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ObjectiveTo utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online.Materials and methodsWe developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data.ResultsOur public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems.Discussion and conclusionsWe present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.

ObjectiveTo explore multi-clinician perspectives on intensive care early mobility, monitoring and to assess the perceived value of technology-generated mobility metrics to provide user feedback to inform research, practice improvement, and technology development.MethodsWe performed a qualitative descriptive study. Three focus groups were conducted with critical care clinicians, including nurses (n = 10), physical therapists (n = 8) and physicians (n = 8) at an academic medical centre that implemented an intensive care early mobility programme in 2012. Qualitative thematic analysis was used to code transcripts and identify overarching themes.FindingsAlong with reaffirming the value of performing early mobility interventions, four themes for improving mobility monitoring emerged, including the need for: 1) standardised indicators for documenting mobility; 2) inclusion of both quantitative and qualitative metrics to measure mobility 3) a balance between quantity and quality of data; and 4) trending mobility metrics over time.ConclusionIntensive care mobility monitoring should be standardised and data generated should be high quality, capable of supporting trend analysis, and meaningful. By improving measurement and monitoring of mobility, future researchers can examine the arc of activity that patients in the intensive care unit undergo and develop models to understand factors that influence successful implementation.

To develop and characterize a machine learning algorithm to discriminate acute respiratory distress syndrome from other causes of respiratory failure using only ventilator waveform data.DesignRetrospective, observational cohort study.SettingAcademic medical center ICU.PatientsAdults admitted to the ICU requiring invasive mechanical ventilation, including 50 patients with acute respiratory distress syndrome and 50 patients with primary indications for mechanical ventilation other than hypoxemic respiratory failure.InterventionsNone.Measurements and main resultsPressure and flow time series data from mechanical ventilation during the first 24-hours after meeting acute respiratory distress syndrome criteria (or first 24-hr of mechanical ventilation for non-acute respiratory distress syndrome patients) were processed to extract nine physiologic features. A random forest machine learning algorithm was trained to discriminate between the patients with and without acute respiratory distress syndrome. Model performance was assessed using the area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value. Analyses examined performance when the model was trained using data from the first 24 hours and tested using withheld data from either the first 24 hours (24/24 model) or 6 hours (24/6 model). Area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.88, 0.90, 0.71, 0.77, and 0.90 (24/24); and 0.89, 0.90, 0.75, 0.83, and 0.83 (24/6).ConclusionsUse of machine learning and physiologic information derived from raw ventilator waveform data may enable acute respiratory distress syndrome screening at early time points after intubation. This approach, combined with traditional diagnostic criteria, could improve timely acute respiratory distress syndrome recognition and enable automated clinical decision support, especially in settings with limited availability of conventional diagnostic tests and electronic health records.

Future healthcare systems will rely heavily on clinical decision support systems (CDSS) to improve the decision-making processes of clinicians. To explore the design of future CDSS, we developed a research-focused CDSS for the management of patients in the intensive care unit that leverages Internet of Things (IoT) devices capable of collecting streaming physiologic data from ventilators and other medical devices. We then created machine learning (ML) models that could analyze the collected physiologic data to determine if the ventilator was delivering potentially harmful therapy and if a deadly respiratory condition, acute respiratory distress syndrome (ARDS), was present. We also present work to aggregate these models into a mobile application that can provide responsive, real-time alerts of changes in ventilation to providers. As illustrated in the recent COVID-19 pandemic, being able to accurately predict ARDS in newly infected patients can assist in prioritizing care. We show that CDSS may be used to analyze physiologic data for clinical event recognition and automated diagnosis, and we also highlight future research avenues for hospital CDSS.

Introduction: Informed consent for research biospecimen donations is traditionally obtained through a face-to-face interaction with research staff and by signing an Institutional Review Board (IRB)-approved printed form. Electronic signatures (eSign) are routinely used in the electronic medical record (EMR) for the consenting of clinical services after patients review printed documentation. Our goal was to develop an electronic self-consenting workflow that mimicked clinical services. Specifically, we tested a research consent process for the biobanking of remnant clinical samples that relies solely on clinical resources in a busy outpatient practice. Materials and Methods: The Biorepositories Core Resource (BCR) unit initiated a new enterprise-wide biobanking infrastructure for consenting patients, termed Biospecimen Use for Research-Related Investigations and Translational Objectives (BURRITO). BURRITO is modeled after an established clinical process called Terms and Conditions of Service (TACOS). The TACOS requires patients to annually review printed documentation and self-consent electronically for clinical services. BURRITO also requires patients to review printed documentation and self-consent with eSign to opt-in for remnant biospecimen banking, but patients must complete this process only once. We captured eSign for consents directly into the EMR without research staff. Results: Patients reviewed the IRB-approved documents and self-consented during their cardiology clinic visit. At checkout, their participation preferences were electronically documented by clinic staff. During a 6-month period, 123 patients agreed to donate. After a review of process, a second 3-month period identified 202 patients agreeing to donate. BURRITO did not require face-to-face interactions with research staff, used a "no-paper" eSign for consent, and created discrete fields in the clinical EMR of the patient's preference. Conclusions: BURRITO electronically documents informed consent using an EMR functionality and the least amount of clinical and research resources. Our results show promise for developing institutionally adopted processes, which could leverage existing clinical workflows for universal research consenting and scalability.

Clinical decision support systems (CDSS) will play increasing role in improving quality of medical care for critically ill patients. However, due to limitations in current informatics infrastructure, CDSS do not always have complete information on state of supporting physiologic monitoring devices, which can limit input data available to CDSS. This is especially true in use case of mechanical ventilation (MV), where current CDSS have no knowledge of critical ventilation settings, such as ventilation mode. To enable MV CDSS make accurate recommendations related to ventilator mode, we developed a highly performant machine learning model that is able to perform per-breath classification of five of most widely used ventilation modes in USA with average F1-score of 97.52%. We also show how our approach makes methodologic improvements over previous work and is highly robust to missing data caused by software/sensor error.

This paper proposes the creation and application of maturity models to guide institutional strategic investment in research informatics and information technology (research IT) and to provide the ability to measure readiness for clinical and research infrastructure as well as sustainability of expertise. Conducting effective and efficient research in health science increasingly relies upon robust research IT systems and capabilities. Academic health centers are increasing investments in health IT systems to address operational pressures, including rapidly growing data, technological advances, and increasing security and regulatory challenges associated with data access requirements. Current approaches for planning and investment in research IT infrastructure vary across institutions and lack comparable guidance for evaluating investments, resulting in inconsistent approaches to research IT implementation across peer academic health centers as well as uncertainty in linking research IT investments to institutional goals. Maturity models address these issues through coupling the assessment of current organizational state with readiness for deployment of potential research IT investment, which can inform leadership strategy. Pilot work in maturity model development has ranged from using them as a catalyst for engaging medical school IT leaders in planning at a single institution to developing initial maturity indices that have been applied and refined across peer medical schools.

A robust biomedical informatics infrastructure is essential for academic health centers engaged in translational research. There are no templates for what such an infrastructure encompasses or how it is funded. An informatics workgroup within the Clinical and Translational Science Awards network conducted an analysis to identify the scope, governance, and funding of this infrastructure. After we identified the essential components of an informatics infrastructure, we surveyed informatics leaders at network institutions about the governance and sustainability of the different components. Results from 42 survey respondents showed significant variations in governance and sustainability; however, some trends also emerged. Core informatics components such as electronic data capture systems, electronic health records data repositories, and related tools had mixed models of funding including, fee-for-service, extramural grants, and institutional support. Several key components such as regulatory systems (e.g., electronic Institutional Review Board [IRB] systems, grants, and contracts), security systems, data warehouses, and clinical trials management systems were overwhelmingly supported as institutional infrastructure. The findings highlighted in this report are worth noting for academic health centers and funding agencies involved in planning current and future informatics infrastructure, which provides the foundation for a robust, data-driven clinical and translational research program.

The Accrual to Clinical Trials (ACT) network is a federated network of sites from the National Clinical and Translational Science Award (CTSA) Consortium that has been created to significantly increase participant accrual to multi-site clinical trials. The ACT network represents an unprecedented collaboration among diverse CTSA sites. The network has created governance and regulatory frameworks and a common data model to harmonize electronic health record (EHR) data, and deployed a set of Informatics for Integrating Biology and the Bedside (i2b2) data repositories that are linked by the Shared Health Research Information Network (SHRINE) platform. It provides investigators the ability to query the network in real time and to obtain aggregate counts of patients who meet clinical trial inclusion and exclusion criteria from sites across the United States. The ACT network infrastructure provides a basis for cohort discovery and for developing new informatics tools to identify and recruit participants for multi-site clinical trials.

BACKGROUND:As healthcare increasingly digitizes, streaming waveform data is being made available from an variety of sources, but there still remains a paucity of performant clinical decision support systems. For example, in the intensive care unit (ICU) existing automated alarm systems typically rely on simple thresholding that result in frequent false positives. Recurrent false positive alerts create distrust of alarm mechanisms that can be directly detrimental to patient health. To improve patient care in the ICU, we need alert systems that are both pervasive, and accurate so as to be informative and trusted by providers. OBJECTIVE:We aimed to develop a machine learning-based classifier to detect abnormal waveform events using the use case of mechanical ventilation waveform analysis, and the detection of harmful forms of ventilation delivery to patients. We specifically focused on detecting injurious subtypes of patient-ventilator asynchrony (PVA). METHODS:Using a dataset of breaths recorded from 35 different patients, we used machine learning to create computational models to automatically detect, and classify two types of injurious PVA, double trigger asynchrony (DTA), breath stacking asynchrony (BSA). We examined the use of synthetic minority over-sampling technique (SMOTE) to overcome class imbalance problems, varied methods for feature selection, and use of ensemble methods to optimize the performance of our model. RESULTS:We created an ensemble classifier that is able to accurately detect DTA at a sensitivity/specificity of 0.960/0.975, BSA at sensitivity/specificity of 0.944/0.987, and non-PVA events at sensitivity/specificity of .967/.980. CONCLUSIONS:Our results suggest that it is possible to create a high-performing machine learning-based model for detecting PVA in mechanical ventilator waveform data in spite of both intra-patient, and inter-patient variability in waveform patterns, and the presence of clinical artifacts like cough and suction procedures. Our work highlights the importance of addressing class imbalance in clinical data sets, and the combined use of statistical methods and expert knowledge in feature selection.

Lack of access to high-frequency, high-volume patient-derived data, such as mechanical ventilator waveform data, has limited the secondary use of these data for research, quality improvement, and decision support. Existing methods for collecting these data are obtrusive, require high levels of technical expertise, and are often cost-prohibitive, limiting their use and scalability for research applications. We describe here the development of an unobtrusive, open-source, scalable, and user-friendly architecture for collecting, transmitting, and storing mechanical ventilator waveform data that is generalizable to other patient care devices. The system implements a software framework that automates and enforces end-to-end data collection and transmission. A web-based data management application facilitates nontechnical end users' abilities to manage data acquisition devices, mitigates data loss and misattribution, and automates data storage. Using this integrated system, we have been able to collect ventilator waveform data from >450 patients as part of an ongoing clinical study.

Healthcare-specific analytic software is needed to process the large volumes of streaming physiologic waveform data increasingly available from life support devices such as mechanical ventilators. Detection of clinically relevant events from these data streams will advance understanding of critical illness, enable real-time clinical decision support, and improve both clinical outcomes and patient experience. We used mechanical ventilation waveform data (VWD) as a use case to address broader issues of data access and analysis including discrimination between true events and waveform artifacts. We developed an open source data acquisition platform to acquire VWD, and a modular, multi-algorithm analytic platform (ventMAP) to enable automated detection of off-target ventilation (OTV) delivery in critically-ill patients. We tested the hypothesis that use of artifact correction logic would improve the specificity of clinical event detection without compromising sensitivity. We showed that ventMAP could accurately detect harmful forms of OTV including excessive tidal volumes and common forms of patient-ventilator asynchrony, and that artifact correction significantly improved the specificity of event detection without decreasing sensitivity. Our multi-disciplinary approach has enabled automated analysis of high-volume streaming patient waveform data for clinical and translational research, and will advance the study and management of critically ill patients requiring mechanical ventilation.

Health leaders in the US and abroad are seeking to aggregate diverse health data from millions of people to enable new architectures for research. The integration of large health data sets raises significant social and ethical questions around the control of health information, human subjects research protection, and access to treatments. Because of the social stakes involved, policy-makers have begun to consult and engage publics with the idea that this might improve the quality, credibility, or relevance of big data health research. While policy-makers aim to engage publics, they might learn from experiments in patient engagement emanating from the private sector and patient advocacy organizations. Three modes of engagement have co-evolved out of new information technology and the cultures of disease advocacy: crowdsourcing, social networking platforms, and dynamic consenting. These modes of engagement are promising avenues of responsible innovation. Together, they project an alternative and more democratic vision of health research emphasizing citizen science, communicative reason, and the engaged research participant.

BackgroundProviding for long-term and consistent public access to scientific data is a growing concern in biomedical research. One aspect of this problem can be demonstrated by evaluating the persistence of supplementary data associated with published biomedical papers.MethodsWe manually evaluated 655 supplementary data links extracted from PubMed abstracts published 1998-2005 (Method 1) as well as a further focused subset of 162 full-text manuscripts published within three representative high-impact biomedical journals between September and December 2004 (Method 2).ResultsFor Method 1 we found that since 2001, only 71 - 92% of supplementary data were still accessible via the links provided, with 93% of these inaccessible links occurring where supplementary data was not stored with the publishing journal. Of the manuscripts evaluated in Method 2, we found that only 83% of these links were available approximately a year after publication, with 55% of these inaccessible links were at locations outside the journal of publication.ConclusionWe conclude that if supplemental data is required to support the publication, journals policies must take-on the responsibility to accept and store such data or require that it be maintained with a credible independent institution or under the terms of a strategic data storage plan specified by the authors. We further recommend that publishers provide automated systems to ensure that supplementary links remain persistent, and that granting bodies such as the NIH develop policies and funding mechanisms to maintain long-term persistent access to these data.

Electrical signals acquired from the cortical surface, or electrocorticography (ECoG), exhibit high spatial and temporal resolution and are valuable for mapping brain activity, detecting irregularities, and controlling a brain-computer interface. As with scalp-recorded EEG, much of the identified information content in ECoG is manifested as amplitude modulations of specific frequency bands. Autoregressive (AR) spectral estimation has proven successful for modeling the well-defined and comparatively limited EEG spectrum. However, because the ECoG spectrum is significantly more extensive with yet undefined dynamics, it cannot be assumed that the ECoG spectrum can be accurately estimated using the same AR model parameters that are valid for analogous EEG studies. This study provides an offline evaluation of AR modeling of ECoG signals for detecting tongue movements. The resulting model parameters can serve as a reference for related AR spectral analysis of ECoG signals. Index Terms--Autoregressive (AR) spectrum estimation, electrocorticography (ECoG).

Abstract Background Providing for long-term and consistent public access to scientific data is a growing concern in biomedical research. One aspect of this problem can be demonstrated by evaluating the persistence of supplementary data associated with published biomedical papers. Methods We manually evaluated 655 supplementary data links extracted from PubMed abstracts published 1998–2005 (Method 1) as well as a further focused subset of 162 full-text manuscripts published within three representative high-impact biomedical journals between September and December 2004 (Method 2). Results For Method 1 we found that since 2001, only 71 – 92% of supplementary data were still accessible via the links provided, with 93% of these inaccessible links occurring where supplementary data was not stored with the publishing journal. Of the manuscripts evaluated in Method 2, we found that only 83% of these links were available approximately a year after publication, with 55% of these inaccessible links were at locations outside the journal of publication. Conclusion We conclude that if supplemental data is required to support the publication, journals policies must take-on the responsibility to accept and store such data or require that it be maintained with a credible independent institution or under the terms of a strategic data storage plan specified by the authors. We further recommend that publishers provide automated systems to ensure that supplementary links remain persistent, and that granting bodies such as the NIH develop policies and funding mechanisms to maintain long-term persistent access to these data.

Dendritic cells (DCs) are the most effective professional antigen-presenting cell. They ferry antigen from the extremities to T cells and are essential for the initiation of an adaptive immune response. Despite interest in how DCs respond to chemical stimuli, there have been few attempts to model DC migration. In this paper, we simulate the motility of DCs by modeling the generation of forces by filopodia and a force balance on the cell. The direction of fliopodial extension is coupled to differential occupancy of cognate chemokine receptors across the cell. Our model simulates chemokinesis and chemotaxis in a variety of chemical and mechanical environments. Simulated DCs undergoing chemokinesis were measured to have a speed of 5.1 ± 0.07 μm·min-1 and a persistence time of 3.2 ± 0.46 min, consistent with experiment. Cells undergoing chemotaxis exhibited a stronger chemotactic response when exposed to lower average chemokine concentrations, also consistent with experiment. We predicted that when placed in two opposing gradients, cells will cluster in a line, which we call the “line of equistimulation;” this clustering has also been observed. We calculated the effect of varying gradient steepness on the line of equistimulation, with steeper gradients resulting in tighter clustering. Moreover, gradients are found to be most potent when cells are in a gradient of chemokine whose mean concentration is close to the binding of the Kd to the receptor, and least potent when the mean concentration is 0.1Kd. Comparing our simulations to experiment, we can give a quantitative measure of the strength of certain chemokines relative to others. Assigning the signal of CCL19 binding CCR7 a baseline strength of 1, we found CCL21 binding CCR7 had a strength of 0.28, and CXCL12 binding CXCR4 had a strength of 0.30. These differences emerge despite both chemokines having virtually the same Kd, suggesting a mechanism of signal amplification in DCs requiring further study. [ABSTRACT FROM AUTHOR]

Clinical decision support systems (CDSS) will play increasing role in improving quality of medical care for critically ill patients. However, due to limitations in current informatics infrastructure, CDSS do not always have complete information on state of supporting physiologic monitoring devices, which can limit input data available to CDSS. This is especially true in use case of mechanical ventilation (MV), where current CDSS have no knowledge of critical ventilation settings, such as ventilation mode. To enable MV CDSS make accurate recommendations related to ventilator mode, we developed a highly performant machine learning model that is able to perform per-breath classification of five of most widely used ventilation modes in USA with average F1-score of 97.52%. We also show how our approach makes methodologic improvements over previous work and is highly robust to missing data caused by software/sensor error. [ABSTRACT FROM AUTHOR]

Leukocyte adhesion is important for the proper functioning of the immune system. While leukocyte homing is mediated by adhesion receptors, the activation of these receptors is modulated by intracellular signaling molecules. In Leukocyte Adhesion Deficiency Type 3, the loss of the kindlin-3 prevents the activation of Leukocyte Function-associated Antigen-1 (LFA-1), which leads to a defect in adhesion, causing recurrent infections and bleeding disorders. Here, we use Integrated Signaling Adhesive Dynamics, a computer model of leukocyte rolling and adhesion combined with a simulated intracellular signaling cascade, to predict the response of T cells to depletion of kindlin-3. Our model predicts that cell adhesion is hypersensitive to the amount of kindlin-3 in the cell, while the rolling velocity is independent of kindlin-3 concentration. In addition, our simulation predicted that the time to stop, an important metric of adhesion, would increase with decreasing kindlin-3 expression. These predictions were confirmed experimentally in experiments using Jurkat cells with reduced expression of kindlin-3. These results suggest that Adhesive Dynamics is a versatile tool for quantifying adhesion in the immune response and predicting the effects of engineering cellular components. [ABSTRACT FROM AUTHOR]

Introduction: The leukocyte adhesion cascade is important for the maintenance of homeostasis and the ability of immune cells to access sites of infection and inflammation. Despite much work identifying the molecular components of the cascade, and numerous simulations to predict the relationship between molecule density, identity, and adhesion, these relationships have not been measured experimentally.Methods: Using surfaces functionalized with recombinant ICAM-1 and/or E-selectin along with immobilized SDF-1α, we used a flow chamber to measure rates of tethering, rolling and arrest of primary naïve human CD4+ T lymphocytes on different surface densities of ligand.Results: Cells required a minimum level of ligand density to progress beyond tethering. E-selectin and ICAM-1 were found to have a synergistic relationship in promoting cell arrest. Surfaces with both ligands had the highest levels of arrest, while surfaces containing only E-selectin hindered the cell’s ability to progress beyond rolling. In contrast, surfaces of ICAM-1 allowed only tethering or arrest. Cells maintained constant rolling velocity and time to stop over large variations in surface density and composition. In addition, surface densities of only O(101) sites/µm2 allowed for rolling while surface densities of O(102) sites/µm2 promoted arrest, approximately equal to previously determined simulated values.Conclusions: We have systematically and experimentally mapped out the state diagram of T cell adhesion under flow, directly demonstrating the quantitative requirements for each dynamic state of adhesion, and showing how multiple adhesion molecules can act in synergy to secure arrest. [ABSTRACT FROM AUTHOR]

ABSTRACT Hydrogel-encapsulating culture systems for ovarian follicles support the in vitro growth of secondary follicles from various species including mouse, non-primate human, and human; however, the growth of early stage follicles (primary and primordial) has been limited. While encapsulation maintains the structure of early stage follicles, feeder cell populations, such as mouse embryonic fibroblasts (MEFs), are required to stimulate growth and development. Hence, in this report, we investigated feeder-free culture environments for early stage follicle development. Mouse ovarian follicles were encapsulated within alginate hydrogels and cultured in various growth medium formulations. Initial studies employed embryonic stem cell medium formulations as a tool to identify factors that influence the survival, growth, and meiotic competence of early stage follicles. The medium formulation that maximized survival and growth was identified as αMEM/F12 supplemented with fetuin, insulin, transferrin, selenium, and follicle stimulating hormone (FSH). This medium stimulated the growth of late primary (average initial diameter of 80 µm) and early secondary (average initial diameter of 90 µm) follicles, which developed antral cavities and increased to terminal diameters exceeding 300 µm in 14 days. Survival ranged from 18% for 80 µm follicles to 36% for 90 µm follicles. Furthermore, 80% of the oocytes from surviving follicles with an initial diameter of 90-100 µm underwent germinal vesicle breakdown (GVBD), and the percentage of metaphase II (MII) eggs was 50%. Follicle/oocyte growth and GVBD/MII rates were not significantly different from MEF co-culture. Survival was reduced relative to MEF co-culture, yet substantially increased relative to the control medium that had been previously used for secondary follicles. Continued development of culture medium could enable mechanistic studies of early stage folliculogenesis and emerging strategies for fertility preservation. Biotechnol. Bioeng. 2013;110: 3258-3268. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Brain computer interfaces are devices that collect signals from a subject's cortical surface and interpret these signals to control a computer. Recently much development has been done on these devices with the help of epilepsy patients and the clinical staff who treat these patients. The types of data collected from epilepsy patients, particularly the invasive data give a unique opportunity to researchers in this area. The clinical staff has a unique opportunity to use the treatment of one patient population to help another. [ABSTRACT FROM AUTHOR]

The agglomeration of ferromagnetic nanoparticles in a fluid is studied using nanoparticle-level Langevin dynamics simulations. The simulations have interdigitation and bridging between ligand coatings included using a computationally-cheap, phenomenological sticking parameter c. The interactions between ligand coatings are shown in this preliminary study to be important in determining the shapes of agglomerates that form. A critical size for the sticking parameter is estimated analytically and via the simulations and indicates where particle agglomerates transition from well-ordered (c is small) to disordered (c is large) shapes. Results are also presented for the hysteresis loops (magnetization versus applied field) for these particle systems in an oscillating magnetic field appropriate for hyperthermia applications. The results show that the clumping of particles has a significant effect on their macroscopic properties, with important consequences on applications. In particular, the work done by an oscillating field on the system has a nonmonotonic dependence on c. [ABSTRACT FROM AUTHOR]

The electroencephalogram (EEG) equipment industry has recently been developing systems that display, not only the raw EEG signal, but also a transformed version of the signal that highlights critical features and can be viewed in a more user friendly manner. A computer automated analysis of the signal is a quantitative approach that can make precise temporal measurements of the signal features, perform digital filtering to allow for identification of specific components of the signal, and statistically analyze the resulting signal. These quantitative analyses have created the potential to decrease the time required for EEG reviewers, allow for seizures to be more accurately detected with a simpler metric, and prevent confusion of symptom detection, thus providing for a more effective and efficient diagnosis. Many companies have addressed this opportunity for development and designed systems, each with their own name and features. This article attempts to explain the techniques for signal transformation that are starting to see wide use and point out some of the benefits of this type of interpretation that have been identified in the literature. [ABSTRACT FROM AUTHOR]

Much of the current pharmacological therapy for chronic heart failure targets neurohormonal activation. In spite of recent advances in drug therapy, the mortality rate for chronic heart failure remains high. Activation of the carotid baroreceptor (BR) reduces sympathetic outflow and augments vagal tone. We investigated the effect of chronic activation of the carotid BR on hemodynamic and neurohormonal parameters and on mortality in dogs with chronic heart failure. Fifteen dogs were instrumented to record hemodynamics. Electrodes were applied around the carotid sinuses to allow for activation of the BR. After 2 weeks of pacing (250 bpm), electrical carotid BR activation was initiated in 7 dogs and continued for the remainder of the study. The start of BR activation was used as a time reference point for the remaining 8 control dogs that did not receive BR activation. Survival was significantly greater for dogs undergoing carotid BR activation compared with control dogs (68.1+/-7.4 versus 37.3+/-3.2 days, respectively; P<0.01), although arterial pressure, resting heart rate, and left ventricular pressure were not different over time in BR-activated versus control dogs. Plasma norepinephrine was lower in dogs receiving BR activation therapy 31 days after the start of BR activation (401.9+/-151.5 versus 1121.9+/-389.1 pg/mL in dogs not receiving activation therapy; P<0.05). Plasma angiotensin II increased less in dogs receiving activation therapy (plasma angiotensin II increased by 157.4+/-58.6 pg/mL in control dogs versus 10.1+/-14.0 pg/mL in dogs receiving activation therapy; P<0.02). We conclude that chronic activation of the carotid BR improves survival and suppresses neurohormonal activation in chronic heart failure. [ABSTRACT FROM AUTHOR]

In the first large study of its kind, we quantified changes in electrocorticographic signals associated with motor movement across 22 subjects with subdural electrode arrays placed for identification of seizure foci. Patients underwent a 5-7 d monitoring period with array placement, before seizure focus resection, and during this time they participated in the study. An interval-based motor-repetition task produced consistent and quantifiable spectral shifts that were mapped on a Talairach-standardized template cortex. Maps were created independently for a high-frequency band (HFB) (76 -100 Hz) and a low-frequency band (LFB) (8 -32 Hz) for several different movement modalities in each subject. The power in relevant electrodes consistently decreased in the LFB with movement, whereas the power in the HFB consistently increased. In addition, the HFB changes were more focal than the LFB changes. Sites of power changes corresponded to stereotactic locations in sensorimotor cortex and to the results of individual clinical electrical cortical mapping. Sensorimotor representation was found to be somatotopic, localized in stereotactic space to rolandic cortex, and typically followed the classic homunculus with limited extrarolandic representation. [ABSTRACT FROM AUTHOR]

We study nonlinear spin motion in one-dimensional magnetic chains. We find significant differences from the classic Fermi-Pasta-Ulam (FPU) problem examining nonlinear elastic motion in a chain. We find that FPU behavior, the transfer of energy among low order eigenmodes, does not occur in magnetic systems with only exchange and external fields, but does exist if a uniaxial anisotropy is also present. The FPU behavior may be altered or turned off through the magnitude and orientation of an external magnetic field. A realistic micromagnetic model shows such behavior could be measurable. [ABSTRACT FROM AUTHOR]

Leukocytes possess the ability to migrate upstream-against the direction of flow-on surfaces of specific chemistry. Upstream migration was first characterized in vitro for T-cells on surfaces comprised of intracellular adhesion molecule-1 (ICAM-1). Upstream migration occurs when the integrin receptor α L β 2 (also known as lymphocyte function-associated antigen-1, or LFA-1) binds to ICAM-1. LFA-1/ICAM-1 interactions are ubiquitous and are widely found in leukocyte trafficking. Upstream migration would be employed after cells come to arrest on the apical surface of the endothelium and might confer an advantage for both trans-endothelial migration and tissue surveillance. It has now been shown that several other motile amoeboid cells which have the responsibility of trafficking from blood vessels into tissues, such as Marginal zone B cells, hematopoietic stem cells, and neutrophils (when macrophage-1 antigen, Mac-1, is blocked), can also migrate upstream on ICAM-1 surfaces. This review will summarize what is known about the basic mechanisms of upstream migration, which cells have displayed this phenomenon, and the possible role of upstream migration in physiology and tissue homeostasis., Competing Interests: NA is a current employee of Carisma Therapeutics (Philadelphia, PA). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Buffone, Hammer, Kim, Anderson, Mochida, Lee and Guin.)

Adoptive cell therapy with genetically modified T cells has generated exciting outcomes in hematologic malignancies, but its application to solid tumors has proven challenging. This gap has spurred the investigation of alternative immune cells as therapeutics. Macrophages are potent immune effector cells whose functional plasticity leads to antitumor as well as protumor function in different settings, and this plasticity has led to notable efforts to deplete or repolarize tumor-associated macrophages. Alternatively, macrophages could be adoptively transferred after ex vivo genetic modification. In this review, we highlight the role of macrophages in solid tumors, the progress made with macrophage-focused immunotherapeutic modalities, and the emergence of chimeric antigen receptor macrophage cell therapy., (©2020 American Association for Cancer Research.)

The recruitment of neutrophils to sites of inflammatory insult is a hallmark of the innate immune response. Neutrophil recruitment is regulated by a multistep process that includes cell rolling, activation, adhesion, and transmigration through the endothelium commonly referred to as the leukocyte adhesion cascade. After selectin-mediated braking, neutrophils migrate along the activated vascular endothelium on which ligands, including intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1), are expressed. Previous studies have shown that two cells that commonly home from blood vessel to tissue-T cells and hematopoietic stem and progenitor cells-use the integrin lymphocyte functional antigen-1 (LFA-1) to migrate against the direction of shear flow once adherent on ICAM-1 surfaces. Like T cells and hematopoietic stem and progenitor cells, neutrophils express LFA-1, but they also express macrophage-1 antigen (Mac-1), which binds to ICAM-1. Previous reports have shown that neutrophils will not migrate against the direction of flow on ICAM-1, but we hypothesized this was due to the influence of Mac-1. Here, we report that both the HL-60 neutrophil-like cell line and primary human neutrophils can migrate against the direction of fluid flow on ICAM-1 surfaces via LFA-1 if Mac-1 is blocked; otherwise, they migrate downstream. We demonstrate this both on ICAM-1 surfaces and on activated endothelium. In sum, both LFA-1 and Mac-1 binding ICAM-1 play a critical role in determining the direction of neutrophil migration along the endothelium, and their interaction may play an important role in controlling neutrophil trafficking during inflammation., (Copyright © 2019 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

The recruitment of immune cells during inflammation is regulated by a multi-step cascade of cell rolling, activation, adhesion and transmigration through the endothelial barrier. Similarly, hematopoietic stem and progenitor cells (HSPCs) use this pathway to migrate and home to the bone marrow. After selectin-mediated braking, HSPCs migrate on adhesion ligands presented by the vascular endothelium including ICAM-1, VCAM-1 or MAdCAM-1. Here, we report that both the KG1a stem cell line and primary bone marrow CD34 + HSPCs can migrate against the direction of fluid flow on surfaces coated with cell adhesion molecules (CAMs), a behavior thus far only reported in T lymphocytes. We demonstrate that KG1a cells and primary HSPCs migrate upstream on surfaces presenting ICAM-1, downstream on surfaces presenting VCAM-1, and both upstream and downstream on surfaces presenting MAdCAM-1. In addition, we demonstrate that KG1a cells and HSPCs display upstream migration both on surfaces with multiple CAMs, as well as on human umbilical vein endothelial cell (HUVEC) monolayers. By blocking with monoclonal antibodies, we show that lymphocyte function-associated antigen-1 (LFA-1) is the key receptor responsible for upstream migration on the endothelium during the trafficking of HSPCs to the bone marrow.This article has an associated First Person interview with the first author of the paper., Competing Interests: Competing interestsThe authors declare no competing or financial interests., (© 2018. Published by The Company of Biologists Ltd.)

The use of in vitro, engineered surrogates in the field of cancer research is of interest for studies involving mechanisms of growth and metastasis, and response to therapeutic intervention. While biomimetic surrogates better model human disease, their complex composition and dimensionality make them challenging to evaluate in a real-time manner. This feature has hindered the broad implementation of these models, particularly in drug discovery. Herein, several methods and approaches for the real-time, non-invasive analysis of cell growth and response to treatment in tissue-engineered, three-dimensional models of breast cancer are presented. The tissue-engineered surrogates used to demonstrate these methods consist of breast cancer epithelial cells and fibroblasts within a three dimensional volume of extracellular matrix and are continuously perfused with nutrients via a bioreactor system. Growth of the surrogates over time was measured using optical in vivo (IVIS) imaging. Morphologic changes in specific cell populations were evaluated by multi-photon confocal microscopy. Response of the surrogates to treatment with paclitaxel was measured by optical imaging and by analysis of lactate dehydrogenase and caspase-cleaved cytokeratin 18 in the perfused medium. Each method described can be repeatedly performed during culture, allowing for real-time, longitudinal analysis of cell populations within engineered tumor models.