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Background Successful tuberculosis (TB) treatment is necessary for disease control. The World Health Organization (WHO) has a target TB treatment success rate of ≥90%. We assessed whether the different types of unfavorable TB treatment outcome had different predictors. Methods Using data from Regional Prospective Observational Research for Tuberculosis-Brazil, we evaluated biological and behavioral factors associated with each component of unsuccessful TB outcomes, recently updated by WHO (death, loss to follow-up [LTFU], and treatment failure). We included culture-confirmed, drug-susceptible, pulmonary TB participants receiving standard treatment in 2015–2019. Multinomial logistic regression models with inverse probability weighting were used to evaluate the distinct determinants of each unsuccessful outcome. Results Of 915 participants included, 727 (79%) were successfully treated, 118 (13%) were LTFU, 44 (5%) had treatment failure, and 26 (3%) died. LTFU was associated with current drug-use (adjusted odds ratio [aOR] = 5.3; 95% confidence interval [CI], 3.0–9.4), current tobacco use (aOR = 2.9; 95% CI, 1.7–4.9), and being a person with HIV (PWH) (aOR = 2.0; 95% CI, 1.1–3.5). Treatment failure was associated with PWH (aOR = 2.7; 95% CI, 1.2–6.2) and having diabetes (aOR = 2.2; 95% CI, 1.1–4.4). Death was associated with anemia (aOR = 5.3; 95% CI, 1.4–19.7), diabetes (aOR = 3.1; 95% CI, 1.4–6.7), and PWH (aOR = 3.9; 95% CI, 1.3–11.4). Direct observed therapy was protective for treatment failure (aOR = 0.5; 95% CI,.3–.9) and death (aOR = 0.5; 95% CI,.2–1.0). Conclusions The treatment success rate was below the WHO target. Behavioral factors were most associated with LTFU, whereas clinical comorbidities were correlated with treatment failure and death. Because determinants of unsuccessful outcomes are distinct, different intervention strategies may be needed to improve TB outcomes. [ABSTRACT FROM AUTHOR]

Background Despite widespread availability of curative therapy, tuberculosis (TB) treatment outcomes remain suboptimal. Clinical prediction models can inform treatment strategies to improve outcomes. Using baseline clinical data, we developed a prediction model for unsuccessful TB treatment outcome and evaluated the incremental value of human immunodeficiency virus (HIV)–related severity and isoniazid acetylator status. Methods Data originated from the Regional Prospective Observational Research for Tuberculosis Brazil cohort, which enrolled newly diagnosed TB patients in Brazil from 2015 through 2019. This analysis included participants with culture-confirmed, drug-susceptible pulmonary TB who started first-line anti-TB therapy and had ≥12 months of follow-up. The end point was unsuccessful TB treatment: composite of death, treatment failure, regimen switch, incomplete treatment, or not evaluated. Missing predictors were imputed. Predictors were chosen via bootstrapped backward selection. Discrimination and calibration were evaluated with c-statistics and calibration plots, respectively. Bootstrap internal validation estimated overfitting, and a shrinkage factor was applied to improve out-of-sample prediction. Incremental value was evaluated with likelihood ratio–based measures. Results Of 944 participants, 191 (20%) had unsuccessful treatment outcomes. The final model included 7 baseline predictors: hemoglobin, HIV infection, drug use, diabetes, age, education, and tobacco use. The model demonstrated good discrimination (c-statistic = 0.77; 95% confidence interval,.73–.80) and was well calibrated (optimism-corrected intercept and slope, –0.12 and 0.89, respectively). HIV-related factors and isoniazid acetylation status did not improve prediction of the final model. Conclusions Using information readily available at treatment initiation, the prediction model performed well in this population. The findings may guide future work to allocate resources or inform targeted interventions for high-risk patients. [ABSTRACT FROM AUTHOR]

A by‐product of energy‐dispersive X‐ray fluorescence (EDXRF)‐analysis consists of using the ratios of selected X‐ray peaks to determine the thickness of multilayered objects. Three different methods were developed in the past, all because the two main K or L X‐lines from an EDXRF spectrum emitted by a chemical element have a distinct energy and are differently attenuated by an overlying layer. This specific subject has many papers dedicated, but only a few considerations were devoted to the limits of these methods, that is, the range, for example, of gold thickness that can be usefully determined by each method. This paper defines these limits in the specific case of thin gilding (with d < 1 μm). Three typical situations were considered, and the following artefacts were studied: two artefacts on gilded lead, where the gold leaf is superimposed to a white lead pigment, both in the painting "La Fornarina" of Raphael, dated 1520 AD, and in the imperial carriage of Dom Pedro II emperor of Brazil. In this last case, the white lead pigment was painted over the wood structure of the carriage; four artefacts on gilded copper, three crowns and a pendant, from the tomb of the Lady of Cao, from the Moche civilization of the North of Peru, dated around 350 AD; finally, an artefact on gilded silver, a vase from the Chavín civilization of the North of Peru, dated around 1000‐200 BC. [ABSTRACT FROM AUTHOR]

Accurate and precise rainfall records are crucial for hydrological applications and water resources management. The accuracy and continuity of ground-based time series rely on the density and distribution of rain gauges over territories. In the context of a decline of rain gauge distribution, how to optimize and design optimal networks is still an unsolved issue. In this work, we present a method to optimize a ground-based rainfall network using satellite-based observations, maximizing the information content of the network. We combine Climate Prediction Center MORPhing technique (CMORPH) observations at ungauged locations with an existing rain gauge network in the Rio das Velhas catchment, in Brazil. We use a greedy ranking algorithm to rank the potential locations to place new sensors, based on their contribution to the joint entropy of the network. Results show that the most informative locations in the catchment correspond to those areas with the highest rainfall variability and that satellite observations can be successfully employed to optimize rainfall monitoring networks. [ABSTRACT FROM AUTHOR]