New models and online calculator for predicting non-sentinel lymph node status in sentinel lymph node positive breast cancer patients

<p>Abstract</p> <p>Background</p> <p>Current practice is to perform a completion axillary lymph node dissection (ALND) for breast cancer patients with tumor-involved sentinel lymph nodes (SLNs), although fewer than half will have non-sentinel node (NSLN) metastasis. Our goal was to develop new models to quantify the risk of NSLN metastasis in SLN-positive patients and to compare predictive capabilities to another widely used model.</p> <p>Methods</p> <p>We constructed three models to predict NSLN status: recursive partitioning with receiver operating characteristic curves (RP-ROC), boosted Classification and Regression Trees (CART), and multivariate logistic regression (MLR) informed by CART. Data were compiled from a multicenter Northern California and Oregon database of 784 patients who prospectively underwent SLN biopsy and completion ALND. We compared the predictive abilities of our best model and the Memorial Sloan-Kettering Breast Cancer Nomogram (Nomogram) in our dataset and an independent dataset from Northwestern University.</p> <p>Results</p> <p>285 patients had positive SLNs, of which 213 had known angiolymphatic invasion status and 171 had complete pathologic data including hormone receptor status. 264 (93%) patients had limited SLN disease (micrometastasis, 70%, or isolated tumor cells, 23%). 101 (35%) of all SLN-positive patients had tumor-involved NSLNs. Three variables (tumor size, angiolymphatic invasion, and SLN metastasis size) predicted risk in all our models. RP-ROC and boosted CART stratified patients into four risk levels. MLR informed by CART was most accurate. Using two composite predictors calculated from three variables, MLR informed by CART was more accurate than the Nomogram computed using eight predictors. In our dataset, area under ROC curve (AUC) was 0.83/0.85 for MLR (n = 213/n = 171) and 0.77 for Nomogram (n = 171). When applied to an independent dataset (n = 77), AUC was 0.74 for our model and 0.62 for Nomogram. The composite predictors in our model were the product of angiolymphatic invasion and size of SLN metastasis, and the product of tumor size and square of SLN metastasis size.</p> <p>Conclusion</p> <p>We present a new model developed from a community-based SLN database that uses only three rather than eight variables to achieve higher accuracy than the Nomogram for predicting NSLN status in two different datasets. </p

American Society for Microbiology provides 2020 Guidelines for Detection and Identification of Group B Streptococcus

Maternal colonization with Group B Streptococcus (GBS) is a primary risk factor for early-onset disease (EOD) GBS infection in infants and intrapartum prophylaxis reduces neonatal infection

On the structure of the stator of the mitochondrial ATP synthase

The structure of most of the peripheral stalk, or stator, of the F-ATPase from bovine mitochondria, determined at 2.8 Å resolution, contains residues 79–183, 3–123 and 5–70 of subunits b, d and F(6), respectively. It consists of a continuous curved α-helix about 160 Å long in the single b-subunit, augmented by the predominantly α-helical d- and F(6)-subunits. The structure occupies most of the peripheral stalk in a low-resolution structure of the F-ATPase. The long helix in subunit b extends from near to the top of the F(1) domain to the surface of the membrane domain, and it probably continues unbroken across the membrane. Its uppermost region interacts with the oligomycin sensitivity conferral protein, bound to the N-terminal region of one α-subunit in the F(1) domain. Various features suggest that the peripheral stalk is probably rigid rather than resembling a flexible rope. It remains unclear whether the transient storage of energy required by the rotary mechanism takes place in the central stalk or in the peripheral stalk or in both domains

Generation of Antigen Microarrays to Screen for Autoantibodies in Heart Failure and Heart Transplantation

<div><p>Autoantibodies directed against endogenous proteins including contractile proteins and endothelial antigens are frequently detected in patients with heart failure and after heart transplantation. There is evidence that these autoantibodies contribute to cardiac dysfunction and correlate with clinical outcomes. Currently, autoantibodies are detected in patient sera using individual ELISA assays (one for each antigen). Thus, screening for many individual autoantibodies is laborious and consumes a large amount of patient sample. To better capture the broad-scale antibody reactivities that occur in heart failure and post-transplant, we developed a custom antigen microarray technique that can simultaneously measure IgM and IgG reactivities against 64 unique antigens using just five microliters of patient serum. We first demonstrated that our antigen microarray technique displayed enhanced sensitivity to detect autoantibodies compared to the traditional ELISA method. We then piloted this technique using two sets of samples that were obtained at our institution. In the first retrospective study, we profiled pre-transplant sera from 24 heart failure patients who subsequently received heart transplants. We identified 8 antibody reactivities that were higher in patients who developed cellular rejection (2 or more episodes of grade 2R rejection in first year after transplant as defined by revised criteria from the International Society for Heart and Lung Transplantation) compared with those who did have not have rejection episodes. In a second retrospective study with 31 patients, we identified 7 IgM reactivities that were higher in heart transplant recipients who developed antibody-mediated rejection (AMR) compared with control recipients, and in time course studies, these reactivities appeared prior to overt graft dysfunction. In conclusion, we demonstrated that the autoantibody microarray technique outperforms traditional ELISAs as it uses less patient sample, has increased sensitivity, and can detect autoantibodies in a multiplex fashion. Furthermore, our results suggest that this autoantibody array technology may help to identify patients at risk of rejection following heart transplantation and identify heart transplant recipients with AMR.</p></div

Heatmap showing reactivities that are higher in pre-transplant serum from rejectors compared with non-rejectors.

<p>Rejectors are indicated in red. Significant differences between rejectors and non-rejectors were detected with the SAM algorithm with q value < 0.05. Four of the rejectors are grouped together at the right of the heatmap and the rejector with the highest reactivities is in a separate group at the left. Scale shows reactivity from low (blue) to high (yellow). Results are representative of two independent array experiments. dsDNA, double-stranded DNA; Hsp60, heat shock protein 60; Hsp27, heat shock protein 27; ssDNA, single-stranded DNA.</p

Time course of ejection fraction and non-HLA antibodies in two patients with AMR.

<p>The solid line plots left ventricular ejection fraction and the dashed lines plot median fluorescence intensity minus background (MFI-B) of various antigens over time. Timing of VAD placement, heart transplant (Tx), and diagnosis of AMR are indicated above the graphs. Day of heart transplant is designated as Day 0. Graph shows mean ± SD for array features. (A) Patient AMR8. This patient developed IgM anti-tropomyosin and to a lesser extent IgM anti-DNA antibodies following transplant, which coincided with a drop in graft function. (B) Patient AMR6. This patient developed IgM anti-IgG antibodies (rheumatoid factor) following VAD placement, which decreased following heart transplant. A severe drop in graft function coincided with reappearance of rheumatoid factor. dsDNA, double-stranded DNA; LVEF, left ventricular ejection fraction; MFI-B, median fluorescence intensity minus background; ssDNA, single-stranded DNA; VAD, ventricular assist device.</p

Heatmap of non-HLA reactivities that are significantly higher in post-transplant serum of AMR patients compared with non-AMR patients.

<p>Significance analysis of microarrays identified seven antigen reactivities that are higher in recipients with AMR compared with non-rejectors. Yellow squares in the heatmap indicate a higher reactivity, whereas blue squares indicate a lower reactivity as shown in scale. Recipients with AMR are labeled in red and are numbered 1–12. Non-rejectors are labeled in black and numbered 1–19. Results are representative of two independent array experiments. ssDNA, single-stranded DNA; dsDNA, double-stranded DNA.</p

Step-wise discriminant analyses of pre-transplant variables that predict rejection outcome (Yes/No) (maximum 3 steps).

<p>Step-wise discriminant analyses of pre-transplant variables that predict rejection outcome (Yes/No) (maximum 3 steps).</p