Does the Method of Biopsy Affect the Incidence of Sentinel Lymph Node Metastases?

More detailed examination of the sentinel lymph node (SLN) in breast cancer has raised concerns about the clinical significance of micrometastases, specifically isolated tumor cells detected only through immunohistochemical (IHC) staining. It has been suggested that these cells do not carry the same biologic implications as true metastatic foci and may represent artifact. A retrospective institutional review board-approved review was conducted on clinically node-negative breast cancer patients who underwent SLN biopsy (SLNB) between 1997 and 2003. Retrospective analysis of tumor characteristics and the method of the initial diagnostic biopsy were correlated with the presence and nature of metastatic disease in the SLN. Of 537 SLNBs, 123 (23%) were hematoxylin-eosin (H&E) positive. SLN positivity strongly correlated with tumor size (p < 0.001) and tumor grade (p = 0.025), but not with the method of biopsy (needle versus excisional biopsy). Prior to July 2002, we routinely evaluated H&E-negative SLNs with IHC ( n  = 381). Of the 291 H&E-negative patients, 26 had IHC-only detected micrometastases (9%). The likelihood of detecting IHC-only metastases did not correlate with tumor size or grade, but was significantly higher in patients undergoing excisional biopsy than core needle biopsy. While the method of biopsy has no demonstrable effect on the likelihood of finding metastases in the SLN by routine serial sectioning and H&E staining, it may significantly impact the likelihood of finding micrometastases by IHC. IHC should not be used routinely in the evaluation of the SLN and caution should be used when basing treatment decisions (completion axillary lymph node dissection or adjuvant therapy) on IHC-only detected micrometastases.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72849/1/j.1075-122X.2006.00179.x.pd

The fine-grained phase-space structure of Cold Dark Matter halos

We present a new and completely general technique for calculating the fine-grained phase-space structure of dark matter throughout the Galactic halo. Our goal is to understand this structure on the scales relevant for direct and indirect detection experiments. Our method is based on evaluating the geodesic deviation equation along the trajectories of individual DM particles. It requires no assumptions about the symmetry or stationarity of the halo formation process. In this paper we study general static potentials which exhibit more complex behaviour than the separable potentials studied previously. For ellipsoidal logarithmic potentials with a core, phase mixing is sensitive to the resonance structure, as indicated by the number of independent orbital frequencies. Regions of chaotic mixing can be identified by the very rapid decrease in the real space density of the associated dark matter streams. We also study the evolution of stream density in ellipsoidal NFW halos with radially varying isopotential shape, showing that if such a model is applied to the Galactic halo, at least $10^5$ streams are expected near the Sun. The most novel aspect of our approach is that general non-static systems can be studied through implementation in a cosmological N-body code. Such an implementation allows a robust and accurate evaluation of the enhancements in annihilation radiation due to fine-scale structure such as caustics. We embed the scheme in the current state-of-the-art code GADGET-3 and present tests which demonstrate that N-body discreteness effects can be kept under control in realistic configurations.Comment: 20 pages, 24 figures, submitted to MNRA

Does organizational formalization facilitate voice and helping organizational citizenship behaviors? It depends on (national) uncertainty norms

Prosocial work behaviors in a globalized environment do not operate in a cultural vacuum. We assess to what extent voice and helping organizational citizenship behaviors (OCBs) vary across cultures, depending on employees’ perceived level of organizational formalization and national uncertainty. We predict that in contexts of uncertainty, cognitive resources are engaged in coping with this uncertainty. Organizational formalization can provide structure that frees up cognitive resources to engage in OCB. In contrast, in contexts of low uncertainty, organizational formalization is not necessary for providing structure and may increase constraints on discretionary behavior. A three-level hierarchical linear modeling analysis of data from 7,537 employees in 267 organizations across 17 countries provides broad support for our hypothesis: perceived organizational formalization is weakly related to OCB, but where uncertainty is high; formalization facilitates voice significantly, helping OCB to a lesser extent. Our findings contribute to clarifying the dynamics between perceptions of norms at organizational and national levels for understanding when employees may engage in helping and voice behaviors. The key implication is that managers can foster OCB through organizational formalization interventions in uncertain environments that are cognitively demanding

The leishmaniases in Kenya: A scoping review.

BackgroundThe leishmaniases are a group of four vector-borne neglected tropical diseases caused by 20 species of protozoan parasites of the genus Leishmania and transmitted through a bite of infected female phlebotomine sandflies. Endemic in over 100 countries, the four types of leishmaniasis-visceral leishmaniasis (VL) (known as kala-azar), cutaneous leishmaniasis (CL), mucocutaneous leishmaniasis (MCL), and post-kala-azar dermal leishmaniasis (PKDL)-put 1.6 billion people at risk. In Kenya, the extent of leishmaniasis research has not yet been systematically described. This knowledge is instrumental in identifying existing research gaps and designing appropriate interventions for diagnosis, treatment, and elimination.Methodology/principal findingsThis study used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to determine the state of leishmaniases research in Kenya and identify research gaps. We searched seven online databases to identify articles published until January 2022 covering VL, CL, MCL, and/or PKDL in Kenya. A total of 7,486 articles were found, of which 479 underwent full-text screening, and 269 met our eligibility criteria. Most articles covered VL only (n = 141, 52%), were published between 1980 and 1994 (n = 108, 39%), and focused on the theme of "vectors" (n = 92, 34%). The most prevalent study types were "epidemiological research" (n = 88, 33%) tied with "clinical research" (n = 88, 33%), then "basic science research" (n = 49, 18%) and "secondary research" (n = 44, 16%).Conclusion/significanceWhile some studies still provide useful guidance today, most leishmaniasis research in Kenya needs to be updated and focused on prevention, co-infections, health systems/policy, and general topics, as these themes combined comprised less than 4% of published articles. Our findings also indicate minimal research on MCL (n = 1, <1%) and PKDL (n = 2, 1%). We urge researchers to renew and expand their focus on these neglected diseases in Kenya

A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis.

Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, and overall survival. We further investigate patient response to therapy, a clinically actionable prediction, and find that patients that are classified as resistant to therapy are harder to predict than responsive patients across the 31 models submitted to the challenge. The top two performing models, which held a high sensitivity to these patients, substantially utilized the proteomics data to make predictions. Using these models, we also identify which signaling proteins were useful in predicting patient therapeutic response

The role of patient outcome and proteomics data in determining prediction accuracy.

<p>A) The probability density of prediction accuracy evaluated separately for CR and Resistant patients. (B) Comparison of individual model accuracy for CR and Resistant patients (right) compared to the distribution over the population (left). The midline of the box plot indicated median accuracy while the lower and upper box edge indicated 25^th and 75^th percentile. (C) The distribution of scores obtained using scrambled RPPA data for the two top performing teams in SC1 (Rank #1 and Rank #2). For each metric, the score obtained using the original RPPA data (not scrambled) is indicated by a diamond. (D) Heat map showing the percent difference in score (average of BAC and AUROC) between predictions obtained using the original RPPA data (not scrambled) and predictions made using data where each protein was scrambled separately over 100 assessments. The y-axis indicates the result for each scrambled protein assessment, 1–100, while the x-axis indicates each protein.</p