With increasing age, the immune system undergoes a remodeling process, affecting the shift of T cell subpopulations and the development of chronic low-grade inflammation. Clinically, this is characterized by increased susceptibility to infections or development of several diseases. Since lifestyle factors can play a significant role in reducing the hallmarks of immune aging and inflammation, we investigated the effect of a 6 week low-dose combined resistance and endurance training program. Forty participants (70.3 ± 5.0 years) were randomly assigned to either a training (TG) or control group (CG) and performed a controlled low-threshold and care-oriented 6-week-long combined resistance and endurance training program. Changes in anthropometrics as well as strength capacity were measured. In subgroups of TG and CG, T cells and their subpopulations (CD4⁺, CD8⁺, naïve, central, effector memory, T-EMRA) were analyzed by flow cytometry. The changes of various plasma cytokines, chemokines, growth factors and adipokines were analyzed by luminex assays. The exercise program was followed by an increase in strength capacities. Participants of TG showed an increase of the CD4⁺/CD8⁺ T cell ratio over time (p < 0.05). Significant decreases in systemic levels of interleukin (IL-) 6, IL-8, IL-10 and vascular endothelial growth factor (VEGF) (p < 0.05) were observed for participants of TG over time. Even short-term and low-threshold training can reduce some of the hallmarks of immune aging in elderly and thus could be beneficial to stimulate immunity. The specific characteristics of the program make it easily accessible to older people, who may benefit in the longer term in terms of their immunocompetence.

Voriconazole (VRC) is used as first line antifungal agent against invasive aspergillosis. Model-based approaches might optimize VRC therapy. This study aimed to investigate the predictive performance of pharmacokinetic models of VRC without pharmacogenetic information for their suitability for model-informed precision dosing. Seven PopPK models were selected from a systematic literature review. A total of 66 measured VRC plasma concentrations from 33 critically ill patients was employed for analysis. The second measurement per patient was used to calculate relative Bias (rBias), mean error (ME), relative root mean squared error (rRMSE) and mean absolute error (MAE) (i) only based on patient characteristics and dosing history (a priori) and (ii) integrating the first measured concentration to predict the second concentration (Bayesian forecasting). The a priori rBias/ME and rRMSE/MAE varied substantially between the models, ranging from −15.4 to 124.6%/−0.70 to 8.01 mg/L and from 89.3 to 139.1%/1.45 to 8.11 mg/L, respectively. The integration of the first TDM sample improved the predictive performance of all models, with the model by Chen (85.0%) showing the best predictive performance (rRMSE: 85.0%; rBias: 4.0%). Our study revealed a certain degree of imprecision for all investigated models, so their sole use is not recommendable. Models with a higher performance would be necessary for clinical use.

• EU guidelines claim the need for widespread availability of TDM. • TDM of antibiotics is recognized as an important element of antibiotic stewardship. • However, availability of analytical services for antibiotic TDM is limited. • ID-LC-MS/MS instruments are still mainly restricted to centralized facilities. • Hospital laboratories should implement ID-LC-MS/MS for TDM of antibiotics.

Background: The MeroRisk-calculator, an easy-to-use tool to determine the risk of meropenem target non-attainment after standard dosing (1000 mg; q8h), uses a patient’s creatinine clearance and the minimum inhibitory concentration (MIC) of the pathogen. In clinical practice, however, the MIC is rarely available. The objectives were to evaluate the MeroRisk-calculator and to extend risk assessment by including general pathogen sensitivity data. Methods: Using a clinical routine dataset (155 patients, 891 samples), a direct data-based evaluation was not feasible. Thus, in step 1, the performance of a pharmacokinetic model was determined for predicting the measured concentrations. In step 2, the PK model was used for a model-based evaluation of the MeroRisk-calculator: risk of target non-attainment was calculated using the PK model and agreement with the MeroRisk-calculator was determined by a visual and statistical (Lin’s concordance correlation coefficient (CCC)) analysis for MIC values 0.125–16 mg/L. The MeroRisk-calculator was extended to include risk assessment based on EUCAST-MIC distributions and cumulative-fraction-of-response analysis. Results: Step 1 showed a negligible bias of the PK model to underpredict concentrations (−0.84 mg/L). Step 2 revealed a high level of agreement between risk of target non-attainment predictions for creatinine clearances >50 mL/min (CCC = 0.990), but considerable deviations for patients <50 mL/min. For 27% of EUCAST-listed pathogens the median cumulative-fraction-of-response for the observed patients receiving standard dosing was < 90%. Conclusions: The MeroRisk-calculator was successfully evaluated: For patients with maintained renal function it allows a reliable and user-friendly risk assessment. The integration of pathogen-based risk assessment substantially increases the applicability of the tool.

The SARS-CoV-2 beta coronavirus is spreading globally with unprecedented consequences for modern societies. The early detection of infected individuals is a pre-requisite for all strategies aiming to contain the virus. Currently, purification of RNA from patient samples followed by RT-PCR is the gold standard to assess the presence of this single-strand RNA virus. However, these procedures are time consuming, require continuous supply of specialized reagents, and are prohibitively expensive in resource-poor settings. Here, we report an improved nucleic-acid-based approach to detect SARS-CoV-2, which alleviates the need to purify RNA, reduces handling steps, minimizes costs, and allows evaluation by non-specialized equipment. The use of unprocessed swap samples and the ability to detect as little as three viral genome equivalents is enabled by employing a heat-stable RNA- and DNA-dependent DNA polymerase, which performs the double task of stringent reverse transcription of RNA at elevated temperatures as well as PCR amplification of a SARS-CoV-2 specific target gene. As results are obtained within 2 hours and can be read-out by a hand-held LED-screen, this novel protocol will be of particular importance for large-scale virus surveillance in economically constrained settings.

Introduction: Angiopoietin II (Ang-II) plays a pivotal role in the development of microcirculatory dysfunction as it provokes endothelial barrier disruption in patients with sepsis or septic shock. In particular, those with acute kidney injury show high Ang-II concentrations. So far, it is unclear which covariates influence Ang-II concentration in the early phase of sepsis, especially if extracorporeal therapies also do. Methods: Ang-II concentrations were measured in 171 patients with sepsis after the first day of antibiotic treatment between 03/2013 and 01/2015. Ang-II was correlated with potential influencing factors (Spearman correlation). A multivariate model was established including the significant correlating parameters. The Mann–Whitney U test and the Kruskal–Wallis test were used to detect significant differences in Ang-II concentration. Results: The median Ang-II concentration was 8015 pg/mL (interquartile range (IQR): 5024–14,185). A total of forty patients were treated with kidney replacement therapy (KRT) and 20 were supported by venovenous extracorporeal membrane oxygenation (vv-ECMO). Sequential organ failure assessment (SOFA) score (r = 0.541), creatinine clearance (r = −0.467), urinary output (r = −0.289), interleukin (IL)-6 (r = 0.529), C-reactive protein (CRP) (r = 0.241), platelet count (r = −0.419), bilirubin (r = 0.565), lactate (r = 0.322), KRT (r = 0.451), and fluid balance (r = 0.373) significantly correlated with Ang-II concentration and were included in the multivariate model. There, creatinine clearance (p < 0.01, b = −26.3, 95% confidence interval (CI) −41.8–−10.8), fluid balance (p = 0.002, b = 0.92, 95% CI 0.33–1.51), and CRP (p = 0.004, b = 127.6, 95% CI 41.6–213.7) were associated with Ang-II concentration. Furthermore, patients with KRT (median: 15,219 pg/mL, IQR: 10,548–20,270) had significantly (p < 0.01) higher Ang-II concentrations than those with vv-ECMO support (median: 6412 pg/mL, IQR: 5246–10,257) or those without extracorporeal therapy (median: 7156 pg/mL, IQR: 4409–12,741). Conclusion: Increased CRP, positive fluid balance, and impaired kidney function were associated with higher Ang-II concentrations in critically ill patients in the early stage of sepsis in this post hoc analysis. In particular, patients with KRT had very high Ang-II concentrations, whereas the use of vv-ECMO was not related to higher Ang-II concentrations. The significance for clinical practice should be clarified by a prospective study with standardized measurements.