Measurable Residual Disease in High-Risk Acute Myeloid Leukemia

Mounting evidence suggests measurable residual disease (MRD) assessments are prognostic in acute myeloid leukemia (AML). High-risk AML encompasses a subset of AML with poor response to therapy and prognosis, with features such as therapy-related AML, an antecedent hematologic disorder, extramedullary disease (in adults), and selected mutations and cytogenetic abnormalities. Historically, few patients with high-risk AML achieved deep and durable remission with conventional chemotherapy; however, newer agents might be more effective in achieving MRD-negative remission. CPX-351 (dual-drug liposomal encapsulation of daunorubicin/cytarabine at a synergistic ratio) demonstrated MRD-negativity rates of 36\u201364% across retrospective studies in adults with newly diagnosed high-risk AML and 84% in pediatric patients with first-relapse AML. Venetoclax (BCL2 inhibitor) demonstrated MRD-negativity rates of 33\u201353% in combination with hypomethylating agents for high-risk subgroups in studies of older adults with newly diagnosed AML who were ineligible for intensive therapy and 65% in combination with chemotherapy in pediatric patients with relapsed/refractory AML. However, there is no consensus on optimal MRD methodology in AML, and the use of different techniques, sample sources, sensitivity thresholds, and the timing of assessments limit comparisons across studies. Robust MRD analyses are needed in future clinical studies, and MRD monitoring should become a routine aspect of AML management

Using metal-ligand binding characteristics to predict metal toxicity: quantitative ion character-activity relationships (QICARs).

Ecological risk assessment can be enhanced with predictive models for metal toxicity. Modelings of published data were done under the simplifying assumption that intermetal trends in toxicity reflect relative metal-ligand complex stabilities. This idea has been invoked successfully since 1904 but has yet to be applied widely in quantitative ecotoxicology. Intermetal trends in toxicity were successfully modeled with ion characteristics reflecting metal binding to ligands for a wide range of effects. Most models were useful for predictive purposes based on an F-ratio criterion and cross-validation, but anomalous predictions did occur if speciation was ignored. In general, models for metals with the same valence (i.e., divalent metals) were better than those combining mono-, di-, and trivalent metals. The softness parameter (sigma p) and the absolute value of the log of the first hydrolysis constant ([symbol: see text] log KOH [symbol: see text]) were especially useful in model construction. Also, delta E0 contributed substantially to several of the two-variable models. In contrast, quantitative attempts to predict metal interactions in binary mixtures based on metal-ligand complex stabilities were not successful

Efficacy of weekly teriparatide does not vary by baseline fracture probability calculated using FRAX

Summary The aim of this study was to determine the efficacy of once-weekly teriparatide as a function of baseline fracture risk. Treatment with once-weekly teriparatide was associated with a statistically significant 79 % decrease in vertebral fractures, and in the cohort as a whole, efficacy was not related to baseline fracture risk. Introduction Previous studies have suggested that the efficacy of some interventions may be greater in the segment of the population at highest fracture risk as assessed by the FRAX® algorithms. The aim of the present study was to determine whether the antifracture efficacy of weekly teriparatide was dependent on the magnitude of fracture risk. Methods Baseline fracture probabilities (using FRAX) were computed from the primary data of a phase 3 study (TOWER) of the effects of weekly teriparatide in 542 men and postmenopausal women with osteoporosis. The outcome variable comprised morphometric vertebral fractures. Interactions between fracture probability and efficacy were explored by Poisson regression. Results The 10-year probability of major osteoporotic fractures (without BMD) ranged from 7.2 to 42.2 %. FRAX-based hip fracture probabilities ranged from 0.9 to 29.3 %. Treatment with teriparatide was associated with a 79 % (95 % CI 52–91 %) decrease in vertebral fractures assessed by semiquantitative morphometry. Relative risk reductions for the effect of teriparatide on the fracture outcome did not change significantly across the range of fracture probabilities (p = 0.28). In a subgroup analysis of 346 (64 %) participants who had FRAX probabilities calculated with the inclusion of BMD, there was a small but significant interaction (p = 0.028) between efficacy and baseline fracture probability such that high fracture probabilities were associated with lower efficacy. Conclusion Weekly teriparatide significantly decreased the risk of morphometric vertebral fractures in men and postmenopausal women with osteoporosis. Overall, the efficacy of teriparatide was not dependent on the level of fracture risk assessed by FRAX in the cohort as a whole

Adding New Tasks to a Single Network with Weight Transformations using Binary Masks

Visual recognition algorithms are required today to exhibit adaptive abilities. Given a deep model trained on a specific, given task, it would be highly desirable to be able to adapt incrementally to new tasks, preserving scalability as the number of new tasks increases, while at the same time avoiding catastrophic forgetting issues. Recent work has shown that masking the internal weights of a given original conv-net through learned binary variables is a promising strategy. We build upon this intuition and take into account more elaborated affine transformations of the convolutional weights that include learned binary masks. We show that with our generalization it is possible to achieve significantly higher levels of adaptation to new tasks, enabling the approach to compete with fine tuning strategies by requiring slightly more than 1 bit per network parameter per additional task. Experiments on two popular benchmarks showcase the power of our approach, that achieves the new state of the art on the Visual Decathlon Challenge

Beyond deficit-based models of learners' cognition: Interpreting engineering students' difficulties with sense-making in terms of fine-grained epistemological and conceptual dynamics

Researchers have argued against deficit-based explanations of students' troubles with mathematical sense-making, pointing instead to factors such as epistemology: students' beliefs about knowledge and learning can hinder them from activating and integrating productive knowledge they have. In this case study of an engineering major solving problems (about content from his introductory physics course) during a clinical interview, we show that "Jim" has all the mathematical and conceptual knowledge he would need to solve a hydrostatic pressure problem that we posed to him. But he reaches and sticks with an incorrect answer that violates common sense. We argue that his lack of mathematical sense-making-specifically, translating and reconciling between mathematical and everyday/common-sense reasoning-stems in part from his epistemological views, i.e., his views about the nature of knowledge and learning. He regards mathematical equations as much more trustworthy than everyday reasoning, and he does not view mathematical equations as expressing meaning that tractably connects to common sense. For these reasons, he does not view reconciling between common sense and mathematical formalism as either necessary or plausible to accomplish. We, however, avoid a potential "deficit trap"-substituting an epistemological deficit for a concepts/skills deficit-by incorporating multiple, context-dependent epistemological stances into Jim's cognitive dynamics. We argue that Jim's epistemological stance contains productive seeds that instructors could build upon to support Jim's mathematical sense-making: He does see common-sense as connected to formalism (though not always tractably so) and in some circumstances this connection is both salient and valued.Comment: Submitted to the Journal of Engineering Educatio

A high incidence of vertebral fracture in women with breast cancer

Because treatment for breast cancer may adversely affect skeletal metabolism, we investigated vertebral fracture risk in women with non-metastatic breast cancer. The prevalence of vertebral fracture was similar in women at the time of first diagnosis to that in an age-matched sample of the general population. The incidence of vertebral fracture, however, was nearly five times greater than normal in women from the time of first diagnosis [odds ratio (OR), 4.7; 95% confidence interval (95% CI), 2.3–9.9], and 20-fold higher in women with soft-tissue metastases without evidence of skeletal metastases (OR, 22.7; 95% CI, 9.1–57.1). We conclude that vertebral fracture risk is markedly increased in women with breast cancer. © 1999 Cancer Research Campaig

Assessment of utility of daily patient results averages as adjunct quality control in a weekday-only satellite chemistry laboratory

ABSTRACT Background: Our department operates a weekday-only (8AM-5PM) satellite laboratory in an infusion center with a menu of 18 chemistry tests on a Roche c501 analyzer. We examined whether daily patient results averages (PRA) in this setting might be useful as a patient-based quality control (PBQC) adjunct to standard daily liquid quality control (LQC) measurements. First, we evaluated the reproducibility (coefficient of variation, CV) of daily PRAs for each analyte, and compared these to CVs of LQC. Second, for select analytes found to have relatively low PRA CVs, we evaluated the extent to which use of daily PRA measurements could improve detection of analytical errors when combined with LQC. Methods: Patient results data for approximately one month (21 weekdays) were obtained from the Sunquest laboratory information system. For calculation of patient results averages (PRA), qualifying results were restricted to those within the reference range for each analyte. PRA and standard deviation (S) of PRA across 21 days was calculated for each analyte. Coefficients of variation for PRA (CV-PRA) were compared to those observed for standard liquid quality control (LQC) measurements (CV-LQC). For those analytes for which CV-PRA was less than CV-LQC, we evaluated the potential advantage of addition of PRA to daily LQC. For each analyte, a presumed PRA shift was determined such that probability of detection (P) was 0.5 when using LQC alone (viz., using high LQC and low LQC measurements), according to criterion that at least one 1-2S deviation from mean was obtained. For this same PRA shift, P = 0.5 for LQC alone was compared to P obtained for LQC + PRA (viz., using high LQC, low LQC, and PRA measurements), according to the same criterion. Results: Across 21 days, the number of results per day per assay ranged from 23 ±4 (uric acid) to 75 ±21 (electrolytes). Qualifying results (results within the reference range) ranged from 70 ± 6 % (LDH) to 99 ± 1 % (Cl). Seven analytes had CV-PRA \u3c CV-LQC (analyte, CV%): albumin, 1.25%; Ca, 0.67%; Cl, 0.62%; CO2, 1.13%; creatinine, 3.44%; K, 1.14%; Na, 0.65%. The remainder did not meet this criterion: ALP, 3.7%; ALT, 5.2%; AST, 5.1%; BUN, 4.6%; glucose, 1.4%; LDH, 2.0%; Mg, 1.4%; P, 2.5%; protein, 0.9%; TBIL, 6.1%; uric acid, 4.3%. Among the seven analytes for which CV-PRA \u3c CV-LQC, probability (P) of shift detection by LQC for circumstances as described in Methods (LQC P = 0.5) was increased substantially by inclusion of PRA (analyte, shift in analyte concentration, P): CO2, ±1.07 mmol/L, 0.97; creatinine, ±0.099 mg/dL, 0.93; albumin, ±0.126 g/dL, 0.85; Ca, ±0.14 mg/dL, 0.80; K, ±0.097 mmol/L, 0.76; Cl, ±1.24 mmol/L, 0.74; Na, ±1.48 mmol/L, 0.68. Conclusions: For 7 analytes, daily PRA demonstrated CVs less than those for LQC. For these analytes, calculations demonstrated that daily PRA can increase probability of detection of small results shifts when used as an adjunct to LQC. Daily PRA is a simple and essentially cost-free form of PBQC that may be useful for certain analytes in part-time laboratory settings

The Case for Dynamic Models of Learners' Ontologies in Physics

In a series of well-known papers, Chi and Slotta (Chi, 1992; Chi & Slotta, 1993; Chi, Slotta & de Leeuw, 1994; Slotta, Chi & Joram, 1995; Chi, 2005; Slotta & Chi, 2006) have contended that a reason for students' difficulties in learning physics is that they think about concepts as things rather than as processes, and that there is a significant barrier between these two ontological categories. We contest this view, arguing that expert and novice reasoning often and productively traverses ontological categories. We cite examples from everyday, classroom, and professional contexts to illustrate this. We agree with Chi and Slotta that instruction should attend to learners' ontologies; but we find these ontologies are better understood as dynamic and context-dependent, rather than as static constraints. To promote one ontological description in physics instruction, as suggested by Slotta and Chi, could undermine novices' access to productive cognitive resources they bring to their studies and inhibit their transition to the dynamic ontological flexibility required of experts.Comment: The Journal of the Learning Sciences (In Press

Memory-Efficient Incremental Learning Through Feature Adaptation

We introduce an approach for incremental learning that preserves feature descriptors of training images from previously learned classes, instead of the images themselves, unlike most existing work. Keeping the much lower-dimensional feature embeddings of images reduces the memory footprint significantly. We assume that the model is updated incrementally for new classes as new data becomes available sequentially.This requires adapting the previously stored feature vectors to the updated feature space without having access to the corresponding original training images. Feature adaptation is learned with a multi-layer perceptron, which is trained on feature pairs corresponding to the outputs of the original and updated network on a training image. We validate experimentally that such a transformation generalizes well to the features of the previous set of classes, and maps features to a discriminative subspace in the feature space. As a result, the classifier is optimized jointly over new and old classes without requiring old class images. Experimental results show that our method achieves state-of-the-art classification accuracy in incremental learning benchmarks, while having at least an order of magnitude lower memory footprint compared to image-preserving strategies