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Traditionally, a high-performance microscope with a large numerical aperture is required to acquire high-resolution images. However, the images’ size is typically tremendous. Therefore, they are not conveniently managed and transferred across a computer network or stored in a limited computer storage system. As a result, image compression is commonly used to reduce image size resulting in poor image resolution. Here, we demonstrate custom convolution neural networks (CNNs) for both super-resolution image enhancement from low-resolution images and characterization of both cells and nuclei from hematoxylin and eosin (H&E) stained breast cancer histopathological images by using a combination of generator and discriminator networks so-called super-resolution generative adversarial network-based on aggregated residual transformation (SRGAN-ResNeXt) to facilitate cancer diagnosis in low resource settings. The results provide high enhancement in image quality where the peak signal-to-noise ratio and structural similarity of our network results are over 30 dB and 0.93, respectively. The derived performance is superior to the results obtained from both the bicubic interpolation and the well-known SRGAN deep-learning methods. In addition, another custom CNN is used to perform image segmentation from the generated high-resolution breast cancer images derived with our model with an average Intersection over Union of 0.869 and an average dice similarity coefficient of 0.893 for the H&E image segmentation results. Finally, we propose the jointly trained SRGAN-ResNeXt and Inception U-net Models, which applied the weights from the individually trained SRGAN-ResNeXt and inception U-net models as the pre-trained weights for transfer learning. The jointly trained model’s results are progressively improved and promising. We anticipate these custom CNNs can help resolve the inaccessibility of advanced microscopes or whole slide imaging (WSI) systems to acquire high-resolution images from low-performance microscopes located in remote-constraint settings.

Nutritional metal ions play critical roles in many important immune processes. Hence, the effective modulation of metal ions may open up new forms of immunotherapy, termed as metalloimmunotherapy. Here, we demonstrate a prototype of cancer metalloimmunotherapy using cyclic dinucleotide (CDN) stimulator of interferon genes (STING) agonists and Mn2+. We screened various metal ions and discovered specific metal ions augmented STING agonist activity, wherein Mn2+ promoted a 12- to 77-fold potentiation effect across the prevalent human STING haplotypes. Notably, Mn2+ coordinated with CDN STING agonists to self-assemble into a nanoparticle (CDN–Mn2+ particle, CMP) that effectively delivered STING agonists to immune cells. The CMP, administered either by local intratumoural or systemic intravenous injection, initiated robust anti-tumour immunity, achieving remarkable therapeutic efficacy with minute doses of STING agonists in multiple murine tumour models. Overall, the CMP offers a new platform for local and systemic cancer treatments, and this work underscores the great potential of coordination nanomedicine for metalloimmunotherapy. The stimulation of interferon genes (STING) pathway with STING agonists such as cyclic dinucleotides (CDNs) has emerged as a promising immunotherapeutic approach. Here, the authors show that Mn2+ can amplify the STING-promoted anti-tumour immune response in challenging murine tumour models by coordinating with CDNs and self-assembling into nanoparticles that can be delivered locally and systemically.