The Ateneo de Manila University’s Business Insights Laboratory for Development (BUILD) is looking at ways for artificial intelligence (AI) to augment human labor in small businesses, which make up the bulk of the Philippine economy.

Recent advances in spatial and single-cell omics have significantly revolutionized biomarker discovery in tumor immunotherapy by addressing critical challenges such as tumor heterogeneity, immune evasion, and variability within the tumor microenvironment (TME). Immunotherapeutic strategies, including immune checkpoint inhibitors and adoptive T-cell transfer, have demonstrated promising clinical outcomes; however, their efficacy is limited by low response rates and the incidence of immune-related adverse events (irAEs). Therefore, the identification of reliable biomarkers is essential for predicting treatment efficacy, minimizing irAEs, and facilitating patient stratification. Spatial omics integrates molecular profiling with spatial localization, thereby providing comprehensive insights into the cellular organization and functional states within the TME. By elucidating the spatial patterns of immune cell infiltration and tumor heterogeneity, this approach enhances the prediction of therapeutic responses. Similarly, single-cell omics enables high-resolution analysis of cellular heterogeneity by capturing transcriptomic, epigenomic, and metabolic signatures at the single-cell level. The integrated application of spatial and single-cell omics has enabled the identification of previously undetected biomarkers, including rare immune cell subsets implicated in resistance mechanisms. In addition to spatial transcriptomics (ST), this technological landscape also includes spatial proteomics (SP) and spatial metabolomics, which further facilitate the study of dynamic tumor-immune interactions. Multi-omics integration provides a comprehensive overview of biomarker landscapes, while the rapid evolution of artificial intelligence (AI)-based approaches enhances the analysis of complex, multidimensional datasets to ultimately enhance predictive potential and clinical utility. Despite substantial progress, several challenges remain in the context of standardization, data integration, and real-time monitoring. Nevertheless, the incorporation of spatial and single-cell omics into biomarker research holds transformative potential for advancing personalized cancer immunotherapy. These emerging strategies pave the way for the development of innovative diagnostic and therapeutic interventions, thereby enabling precision oncology and improving treatment outcomes across a wide range of tumor profiles.

This review aims to provide a comprehensive overview of the integration of spatial omics with single-cell omics in the discovery of biomarkers for tumor immunotherapy. Specifically, it examines the strategies by which these emerging technologies address the challenges related to tumor heterogeneity, immune evasion, and the dynamic nature of the TME. By elaborating on the principles, applications, and clinical potential of these technologies, this review also critically evaluates their limitations, challenges, and the current gaps in clinical translation.

In the global trend of vigorously developing hydrogen energy, proton-conducting solid oxide electrolysis cells (P-SOECs) have attracted significant attention due to their advantages of high efficiency and not requiring precious metals. However, the application of P-SOECs faces challenges, particularly in developing high-performance anodes possessing both high catalytic activity and ionic conductivity. In this study, La_0.9Ba_0.1Co_0.7Ni_0.3O_3−δ (LBCN9173) and La_0.9Ca_0.1Co_0.7Ni_0.3O_3−δ (LCCN9173) oxides are tailored as promising anodes by machine learning model, achieving the synergistic enhancement of water oxidation reaction kinetics and proton conduction, which is confirmed by comprehensively analyzing experiment and density functional theory calculation results. Furthermore, the anodic reaction mechanisms for P-SOECs with these anodes are elucidated by analyzing distribution of relaxation time spectra and Gibbs energy of water oxidation reaction, manifesting that the dissociation of H₂O is facilitated on LBCN9173 anode. As a result, P-SOEC with LBCN9173 anode demonstrates a top-rank current density of 2.45 A cm⁻² at 1.3 V and an extremely low polarization resistance of 0.05 Ω cm² at 650 °C. This multi-scale, multi-faceted research approach not only discovered a high-performance anode but also proved the robust framework for the machine learning-assisted design of anodes for P-SOECs.