BackgroundThe incidence of tuberculosis (TB) is high among human immunodeficiency virus (HIV) infected Ugandans. Recent evidence suggests that Chronic Pulmonary Aspergillosis and Aspergillus sensitisation might be responsible for significant mortality in patients treated for tuberculosis in Uganda.MethodsWe retrieved and tested paired serum aliquots for 101 HIV-TB co-infected patients at the beginning and week 24 of TB treatment. We tested samples for Aspergillus-specific immunoglobulin G (IgG) and immunoglobulin E (IgE) using ImmunoCAP®; and Aspergillus-specific IgG and total serum IgE using Immulite® immunoassays. We compared antibody levels between baseline and week 24, relating them to selected baseline characteristics.Results10% of the patients had elevated Aspergillus-specific IgE (Aspergillus sensitization) and Aspergillus-specific IgG antibodies were elevated in 9% of the patients at the end of TB treatment. There was a significant fall in the Aspergillus-specific IgG antibody levels between baseline and week 24 (P = 0.02). Patients with cluster of differentiation 4 (CD4) T-cell count <100 cells/μl and those who were not on anti-retroviral therapy at baseline had more elevated Aspergillus-specific IgG antibodies (P = 0.01, P = 0.03). The ImmunoCAP® Aspergillus-specific IgG antibody titres were higher at week 24 than baseline with more positives at week 24; even though the difference in means was small. However, this difference was statistically significant (P = 0.02). Pulmonary infiltrates were the commonest x-ray abnormality and only 5% of the patients had pulmonary cavities on chest x-ray at week 24.ConclusionThese results suggest that Aspergillus infection may complicate active pulmonary TB and further studies including fungal culture and thoracic imaging may now be indicated to measure the prevalence of pulmonary aspergillosis complicating tuberculosis.Trial registrationThe SOUTH trial was registered prospectively. ClinicalTrials.gov Identifier: NCT01782950; Registration date: 4th February 2013; Last verified: 13th April 2015.Electronic supplementary materialThe online version of this article (10.1186/s12890-017-0500-9) contains supplementary material, which is available to authorized users.

Antituberculosis drugs display large pharmacokinetic variability, which may be influenced by several factors, including body size, genetic differences, and drug-drug interactions. We set out to determine these factors, quantify their effect, and determine the dose adjustments necessary for optimal drug concentrations. HIVinfected Ugandan adults with pulmonary tuberculosis treated according to international weight-based dosing guidelines underwent pharmacokinetic sampling (1, 2, and 4 h after drug intake) 2, 8, and 24 weeks after treatment initiation. Between May 2013 and November 2015, we enrolled 268 patients (148 males) with a median weight of 53.5 (interquartile range [IQR], 47.5 to 59.0) kg and a median age of 35 (IQR, 29 to 40) years. Population pharmacokinetic modeling was used to interpret the data and revealed that patients weighing Ͻ55 kg achieved lower concentrations than those in higher weight bands for all drugs in the regimen. The models predicted that this imbalance could be solved with a dose increment of one fixed-dose combination (FDC) tablet for the weight bands of 30 to 37 and 38 to 54 kg. Additionally, the concomitant use of efavirenz increased isoniazid clearance by 24.1%, while bioavailability and absorption of rifampin and isoniazid varied up to 30% in patients on different formulations. Current dosing guidelines lead to lower drug exposure in patients in the lower weight bands. Simply adding one FDC tablet to current weight band-based dosing would address these differences in exposure and possibly improve outcomes. Lower isoniazid exposures due to efavirenz deserve further attention, as does the quality of currently used drug formulations of anti-TB drugs. (This study has been registered at ClinicalTrials.gov under identifier NCT01782950.)

Deep Learning analysis of CXR photographs is a promising tool. Further efforts are needed to build larger, high-quality data sets to achieve better diagnostic performance.