In Situ MicroRNA Profiling of Single Tumor Extracellular Vesicles for Precise Prostate Cancer Diagnosis.

MicroRNAs (miRNAs) packaged within extracellular vesicles (EV) exhibit remarkable stability in circulation and reflect the genetic and epigenetic characteristics of their parent cells, making them promising biomarkers for cancer diagnosis. However, the intrinsic heterogeneity of EV populations and the low abundance of miRNAs in early stage cancer pose a challenge in the sensitive detection of miRNAs in tumor-cell-derived EV (TEV). Herein, we present a one-pot strategy named miR-nSTEV for specific recognition and in situ miRNA profiling of TEV at the single-particle level for precise prostate cancer (PCa) diagnosis.

Orthogonal Coding-Guided Liposomal Nanoreactor for One-Pot Precise Proteomic Profiling of Tumor-Derived Extracellular Vesicle Subpopulations.

The protein cargoes of tumor-derived extracellular vesicles (TEVs) are promising biomarkers for cancer diagnosis, yet their application is constrained by EV heterogeneity and complex assay procedures. Here, we developed an orthogonal coding-guided liposomal enzyme nanoreactor (OLEN) for the one-pot proteomic profiling of TEVs. It integrates a DNA computation module to selectively recognize CD63EpCAM TEVs via dual-marker orthogonal coding.

An orthogonal barcoding enabled smart nanodevice for highly efficient isolation and proteomic profiling of tumor-derived extracellular vesicles.

Tumor-derived extracellular vesicles (T-EVs) are small, membrane-bound particles secreted by cancer cells into the extracellular environment. These vesicles carry tumor-specific molecules, making them promising candidates as biomarkers for cancer diagnosis and monitoring. Among the various molecular components of T-EVs, such as nucleic acids and lipids, proteins stand out due to their unique characteristics and functional significance in cancer progression, diagnosis, and therapy.

OpenDeID Pipeline for Unstructured Electronic Health Record Text Notes Based on Rules and Transformers: Deidentification Algorithm Development and Validation Study.

Background: Electronic health records (EHRs) in unstructured formats are valuable sources of information for research in both the clinical and biomedical domains. However, before such records can be used for research purposes, sensitive health information (SHI) must be removed in several cases to protect patient privacy. Rule-based and machine learning-based methods have been shown to be effective in deidentification.

Simultaneous quantification of exosomal MMP14 expression and proteolysis activity on a spherical dual-probe-based fluorescent nanosensor.

Among various exosomal proteins, matrix metalloproteinases (MMPs) are a family of membrane associated endopeptidases and have been considered as potential biomarkers in liquid biopsy owing to their multiple roles in pathological processes. However, the potential of MMP14 expression (MMP14-E) and MMP14 proteolytic activity (MMP14-A) in clinical diagnosis is still not clear due to the lack of sensitive and simultaneous detection techniques. Herein, we propose a fluorescent nanosensor for the simultaneous detection of MMP14-E and MMP14-A using a spherical aptamer/peptide dual-probe strategy.

Efficient exosome subpopulation isolation and proteomic profiling using a Sub-ExoProfile chip towards cancer diagnosis and treatment.

Exosomes have been extensively studied as liquid biopsy biomarkers in the past decade. However, the origin and molecular heterogeneity of exosomes hinder the research development moving from proof-of-concept to clinical applications. Herein, we report an integrated microfluidic platform termed Sub-ExoProfile chip, to achieve the selective isolation and subsequent proteomic profiling of multiplex exosome subpopulations simultaneously.

The OpenDeID corpus for patient de-identification.

For research purposes, protected health information is often redacted from unstructured electronic health records to preserve patient privacy and confidentiality. The OpenDeID corpus is designed to assist development of automatic methods to redact sensitive information from unstructured electronic health records. We retrieved 4548 unstructured surgical pathology reports from four urban Australian hospitals.

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge.

Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia.

Generation of Surrogates for De-Identification of Electronic Health Records.

Unstructured electronic health records are valuable resources for research. Before they are shared with researchers, protected health information needs to be removed from these unstructured documents to protect patient privacy. The main steps involved in removing protected health information are accurately identifying sensitive information in the documents and removing the identified information.