Indirubin-3'-monoxime exhibits potent antiviral and anti-inflammatory effects against human adenoviruses in vitro and in vivo.

Human adenovirus (HAdV) infection is a major cause of respiratory disease, yet no antiviral drugs have been approved for its treatment. Herein, we evaluated the antiviral and anti-inflammatory effects of cyclin-dependent protein kinase (CDK) inhibitor indirubin-3'-monoxime (IM) against HAdV infection in cells and a transgenic mouse model. After evaluating its cytotoxicity, cytopathic effect reduction, antiviral replication kinetics, and viral yield reduction assays were performed to assess the anti-HAdV activity of IM. Quantitative real-time polymerase chain reaction (qPCR), quantitative reverse transcription PCR (qRT-PCR), and western blotting were used to assess the effects of IM on HAdV DNA replication, transcription, and protein expression, respectively. IM significantly inhibited HAdV DNA replication as well as E1A and Hexon transcription, in addition to significantly suppressing the phosphorylation of the RNA polymerase II C-terminal domain (CTD). IM mitigated body weight loss, reduced viral burden, and lung injury, decreasing cytokine and chemokine secretion to a greater extent than cidofovir. Altogether, IM inhibits HAdV replication by downregulating CTD phosphorylation to suppress viral infection and corresponding innate immune reactions as a promising therapeutic agent.

Comparisons of lymphocytes profiles and inflammatory cytokines levels in blood of patients with differed severity of infection by human adenovirus type 7.

BACKGROUND: Human adenovirus (HAdV) infection outbreak causes community-acquired pneumonia. Cellular immune dysfunction and hypercytokinemia play important roles in the pathogenesis of adenovirus respiratory infection. Some soluble factors in peripheral blood can assist in judging the virus-induced disease severity. The expression levels of inflammatory cytokines differ among patients with different disease severity. However, whether and how HAdV-7 infection influences the composition of blood immune cells and serum cytokine levels in patients at different disease stages, as well as the diagnosis values of these parameters, have rarely been intensively studied. We aimed to investigate lymphocytes profiles and cytokines levels in blood of patients at different disease stages upon human adenovirus type 7 (HAdV-7) infections, and explored the diagnosis values of the investigated parameters. METHODS: Patients from two outbreaks of HAdV-7 in military of China were categorized into upper respiratory infection (URI) group, common pneumonia (CP) group and severe pneumonia (SP) group according to disease severity. Peripheral blood samples were subjected to routine laboratory tests, while flow cytometry and ELISA were used to measure the lymphocyte subsets and cytokines in blood, respectively. The receiver operating characteristic (ROC) curves were performed to examine the diagnostic of these blood parameters. RESULTS: Signs of imbalanced lymphocytes composition and hypercytokinemia were observed in HAdV-7-infected patients. The percentages of CD3+ T cells and NK cells were significantly decreased along with the aggravation of the disease, particularly for NK cells and CD4+ T cells. The neutrophil to lymphocyte ratio (NLR) increased significantly in patients with more severe disease. In addition, the levels of serum CXCL10, IL-2 and TNF-α were positively correlated with disease severity, while reduced levels of IFN-γ and IL-10 were found in SP patients. Furthermore, analysis of ROC showed that multiple parameters including the percentage of blood CD3+ cells and serum CXCL10 level could predict the progression of HAdV-7 infection. CONCLUSION: Imbalance of immune state with hypercytokinemia occurred during HAdV-7 infection. The percentages of blood immune cells such as CD3+ T cells and the levels of serum cytokines such as CXCL10 showed potential diagnosis values in HAdV-7 infection.

GLaMST: grow lineages along minimum spanning tree for b cell receptor sequencing data.

BACKGROUND: B cell affinity maturation enables B cells to generate high-affinity antibodies. This process involves somatic hypermutation of B cell immunoglobulin receptor (BCR) genes and selection by their ability to bind antigens. Lineage trees are used to describe this microevolution of B cell immunoglobulin genes. In a lineage tree, each node is one BCR sequence that mutated from the germinal center and each directed edge represents a single base mutation, insertion or deletion. In BCR sequencing data, the observed data only contains a subset of BCR sequences in this microevolution process. Therefore, reconstructing the lineage tree from experimental data requires algorithms to build the tree based on partially observed tree nodes. RESULTS: We developed a new algorithm named Grow Lineages along Minimum Spanning Tree (GLaMST), which efficiently reconstruct the lineage tree given observed BCR sequences that correspond to a subset of the tree nodes. Through comparison using simulated and real data, GLaMST outperforms existing algorithms in simulations with high rates of mutation, insertion and deletion, and generates lineage trees with smaller size and closer to ground truth according to tree features that highly correlated with selection pressure. CONCLUSIONS: GLaMST outperforms state-of-art in reconstruction of the BCR lineage tree in both efficiency and accuracy. Integrating it into existing BCR sequencing analysis frameworks can significant improve lineage tree reconstruction aspect of the analysis.

Accurately tracking single-cell movement trajectories in microfluidic cell sorting devices.

Microfluidics are routinely used to study cellular properties, including the efficient quantification of single-cell biomechanics and label-free cell sorting based on the biomechanical properties, such as elasticity, viscosity, stiffness, and adhesion. Both quantification and sorting applications require optimal design of the microfluidic devices and mathematical modeling of the interactions between cells, fluid, and the channel of the device. As a first step toward building such a mathematical model, we collected video recordings of cells moving through a ridged microfluidic channel designed to compress and redirect cells according to cell biomechanics. We developed an efficient algorithm that automatically and accurately tracked the cell trajectories in the recordings. We tested the algorithm on recordings of cells with different stiffness, and showed the correlation between cell stiffness and the tracked trajectories. Moreover, the tracking algorithm successfully picked up subtle differences of cell motion when passing through consecutive ridges. The algorithm for accurately tracking cell trajectories paves the way for future efforts of modeling the flow, forces, and dynamics of cell properties in microfluidics applications.

Efficient Computing Budget Allocation for Finding Simplest Good Designs.

In many applications some designs are easier to implement, require less training data and shorter training time, and consume less storage than the others. Such designs are called simple designs, and are usually preferred over complex ones when they all have good performance. Despite the abundant existing studies on how to find good designs in simulation-based optimization (SBO), there exist few studies on finding simplest good designs. We consider this important problem in this paper, and make the following contributions. First, we provide lower bounds for the probabilities of correctly selecting the m simplest designs with top performance, and selecting the best m such simplest good designs, respectively. Second, we develop two efficient computing budget allocation methods to find m simplest good designs and to find the best m such designs, respectively; and show their asymptotic optimalities. Third, we compare the performance of the two methods with equal allocations over 6 academic examples and a smoke detection problem in wireless sensor networks. We hope that this work brings insight to finding the simplest good designs in general.

Do unexpected panic attacks occur spontaneously?

BACKGROUND: Spontaneous or unexpected panic attacks, per definition, occur "out of the blue," in the absence of cues or triggers. Accordingly, physiological arousal or instability should occur at the onset of, or during, the attack, but not preceding it. To test this hypothesis, we examined if points of significant autonomic changes preceded the onset of spontaneous panic attacks. METHODS: Forty-three panic disorder patients underwent repeated 24-hour ambulatory monitoring. Thirteen natural panic attacks were recorded during 1960 hours of monitoring. Minute-by-minute epochs beginning 60 minutes before and continuing to 10 minutes after the onset of individual attacks were examined for respiration, heart rate, and skin conductance level. Measures were controlled for physical activity and vocalization and compared with time matched control periods within the same person. RESULTS: Significant patterns of instability across a number of autonomic and respiratory variables were detected as early as 47 minutes before panic onset. The final minutes before onset were dominated by respiratory changes, with significant decreases in tidal volume followed by abrupt carbon dioxide partial pressure increases. Panic attack onset was characterized by heart rate and tidal volume increases and a drop in carbon dioxide partial pressure. Symptom report was consistent with these changes. Skin conductance levels were generally elevated in the hour before, and during, the attacks. Changes in the matched control periods were largely absent. CONCLUSIONS: Significant autonomic irregularities preceded the onset of attacks that were reported as abrupt and unexpected. The findings invite reconsideration of the current diagnostic distinction between uncued and cued panic attacks.

Change point analysis for longitudinal physiological data: detection of cardio-respiratory changes preceding panic attacks.

Statistical methods for detecting changes in longitudinal time series of psychophysiological data are limited. ANOVA and mixed models are not designed to detect the existence, timing, or duration of unknown changes in such data. Change point (CP) analysis was developed to detect distinct changes in time series data. Preliminary reports using CP analysis for fMRI data are promising. Here, we illustrate the application of CP analysis for detecting discrete changes in ambulatory, peripheral physiological data leading up to naturally occurring panic attacks (PAs). The CP method was successful in detecting cardio-respiratory changes that preceded the onset of reported PAs. Furthermore, the changes were unique to the pre-PA period, and were not detected in matched non-PA control periods. The efficacy of our CP method was further validated by detecting patterns of change that were consistent with prominent respiratory theories of panic positing a relation between aberrant respiration and panic etiology.