Organ transplantation is the most effective therapy for patients with end-stage disease. Preservation solutions and techniques are crucial for donor organ quality, which is directly related to morbidity and survival after transplantation. Currently, static cold storage (SCS) is the standard method for organ preservation. However, preservation time with SCS is limited as prolonged cold storage increases the risk of early graft dysfunction that contributes to chronic complications. Furthermore, the growing demand for the use of marginal donor organs requires methods for organ assessment and repair. Machine perfusion has resurfaced and dominates current research on organ preservation. It is credited to its dynamic nature and physiological-like environment. The development of more sophisticated machine perfusion techniques and better perfusates may lead to organ repair/reconditioning. This review describes the history of organ preservation, summarizes the progresses that has been made to date, and discusses future directions for organ preservation.

BackgroundTransient protein-protein interactions (PPIs), which underly most biological processes, are a prime target for therapeutic development. Immense progress has been made towards computational prediction of PPIs using methods such as protein docking and sequence analysis. However, docking generally requires high resolution structures of both of the binding partners and sequence analysis requires that a significant number of recurrent patterns exist for the identification of a potential binding site. Researchers have turned to machine learning to overcome some of the other methods’ restrictions by generalising interface sites with sets of descriptive features. Best practices for dataset generation, features, and learning algorithms have not yet been identified or agreed upon, and an analysis of the overall efficacy of machine learning based PPI predictors is due, in order to highlight potential areas for improvement.ResultsThe presence of unknown interaction sites as a result of limited knowledge about protein interactions in the testing set dramatically reduces prediction accuracy. Greater accuracy in labelling the data by enforcing higher interface site rates per domain resulted in an average 44% improvement across multiple machine learning algorithms. A set of 10 biologically unrelated proteins that were consistently predicted on with high accuracy emerged through our analysis. We identify seven features with the most predictive power over multiple datasets and machine learning algorithms. Through our analysis, we created a new predictor, RAD-T, that outperforms existing non-structurally specializing machine learning protein interface predictors, with an average 59% increase in MCC score on a dataset with a high number of interactions.ConclusionCurrent methods of evaluating machine-learning based PPI predictors tend to undervalue their performance, which may be artificially decreased by the presence of un-identified interaction sites. Changes to predictors’ training sets will be integral to the future progress of interface prediction by machine learning methods. We reveal the need for a larger test set of well studied proteins or domain-specific scoring algorithms to compensate for poor interaction site identification on proteins in general.

Polycomb Repressive Complex 2 (PRC2) is an epigenetic regulator induced in many cancers. It is thought to drive tumorigenesis by repressing division, stemness, and/or developmental regulators. Cancers evade immune detection, and diverse immune regulators are perturbed in different tumors. It is unclear how such cell-specific effects are coordinated. Here, we show a profound and cancer-selective role for PRC2 in repressing multiple cytokine pathways. We find that PRC2 represses hundreds of IFNγ stimulated genes (ISGs), cytokines and cytokine receptors. This target repertoire is significantly broadened in cancer vs non-cancer cells, and is distinct in different cancer types. PRC2 is therefore a higher order regulator of the immune program in cancer cells. Inhibiting PRC2 with either RNAi or EZH2 inhibitors activates cytokine/cytokine receptor promoters marked with bivalent H3K27me3/H3K4me3 chromatin, and augments responsiveness to diverse immune signals. PRC2 inhibition rescues immune gene induction even in the absence of SWI/SNF, a tumor suppressor defective in ~20% of human cancers. This novel PRC2 function in tumor cells could profoundly impact the mechanism of action and efficacy of EZH2 inhibitors in cancer treatment.

Social distancing is the core policy response to COVID-19. But as federal, state and local governments begin opening businesses and relaxing shelter-in-place orders worldwide, we lack quantitative evidence on how policies in one region affect mobility and social distancing in other regions and the consequences of uncoordinated regional policies adopted in the presence of such spillovers. We therefore combined daily, county-level data on shelter-in-place and business closure policies with movement data from over 27 million mobile devices, social network connections among over 220 million of Facebook users, daily temperature and precipitation data from 62,000 weather stations and county-level census data on population demographics to estimate the geographic and social network spillovers created by regional policies across the United States. Our analysis showed the contact patterns of people in a given region are significantly influenced by the policies and behaviors of people in other, sometimes distant, regions. When just one third of a state’s social and geographic peer states adopt shelter in place policies, it creates a reduction in mobility equal to the state’s own policy decisions. These spillovers are mediated by peer travel and distancing behaviors in those states. A simple analytical model calibrated with our empirical estimates demonstrated that the “loss from anarchy” in uncoordinated state policies is increasing in the number of non cooperating states and the size of social and geographic spillovers. These results suggest a substantial cost of uncoordinated government responses to COVID-19 when people, ideas, and media move across borders.

Development of nanoparticle-based drug delivery systems has been attempted for the treatment of cancer over the past decade. The enhanced permeability and retention (EPR) effect is the major mechanism to passively deliver nanodrugs to tumor tissue. However, a recent systematic review demonstrated limited success of these studies, with the clearance of nanoparticles by the mononuclear phagocytic system (MPS) being a major hurdle. Herein, we propose that nanotechnologists should reconsider their research focuses, aiming for therapeutic targets other than cancer. Treatments for diseases that do not (or less) rely on EPR should be considered, such as active targeting or MPS evasion systems. For example, systemic delivery of drugs through intravenous injection can be used to treat sepsis, multi-organ failure, metabolic disorders, blood diseases, immune and autoimmune diseases, etc. Local delivery of nanodrugs to organs such as the lung, rectum, or bladder may enhance the local drug concentration with less clearance via MPS. In transplant settings, ex vivo organ perfusion provides a new route to repair injury of isolated organs in the absence of MPS. Based on a similar concept, chemotherapy with in vivo lung perfusion techniques and other isolated organ perfusion provides opportunities for cancer therapy.

A new expanded porphycene with 26 π-electrons has been prepared by the McMurry coupling of 1,4-bis(3,4-diethyl-2-pyrryl)benzene dialdehyde. Expansion of the porphycene framework provides a ligand capable of stabilizing a bis(rhodium) and a monoruthenium complex. These new porphycene derivatives absorb strongly in the NIR spectral region, with appreciable absorptivity up to 1300 nm. On the basis of their ground- and excited-state spectroscopic features and structural parameters, both the free-base system and the bis(rhodium) complex are considered to be Hückel-type aromatic systems. This conclusion is supported by DFT calculations.

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We report here strategic functionalization of the FDA approved chelator deferasirox (1) in an effort to produce organelle-targeting iron chelators with enhanced activity against A549 lung cancer cells. Derivative 8...