Abstract Solar‐driven high‐efficiency and direct conversion of methane into high‐value‐added liquid oxygenates against overoxidation remains a great challenge. Herein, facile and mass fabrication of low‐cost tungsten single‐atom photocatalysts is achieved by directly calcining urea and sodium tungstate under atmosphere (W‐SA‐PCN‐ m , urea amount m = 7.5, 15, 30, and 150 g). The single‐atom photocatalysts can manage H 2 O 2 in situ generation and decomposition into ·OH, thus achieving highly efficient CH 4 photooxidation in water vapor under mild conditions. Systematic investigations demonstrate that integration of multifunctions of methane activation, H 2 O 2 generation, and decomposition into one photocatalyst can dramatically promote methane conversion to C1 oxygenates with a yield as high as 4956 µmol g cat −1 , superior to that of the most reported non‐precious photocatalysts. Liquid–solid phase transition can induce the products to facilely switch in from HCOOH to CH 3 OH by pulling the catalyst above water with CH 3 OH/HCOOH ratio from 10% (in H 2 O) to 80% (above H 2 O).

<p>Expression of BaC-associated genes in murine mammary epithelial subpopulations</p>

<p>Histologic expression of basal markers in HR-positive breast tumors at different tumor stages. <b>A,</b> Representative sections of HR-positive samples at different tumor stages (LGDCIS, DCIS, and IDC) stained with the indicated basal markers. Scale bar, 50 μm. <b>B,</b> Bar plots representing the percentage of cells expressing each marker in in HR-positive samples across the different tumor stages. Statistics calculated two-way ANOVA. *, <i>P</i> < 0.05; **, <i>P</i> < 0.005; ns, not significant.</p>

<p>Primary antibodies used for immunohistochemistry</p>

<p>Correlation between ACTA2, TAGLN, and TPM2 expression and drug sensitivity of selected compounds of drug screening</p>

<p>TAGL as a predictive marker for dasatinib sensitivity. <b>A,</b> Western blot of control and <i>TAGLN</i>-KO cells in Hs578T (top) and BT-549 (bottom) cells. <b>B,</b> Representative immunofluorescence of Hs578T and BT549 control and <i>TAGLN</i>-KO cells, stained with αTAGL (green) and DAPI (blue). Scale bar, 50 μm. <b>C,</b> Cell proliferation in control (black) and <i>TAGLN</i>-KO (purple) cells measured by MTT assays over 72 hours. <b>D,</b> Relative percentage of migrated cells in control and <i>TAGLN</i>-KO cells, assessed by transwell assays. In <b>C</b> and <b>D</b>, values represent the mean ± SEM of at least three experimental replicates, and statistical significance was determined using one-way ANOVA. <b>E,</b> Drug–response curves for cell viability of control and <i>TAGLN</i>-KO cells treated with dasatinib at increasing concentrations. <b>F,</b> Western blot of known dasatinib targets in control and <i>TAGLN</i>-KO cells. <b>G,</b> Data from the <i>DepMap</i> portal showing the correlation between <i>TAGLN</i> and <i>PDGFRB</i> in breast cancer cell lines. <b>H,</b> Western blot of PDGFRβ in <i>TAGLN</i>-KO and cells and clones constitutively expressing <i>PDGFRB</i>. <b>I,</b> Drug–response curves for cell viability of <i>TAGLN</i>-KO and <i>TAGLN</i>-KO/<i>PBGFRB</i> cells treated with dasatinib at increasing concentrations. In <b>E</b> and <b>I</b>, solid lines represent the mean of three biological replicates performed in technical replicates. The dashed line indicates their IC<sub>50</sub> value. **, <i>P</i> < 0.01; ****, <i>P</i> < 0.0001; ns, not significant.</p>

<p>Immunohistochemical validation of basal markers in TNBC samples</p>

<p>Secondary antibodies used for IHC, IF, and WB assays</p>

<p>Evaluation of dasatinib sensitivity in tB-TNBC models <i>in vitro</i> and <i>in vivo</i>. <b>A,</b> Dose–response curves showing cell viability of indicated cell lines treated with increasing concentrations of dasatinib for 72 hours. Solid lines represent the mean of three biological replicates performed in technical replicates normalized to untreated controls, and IC<sub>50</sub> values are indicated. <b>B,</b> Representative IHC images of tB-TNBC PDXs (PDX_01 and PDX_02) showing expression of SMA, TAGL, and TPM2. Scale bars, 100 μm. <b>C,</b> Quantification of positive tumor cells for each tB-marker in PDX_01 and PDX_02. Data are shown as mean ± SEM of different tumor areas. <b>D,</b> Schematic representation of the dasatinib treatment protocol. <b>E,</b> Tumor growth curves of the tB-TNBC PDXs treated with vehicle (black) or dasatinib (red). Error bars, ± SEM. <i>P</i> values calculated by Wilcoxon test. <b>F,</b> Bar plot showing tumor weight at the end point. Data are shown as mean ± SEM. <i>P</i> values calculated by two-tailed Student <i>t</i> test. **, <i>P</i> < 0.01. <b>D,</b> Created in BioRender. Rodilla, V. (2026) <a href="https://BioRender.com/qpb7bux" target="_blank">https://BioRender.com/qpb7bux</a>.</p>

<p>Distribution of clinical and pathological features in the validation cohort of Hospital Universitario Santa Lucía, Cartagena</p>

<p>Integration of three scRNA-seq datasets from healthy human mammary gland</p>

<p>Integration of three scRNA-seq datasets from adult murine mammary gland</p>

<p>Identification of candidate compounds selectively targeting tB-TNBC cells. <b>A,</b> Heatmap showing the transcriptomic expression of tB-markers in a panel of breast cancer cell lines. <b>B,</b> Schematic representation of the FDA-approved drug library screening workflow. Cell viability heatmap shows the response of each cell line to the 3,200 tested compounds. ***, <i>P</i> < 0.001. <b>C,</b> Dot plot of average cell viability (%) in tB-TNBC (<i>x</i>-axis) and nB-TNBC (<i>y</i>-axis) cell lines after treatment. Dotted red and blue lines indicate the viability thresholds used for compound selection (<20% viability in tB-TNBC and >20% in nB-TNBC). Colored dots highlight the selective compounds. <b>D,</b> Heatmap showing the viability percentages of the selected compounds across the four TNBC cell lines used in the screen. <b>E,</b> Correlation plots showing the association between <i>ACTA2</i>, <i>TAGLN</i>, and <i>TPM2</i> expression (log<sub>2</sub> TPM + 1) and dasatinib sensitivity (AUC) in breast cancer cell lines. Red lines, regression fit; shaded areas show 95% confidence intervals. <i>P</i> values calculated by Pearson correlation. <b>B,</b> Created in BioRender. Rodilla, V. (2026) <a href="https://BioRender.com/qpb7bux" target="_blank">https://BioRender.com/qpb7bux</a>.</p>

<p>Prognostic value of ACTA2, TAGLN, and TPM2</p>

In today’s digital age, people make use of digital media platforms to interact and ‘influence’ each other on social media (Brown and Hayes, 2007). Social media influencers (SMI) are such users who have attained a celebrity-like status and secured a considerable network of followers by developing engaging content based on expertise in a specific domain, and who directly influence the behavior of their followers (Hearn and Schonhoff, 2016; De Veriman et al., 2017; Ge and Gretzel, 2018). VI, also termed as a virtual celebrity, digital personality, CGI influencer (Mrad et al., 2022), or AI influencer (Thomas and Fowler, 2021) refer to fictional personalities created through computer imagery (Miao et al., 2022). They possess human- like characteristics (Burden and Savin-Baden, 2019), physical features and personality (Moustakas et al., 2020), and indulge in online activity (Hudders et al., 2021; Moustakas et al., 2020). VI exhibit anthropomorphism (Moustakas et al., 2020), ranging from anime-like to almost human-like appearance (Kim et al., 2023), and akin to human influencers, can be effective as they can look and behave like human influencers (Sands et al., 2020). In the case of human SMIs, scholars have adopted the opinion leadership framework to analyze the persuasive traits of an endorser. Katz and Lazersfeld (1955) have highlighted the significant role played by the media spokesperson in persuading audience into use sponsors’ product or services, which they termed as the ‘opinion leader’. Thus, trust in an endorser is of utmost importance. Consumer segments are heterogeneous and factors which contribute or detract viewer trust in VI can also differ. VI phenomena are at the introductory stage in emerging economies like India, and we aim to gain insight on viewers perception of VI as new endorser.

BACKGROUND: Accurate locoregional staging is critical in the management of T1 rectal cancer and guides organ-preserving strategies. Despite guideline recommendations, the real-world performance of magnetic resonance imaging (MRI) and endoscopic ultrasound (EUS) remains unclear. This study aims to evaluate the use and accuracy of locoregional staging in T1 rectal cancer. METHODS: Retrospective analysis was performed from a nationwide multicentre T1 colorectal cancer cohort (EpiT1 Consortium) between 2007 and 2018 with a 5-year follow-up. Locoregional staging methods included MRI and EUS. Multivariable logistic regression identified factors associated with locoregional imaging. The accuracy of each technique and their combination for T and N staging was assessed. RESULTS: Among 3161 patients with T1 colorectal cancer, 681 had rectal cancer, of which 424 (62.3%) underwent locoregional staging: 234 (55.2%) with MRI only, 131 (30.9%) with both MRI and EUS, and 59 (13.9%) with EUS only. Factors associated with imaging (MRI and EUS) included management at a referral centre (odds ratio 2.9, 95% confidence interval 1.5 to 5.7), tumour location in the low/middle rectum (odds ratio 3.2, 1.8 to 5.7), suspicion of invasive carcinoma at colonoscopy (odds ratio 2.4, 1.3 to 4.5), and high-risk histological features (odds ratio 3.7, 1.8 to 7.4). MRI accurately staged T in 28.3%, whereas EUS achieved an accuracy of 59% for T staging. Combining modalities overstaged 67.1% of tumours for T staging. N staging was detected with ≤ 16% sensitivity across all strategies; in the surgical group (MRI and/or EUS), overall accuracy was 78%. CONCLUSION: Locoregional staging varied widely and was influenced by non-tumour-related factors. MRI and EUS showed modest accuracy, overstaging T, and low sensitivity for N. These findings highlight the need to improve pretreatment evaluation of T1 rectal cancer.

Abstract For heterogeneous catalysts, the active sites exposed on the surface have been investigated intensively, yet the effect of the subsurface‐underlying atoms is much less scrutinized. Here, a surface‐engineering strategy to dope Ru into the subsurface/surface of Co matrix is reported, which alters the electronic structure and lattice strain of the catalyst surface. Using hydrogen evolution (HER) as a model reaction, it is found that the subsurface doping Ru can optimize the hydrogen adsorption energy and improve the catalytic performance, with overpotentials of 28 and 45 mV at 10 mA cm −2 in alkaline and acidic media, respectively, and in particular, 28 mV in neutral electrolyte. The experimental results and theoretical calculations indicate that the subsurface/surface doping Ru improves the HER efficiency in terms of both thermodynamics and kinetics. The approach here stands as an effective strategy for catalyst design via subsurface engineering at the atomic level.

Abstract Although much effort has been devoted to improving photoelectrochemical water splitting of hematite (α-Fe 2 O 3 ) due to its high theoretical solar-to-hydrogen conversion efficiency of 15.5%, the low applied bias photon-to-current efficiency remains a huge challenge for practical applications. Herein, we introduce single platinum atom sites coordination with oxygen atom (Pt-O/Pt-O-Fe) sites into single crystalline α-Fe 2 O 3 nanoflakes photoanodes (SAs Pt:Fe 2 O 3 -Ov). The single-atom Pt doping of α-Fe 2 O 3 can induce few electron trapping sites, enhance carrier separation capability, and boost charge transfer lifetime in the bulk structure as well as improve charge carrier injection efficiency at the semiconductor/electrolyte interface. Further introduction of surface oxygen vacancies can suppress charge carrier recombination and promote surface reaction kinetics, especially at low potential. Accordingly, the optimum SAs Pt:Fe 2 O 3 -Ov photoanode exhibits the photoelectrochemical performance of 3.65 and 5.30 mA cm −2 at 1.23 and 1.5 V RHE , respectively, with an applied bias photon-to-current efficiency of 0.68% for the hematite-based photoanodes. This study opens an avenue for designing highly efficient atomic-level engineering on single crystalline semiconductors for feasible photoelectrochemical applications.