High-volume centers are associated with higher receipt of combined therapy in stage III pancreatic cancer.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed at a locally advanced stage with vascular involvement which was previously viewed as a contraindication to resection. However, high-volume centers are increasingly capable of resecting complex tumors. We aimed to explore patterns of treatment that are uncharacterized on a population level. METHODS: A statewide registry was queried from 2003 to 2018 for stage III PDAC. Stepwise logistic regression and Kaplan-Meier were used for statistical analysis. RESULTS: We identified 424 eligible patients. 348 (82%) received chemotherapy, 17 (4.0%) received resection, and 59 (13.9%) received both; median survival was 10.7, 8.7, and 22.7 months, respectively (P < 0.001). High-volume centers (≥20 cases per year; OR 5.40 [95% CI: 2.76, 10.58], P < 0.001) and later year of diagnosis (OR 1.12/year [95% CI: 1.04, 1.20], P = 0.004) were associated with higher odds of receiving combined therapy. CONCLUSION: PDAC patients with vascular involvement who receive both systemic chemotherapy and surgical resection have improved overall survival. High-volume centers are independently associated with higher odds of receiving combined systemic therapy and surgical resection.

Complications in Distal Pancreatectomy versus Radical Antegrade Modular Pancreatosplenectomy: A Disease Risk Score Analysis Utilizing National Surgical Quality Improvement Project Data.

INTRODUCTION: Radical antegrade modular pancreatosplenectomy (RAMPS) was developed to improve R0 resections and lymph node harvests versus distal pancreatectomy (DP) in pancreatic adenocarcinoma (PDAC); relative complication rates are understudied. METHODS: Patients undergoing distal pancreas resections from 2006 to 2020 were identified from our institutional NSQIP database, grouped by resection method, and evaluated for the following outcomes: postoperative pancreatic fistula (POPF), clinically relevant POPF (crPOPF), incisional surgical site infection (iSSI), organ space SSI (osSSI), and Clavien-Dindo grade ≥ 3 (CD ≥ 3) complications using logistic regression. Patients were matched 1:1 based on disease risk score. RESULTS: Two-hundred-thirty-six and 117 patients underwent DP and RAMPS, respectively. POPF, crPOPF, CD ≥ 3 complications, iSSI, and osSSIs occurred in 105 (30%), 43 (12%), 74 (21%), 34 (10%) and 52 (15%) patients, respectively. Disease risk score matching yielded 89 similar patients per group. On multivariable analysis, patients undergoing RAMPS were not significantly more likely to experience POPF (OR 0.69, P = 0.26), crPOPF (OR 0.41, P = 0.72), CD ≥ 3 complication (OR 0.78, P = 0.44), iSSI (OR 0.58, P = 0.27), or osSSI (OR 0.93, P = 0.86). Of patients with PDAC (n = 108) mean nodal harvest were 14.8 (SD 11.30) and 19.4 (SD 7.19) nodes for patients undergoing DP and RAMPS, respectively (P = 0.01). Six patients (20%) undergoing DP had positive margins versus 12 (15%) undergoing RAMPS (P = 0.56). At a median follow-up of 17 months, there was no difference in locoregional recurrence-free survival (P = 0.32) or overall survival (P = 0.92) on Kaplan-Meier analysis. CONCLUSION: RAMPS does not result in increased complications compared to DP and routine use is encouraged in pancreatic malignancies.

The power of suction: Theory and practice in closed suction vs gravity drains and postoperative pancreatic fistulas.

BACKGROUND: Postoperative pancreatic fistula (POPF) is a feared complication in pancreatic resection. Gravity drainage (GD) is hypothesized to reduce POPF versus closed-suction drainage (CSD). We sought to evaluate this theory. METHODS: Six-hundred-twenty-nine patients undergoing pancreatic resection between 2013 and 2020 were analyzed with multivariable logistic regression for the outcomes of POPF and clinically-relevant POPF (crPOPF). RESULTS: Three-hundred-ninety-seven patients (63.1%) underwent pancreaticoduodenectomy and 232 (36.9%) underwent distal pancreatectomy. Suction drains were placed in 588 patients (93.5%) whereas 41 (6.5%) had GDs. One-hundred-twenty-five (27.6%) experienced a POPF; 49 (10%) crPOPFs. On multivariable analysis, suction drainage was not associated with increased risk of POPF (OR 0.76, 95% CI 0.30-1.93, P = 0.57) or crPOPF (OR 0.99, 95% CI 0.30-3.26, P = 0.98). CONCLUSION: Suction drainage does not promote POPF when compared to GDs. Drain type should be determined by surgeon preference, while taking into account nursing and patient-specific considerations especially when patients are discharged with drains.

Risk factors for postoperative pancreatic fistula in the Era of pasireotide.

INTRODUCTION: Postoperative pancreatic fistulas (POPFs) remain common; POPFs frequently require intervention, termed clinically-relevant POPFs (crPOPFs). Pasireotide is increasingly used to prevent POPF, however, risk factors for POPF in this population remain unexplored. METHODS: Patients undergoing pancreatectomy with perioperative pasireotide from 2013 to 2020 were identified from our institutional National Surgical Quality Improvement Project database. Logistic regression was utilized to identify risk factors associated with POPF. RESULTS: One-hundred patients were identified; 26 (26%) underwent distal pancreatectomy with the remainder undergoing pancreaticoduodenectomy. Thirty (30%) experienced POPF, with 21 crPOPFs. Only current smoking was significantly associated with crPOPF (OR 3.79, p = 0.04). Of 30 patients with a firm gland, none experienced crPOPF. Twenty-five received a partial course of pasireotide; 7/25 (28%) crPOPFs occurred versus 14/75 (19%) in patients receiving a full course (p = 0.38). CONCLUSION: Shortened courses of pasireotide do not increase crPOPF risk; selective discontinuation may be suitable in low-risk patients. Smoking cessation should be encouraged.

Intensive care unit observation after pancreatectomy: Treating the patient or the surgeon?

BACKGROUND: Routine intensive care unit admission (ICUA) is commonplace following pancreatectomy, particularly pancreaticoduodenectomy. The value of this practice in avoiding failure-to-rescue is poorly studied. METHODS: We queried our institutional National Surgical Quality Improvement Project database for patients undergoing pancreatectomy from 2013 to 2020. Postoperative dispositions, ICU courses, and hospital cost data in United States Dollars (USD) were captured. Data were analyzed with multivariable logistic regression. RESULTS: Six-hundred-thirty-seven patients were identified; 404 (63%) underwent pancreaticoduodenectomy. Postoperatively, 398 (99%) pancreaticoduodenectomies and 110 (47%) distal pancreatectomies had ICUA; two-thirds (n = 318, 63%) did not require immediate postoperative ICU-level interventions at ICUA. Of these, 17 (5.3%) subsequently required ICU-level interventions during initial ICUA, most commonly antiarrhythmic infusion (n = 12). Thirty-day and 90-day mortality in patients requiring immediate ICU-level interventions was 5% (n = 10) and 8% (n = 16) versus 0.3% (n = 1) and 1.2% (n = 4) in those without, respectively. Hospital length of stay was significantly longer with initial ICU-level interventions (median 11 vs. 9 days, p < 0.001), as were total ICU costs (mean 8683 vs. 14611 USD, p < 0.001). CONCLUSION: At high-volume pancreas centers, patients without immediate postoperative ICU-level interventions are very low risk for failure-to-rescue. Ward admission with a low threshold for care escalation presents a significant opportunity for cost-savings and un-burdening ICUs.

Agent-Based Modeling Demonstrates How Local Chemotactic Behavior Can Shape Biofilm Architecture.

Bacteria are often found living in aggregated multicellular communities known as biofilms. Biofilms are three-dimensional structures that confer distinct physical and biological properties to the collective of cells living within them. We used agent-based modeling to explore whether local cellular interactions were sufficient to give rise to global structural features of biofilms. Specifically, we asked whether chemorepulsion from a self-produced quorum-sensing molecule, autoinducer-2 (AI-2), was sufficient to recapitulate biofilm growth and cellular organization observed for biofilms of Helicobacter pylori, a common bacterial resident of human stomachs. To carry out this modeling, we modified an existing platform, Individual-based Dynamics of Microbial Communities Simulator (iDynoMiCS), to incorporate three-dimensional chemotaxis, planktonic cells that could join or leave the biofilm structure, and cellular production of AI-2. We simulated biofilm growth of previously characterized H. pylori strains with various AI-2 production and sensing capacities. Using biologically plausible parameters, we were able to recapitulate both the variation in biofilm mass and cellular distributions observed with these strains. Specifically, the strains that were competent to chemotax away from AI-2 produced smaller and more heterogeneously spaced biofilms, whereas the AI-2 chemotaxis-defective strains produced larger and more homogeneously spaced biofilms. The model also provided new insights into the cellular demographics contributing to the biofilm patterning of each strain. Our analysis supports the idea that cellular interactions at small spatial and temporal scales are sufficient to give rise to larger-scale emergent properties of biofilms.IMPORTANCE Most bacteria exist in aggregated, three-dimensional structures called biofilms. Although biofilms play important ecological roles in natural and engineered settings, they can also pose societal problems, for example, when they grow in plumbing systems or on medical implants. Understanding the processes that promote the growth and disassembly of biofilms could lead to better strategies to manage these structures. We had previously shown that Helicobacter pylori bacteria are repulsed by high concentrations of a self-produced molecule, AI-2, and that H. pylori mutants deficient in AI-2 sensing form larger and more homogeneously spaced biofilms. Here, we used computer simulations of biofilm formation to show that local H. pylori behavior of repulsion from high AI-2 could explain the overall architecture of H. pylori biofilms. Our findings demonstrate that it is possible to change global biofilm organization by manipulating local cell behaviors, which suggests that simple strategies targeting cells at local scales could be useful for controlling biofilms in industrial and medical settings.

Stereospecific Synthesis of Alkenes by Eliminative Cross-Coupling of Enantioenriched sp(3) -Hybridized Carbenoids.

1-Aryl-1,2-dialkylethenes were generated by a sequence of electrophilic substitution, 1,2-metalate rearrangement, and ß-elimination initiated by the addition of enantioenriched α-(carbamoyloxy)alkylboronates to enantioenriched lithiated carbamates. The carbenoid stereochemical pairing [i.e., "like"=(S)+(S) or "unlike"=(S)+(R)] and the elimination mechanism (syn or anti), not substituent effects, determined the configuration of the trisubstituted alkene target. For example, (Z)-2,5-diphenyl-2-pentene was produced in 70 % yield with E/Z=5:95 by a like combination of Li and B carbenoids and syn (thermal) elimination whereas the E isomer was obtained in the same yield with E/Z>98:2 by an otherwise identical process involving an unlike stereochemical pairing. The concept elaborated overcomes an intrinsic limitation of traditional strategies for direct connective alkene synthesis, which cannot realize meaningful stereochemical bias unless the alkene substituents are strongly differentiated.

Surface boxplots.

In this paper, we introduce a surface boxplot as a tool for visualization and exploratory analysis of samples of images. First, we use the notion of volume depth to order the images viewed as surfaces. In particular, we define the median image. We use an exact and fast algorithm for the ranking of the images. This allows us to detect potential outlying images that often contain interesting features not present in most of the images. Second, we build a graphical tool to visualize the surface boxplot and its various characteristics. A graph and histogram of the volume depth values allow us to identify images of interest. The code is available in the supporting information of this paper. We apply our surface boxplot to a sample of brain images and to a sample of climate model outputs.

Uncertainty Visualization in Forward and Inverse Cardiac Models.

Quantification and visualization of uncertainty in cardiac forward and inverse problems with complex geometries is subject to various challenges. Specific to visualization is the observation that occlusion and clutter obscure important regions of interest, making visual assessment difficult. In order to overcome these limitations in uncertainty visualization, we have developed and implemented a collection of novel approaches. To highlight the utility of these techniques, we evaluated the uncertainty associated with two examples of modeling myocardial activity. In one case we studied cardiac potentials during the repolarization phase as a function of variability in tissue conductivities of the ischemic heart (forward case). In a second case, we evaluated uncertainty in reconstructed activation times on the epicardium resulting from variation in the control parameter of Tikhonov regularization (inverse case). To overcome difficulties associated with uncertainty visualization, we implemented linked-view windows and interactive animation to the two respective cases. Through dimensionality reduction and superimposed mean and standard deviation measures over time, we were able to display key features in large ensembles of data and highlight regions of interest where larger uncertainties exist.

Visualization of uncertainty without a mean.

As dataset size and complexity steadily increase, uncertainty is becoming an important data aspect. So, today's visualizations need to incorporate indications of uncertainty. However, characterizing uncertainty for visualization isn't always straightforward. Entropy, in the information-theoretic sense, can be a measure for uncertainty in categorical datasets. The authors discuss the mathematical formulation, interpretation, and use of entropy in visualizations. This research aims to demonstrate entropy as a metric and expand the vocabulary of uncertainty measures for visualization.

From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches.

Quantifying uncertainty is an increasingly important topic across many domains. The uncertainties present in data come with many diverse representations having originated from a wide variety of disciplines. Communicating these uncertainties is a task often left to visualization without clear connection between the quantification and visualization. In this paper, we first identify frequently occurring types of uncertainty. Second, we connect those uncertainty representations to ones commonly used in visualization. We then look at various approaches to visualizing this uncertainty by partitioning the work based on the dimensionality of the data and the dimensionality of the uncertainty. We also discuss noteworthy exceptions to our taxonomy along with future research directions for the uncertainty visualization community.

INTERACTIVE VISUALIZATION OF PROBABILITY AND CUMULATIVE DENSITY FUNCTIONS.

The probability density function (PDF), and its corresponding cumulative density function (CDF), provide direct statistical insight into the characterization of a random process or field. Typically displayed as a histogram, one can infer probabilities of the occurrence of particular events. When examining a field over some two-dimensional domain in which at each point a PDF of the function values is available, it is challenging to assess the global (stochastic) features present within the field. In this paper, we present a visualization system that allows the user to examine two-dimensional data sets in which PDF (or CDF) information is available at any position within the domain. The tool provides a contour display showing the normed difference between the PDFs and an ansatz PDF selected by the user and, furthermore, allows the user to interactively examine the PDF at any particular position. Canonical examples of the tool are provided to help guide the reader into the mapping of stochastic information to visual cues along with a description of the use of the tool for examining data generated from an uncertainty quantification exercise accomplished within the field of electrophysiology.

Uvf - Unified Volume Format: A General System for Efficient Handling of Large Volumetric Datasets.

With the continual increase in computing power, volumetric datasets with sizes ranging from only a few megabytes to petascale are generated thousands of times per day. Such data may come from an ordinary source such as simple everyday medical imaging procedures, while larger datasets may be generated from cluster-based scientific simulations or measurements of large scale experiments. In computer science an incredible amount of work worldwide is put into the efficient visualization of these datasets. As researchers in the field of scientific visualization, we often have to face the task of handling very large data from various sources. This data usually comes in many different data formats. In medical imaging, the DICOM standard is well established, however, most research labs use their own data formats to store and process data. To simplify the task of reading the many different formats used with all of the different visualization programs, we present a system for the efficient handling of many types of large scientific datasets (see Figure 1 for just a few examples). While primarily targeted at structured volumetric data, UVF can store just about any type of structured and unstructured data. The system is composed of a file format specification with a reference implementation of a reader. It is not only a common, easy to implement format but also allows for efficient rendering of most datasets without the need to convert the data in memory.

IStar: a raster representation for scalable image and volume data.

Topology has been an important tool for analyzing scalar data and flow fields in visualization. In this work, we analyze the topology of multivariate image and volume data sets with discontinuities in order to create an efficient, raster-based representation we call IStar. Specifically, the topology information is used to create a dual structure that contains nodes and connectivity information for every segmentable region in the original data set. This graph structure, along with a sampled representation of the segmented data set, is embedded into a standard raster image which can then be substantially downsampled and compressed. During rendering, the raster image is upsampled and the dual graph is used to reconstruct the original function. Unlike traditional raster approaches, our representation can preserve sharp discontinuities at any level of magnification, much like scalable vector graphics. However, because our representation is raster-based, it is well suited to the real-time rendering pipeline. We demonstrate this by reconstructing our data sets on graphics hardware at real-time rates.