5,893 research outputs found

    Efficacy of immune checkpoint inhibitors for metastatic colorectal cancer with microsatellite instability in second or latter line using synthetic control arms: A non-randomised evaluation

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    Purpose Immune checkpoint inhibitors (ICIs) appeared active in single-arm trials for patients with chemoresistant metastatic colorectal cancer (mCRC) harboring microsatellite instability (MSI). Given the paucity of randomised controlled trials (RCTs) in this setting, we evaluated the effect size of ICIs using intra-patients comparison and ARCAD database as historical controls. Patients and methods Individual-patient data from NIPICOL and CheckMate 142 phases II trials that evaluated a combination of ICIs for MSI mCRC patients (N=176) and from five non-ICI mCRC historical RCTs in second-line or latter (N=4026) were analyzed. Firstly, promising of ICIs was identified using intra-patient comparison based on growth modulation index (GMI). Survival outcomes of ICIs-treated patients were then compared with those matched non-ICIs treated from ARCAD database historical RCTs. Results Among ICIs-treated patients, median progression-free survival (PFS) on ICIs was 32.66 (range 0.10-74.25) versus 4.07 months (range 0.7-49.87) on prior therapy, resulting on median GMI of 4.97 (range 0.07-59.51; hazard-ratio (HR)=0.16 (95%CI=0.11-0.22, P<0.001)). Compared to matched non-ICI patients, in third-line, median overall survival (OS) was not reached with ICIs versus 3.52 months with placebo (HR=0.20, 95%CI=0.10-0.41, P<0.001), and 6.51 months with active drugs (HR=0.30, 95%CI=0.15-0.60, P=0.001). In second-line, median OS was not reached with ICIs versus 11.7 months with chemotherapy+placebo (HR=0.12, 95%CI=0.07-0.22, P<0.001), and 16.3 months with chemotherapy+targeted therapy (HR=0.10, 95%CI=0.05-0.19, P<0.001). Conclusion ICIs demonstrates high effect size for MSI mCRC patients in second-line and later. This work might be useful as an example of methodology to avoid RCTs when benefit from experimental therapy is likely to be high

    Exploring the Dispersion and Electrostatic Components in Arene–Arene Interactions between Ligands and G4 DNA to Develop G4-Ligands

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    G-Quadruplex (G4) DNA structures are important regulatory elements in central biological processes. Small molecules that selectively bind and stabilize G4 structures have therapeutic potential, and there are currently >1000 known G4 ligands. Despite this, only two G4 ligands ever made it to clinical trials. In this work, we synthesized several heterocyclic G4 ligands and studied their interactions with G4s (e.g., G4s from the c-MYC, c-KIT, and BCL-2 promoters) using biochemical assays. We further studied the effect of selected compounds on cell viability, the effect on the number of G4s in cells, and their pharmacokinetic properties. This identified potent G4 ligands with suitable properties and further revealed that the dispersion component in arene–arene interactions in combination with electron-deficient electrostatics is central for the ligand to bind with the G4 efficiently. The presented design strategy can be applied in the further development of G4-ligands with suitable properties to explore G4s as therapeutic targets

    Exploring the Dispersion and Electrostatic Components in Arene–Arene Interactions between Ligands and G4 DNA to Develop G4-Ligands

    No full text
    G-Quadruplex (G4) DNA structures are important regulatory elements in central biological processes. Small molecules that selectively bind and stabilize G4 structures have therapeutic potential, and there are currently >1000 known G4 ligands. Despite this, only two G4 ligands ever made it to clinical trials. In this work, we synthesized several heterocyclic G4 ligands and studied their interactions with G4s (e.g., G4s from the c-MYC, c-KIT, and BCL-2 promoters) using biochemical assays. We further studied the effect of selected compounds on cell viability, the effect on the number of G4s in cells, and their pharmacokinetic properties. This identified potent G4 ligands with suitable properties and further revealed that the dispersion component in arene–arene interactions in combination with electron-deficient electrostatics is central for the ligand to bind with the G4 efficiently. The presented design strategy can be applied in the further development of G4-ligands with suitable properties to explore G4s as therapeutic targets

    Spatial disparities across labour markets

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    We consider disparities across local labour markets in Great Britain. Disparities in wages and employment rates are large and persistent, although smaller than 20 years ago. These disparities largely reflect the concentration of high-skilled workers, who would have better labour market outcomes wherever they live. This concentration is driven by differences in the demand for, and supply of, skills and the self-reinforcing interaction between the two, which is particularly pronounced in the highest-wage areas and at the upper end of the wage distribution. The highest-paid jobs are concentrated in London and a handful of other areas and wage disparities are mostly driven by the higher-paid. Places that offer higher earnings also have higher rents, which may entirely offset gains in earnings. Consistent with this, people in higher-paid places are no happier than those in lower-paid places

    Comparison of “valve” genes to published ST/single cell RNASeq datasets.

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    A) Comparison of the top 20 genes in our valve cluster with the human embryonic “atrioventricular mesenchyme and valve” ST cluster from Asp et al (2019) reveals 5 shared genes. Similarly, comparison of our top 30 valve cluster genes with scRNASeq “VIC” cluster data from P7 and P30 mouse atrioventricular and arterial valves (Hulin et al, 2019) reveals 7 genes in common. Notably, LGALS1, which has not previously been reported to be specific to the arterial valves, is found in all 3 datasets. B) Immunohistochemistry for LGALS1 in human and mouse embryonic hearts. White boxes denote the area covered by the high-power images (b,d). LGALS1 is expressed at high level in the developing aorto-pulmonary septum and at lower level in the mesenchyme of the distal and proximal outflow tract cushions of the human CS16 heart. In the E11.5 mouse heart, Lgals1 is found at high level in the aortopulmonary septum and throughout the distal and proximal cushions. LGALS1/Lgals1 is found at only low level in the endocardium in both species (arrowheads). C) Circos plot showing the top 30 genes in our valve cluster mapped to GO terms. D) STX10, HES4 and MRXA5, none of which are found in the mouse genome, are expressed (red dots) in the developing outflow tract at CS16. White boxes denote the area covered by the high-power images (d,e,f). MXRA5 is expressed at high level in the aortopulmonary septum (APS) and the proximal cushions, whereas HES4 is restricted to the APS and STX10 is found at lower level in both tissues. STX10 and HES4 are strongly expressed in the forming walls of the arterial roots, whereas MXRA5 is found only at low level in this tissue. Whereas all three genes are found in the walls of the forming arterial roots, only STX10 and HES4 are also found in the endocardium in this region. Ao = aortic valve; APS = aortopulmonary septum; cm = cushion mesenchyme; doftc = distal outflow tract cushions; end = endocardium; oftw = outflow tract wall; poftc = proximal outflow tract cushions; Pu = pulmonary valve. Scale bar in B = 100μm in a,c, 20μm in b,d. Scale bar in D = 300μm in a-c, 40μm in d-f.</p

    Functional associations of valve cluster genes generated by filtered IPA analysis.

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    A) Top 20 regulators of genes in the valve cluster. B) Circos plot linking valve regulators to cellular processes. The strongest associations relate to cell movement and growth/proliferation but cardiovascular disease is also a string association for this group of valve regulatory genes. C) Following filtering for the terms “cardiac” and “development” regulation of EMT was the only pathway significantly upregulated in the valve dataset compared to the myocardial and blood clusters. Notch and Wnt were the likely upstream regulators. Pathways highlight the genes in the Notch and Wnt pathways that are positively associated regulators of EMT for the valve cluster (dark green genes are the most differentially upregulated, with mid green and light green progressively less so). D,E) IPA pathway analysis shows that for disease and biological processes, developmental terms such as development of trunk, genitourinary system, vasculature and neurons were strongly associated with the valve cluster. Negative associations were also found, the most striking being those to familial heart/cardiovascular disease, and congenital heart/cardiovascular disease/anomaly. These terms were positively associated with the myocardial cluster dataset (red box).</p

    Correction to: Comparison of fracture risk assessment tools in older men without prior hip or spine fracture: the MrOS study

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    The italicized text in the following sentence was reversed in the original paper. The corrected sentence is below: “The FRAX, Garvan tool, and QFracture were poorly calibrated; calibration plots revealed that the risk scores overestimated observed hip fracture incidence in the lowest deciles of scores and underestimated observed hip fracture incidence in the highest deciles of scores (Fig. 1).” The authors regret their error. The original article has been corrected

    Neural distinctiveness and discriminability underlying unitization and associative memory in aging

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    Previous work has suggested unitized pairs behave as a single unit and more critically, are processed neurally different than those of associative memories. The current works examines the neural differences between unitization and non-unitized memory using fMRI and multivoxel analyses. Specifically, we examined the differences across face-occupation pairings as a function of whether the pairing was viewed as a person performing the given job (unitized binding) or a person saying they knew someone who had a particular job (non-unitized binding). The results show that at encoding and retrieval, the angular gyrus can discriminate between unitized and non-unitized target trials. Additionally, during encoding, the medial temporal lobe (hippocampus and perirhinal cortex), frontal parietal regions (angular gyrus and medial frontal gyrus) and visual regions (middle occipital cortex) exhibit distinct neural patterns to recollected unitized and non-unitized targets. Furthermore, the perirhinal cortex and medial frontal gyrus show greater neural similarity within subsequently recollected unitized trials compared to non-unitized trials. We conclude that an encoding based strategy to elicit unitization can produce greater associative memory compared to non-unitized trials in older adults. Additionally, when unitized trials are subsequently recollected in the perirhinal cortex older adults show greater neural similarity within unitized trials compared to non-unitized trials

    Cluster analysis.

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    A) UMAP projection showing overlap of data between sections from the CS16 (orange dots) and CS19 (turquoise dots) embryos. B) UMAP projection showing clustering of combined CS16 and CS19 data, again showing three clusters (red, blue and orange dots). C) Heat map showing top 10 genes in each cluster, highlighting the gene expression differences between the three clusters. At this stage, based on the expressed genes, putative tissue types can be proposed as myocardium (orange), cushion (blue) and blood (red). D) Perfect marker analysis using recognised markers for specific cardiac cell types confirms the orange cluster as myocardium, the red cluster as blood cells, and shows that the putative cushion cluster has characteristics of cushions, bone and fibrous tissue, and thus should be better named as “valve” as this implies the entire structure, not just the developing leaflets. E) Mapping “perfect markers” back to the H&E sections shows that they localise to the expected areas (compare to Fig 1D). The H&E section shows the identity of the tissues and structures contained within the section. oft = outflow tract, oftw = outflow tract wall, rbc = red blood cells (in lumen), v = ventricular myocardium, vlp = valve leaflet primordia.</p
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