Applications In Oncology Research Articles

Role of next‐generation sequencing in revolutionizing healthcare for cancer management

AbstractNext‐generation sequencing (NGS) has emerged as a transformative technology in oncology, revolutionizing cancer diagnostics and personalized treatment strategies. By providing comprehensive insights into the genetic landscape of tumors, NGS enables the identification of critical somatic and germline mutations, copy number variations (CNVs), and gene fusions. Over the past decade, advancements in NGS platforms have led to greater accuracy, speed, and cost‐effectiveness, making it an integral part of cancer research and clinical diagnostics. Despite its widespread adoption, significant challenges remain, including the need for improved methods to detect minimal residual disease (MRD) and accurately profile tumor heterogeneity. This review explores the evolution of NGS technologies and their pivotal role in cancer biology, from early diagnostics to therapeutic guidance. It delves into the application of NGS in identifying CNVs and gene fusions, monitoring MRD, and the increasing relevance of targeted NGS and spatial genomics. Furthermore, the integration of spatial transcriptomics is highlighted as a frontier in understanding the tumor microenvironment. By addressing these critical aspects, this review provides a comprehensive overview of how NGS is shaping the future of cancer research and treatment, offering a complete overview of potential NGS applications in scientific and clinical oncology.

Exploring exosomes: novel diagnostic and therapeutic frontiers in thyroid cancer.

In recent years, the incidence of thyroid cancer has surged globally, posing significant challenges in its diagnosis, treatment, and prognosis. Exosomes, as a class of extracellular vesicles, are secreted by nearly all cell types and encapsulate a variety of nucleic acids and proteins reflective of their cell of origin, thereby facilitating critical intercellular communication. Recent advancements in understanding these exosomes have catalyzed their application in oncology, particularly through uncovering their roles in the pathogenesis, diagnosis, and therapy of cancers. Notably, the latest literature highlights the integral role of exosomes in refining diagnostic techniques, enhancing targeted therapies, optimizing radiotherapy outcomes, and advancing immunotherapeutic approaches in thyroid cancer management. This review provides a current synthesis of the implications of exosomes in thyroid cancer tumorigenesis and progression, as well as their emerging applications in diagnosis and treatment strategies. Furthermore, we discuss the profound clinical potential of exosome-based interventions in managing thyroid cancer, serving as a foundational reference for future therapeutic developments.

The concept of the liquid biopsy in the treatment of malignant eye tumours

The liquid biopsy is acutting-edge technique that involves analysing non-solid biological tissues, primarily blood but also ocular fluids, for the presence of cancer cells or fragments of tumour DNA. Unlike traditional biopsies, liquid biopsies are usually minimally invasive and can be performed more frequently, enabling continuous monitoring of disease progression and treatment efficacy. This article (and the associated series of articles) outlines the key developments in liquid biopsy, which include the analysis of circulating tumor DNA (ctDNA), circulating tumor cells (CTC) and exosomal RNA and protein biomarkers. Techniques, such as digital droplet PCR and next-generation sequencing (NGS) have made it possible to detect even very low levels of ctDNA, which is crucial for early cancer detection and monitoring minimal residual disease. The detection of rare CTCs is enhanced by techniques, such as microfluidic devices and immunomagnetic separation. Multiomic approaches, whereby exosomal RNA, protein and ctDNA analyses are combined, help to create amore comprehensive picture of tumour biology, including insights into tumour heterogeneity, potentially leading to better diagnostic and prognostic tools and helping to predict treatment response and resistance. The challenges of liquid biopsy application, which will be described in the following article, include (a)standardization, (b)cost and accessibility, (c)validation and clinical utility. However, the liquid biopsy represents apromising frontier in the application of precision ocular oncology, with ongoing research likely to expand its applications and improve its effectiveness in the coming years.

Mixed reality applications in upper extremity surgery: the future is now.

Mixed reality refers to the integration of virtual reality into the real-world environment. This digital content can be interacted with in real time. The emergence of mixed reality technology has been made possible by the introduction of head-mounted displays, which are being utilized across multiple surgical specialties. In upper extremity surgery, mixed reality has widespread applications in trauma, corrective surgery, arthroplasty, arthroscopy, and oncology. Preoperatively, mixed reality allows for complex 3D planning. Intraoperatively, surgeons can access this 3D data in a sterile environment. While in its infant stages, mixed reality is likely to become a powerful tool for intraoperative guidance and navigation. Mixed reality can change the paradigm of communication, as it allows the sharing of visual data from the surgeon's perspective, enabling remote assistance and participation.

Advanced Nanomaterials for Cancer Therapy: Gold, Silver, and Iron Oxide Nanoparticles in Oncological Applications.

Cancer remains one of the most challenging health issues globally, demanding innovative therapeutic approaches for effective treatment. Nanoparticles, particularly those composed of gold, silver, and iron oxide, have emerged as promising candidates for changing cancer therapy. This comprehensive review demonstrates the landscape of nanoparticle-based oncological interventions, focusing on the remarkable advancements and therapeutic potentials of gold, silver, and iron oxide nanoparticles. Gold nanoparticles have garnered significant attention for their exceptional biocompatibility, tunable surface chemistry, and distinctive optical properties, rendering them ideal candidates for various cancer diagnostic and therapeutic strategies. Silver nanoparticles, renowned for their antimicrobial properties, exhibit remarkable potential in cancer therapy through multiple mechanisms, including apoptosis induction, angiogenesis inhibition, and drug delivery enhancement. With their magnetic properties and biocompatibility, iron oxide nanoparticles offer unique cancer diagnosis and targeted therapy opportunities. This review critically examines the recent advancements in the synthesis, functionalization, and biomedical applications of these nanoparticles in cancer therapy. Moreover, the challenges are discussed, including toxicity concerns, immunogenicity, and translational barriers, and ongoing efforts to overcome these hurdles are highlighted. Finally, insights into the future directions of nanoparticle-based cancer therapy and regulatory considerations, are provided aiming to accelerate the translation of these promising technologies from bench to bedside.

Protective Effect of Biochanin A on Gamma Radiation-Induced Oxidative Stress, Antioxidant Status, Apoptotic, and DNA Repairing Molecules in Swiss Albino Mice.

Radiation therapy is indispensable in medical practice but often causes adverse effects on healthy tissues, necessitating the search for natural radioprotectors. This study investigates the protective effect of Biochanin A (BCA) against gamma radiation-induced oxidative stress and DNA damage in Swiss albino mice. Gamma radiation, a potent ionizing source, generates reactive oxygen species (ROS) that damage cellular biomolecules, including DNA. Antioxidants play a crucial role in neutralizing ROS and preventing oxidative damage. Swiss albino mice were divided into control, BCA control (10 mg/kg body weight), radiation alone (7 Gy), and radiation+ BCA pretreatment groups. BCA, a natural isoflavone with known antioxidant and cytoprotective properties, was administered intraperitoneally before radiation exposure. After irradiation, lipid peroxidation levels, antioxidant enzyme activities/level (superoxide dismutase, catalase, glutathione peroxidase and reduced glutathione), expression levels of DNA repair genes (P53, P21, GADD45α), apoptotic markers (Bax, Bcl-2, Caspase-3, -9 and Cytochrome-C), and inflammatory marker (NF-κB) were analyzed in small intestine tissue. Our findings indicate that gamma radiation significantly elevated lipid peroxidation levels and altered antioxidant enzyme activities, indicating oxidative stress. However, BCA pretreatment mitigated these effects by bolstering antioxidant defences, reducing radiation-induced oxidative damage. Additionally, BCA altered apoptotic markers, NF-κB expression, promoting cell survival mechanisms. At the molecular level, BCA pretreatment upregulated key DNA repair genes (P53, P21, GADD45α), crucial for repairing radiation-induced DNA damage and maintaining genomic stability. These results underscore BCA potential as a radioprotector, suggesting its efficacy in mitigating radiation-induced oxidative stress and preserving cellular integrity. In conclusion, BCA demonstrates promising radioprotective properties by attenuating oxidative stress, enhancing antioxidant defences, modulating apoptotic pathways, and promoting DNA repair mechanisms following gamma radiation exposure. Further research is necessary to elucidate its precise mechanisms of action and explore its potential therapeutic applications in radiation oncology and environmental radioprotection.

The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning.

Artificial intelligence (AI), the wide spectrum of technologies aiming to give machines or computers the ability to perform human-like cognitive functions, began in the 1940s with the first abstract models of intelligent machines. Soon after, in the 1950s and 1960s, machine learning algorithms such as neural networks and decision trees ignited significant enthusiasm. More recent advancements include the refinement of learning algorithms, the development of convolutional neural networks to efficiently analyze images, and methods to synthesize new images. This renewed enthusiasm was also due to the increase in computational power with graphical processing units and the availability of large digital databases to be mined by neural networks. AI soon began to be applied in medicine, first through expert systems designed to support the clinician's decision and later with neural networks for the detection, classification, or segmentation of malignant lesions in medical images. A recent prospective clinical trial demonstrated the non-inferiority of AI alone compared with a double reading by two radiologists on screening mammography. Natural language processing, recurrent neural networks, transformers, and generative models have both improved the capabilities of making an automated reading of medical images and moved AI to new domains, including the text analysis of electronic health records, image self-labeling, and self-reporting. The availability of open-source and free libraries, as well as powerful computing resources, has greatly facilitated the adoption of deep learning by researchers and clinicians. Key concerns surrounding AI in healthcare include the need for clinical trials to demonstrate efficacy, the perception of AI tools as 'black boxes' that require greater interpretability and explainability, and ethical issues related to ensuring fairness and trustworthiness in AI systems. Thanks to its versatility and impressive results, AI is one of the most promising resources for frontier research and applications in medicine, in particular for oncological applications.

Microdissected tumor cuboids: a microscale cancer model for large-scale testing that retains a complex tumor microenvironment.

To bridge the gap between bench and bedside, there is a need for more faithful models of human cancers that can recapitulate key features of the human tumor microenvironment (TME) and simultaneously facilitate large-scale drug tests. Our recently developed microdissection method optimizes the yield of large numbers of cuboidal microtissues (″cuboids″, ~(400 µm) 3 ) from a tumor biopsy. Here we demonstrate that cuboids from syngeneic mouse tumor models and human tumors retain a complex TME, making them amenable for drug and immunotherapy evaluation. We characterize relevant TME parameters, such as cellular architecture, cytokine secretion, proteomics profiles, and response to drug panels in multi-well arrays. Despite the cutting procedure and the time spent in culture (up to 7 days), the cuboids display strong cytokine expression and drug responses, including to immunotherapy. Overall, our results suggest that cuboids could provide essential therapeutic information for personalized oncology applications and could help the development of TME-dependent therapeutics and cancer disease models, including for clinical trials.

Correlation Analysis Among the Chemical Composition and Cytotoxic and Antioxidative Activities of a Tessaria absinthioides Decoction for Endorsing Its Potential Application in Oncology.

Historically, botanical preparations have been used to improve human health. Their active ingredients are influenced by multiple factors such as intraspecies variations, environmental conditions, collection time and methods, and the part of the plant used. To ensure the efficiency and safety of these herbal drugs, qualitative and quantitative analyses are required. A Tessaria absinthioides decoction (DETa) was reported as having hypocholesterolemic, anti-inflammatory, cytotoxic, antitumor, and antioxidative properties. This work aimed to analyze DETa by correlating its chemical composition with cytotoxic and antioxidative properties, with the aim of promoting research on it as an anticancer agent. DETa collections (2017, 2018, 2019, and 2022) were analyzed by UHPLC-DAD, UHPLC-DAD-FLD, and UPLC-MS/MS; cytotoxicity was assessed on the MCF-7 breast cancer cell line; antioxidative capacity was evaluated by the DPPH and FRAP methods; and correlation analysis was used to determine biological and chemical markers. The results provide evidence that biological activities were consistent across the collections. Among the quantified compounds, apigenin, naringin, gallocatechin gallate, ginnalin A, myricetin, epicatechin, OH-tyrosol, quercetin, and chlorogenic, tessaric, p-coumaric, vanillic, caffeic, caftaric, ellagic, and rosmarinic acids correlated as bioactive and chemical markers. Moreover, tessaric acid could be established as a species marker. Altogether, these findings add relevant information to DETa properties, encouraging further exploration of its potential application as an anticancer botanical.

AI-Guided Cancer Therapy for Patients with Coexisting Migraines.

Background: Cancer remains a leading cause of death worldwide. Progress in its effective treatment has been hampered by challenges in personalized therapy, particularly in patients with comorbid conditions. The integration of artificial intelligence (AI) into patient profiling offers a promising approach to enhancing individualized anticancer therapy. Objective: This narrative review explores the role of AI in refining anticancer therapy through personalized profiling, with a specific focus on cancer patients with comorbid migraine. Methods: A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, and Google Scholar. Studies were selected based on their relevance to AI applications in oncology and migraine management, with a focus on personalized medicine and predictive modeling. Key themes were synthesized to provide an overview of recent developments, challenges, and emerging directions. Results: AI technologies, such as machine learning (ML), deep learning (DL), and natural language processing (NLP), have become instrumental in the discovery of genetic and molecular biomarkers of cancer and migraine. These technologies also enable predictive analytics for assessing the impact of migraine on cancer therapy in comorbid cases, predicting outcomes and provide clinical decision support systems (CDSS) for real-time treatment adjustments. Conclusions: AI holds significant potential to improve the precision and effectiveness of the management and therapy of cancer patients with comorbid migraine. Nevertheless, challenges remain over data integration, clinical validation, and ethical consideration, which must be addressed to appreciate the full potential for the approach outlined herein.

Patients’, parents’, and survivors’ perspective about AI applications in pediatric oncology

Patients, parents and survivors of childhood cancer are those who represent the critical link between the clinical and technical research spheres. Thus, UNICA4EU worked towards a patient- centric approach to integrate AI in the care pathways for childhood cancer, with evidence-based patient advocacy at its core to build trust while protecting and guaranteeing patients’ fundamental rights [1].The task “Increase Knowledge and Transparency about AI among patients, parents and survivors” was led by CCI Europe as the biggest pan-European childhood cancer parents’ and survivors’ organization which represents childhood cancer parents´ and survivors´ groups and other childhood cancer organizations and reunites 63 member-organizations in 34 countries [2].To investigate the knowledge base of AI application in pediatric oncology among those who are affected, a survey was conducted. The survey translated into nine European languages, gathered responses from 332 individuals. To delve deeper into the survey findings, discussions were held with a diverse focus group, including four parents of childhood cancer former patients (survivors), three childhood cancer survivors, and one bereaved parent, each representing different backgrounds, age groups, and countries.Insights and outcomes of this study produced a report for guiding the multi-stakeholder board of the project when defining the governance structures reg. data sharing, ownership, protection, access and usage.Perspective of parents, patients and survivors of pediatric cancer regarding AI applications in Pediatric Oncology focused in six areas of interest including: data anonymization and data protection, data ownership, data withdrawal, ethical concerns of use of data, data types and, additionally, informed consents. This paper summarizes the respective results, along concluding policy recommendations.

Drug-Induced Differential Gene Expression Analysis on Nanoliter Droplet Microarrays: Enabling Tool for Functional Precision Oncology.

Drug-induced differential gene expression analysis (DGEA) is essential for uncovering the molecular basis of cell phenotypic changes and understanding individual tumor responses to anticancer drugs. Performing high throughput DGEA is challenging due to the high cost and labor-intensive multi-step sample preparation protocols. In particular, performing drug-induced DGEA on cancer cells derived from patient biopsies is even more challenging due to the scarcity of available cells. A novel, miniaturized, nanoliter-scale method for drug-induced DGEA is introduced, enabling high-throughput and parallel analysis of patient-derived cell drug responses, overcoming the limitations and laborious nature of traditional protocols. The method is based on the Droplet Microarray (DMA), a microscope glass slide with hydrophilic spots on a superhydrophobic background, facilitating droplet formation for cell testing. DMA allows microscopy-based phenotypic analysis, cDNA extraction, and DGEA. The procedure includes cell lysis for mRNA isolation and cDNA conversion followed by droplet pooling for qPCR analysis. In this study, the drug-induced DGEA protocol on the DMA platform is demonstrated using patient-derived chronic lymphocytic leukemia (CLL) cells. This methodology is critical for DGEA with limited cell numbers and promise for applications in functional precision oncology. This method enables molecular profiling of patient-derived samples after drug treatment, crucial for understanding individual tumor responses to anticancer drugs.

Innovative approaches to cage reconstructions in orthopedic limb surgery: Advances and insights with two cases

BackgroundLimb tumors, though rare, pose significant challenges to surgical management due to their impact on limb function and quality of life. Effective treatment requires precise tumor resection alongside advanced reconstructive techniques to restore limb function, particularly in cases of compromised bone healing. Titanium cages, traditionally used in spinal surgeries, have recently gained attention for their potential in limb reconstruction within orthopedic oncology, offering a novel approach to managing complex bone defects. Case PresentationThis retrospective case series presents two patients treated with titanium cages for limb reconstruction following tumor resection.Case 1: A 53-year-old female with a solitary plasmacytoma of the femur experienced a pathological fracture and nonunion after initial treatment. A titanium cage was employed to bridge the resected bone segment, leading to successful callus formation at the six-month follow-up and restored limb function.Case 2: A 31-year-old female with a giant cell tumor and hemangioma of the foot underwent en bloc resection followed by reconstruction using a vertebral cage and iliac corticospongious graft. Six months postoperatively, the patient had resumed full weight-bearing with no complications. ConclusionTitanium cages offer a promising solution for limb reconstruction in patients with tumors, particularly for large bone defects where traditional methods may be inadequate. This case series demonstrates successful outcomes in limb preservation and functional recovery using titanium cages. Their use in the adult population shows potential for broader application in orthopedic oncology, warranting further clinical investigation.

Development trends and knowledge framework of artificial intelligence (AI) applications in oncology by years: a bibliometric analysis from 1992 to 2022

PurposeOncology is the primary field in medicine with a high rate of artificial intelligence (AI) use. Thus, this study aimed to investigate the trends of AI in oncology, evaluating the bibliographic characteristics of articles. We evaluated the related research on the knowledge framework of Artificial Intelligence (AI) applications in Oncology through bibliometrics analysis and explored the research hotspots and current status from 1992 to 2022.MethodsThe research employed a scientometric methodology and leveraged scientific visualization tools such as Bibliometrix R Package Software, VOSviewer, and Litmaps for comprehensive data analysis. Scientific AI-related publications in oncology were retrieved from the Web of Science (WoS) and InCites from 1992 to 2022.ResultsA total of 7,815 articles authored by 35,098 authors and published in 1,492 journals were included in the final analysis. The most prolific authors were Esteva A (citaition = 5,821) and Gillies RJ (citaition = 4288). The most active institutions were the Chinese Academy of Science and Harward University. The leading journals were Frontiers ın Oncology and Scientific Reports. The most Frequent Author Keywords are "machine learning", "deep learning," "radiomics", "breast cancer", “melanoma” and "artificial intelligence," which are the research hotspots in this field. A total of 10,866 Authors' keywords were investigated. The average number of citations per document is 23. After 2015, the number of publications proliferated.ConclusionThe investigation of Artificial Intelligence (AI) applications in the field of Oncology is still in its early phases especially for genomics, proteomics, and clinicomics, with extensive studies focused on biology, diagnosis, treatment, and cancer risk assessment. This bibliometric analysis offered valuable perspectives into AI's role in Oncology research, shedding light on emerging research paths. Notably, a significant portion of these publications originated from developed nations. These findings could prove beneficial for both researchers and policymakers seeking to navigate this field.

The effect of knowledge and person-related factors on breast cancer susceptibility genes (BRCA1/2) testing perception in Turkish women.

Genetic testing for breast cancer susceptibility genes (BRCA1/2) plays a pivotal role in risk assessment and preventive interventions. However, individuals' awareness, knowledge, and attitudes toward genetic testing can vary across different societies. This study focuses on understanding Turkish women's knowledge, perceptions, and attitudes toward BRCA1/2 testing, considering demographic factors and awareness. In this cross-sectional study, 301 Turkish participants, including breast/ovarian cancer patients and their first-degree relatives, were surveyed. Information on sociodemographics, cancer history, awareness, knowledge, and perceptions was collected. The study aimed to assess knowledge levels about breast cancer inheritance and BRCA1/2 testing, describe perspectives about testing in women with a family history of breast or ovarian cancer, and determine associations between knowledge, personal factors, anxiety, and genetic testing perspectives. Results showed a wide range in correct responses (31.6%-96.7%) for knowledge items. No significant relationship between knowledge levels and positive perception was observed. However, participants answering a specific question incorrectly showed higher negative perceptions. While most participants recognized the benefits of genetic testing, concerns centered around passing the genes to future generations. Participants who were younger, more educated, had higher income, were employed, at an earlier disease stage, and were social media users demonstrated more positive attitudes. Negative perceptions were higher among younger patients, physicians, and healthcare professionals. Interestingly, anxiety in cancer patients did not correlate with either positive or negative perceptions. In conclusion, this study identifies participant-related factors influencing perceptions of hereditary genetic tests. Understanding these factors and addressing associated issues can enhance the utilization of genetic testing and promote preventive oncology applications.

Advancements in genetic analysis: Insights from a case study and review of next-generation sequencing techniques for veterinary oncology applications.

Acute myeloid leukemia (AML) poses significant challenges in veterinary medicine, with limited treatment options and poor survival rates. While substantial progress has been made in characterizing human AML, translating these advancements to veterinary practice has been hindered by limited molecular understanding and diagnostic tools. The case study presented illustrates the application of whole genome sequencing in diagnosing AML in a dog, showcasing its potential in veterinary oncology. Our approach facilitated comprehensive genomic analysis, identifying mutations in genes that may be associated with AML pathogenesis in dogs, such as KRAS, IKZF1, and RUNX1. However, without supportive evidence of its clinical utility (eg, association with response to treatment or prognosis), the information is limited to exploration. This article reviews the comparative features of canine AML with human AML and discusses strategies to shrink the knowledge gap between human and veterinary medicine with cost-effective next-generation sequencing (NGS) techniques. By utilizing these approaches, the unique and shared molecular features with human AML can be identified, aiding in molecular classification and therapeutic development for both species. Despite the promise of NGS, challenges exist in implementing it into routine veterinary diagnostics. Cost considerations, turnaround times, and the need for robust bioinformatics pipelines and quality control measures must be addressed. Most importantly, analytical and clinical validation processes are essential to ensure the reliability and clinical utility of NGS-based assays. Overall, integrating NGS technologies into veterinary oncology holds great potential for advancing our understanding of AML and improving disease stratification, in hopes of improving clinical outcomes.

Geldanamycin, a Naturally Occurring Inhibitor of Hsp90 and a Lead Compound for Medicinal Chemistry.

Geldanamycin remains a driver in the medicinal chemistry of heat shock protein 90 (Hsp90) inhibition, even half a century after its original isolation from nature. This Perspective focuses on the properties of the benzoquinone ring of the natural product that enable a range of functionalization reactions to take place. Therefore, inherent reactivity at C-17, where the methoxy group serves as a vinylogous ester, and at C-19 that demonstrates nucleophilic, enamide-type character toward electrophiles, and also as a conjugate acceptor to react with nucleophiles, has facilitated the synthesis of semisynthetic derivatives. Thus, a range of C-17-substituted amine derivatives has been investigated in oncology applications, with a number of compounds in this series reaching clinical trials. In contrast, the 19-position of geldanamycin has received less attention, although 19-substituted derivatives offer promise with markedly reduced toxicity compared to geldanamycin itself, while retaining Hsp90 inhibitory activity albeit with diminished potency in cellular studies.

Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature.

Interventional oncology provides image-guided therapies, including transarterial tumor embolization and percutaneous tumor ablation, for malignant tumors in a minimally invasive manner. As in other medical fields, the application of artificial intelligence (AI) in interventional oncology has garnered significant attention. This narrative review describes the current state of AI applications in interventional oncology based on recent literature. A literature search revealed a rapid increase in the number of studies relevant to this topic recently. Investigators have attempted to use AI for various tasks, including automatic segmentation of organs, tumors, and treatment areas; treatment simulation; improvement of intraprocedural image quality; prediction of treatment outcomes; and detection of post-treatment recurrence. Among these, the AI-based prediction of treatment outcomes has been the most studied. Various deep and conventional machine learning algorithms have been proposed for these tasks. Radiomics has often been incorporated into prediction and detection models. Current literature suggests that AI is potentially useful in various aspects of interventional oncology, from treatment planning to post-treatment follow-up. However, most AI-based methods discussed in this review are still at the research stage, and few have been implemented in clinical practice. To achieve widespread adoption of AI technologies in interventional oncology procedures, further research on their reliability and clinical utility is necessary. Nevertheless, considering the rapid research progress in this field, various AI technologies will be integrated into interventional oncology practices in the near future.

Photodynamic Therapy Review: Past, Present, Future, Opportunities and Challenges

Photodynamic therapy (PDT) is a medical treatment that utilizes photosensitizing agents, along with light, to produce reactive oxygen species that can kill nearby cells. When the photosensitizer is exposed to a specific wavelength of light, it becomes activated and generates reactive oxygen that can destroy cancer cells, bacteria, and other pathogenic micro-organisms. PDT is commonly used in dermatology for treating actinic keratosis, basal cell carcinoma, and other skin conditions. It is also being explored for applications in oncology, such as treating esophageal and lung cancers, as well as in ophthalmology for age-related macular degeneration. In this study, we provide a comprehensive review of PDT, covering its fundamental principles and mechanisms, as well as the critical components for its function. We examine key aspects of PDT, including its current clinical applications and potential future developments. Additionally, we discuss the advantages and disadvantages of PDT, addressing the various challenges associated with its implementation and optimization. This review aims to offer a thorough understanding of PDT, highlighting its transformative potential in medical treatments while acknowledging the areas requiring further research and development.

Stepwise Isolation of Diverse Metabolic Cell Populations Using Sorting by Interfacial Tension (SIFT).

We present here a passive and label-free droplet microfluidic platform to sort cells stepwise by lactate and proton secretion from glycolysis. A technology developed in our lab, Sorting by Interfacial Tension (SIFT), sorts droplets containing single cells into two populations based on pH by using interfacial tension. Cellular glycolysis lowers the pH of droplets through proton secretion, enabling passive selection based on interfacial tension and hence single-cell glycolysis. The SIFT technique is expanded here by exploiting the dynamic droplet acidification from surfactant adsorption that leads to a concurrent increase in interfacial tension. This allows multiple microfabricated rails at different downstream positions to isolate cells with distinct glycolytic levels. The device is used to correlate sorted cells with three levels of glycolysis with a conventional surface marker for T-cell activation. As glycolysis is associated with both disease and cell state, this technology facilitates the sorting and analysis of crucial cell subpopulations for applications in oncology, immunology and immunotherapy.