A three-dimensional liquid diode for soft, integrated permeable electronics.

Wearable electronics with great breathability enable a comfortable wearing experience and facilitate continuous biosignal monitoring over extended periods1-3. However, current research on permeable electronics is predominantly at the stage of electrode and substrate development, which is far behind practical applications with comprehensive integration with diverse electronic components (for example, circuitry, electronics, encapsulation)4-8. Achieving permeability and multifunctionality in a singular, integrated wearable electronic system remains a formidable challenge. Here we present a general strategy for integrated moisture-permeable wearable electronics based on three-dimensional liquid diode (3D LD) configurations. By constructing spatially heterogeneous wettability, the 3D LD unidirectionally self-pumps the sweat from the skin to the outlet at a maximum flow rate of 11.6 ml cm-2 min-1, 4,000 times greater than the physiological sweat rate during exercise, presenting exceptional skin-friendliness, user comfort and stable signal-reading behaviour even under sweating conditions. A detachable design incorporating a replaceable vapour/sweat-discharging substrate enables the reuse of soft circuitry/electronics, increasing its sustainability and cost-effectiveness. We demonstrated this fundamental technology in both advanced skin-integrated electronics and textile-integrated electronics, highlighting its potential for scalable, user-friendly wearable devices.

Biofilm Potentiates Cancer-Promoting Effects of Tumor-Associated Macrophages in a 3D Multi-Faceted Tumor Model.

Components of the tumor microenvironment (TME), such as tumor-associated macrophages (TAMs), influence tumor progression. The specific polarization and phenotypic transition of TAMs in the tumor microenvironment lead to two-pronged impacts that can promote or hinder cancer development and treatment. Here, a novel microfluidic multi-faceted bladder tumor model (TAMPIEB ) is developed incorporating TAMs and cancer cells to evaluate the impact of bacterial distribution on immunomodulation within the tumor microenvironment in vivo. It is demonstrated for the first time that biofilm-induced inflammatory conditions within tumors promote the transition of macrophages from a pro-inflammatory M1-like to an anti-inflammatory/pro-tumor M2-like state. Consequently, multiple roles and mechanisms by which biofilms promote cancer by inducing pro-tumor phenotypic switch of TAMs are identified, including cancer hallmarks such as reducing susceptibility to apoptosis, enhancing cell viability, and promoting epithelial-mesenchymal transition and metastasis. Furthermore, biofilms formed by extratumoral bacteria can shield tumors from immune attack by TAMs, which can be visualized through various imaging assays in situ. The study sheds light on the underlying mechanism of biofilm-mediated inflammation on tumor progression and provides new insights into combined anti-biofilm therapy and immunotherapy strategies in clinical trials.

Liquid biopsy technologies for hematological diseases.

Since the discovery of circulating tumor cells in 1869, technological advances in studying circulating biomarkers from patients' blood have made the diagnosis of nonhematologic cancers less invasive. Technological advances in the detection and analysis of biomarkers provide new opportunities for the characterization of other disease types. When compared with traditional biopsies, liquid biopsy markers, such as exfoliated bladder cancer cells, circulating cell-free DNA (cfDNA), and extracellular vesicles (EV), are considered more convenient than conventional biopsies. Liquid biopsy markers undoubtedly have the potential to influence disease management and treatment dynamics. Our main focuses of this review will be the cell-based, gene-based, and protein-based key liquid biopsy markers (including EV and cfDNA) in disease detection, and discuss the research progress of these biomarkers used in conjunction with liquid biopsy. First, we highlighted the key technologies that have been broadly adopted used in hematological diseases. Second, we introduced the latest technological developments for the specific detection of cardiovascular disease, leukemia, and coronavirus disease. Finally, we concluded with perspectives on these research areas, focusing on the role of microfluidic technology and artificial intelligence in point-of-care medical applications. We believe that the noninvasive capabilities of these technologies have great potential in the development of diagnostics and can influence treatment options, thereby advancing precision disease management.

Fasting to enhance Cancer treatment in models: the next steps.

Short-term fasting (STF) is a technique to reduce nutrient intake for a specific period. Since metabolism plays a pivotal role in tumor progression, it can be hypothesized that STF can improve the efficacy of chemotherapy. Recent studies have demonstrated the efficacy of STF in cell and animal tumor models. However, large-scale clinical trials must be conducted to verify the safety and effectiveness of these diets. In this review, we re-examine the concept of how metabolism affects pathophysiological pathways. Next, we provided a comprehensive discussion of the specific mechanisms of STF on tumor progression, derived through studies carried out with tumor models. There are currently at least four active clinical trials on fasting and cancer treatment. Based on these studies, we highlight the potential caveats of fasting in clinical applications, including the onset of metabolic syndrome and other metabolic complications during chemotherapy, with a particular focus on the regulation of the epithelial to mesenchymal pathway and cancer heterogeneity. We further discuss the advantages and disadvantages of the current state-of-art tumor models for assessing the impact of STF on cancer treatment. Finally, we explored upcoming fasting strategies that could complement existing chemotherapy and immunotherapy strategies to enable personalized medicine. Overall, these studies have the potential for breakthroughs in cancer management.

Low-dose anti-inflammatory combinatorial therapy reduced cancer stem cell formation in patient-derived preclinical models for tumour relapse prevention.

BACKGROUND: Emergence of drug-resistant cancer phenotypes is a challenge for anti-cancer therapy. Cancer stem cells are identified as one of the ways by which chemoresistance develops. METHOD: We investigated the anti-inflammatory combinatorial treatment (DA) of doxorubicin and aspirin using a preclinical microfluidic model on cancer cell lines and patient-derived circulating tumour cell clusters. The model had been previously demonstrated to predict patient overall prognosis. RESULTS: We demonstrated that low-dose aspirin with a sub-optimal dose of doxorubicin for 72 h could generate higher killing efficacy and enhanced apoptosis. Seven days of DA treatment significantly reduced the proportion of cancer stem cells and colony-forming ability. DA treatment delayed the inhibition of interleukin-6 secretion, which is mediated by both COX-dependent and independent pathways. The response of patients varied due to clinical heterogeneity, with 62.5% and 64.7% of samples demonstrating higher killing efficacy or reduction in cancer stem cell (CSC) proportions after DA treatment, respectively. These results highlight the importance of using patient-derived models for drug discovery. CONCLUSIONS: This preclinical proof of concept seeks to reduce the onset of CSCs generated post treatment by stressful stimuli. Our study will promote a better understanding of anti-inflammatory treatments for cancer and reduce the risk of relapse in patients.

Characterizing Deformability and Electrical Impedance of Cancer Cells in a Microfluidic Device.

Mechanical properties of cells, reflective of various biochemical characteristics such as gene expression and cytoskeleton, are promising label-free biomarkers for studying and characterizing cells. Electrical properties of cells, dependent on the cellular structure and content, are also label-free indicators of cell states and phenotypes. In this work, we have developed a microfluidic device that is able to simultaneously characterize the mechanical and electrical properties of individual biological cells in a high-throughput manner (>1000 cells/min). The deformability of MCF-7 breast cancer cells was characterized based on the passage time required for an individual cell to pass through a constriction smaller than the cell size. The total passage time can be divided into two components: the entry time required for a cell to deform and enter a constriction, which is dominated by the deformability of cells, and the transit time required for the fully deformed cell to travel inside the constriction, which mainly relies on the surface friction between cells and the channel wall. The two time durations for individual cells to pass through the entry region and transit region have both been investigated. In addition, undeformed cells and fully deformed cells were simultaneously characterized via electrical impedance spectroscopy technique. The combination of mechanical and electrical properties serves as a unique set of intrinsic cellular biomarkers for single-cell analysis, providing better differentiation of cellular phenotypes, which are not easily discernible via single-marker analysis.

Single-cell profiling approaches to probing tumor heterogeneity.

Tumor heterogeneity is a major hindrance in cancer classification, diagnosis and treatment. Recent technological advances have begun to reveal the true extent of its heterogeneity. Single-cell analysis (SCA) is emerging as an important approach to detect variations in morphology, genetic or proteomic expression. In this review, we revisit the issue of inter- and intra-tumor heterogeneity, and list various modes of SCA techniques (cell-based, nucleic acid-based, protein-based, metabolite-based and lipid-based) presently used for cancer characterization. We further discuss the advantages of SCA over pooled cell analysis, as well as the limitations of conventional techniques. Emerging trends, such as high-throughput sequencing, are also mentioned as improved means for cancer profiling. Collectively, these applications have the potential for breakthroughs in cancer treatment.

Intraoperative cell salvage in metastatic spine tumour surgery reduces potential for reinfusion of viable cancer cells.

PURPOSE: This study aimed at evaluating our hypothesis that tumour cells, which pass through the intraoperative cell salvage (IOCS) machine, lose viability due to possible injury to the cell membrane during centrifugation and filtration, enabling safe reinfusion even without filtration. METHODS: Thirteen patients who underwent metastatic spine tumour surgery (MSTS) at our institution were recruited. Blood samples (5 ml each) were collected at five different stages during surgery, namely, stage A and B: from patients' vein during induction and at the time of maximum tumour manipulation; stage C, D and E: from the operative blood prior to IOCS processing, after IOCS processing and after IOCS-LDF (leucocyte depletion filter) processing, respectively. The samples were then analysed for viability of tumour cells using microwell-based culture. RESULTS: The median age of the patients was 65 years (range 37-77 years). The most common primary tumour was lung, followed by breast, hepatocellular and renal cell carcinoma. The median blood loss was 680 ml (range 300-1500 ml). Analysis of cultured blood samples showed that CTC-containing clusters were developed from some samples before IOCS-LDF processing (stage A: three patients, stage B: three patients and stage C: one patient). None of the samples from stages D and E generated clusters after culture, suggesting the absence of viable cancer cells after IOCS processing. CONCLUSIONS: The salvaged blood may contain some tumour cells after processing with IOCS machine, but these cells are damaged and hence unable to replicate and unlikely to metastasise. The results of this study support the hypothesis that salvaged blood in MSTS is safe for transfusion.

An ultra-high-throughput spiral microfluidic biochip for the enrichment of circulating tumor cells.

The detection and characterization of rare circulating tumor cells (CTCs) from the blood of cancer patients can potentially provide critical insights into tumor biology and hold great promise for cancer management. The ability to collect a large number of viable CTCs for various downstream assays such as quantitative measurements of specific biomarkers or targeted somatic mutation analysis is increasingly important in medical oncology. Here, we present a simple yet reliable microfluidic device for the ultra-high-throughput, label-free, size-based isolation of CTCs from clinically relevant blood volumes. The fast processing time of the technique (7.5 mL blood in less than 10 min) and the ability to collect more CTCs from larger blood volumes lends itself to a broad range of potential genomic and transcriptomic applications. A critical advantage of this protocol is the ability to return all fractions of blood (i.e., plasma (centrifugation), CTCs and white blood cells (WBCs) (size-based sorting)) that can be utilized for diverse biomarker studies or time-sensitive molecular assays such as RT-PCR. The clinical use of this biochip was demonstrated by detecting CTCs from 100% (10/10) of blood samples collected from patients with advanced-stage metastatic breast and lung cancers. The CTC recovery rate ranged from 20 to 135 CTCs mL(-1) and obtained under high purity (of 1 CTC out of every 30-100 WBCs which gives ∼4 log depletion of WBCs). They were identified with immunofluorescence assays (pan-cytokeratin+/CD45-) and molecular probes such as HER2/neu.

Oxidative stress induced by Etoposide anti-cancer chemotherapy drives the emergence of tumor-associated bacteria resistance to fluoroquinolones.

INTRODUCTION: Antibiotic-resistant bacterial infections, such as Pseudomonas aeruginosa and Staphylococcus aureus, are prevalent in lung cancer patients, resulting in poor clinical outcomes and high mortality. Etoposide (ETO) is an FDA-approved chemotherapy drug that kills cancer cells by damaging DNA through oxidative stress. However, it is unclear if ETO can cause unintentional side effects on tumor-associated microbial pathogens, such as inducing antibiotic resistance. OBJECTIVES: We aimed to show that prolonged ETO treatment could unintendedly confer fluoroquinolone antibiotic resistance to P. aeruginosa, and evaluate the effect of tumor-associated P. aeruginosa on tumor progression. METHODS: We employed experimental evolution assay to treat P. aeruginosa with prolonged ETO exposure, evaluated the ciprofloxacin resistance, and elucidated the gene mutations by DNA sequencing. We also established a lung tumor-P. aeruginosa bacterial model to study the role of ETO-evolved intra-tumoral bacteria in tumor progression using immunostaining and confocal microscopy. RESULTS: ETO could generate oxidative stress and lead to gene mutations in P. aeruginosa, especially the gyrase (gyrA) gene, resulting in acquired fluoroquinolone resistance. We further demonstrated using a microfluidic-based lung tumor-P. aeruginosa coculture model that bacteria can evolve ciprofloxacin (CIP) resistance in a tumor microenvironment. Moreover, ETO-induced CIP-resistant (EICR) mutants could form multicellular biofilms which protected tumor cells from ETO killing and enabled tumor progression. CONCLUSION: Overall, our preclinical proof-of-concept provides insights into how anti-cancer chemotherapy could inadvertently allow tumor-associated bacteria to acquire antibiotic resistance mutations and shed new light on the development of novel anti-cancer treatments based on anti-bacterial strategies.

Dual-species proteomics and targeted intervention of animal-pathogen interactions.

INTRODUCTION: Host-microbe interactions are important to human health and ecosystems globally, so elucidating the complex host-microbe interactions and associated protein expressions drives the need to develop sensitive and accurate biochemical techniques. Current proteomics techniques reveal information from the point of view of either the host or microbe, but do not provide data on the corresponding partner. Moreover, it remains challenging to simultaneously study host-microbe proteomes that reflect the direct competition between host and microbe. This raises the need to develop a dual-species proteomics method for host-microbe interactions. OBJECTIVES: We aim to establish a forward + reverse Stable Isotope Labeling with Amino acids in Cell culture (SILAC) proteomics approach to simultaneously label and quantify newly-expressed proteins of host and microbe without physical isolation, for investigating mechanisms in direct host-microbe interactions. METHODS: Using Caenorhabditis elegans-Pseudomonas aeruginosa infection model as proof-of-concept, we employed SILAC proteomics and molecular pathway analysis to characterize the differentially-expressed microbial and host proteins. We then used molecular docking and chemical characterization to identify chemical inhibitors that intercept host-microbe interactions and eliminate microbial infection. RESULTS: Based on our proteomics results, we studied the iron competition between pathogen iron scavenger and host iron uptake protein, where P. aeruginosa upregulated pyoverdine synthesis protein (PvdA) (fold-change of 5.2313) and secreted pyoverdine, and C. elegans expressed ferritin (FTN-2) (fold-change of 3.4057). Targeted intervention of iron competition was achieved using Galangin, a ginger-derived phytochemical that inhibited pyoverdine production and biofilm formation in P. aeruginosa. The Galangin-ciprofloxacin combinatorial therapy could eliminate P. aeruginosa biofilms in a fish wound infection model, and enabled animal survival. CONCLUSION: Our work provides a novel SILAC-based proteomics method that can simultaneously evaluate host and microbe proteomes, with future applications in higher host organisms and other microbial species. It also provides insights into the mechanisms dictating host-microbe interactions, offering novel strategies for anti-infective therapy.

Bacterial Iron Siderophore Drives Tumor Survival and Ferroptosis Resistance in a Biofilm-Tumor Spheroid Coculture Model.

Interactions between tumoral cells and tumor-associated bacteria within the tumor microenvironment play a significant role in tumor survival and progression, potentially impacting cancer treatment outcomes. In lung cancer patients, the Gram-negative pathogen Pseudomonas aeruginosa raises questions about its role in tumor survival. Here, a microfluidic-based 3D-human lung tumor spheroid-P. aeruginosa model is developed to study the bacteria's impact on tumor survival. P. aeruginosa forms a tumor-associated biofilm by producing Psl exopolysaccharide and secreting iron-scavenging pyoverdine, which is critical for establishing a bacterial community in tumors. Consequently, pyoverdine promotes cancer progression by reducing susceptibility to iron-induced death (ferroptosis), enhancing cell viability, and facilitating several cancer hallmarks, including epithelial-mesenchymal transition and metastasis. A promising combinatorial therapy approach using antimicrobial tobramycin, ferroptosis-inducing thiostrepton, and anti-cancer doxorubicin could eradicate biofilms and tumors. This work unveils a novel phenomenon of cross-kingdom cooperation, where bacteria protect tumors from death, and it paves the way for future research in developing antibiofilm cancer therapies. Understanding these interactions offers potential new strategies for combatting cancer and enhancing treatment efficacy.

Juglone as a Natural Quorum Sensing Inhibitor against Pseudomonas aeruginosa pqs-Mediated Virulence and Biofilms.

Pseudomonas aeruginosa is a notorious opportunistic pathogen associated with chronic biofilm-related infections, posing a significant challenge to effective treatment strategies. Quorum sensing (QS) and biofilm formation are critical virulence factors employed by P. aeruginosa, contributing to its pathogenicity and antibiotic resistance. Other than the homoserine-based QS systems, P. aeruginosa also possesses the quinolone-based Pseudomonas quinolone signal (PQS) QS signaling. Synthesis of the PQS signaling molecule is achieved by the pqsABCDEH operon, whereas the PQS signaling response was mediated by the PqsR receptor. In this study, we report the discovery of a novel natural compound, Juglone, with potent inhibitory effects on pqs QS and biofilm formation in P. aeruginosa. Through an extensive screening of natural compounds from diverse sources, we identified Juglone, a natural compound from walnut, as a promising candidate. We showed that Juglone could inhibit PqsR and the molecular docking results revealed that Juglone could potentially bind to the PqsR active site. Furthermore, Juglone could inhibit pqs-regulated virulence factors, such as pyocyanin and the PQS QS signaling molecule. Juglone could also significantly reduce both the quantity and quality of P. aeruginosa biofilms. Notably, this compound exhibited minimal cytotoxicity toward mammalian cells, suggesting its potential safety for therapeutic applications. To explore the clinical relevance of Juglone, we investigated its combinatorial effects with colistin, a commonly used antibiotic against P. aeruginosa infections. The Juglone-colistin combinatorial treatment could eliminate biofilms formed by wild-type P. aeruginosa PAO1 and its clinical isolates collected from cystic fibrosis patients. The Juglone-colistin combinatorial therapy dramatically improved colistin efficacy and reduced inflammation in a wound infection model, indicating its potential for clinical utility. In conclusion, the discovery of Juglone provides insights into the development of innovative antivirulence therapeutic strategies to combat P. aeruginosa biofilm-associated infections.

Microfluidics-based patient-derived disease detection tool for deep learning-assisted precision medicine.

Cancer spatial and temporal heterogeneity fuels resistance to therapies. To realize the routine assessment of cancer prognosis and treatment, we demonstrate the development of an Intelligent Disease Detection Tool (IDDT), a microfluidic-based tumor model integrated with deep learning-assisted algorithmic analysis. IDDT was clinically validated with liquid blood biopsy samples (n = 71) from patients with various types of cancers (e.g., breast, gastric, and lung cancer) and healthy donors, requiring low sample volume (∼200 µl) and a high-throughput 3D tumor culturing system (∼300 tumor clusters). To support automated algorithmic analysis, intelligent decision-making, and precise segmentation, we designed and developed an integrative deep neural network, which includes Mask Region-Based Convolutional Neural Network (Mask R-CNN), vision transformer, and Segment Anything Model (SAM). Our approach significantly reduces the manual labeling time by up to 90% with a high mean Intersection Over Union (mIoU) of 0.902 and immediate results (<2 s per image) for clinical cohort classification. The IDDT can accurately stratify healthy donors (n = 12) and cancer patients (n = 55) within their respective treatment cycle and cancer stage, resulting in high precision (∼99.3%) and high sensitivity (∼98%). We envision that our patient-centric IDDT provides an intelligent, label-free, and cost-effective approach to help clinicians make precise medical decisions and tailor treatment strategies for each patient.

AIEgen-deep: Deep learning of single AIEgen-imaging pattern for cancer cell discrimination and preclinical diagnosis.

This study introduces AIEgen-Deep, an innovative classification program combining AIEgen fluorescent dyes, deep learning algorithms, and the Segment Anything Model (SAM) for accurate cancer cell identification. Our approach significantly reduces manual annotation efforts by 80%-90%. AIEgen-Deep demonstrates remarkable accuracy in recognizing cancer cell morphology, achieving a 75.9% accuracy rate across 26,693 images of eight different cell types. In binary classifications of healthy versus cancerous cells, it shows enhanced performance with an accuracy of 88.3% and a recall rate of 79.9%. The model effectively distinguishes between healthy cells (fibroblast and WBC) and various cancer cells (breast, bladder, and mesothelial), with accuracies of 89.0%, 88.6%, and 83.1%, respectively. Our method's broad applicability across different cancer types is anticipated to significantly contribute to early cancer detection and improve patient survival rates.

One-step high-speed thermal-electric aerosol printing of piezoelectric bio-organic films for wirelessly powering bioelectronics.

Piezoelectric biomaterials hold a pivotal role in the progression of bioelectronics and biomedicine, owing to their remarkable electromechanical properties, biocompatibility, and bioresorbability. However, their technological potential is restrained by certain challenges, including precise manipulation of nanobiomolecules, controlling their growth across nano-to-macro hierarchy, and tuning desirable mechanical properties. We report a high-speed thermal-electric driven aerosol (TEA) printing method capable of fabricating piezoelectric biofilms in a singular step. Electrohydrodynamic aerosolizing and in situ electrical poling allow instantaneous tuning of the spatial organization of biomolecular inks. We demonstrate TEA printing of ß-glycine/polyvinylpyrrolidone films, and such films exhibit the piezoelectric voltage coefficient of 190 × 10-3 volt-meters per newton, surpassing that of industry-standard lead zirconate titanate by approximately 10-fold. Furthermore, these films demonstrate nearly two orders of magnitude improvement in mechanical flexibility compared to glycine crystals. We also demonstrate the ultrasonic energy harvesters based on the biofilms, providing the possibility of wirelessly powering bioelectronics.

Subtyping based on immune cell fractions reveal heterogeneity of cardiac fibrosis in end-stage heart failure.

Background: A central issue hindering the development of effective anti-fibrosis drugs for heart failure is the unclear interrelationship between fibrosis and the immune cells. This study aims at providing precise subtyping of heart failure based on immune cell fractions, elaborating their differences in fibrotic mechanisms, and proposing a biomarker panel for evaluating intrinsic features of patients' physiological statuses through subtype classification, thereby promoting the precision medicine for cardiac fibrosis. Methods: We inferred immune cell type abundance of the ventricular samples by a computational method (CIBERSORTx) based on ventricular tissue samples from 103 patients with heart failure, and applied K-means clustering to divide patients into two subtypes based on their immune cell type abundance. We also designed a novel analytic strategy: Large-Scale Functional Score and Association Analysis (LAFSAA), to study fibrotic mechanisms in the two subtypes. Results: Two subtypes of immune cell fractions: pro-inflammatory and pro-remodeling subtypes, were identified. LAFSAA identified 11 subtype-specific pro-fibrotic functional gene sets as the basis for personalised targeted treatments. Based on feature selection, a 30-gene biomarker panel (ImmunCard30) established for diagnosing patient subtypes achieved high classification performance, with the area under the receiver operator characteristic curve corresponding to 0.954 and 0.803 for the discovery and validation sets, respectively. Conclusion: Patients with the two subtypes of cardiac immune cell fractions were likely having different fibrotic mechanisms. Patients' subtypes can be predicted based on the ImmunCard30 biomarker panel. We envision that our unique stratification strategy revealed in this study will unravel advance diagnostic techniques for personalised anti-fibrotic therapy.

Biofilms exacerbate atherogenesis through macrophage-induced inflammatory responses in a fibrous plaque microsystem model.

BACKGROUND: Microbes have been implicated in atherosclerosis development and progression, but the impact of bacterial-based biofilms on fibrous plaque rupture remains poorly understood. RESULTS: Here, we developed a comprehensive atherosclerotic model to reflect the progression of fibrous plaque under biofilm-induced inflammation (FP-I). High expressions of biofilm-specific biomarkers algD, pelA and pslB validated the presence of biofilms. Biofilm promotes the polarization of macrophages towards a pro-inflammatory (M1) phenotype, as demonstrated by an increase in M1 macrophage-specific marker CD80 expression in CD68+ macrophages. The increase in the number of intracellular lipid droplets (LDs) and foam cell percentage highlighted the potential role of biofilms on lipid synthesis or metabolic pathways in macrophage-derived foam cells. In addition, collagen I production by myofibroblasts associated with the fibrous cap was significantly reduced along with the promotion of apoptosis of myofibroblasts, indicating that biofilms affect the structural integrity of the fibrous cap and potentially undermine its strength. CONCLUSION: We validated the unique role of biofilm-based inflammation in exacerbating fibrous plaque damage in the FP-I model, increasing fibrous plaque instability and risk of thrombosis. Our results lay the foundation for mechanistic studies of the role of biofilms in fibrous plaques, allowing the evaluation of preclinical combination strategies for drug therapy. STATEMENT OF SIGNIFICANCE: A microsystem-based model was developed to reveal interactions in fibrous plaque during biofilm-induced inflammation (FP-I). Real-time assessment of biofilm formation and its role in fibrous plaque progression was achieved. The presence of biofilms enhanced the expression of pro-inflammatory (M1) specific marker CD80, lipid droplets, and foam cells and reduced anti-inflammatory (M2) specific marker CD206 expression. Fibrous plaque exposure to biofilm-based inflammation reduced collagen I expression and increased apoptosis marker Caspase-3 expression significantly. Overall, we demonstrate the unique role of biofilm-based inflammation in exacerbating fibrous plaque damage in the FP-I model, promoting fibrous plaque instability and enhanced thrombosis risk. Our findings lay the groundwork for mechanistic studies, facilitating the evaluation of preclinical drug combination strategies.

Distinct bacterial population dynamics and disease dissemination after biofilm dispersal and disassembly.

Microbial communities that form surface-attached biofilms must release and disperse their constituent cells into the environment to colonize fresh sites for continued survival of their species. For pathogens, biofilm dispersal is crucial for microbial transmission from environmental reservoirs to hosts, cross-host transmission, and dissemination of infections across tissues within the host. However, research on biofilm dispersal and its consequences in colonization of fresh sites remain poorly understood. Bacterial cells can depart from biofilms via stimuli-induced dispersal or disassembly due to direct degradation of the biofilm matrix, but the complex heterogeneity of bacterial populations released from biofilms rendered their study difficult. Using a novel 3D-bacterial "biofilm-dispersal-then-recolonization" (BDR) microfluidic model, we demonstrated that Pseudomonas aeruginosa biofilms undergo distinct spatiotemporal dynamics during chemical-induced dispersal (CID) and enzymatic disassembly (EDA), with contrasting consequences in recolonization and disease dissemination. Active CID required bacteria to employ bdlA dispersal gene and flagella to depart from biofilms as single cells at consistent velocities but could not recolonize fresh surfaces. This prevented the disseminated bacteria cells from infecting lung spheroids and Caenorhabditis elegans in on-chip coculture experiments. In contrast, EDA by degradation of a major biofilm exopolysaccharide (Psl) released immotile aggregates at high initial velocities, enabling the bacteria to recolonize fresh surfaces and cause infections in the hosts efficiently. Hence, biofilm dispersal is more complex than previously thought, where bacterial populations adopting distinct behavior after biofilm departure may be the key to survival of bacterial species and dissemination of diseases.

A deformability-based biochip for precise label-free stratification of metastatic subtypes using deep learning.

Cellular deformability is a promising biomarker for evaluating the physiological state of cells in medical applications. Microfluidics has emerged as a powerful technique for measuring cellular deformability. However, existing microfluidic-based assays for measuring cellular deformability rely heavily on image analysis, which can limit their scalability for high-throughput applications. Here, we develop a parallel constriction-based microfluidic flow cytometry device and an integrated computational framework (ATMQcD). The ATMQcD framework includes automatic training set generation, multiple object tracking, segmentation, and cellular deformability quantification. The system was validated using cancer cell lines of varying metastatic potential, achieving a classification accuracy of 92.4% for invasiveness assessment and stratifying cancer cells before and after hypoxia treatment. The ATMQcD system also demonstrated excellent performance in distinguishing cancer cells from leukocytes (accuracy = 89.5%). We developed a mechanical model based on power-law rheology to quantify stiffness, which was fitted with measured data directly. The model evaluated metastatic potentials for multiple cancer types and mixed cell populations, even under real-world clinical conditions. Our study presents a highly robust and transferable computational framework for multiobject tracking and deformation measurement tasks in microfluidics. We believe that this platform has the potential to pave the way for high-throughput analysis in clinical applications, providing a powerful tool for evaluating cellular deformability and assessing the physiological state of cells.