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ObjectiveTo determine if chikungunya virus persists in synovial fluid after infection, potentially acting as a causative mechanism of persistent arthritis.MethodsWe conducted a cross-sectional study of 38 Colombian participants with clinical chikungunya virus infection during the 2014-2015 epidemic who reported chronic arthritis and 10 location-matched controls without chikungunya virus or arthritis. Prior chikungunya virus infection status was serologically confirmed, and the presence of synovial fluid chikungunya virus, viral RNA, and viral proteins was determined by viral culture, quantitative reverse transcription-polymerase chain reaction (qRT-PCR), and mass spectrometry, respectively. Biomarkers were assessed by multiplex analysis.ResultsPatients with serologically confirmed chikungunya arthritis (33 of 38 [87%]) were predominantly female (82%) and African Colombian (55%) or white Colombian (33%), with moderate disease activity (mean ± SD Disease Activity Score in 28 joints 4.52 ± 0.77) a median of 22 months after infection (interquartile range 21-23 months). Initial symptoms of chikungunya virus infection included joint pain (97%), swelling (97%), stiffness (91%), and fever (91%). The most commonly affected joints were the knees (87%), elbows (76%), wrists (75%), ankles (56%), fingers (56%), and toes (56%). Synovial fluid samples from all patients with chikungunya arthritis were negative for chikungunya virus on qRT-PCR, showed no viral proteins on mass spectrometry, and cultures were negative. Case and control plasma cytokine and chemokine concentrations did not differ significantly.ConclusionThis is one of the largest observational studies involving analysis of the synovial fluid of chikungunya arthritis patients. Synovial fluid analysis revealed no detectable chikungunya virus. This finding suggests that chikungunya virus may cause arthritis through induction of potential host autoimmunity, suggesting a role for immunomodulating agents in the treatment of chikungunya arthritis, or that low-level viral persistence exists in synovial tissue only and is undetectable in synovial fluid.

Zika virus is a member of the Spondweni serogroup, family Flaviviridae, genus Flavivirus (1,2). Since the initial isolation of the virus in 1947 (3), intermittent reports of Zika virus infection [...]

Abstract Background Time-of-flight mass spectrometry (TOF-MS) has the potential to provide non-invasive, high-throughput screening for cancers and other serious diseases via detection of protein biomarkers in blood or other accessible biologic samples. Unfortunately, this potential has largely been unrealized to date due to the high variability of measurements, uncertainties in the distribution of proteins in a given population, and the difficulty of extracting repeatable diagnostic markers using current statistical tools. With studies consisting of perhaps only dozens of samples, and possibly hundreds of variables, overfitting is a serious complication. To overcome these difficulties, we have developed a Bayesian inductive method which uses model-independent methods of discovering relationships between spectral features. This method appears to efficiently discover network models which not only identify connections between the disease and key features, but also organizes relationships between features--and furthermore creates a stable classifier that categorizes new data at predicted error rates. Results The method was applied to artificial data with known feature relationships and typical TOF-MS variability introduced, and was able to recover those relationships nearly perfectly. It was also applied to blood sera data from a 2004 leukemia study, and showed high stability of selected features under cross-validation. Verification of results using withheld data showed excellent predictive power. The method showed improvement over traditional techniques, and naturally incorporated measurement uncertainties. The relationships discovered between features allowed preliminary identification of a protein biomarker which was consistent with other cancer studies and later verified experimentally. Conclusions This method appears to avoid overfitting in biologic data and produce stable feature sets in a network model. The network structure provides additional information about the relationships among features that is useful to guide further biochemical analysis. In addition, when used to classify new data, these feature sets are far more consistent than those produced by many traditional techniques.

Tuberculosis (TB) is a major global health threat, resulting in an urgent unmet need for a rapid, non-sputum-based quantitative test to detect active Mycobacterium tuberculosis (Mtb) infections in clinically diverse populations and quickly assess Mtb treatment responses for emerging drug-resistant strains. We have identified Mtb-specific peptide fragments and developed a method to rapidly quantify their serum concentrations, using antibody-labeled and energy-focusing porous discoidal silicon nanoparticles (nanodisks) and high-throughput mass spectrometry (MS) to enhance sensitivity and specificity. NanoDisk-MS diagnosed active Mtb cases with high sensitivity and specificity in a case-control study with cohorts reflecting the complexity of clinical practice. Similar robust sensitivities were obtained for cases of culture-positive pulmonary TB (PTB; 91.3%) and extrapulmonary TB (EPTB; 92.3%), and the sensitivities obtained for culture-negative PTB (82.4%) and EPTB (75.0%) in HIV-positive patients significantly outperformed those reported for other available assays. NanoDisk-MS also exhibited high specificity (87.1-100%) in both healthy and high-risk groups. Absolute quantification of serum Mtb antigen concentration was informative in assessing responses to antimycobacterial treatment. Thus, a NanoDisk-MS assay approach could significantly improve the diagnosis and management of active TB cases, and perhaps other infectious diseases as well. [ABSTRACT FROM AUTHOR]