Derivation of internal dose-based thresholds of toxicological concern for occupational inhalation exposure to systemically acting organic chemicals

This study aimed at deriving occupational thresholds of toxicological concern for inhalation exposure to systemically-acting organic chemicals using predicted internal doses. The latter were also used to evaluate the quantitative relationship between occupational exposure limit and internal dose. Three internal dose measures were identified for investigation: (i) the daily area under the venous blood concentration vs. time curve, (ii) the daily rate of the amount of parent chemical metabolized, and (iii) the maximum venous blood concentration at the end of an 8-hr work shift. A dataset of 276 organic chemicals with 8-hr threshold limit values-time-weighted average was compiled along with their molecular structure and Cramer classes (Class I: low toxicity, Class II: intermediate toxicity, Class III: suggestive of significant toxicity). Using a human physiologically-based pharmacokinetic model, the three identified dose metrics were predicted for an 8-hr occupational inhalation exposure to the threshold limit value for each chemical. Distributional analyses of the predicted dose metrics were performed to identify the percentile values corresponding to the occupational thresholds of toxicological concern. Also, simple linear regression analyses were performed to evaluate the relationship between the 8-hr threshold limit value and each of the predicted dose metrics, respectively. No threshold of toxicological concern could be derived for class II due to few chemicals. Based on the daily rate of the amount of parent chemical metabolized, the proposed internal dose-based occupational thresholds of toxicological concern were 5.61?×?10^?2 and 9?×?10^?4 mmol/d at the 10th percentile level for classes I and III, respectively, while they were 4.55?×?10^?1 and 8.5?×?10^?3 mmol/d at the 25th percentile level. Even though high and significant correlations were observed between the 8-hr threshold limit values and the predicted dose metrics, the one with the rate of the amount of chemical metabolized was remarkable regardless of the Cramer class (r² = 0.81; n = 276). The proposed internal dose-based occupational thresholds of toxicological concern are potentially useful for screening-level assessments as well as prioritization within an integrated occupational risk assessment framework.     

Pump Speed Optimization in Patients Implanted With the HeartMate 3 Device

Background

Pump speed optimization in patients implanted with a ventricular assist device represents a major challenge during the follow-up period. We present our findings on whether combined invasive hemodynamic ramp tests and cardiopulmonary exercise testing (CPX) can help optimize patient management.

Biomarkers on patient T cells diagnose active tuberculosis and monitor treatment response

BACKGROUND. The identification and treatment of individuals with tuberculosis (TB) is a global public health priority. Accurate diagnosis of pulmonary active TB (ATB) disease remains challenging and relies on extensive medical evaluation and detection of Mycobacterium tuberculosis (Mtb) in the patient’s sputum. Further, the response to treatment is monitored by sputum culture conversion, which takes several weeks for results. Here, we sought to identify blood-based host biomarkers associated with ATB and hypothesized that immune activation markers on Mtb-specific CD4⁺ T cells would be associated with Mtb load in vivo and could thus provide a gauge of Mtb infection.METHODS. Using polychromatic flow cytometry, we evaluated the expression of immune activation markers on Mtb-specific CD4⁺ T cells from individuals with asymptomatic latent Mtb infection (LTBI) and ATB as well as from ATB patients undergoing anti-TB treatment.RESULTS. Frequencies of Mtb-specific IFN-γ⁺CD4⁺ T cells that expressed immune activation markers CD38 and HLA-DR as well as intracellular proliferation marker Ki-67 were substantially higher in subjects with ATB compared with those with LTBI. These markers accurately classified ATB and LTBI status, with cutoff values of 18%, 60%, and 5% for CD38⁺IFN-γ⁺, HLA-DR⁺IFN-γ⁺, and Ki-67⁺IFN-γ⁺, respectively, with 100% specificity and greater than 96% sensitivity. These markers also distinguished individuals with untreated ATB from those who had successfully completed anti-TB treatment and correlated with decreasing mycobacterial loads during treatment.CONCLUSION. We have identified host blood-based biomarkers on Mtb-specific CD4⁺ T cells that discriminate between ATB and LTBI and provide a set of tools for monitoring treatment response and cure.TRIAL REGISTRATION. Registration is not required for observational studies.FUNDING. This study was funded by Emory University, the NIH, and the Yerkes National Primate Center.     

Variation of resting energy expenditure after the first chemotherapy cycle in acute leukemia patients

Changes in resting energy expenditure (REE) of cancer patients vary depending on type of tumor, treatment time point and kind of treatment. Little is known about REE of acute leukemia adult patients after treatment, especially with results related to body weight or fat free mass (FFM). This study aimed to assess changes in REE of acute leukemia adult patients before and after the first remission induction. Evaluation of REE was performed by indirect calorimetry and predicted REE was calculated by Harris-Benedict equation. Weight and height were measured and compared to a control group of healthy individuals. FFM was assessed by bioelectrical impedance for adjusting REE values. We evaluated 18 patients and 26 healthy individuals. At diagnosis, patients presented REE, REE/weight, and REE/FFM higher than the controls. Reductions of REE, REE/weight, and REE/FFM were also observed in patients after the first cycle of chemotherapy. The predicted REE for the patients group showed significant lower value compared with measured REE. Before the first cycle of chemotherapy REE was increased but undergoes a reduction after treatment, reaching values similar to the controls. For predictive Harris-Benedict equation, stress factors should be added to avoid underestimation of REE before and after chemotherapy.