Resultados de investigación

S2 Table. Primers used in this study., Peer reviewed

S3 Table. Raw numerical data of all the figures., Peer reviewed

Table S1. Treatment allocation based on the molecular profile. Table S2. Median PFS and OS and parameters for exponential extrapolation. Table S3. Dosage and estimated monthly costs of the treatments included in the analysis. References only cited in the supplementary material., To assess the cost-effectiveness of using next-generation sequencing (NGS) compared to sequential single-testing (SST) for molecular diagnostic and treatment of patients with advanced non-small cell lung cancer (NSCLC) from a Spanish single-center perspective, the Hospital Universitario Virgen del Rocio (HUVR). A decision-tree model was developed to assess the alterations detection alterations and diagnostic cost in patients with advanced NSCLC, comparing NGS versus SST. Model inputs such as testing, positivity rates, or treatment allocation were obtained from the literature and the clinical practice of HUVR experts through consultation. Several sensitivity analyses were performed to test the robustness of the model. Using NGS for molecular diagnosis of a 100-patients hypothetical cohort, 30 more alterations could be detected and 3 more patients could be enrolled in clinical-trials than using SST. On the other hand, diagnostic costs were increased up to €20,072 using NGS instead of SST. Using NGS time-to-results would be reduced from 16.7 to 9 days. The implementation of NGS at HUVR for the diagnostic of patients with advanced NSCLC provides significant clinical benefits compared to SST in terms of alterations detected, treatment with targeted-therapies and clinical-trial enrollment, and could be considered a cost-effective strategy., This work was funded by Roche Farma S.A., Peer reviewed

Peer reviewed

Intracellular trafficking through the secretory organelles depends on transient interactions between cargo proteins and transport machinery. Cytosolic coat protein complexes capture specific luminal cargo proteins for incorporation into transport vesicles by interacting with them indirectly through a transmembrane adaptor or cargo receptor. Due to their transient nature, it is difficult to study these specific ternary protein interactions just using conventional native co-immunoprecipitation. To overcome this technical challenge, we have applied a crosslinking assay to stabilize the transient and/or weak protein interactions. Here, we describe a protocol of protein cross-linking and co-immunoprecipitation, which was employed to prove the indirect interaction in the endoplasmic reticulum of a luminal secretory protein with a selective subunit of the cytosolic COPII coat through a specific transmembrane cargo receptor. This method can be extended to address other transient ternary interactions between cytosolic proteins and luminal or extracellular proteins through a transmembrane receptor within the endomembrane system., Peer reviewed

[How to contribute] For any problems downloading the data or for additional information, please send us an email and we will provide you with the necessary information. We would also appreciate your feedback regarding potential errors., [Ethics statement] All procedures were conducted according to relevant Spanish national (Decreto 105/2011, 19 de abril) and regional guidelines. Necessary permits for hoopoe manipulation were provided by Consejería de Medio Ambiente de la Junta de Andalucía, Spain (Ref: SGYB/FOA/AFR/CFS and SGMN/GyB/JMIF). All applicable guidelines for the care and use of animals were followed., The research group received funds from the projects PID2020-117429GB-C21 and PID2020-117429GB-C22, funded by the Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación/10.13039/501100011033 and by “Fondo Europeo de Desarrollo Regional, a way of making Europe”. We also benefited from facilities, including an apartment, provided by the city authorities of Guadix., Peer reviewed

16 pages. -- PDF file includes: 1.Characterization. -- 2. Electrocatalysis measurement. -- 3. Photocatalytic reduction of oxygen to hydrogen peroxide. -- 4. RRDE test. -- 5. Computational method. -- 6. Apparent quantum yield (AQY) calculations. -- 7. Structural characterization. -- 8. Elemental analysis. -- 9. Band structure. -- 10. Surface area and porosity. -- 11. Calibration for H2O2 quantification. -- 12. Photocatalytic activity. -- 13. Linear sweep voltammetry. -- 14. DFT calculation results. -- 15. Reaction mechanisms., Peer reviewed

62 pages. -- PDF file includes: 1. Experimental section. -- Figure S1. XRD patterns of pure ZIF-67 (a), Co(OH)2 (b) and Co3O4 (c). -- Figure S2. SEM images of Co3O4 single-shelled nanocages and EDS chemical mapping. -- Figure S3. SEM images of and EDS chemical mapping of CoP-HS (a); CoP2-HS (b); CoP3-HS (c). -- Figure S4. Nitrogen absorption–desorption isotherms and pore size distributions of three cobalt phosphides, CoP-HS (a); CoP2-HS (b); CoP3-HS (c). -- Figure S5. XPS spectra of the XPS full scan for CoP-HS, CoP2-HS and CoP3-HS. -- Figure S6. The CV curves of CoP-HS, CoP2-HS, and CoP3-HS obtained at the 1st (a), 3rd (b), 5th (c), and 10th (d) cycles at a scan rate of 10 mV/s in a 1.0 M KOH solution. -- Figure S7. The OER activities of CoP-HS, CoP2-HS and CoP3-HS were tested by both forward and reverse scan. -- Figure S8. (a) The CV of the CoPx. (b) The double layer capacitance (CDL) was determined as the half of the slope from the plot of the capacitive current vs. scan rate plot. -- Figure S9. Chronopotentiometry responses of activity stabilized CoPx in 1.0 M KOH in the catalytic turnover region. -- Figure S10. (a-c) OER LSV curves with (red) and without (blue) 100% iR drop correction. (d) Corresponding Tafel lines. -- Figure S11. SEM images of CoP-HS (a), CoP2-HS (b) and CoP3-HS (c) single-shelled nanocages after 100 h OER stability measurement. (d) The changed ratio of Co:P before and after stability test. -- Figure S12.SEM of post-OER CoP (a) before HCl wash, (b) after HCl washed. -- Figure S13. The LSV curves of CoP-HS, CoP2-HS, CoP3-HS, Co3O4-HS and Co(OH)2-HS measured in 1.0 M KOH solution toward OER at a scan rate 10 mV/s after activation by 50 CV cycles between 0.0 V and 0.85 V (vs. Hg/HgO) at a scan rate 50 mV/s. -- Figure S14. (a) The LSV curves of carbon paper measured in 1.0 M KOH toward HER at scan rate 10 mV/s. (b) The data of CoP-HS, CoP2-HS and CoP3-HS test in 1.0 M KOH. -- Figure S15. Chronopotentiometry responses of activity stabilized CoPx in 1.0 M KOH in the catalytic turnover region. -- Figure S16. (a-c) HER LSV curves with (red) and without (blue) 100% iR drop correction. (d) Corresponding Tafel lines. -- Figure S17. The CV curves of CoP-HS, CoP2-HS and CoP3-HS measured in 1.0 M KOH solution for 1st (a), 3rd (b), 5th (c), and 10th (d) cycles at a scan rate 10 mV/s. -- Figure S18. (a) The XPS spectra, and (b) the SEM image and EDS chemical mapping of CoP-HS after 100 h HER stability measurement in 1 M KOH. -- Figure S19. TEM images of CoP-HS after HER stability test (a). Elements mapping and SAED of CoP-HS after HER stability test (b-f). -- Figure S20. (a) The LSV curves of CoP-HS, CoP2-HS, CoP3-HS and Pt/C measured in 0.5 M H2SO4 toward HER at scan rate 10 mV/s. (b) The corresponding Tafel plots for the samples in 0.5 M H2SO4. (c) Nyquist plots of CoP-HS, CoP2-HS, CoP3-HS in 0.5 M H2SO4. (All the tests were taken on carbon paper). -- Figure S21. (a) The LSV curves of carbon paper measured in 0.5 M H2SO4 toward HER at scan rate 10 mV/s. (b) The data of CoP-HS, CoP2-HS and CoP3-HS test in 0.5 M H2SO4. -- Figure S22. The CV curves of CoP-HS, CoP2-HS and CoP3-HS measured in 0.5 M H2SO4 solution for 1st (a), 3rd (b), 5th (c), and 10th (d) cycles at a scan rate 10 mV/s. -- Figure S23. (a) The chronopotentiometry curve of CoP at the current density of -20 mA cm-2 for 100 h in 0.5 M H2SO4. (b) The SEM image and EDS chemical mapping (d) of CoP single-shelled nanocages after 100 h HER stability measurement. -- Figure S24. Overall water splitting activities of CoP||CoP and Pt/C||IrO2. -- Figure S25. (a, b, c, d, e, f) Corresponding levels of oxygen and hydrogen gas generated at 0 s, 200 s, 400 s, 600 s, 800 s, 1000 s. -- Figure S26. Optimized configuration of CoP-HS adsorbed with H. -- Figure S27. Optimized configuration of CoP2-HS adsorbed with H. -- Figure S28. Optimized configurations of CoP3-HS adsorbed with H. -- Figure S29. HER free energy changes of CoP-HS, CoP2-HS and CoP3-HS at P-sites and Co-site. in 0.5 M H2SO4. (c) P(2p) XPS spectra of CoP-HS after 100 h HER stability. -- Figure S30. The normalized LSV curves of CoP-HS, CoP2-HS, and CoP3-HS. -- Figure S31. The correlation between the HER free energy changes based on Co-sites of CoPx-HS and the normalized overpotential as well as Tafel slope measurement. -- Table S1. Elemental composition of Co and P in the different cobalt phosphides. -- Table S2. Comparison of the alkaline OER efficiency of those cobalt phosphides with other reported advanced cathodic materials. -- Table S3. Comparison of the alkaline HER efficiency of this CoP with other reported advanced cathodic materials. -- Table S4. Comparison of the acidic HER efficiency of this CoP with other reported advanced cathodic materials. -- Table S5. Comparison of the alkaline overall water-splitting efficiency of this CoP with other reported advanced bifunctional catalysts., Peer reviewed

Additional File 1: Table S1. Demographic and neuropathological characteristics of study subjects. Description: Abbreviations: ADNC = AD neuropathological changes; APOE = Apolipoprotein E genotype; CAA = cerebral amyloid angiopathy; CVD = cerebrovascular disease; F = female; LBD = Lewy body disease; M = male; NA = Not available/applicable; NOS = not otherwise specified; NP Dx = neuropathological diagnosis. Table S2. Primary and secondary antibodies used in this study and sequence of immunohistochemistry cycles. Description: Note: GFAP and DAPI detection are needed in all the cycles to guarantee an adequate alignment of the images. Abbreviations: AF488 = AlexaFluor 488; Cy = cyanine; Dk = donkey; Gt = goat; Ms = mouse; Rb = rabbit. All secondary antibodies were purchased from Jackson ImmunoResearch Labs, West Grove, PA. Table S3. Results of mixed effects regression models. Description: Results of mixed effects regression models with diagnosis (CTRL vs. AD) or state (homeostatic vs. intermediate vs. reactive) as a fixed effect, respectively, and subject ID as random effect in both cases, are reported. Table S4. Model performance statistics for CTRL vs. AD binary classifiers. Description: Model performance statistics for the binary classification task of CTRL vs. AD for both the gradient boosting machine (GBM) and the convolutional neural network (CNN) machine learning models are reported. For all heuristics except for AUC and AUCPR (which are not threshold-dependent), the threshold was chosen by maximizing the accuracy. 95% confidence intervals were estimated by bootstrapping the hold-out test set across 500 iterations. Table S5. Results of Bayesian hyperparameter optimization. Description: The final hyperparameters determined by the Optuna hyperparameter tuning framework are reported. The Optuna optimizer maximized the out-of-sample area under the receiver operating characteristic (ROC) curve (AUC), which in turn was determined by 3-fold cross-validation for each trial., Ministerio de Ciencia, Innovación y Universidades Real Colegio Complutense National Institute on Aging Alzheimer's Association., Peer reviewed

Additional File 2: Figure S1. A β pathology in the temporal pole cortex. Description: Immunohistochemistry for Aβ (mouse monoclonal antibody, clone 6F/3D, Agilent, #M0872, 1:600) with peroxidase/DAB was performed in nearly-adjacent sections to those used for cyclic multiplex fluorescent immunohistochemistry in a Leica BOND-III automated stainer. Sections were counterstained with hematoxylin. Scale bars: 5 mm, insets 200 μm. Figure S2. Phospho-tau pathology in the temporal pole cortex. Description: Immunohistochemsitry for phospho-tauSer202/Thr205(mouse monoclonal antibody, clone AT8, Thermo-Scientific, #MN1020, 1:10,000) with peroxidase/DAB was performed in nearly-adjacent sections to those used for cyclic multiplex fluorescent immunohistochemistry in a Leica BOND-III automated stainer. Sections were counterstained with hematoxylin. Scale bars: 5 mm, insets 200 μm. Figure S3. Expression levels of selected markers across astrocytic and microglial subclusters from public single-nuclei RNA-seq studies. Description: Bubble plots illustrate the percent of nuclei (bubble size) and the gene expression levels (z-scores, color bar) of the astrocytic and microglial markers used in our cyclic multiplex fluorescent immunohistochemistry protocol across the astrocytic and microglial subclusters rendered by several published single-nuclei RNA-seq data sets. Note that our set of markers discriminates some of these transcriptomic subclusters. Figure S4. Characterization of astrocytes and microglia in AD vs. CTRL by cortical layer. Description: Box and whisker plots illustrate the distribution (box: median and interquartile range [IQR]; whiskers: 1.5 × IQR) of mean gray intensity (MGI) z-scores for (a) each astrocytic marker and (b) each microglial marker across the CTRL and AD groups by cortical layer. Only layers II to VI were included in this study. Figure S5. Characterization of astrocytic and microglial states by cortical layer. Description: Box and whisker plots show the distribution (box: median and interquartile range [IQR]; whiskers: 1.5 × IQR) of mean gray intensity (MGI) z-scores for each astrocytic (a) or microglial (b) marker across the three phenotypes by cortical layer. Only layers II to VI were included in this study. Figure S6. Effects of proximity to AD neuropathological changes on astrocytic and microglial phenotypes from two CTRL subjects with abundant Aβ plaques. Description: (a) Representative high-plex image of astrocytes from a CTRL subject with abundant Aβ plaques; note the differences with AD astrocytes in Fig. 5a. For clarity, only ALDH1L1, EAAT2, and GFAP markers are shown together with Aβ. Scale bar: 100 µm, insets a1–a3: 10 µm. (b) Histograms show the proportion of each astrocyte phenotype in n=2 CTRL subjects with abundant Aβ plaques relative to all their astrocytes as a function of their distance (µm, x axis) to the nearest Aβ plaque. Note that there are equal numbers of astrocytes within 25 µm from the nearest Aβ plaque classified as homeostatic, intermediate, or reactive. (c) Representative high-plex image of microglia from the same field of the same CTRL with abundant Aβ plaques; note the differences when compared to AD microglia in Fig. 5c. For clarity, only IBA1, TMEM119, and CD68 markers are shown together with Aβ. Scale bar: 100 µm, insets c1–c3: 10 µm. (d) Histograms indicate the proportion of each microglial phenotype in n=2 CTRL subjects with abundant Aβ plaques relative to all their microglial profiles as a function of their distance (µm, x axis) to the nearest Aβ plaque. Note that most microglia in the vicinity of Aβ plaques were classified as homeostatic, suggesting that their phenotypic transition to intermediate and reactive had not yet occurred. Figure S7. Differences in neuritic component of Aβ plaques from CTRL and AD subjects. Description: Representative images of Aβ and phospho-tau (PHF1) immunohistochemistry corresponding to the same fields of the AD and CTRL subjects shown in Fig. 5 and Fig. S6, respectively. Note the differences in the PHF1+ neuritic changes between CTRL and AD Aβ plaques. Scale bar: 100 µm, insets a1 and b1: 10 µm. Figure S8. Gradient boosting machine models accurately discriminate between glial phenotypes. Description: Receiver operating characteristic (ROC) curves demonstrate the high discriminative power of the gradient boosting machine (GBM) models to discern between states (i.e., homeostatic vs. intermediate vs. reactive) of (a) astrocytes and (b) microglia based on mean gray intensity (MGI) data from thousands of high-plex single-cell profiles. Rankings of the variable importance scores shown in the horizontal bar plots reveal the most relevant markers for each classification task, respectively. Figure S9. Application of deep learning model interpretability functions to astrocytes with extreme classification probabilities. Description: Examples of the convolutional neural network (CNN) model interpretability functions applied to astrocytes with extreme classification probabilities (i.e., confident and correct predictions). Columns 1 and 5 show DAPI and all astrocyte markers of the high-plex image of a single astrocyte cell body from a CTRL and an AD subject, respectively, after performing the CNN normalization steps described (i.e., segmentation, interpolation, channel-level z-score). Hence, the signal intensity is represented by dynamic range rather than by pixel intensity. Columns 2–4 and 6–8 show the saliency (2 and 6), integrated gradient (3 and 7), and GradCAM (4 and 8) maps, which illustrate the pixels of each marker that the CNN considered most important for the classification of these two astrocytes as CTRL or AD. Figure S10. Application of deep learning model interpretability functions to microglia with extreme classification probabilities. Description: Examples of the convolutional neural network (CNN) model interpretability functions applied to microglia with extreme classification probabilities (i.e., confident and correct predictions). Columns 1 and 5 show DAPI and all microglial markers of the high-plex image of a single microglial cell from a CTRL and an AD subject, respectively, after performing the CNN normalization steps described (i.e., segmentation, interpolation, channel-level z-score). Hence, the signal intensity is represented by dynamic range rather than by pixel intensity. Columns 2–4 and 6–8 show the saliency (2 and 6), integrated gradient (3 and 7), and GradCAM (4 and 8) maps, which illustrate the pixels of each marker that the CNN considered most important for the classification of these two microglia as CTRL or AD., Ministerio de Ciencia, Innovación y Universidades Real Colegio Complutense National Institute on Aging Alzheimer's Association., Peer reviewed