Neratinib could be effective as monotherapy or in combination with trastuzumab in HER2-low breast cancer cells and organoid models.

BACKGROUND: Previous studies have suggested that patients with HER2-low breast cancers do not benefit from trastuzumab treatment although the reasons remain unclear. METHODS: We investigated the effect of trastuzumab monotherapy and its combination with different HER2 targeting treatments in a panel of breast cancer cell lines and patient-derived organoids (PDOs) using biochemical methods and cell viability assays. RESULTS: Compared to sensitive HER2 over-expressing (IHC3 + ) breast cancer cells, increasing doses of trastuzumab could not achieve IC50 in MDA-MB-361 (IHC 2 + FISH + ) and MDA-MB-453 (IHC 2 + FISH-) cells which showed an intermediate response to trastuzumab. Trastuzumab treatment induced upregulation of HER ligand release, resulting in the activation of HER receptors in these cells, which could account for their trastuzumab insensitivity. Adding a dual ADAM10/17 inhibitor to inhibit the shedding of HER ligands in combination with trastuzumab only showed a modest decrease in the cell viability of HER2-low breast cancer cells and PDOs. However, the panHER inhibitor neratinib was an effective monotherapy in HER2-low breast cancer cells and PDOs, and showed additive effects when combined with trastuzumab. CONCLUSION: This study demonstrates that neratinib in combination with trastuzumab may be effective in a subset of HER2-low breast cancers although further validation is required in a larger panel of PDOs and in future clinical studies.

4D-STEM Mapping of Nanocrystal Reaction Dynamics and Heterogeneity in a Graphene Liquid Cell.

Chemical reaction kinetics at the nanoscale are intertwined with heterogeneity in structure and composition. However, mapping such heterogeneity in a liquid environment is extremely challenging. Here we integrate graphene liquid cell (GLC) transmission electron microscopy and four-dimensional scanning transmission electron microscopy to image the etching dynamics of gold nanorods in the reaction media. Critical to our experiment is the small liquid thickness in a GLC that allows the collection of high-quality electron diffraction patterns at low dose conditions. Machine learning-based data-mining of the diffraction patterns maps the three-dimensional nanocrystal orientation, groups spatial domains of various species in the GLC, and identifies newly generated nanocrystallites during reaction, offering a comprehensive understanding on the reaction mechanism inside a nanoenvironment. This work opens opportunities in probing the interplay of structural properties such as phase and strain with solution-phase reaction dynamics, which is important for applications in catalysis, energy storage, and self-assembly.

Framework of compressive sensing and data compression for 4D-STEM.

Four-dimensional Scanning Transmission Electron Microscopy (4D-STEM) is a powerful technique for high-resolution and high-precision materials characterization at multiple length scales, including the characterization of beam-sensitive materials. However, the field of view of 4D-STEM is relatively small, which in absence of live processing is limited by the data size required for storage. Furthermore, the rectilinear scan approach currently employed in 4D-STEM places a resolution- and signal-dependent dose limit for the study of beam sensitive materials. Improving 4D-STEM data and dose efficiency, by keeping the data size manageable while limiting the amount of electron dose, is thus critical for broader applications. Here we introduce a general method for reconstructing 4D-STEM data with subsampling in both real and reciprocal spaces at high fidelity. The approach is first tested on the subsampled datasets created from a full 4D-STEM dataset, and then demonstrated experimentally using random scan in real-space. The same reconstruction algorithm can also be used for compression of 4D-STEM datasets, leading to a large reduction (100 times or more) in data size, while retaining the fine features of 4D-STEM imaging, for crystalline samples.

Nasal mucosal IgA levels against SARS-CoV-2 and seasonal coronaviruses are low in children but boosted by reinfection.

Repeated coronavirus infections in childhood drive progressive maturation of systemic immune responses into adulthood. Analyses of immune responses in children have focused primarily upon systemic assessment but the importance of mucosal immunity is increasingly recognised. We studied virus-specific antibody responses in contemporaneous nasal swabs and blood samples from 99 children (4-15 years) and 28 adults (22-56 years), all of whom had prior SARS-CoV-2 infection. Whilst mucosal IgA titres against Influenza and Respiratory Syncytial virus were comparable between children and adults, those against all coronaviruses, including SARS-CoV-2, were lower in children. Mucosal IgA antibodies demonstrated comparable relative neutralisation capacity in both groups and retained activity against recent omicron variants such as XBB.1 which are highly evasive of IgG neutralisation. SARS-CoV-2 reinfection preferentially enhanced mucosal IgA responses whilst the impact of vaccination was more modest. Nasal IgA levels against coronaviruses thus display a pattern of incremental response to reinfection which likely determines the natural history of reinfection. This highlights the particular significance of developing mucosal vaccines against coronaviruses in children.

Quantifying Atomically Dispersed Catalysts Using Deep Learning Assisted Microscopy.

The catalytic performance of atomically dispersed catalysts (ADCs) is greatly influenced by their atomic configurations, such as atom-atom distances, clustering of atoms into dimers and trimers, and their distributions. Scanning transmission electron microscopy (STEM) is a powerful technique for imaging ADCs at the atomic scale; however, most STEM analyses of ADCs thus far have relied on human labeling, making it difficult to analyze large data sets. Here, we introduce a convolutional neural network (CNN)-based algorithm capable of quantifying the spatial arrangement of different adatom configurations. The algorithm was tested on different ADCs with varying support crystallinity and homogeneity. Results show that our algorithm can accurately identify atom positions and effectively analyze large data sets. This work provides a robust method to overcome a major bottleneck in STEM analysis for ADC catalyst research. We highlight the potential of this method to serve as an on-the-fly analysis tool for catalysts in future in situ microscopy experiments.