An interpretable multi-task system for clinically applicable COVID-19 diagnosis using CXR.

BACKGROUND: With the emergence of continuously mutating variants of coronavirus, it is urgent to develop a deep learning model for automatic COVID-19 diagnosis at early stages from chest X-ray images. Since laboratory testing is time-consuming and requires trained laboratory personal, diagnosis using chest X-ray (CXR) is a befitting option. OBJECTIVE: In this study, we proposed an interpretable multi-task system for automatic lung detection and COVID-19 screening in chest X-rays to find an alternate method of testing which are reliable, fast and easily accessible, and able to generate interpretable predictions that are strongly correlated with radiological findings. METHODS: The proposed system consists of image preprocessing and an unsupervised machine learning (UML) algorithm for lung region detection, as well as a truncated CNN model based on deep transfer learning (DTL) to classify chest X-rays into three classes of COVID-19, pneumonia, and normal. The Grad-CAM technique was applied to create class-specific heatmap images in order to establish trust in the medical AI system. RESULTS: Experiments were performed with 15,884 frontal CXR images to show that the proposed system achieves an accuracy of 91.94% in a test dataset with 2,680 images including a sensitivity of 94.48% on COVID-19 cases, a specificity of 88.46% on normal cases, and a precision of 88.01% on pneumonia cases. Our system also produced state-of-the-art outcomes with a sensitivity of 97.40% on public test data and 88.23% on a previously unseen clinical data (1,000 cases) for binary classification of COVID-19-positive and COVID-19-negative films. CONCLUSION: Our automatic computerized evaluation for grading lung infections exhibited sensitivity comparable to that of radiologist interpretation in clinical applicability. Therefore, the proposed solution can be used as one element of patient evaluation along with gold-standard clinical and laboratory testing.

Western diet induces colonic nitrergic myenteric neuropathy and dysmotility in mice via saturated fatty acid- and lipopolysaccharide-induced TLR4 signalling.

KEY POINTS: A high-fat diet (60% kcal from fat) is associated with motility disorders inducing constipation and loss of nitrergic myenteric neurons in the proximal colon. Gut microbiota dysbiosis, which occurs in response to HFD, contributes to endotoxaemia. High levels of lipopolysaccharide lead to apoptosis in cultured myenteric neurons that express Toll-like receptor 4 (TLR4). Consumption of a Western diet (WD) (35% kcal from fat) for 6 weeks leads to gut microbiota dysbiosis associated with altered bacterial metabolites and increased levels of plasma free fatty acids. These disorders precede the nitrergic myenteric cell loss observed in the proximal colon. Mice lacking TLR4 did not exhibit WD-induced myenteric cell loss and dysmotility. Lipopolysaccharide-induced in vitro enteric neurodegeneration requires the presence of palmitate and may be a result of enhanced NO production. The present study highlights the critical role of plasma saturated free fatty acids that are abundant in the WD with respect to driving enteric neuropathy and colonic dysmotility. ABSTRACT: The consumption of a high-fat diet (HFD) is associated with myenteric neurodegeneration, which in turn is associated with delayed colonic transit and constipation. We examined the hypothesis that an inherent increase in plasma free fatty acids (FFA) in the HFD together with an HFD-induced alteration in gut microbiota contributes to the pathophysiology of these disorders. C57BL/6 mice were fed a Western diet (WD) (35% kcal from fat enriched in palmitate) or a purified regular diet (16.9% kcal from fat) for 3, 6, 9 and 12 weeks. Gut microbiota dysbiosis was investigated by fecal lipopolysaccharide (LPS) measurement and metabolomics (linear trap quadrupole-Fourier transform mass spectrometer) analysis. Plasma FFA and LPS levels were assessed, in addition to colonic and ileal nitrergic myenteric neuron quantifications and motility. Compared to regular diet-fed control mice, WD-fed mice gained significantly more weight without blood glucose alteration. Dysbiosis was exhibited after 6 weeks of feeding, as reflected by increased fecal LPS and bacterial metabolites and concomitant higher plasma FFA. The numbers of nitrergic myenteric neurons were reduced in the proximal colon after 9 and 12 weeks of WD and this was also associated with delayed colonic transit. WD-fed Toll-like receptor 4 (TLR4)-/- mice did not exhibit myenteric cell loss or dysmotility. Finally, LPS (0.5-2 ng·ml-1 ) and palmitate (20 and 30 µm) acted synergistically to induce neuronal cell death in vitro, which was prevented by the nitric oxide synthase inhibitor NG-nitro-l-arginine methyl ester. In conclusion, WD-feeding results in increased levels of FFA and microbiota that, even in absence of hyperglycaemia or overt endotoxaemia, synergistically induce TLR4-mediated neurodegeneration and dysmotility.

High-resolution metabolomics to discover potential parasite-specific biomarkers in a Plasmodium falciparum erythrocytic stage culture system.

BACKGROUND: Current available malaria diagnostic methods each have some limitations to meet the need for real-time and large-scale screening of asymptomatic and low density malaria infection at community level. It was proposed that malaria parasite-specific low molecular-weight metabolites could be used as biomarkers for the development of a malaria diagnostic tool aimed to address this diagnostic challenge. In this study, high resolution metabolomics (HRM) was employed to identify malaria parasite-specific metabolites in Plasmodium falciparum in vitro culture samples. METHODS: Supernatants were collected at 12 hours interval from 3% haematocrit in vitro 48-hour time-course asynchronized culture system of P. falciparum. Liquid chromatography coupled with high resolution mass spectrometry was applied to discover potential parasite-specific metabolites in the cell culture supernatant. A metabolome-wide association study was performed to extract metabolites using Manhattan plot with false discovery rate (FDR) and hierarchical cluster analysis. The significant metabolites based on FDR cutoff were annotated using Metlin database. Standard curves were created using corresponding chemical compounds to accurately quantify potential Plasmodium-specific metabolites in culture supernatants. RESULTS: The number of significant metabolite features was 1025 in the supernatant of the Plasmodium infected culture based on Manhattan plot with FDR q=0.05. A two way hierarchical cluster analysis showed a clear segregation of the metabolic profile of parasite infected supernatant from non-infected supernatant at four time points during the 48 hour culture. Among the 1025 annotated metabolites, the intensities of four molecules were significantly increased with culture time suggesting a positive association between the quantity of these molecules and level of parasitaemia: i) 3-methylindole, a mosquito attractant, ii) succinylacetone, a haem biosynthesis inhibitor, iii) S-methyl-L-thiocitrulline, a nitric oxide synthase inhibitor, and iv) O-arachidonoyl glycidol, a fatty acid amide hydrolase inhibitor, The highest concentrations of 3-methylindole and succinylacetone were 178 ± 18.7 pmoles at 36 hours and 157±30.5 pmoles at 48 hours respectively in parasite infected supernatant. CONCLUSION: HRM with bioinformatics identified four potential parasite-specific metabolite biomarkers using in vitro culture supernatants. Further study in malaria infected human is needed to determine presence of the molecules and its relationship with parasite densities.

Silicon substrate as a novel cell culture device for myoblast cells.

BACKGROUND: Tissue and organ regeneration via transplantation of cell bodies in-situ has become an interesting strategy in regenerative medicine. Developments of cell carriers to systematically deliver cell bodies in the damage site have fall shorten on effectively meet this purpose due to inappropriate release control. Thus, there is still need of novel substrate to achieve targeted cell delivery with appropriate vehicles. In the present study, silicon based photovoltaic (PV) devices are used as a cell culturing substrate for the expansion of myoblast mouse cell (C2C12 cells) that offers an atmosphere for regular cell growth in vitro. The adherence, viability and proliferation of the cells on the silicon surface were examined by direct cell counting and fluorescence microscopy. RESULTS: It was found that on the silicon surface, cells proliferated over 7 days showing normal morphology, and expressed their biological activities. Cell culture on silicon substrate reveals their attachment and proliferation over the surface of the PV device. After first day of culture, cell viability was 88% and cell survival remained above 86% as compared to the seeding day after the seventh day. Furthermore, the DAPI staining revealed that the initially scattered cells were able to eventually build a cellular monolayer on top of the silicon substrate. CONCLUSIONS: This study explored the biological applications of silicon based PV devices, demonstrating its biocompatibility properties and found useful for culture of cells on porous 2-D surface. The incorporation of silicon substrate has been efficaciously revealed as a potential cell carrier or vehicle in cell growth technology, allowing for their use in cell based gene therapy, tissue engineering, and therapeutic angiogenesis.

Multi-modal learning for inpatient length of stay prediction.

Predicting inpatient length of stay (LoS) is important for hospitals aiming to improve service efficiency and enhance management capabilities. Patient medical records are strongly associated with LoS. However, due to diverse modalities, heterogeneity, and complexity of data, it becomes challenging to effectively leverage these heterogeneous data to put forth a predictive model that can accurately predict LoS. To address the challenge, this study aims to establish a novel data-fusion model, termed as DF-Mdl, to integrate heterogeneous clinical data for predicting the LoS of inpatients between hospital discharge and admission. Multi-modal data such as demographic data, clinical notes, laboratory test results, and medical images are utilized in our proposed methodology with individual "basic" sub-models separately applied to each different data modality. Specifically, a convolutional neural network (CNN) model, which we termed CRXMDL, is designed for chest X-ray (CXR) image data, two long short-term memory networks are used to extract features from long text data, and a novel attention-embedded 1D convolutional neural network is developed to extract useful information from numerical data. Finally, these basic models are integrated to form a new data-fusion model (DF-Mdl) for inpatient LoS prediction. The proposed method attains the best R2 and EVAR values of 0.6039 and 0.6042 among competitors for the LoS prediction on the Medical Information Mart for Intensive Care (MIMIC)-IV test dataset. Empirical evidence suggests better performance compared with other state-of-the-art (SOTA) methods, which demonstrates the effectiveness and feasibility of the proposed approach.

Time-to-event modeling for hospital length of stay prediction for COVID-19 patients.

Providing timely patient care while maintaining optimal resource utilization is one of the central operational challenges hospitals have been facing throughout the pandemic. Hospital length of stay (LOS) is an important indicator of hospital efficiency, quality of patient care, and operational resilience. Numerous researchers have developed regression or classification models to predict LOS. However, conventional models suffer from the lack of capability to make use of typically censored clinical data. We propose to use time-to-event modeling techniques, also known as survival analysis, to predict the LOS for patients based on individualized information collected from multiple sources. The performance of six proposed survival models is evaluated and compared based on clinical data from COVID-19 patients.

Phytochelatin database: a resource for phytochelatin complexes of nutritional and environmental metals.

Phytochelatins (PyCs) are a diverse set of plant compounds that chelate metals, protect against metal toxicity and function in metal homeostasis. PyCs are present in plants consumed as food by humans and could, in principle, impact absorption and utilization of essential and toxic metals such as selenium and cadmium, respectively. PyCs vary in terminal amino acid composition and chain length, exist in multiple oxidation states and reversibly bind multiple metals; consequently, PyCs include a large set of possible structures. Although individual PyC-metal complexes have been studied, no resource exists to characterize the diversity of PyCs and PyC-metal complexes. We used the scientific literature to develop a database of elemental formulas for polymer forms varying in chain length from 2 to 11 glutamyl-cysteine repeats. Using elemental formulas, we calculated monoisotopic masses using the most abundant isotopes of each element and calculated masses for complexes with 13 metals of nutritional and toxicological significance. The resulting phytochelatin database (PyCDB) contains 46 260 unique elemental formulas for PyC and PyC-metal complexes. The database is available online for download as well as for direct mass queries for mass spectrometry using an accurate mass annotation tool for user-selected PyC types, metals and adducts of interest. We performed studies of a commonly consumed food-onion-to validate the database and test utility of the tool. Onion samples were analyzed using ultra-high resolution mass spectrometry-based metabolomics. Mass spectral features were annotated using the PyCDB web tool and the R package, xMSannotator; annotated features were further validated by collision-induced dissociation mass spectrometry. The results establish use and a workflow for PyCDB as a resource for characterization of PyCs and PyC-metal complexes.

Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks.

Deep learning techniques have been extensively used in computerized pulmonary nodule analysis in recent years. Many reported studies still utilized hybrid methods for diagnosis, in which convolutional neural networks (CNNs) are used only as one part of the pipeline, and the whole system still needs either traditional image processing modules or human intervention to obtain final results. In this paper, we introduced a fast and fully-automated end-to-end system that can efficiently segment precise lung nodule contours from raw thoracic CT scans. Our proposed system has four major modules: candidate nodule detection with Faster regional-CNN (R-CNN), candidate merging, false positive (FP) reduction with CNN, and nodule segmentation with customized fully convolutional neural network (FCN). The entire system has no human interaction or database specific design. The average runtime is about 16 s per scan on a standard workstation. The nodule detection accuracy is 91.4% and 94.6% with an average of 1 and 4 false positives (FPs) per scan. The average dice coefficient of nodule segmentation compared to the groundtruth is 0.793.

Immunogenicity and safety of a trivalent inactivated influenza vaccine produced in Shenzhen, China versus a comparator influenza vaccine: a phase IV randomized study.

Seasonal influenza causes substantial morbidity and mortality in China, which largely results from limited vaccine accessibility and poor vaccination coverage. Since 2013, Sanofi Pasteur's facilities in Shenzhen, China have produced a trivalent inactivated influenza vaccine (Shz-IIV3) for each influenza season according to Chinese pharmacopeia requirements. However, the immunogenicity of Shz-IIV3 has not been compared to existing Chinese trivalent inactivated influenza vaccines (IIV3s). Here, we describe the results of a phase IV, observer-blind, randomized study to evaluate whether the immunogenicity of Shz-IIV3 was non-inferior to a comparator IIV3 (Hualan Biological Engineering Inc) also manufactured and licensed in China. Healthy adults aged 18-59 years were randomly assigned in a 1:1 ratio to receive a single 0.5-mL intramuscular injection of the 2017-2018 Northern Hemisphere formulation of Shz-IIV3 (n = 800) or the comparator IIV3 (n = 799). Between baseline and day 28 after vaccination, hemagglutination inhibition titers for the three vaccine strains increased by at least 4-fold and were of similar magnitude in Shz-IIV3 and comparator IIV3 recipients. The rate of seroconversion or significant increase in titers was 62% to 92% in Shz-IIV3 recipients, and 63% to 91% in comparator IIV3 recipients. Post-vaccination hemagglutination inhibition titers and seroconversion rates for Shz-IIV3 were statistically non-inferior to the comparator IIV3 for all three influenza vaccine strains. Rates of solicited and unsolicited vaccine-related adverse events were similar between the two vaccine groups. These results demonstrated that Shz-IIV3 was as immunogenic and safe in adults as a comparator Chinese IIV3, and support the continued use of Shz-IIV3 in China.

Cyclic O3 exposure synergizes with aging leading to memory impairment in male APOE ε3, but not APOE ε4, targeted replacement mice.

The etiology of late-onset Alzheimer's disease is unknown. Recent epidemiological studies suggest that exposure to high levels of ozone (O3) may be a risk factor for late-onset Alzheimer's disease. Nonetheless, whether and how O3 exposure contributes to AD development remains to be determined. In this study, we tested the hypothesis that O3 exposure synergizes with the genetic risk factor APOE ε4 and aging leading to AD, using male apolipoprotein E (apoE)4 and apoE3 targeted replacement mice as men have increased risk exposure to high levels of O3 via working environments and few studies have addressed APOE ε4 effects on males. Surprisingly, our results show that O3 exposure impairs memory in old apoE3, but not old apoE4 or young apoE3 and apoE4, male mice. Further studies show that old apoE4 mice have increased hippocampal activities or expression of some enzymes involved in antioxidant defense, diminished protein oxidative modification, and neuroinflammation following O3 exposure compared with old apoE3 mice. These novel findings highlight the complexity of interactions between APOE genotype, age, and environmental exposure in AD development.

A new near-term breast cancer risk prediction scheme based on the quantitative analysis of ipsilateral view mammograms.

PURPOSE: To help improve efficacy of screening mammography and eventually establish an optimal personalized screening paradigm, this study aimed to develop and test a new near-term breast cancer risk prediction scheme based on the quantitative analysis of ipsilateral view of the negative screening mammograms. METHODS: The dataset includes digital mammograms acquired from 392 women with two sequential full-field digital mammography examinations. All the first ("prior") sets of mammograms were interpreted as negative during the original reading. In the sequential ("current") screening, 202 were proved positive and 190 remained negative/benign. For each pair of the "prior" ipsilateral mammograms, we adaptively fused the image features computed from two views. Using four different types of image features, we built four elastic net support vector machine (EnSVM) based classifiers. Then, the initial prediction scores form the 4 EnSVMs were combined to build a final artificial neural network (ANN) classifier that produces the final risk prediction score. The performance of the new scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). RESULTS: A total number of 466 features were initially extracted from each pair of ipsilateral mammograms. Among them, 51 were selected to build the EnSVM based prediction scheme. The AUC = 0.737 ±â€¯0.052 was yielded using the new scheme. Applying an optimal operating threshold, the prediction sensitivity was 60.4% (122 of 202) and the specificity was 79.0% (150 of 190). CONCLUSION: The study results showed moderately high positive association between computed risk scores using the "prior" negative mammograms and the actual outcome of the image-detectable breast cancers in the next subsequent screening examinations. The study also demonstrated that quantitative analysis of the ipsilateral views of the mammograms enabled to provide useful information in predicting near-term breast cancer risk.

Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data.

In this study we developed a graph based semi-supervised learning (SSL) scheme using deep convolutional neural network (CNN) for breast cancer diagnosis. CNN usually needs a large amount of labeled data for training and fine tuning the parameters, and our proposed scheme only requires a small portion of labeled data in training set. Four modules were included in the diagnosis system: data weighing, feature selection, dividing co-training data labeling, and CNN. 3158 region of interests (ROIs) with each containing a mass extracted from 1874 pairs of mammogram images were used for this study. Among them 100 ROIs were treated as labeled data while the rest were treated as unlabeled. The area under the curve (AUC) observed in our study was 0.8818, and the accuracy of CNN is 0.8243 using the mixed labeled and unlabeled data.

Rough set based rule induction in decision making using credible classification and preference from medical application perspective.

This paper presents a new heuristic algorithm for reduct selection based on credible index in the rough set theory (RST) applications. This algorithm is efficient and effective in selecting the decision rules particularly the problem to be solved in a large scale. This algorithm is capable to derive the rules with multi-outcomes and identify the most significant features simultaneously, which is unique and useful in solving predictive medical problems. The end results of the proposed approach are a set of decision rules that illustrates the causes for solitary pulmonary nodule and results of the long term treatment.

Photovoltaic surfaces enable clonal myoblastic cell release using visible light as external stimulation.

Many new biomedical approaches to treating disease require the supply of cells delivered to an injured or diseased organ either individually, collectively as aggregates or sheets, or encapsulated with a scaffold. The collection of cells is accomplished by using enzymatic digestion witch suffer from the need to remove the enzymes after digestion. In addition, enzymatic methods are not applicable for all cells, cell aggregates, cell sheets or 3D structures. The objective of this study was to investigate the release of cultured cells from silicon based Photovoltaic (PV) surfaces using a light source as external stimulation. C2C12 myoblasts were cultured on the negative surface of a PV device and upon confluence they were exposed to light. The amount of released cells was quantified as a function light exposure. It was found that light exposure at 25,000 lux for one hour caused equivalent cell release from the PV surface than trypsination. The released cells are viable and can be re-cultured if needed. This mechanism may offer an alternative method to release excitable cells without using an enzymatic agent. This may be important for cell therapy if larger cell structures such as sheets need to be collected.

Computerized breast cancer analysis system using three stage semi-supervised learning method.

BACKGROUND AND OBJECTIVE: A large number of labeled medical image data is usually a requirement to train a well-performed computer-aided detection (CAD) system. But the process of data labeling is time consuming, and potential ethical and logistical problems may also present complications. As a result, incorporating unlabeled data into CAD system can be a feasible way to combat these obstacles. METHODS: In this study we developed a three stage semi-supervised learning (SSL) scheme that combines a small amount of labeled data and larger amount of unlabeled data. The scheme was modified on our existing CAD system using the following three stages: data weighing, feature selection, and newly proposed dividing co-training data labeling algorithm. Global density asymmetry features were incorporated to the feature pool to reduce the false positive rate. Area under the curve (AUC) and accuracy were computed using 10 fold cross validation method to evaluate the performance of our CAD system. The image dataset includes mammograms from 400 women who underwent routine screening examinations, and each pair contains either two cranio-caudal (CC) or two mediolateral-oblique (MLO) view mammograms from the right and the left breasts. From these mammograms 512 regions were extracted and used in this study, and among them 90 regions were treated as labeled while the rest were treated as unlabeled. RESULTS: Using our proposed scheme, the highest AUC observed in our research was 0.841, which included the 90 labeled data and all the unlabeled data. It was 7.4% higher than using labeled data only. With the increasing amount of labeled data, AUC difference between using mixed data and using labeled data only reached its peak when the amount of labeled data was around 60. CONCLUSIONS: This study demonstrated that our proposed three stage semi-supervised learning can improve the CAD performance by incorporating unlabeled data. Using unlabeled data is promising in computerized cancer research and may have a significant impact for future CAD system applications.

Using multiscale texture and density features for near-term breast cancer risk analysis.

PURPOSE: To help improve efficacy of screening mammography by eventually establishing a new optimal personalized screening paradigm, the authors investigated the potential of using the quantitative multiscale texture and density feature analysis of digital mammograms to predict near-term breast cancer risk. METHODS: The authors' dataset includes digital mammograms acquired from 340 women. Among them, 141 were positive and 199 were negative/benign cases. The negative digital mammograms acquired from the "prior" screening examinations were used in the study. Based on the intensity value distributions, five subregions at different scales were extracted from each mammogram. Five groups of features, including density and texture features, were developed and calculated on every one of the subregions. Sequential forward floating selection was used to search for the effective combinations. Using the selected features, a support vector machine (SVM) was optimized using a tenfold validation method to predict the risk of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) was used as the performance assessment index. RESULTS: From a total number of 765 features computed from multiscale subregions, an optimal feature set of 12 features was selected. Applying this feature set, a SVM classifier yielded performance of AUC = 0.729 ± 0.021. The positive predictive value was 0.657 (92 of 140) and the negative predictive value was 0.755 (151 of 200). CONCLUSIONS: The study results demonstrated a moderately high positive association between risk prediction scores generated by the quantitative multiscale mammographic image feature analysis and the actual risk of a woman having an image-detectable breast cancer in the next subsequent examinations.

Long-term exposure to AZT, but not d4T, increases endothelial cell oxidative stress and mitochondrial dysfunction.

Nucleoside reverse transcriptase inhibitors (NRTIs), such as zidovudine (AZT) and stavudine (d4T), cause toxicities to numerous tissues, including the liver and vasculature. While much is known about hepatic NRTI toxicity, the mechanism of toxicity in endothelial cells is incompletely understood. Human aortic endothelial and HepG2 liver cells were exposed to 1 muM AZT or d4T for up to 5 weeks. Markers of oxidative stress, mitochondrial function, NRTI phosphorylation, mitochondrial DNA (mtDNA) levels, and cytotoxicity were monitored over time. In endothelial cells, AZT significantly oxidized glutathione redox potential, increased total cellular and mitochondrial-specific superoxide, decreased mitochondrial membrane potential, increased lactate release, and caused cell death from weeks 3 through 5. Toxicity occurred in the absence of di- and tri-phosphorylated AZT and mtDNA depletion. These data show that oxidative stress and mitochondrial dysfunction in endothelial cells occur with a physiologically relevant concentration of AZT, and require long-term exposure to develop. In contrast, d4T did not induce endothelial oxidative stress, mitochondrial dysfunction, or cytotoxicity despite the presence of d4T-triphosphate. Both drugs depleted mtDNA in HepG2 cells without causing cell death. Endothelial cells are more susceptible to AZT-induced toxicity than HepG2 cells, and AZT caused greater endothelial dysfunction than d4T because of its pro-oxidative effects.

Vascular oxidative stress and nitric oxide depletion in HIV-1 transgenic rats are reversed by glutathione restoration.

Human immunodeficiency virus (HIV)-infected patients have a higher incidence of oxidative stress, endothelial dysfunction, and cardiovascular disease than uninfected individuals. Recent reports have demonstrated that viral proteins upregulate reactive oxygen species, which may contribute to elevated cardiovascular risk in HIV-1 patients. In this study we employed an HIV-1 transgenic rat model to investigate the physiological effects of viral protein expression on the vasculature. Markers of oxidative stress in wild-type and HIV-1 transgenic rats were measured using electron spin resonance, fluorescence microscopy, and various molecular techniques. Relaxation studies were completed on isolated aortic rings, and mRNA and protein were collected to measure changes in expression of nitric oxide (NO) and superoxide sources. HIV-1 transgenic rats displayed significantly less NO-hemoglobin, serum nitrite, serum S-nitrosothiols, aortic tissue NO, and impaired endothelium-dependent vasorelaxation than wild-type rats. NO reduction was not attributed to differences in endothelial NO synthase (eNOS) protein expression, eNOS-Ser1177 phosphorylation, or tetrahydrobiopterin availability. Aortas from HIV-1 transgenic rats had higher levels of superoxide and 3-nitrotyrosine but did not differ in expression of superoxide-generating sources NADPH oxidase or xanthine oxidase. However, transgenic aortas displayed decreased superoxide dismutase and glutathione. Administering the glutathione precursor procysteine decreased superoxide, restored aortic NO levels and NO-hemoglobin, and improved endothelium-dependent relaxation in HIV-1 transgenic rats. These results show that HIV-1 protein expression decreases NO and causes endothelial dysfunction. Diminished antioxidant capacity increases vascular superoxide levels, which reduce NO bioavailability and promote peroxynitrite generation. Restoring glutathione levels reverses HIV-1 protein-mediated effects on superoxide, NO, and vasorelaxation.

Alterations in ionotropic glutamate receptor subunits during binge cocaine self-administration and withdrawal in rats.

Chronic cocaine use in humans and animal models is known to lead to pronounced alterations in glutamatergic function in brain regions associated with reinforcement. Previous studies have examined ionotropic glutamate receptor (iGluR) subunit protein level changes following acute and chronic experimenter-administered cocaine or after withdrawal periods from experimenter-administered cocaine. To evaluate whether alterations in expression of iGluRs are associated with cocaine reinforcement, protein levels were assessed after binge (8 h/day, 15 days; 24-h access, days 16-21) cocaine self-administration and following 2 weeks of abstinence from this binge. Western blotting was used to compare levels of iGluR protein expression (NR1-3B, GluR1-7, KA2) in the ventral tegmental area (VTA), substantia nigra (SN), nucleus accumbens (NAc), striatum and prefrontal cortex (PFC) of rats. iGluR subunits were altered in a time-dependent manner in all brain regions studied; however, selective alterations in certain iGluR subtypes appeared to be associated with binge cocaine self-administration and withdrawal in a region-specific manner. In the SN and VTA, alterations in iGluR protein levels compared with controls occurred only following binge access, whereas in the striatum and PFC, iGluR alterations occurred with binge access and following withdrawal. In the NAc, GluR2/3 levels were increased following withdrawal compared with binge access, and were the only changes observed in this region. Because subunit composition determines the functional properties of iGluRs, the observed changes may indicate alterations in the excitability of dopamine transmission underlying long-term biochemical and behavioral effects of cocaine.