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BackgroundSimple biomarkers are required to identify TB in both HIV−TB+ and HIV+TB+ patients. Earlier studies have identified the M. tuberculosis Malate Synthase (MS) and MPT51 as immunodominant antigens in TB patients. One goal of these investigations was to evaluate the sensitivity and specificity of anti-MS and –MPT51 antibodies as biomarkers for TB in HIV−TB+ and HIV+TB+ patients from a TB-endemic setting. Earlier studies also demonstrated the presence of these biomarkers during incipient subclinical TB. If these biomarkers correlate with incipient TB, their prevalence should be higher in asymptomatic HIV+ subjects who are at a high-risk for TB. The second goal was to compare the prevalence of these biomarkers in asymptomatic, CD4+ T cell-matched HIV+TB− subjects from India who are at high-risk for TB with similar subjects from US who are at low-risk for TB.Methods and ResultsAnti-MS and -MPT51 antibodies were assessed in sera from 480 subjects including PPD+ or PPD− healthy subjects, healthy community members, and HIV−TB+ and HIV+TB+ patients from India. Results demonstrate high sensitivity (∼80%) of detection of smear-positive HIV−TB+ and HIV+TB+ patients, and high specificity (>97%) with PPD+ subjects and endemic controls. While ∼45% of the asymptomatic HIV+TB− patients at high-risk for TB tested biomarker-positive, >97% of the HIV+TB− subjects at low risk for TB tested negative. Although the current studies are hampered by lack of knowledge of the outcome, these results provide strong support for the potential of these biomarkers to detect incipient, subclinical TB in HIV+ subjects.ConclusionsThese biomarkers provide high sensitivity and specificity for TB diagnosis in a TB endemic setting. Their performance is not compromised by concurrent HIV infection, site of TB and absence of pulmonary manifestations in HIV+TB+ patients. Results also demonstrate the potential of these biomarkers for identifying incipient subclinical TB in HIV+TB− subjects at high-risk for TB.

Abstract:The battery critical functions such as State-of-Charge (SoC) and State-of-Health (SoH) estimations, over-current, and over-/under-voltage protections mainly depend on current and voltage sensor measurements. Therefore, it is imperative to develop a reliable sensor fault diagnosis scheme to guarantee the battery performance, safety and life. This paper presents a systematic model-based fault diagnosis scheme for a battery cell to detect current or voltage sensor faults. The battery model is developed based on the equivalent circuit technique. For the diagnostic scheme implementation, the extended Kalman filter (EKF) is used to estimate the terminal voltage of battery cell, and the residual carrying fault information is then generated by comparing the measured and estimated voltage. Further, the residual is evaluated by a statistical inference method that determines the presence of a fault. To highlight the importance of battery sensor fault diagnosis, the effects of sensors faults on battery SoC estimation and possible influences are analyzed. Finally, the effectiveness of the proposed diagnostic scheme is experimentally validated, and the results show that the current or voltage sensor fault can be accurately detected.