BUSCADOR RECOLECTA

Table 1: Examples of feedback from clinicians in relation to dominant themes from analysis of model evaluation., Background: Post-infarct ventricular septal defect (PIVSD) is a serious complication of myocardial infarction. We evaluated 3D-printing models in PIVSD clinical assessment and the feasibility of statistical shape modeling for morphological analysis of the defects. Methods: Models (n = 15) reconstructed from computed tomography data were evaluated by clinicians (n = 8). Statistical shape modeling was performed on 3D meshes to calculate the mean morphological configuration of the defects. Results: Clinicians’ evaluation highlighted the models’ utility in displaying defects for interventional/surgical planning, education/training and device development. However, models lack dynamic representation. Morphological analysis was feasible and revealed oval-shaped (n = 12) and complex channel-like (n = 3) defects. Conclusion: 3D-PIVSD models can complement imaging data for teaching and procedural planning. Statistical shape modeling is feasible in this scenario., The authors gratefully acknowledge the support of the British Heart Foundation (CH/17/1/32804), the Bristol BHF Accelerator Award (AA/18/1/34219), The Grand Appeal (Bristol Children’s Hospital Charity), and the Bristol National Institute for Health Research (NIHR) Biomedical Research Centre (BRC). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed., Peer reviewed

(A) Schematic depicting key stages in postembryonic zebrafish liver development. (B) Experimental schematics of long-term lineage tracing experiments using fraeppli-nls embryos, inducing recombination by heat shock at 26 hpf to label hepatoblasts. At 120 hpf, embryos were screened by live imaging at the confocal microscope, and only sparsely labelled embryos were raised and fixed in either juvenile or adult stages. (C-H) Recombined livers showed different cluster topologies: clusters along central veins (C-C’) (n = 9 livers), proximal–distal stripes (D) (n = 23 livers) or giant clusters in the ventral lobe in adult (F-G’) (n = 3 livers). Large clusters in the ventral lobe can originate from one single-labelled cell at 5 dpf (n = 1 liver) (E). (F) Stereomicroscope image showing the spatial location of the giant clone originating from a single recombined cell (H). Recombined livers show a range of cluster sizes from small (H’) to medium (H”). (I) Schematics of characteristic cluster topologies in recombined livers. Red lines indicate the blood vessel orientation in the liver. (C-H) Total numbers are (N = 9, n = 79 livers). A, anterior; P, posterior; R, right; L, left; RL, right lobe; LL, left lobe; VL, ventral lobe., Peer reviewed

(A) Frequency of manually assigned pure hepatocyte clone sizes (N = 6, n = 190 clones). (B) Distribution of the corresponding number of cell divisions for each pure hepatocyte clone (N = 6, n = 190 clones). (A, B) Clone colours are plotted in blue (TagBFP), turquoise (mTFP1), magenta (mKate2), and orange (E2-Orange); the mean of all colours is represented in black. (C) Whole-mount of a 100-hpf liver showing several clones, including a mKate2+ 1-cell clone (N = 6, n = 15 livers). (D) Liver with a medium size 12-cell mTFP1+ clone (N = 6, n = 7 livers). (E) Whole-mount of a 100-hpf liver with a large 33-cell TagBFP+ clone (N = 1, n = 1 livers). (C-E) Labelled cells are represented as segmented nuclei, and an overall segmentation of the whole liver tissue is shown in transparent grey. The numerical values that were used to generate the graphs in (A, B) can be found in S1 Data., Peer reviewed

Supplementary table 1. Analysis of CD10, CD184, CD19, CD133, CD166 and CD44 positive subpopulations by FACS in HNSCC cell lines.-- Supplementary Table 2. Differential genes common to CD10, CD184, CD19 and NAMPT subpopulations obtained from transcriptomic analysis. All the genes in color are related to tumorigenic process, acting as oncogenes (in red), tumor suppressor genes (in blue), ambiguous genes depending on the type of the tumor (in green).-- Supplementary Table 3: IC50s for NAMPT inhibitors in parental and NAMPT CRISPR clones of both cell lines.-- Supplementary figure 1: Verification of NAMPT overexpression in RPMI and Detroit HNSCC cell lines. A: Western blot showing increased NAMPT ectopic overexpression. B: increased NAD total and NAD+ pools in cells overexpressing NAMPT (in orange).-- Supplementary Figure 2. Overall survival of HNSCC patients from the TCGA database. Kaplan-Meier curves show the overall survival of HNSCC patients with high and low expression levels of CD10, CD184, CD19, CD133, CD166 and NAMPT genes from the TCGA (The Cancer Genome Atlas) database, N=520.-- Supplementary Figure 3. GO term analysis. Analysis of the genes by the terms GO biological process, molecular function and cellular component (p<0.05).-- Supplementary Figure 4. Transcriptomic analysis of differential genes common to CD10, CD184, CD19 and NAMPT subpopulations in HNSCC cell lines. Venn diagram represents the differential genes common to positive and negative CD10, CD184 and CD19 populations and NAMPT overexpression and CRISPRs in RPMI-2650 and Detroit-562 cell lines obtained by NGS sequencing.-- Supplementary Figure 5: Densitometric quantification of NAMPT expression in the WB of figure 2C.-- Supplementary Figure 6: Densitometric quantification of NAMPT and NAPRT expression in the WB of Figure 5C.-- Supplementary materials: Original images of western blots., Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous group of tumors that affect different anatomical locations. Despite this heterogeneity, HNSCC treatment depends on the anatomical location, TNM stage and resectability of the tumor. Classical chemotherapy is based on platinum-derived drugs (cisplatin, carboplatin and oxaliplatin), taxanes (docetaxel, paclitaxel) and 5-fluorouracil1. Despite advances in HNSCC treatment, the rate of tumor recurrence and patient mortality remain high. Therefore, the search for new prognostic identifiers and treatments targeting therapy-resistant tumor cells is vital. Our work demonstrates that there are different subgroups with high phenotypic plasticity within the CSC population in HNSCC. CD10, CD184, and CD166 may identify some of these CSC subpopulations with NAMPT as a common metabolic gene for the resilient cells of these subpopulations. We observed that NAMPT reduction causes a decrease in tumorigenic and stemness properties, migration capacity and CSC phenotype through NAD pool depletion. However, NAMPT-inhibited cells can acquire resistance by activating the NAPRT enzyme of the Preiss-Handler pathway. We observed that coadministration of the NAMPT inhibitor with the NAPRT inhibitor cooperated inhibiting tumor growth. The use of an NAPRT inhibitor as an adjuvant improved NAMPT inhibitor efficacy and reduced the dose and toxicity of these inhibitors. Therefore, it seems that the reduction in the NAD pool could have efficacy in tumor therapy. This was confirmed by in vitro assays supplying the cells with products of inhibited enzymes (NA, NMN or NAD) and restoring their tumorigenic and stemness properties. In conclusion, the coinhibition of NAMPT and NAPRT improved the efficacy of antitumor treatment, indicating that the reduction in the NAD pool is important to prevent tumor growth., This research was funded by Grants RTI2018-097455-B-I00 and PID2021-122629OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, by the “European Union”. Additional grants from CIBER de Cáncer (CB16/12/00275), from Consejeria de Salud (PI-0397–2017) and Project P18-RT-2501 from 2018 competitive research projects call within the scope of PAIDI 2020—80% co-financed by the European Regional Development Fund (ERDF) from the Regional Ministry of Economic Transformation, Industry, Knowledge and Universities. Junta de Andalucía. Special thanks to the AECC (Spanish Association of Cancer Research) Founding Ref. GC16173720CARR for supporting this work. AES was funded by a grant from the Fundación AECC. EMVS was funded by a postdoctoral fellowship from Junta de Andalucía (CTEICU/PAIDI 2020). LEN, ES-M and LS-D were funded by Spanish ministry of education (FPU16/0290; FPU17/02173; FPU18/01009)., Peer reviewed

(A) Schematic of FRaeppli-NLS cassette including attB and attP sites for PhiC31-mediated recombination and the 4 FRaeppli FPs: TagBFP, mTFP1, mKate2, and E2-Orange. Recombination is induced by combining fraeppli-nls with hsp70l:phiC31; prox1a:kalTA4; see S3A Fig. (B) Key steps of liver development in zebrafish: After hepatoblast specification, the differentiation into BECs and hepatocytes is initiated at around 42 hpf. Differentiated cells acquire polarity and form a functional architecture by 120 hpf. (C) Experimental strategy for tracing progeny of individual hepatoblasts using fraeppli-nls: Heat shock at 26 hpf controls PhiC31 expression followed by attB-attP recombination. Embryos were fixed at 100 hpf for analysis. (D-F) Whole-mount livers at 100 hpf showing (D) mixed clone composed of hepatocytes and BECs (D’) (N = 6, n = 23 clones); (E) clones formed by pure hepatocytes (E’-E”) (N = 6, n = 190 clones); and (F) example of pure BEC clone coexpressing TagBFP and mTFP1 (white, coexpressing cells were manually segmented and masked). (F’) (N = 2, n = 2 clones). (D-F) An overall segmentation of the whole liver tissue is shown in transparent grey. (G) Pie charts showing the total number of labelled embryos and clones with manually assigned lineage contributions (N = 6, n = 214 clones; in 2 of the 6 experiments, nuclear shape indicated BEC fate). The numerical values that were used to generate the graphs in (G) can be found in S1 Data. BEC, biliary epithelial cell; FP, fluorescent protein; hpf, hours post fertilization., Peer reviewed

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(A) Schematic of a 5-dpf liver, highlighting the biliary network. (B-B’) Maximum projection (200 μm z-stack) of a 120-hpf liver expressing tp1:H2B-mCherry (BEC) and stained for Hnf4ɑ (hepatocyte). Autofluorescent blood cells appear in bright white. (N = 4, n ≥ 12 livers) (C) Relative distribution of BECs and hepatocytes at 120 hpf (N = 4, n ≥ 12 livers). (D-F) Mathematical models simulating hepatoblast differentiation employing different parameter combinations: proliferation rates of differentiated cell types is equal (D, F) or slower in BECs (E). Hepatoblasts either are all bipotent (D, E) or represent a heterogeneous population with mixed probabilities for uni-or bipotent differentiation (F). Plots showing the simulated cell proportions over simulation time (n = 10) and the final cell type ratio in bar graphs. The numerical values that were used to generate the graphs in (C-F) can be found in S1 Data. BEC, biliary epithelial cell; dpf, day postfertilization; Hb, hepatoblast; Hc, hepatocyte; hpf, hours post fertilization., Peer reviewed

The Bible in 11 languages and a historical corpus of English works., Peer reviewed

(A, B) Confocal images of the same liver showing the embryonic left liver lobe at 5 dpf with a 3-cell mKate2+ clone (A) and at juvenile stage (SL = 14.4 mm) including a continuous Kate2+ clone in the ventral lobe (N = 1, n = 1 liver). (C, D) Juvenile livers (C–SL = 8.46 mm and D–SL = 10.93 mm) with connected clusters that are oriented along the tissue edge and spread through the left and the ventral lobe. Arrows indicate cluster growth direction (N = 4, n = 14 livers). (E-P) Brightfield images of stages I-VI livers in loco within the fish (E, G, I, K, M, O) or dissected out (F, H, J, L, N, P). In (M), the liver is removed and the gut bend is visible. A, anterior; P, posterior; R, right; L, left; RL, right lobe; LL, left lobe; VL, ventral lobe., Peer reviewed

(A) Fish standard length (SL) plotted against fish age. (B, C) Fish weight (B) and liver weight (C) increases with SL represented in a semi-log plot. (D) Liver-to-body weight ratio during postembryonic growth is constant in adult fish. (N > 10, n ≥ 300 fish). Gender of the corresponding samples is colour coded: male (blue), female (pink), and ND (green). The numerical values that were used to generate the graphs can be found in S1 Data., Peer reviewed