Multiscale Modeling of Skeletal Muscle Active Contraction in Relation to Mechanochemical Coupling of Molecular Motors

In this work, a mathematical model was developed to relate the mechanochemical characterizations of molecular motors with the macroscopic manifestation of muscle contraction. Non-equilibrium statistical mechanics were used to study the collective behavior of myosin molecular motors in terms of the complex conformation change and multiple chemical states in one working cycle. The stochastic evolution of molecular motor probability density distribution during the contraction of sarcomere was characterized by the Fokker-Planck Equation. Quick muscle contraction was demonstrated by the collective dynamic behavior of myosin motors, the muscle contraction force, and the muscle contraction velocity-force relation. Our results are validated against published experiments, as well as the predictions from the Hill’s model. The quantitative relation between myosin molecular motors and muscle contraction provides a novel way to unravel the mechanism of force generation

Risk Assessment and Prediction of Aflatoxin in Agro-Products

Aflatoxin (AFT), highly toxic and carcinogenic to humans, seriously threatens consumption safety of agro-products. It is necessary to conduct risk assessment of aflatoxin contamination in agro-food products to find out critical control points (CCPs) and develop prediction, prevention and control theories and technologies. In this chapter, risk assessment and prediction of aflatoxin contamination in peanut were taken as an example. The values under the limit of detection (LOD) were replaced by zero, 1/2 LOD or LOD according to their respective proportion, and the distribution of values higher than the LOD was fitted by @RISK software. AFB1 dietary exposure was evaluated based on non-parametric probability risk assessment and margin of exposure (MOE). A risk ranking method was adopted for mycotoxins based on food risk expectation ranking. Spatial analysis of AFB1 contamination was conducted using geographic information system (GIS). Average climatic conditions were calculated by Thiessen polygon method and the relationship between AFB1 concentration and average pre-harvest climatic conditions was obtained through multiple regression. To fulfill the purposes of reducing cost, increasing efficiency, maximizing the role of risk assessment and prediction, and improving the quality and safety of agricultural products, we will continuously focus on developing advanced and integrated technologies and solutions

Scaffold Structural Microenvironmental Cues to Guide Tissue Regeneration in Bone Tissue Applications

In the process of bone regeneration, new bone formation is largely affected by physico-chemical cues in the surrounding microenvironment. Tissue cells reside in a complex scaffold physiological microenvironment. The scaffold should provide certain circumstance full of structural cues to enhance multipotent mesenchymal stem cell (MSC) differentiation, osteoblast growth, extracellular matrix (ECM) deposition, and subsequent new bone formation. This article reviewed advances in fabrication technology that enable the creation of biomaterials with well-defined pore structure and surface topography, which can be sensed by host tissue cells (esp., stem cells) and subsequently determine cell fates during differentiation. Three important cues, including scaffold pore structure (i.e., porosity and pore size), grain size, and surface topography were studied. These findings improve our understanding of how the mechanism scaffold microenvironmental cues guide bone tissue regeneration

Scalable Culturing of Primary Human Glioblastoma Tumor- Initiating Cells with a Cell-Friendly Culture System

Glioblastoma is the most aggressive and deadly brain cancer. There is growing interest to develop drugs that specifically target to glioblastoma tumor-initiating cells (TICs). However, the cost-effective production of large numbers of high quality glioblastoma TICs for drug discovery with current cell culturing technologies remains very challenging. Here, we report a new method that cultures glioblastoma TICs in microscale alginate hydrogel tubes (or AlgTubes). The AlgTubes allowed long-term culturing (~50 days, 10 passages) of glioblastoma TICs with high growth rate (~700-fold expansion/14 days), high cell viability and high volumetric yield (~3.0 × 108 cells/mL) without losing the stem cell properties, all offered large advancements over current culturing methods. This method can be applied for the scalable production of glioblastoma TICs at affordable cost for drug discovery

Engineered Microenvironment for Manufacturing Human Pluripotent Stem Cell-Derived Vascular Smooth Muscle Cells

Human pluripotent stem cell-derived vascular smooth muscle cells (hPSC-VSMCs) are of great value for disease modeling, drug screening, cell therapies, and tissue engineering. However, producing a high quantity of hPSC-VSMCs with current cell culture technologies remains very challenging. Here, we report a scalable method for manufacturing hPSC-VSMCs in alginate hydrogel microtubes (i.e., AlgTubes), which protect cells from hydrodynamic stresses and limit cell mass to \u3c400 \u3eμm ensure efficient mass transport. The tubes provide cells a friendly microenvironment, leading to extremely high culture efficiency.We have shown that hPSC-VSMCs can be generated in 10 days with high viability, high purity, and high yield (~5.0 x 108 cells/mL). Phenotype and gene expression showed that VSMCs made in AlgTubes and VSMCs made in 2D cultures were similar overall. However, AlgTube-VSMCs had higher expression of genes related to vasculature development and angiogenesis, and 2D-VSMCs had higher expression of genes related to cell death and biosynthetic processes

Scalable Culturing of Primary Human Glioblastoma Tumor- Initiating Cells with a Cell-Friendly Culture System

Glioblastoma is the most aggressive and deadly brain cancer. There is growing interest to develop drugs that specifically target to glioblastoma tumor-initiating cells (TICs). However, the cost-effective production of large numbers of high quality glioblastoma TICs for drug discovery with current cell culturing technologies remains very challenging. Here, we report a new method that cultures glioblastoma TICs in microscale alginate hydrogel tubes (or AlgTubes). The AlgTubes allowed long-term culturing (~50 days, 10 passages) of glioblastoma TICs with high growth rate (~700-fold expansion/14 days), high cell viability and high volumetric yield (~3.0 × 108 cells/mL) without losing the stem cell properties, all offered large advancements over current culturing methods. This method can be applied for the scalable production of glioblastoma TICs at affordable cost for drug discovery

Scalable Culturing of Primary Human Glioblastoma Tumor- Initiating Cells with a Cell-Friendly Culture System

Glioblastoma is the most aggressive and deadly brain cancer. There is growing interest to develop drugs that specifically target to glioblastoma tumor-initiating cells (TICs). However, the cost-effective production of large numbers of high quality glioblastoma TICs for drug discovery with current cell culturing technologies remains very challenging. Here, we report a new method that cultures glioblastoma TICs in microscale alginate hydrogel tubes (or AlgTubes). The AlgTubes allowed long-term culturing (~50 days, 10 passages) of glioblastoma TICs with high growth rate (~700-fold expansion/14 days), high cell viability and high volumetric yield (~3.0 × 108 cells/mL) without losing the stem cell properties, all offered large advancements over current culturing methods. This method can be applied for the scalable production of glioblastoma TICs at affordable cost for drug discovery

Scalable Culturing of Primary Human Glioblastoma Tumor- Initiating Cells with a Cell-Friendly Culture System

Glioblastoma is the most aggressive and deadly brain cancer. There is growing interest to develop drugs that specifically target to glioblastoma tumor-initiating cells (TICs). However, the cost-effective production of large numbers of high quality glioblastoma TICs for drug discovery with current cell culturing technologies remains very challenging. Here, we report a new method that cultures glioblastoma TICs in microscale alginate hydrogel tubes (or AlgTubes). The AlgTubes allowed long-term culturing (~50 days, 10 passages) of glioblastoma TICs with high growth rate (~700-fold expansion/14 days), high cell viability and high volumetric yield (~3.0 × 108 cells/mL) without losing the stem cell properties, all offered large advancements over current culturing methods. This method can be applied for the scalable production of glioblastoma TICs at affordable cost for drug discovery

Clinical-radiomics-based treatment decision support for KIT Exon 11 deletion in gastrointestinal stromal tumors: a multi-institutional retrospective study

Objectivegastrointestinal stromal tumors (GISTs) with KIT exon 11 deletions have more malignant clinical outcomes. A radiomics model was constructed for the preoperative prediction of KIT exon 11 deletion in GISTs.MethodsOverall, 126 patients with GISTs who underwent preoperative enhanced CT were included. GISTs were manually segmented using ITK-SNAP in the arterial phase (AP) and portal venous phase (PVP) images of enhanced CT. Features were extracted using Anaconda (version 4.2.0) with PyRadiomics. Radiomics models were constructed by LASSO. The clinical-radiomics model (combined model) was constructed by combining the clinical model with the best diagnostic effective radiomics model. ROC curves were used to compare the diagnostic effectiveness of radiomics model, clinical model, and combined model. Diagnostic effectiveness among radiomics model, clinical model and combine model were analyzed in external cohort (n=57). Statistics were carried out using R 3.6.1.ResultsThe Radscore showed favorable diagnostic efficacy. Among all radiomics models, the AP-PVP radiomics model exhibited excellent performance in the training cohort, with an AUC of 0.787 (95% CI: 0.687-0.866), which was verified in the test cohort (AUC=0.775, 95% CI: 0.608-0.895). Clinical features were also analyzed. Among the radiomics, clinical and combined models, the combined model showed favorable diagnostic efficacy in the training (AUC=0.863) and test cohorts (AUC=0.851). The combined model yielded the largest AUC of 0.829 (95% CI, 0.621–0.950) for the external validation of the combined model. GIST patients could be divided into high or low risk subgroups of recurrence and mortality by the Radscore.ConclusionThe radiomics models based on enhanced CT for predicting KIT exon 11 deletion mutations have good diagnostic performance

A prediction model for short-term neurodevelopmental impairment in preterm infants with gestational age less than 32 weeks

IntroductionEarly identification and intervention of neurodevelopmental impairment in preterm infants may significantly improve their outcomes. This study aimed to build a prediction model for short-term neurodevelopmental impairment in preterm infants using machine learning method.MethodsPreterm infants with gestational age  < 32 weeks who were hospitalized in The Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, and were followed-up to 18 months corrected age were included to build the prediction model. The training set and test set are divided according to 8:2 randomly by Microsoft Excel. We firstly established a logistic regression model to screen out the indicators that have a significant effect on predicting neurodevelopmental impairment. The normalized weights of each indicator were obtained by building a Support Vector Machine, in order to measure the importance of each predictor, then the dimension of the indicators was further reduced by principal component analysis methods. Both discrimination and calibration were assessed with a bootstrap of 505 resamples.ResultsIn total, 387 eligible cases were collected, 78 were randomly selected for external validation. Multivariate logistic regression demonstrated that gestational age(p = 0.0004), extrauterine growth restriction (p = 0.0367), vaginal delivery (p = 0.0009), and hyperbilirubinemia (0.0015) were more important to predict the occurrence of neurodevelopmental impairment in preterm infants. The Support Vector Machine had an area under the curve of 0.9800 on the training set. The results of the model were exported based on 10-fold cross-validation. In addition, the area under the curve on the test set is 0.70. The external validation proves the reliability of the prediction model.ConclusionA support vector machine based on perinatal factors was developed to predict the occurrence of neurodevelopmental impairment in preterm infants with gestational age  < 32 weeks. The prediction model provides clinicians with an accurate and effective tool for the prevention and early intervention of neurodevelopmental impairment in this population