Tumor cell-based liquid biopsy using high-throughput microfluidic enrichment of entire leukapheresis product.

Circulating Tumor Cells (CTCs) in blood encompass DNA, RNA, and protein biomarkers, but clinical utility is limited by their rarity. To enable tumor epitope-agnostic interrogation of large blood volumes, we developed a high-throughput microfluidic device, depleting hematopoietic cells through high-flow channels and force-amplifying magnetic lenses. Here, we apply this technology to analyze patient-derived leukapheresis products, interrogating a mean blood volume of 5.

Is There an Ideal Concentration of Ozonized Oil for the Prevention and Modulation of Zoledronate-Induced Mandibular Osteonecrosis? A Study on Senescent Rats.

This study aimed to identify whether there is an ideal concentration for applying ozonized oil (OZ) in the post-exodontic alveoli of senescent rats treated with zoledronate (ZOL). Thirty-five female rats, aged 18 months, were divided into five groups: ZOL; ZOL+OZ500; ZOL+OZ600; ZOL+OZ700; and SAL. The groups treated with ZOL, and other concentrations of OZ received applications at a dose of 100 μg/kg, while the SAL group received saline.

A TROP2/Claudin Program Mediates Immune Exclusion to Impede Checkpoint Blockade in Breast Cancer.

Immune exclusion inhibits anti-tumor immunity and response to immunotherapy, but its mechanisms remain poorly defined. Here, we demonstrate that Trophoblast Cell-Surface Antigen 2 (TROP2), a key target of emerging anti-cancer Antibody Drug Conjugates (ADCs), controls barrier-mediated immune exclusion in Triple-Negative Breast Cancer (TNBC) through Claudin 7 association and tight junction regulation. TROP2 expression is inversely correlated with T cell infiltration and strongly associated with outcomes in TNBC.

Establishing responsible use of AI guidelines: a comprehensive case study for healthcare institutions.

This report presents a comprehensive case study for the responsible integration of artificial intelligence (AI) into healthcare settings. Recognizing the rapid advancement of AI technologies and their potential to transform healthcare delivery, we propose a set of guidelines emphasizing fairness, robustness, privacy, safety, transparency, explainability, accountability, and benefit. Through a multidisciplinary collaboration, we developed and operationalized these guidelines within a healthcare system, highlighting a case study on ambient documentation to demonstrate the practical application and challenges of implementing generative AI in clinical environments.

Cancer cells restrict immunogenicity of retrotransposon expression via distinct mechanisms.

To thrive, cancer cells must navigate acute inflammatory signaling accompanying oncogenic transformation, such as via overexpression of repeat elements. We examined the relationship between immunostimulatory repeat expression, tumor evolution, and the tumor-immune microenvironment. Integration of multimodal data from a cohort of pancreatic ductal adenocarcinoma (PDAC) patients revealed expression of specific Alu repeats predicted to form double-stranded RNAs (dsRNAs) and trigger retinoic-acid-inducible gene I (RIG-I)-like-receptor (RLR)-associated type-I interferon (IFN) signaling.

Dynamic Evolution of Fibroblasts Revealed by Single-Cell RNA Sequencing of Human Pancreatic Cancer.

Pancreatic cancer remains a high unmet medical need. Understanding the interactions between stroma and cancer cells in this disease may unveil new opportunities for therapeutic intervention.

Implementation of Artificial Intelligence in Retinopathy of Prematurity Care: Challenges and Opportunities.

The diagnosis of retinopathy of prematurity (ROP) is primarily image-based and suitable for implementation of artificial intelligence (AI) systems. Increasing incidence of ROP, especially in low and middle-income countries, has also put tremendous stress on health care systems. Barriers to the implementation of AI include infrastructure, regulatory, legal, cost, sustainability, and scalability.

A look at the emerging trends of large language models in ophthalmology.

Purpose Of Review: As the surge in large language models (LLMs) and generative artificial intelligence (AI) applications in ophthalmology continue to expand, this review seeks to update physicians of the current progress, to catalyze further work to harness its capabilities to enhance healthcare delivery in ophthalmology.

Recent Findings: Generative AI applications have shown promising performance in Ophthalmology. Beyond native LLMs and question-answering based tasks, there has been increasing work in employing novel LLM techniques and exploring wider use case applications.

Privacy-Preserving Technology Using Federated Learning and Blockchain in Protecting against Adversarial Attacks for Retinal Imaging.

Purpose: Collaboration provides valuable data for robust artificial intelligence (AI) model development. Federated learning (FL) is a privacy-enhancing technology that allows collaboration while respecting privacy via the development of models without raw data transfer. However state-of-the-art FL models still face challenges in non-independent and identically distributed (non-IID) health care settings and remain susceptible to privacy breaches.

HCC spatial transcriptomic profiling reveals significant and potentially targetable cancer-endothelial interactions.

Background: HCC is a highly vascular tumor, and many effective drug regimens target the tumor blood vessels. Prior bulk HCC subtyping data used bulk transcriptomes, which contained a mixture of parenchymal and stromal contributions.

Methods: We utilized computational deconvolution and cell-cell interaction analyses to cell type-specific (tumor-enriched and vessel-enriched) spatial transcriptomic data collected from 41 resected HCC tissue specimens.

A Competition for the Diagnosis of Myopic Maculopathy by Artificial Intelligence Algorithms.

Importance: Myopic maculopathy (MM) is a major cause of vision impairment globally. Artificial intelligence (AI) and deep learning (DL) algorithms for detecting MM from fundus images could potentially improve diagnosis and assist screening in a variety of health care settings.

Objectives: To evaluate DL algorithms for MM classification and segmentation and compare their performance with that of ophthalmologists.

Antimicrobial resistance: a concise update.

Antimicrobial resistance (AMR) is a serious threat to global public health, with approximately 5 million deaths associated with bacterial AMR in 2019. Tackling AMR requires a multifaceted and cohesive approach that ranges from increased understanding of mechanisms and drivers at the individual and population levels, AMR surveillance, antimicrobial stewardship, improved infection prevention and control measures, and strengthened global policies and funding to development of novel antimicrobial therapeutic strategies. In this rapidly advancing field, this Review provides a concise update on AMR, encompassing epidemiology, evolution, underlying mechanisms (primarily those related to last-line or newer generation of antibiotics), infection prevention and control measures, access to antibiotics, antimicrobial stewardship, AMR surveillance, and emerging non-antibiotic therapeutic approaches.

Artificial intelligence applications in cataract and refractive surgeries.

Purpose Of Review: This review highlights the recent advancements in the applications of artificial intelligence within the field of cataract and refractive surgeries. Given the rapid evolution of artificial intelligence technologies, it is essential to provide an updated overview of the significant strides and emerging trends in this field.

Recent Findings: Key themes include artificial intelligence-assisted diagnostics and intraoperative support, image analysis for anterior segment surgeries, development of artificial intelligence-based diagnostic scores and calculators for early disease detection and treatment planning, and integration of generative artificial intelligence for patient education and postoperative monitoring.

Spatially resolved analysis of pancreatic cancer identifies therapy-associated remodeling of the tumor microenvironment.

In combination with cell-intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell-cell interactions in human pancreatic cancer associated with neoadjuvant chemotherapy and radiotherapy. We developed spatially constrained optimal transport interaction analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand-receptor gene expression.