Isolated unilateral absence of the right pulmo nary artery.

A 40-year-old man was referred for evaluation of cough of a few weeks duration and a history of recurrent respiratory tract infections for several years. Clinical examination revealed no abnormalities. Routine hematologic and biochemical evaluation were normal. Contrast-enhanced computed tomography of the thorax showed a hypoplastic right lung, hyperinflation of the left lung with cardiomediastinal shift to the right. Absence of the right pulmonary artery was noted and replaced by an extensive collateral network of hypertrophied vessels originating from bronchial, intercostal and mammaria interna arteries, and right arteria subclavia (Fig. A, B). Discrete bronchiectasis with thickened bronchial walls in a hypoplastic right lung was noted (Fig. C). A normal bronchial tree and normal parenchyma in the left lung was seen

Transform-domain analysis of packet delay in network nodes with QoS-aware scheduling

In order to differentiate the perceived QoS between traffic classes in heterogeneous packet networks, equipment discriminates incoming packets based on their class, particularly in the way queued packets are scheduled for further transmission. We review a common stochastic modelling framework in which scheduling mechanisms can be evaluated, especially with regard to the resulting per-class delay distribution. For this, a discrete-time single-server queue is considered with two classes of packet arrivals, either delay-sensitive (1) or delay-tolerant (2). The steady-state analysis relies on the use of well-chosen supplementary variables and is mainly done in the transform domain. Secondly, we propose and analyse a new type of scheduling mechanism that allows precise control over the amount of delay differentiation between the classes. The idea is to introduce N reserved places in the queue, intended for future arrivals of class 1

Unsupervised analysis approach reveals increased endometrial NKp30 frequency in patients with idiopathic RPL

Research into fetal development and medicin

Single-cell Analysis of the Neonatal Immune System Across the Gestational Age Continuum

Although most causes of death and morbidity in premature infants are related to immune maladaptation, the premature immune system remains poorly understood. We provide a comprehensive single-cell depiction of the neonatal immune system at birth across the spectrum of viable gestational age (GA), ranging from 25 weeks to term. A mass cytometry immunoassay interrogated all major immune cell subsets, including signaling activity and responsiveness to stimulation. An elastic net model described the relationship between GA and immunome (R=0.85, p=8.75e-14), and unsupervised clustering highlighted previously unrecognized GA-dependent immune dynamics, including decreasing basal MAP-kinase/NFkB signaling in antigen presenting cells; increasing responsiveness of cytotoxic lymphocytes to interferon-a; and decreasing frequency of regulatory and invariant T cells, including NKT cells and MAIT cells. Knowledge gained from the analysis of the neonatal immune landscape across GA provides a mechanistic framework to understand the unique susceptibility of preterm infants to both hyper-inflammatory diseases and infections

Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset

Estimating the time of delivery is of high clinical importance because pre- and postterm deviations are associated with complications for the mother and her offspring. However, current estimations are inaccurate. As pregnancy progresses toward labor, major transitions occur in fetomaternal immune, metabolic, and endocrine systems that culminate in birth. The comprehensive characterization of maternal biology that precedes labor is key to understanding these physiological transitions and identifying predictive biomarkers of delivery. Here, a longitudinal study was conducted in 63 women who went into labor spontaneously. More than 7000 plasma analytes and peripheral immune cell responses were analyzed using untargeted mass spectrometry, aptamer-based proteomic technology, and single-cell mass cytometry in serial blood samples collected during the last 100 days of pregnancy. The high-dimensional dataset was integrated into a multiomic model that predicted the time to spontaneous labor [R = 0.85, 95% confidence interval (CI) [0.79 to 0.89], P = 1.2 × 10−40, N = 53, training set; R = 0.81, 95% CI [0.61 to 0.91], P = 3.9 × 10−7, N = 10, independent test set]. Coordinated alterations in maternal metabolome, proteome, and immunome marked a molecular shift from pregnancy maintenance to prelabor biology 2 to 4 weeks before delivery. A surge in steroid hormone metabolites and interleukin-1 receptor type 4 that preceded labor coincided with a switch from immune activation to regulation of inflammatory responses. Our study lays the groundwork for developing blood-based methods for predicting the day of labor, anchored in mechanisms shared in preterm and term pregnancies

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

The biological determinants of the wide spectrum of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, basal signaling activity, and signaling responses to inflammatory ligands were assessed in peripheral blood from patients with mild, moderate, and severe COVID-19, at the time of diagnosis. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identified and independently validated a multivariate model classifying COVID-19 severity (multi-class AUCtraining = 0.799, p-value = 4.2e-6; multi-class AUCvalidation = 0.773, p-value = 7.7e-6). Features of this high-dimensional model recapitulated recent COVID-19 related observations of immune perturbations, and revealed novel biological signatures of severity, including the mobilization of elements of the renin-angiotensin system and primary hemostasis, as well as dysregulation of JAK/STAT, MAPK/mTOR, and NF-κB immune signaling networks. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for the prevention of COVID-19 progression

Multiomics Longitudinal Modeling of Preeclamptic Pregnancies

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear and that poses a threat to both mothers and infants. Specific complex changes in women\u27s physiology precede a diagnosis of preeclampsia. Understanding multiple aspects of such a complex changes at different levels of biology, can be enabled by simultaneous application of multiple assays. We developed prediction models for preeclampsia risk by analyzing six omics datasets from a longitudinal cohort of pregnant women. A machine learning-based multiomics model had high accuracy (area under the receiver operating characteristics curve (AUC) of 0.94, 95% confidence intervals (CI):[0.90, 0.99]). A prediction model using only ten urine metabolites provided an accuracy of the whole metabolomic dataset and was validated using an independent cohort of 16 women (AUC= 0.87, 95% CI:[0.76, 0.99]). Integration with clinical variables further improved prediction accuracy of the urine metabolome model (AUC= 0.90, 95% CI:[0.80, 0.99], urine metabolome, validated). We identified several biological pathways to be associated with preeclampsia. The findings derived from models were integrated with immune system cytometry data, confirming known physiological alterations associated with preeclampsia and suggesting novel associations between the immune and proteomic dynamics. While further validation in larger populations is necessary, these encouraging results will serve as a basis for a simple, early diagnostic test for preeclampsia

Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning

Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand. Additionally, by integrating single cell immunological profiling of underlying biological processes, the effects of stress-based therapeutics may be measurable, facilitating the development of precision medicine approaches.ObjectivesThe primary objectives were to jointly model multiple APOs and their connection to stress early in pregnancy, and to explore the underlying biology to guide development of accessible and measurable interventions.Materials and MethodsIn a prospective cohort study, PSFs were assessed during the first trimester with an extensive self-filled questionnaire for 200 women. We used MML to simultaneously model, and predict APOs (severe preeclampsia, superimposed preeclampsia, gestational diabetes and early gestational age) as well as several risk factors (BMI, diabetes, hypertension) for these patients based on PSFs. Strongly interrelated stressors were categorized to identify potential therapeutic targets. Furthermore, for a subset of 14 women, we modeled the connection of PSFs to the maternal immune system to APOs by building corresponding ML models based on an extensive single cell immune dataset generated by mass cytometry time of flight (CyTOF).ResultsJointly modeling APOs in a MML setting significantly increased modeling capabilities and yielded a highly predictive integrated model of APOs underscoring their interconnectedness. Most APOs were associated with mental health, life stress, and perceived health risks. Biologically, stressors were associated with specific immune characteristics revolving around CD4/CD8 T cells. Immune characteristics predicted based on stress were in turn found to be associated with APOs.ConclusionsElucidating connections among stress, multiple APOs simultaneously, and immune characteristics has the potential to facilitate the implementation of ML-based, individualized, integrative models of pregnancy in clinical decision making. The modifiable nature of stressors may enable the development of accessible interventions, with success tracked through immune characteristics

Striking Essential Oil: Novel tools and new insights to study the biological activities of essential oils (EOs) and their components (EOCs)

The increasing level of antimicrobial resistance poses the threat that no suitable antimicrobials will be available for systematic treatment of common diseases. More and more Candida infections are caused by intrinsically and multi-resistant Candida isolates. There is clearly renewed scientific interest in discovering drugs from natural sources, although natural product-based drug discovery is considered intrinsically complex and requires a highly integrated interdisciplinary approach. Medicinal plants that are used to treat infectious diseases appear to be an abundant source of new bioactive secondary metabolites. One such class of plant-based molecules are essential oils which have been used therapeutically for millennia as they have a broad range of biological activities. Reanalyzing the data of a large antimicrobial screening (2012) of essential oils in microtiter plates systematically showed false-positive hits. We discovered that these false-positives were mediated by the vapor-phase of some essential oils in adjacent wells. The challenge was to circumvent this problem before we could start screening our latest essential oil (component) collection against two potentially disease-causing fungi i.e. C. albicans and C. glabrata. We developed an alternative plate set-up for the new screening in a way that it intrinsically does not modify the standardized protocol, while controlling for false positive results caused by the vapor-phase-mediated antimicrobial activity of the tested essential oils and their components. However, the alternative plate set-up and its derivatives are not limited to the testing of essential oils and their components but can be used with any volatile compound; nor is the set-up limited to testing antimicrobial activity but can essentially be extended to any biological activity tested in a microtiter plate. Therefore, we recommend these adaptations as good laboratory practices when working with volatiles in microtiter plates. An in vitro assay to test the vapor-phase-mediated antimicrobial activity of volatiles did not exist. From a drug discovery point such an assay is interesting as a model for testing compounds for e.g. the treatment of tract-related infections. We hypothesized that if we can detect false-positives in microtiter plates caused by the vapor-phase of volatiles, it should be possible to develop a microtiter plate-based assay. We developped the qualitative vapor-phase-mediated patch assay, a relatively simple test to detect the vapor-phase-mediated activity of volatiles such as essential oils and their components. We conceptualized a quantitative version of the aforementioned assay, i.e. the vapor-phase-mediated susceptibility assay, which we then developed further. Subsequently we quantified with this assay the vapor-phase-mediated antimicrobial activity of a collection of 175 commercial essential oils and 37 reference essential oil components against C. albicans and C. glabrata. About half of the essential oils and their components had a growth-inhibitory vapor-phase-mediated antimicrobial activity. On average, a stronger activity was observed against the intrinsically more resistant C. glabrata, with essential oil component citronellal showing the largest significant difference in vapor-phase-mediated antimicrobial activity. In contrast, representatives of each class of antifungals currently used in clinical practice did not exhibit any vapor-phase-mediated antimicrobial activity. The vapor-phase-mediated susceptibility assay can advance the search for novel (applications of existing) antimicrobials. Furthermore, our study is the first comprehensive characterization of essential oils and their components as a unique class of antifungals with properties distinct from existing antifungal classes. We conducted a pairwise comparison between the inhibitory vapor-phase-mediated anti-Candida activity of 33 certified organic essential oils and as many equivalent essential oils without such certification. It is believed among essential oil consumers that essential oils from organic cultivation, which includes plants that are harvested in the wild, are therapeutically more potent than those from conventional cultivation. However, we did not discern a significant difference in inhibitory vapor-phase-mediated anti-Candida activity between certified organic essential oils and those without such certification. Although essential oils have been used therapeutically for millennia they continue to be considered as products mainly used in complementary and alternative medicine. Few have made the transition to drugs despite a shared Nobel Prize in 2015 for essential oil-related research of Dr. Youyou Tu for her discoveries concerning a novel therapy against malaria. In the past decades, natural products such as essential oils and their components have somehow been avoided in drug discovery because of inter alia possible technical problems during processing. However, is this still justified with our current understanding of drug discovery? Therefore, we studied selected physicochemical parameters, used in conventional drug discovery, from the components of a collection of 142 essential oils. We demonstrated that, contrary to generally held belief, most essential oil components satisfy current-day requirements of medicinal chemistry for good drug candidates. Therefore, their therapeutic potential remains vastly under-used and should be more vigorously explored with modern methods. Furthermore, it seems that they offer striking opportunities for lead optimization or even fragment-based drug discovery.status: publishe