Cancer remains a complex and challenging medical problem, driving extensive research efforts. Despite significant progress in understanding its genetic and molecular aspects, the quest for effective treatments continues. Nanomedicines have shown great potential for revolutionizing cancer treatment by offering targeted and controlled drug delivery, reducing side effects, and improving patient outcomes. Accordingly, nanomedicines have been the focus of extensive research and development for clinical translation. As of September 2024, a search on the ClinicalTrials.gov website using the term "nanoparticles" revealed numerous ongoing and planned clinical trials. Motivated by recent advances in the field, this review explores the current frontier of cancer nanomedicine. Nanomedicines have supported chemotherapy, phototherapy and sonodynamic therapy, nucleic acid therapy, and immunotherapy. However, translating nanomedicines into practice has been challenged by complex interactions between nanoparticles and biological systems, variable permeability and retention of nanoparticles in tumors, safety concerns, difficulty achieving targeted delivery, and issues with scaling up manufacturing. Perspectives on addressing these challenges are offered. Future opportunities for cancer nanomedicines, including modifying the tumor microenvironment, integrating artificial intelligence and big data, and targeting new medical areas, are also discussed. This review underscores the potential of cancer nanomedicines to revolutionize treatment from a clinical standpoint.

Single-cell analysis of phenotypic plasticity could improve the development of more effective therapeutics. Still, the development of tools to measure single-cell heterogeneity has lagged due to difficulties in manipulating and culturing single cells. Here, we describe a single-cell culture and phenotyping platform that employs a starburst microfluidic network and automatic liquid handling system to capture single cells for long-term culture and multi-dimensional analysis and quantify their clonal properties via their surface biomarker and secreted cytokine/growth factor profiles. Studies performed on this platform found that cells derived from single-cell cultures maintained phenotypic equilibria similar to their parental populations. Single-cell cultures exposed to chemotherapeutic drugs stochastically disrupted this balance to favor stem-like cells. They had enhanced expression of mRNAs and secreted factors associated with cell signaling, survival, and differentiation. This single-cell analysis approach can be extended to analyze more complex phenotypes and screen responses to therapeutic targets.

The diagnosis of hematological disorders is currently established from the combined results of different tests, including those assessing morphology (M), immunophenotype (I), cytogenetics (C), and molecular biology (M) (collectively known as the MICM classification). In this workflow, most of the results are interpreted manually (i.e., by a human, without automation), which is expertise-dependent, labor-intensive, time-consuming, and with inherent interobserver variability. Also, with advances in instruments and technologies, the data is gaining higher dimensionality and throughput, making additional challenges for manual analysis. Recently, artificial intelligence (AI) has emerged as a promising tool in clinical hematology to ensure timely diagnosis, precise risk stratification, and treatment success. In this review, we summarize the current advances, limitations, and challenges of AI models and raise potential strategies for improving their performance in each sector of the MICM pipeline. Finally, we share perspectives, highlight future directions, and call for extensive interdisciplinary cooperation to perfect AI with wise human-level strategies and promote its integration into the clinical workflow.

Extracellular vesicles (EVs) are secreted by both eukaryotes and prokaryotes, and are present in all biological fluids of vertebrates, where they transfer DNA, RNA, proteins, lipids, and metabolites from donor to recipient cells in cell-to-cell communication. Some EV components can also indicate the type and biological status of their parent cells and serve as diagnostic targets for liquid biopsy. EVs can also natively carry or be modified to contain therapeutic agents (e.g., nucleic acids, proteins, polysaccharides, and small molecules) by physical, chemical, or bioengineering strategies. Due to their excellent biocompatibility and stability, EVs are ideal nanocarriers for bioactive ingredients to induce signal transduction, immunoregulation, or other therapeutic effects, which can be targeted to specific cell types. Herein, we review EV classification, intercellular communication, isolation, and characterization strategies as they apply to EV therapeutics. This review focuses on recent advances in EV applications as therapeutic carriers from in vitro research towards in vivo animal models and early clinical applications, using representative examples in the fields of cancer chemotherapeutic drug, cancer vaccine, infectious disease vaccines, regenerative medicine and gene therapy. Finally, we discuss current challenges for EV therapeutics and their future development.

Macrophages are typically identified as classically activated (M1) macrophages and alternatively activated (M2) macrophages, which respectively exhibit pro- and anti-inflammatory phenotypes, and the balance between these two subtypes plays a critical role in the regulation of tissue inflammation, injury, and repair processes. Recent studies indicate that tissue cells and macrophages interact

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipe-line and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to gen-erate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.