Advanced thermal management technologies represent an important research frontier because such materials and systems show promise for enhancing personal physiological comfort and reducing building energy consumption. These technologies typically offer the advantages of excellent portability, user-friendly tunability, energy efficiency, and straightforward manufacturability, but they frequently suffer from critical challenges associated with poor breathability, inadequate wash stability, and difficult fabric integration. Within this broader context, our laboratory has previously developed heat-managing composite materials by drawing inspiration from the color-changing skin of the common squid. Herein, we describe the design, fabrication, and testing of breathable, washable, and fabric-integrated variants of our composite materials, which demonstrate state-of-the-art adaptive infrared properties and dynamic thermoregulatory functionalities. The combined findings directly advance the performance and applications scope of our bioinspired thermoregulatory composites and ultimately may guide the incorporation of desirable multifunctionality into other wearable technologies.

Cancer nanotechnology is a promising area of cross-disciplinary research aiming to develop facile, effective, and noninvasive strategies to improve cancer diagnosis and treatment. Catalytic therapy based on exogenous stimulus-responsive semiconductor nanomaterials has shown its potential to address the challenges under the most global medical needs. Semiconductor nanocatalytic therapy is usually triggered by the catalytic action of hot electrons and holes during local redox reactions within the tumor, which represent the response of nontoxic semiconductor nanocatalysts to pertinent internal or external stimuli. However, careful architecture design of semiconductor nanocatalysts has been the major focus since the catalytic efficiency is often limited by facile hot electron/hole recombination. Addressing these challenges is vital for the progress of cancer catalytic therapy. In recent years, diverse strategies have been developed, with heterojunctions emerging as a prominent and extensively explored method. The efficiency of charge separation under exogenous stimulation can be heightened by manipulating the semiconducting performance of materials through heterojunction structures, thereby enhancing catalytic capabilities. This review summarizes the recent applications of exogenous stimulus-responsive semiconducting nanoheterojunctions for cancer theranostics. The first part of the review outlines the construction of different heterojunction types. The next section summarizes recent designs, properties, and catalytic mechanisms of various semiconductor heterojunctions in tumor therapy. The review concludes by discussing the challenges and providing insights into their prospects within this dynamic and continuously evolving field of research.

Advanced models of the brain have evolved in recent years from traditional two-dimensional (2D) ones, based on rodent derived cells, to three-dimensional (3D) ones, based on human neurons derived from induced pluripotent stem cells. To address the dynamic changes of the tissue microenvironment, bioreactors are used to control the microenvironment for viability, repeatability, and standardization. However, in neuronal tissue engineering, bioreactors have primarily been used for cell expansion purposes, while microfluidic systems have mainly been employed for culturing organoids. In this study, we explored the use of a commercial perfusion bioreactor to control the culture microenvironment of neuronal cells in both 2D and 3D cultures. Namely, neurons differentiated from human induced pluripotent stem cells (iNeurons) were cultured in 2D under different constant flow rates for 72 h. The impact of different flow rates on early-stage neuronal development and synaptogenesis was assessed by morphometric characterization and synaptic analysis. Based on these results, two involving variable flow rates were developed and applied again in 2D culture. The most effective protocol, in terms of positive impact on neuronal development, was then used for a preliminary study on the application of dynamic culturing conditions to neuronal cells in 3D. To this purpose, both iNeurons, co-cultured with astrocytes, and the human neuroblastoma cells SH-SY5Y were embedded into a hydrogel and maintained under perfusion for up to 28 days. A qualitative evaluation by immunocytochemistry and confocal microscopy was carried out to assess cell morphology and the formation of a 3D neuronal network.

Smooth muscle cell (SMC) phenotypic switching is a hallmark of many vascular diseases. Although prior work has established that chemical and mechanical cues contribute to SMC phenotypic switching, the impact of three-dimensional (3D) confinement on this process remains elusive. Yet, , arterial SMCs reside within confined environments. In this study, we designed a microfluidic assay to investigate the interplay between 3D confinement and different environmental stimuli in SMC function. Our results show that tightly, but not moderately, confined SMCs acquire a contractile phenotype when exposed to collagen I. Elevated compressive forces induced by hydrostatic pressure abolish this upregulation of the contractile phenotype and compromise SMC survival, particularly in tightly confined spaces. Transforming growth factor beta 1, which promotes the contractile state in moderate confinement, fails to enhance the contractility of tightly confined cells. Fibronectin and engagement of cadherin 2 suppress the contractile phenotype of SMCs regardless of the degree of confinement. In contrast, homophilic engagement of cadherin 11 upregulates SMC-specific genes and enhances contractility in both moderately and tightly confined cells. Overall, our work introduces 3D confinement as a regulator of SMC phenotypic responses to chemical and mechanical signals.

RNA translation to protein is paramount to creating life, yet RNA and protein correlations vary widely across tissues, cells, and species. To investigate these perplexing results, we utilize a time-series fixation method that combines static stimulation and a programmable formaldehyde perfusion to map pseudo-Signaling with Omics signatures (pSigOmics) of single-cell data from hundreds of thousands of cells. Using the widely studied nuclear factor kappa B (NFκB) mammalian signaling pathway in mouse fibroblasts, we discovered a novel asynchronous pseudotime regulation (APR) between RNA and protein levels in the quintessential NFκB p65 protein using single molecule spatial imaging. Prototypical NFκB dynamics are successfully confirmed by the rise and fall of NFκB response as well as A20 negative inhibitor activity by 90 min. The observed p65 translational APR is evident in both statically sampled timepoints and dynamic response gradients from programmable formaldehyde fixation, which successfully creates continuous response measurements. Finally, we implement a graph neural network model capable of predicting APR cell subpopulations from GAPDH RNA spatial expression, which is strongly correlated with p65 RNA signatures. Successful decision tree classifiers on Potential of Heat-diffusion for Affinity-based Trajectory Embedding embeddings of our data, which illustrate partitions of APR cell subpopulations in latent space, further confirm the APR patterns. Together, our data suggest an RNA-protein regulatory framework in which translation adapts to signaling events and illuminates how immune signaling is timed across various cell subpopulations.

The advent of advanced robotic platforms and workflow automation tools has revolutionized the landscape of biological research, offering unprecedented levels of precision, reproducibility, and versatility in experimental design. In this work, we present an automated and modular workflow for exploring cell behavior in two-dimensional culture systems. By integrating the BioAssemblyBot (BAB) robotic platform and the BioApps™ workflow automater with live-cell fluorescence microscopy, our workflow facilitates execution and analysis of migration and proliferation assays. Robotic assistance and automation allow for the precise and reproducible creation of highly customizable cell-free zones (CFZs), or wounds, in cell monolayers and "hands-free," schedulable integration with real-time monitoring systems for cellular dynamics. CFZs are designed as computer-aided design models and recreated in confluent cell layers by the BAB 3D-Bioprinting tool. The dynamics of migration and proliferation are evaluated in individual cells using live-cell fluorescence microscopy and an in-house pipeline for image processing and single-cell tracking. Our robotics-assisted approach outperforms manual scratch assays with enhanced reproducibility, adaptability, and precision. The incorporation of automation further facilitates increased flexibility in wound geometry and allows for many experimental conditions to be analyzed in parallel. Unlike traditional cell migration assays, our workflow offers an adjustable platform that can be tailored to a wide range of applications with high-throughput capability. The key features of this system, including its scalability, versatility, and the ability to maintain a high degree of experimental control, position it as a valuable tool for researchers across various disciplines.

Image-based patient-specific rupture risk analysis for abdominal aortic aneurysms (AAAs) has shown considerable promise. However, clinical translation has been hampered by the use of invasive and costly imaging modalities. Despite being a promising alternative, ultrasound (US) makes a full analysis, including intraluminal thrombus (ILT), not trivial. This study explored the feasibility of assessing AAA rupture risk parameters, e.g., peak wall stress (PWS) and peak wall rupture index (PWRI), using US-based models of the AAA wall, finally including ILT. Three-dimensional US data were segmented from a group of AAA patients whose CT data were available within 30 days. The segmented vessel wall and ILT boundaries were converted into a mesh including and excluding ILT to evaluate the effect of adding ILT on the model output. US-based rupture risk parameters (PWS and PWRI) were compared to CT-based results. The US-based PWS and PWRI, including ILT, showed good agreement with CT-based results, and the model excluding ILT showed no significant bias in wall stress or rupture index. When including ILT, a lower US-based wall stress and rupture index of 7.2% and 3.8% were found, respectively. The intraclass correlation coefficient (ICC) of PWS was 0.60. The highest ICC was found for the PWRI (ICC = 0.86), indicating good absolute agreement. This study showed that PWRI can be estimated with US when including the ILT, yielding comparable results to CT, and good absolute agreement. Future work should focus on improving the contrast of ILT in US, since this will be essential to performing large-scale studies in AAA cohorts.

This study explores the mechanisms of glucocorticoid-induced osteoporosis (OP) and Rheumatoid arthritis (RA), focusing on apoptosis and its role in the progression from RA to OP. Using microarray data from the GEO database, differential gene expression analysis was conducted with the limma package, identifying significant genes in RA and OP. Weighted Gene Co-expression Network Analysis (WGCNA) further examined gene relationships with the disease status, identifying co-expression patterns. Key genes were pinpointed by intersecting differentially expressed genes from RA and OP datasets with WGCNA module genes. Functional enrichment analysis using the "clusterProfiler" package focused on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. Machine learning methods, including Lasso and Random Forest, refined the selection of key genes related to apoptosis. Immune infiltration analysis using CIBERSORT assessed immune cell differences between disease and normal samples. The study highlighted two crucial genes: ATXN2L and MMP14. These genes were identified through various analyses and found to be significantly associated with the progression of RA and OP. Gene Set Enrichment Analysis of ATXN2L and MMP14 revealed their involvement in specific biological processes and pathways. Correlation analysis between these key genes and immune cell infiltration showed significant associations. The ROC analysis evaluated the diagnostic performance of ATXN2L and MMP14, with miRNA regulatory networks related to these genes also predicted. In summary, this research provides valuable insights into the molecular mechanisms of RA and OP, emphasizing the importance of apoptosis and immune processes.

While electroencephalography (EEG) and magnetoencephalography (MEG) are well-established noninvasive methods in neuroscience and clinical medicine, they suffer from low spatial resolution. Electrophysiological source imaging (ESI) addresses this by noninvasively exploring the neuronal origins of M/EEG signals. Although subcortical structures are crucial to many brain functions and neuronal diseases, accurately localizing subcortical sources of M/EEG remains particularly challenging, and the feasibility is still a subject of debate. Traditional ESIs, which depend on explicitly defined regularization priors, have struggled to set optimal priors and accurately localize brain sources. To overcome this, we introduced a data-driven, deep learning-based ESI approach without the need for these priors. We proposed a four-layered convolutional neural network (4LCNN) designed to locate both subcortical and cortical sources underlying M/EEG signals. We also employed a sophisticated realistic head conductivity model using the state-of-the-art segmentation method of ten different head tissues from individual MRI data to generate realistic training data. This is the first attempt at deep learning-based ESI targeting subcortical regions. Our method showed excellent accuracy in source localization, particularly in subcortical areas compared to other methods. This was validated through M/EEG simulations, evoked responses, and invasive recordings. The potential for accurate source localization of the 4LCNNs demonstrated in this study suggests future contributions to various research endeavors such as the clinical diagnosis, understanding of the pathophysiology of various neuronal diseases, and basic brain functions.

Understanding the detailed mechanisms driving fibroblast migration within native tissue settings during pathophysiological events presents a critical research challenge. In this study, we elucidate how stromal cells migrate and contribute to the development of three-dimensional (3D) cellular aggregates within confined microcavities. Integrin α5β1 and β-catenin (β-cat) are central in guiding this collective migration and achieving optimal filling of the microcavity. When β-cat is suppressed, cells tend to migrate more sporadically, leading to less efficient cellular organization. Furthermore, we also detail the pivotal roles of Cx43 and N-cadherin (N-cad) in orchestrating collective migration and in shaping efficient cellular stacking. Suppressing gap junctions, especially Cx43, significantly impacts the extracellular matrix expression, integrin α5 and β1, and other elements in the 3D construct, emphasizing the importance of physicochemical cell-cell interactions. The distribution patterns of N-cad and focal adhesion kinase (FAK) further corroborate the essential roles in forming cell-cell junctions and FAK in establishing the foundational layer that underpins the cell stacking within the microcavity. Interestingly, neither Rho-associated protein kinase (ROCK) nor RhoA significantly alter the cell migration pattern toward microcavity. These findings provide fresh perspectives on fibroblast activities in 3D space, enriching our understanding and offering implications for advancements in wound healing and tissue engineering.

Gas vesicles (GVs) based on acoustic reporter genes have emerged as potent contrast agents for cellular and molecular ultrasound imaging. These air-filled, genetically encoded protein nanostructures can be expressed in a variety of cell types to visualize cell location and activity or injected systemically to label and monitor tissue function. Distinguishing GV signal from tissue deep inside intact organisms requires imaging approaches such as amplitude modulation (AM) or collapse-based pulse sequences. However, these approaches have limitations either in sensitivity or require the destruction of GVs, restricting the imaging of dynamic cellular processes. To address these limitations, we developed harmonic imaging to enhance the sensitivity of nondestructive GV imaging. We hypothesized that harmonic imaging, integrated with AM, could significantly elevate GV detection sensitivity by leveraging the nonlinear acoustic response of GVs. We tested this hypothesis by imaging tissue-mimicking phantoms embedded with purified GVs, mammalian cells genetically modified to express GVs, and mice liver post-systemic infusion of GVs. Our findings reveal that harmonic cross-propagating wave AM (HxAM) imaging markedly surpasses traditional xAM in isolating GVs' nonlinear acoustic signature, demonstrating significant (p < 0.05) enhancements in imaging performance. HxAM imaging improves detection of GV producing cells up to three folds , enhances imaging performance by over 10 dB, while extending imaging depth by up to 20%. Investigation into the backscattered spectra further elucidates the advantages of harmonic imaging. These advancements bolster ultrasound's capability in molecular and cellular imaging, underscoring the potential of harmonic signals to improve GV detection.

Type 2 diabetes mellitus (T2DM) is the most common type of diabetes, accounting for around 90% of all diabetes. Studies have found that dietary habits and biochemical metabolic changes are closely related to T2DM disease surveillance, but early surveillance tools are not specific and have lower accuracy. This paper aimed to provide a reliable artificial intelligence model with high accuracy for the clinical diagnosis of T2DM. A cross-sectional dataset comprising 8981 individuals from the First Affiliated Hospital of Guangxi Medical University was analyzed by a model fusion framework. The model includes four machine learning (ML) models, which used the stacking method. The ability to leverage the strengths of different algorithms to capture complex patterns in the data can effectively combine questionnaire data and blood test data to predict diabetes. The experimental results show that the stacking model achieves significant prediction results in diabetes detection. Compared with the single machine learning algorithm, the stacking model has improved in the metrics of accuracy, recall, and F1-score. The test set accuracy is 0.90, and the precision, recall, F1-score, area under the curve, and average precision (AP) are 0.91, 0.90, 0.90, 0.90, and 0.85, respectively. Additionally, this study showed that HbA1c (P < 0.001,OR = 2.203), fasting blood glucose (FBG) (P < 0.001,OR = 1.586), Ph2BG (P < 0.001,OR = 1.190), age (P < 0.001,OR = 1.018), Han nationality (P < 0.001,OR = 1.484), and carbonate beverages (P = 0.001,OR = 1.347) were important predictors of T2DM. This study demonstrates that stacking models show great potential in diabetes detection, and by integrating multiple machine learning algorithms, stacking models can significantly improve the accuracy and stability of diabetes prediction and provide strong support for disease prevention, early diagnosis, and individualized treatment.

Recent advances in scaffold-free three-dimensional (3D) culture methods have significantly enhanced the potential of stem cell-based therapies in regenerative medicine. This cutting-edge technology circumvents the use of exogenous biomaterial and prevents its associated complications. The 3D culture system preserves crucial intercellular interactions and extracellular matrix support, closely mimicking natural biological niches. Therefore, stem cells cultured in 3D formats exhibit distinct characteristics, showcasing their capabilities in promoting angiogenesis and immunomodulation. This review aims to elucidate foundational technologies and recent breakthroughs in 3D scaffold-free stem cell engineering, offering comprehensive guidance for researchers to advance this technology across various clinical applications. We first introduce the various sources of stem cells and provide a comparative analysis of two-dimensional (2D) and 3D culture systems. Given the advantages of 3D culture systems, we delve into the specific fabrication and harvesting techniques for cell sheets and spheroids. Furthermore, we explore their applications in pre-clinical studies, particularly in large animal models and clinical trials. We also discuss multidisciplinary strategies to overcome existing limitations such as insufficient efficacy, hostile microenvironments, and the need for scalability and standardization of stem cell-based products.

In cardiovascular therapeutics, procedures such as heart transplants and coronary artery bypass graft are pivotal. However, an acute shortage of organ donors increases waiting times of patients, which is reflected in negative effects on the outcome for the patient. Post-procedural complications such as thrombotic events and atherosclerotic developments may also have grave clinical implications. To address these challenges, tissue engineering is emerging as a solution, using textile technologies to synthesize biomimetic scaffolds resembling natural tissues. This comprehensive analysis explains methodologies including electrospinning, electrostatic flocking, and advanced textile techniques developed from weaving, knitting, and braiding. These techniques are evaluated in the context of fabricating cardiac patches, vascular graft constructs, stent designs, and state-of-the-art wearable sensors. We also closely examine the interaction of distinct process parameters with the biomechanical and morphological attributes of the resultant scaffolds. The research concludes by combining current findings and recommendations for subsequent investigation.

Regenerative medicine is moving from the nascent to the transitional stage as researchers are actively engaged in creating mini-organs from pluripotent stem cells to construct artificial models of physiological and pathological conditions. Currently, mini-organs can express higher-order functions, but their size is limited to the order of a few millimeters. Therefore, one of the ultimate goals of regenerative medicine, "organ replication and transplantation with organoid," remains a major obstacle. Three-dimensional (3D) bioprinting technology is expected to be an innovative breakthrough in this field, but various issues have been raised, such as cell damage, versatility of bioink, and printing time. In this study, we established a method for fabricating, connecting, and assembling organoid units of various shapes independent of cell type, extracellular matrix, and adhesive composition (unit construction method). We also fabricated kidney tissue-like structures using three types of parenchymal and interstitial cells that compose the human kidney and obtained findings suggesting the possibility of crosstalk between the units. This study mainly focuses on methods for reproducing the structure of organs, and there are still issues to be addressed in terms of the expression of their higher-order functions. We anticipate that engineering innovation based on this technique will bring us closer to the realization of highly efficient and rapid fabrication of full-scale organoids that can withstand organ transplantation.

Three-dimensionality (3D) was proven essential for developing reliable models for different anatomical compartments and many diseases. However, the neuronal compartment still poses a great challenge as we still do not understand precisely how the brain computes information and how the complex chain of neuronal events can generate conscious behavior. Therefore, a comprehensive model of neuronal tissue has not yet been found. The present work was conceived in this framework: we aimed to contribute to what must be a collective effort by filling in some information on possible 3D strategies to pursue. We compared directly different kinds of scaffolds (i.e., PDMS sponges, thermally crosslinked hydrogels, and glass microbeads) in their effect on neuronal network activity recorded using micro-electrode arrays. While the overall rate of spiking activity remained consistent, the type of scaffold had a notable impact on bursting dynamics. The frequency, density of bursts, and occurrence of random spikes were all affected. The examination of inter-burst intervals revealed distinct burst generation patterns unique to different scaffold types. Network burst propagation unveiled divergent trends among configurations. Notably, it showed the most differences, underlying that functional variations may arise from a different 3D spatial organization. This evidence suggests that not all 3D neuronal constructs can sustain the same level of richness of activity. Furthermore, we commented on the reproducibility, efficacy, and scalability of the methods, where the beads still offer superior performances. By comparing different 3D scaffolds, our results move toward understanding the best strategies to develop functional 3D neuronal units for reliable pre-clinical studies.

Cancer cell invasion relies on an equilibrium between cell deformability and the biophysical constraints imposed by the extracellular matrix (ECM). However, there is little consensus on the nature of the local biomechanical alterations in cancer cell dissemination in the context of three-dimensional (3D) tumor microenvironments (TMEs). While the shortcomings of two-dimensional (2D) models in replicating cell behavior are well known, 3D TME models remain underutilized because contemporary mechanical quantification tools are limited to surface measurements. Here, we overcome this major challenge by quantifying local mechanics of cancer cell spheroids in 3D TMEs. We achieve this using multimodal mechano-microscopy, integrating optical coherence microscopy-based elasticity imaging with confocal fluorescence microscopy. We observe that non-metastatic cancer spheroids show no invasion while showing increased peripheral cell elasticity in both stiff and soft environments. Metastatic cancer spheroids, however, show ECM-mediated softening in a stiff microenvironment and, in a soft environment, initiate cell invasion with peripheral softening associated with early metastatic dissemination. This exemplar of live-cell 3D mechanotyping supports that invasion increases cell deformability in a 3D context, illustrating the power of multimodal mechano-microscopy for quantitative mechanobiology .

Lab-on-a-Chip microfluidic devices present an innovative and cost-effective platform in the current trend of miniaturization and simplification of imaging flow cytometry; they are excellent candidates for high-throughput single-cell analysis. In such microfluidic platforms, cell tracking becomes a fundamental tool for investigating biophysical processes, from intracellular dynamics to the characterization of cell motility and migration. However, high-throughput and long-term cell tracking puts a high demand on the consumption of computing resources. Here, we propose a novel strategy to achieve rapid 3D cell localizations along the microfluidic channel. This method is based on the spatiotemporal manipulation of recorded holographic interference fringes, and it allows fast and precise localization of cells without performing complete holographic reconstruction. Conventional holographic tracking is typically based on the phase contrast obtained by decoupling the calculation of optical axial and transverse coordinates. Computing time and resource consumption may increase because all the frames need to be calculated in the Fourier domain. In our proposed method, the 2D transverse positions are directly located by morphological calculation based on the hologram. The complex-amplitude wavefronts are directly reconstructed by spatiotemporal phase shifting to calculate the axial position by the refocusing criterion. Only spatial calculation is considered in the proposed method. We demonstrate that the computational time of transverse tracking is only one-tenth of the conventional method, while the total computational time of the proposed method decreases up to 54% with respect to the conventional approach. The proposed approach can open the route for analyzing flow cytometry in quantitative phase microscopy assays.

Gene therapy has emerged as a highly promising strategy for the clinical treatment of large segmental bone defects and non-union fractures, which is a common clinical need. Meanwhile, many preclinical data have demonstrated that gene and cell therapies combined with optimal scaffold biomaterials could be used to solve these tough issues. Bone tissue engineering, an interdisciplinary field combining cells, biomaterials, and molecules with stimulatory capability, provides promising alternatives to enhance bone regeneration. To deliver and localize growth factors and associated intracellular signaling components into the defect site, gene therapy strategies combined with bioengineering could achieve a uniform distribution and sustained release to ensure mesenchymal stem cell osteogenesis. In this review, we will describe the process and cell molecular changes during normal fracture healing, followed by the advantages and disadvantages of various gene therapy vectors combined with bone tissue engineering. The growth factors and other bioactive peptides in bone regeneration will be particularly discussed. Finally, gene-activated biomaterials for bone regeneration will be illustrated through a description of characteristics and synthetic methods.

Quantitative micro-elastography (QME) is a compression-based optical coherence elastography technique enabling the estimation of tissue mechanical properties on the micro-scale. QME utilizes a compliant layer as an optical stress sensor, placed between an imaging window and tissue, providing quantitative estimation of elasticity. However, the implementation of the layer is challenging and introduces unpredictable friction conditions at the contact boundaries, deteriorating the accuracy and reliability of elasticity estimation. This has largely limited the use of QME to studies and is a barrier to clinical translation. In this work, we present a novel implementation by affixing the stress sensing layer to the imaging window and optimizing the layer thickness, enhancing the practical use of QME for applications by eliminating the requirement for manual placement of the layer, and significantly reducing variations in the friction conditions, leading to substantial improvement in the accuracy and repeatability of elasticity estimation. We performed a systematic validation of the integrated layer, demonstrating >30% improvement in sensitivity and the ability to provide mechanical contrast in a mechanically heterogeneous phantom. In addition, we demonstrate the ability to obtain accurate estimation of elasticity (<6% error compared to <14% achieved using existing QME) in homogeneous phantoms with mechanical properties ranging from 40 to 130 kPa. Furthermore, we show the integrated layer to be more robust, exhibiting increased temporal stability, as well as improved conformity to variations in sample surface topography, allowing for accurate estimation of elasticity over acquisition times 3× longer than current methods. Finally, when applied to human breast tissue, we demonstrate the ability to distinguish between healthy and diseased tissue features, such as stroma and cancer, confirmed by co-registered histology, showcasing the potential for routine use in biomedical applications.

Conducting polymer hydrogels (CPHs) are composite polymeric materials with unique properties that combine the electrical capabilities of conducting polymers (CPs) with the excellent mechanical properties and biocompatibility of traditional hydrogels. This review aims to highlight how the unique properties CPHs have from combining their two constituent materials are utilized within the biomedical field. First, the synthesis approaches and applications of non-CPH conductive hydrogels are discussed briefly, contrasting CPH-based systems. The synthesis routes of hydrogels, CPs, and CPHs are then discussed. This review also provides a comprehensive overview of the recent advancements and applications of CPHs in the biomedical field, encompassing their applications as biosensors, drug delivery scaffolds (DDSs), and tissue engineering platforms. Regarding their applications within tissue engineering, a comprehensive discussion of the usage of CPHs for skeletal muscle prosthetics and regeneration, cardiac regeneration, epithelial regeneration and wound healing, bone and cartilage regeneration, and neural prosthetics and regeneration is provided. Finally, critical challenges and future perspectives are also addressed, emphasizing the need for continued research; however, this fascinating class of materials holds promise within the vastly evolving field of biomedicine.