Mathematical modeling of the molecular switch of TNFR1-mediated signaling pathways applying Petri net formalism and in silico knockout analysis

The paper describes a mathematical model of the molecular switches of cell survival, apoptosis, and necroptosis in cellular signaling pathways initiated by tumor necrosis factor 1. Based on experimental findings in the literature, we constructed a Petri net model based on detailed molecular reactions of the molecular players, protein complexes, post-translational modifications, and cross talk. The model comprises 118 biochemical entities, 130 reactions, and 299 edges. We verified the model by evaluating invariant properties of the system at steady state and by in silico knockout analysis. Applying Petri net analysis techniques, we found 279 pathways, which describe signal flows from receptor activation to cellular response, representing the combinatorial diversity of functional pathways.120 pathways steered the cell to survival, whereas 58 and 35 pathways led to apoptosis and necroptosis, respectively. For 65 pathways, the triggered response was not deterministic and led to multiple possible outcomes. We investigated the in silico knockout behavior and identified important checkpoints of the TNFR1 signaling pathway in terms of ubiquitination within complex I and the gene expression dependent on NF-κB, which controls the caspase activity in complex II and apoptosis induction. Despite not knowing enough kinetic data of sufficient quality, we estimated system’s dynamics using a discrete, semi-quantitative Petri net model.

It is still a challenge to develop mechanistic models for big molecular systems without the knowledge of enough kinetic parameters of sufficient quality. At the same time, more qualitative and semi-quantitative data have been produced in increasing numbers, e.g., by new high-throughput technologies. This has generated demands for new concepts at appropriate abstraction levels. The Petri net formalism enables the integration of qualitative as well as quantitative data and provides algorithms and methods for model verification and model simulation. Moreover, Petri nets exhibit a clear and coherent visualization. Here, we modeled the molecular switches between cell survival, apoptosis, and necroptosis induced by tumor necrosis factor 1. We were interested not only in an exhaustive exploration of all possible signaling pathways, but also in finding the system’s checkpoints. Our Petri net model comprises 118 biochemical entities, 130 reactions, and 299 edges. We found 279 pathways that describe signal flows from receptor activation to cellular response.120 pathways steered the cell to survival, whereas 58 and 35 pathways led to apoptosis and necroptosis, respectively. For 65 pathways, the triggered response was not deterministic, leading to multiple possible outcomes. We applied in silico knockout analyses to the Petri net model and could identify important checkpoints of the tumor necrosis factor 1 signaling pathway.

The Disruption of Trust in the Digital Transformation Leading to Health 4.0

The specification and application of policies and guidelines for public health, medical education and training, and screening programmes for preventative medicine are all predicated on trust relationships between medical authorities, health practitioners and patients. These relationships are in turn predicated on a verbal contract that is over two thousand years old. The impact of information and communication technology (ICT), underpinning Health 4.0, has the potential to disrupt this analog relationship in several dimensions; but it also presents an opportunity to strengthen it, and so to increase the take-up and effectiveness of new policies. This paper develops an analytic framework for the trust relationships in Health 4.0, and through three use cases, assesses a medical policy, the introduction of a new technology, and the implications of that technology for the trust relationships. We integrate this assessment in a set of actionable recommendations, in particular that the trust framework should be part of the design methodology for developing and deploying medical applications. In a concluding discussion, we advocate that, in a post-pandemic world, IT to support policies and programmes to address widespread socio-medical problems with mental health, long Covid, physical inactivity and vaccine misinformation will be essential, and for that, strong trust relationships between all the stakeholders are absolutely critical.

Identifying Heart Failure in ECG Data With Artificial Intelligence-A Meta-Analysis

Introduction: Electrocardiography (ECG) is a quick and easily accessible method for diagnosis and screening of cardiovascular diseases including heart failure (HF). Artificial intelligence (AI) can be used for semi-automated ECG analysis. The aim of this evaluation was to provide an overview of AI use in HF detection from ECG signals and to perform a meta-analysis of available studies.

Methods and Results: An independent comprehensive search of the PubMed and Google Scholar database was conducted for articles dealing with the ability of AI to predict HF based on ECG signals. Only original articles published in peer-reviewed journals were considered. A total of five reports including 57,027 patients and 579,134 ECG datasets were identified including two sets of patient-level data and three with ECG-based datasets. The AI-processed ECG data yielded areas under the receiver operator characteristics curves between 0.92 and 0.99 to identify HF with higher values in ECG-based datasets. Applying a random-effects model, an sROC of 0.987 was calculated. Using the contingency tables led to diagnostic odds ratios ranging from 3.44 [95% confidence interval (CI) = 3.12–3.76] to 13.61 (95% CI = 13.14–14.08) also with lower values in patient-level datasets. The meta-analysis diagnostic odds ratio was 7.59 (95% CI = 5.85–9.34).

Conclusions: The present meta-analysis confirms the ability of AI to predict HF from standard 12-lead ECG signals underlining the potential of such an approach. The observed overestimation of the diagnostic ability in artificial ECG databases compared to patient-level data stipulate the need for robust prospective studies.

A data-pipeline processing electrocardiogram recordings for use in artificial intelligence algorithms

Introduction

Artificial intelligence (AI) can be used for various tasks in medicine and specifically in cardiology. Medical data such as electrocardiogram recordings (ECGs) are widely used and universally accepted as diagnostic and prognostic tools. It has been shown that deep learning methods using ECGs yield excellent results detecting cardiac pathologies. A significant amount of reliable data is required for supervised learning algorithms such as deep learning models. However, only a small fraction of ECG data generated in daily practice is available in a fully digital and machine-readable format, such as XML. Frequently, used ECG devices produce PDF files or even paper-based print outs, which need to be digitised later for inclusion in clinical information systems. Such ECGs cannot be used without further effort for training or application of deep learning models. Therefore, aim of the present project was to develop a data-pipeline that generates machine-readable ECG data for AI use data irrespective of the initial ECG format.

Methods

We propose an end-to-end pipeline that can not only process data from modern digital ECG devices but is also capable of extracting all necessary information from PDF files (both scanned hard copies and digitally generated PDFs) (see Figure 1). By using different techniques including adaption of open source libraries for vectorisation of image data, and modern computer vision technologies, such as optical character recognition (OCR), our pipeline is able to flexibly process data from different recording devices and read both data in PDF format and data from native digital devices delivered in XML. The processed files from various sources are either saved as a common and easily accessible CSV file format, or are processed directly with deep learning models (see Figure 2).

Results

The developed data-pipeline was validated using data from a set of 113 12-lead ECGs for which data was available in multiple formats. Each format dataset was separately processed by our pipeline and then used for training and validation of a deep learning architecture for myocardial scar detection based on raw ECG signals. The quality of the extraction process by our pipeline was assessed by the respective deep learning models with their prediction capability depicted by receiver operator characteristic analyses (ROC). Comparing the benchmark model that was generated from XML data against a model that was purely trained on PDF data processed by the pipeline shows that both models produced comparable results, reaching area under the curve (AUC) values of 0:79±0:10 (XML) and 0:83±0:07 (PDF).

Conclusion

The data pipeline facilitates acceleration of ECG-based AI research and application of AI algorithms by providing access to ECG data irrespective of the format of the stored ECG. Future work will focus on independent validation as well as expanding this pipeline to include additional ECG types.

Funding Acknowledgement

Type of funding sources: Public Institution(s). Main funding source(s): Flexi Funds by Forschungscampus Mittelhessen

On the importance of representative datasets in ECG-based artificial intelligence

Background/Introduction

ECG-based artificial intelligence (AI) is an emerging field in digital cardiology. Training on diseased records vs. healthy controls is common practice. We aimed to evaluate if such an approach can lead to unwanted behaviour in real-world settings and thus unnecessarily reduce diagnostic precision of the developed AI model.

Purpose

Several studies have shown that deep neural networks are able to exceed performance of medical experts. However, when these models are applied to different cohorts, results vary strongly. We hypothesise that this is because the datasets used for training were not representative for the target population.

Methods

Based on the public ECG database PTB-XL we sampled three distinct subsets of n=150 records representing ECG groups labelled for diagnoses 'old myocardial infarction' (M), 'normal ECG' (N), or 'other cardiac abnormality' (O). These groups were combined to three datasets ([M, N] (n=300), [M, O] (n=300), [M, N, O] (n=450)), representing different approaches to data sampling. On each dataset, we trained a separate but equally structured deep neural network using 100-fold bootstrapping. The diagnostic performance of each model was validated on unseen data from all datasets with sensitivity, specificity and area under the receiver operator characteristic curve.

Results

Evaluation of the three differently trained models shows best diagnostic performance on the M vs. N records and worst on the M vs. O records. However, in the out-of-dataset setting, the best-performing model (trained on [M, N]) shows weaker performance on the [M, N, O] and [M, O] datasets. Sensitivity for the same model remained equal, as identical M records were used throughout corresponding bootstrapping folds. Detailed results are presented in Table 1.

Conclusions

Our results suggest that the model trained on a dataset including only diseased records vs. healthy controls [M, N] learned to recognise healthy (N) instead of diseased (M) records, which explains why it performed poorly on datasets including records showing other cardiac abnormalities (O). Such behaviour is a common problem in AI and requires special attention in dataset sampling. For small cohorts, it is tempting to increase the amount of training data by using healthy controls. However, we have shown that this can be a poor option, since classifiers can more easily rely on features that are not actually related to the target disease. Training and validation of classifiers should therefore be performed on representative datasets that are as close as possible to the target population.

Funding Acknowledgement

Type of funding sources: Public Institution(s). Main funding source(s): Forschungscampus Mittelhessen, Flexi Funds Table 1

Predicting mortality in cardiovascular patients using electrocardiogram data and artificial intelligence

Background/Introduction

The electrocardiogram (ECG) is an ubiquitously used non-invasive tool for diagnosis and risk prediction in cardiology, granting deep extensive insights into the heart. Artificial intelligence (AI) is a modern resource allowing the processing of vast complex datasets in a way that is comparable to humans. Risk stratification in cardiovascular patients is mainly based on scoring systems, such as the ESC-SCORE, relying on traditional risk variables like cholesterol levels or arterial hypertension, rather than actual cardiac structure and function. Goal of this project was to predict mortality using AI in patients with cardiovascular risk based on the current cardiac situation represented by a standard 12-lead ECG recording.

Methods

The study population is based on an ongoing registry that started in 2010 and enrolled patients scheduled for an invasive coronary angiography due to suspected chronic coronary syndrome. Data of the following study patients were analysed: enrolment within the first two study years with available long-term follow-up data on the outcome measure overall mortality, availability of an ECG at admission without pacemaker stimulation and availability of all variables needed to calculate the ESC-SCORE (in the version weighed for a German population) as comparison. This led to a cohort of 720 patients, of whom 70 died within the follow-up period. Information on presence of a relevant coronary artery disease (CAD) was available for all patients, to differentiate between primary and secondary prevention. A deep learning architecture that was previously developed to detect myocardial scar in raw ECG time-series data was used. This model was trained with 1400 ECG recordings, from the publicly available PTB-XL dataset with 700 of those ECGs labelled for acute, recent or old myocardial infarction while 700 were labelled as healthy. This pre-trained model was then applied to our study cohort to predict long-term mortality based on a single 12-lead ECG obtained at admission.

Results

For mortality prediction in patients without CAD (primary prevention) the AI model compares to the ESC-SCORE with an AUROC of 0.606 vs 0.584. For CAD patients (secondary prevention) the AI model compares with an AUROC of 0.612 vs 0.658. Detailed results are presented in Table 1.

Conclusion(s)

Our data underlines the potential of an AI based approach, predicting mortality in cardiovascular patients using only single 12-lead ECG recordings. Additionally, our model achieved similar predictive information to established risk classification systems, such as the ESC-SCORE. Since data acquisition is still ongoing, we will continue to improve our model. In future work training AI to specifically predict mortality while also exploring explainable AI could lead to breakthrough findings in ECG interpretation.

Funding Acknowledgement

Type of funding sources: Public Institution(s). Main funding source(s): FlexiFunds by Forschungscampus Mittelhessen

Detecting a broader spectrum of cardiac pathologies in electrocardiogram data by applying a deep neural network designed to detect a specific cardiac disease

Background/Introduction

The electrocardiogram (ECG) is a widely used and inexpensive tool that provides extensive insights into the cardiac structure and function. Artificial intelligence (AI) algorithms, especially deep learning (DL) models, are efficient computer based instruments with which large and complex datasets can be processed for identification of e.g. specific diseases. PhysioNet is a NIH research resource for complex signals including a large amount of labelled ECG time-series data. Our aim was to evaluate the diagnostic performance of an AI architecture developed to detect a specific cardiac pathology in a large ECG data set including a broad range of cardiac abnormalities.

Methods

The PhysioNet ECG dataset provided as part of the PhysioNet Challenge 2020 consists of five distinct databases with a total of 43100 12-Lead ECG recordings of varying length stemming from patients from China, Russia, Europe and the United States. Each ECG recording is annotated with diagnoses based on a set of 111 possible labels, which express either a cardiac pathology, e.g. atrial flutter or anterior wall ischemia, or unspecific changes in the ECG, e.g. a prolonged qt interval or low qrs voltages. Based on these labels we defined 10 groups merging PhysioNet labels describing related cardiac abnormalities (see Table 1). We adapted a recently published DL model which used raw ECG time-series data of all 12-leads rather than extracted features as model input. This DL model was adapted to the larger number of output variables and then trained on 80% (n=34480 ECGs) of the PhysioNet dataset. The remaining 20% (n=8620 ECGs) of the PhysioNet dataset were used to evaluate the diagnostic performance of the AI model. Sensitivities, specificities and the areas under the receiver operator characteristic curves (AUROC) were used as performance metrices.

Results

The AI model, that was initially designed to detect a specific cardiac pathology, performed well in the large PhysioNet dataset providing AUROCs ranging from 0.78 to 0.95 to detect the defined 10 cardiac abnormality groups. Interestingly, the AI model was able to detect disease groups with changes in the chronological sequence of the ECG, e.g. arrhythmia, with comparable precision as disease groups associated primarily with changes in the ECG amplitude like e.g. ischemia. Detailed results are presented in Table 2.

Conclusion(s)

Our evaluation shows that an AI model that uses raw ECG time-series data rather than extracted features as model input can be easily transferred to other large datasets with different prediction variables. This might also serve as a proof of concept that raw data instead of pre-selected features should be used as model input if developing AI applications for medical use cases.

Funding Acknowledgement

Type of funding sources: Public Institution(s). Main funding source(s): FlexiFunds by Forschungscampus Mittelhessen

Detecting myocardial scar using electrocardiogram data and deep neural networks

Ischaemic heart disease is among the most frequent causes of death. Early detection of myocardial pathologies can increase the benefit of therapy and reduce the number of lethal cases. Presence of myocardial scar is an indicator for developing ischaemic heart disease and can be detected with high diagnostic precision by magnetic resonance imaging. However, magnetic resonance imaging scanners are expensive and of limited availability. It is known that presence of myocardial scar has an impact on the well-established, reasonably low cost, and almost ubiquitously available electrocardiogram. However, this impact is non-specific and often hard to detect by a physician. We present an artificial intelligence based approach - namely a deep learning model - for the prediction of myocardial scar based on an electrocardiogram and additional clinical parameters. The model was trained and evaluated by applying 6-fold cross-validation to a dataset of 12-lead electrocardiogram time series together with clinical parameters. The proposed model for predicting the presence of scar tissue achieved an area under the curve score, sensitivity, specificity, and accuracy of 0.89, 70.0, 84.3, and 78.0%, respectively. This promisingly high diagnostic precision of our electrocardiogram-based deep learning models for myocardial scar detection may support a novel, comprehensible screening method.

Bioinformatics analysis of whole slide images reveals significant neighborhood preferences of tumor cells in Hodgkin lymphoma

In pathology, tissue images are evaluated using a light microscope, relying on the expertise and experience of pathologists. There is a great need for computational methods to quantify and standardize histological observations. Computational quantification methods become more and more essential to evaluate tissue images. In particular, the distribution of tumor cells and their microenvironment are of special interest. Here, we systematically investigated tumor cell properties and their spatial neighborhood relations by a new application of statistical analysis to whole slide images of Hodgkin lymphoma, a tumor arising in lymph nodes, and inflammation of lymph nodes called lymphadenitis. We considered properties of more than 400, 000 immunohistochemically stained, CD30-positive cells in 35 whole slide images of tissue sections from subtypes of the classical Hodgkin lymphoma, nodular sclerosis and mixed cellularity, as well as from lymphadenitis. We found that cells of specific morphology exhibited significantly favored and unfavored spatial neighborhood relations of cells in dependence of their morphology. This information is important to evaluate differences between Hodgkin lymph nodes infiltrated by tumor cells (Hodgkin lymphoma) and inflamed lymph nodes, concerning the neighborhood relations of cells and the sizes of cells. The quantification of neighborhood relations revealed new insights of relations of CD30-positive cells in different diagnosis cases. The approach is general and can easily be applied to whole slide image analysis of other tumor types.

In pathology, histological diagnosis is still challenging, in particular, for tumor diseases. Pathologists diagnose the disease and its stage of development on the basis of evaluation and interpretation of images of tissue sections. The quantification of experimental data to support decisions of diagnosis and prognosis, applying bioinformatics methods, is an important issue. Here, we introduce a new, general approach to analyze tissue images of tumor and non-tumor patients and to evaluate the distribution of tumor cells in the tissue. Moreover, we consider neighborhood relations between immunostained cells of different cell morphology. We focus on a special type of lymph node tumor, the Hodgkin lymphoma, exploring the two main types of the classical Hodgkin lymphoma, the nodular sclerosis and the mixed cellularity, and the non-tumor case, the lymphadenitis, representing an inflammation of the lymph node. We considered more than 400, 000 cells immunohistochemically stained with CD30 in 35 whole slide images of tissue sections. We found that cells of specific morphology exhibited significant relations to cells of certain morphology as spatial nearest neighbor. We could show different neighborhood patterns of CD30-positive cells between tumor and non-tumor. The approach is general and can easily be applied to other tumor types.

Nonparametric generalized fiducial inference for survival functions under censoring

Since the introduction of fiducial inference by Fisher in the 1930s, its application has been largely confined to relatively simple, parametric problems. In this paper, we present what might be the first time fiducial inference is systematically applied to estimation of a nonparametric survival function under right censoring. We find that the resulting fiducial distribution gives rise to surprisingly good statistical procedures applicable to both one-sample and two-sample problems. In particular, we use the fiducial distribution of a survival function to construct pointwise and curvewise confidence intervals for the survival function, and propose tests based on the curvewise confidence interval. We establish a functional Bernstein–von Mises theorem, and perform thorough simulation studies in scenarios with different levels of censoring. The proposed fiducial-based confidence intervals maintain coverage in situations where asymptotic methods often have substantial coverage problems. Furthermore, the average length of the proposed confidence intervals is often shorter than the length of confidence intervals for competing methods that maintain coverage. Finally, the proposed fiducial test is more powerful than various types of log-rank tests and sup log-rank tests in some scenarios. We illustrate the proposed fiducial test by comparing chemotherapy against chemotherapy combined with radiotherapy, using data from the treatment of locally unresectable gastric cancer.

isiKnock: in silico knockouts in signaling pathways

SUMMARY

isiKnock is a new software that automatically conducts in silico knockouts for mathematical models of signaling pathways. The software allows for the prediction of the behavior of biological systems after single or multiple knockout. The implemented algorithm applies transition invariants and the novel concept of Manatee invariants. A knockout matrix visualizes the results. The tool enables the analysis of dependencies, for example, in signal flows from the receptor activation to the cell response at steady state.

AVAILABILITY AND IMPLEMENTATION

isiKnock is an open-source tool, freely available at http://www.bioinformatik.uni-frankfurt.de/tools/isiKnock/. It requires at least Java 8 and runs under Microsoft Windows, Linux, and Mac OS.

Biophysical injury mechanisms in electrical shock trauma

Electrical shock trauma tends to produce a very complex pattern of injury, mainly because of the multiple modes of frequency-dependent tissue-field interactions. Historically, Joule heating was thought to be the only cause of electrical injuries to tissue by commercial-frequency electrical shocks. In the last 15 years, biomedical engineering research has improved the understanding of the underlying biophysical injury mechanisms. Besides thermal burns secondary to Joule heating, permeabilization of cell membranes and direct electroconformational denaturation of macromolecules such as proteins have also been identified as tissue-damage mechanisms. This review summarizes the physics of tissue injury caused by contact with commercial-frequency power lines, as well as exposure to lightning and radio frequency (RF), microwave, and ionizing radiation. In addition, we describe the anatomic patterns of the resultant tissue injury from these modes of electromagnetic exposures.

Surfactant sealing of membranes permeabilized by ionizing radiation

Acute tissue injury and subsequent inflammation, including tissue edema and erythema, can be caused by sufficiently high levels of exposure to gamma radiation. The mechanism of this tissue injury is related to the generation of reactive oxygen intermediates (ROI) which chemically alter biological molecules and cell physiology. Cell membrane lipids are vulnerable to ROI-mediated lipid peroxidation that then leads to many of the acute tissue effects. We hypothesize that increased cell membrane permeability leading to osmotic swelling and vascular transudation is one of these effects. Thus we used adult postmitotic rhabdomyocytes in culture and microscopic fluorescence techniques to quantify radiation-induced changes in cell membrane permeability. Based on time-resolved dye flux measurements, a characteristic lag time of 34 +/- 3 min was determined between exposure to 160 Gy of gamma radiation and the decrease in membrane permeability. Administration of 0.1 mM nonionic surfactant Poloxamer 188 added to the cell medium after irradiation completely inhibited the dye loss over the time course of 2 h. Thus a reproducible model was developed for studying the mechanism of acute radiation injury and the efficacy of membrane-sealing agents. As only supportive measures now exist for treating the acute, nonlethal injuries from high-dose radiation exposure, agents that can restore cell membrane function after radiation damage may offer an important tool for therapy.

Oxidative cell membrane alteration. Evidence for surfactant-mediated sealing

Exposure to very intense ionizing irradiation produces acute tissue sequelae including inflammation, pain, and swelling that often results in tissue fibrosis and/or necrosis. Acute tissue necrosis occurs in hours when sufficiently rapid damage to membrane lipids and proteins leads to altered membrane structure, disrupting the vital electrochemical diffusion barrier necessary for cell survival. This damage mechanism is thought to underlie the interphase death of lethally irradiated postmitotic cells such as neurons, but it has also been implicated in the rapid cell death of lymphocytes and acute vascular changes due to capillary epithelium dysfunction. It is not known whether sealing of radiation-permeabilized cell membranes will prolong survival of lethally irradiated cells or perhaps lead to repair of damaged nucleic acids. The purpose of this study is to begin to address the first question.

Pharmaceutical therapies for sealing of permeabilized cell membranes in electrical injuries

Several years ago, we proposed that loss of cell membrane structural integrity by electroporation is a substantial cause of tissue necrosis in victims of electrical trauma. Specifically, this involves the permeabilization of the lipid bilayer by thermal and electrical forces. We further suggested that certain mild surfactants in low concentration could induce sealing of permeabilized lipid bilayers and salvage of cells that had not been extensively heat-damaged. Successful restoration of membrane transport properties using the surfactant poloxamer 188 was reported in 1992. The purpose of this study is to further examine the response of electroporated rat skeletal muscle membranes to poloxamer 188 (P188) therapy by direct assay of membrane transport properties. Experimental evidence accumulated to date suggests that P188 is effective in sealing permeabilized cell membranes both in vitro and in vivo.

Poloxamine 1107 sealing of radiopermeabilized erythrocyte membranes

PURPOSE

Lipid peroxidation-mediated permeabilization of cell membranes following intense ionizing irradiation is well documented. This form of membrane radiopermeabilization leads to rapid exhaustion of cellular high-energy compounds, resulting in the acute onset of cellular necrosis. Strategies to reverse the process of necrosis and preserve cell viability require membrane sealing. This report documents the relative efficacy of Poloxamine 1107, a non-ionic surfactant, compared with other polymers, in sealing radiopermeabilized cell membranes.

MATERIALS AND METHODS

Isolated erythrocytes were exposed to 600 Gy 60Co irradiation at a dose rate of 1.3 Gy/s. Different polymer compounds were added 10 min later to the irradiated cell suspensions. At 2 h later the haemoglobin content in the supernatants was determined spectrophotometrically.

RESULTS

Compared with the non-treated irradiated control, Poloxamine 1107 significantly reduced the leakage of haemoglobin from irradiated erythrocytes. Poloxamer 188 and dextran at equal concentrations had no significant reverse effect on the irradiation-mediated increased membrane permeability. The amount of haemoglobin released from irradiated erythrocytes was inversely related to the Poloxamine 1107 concentration.

CONCLUSIONS

This study demonstrates the capability of Poloxamine 1107 to seal radiopermeabilized cell membranes. Thus, surfactants such as Poloxamine 1107 might be useful as a therapeutic agent in the treatment of high-dose radiation injuries since cellular necrosis due to metabolic exhaustion following radiopermeabilization of their membranes might be prevented.

Diagnosis of human immunodeficiency virus infection using citrated whole blood

Standard isolation of human immunodeficiency virus type 1 (HIV-1) from peripheral blood mononuclear cells (PBMC) requires 5 to 20 ml of blood, and the centrifugal separation of PBMC is expensive and time-consuming. Whole-blood coculture techniques use small sample volumes, do not require centrifugation, and allow measurement of the total viral burden in peripheral circulation. We compared the results of citrated whole-blood coculture with those obtained by the standard AIDS Clinical Trials Group PBMC semiquantitative culture method and reverse transcription-PCR quantitation of plasma HIV-1 RNA levels. PBMC cocultures were also set up with added erythrocytes (RBCs) to determine if the presence of RBCs affects the replication of HIV-1 in vitro. The mean number of cells required for a p24-positive PBMC coculture was approximately seven times greater than that required for a positive citrated whole-blood coculture (P < 0.01). At volumes of 100, 50, and 25 microl, the sensitivities of the whole-blood coculture were 94.5, 93.6, and 87.3%, respectively. The PBMC culture in the presence of added RBCs was more sensitive than PBMC coculture alone. The citrated whole-blood coculture was simple to perform, produced a reliable diagnosis of HIV infection in adult volunteers, was more sensitive than previously reported techniques even in half the culture time, and showed less variability than the PBMC coculture. Citrated whole-blood coculture may be a useful and efficient tool for diagnosing infection with HIV-1.

Human erythrocytes bearing electroinserted CD4 neutralize infection in vitro by primary isolates of human immunodeficiency virus type 1

Human erythrocytes bearing electroinserted full-length CD4 (RBC-CD4) can bind and fuse with a laboratory strain of human immunodeficiency virus type 1 (HIV-1) or with T cells infected by HIV-1. Here we show that RBC-CD4 neutralize primary HIV-1 strains in an assay of cocultivation of peripheral blood mononuclear cells (PBMC) from HIV-1-infected persons with uninfected PBMC. RBC-CD4 inhibited viral p24 core antigen accumulation in these cocultures up to 10,000-fold compared with RBC alone. Viral p24 accumulation was inhibited equally well when measured in culture supernatants or in call extracts. The inhibition was dose-dependent and long-lived. Two types of recombinant CD4 tested in parallel were largely ineffective. The neutralization of primary HIV-1 by RBC-CD4 in vitro was demonstrated in PBMC cultures from 21 of a total of 23 patients tested at two independent sites. RBC-CD4 may offer a route to blocking HIV-1 infection in vivo.

Low-oxygen-affinity red cells produced in a large-volume, continuous-flow electroporation system

BACKGROUND

Human red cells containing inositol hexaphosphate (IHP) have a lowered O2 affinity, though they are able to bind and carry about the same amount of oxygen as native cells. These modified cells therefore deliver oxygen more efficiently to the tissues, which is a property of potential clinical utility. Investigators set out to devise a system and procedure by which large volumes of IHP-containing red cells, suitable for transfusion, could be produced quickly and efficiently.

STUDY DESIGN AND METHODS

The encapsulation of IHP into human red cells by use of several variations of static electroporation was performed to define the conditions necessary for optimal IHP incorporation and cell survival. These conditions were used as a starting point for optimization of a flow electroporation system.

RESULTS

When fresh human red cells in a 35 mM IHP solution are subjected to three exponential pulses of field strength of 2.98 +/- 0.064 kV per cm per pulse and pulse length of 2.0 +/- 0.2 msec per pulse while flowing through a cooled electroporation chamber, the condition of the resultant cells, according to the criteria used here, is optimized. After storage for 24 hours in plasma at 37 degrees C, the cells show more than 85-percent survival (in vitro) and hematologic indices nearly identical to those of unpulsed control cells. The p50 value of these cells, however, has doubled to 50.4 +/- 2.0 torr. The processing time for 1 unit of blood is 90 minutes.

CONCLUSION

These data indicate that the system described here can efficiently produce low-oxygen-affinity red cells in volumes that are useful in clinical applications.

Stability and immunological reactivity of recombinant membrane CD4 electroinserted into the plasma membrane of erythrocytes

Concentration-dependent electroinsertion of recombinant human membrane CD4 in human erythrocytes shows a saturation at an average of about 3,500 inserted CD4 epitopes per cell, detectable by flow cytometry. The erythrocyte recovery drops to 10% at this high level of electroinsertion. Experimentally an optimum for cell recovery and insertion rate was found at about 2,500 CD4 epitopes per red blood cell. In vitro stability assay by flow cytometry indicated a temperature- and medium-dependent decrease in the number of CD4 epitopes inserted per cell. This decrease is biphasic, with an exponential part during the first 24 h after electroinsertion followed by a much slower linear decay.