Effect and Mechanism of Mycobacterium avium MAV-5183 on Apoptosis of Mouse Ana-1 Macrophages.

To investigate the effects and mechanisms of Mycobacterium avium MAV-5183 protein on apoptosis in mouse Ana-1 macrophages. A pET-21a-MAV-5183 recombinant plasmid was constructed. The recombinant MAV-5183 protein was cloned, expressed, purified, and identified using an anti-His-tagged antibody. Rabbits were immunized to obtain antiserum, and its potency and immunoreactivity were assessed through WB. Mouse Ana-1 macrophages were incubated with varying concentrations of MAV-5183 protein. Flow cytometry, following ANNEXIN V-FITC/PI double staining, detected apoptosis. Western Blot analysis was conducted to identify apoptosis-related molecules Caspase-9/8/3 and vesicle-related molecules ASC, NLRP3, and Cleaved-casp1. ELISA measured TNF-α and IL-6 levels in the culture supernatant. LDH activity and ROS levels were analyzed separately. RT-qPCR measured mRNA levels of Caspase-9/8/3, ASC, NLRP3, Caspase-1, IL-1ß, Bax, MAPK-p38, Bcl-2, TNF-α, and IL-6. MAV-5183 protein was successfully cloned, purified, and identified. In in vitro studies on Ana-1 macrophages, MAV-5183 protein increased the expression of Caspase-9/8/3, ASC, NLRP3 (P < 0.01), induced ROS secretion (P < 0.05), and promoted inflammatory cytokine secretion (TNF-α, IL-6, P < 0.0001); however, it did not significantly affect LDH (P > 0.05). MAV-5183 also induced apoptosis in Ana-1 macrophages (P < 0.05). RT-qPCR results indicated a significant increase in mRNA expression of Caspase-9/8/3, ASC, NLRP3, TNF-α, IL-6, MAPK-p38, and pro-apoptotic factor Bax (P < 0.01), with no significant effect on Bcl-2 and IL-1ß mRNA (P > 0.05). The data indicate that MAV-5183 induces macrophage apoptosis through a caspase-dependent pathway and promotes inflammatory cytokine secretion via ROS.

Semi-Supervised Deep Learning for Cell Type Identification From Single-Cell Transcriptomic Data.

Cell type identification from single-cell transcriptomic data is a common goal of single-cell RNA sequencing (scRNAseq) data analysis. Deep neural networks have been employed to identify cell types from scRNAseq data with high performance. However, it requires a large mount of individual cells with accurate and unbiased annotated types to train the identification models. Unfortunately, labeling the scRNAseq data is cumbersome and time-consuming as it involves manual inspection of marker genes. To overcome this challenge, we propose a semi-supervised learning model "SemiRNet" to use unlabeled scRNAseq cells and a limited amount of labeled scRNAseq cells to implement cell identification. The proposed model is based on recurrent convolutional neural networks (RCNN), which includes a shared network, a supervised network and an unsupervised network. The proposed model is evaluated on two large scale single-cell transcriptomic datasets. It is observed that the proposed model is able to achieve encouraging performance by learning on the very limited amount of labeled scRNAseq cells together with a large number of unlabeled scRNAseq cells.

Calibrated bagging deep learning for image semantic segmentation: A case study on COVID-19 chest X-ray image.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). Imaging tests such as chest X-ray (CXR) and computed tomography (CT) can provide useful information to clinical staff for facilitating a diagnosis of COVID-19 in a more efficient and comprehensive manner. As a breakthrough of artificial intelligence (AI), deep learning has been applied to perform COVID-19 infection region segmentation and disease classification by analyzing CXR and CT data. However, prediction uncertainty of deep learning models for these tasks, which is very important to safety-critical applications like medical image processing, has not been comprehensively investigated. In this work, we propose a novel ensemble deep learning model through integrating bagging deep learning and model calibration to not only enhance segmentation performance, but also reduce prediction uncertainty. The proposed method has been validated on a large dataset that is associated with CXR image segmentation. Experimental results demonstrate that the proposed method can improve the segmentation performance, as well as decrease prediction uncertainty.

Effect of Displacement Pressure on Oil-Water Relative Permeability for Extra-Low-Permeability Reservoirs.

The oil-water relative permeability is an important parameter to characterize the seepage law of fluid in extra-low-permeability reservoirs, and it is of vital significance for the prediction and evaluation of the production. The pore throat size of extra-low-permeability reservoirs is relatively small, and the threshold pressure gradient and capillary pressure cannot be negligible. In this study, the oil-water relative permeability experiments with three different displacement pressures were carried out on the same core from the extra-low-permeability reservoir of Chang 4+5 formation in Ordos basin by the unsteady experimental method. The results show that the relative permeability of oil increases, while the relative permeability of water remains unchanged considering the capillary pressure and oil threshold pressure gradient compared with the JBN method. As the displacement pressure enlarges, the relative permeability of oil and water both increases; the residual oil saturation decreases, therefore the range of the two-phase flow zone is improved. Moreover, the isotonic point of water-oil relative permeability curves moves to the upper right region, and the reference permeability improves as well with the increasing pressure.

A Novel Fluorescent Probe Based on Pyrazole-Pyrazoline for Fe (III) Ions Recognition.

Firstly, a novel pyrazole-pyrazoline fluorescent probe was developed and synthesized. The probe can be used to determine Fe3+ ions in a series of cations in tetrahydrofuran aqueous solution with high selectivity and high sensitivity. After the addition of iron ions, the fluorescence intensity is significantly reduced, Its structure was characterized by 1H NMR, 13C NMR and HR-ESI-MS. UV absorption spectra and Fluorescence spectroscopy were used to study the selective recognition of probe M on metal ions. The probe M can selectivity and sensitivity to distinguish the target ion from other ions through different fluorescence phenomena. In addition, the binding modes of M with Fe3+ were proved to be 1:1 stoichiometry in the complexes by Job's plot, IR results. The combination of probe M and iron ions is 1:1, and the detection limit is 3.9 × 10-10 M. The binding mode and sensing mechanism of M with Fe3+ was verified by theoretical calculations using Gaussian 09 based on B3LYP/6-31G(d) basis.

Mesoscopic method to study water flow in nanochannels with different wettability.

Molecular dynamics (MD) simulations is currently the most popular and credible tool to model water flow in nanoscale where the conventional continuum equations break down due to the dominance of fluid-surface interactions. However, current MD simulations are computationally challenging for the water flow in complex tube geometries or a network of nanopores, e.g., membrane, shale matrix, and aquaporins. We present a novel mesoscopic lattice Boltzmann method (LBM) for capturing fluctuated density distribution and a nonparabolic velocity profile of water flow through nanochannels. We incorporated molecular interactions between water and the solid inner wall into LBM formulations. Details of the molecular interactions were translated into true and apparent slippage, which were both correlated to the surface wettability, e.g., contact angle. Our proposed LBM was tested against 47 published cases of water flow through infinite-length nanochannels made of different materials and dimensions-flow rates as high as seven orders of magnitude when compared with predictions of the classical no-slip Hagen-Poiseuille (HP) flow. Using the developed LBM model, we also studied water flow through finite-length nanochannels with tube entrance and exit effects. Results were found to be in good agreement with 44 published finite-length cases in the literature. The proposed LBM model is nearly as accurate as MD simulations for a nanochannel, while being computationally efficient enough to allow implications for much larger and more complex geometrical nanostructures.

Nanoconfinement Effect on Surface Tension: Perspectives from Molecular Potential Theory.

Liquid-vapor surface tension (ST) in nanopores attracts great attention in many industries because of the prosperity of nanoscience and nanotechnology. Here, considering the important emerging new physical phenomena induced by nanoconfinement effects, including curvature-dependent and shift-critical temperature (Tc)-dependent effects, the anomalous variation of ST in nanopores is captured from the molecular potential perspective. Furthermore, a simple analytical model is proposed to determine the ST in nanopores by correlating these two effects with an easily accessible parameter, that is, normalized pore dimension, which is the ratio of the pore radius to Lennard-Jones size parameter. The model is validated to be reliable for determining the STs of different substances both in the bulk phase as well as nanopores through comparison with the experimental results and molecular simulations. Our results show that the reduction of ST induced by the nanoconfinement effects is visible when the pore diameter is within tens of nanometers, and the reduction is more sensitive as the pore size decreases. In detail, the curvature-dependent effect is remarkable in the pores with diameters ranging from a few nanometers to tens of nanometers. Moreover, a simply generalized formula is obtained to determine the curvature-dependent effect and the Tolman length for different substances. The shift-Tc-dependent effect is not only related to the pore dimension but also depends on the temperature. As the pore size decreases, the critical temperature of confined fluids diverges significantly from the bulk values. While at high temperatures, the range of pore size impacted by the shift-Tc-dependent effect is enlarged. Additionally, the nanoconfined STs of different substances are calculated and compared. Overall, the new model captures the underlying physics behind the variation of STs in nanopores and can determine the nanoconfined STs reasonably. Moreover, the simple formulation of the model is beneficial to the practical applications in many chemical engineering processes, such as chemical separation, nucleation phenomenon, and capillary condensation.

Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN.

Specific entity terms such as disease, test, symptom, and genes in Electronic Medical Record (EMR) can be extracted by Named Entity Recognition (NER). However, limited resources of labeled EMR pose a great challenge for mining medical entity terms. In this study, a novel multitask bi-directional RNN model combined with deep transfer learning is proposed as a potential solution of transferring knowledge and data augmentation to enhance NER performance with limited data. The proposed model has been evaluated using micro average F-score, macro average F-score and accuracy. It is observed that the proposed model outperforms the baseline model in the case of discharge datasets. For instance, for the case of discharge summary, the micro average F-score is improved by 2.55% and the overall accuracy is improved by 7.53%. For the case of progress notes, the micro average F-score and the overall accuracy are improved by 1.63% and 5.63%, respectively.

A multitask bi-directional RNN model for named entity recognition on Chinese electronic medical records.

BACKGROUND: Electronic Medical Record (EMR) comprises patients' medical information gathered by medical stuff for providing better health care. Named Entity Recognition (NER) is a sub-field of information extraction aimed at identifying specific entity terms such as disease, test, symptom, genes etc. NER can be a relief for healthcare providers and medical specialists to extract useful information automatically and avoid unnecessary and unrelated information in EMR. However, limited resources of available EMR pose a great challenge for mining entity terms. Therefore, a multitask bi-directional RNN model is proposed here as a potential solution of data augmentation to enhance NER performance with limited data. METHODS: A multitask bi-directional RNN model is proposed for extracting entity terms from Chinese EMR. The proposed model can be divided into a shared layer and a task specific layer. Firstly, vector representation of each word is obtained as a concatenation of word embedding and character embedding. Then Bi-directional RNN is used to extract context information from sentence. After that, all these layers are shared by two different task layers, namely the parts-of-speech tagging task layer and the named entity recognition task layer. These two tasks layers are trained alternatively so that the knowledge learned from named entity recognition task can be enhanced by the knowledge gained from parts-of-speech tagging task. RESULTS: The performance of our proposed model has been evaluated in terms of micro average F-score, macro average F-score and accuracy. It is observed that the proposed model outperforms the baseline model in all cases. For instance, experimental results conducted on the discharge summaries show that the micro average F-score and the macro average F-score are improved by 2.41% point and 4.16% point, respectively, and the overall accuracy is improved by 5.66% point. CONCLUSIONS: In this paper, a novel multitask bi-directional RNN model is proposed for improving the performance of named entity recognition in EMR. Evaluation results using real datasets demonstrate the effectiveness of the proposed model.

Multiscale Tumor Modeling With Drug Pharmacokinetic and Pharmacodynamic Profile Using Stochastic Hybrid System.

Effective cancer treatment strategy requires an understanding of cancer behavior and development across multiple temporal and spatial scales. This has resulted into a growing interest in developing multiscale mathematical models that can simulate cancer growth, development, and response to drug treatments. This study thus investigates multiscale tumor modeling that integrates drug pharmacokinetic and pharmacodynamic (PK/PD) information using stochastic hybrid system modeling framework. Specifically, (1) pathways modeled by differential equations are adopted for gene regulations at the molecular level; (2) cellular automata (CA) model is proposed for the cellular and multicellular scales. Markov chains are used to model the cell behaviors by taking into account the gene expression levels, cell cycle, and the microenvironment. The proposed model enables the prediction of tumor growth under given molecular properties, microenvironment conditions, and drug PK/PD profile. Simulation results demonstrate the effectiveness of the proposed approach and the results agree with observed tumor behaviors.

Anomalous Capillary Rise under Nanoconfinement: A View of Molecular Kinetic Theory.

Understanding the capillary filling behaviors in nanopores is crucial for many science and engineering problems. Compared with the classical Bell-Cameron-Lucas-Washburn (BCLW) theory, anomalous coefficient is always observed because of the increasing role of surfaces. Here, a molecular kinetics approach is adopted to explain the mechanism of anomalous behaviors at the molecular level; a unified model taking account of the confined liquid properties (viscosity and density) and slip boundary condition is proposed to demonstrate the macroscopic consequences, and the model results are successfully validated against the published literature. The results show that (1) the effective viscosity induced by the interaction from the pore wall, as a function of wettability and the pore dimension (nanoslit height or nanotube diameter), may remarkably slow down the capillary filling process more than theoretically predicted. (2) The true slip, where water molecules directly slide on the walls, strongly depends on the wettability and will increase as the contact angle increases. In the hydrophilic nanopores, though, the magnitude may be comparable with the pore dimensions and promote the capillary filling compared with the classical BCLW model. (3) Compared with the other model, the proposed model can successfully predict the capillary filling for both faster or slower capillary filling process; meanwhile, it can capture the underlying physics behind these behaviors at the molecular level based on the effective viscosity and slippage. (4) The surface effects have different influence on the capillary filling in nanoslits and nanotubes, and the relative magnitude will change with the variation of wettability as well as the pore dimension.

Manipulating the Flow of Nanoconfined Water by Temperature Stimulation.

The manipulation of a nanoconfined fluid flow is a great challenge and is critical in both fundamental research and practical applications. Compared with chemical or biochemical stimulation, the use of temperature as controllable, physical stimulation possesses huge advantages, such as low cost, easy operation, reversibility, and no contamination. We demonstrate an elegant, simple strategy by which temperature stimulation can readily manipulate the nanoconfined water flow by tuning interfacial and viscous resistances. We show that with an increase in temperature, the water fluidity is decreased in hydrophilic nanopores, whereas it is enhanced by at least four orders of magnitude in hydrophobic nanopores, especially in carbon nanotubes with a controlled size and atomically smooth walls. We attribute these opposing trends to a dramatic difference in varying surface wettability that results from a small temperature variation.

A mechanistic model of heat transfer for gas-liquid flow in vertical wellbore annuli.

The most prominent aspect of multiphase flow is the variation in the physical distribution of the phases in the flow conduit known as the flow pattern. Several different flow patterns can exist under different flow conditions which have significant effects on liquid holdup, pressure gradient and heat transfer. Gas-liquid two-phase flow in an annulus can be found in a variety of practical situations. In high rate oil and gas production, it may be beneficial to flow fluids vertically through the annulus configuration between well tubing and casing. The flow patterns in annuli are different from pipe flow. There are both casing and tubing liquid films in slug flow and annular flow in the annulus. Multiphase heat transfer depends on the hydrodynamic behavior of the flow. There are very limited research results that can be found in the open literature for multiphase heat transfer in wellbore annuli. A mechanistic model of multiphase heat transfer is developed for different flow patterns of upward gas-liquid flow in vertical annuli. The required local flow parameters are predicted by use of the hydraulic model of steady-state multiphase flow in wellbore annuli recently developed by Yin et al. The modified heat-transfer model for single gas or liquid flow is verified by comparison with Manabe's experimental results. For different flow patterns, it is compared with modified unified Zhang et al. model based on representative diameters.

Sequential Therapeutic Response Modeling for Tumor Treatment Using Computational Hybrid Control Systems Approach.

OBJECTIVE: Tumorigenesis is due to uncontrolled cell division arising from mutations and alterations in the proliferative controls of the cell population. The fight against tumor growth and development has often relied on combination therapy that has been acclaimed as one of the main standards of care in cancer therapeutics and prevention of drug-related resistances. The toxicity of the combinatorial drugs raises a significant concern whenever patients take two or more drugs concurrently at the maximum tolerated dose. A promising solution in tumor treatment involves the administration of the drugs in an alternating or sequential fashion rather than a simultaneous manner. In this paper, we investigate how feasible such an approach is from a mathematical perspective and propose a switched hybrid control systems framework. METHODS: We explore the response of tumor cells dynamics to sequential drugs administration with the aid of a time-dependent switching strategy. A transit compartmentalized model is employed to describe the tumor cells progression to death. RESULTS: The design of the time-based drug switching logic ensures the proliferating tumor cells are repressed. CONCLUSIONS: Simulation results are provided using the tumor growth dynamics with sequential drugs intake to demonstrate the effectiveness of the proposed method in reducing the tumor size. SIGNIFICANCE: This paper is the first attempt to provide a switched hybrid control systems framework on sequential drug administration to biomedical researchers and clinicians.

Review of stochastic hybrid systems with applications in biological systems modeling and analysis.

Stochastic hybrid systems (SHS) have attracted a lot of research interests in recent years. In this paper, we review some of the recent applications of SHS to biological systems modeling and analysis. Due to the nature of molecular interactions, many biological processes can be conveniently described as a mixture of continuous and discrete phenomena employing SHS models. With the advancement of SHS theory, it is expected that insights can be obtained about biological processes such as drug effects on gene regulation. Furthermore, combining with advanced experimental methods, in silico simulations using SHS modeling techniques can be carried out for massive and rapid verification or falsification of biological hypotheses. The hope is to substitute costly and time-consuming in vitro or in vivo experiments or provide guidance for those experiments and generate better hypotheses.

Time-Based Switching Control of Genetic Regulatory Networks: Toward Sequential Drug Intake for Cancer Therapy.

As cancer growth and development typically involves multiple genes and pathways, combination therapy has been touted as the standard of care in the treatment of cancer. However, drug toxicity becomes a major concern whenever a patient takes 2 or more drugs simultaneously at the maximum tolerable dosage. A potential solution would be administering the drugs in a sequential or alternating manner rather than concurrently. This study therefore examines the feasibility of such an approach from a switched system control perspective. Particularly, we study how genetic regulatory systems respond to sequential (switched) drug inputs using the time-based switching mechanism. The design of the time-driven drug switching function guarantees the stability of the genetic regulatory system and the repression of the diseased genes. Simulation results using proof-of-concept models and the proliferation and survival pathways with sequential drug inputs show the effectiveness of the proposed approach.

A 2-Year Holistic Health and Stress Intervention: Results of an RCT in Clergy.

INTRODUCTION: This study sought to determine the effect of a 2-year, multicomponent health intervention (Spirited Life) targeting metabolic syndrome and stress simultaneously. DESIGN: An RCT using a three-cohort multiple baseline design was conducted in 2010-2014. SETTING/PARTICIPANTS: Participants were United Methodist clergy in North Carolina, U.S., in 2010, invited based on occupational status. Of invited 1,745 clergy, 1,114 consented, provided baseline data, and were randomly assigned to immediate intervention (n=395), 1-year waitlist (n=283), or 2-year waitlist (n=436) cohorts for a 48-month trial duration. INTERVENTION: The 2-year intervention consisted of personal goal setting and encouragement to engage in monthly health coaching, an online weight loss intervention, a small grant, and three workshops delivering stress management and theological content supporting healthy behaviors. Participants were not blinded to intervention. MAIN OUTCOME MEASURES: Trial outcomes were metabolic syndrome (primary) and self-reported stress and depressive symptoms (secondary). Intervention effects were estimated in 2016 in an intention-to-treat framework using generalized estimating equations with adjustment for baseline level of the outcome and follow-up time points. Log-link Poisson generalized estimating equations with robust SEs was used to estimate prevalence ratios (PRs) for binary outcomes; mean differences were used for continuous/score outcomes. RESULTS: Baseline prevalence of metabolic syndrome was 50.9% and depression was 11.4%. The 12-month intervention effect showed a benefit for metabolic syndrome (PR=0.86, 95% CI=0.79, 0.94, p<0.001). This benefit was sustained at 24 months of intervention (PR=0.88; 95% CI=0.78, 1.00, p=0.04). There was no significant effect on depression or stress scores. CONCLUSIONS: The Spirited Life intervention improved metabolic syndrome prevalence in a population of U.S. Christian clergy and sustained improvements during 24 months of intervention. These findings offer support for long-duration behavior change interventions and population-level interventions that allow participants to set their own health goals. TRIAL REGISTRATION: This study is registered at www.clinicaltrials.gov NCT01564719.

Wettability effect on nanoconfined water flow.

Understanding and controlling the flow of water confined in nanopores has tremendous implications in theoretical studies and industrial applications. Here, we propose a simple model for the confined water flow based on the concept of effective slip, which is a linear sum of true slip, depending on a contact angle, and apparent slip, caused by a spatial variation of the confined water viscosity as a function of wettability as well as the nanopore dimension. Results from this model show that the flow capacity of confined water is 10-1∼107 times that calculated by the no-slip Hagen-Poiseuille equation for nanopores with various contact angles and dimensions, in agreement with the majority of 53 different study cases from the literature. This work further sheds light on a controversy over an increase or decrease in flow capacity from molecular dynamics simulations and experiments.

Methane storage in nanoporous material at supercritical temperature over a wide range of pressures.

The methane storage behavior in nanoporous material is significantly different from that of a bulk phase, and has a fundamental role in methane extraction from shale and its storage for vehicular applications. Here we show that the behavior and mechanisms of the methane storage are mainly dominated by the ratio of the interaction between methane molecules and nanopores walls to the methane intermolecular interaction, and a geometric constraint. By linking the macroscopic properties of the methane storage to the microscopic properties of a system of methane molecules-nanopores walls, we develop an equation of state for methane at supercritical temperature over a wide range of pressures. Molecular dynamic simulation data demonstrates that this equation is able to relate very well the methane storage behavior with each of the key physical parameters, including a pore size and shape and wall chemistry and roughness. Moreover, this equation only requires one fitted parameter, and is simple, reliable and powerful in application.

Integrating Multiscale Modeling with Drug Effects for Cancer Treatment.

In this paper, we review multiscale modeling for cancer treatment with the incorporation of drug effects from an applied system's pharmacology perspective. Both the classical pharmacology and systems biology are inherently quantitative; however, systems biology focuses more on networks and multi factorial controls over biological processes rather than on drugs and targets in isolation, whereas systems pharmacology has a strong focus on studying drugs with regard to the pharmacokinetic (PK) and pharmacodynamic (PD) relations accompanying drug interactions with multiscale physiology as well as the prediction of dosage-exposure responses and economic potentials of drugs. Thus, it requires multiscale methods to address the need for integrating models from the molecular levels to the cellular, tissue, and organism levels. It is a common belief that tumorigenesis and tumor growth can be best understood and tackled by employing and integrating a multifaceted approach that includes in vivo and in vitro experiments, in silico models, multiscale tumor modeling, continuous/discrete modeling, agent-based modeling, and multiscale modeling with PK/PD drug effect inputs. We provide an example application of multiscale modeling employing stochastic hybrid system for a colon cancer cell line HCT-116 with the application of Lapatinib drug. It is observed that the simulation results are similar to those observed from the setup of the wet-lab experiments at the Translational Genomics Research Institute.