Explore the vast range of titles available.

Extracellular vesicles (EV) have been emerging as potential biomarkers for disease monitoring. In particular, tumor‐derived EV (TDE) are known to carry oncogenic miRNA, so they can be used for diagnosis of early cancer by analyzing the expression levels of EV‐miRNA circulating in the blood. Here, using our novel microfluidic device, we rapidly and selectively isolate cancerous EV expressing breast cancer‐derived surface markers CD49f and EpCAM within 2 minutes. Based on seven candidates of miRNA nominated from The Cancer Genome Atlas (TCGA) database, the expression levels of miRNA in TDE were validated in a total of 82 individuals, including 62 breast cancer patients and 20 healthy controls. Among seven candidates, four miRNAs (miR‐9, miR‐16, miR‐21, and miR‐429) from the EV were highly elevated in early‐stage breast cancer patients compared with healthy donors. The combination of significant miRNAs from specific EV has high sensitivities of 0.90, 0.86, 0.88, and 0.84 of the area under the receiver operating characteristic curve (AUC) in each subtype (luminal A, luminal B, HER‐2, and triple‐negative) of early‐stage breast cancer. Our results suggest that the combination of four miRNA signatures of specific EV could serve as a sensitive and specific biomarker and enable early diagnosis of breast cancer using liquid biopsy. A new combination of miRNA from cancer‐specific extracellular vesicles was proposed for the highly sensitive and specific diagnosis of early‐stage breast cancer in association with a microfluidic‐based immuno‐isolation technique. These findings suggest that miRNA in EV could be used as important biomarkers for the early detection of breast cancer and identification of cancer subtype.

The response of a cell population is often delayed relative to drug injection, and individual cells in a population of cells have a specific age distribution. The application of transit compartment models (TCMs) is a common approach for describing this delay. In this paper, we propose a TCM in which damaged cells caused by a drug are given by a single fractional derivative equation. This model describes the delay as a single equation composed of fractional and ordinary derivatives, instead of a system of ODEs expressed in multiple compartments, applicable to the use of the PK concentration in the model. This model tunes the number of compartments in the existing model and expresses the delay in detail by estimating an appropriate fractional order. We perform model robustness, sensitivity analysis, and change of parameters based on the amount of data. Additionally, we resolve the difficulty in parameter estimation and model simulation using a semigroup property, consisting of a system with a mixture of fractional and ordinary derivatives. This model provides an alternative way to express the delays by estimating an appropriate fractional order without determining the pre-specified number of compartments.

Synchronization plays a significant role in information transfer and decision-making by neurons and brain neural networks. The development of control strategies for synchronizing a network of chaotic neurons with time delays, different direction-dependent coupling (unidirectional and bidirectional), and noise, particularly under external disturbances, is an essential and very challenging task. Researchers have extensively studied the synchronization mechanism of two coupled time-delayed neurons with bidirectional coupling and without incorporating the effect of noise, but not for time-delayed neural networks. To overcome these limitations, this study investigates the synchronization problem in a network of coupled FitzHugh–Nagumo (FHN) neurons by incorporating time delays, different direction-dependent coupling (unidirectional and bidirectional), noise, and ionic and external disturbances in the mathematical models. More specifically, this study investigates the synchronization of time-delayed unidirectional and bidirectional ring-structured FHN neuronal systems with and without external noise. Different gap junctions and delay parameters are used to incorporate time-delay dynamics in both neuronal networks. We also investigate the influence of the time delays between connected neurons on synchronization conditions. Further, to ensure the synchronization of the time-delayed FHN neuronal networks, different adaptive control laws are proposed for both unidirectional and bidirectional neuronal networks. In addition, necessary and sufficient conditions to achieve synchronization are provided by employing the Lyapunov stability theory. The results of numerical simulations conducted for different-sized multiple networks of time-delayed FHN neurons verify the effectiveness of the proposed adaptive control schemes.

Current methods for detecting Mycobacterium tuberculosis (M.tb) in centralized medical facilities are a bottleneck in TB surveillance, particularly in resource-constrained regions. In response, we present a groundbreaking portable bio-tool, the lab-in-a-cartridge (LIC) system, designed for on-site detection of lipoarabinomannan (LAM) in trace amounts within the urine. The innovative design combines pumpless liquid handling and magnetic force-based enrichment with horseradish peroxidase polymer amplification to precisely quantify low biomarker levels. Employing a tetramethylbenzidine-based colorimetric reaction, the LIC enables semi-quantitative LAM detection. This LIC incorporates all necessary reagents, achieving a detection threshold of as low as 0.01 pg/mL in pooled urine samples within 40 minutes. The LIC distinguishes TB patients in clinical urine samples with 92% sensitivity and 88% specificity. This pioneering device not only sets an improved standard for detecting low LAM concentrations but also holds the potential to realize a decentralized diagnosis of TB. Tuberculosis detection methods in centralized facilities are bottlenecks in low resource areas. Here, the authors develop a portable lab-in-a-cartridge system that detects TB biomarkers in urine with 92% sensitivity in 40 minutes enabling decentralized TB diagnosis.

Several image-based biomedical diagnoses require high-resolution imaging capabilities at large spatial scales. However, conventional microscopes exhibit an inherent trade-off between depth-of-field (DoF) and spatial resolution, and thus require objects to be refocused at each lateral location, which is time consuming. Here, we present a computational imaging platform, termed E2E-BPF microscope, which enables large-area, high-resolution imaging of large-scale objects without serial refocusing. This method involves a physics-incorporated, deep-learned design of binary phase filter (BPF) and jointly optimized deconvolution neural network, which altogether produces high-resolution, high-contrast images over extended depth ranges. We demonstrate the method through numerical simulations and experiments with fluorescently labeled beads, cells and tissue section, and present high-resolution imaging capability over a 15.5-fold larger DoF than the conventional microscope. Our method provides highly effective and scalable strategy for DoF-extended optical imaging system, and is expected to find numerous applications in rapid image-based diagnosis, optical vision, and metrology. The E2E-BPF microscope, a DoF-extension computational imaging platform, enabled by an end-to-end optimized binary phase filter and image reconstruction, facilitates high-resolution imaging over an extended DoF without refocusing.

The efficacy evaluation of anticancer drugs derived from natural products has traditionally relied on animal models, highlighting the need for more efficient preclinical assessment platforms. In this study, a binary tumor‐microenvironment‐on‐a‐chip (T‐MOC) system is introduced to assess the therapeutic potential of illudin S and roridin E, two cytotoxic compounds derived from Omphalotus japonicus and Podostroma cornu‐damae, respectively. The binary T‐MOC model integrates independently developed vascular and invasive ductal carcinoma compartments, effectively mimicking in vivo drug delivery barriers and physiological dynamics. Using this model, illudin S demonstrates strong anticancer effects but exhibits high toxicity, particularly in the lung and liver, indicating a narrow therapeutic window. Roridin E demonstrates potent activity at low concentrations but exhibits high toxicity, especially in the liver and skin. Additionally, morphological analysis is performed to predict drug delivery and distribution characteristics, revealing anisotropic remission and the influence of microenvironmental factors on drug response. This study underscores the potential of the binary T‐MOC system as an alternative platform for anticancer drug evaluation, enabling efficient preclinical validation while reducing reliance on animal models. This study presents a binary tumor‐microenvironment‐on‐a‐chip (T‐MOC) system incorporating multicellular tumor spheroids (MCTs) as an alternative preclinical platform to evaluate the efficacy of anticancer natural products. The T‐MOC model reproduces in vivo drug delivery barriers and physiological conditions, enabling morphological analysis to predict drug delivery and distribution characteristics, thereby highlighting its potential to reduce reliance on animal testing.

Liquid biopsies are easier to acquire patient derived samples than conventional tissue biopsies, and their use enables real-time monitoring of the disease through continuous sampling after initial diagnosis, resulting in a paradigm shift to customized treatment according to the patient’s prognosis. Among the various liquid biopsy samples, saliva is easily obtained by spitting or swab sucking without needing an expert for sample collection. In addition, it is known that disease related biomarkers that exist in the blood and have undergone extensive research exist in saliva even at a lower concentration than the blood. Thus, interest in the use of saliva as a liquid biopsy has increased. In this review, we focused on the salivary exosome and cell-free DNA (cfDNA) among the various biomarkers in saliva. Since the exosome and cfDNA in saliva are present at lower concentrations than the biomarkers in blood, it is important to separate and concentrate them before conducting down-stream analyses such as exosome cargo analysis, quantitative polymerase chain reaction (qPCR), and sequencing. However, saliva is difficult to apply directly to microfluidics-based systems for separation because of its high viscosity and the presence of various foreign substances. Therefore, we reviewed the microfluidics-based saliva pretreatment method and then compared the commercially available kit and the microfluidic chip for isolation and enrichment of the exosome and cfDNA in saliva.

Due to its similarity to in vivo conditions, tumor spheroids are actively used in research areas, such as drug screening and cell–cell interactions. A substantial quantity of spheroids is crucial for obtaining dependable results in high-throughput screening. Conventional fabrication methods of spheroid have limitations in low yield and morphological variation. Droplet-based microfluidic system capable of mass-producing uniformed spheroids can overcome these limitations. In this study, we investigated the optimal culture conditions, which allows to researchers provide guidelines for producing spheroids with the desired diameter and quantity. Mass-produced spheroids were employed to analyze compaction, which is crucial for evaluating the remission effects of drugs, as well as the formation of a necrotic core, which induces a bias in the analysis of drug response and viability. The time point at which compaction is completed and the diameter begins to increase was measured using spheroids with diameters of both > 400 μm and < 400 μm, and spheroids do not proliferate a linear growth trend. Spheroid with diameters ranging from 73.4 ± 11.42 μm to 371 ± 5.11 μm was used to predict the formation of the necrotic core based on live cell counting, and diameter of 300–330 μm was mathematically calculated as the diameter where a necrotic core forms. Additionally, the use of artificial intelligence (AI) for high-throughput analysis is crucial for obtaining time-saving and reproducible data. We produced BT474 and MCF-7 spheroids with diameters of 100, 200, and 300 μm and obtained morphological indicators from an AI-based program to compare the differences in heterogeneous breast tumor spheroids. Through this study, we optimized the diameter of spheroids and the initiation timing for drug screening and emphasized the importance of AI-based morphological analysis in high-throughput screening.

Circulating tumor cells (CTCs) are a popular topic in cancer research because they can be obtained by liquid biopsy, a minimally invasive procedure with more sample accessibility than tissue biopsy, to monitor a patient’s condition. Over the past decades, CTC research has covered a wide variety of topics such as enumeration, profiling, and correlation between CTC number and patient overall survival. It is important to isolate and enrich CTCs before performing CTC analysis because CTCs in the blood stream are very rare (0–10 CTCs/mL of blood). Among the various approaches to separating CTCs, here, we review the research trends in the isolation and analysis of CTCs using microfluidics. Microfluidics provides many attractive advantages for CTC studies such as continuous sample processing to reduce target cell loss and easy integration of various functions into a chip, making “do-everything-on-a-chip” possible. However, tumor cells obtained from different sites within a tumor exhibit heterogenetic features. Thus, heterogeneous CTC profiling should be conducted at a single-cell level after isolation to guide the optimal therapeutic path. We describe the studies on single-CTC analysis based on microfluidic devices. Additionally, as a critical concern in CTC studies, we explain the use of CTCs in cancer research, despite their rarity and heterogeneity, compared with other currently emerging circulating biomarkers, including exosomes and cell-free DNA (cfDNA). Finally, the commercialization of products for CTC separation and analysis is discussed.

Stem cells produce nanosized particles known as extracellular vesicles (SC-EVs), which therapeutically affect stem cells. EVs are more abundantly produced, exhibit better stability, and possess lower immune rejection rates than stem cells. However, the traditional methods of isolating EVs, such as ultracentrifugation, possess limitations that require a complex process and consume more time. Moreover, it is difficult to isolate specific EVs that have target surface proteins that affect regenerative effects. To address these limitations, a new dual-mode horseshoe-shaped orifice micromixer (DM-HOMM) chip that can bind antibody-conjugated micromagnetic beads and SC-EVs and sequentially elute specific SC-EVs on the beads using an eluent was developed. For effective elution from the microbead-SC-EV complex, four types of eluents were used to control pH and ionic strength between antibodies and surface proteins in EVs. In addition, we investigated the reusability of antibody-conjugated micromagnetic beads. The beads indicated identical binding efficiencies between the antibodies and specific SC-EVs for three repeated cycles using the dual-mode chip. CD63 + EVs collected by the chip exhibited higher cell viability and regeneration effects than untreated and total EVs. This SC-EVs’ isolation method possesses the potential for targeted therapeutic applications and enhanced regenerative effects.