Jianwei Shuai

Parkinson's disease (PD) is characterized by progressive dopaminergic neuron loss compounded by oxidative stress and neuroinflammation within a self‐perpetuating pathological brain microenvironment. Here, we report a bioactive injectable self‐healing hydrogel (COPRP) constructed by crosslinking oxidized Polygonati rhizoma polysaccharide (OPRP) with carboxymethyl chitosan via dynamic Schiff base linkages. OPRP is structurally identified as an inulin neoseries‐type fructan with relevance to TGF‐β/Smad pathway regulation. Without levodopa loading, COPRP reduced ROS accumulation and M1‐associated inflammatory markers in LPS‐stimulated microglia. It also improved cell viability, preserved mitochondrial membrane potential, and maintained TH expression in 6‐OHDA‐challenged SH‐SY5Y cells, supporting the intrinsic bioactivity of the hydrogel matrix. In a 6‐OHDA rat PD model, COPRP significantly ameliorated motor deficits and preserved TH‐positive neurons. Incorporating levodopa into COPRP creates a dual‐mechanism platform combining active microenvironmental remodeling with passive dopaminergic supplementation. Proteomic analysis, corroborated by western blotting, identified the TGF‐β/Smad signaling axis as the principal mechanistic mediator. Pharmacological intervention with pirfenidone attenuated TGF‐β2/Smad2/3 activation and partially reduced the behavioral and neuroprotective effects associated with COPRP, supporting the functional involvement of this pathway. This work delineates how a defined polysaccharide structure modulates the intracranial pathological microenvironment, offering insights for the rational design of bioactive biomaterials targeting neurodegenerative disease.

Meta‐analysis is fundamental to evidence‐based medicine, yet traditional workflows remain labor‐intensive and susceptible to bias. Although LLM‐based research agents offer opportunities for workflow automation, they often lack the data fidelity and methodological traceability required for rigorous quantitative evidence synthesis, particularly when parsing multimodal scientific charts. To address this challenge, we introduce MacAma, a semi‐automated multi‐agent framework for protocol‐constrained and human‐verifiable meta‐analysis. MacAma operationalizes selected PRISMA 2020 reporting items, PICOS‐based eligibility logic, and SYRCLE risk‐of‐bias domains as structured prompts, decision rules, output fields, and audit records. Critically, MacAma adopts a risk‐aware automation strategy: Lower risk, repetitive, and protocol‐driven tasks, such as literature screening and drafting, are delegated to AI agents, whereas high‐impact steps that directly affect effect‐size estimation and statistical conclusions, such as quantitative chart‐data extraction, remain subject to expert verification. In a preclinical radiotherapy case study evaluating tumor‐related immune outcomes and metastatic potential mediated by circulating tumor cells, MacAma achieved competitive screening performance in the evaluated benchmark and reduced the manual screening burden by over 80% within the current workflow. The case study further demonstrates how structured agent outputs, predefined criteria, and audit records can support transparent screening, data extraction, statistical synthesis, and manuscript drafting . These results suggest that MacAma may provide a scalable and auditable framework for AI‐assisted meta‐analysis, although important limitations remain in full‐text access, quantitative chart data extraction, and expert interpretation of heterogeneity. MacAma is open‐source and available at https://github.com/YilinYuan/MacAma .

Sleep deprivation induces systemic physiological stress accompanied by transcriptomic remodelling and immune dysregulation, yet objective molecular indicators for its assessment remain insufficient. This study integrated blood transcriptomic analysis with an interpretable machine learning framework to identify and validate candidate molecular signatures associated with sleep deprivation and their potential relevance to insomnia. Publicly available Gene Expression Omnibus datasets were used to construct an acute sleep deprivation training cohort, an independent sleep deprivation validation cohort, and a chronic insomnia validation cohort. Differentially expressed genes were first identified, followed by feature selection using six machine learning algorithms and Shapley additive explanations to improve model interpretability. Immune cell composition was inferred using CIBERSORT, and associations between candidate genes and immune cell subsets were further evaluated. Twenty‐five differentially expressed genes were identified in the training cohort, from which eight high‐priority candidate genes were selected by the interpretable machine learning framework. Among them, S100A3 showed consistent discriminatory performance across the training cohort, the independent sleep deprivation cohort, and the insomnia cohort, whereas VEGFB exhibited notable diagnostic potential, particularly in insomnia. Immune infiltration analysis indicated that sleep deprivation was associated with altered peripheral immune composition, including reduced resting natural killer cells and activated dendritic cells, together with changes in regulatory and naïve immune cell populations. Expression levels of S100A3 and VEGFB were significantly correlated with specific immune cell subsets, suggesting a link between these molecular signatures and stress‐related immunomodulation. These findings identify S100A3 as a robust candidate biomarker shared by acute sleep deprivation and chronic insomnia, while VEGFB may reflect chronic metabolic or inflammatory adaptation. The proposed interpretable transcriptomic‐machine learning framework provides a non‐invasive strategy for discovering molecular indicators of sleep‐related physiological stress and may support future risk stratification in sleep medicine.

Vesicoureteral reflux (VUR) can cause retrograde urine flow under voiding pressure, facilitating ascending bacterial colonisation and recurrent inflammatory responses. These processes trigger a cascade of cellular and molecular events—innate immune activation, pro‐inflammatory cytokine release, oxidative stress, apoptosis and extracellular matrix deposition—thereby promoting tubulointerstitial remodelling and increasing the risk of renal parenchymal injury and scarring. Static renal 99mTc‐DMSA scintigraphy primarily reflects tracer uptake by proximal tubular cells in the renal cortex and can serve as an integrated phenotypic readout of tubular dysfunction and focal cortical involvement. However, its clinical interpretation remains experience‐dependent and lacks reproducible quantitative criteria, while voiding cystourethrography (VCUG), the diagnostic and grading gold standard for VUR, is limited by invasiveness and procedural burden. In this study, we collected DMSA data from 346 children with febrile urinary tract infection treated at the Second Affiliated Hospital of Wenzhou Medical University between January 2019 and January 2023 and developed a deep learning model (MedSwinNet) for VUR risk stratification. Built on a Swin Transformer backbone and enhanced with multi‐scale representation fusion, a convolutional block attention module and a gated selection strategy, MedSwinNet was designed to sensitively capture phenotypic signals such as reduced proximal tubular uptake and focal cortical defects while improving robustness. On the test set, the model achieved accuracies of 0.8290 under the severe‐side input setting and 0.7997 under the bilateral‐side input setting, demonstrating stable discriminative performance and favourable generalisation. Quality control analyses indicated broadly consistent distributions of key image quality metrics across data splits, mitigating potential bias from dataset shift. Collectively, deep learning–based decoding of tubular dysfunction–related phenotypic readouts enables noninvasive quantification of VUR‐associated renal involvement, supports decision‐making on whether VCUG is warranted and may reduce unnecessary invasive procedures while improving clinical risk‐stratified management.

Artificial intelligence (AI) is catalyzing a paradigm shift in medical robotics, transforming medical robots from teleoperated tools into intelligent partners across clinical domains. This evolution is pivotal in addressing global challenges like aging populations, driven by core AI pillars—including computer vision (CV), deep reinforcement learning, and large language models (LLMs)—that support perception, decision‐making, and naturalistic communication, enabling varying degrees of autonomy and adaptive care. However, the literature still lacks a holistic analysis that integrates these advances and tackles the translational challenges hindering clinical adoption. This review bridges this gap by systematically charting the evolution of AI‐driven robotics across intelligent surgery, adaptive rehabilitation, and multimodal healthcare delivery. We dissect the core technologies powering this revolution, from digital twins for surgical simulation to LLMs for enhanced human–robot interaction, and critically analyze the associated technical, ethical, and regulatory hurdles. By synthesizing current progress and outlining future frontiers, including embodied AI, nanorobotics, and the concept of the AI‐augmented surgeon, this review provides a comprehensive roadmap for accelerating the translation of intelligent medical robotics into routine clinical practice.

Biphasic dynamics, the variable-dependent ability to enhance or restrain biological function, is prevalent in natural systems. Accompanied by biphasic dynamics, necroptosis signaling also appears emergent and coexistent dynamics. However, it remains elusive how the properties of these dynamics are characterized by specific circuit structures and components. Starting with necroptosis circuit modeling, we systematically analyzed the network topology for achieving RIP1-dependent biphasic, emergent, and coexistent (BEC) dynamics. RIP1-RIP3-Caspase-8 (C8) incoherent feedforward loop embedded with positive feedback of RIP3 to RIP1 is identified as the core topology. The peak value of RIP3 phosphorylation is determined to present a scale-invariant feature, dictating BEC dynamics and the bell-shaped regulation of necroptosis biphasic dynamics. To quantitatively determine the uncertainty of necroptosis coexistent dynamics, potential landscape and Shannon entropy that measure entropy production during cell death are introduced for the first time. Further random necroptosis circuit analysis identifies the bell-shaped regulation of necroptosis biphasic dynamics by RIP3 auto-phosphorylation, which acts as a complementary process for robustly attaining BEC dynamics. Finally, we searched all possible two- and three-node circuit topologies to screen those that could perform BEC dynamics. A complete atlas of three-node circuit BEC dynamics is generated and only three minimal circuits emerge as robust solutions, confirming incoherent feedforward loop is the core topology. Analysis of the association between the minimal circuit structure and robustness proves that the identified optimal functional achievement structure is highly consistent with the experimental observed RIP1-RIP3-C8 topology. Overall, through highlighting a finite set of circuits, this study yields guiding principles that enable the mapping, modulation, and design of circuits for BEC dynamics in diverse synthetic biology applications.

Necrosome assembly is essential for necroptosis, a process implicated in neurodegeneration, ischemic injury, and inflammatory diseases. Yet the spatiotemporal rules governing this assembly remain elusive. Leveraging quantitative STORM and mathematical modeling, we define an approximately 3:1 ratio of RIP3 to RIP1 in necrosomes as the optimal stoichiometry for necroptosis, enabling signal amplification and a threshold response. Surprisingly, excessive RIP3 oligomerization attenuates signaling, acting as an intrinsic size control mechanism. RIP3 assembly is dynamically regulated: it is constrained by stimulation and RIP1, promoted by RIP3 itself, and unexpectedly limited by downstream MLKL. A complementary balance between necrosome quantity and RIP3 assembly degree ensures efficient MLKL phosphorylation. In contrast, Caspase-8 assembly is limited by c-FLIP and recruited linearly by RIP1, while its distinct behavior from RIP3 underlies the biphasic necroptotic response to RIP1. These findings uncover the flexible, multi-strategic nature of signalosomes and offer valuable insights for therapeutic and synthetic biology.

As high-throughput omics technologies continue to advance, researchers are facing the challenge of a rapid surge in proteomics, metabolomics, and genomics data. This growth not only necessitates more disk space and network bandwidth, but also complicates data sharing and subsequent analysis. To address this challenge and enhance the analytical efficiency of downstream software, we propose Dear-OMG, a unified, compact, flexible, and high-performance metadata storage solution. Dear-OMG introduces a novel file storage structure and utilizes the Elias-Fano encoding algorithm to compress and store proteomics, genomics, and metabolomics metadata into the unified OMG format. The OMG format not only demonstrates remarkably high compression and decompression speeds, but also enables parallel random access to any data block. Test results reveal that, compared to the commonly used proteomics formats of mzXML and mzML, the OMG format achieves an 80% reduction in storage space, a 90% decrease in conversion time, and approximately a tenfold speed improvement with support for parallel random access. Dear-OMG is freely available at https://github.com/jianweishuai/Dear-OMG. Graphical Abstract In omics data, proteomics and metabolomics apply high-throughput MS techniques to acquire mass spectral data for analyzing biological samples. These three-dimensional or four-dimensional mass spectral data are typically stored in vendor-specific formats (such as .d, .wiff, and .raw formats) or community formats (such as mzML, mzXML, imzML, and mzMLb formats). Single-cell genomics, on the other hand, studies gene expression through single-cell sequencing technology, with sequencing data stored in FASTQ files containing identifiers, gene sequences, and quality scores. Dear-OMG converts various omics storage formats into the unified OMG format. It offers three optional storage modes and two reading modes. Dear-OMG boasts several advantages, including minimal volume, fast encoding and decoding speeds, and support for random access and short conversion times.