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Human cytomegalovirus (HCMV) latency is typically harmless but reactivation can be largely detrimental to immune compromised hosts. We modeled latency and reactivation using a traceable HCMV laboratory strain expressing the Gaussia luciferase reporter gene (HCMV/GLuc) in order to interrogate the viral modulatory effects on the human adaptive immunity. Humanized mice with long-term (more than 17 weeks) steady human T and B cell immune reconstitutions were infected with HCMV/GLuc and 7 weeks later were further treated with granulocyte-colony stimulating factor (G-CSF) to induce viral reactivations. Whole body bio-luminescence imaging analyses clearly differentiated mice with latent viral infections vs. reactivations. Foci of vigorous viral reactivations were detectable in liver, lymph nodes and salivary glands. The number of viral genome copies in various tissues increased upon reactivations and were detectable in sorted human CD14+, CD169+, and CD34+ cells. Compared with non-infected controls, mice after infections and reactivations showed higher thymopoiesis, systemic expansion of Th, CTL, Treg, and Tfh cells and functional antiviral T cell responses. Latent infections promoted vast development of memory CD4+ T cells while reactivations triggered a shift toward effector T cells expressing PD-1. Further, reactivations prompted a marked development of B cells, maturation of IgG+ plasma cells, and HCMV-specific antibody responses. Multivariate statistical methods were employed using T and B cell immune phenotypic profiles obtained with cells from several tissues of individual mice. The data was used to identify combinations of markers that could predict an HCMV infection vs. reactivation status. In spleen, but not in lymph nodes, higher frequencies of effector CD4+ T cells expressing PD-1 were among the factors most suited to distinguish HCMV reactivations from infections. These results suggest a shift from a T cell dominated immune response during latent infections toward an exhausted T cell phenotype and active humoral immune response upon reactivations. In sum, this novel in vivo humanized model combined with advanced analyses highlights a dynamic system clearly specifying the immunological spatial signatures of HCMV latency and reactivations. These signatures can be merged as predictive biomarker clusters that can be applied in the clinical translation of new therapies for the control of HCMV reactivation.
Background
Few studies have assessed trajectories of alcohol use in the general population, and even fewer studies have assessed the impact of brief intervention on the trajectories. Especially for low-risk drinkers, it is unclear what trajectories occur, whether they benefit from intervention, and if so, when and how long. The aims were first, to identify alcohol use trajectories among at-risk and among low-risk drinkers, second, to explore potential effects of brief alcohol intervention and, third, to identify predictors of trajectories.
Methods
Adults aged 18-64 years were screened for alcohol use at a municipal registration office. Those with alcohol use in the past 12 months (N = 1646; participation rate: 67%) were randomized to assessment plus computer-generated individualized feedback letters or assessment only. Outcome was drinks/week assessed at months 3, 6, 12, and 36. Alcohol risk group (at-risk/low-risk) was determined using the Alcohol Use Disorders Identification Test–Consumption. Latent class growth models were estimated to identify alcohol use trajectories among each alcohol risk group. Sex, age, school education, employment status, self-reported health, and smoking status were tested as predictors.
Results
For at-risk drinkers, a light-stable class (46%), a medium-stable class (46%), and a high-decreasing class (8%) emerged. The light-stable class tended to benefit from intervention after 3 years (Incidence Rate Ratio, IRR=1.96; 95% Confidence Interval, CI: 1.14–3.37). Male sex, higher age, more years of school, and current smoking decreased the probability of belonging to the light-stable class (p-values<0.05). For low-risk drinkers, a very light-slightly increasing class (72%) and a light-increasing class (28%) emerged. The very light-slightly increasing class tended to benefit from intervention after 6 months (IRR=1.60; 95% CI: 1.12–2.28). Male sex and more years of school increased the probability of belonging to the light-increasing class (p-value < 0.05).
Conclusion
Most at-risk drinkers did not change, whereas the majority of low-risk drinkers increased alcohol use. There may be effects of alcohol feedback, with greater long-term benefits among persons with low drinking amounts. Our findings may help to identify refinements in the development of individualized interventions to reduce alcohol use.
Background
Missing data are ubiquitous in randomised controlled trials. Although sensitivity analyses for different missing data mechanisms (missing at random vs. missing not at random) are widely recommended, they are rarely conducted in practice. The aim of the present study was to demonstrate sensitivity analyses for different assumptions regarding the missing data mechanism for randomised controlled trials using latent growth modelling (LGM).
Methods
Data from a randomised controlled brief alcohol intervention trial was used. The sample included 1646 adults (56% female; mean age = 31.0 years) from the general population who had received up to three individualized alcohol feedback letters or assessment-only. Follow-up interviews were conducted after 12 and 36 months via telephone. The main outcome for the analysis was change in alcohol use over time. A three-step LGM approach was used. First, evidence about the process that generated the missing data was accumulated by analysing the extent of missing values in both study conditions, missing data patterns, and baseline variables that predicted participation in the two follow-up assessments using logistic regression. Second, growth models were calculated to analyse intervention effects over time. These models assumed that data were missing at random and applied full-information maximum likelihood estimation. Third, the findings were safeguarded by incorporating model components to account for the possibility that data were missing not at random. For that purpose, Diggle-Kenward selection, Wu-Carroll shared parameter and pattern mixture models were implemented.
Results
Although the true data generating process remained unknown, the evidence was unequivocal: both the intervention and control group reduced their alcohol use over time, but no significant group differences emerged. There was no clear evidence for intervention efficacy, neither in the growth models that assumed the missing data to be at random nor those that assumed the missing data to be not at random.
Conclusion
The illustrated approach allows the assessment of how sensitive conclusions about the efficacy of an intervention are to different assumptions regarding the missing data mechanism. For researchers familiar with LGM, it is a valuable statistical supplement to safeguard their findings against the possibility of nonignorable missingness.
Despite their very close structural similarity, CxxC/S-type (class I) glutaredoxins (Grxs) actas oxidoreductases, while CGFS-type (class II) Grxs act as FeS cluster transferases. Here weshow that the key determinant of Grx function is a distinct loop structure adjacent to theactive site. Engineering of a CxxC/S-type Grx with a CGFS-type loop switched its functionfrom oxidoreductase to FeS transferase. Engineering of a CGFS-type Grx with a CxxC/S-typeloop abolished FeS transferase activity and activated the oxidative half reaction of the oxi-doreductase. The reductive half-reaction, requiring the interaction with a second GSHmolecule, was enabled by switching additional residues in the active site. We explain howsubtle structural differences, mostly depending on the structure of one particular loop, act inconcert to determine Grx function.
Synopsis
C+60 has been proposed to be responsible for two of the diffuse interstellar bands (DIBs), the absorption features observed in the visible-to-near-infrared spectra of the interstellar medium. However, a confirmation requires laboratory gas-phase spectra, which are so far not available. We plan to develop a novel spectroscopy technique that will allow us to obtain the first gas-phase spectra of C+60, and that will be applicable to other complex organic molecules such as polycyclic aromatic hydrocarbons. The current status of the experimental setup, the ideas behind the measurement scheme and the preparatory work toward its implementation will be presented.
Abstract
Type 1 diabetes mellitus (T1DM) represents one of the most common chronic diseases in childhood. It is associated with high morbidity and mortality rates due to metabolic dysregulation, immunosuppressive effects, and a predisposition to fungal infections. Candidiasis is a severe infection and its prevalence has increased throughout the last decades. We report the case of a 19‐year‐old female patient admitted to our intensive care unit with T1DM and Candida infection associated with severe metabolic acidosis. In the absence of response to high dose catecholamine cardiovascular therapy and the presence of severe metabolic acidosis, a CytoSorb cartridge was implemented into the extracorporeal dialysis circuit resulting in a stabilization of hemodynamics accompanied by a tremendous decrease in vasopressor requirements, control of the hyperinflammatory response, as well as a resolution of metabolic acidosis and regeneration of renal function. Treatment with CytoSorb was safe and feasible without technical problems. Notably, this is the first case description reporting on the effects of CytoSorb in a patient with Candida infection as part of T1DM.
Background
In combination with systematic routine screening, brief alcohol interventions have the potential to promote population health. Little is known on the optimal screening interval. Therefore, this study pursued 2 research questions: (i) How stable are screening results for at‐risk drinking over 12 months? (ii) Can the transition from low‐risk to at‐risk drinking be predicted by gender, age, school education, employment, or past week alcohol use?
Methods
A sample of 831 adults (55% female; mean age = 30.8 years) from the general population was assessed 4 times over 12 months. The Alcohol Use Disorders Identification Test—Consumption was used to screen for at‐risk drinking each time. Participants were categorized either as low‐risk or at‐risk drinkers at baseline, 3, 6, and 12 months later. Stable and instable risk status trajectories were analyzed descriptively and graphically. Transitioning from low‐risk drinking at baseline to at‐risk drinking at any follow‐up was predicted using a logistic regression model.
Results
Consistent screening results over time were observed in 509 participants (61%). Of all baseline low‐risk drinkers, 113 (21%) received a positive screening result in 1 or more follow‐up assessments. Females (vs. males; OR = 1.66; 95% confidence intervals [95% CI] = 1.04; 2.64), 18‐ to 29‐year‐olds (vs. 30‐ to 45‐year‐olds; OR = 2.30; 95% CI = 1.26; 4.20), and those reporting 2 or more drinking days (vs. less than 2; OR = 3.11; 95% CI = 1.93; 5.01) and heavy episodic drinking (vs. none; OR = 2.35; 95% CI = 1.06; 5.20) in the week prior to the baseline assessment had increased odds for a transition to at‐risk drinking.
Conclusions
Our findings suggest that the widely used time frame of 1 year may be ambiguous regarding the screening for at‐risk alcohol use although generalizability may be limited due to higher‐educated people being overrepresented in our sample.