Identifying temporal molecular signatures underlying cardiovascular diseases: A data science platform.

OBJECTIVE: During cardiovascular disease progression, molecular systems of myocardium (e.g., a proteome) undergo diverse and distinct changes. Dynamic, temporally-regulated alterations of individual molecules underlie the collective response of the heart to pathological drivers and the ultimate development of pathogenesis. Advances in high-throughput omics technologies have enabled cost-effective, temporal profiling of targeted systems in animal models of human diseases. However, computational analysis of temporal patterns from omics data remains challenging. In particular, bioinformatic pipelines involving unsupervised statistical approaches to support cardiovascular investigations are lacking, which hinders one's ability to extract biomedical insights from these complex datasets. APPROACH AND RESULTS: We developed a non-parametric data analysis platform to resolve computational challenges unique to temporal omics datasets. Our platform consists of three modules. Module I preprocesses the temporal data using either cubic splines or principal component analysis (PCA), and it simultaneously accomplishes the tasks on missing data imputation and denoising. Module II performs an unsupervised classification by K-means or hierarchical clustering. Module III evaluates and identifies biological entities (e.g., molecular events) that exhibit strong associations to specific temporal patterns. The jackstraw method for cluster membership has been applied to estimate p-values and posterior inclusion probabilities (PIPs), both of which guided feature selection. To demonstrate the utility of the analysis platform, we employed a temporal proteomics dataset that captured the proteome-wide dynamics of oxidative stress induced post-translational modifications (O-PTMs) in mouse hearts undergoing isoproterenol (ISO)-induced hypertrophy. CONCLUSION: We have created a platform, CV.Signature.TCP, to identify distinct temporal clusters in omics datasets. We presented a cardiovascular use case to demonstrate its utility in unveiling biological insights underlying O-PTM regulations in cardiac remodeling. This platform is implemented in an open source R package (https://github.com/UCLA-BD2K/CV.Signature.TCP).

Unsupervised classification of multi-omics data during cardiac remodeling using deep learning.

Integration of multi-omics in cardiovascular diseases (CVDs) presents high potentials for translational discoveries. By analyzing abundance levels of heterogeneous molecules over time, we may uncover biological interactions and networks that were previously unidentifiable. However, to effectively perform integrative analysis of temporal multi-omics, computational methods must account for the heterogeneity and complexity in the data. To this end, we performed unsupervised classification of proteins and metabolites in mice during cardiac remodeling using two innovative deep learning (DL) approaches. First, long short-term memory (LSTM)-based variational autoencoder (LSTM-VAE) was trained on time-series numeric data. The low-dimensional embeddings extracted from LSTM-VAE were then used for clustering. Second, deep convolutional embedded clustering (DCEC) was applied on images of temporal trends. Instead of a two-step procedure, DCEC performes a joint optimization for image reconstruction and cluster assignment. Additionally, we performed K-means clustering, partitioning around medoids (PAM), and hierarchical clustering. Pathway enrichment analysis using the Reactome knowledgebase demonstrated that DL methods yielded higher numbers of significant biological pathways than conventional clustering algorithms. In particular, DCEC resulted in the highest number of enriched pathways, suggesting the strength of its unified framework based on visual similarities. Overall, unsupervised DL is shown to be a promising analytical approach for integrative analysis of temporal multi-omics.

Integrated Dissection of Cysteine Oxidative Post-translational Modification Proteome During Cardiac Hypertrophy.

Cysteine oxidative modification of cellular proteins is crucial for many aspects of cardiac hypertrophy development. However, integrated dissection of multiple types of cysteine oxidative post-translational modifications (O-PTM) of proteomes in cardiac hypertrophy is currently missing. Here we developed a novel discovery platform that encompasses a customized biotin switch-based quantitative proteomics pipeline and an advanced analytic workflow to comprehensively profile the landscape of cysteine O-PTM in an ISO-induced cardiac hypertrophy mouse model. Specifically, we identified a total of 1655 proteins containing 3324 oxidized cysteine sites by at least one of the following three modifications: reversible cysteine O-PTM, cysteine sulfinylation (CysSO2H), and cysteine sulfonylation (CysSO3H). Analyzing the hypertrophy signatures that are reproducibly discovered from this computational workflow unveiled four biological processes with increased cysteine O-PTM. Among them, protein phosphorylation, creatine metabolism, and response to elevated Ca2+ pathways exhibited an elevation of cysteine O-PTM in early stages, whereas glucose metabolism enzymes were increasingly modified in later stages, illustrating a temporal regulatory map in cardiac hypertrophy. Our cysteine O-PTM platform depicts a dynamic and integrated landscape of the cysteine oxidative proteome, through the extracted molecular signatures, and provides critical mechanistic insights in cardiac hypertrophy. Data are available via ProteomeXchange with identifier PXD010336.

Phrase mining of textual data to analyze extracellular matrix protein patterns across cardiovascular disease.

Extracellular matrix (ECM) proteins have been shown to play important roles regulating multiple biological processes in an array of organ systems, including the cardiovascular system. Using a novel bioinformatics text-mining tool, we studied six categories of cardiovascular disease (CVD), namely, ischemic heart disease, cardiomyopathies, cerebrovascular accident, congenital heart disease, arrhythmias, and valve disease, anticipating novel ECM protein-disease and protein-protein relationships hidden within vast quantities of textual data. We conducted a phrase-mining analysis, delineating the relationships of 709 ECM proteins with the 6 groups of CVDs reported in 1,099,254 abstracts. The technology pipeline known as Context-Aware Semantic Online Analytical Processing was applied to semantically rank the association of proteins to each CVD and all six CVDs, performing analyses to quantify each protein-disease relationship. We performed principal component analysis and hierarchical clustering of the data, where each protein was visualized as a six-dimensional vector. We found that ECM proteins display variable degrees of association with the six CVDs; certain CVDs share groups of associated proteins, whereas others have divergent protein associations. We identified 82 ECM proteins sharing associations with all 6 CVDs. Our bioinformatics analysis ascribed distinct ECM pathways (via Reactome) from this subset of proteins, namely, insulin-like growth factor regulation and interleukin-4 and interleukin-13 signaling, suggesting their contribution to the pathogenesis of all six CVDs. Finally, we performed hierarchical clustering analysis and identified protein clusters predominantly associated with a targeted CVD; analyses of these proteins revealed unexpected insights underlying the key ECM-related molecular pathogenesis of each CVD, including virus assembly and release in arrhythmias. NEW & NOTEWORTHY The present study is the first application of a text-mining algorithm to characterize the relationships of 709 extracellular matrix-related proteins with 6 categories of cardiovascular disease described in 1,099,254 abstracts. Our analysis informed unexpected extracellular matrix functions, pathways, and molecular relationships implicated in the six cardiovascular diseases.

The human cardiac and skeletal muscle proteomes defined by transcriptomics and antibody-based profiling.

BACKGROUND: To understand cardiac and skeletal muscle function, it is important to define and explore their molecular constituents and also to identify similarities and differences in the gene expression in these two different striated muscle tissues. Here, we have investigated the genes and proteins with elevated expression in cardiac and skeletal muscle in relation to all other major human tissues and organs using a global transcriptomics analysis complemented with antibody-based profiling to localize the corresponding proteins on a single cell level. RESULTS: Our study identified a comprehensive list of genes expressed in cardiac and skeletal muscle. The genes with elevated expression were further stratified according to their global expression pattern across the human body as well as their precise localization in the muscle tissues. The functions of the proteins encoded by the elevated genes are well in line with the physiological functions of cardiac and skeletal muscle, such as contraction, ion transport, regulation of membrane potential and actomyosin structure organization. A large fraction of the transcripts in both cardiac and skeletal muscle correspond to mitochondrial proteins involved in energy metabolism, which demonstrates the extreme specialization of these muscle tissues to provide energy for contraction. CONCLUSIONS: Our results provide a comprehensive list of genes and proteins elevated in striated muscles. A number of proteins not previously characterized in cardiac and skeletal muscle were identified and localized to specific cellular subcompartments. These proteins represent an interesting starting point for further functional analysis of their role in muscle biology and disease.

QuantiFERON-TB gold in-tube implementation for latent tuberculosis diagnosis in a public health clinic: a cost-effectiveness analysis.

BACKGROUND: The tuberculin skin test (TST) has limitations for latent tuberculosis infection (LTBI) diagnosis in low-prevalence settings. Previously, all TST-positive individuals referred from the community to Baltimore City Health Department (BCHD) were offered LTBI treatment, after active TB was excluded. In 2010, BCHD introduced adjunctive QuantiFERON-TB Gold In-Tube (QFT-GIT) testing for TST-positive referrals. We evaluated costs and cost-effectiveness of this new diagnostic algorithm. METHODS: A decision-analysis model compared the strategy of treating all TST-positive referrals versus only those with positive results on adjunctive QFT-GIT testing. Costs were collected at BCHD, and Incremental Cost-Effectiveness Ratios (ICERs) were utilized to report on cost-effectiveness. RESULTS: QFT-GIT testing at BCHD cost $43.51 per test. Implementation of QFT-GIT testing was associated with an ICER of $1,202 per quality-adjusted life-year gained and was considered highly cost-effective. In sensitivity analysis, the QFT-GIT strategy became cost-saving if QFT-GIT sensitivity increased above 92% or if less than 3.5% of individuals with LTBI progress to active TB disease. CONCLUSIONS: LTBI screening with TST in low-prevalence settings may lead to overtreatment and increased expenditures. In this public health clinic, additional QFT-GIT testing of individuals referred for a positive TST was cost-effective.

Cardiovascular informatics: building a bridge to data harmony.

The search for new strategies for better understanding cardiovascular (CV) disease is a constant one, spanning multitudinous types of observations and studies. A comprehensive characterization of each disease state and its biomolecular underpinnings relies upon insights gleaned from extensive information collection of various types of data. Researchers and clinicians in CV biomedicine repeatedly face questions regarding which types of data may best answer their questions, how to integrate information from multiple datasets of various types, and how to adapt emerging advances in machine learning and/or artificial intelligence to their needs in data processing. Frequently lauded as a field with great practical and translational potential, the interface between biomedical informatics and CV medicine is challenged with staggeringly massive datasets. Successful application of computational approaches to decode these complex and gigantic amounts of information becomes an essential step toward realizing the desired benefits. In this review, we examine recent efforts to adapt informatics strategies to CV biomedical research: automated information extraction and unification of multifaceted -omics data. We discuss how and why this interdisciplinary space of CV Informatics is particularly relevant to and supportive of current experimental and clinical research. We describe in detail how open data sources and methods can drive discovery while demanding few initial resources, an advantage afforded by widespread availability of cloud computing-driven platforms. Subsequently, we provide examples of how interoperable computational systems facilitate exploration of data from multiple sources, including both consistently formatted structured data and unstructured data. Taken together, these approaches for achieving data harmony enable molecular phenotyping of CV diseases and unification of CV knowledge.

Heterogeneity of proteome dynamics between connective tissue phases of adult tendon.

Maintenance of connective tissue integrity is fundamental to sustain function, requiring protein turnover to repair damaged tissue. However, connective tissue proteome dynamics remain largely undefined, as do differences in turnover rates of individual proteins in the collagen and glycoprotein phases of connective tissue extracellular matrix (ECM). Here, we investigate proteome dynamics in the collagen and glycoprotein phases of connective tissues by exploiting the spatially distinct fascicular (collagen-rich) and interfascicular (glycoprotein-rich) ECM phases of tendon. Using isotope labelling, mass spectrometry and bioinformatics, we calculate turnover rates of individual proteins within rat Achilles tendon and its ECM phases. Our results demonstrate complex proteome dynamics in tendon, with ~1000 fold differences in protein turnover rates, and overall faster protein turnover within the glycoprotein-rich interfascicular matrix compared to the collagen-rich fascicular matrix. These data provide insights into the complexity of proteome dynamics in tendon, likely required to maintain tissue homeostasis.

Muscles are anchored to bones through specialized tissues called tendons. Made of bundles of fibers (or fascicles) linked together by an 'interfascicular' matrix, healthy tendons are required for organisms to move properly. Yet, these structures are constantly exposed to damage: the interfascicular matrix, in particular, is highly susceptible to injury as it allows the fascicles to slide on each other. One way to avoid damage could be for the body to continually replace proteins in tendons before they become too impaired. However, the way proteins are renewed in these structures is currently not well understood ­ indeed, it has long been assumed that almost no protein turnover occurs in tendons. In particular, it is unknown whether proteins in the interfascicular matrix have a higher turn over than those in the fascicles. To investigate, Choi, Simpson et al. fed rats on water carrying a molecular label that becomes integrated into new proteins. Analysis of individual proteins from the rats' tendons showed great variation in protein turnover, with some replaced every few days and others only over several years. This suggests that protein turnover is actually an important part of tendon health. In particular, the results show that turnover is higher in the interfascicular matrix, where damage is expected to be more likely. Protein turnover also plays a part in conditions such as cancer, heart disease and kidney disease. Using approaches like the one developed by Choi, Simpson et al. could help to understand how individual proteins are renewed in a range of diseases, and how to design new treatments.

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues.

Flow-Responsive Vascular Endothelial Growth Factor Receptor-Protein Kinase C Isoform Epsilon Signaling Mediates Glycolytic Metabolites for Vascular Repair.

AIMS: Hemodynamic shear stress participates in maintaining vascular redox status. Elucidating flow-mediated endothelial metabolites enables us to discover metabolic biomarkers and therapeutic targets. We posited that flow-responsive vascular endothelial growth factor receptor (VEGFR)-protein kinase C isoform epsilon (PKCɛ)-6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3) signaling modulates glycolytic metabolites for vascular repair. RESULTS: Bidirectional oscillatory flow (oscillatory shear stress [OSS]: 0.1 ± 3 dyne·cm-2 at 1 Hz) upregulated VEGFR-dependent PKCɛ expression to a greater degree than did unidirectional pulsatile flow (pulsatile shear stress [PSS]: 23 ± 8 dyne·cm-2 at 1 Hz) in human aortic endothelial cells (p < 0.05, n = 3). PSS and OSS further upregulated PKCɛ-dependent PFKFB3 expression for glycolysis (p < 0.05, n = 4). Constitutively active PKCɛ increased, whereas dominant-negative PKCɛ reduced both basal and maximal extracellular acidification rates for glycolytic flux (p < 0.01, n = 4). Metabolomic analysis demonstrated an increase in PKCɛ-dependent glycolytic metabolite, dihydroxyacetone (DHA), but a decrease in gluconeogenic metabolite, aspartic acid (p < 0.05 vs. control, n = 6). In a New Zealand White rabbit model, both PKCɛ and PFKFB3 immunostaining was prominent in the PSS- and OSS-exposed aortic arch and descending aorta. In a transgenic Tg(flk-1:EGFP) zebrafish model, GATA-1a morpholino oligonucleotide injection (to reduce viscosity-dependent shear stress) impaired vascular regeneration after tail amputation (p < 0.01, n = 20), which was restored with PKCɛ messenger RNA (mRNA) rescue (p < 0.05, n = 5). As a corollary, siPKCɛ inhibited tube formation and vascular repair, which were restored by DHA treatment in our Matrigel and zebrafish models. Innovation and Conclusion: Flow-sensitive VEGFR-PKCɛ-PFKFB3 signaling increases the glycolytic metabolite, dihydroxyacetone, to promote vascular repair. Antioxid. Redox Signal. 28, 31-43.

Respiratory abnormalities resulting from midcervical spinal cord injury and their reversal by serotonin 1A agonists in conscious rats.

Respiratory dysfunction after cervical spinal cord injury (SCI) has not been examined experimentally using conscious animals, although clinical SCI most frequently occurs in midcervical segments. Here, we report a C5 hemicontusion SCI model in rats with abnormalities that emulate human post-SCI pathophysiology, including spontaneous recovery processes. Post-C5 SCI rats demonstrated deficits in minute ventilation (Ve) responses to a 7% CO2 challenge that correlated significantly with lesion severities (no injury or 12.5, 25, or 50 mm x 10 g weight drop; New York University impactor; p < 0.001) and ipsilateral motor neuron loss (p = 0.016). Importantly, C5 SCI resulted in at least 4 weeks of respiratory abnormalities that ultimately recovered afterward. Because serotonin is involved in respiration-related neuroplasticity, we investigated the impact of activating 5-HT1A receptors on post-C5 SCI respiratory dysfunction. Treatment with the 5-HT1A agonist 8-hydroxy-2-(di-n-propylmino)tetralin (8-OH DPAT) (250 microg/kg, i.p.) restored hypercapnic Ve at 2 and 4 weeks after injury (i.e., approximately 39.2% increase vs post-SCI baseline; p < or = 0.033). Improvements in hypercapnic Ve response after single administration of 8-OH DPAT were dose dependent and lasted for approximately 4 h(p < or = 0.038 and p < or = 0.024, respectively). Treatment with another 5-HT1A receptor agonist, buspirone (1.5 mg/kg, i.p.), replicated the results, whereas pretreatment with a 5-HT1A-specific antagonist, 4-iodo-N-[2-[4(methoxyphenyl)-1-piperazinyl]ethyl]-N-2-pyridinyl-benzamide (3 mg/kg, i.p.) given 20 min before 8-OH DPAT negated the effect of 8-OH DPAT. These results imply a potential clinical use of 5-HT1A agonists for post-SCI respiratory disorders.

Therapeutic effects of clenbuterol in a murine model of amyotrophic lateral sclerosis.

We investigated the effects of clenbuterol, a beta2-adrenoceptor agonist with known anabolic and neuroprotective properties, on G93A-SOD1 mice, a transgenic murine model of familial amyotrophic lateral sclerosis (ALS). Relative to saline-treated vehicle controls (0.2 ml/kg/day; i.p.), early pathologic G93A-SOD1 mice treated with clenbuterol (1.5 mg/kg/day; i.p.) demonstrated a delayed onset of hindlimb signs as measured by rotarod performance, slowed disease progression, as well as trends toward mitigated losses of lumbar motoneurons and body weight. Responses in female G93A-SOD1 mice were favorable to those of males, suggesting synergistic effects between clenbuterol and sex-specific factors. Overall, our data suggest that clenbuterol offers therapeutic effects on ALS-related neuromuscular degeneration.

Public transportation and tuberculosis transmission in a high incidence setting.

BACKGROUND: Tuberculosis (TB) transmission may occur with exposure to an infectious contact often in the setting of household environments, but extra-domiciliary transmission also may happen. We evaluated if using buses and/or minibuses as public transportation was associated with acquiring TB in a high incidence urban district in Lima, Peru. METHODS: Newly diagnosed TB cases with no history of previous treatment and community controls were recruited from August to December 2008 for a case-control study. Crude and adjusted odd ratios (OR) and 95% confidence intervals (CI) were calculated using logistic regression to study the association between bus/minibus use and TB risk. RESULTS: One hundred forty TB cases and 80 controls were included. The overall use of buses/minibuses was 44.9%; 53.3% (72/135) among cases and 30.4% (24/79) among controls [OR: 3.50, (95% CI: 1.60-7.64)]. In the TB group, 25.7% (36/140) of subjects reported having had a recent household TB contact, and 13% (18/139) reported having had a workplace TB contact; corresponding figures for controls were 3.8% (3/80) and 4.1% (3/73), respectively[OR: 8.88 (95% CI: 2.64-29.92), and OR: 3.89 (95% CI: 1.10-13.70)]. In multivariate analyses, age, household income, household contact and using buses/minibuses to commute to work were independently associated with TB [OR for bus/minibus use: 11.8 (95% CI: 1.45-96.07)]. CONCLUSIONS: Bus/minibus use to commute to work is associated with TB risk in this high-incidence, urban population in Lima, Peru. Measures should be implemented to prevent TB transmission through this exposure.

ProteinInferencer: Confident protein identification and multiple experiment comparison for large scale proteomics projects.

Shotgun proteomics generates valuable information from large-scale and target protein characterizations, including protein expression, protein quantification, protein post-translational modifications (PTMs), protein localization, and protein-protein interactions. Typically, peptides derived from proteolytic digestion, rather than intact proteins, are analyzed by mass spectrometers because peptides are more readily separated, ionized and fragmented. The amino acid sequences of peptides can be interpreted by matching the observed tandem mass spectra to theoretical spectra derived from a protein sequence database. Identified peptides serve as surrogates for their proteins and are often used to establish what proteins were present in the original mixture and to quantify protein abundance. Two major issues exist for assigning peptides to their originating protein. The first issue is maintaining a desired false discovery rate (FDR) when comparing or combining multiple large datasets generated by shotgun analysis and the second issue is properly assigning peptides to proteins when homologous proteins are present in the database. Herein we demonstrate a new computational tool, ProteinInferencer, which can be used for protein inference with both small- or large-scale data sets to produce a well-controlled protein FDR. In addition, ProteinInferencer introduces confidence scoring for individual proteins, which makes protein identifications evaluable. This article is part of a Special Issue entitled: Computational Proteomics.

Lysine ubiquitination and acetylation of human cardiac 20S proteasomes.

PURPOSE: Altered proteasome functions are associated with multiple cardiomyopathies. While the proteasome targets polyubiquitinated proteins for destruction, it itself is modifiable by ubiquitination. We aim to identify the exact ubiquitination sites on cardiac proteasomes and examine whether they are also subject to acetylations. EXPERIMENTAL DESIGN: Assembled cardiac 20S proteasome complexes were purified from five human hearts with ischemic cardiomyopathy, then analyzed by high-resolution MS to identify ubiquitination and acetylation sites. We developed a library search strategy that may be used to complement database search in identifying PTM in different samples. RESULTS: We identified 63 ubiquitinated lysines from intact human cardiac 20S proteasomes. In parallel, 65 acetylated residues were also discovered, 39 of which shared with ubiquitination sites. CONCLUSION AND CLINICAL RELEVANCE: This is the most comprehensive characterization of cardiac proteasome ubiquitination to date. There are significant overlaps between the discovered ubiquitination and acetylation sites, permitting potential crosstalk in regulating proteasome functions. The information presented here will aid future therapeutic strategies aimed at regulating the functions of cardiac proteasomes.

Immediate hypersensitivity reactions following monovalent 2009 pandemic influenza A (H1N1) vaccines: reports to VAERS.

BACKGROUND: Hypersensitivity disorders following vaccinations are a cause for concern. OBJECTIVE: To determine the type and rate by age, gender, and vaccine received for reported hypersensitivity reactions following monovalent 2009 pandemic influenza A (H1N1) vaccines. DESIGN: A systematic review of reports to the Vaccine Adverse Event Reporting System (VAERS) following monovalent 2009 pandemic influenza A (H1N1) vaccines. SETTING/PATIENTS: US Civilian reports following vaccine received from October 1, 2009 through May 31, 2010. MEASUREMENTS: Age, gender, vaccines received, diagnoses, clinical signs, and treatment were reviewed by nurses and physicians with expertise in vaccine adverse events. A panel of experts, including seven allergists reviewed complex illnesses and those with conflicting evidence for classification of the event. RESULTS: Of 1984 reports, 1286 were consistent with immediate hypersensitivity disorders and 698 were attributed to anxiety reactions, syncope, or other illnesses. The female-to-male ratio was ≥4:1 for persons 20-to-59 years of age, but approximately equal for children under 10. One hundred eleven reports met Brighton Collaboration criteria for anaphylaxis; only one-half received epinephrine for initial therapy. The overall rate of reported hypersensitivity reactions was 10.7 per million vaccine doses distributed, with a 2-fold higher rate for live vaccine. LIMITATIONS: Underreporting, especially of mild events, would result in an underestimate of the true rate of immediate hypersensitivity reactions. Selective reporting of events in adult females could have resulted in higher rates than reported for males. CONCLUSIONS: Adult females may be at higher risk of hypersensitivity reactions after influenza vaccination than men. Although the risk of hypersensitivity reactions following 2009 pandemic influenza A (H1N1) vaccines was low, all clinics administering vaccines should be familiar with treatment guidelines for these adverse events, including the use of intramuscular epinephrine early in the course of serious hypersensitivity reactions.

Self-reported risks for multiple-drug resistance among new tuberculosis cases: implications for drug susceptibility screening and treatment.

BACKGROUND: Multiple drug-resistance in new tuberculosis (TB) cases accounts for the majority of all multiple drug-resistant TB (MDR-TB) worldwide. Effective control requires determining which new TB patients should be tested for MDR disease, yet the effectiveness of global screening recommendations of high-risk groups is unknown. METHODS: Sixty MDR-TB cases with no history of previous TB treatment, 80 drug-sensitive TB and 80 community-based controls were recruited in Lima, Peru between August and December, 2008 to investigate whether recommended screening practices identify individuals presenting with MDR-TB. Odd ratios (OR) and 95% confidence intervals (CI) were calculated using logistic regression to study the association of potential risk factors with case/control variables. RESULTS: MDR-TB cases did not differ from drug-sensitive TB and community controls in rates of human immunodeficiency virus infection, reported hospital or prison visits in the 3 years prior to diagnosis. MDR-TB cases were more likely than drug-sensitive TB controls to have had a recent MDR-TB household contact (OR 4.66, (95% CI 1.56-13.87)); however, only 15 cases (28.3%) reported this exposure. In multivariate modeling, recent TB household contact, but not contact with an MDR-TB case, remained predictive of MDR-TB, OR 7.47, (95% CI 1.91-29.3). Living with a partner rather than parents was associated with a lower risk of MDR-TB, OR 0.15, (95% CI 0.04-0.51). CONCLUSION: Targeted drug susceptibility testing (DST) linked to reported MDR-TB contact or other high-risk exposures does not identify the majority of new TB cases with MDR disease in Lima where it is endemic. All new TB cases should be screened with DST to identify MDR patients. These findings are likely applicable to other regions with endemic MDR-TB.

Overview of the Clinical Consult Case Review of adverse events following immunization: Clinical Immunization Safety Assessment (CISA) network 2004-2009.

BACKGROUND: In 2004 the Clinical Consult Case Review (CCCR) working group was formed within the CDC-funded Clinical Immunization Safety Assessment (CISA) Network to review individual cases of adverse events following immunizations (AEFI). METHODS: Cases were referred by practitioners, health departments, or CDC employees. Vaccine Adverse Event Reporting System (VAERS) searches and literature reviews for similar cases were performed prior to review. After CCCR discussion, AEFI were assessed for a causal relationship with vaccination and recommendations regarding future immunizations were relayed back to the referring physicians. In 2010, surveys were sent to referring physicians to determine the utility and effectiveness of the CCCR service. RESULTS: CISA investigators reviewed 76 cases during 68 conference calls between April 2004 and December 2009. Almost half of the cases (35/76) were neurological in nature. Similar AEFI for the specific vaccines received were discovered for 63 cases through VAERS searches and for 38 cases through PubMed searches. Causality assessment using the modified WHO criteria resulted in classifying 3 cases as definitely related to vaccine administration, 12 as probably related, 16 as possibly related, 18 as unlikely related, 10 as unrelated, and 17 had insufficient information to assign causality. The physician satisfaction survey was returned by 30 (57.7%) of those surveyed and a majority of respondents (93.3%) felt that the CCCR service was useful. CONCLUSIONS: The CCCR provides advice about AEFI to practitioners, assigns potential causality, and contributes to an improved understanding of adverse health events following immunizations.

Functional recovery in T13-L1 hemisected rats resulting from peripheral nerve rerouting: role of central neuroplasticity.

BACKGROUND: Functional improvements after spinal cord injury (SCI) have been reported anecdotally following neurotization, in other words, rerouting nerves proximal to injured cord segments to distal neuromuscular targets, although the underlying mechanisms remain largely unknown. AIM: To test our hypothesis that neurotization-mediated recovery is primarily attributable to CNS neuroplasticity that therefore manifests optimal response during particular therapeutic windows, we anastomosed the T12 intercostal nerve to the ipsilateral L3 nerve root 1-4 weeks after T13-L1 midline hemisection in rats. RESULTS: While axonal tracing and electromyography revealed limited reinnervation in the target muscles, neurobehavioral function, as assessed by locomotion, extensor postural thrust and sciatic functional index of SCI rats receiving neurotization 7-10 days postinjury (n = 11), recovered to levels close to non-SCI controls with neurotization only (n = 3), beginning 3-5 weeks postanastomosis. Conversely, hindlimb deficits were unchanged in hemisected controls with sham neurotization (n = 7) or 4 weeks-delayed neurotization (n = 3) and in rats that had undergone T13-L1 transection plus bilateral anastomoses (n = 6). CONCLUSION: Neurotized SCI animals demonstrated multiparameters of neural reorganization in the distal lumbar cord, including enhanced proliferation of endogenous neural stem cells, increased immunoreactivity of serotonin and synaptophysin, and neurite growth/sprouting, suggesting that anastomosing functional nerves with the nerve stump emerging distal to the hemisection stimulates neuroplasticity in the dysfunctional spinal cord. Our conclusion is validated by the fact that severance of the T13-L1 contralateral cord abolished the postanastomosis functional recovery. Neurotization and its neuroplastic sequelae need to be explored further to optimize clinical strategies of post-SCI functional repair.

Single muscle fiber size and contractility after spinal cord injury in rats.

Spinal cord injury (SCI) results in muscle weakness but the degree of impairment at the level of single fibers is not known. The purpose of this study was to examine the effects of T9-level SCI on single muscle fibers from the tibialis anterior of rats. Significant decreases in cross-sectional area (CSA), maximal force (Po), and specific force (SF = Po/CSA) were noted at 2 weeks. Atrophy and force-generating capacity were reversed at 4 weeks, but SF remained impaired. Maximum shortening velocity (Vo) did not change after injury. SCI thus appears to affect various contractile properties of single muscle fibers differently. Normal cage activity may partially restore function but new interventions are needed to restore muscle fiber quality.