Early prediction of mortality at sepsis diagnosis time in critically ill patients by using interpretable machine learning

This study applied machine learning for the early prediction of 30-day mortality at sepsis diagnosis time in critically ill patients. Retrospective study using data collected from the Medical Information Mart for Intensive Care IV database. The data of the patient cohort was divided on the basis of the year of hospitalization, into training (2008-2013), validation (2014-2016), and testing (2017-2019) datasets. 24,377 patients with the sepsis diagnosis time < 24 h after intensive care unit (ICU) admission were included. A gradient boosting tree-based algorithm (XGBoost) was used for training the machine learning model to predict 30-day mortality at sepsis diagnosis time in critically ill patients. Model performance was measured in both discrimination and calibration aspects. The model was interpreted using the SHapley Additive exPlanations (SHAP) module. The 30-day mortality rate of the testing dataset was 17.9%, and 39 features were selected for the machine learning model. Model performance on the testing dataset achieved an area under the receiver operating characteristic curve (AUROC) of 0.853 (95% CI 0.837-0.868) and an area under the precision-recall curves of 0.581 (95% CI 0.541-0.619). The calibration plot for the model revealed a slope of 1.03 (95% CI 0.94-1.12) and intercept of 0.14 (95% CI 0.04-0.25). The SHAP revealed the top three most significant features, namely age, increased red blood cell distribution width, and respiratory rate. Our study demonstrated the feasibility of using the interpretable machine learning model to predict mortality at sepsis diagnosis time.

Drop the shortcuts: image augmentation improves fairness and decreases AI detection of race and other demographics from medical images

BACKGROUND

It has been shown that AI models can learn race on medical images, leading to algorithmic bias. Our aim in this study was to enhance the fairness of medical image models by eliminating bias related to race, age, and sex. We hypothesise models may be learning demographics via shortcut learning and combat this using image augmentation.

METHODS

This study included 44,953 patients who identified as Asian, Black, or White (mean age, 60.68 years ±18.21; 23,499 women) for a total of 194,359 chest X-rays (CXRs) from MIMIC-CXR database. The included CheXpert images comprised 45,095 patients (mean age 63.10 years ±18.14; 20,437 women) for a total of 134,300 CXRs were used for external validation. We also collected 1195 3D brain magnetic resonance imaging (MRI) data from the ADNI database, which included 273 participants with an average age of 76.97 years ±14.22, and 142 females. DL models were trained on either non-augmented or augmented images and assessed using disparity metrics. The features learned by the models were analysed using task transfer experiments and model visualisation techniques.

FINDINGS

In the detection of radiological findings, training a model using augmented CXR images was shown to reduce disparities in error rate among racial groups (-5.45%), age groups (-13.94%), and sex (-22.22%). For AD detection, the model trained with augmented MRI images was shown 53.11% and 31.01% reduction of disparities in error rate among age and sex groups, respectively. Image augmentation led to a reduction in the model's ability to identify demographic attributes and resulted in the model trained for clinical purposes incorporating fewer demographic features.

INTERPRETATION

The model trained using the augmented images was less likely to be influenced by demographic information in detecting image labels. These results demonstrate that the proposed augmentation scheme could enhance the fairness of interpretations by DL models when dealing with data from patients with different demographic backgrounds.

FUNDING

National Science and Technology Council (Taiwan), National Institutes of Health.

Enabling chronic obstructive pulmonary disease diagnosis through chest X-rays: A multi-site and multi-modality study

PURPOSE

Chronic obstructive pulmonary disease (COPD) is one of the most common chronic illnesses in the world. Unfortunately, COPD is often difficult to diagnose early when interventions can alter the disease course, and it is underdiagnosed or only diagnosed too late for effective treatment. Currently, spirometry is the gold standard for diagnosing COPD but it can be challenging to obtain, especially in resource-poor countries. Chest X-rays (CXRs), however, are readily available and may have the potential as a screening tool to identify patients with COPD who should undergo further testing or intervention. In this study, we used three CXR datasets alongside their respective electronic health records (EHR) to develop and externally validate our models.

METHOD

To leverage the performance of convolutional neural network models, we proposed two fusion schemes: (1) model-level fusion, using Bootstrap aggregating to aggregate predictions from two models, (2) data-level fusion, using CXR image data from different institutions or multi-modal data, CXR image data, and EHR data for model training. Fairness analysis was then performed to evaluate the models across different demographic groups.

RESULTS

Our results demonstrate that DL models can detect COPD using CXRs with an area under the curve of over 0.75, which could facilitate patient screening for COPD, especially in low-resource regions where CXRs are more accessible than spirometry.

CONCLUSIONS

By using a ubiquitous test, future research could build on this work to detect COPD in patients early who would not otherwise have been diagnosed or treated, altering the course of this highly morbid disease.

Deep learning with test-time augmentation for radial endobronchial ultrasound image differentiation: a multicentre verification study

PURPOSE

Despite the importance of radial endobronchial ultrasound (rEBUS) in transbronchial biopsy, researchers have yet to apply artificial intelligence to the analysis of rEBUS images.

MATERIALS AND METHODS

This study developed a convolutional neural network (CNN) to differentiate between malignant and benign tumours in rEBUS images. This study retrospectively collected rEBUS images from medical centres in Taiwan, including 769 from National Taiwan University Hospital Hsin-Chu Branch, Hsinchu Hospital for model training (615 images) and internal validation (154 images) as well as 300 from National Taiwan University Hospital (NTUH-TPE) and 92 images were obtained from National Taiwan University Hospital Hsin-Chu Branch, Biomedical Park Hospital (NTUH-BIO) for external validation. Further assessments of the model were performed using image augmentation in the training phase and test-time augmentation (TTA).

RESULTS

Using the internal validation dataset, the results were as follows: area under the curve (AUC) (0.88 (95% CI 0.83 to 0.92)), sensitivity (0.80 (95% CI 0.73 to 0.88)), specificity (0.75 (95% CI 0.66 to 0.83)). Using the NTUH-TPE external validation dataset, the results were as follows: AUC (0.76 (95% CI 0.71 to 0.80)), sensitivity (0.58 (95% CI 0.50 to 0.65)), specificity (0.92 (95% CI 0.88 to 0.97)). Using the NTUH-BIO external validation dataset, the results were as follows: AUC (0.72 (95% CI 0.64 to 0.82)), sensitivity (0.71 (95% CI 0.55 to 0.86)), specificity (0.76 (95% CI 0.64 to 0.87)). After fine-tuning, the AUC values for the external validation cohorts were as follows: NTUH-TPE (0.78) and NTUH-BIO (0.82). Our findings also demonstrated the feasibility of the model in differentiating between lung cancer subtypes, as indicated by the following AUC values: adenocarcinoma (0.70; 95% CI 0.64 to 0.76), squamous cell carcinoma (0.64; 95% CI 0.54 to 0.74) and small cell lung cancer (0.52; 95% CI 0.32 to 0.72).

CONCLUSIONS

Our results demonstrate the feasibility of the proposed CNN-based algorithm in differentiating between malignant and benign lesions in rEBUS images.

Automatic segmentation and radiomic texture analysis for osteoporosis screening using chest low-dose computed tomography

OBJECTIVE

This study developed a diagnostic tool combining machine learning (ML) segmentation and radiomic texture analysis (RTA) for bone density screening using chest low-dose computed tomography (LDCT).

METHODS

A total of 197 patients who underwent LDCT followed by dual-energy X-ray absorptiometry were analyzed. First, an autosegmentation model was trained using LDCT to delineate the thoracic vertebral body (VB). Second, a two-level classifier was developed using radiomic features extracted from VBs for the hierarchical pairwise classification of each patient's bone status. All the patients were initially classified as either normal or abnormal, and all patients with abnormal bone density were then subdivided into an osteopenia group and an osteoporosis group. The performance of the classifier was evaluated through fivefold cross-validation.

RESULTS

The model for automated VB segmentation achieved a Sorenson-Dice coefficient of 0.87 ± 0.01. Furthermore, the area under the receiver operating characteristic curve scores for the two-level classifier were 0.96 ± 0.01 for detecting abnormal bone density (accuracy = 0.91 ± 0.02; sensitivity = 0.93 ± 0.03; specificity = 0.89 ± 0.03) and 0.98 ± 0.01 for distinguishing osteoporosis (accuracy = 0.94 ± 0.02; sensitivity = 0.95 ± 0.03; specificity = 0.93 ± 0.03). The testing prediction accuracy levels for the first- and second-level classifiers were 0.92 ± 0.04 and 0.94 ± 0.05, respectively. The overall testing prediction accuracy of our method was 0.90 ± 0.05.

CONCLUSION

The combination of ML segmentation and RTA for automated bone density prediction based on LDCT scans is a feasible approach that could be valuable for osteoporosis screening during lung cancer screening.

KEY POINTS

• This study developed an automatic diagnostic tool combining machine learning-based segmentation and radiomic texture analysis for bone density screening using chest low-dose computed tomography. • The developed method enables opportunistic screening without quantitative computed tomography or a dedicated phantom. • The developed method could be integrated into the current clinical workflow and used as an adjunct for opportunistic screening or for patients who are ineligible for screening with dual-energy X-ray absorptiometry.

Efficient conversion of waste-to-SNG via hybrid renewable energy systems for circular economy: Process design, energy, and environmental analysis

Developing an efficient and environment-friendly route for waste valorization is extremely significant in accelerating the transition toward a circular economy. A novel waste-to-synthetic natural gas (SNG) conversion process comprising hybrid renewable energy systems is proposed for this purpose. This includes thermochemical waste conversion and power-to-gas technologies for simultaneous waste utilization and renewable energy storage applications. The energy and environmental performances of the proposed waste-to-SNG plant are assessed and optimized. Results indicated that the implementation of a thermal pretreatment unit prior to the plasma gasification (two-step) is beneficial to improve the yield of hydrogen in the syngas, thereby leading to less renewable energy requirement for green hydrogen production used in the methanation process. This also enhances SNG yield by a factor of 30% as compared to the case without thermal pretreatment (one-step). The overall energy efficiency (OE) of the proposed waste-to-SNG plant is in the range of 61.36-77.73%, while the energy return on investment (EROI) ranges between 2.66 and 6.11. Most environmental impacts are mainly contributed by the indirect carbon emissions as a consequence of the power requirement for thermal pretreatment, plasma gasifier, and auxiliary equipment. The value of specific electricity consumption for SNG production of the treated RDF exhibits 1.70-9.25 % less than that of raw RDF when the pretreatment temperature is less than 300 °C. The OE of the system declines by 4.52% when 50 wt% of biomass is mixed in the fuel, whereas an enhancement of 18.33% in EROI and a reduction of 16.19% in specific CO₂ emissions are obtained.

A deep learning model using chest X-ray for identifying TB and NTM-LD patients: a cross-sectional study

BACKGROUND

Timely differentiating between pulmonary tuberculosis (TB) and nontuberculous mycobacterial lung disease (NTM-LD), which are radiographically similar, is important because infectiousness and treatment differ. This study aimed to evaluate whether artificial intelligence could distinguish between TB or NTM-LD patients by chest X-rays (CXRs) from suspects of mycobacterial lung disease.

METHODS

A total of 1500 CXRs, including 500 each from patients with pulmonary TB, NTM-LD, and patients with clinical suspicion but negative mycobacterial culture (Imitator) from two hospitals, were retrospectively collected and evaluated in this study. We developed a deep neural network (DNN) and evaluated model performance using the area under the receiver operating characteristic curves (AUC) in both internal and external test sets. Furthermore, we conducted a reader study and tested our model under three scenarios of different mycobacteria prevalence.

RESULTS

Among the internal and external test sets, the AUCs of our DNN model were 0.83 ± 0.005 and 0.76 ± 0.006 for pulmonary TB, 0.86 ± 0.006 and 0.64 ± 0.017 for NTM-LD, and 0.77 ± 0.007 and 0.74 ± 0.005 for Imitator. The DNN model showed higher performance on the internal test set in classification accuracy (66.5 ± 2.5%) than senior (50.8 ± 3.0%, p < 0.001) and junior pulmonologists (47.5 ± 2.8%, p < 0.001). Among different prevalence scenarios, the DNN model has stable performance in terms of AUC to detect TB and mycobacterial lung disease.

CONCLUSION

DNN model had satisfactory performance and a higher accuracy than pulmonologists on classifying patients with presumptive mycobacterial lung diseases. DNN model could be a complementary first-line screening tool.

Developing and Validating Multi-Modal Models for Mortality Prediction in COVID-19 Patients: a Multi-center Retrospective Study

The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2 infections with the goal of helping health systems allocate resources. Machine learning models, in particular, hold promise for their ability to leverage patient clinical information and medical images for prediction. However, most of the published COVID-19 prediction models thus far have little clinical utility due to methodological flaws and lack of appropriate validation. In this paper, we describe our methodology to develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. The models for COVID-19 mortality prediction were developed using retrospective data from Madrid, Spain (N = 2547) and were externally validated in patient cohorts from a community hospital in New Jersey, USA (N = 242) and an academic center in Seoul, Republic of Korea (N = 336). The models we developed performed differently across various clinical settings, underscoring the need for a guided strategy when employing machine learning for clinical decision-making. We demonstrated that using features from both the structured electronic health records and chest X-ray imaging data resulted in better 30-day mortality prediction performance across all three datasets (areas under the receiver operating characteristic curves: 0.85 (95% confidence interval: 0.83-0.87), 0.76 (0.70-0.82), and 0.95 (0.92-0.98)). We discuss the rationale for the decisions made at every step in developing the models and have made our code available to the research community. We employed the best machine learning practices for clinical model development. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification, and/or optimization.

Decision thresholding on fMRI activation maps using the Hilbert-Huang transform

OBJECTIVE

Functional magnetic resonance imaging (fMRI) requires thresholds by which to identify brain regions with significant activation, particularly for experiments involving real-life paradigms. One conventional non-parametric approach to generating surrogate data involves decomposition of the original fMRI time series using the Fourier transform, after which the phase is randomized without altering the magnitude of individual frequency components. However, it has been reported that spontaneous brain signals could be non-stationary, which, if true, could lead to false-positive results.

APPROACH

This paper introduces a randomization procedure based on the Hilbert-Huang transform by which to account for non-stationarity in fMRI time series derived from two fMRI datasets (stationary or non-stationary). The significance of individual voxels was determined by comparing the distribution of empirical data versus a surrogate distribution.

MAIN RESULTS

In a comparison with conventional phase-randomization and wavelet-based permutation methods, the proposed method proved highly effective in generating activation maps indicating essential brain regions, while filtering out noise in the white matter.

SIGNIFICANCE

This work demonstrated the importance of considering the non-stationary nature of fMRI time series when selecting resampling methods by which to probe brain activity or identify functional networks in real-life fMRI experiments. We propose a statistical testing method to deal with the non-stationarity of continuous brain signals.

Clinically Interpretable Machine Learning Models for Early Prediction of Mortality in Older Patients with Multiple Organ Dysfunction Syndrome (MODS): An International Multicenter Retrospective Study

BACKGROUND

Multiple organ dysfunction syndrome (MODS) is associated with a high risk of mortality among older patients. Current severity scores are limited in their ability to assist clinicians with triage and management decisions. We aim to develop mortality prediction models for older patients with MODS admitted to the ICU.

METHODS

The study analyzed older patients from 197 hospitals in the US and one hospital in the Netherlands. The cohort was divided into the young-old (65-80 years) and old-old (≥80 years), which were separately used to develop and evaluate models including internal, external and temporal validation. Demographic characteristics, comorbidities, vital signs, laboratory measurements, and treatments were used as predictors. We used the XGBoost algorithm to train models, and the SHAP method to interpret predictions.

RESULTS

34,497 young-old (11.3% mortality) and 21,330 old-old (15.7% mortality) patients were analyzed. Discrimination AUROC of internal validation models in 9,046 U.S. patients was as follows: 0.87 and 0.82, respectively; Discrimination of external validation models in 1,905 EUR patients was as follows: 0.86 and 0.85, respectively; and of temporal validation models in 8,690 U.S. patients: 0.85 and 0.78, respectively. These models outperformed standard clinical scores like SOFA and APSIII. The GCS, Charlson Comorbidity Index, and Code Status emerged as top predictors of mortality.

CONCLUSIONS

Our models integrate data spanning physiologic and geriatric-relevant variables that outperform existing scores used in older adults with MODS, which represents a proof of concept of how machine learning can streamline data analysis for busy ICU clinicians to potentially optimize prognostication and decision making.

AI recognition of patient race in medical imaging: a modelling study

BACKGROUND

Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images.

METHODS

Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race.

FINDINGS

In our study, we show that standard AI deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities, which was sustained under external validation conditions (x-ray imaging [area under the receiver operating characteristics curve (AUC) range 0·91-0·99], CT chest imaging [0·87-0·96], and mammography [0·81]). We also showed that this detection is not due to proxies or imaging-related surrogate covariates for race (eg, performance of possible confounders: body-mass index [AUC 0·55], disease distribution [0·61], and breast density [0·61]). Finally, we provide evidence to show that the ability of AI deep learning models persisted over all anatomical regions and frequency spectrums of the images, suggesting the efforts to control this behaviour when it is undesirable will be challenging and demand further study.

INTERPRETATION

The results from our study emphasise that the ability of AI deep learning models to predict self-reported race is itself not the issue of importance. However, our finding that AI can accurately predict self-reported race, even from corrupted, cropped, and noised medical images, often when clinical experts cannot, creates an enormous risk for all model deployments in medical imaging.

FUNDING

National Institute of Biomedical Imaging and Bioengineering, MIDRC grant of National Institutes of Health, US National Science Foundation, National Library of Medicine of the National Institutes of Health, and Taiwan Ministry of Science and Technology.

TOP-Net Prediction Model Using Bidirectional Long Short-term Memory and Medical-Grade Wearable Multisensor System for Tachycardia Onset: Algorithm Development Study

Background

Without timely diagnosis and treatment, tachycardia, also called tachyarrhythmia, can cause serious complications such as heart failure, cardiac arrest, and even death. The predictive performance of conventional clinical diagnostic procedures needs improvement in order to assist physicians in detecting risk early on.

Objective

We aimed to develop a deep tachycardia onset prediction (TOP-Net) model based on deep learning (ie, bidirectional long short-term memory) for early tachycardia diagnosis with easily accessible data.

Methods

TOP-Net leverages 2 easily accessible data sources: vital signs, including heart rate, respiratory rate, and blood oxygen saturation (SpO₂) acquired continuously by wearable embedded systems, and electronic health records, containing age, gender, admission type, first care unit, and cardiovascular disease history. The model was trained with a large data set from an intensive care unit and then transferred to a real-world scenario in the general ward. In this study, 3 experiments incorporated merging patients’ personal information, temporal memory, and different feature combinations. Six metrics (area under the receiver operating characteristic curve [AUROC], sensitivity, specificity, accuracy, F1 score, and precision) were used to evaluate predictive performance.

Results

TOP-Net outperformed the baseline models on the large critical care data set (AUROC 0.796, 95% CI 0.768-0.824; sensitivity 0.753, 95% CI 0.663-0.793; specificity 0.720, 95% CI 0.645-0.758; accuracy 0.721; F1 score 0.718; precision 0.686) when predicting tachycardia onset 6 hours in advance. When predicting tachycardia onset 2 hours in advance with data acquired from our hospital using the transferred TOP-Net, the 6 metrics were 0.965, 0.955, 0.881, 0.937, 0.793, and 0.680, respectively. The best performance was achieved using comprehensive vital signs (heart rate, respiratory rate, and SpO₂) statistical information.

Conclusions

TOP-Net is an early tachycardia prediction model that uses 8 types of data from wearable sensors and electronic health records. When validated in clinical scenarios, the model achieved a prediction performance that outperformed baseline models 0 to 6 hours before tachycardia onset in the intensive care unit and 2 hours before tachycardia onset in the general ward. Because of the model’s implementation and use of easily accessible data from wearable sensors, the model can assist physicians with early discovery of patients at risk in general wards and houses.

Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph

Image-based teleconsultation using smartphones has become increasingly popular. In parallel, deep learning algorithms have been developed to detect radiological findings in chest X-rays (CXRs). However, the feasibility of using smartphones to automate this process has yet to be evaluated. This study developed a recalibration method to build deep learning models to detect radiological findings on CXR photographs. Two publicly available databases (MIMIC-CXR and CheXpert) were used to build the models, and four derivative datasets containing 6453 CXR photographs were collected to evaluate model performance. After recalibration, the model achieved areas under the receiver operating characteristic curve of 0.80 (95% confidence interval: 0.78-0.82), 0.88 (0.86-0.90), 0.81 (0.79-0.84), 0.79 (0.77-0.81), 0.84 (0.80-0.88), and 0.90 (0.88-0.92), respectively, for detecting cardiomegaly, edema, consolidation, atelectasis, pneumothorax, and pleural effusion. The recalibration strategy, respectively, recovered 84.9%, 83.5%, 53.2%, 57.8%, 69.9%, and 83.0% of performance losses of the uncalibrated model. We conclude that the recalibration method can transfer models from digital CXRs to CXR photographs, which is expected to help physicians' clinical works.

Plasma gasification performances of various raw and torrefied biomass materials using different gasifying agents

Plasma gasification of raw and torrefied woody, non-woody, and algal biomass using three different gasifying agents (air, steam, and CO₂) is conducted through a thermodynamic analysis. The impacts of feedstock and reaction atmosphere on various performance indices such as syngas yield, pollutant emissions, plasma energy to syngas production ratio (PSR), and plasma gasification efficiency (PGE) are studied. Results show that CO₂ plasma gasification gives the lowest PSR, thereby leading to the highest PGE among the three reaction atmospheres. Torrefied biomass displays increased syngas yield and PGE, but is more likely to have a negative environmental impact of N/S pollutants in comparison with raw one, especially for rice straw. However, the exception is for torrefied grape marc and macroalgae which produce lower amounts of S-species under steam and CO₂ atmospheres. Overall, torrefied pine wood has the best performance for producing high quality syngas containing low impurities among the investigated feedstocks.

The crystal structure of protein-transporting chaperone BCP1 from Saccharomyces cerevisiae

BCP1 is a protein enriched in the nucleus that is required for Mss4 nuclear export and identified as the chaperone of ribosomal protein Rpl23 in Saccharomyces cerevisiae. According to sequence homology, BCP1 is related to the mammalian BRCA2-interacting protein BCCIP and belongs to the BCIP protein family (PF13862) in the Pfam database. However, the BCIP family has no discernible similarity to proteins with known structure. Here, we report the crystal structure of BCP1, presenting an α/β fold in which the central antiparallel β-sheet is flanked by helices. Protein structural classification revealed that BCP1 has similarity to the GNAT superfamily but no conserved substrate-binding residues. Further modeling and protein-protein docking work provide a plausible model to explain the interaction between BCP1 and Rpl23. Our structural analysis presents the first structure of BCIP family and provides a foundation for understanding the molecular basis of BCP1 as a chaperone of Rpl23 for ribosome biosynthesis.

Machine learning-based segmentation of ischemic penumbra by using diffusion tensor metrics in a rat model

Background

Recent trials have shown promise in intra-arterial thrombectomy after the first 6–24 h of stroke onset. Quick and precise identification of the salvageable tissue is essential for successful stroke management. In this study, we examined the feasibility of machine learning (ML) approaches for differentiating the ischemic penumbra (IP) from the infarct core (IC) by using diffusion tensor imaging (DTI)-derived metrics.

Methods

Fourteen male rats subjected to permanent middle cerebral artery occlusion (pMCAO) were included in this study. Using a 7 T magnetic resonance imaging, DTI metrics such as fractional anisotropy, pure anisotropy, diffusion magnitude, mean diffusivity (MD), axial diffusivity, and radial diffusivity were derived. The MD and relative cerebral blood flow maps were coregistered to define the IP and IC at 0.5 h after pMCAO. A 2-level classifier was proposed based on DTI-derived metrics to classify stroke hemispheres into the IP, IC, and normal tissue (NT). The classification performance was evaluated using leave-one-out cross validation.

Results

The IC and non-IC can be accurately segmented by the proposed 2-level classifier with an area under the receiver operating characteristic curve (AUC) between 0.99 and 1.00, and with accuracies between 96.3 and 96.7%. For the training dataset, the non-IC can be further classified into the IP and NT with an AUC between 0.96 and 0.98, and with accuracies between 95.0 and 95.9%. For the testing dataset, the classification accuracy for IC and non-IC was 96.0 ± 2.3% whereas for IP and NT, it was 80.1 ± 8.0%. Overall, we achieved the accuracy of 88.1 ± 6.7% for classifying three tissue subtypes (IP, IC, and NT) in the stroke hemisphere and the estimated lesion volumes were not significantly different from those of the ground truth (p = .56, .94, and .78, respectively).

Conclusions

Our method achieved comparable results to the conventional approach using perfusion–diffusion mismatch. We suggest that a single DTI sequence along with ML algorithms is capable of dichotomizing ischemic tissue into the IC and IP.

Brain dynamics and connectivity networks under natural auditory stimulation

The analysis of functional magnetic resonance imaging (fMRI) data is challenging when subjects are under exposure to natural sensory stimulation. In this study, a two-stage approach was developed to enable the identification of connectivity networks involved in the processing of information in the brain under natural sensory stimulation. In the first stage, the degree of concordance between the results of inter-subject and intra-subject correlation analyses is assessed statistically. The microstructurally (i.e., cytoarchitectonically) defined brain areas are designated either as concordant in which the results of both correlation analyses are in agreement, or as discordant in which one analysis method shows a higher proportion of supra-threshold voxels than does the other. In the second stage, connectivity networks are identified using the time courses of supra-threshold voxels in brain areas contingent upon the classifications derived in the first stage. In an empirical study, fMRI data were collected from 40 young adults (19 males, average age 22.76 ± 3.25), who underwent auditory stimulation involving sound clips of human voices and animal vocalizations under two operational conditions (i.e., eyes-closed and eyes-open). The operational conditions were designed to assess confounding effects due to auditory instructions or visual perception. The proposed two-stage analysis demonstrated that stress modulation (affective) and language networks in the limbic and cortical structures were respectively engaged during sound stimulation, and presented considerable variability among subjects. The network involved in regulating visuomotor control was sensitive to the eyes-open instruction, and presented only small variations among subjects. A high degree of concordance was observed between the two analyses in the primary auditory cortex which was highly sensitive to the pitch of sound clips. Our results have indicated that brain areas can be identified as concordant or discordant based on the two correlation analyses. This may further facilitate the search for connectivity networks involved in the processing of information under natural sensory stimulation.

Interactions of BDNF Val66Met Polymorphism and Menstrual Pain on Brain Complexity

The irregularity and uncertainty of neurophysiologic signals across different time scales can be regarded as neural complexity, which is related to the adaptability of the nervous system and the information processing between neurons. We recently reported general loss of brain complexity, as measured by multiscale sample entropy (MSE), at pain-related regions in females with primary dysmenorrhea (PDM). However, it is unclear whether this loss of brain complexity is associated with inter-subject genetic variations. Brain-derived neurotrophic factor (BDNF) is a widely expressed neurotrophin in the brain and is crucial to neural plasticity. The BDNF Val66Met single-nucleotide polymorphism (SNP) is associated with mood, stress, and pain conditions. Therefore, we aimed to examine the interactions of BDNF Val66Met polymorphism and long-term menstrual pain experience on brain complexity. We genotyped BDNF Val66Met SNP in 80 PDM females (20 Val/Val, 31 Val/Met, 29 Met/Met) and 76 healthy female controls (25 Val/Val, 36 Val/Met, 15 Met/Met). MSE analysis was applied to neural source activity estimated from resting-state magnetoencephalography (MEG) signals during pain-free state. We found that brain complexity alterations were associated with the interactions of BDNF Val66Met polymorphism and menstrual pain experience. In healthy female controls, Met carriers (Val/Met and Met/Met) demonstrated lower brain complexity than Val/Val homozygotes in extensive brain regions, suggesting a possible protective role of Val/Val homozygosity in brain complexity. However, after experiencing long-term menstrual pain, the complexity differences between different genotypes in healthy controls were greatly diminished in PDM females, especially in the limbic system, including the hippocampus and amygdala. Our results suggest that pain experience preponderantly affects the effect of BDNF Val66Met polymorphism on brain complexity. The results of the present study also highlight the potential utilization of resting-state brain complexity for the development of new therapeutic strategies in patients with chronic pain.

Challenges and Future Perspectives on Electroencephalogram-Based Biometrics in Person Recognition

The emergence of the digital world has greatly increased the number of accounts and passwords that users must remember. It has also increased the need for secure access to personal information in the cloud. Biometrics is one approach to person recognition, which can be used in identification as well as authentication. Among the various modalities that have been developed, electroencephalography (EEG)-based biometrics features unparalleled universality, distinctiveness and collectability, while minimizing the risk of circumvention. However, commercializing EEG-based person recognition poses a number of challenges. This article reviews the various systems proposed over the past few years with a focus on the shortcomings that have prevented wide-scale implementation, including issues pertaining to temporal stability, psychological and physiological changes, protocol design, equipment and performance evaluation. We also examine several directions for the further development of usable EEG-based recognition systems as well as the niche markets to which they could be applied. It is expected that rapid advancements in EEG instrumentation, on-device processing and machine learning techniques will lead to the emergence of commercialized person recognition systems in the near future.

Manifold decoding for neural representations of face viewpoint and gaze direction using magnetoencephalographic data

The main challenge in decoding neural representations lies in linking neural activity to representational content or abstract concepts. The transformation from a neural-based to a low-dimensional representation may hold the key to encoding perceptual processes in the human brain. In this study, we developed a novel model by which to represent two changeable features of faces: face viewpoint and gaze direction. These features are embedded in spatiotemporal brain activity derived from magnetoencephalographic data. Our decoding results demonstrate that face viewpoint and gaze direction can be represented by manifold structures constructed from brain responses in the bilateral occipital face area and right superior temporal sulcus, respectively. Our results also show that the superposition of brain activity in the manifold space reveals the viewpoints of faces as well as directions of gazes as perceived by the subject. The proposed manifold representation model provides a novel opportunity to gain further insight into the processing of information in the human brain.

Impact of hospital transfer on surgical outcomes of intestinal atresia

BACKGROUND

Examine effects of hospital transfer into a quaternary care center on surgical outcomes of intestinal atresia.

METHODS

Children <1 yo principally diagnosed with intestinal atresia were identified using the Kids' Inpatient Database (2012). Exposure variable was patient transfer status. Outcomes measured were inpatient mortality, hospital length of stay (LOS) and discharge status. Linearized standard errors, design-based F tests, and multivariable logistic regression were performed.

RESULTS

1672 weighted discharges represented a national cohort. The highest income group and those with private insurance had significantly lower odds of transfer (OR:0.53 and 0.74, p < 0.05). Rural patients had significantly higher transfer rates (OR: 2.73, p < 0.05). Multivariate analysis revealed no difference in mortality (OR:0.71, p = 0.464) or non-home discharge (OR: 0.79, p = 0.166), but showed prolonged LOS (OR:1.79, p < 0.05) amongst transferred patients.

CONCLUSIONS

Significant differences in hospital LOS and treatment access reveal a potential healthcare gap. Post-acute care resources should be improved for transferred patients.

Decoding the perception of endogenous pain from resting-state MEG

Decoding the neural representations of pain is essential to obtaining an objective assessment as well as an understanding of its underlying mechanisms. The complexities involved in the subjective experience of pain make it difficult to obtain a quantitative assessment from the induced spatiotemporal patterns of brain activity of high dimensionality. Most previous studies have investigated the perception of pain by analyzing the amplitude or spatial patterns in the response of the brain to external stimulation. This study investigated the decoding of endogenous pain perceptions according to resting-state magnetoencephalographic (MEG) recordings. In our experiments, we applied a beamforming method to calculate the brain activity for every brain region and examined temporal and spectral features of brain activity for predicting the intensity of perceived pain in patients with primary dysmenorrhea undergoing menstrual pain. Our results show that the asymmetric index of sample entropy in the precuneus and the sample entropy in the left posterior cingulate gyrus were the most informative characteristics associated with the perception of menstrual pain. The correlation coefficient (ρ=0.64, p<0.001) between the predicted and self-reported pain scores demonstrated the high prediction accuracy. In addition to the estimated brain activity, we were able to predict accurate pain scores directly from MEG channel signals (ρ=0.65, p<0.001). These findings suggest the possibility of using the proposed model based on resting-state MEG to predict the perceived intensity of endogenous pain.

Supervised learning for neural manifold using spatiotemporal brain activity

OBJECTIVE

Determining the means by which perceived stimuli are compactly represented in the human brain is a difficult task. This study aimed to develop techniques for the construction of the neural manifold as a representation of visual stimuli.

APPROACH

We propose a supervised locally linear embedding method to construct the embedded manifold from brain activity, taking into account similarities between corresponding stimuli. In our experiments, photographic portraits were used as visual stimuli and brain activity was calculated from magnetoencephalographic data using a source localization method.

MAIN RESULTS

The results of 10 × 10-fold cross-validation revealed a strong correlation between manifolds of brain activity and the orientation of faces in the presented images, suggesting that high-level information related to image content can be revealed in the brain responses represented in the manifold.

SIGNIFICANCE

Our experiments demonstrate that the proposed method is applicable to investigation into the inherent patterns of brain activity.

Osteopontin neutralisation abrogates the liver progenitor cell response and fibrogenesis in mice

BACKGROUND

Chronic liver injury triggers a progenitor cell repair response, and liver fibrosis occurs when repair becomes deregulated. Previously, we reported that reactivation of the hedgehog pathway promotes fibrogenic liver repair. Osteopontin (OPN) is a hedgehog-target, and a cytokine that is highly upregulated in fibrotic tissues, and regulates stem-cell fate. Thus, we hypothesised that OPN may modulate liver progenitor cell response, and thereby, modulate fibrotic outcomes. We further evaluated the impact of OPN-neutralisation on murine liver fibrosis.

METHODS

Liver progenitors (603B and bipotential mouse oval liver) were treated with OPN-neutralising aptamers in the presence or absence of transforming growth factor (TGF)-β, to determine if (and how) OPN modulates liver progenitor function. Effects of OPN-neutralisation (using OPN-aptamers or OPN-neutralising antibodies) on liver progenitor cell response and fibrogenesis were assessed in three models of liver fibrosis (carbon tetrachloride, methionine-choline deficient diet, 3,5,-diethoxycarbonyl-1,4-dihydrocollidine diet) by quantitative real time (qRT) PCR, Sirius-Red staining, hydroxyproline assay, and semiquantitative double-immunohistochemistry. Finally, OPN expression and liver progenitor response were corroborated in liver tissues obtained from patients with chronic liver disease.

RESULTS

OPN is overexpressed by liver progenitors in humans and mice. In cultured progenitors, OPN enhances viability and wound healing by modulating TGF-β signalling. In vivo, OPN-neutralisation attenuates the liver progenitor cell response, reverses epithelial-mesenchymal-transition in Sox9+ cells, and abrogates liver fibrogenesis.

CONCLUSIONS

OPN upregulation during liver injury is a conserved repair response, and influences liver progenitor cell function. OPN-neutralisation abrogates the liver progenitor cell response and fibrogenesis in mouse models of liver fibrosis.

Osteopontin mediates an MZF1-TGF-β1-dependent transformation of mesenchymal stem cells into cancer-associated fibroblasts in breast cancer

Interactions between tumor cells and cancer-associated fibroblasts (CAFs) in the tumor microenvironment (TMEN) significantly influence cancer growth and metastasis. Transforming growth factor-β (TGF-β) is known to be a critical mediator of the CAF phenotype, and osteopontin (OPN) expression in tumors is associated with more aggressive phenotypes and poor patient outcomes. The potential link between these two pathways has not been previously addressed. Utilizing in vitro studies using human mesenchymal stem cells (MSCs) and MDA-MB231 (OPN+) and MCF7 (OPN−) human breast cancer cell lines, we demonstrate that OPN induces integrin-dependent MSC expression of TGF-β1 to mediate adoption of the CAF phenotype. This OPN-TGF-β1 pathway requires the transcription factor, myeloid zinc finger 1 (MZF1). In vivo studies with xenotransplant models in NOD-scid mice showed that OPN expression increases cancer growth and metastasis by mediating MSC-to-CAF transformation in a process that is MZF1- and TGF-β1-dependent. We conclude that tumor-derived OPN engenders MSC-to-CAF transformation in the microenvironment to promote tumor growth and metastasis via the OPN-MZF1-TGF-β1 pathway.

Duplication and diversification of the spermidine/spermine N1-acetyltransferase 1 genes in zebrafish

Spermidine/spermine N(1)-acetyltransferase 1 (Ssat1) is a key enzyme in the polyamine interconversion pathway, which maintains polyamine homeostasis. In addition, mammalian Ssat1 is also involved in many physiological and pathological events such as hypoxia, cell migration, and carcinogenesis. Using cross-genomic bioinformatic analysis in 10 deuterostomes, we found that ssat1 only exists in vertebrates. Comparing with mammalian, zebrafish, an evolutionarily distant vertebrate, contains 3 homologous ssat1 genes, named ssat1a, ssat1b, and ssat1c. All zebrafish homologues could be transcribed and produce active enzymes. Despite the long history since their evolutionary diversification, some features of human SSAT1 are conserved and subfunctionalized in the zebrafish family of Ssat1 proteins. The polyamine-dependent protein synthesis was only found in Ssat1b and Ssat1c, not in Ssat1a. Further study indicated that both 5' and 3' sequences of ssat1b mediate such kind of translational regulation inside the open reading frame (ORF). The polyamine-dependent protein stabilization was only observed in Ssat1b. The last 70 residues of Ssat1b were crucial for its rapid degradation and polyamine-induced stabilization. It is worth noting that only Ssat1b and Ssat1c, but not the polyamine-insensitive Ssat1a, were able to interact with integrin α9 and Hif-1α. Thus, Ssat1b and Ssat1c might not only be a polyamine metabolic enzyme but also simultaneously respond to polyamine levels and engage in cross-talk with other signaling pathways. Our data revealed some correlations between the sequences and functions of the zebrafish family of Ssat1 proteins, which may provide valuable information for studies of their translational regulatory mechanism, protein stability, and physiological functions.

Effect of Ag underlayer on microstructures and perpendicular magnetic properties of CoPt nanocomposite thin films

CoPt/Ag films were prepared by magnetron sputtering on glass substrates and subsequent annealing. The dependence of degree of ordering and magnetic properties on Ag film thickness and annealing conditions were investigated. It was found that the Ag underlayer played a dominant role in inducing the (001) texture of the CoPt film after annealing. CoPt films with a thickness about 20 nm and Ag underlayers with a thickness about 70 nm are easy to obtain a large degree of ordering and a perpendicular magnetic anisotropy after annealing at 700 degrees C for 30 min. CoPt/Ag films with out-of-plane coercivity (Hc (perpendicular)) in the range of 13.5-14.0 kOe and a out-of-plane squareness (S(perpendicular)) of 0.97 were obtained after annealing at 700 degrees C for 30 min. Ag underlayer is beneficial to enhance the Hc(perpendicular)and S(perpendicular) of CoPt film significantly. The degree of ordering and perpendicular magnetic properties of the CoPt films which deposited on Ag underlayer are larger than those of the single layer CoPt films.

Crystallization mechanisms of phase change (GeSbSn)(100-x)Co(x) optical recording films

In this study, the (GeSbSn)(100-x0Co(x) films (x = 0-13.3) were deposited on natural oxidized silicon wafer and glass substrate by dc magnetron co-sputtering of GeSbSn and Co targets. The ZnS-SiO2 films were used as protective layers. The thicknesses of the (GeSbSn)(100-x)Co(x) films and protective layer were 100 nm and 30 nm, respectively. We investigated the effects of Co addition on the thermal property, crystallization kinetics, and crystallization mechanism of the GeSbSn recording film. The crystallization temperatures of (GeSbSn)(100-x)Co(x) films were decreased with Co content. It was found that the activation energy of the (GeSbSn)(100-x)Co(x) films will decrease from 1.53 eV to 0.55 eV as Co content increased from 0 at.% to 13.3 at.%.

Microstructure and crystallization kinetics analysis of the (In15Sb85)(100-x)Zn(x) phase change recording thin films

The (In15Sb85)(100-x)Zn(x) films (x = 0 - 17.4) were deposited on nature oxidized Si wafer and glass substrate at room temperature by magnetron co-sputtering of Sb target and InZn composite target. The thermal property of the films was examined by a homemade reflectivity thermal analyzer. Microstructures of the films were analyzed by transmission electron microscope (TEM). We examined the effects of Zn addition on the thermal property, crystallization kinetics, and crystallization mechanism of the In15Sb85 recording film. As x = 0 - 17.4, thermal analysis shows that the (In15Sb85)(100-x)Zn(x) films have two phase transition temperature ranges which are 189 degrees C-215 degrees C and 300 degrees C-350 degrees C. It is found that the activation energy is increased with Zn content. This indicates that the thermal stability of amorphous state is improved by doping Zn. The optical contrasts of the films are all larger than 15%, as x = 0 - 6.2, indicating that the films have the potential in blue laser optical recording media application.

One-year results with extended-release tacrolimus/MMF, tacrolimus/MMF and cyclosporine/MMF in de novo kidney transplant recipients

Once-daily tacrolimus extended-release formulation (Prograf XL, formerly referred to as MR or MR4) was compared with the twice-a-day tacrolimus formulation (TAC) and cyclosporine microemulsion (CsA), all administered in combination with mycophenolate mofetil (MMF), corticosteroids and basiliximab induction, in a phase 3, randomized (1:1:1), open-label trial in 638 de novo kidney transplant recipients. In combination with MMF and corticosteroids, XL had an efficacy profile comparable to TAC and CsA. XL/MMF and TAC/MMF were statistically noninferior at 1-year posttransplantation to CsA/MMF for the primary efficacy endpoint, efficacy failure (death, graft loss, biopsy-confirmed acute rejection (BCAR) or lost to follow-up). One-year patient and graft survival were 98.6% and 96.7% in the XL/MMF group, 95.7% and 92.9% in TAC/MMF group and 97.6% and 95.7% in CsA/MMF group. The safety profile of XL in comparison with CsA was similar to that observed with TAC in this study and consistent with previously published reports of TAC in comparison with CsA. The results support the safety and efficacy of tacrolimus in combination with MMF, corticosteroids and basiliximab induction, as well as XL as a safe and effective once-daily dosing alternative.

One-year results with extended-release tacrolimus/MMF, tacrolimus/MMF and cyclosporine/MMF in de novo kidney transplant recipients

Once-daily tacrolimus extended-release formulation (Prograf XL, formerly referred to as MR or MR4) was compared with the twice-a-day tacrolimus formulation (TAC) and cyclosporine microemulsion (CsA), all administered in combination with mycophenolate mofetil (MMF), corticosteroids and basiliximab induction, in a phase 3, randomized (1:1:1), open-label trial in 638 de novo kidney transplant recipients. In combination with MMF and corticosteroids, XL had an efficacy profile comparable to TAC and CsA. XL/MMF and TAC/MMF were statistically noninferior at 1-year posttransplantation to CsA/MMF for the primary efficacy endpoint, efficacy failure (death, graft loss, biopsy-confirmed acute rejection (BCAR) or lost to follow-up). One-year patient and graft survival were 98.6% and 96.7% in the XL/MMF group, 95.7% and 92.9% in TAC/MMF group and 97.6% and 95.7% in CsA/MMF group. The safety profile of XL in comparison with CsA was similar to that observed with TAC in this study and consistent with previously published reports of TAC in comparison with CsA. The results support the safety and efficacy of tacrolimus in combination with MMF, corticosteroids and basiliximab induction, as well as XL as a safe and effective once-daily dosing alternative.

Obesity predicts increased overall complications following pancreas transplantation

PURPOSE

We sought to evaluate the role of recipient body mass index (BMI) on postoperative complications in patients receiving pancreas transplants.

METHODS

A single-institution retrospective study of 145 consecutive patients undergoing either simultaneous kidney pancreas (SPK) or pancreas after kidney (PAK) transplantation from January 1997 through December 2003. Variables analyzed included: age, sex, BMI, number of prior transplants, cytomegalovirus status of donor and recipient, postoperative insulin resistance, complications, and overall patient and graft survival. Differences in continuous variables and dichotomous variables were evaluated using two-tailed t test and Fisher exact test, respectively. Univariate and multivariate logistic regression analyses were employed to identify predictors of overall complications following surgery.

RESULTS

Obesity was defined by a BMI > or = 30. Of the 145 patients, 33 (23%) had a BMI > or = 30 and 112 (77%) had a BMI < 30. There was no significant difference in age or sex between obese and nonobese patients (P = .98 and P = .56, respectively). The type of transplantation, SPK or PAK, did not affect the complication rate (P = .36). Overall complications (infection, dehiscence, evisceration, ventral hernia, allograft failure, gangrene, necrotizing fasciitis, postoperative bleeding, or death) were significantly higher in the obese group (81% vs 40%, P < .001). Obesity was specifically associated with increased frequency of dehiscence, ventral hernia, intra-abdominal infection, gangrene, necrotizing fasciitis, and repeat laparotomy. Obese patients also had a threefold higher rate of graft pancreatitis/enteric leak. Multivariate logistic regression analysis identified age > or = 50 and BMI > or = 30 as independent predictors of overall complications following surgery (odds ratio 4.0, P = .014 and OR 6.8, P < .001, respectively). There was no difference identified between groups with regards to allograft failure, posttransplant insulin resistance, and death.

CONCLUSION

Obese patients are at increased risk of overall complications following pancreas transplantation. Specifically, obese patients experience higher frequency of dehiscence, ventral hernia, intra-abdominal infection, gangrene, and necrotizing fasciitis. This study demonstrates the need for careful postoperative monitoring in the obese patient.

Pharmacokinetics and biochemical effects of hepapoietin in patients with chronic liver disease

BACKGROUND

Hepapoietin is a naturally occurring cytokine that promotes hepatocyte growth. Animal studies have suggested that hepapoietin and hepatocyte growth factor have a potential role in the prevention and management of liver diseases. However, human studies have been lacking.

AIM

To evaluate the safety and pharmacokinetics of single escalating doses of hepapoietin in patients with chronic liver disease.

METHODS

An open-label, single escalating dose trial with five different doses of hepapoietin (1, 3, 10, 30 and 100 mg) was performed. Adults with chronic, compensated, non-viral liver disease were included. Liver function tests were obtained before dosing, 24 h after hepapoietin administration and on days 4, 7, 30 and 45. All patients were followed for 45 days.

RESULTS

Twenty-five subjects received hepapoietin, with five subjects each at 1, 3, 10, 30 and 100 mg of hepapoietin. Significant decreases occurred in total bilirubin, ammonia, partial thromboplastin time and cholesterol levels overall, and both high-density and low-density lipoprotein cholesterol showed a downward trend. An increase in albumin was observed at the 30 mg dose level. Slight decreases in haemoglobin and red blood cell levels were observed at day 4, but returned to normal levels immediately thereafter. Child-Pugh scores from day 0 to day 7 were improved in 24%, stable in 64% and worse in 12% of patients. Hepatic encephalopathy displayed changes from day 0 to day 45 with improvement in 16%, no change in 80% and worsening in 4%.

CONCLUSIONS

Hepapoietin in doses up to 100 mg is safe for use in humans. Potential benefits are suggested by significant decreases in bilirubin, ammonia, partial thromboplastin time and cholesterol levels and an increase in albumin. Further studies with multiple dosing regimens are needed to identify the clinical utility of hepapoietin in the management of chronic liver disease.

Nitric oxide inhibits expression of cytochrome B in endotoxin-stimulated murine macrophages

In LPS-mediated states of sepsis, inducible nitric oxide synthase (iNOS) expression and nitric oxide (NO) production inhibit cellular respiration and mitochondrial electron transport. NO has been demonstrated to inhibit mitochondrial respiration by nitrosylation of the iron-sulfur centers of aconitase, complex I (NADH-ubiquinone oxidoreductase), complex II (succinate-ubiquinone oxidoreductase), and complex IV (cytochrome c oxidase). However, little is known of the effect of NO on expression of critical proteins in the electron transport chain. In ANA-1 murine macrophages, LPS-mediated NO synthesis decreases Cyt b protein expression and steady-state mRNA levels. Mitochondrial run-on analysis demonstrates unaltered Cyt b mitochondrial gene transcription. In this study utilizing LPS-stimulated ANA-1 murine macrophages, we demonstrate that expression of the mitochondrial protein, Cyt b, is significantly decreased as the result of a unique and previously unknown, NO-dependent posttranscriptional regulatory mechanism. (c)2001 Elsevier Science.

Transplantation of hepatitis C-positive livers in hepatitis C-positive patients is equivalent to transplanting hepatitis C-negative livers

A significant number of patients with end-stage liver disease secondary to hepatitis C die of disease-related complications. Liver transplantation offers the only effective alternative. Unfortunately, organ demand exceeds supply. Consequently, some transplant centers have used hepatitis C virus-positive (HCV(+)) donor livers for HCV(+) recipients. This study reviews the clinical outcome of a large series of HCV(+) recipients of HCV(+) liver allografts and compares their course with that of HCV(+) recipients of HCV-negative (HCV(-)) allografts. The United Network for Organ Sharing Scientific Registry was reviewed for the period from April 1, 1994, to June 30, 1997. All HCV(+) transplant recipients were analyzed. Two groups were identified: a group of HCV(+) recipients of HCV(+) donor livers (n = 96), and a group of HCV(+) recipients of HCV(-) donor livers (n = 2,827). A multivariate logistic regression model was used to determine the odds of graft failure and patient mortality, and unadjusted graft and patient survival were determined using the Kaplan-Meier method. There were no differences in demographic criteria between the groups. A greater percentage of patients with hepatocellular carcinoma received an HCV(+) allograft (8.3% v 3.1%; P =.01). Patient survival showed a significant difference for the HCV(+) group compared with the HCV(-) group (90% v 77%; P =.01). Blood type group A, group B, group O incompatibility was significant, with 4.2% incompatibility in the HCV(+) group and only 1.3% in the HCV(-) group (P =.04). Donor hepatitis C status does not impact on graft or patient survival after liver transplantation for HCV(+) recipients. Their survival was equivalent, if not better, compared with the control group. Using HCV(+) donor livers for transplantation in HCV(+) recipients safely and effectively expands the organ donor pool.

Candida fasciitis following renal transplantation

BACKGROUND

We describe a rare case of necrotizing fasciitis involving Candida albicans, an organism that has been reported to have a minimal potential for invasive soft tissue infection. In this case, immunosuppression, chronic renal failure, and a history of diabetes mellitus were predisposing factors.

METHODS

The medical record and histopathologic material were examined. The clinical literature was reviewed for previous cases of C albicans necrotizing fasciitis.

RESULTS

A review of the literature showed that in solid organ transplant recipients, localized fungal soft tissue infection is infrequent, with only 35 cases reported between 1974 and 1992. Necrotizing fasciitis caused by C albicans is extremely rare in the modern era of solid organ transplantation.

CONCLUSIONS

The management of transplant patients at risk for invasive fungal infection warrants a high index of suspicion for fungal necrotizing fasciitis in the setting of wound infection and merits a thorough investigation for atypical pathogens.

Outcome of extra-anatomic vascular reconstruction in orthotopic liver transplantation

BACKGROUND

Portal venous and hepatic arterial reconstruction are critical to successful outcomes in orthotopic liver transplantation (OLT). With portal vein thrombosis or inadequate hepatic arterial inflow, extra-anatomic vascular reconstruction is required. However, the clinical outcomes following extra-anatomic vascular reconstruction are largely unknown.

METHODS

To determine the outcomes associated with extra-anatomic vascular reconstruction, we performed a retrospective review of 205 OLT recipients transplanted between 1995 and 2000.

RESULTS

Extra-anatomic portal venous inflow was based upon the recipient superior mesenteric vein using donor iliac vein graft in a retrogastric position (n = 12). Extra-anatomic arterial inflow was based on recipient infrarenal aorta using donor iliac artery graft through the transverse mesocolon (n = 25). OLT with routine anatomic vascular construction served as control (n = 168). Extra-anatomic vascular reconstruction was not associated with increased morbidity, mortality, operating room time, length of stay, or thrombosis.

CONCLUSION

We conclude that extra-anatomic vascular conduits are associated with excellent long-term outcomes and provide acceptable alternatives for vascular reconstruction in OLT.

Transplantation in the HIV+ patient

The historical exclusion from transplantation of HIV-infected people was based on the logical premise that immunosuppression required for organ transplantation would exacerbate an immunocompromised state. However, the prognosis for people with HIV infection has dramatically improved with the clinical use of highly active antiretroviral (ARV) therapy (HAART). Clinical trials of ARV agents have demonstrated significant virologic, immunologic and survival benefits associated with the use of protease inhibitor (PI) or non-nucleoside reverse transcriptase inhibitor (NNRTI) containing regimens, when combined with two nucleoside analogues. The incidence of opportunistic infections and hospitalizations has decreased with the use of HAART. In combination with historical data suggesting that a subpopulation of HIV+ transplant recipients tolerate immunosuppression and have allograft survival comparable to that of HIV- transplant recipients, these results indicate that the medical community should readdress HIV infection as a contraindication to transplantation.

Osteopontin is a negative feedback regulator of nitric oxide synthesis in murine macrophages

In a system of endotoxin (LPS)-mediated NO production in ANA-1 murine macrophages, suppression subtractive hybridization was used to identify genes up-regulated by NO. Osteopontin (OPN), a secreted acidic phosphoprotein that binds to a cell surface RGD integrin-binding motif, was found to be differentially expressed in the presence of NO. OPN has been demonstrated to inhibit NO production in a variety of cell types. Northern blot and nuclear run-on analyses demonstrated that OPN mRNA levels and gene transcription were significantly increased in the presence of LPS-induced NO synthesis. Transient transfection of an OPN promoter-luciferase reporter plasmid construct showed that promoter activity is increased in the presence of LPS and NO. Immunoblot analysis showed that OPN protein is secreted into the extracellular fluid. Similar results were noted with an alternative cell system, RAW 264.7 macrophages, and alternative inducers of NO synthesis, IFN-gamma and IL-1beta. In the presence of GRGDSP, a hexapeptide that blocks binding of RGD-containing proteins to cell surface integrins, NO production is significantly increased in the presence of LPS stimulation. These data suggest a unique trans-regulatory mechanism in which LPS-induced NO synthesis feedback regulates itself through up-regulation of OPN promoter activity and gene transcription.

Pulmonary expression of iNOS and HO-1 protein is upregulated in a rat model of prehepatic portal hypertension

Portal hypertension is associated with a wide range of pulmonary pathophysiologies, ranging from portopulmonary hypertension to hepatopulmonary syndrome. Although the clinical and pathological features of pulmonary dysfunction in this setting have been extensively characterized, the underlying biology is not well understood. Specifically, the role of mediators that regulate mesenteric vascular hemodynamics in portal hypertension, such as nitric oxide and endothelin, have not been studied in the lung. Using a rat model of prehepatic portal hypertension with preserved hepatic function, we examined pulmonary elaboration of endothelial nitric oxide synthase (NOS), inducible NOS, heme oxygenase- 1 (HO-1), heme oxygenase-2 (HO-2), endothelin-1 mRNA, and protein. In comparison to sham controls, portal hypertensive animals exhibited significantly increased pulmonary iNOS and HO-1 mRNA and protein. Cyclic GMP was significantly increased in portal hypertensive lung tissue, suggesting activation of guanylyl cyclase by the endproducts of iNOS and/or HO-1 activity. Using immunohistochemical analysis, iNOS expression was localized to the vascular endothelium, while HO-1 localized to bronchiolar epithelium and macrophages. These results suggest that production of nitric oxide and carbon monoxide may contribute to the pulmonary pathology associated with portal hypertension.

Endotoxin and NO induce MIP-1gamma gene transcription in ANA-1 murine macrophages

The host response to gram-negative endotoxin is characterized by an influx of inflammatory cells into host tissues, mediated in part by localized production of chemokines. In this study, using subtractive suppression hybridization analysis, we demonstrate that ANA-1 murine macrophages produce the CC chemokine, MIP-1gamma, in response to LPS-mediated NO production. Gene transcription and protein translation are upregulated in the setting of LPS-induced NO synthesis. However, NO alone is a necessary but insufficient cofactor for induction of MIP-1gamma protein expression; an NO-independent LPS signalling pathway is also required. This study suggests a novel mechanism by which NO modulates the host inflammatory response to endotoxin.

Nitric oxide-dependent ribosomal RNA cleavage is associated with inhibition of ribosomal peptidyl transferase activity in ANA-1 murine macrophages

NO can regulate specific cellular functions by altering transcriptional programs and protein reactivity. With respect to global cellular processes, NO has also been demonstrated to inhibit total protein synthesis and cell proliferation. The underlying mechanisms are unknown. In a system of ANA-1 murine macrophages, iNOS expression and NO production were induced by exposure to endotoxin (LPS). In selected instances, cells were exposed to an exogenous NO donor, S-nitroso-N-acetylpenicillamine or a substrate inhibitor of NO synthesis. Cellular exposure to NO, from both endogenous and exogenous sources, was associated with a significant time-dependent decrease in total protein synthesis and cell proliferation. Gene transcription was unaltered. In parallel with decreased protein synthesis, cells exhibited a distinctive cleavage pattern of 28S and 18S rRNA that were the result of two distinct cuts in both 28S and 18S rRNA. Total levels of intact 28S rRNA, 18S rRNA, and the composite 60S ribosome were significantly decreased in the setting of cell exposure to NO. Finally, 60S ribosome-associated peptidyl transferase activity, a key enzyme for peptide chain elongation, was also significantly decreased. Our data suggest that NO-mediated cleavage of 28S and 18S rRNA results in decreased 60S ribosome associated peptidyl transferase activity and inhibition of total protein synthesis.

Use of aerosolized inhaled epoprostenol in the treatment of portopulmonary hypertension

BACKGROUND

Portopulmonary hypertension is a known complication in the liver transplant candidate. Intravenous epoprostenol has been demonstrated to decrease pulmonary artery pressures and possibly remodel right ventricle geometry.

METHODS

In this report, we document the efficacy of inhaled aerosolized epoprostenol in a patient with portopulmonary hypertension. The effect was of rapid onset and offset.

RESULTS

After 10 min of delivery, mean pulmonary artery pressure decreased 26%; cardiac output increased by 22%; pulmonary vascular resistance decreased by 42%; and the transpulmonary gradient decreased by 29%. There were no untoward side effects.

CONCLUSION

The inhaled route of delivery of epoprostenol is potential alternative for the acute therapy of portpulmonary hypertension.