Nidogen 1 regulates proliferation and migration/invasion in murine claudin-low mammary tumor cells

Nidogen 1 (NID1) is a glycoprotein found in basement membranes involved in cross-linking collagen IV and laminin. The role of NID in breast cancer has only been evaluated in a small number of studies and the findings of these studies have been inconsistent. Our previous work revealed that highly tumorigenic murine mammary tumor cells express high levels of Nid1 while weakly tumorigenic mammary tumor cells express low levels of Nid1. To investigate Nid1, two stable knockdown lines were created, and Nid1 knockdown was confirmed at both the mRNA and protein level. Nid1 knockdown significantly reduced cell proliferation and migration/invasion and these reductions in proliferation and migration/invasion could be rescued by conditioned media containing NID1 protein. The reduced migration/invasion observed in the Nid1 knockdown cells was not associated with significant alterations in the epithelial gene Cdh1 or the mesenchymal genes Snai1, Snai2, Twist1, Twist2, Zeb1 and Zeb2. Therefore, suppression of Nid1 expression reduces proliferation and migration/invasion in claudin-low murine mammary tumor cells.     

Acute myeloid leukemia: current progress and future directions

Progress in the understanding of the biology and therapy of acute myeloid leukemia (AML) is occurring rapidly. Since 2017, nine agents have been approved for various indications in AML. These included several targeted therapies like venetoclax, FLT3 inhibitors, IDH inhibitors, and others. The management of AML is complicated, highlighting the need for expertise in order to deliver optimal therapy and achieve optimal outcomes. The multiple subentities in AML require very different therapies. In this review, we summarize the important pathophysiologies driving AML, review current therapies in standard practice, and address present and future research directions.Subject terms: Prognosis, Health services     

Validation of PATHFx 2.0: An open-source tool for estimating survival in patients undergoing pathologic fracture fixation

Treatment decisions in patients with metastatic bone disease rely on accurate survival estimation. We developed the original PATHFx models using expensive, proprietary software and now seek to provide a more cost-effective solution. Using open-source machine learning software to create PATHFx version 2.0, we asked whether PATHFx 2.0 could be created using open-source methods and externally validated in two unique patient populations. The training set of a well-characterized, database records of 189 patients and the bnlearn package within R Version 3.5.1 (R Foundation for Statistical Computing), was used to establish a series of Bayesian belief network models designed to predict survival at 1, 3, 6, 12, 18, and 24 months. Each was externally validated in both a Scandinavian (n = 815 patients) and a Japanese (n = 261 patients) data set. Brier scores and receiver operating characteristic curves to assessed discriminatory ability. Decision curve analysis (DCA) evaluated whether models should be used clinically. DCA showed that the model should be used clinically at all time points in the Scandinavian data set. For the 1-month time point, DCA of the Japanese data set suggested to expect better outcomes assuming all patients will survive greater than 1 month. Brier scores for each curve demonstrate that the models are accurate at each time point. Statement of Clinical Significance: we successfully transitioned to PATHFx 2.0 using open-source software and externally validated it in two unique patient populations, which can be used as a cost-effective option to guide surgical decisions in patients with metastatic bone disease.     

Assessment of backward walking unmasks mobility impairments in post-stroke community ambulators

Background: While over half of stroke survivors recover the ability to walk without assistance, deficits persist in the performance of walking adaptations necessary for safe home and community mobility. One such adaptation is the ability to walk or step backward. Post-stroke rehabilitation rarely includes backward walking (BW) assessment and BW deficits have not been quantified in post-stroke community ambulators.

Objective: To quantify spatiotemporal and kinematic BW characteristics in post-stroke community ambulators and compare their performance to controls.

Methods: Individuals post-stroke (n = 15, 60.1 ± 12.9 years, forward speed: 1.13 ± 0.23 m/s) and healthy adults (n = 12, 61.2 ± 16.2 years, forward speed: 1.40 ± 0.13 m/s) performed forward walking (FW) and BW during a single session. Step characteristics and peak lower extremity joint angles were extracted using 3D motion analysis and analyzed with mixed-method ANOVAs (group, walking condition).

Results: The stroke group demonstrated greater reductions in speed, step length and cadence and a greater increase in double-support time during BW compared to FW (p < .01). Compared to FW, the post-stroke group demonstrated greater reductions in hip extension and knee flexion during BW (p < .05). The control group demonstrated decreased plantarflexion and increased dorsiflexion during BW, but these increases were attenuated in the post-stroke group (p < .05).

Conclusions: Assessment of BW can unmask post-stroke walking impairments not detected during typical FW. BW impairments may contribute to the mobility difficulties reported by adults post-stroke. Therefore, BW should be assessed when determining readiness for home and community ambulation.