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.

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.

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.

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.

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.

Green Fabrication of Freestanding Piezoceramic Films for Energy Harvesting and Virus Detection.

Most electronics such as sensors, actuators and energy harvesters need piezoceramic films to interconvert mechanical and electrical energy. Transferring the ceramic films from their growth substrates for assembling electronic devices commonly requires chemical or physical etching, which comes at the sacrifice of the substrate materials, film cracks, and environmental contamination. Here, we introduce a van der Waals stripping method to fabricate large-area and freestanding piezoceramic thin films in a simple, green, and cost-effective manner. The introduction of the quasi van der Waals epitaxial platinum layer enables the capillary force of water to drive the separation process of the film and substrate interface. The fabricated lead-free film, [Formula: see text] (BCZT), shows a high piezoelectric coefficient d33 = 209 ± 10 pm V-1 and outstanding flexibility of maximum strain 2%. The freestanding feature enables a wide application scenario, including micro energy harvesting, and covid-19 spike protein detection. We further conduct a life cycle analysis and quantify the low energy consumption and low pollution of the water-based stripping film method.

3D Biomimetic Models to Reconstitute Tumor Microenvironment In Vitro: Spheroids, Organoids, and Tumor-on-a-Chip.

Decades of efforts in engineering in vitro cancer models have advanced drug discovery and the insight into cancer biology. However, the establishment of preclinical models that enable fully recapitulating the tumor microenvironment remains challenging owing to its intrinsic complexity. Recent progress in engineering techniques has allowed the development of a new generation of in vitro preclinical models that can recreate complex in vivo tumor microenvironments and accurately predict drug responses, including spheroids, organoids, and tumor-on-a-chip. These biomimetic 3D tumor models are of particular interest as they pave the way for better understanding of cancer biology and accelerating the development of new anticancer therapeutics with reducing animal use. Here, the recent advances in developing these in vitro platforms for cancer modeling and preclinical drug screening, focusing on incorporating hydrogels are reviewed to reconstitute physiologically relevant microenvironments. The combination of spheroids/organoids with microfluidic technologies is also highlighted to better mimic in vivo tumors and discuss the challenges and future directions in the clinical translation of such models for drug screening and personalized medicine.

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.

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.

Sensitive detection of microRNAs using polyadenine-mediated fluorescent spherical nucleic acids and a microfluidic electrokinetic signal amplification chip.

The identification of tumor-related microRNAs (miRNAs) exhibits excellent promise for the early diagnosis of cancer and other bioanalytical applications. Therefore, we developed a sensitive and efficient biosensor using polyadenine (polyA)-mediated fluorescent spherical nucleic acid (FSNA) for miRNA analysis based on strand displacement reactions on gold nanoparticle (AuNP) surfaces and electrokinetic signal amplification (ESA) on a microfluidic chip. In this FSNA, polyA-DNA biosensor was anchored on AuNP surfaces via intrinsic affinity between adenine and Au. The upright conformational polyA-DNA recognition block hybridized with 6-carboxyfluorescein-labeled reporter-DNA, resulting in fluorescence quenching of FSNA probes induced by AuNP-based resonance energy transfer. Reporter DNA was replaced in the presence of target miRNA, leading to the recovery of reporter-DNA fluorescence. Subsequently, reporter-DNAs were accumulated and detected in the front of with Nafion membrane in the microchannel by ESA. Our method showed high selectivity and sensitivity with a limit of detection of 1.3 pM. This method could also be used to detect miRNA-21 in human serum and urine samples, with recoveries of 104.0%-113.3% and 104.9%-108.0%, respectively. Furthermore, we constructed a chip with three parallel channels for the simultaneous detection of multiple tumor-related miRNAs (miRNA-21, miRNA-141, and miRNA-375), which increased the detection efficiency. Our universal method can be applied to other DNA/RNA analyses by altering recognition sequences.

Label-free enrichment of human blast cells from whole blood for leukemia monitoring.

Liquid biopsy is an alternative to invasive bone marrow biopsy for leukemia detection and management. However, no robust technology is available for enriching leukemic blast cells from the blood. Here, we present a simple and effective protocol for vigorous enrichment of blast cells from whole blood using a one-step microfluidic blast cell biochip (BCB) that exploits distinct cell mechanical properties between diseased and healthy leukocytes. The BCB system achieves higher sensitivity than flow cytometry in detecting blasts. For complete details on the use and execution of this protocol, please refer to Khoo et al. (2019).

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.

Label-free biosensor of phagocytosis for diagnosing bacterial infections.

Phagocytic cells recognize and phagocytose invading microbes for destruction. However, bacterial pathogens can remain hidden at low levels from conventional detection or replicate intracellularly after being phagocytosed by immune cells. Current phagocytosis-detection approaches involve flow cytometry or microscopic search for rare bacteria-internalized phagocytes among large populations of uninfected cells, which poses significant challenges in research and clinical settings. Hence it is imperative to develop a rapid, non-disruptive, and label-free phagocytosis detection approach. Using deformability assays and microscopic imaging, we have demonstrated for the first time that the presence of intracellular bacteria in phagocytic blood cells led to aberrant physical properties. Specifically, human monocytes with internalized bacteria of various species were stiffer and larger compared with uninfected monocytes. Taking advantage of these physical differences, a novel microfluidics-based biosensor platform was developed to passively sort, concentrate and quantify rare monocytes with internalized pathogens (MIP) from uninfected monocyte populations for phagocytosis detection. The clinical utility of the MIP platform was demonstrated by enriching and detecting bacteria-internalized monocytes from spiked human blood samples within 1.5 h. Patient-derived clinical isolates were used to validate the utility of the MIP platform further. This proof-of-concept presents a phagocytosis detection platform that could be used to rapidly diagnose microbial infections, especially in bloodstream infections (BSIs), thereby improving the clinical outcomes for point-of-care management.

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.