached to cancer cells. Our approach to studying this phenomenon is to use similarly functionalized metallic nanoparticles that are specific for the T47D breast cancer cell line, exposing these nanoparticle cell conjugates to a nanosecond pulsed electric field. Using fluorescent, polystyrene-coated, iron-oxide nanoparticles, the results of our pilot study indicated that we were able to ablate up to approximately 80% of the cells using 60 ns pulses in increasing numbers of pulses and up to approximately 90% of the cells using 300 ns pulses in increasing numbers of pulses. These quantities of ablated cells were achieved using a cumulative exposure time 6 orders of magnitude less than most in vitro constant electric field studies.

Total internal reflection photoacoustic spectroscopy for the detection of ß-hematin.

Evanescent field sensing methods are currently used to detect many different types of disease markers and biologically important chemicals such as the HER2 breast cancer receptor. Hinoue et al. used Total Internal Reflection Photoacoustic Spectroscopy (TIRPAS) as a method of using the evanescent field to detect an optically opaque dye at a sample interface. Although their methods were successful at detecting dyes, the results at that time did not show a very practical spectroscopic technique, which was due to the less than typical sensitivity of TIRPAS as a spectroscopy modality given the low power (≈ 1 to 2 W) lasers being used. Contrarily, we have used an Nd:YAG laser with a five nanosecond pulse that gives peak power of 1 MW coupled with the TIRPAS system to increase the sensitivity of this technique for biological material sensing. All efforts were focused on the eventual detection of the optically absorbing material, hemozoin, which is created as a byproduct of a malarial infection in blood. We used an optically analogous material, ß-hematin, to determine the potential for detection in the TIRPAS system. In addition, four properties which control the sensitivity were investigated to increase understanding about the sensor's function as a biosensing method.