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.

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.

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.

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.

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.

Rapid detection of microorganisms in a fish infection microfluidics platform.

Inadequate access to clean water is detrimental to human health and aquatic industries. Waterborne pathogens can survive prolonged periods in aquatic bodies, infect commercially important seafood, and resist water disinfection, resulting in human infections. Environmental agencies and research laboratories require a relevant, portable, and cost-effective platform to monitor microbial pathogens and assess their risk of infection on a large scale. Advances in microfluidics enable better control and higher precision than traditional culture-based pathogen monitoring approaches. We demonstrated a rapid, high-throughput fish-based teleost (fish)-microbe (TelM) microfluidic-based device that simultaneously monitors waterborne pathogens in contaminated waters and assesses their infection potential under well-defined settings. A chamber-associated port allows direct access to the animal, while the transparency of the TelM platform enables clear observation of sensor readouts. As proof-of-concept, we established a wound infection model using Pseudomonas aeruginosa-contaminated water in the TelM platform, where bacteria formed biofilms on the wound and secreted a biofilm metabolite, pyoverdine. Pyoverdine was used as fluorescent sensor to correlate P. aeruginosa contamination to infection. The TelM platform was validated with environmental waterborne microbes from marine samples. Overall, the TelM platform can be readily applied to assess microbial and chemical risk in aquatic bodies in resource-constrained settings.

Early Predictor Tool of Disease Using Label-Free Liquid Biopsy-Based Platforms for Patient-Centric Healthcare.

Cancer cells undergo phenotypic changes or mutations during treatment, making detecting protein-based or gene-based biomarkers challenging. Here, we used algorithmic analysis combined with patient-derived tumor models to derive an early prediction tool using patient-derived cell clusters from liquid biopsy (LIQBP) for cancer prognosis in a label-free manner. The LIQBP platform incorporated a customized microfluidic biochip that mimicked the tumor microenvironment to establish patient clusters, and extracted physical parameters from images of each sample, including size, thickness, roughness, and thickness per area (n = 31). Samples from healthy volunteers (n = 5) and cancer patients (pretreatment; n = 4) could be easily distinguished with high sensitivity (91.16 ± 1.56%) and specificity (71.01 ± 9.95%). Furthermore, we demonstrated that the multiple unique quantitative parameters reflected patient responses. Among these, the ratio of normalized gray value to cluster size (RGVS) was the most significant parameter correlated with cancer stage and treatment duration. Overall, our work presented a novel and less invasive approach for the label-free prediction of disease prognosis to identify patients who require adjustments to their treatment regime. We envisioned that such efforts would promote the management of personalized patient care conveniently and cost effectively.

The effects of biofilms on tumor progression in a 3D cancer-biofilm microfluidic model.

Components within the tumor microenvironment, such as intratumoral bacteria (IB; within tumors), affect tumor progression. However, current experimental models have not explored the effects of extratumoral bacteria (EB; outside tumors) on cancer progression. Here, we developed a microfluidic platform to analyze the influence of bacterial distribution on bladder cancer progression under defined conditions, using uropathogenic Escherichia coli. This was achieved by establishing coating (CT) and colonizing (CL) models to simulate the different invasion and colonization modes of IB and EB in tumor tissues. We demonstrated that both EB and IB induced closer cell-cell contacts within the tumor cluster, but cancer cell viability was reduced only in the presence of IB. Interestingly, cancer stem cell counts increased significantly in the presence of EB. These outcomes were due to the formation of extracellular DNA-based biofilms by EB. Triple therapy of DNase (anti-biofilm agent), ciprofloxacin (antibiotic), and doxorubicin (anti-cancer drug) could effectively eradicate biofilms and tumors simultaneously. Our preclinical proof-of-concept provides insights on how bacteria can influence tumor progression and facilitate future research on anti-biofilm cancer management therapies.

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.