BUSCADOR RECOLECTA

Tables summarizing the Ru NP morphology and size obtained for different experimental conditions; figures showing TEM images, STEM-HAADF images, and high-resolution STEM-HAADF images of Ru NPs; local EDS analyses of Ru icosahedra; high-resolution XPS spectra of Ru NPs in the C 1s and Ru 3d energy range; X-ray photoelectron spectra of Ru NPs; table summarizing the DFT energies of Ru polyhedra of 147 atoms (hcp, ico, cubo); and the link to interactive three-dimensional (3D) models., Peer reviewed

Ex vivo physiological systems for the study of SARS-CoV-2-host interactions are scarce. We establish a method using primary human lung tissue (HLT) cells for the rapid analysis of cell tropism and identification of therapeutics. Main findings: i) HLT cells preserve main cell subpopulations, including alveolar type-II cells, and expression of SARS-CoV-2 entry factors ACE2, CD147, TMPRSS2 and AXL. ii) HLT cells are readily susceptible to SARS-CoV-2 infection without the need of cell isolation or further cell differentiation. iii) Antiviral testing in HLT cells allows the rapid identification of new drug candidates against SARS-CoV-2 variants, missed by conventional systems. iv) Local inflammation is supported in HLT cells and offers the identification of relevant anti-inflammatory compounds for SARS-CoV-2 infection., Peer reviewed

3 pages. -- 1 table and 1 figure. -- Fig. S1 Representative voltammograms obtained after measurement process with the integrated system in different conditions: (a) laboratory measurement of pure water sample spiked with 50 and 200 µg/L for Pb and Cu respectively. The measured areas were 0.454 µAV (left peak) and 1.816 µAV (right peak) for Pb and Cu, respectively; (b) laboratory measurement in the water tank (500 L) filled with Danube water spiked with 25 µg/L and 100 µg/L for Pb and Cu respectively. The measured areas were 0.396 µAV (left peak) and 1.244 µAV (right peak) for Pb and Cu respectively; (c) laboratory measurement in the water tank (500 L) filled with ground water (bank filtration of Danube) spiked with 25 µg/L and 100 µg/L for Pb and Cu respectively. The measured areas were 1.46 µAV (left peak) and 1.27 µAV (right peak) for Pb and Cu respectively; (d) field measurement of the Danube River. The measured areas were 0.018 µAV for Pb and Cu was not detected (, The ICN2 is funded by the CERCA Programme/Generalitat de Catalunya. The ICN2 is supported by the Severo Ochoa program of the Spanish Ministry of Economy, Industry, and Competitiveness (MINECO, Grant No. SEV-2017–0706)., Peer reviewed

10 pages. -- Figures and tables. -- Figure S1: SEM image of (a) bulk g-C3N4 and (b) ultrathin g-C3N4, (c) N2 adsorption-desorption isotherms of bCN and uCN. -- Figure S2: FTIR spectra of OAC, OLMA and TiO2 before and after ligands remove. -- Figure S3: Zeta potential distribution spectrum of TiO2 after ligands removal (a) and uCN (b). -- Figure S4: SEM image and EDS compositional maps of a T1/uCN1 composite. -- Figure S5: SEM image of T1/uCN2 and corresponding EDS spectrum. -- Figure S6: SEM image of T1/uCN2 and corresponding EDS spectrum. -- Figure S7: SEM image of T1/uCN2 and corresponding EDS spectrum; Figure S8: Chromatogram plots for 0.5 ml of standard hydrogen injected every half hour. -- Table S1: Gas Chromatography Peak Processing Data based on figure S8. -- Figure S9: Standard hydrogen curve for gas chromatography. -- Table S2: Exponential decay-fitted parameters of fluorescence lifetime of uCN, TiO2 and T1/uCN1. -- Figure S10: Photocatalytic hydrogen generation amount on bCN, TiO2 and T1/bCN1 during 4 h under simulated solar light irradiation; Table S3: Photocatalytic hydrogen production about TiO2/g-C3N4 based catalysts. -- Table S4: The AQE values with different incident light wavelengths for T1/uCN1. -- Figure S11: (a) Stability cycles of the T1/uCN1 for H2 evolution under simulated solar light irradiation; (b) TEM image of T1/uCN1 after 20 h photocatalytic H2 evolution reaction and (c) XRD pattern of T1/uCN1 before and after 20 h photocatalytic H2O2 evolution reaction., CN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and is funded by the CERCAProgramme / Generalitat de Catalunya., Peer reviewed

A) Percentage of enriched AT-II cells co-expressing the entry factors ACE2 and CD147 (in blue), and ACE2 and TMPRSS2 (in purple). (B) t-distributed Stochastic Neighbor Embedding (tSNE) representation for AXL expression in CD45+ and CD45-EpCAM+ fractions from a representative lung tissue. Right graphs show the percentage of expression of the AXL entry factor in the different cell populations, which were identified as in Fig 1A. (C) Bar plots showing the percentage of viral entry inhibition on HLT cells in the presence of anti-ACE2 antibody (15μg/ml) or recombinant human AXL (50μg/ml) after cell challenge with VSV*ΔG(Luc)-Spike. Data were analyzed by one sample t-test; *p<0.05, **p<0.01. (D) Frequency of each subset relative to live cells at 0h and 24h with and without the presence of virus. All cell subsets were identified as shown in S2A Fig. (E) EC50 values in the HLT model obtained from 3 different lung donors and performed in replicates. (F) Cells from 1 donor were cultured with 20 μM of selected drugs for 48h, and cell toxicity was measured using the CellTiter-Glo Luminescent kit (Promega), following the manufacturer’s instructions. Data was normalized to the untreated control. Mean±SEM is shown for all graphs, Peer reviewed

Fig. S1. KSR1ΔCA1 localizes to the plasma membrane and binds BRAF. Fig. S2. 3D model of the mKSR1 kinase domain. Fig. S3. Purification of recombinant KSR1 protein. Fig. S4. RAS-independent proliferation in the absence of p53 does not involve KSR. Fig. S5. Western blot analysis of KSR1 expression levels in parental and resistant MIA PaCa-2 (A) as well as PDX-dc1 (B) cell lines. Fig. S6. Model of KSR-driven proliferation in RASless cells., Peer reviewed

Peer reviewed

Figure A.1: Selection of Comparator Arms for ITC Analyses Figure A.2: Results of sensitivity analyses with OIs removed for OS at all (A) and first (B) index dates Figure A.3: Results of sensitivity analyses with LocoMMotion removed for OS at all (A) and first (B) index dates, and PF at first index dates (C) Table A.1: Characteristics of Data Sources for PCT arms in ITCs Table A.2: Published ITC Results and Augmented Results Included in Meta-analyses (All Index Dates) Table A.3: Published ITC Results and Augmented Results Included in Meta-analyses (First Index Dates) Table A.4: Baseline Covariates After Adjustment (mITT Populations; All Index Dates) Table A.5: Baseline Covariates After Adjustment (mITT Populations; First Index Dates) Table A.6: Outcome Definitions in ITC Analyses, [Objective]: In the absence of head-to-head trials, indirect treatment comparisons (ITCs) between ciltacabtagene autoleucel (cilta-cel; in CARTITUDE-1) and treatments used in real-world clinical practice (physician’s choice of treatment [PCT]), were previously conducted. We conducted multiple meta-analyses using available ITC data to consolidate the effectiveness of cilta-cel versus PCT for patients with triple-class exposed relapsed or refractory multiple myeloma (RRMM). [Methods]: Five ITCs were assessed for similarity to ensure robust comparisons using meta-analysis. Effectiveness outcomes were overall survival (OS), progression-free survival (PFS), time to next treatment (TTNT), and overall response rate (ORR). A robust variance estimator was used to account for the use of CARTITUDE-1 in each pairwise ITC. Analyses were conducted in both treated and enrolled populations of CARTITUDE-1. [Results]: Four ITCs were combined for evaluation of OS. Results were statistically significantly in favor of cilta-cel versus PCT in treated patients (hazard ratio [HR]: 0.24, 95% confidence interval [CI]: 0.22–0.26). Three ITCs were combined for evaluation of PFS and TTNT. Cilta-cel reduced the risk of progression and receiving a subsequent treatment by 80% (HR: 0.20 [95% CI: 0.06, 0.70]) and 83% (HR: 0.17 [95% CI: 0.12, 0.26]), respectively. Three ITCs were combined for evaluation of ORR. Cilta-cel increased the odds of achieving an overall response by 86-times versus PCT in treated patients. Findings were consistent in the enrolled populations and across sensitivity analyses. [Conclusions]: Evaluating multiple indirect comparisons, cilta-cel demonstrated a significantly superior advantage over PCT, highlighting its effectiveness as a therapy in patients with triple-class exposed RRMM., Peer reviewed

Supplementary Notes 1: Case Reports. Supplementary Notes 2: 3D modeling analysis of the identified Thyroid Peroxidase (TPO), Dual Oxidase 2 (DUOX2) and Iodothyrosine Deiodinase I IYD variants. Supplementary Table 1. Human thyroid peroxidase, dual oxidase 2 and iodothyrosine deiodinase missense variants identified and their analysis with amino acid substitution prediction tools. Supplementary Table 2. Identification of human Thyroid Peroxidase (TPO) variants by Next-Generation Sequencing (NGS). Supplementary Table 3. Identification of human Dual Oxidase 2 (DUOX2) variants by Next-Generation Sequencing (NGS). Supplementary Figure 1. Differential diagnosis in patients with thyroid dyshormonogenesis. Supplementary Figure 2. Partial protein alignment of the Homo sapiens, Camelus dromedarius, Rattus norvegicus, Mus musculus, Culex quinquefasciatus, Sus scrofa, Lynx Canadensis, Xenopus tropicalis and Canis lupus familiaris thyroid peroxidase (TPO) species. Supplementary Figure 3. Partial protein alignment of the Homo sapiens, Mustela erminea, Sus scrofa, Mus musculus, Lynx canadensis, Bos taurus, Gallus gallus, Pan troglodytes dual oxidase 2 (DUOX2) species., Peer reviewed

(A) General gating strategy used to identify different cell subsets in lung samples. A gate based on FSC vs. SSC was followed by doublet and dead cells exclusion. From live CD45- cells, endothelial cells (CD31+, purple) and epithelial cells (EpCAM+, grey) were gated, and within epithelial cells, AT-II cells (EpCAM+ and HLA-DR+, pink) were identified. Out of live CD45+ cells and based on FSC vs. SSC, we identified a lymphocyte population in which we distinguished between non-T lymphocytes (turquoise) and T cells (dark green) based on CD3 expression; and big cells, where we identified three major subsets based on their expression of CD11b and CD11c and, subsequently, CD14 and HLA-DR markers. We identified alveolar macrophages (blue), monocytes (violet), myeloid dendritic cells (mDCs, fuchsia) and neutrophils (orange)., Peer reviewed