Summary
Neonates show an impaired anti‐microbial host defence, but the underlying immune mechanisms are not understood fully. Myeloid‐derived suppressor cells (MDSCs) represent an innate immune cell ...subset characterized by their capacity to suppress T cell immunity. In this study we demonstrate that a distinct MDSC subset with a neutrophilic/granulocytic phenotype (Gr‐MDSCs) is highly increased in cord blood compared to peripheral blood of children and adults. Functionally, cord blood isolated Gr‐MDSCs suppressed T cell proliferation efficiently as well as T helper type 1 (Th1), Th2 and Th17 cytokine secretion. Beyond T cells, cord blood Gr‐MDSCs controlled natural killer (NK) cell cytotoxicity in a cell contact‐dependent manner. These studies establish neutrophilic Gr‐MDSCs as a novel immunosuppressive cell subset that controls innate (NK) and adaptive (T cell) immune responses in neonates. Increased MDSC activity in cord blood might serve as key fetomaternal immunosuppressive mechanism impairing neonatal host defence. Gr‐MDSCs in cord blood might therefore represent a therapeutic target in neonatal infections.
Introduction
Depression is a leading cause of disability worldwide despite dozens of approved antidepressants. There are currently no clear guidelines to assist the physician in their choice of drug, ...with existing tools limited to pharmacogenetics that have shown suboptimal response prediction outcomes resulting in a subscription process that is largely a trial and error one. Consequently, the majority of depressed patients do not respond to their first prescribed antidepressant, with >30% not responding to subsequent drugs. We report here on molecular readouts from an in vitro-based platform that provides patient-specific information on antidepressant mechanisms using cortical neurons derived individually from each patient.
Objectives
To assess gene expression differences in prefrontal cortex neurons derived from responders and non-responders to two commonly used antidepressants, the selective serotonin reuptake inhibitor Citalopram and the atypical antidepressant Bupropion.
Methods
Patient-derived lymphoblastoid cell lines from the Sequenced Treatment Alternatives to Relieve Depression (STARD) study with known response to Citalopram or Bupropion were reprogrammed and then differentiated to cortical neurons. Differential gene expression analysis was preformed to identify genes that are differentially expressed between drug responders and non-responders.
Results
Significant differential expression was shown in 359 genes between Bupropion responders and non-responders (Fig1A) and 12 genes between Citalopram responders and non-responders (Fig1B). Clustering on the differentially expressed genes showed high agreement with the known response to both drugs (Fig1). Functional enrichment analysis revealed biologically relevant pathways that differ between responders and non-responders in Bupropion versus Citalopram.
Image:
Figure 1.
Heatmap of the expression of genes that show significant differential expression between neurons derived from Bupropion (A) and Citalopram (B) responders and non-responders. Color is the scaled gene expression; lines are genes and columns are samples. Column side colors represent the known response of the patient. Colum and line dendrograms are unsupervised hierarchical clustering.
Conclusions
Gene expression patterns of neurons derived from patients with depression differ according to their response to two common antidepressants from different groups. The identification of distinct drug response dependent expression patterns in derived neurons can help elucidate mechanisms underlying antidepressant activity, supporting new drug development and response prediction.
Disclosure of Interest
None Declared
We demonstrate a novel array-based diagnostic platform comprising lipid/polydiacetylene (PDA) vesicles embedded within a transparent silica-gel matrix. The diagnostic scheme is based upon the unique ...chromatic properties of PDA, which undergoes blue-red transformations induced by interactions with amphiphilic or membrane-active analytes. We show that constructing a gel matrix array hosting PDA vesicles with different lipid compositions and applying to blood plasma obtained from healthy individuals and from patients suffering from disease, respectively, allow distinguishing among the disease conditions through application of a simple machine-learning algorithm, using the colorimetric response of the lipid/PDA/gel matrix as the input. Importantly, the new colorimetric diagnostic approach does not require a priori knowledge on the exact metabolite compositions of the blood plasma, since the concept relies only on identifying statistically significant changes in overall disease-induced chromatic response. The chromatic lipid/PDA/gel array-based "fingerprinting" concept is generic, easy to apply, and could be implemented for varied diagnostic and screening applications.
The focal-plane module is the key component of the DEPFET sensor with signal compression (DSSC) mega-pixel X-ray imager and handles the data of 128 <inline-formula> <tex-math ...notation="LaTeX">\times512 </tex-math></inline-formula> pixels. We report on assembly-related aspects, discuss the experimental investigation of bonding behavior of different adhesives, and present the metrology and electrical test results of the production. The module consists of two silicon (Si) sensors with flip-chip connected CMOS integrated circuits, a Si-heat spreader, a low-temperature co-fired ceramics circuit board, and a molybdenum frame. A low-modulus urethane-film adhesive fills the gaps between on-board components and frame. It is also used between board and heat spreader, reduces the misfit strain, and minimizes the module warpage very efficiently. The heat spreader reduces the on-board temperature gradient by about one order of magnitude. The placement precision of the bare modules to each other and the frame is characterized by a standard deviation below 10 and 65 <inline-formula> <tex-math notation="LaTeX">\mu \text{m} </tex-math></inline-formula>, respectively. The displacement due to the in-plane rotation and vertical tilting errors remains below 80 and 50 <inline-formula> <tex-math notation="LaTeX">\mu \text{m} </tex-math></inline-formula>, respectively. The deflection of the sensor plane shows a mean value below 30 <inline-formula> <tex-math notation="LaTeX">\mu \text{m} </tex-math></inline-formula> with a standard deviation below 15 <inline-formula> <tex-math notation="LaTeX">\mu \text{m} </tex-math></inline-formula>. Less than 4% of the application-specified integrated circuits (ASICs) exhibit a malfunction. More than two-thirds of the sensors have a maximum leakage current below 1 <inline-formula> <tex-math notation="LaTeX">\mu \text{A} </tex-math></inline-formula>.
Non-alcoholic alternatives are gaining growing significance within the German beverage sector. In this context, the German wine industry is increasingly focusing on non-alcoholic wines, whose market ...has developed dynamically in recent years. While the technologies used, the sensory characteristics and the marketing of the products are frequently addressed in the literature, the consideration of sustainability impacts has so far been largely neglected. This applies in particular to the view of all three dimensions of sustainability. These are examined more closely in this review with regard to tradeoffs, which indicate that positive aspects in one dimension go hand in hand with a loss in the other. It can be shown that tradeoffs in the production and marketing of non-alcoholic wines arise both within and between the three sustainability dimensions. Exemplary of this is the increased use of resources in the course of alcohol removal. At the same time, an emerging market segment holds positive aspects from an economic perspective. Ultimately, the consideration of social sustainability is marked by the health science and political debate around the reduction in alcohol consumption and the simultaneous increase in the consumption of non-alcoholic alternatives.
The higher luminosity that is expected for the LHC after future upgrades will require better performance by the data acquisition system, especially in terms of throughput. In particular, during the ...first shutdown of the LHC collider in 2013/14, the ATLAS Pixel Detector will be equipped with a fourth layer – the Insertable B-Layer or IBL – located at a radius smaller than the present three layers. Consequently, a new front end ASIC (FE-I4) was designed as well as a new off-detector chain. The latter is composed mainly of two 9U-VME cards called the Back-Of-Crate (BOC) and Read-Out Driver (ROD). The ROD is used for data and event formatting and for configuration and control of the overall read-out electronics. After some prototyping samples were completed, a pre-production batch of 5 ROD cards was delivered with the final layout. Actual production of another 15 ROD cards is ongoing in Fall 2013, and commissioning is scheduled in 2014. Altogether 14 cards are necessary for the 14 staves of the IBL detector, one additional card is required by the Diamond Beam Monitor (DBM), and additional spare ROD cards will be produced for a total of 20 boards. This paper describes some integration tests that were performed and our plan to test the production of the ROD cards. Slices of the IBL read-out chain have been instrumented, and ROD performance is verified on a test bench mimicking a small-sized final setup. This contribution will report also one view on the possible adoption of the IBL ROD for ATLAS Pixel Detector Layer 2 (firstly) and, possibly, in the future, for Layer 1.
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•Early detection of COVID-19 resurgence is achievable using syndromic surveillance.•Outbreaks of diseases affect in-hospital symptom prevalence distributions.•Autoencoder-based ...aberration detection can be used to detect outbreaks early.•Autoencoder approach can differentiate between diseases with similar syndromes.•Signals present more than one month prior to first laboratory confirmed case.
Coronavirus Disease 2019 has emerged as a significant global concern, triggering harsh public health restrictions in a successful bid to curb its exponential growth. As discussion shifts towards relaxation of these restrictions, there is significant concern of second-wave resurgence. The key to managing these outbreaks is early detection and intervention, and yet there is a significant lag time associated with usage of laboratory confirmed cases for surveillance purposes. To address this, syndromic surveillance can be considered to provide a timelier alternative for first-line screening. Existing syndromic surveillance solutions are however typically focused around a known disease and have limited capability to distinguish between outbreaks of individual diseases sharing similar syndromes. This poses a challenge for surveillance of COVID-19 as its active periods tend to overlap temporally with other influenza-like illnesses. In this study we explore performing sentinel syndromic surveillance for COVID-19 and other influenza-like illnesses using a deep learning-based approach. Our methods are based on aberration detection utilizing autoencoders that leverages symptom prevalence distributions to distinguish outbreaks of two ongoing diseases that share similar syndromes, even if they occur concurrently. We first demonstrate that this approach works for detection of outbreaks of influenza, which has known temporal boundaries. We then demonstrate that the autoencoder can be trained to not alert on known and well-managed influenza-like illnesses such as the common cold and influenza. Finally, we applied our approach to 2019–2020 data in the context of a COVID-19 syndromic surveillance task to demonstrate how implementation of such a system could have provided early warning of an outbreak of a novel influenza-like illness that did not match the symptom prevalence profile of influenza and other known influenza-like illnesses.
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