Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine

Exploring therapeutic and diagnostic potential of cysteine cathepsin as targets for cancer therapy with nanomedicine

Cysteine cathepsins have been discovered to be substantially expressed in multiple types of cancer. They play a key role in the progression and growth of these cancers, rendering them appealing targets for nanoscale delivery and noninvasive diagnostic imaging. This review explores cathepsins from the papain-like enzyme family (C1) within the cysteine peptidase clan (CA), emphasizing the role of cathepsin-responsive nanoparticles in tumor growth. Furthermore, it also explores how nanotechnology can harness cathepsin activity to enable targeted drug delivery, improve tumor imaging, and reduce systemic toxicity. By examining the molecular mechanisms governing cathepsin function and evaluating different nanocarrier systems, this work aims to enhance our understanding of targeted cancer treatment. Despite significant advances, challenges remain in translating these nanomedicine platforms into clinical use, including improving delivery efficiency, biocompatibility, long-term safety, and addressing issues such as interspecies protease variability and scalable nanomanufacturing. Future advancement, integrating advanced biomaterials, patient-derived organoid models, bispecific immune-protease targeting, CRISPR-based cathepsin editing, and artificial intelligence-driven pharmacokinetic modeling and analysis will be critical to fully realizing the clinical potential of cathepsin targeted nanomedicines. These innovations hold promises for advancing precision oncology by overcoming current limitations and improving patient outcomes.

Engineered nanoparticles for imaging and targeted drug delivery in hepatocellular carcinoma

Liver cancer, notably hepatocellular carcinoma (HCC), poses a significant global health burden due to its high fatality rates. Conventional antitumor medications face challenges, including poor targeting, high toxicity, and drug resistance, leading to suboptimal clinical outcomes. This review focused on nanoparticle use in diagnosing and delivering medication for HCC, aiming to advance the development of nanomedicines for improved treatment outcomes. As an emerging frontier science and technology, nanotechnology has shown great potential, especially in precision medicine and personalized treatment. The success of nanosystems is attributable to their smaller size, biocompatibility, selective tumor accumulation, and lower toxicity. Nanoparticles, as a central part of nanotechnology innovation, have emerged in the field of medical diagnostics and therapeutics to overcome the various limitations of conventional chemotherapy, thus offering promising applications for improved selectivity, earlier and more precise diagnosis of cancers, personalized treatment, and overcoming drug resistance. Nanoparticles play a crucial role in drug delivery and imaging of HCC, with the body acting as a delivery system to target and deliver drugs or diagnostic reagents to specific organs or tissues, helping to accurately diagnose and target therapies while minimizing damage to healthy tissues. They protect drugs from early degradation and increase their biological half-life.

Comparative examination of the chemistry and biology of AI-driven gold NPs in Theranostics: New insights into biosensing, bioimaging, genomics, diagnostics, and therapy

Integrating artificial intelligence (AI) with nanomedicine is transforming Theranostics, driving advances in biosensing, bioimaging, genomics, diagnostics, and treatment. This review highlights the latest advancements in AI-driven nanomedicine, focusing on its transformative impact on healthcare. AI-integrated biosensors offer ultra-sensitive, real-time biomaterial detection, reducing false positives by 40 %. In bioimaging, AI algorithms improve resolution to 10 nm, particularly in gold nanoparticles (AuNP)-based imaging. AuNPs, leveraging surface plasmon resonance (SPR), act as contrast agents for early disease detection. AI accelerates genomic analysis, increasing sequencing accuracy by 30 %, enhancing biomarker identification for personalized medicine. AI powered diagnostics ensure rapid, non-invasive pathogen detection within 30 min with 95 % accuracy. AI-driven drug delivery systems enable precise, controlled release, reducing side effects by 20 %. This review explores AI-enhanced AuNPs in biosensing, bioimaging, genomics, diagnostics, and therapy while addressing challenges like scalability, biocompatibility. AI's role in Nanomedicine underscores its potential to revolutionize personalized medicine and future healthcare innovations.

Cancer chemoprevention: signaling pathways and strategic approaches

Although cancer chemopreventive agents have been confirmed to effectively protect high-risk populations from cancer invasion or recurrence, only over ten drugs have been approved by the U.S. Food and Drug Administration. Therefore, screening potent cancer chemopreventive agents is crucial to reduce the constantly increasing incidence and mortality rate of cancer. Considering the lengthy prevention process, an ideal chemopreventive agent should be nontoxic, inexpensive, and oral. Natural compounds have become a natural treasure reservoir for cancer chemoprevention because of their superior ease of availability, cost-effectiveness, and safety. The benefits of natural compounds as chemopreventive agents in cancer prevention have been confirmed in various studies. In light of this, the present review is intended to fully delineate the entire scope of cancer chemoprevention, and primarily focuses on various aspects of cancer chemoprevention based on natural compounds, specifically focusing on the mechanism of action of natural compounds in cancer prevention, and discussing in detail how they exert cancer prevention effects by affecting classical signaling pathways, immune checkpoints, and gut microbiome. We also introduce novel cancer chemoprevention strategies and summarize the role of natural compounds in improving chemotherapy regimens. Furthermore, we describe strategies for discovering anticancer compounds with low abundance and high activity, revealing the broad prospects of natural compounds in drug discovery for cancer chemoprevention. Moreover, we associate cancer chemoprevention with precision medicine, and discuss the challenges encountered in cancer chemoprevention. Finally, we emphasize the transformative potential of natural compounds in advancing the field of cancer chemoprevention and their ability to introduce more effective and less toxic preventive options for oncology.

Nanocarriers for cutting-edge cancer immunotherapies

Cancer immunotherapy aims to harness the body's own immune system for effective and long-lasting elimination of malignant neoplastic tissues. Owing to the advance in understanding of cancer pathology and immunology, many novel strategies for enhancing immunological responses against various cancers have been successfully developed, and some have translated into excellent clinical outcomes. As one promising strategy for the next generation of immunotherapies, activating the multi-cellular network (MCN) within the tumor microenvironment (TME) to deploy multiple mechanisms of action (MOAs) has attracted significant attention. To achieve this effectively and safely, delivering multiple or pleiotropic therapeutic cargoes to the targeted sites of cancerous tissues, cells, and intracellular organelles is critical, for which numerous nanocarriers have been developed and leveraged. In this review, we first introduce therapeutic payloads categorized according to their predicted functions in cancer immunotherapy and their physicochemical structures and forms. Then, various nanocarriers, along with their unique characteristics, properties, advantages, and limitations, are introduced with notable recent applications in cancer immunotherapy. Following discussions on targeting strategies, a summary of each nanocarrier matching with suitable therapeutic cargoes is provided with comprehensive background information for designing cancer immunotherapy regimens.

Recent advances in 3D printing applications for CNS tumours

Three-dimensional printing (3DP) has emerged as a transformative technology in the field of central nervous system (CNS) tumours, offering innovative advancements in various aspects of diagnosis, treatment and education. By precisely replicating the microenvironment of CNS tumours, modelling tumour vascularisation, and capturing genetic heterogeneity, 3DP enables the development of targeted therapies and personalised treatment strategies. The technology has markedly enhanced preoperative planning and intraoperative guidance, providing highly accurate, patient-specific models that improve tumour localisation, facilitate tailored surgical planning, and offer superior visualisation of complex anatomical structures. Furthermore, 3DP has revolutionised education and training for neurosurgeons, trainees, and patients by delivering realistic simulations that enhance surgical skills and decision-making. Despite its transformative potential, the widespread adoption of 3DP faces challenges, including material biocompatibility issues, high costs, and technical limitations. Furthermore, the ethical and regulatory landscape for 3DP in clinical practice requires further development. This review concludes that while 3DP offers significant promise for advancing CNS tumour treatment, ongoing research is essential to address these challenges and optimising its clinical impact.

Nanotechnology-based drug delivery system for the diagnosis and treatment of ovarian cancer

Current research in nanotechnology is improving or developing novel applications that could improve disease diagnosis or treatment. This study highlights several nanoscale drug delivery technologies, such as nano micelles, nanocapsules, nanoparticles, liposomes, branching dendrimers, and nanostructured lipid formulations for the targeted therapy of ovarian cancer (OC), to overcome the limitations of traditional delivery. Because traditional drug delivery to malignant cells has intrinsic flaws, new nanotechnological-based treatments have been developed to address these conditions. Ovarian cancer is the most common gynecological cancer and has a higher death rate because of its late diagnosis and recurrence. This review emphasizes the discipline of medical nanotechnology, which has made great strides in recent years to solve current issues and enhance the detection and treatment of many diseases, including cancer. This system has the potential to provide real-time monitoring and diagnostics for ovarian cancer treatment, as well as simultaneous delivery of therapeutic agents.

Smart nanomedicines powered by artificial intelligence: a breakthrough in lung cancer diagnosis and treatment

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, primarily due to challenges in early detection, suboptimal therapeutic efficacy, and severe adverse effects associated with conventional treatments. The convergence of nanotechnology and artificial intelligence (AI) offers transformative potential in precision oncology, enabling innovative solutions for lung cancer diagnosis and therapy. Intelligent nanomedicines facilitate targeted drug delivery, enhanced imaging, and theranostic applications, while AI-driven models harness big biomedical data to optimize nanomedicine design, functionality, and clinical application. This review explores the synergistic integration of AI and nanotechnology in lung cancer care, highlighting recent advancements, key challenges, and future directions for clinical translation. Ethical considerations, including data standardization and privacy concerns, are also addressed, providing a comprehensive roadmap to overcome current barriers and advance the adoption of AI-driven intelligent nanomedicines in precision oncology. This synthesis underscores the critical role of emerging technologies in revolutionizing lung cancer management.

Cutting-edge nanotechnology: unveiling the role of zinc oxide nanoparticles in combating deadly gastrointestinal tumors

Zinc oxide nanoparticles (ZnO-NPs) have gained significant attention in cancer therapy due to their unique physical and chemical properties, particularly in treating gastrointestinal (GI) cancers such as gastric, colorectal, and hepatocellular carcinoma. These nanoparticles generate reactive oxygen species (ROS) upon entering cancer cells, causing oxidative stress that leads to cellular damage, DNA fragmentation, and apoptosis. ZnO-NPs affect the expression of key proteins involved in apoptosis, including p53, Bax, and Bcl-2, which regulate cell cycle arrest and programmed cell death. Additionally, ZnO-NPs can reduce mitochondrial membrane potential, further enhancing apoptosis in cancer cells. Furthermore, ZnO-NPs inhibit cancer cell proliferation by interfering with cell cycle progression. They reduce levels of cyclins and cyclin-dependent kinases (CDKs), leading to cell cycle arrest. ZnO-NPs also exhibit anti-metastatic properties by inhibiting the migration and invasion of cancer cells through modulation of signaling pathways that affect cell adhesion and cytoskeletal dynamics. The efficacy of ZnO-NPs in overcoming chemotherapy resistance has been demonstrated by their ability to reduce the IC50 values of chemotherapeutic agents, making cancer cells more susceptible to drug-induced cell death. In this review, we summarize the mechanisms by which ZnO-NPs exert anticancer effects in GI cancers, focusing on apoptosis, cell cycle regulation, and metastasis inhibition, while also highlighting the current limitations in translating these findings into effective clinical treatments.

Nanobiotechnology: traditional re-interpreting personalized medicine through targeted therapies and regenerative solutions

Nanobiotechnology is transforming personalized medicine by leveraging the unique properties of nanomaterials to address key challenges in targeted drug delivery, regenerative medicine, and diagnostics. The development of nanocarriers, such as liposomes, polymeric nanoparticles, dendrimers, and metallic nanoparticles, has enabled precise drug delivery with enhanced bioavailability and reduced systemic toxicity. Concurrently, nanostructured scaffolds have advanced regenerative medicine by supporting stem cell differentiation, modulating cellular microenvironments, and enhancing tissue repair. These nanoscale innovations have also led to highly sensitive biosensors and imaging agents, significantly improving early disease detection and biomarker monitoring. Despite these advancements, challenges persist, including nanoparticle-induced cytotoxicity, immunogenicity, scalability issues, and regulatory hurdles requiring extensive evaluation of long-term biocompatibility and pharmacokinetics. Addressing these limitations, recent breakthroughs in AI-assisted nanotechnology and CRISPR-Cas9-mediated gene editing are driving next-generation precision medicine, integrating nanoscale therapeutics with computational approaches to enhance efficacy. Future directions focus on nanorobotics, bioengineered nanovaccines, and theranostic platforms capable of simultaneous diagnosis and treatment, paving the way for real-time, patient-specific interventions. The successful translation of nanomedicine into clinical practice will require interdisciplinary collaboration across nanoscience, bioengineering, and translational medicine to refine nanoparticle functionalization, optimize safety profiles, and ensure equitable access to nanotherapeutics. This review provides a comprehensive overview of these advancements, challenges, and emerging opportunities in nanobiotechnology-driven precision medicine.

Nanomedicines Targeting Metabolic Pathways in the Tumor Microenvironment: Future Perspectives and the Role of AI

Background: Tumor cells engage in continuous self-replication by utilizing a large number of resources and capabilities, typically within an aberrant metabolic regulatory network to meet their own demands. This metabolic dysregulation leads to the formation of the tumor microenvironment (TME) in most solid tumors. Nanomedicines, due to their unique physicochemical properties, can achieve passive targeting in certain solid tumors through the enhanced permeability and retention (EPR) effect, or active targeting through deliberate design optimization, resulting in accumulation within the TME. The use of nanomedicines to target critical metabolic pathways in tumors holds significant promise. However, the design of nanomedicines requires the careful selection of relevant drugs and materials, taking into account multiple factors. The traditional trial-and-error process is relatively inefficient. Artificial intelligence (AI) can integrate big data to evaluate the accumulation and delivery efficiency of nanomedicines, thereby assisting in the design of nanodrugs. Methods: We have conducted a detailed review of key papers from databases, such as ScienceDirect, Scopus, Wiley, Web of Science, and PubMed, focusing on tumor metabolic reprogramming, the mechanisms of action of nanomedicines, the development of nanomedicines targeting tumor metabolism, and the application of AI in empowering nanomedicines. We have integrated the relevant content to present the current status of research on nanomedicines targeting tumor metabolism and potential future directions in this field. Results: Nanomedicines possess excellent TME targeting properties, which can be utilized to disrupt key metabolic pathways in tumor cells, including glycolysis, lipid metabolism, amino acid metabolism, and nucleotide metabolism. This disruption leads to the selective killing of tumor cells and disturbance of the TME. Extensive research has demonstrated that AI-driven methodologies have revolutionized nanomedicine development, while concurrently enabling the precise identification of critical molecular regulators involved in oncogenic metabolic reprogramming pathways, thereby catalyzing transformative innovations in targeted cancer therapeutics. Conclusions: The development of nanomedicines targeting tumor metabolic pathways holds great promise. Additionally, AI will accelerate the discovery of metabolism-related targets, empower the design and optimization of nanomedicines, and help minimize their toxicity, thereby providing a new paradigm for future nanomedicine development.

Global trends and emerging research in nanotechnology for esophageal cancer: a comprehensive bibliometric analysis

BACKGROUND

Despite the growing body of research on nanotechnology for esophageal cancer (EC), a comprehensive bibliometric analysis in this field has yet to be conducted. This study aims to fill this gap by analyzing global research trends, key contributors, and emerging themes in nanotechnology for EC.

METHODS

A bibliometric analysis was performed on publications from 1980 to 2024, using data from the Web of Science Core Collection. The analysis was conducted using VOSviewer, CiteSpace, and the R package 'bibliometrix' to visualize research trends, collaboration networks, and thematic areas.

RESULTS

The analysis included 419 documents authored by 2952 researchers from 44 countries. A significant increase in publications was observed, particularly after 2011, with China, the United States, and Japan leading the contributions. Prominent institutions, including Zhengzhou University and the Chinese Academy of Sciences, were identified as key players. The research predominantly focused on drug delivery systems, nanomedicine, and cancer treatment mechanisms, with emerging trends in the development of advanced nanomaterials for personalized therapies.

CONCLUSION

This comprehensive bibliometric analysis of nanotechnology applications in EC highlights global research trends, key contributors, and emerging research areas. The findings underscore the crucial role of nanotechnology in advancing treatment strategies for EC and identify areas for future research and interdisciplinary collaboration.

DeepRaman: Implementing surface-enhanced Raman scattering together with cutting-edge machine learning for the differentiation and classification of bacterial endotoxins

To classify raw SERS Raman spectra from biological materials, we propose DeepRaman, a new architecture inspired by the Progressive Fourier Transform and integrated with the scalogram transformation approach. Unlike standard machine learning approaches such as PCA, LDA, SVM, RF, GBM etc, DeepRaman functions independently, requiring no human interaction, and can be used to much smaller datasets than traditional CNNs. Performance of DeepRaman on 14 endotoxins bacteria and on a public data achieved an extraordinary accuracy of 99 percent. This provides exact endotoxin classification and has tremendous potential for accelerated medical diagnostics and treatment decision-making in cases of pathogenic infections.

Background

Bacterial endotoxin, a lipopolysaccharide exuded by bacteria during their growth and infection process, serves as a valuable biomarker for bacterial identification. It is a vital component of the outer membrane layer in Gram-negative bacteria. By employing silver nanorod-based array substrates, surface-enhanced Raman scattering (SERS) spectra were obtained for two separate datasets: Eleven endotoxins produced by bacteria, each having an 8.75 pg average detection quantity per measurement, and three controls chitin, lipoteichoic acid (LTA), bacterial peptidoglycan (PGN), because their structures differ greatly from those of LPS.

Objective

This study utilized various classical machine learning techniques, such as support vector machines, k-nearest neighbors, and random forests, in conjunction with a modified deep learning approach called DeepRaman. These algorithms were employed to distinguish and categorize bacterial endotoxins, following appropriate spectral pre-processing, which involved novel filtering techniques and advanced feature extraction methods.

Result

Most traditional machine learning algorithms achieved distinction accuracies of over 99 percent, whereas DeepRaman demonstrated an exceptional accuracy of 100 percent. This method offers precise endotoxin classification and holds significant potential for expedited medical diagnoses and therapeutic decision-making in cases of pathogenic infections.

Conclusion

We present the effectiveness of DeepRaman, an innovative architecture inspired by the Progressive Fourier Transform and integrated with the scalogram transformation method, in classifying raw SERS Raman spectral data from biological specimens with unparalleled accuracy relative to conventional machine learning algorithms. Notably, this Convolutional Neural Network (CNN) operates autonomously, requiring no human intervention, and can be applied with substantially smaller datasets than traditional CNNs. Furthermore, it exhibits remarkable proficiency in managing challenging baseline scenarios that often lead to failures in other techniques, thereby promoting the broader clinical adoption of Raman spectroscopy.

A review of metal-organic frameworks and polymers in mixed matrix membranes for CO2 capture

Polymeric membranes offer an appealing solution for sustainable CO2 capture, with potential for large-scale deployment. However, balancing high permeability and selectivity is an inherent challenge for pristine membranes. To address this challenge, the development of mixed matrix membranes (MMMs) is a promising strategy. MMMs are obtained by carefully integrating porous nano-fillers into polymeric matrices, enabling the simultaneous enhancement of selectivity and permeability. In particular, metal-organic frameworks (MOFs) have gained recognition as MMM fillers for CO2 capture. Here, a review of the current state, recent advancements, and challenges in the fabrication and engineering of MMMs with MOFs for selective CO2 capture is proposed. Key considerations and promising research directions to fully exploit the gas separation potential of MOF-based MMMs in CO2 capture applications are highlighted.

Pooled Nanoparticle Screening Using a Chemical Barcoding Approach

We report the development of a small molecule-based barcoding platform for pooled screening of nanoparticle delivery. Using aryl halide-based tags (halocodes), we achieve high-sensitivity detection via gas chromatography coupled with mass spectrometry or electron capture. This enables barcoding and tracking of nanoparticles with minimal halocode concentrations and without altering their physicochemical properties. To demonstrate the utility of our platform for pooled screening, we synthesized a halocoded library of polylactide-co-glycolide (PLGA) nanoparticles and quantified uptake in ovarian cancer cells in a pooled manner. Our findings correlate with conventional fluorescence-based assays. Additionally, we demonstrate the potential of halocodes for spatial mapping of nanoparticles using mass spectrometry imaging (MSI). Halocoding presents an accessible and modular nanoparticle screening platform capable of quantifying delivery of pooled nanocarrier libraries in a range of biological settings.

Harnessing nanotechnology for cancer treatment

Nanotechnology has become a groundbreaking innovation force in cancer therapy, offering innovative solutions to the limitations of conventional treatments such as chemotherapy and radiation. By manipulating materials at the nanoscale, researchers have developed nanocarriers capable of targeted drug delivery, improving therapeutic efficacy while reducing systemic toxicity. Nanoparticles like liposomes, dendrimers, and polymeric nanomaterials have shown significant promise in delivering chemotherapeutic agents directly to tumor sites, enhancing drug bioavailability and minimizing damage to healthy tissues. In addition to drug delivery, with the utilization of tools such as quantum dots and nanosensors that enables more precise identification of cancer biomarkers, nanotechnology is also playing a pivotal role in early cancer detection and diagnosis. Furthermore, nanotechnology-based therapeutic strategies, including photothermal therapy, gene therapy and immunotherapy are offering novel ways to combat cancer by selectively targeting tumor cells and enhancing the immune response. Nevertheless, despite these progressions, obstacles still persist, particularly in the clinical translation of these technologies. Issues such as nanoparticle toxicity, biocompatibility, and the complexity of regulatory approval hinder the widespread adoption of nanomedicine in oncology. This review discusses different applications of nanotechnology in cancer therapy, highlighting its potential and the hurdles to its clinical implementation. Future research needs to concentrate on addressing these obstacles to unlock the full potential of nanotechnology in providing personalized, effective, and minimally invasive cancer treatments.

Overview of Deep Learning and Nondestructive Detection Technology for Quality Assessment of Tomatoes

Tomato, as the vegetable queen, is cultivated worldwide due to its rich nutrient content and unique flavor. Nondestructive technology provides efficient and noninvasive solutions for the quality assessment of tomatoes. However, processing the substantial datasets to achieve a robust model and enhance detection performance for nondestructive technology is a great challenge until deep learning is developed. The aim of this paper is to provide a systematical overview of the principles and application for three categories of nondestructive detection techniques based on mechanical characterization, electromagnetic characterization, as well as electrochemical sensors. Tomato quality assessment is analyzed, and the characteristics of different nondestructive techniques are compared. Various data analysis methods based on deep learning are explored and the applications in tomato assessment using nondestructive techniques with deep learning are also summarized. Limitations and future expectations for the quality assessment of the tomato industry by nondestructive techniques along with deep learning are discussed. The ongoing advancements in optical equipment and deep learning methods lead to a promising outlook for the application in the tomato industry and agricultural engineering.

The optimization and application of photodynamic diagnosis and autofluorescence imaging in tumor diagnosis and guided surgery: current status and future prospects

Photodynamic diagnosis (PDD) and autofluorescence imaging (AFI) are emerging cancer diagnostic technologies that offer significant advantages over traditional white-light endoscopy in detecting precancerous lesions and early-stage cancers; moreover, they hold promising potential in fluorescence-guided surgery (FGS) for tumors. However, their shortcomings have somewhat hindered the clinical application of PDD and AFI. Therefore, it is imperative to enhance the efficacy of PDD and AFI, thereby maximizing their potential for practical clinical use. This article reviews the principles, characteristics, current research status, and advancements of PDD and AFI, focusing on analyzing and discussing the optimization strategies of PDD and AFI in tumor diagnosis and FGS scenarios. Considering the practical and technical feasibility, optimizing PDD and AFI may result in an effective real-time diagnostic tool to guide clinicians in tumor diagnosis and surgical guidance to achieve the best results.

Musculoskeletal Organs-on-Chips: An Emerging Platform for Studying the Nanotechnology-Biology Interface

Nanotechnology-based approaches are promising for the treatment of musculoskeletal (MSK) disorders, which present significant clinical burdens and challenges, but their clinical translation requires a deep understanding of the complex interplay between nanotechnology and MSK biology. Organ-on-a-chip (OoC) systems have emerged as an innovative and versatile microphysiological platform to replicate the dynamics of tissue microenvironment for studying nanotechnology-biology interactions. This review first covers recent advances and applications of MSK OoCs and their ability to mimic the biophysical and biochemical stimuli encountered by MSK tissues. Next, by integrating nanotechnology into MSK OoCs, cellular responses and tissue behaviors may be investigated by precisely controlling and manipulating the nanoscale environment. Analysis of MSK disease mechanisms, particularly bone, joint, and muscle tissue degeneration, and drug screening and development of personalized medicine may be greatly facilitated using MSK OoCs. Finally, future challenges and directions are outlined for the field, including advanced sensing technologies, integration of immune-active components, and enhancement of biomimetic functionality. By highlighting the emerging applications of MSK OoCs, this review aims to advance the understanding of the intricate nanotechnology-MSK biology interface and its significance in MSK disease management, and the development of innovative and personalized therapeutic and interventional strategies.

Microfluidic Nanoparticle Separation for Precision Medicine

A deeper understanding of disease heterogeneity highlights the urgent need for precision medicine. Microfluidics, with its unique advantages, such as high adjustability, diverse material selection, low cost, high processing efficiency, and minimal sample requirements, presents an ideal platform for precision medicine applications. As nanoparticles, both of biological origin and for therapeutic purposes, become increasingly important in precision medicine, microfluidic nanoparticle separation proves particularly advantageous for handling valuable samples in personalized medicine. This technology not only enhances detection, diagnosis, monitoring, and treatment accuracy, but also reduces invasiveness in medical procedures. This review summarizes the fundamentals of microfluidic nanoparticle separation techniques for precision medicine, starting with an examination of nanoparticle properties essential for separation and the core principles that guide various microfluidic methods. It then explores passive, active, and hybrid separation techniques, detailing their principles, structures, and applications. Furthermore, the review highlights their contributions to advancements in liquid biopsy and nanomedicine. Finally, it addresses existing challenges and envisions future development spurred by emerging technologies such as advanced materials science, 3D printing, and artificial intelligence. These interdisciplinary collaborations are anticipated to propel the platformization of microfluidic separation techniques, significantly expanding their potential in precision medicine.

In Vivo Interactions of Nucleic Acid Nanostructures With Cells

Nucleic acid nanostructures, derived from the assembly of nucleic acid building blocks (e.g., plasmids and oligonucleotides), are important intracellular carriers of therapeutic cargoes widely utilized in preclinical nanomedicine applications, yet their clinical translation remains scarce. In the era of "translational nucleic acid nanotechnology", a deeper mechanistic understanding of the interactions of nucleic acid nanostructures with cells in vivo will guide the development of more efficacious nanomedicines. This review showcases the recent progress in dissecting the in vivo interactions of four key types of nucleic acid nanostructures (i.e., tile-based, origami, spherical nucleic acid, and nucleic acid nanogel) with cells in rodents over the past five years. Emphasis lies on the cellular-level distribution of nucleic acid nanostructures in various organs and tissues and the cellular responses induced by their cellular entry. Next, in the spirit of preclinical translation, this review features the latest interactions of nucleic acid nanostructures with cells in large animals and humans. Finally, the review offers directions for studying the interactions of nucleic acid nanostructures with cells from both materials and biology perspectives and concludes with some regulatory updates.

Artificial intelligence for personalized nanomedicine; from material selection to patient outcomes

INTRODUCTION

Artificial intelligence (AI) is changing the field of nanomedicine by exploring novel nanomaterials for developing therapies of high efficacy. AI works on larger datasets, finding sought-after nano-properties for different therapeutic aims and eventually enhancing nanomaterials' safety and effectiveness. AI leverages patient clinical and genetic data to predict outcomes, guide treatments, and optimize drug dosages and forms, enhancing benefits while minimizing side effects. AI-supported nanomedicine faces challenges like data fusion, ethics, and regulation, requiring better tools and interdisciplinary collaboration. This review highlights the importance of AI regarding patient care and urges scientists, medical professionals, and regulators to adopt AI for better outcomes.

AREAS COVERED

Personalized Nanomedicine, Material Discovery, AI-Driven Therapeutics, Data Integration, Drug Delivery, Patient Centric Care.

EXPERT OPINION

Today, AI can improve personalized health wellness through the discovery of new types of drug nanocarriers, nanomedicine of specific properties to tackle targeted medical needs, and an increment in efficacy along with safety. Nevertheless, problems such as ethical issues, data security, or unbalanced data sets need to be addressed. Potential future developments involve using AI and quantum computing together and exploring telemedicine i.e. the Internet-of-Medical-Things (IoMT) approach can enhance the quality of patient care in a personalized manner by timely decision-making.

The future of plant based green carbon dots as cancer Nanomedicine: From current progress to future Perspectives and beyond

BACKGROUND

The emergence of carbon dots (CDs) as anticancer agents had sparked a transformation in cancer research and treatment strategies. These fluorescent CDs, initially introduced in the early 2000 s, possess exceptional biocompatibility, tunable fluorescence, and surface modification capabilities, positioning them as promising tools in biomedical applications.

AIM OF REVIEW

The review encapsulates the transformative trajectory of green CDs as future anticancer nanomedicine, poised to redefine the strategies employed in the ongoing fight against cancer.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The versatility of CDs was rooted in their various synthesis approaches and sustainable strategies, enabling their adaptability for diverse therapeutic uses. In vitro studies had showcased CDs' selective cytotoxicity against cancer cells while sparing healthy counterparts, forming the basis for targeted therapeutic potential. This selectivity had been attributed to the reactive oxygen species (ROS) generation, which opened avenues for targeted interventions. The role of CDs in combination therapies, synergizing with chemotherapy, radiotherapy, and targeted approaches was then investigated to heighten their anticancer efficacy. Notably, in vivo studies highlight CDs' remarkable biocompatibility and minimal side effects, endorsing their translational promise. Integration with conventional cancer treatments such as chemotherapy, radiotherapy, and immunotherapy amplified the versatility and effectiveness of CDs. The exploration of CDs' applications in photo-induced treatments further solidified their significance, positioning them as photosensitizers (PS) in photodynamic therapy (PDT) and photothermal agents (PA) in photothermal therapy (PTT). In PDT, CDs triggered the generation of ROS upon light exposure, facilitating cancer cell elimination, while in PTT, they induced localized hyperthermia within cancer cells, enhancing therapeutic outcomes. In vitro and in vivo investigations validated CDs' efficacy in PDT and PTT, affirming their potential for integration into combination therapies. Looking ahead, the future of CDs in anticancer treatment encompasses bioavailability, biocompatibility, synergistic treatments, tumor targeting, artificial intelligence (AI) and robotics integration, personalized medicine, and clinical translation. This transformative odyssey of CDs as future anticancer agents is poised to redefine the paradigm of cancer treatment strategies.

Nanoparticle Strategies for Treating CNS Disorders: A Comprehensive Review of Drug Delivery and Theranostic Applications

This review aims to address the significant challenges of treating central nervous system (CNS) disorders such as neurodegenerative diseases, strokes, spinal cord injuries, and brain tumors. These disorders are difficult to manage due to the complexity of disease mechanisms and the protective blood-brain barrier (BBB), which restricts drug delivery. Recent advancements in nanoparticle (NP) technologies offer promising solutions, with potential applications in drug delivery, neuroprotection, and neuroregeneration. By examining current research, we explore how NPs can cross the BBB, deliver medications directly to targeted CNS regions, and enhance both diagnostics and treatment. Key NP strategies, such as passive targeting, receptor-mediated transport, and stimuli-responsive systems, demonstrate encouraging results. Studies show that NPs may improve drug delivery, minimize side effects, and increase therapeutic effectiveness in models of Alzheimer's, Parkinson's, stroke, and glioblastoma. NP technologies thus represent a promising approach for CNS disorder management, combining drug delivery and diagnostic capabilities to enable more precise and effective treatments that could significantly benefit patient outcomes.

From Concept to Cure: The Road Ahead for Ruthenium-Based Anticancer Drugs

The evolution of chemotherapy, especially the dawn of metal-based drugs, represents a transformative era in cancer treatment. From the serendipitous discovery of mustard gas's cytotoxic effects to the sophisticated development of targeted therapies, chemotherapy has significantly refined. Central to this progression is the incorporation of metal-based compounds, such as platinum (Pt), ruthenium (Ru), and gold (Au), which offer unique mechanisms of action, distinguishing them from organic therapeutics. Among these, Ru complexes, exemplified by BOLD-100 and TLD1433, have shown exceptional promise due to their selective activity, lower propensity for resistance, and the ability to target spescific cellular pathways. This paper explores the journey of such Ru candidates, focusing on the mechanisms, efficacy, and clinical potential of these Ru-based drugs, which stand at the forefront of current research, aiming to provide more targeted, less toxic, and highly effective cancer treatments.

Artificial intelligence in nanotechnology for treatment of diseases

Nano-based drug delivery systems (DDSs) have demonstrated the ability to address challenges posed by therapeutic agents, enhancing drug efficiency and reducing side effects. Various nanoparticles (NPs) are utilised as DDSs with unique characteristics, leading to diverse applications across different diseases. However, the complexity, cost and time-consuming nature of laboratory processes, the large volume of data, and the challenges in data analysis have prompted the integration of artificial intelligence (AI) tools. AI has been employed in designing, characterising and manufacturing drug delivery nanosystems, as well as in predicting treatment efficiency. AI's potential to personalise drug delivery based on individual patient factors, optimise formulation design and predict drug properties has been highlighted. By leveraging AI and large datasets, developing safe and effective DDSs can be accelerated, ultimately improving patient outcomes and advancing pharmaceutical sciences. This review article investigates the role of AI in the development of nano-DDSs, with a focus on their therapeutic applications. The use of AI in DDSs has the potential to revolutionise treatment optimisation and improve patient care.

Omics-Enhanced Nanomedicine for Cancer Therapy

Cancer nanomedicine has emerged as a promising approach to overcome the limitations of conventional cancer therapies, offering enhanced efficacy and safety in cancer management. However, the inherent heterogeneity of tumors presents increasing challenges for the application of cancer nanomedicine in both diagnosis and treatment. This heterogeneity necessitates the integration of advanced and high-throughput analytical techniques to tailor nanomedicine strategies to individual tumor profiles. Omics technologies, encompassing genomics, epigenomics, transcriptomics, proteomics, metabolomics, and more, provide unparalleled insights into the molecular and cellular mechanisms underlying cancer. By dissecting tumor heterogeneity across multiple levels, these technologies offer robust support for the development of personalized and precise cancer nanomedicine strategies. In this review, the principles, techniques, and applications of key omics technologies are summarized. Especially, the synergistic integration of omics and nanomedicine in cancer therapy is explored, focusing on enhanced diagnostic accuracy, optimized therapeutic strategies and the assessment of nanomedicine-mediated biological responses. Moreover, this review addresses current challenges and outlines future directions in the field of omics-enhanced nanomedicine. By offering valuable insights and guidance, this review aims to advance the integration of omics with nanomedicine, ultimately driving improved diagnostic and therapeutic strategies for cancer.

Antimicrobial Feature of Nanoparticles in the Antibiotic Resistance Era: From Mechanism to Application

The growth of nanoscale sciences enables us to define and design new methods and materials for a better life. Health and disease prevention are the main issues in the human lifespan. Some nanoparticles (NPs) have antimicrobial properties that make them useful in many applications. In recent years, NPs have been used as antibiotics to overcome drug resistance or as drug carriers with antimicrobial features. They can also serve as antimicrobial coatings for implants in different body areas. The antimicrobial feature of NPs is based on different mechanisms. For example, the oxidative functions of NPs can inhibit nucleic acid replication and destroy the microbial cell membrane as well as interfere with their cellular functions and biochemical cycles. On the other hand, NPs can disrupt the pathogens' lifecycle by interrupting vital points of their life, such as virus uncoating and entry into human cells. Many types of NPs have been tested by different scientists for these purposes. Silver, gold, copper, and titanium have shown the most ability to inhibit and remove pathogens inside and outside the body. In this review, the authors endeavor to comprehensively describe the antimicrobial features of NPs and their applications for different biomedical goals.

Rational strategies for improving the efficiency of design and discovery of nanomedicines

The rise of rational strategies in nanomedicine development, such as high-throughput methods and computer-aided techniques, has led to a shift in the design and discovery patterns of nanomedicines from a trial-and-error mode to a rational mode. This transition facilitates the enhancement of efficiency in the preclinical discovery pipeline of nanomaterials, particularly in improving the hit rate of nanomaterials and the optimization efficiency of promising candidates. Herein, we describe a directed evolution mode of nanomedicines driven by data to accelerate the discovery of nanomaterials with high delivery efficiency. Computer-aided design strategies are introduced in detail as one of the cutting-edge directions for the development of nanomedicines. Ultimately, we look forward to expanding the tools for the rational design and discovery of nanomaterials using multidisciplinary approaches. Rational design strategies may potentially boost the delivery efficiency of next-generation nanomedicines.

Convergence of Nanotechnology and Machine Learning: The State of the Art, Challenges, and Perspectives

Nanotechnology and machine learning (ML) are rapidly emerging fields with numerous real-world applications in medicine, materials science, computer engineering, and data processing. ML enhances nanotechnology by facilitating the processing of dataset in nanomaterial synthesis, characterization, and optimization of nanoscale properties. Conversely, nanotechnology improves the speed and efficiency of computing power, which is crucial for ML algorithms. Although the capabilities of nanotechnology and ML are still in their infancy, a review of the research literature provides insights into the exciting frontiers of these fields and suggests that their integration can be transformative. Future research directions include developing tools for manipulating nanomaterials and ensuring ethical and unbiased data collection for ML models. This review emphasizes the importance of the coevolution of these technologies and their mutual reinforcement to advance scientific and societal goals.

Next-Generation Immunotherapy: Advancing Clinical Applications in Cancer Treatment

Next-generation immunotherapies have revolutionized cancer treatment, offering hope for patients with hard-to-treat tumors. This review focuses on the clinical applications and advancements of key immune-based therapies, including immune checkpoint inhibitors, CAR-T cell therapy, and new cancer vaccines designed to harness the immune system to combat malignancies. A prime example is the success of pembrolizumab in the treatment of advanced melanoma, underscoring the transformative impact of these therapies. Combination treatments, integrating immunotherapy with chemotherapy, radiation, and targeted therapies, are demonstrating synergistic benefits and improving patient outcomes. This review also explores the evolving role of personalized immunotherapy, guided by biomarkers, genomic data, and the tumor environment, to better target individual tumors. Although significant progress has been made, challenges such as resistance, side effects, and high treatment costs persist. Technological innovations, including nanotechnology and artificial intelligence, are explored as future enablers of these therapies. The review evaluates key clinical trials, breakthroughs, and the emerging immune-modulating agents and advanced delivery systems that hold great promise for enhancing treatment efficacy, reducing toxicity, and expanding access to immunotherapy. In conclusion, this review highlights the ongoing advancements in immunotherapy that are reshaping cancer care, with future strategies poised to overcome current challenges and further extend therapeutic reach.

A heterogeneous graph transformer framework for accurate cancer driver gene prediction and downstream analysis

Accurately predicting cancer driver genes remains a formidable challenge amidst the burgeoning volume and intricacy of cancer genomic data. In this investigation, we propose HGTDG, an innovative heterogeneous graph transformer framework tailored for precisely predicting cancer driver genes and exploring downstream tasks. A heterogeneous graph construction module is central to the framework, which assembles a gene-protein heterogeneous network leveraging the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and protein-protein interactions sourced from the STRING (search tool for recurring instances of neighboring genes) database. Moreover, our framework introduces a pioneering heterogeneous graph transformer module, harnessing multi-head attention mechanisms for nuanced node embedding. This transformative module proficiently captures distinct representations for both nodes and edges, thereby enriching the model's predictive capacity. Subsequently, the generated node embeddings are seamlessly integrated into a classification module, facilitating the discrimination between driver and non-driver genes. Our experimental findings evince the superiority of HGTDG over existing methodologies, as evidenced by the enhanced performance metrics, including the area under the receiver operating characteristic curves (AUROC) and the area under the precision-recall curves (AUPRC). Furthermore, the downstream analysis utilizing the newly identified cancer driver genes underscores the efficacy and versatility of our proposed framework.

Analyzing research trends in glioblastoma metabolism: a bibliometric review

Background

A bibliometric and visual analysis of articles related to glioblastoma metabolism was conducted to reveal the dynamics of scientific development and to assist researchers in gaining a global perspective when exploring hotspots and trends.

Methods

The Web of Science Core Collection (WoSCC) was employed to search, screen, and download articles about glioblastoma metabolism published between 2014 and 2024. The relevant literature was analyzed using CiteSpace, VOSviewer and Microsoft Excel.

Results

A total of 729 articles were included for bibliometric analysis between 2014 and 2024, and the number of articles published each year showed an overall increasing trend, except for a decrease in the number of articles published in 2018 compared to 2017. Collaboration network analysis showed that the United States, Germany and China are influential countries in this field, with a high number of articles published, citations and collaborations with other countries. The journal with the largest number of published articles is the International Journal of Molecular Sciences. Mischel PS is the most prolific author with 14 articles, and Guo DL received the most citations with 104 citations. Keyword analysis of the literature showed that the "Warburg effect" achieved the highest burst intensity, and "central nervous system", "classification" and "fatty acids" showed stronger citation bursts in 2024, indicating that they are still popular topics so far.

Conclusion

This article elucidates the research trends and focal points in the field of glioblastoma metabolism, furnishes invaluable insights into the historical and contemporary status of this field, and offers guidance for future research. Further research into glioblastoma metabolism will undoubtedly yield new insights that will inform the diagnosis and treatment of this disease.

Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine

Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. While the integration of AI promises to enhance efficiency, reduce costs, and improve both medicines and patient health, it also raises important questions from a regulatory point of view. In this review article, we will present a comprehensive overview of AI's applications in the pharmaceutical industry, covering areas such as drug discovery, target optimization, personalized medicine, drug safety, and more. By analyzing current research trends and case studies, we aim to shed light on AI's transformative impact on the pharmaceutical industry and its broader implications for healthcare.

Supramolecular nanomedicine in the intelligent cancer therapy: recent advances and future

In recent years, the incidence of cancer has been increasing year by year, and the burden of the disease and the economic burden caused by it has been worsening. Although chemotherapy, immunotherapy, targeted therapy and other therapeutic means continue to progress, they still inevitably have problems such as high toxicity and side effects, susceptibility to drug resistance, and high price. Photothermal therapy and photodynamic therapy have demonstrated considerable advantages in cancer imaging and treatment due to their minimally invasive and selective nature. However, their development has been constrained by challenges related to drug delivery. In recent times, drug delivery systems constructed based on supramolecular chemistry have been the subject of considerable interest, particularly in view of their compatibility with the high permeability and long retention effect of tumors. Furthermore, the advantage of dissociating the active ingredient under pH, light and other stimuli makes them unique in cancer therapy. This paper reviews the current status of supramolecular nanomedicines in cancer therapy, elucidating the challenges faced and providing a theoretical basis for the efficient and precise treatment of malignant tumors.

PEGylation-Dependent Cell Uptake of Lipid Nanoparticles Revealed by Spatiotemporal Correlation Spectroscopy

Polyethylene glycol (PEG) is a common surface modification for lipid nanoparticles (LNPs) to improve their stability and in vivo circulation time. However, the impact of PEGylation on LNP cellular uptake remains poorly understood. To tackle this issue, we systematically compared plain and PEGylated LNPs by combining dynamic light scattering, electrophoretic light scattering, and synchrotron small-angle X-ray scattering (SAXS) that unveils a striking similarity in size and core structure but a significant reduction in surface charge. Upon administration to human embryonic kidney (HEK 293) cells, plain and PEGylated LNPs were internalized through different endocytic routes, as revealed by spatiotemporal correlation spectroscopy. An imaging-derived mean square displacement (iMSD) analysis shows that PEGylated LNPs exhibit a significantly stronger preference for caveolae-mediated endocytosis (CAV) and clathrin-mediated endocytosis (CME) pathways compared to plain LNPs, with these latter being better tailored to MCR-dependent internalization and trafficking. This suggests that PEG plays a crucial role in directing LNPs toward specific cellular uptake routes. Further studies should explore how PEG-mediated endocytosis impacts intracellular trafficking and ultimately translates to therapeutic efficacy, guiding the design of next-generation LNP delivery systems.

Liver cirrhosis: molecular mechanisms and therapeutic interventions

Liver cirrhosis is the end-stage of chronic liver disease, characterized by inflammation, necrosis, advanced fibrosis, and regenerative nodule formation. Long-term inflammation can cause continuous damage to liver tissues and hepatocytes, along with increased vascular tone and portal hypertension. Among them, fibrosis is the necessary stage and essential feature of liver cirrhosis, and effective antifibrosis strategies are commonly considered the key to treating liver cirrhosis. Although different therapeutic strategies aimed at reversing or preventing fibrosis have been developed, the effects have not be more satisfactory. In this review, we discussed abnormal changes in the liver microenvironment that contribute to the progression of liver cirrhosis and highlighted the importance of recent therapeutic strategies, including lifestyle improvement, small molecular agents, traditional Chinese medicine, stem cells, extracellular vesicles, and gut remediation, that regulate liver fibrosis and liver cirrhosis. Meanwhile, therapeutic strategies for nanoparticles are discussed, as are their possible underlying broad application and prospects for ameliorating liver cirrhosis. Finally, we also reviewed the major challenges and opportunities of nanomedicine‒biological environment interactions. We hope this review will provide insights into the pathogenesis and molecular mechanisms of liver cirrhosis, thus facilitating new methods, drug discovery, and better treatment of liver cirrhosis.

Pooled nanoparticle screening using a chemical barcoding approach

We report the development of a small molecule-based barcoding platform for pooled screening of nanoparticle delivery. Using aryl halide-based tags (halocodes), we achieve high-sensitivity detection via gas chromatography coupled with mass spectrometry or electron capture. This enables barcoding and tracking of nanoparticles with minimal halocode concentrations and without altering their physicochemical properties. To demonstrate the utility of our platform for pooled screening, we synthesized a halocoded library of polylactide-co-glycolide (PLGA) nanoparticles and quantified uptake in ovarian cancer cells in a pooled manner. Our findings correlate with conventional fluorescence-based assays. Additionally, we demonstrate the potential of halocodes for spatial mapping of nanoparticles using mass spectrometry imaging (MSI). Halocoding presents an accessible and modular nanoparticle screening platform capable of quantifying delivery of pooled nanocarrier libraries in a range of biological settings.

Evaluating the Effects of BSA-Coated Gold Nanorods on Cell Migration Potential and Inflammatory Mediators in Human Dermal Fibroblasts

Albumin-coated gold nanoparticles display potential biomedical applications, including cancer research, infection treatment, and wound healing; however, elucidating their interaction with normal cells remains an area with limited exploration. In this study, gold nanorods (GNR) were prepared and coated with bovine serum albumin (BSA) to produce GNR-BSA. The functionalized nanoparticles were characterized based on their optical absorption spectra, morphology, surface charge, and quantity of attached protein. The interaction between GNR-BSA and BSA with normal cells was investigated using human dermal fibroblasts. The cytotoxicity test indicated cell viability between ~63-95% for GNR-BSA over concentrations from 30.0 to 0.47 μg/mL and ~85-98% for BSA over concentrations from 4.0 to 0.0625 mg/mL. The impact of the GNR-BSA and BSA on cell migration potential and wound healing was assessed using scratch assay, and the modulation of cytokine release was explored by quantifying a panel of cytokines using Multiplex technology. The results indicated that GNR-BSA, at 10 μg/mL, delayed the cell migration and wound healing 24 h post-treatment compared to the BSA or the control group with an average wound closure percentage of 6% and 16% at 6 and 24 h post-treatment, respectively. Multiplex analysis revealed that while GNR-BSA reduced the release of the pro-inflammatory marker IL-12 from the activated fibroblasts 24 h post-treatment, they significantly reduced the release of IL-8 (p < 0.001), and CCL2 (p < 0.01), which are crucial for the inflammation response, cell adhesion, proliferation, migration, and angiogenesis. Although GNR-BSA exhibited relatively high cell viability towards human dermal fibroblasts and promising therapeutic applications, toxicity aspects related to cell motility and migration must be considered.

Illuminating the future of precision cancer surgery with fluorescence imaging and artificial intelligence convergence

Real-time and accurate guidance for tumor resection has long been anticipated by surgeons. In the past decade, the flourishing material science has made impressive progress in near-infrared fluorophores that may fulfill this purpose. Fluorescence imaging-guided surgery shows great promise for clinical application and has undergone widespread evaluations, though it still requires continuous improvements to transition this technique from bench to bedside. Concurrently, the rapid progress of artificial intelligence (AI) has revolutionized medicine, aiding in the screening, diagnosis, and treatment of human doctors. Incorporating AI helps enhance fluorescence imaging and is poised to bring major innovations to surgical guidance, thereby realizing precision cancer surgery. This review provides an overview of the principles and clinical evaluations of fluorescence-guided surgery. Furthermore, recent endeavors to synergize AI with fluorescence imaging were presented, and the benefits of this interdisciplinary convergence were discussed. Finally, several implementation strategies to overcome technical hurdles were proposed to encourage and inspire future research to expedite the clinical application of these revolutionary technologies.

Recent Advances in Research from Nanoparticle to Nano-Assembly: A Review

The careful arrangement of nanomaterials (NMs) holds promise for revolutionizing various fields, from electronics and biosensing to medicine and optics. This review delves into the intricacies of nano-assembly (NA) techniques, focusing on oriented-assembly methodologies and stimuli-dependent approaches. The introduction provides a comprehensive overview of the significance and potential applications of NA, setting the stage for review. The oriented-assembly section elucidates methodologies for the precise alignment and organization of NMs, crucial for achieving desired functionalities. The subsequent section delves into stimuli-dependent techniques, categorizing them into chemical and physical stimuli-based approaches. Chemical stimuli-based self-assembly methods, including solvent, acid-base, biomolecule, metal ion, and gas-induced assembly, are discussed in detail by presenting examples. Additionally, physical stimuli such as light, magnetic fields, electric fields, and temperature are examined for their role in driving self-assembly processes. Looking ahead, the review outlines futuristic scopes and perspectives in NA, highlighting emerging trends and potential breakthroughs. Finally, concluding remarks summarize key findings and underscore the significance of NA in shaping future technologies. This comprehensive review serves as a valuable resource for researchers and practitioners, offering insights into the diverse methodologies and potential applications of NA in interdisciplinary research fields.

Advances in manufacturing chimeric antigen receptor immune cell therapies

Biomedical research has witnessed significant strides in manufacturing chimeric antigen receptor T cell (CAR-T) therapies, marking a transformative era in cellular immunotherapy. Nevertheless, existing manufacturing methods for autologous cell therapies still pose several challenges related to cost, immune cell source, safety risks, and scalability. These challenges have motivated recent efforts to optimize process development and manufacturing for cell therapies using automated closed-system bioreactors and models created using artificial intelligence. Simultaneously, non-viral gene transfer methods like mRNA, CRISPR genome editing, and transposons are being applied to engineer T cells and other immune cells like macrophages and natural killer cells. Alternative sources of primary immune cells and stem cells are being developed to generate universal, allogeneic therapies, signaling a shift away from the current autologous paradigm. These multifaceted innovations in manufacturing underscore a collective effort to propel this therapeutic approach toward broader clinical adoption and improved patient outcomes in the evolving landscape of cancer treatment. Here, we review current CAR immune cell manufacturing strategies and highlight recent advancements in cell therapy scale-up, automation, process development, and engineering.

Advancements in nanomaterials for nanosensors: a comprehensive review

Nanomaterials (NMs) exhibit unique properties that render them highly suitable for developing sensitive and selective nanosensors across various domains. This review aims to provide a comprehensive overview of nanomaterial-based nanosensors, highlighting their applications and the classification of frequently employed NMs to enhance sensitivity and selectivity. The review introduces various classifications of NMs commonly used in nanosensors, such as carbon-based NMs, metal-based NMs, and others, elucidating their exceptional properties, including high thermal and electrical conductivity, large surface area-to-volume ratio and good biocompatibility. A thorough examination of literature sources was conducted to gather information on NMs-based nanosensors' characteristics, properties, and fabrication methods and their application in diverse sectors such as healthcare, environmental monitoring, industrial processes, and security. Additionally, advanced applications incorporating machine learning techniques were analyzed to enhance the sensor's performance. This review advances the understanding and development of nanosensor technologies by providing insights into fabrication techniques, characterization methods, applications, and future outlook. Key challenges such as robustness, biocompatibility, and scalable manufacturing are also discussed, offering avenues for future research and development in this field.

Nanomaterial-Based Repurposing of Macrophage Metabolism and Its Applications

Macrophage immunotherapy represents an emerging therapeutic approach aimed at modulating the immune response to alleviate disease symptoms. Nanomaterials (NMs) have been engineered to monitor macrophage metabolism, enabling the evaluation of disease progression and the replication of intricate physiological signal patterns. They achieve this either directly or by delivering regulatory signals, thereby mapping phenotype to effector functions through metabolic repurposing to customize macrophage fate for therapy. However, a comprehensive summary regarding NM-mediated macrophage visualization and coordinated metabolic rewiring to maintain phenotypic equilibrium is currently lacking. This review aims to address this gap by outlining recent advancements in NM-based metabolic immunotherapy. We initially explore the relationship between metabolism, polarization, and disease, before delving into recent NM innovations that visualize macrophage activity to elucidate disease onset and fine-tune its fate through metabolic remodeling for macrophage-centered immunotherapy. Finally, we discuss the prospects and challenges of NM-mediated metabolic immunotherapy, aiming to accelerate clinical translation. We anticipate that this review will serve as a valuable reference for researchers seeking to leverage novel metabolic intervention-matched immunomodulators in macrophages or other fields of immune engineering.

Application of Photoactive Compounds in Cancer Theranostics: Review on Recent Trends from Photoactive Chemistry to Artificial Intelligence

According to the World Health Organization (WHO) and the International Agency for Research on Cancer (IARC), the number of cancer cases and deaths worldwide is predicted to nearly double by 2030, reaching 21.7 million cases and 13 million fatalities. The increase in cancer mortality is due to limitations in the diagnosis and treatment options that are currently available. The close relationship between diagnostics and medicine has made it possible for cancer patients to receive precise diagnoses and individualized care. This article discusses newly developed compounds with potential for photodynamic therapy and diagnostic applications, as well as those already in use. In addition, it discusses the use of artificial intelligence in the analysis of diagnostic images obtained using, among other things, theranostic agents.

Peptide-Hitchhiking for the Development of Nanosystems in Glioblastoma

Glioblastoma (GBM) remains the epitome of aggressiveness and lethality in the spectrum of brain tumors, primarily due to the blood-brain barrier (BBB) that hinders effective treatment delivery, tumor heterogeneity, and the presence of treatment-resistant stem cells that contribute to tumor recurrence. Nanoparticles (NPs) have been used to overcome these obstacles by attaching targeting ligands to enhance therapeutic efficacy. Among these ligands, peptides stand out due to their ease of synthesis and high selectivity. This article aims to review single and multiligand strategies critically. In addition, it highlights other strategies that integrate the effects of external stimuli, biomimetic approaches, and chemical approaches as nanocatalytic medicine, revealing their significant potential in treating GBM with peptide-functionalized NPs. Alternative routes of parenteral administration, specifically nose-to-brain delivery and local treatment within the resected tumor cavity, are also discussed. Finally, an overview of the significant obstacles and potential strategies to overcome them are discussed to provide a perspective on this promising field of GBM therapy.

Precise Capture and Dynamic Release of Circulating Liver Cancer Cells with Dual-Histidine-Based Cell Imprinted Hydrogels

Circulating tumor cells (CTCs) detection presents significant advantages in diagnosing liver cancer due to its noninvasiveness, real-time monitoring, and dynamic tracking. However, the clinical application of CTCs-based diagnosis is largely limited by the challenges of capturing low-abundance CTCs within a complex blood environment while ensuring them alive. Here, an ultrastrong ligand, l-histidine-l-histidine (HH), specifically targeting sialylated glycans on the surface of CTCs, is designed. Furthermore, HH is integrated into a cell-imprinted polymer, constructing a hydrogel with precise CTCs imprinting, high elasticity, satisfactory blood compatibility, and robust anti-interference capacities. These features endow the hydrogel with excellent capture efficiency (>95%) for CTCs in peripheral blood, as well as the ability to release CTCs controllably and alive. Clinical tests substantiate the accurate differentiation between liver cancer, cirrhosis, and healthy groups using this method. The remarkable diagnostic accuracy (94%), lossless release of CTCs, material reversibility, and cost-effectiveness ($6.68 per sample) make the HH-based hydrogel a potentially revolutionary technology for liver cancer diagnosis and single-cell analysis.