Pathogenic variants in the rhodopsin (RHO) gene are the most common cause of autosomal dominant retinitis pigmentosa leading to photoreceptor degeneration. Quantitative imaging using adaptive optics (AO) revealed irregularities in cone organization, even in relatively well-preserved retinal locations. At the leading disease front, rod density was disrupted to a greater extent than cones. Repeated longitudinal measurements demonstrate the possibility of using cone-based metrics for treatment trials to preserve photoreceptor structure.
In glioma, accurate prediction of recurrence and survival is essential for clinical decision-making and individualized management. However, current prognostic tools do not fully capture outcome heterogeneity, and convenient approaches for early postoperative prognostic evaluation are lacking. We investigated whether terahertz (THz) spectroscopy could provide label-free prognostic information. In this single-center retrospective study, 56 patients were assigned to training (n = 33) and held-out internal testing (n = 23) cohorts. A total of 443 frozen sections were measured using THz time-domain spectroscopy, and patient-level features were derived from six spectral parameters across 0.2-1.4 THz. Separate progression-free survival (PFS) and overall survival (OS) signatures were developed using univariable Cox screening, LASSO-Cox selection, and multivariable Cox modeling and validated in the testing cohort. The THz-based risk score (THz-RS) significantly stratified PFS and OS in both cohorts, showed good time-dependent discrimination, and retained prognostic value after multivariable adjustment for clinicomolecular variables. Model interpretation identified the 1.2-1.3 THz band (B11) as a major contributor, while targeted LC-MS/MS revealed associations between tissue glutamate abundance and B11-derived THz features, providing preliminary biochemical support for this spectral region. These findings support THz spectroscopy as a label-free complementary tool for perioperative prognostic assessment in glioma, pending validation in larger multicenter cohorts.
Abstract sEMGCareHCI presents a low-cost, non-invasive surface electromyography (sEMG)-based system for recognizing finger positions and gestures from forearm muscle activity. The proposed “Spatio-Temporal Attention Model (STAM)” combines handcrafted time-domain features, autoencoder-derived latent representations, and continuous wavelet transform (CWT) scalograms to predict accuracy and robustness, demonstrating strong potential for prosthetic control, rehabilitation, and human-computer interaction applications. These heterogeneous representations are projected into a shared feature space, where graph convolutional layers model inter-electrode spatial relationships, CNN-TCN modules capture temporal muscle activation patterns, and an attention-based fusion mechanism adaptively weights spatial, temporal, and time-frequency information for gesture-specific classification. To establish this design, the first benchmark five classical classifiers (SVM, KNN, Decision Tree, Random Forest, MLP) trained on handcrafted features, identifying SVM with the Waveform Length feature as the strongest baseline (84.1% ± 1.9 offline cross-validation accuracy). STAM is evaluated against this baseline and against several deep-learning alternatives (CNN–BiLSTM, TCN, Transformer Encoder, GNN, Ensemble Fusion). The framework enhances signal quality and analog-to-digital conversion accuracy, with the observed features conceptually explained using a Lagrangian dynamics-based biomechanical model. The proposed STAM architecture produced continuous gesture predictions with an accuracy of 90.4%, demonstrating its potential for amputee-centric real-time human-computer interaction and assistive control applications.
Abstract Native tissue crosstalk depends on spatial organization, temporal dynamics, and mechanical context, yet 2D cultures and stochastic organoids provide limited control over multicellular and matrix interfaces. This review categorizes biological crosstalk into intercellular, cell–extracellular, and intertissue communication and surveys biofabrication strategies that reconstruct these interactions. We propose a framework for designing physiologically relevant tissue models for mechanistic disease studies and therapeutic discovery.
Brain tumors remain among the most challenging malignancies, necessitating advances in diagnostic accuracy and treatment precision. Integrated multimodal surgical platforms combining structural, functional, and molecular data are transforming neuro-oncology by improving surgical planning, intraoperative guidance, and patient monitoring. This perspective reviews current and emerging imaging modalities including optical techniques and AI-integrated platforms, while discussing their integration within unified clinical workflows that may advance personalized brain tumor care and surgical outcomes.
Neurological disorders pose a growing global health burden, motivating advances in neural interfacing and artificial intelligence (AI). This review surveys state-of-the-art approaches for neural recording, stimulation and signal decoding and encoding across brain-computer interfaces, neuroprosthetics, and neuromodulation systems aimed at restoring function and treating neurological disorders. It highlights recent progress in AI, particularly neuromorphic computing and spiking neural networks (SNNs), and introduces Brain-Inspired Brain-Computer Interfaces (BI-BCIs) as a unifying framework for low-power, closed-loop, and miniaturized neuromorphic neurotechnologies.
Bidirectional optogenetics enables simultaneous optical readout and manipulation of neural activity. However, optical crosstalk between actuators and indicators impedes clean observation and control. This review surveys genetically encoded indicators and optogenetic actuators, and the mechanisms of crosstalk, then catalogues reported crosstalk-free pairings and trade-offs. Finally, we propose best-practice guidelines and identify future directions, including near-infra-red and two-photon modalities, protein engineering and computational correction.
A label-free high-frequency bioelectrical impedance spectroscopy method, coupled with supervised machine learning, was evaluated as an adjunct to histopathology by generating probability heat maps of excised dermal specimens to estimate the risk of basal cell carcinoma at the surgical margin during Mohs micrographic surgery. In an IRB-approved study of 98 specimens from 55 patients with nodular BCC, a two-step cascade machine-learning algorithm identified cancer-positive tissue locations (ROC AUC 0.878 ± 0.048). To account for tumor-boundary label noise from manual histology misregistration, spatial-tolerance scoring yielded an ROC AUC of 0.993 ± 0.003, with sensitivity of 95.6% ± 4.7% and specificity of 96.1% ± 5.0%. Clinical relevance was addressed by recognizing that, with a median acquisition time of 3.7 min (IQR 2.6-5.5) per specimen, this technology is a non-destructive mapping tool that is easily integrated into the workflow to provide early surgical guidance while preserving frozen sections as the definitive clearance assessment.
Advancing neural interfaces requires large-scale, high-density recording technologies capable of capturing full-spectrum neural activity across cortical and subcortical regions. Here, we present a scalable approach to integrate neural electrodes with advanced application-specific integrated circuits (ASICs). Specifically, we custom-designed an ASIC with 5376 simultaneous channels, each sampling at 20 kS/s and enabling >1.3 Gb/s total data streaming throughput. The ASIC incorporates in-pixel amplification, time-division multiplexed ADCs, and on-chip stimulation capabilities, ensuring precise signal acquisition with minimal power consumption while maintaining a low noise level of 5.5 µVrms. We further developed an interconnect strategy using gold bump bonding, which allows for high-density integration of the flexible probe and rigid chip. We demonstrate the capacity of this platform through the integration with a flexible μECoG array. The resulting device allows for the high-resolution mapping of in vivo field potentials on the cortical surfaces of rat brains, supported by the precise localization of evoked sensory activities. These results prove an effective approach towards highly integrated neural interfaces with applications in brain-computer interfaces, neuroprosthetics, and large-scale functional brain mapping.
For orally administered drugs, intestinal first-pass metabolism influences systemic exposure and accounts for inter-individual pharmacokinetic differences. However, the mechanistic roles of gut microbiome-mediated biotransformation and patient-specific intestinal variability remain underrepresented in current regulatory frameworks. This Perspective explores how human-relevant culturomics platforms, including Gut-on-a-Chip microphysiological systems, allow for quantitative analysis of host-microbiome-drug interactions. We also discuss the potential of predictive intestinal ecosystem models for personalized pharmacomicrobiomics and next-generation translational drug development.
Radiomics provides an appealing, non-invasive approach to probing tumor biology for potential diagnostic and prognostic applications. However, its clinical adoption is limited by challenges in interpretability, which in turn compromise its robustness. To uncover the underlying causation, we developed an ultra-large-scale (ULS) computational model that simulates heterogeneous, vascularized tumor growth under physical constraints to a scale that can be visualized in medical images. Our study revealed the pivotal role of tumor proliferation rate in driving necrosis and tissue heterogeneity and the dominant impact of oxygen consumption rate on vascularization level. Analysis of the resultant tumor Radiomics shows a causal relationship between tumor biophysical parameters and imaging features. Specifically, differences in proliferation and oxygen consumption rates result in distinct changes in radiomic image features, identifying suitable imaging modalities and quantitative imaging metrics for studying these biophysical parameters. We thus reverse-engineer the building blocks of Radiomics as a means to understand their respective biological underpinnings. This work introduces what we believe to be the first computational framework that explicitly links tumor cell biology to macroscopic imaging features-an area traditionally explored through radiomics.
Osteoarthritis (OA) is a leading cause of chronic pain and disability, and lacks disease-modifying therapies. Unlike conventional hydrogels, which suffer from poor mechanical robustness and limited retention, cryogels overcome existing limitations through their interconnected pore architecture, shape-memory behavior, and fatigue-resistant mechanics. Here, we review how cryogels are being engineered as platforms for stem cell delivery, bioactive molecule release, localized gene activation, and immunomodulation, and discuss key translation challenges.
Ancestry-associated immune differences influence fibrosis risk, however how fibrosis-associated pathways vary across individuals remains poorly understood. Fibroblasts are a main cell type involved in fibrosis. The fibroblast response is shaped by cytokine signaling and macrophage activation. The extent to which these pathways vary across individuals, and how ancestry-associated immune differences influence fibrosis risk, remains poorly understood. Here, a poly(ethylene glycol) (PEG)-based hydrogel microphysiological system was leveraged to model fibroblast-macrophage interactions following oxidative stress and to integrate donor-specific immune signals using matched macrophages and serum. Individuals of self-reported African ancestry exhibited higher monocyte expression of CCL4, lower monocyte expression of OXER1, and increased serum IL-10, compared to individuals of European ancestry. Within the hydrogel, oxidative stress reduced fibroblast prevalence while inducing Ki67 and p16. Exogenous TGF-β1 increased fibroblast prevalence and collagen 3 production but did not independently increase α-SMA. Incorporating donor-specific macrophages and serum revealed that cultures from individuals of European ancestry demonstrated higher fibroblast α-SMA and p16 expression. Pharmacologic inhibition of IL-10 further increased α-SMA expression, particularly in African ancestry-derived cultures, identifying IL-10 as a key protective signal limiting fibroblast activation. This hydrogel system provides a platform for dissecting inter-individual immune variation and identifying mechanisms underlying ancestry-associated fibrosis risk.
Clinical treatment of inflammatory bowel diseases (IBD) remains challenging due to the complex interplay between the epithelial barrier, immune system, and gut microbiota. While in vitro models are pivotal for studying barrier dysfunction, developing a standardized and functionally relevant system for IBD remains challenging. To overcome this, we established an immunocompetent murine colon epithelium monolayer to model IBD-like conditions. Colons from wild-type mice were digested into single cells and seeded onto Matrigel-coated transwells. Within seven days, monolayers showed strong barrier properties and displayed epithelial cell lineage, including goblet, stem, and enteroendocrine cells. However, exposure to pro-inflammatory cytokines as well as infection with pathogenic bacteria, including Clostridium rodentium and Salmonella Typhimurium, disrupted epithelial integrity. To better reflect the in vivo state, polarized T cells and macrophages were co-cultured with the epithelium. Pro-inflammatory Th1 and Th17 cells impaired barrier function, while M0 and M2 macrophages maintained it, representing both homeostatic and disrupted conditions of the gut. Upon Salmonella Typhimurium infection, M1 macrophages produced IFN-γ, and M2 macrophages secreted IL-10 and enhanced ZO-1 expression. Overall, our model presents a promising platform to study epithelial barrier dysfunction, immune-epithelial cross-talk, and host-pathogen interactions, offering valuable insights into IBD mechanisms and potential therapeutic approaches.
Blast traumatic brain injury (bTBI) is a significant clinical challenge particularly in military populations, yet the biological mechanisms linking cavitation-induced injuries in brain tissue remain poorly understood and effective treatments are limited. Intracranial cavitation may arise from blast wave exposure or other sources of mechanical and acoustic energy, and has been proposed to contribute to brain injury, but the cellular response to cavitation has been difficult to examine in controlled, physiologically relevant experimental systems. To address this limitation, we present a human brain microphysiological system to investigate cavitation-induced injury in a multicellular neural tissue environment. The platform consists of three-dimensional engineered brain tissue constructs containing human neurons, astrocytes, and microglia, and uses focused ultrasound to generate localized cavitation within the tissue. Cavitation injury produces dose- and time-dependent cytotoxic and excitotoxic cellular responses, including the release of clinically relevant neurotrauma biomarkers consistent with patterns reported in experimental and clinical TBI. Protein and functional analyses further reveal disruption of mechanotransduction-associated signaling, cytoskeletal integrity, and network activity. Together, these results demonstrate a scalable human multicellular brain tissue platform for mechanistic studies and evaluation of candidate therapeutics or protective strategies relevant to bTBI and other conditions where intracranial cavitation is implicated.
Abstract The HeMonitor study evaluated the feasibility and accuracy of non-invasive hemoglobin (Hb) assessment using image-based techniques and machine learning in patients with hematologic malignancies. A total of 367 patients with hematologic malignancies and 184 healśśthy donors were enrolled, with fingernail and eyelid photographs collected and analyzed using Light Gradient-Boosting Machine (LightGBM) regression models. The best-performing model achieved a residual standard deviation of ±1.02 mmol/L for Hb prediction. Our framework further explored a two-stage concept combining (i) a non-invasive image-based Hb predictor and (ii) a post hoc, rule-basśed corridor aggregation layer integrating EORTC Global Health and Fatigue categories. This exploratory layer was designed to contextualize Hb estimates with patient-reported symptom burden and well-being. Visual analyses suggested that lower Hb levels were generally associated with impaired quality-of-life measures, consistent with the known clinical burden of anemia. Within the QoL subset, the integrated framework showed encouraging concordance with clinician assessments, particularly in borderline Hb ranges. These findings support the feasibility of combining digital biomarkers with patient-reported outcomes for future patient-centered home monitoring strategies, while prospective validation remains necessary.
Disruption of the intestinal epithelial barrier is a hallmark of Inflammatory Bowel Disease (IBD), yet mechanisms of epithelial repair remain poorly understood. We developed a scalable human colon organoid-derived model in a 96-well Transwell system that recapitulates the cellular diversity and barrier function of the native epithelium. Using this platform, we investigated how various growth factors and cytokines influence epithelial maturation and repair following simulated inflammatory damage. Our results demonstrate that restorative factors, particularly EGF and TGFα, significantly enhance barrier integrity by reducing cell death and supporting proliferation under both chronic and acute inflammatory conditions. We also showed direct protective effects of immunomodulatory cytokines IL-2 and IL-10 on epithelial cells. This robust model provides a powerful high-throughput tool for dissecting mechanisms of intestinal repair and screening potential therapeutics to promote mucosal healing in IBD.
The ability to distinguish cancerous lesions based on aggressiveness using noninvasive molecular imaging techniques enables more precise and accurate diagnosis. In colorectal cancer screening, current approaches such as blood or fecal tests and endoscopic examination are widely used. However, reliably differentiating malignant adenomatous lesions from benign lesions, particularly those ≤5 mm in size, remains a significant clinical challenge. We synthesized, optimized, and validated a small-molecule near-infrared (NIR) activated photoacoustic dye conjugated to a tripeptide substrate specific for urokinase plasminogen activator (uPA), a protease that is upregulated in colorectal cancer tissues. The probe was designed to produce an "ON-OFF" photoacoustic signal upon activation by uPA. Specificity of the probe towards aggressiveness was evaluated using two colorectal cancer cell lines with differential uPA and cathepsin B expression. The uPA-responsive photoacoustic probe demonstrated high sensitivity and a clear "ON-OFF" activation signal in response to uPA activity. It showed strong specificity between colorectal cancer cell lines with different levels of uPA expression, confirming its selective activation. Noninvasive monitoring of extracellular uPA activity using photoacoustic imaging shows promise as a predictive screening approach for distinguishing malignant and premalignant colorectal lesions, particularly those that are small and difficult to classify using current screening methods.
The electrical stimulation of the nervous system has shown great clinical potential in injury and pathology, yet experimentally driven practice makes it challenging to identify effective design choices and personalized stimulation protocols. This review outlines emerging model-based optimization frameworks that address these challenges by leveraging biophysical digital twins of neural interfaces. Enabling acceleration strategies and complementary data-driven approaches are also highlighted, along with key factors that currently limit clinical translation.
Peptide-based strategies offer promising solutions to overcome the complex, multi-tissue barriers of osteoarthritis. Their tunability, specificity, and versatility enable targeted drug delivery to cartilage, synovium, and subchondral bone, while some therapeutic peptides provide intrinsic anti-inflammatory, regenerative, or analgesic effects. Advances in peptide design, stability engineering, and in silico screening, alongside emerging human joint-on-chip models, are accelerating the development of targeted, stable, multi-tissue OA therapies.
