The stories about racism and health: the development of a framework for racism narratives in medical literature using a computational grounded theory approach.

INTRODUCTION: The scientific study of racism as a root cause of health inequities has been hampered by the policies and practices of medical journals. Monitoring the discourse around racism and health inequities (i.e., racism narratives) in scientific publications is a critical aspect of understanding, confronting, and ultimately dismantling racism in medicine. A conceptual framework and multi-level construct is needed to evaluate the changes in the prevalence and composition of racism over time and across journals. OBJECTIVE: To develop a framework for classifying racism narratives in scientific medical journals. METHODS: We constructed an initial set of racism narratives based on an exploratory literature search. Using a computational grounded theory approach, we analyzed a targeted sample of 31 articles in four top medical journals which mentioned the word 'racism'. We compiled and evaluated 80 excerpts of text that illustrate racism narratives. Two coders grouped and ordered the excerpts, iteratively revising and refining racism narratives. RESULTS: We developed a qualitative framework of racism narratives, ordered on an anti-racism spectrum from impeding anti-racism to strong anti-racism, consisting of 4 broad categories and 12 granular modalities for classifying racism narratives. The broad narratives were "dismissal," "person-level," "societal," and "actionable." Granular modalities further specified how race-related health differences were related to racism (e.g., natural, aberrant, or structurally modifiable). We curated a "reference set" of example sentences to empirically ground each label. CONCLUSION: We demonstrated racism narratives of dismissal, person-level, societal, and actionable explanations within influential medical articles. Our framework can help clinicians, researchers, and educators gain insight into which narratives have been used to describe the causes of racial and ethnic health inequities, and to evaluate medical literature more critically. This work is a first step towards monitoring racism narratives over time, which can more clearly expose the limits of how the medical community has come to understand the root causes of health inequities. This is a fundamental aspect of medicine's long-term trajectory towards racial justice and health equity.

Resolving Non-identifiability Mitigates Bias in Models of Neural Tuning and Functional Coupling.

In the brain, all neurons are driven by the activity of other neurons, some of which maybe simultaneously recorded, but most are not. As such, models of neuronal activity need to account for simultaneously recorded neurons and the influences of unmeasured neurons. This can be done through inclusion of model terms for observed external variables (e.g., tuning to stimuli) as well as terms for latent sources of variability. Determining the influence of groups of neurons on each other relative to other influences is important to understand brain functioning. The parameters of statistical models fit to data are commonly used to gain insight into the relative importance of those influences. Scientific interpretation of models hinge upon unbiased parameter estimates. However, evaluation of biased inference is rarely performed and sources of bias are poorly understood. Through extensive numerical study and analytic calculation, we show that common inference procedures and models are typically biased. We demonstrate that accurate parameter selection before estimation resolves model non-identifiability and mitigates bias. In diverse neurophysiology data sets, we found that contributions of coupling to other neurons are often overestimated while tuning to exogenous variables are underestimated in common methods. We explain heterogeneity in observed biases across data sets in terms of data statistics. Finally, counter to common intuition, we found that model non-identifiability contributes to bias, not variance, making it a particularly insidious form of statistical error. Together, our results identify the causes of statistical biases in common models of neural data, provide inference procedures to mitigate that bias, and reveal and explain the impact of those biases in diverse neural data sets.

Improved inference in coupling, encoding, and decoding models and its consequence for neuroscientific interpretation.

BACKGROUND: A central goal of systems neuroscience is to understand the relationships amongst constituent units in neural populations, and their modulation by external factors, using high-dimensional and stochastic neural recordings. Parametric statistical models (e.g., coupling, encoding, and decoding models), play an instrumental role in accomplishing this goal. However, extracting conclusions from a parametric model requires that it is fit using an inference algorithm capable of selecting the correct parameters and properly estimating their values. Traditional approaches to parameter inference have been shown to suffer from failures in both selection and estimation. The recent development of algorithms that ameliorate these deficiencies raises the question of whether past work relying on such inference procedures have produced inaccurate systems neuroscience models, thereby impairing their interpretation. NEW METHOD: We used algorithms based on Union of Intersections, a statistical inference framework based on stability principles, capable of improved selection and estimation. COMPARISON: We fit functional coupling, encoding, and decoding models across a battery of neural datasets using both UoI and baseline inference procedures (e.g., ℓ1-penalized GLMs), and compared the structure of their fitted parameters. RESULTS: Across recording modality, brain region, and task, we found that UoI inferred models with increased sparsity, improved stability, and qualitatively different parameter distributions, while maintaining predictive performance. We obtained highly sparse functional coupling networks with substantially different community structure, more parsimonious encoding models, and decoding models that relied on fewer single-units. CONCLUSIONS: Together, these results demonstrate that improved parameter inference, achieved via UoI, reshapes interpretation in diverse neuroscience contexts.

Heterogeneous Synaptic Weighting Improves Neural Coding in the Presence of Common Noise.

Simultaneous recordings from the cortex have revealed that neural activity is highly variable and that some variability is shared across neurons in a population. Further experimental work has demonstrated that the shared component of a neuronal population's variability is typically comparable to or larger than its private component. Meanwhile, an abundance of theoretical work has assessed the impact that shared variability has on a population code. For example, shared input noise is understood to have a detrimental impact on a neural population's coding fidelity. However, other contributions to variability, such as common noise, can also play a role in shaping correlated variability. We present a network of linear-nonlinear neurons in which we introduce a common noise input to model-for instance, variability resulting from upstream action potentials that are irrelevant to the task at hand. We show that by applying a heterogeneous set of synaptic weights to the neural inputs carrying the common noise, the network can improve its coding ability as measured by both Fisher information and Shannon mutual information, even in cases where this results in amplification of the common noise. With a broad and heterogeneous distribution of synaptic weights, a population of neurons can remove the harmful effects imposed by afferents that are uninformative about a stimulus. We demonstrate that some nonlinear networks benefit from weight diversification up to a certain population size, above which the drawbacks from amplified noise dominate over the benefits of diversification. We further characterize these benefits in terms of the relative strength of shared and private variability sources. Finally, we studied the asymptotic behavior of the mutual information and Fisher information analytically in our various networks as a function of population size. We find some surprising qualitative changes in the asymptotic behavior as we make seemingly minor changes in the synaptic weight distributions.

Sparse, Predictive, and Interpretable Functional Connectomics with UoILasso.

Network formation from neural activity is a foundational problem in systems neuroscience. Functional networks, after downstream analysis, can provide key insights into the nature of neurobiological structure and computation. The validity of such insights hinges on accurate selection and estimation of the edges connecting nodes. However, commonly used statistical inference procedures generally fail to identify the correct features, and further introduce consequential bias in the estimates. To address these issues, we developed Union of Intersections (UoI), a flexible, modular, and scalable framework for enhanced statistical feature selection and estimation. Methods based on UoI perform feature selection and feature estimation through intersection and union operations, respectively. In the context of linear regression (specifically UoILasso), we summarize extensive numerical investigation on synthetic data to demonstrate tight control of false-positives and false-negatives in feature selection with low-bias and low-variance estimates of selected parameters, while maintaining high-quality prediction accuracy. We demonstrate, with UoILasso, the extraction of sparse, predictive, and interpretable functional networks from human electrocorticography recordings during speech production and the inference of parsimonious coupling models from nonhuman primate single-unit recordings during reaching tasks. Our results establish that UoILasso generates interpretable and predictive functional connectivity networks.

Enhanced anticancer activity of gemcitabine in combination with noscapine via antiangiogenic and apoptotic pathway against non-small cell lung cancer.

BACKGROUND: The aim of this investigation was to evaluate the anticancer activity of Noscapine (Nos) and Gemcitabine (Gem) combination (NGC) against non-small cell lung cancer (NSCLC) and to elucidate the underlying mechanism of action. METHODS: Isobolographic method was used to calculate combination index values from cytotoxicity data. In vitro antiangiogenic and apoptotic activity of Nos, Gem and NGC was evaluated. For in vivo studies, female athymic Nu/nu mice were xenografted with H460 tumors and the efficacy of Nos, Gem, or NGC was determined. Protein expressions by immunohistochemical staining were evaluated in harvested tumor tissues. RESULTS: The CI values (<0.59) were suggestive of synergistic behavior between Nos and Gem. NGC treatment showed significantly inhibited tube formation and increased percentage of apoptotic cells. NGC, Gem and Nos treatment reduced tumor volume by 82.9±4.5 percent, 39.4±5.8 percent and 34.2±5.7 percent respectively. Specifically, NGC treatment decreased expression cell survival proteins; VEGF, CD31 staining and microvessel density and enhanced DNA fragmentation and cleaved caspase 3 levels compared to single agent treated and control groups. CONCLUSION: Nos potentiated the anticancer activity of Gem in an additive to synergistic manner against lung cancer via antiangiogenic and apoptotic pathways. These findings suggest potential benefit for use of NGC chemotherapy for treatment of lung cancer.

Anticancer activity of Noscapine, an opioid alkaloid in combination with Cisplatin in human non-small cell lung cancer.

The purpose of this study was to examine the efficacy of Noscapine (Nos) and Cisplatin (Cis) combination treatment in vitro in A549 and H460 lung cancer cells, in vivo in murine xenograft model and to investigate the underlying mechanism. The combination index values (< 0.6) suggested synergistic effects of Nos+Cis and resulted in the highest increase in percentage of apoptotic NSCLC cells and increased expression of p53, p21, caspase 3, cleaved caspase 3, cleaved PARP, Bax, and decreased expression of Bcl2 and surviving proteins compared with treatment with either agent. Nos+Cis treatment reduced tumor volume by 78.1 ± 7.5% compared with 38.2 ± 6.8% by Cis or 35.4 ± 6.9% by Nos alone in murine xenograft lung cancer model. Nos+Cis treatment decreased expression of pAkt, Akt, cyclin D1, survivin, PARP, Bcl2, and increased expression of p53, p21, Bax, cleaved PARP, caspase 3, cleaved caspase 3, cleaved caspase 8, caspase 8, cleaved caspase 9 and caspase 9 compared to single-agent treated and control groups. Our results suggest that Nos enhanced the anticancer activity of Cis in an additive to synergistic manner by activating multiple signaling pathways including apoptosis. These findings suggest potential benefit for use of Nos and Cis combination in treatment of lung cancer.