Advanced Science

The pursuit of continuous, cuffless blood pressure (BP) monitoring is constrained by the superficial sensing depth of photoplethysmography (PPG). Impedance plethysmography (IPG) offers deeper tissue penetration, but its comparative value over PPG remains unquantified at scale. In this comparative study of 261 participants (130 hypertensive, 131 non-hypertensive), we utilized a custom dual-modality wearable prototype to capture simultaneous IPG and PPG signals. Over 150,000 cardiac cycles were analyzed using an unsupervised archetype discovery pipeline to quantify beat-to-beat morphological heterogeneity. IPG resolved up to three distinct morphological modes per participant, whereas co-located PPG converged into highly conserved, uniform profiles. IPG captured specific signatures of pathological arterial remodeling and physiological habitus; ventral forearm IPG pulse amplitude exhibited a significant main effect for BP status (p = 0.024), a relationship absent in the co-located PPG signal. Furthermore, increasing body mass index (BMI) significantly attenuated the prevalence of steep-upstroke archetypes in IPG (p = 0.035), quantifying a likely damping effect of adipose tissue. Deep-tissue bioimpedance captures rich, heterogeneous hemodynamic signatures including arterial-dominant morphologies that are invisible to optical sensors. Transitioning from optical pulse wave analysis to bioimpedance-based models may offer a promising pathway for accurate wearable cardiovascular monitoring.

The anatomical complexity and distinctive tissue environment of the human oral cavity pose major challenges to modeling oral infection and host-microbe interactions in preclinical laboratory settings. Here we present a bioengineered oral microphysiological system comprising vascularized human gingival tissue integrated with tooth analogs that together recreate a functional unit of the human oral cavity. We incorporated Streptococcus mutans and Candida albicans into this system to model cross-kingdom biofilm formation, microbial dissemination, and host-microbial interactions at the gingival-tooth interface. Single-cell RNA sequencing and global metabolomics analysis revealed that fungal colonization induces epithelial-to-mesenchymal transition associated with distinct transcriptional and metabolic signatures. Our platform also allowed us to simulate SARS-CoV-2 infection and examine gingival responses to live-virus challenge. Finally, we integrated the engineered gingival tissue with controlled human saliva flow to show that hyposalivation potentiates the pathogenic capacity of fungal infection. This work demonstrates the potential of oral microphysiological systems as an experimental platform for in vitro modeling and mechanistic investigation of host-microbe interactions under controlled, human-relevant conditions.

Targeting the overwhelming inflammation driven by neutrophil extracellular traps (NETs) during infection provides an opportunity to manage severe sepsis. This potential needs to be realized by exploring selective NET-neutralization materials, which remains a challenge. Herein, we report a multivalent macromolecular strategy that targets NET-associated DNA-histone chromatin complexes while preserving antibacterial activity of aminoglycoside. We identify 8-arm PEG-conjugated netilmicin (8-arm Netil) as a lead NETs-neutralizer from a library of multivalent aminoglycoside-displayed materials. When compared with 2- and 4-arm counterparts, 8-arm Netil exhibits potent antibacterial activity and high-affinity binding to DNA-histone chromatin complexes through stable multivalent noncovalent interactions, thereby suppressing NET-induced TLR4/TLR9 activation and macrophage inflammatory responses. In severe septic mice, intravenously administered 8-arm Netil preferentially accumulates in inflamed tissues, leading to improved survival protection, owing to the reduction of bacterial dissemination, NET accumulation, systemic cytokine production, and multiple-organ injury. These findings establish NET-associated DNA-histone chromatin complexes as actionable extracellular targets and demonstrate multivalent chromatin targeting as a rational material design strategy for selective NET neutralization and inflammation control in severe sepsis.

The intermittent energy restriction (iER) represents an effective dietary strategy for improving metabolic diseases including metabolic dysfunction-associated steatotic liver disease (MASLD) and type 2 diabetes mellitus (T2DM), yet the underlying mechanisms remain elusive. In this study, we integrated human clinical data, mouse models, and in vitro experiments to investigate the role of iER in modulating the gut-liver axis in comorbid MASLD and T2DM. We demonstrate that an iER diet improves hyperglycemia, hepatic steatosis and decreases the abundance of gut pathogen Klebsiella pneumoniae, which is strongly associated with reductions in blood endotoxin, lipopolysaccharide (LPS) levels, suggesting a potential role of K. pneumoniae-derived LPS in mediating effects of the iER on hepatometabolic improvements. We confirm that K. pneumoniae-derived LPS exacerbates lipid accumulation and inflammation using an in vitro model. Mechanistically, we reveal a core target of protein lysine acetylation (Kac), hydroxyacyl-CoA dehydrogenase -subunit (HADHA) Lys353 in the liver of db/db mice through a multi-omics analysis. The iER decreases HADHA-K353 acetylation and enhances its enzyme activity. A Kac-mimicking mutation (K353R) increases its enzyme activity and stability, blocks its binding to the inflammasome adaptor ASC, and alleviates lipid accumulation and inflammation in K. pneumoniae-derived LPS induced in vitro model. This study provides novel insights into the potential benefits of the iER in comorbid MASLD and T2DM.

Nonunion fractures remain a costly and persistent challenge in regenerative medicine, with current treatments limited by donor-site morbidity, restricted graft availability, and severe adverse effects associated with supraphysiological bone morphogenetic protein 2 (BMP-2) delivery, including ectopic ossification and inflammation. Endogenous BMP-2 signaling is tightly regulated in adult tissues, constraining the precision and scalability of approaches based on transcriptional upregulation or bolus growth factor administration. To address these limitations, we developed a two-phase integrated computational-experimental pipeline for the de novo design of protein binders targeting the BMP-2 knuckle epitope, a receptor-binding surface corresponding to BMPR-II engagement, enabling affinity-tuned modulation of BMP-2 activity rather than uncontrolled pathway activation. Phase I employed PyRosetta-based {beta}-strand motif grafting and physics-based docking protocols to generate 264 candidate binders, followed by deep-learning-driven refinement in Phase II using partial RFDiffusion and ProteinMPNN with AlphaFold2 validation, yielding 22 candidates with stable {beta}-sheet architectures consistent with knuckle-epitope targeting. Experimental validation demonstrated dose-dependent BMP-2 binding, with the lead construct exhibiting an apparent KD of 2.07 nM toward BMP-2. Targeted alanine substitutions revealed differential residue contributions, with mutation of T42 significantly disrupting binding, while other substitutions had more modest effects, indicating a partially hotspot-driven interface supported by other interactions.

Although microplastics and nanoplastics (MP/NP) are pervasive environmental contaminants, our understanding of cellular toxicity remains incomplete, as adverse effects are often attributed to long-term intracellular accumulation, while the spatiotemporal onset of cellular damage remains poorly defined. Here, we employ chemical-bond-selective stimulated Raman scattering (SRS) microscopy and cell models that decouple continuous exposure from intracellular retention to directly visualize clinically derived MP/NP-cell interactions. Cellular stress occurs primarily during MP/NP exposure, accompanied by alterations in lipid droplet (LD) composition. In contrast, following extracellular removal, intracellularly retained MP/NP become largely inert, with recovery of lipid metabolism and cellular functions. Lipidomics identifies arachidonic acid (AA) as a key dysregulated metabolite, and SRS imaging further reveals transient, spatially confined AA enrichment in MP/NP-proximal LDs during uptake. Importantly, phospholipid coating of MP/NP attenuates LD alterations and cytotoxicity while preserving particle internalization, establishing uptake-driven metabolic stress, rather than long-term intracellular retention, as primary source of MP/NP-induced damage.

Artificial skin substitutes that simultaneously achieve mechanical robustness, regenerative bioactivity, and transplantation-scale tissue integration remain challenging to engineer. Here we report a mechanically adaptive bilayer artificial skin based on entanglement-mediated protein networks. By integrating protein chain entanglement, flexible molecular linkers, and photo-triggered intermolecular crosslinking, we establish a hierarchically organized protein matrix with enhanced toughness, structural adaptability, and regenerative compatibility. Spatial biofunctionalization further enables integration of an antibacterial Zn{superscript 2}-coordinated epidermal layer and a regenerative CLP-EGF-functionalized dermal layer within a unified construct. The engineered skin promotes cellular proliferation through PI3K-AKT-mTOR activation, exhibits sustained antibacterial activity, and supports large-area full-thickness skin replacement covering approximately 40% of the dorsal skin surface in mice. The construct further accelerates diabetic wound repair and extracellular matrix remodeling in vivo. These findings establish entanglement-mediated protein engineering as a strategy for mechanically adaptive regenerative biomaterials and provide a platform for transplantation-scale skin regeneration.

Oxytocin-mediated milk ejection (ME) is pivotal to effective breastfeeding and productive health, yet behaviorally decoding and revealing neural mechanisms of ME remains challenging. Here, we combined in vivo calcium imaging and intramammary pressure recording to uncover the temporal connections between episodic activity of oxytocin neurons and ME in conscious lactating rats. Leveraging the association and behavioral responses in dam and pup, we developed a supervised machine learning framework (ME Decoder) to enable automated analyses of ME. Inspired by its interpretable features, we defined the activity-coupled dam-pup interactions (ADPI), manifested by high kyphosis of the dam followed by pup treading and stretch, as the behavioral signatures of ME. By ME Decoder and ADPI analyses, we detected reduced ME but unaffected activity of oxytocinergic neurons after systemic blockade of oxytocin receptor. Our study uncovers the oxytocinergic and behavioral signatures of ME and provides a generalizable approach for further investigation.

Intracortical microstimulation (ICMS) with ultraflexible neural electrodes enables low-threshold, chronically stable, and high-resolution modulation of neural circuits, providing a promising strategy for sensory restoration and closed-loop neuromodulation. However, the microscopic mechanisms delineating its safe and effective current range remain unclear. Here, we combine intravital two-photon (2P) imaging and electrophysiology in awake mice to examine the current-dependent neurovascular outcomes of charge-balanced stimulation via ultraflexible arrays. We observed gas bubbles formed along the electrode during ICMS, with bubble size increasing quadratically with current amplitude, consistent with a Faradaic bubble-growth model. Intravital 2P imaging reveals that at low-to-moderate currents (20-40 {micro}A), vascular leakage is small, spatially confined, and largely reversible, whereas higher currents ([≥]60 {micro}A) induce a sharp transition to extensive, field-dominated extravasation and secondary vessel disruption. This transition coincides with immediate, stimulus-locked motor responses and the onset of electrode degradation. Multiphysics simulations reproduce the observed nonlinear leakage-current relationship by incorporating gas bubble-induced electric field redistribution and voltage-dependent vessel wall permeability. The model indicates that gas bubbles act as local electric-field modulators, concentrating suprathreshold fields near the bubble boundary at lower currents while shielding more distant vessel segments; at higher currents, this confinement breaks down and the system enters a field-dominated damage regime. Collectively, these findings define a mechanistically informed safety window for ICMS with flexible neural interfaces and identify bubble-assisted vascular permeabilization as a key failure mode at high currents, crucial for the design of future bidirectional brain-computer interfaces and high-precision neuroprosthetic protocols.

Brain organoids offer an invaluable model system for studying human brain development and disease. However, the establishment of high-fidelity brain organoids with multiple cell lineages including vasculature and immune cells remains a huge challenge. Here, we present a new strategy to generate human cerebral organoids with vasculature and microglia-like cells using genetic code expansion technology (GCE-T) via site-specific protein engineering. The strategy integrates orthogonal genetic translation machinery in hPSCs via PiggyBac transposon system, enabling temporally control of ETV2 expression and endothelial differentiation in hPSC-derived cerebral organoids. The vascularized human cerebral organoids (vhCOs) exhibit coordinated development of multiple cell lineages and blood-brain barrier (BBB) features. Moreover, vhCOs form perfusable vascular network after transplanted in the immune-deficient mice. Single-nucleus RNA sequencing reveals enhanced neurovascular interactions, multi-brain-regional identities, diverse neuronal subtypes and specialized endothelial subclusters in vhCOs, closely resembling human fetal brain. Strikingly, we identify enriched microglia-like cells comprising three distinct subtypes in vhCOs, which contribute to microglia-vascular interactions and synergistically modulate vascular development. Upon Zika virus (ZIKV) infection, vhCOs show neurovascular dysfunction and impaired microglia development, offering new insights into viral-induced neurodevelopmental disorders. This study offers a unique platform for producing more valuable brain organoids with vasculature and immune components, opening a new avenue to advance organoid research and applications.

Capabilities for quantitative monitoring of chronic wounds remain an unmet clinical need, as existing diagnostic approaches rely on semiquantitative evaluation of symptoms that lack sensitivity especially during early stages of infection. Here we present a scheme for tracking wound physiology that leverages a miniature, wireless skin-interfaced device for non-contact, transient measurements of the flux of volatile organic compounds (VOCs) and water vapor from the wound microenvironment. Unlike emerging smart bandage platforms that rely on physical contact with the fragile wound bed to interrogate liquid-phase biomarkers, this strategy uses an engineered microclimate and suspended suite of sensors to measure the diffusive transport of wound-derived gases across the wound surface but separated from it. The result enables quantitative evaluation of metabolic activity and healing progression without perturbing the healing tissues. In biofilm growth models of Staphylococcus aureus, measurements demonstrate that trends in VOC flux correlate strongly with bacterial growth kinetics and precede any visible biofilm formation. Longitudinal monitoring in infected murine wound healing models shows that concurrent measurements of water vapor and VOC flux provide complementary physiological insights, capturing both the trajectory of barrier restoration and the dynamics of bacterial burden. The findings establish this non-contact sensing scheme as a distinct and clinically translatable paradigm for wound monitoring, with broad implications for non-invasive surveillance of disease states in which tissue metabolic activity and skin barrier integrity serve as actionable physiological readouts. Significance StatementLimited capabilities in continuous, quantitative assessment of a wound make early diagnosis and effective management challenging, particularly in cases of infection that rapidly progress before symptoms appear. Non-contact approaches for wound monitoring that preserve fragile tissue can transform wound care. In this context, gaseous flux from the wound bed provides an integrative measure of microbial activity and barrier restoration. This study establishes a wearable sensing platform that quantifies these fluxes in real time, enabling early infection detection and temporal tracking of wound healing. These results highlight a path toward personalized treatment strategies and reduced reliance on episodic clinical evaluation.

Non-invasive neuromodulation can influence memory, but whether peripheral stimulation can engage hippocampal plasticity through a defined mechanism and translate across species remains unclear. Here we provide, to our knowledge, the first cross-species evidence linking trigeminal nerve stimulation to sustained hippocampal plasticity, direct human hippocampal engagement and associative-memory benefit. In rats, intermittent 200 Hz TNS produced persistent CA1 fEPSP potentiation and prolonged neuronal firing despite substantially lower cumulative charge than continuous 100-Hz stimulation. LC inhibition strongly suppressed these responses. In patients undergoing stereo-EEG monitoring, i200-TNS evoked prominent hippocampal and thalamic responses and increased hippocampal theta-gamma coupling. In a randomized active-sham crossover study, i200-TNS was associated with improved delayed occupation recall and accompanying EEG changes. These results link patterned trigeminal stimulation to hippocampal physiology across species and support its potential for engaging human memory-related networks.

Vascular chips have advanced endothelial mechanobiology by enabling controlled responses to hemodynamic cues, yet disease-relevant wall shear stress (WSS) modeling remains limited. Simplified one-dimensional flow shear systems, designed mainly for physiological mechanobiology, miss the topological organization of pathological flow, whereas patient-specific vascular models capture complex hemodynamics but sacrifice generality and imaging compatibility. Here we develop a programmable vascular chip that converts disease-associated WSS topology into a physiologically parameterized experimental input. The device reconstructs a representative pathological shear-topology field on endothelial layer, supports stationary and physiologically paced oscillatory flow modes, and integrates matched unidirectional-shear references within the same chip. Using this system, we show that oscillatory WSS topology destabilizes endothelial monolayers, drives asymmetric collective emergent behaviors, impairs actin-nuclear mechanotransduction, accompanied by nuclear softening and enhanced perinuclear nanoparticle uptake. Integrated live-cell imaging, fluorescence analysis, Brillouin microscopy, and transport assays enable multimodal phenotyping across collective, subcellular mechanical and functional scales. By making disease-relevant WSS topology experimentally controllable, this vascular-chip framework bridges computational hemodynamics and experimental mechanomedicine, supporting standardized vascular disease modeling and functional screening.

Abstract The gayal (Bos frontalis) is an endangered semi-domesticated bovine species renowned for its high-quality beef. However, its semi-feral lifestyle, ongoing habitat fragmentation, and extensive genetic introgression from sympatric local cattle have led to dramatic population decline and severe erosion of purebred genetic integrity, posing substantial challenges to its conservation and utilization. To address the urgent demand for rapid, non-invasive, and field-compatible germplasm identification, we developed an integrated artificial intelligence (AI) framework that predicts genomic admixture composition from external morphological images. We constructed a comprehensive dataset comprising 6,245 morphological images and matched genomic sequences from 52 gayals maintained at the Yunnan Provincial Gayal Conservation Farms. Following a preliminary evaluation of nine deep learning models, five were incorporated into a anatomical segment-based multi-modal pipeline, among which Inception_V3 delivered the optimal overall performance. To enhance simultaneous extraction of local fine-grained features and global structural information, we further designed an innovative HybridInceptionViT model by integrating the multi-scale Inception module with the Vision Transformer (ViT) framework. This hybrid model significantly outperformed the baseline Inception_V3, boosting the accuracy of phenotype-derived prediction against genomic admixture estimate from 69.69% to 87.87% (absolute error <15%). This study establishes a practical, low-cost "phenotype-to-genotype" tool for rapid on-site gayal germplasm screening, offering a scalable strategy for the conservation and breeding management of endangered livestock, and holds broad application prospects for agricultural and livestock production systems.

Endothelial cells exhibit organotypic specialization, yet the tools to decode whether hemodynamic shear shapes this diversity across vascular beds have remained limited. Here we present PROPEL, a tubing-free, magnetic stirrer-driven platform that delivers programmable laminar shear stress (2 to 60 dyn/cm{superscript 2}) to multiple cell types in parallel within the confines of a standard Petri dish, with the modular flexibility to easily introduce different substrate geometries including 3D vessel formats. Using this platform, we profiled six human endothelial subtypes across static, low, intermediate, and high shear. Phenotypic profiling revealed subtype-specific thresholds for alignment, elongation, and Golgi-nuclear polarization, including a dissociation between elongation and polarization in dermal microvascular and saphenous venous endothelial cells at low shear. Bulk RNA sequencing showed conserved transcriptional programs that shift progressively with shear magnitude, including induction of mechanotransduction pathways alongside suppression of proliferative programs. Phenotype-informed transcriptional comparisons further linked alignment transitions to engagement of cytoskeletal and metabolic signatures. In addition to conserved responses, organotypic-specific responses were also observed. Together, these findings establish PROPEL as a scalable platform for multi-endothelial, multi-shear transcriptomic and phenotypic profiling, and reveal that endothelial cells engage shear adaptation along two organotypic axes--a threshold axis governing when a subtype responds and a signature axis governing which the shear stress response--with implications for vascular bed-specific disease susceptibility.

Hair thinning arises from multi-faceted dysfunction within the hair follicle, driven by both intrinsic cellular pathways and pathways responding to extrinsic hormonal and microenvironmental cues. Here, we present an AI-enabled discovery framework to discover small molecules that promote hair follicle rejuvenation. This framework integrates graph neural networks trained on phenotypic screening data with structure-based virtual screening to prioritize compounds that modulate complementary biological pathways. Through AI-enabled screening, hit-to-lead optimization, and medicinal chemistry, we identified four compounds that increase follicle dermal papilla cell viability, stabilize hypoxia signaling by inhibiting prolyl hydroxylase domain protein 2 (PHD2), and suppress androgen-mediated follicular miniaturization by inhibiting 5-reductases (5-ARs). RNA sequencing analyses confirmed pathway engagement, and functional validation across primary cells and a 3D hair follicle organoid model demonstrated high activity and cellular specificity. The lead compounds were incorporated into a water-based formulation, where they demonstrated robust solubility and combinatorial efficacy to increase sprouting length of follicle organoids. These results establish an AI-enabled platform for discovering multi-pathway modulators of hair follicle rejuvenation.

Postoperative resilience varies widely among older adults, yet the biological drivers of recovery remain unclear. We evaluated whether preoperative immune profiles, measured in plasma and through ex vivo whole blood stimulation, predict resilience to the acute stress of total knee arthroplasty. A total of 152 adults (greater or equal to 60 years) in the PRIME KNEE cohort underwent elective total knee arthroplasty and had available blood samples for measurement of 45 immune biomarkers, quantified in plasma and in whole blood stimulated ex vivo for 24 hours with lipopolysaccharide (LPS) or influenza antigen (FLU). Resilience was assessed using Expected Recovery Differential (ERD) and Resilience Trajectory (RT) across pain severity, pain interference, lower extremity physical activities of daily living (LE PADLs), and step counts. An exploratory stability selection framework using LASSO identified biomarker predictors of postoperative outcomes. Plasma and stimulated biomarkers showed broadly similar predictive performance. A shared set of biomarkers, including LBP, leptin, TNFR1, CD30, and LIF, was consistently selected across models. Immune predictors explained ~12-24% of the variance in resilience outcomes. Distinct immune signatures emerged for pain versus functional recovery: pain related predictors mapped to local inflammatory and neuroimmune pathways, whereas function related predictors reflected systemic inflammatory load and cytokine signaling. Preoperative immune biomarkers, whether measured in plasma or after ex vivo stimulation, capture meaningful variance in postoperative resilience. The divergence between pain related and function related immune signatures highlights biologically distinct pathways underlying different dimensions of recovery and supports further development of immune based perioperative risk assessment.

Extracellular vesicle (EV)-derived microRNAs serve as important biomarkers for cancer diagnosis, yet their accurate detection remains limited by insufficient control of nucleic acid recognition and signal activation. Here, we identified a previously unrecognized feature of CRISPR/Cas12a, in which incorporation of ribonucleotides into single stranded DNA targets modulates Cas12a activation efficiency, revealing a hybrid DNA/RNA-dependent regulation of Cas12a activity. Leveraging this mechanism, we established a programmable detection strategy that enables sequence dependent tuning of Cas12a activation without the need for target amplification. By coupling DNAzyme mediated cleavage with Cas12a trans-cleavage, a cascade signal amplification system was established, enabling highly sensitive and selective detection of miRNAs. To facilitate clinical applications, an EV-based sample processing strategy was integrated to simplify isolation of EV associated miRNAs and allow direct miRNA detection without conventional RNA extraction. The resulting platform demonstrated robust discrimination of multiple miRNA targets in clinical cohorts and supported accurate classification of cancer subtypes according to expression signatures. By integrating machine learning analysis, the system accurate distinguished breast cancer (BC) patients from healthy donors (HD), as well as triple-negative breast cancer (TNBC) from BC. This study provides a mechanism-guided strategy for programmable CRISPR-based nucleic acid detection in complex biological samples.

Multimorbidity is the dominant clinical reality of primary care, yet the temporal dynamics governing when and how persistent comorbidity associations emerge remain poorly characterised. Most large-scale comorbidity studies adopt a single observation window after an index diagnosis, implicitly assuming that associations detectable at one year are equally detectable at five. Using 11 years of electronic health records from 5,821,197 individuals in Catalan primary care, we applied a matched cohort design across nine complementary follow-up windows, five cumulative (0-1 to 0-5 years) and four conditional (1-2 to 4-5 years), to 1,315 index diseases, identifying 144,030 significant directed comorbidity associations in the five-year network. We found that 60.1% of these associations required at least three years of follow-up and were undetectable in shorter-window analyses, demonstrating that observation window length is a primary determinant of which comorbidities can be observed. To organise this temporal heterogeneity, we introduce the biological clock of multimorbidity: a two-dimensional framework that positions ICD-10 disease categories according to their rates of cumulative signal attenuation and the persistence of conditional risk. This framework identifies four reproducible temporal patterns (episodic, chronic stable, chronic progressive, and transient-persistent) that are robust under bootstrap resampling, leave-one-disease-out sensitivity analysis, and alternative clustering approaches. The biological clock is systematically modulated by sex, with Blood/Immune and Musculoskeletal disorders showing the largest sex differences in temporal dynamics. Network analysis identified 19 disease "initiators" that generate broad downstream comorbidity burdens and 21 "sinks" representing convergent endpoints of multiple disease trajectories. Comparison with hospital-based Danish data from 6,909,676 individuals showed that shared associations were 2.7-fold enriched over chance expectation (hypergeometric test, p<10-300) and showed moderate concordance of effect sizes (Spearman {rho}=0.460), confirming that the comorbidity structure identified here reflects genuine, generalisable signal; nonetheless, only 3.6% of primary care associations were replicated in the hospital network, indicating that the two settings capture largely complementary segments of the disease co-occurrence landscape. Together, these findings establish the observation window length as a principal design parameter in EHR-based multimorbidity research and the biological clock as a framework for understanding how and over what timescale disease associations emerge, persist, and resolve.

Pelvic organ prolapse (POP) reconstruction is increasingly performed utilizing knitted silk meshes (KSM), yet tracking in vivo degradation kinetics remains challenging due to complex host tissue integration. This study developed an AI-driven semi-empirical framework utilizing Gaussian Process Regression (GPR) to bridge the kinetic mismatch between in vitro and in vivo environments. KSM scaffolds underwent 32 weeks of accelerated in vitro enzymatic degradation, with morphology (SEM), molecular conformation (FTIR), and mass loss being coupled with mechanical decay to train the GPR model. In vitro results revealed a multi-stage physical disintegration via a topochemical erosion pathway that preserved crystalline {beta}-sheet structures despite macro-scale mass and mechanical loss. When validated in a rat abdominal wall defect model, traditional tracking metrics encountered severe bottlenecks. Heterogeneous dye labeling caused premature fluorescence quenching by Week 16, while extensive tissue ingrowth masked gravimetric and SEM signatures. Intriguingly, a bi-phasic in vivo mechanical trajectory was identified, where initial degradation-led failure was followed by a secondary mechanical recovery driven by biomechanical synergy with neo-muscular tissue. Importantly, despite premature quenching, this work presents the first optical imaging approach to visually mapping the complete chronological breakdown of the scaffolds peripheral boundary layer in vivo, proving that outer functionalized layers eroded prior to internal silk cores. Furthermore, our GPR framework elegantly resolved the perennial technical barrier of tissue-mesh overlapping. By mathematically decoupling intrinsic polymer degradation from confounding tissue ingrowth, the model successfully achieved a first-of-its-kind prediction of the bare scaffolds long-term structural fate in a non-adhered state, providing a robust digital twin methodology for lifetime predictions of degradable biomaterials.