SUMMARY Development of cancer has been linked to chronic inflammation, particularly via interleukin-23 (IL-23) and IL-17 signaling pathways. However, the cellular source of IL-17 and underlying mechanisms by which IL-17-producing cells promote human colorectal cancer (CRC) remain poorly defined. Here, we demonstrate that innate γδT (γδT17) cells are the major cellular source of IL-17 in human CRC. Microbial products elicited by tumorous epithelial barrier disruption correlated with inflammatory dendritic cell (inf-DC) accumulation and γδT17 polarization in human tumors. Activated inf-DCs induced γδT17 cells to secrete IL-8, tumor necrosis factor alpha, and GM-CSF with a concomitant accumulation of immunosuppressive PMN-MDSCs in the tumor. Importantly, γδT17 cell infiltration positively correlated with tumor stages and other clinicopathological features. Our study uncovers an inf-DC-γδT17-PMN-MDSC regulatory axis in human CRC that correlates MDSC-meditated immunosuppression with tumor-elicited inflammation. These findings suggest that γδT17 cells might be key players in human CRC progression and have the potential for treatment or prognosis prediction.

Metabolic reprogramming is a hallmark of cancer cells and is used by cancer cells for growth and survival. Pyruvate kinase muscle isozyme M2 (PKM2) is a limiting glycolytic enzyme that catalyzes the final step in glycolysis, which is key in tumor metabolism and growth. The present review discusses the expression and regulation of PKM2, and reports the dominant role that PKM2 plays in glycolysis to achieve the nutrient demands of cancer cell proliferation. In addition, the present study discusses the non-metabolic function of PKM2, and its role as a coactivator and protein kinase, which contributes to tumorigenesis. Furthermore, conflicting studies concerning the role of PKM2 as a therapeutic target are reviewed. The improved understanding of PKM2 may provide a noval approach for cancer treatment.

The bottom-up prediction of the properties of polymeric materials based on molecular dynamics simulation is a major challenge in soft matter physics. Coarse-grained (CG) models are often employed to access greater spatiotemporal scales required for many applications, but these models normally experience significantly altered thermodynamics and highly accelerated dynamics due to the reduced number of degrees of freedom upon coarse-graining. While CG models can be calibrated to meet certain properties at particular state points, there is unfortunately no temperature transferable and chemically specific coarse-graining method that allows for modeling of polymer dynamics over a wide temperature range. Here, we pragmatically address this problem by “correcting” for deviations in activation free energies that occur upon coarse-graining the dynamics of a model polymeric material (polystyrene). In particular, we propose a new strategy based on concepts drawn from the Adam−Gibbs (AG) theory of glass formation. Namely we renormalize the cohesive interaction strength and effective interaction length-scale parameters to modify the activation free energy. We show that this energy-renormalization method for CG modeling allows accurate prediction of atomistic dynamics over the Arrhenius regime, the non-Arrhenius regime of glass formation, and even the non-equilibrium glassy regime, thus allowing for the predictive modeling of dynamic properties of polymer over the entire range of glass formation. Our work provides a practical scheme for establishing temperature transferable coarse-grained models for predicting and designing the properties of polymeric materials.

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Precision medicine and personalized medicine are based on the development of biomarkers, and liquid biopsy has been reported to be able to detect biomarkers that carry information on tumor development and progression. Compared with traditional ‘solid biopsy’, which cannot always be performed to determine tumor dynamics, liquid biopsy has notable advantages in that it is a noninvasive modality that can provide diagnostic and prognostic information prior to treatment, during treatment and during progression. In this review, we describe the source, characteristics, technology for detection and current situation of circulating tumor cells, circulating free DNA and exosomes used for diagnosis, recurrence monitoring, prognosis assessment and medication planning.

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We present a systematic, two-bead per monomer coarse-graining strategy allowing for the prediction of the thermomechanical behavior of polystyrene. Analytical bonded potentials optimized to match atomistic bonded distributions for different stereochemistries emulate local structure. Alternatively, the backbone torsional potential is leveraged to match the chain stiffness in a direct approach. Nonbonded potentials using a temperature-dependent density correction term demonstrate transferability of the temperature-dependent modulus. Flory–Fox constants of the T g-optimized CG model are commensurate with all-atomistic and experimental results. The thermomechanically consistent coarse-graining (TCCG) procedure is demonstrated using polystyrene as a benchmark system to be a robust and effective technique to extend the computational prediction of the thermomechanical behavior of polymers to the mesoscale.

We present a versatile systematic two-bead-per-monomer coarse-grain modeling strategy for simulating the thermomechanical behavior of methacrylate polymers at length and time scales far exceeding atomistic simulations. We establish generic bonded interaction parameters via Boltzmann inversion of probability distributions obtained from the common coarse-grain bead center locations of fi ve different methacrylate polymers. Distinguishing features of each monomer side-chain group are captured using Lennard-Jones nonbonded potentials with parameters specifi ed to match the density and glass-transition temperature values obtained from all-atomistic simulations. The developed force fi eld is validated using Flory-Fox scaling relationships, self-diffusion coeffi cients of monomers, and modulus of elasticity for p (MMA). Our approach establishes a transferable, effi cient, and accurate scalebridging strategy for investigating the thermomechanics of copolymers, polymer blends, and nanocomposites.