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Dark souls : the breath of Andolus
As her kingdom collapses into chaos and death, battle-hardened warrior, Fira, embarks on a perilous, last-ditch quest to save it. Allies are few. Campfires are burned to embers and countless hordes of demonic and draconic foes stand in her way. Only with the aid of a duplicitous scryer can rekindle the flame that will return light to her world, if she doesn't perish in the attempt.
Book
Negative spillover due to constraints on care delivery: a potential source of bias in pragmatic clinical trials
Background
Pragmatic clinical trials evaluate the effectiveness of health interventions in real-world settings. Negative spillover can arise in a pragmatic trial if the study intervention affects how scarce resources are allocated across patients in the intervention and comparison groups.
Main body
Negative spillover can lead to overestimation of treatment effect and harm to patients assigned to usual care in trials of diverse health interventions. While this type of spillover has been addressed in trials of social welfare and public health interventions, there is little recognition of this source of bias in the medical literature. In this commentary, I examine what causes negative spillover and how it may have led clinical trial investigators to overestimate the effect of patient navigation, AI-based physiological alarms, and elective induction of labor. Trials discussed here are a convenience sample and not the result of a systematic review. I also suggest ways to detect negative spillover and design trials that avoid this potential source of bias.
Conclusion
As new clinical practices and technologies that affect care delivery are considered for widespread adoption, well-designed trials are needed to provide valid evidence on their risks and benefits. Understanding all sources of bias that could affect these trials, including negative spillover, is a critical part of this effort. Future guidance on clinical trial design should consider addressing this form of spillover, just as current guidance often discusses bias due to lack of blinding, differential attrition, or contamination.
Journal Article
Rapid and high-yield recovery of plasma-derived extracellular vesicles using modified chromatography with soluble protein depletion for biomarker discovery
Extracellular vesicles (EVs) are critical mediators of intercellular communication by transferring proteins, lipid and nucleic acids between cells. EVs in biofluids, particularly blood, have gathered significant interest as potential biomarkers for disease diagnosis. However, isolating EVs from blood poses a challenge due to the high concentration of plasma proteins, which obscure the detection of low abundant EV-associated proteins. Here, we optimized a simplified and efficient method for isolating plasma-derived EVs by combining size exclusion chromatography (SEC) with flow-through chromatography using Capto Core 700 beads. A brief incubation of SEC-derived EV fractions with Capto Core beads (qEV + CC) enabled us to isolate intact, high-purity EVs with reduced soluble plasma protein contamination. As a comparison, MagReSyn-based method was not compatible with elution of intact EVs after the purification and showed significant contamination of soluble plasma proteins. Data-independent acquisition-based liquid chromatography-mass spectrometry of isolated plasma-EVs using the qEV + CC approach identified over 1,000 EV-associated proteins, including an increased presence of brain derived proteins and markers linked to neurodegenerative diseases, such as amyloid precursor protein and apolipoprotein E. These findings were further validated by super-resolution microscopy at a single EV resolution. Bioinformatic pathway and network analyses revealed enrichment of pathways involved in RNA processing, cell adhesion and synaptic function, highlighting the potential of EV molecules for broad disease biomarker discovery. Our findings present an optimized method for efficient purification of plasma-derived EVs, providing a valuable tool for advancing EV-based biomarker development.
Graphical abstract
Journal Article
Artificial intelligence applications used in the clinical response to COVID-19: A scoping review
Research into using artificial intelligence (AI) in health care is growing and several observers predicted that AI would play a key role in the clinical response to the COVID-19. Many AI models have been proposed though previous reviews have identified only a few applications used in clinical practice. In this study, we aim to (1) identify and characterize AI applications used in the clinical response to COVID-19; (2) examine the timing, location, and extent of their use; (3) examine how they relate to pre-pandemic applications and the U.S. regulatory approval process; and (4) characterize the evidence that is available to support their use. We searched academic and grey literature sources to identify 66 AI applications that performed a wide range of diagnostic, prognostic, and triage functions in the clinical response to COVID-19. Many were deployed early in the pandemic and most were used in the U.S., other high-income countries, or China. While some applications were used to care for hundreds of thousands of patients, others were used to an unknown or limited extent. We found studies supporting the use of 39 applications, though few of these were independent evaluations and we found no clinical trials evaluating any application’s impact on patient health. Due to limited evidence, it is impossible to determine the extent to which the clinical use of AI in the pandemic response has benefited patients overall. Further research is needed, particularly independent evaluations on AI application performance and health impacts in real-world care settings.
Journal Article
Basic Science and Pathogenesis
Tauopathies are a group of neurodegenerative disorders which are characterized by the accumulation of abnormal tau protein in the brain. However, the mechanistic understanding of pathogenic tau formation and spread within the brain remains elusive. Astrocytes are major immune reactive cells in the brain and have been implicated in exacerbating tau pathology by releasing extracellular vesicles (AEVs) containing pro-inflammatory cytokines and chemokines upon activation. Our prior investigation revealed a significant association between AEVs and tau pathology development, as well as cognitive function, by analyzing brain-derived EV proteins from AD patients. In this study, we explore the potential roles of AEVs in tau pathogenesis using a human induced pluripotent stem cell (iPSC) model.
We obtained two male P301L tau mutant iPSC lines from a Polish family with frontotemporal dementia. By including two male control lines, these iPSCs were differentiated into astrocytes (iAs) and characterized by immunocytochemistry and subjected for bulk RNAseq. Bioinformatics analysis was conducted to compare the transcriptome profile between wild-type (WT) and P301L iAs. EVs from WT and P301L iAs were isolated by ultracentrifugation combined with size exclusion chromatography. Characterization of WT and P301L iAEVs involved nanoparticle tracking analysis, nano-flow cytometry and super-resolution microscopy.
We successfully differentiated WT and P301L mutant iPSC lines into astrocytes with >99% purity. P301L iAs displayed distinctive astrocyte reactivity compared to WT cells, with elevated levels of pan-reactive astrocyte genes (e.g., GFAP, CD44) and decreased expression of neuroprotective A2 astrocyte-specific genes (e.g., TM4SF1, PTGS2). Additionally, gene enrichment set analysis of RNAseq data revealed dysregulation in the endo-lysosomal pathway and extracellular matrix in P301L iAs compared to WT iAs. The count of intraluminal vesicles marked by CD9+ were reduced in P301L iAs compared to WT cells. Moreover, we observed a significant increase in the internalization of Tau by P301L iAs compared to WT iAs following incubation with preformed Tau fibrils, resulting in an augmented release of tau-containing EVs from P301L iAs.
Our findings suggest a potential alteration in EV biogenesis in P301L iAs, potentially contributing to astrocyte-mediated tau pathology. Future investigations will focus on understanding how AEVs contribute to tau propagation and accumulation.
Journal Article
SAMoSSA: Multivariate Singular Spectrum Analysiswith Stochastic Autoregressive Noise
The well-established practice of time series analysis involves (i) estimating deterministic, non-stationary trend and seasonality components, followed by (ii) learning the residual stochastic, stationary components. Recently, it has been shown that one can learn the deterministic non-stationary components accurately using multivariate Singular Spectrum Analysis (mSSA) in the absence of a correlated stationary component; meanwhile, in the absence of deterministic non-stationary components, the Autoregressive (AR) stationary component can also be learnt readily, e.g. via Ordinary Least Squares (OLS). However, a theoretical underpinning of multi-stage learning algorithms involving both deterministic and stationary components has been absent in the literature despite its pervasiveness. We tackle this issue by establishing desirable theoretical guarantees for a natural two-stage algorithm, where mSSA is first applied to estimate the non-stationary components despite the presence of a correlated stationary AR component, which is subsequently learned from the residual time series. We provide a finite-sample forecasting consistency bound for the proposed algorithm, SAMoSSA, which is data-driven and thus requires minimal parameter tuning. To establish theoretical guarantees, we overcome three hurdles: (i) we characterize the spectra of Page matrices of stable AR processes, thus extending the analysis of mSSA; (ii) we extend the analysis of AR process identification in the presence of arbitrary bounded perturbations; (iii) we characterize the out-of-sample or forecasting error, as opposed to solely considering model identification. Through representative empirical studies, we validate the superior performance of SAMoSSA compared to existing baselines. Notably, SAMoSSA’s ability to account for AR noise structure yields improvements ranging from 5% to 37% across various benchmark datasets.
Dissertation
The P301L tau mutation alters extracellular vesicle biogenesis in astrocyte and contributes to astrocyte‐mediated tau pathology in a human iPSC‐derived model of tauopathies
Background Tauopathies are a group of neurodegenerative disorders which are characterized by the accumulation of abnormal tau protein in the brain. However, the mechanistic understanding of pathogenic tau formation and spread within the brain remains elusive. Astrocytes are major immune reactive cells in the brain and have been implicated in exacerbating tau pathology by releasing extracellular vesicles (AEVs) containing pro‐inflammatory cytokines and chemokines upon activation. Our prior investigation revealed a significant association between AEVs and tau pathology development, as well as cognitive function, by analyzing brain‐derived EV proteins from AD patients. In this study, we explore the potential roles of AEVs in tau pathogenesis using a human induced pluripotent stem cell (iPSC) model. Method We obtained two male P301L tau mutant iPSC lines from a Polish family with frontotemporal dementia. By including two male control lines, these iPSCs were differentiated into astrocytes (iAs) and characterized by immunocytochemistry and subjected for bulk RNAseq. Bioinformatics analysis was conducted to compare the transcriptome profile between wild‐type (WT) and P301L iAs. EVs from WT and P301L iAs were isolated by ultracentrifugation combined with size exclusion chromatography. Characterization of WT and P301L iAEVs involved nanoparticle tracking analysis, nano‐flow cytometry and super‐resolution microscopy. Result We successfully differentiated WT and P301L mutant iPSC lines into astrocytes with >99% purity. P301L iAs displayed distinctive astrocyte reactivity compared to WT cells, with elevated levels of pan‐reactive astrocyte genes (e.g., GFAP, CD44) and decreased expression of neuroprotective A2 astrocyte‐specific genes (e.g., TM4SF1, PTGS2). Additionally, gene enrichment set analysis of RNAseq data revealed dysregulation in the endo‐lysosomal pathway and extracellular matrix in P301L iAs compared to WT iAs. The count of intraluminal vesicles marked by CD9+ were reduced in P301L iAs compared to WT cells. Moreover, we observed a significant increase in the internalization of Tau by P301L iAs compared to WT iAs following incubation with preformed Tau fibrils, resulting in an augmented release of tau‐containing EVs from P301L iAs. Conclusion Our findings suggest a potential alteration in EV biogenesis in P301L iAs, potentially contributing to astrocyte‐mediated tau pathology. Future investigations will focus on understanding how AEVs contribute to tau propagation and accumulation.
Journal Article
Artificial intelligence applications used in the clinical response to COVID-19: A scoping review
Research into using artificial intelligence (AI) in health care is growing and several observers predicted that AI would play a key role in the clinical response to the COVID-19. Many AI models have been proposed though previous reviews have identified only a few applications used in clinical practice. In this study, we aim to (1) identify and characterize AI applications used in the clinical response to COVID-19; (2) examine the timing, location, and extent of their use; (3) examine how they relate to pre-pandemic applications and the U.S. regulatory approval process; and (4) characterize the evidence that is available to support their use. We searched academic and grey literature sources to identify 66 AI applications that performed a wide range of diagnostic, prognostic, and triage functions in the clinical response to COVID-19. Many were deployed early in the pandemic and most were used in the U.S., other high-income countries, or China. While some applications were used to care for hundreds of thousands of patients, others were used to an unknown or limited extent. We found studies supporting the use of 39 applications, though few of these were independent evaluations and we found no clinical trials evaluating any application’s impact on patient health. Due to limited evidence, it is impossible to determine the extent to which the clinical use of AI in the pandemic response has benefited patients overall. Further research is needed, particularly independent evaluations on AI application performance and health impacts in real-world care settings. Author summary In this study we describe the use of artificial intelligence (AI) in the clinical response to COVID-19. AI has been variously predicted to play a key role during the pandemic or has been reported to have had little or no impact on patient care. Our findings support a balanced view. We identified 66 applications—specific AI products or tools—used in a variety of ways to diagnose, guide treatment, or prioritize patients during the pandemic response. Many were deployed early in 2020 and most were used in the U.S., other high-income countries, or China. Some were used to care for hundreds of thousands of patients though most were adopted at smaller scales. We found evaluation studies that supported the use of 39 of these applications, though few of these evaluations were written by independent authors, not affiliated with application developers. We found no clinical trials that evaluated the effect of using an AI application on patient health outcomes. Future research is needed to better understand the impact of using AI in clinical care.
Journal Article
Negative Spillover: A Potential Source of Bias in Pragmatic Clinical Trials
Pragmatic clinical trials evaluate the effectiveness of health interventions in real-world settings. Negative spillover can arise in a pragmatic trial if the study intervention affects how scarce resources are allocated between patients in the intervention and comparison groups. This can harm patients assigned to the control group and lead to overestimation of treatment effect. While this type of negative spillover is often addressed in trials of social welfare and public health interventions, there is little recognition of this source of bias in the medical literature. In this article, I examine what causes negative spillover and how it may have led clinical trial investigators to overestimate the effect of patient navigation, AI-based physiological alarms, and elective induction of labor. I also suggest ways to detect negative spillover and design trials that avoid this potential source of bias.
Paper
Impact of World Trade Center-Related Health Research: An Application of the NIEHS Translational Framework
The World Trade Center Health Program (WTCHP) has a research mission to identify physical and mental health conditions that may be related to the 9/11 terrorist attacks as well as effective diagnostic procedures and treatments for WTC-related health conditions. The ability of the WTCHP to serve its members and realize positive impacts on all of its stakeholders depends on effective translation of research findings. As part of an ongoing assessment of the translational impact of World Trade Center (WTC)-related research, we applied the National Institute of Environmental Health Sciences (NIEHS) translational framework to two case studies: WTC-related research on post-traumatic stress disorder (PTSD) and cancer. We conducted a review of 9/11 health-related research in the peer-reviewed literature through October 2017, grey literature, and WTCHP program documentation. We mapped peer-reviewed studies in the literature to the NIEHS framework and used WTCHP program documentation and grey literature to find evidence of translation of research into clinical practice and policy. Using the NIEHS framework, we identified numerous translational milestones and bridges, as well as areas of opportunity, within each case study. This application demonstrates the utility of the NIEHS framework for documenting progress toward public health impact and for setting future research goals.
Journal Article