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28 June 2026, Volume 31 Issue 3 Previous Issue   
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Celebrating 30 Years


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Cover and Table of Contents 2026, 31 (3):  0.  Abstract ( 25 )   PDF (50686KB) ( 58 )   Related Articles | Metrics

Celebrating 30 Years

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Exploring the Biosynthesis of Cyclic Peptide Catalyzed by the PBP-Type Thioesterase Ulm16 Li Hanbing, Xu Chang, Xia Yilinting, Tang Zhixuan, Zhou Yongjun, Shi Ting 2026, 31 (3):  537-547.  doi: 10.1007/s12204-026-2928-7 Abstract ( 50 )   PDF (1440KB) ( 15 )   Cyclopeptide compounds have emerged as a significant source of drug candidates in pharmaceutical development due to their unique cyclic structures, enhanced metabolic stability, and exceptional biological activities. Ulm16, a penicillin-binding protein-type thioesterase, promiscuously catalyzes the macrocyclization of linear peptides of different sizes between the N- and C-terminal residues with L- and D-configurations, respectively. However, the mechanism governing its selectivity towards substrates with various lengths remains unclear. This study comparatively investigated the structural differences in the interaction modes between Ulm16 and its linear substrates—hexapeptide (WLA-B1) and octapeptide (SGM)—by integrating molecular docking, molecular dynamics simulations, and rational design approaches. Findings revealed that the cooperative interactions with the oxygen anion cavity (Ser71/Thr299) and Arg431 in the active pockets stabilize WLA-B1 at both the C- and N-termini, which is crucial for its efficient macrocyclization. Conversely, SGM exhibits impaired pre-reaction state proportion due to the absence of key hydrogen-bonding networks. Based on structural analysis, we designed a series of mutants (A225S, G226E, D147V, S144F, and L300G) to optimize substrate binding by enhancing the negative charge density within the active pocket. Among them, A225S variant demonstrated a slight improvement in the yield of cyclized WLA-B1 (1.3-fold higher than the wild type). This study established a computational methodology spanning from substrate loading to catalytic macrocyclization, elucidated the molecular mechanism of substrate selectivity with Ulm16, and provided crucial theoretical foundations for rationally designing highly efficient cyclic peptide biosynthetic enzymes. References | Related Articles | Metrics

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Thermally Responsive Single-Compartment Polysaccharide Microreactors for Molecular Diagnostics Cui Yaxin, Dou Hongjing 2026, 31 (3):  548-555.  doi: 10.1007/s12204-026-2938-5 Abstract ( 29 )   PDF (943KB) ( 14 )   Molecular diagnostics based on reverse transcription quantitative polymerase chain reaction (RT-qPCR) relies on temperature-dependent enzymatic reactions that require compatibility with thermal cycling processes. In this study, thermally responsive single-compartment polysaccharide microreactors were fabricated using a water-in-oil emulsion strategy combined with in situ agarose gelation. The effects of oil phase composition and agarose concentration on microsphere formation, size distribution, and protein encapsulation were systematically investigated, and isooctanol was identified as an effective oil phase for achieving stable emulsification with reduced interfacial leakage. Reverse transcriptase and DNA polymerase were subsequently encapsulated to construct single-compartment microreactors, and the biochemical compatibility of these microreactors was evaluated using a one-step RT-qPCR assay. The encapsulated enzyme systems exhibited amplification curves and cycle threshold values comparable to those of free enzyme controls, without detectable inhibition or kinetic interference. Moreover, multiplex RT-qPCR assays confirmed preserved detection specificity without apparent cross-reactivity. These results demonstrate that thermally responsive agarose microreactors provide a structurally stable and functionally neutral platform that is compatible with RT-qPCR workflows for molecular diagnostic applications. References | Related Articles | Metrics

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Reconstruction of Tumor Evolution: Bulk Versus Single-Cell Approach Xiao Jing, He Kunyan, Su Peipei, Zhu Jiahui, Wang Na, Han Zeguang, Su Xianbin 2026, 31 (3):  556-567.  doi: 10.1007/s12204-026-2914-0 Abstract ( 31 )   PDF (487KB) ( 17 )   Tumor is generally believed to originate from a single cell, which develops into a genetically heterogeneous population after continuous cell divisions under the principle of “survival of the fittest”. Tumor evolution is a dynamic process, and reconstruction of the disappeared history is challenging but crucial for both understanding the mechanism and personalized therapy. Genetic alterations can be used as molecular clues to tumor evolution, with somatic mutations as a key tracer. Although bulk-level sequencing of a large number of tumor specimens has provided useful information on the mutational landscape, it is not easy to utilize such data for evolutionary history study. This is because bulk-level mutation prevalence may be confounded by various factors, such as tumor purity, copy number alterations, and more severely, the overlapping of prevalence ranges for mutations from similarly sized subclones. Single-cell mutational profiling overcomes such challenges, enabling precise identification of co-mutation groups and tumor subclones. This further enables successful reconstruction of tumor evolution, even for a solid tumor sample collected at a single time point. Technical and analytical advancements in single-cell profiling, such as mutation calling from single-cell RNA-Seq or spatial transcriptomic data, will provide further insights into tumor evolution and cancer treatment. References | Related Articles | Metrics

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Application of Menstrual Blood in the Diagnosis of Female Genital Tract Diseases Li Qiuhong, Huang Wanqiu, Jia Xinyu, Xu Jie, Zou Rong, Li Yun, Deng Yuliang, Huang Jian, Han Ying 2026, 31 (3):  568-583.  doi: 10.1007/s12204-026-2944-7 Abstract ( 43 )   PDF (767KB) ( 10 )   In recent years, menstrual blood has emerged as a promising non-invasive biological specimen for the diagnosis of female genital tract disorders because it provides rich molecular information while avoiding several limitations associated with conventional diagnostic approaches. Traditional diagnostic methods often rely on invasive procedures, which may impose physical discomfort and psychological distress on patients and limit the feasibility of repeated sampling and large-scale population screening. In contrast, menstrual blood collection is non-invasive, readily repeatable, suitable for self-collection, and associated with high patient acceptability, making it particularly well suited for longitudinal monitoring and large-scale screening programs. This review systematically summarizes recent advances in the application of menstrual blood for the diagnosis of major female genital tract diseases, including lower genital tract infections, endometriosis, endometrial carcinoma, and cervical cancer. The diagnostic utility and current level of clinical evidence for different molecular biomarkers are critically compared. In addition, key pre-analytical and analytical factors affecting assay performance are discussed, including sample collection protocols, timing and handling procedures, and detection methodologies. This review also addresses major translational challenges, including the lack of standardized collection and processing protocols, insufficient prospective clinical validation, and unresolved issues regarding applicability across diverse populations. Overall, menstrual blood represents a novel liquid biopsy specimen with considerable potential for the early detection and risk stratification of female genital tract diseases. However, successful clinical translation will require large multicenter prospective studies and the establishment of harmonized technical and regulatory standards. References | Related Articles | Metrics

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Genetically Engineered Natural Killer Cells in Cancer Therapy: Advances, Challenges, and Future Directions You Yiqi, Dong Han, Xu Jie 2026, 31 (3):  584-603.  doi: 10.1007/s12204-026-2936-7 Abstract ( 32 )   PDF (977KB) ( 14 )   A cure of cancer remains challenging. With the advancements in gene editing techniques, immune cell-based treatments (e.g., natural killer (NK) cell therapy) have emerged as potent therapeutic modalities, aiming to address clinical bottlenecks associated with traditional approaches. This review recapitulates the inherent characteristics of NK cells as a safe allogeneic tool against cancers, as well as available gene-editing systems for enhancing NK-cell activity, improving persistence, and increasing safety. Furthermore, it summarizes the preclinical and clinical practices of genetically engineered NK cell therapies and highlights their potential, challenges, and future perspectives in cancer treatment. Overall, we provide a comprehensive insight into NK cell-based immunotherapies as a promising approach for cancer treatment. References | Related Articles | Metrics

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Computational Modeling and Experimental Validation of MRI-Induced RF Heating for a Transcranial Puncture Needle at 3 T Gao Zhong, Guo Ran, Li Chengling, Gao Anzhu, Zhu Weiran, Kong Jilie 2026, 31 (3):  604-610.  doi: 10.1007/s12204-026-2932-y Abstract ( 34 )   PDF (1418KB) ( 13 )   This study presents a computational and experimental framework for assessing magnetic resonance imaging (MRI)-induced radiofrequency (RF) heating risks for a transcranial puncture needle at 3T MRI. Due to the one-dimensional structure of the puncture needle, as well as its clinical interventional use, the transfer function method was used to evaluate the needle’s electromagnetic model. Multi-variable computational simulations encompassing over 100 000 tests were performed using five anatomical virtual human models, including various coil configurations and clinical implantation trajectories. The in vivo RF heating was predicted by convolving the simulated tangential electric field along each path with the measured transfer function, scaled by an experimentally validated factor. The maximum temperature rise of the puncture needle in the human body does not exceed 6 °C after a 15-min scan under a given power limit exposure under MRI. This integrated approach provides a robust safety assessment for transcranial MRI-guided intervention. References | Related Articles | Metrics

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Deep Learning-Based Reconstruction of Temporomandibular Joint MRI at 1.5-T and 3-T Zhou Xin, Li Xiaomin, Zhang Zhengjia, Ma Hairong, Cao Ran, Wu Jianwei, Sun Qi, Ai Songtao 2026, 31 (3):  611-621.  doi: 10.1007/s12204-026-2927-8 Abstract ( 26 )   PDF (1724KB) ( 11 )   Image quality and diagnostic performance between a fast spin-echo sequence with standard parameters (FSE-SD) and a deep learning-reconstructed fast spin echo with low parameters (FSE-DL) are compared for temporomandibular joint (TMJ) magnetic resonance imaging (MRI) at 1.5-T and 3-T scanners. This study enrolled 183 patients (94 scanned at 1.5-T and 89 at 3-T) who underwent both FSE-SD and FSE-DL acquisitions for temporomandibular disorders. Scan time, subjective image quality, detection rates of pathological features, and the inter-protocol and inter-reader agreement were assessed by two readers. The Wilcoxon signed-rank test, McNemar test, and unweighted/linearly weighted Cohen’s κ statistics were used. Using the deep learning algorithm, total scan time at 1.5-T was reduced from 316 s (FSE-SD) to 211 s (FSE-DL), representing a 33.23% reduction. At 3-T, scan time was similarly shortened from 329 s (FSE-SD) to 189 s (FSE-DL), with a 42.55% decrease. Compared with FSE-SD, FSE-DL showed lower noise and fewer artifacts, with overall image quality similar or slightly improved at both 1.5-T and 3-T; most of these differences were statistically significant. Diagnostic confidence was not significantly different between the two protocols. Interreader and interprotocol agreement ranged from moderate to almost perfect (κ values ranging from 0.602 to 1.000). Detection rates of major TMJ pathologies were similar between the two protocols for both readers at 1.5-T and 3-T (p ⩾ 0.375). FSE-DL achieved a significant reduction in scan time for TMJ MRI at both 1.5-T and 3-T while maintaining overall image quality, and demonstrated pathological feature detection performance comparable to that of the FSE-SD. References | Related Articles | Metrics

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Accuracy of AI-Assisted Preoperative Templating in Total Hip Arthroplasty: A Comparison Between Anteroposterior Pelvic Radiographs and Robotic Advanced X-Ray Machine-Acquired Long-Leg Radiographs Liao Jiabo, Xu Yufan, Wang Yicang, Ding Huan, Xie Kai, Jiang Xu, Li Xing, Wang Liao, Yan Mengning 2026, 31 (3):  622-630.  doi: 10.1007/s12204-026-2937-6 Abstract ( 20 )   PDF (1542KB) ( 12 )   To investigate the prosthesis prediction accuracy of an artificial intelligence (AI)-assisted preoperative planning system, this study compared standard anteroposterior (AP) pelvic radiographs with long-leg radiographs (LLRs) acquired by a robotic advanced X-ray (RAX) system (Multitom Rax). A total of 29 patients (29 hips) undergoing primary total hip arthroplasty (THA) were retrospectively analyzed, excluding those with severe structural anomalies such as Crowe type IV developmental dysplasia of the hip. Preoperative planning was performed using the “Cuantian” AI system across four groups: AP pelvic radiographs with and without calibration markers, and RAX-acquired LLRs with and without calibration markers. The actual implanted component sizes recorded in intraoperative logs served as the standard. The results demonstrated that for AP pelvic radiographs, the inclusion of calibration markers was associated with improved prediction accuracy, achieving a 100% compliance rate compared with 51.7%–82.8% without calibration. Conversely, RAX-acquired LLRs maintained a 100% compliance rate and higher perfect-match rates (72.4%–75.9% for femoral stems; 55.2%–58.6% for acetabular cups) in this cohort. Regardless of the imaging modality employed, femoral stem size prediction remained consistently more accurate than acetabular cup prediction. The 300 cm source-to-image distance of the RAX system may reduce magnification distortion, making it a potentially useful imaging option for 2D planning in THA. For primary hospitals without access to RAX imaging, standard AP pelvic radiographs with calibration markers remain a more economical and pragmatic choice. The AI-assisted workflow may provide standardized support for preoperative templating, although further validation in larger and more anatomically diverse cohorts is still required. References | Related Articles | Metrics

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Unsupervised Anomaly Detection in 3D Medical Imaging via Normal Sample Learning: A Survey and Benchmark Yang Tao, Chen Ningxin, Yao Liguo, Wang Lisheng 2026, 31 (3):  631-645.  doi: 10.1007/s12204-026-2910-4 Abstract ( 34 )   PDF (930KB) ( 8 )   How computers can acquire the capability of radiologists to identify various anomalies within 3D medical images remains a central and challenging problem in intelligent imaging diagnosis. Supervised learning methods relying on lesion-level annotations are restricted to detecting a limited set of lesion types similar to those in the training data. Furthermore, obtaining voxel-level annotations is notoriously labor-intensive and time-consuming. To address these challenges, researchers have proposed unsupervised anomaly detection (UAD) methods for 3D medical images that learn from normal (healthy) samples. These approaches aim to identify any lesion that deviates from the learned normal anatomical distribution. Centered on this principle, three distinct UAD paradigms have emerged: self-supervised learning-based methods that train supervised models by constructing synthetic anomalies; deep feature embedding-based methods that measure the distance between test features and normal feature distribution; and reconstruction-based methods that transform abnormal images into pseudo-healthy images based on learned normal patterns. This paper provides a comprehensive review, analysis, and comparison of key studies within these three categories, highlighting their respective strengths, limitations, and potential research directions. Moreover, existing UAD methods are often validated using inconsistent datasets or preprocessing pipelines, making fair performance comparison challenging. To address this issue, we establish a unified benchmark for evaluating UAD methods on 3D medical images, comprising nine brain MRI datasets (4365 cases) and six liver CT datasets (376 cases). Using this benchmark, we evaluate the performance of 21 representative state-of-the-art algorithms on the 3D voxel-level anomaly localization task, objectively revealing the advantages and limitations of these methods. References | Related Articles | Metrics

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Deep Residual Learning-Based AprilTag Surgical Instrument Localization and Tracking Zhong Jihao, Liao Shenghui, Jiang Wenbo, Li Jianfeng, Liu Lihong, Kui Xiaoyan 2026, 31 (3):  646-659.  doi: 10.1007/s12204-026-2931-z Abstract ( 28 )   PDF (1980KB) ( 13 )   Surgical navigation technology provides surgeons with precise guidance through real-time tracking of the spatial pose of surgical instruments and serves as a crucial technical foundation for modern precision medicine. As a visual reference marker system, AprilTag demonstrates the advantages of low cost and flexible deployment for surgical instrument localization. However, traditional geometric constraint-based pose estimation methods face challenges such as cumulative detection errors and environmental interference sensitivity in complex surgical environments, making it difficult to meet the requirements of high-precision surgical navigation. This study proposes a novel AprilTag pose tracking method that integrates geometric and deep learning approaches. By constructing a deep residual learning network, the method learns the influence patterns of image features on pose errors to achieve intelligent correction of traditional geometric methods. The approach employs ResNet18 to extract deep image features, encodes the AprilTag’s pose information, and utilizes a dual-branch fusion network to learn the residual error between geometric predictions and actual instrument pose. A weighted loss function is designed to address rotation and translation errors. Experimental results on real surgical instrument datasets show that compared to traditional geometric methods, this approach reduces the average position error by 48.44% and the average rotation error by 9.66%. Comparative tests with other visual positioning methods further demonstrate the method’s superior comprehensive performance in complex scenarios. References | Related Articles | Metrics

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Hybrid Supervised Fine-Tuning Method for Medical Language Models via Explicit Reasoning Modeling Wang Xu, Tao Wei, Nan Zhuojiang, Wan Song 2026, 31 (3):  660-670.  doi: 10.1007/s12204-026-2929-6 Abstract ( 39 )   PDF (879KB) ( 11 )   To enhance the reasoning stability of small-parameter medical large language models in internal medicine question-answering tasks, this paper proposes a training methodology based on explicit chain-of-thought (CoT) modeling and hybrid supervised fine-tuning (SFT). First, a hierarchical dataset comprising general internal medicine instructions and explicit CoT data was constructed. On this basis, a two-stage hybrid SFT process was implemented, incorporating direct preference optimization to align the model with clinical preferences. Experimental results demonstrate that the proposed method improves the accuracy on Chinese medical benchmarks while reducing the proportion of redundant reasoning, effectively enhancing the logical rigor of complex clinical inquiries. Furthermore, these findings validate the potential of this approach for deploying low-cost, highly reliable, and localized auxiliary diagnostic systems in privacy-sensitive and compute-constrained clinical scenarios. References | Related Articles | Metrics

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Research Progress and Trends of Intelligent Speech in Pathological Healthcare Gao Yingming, Wu Yangqing, Yang Fei, Zhou Yingying, Li Ya, Wu Mengyue 2026, 31 (3):  671-692.  doi: 10.1007/s12204-026-2943-8 Abstract ( 31 )   PDF (741KB) ( 11 )   Intelligent speech technology, rooted in the ancient medical practice of “auscultation and interrogation”, is emerging as a transformative tool in modern pathological healthcare. By analyzing acoustic biomarkers within speech, voice, cough, breath sounds, and heart sounds, it offers a non-invasive, cost-effective avenue for early screening, auxiliary diagnosis, monitoring, and rehabilitation assessment across a wide spectrum of conditions, including mental disorders, neurodegenerative diseases, respiratory illnesses, cardiovascular diseases, and laryngeal or vocal tract pathologies. This study comprehensively reviews the research progress and prevailing trends in this interdisciplinary field. It begins by elucidating the physiological basis of pathological acoustics, including neural dysregulation, airway and pulmonary abnormalities, hemodynamic disturbances, and laryngeal or vocal tract dysfunction, and then discusses its integration with AI-driven diagnostics. The core of the review systematically details advances in two pillars: data resource construction (encompassing datasets for various diseases and standardization efforts) and methodological innovation (tracking the paradigm shift from feature-based machine learning to deep learning, self-supervised models, and multimodal large language models). Furthermore, it explores the development of intelligent speech-driven intervention systems for mental health. The analysis identifies key dynamic trends: the evolution from single-modality to multimodal analysis, the shift from strong to weak/self-supervised learning, the transition from controlled lab settings to naturalistic scenarios, the growing priority of model interpretability, and the move towards multi-disease coexistence modeling. Despite promising clinical potential, significant challenges persist, including data scarcity, algorithmic robustness, and clinical integration bottlenecks. The study concludes by outlining critical future directions: fostering federated learning and multi-center validation, enhancing explainable AI fused with medical knowledge, improving cross-device and cross-environment robustness through hardware-software co-design, refining human-AI collaborative diagnostic paradigms, and establishing comprehensive standardization and regulatory frameworks. Overcoming these hurdles through concerted interdisciplinary efforts is essential to realize a full-cycle intelligent health ecosystem, advancing precision medicine and equitable healthcare delivery. References | Related Articles | Metrics

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Progress in Advancing Mechanisms for Medical Continuum Robots Yang Junlin, Yin Minyi, Wei Weiqing, Qiu Peng, Lu Xinzhan, Gao Anzhu 2026, 31 (3):  693-706.  doi: 10.1007/s12204-026-2933-x Abstract ( 32 )   PDF (2498KB) ( 14 )   Continuum robots (CRs) have shown significant clinical potential in minimally invasive surgery (MIS) due to their inherent compliance and high dexterity. However, achieving independently decoupled control and long-distance, stable transmission within strictly confined anatomical spaces remains a key challenge for proximal advancing mechanisms. This study presents a systematic review of advancing mechanisms for CRs and their representative applications. Advancing mechanisms are categorized into coupled-motion and decoupled-feed configurations, where the former translates the entire drive module to provide high kinematic rigidity and motion accuracy, and the latter employs friction-wheel or spooling mechanisms with decoupled transmission to enable compact architectures and extended stroke capabilities. Application-driven design strategies are analyzed across diverse surgical scenarios, highlighting how configuration selection and topology optimization can be tailored to address anatomical and environmental constraints. Key challenges, including transmission inaccuracies from nonlinear friction, sterility and modularity constraints, and embodied integration, are discussed. Future directions focus on high-fidelity force feedback, modular quick-release architectures, and integrated embodied sensing-actuation units to advance the next generation of continuum robotic systems. References | Related Articles | Metrics

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Application Principles and Technical Strategies of 3D Printing-Based Digital Technologies in Medical–Engineering Interaction for Precision Treatment of Foot and Ankle Surgery Guo Yu, Chang Hanwen, Chen Hongyu, Yang Tianxing, Xu Chen, Zhang Henghui, Wang Shijin, Jiang Wenbo, Dai Kerong, Gan Yaokai 2026, 31 (3):  707-720.  doi: 10.1007/s12204-026-2925-x Abstract ( 28 )   PDF (5300KB) ( 10 )   To investigate and analyze the application techniques and principles of 3D printing-based medical–engineering interaction for precision surgery of the foot and ankle, we summarized 216 patients treated at the Foot and Ankle Surgery Team of the Department of Orthopaedic Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, between February 2020 and March 2025, who underwent 3D printing-assisted interventions under medical–engineering interaction. Among them, 152 patients underwent pre-operative 3D reconstruction based solely on CT/MRI imaging, 32 patients underwent deformity localization and quantitative measurements performed in the regions of interest based on these reconstruction, and 26 patients received personalized 3D-printed surgical guides for osteotomy, lesion localization, or broken implant removal. Additionally, 3 patients received customized fixation and rehabilitation orthoses, and 3 patients received personalized 3D-printed prostheses. Using 3D reconstruction, deformity localization and quantitative measurements, personalized 3D-printed surgical guides, and personalized 3D-printed prostheses, individualized treatment strategies were formulated for selected foot and ankle patients, accounting for 17.2% of all surgical cases. The doctors’ satisfaction rate with 3D reconstruction was 97.4%, with the deformity localization and quantitative measurements was 100%, with the guide plate was 92.3%, and with the rehabilitation orthoses and 3D-printed prostheses was 100%. When using the talus-targeting lesion guide plate, 87.5% of patients were able to achieve high-precision localization; and the average number of fluoroscopies during surgery was (3.8±0.8) times, consistent with the preoperative plan. Through continuous medical–engineering interactions and learning, digital technologies and precision treatment plans are being increasingly adopted. Medical–engineering interaction and 3D printing have been fully recognized and applied in foot and ankle surgery. They offer unparalleled advantages, especially for complex orthopedic cases and those requiring precise positioning, providing excellent solutions for the minimally invasive and precise treatment of complex foot and ankle diseases. References | Related Articles | Metrics

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Novel Robotic System with Semi-Automatic Planning and Collision Detection for Long Bone Fracture Reduction Wang Zhelong, Li Haitao, Shi Haochen, Chen Xiaojun 2026, 31 (3):  721-733.  doi: 10.1007/s12204-026-2913-1 Abstract ( 26 )   PDF (2600KB) ( 8 )   A novel robotic system for long bone fracture reduction is developed to address the drawbacks of reduction surgery in clinical practice such as infection, non-union, and malunion. Compared to existing robotic systems, the proposed robotic system integrates a novel semi-automatic surgical planning method and can perform the reduction automatically under supervision, thereby assisting surgeons throughout the whole clinical process. Besides, the proposed robotic system utilizes both optical tracking data and robot kinematics to calculate the position of fragments during the execution of fracture reduction, thereby increasing tolerance to line-of-sight interruption. Radiation exposure to surgeons is minimized since C-arm devices are not required. Trajectory planning experiments using clinical data from tibial, femoral, and fibular fractures are conducted to validate the surgical planning method, in which the generated path length is shorter than that achieved by existing methods. Phantom experiments are performed to evaluate the system’s accuracy, where the mean translational error is reduced to 0.96 mm and the rotational error about the z-axis is 0.26◦, both meeting clinical requirements, indicating that the proposed system has the potential to assist surgeons with more precise operations, thereby achieving higher reduction accuracy and minimizing the reduction path length for patients. References | Related Articles | Metrics

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Magnetic Biomimetic Cilia Arrays Enable Dynamic Control of Microfluids Yang Pufan, Zhang Zhinan 2026, 31 (3):  734-741.  doi: 10.1007/s12204-025-2876-7 Abstract ( 31 )   PDF (1298KB) ( 16 )   This study introduces an innovative method for dynamic control of microfluids using bionic magnetic cilia arrays, combining theoretical analysis with experimental verification to achieve precise and localized manipulation of microfluidic motion driven by magnetic force. Leveraging 3D printing technology, a microcolumn array mold was fabricated, and the dynamic response of magnetic microcolumns was systematically analyzed using polydimethylsiloxane (PDMS) and Ecoflex as substrate materials. The effects of substrate material properties, doping ratios, and additives on the performance of the cilia arrays were investigated. Experimental results reveal that the mechanical properties of the substrate, particularly elastic modulus, are the primary factors influencing the dynamic response angle. Notably, the Ecoflex substrate demonstrated significantly superior performance compared to PDMS, and the incorporation of graphene oxide enhanced the functionality of the magnetic microcolumns. Furthermore, in fluid pumping scenarios, a dimensionless coefficient evaluation of pumping capacity was implemented, and the doping ratios of magnetic materials and additives were optimized. Through experimentally-informed analysis, this study advances the design of microfluidic systems, providing a robust foundation for applications in precision medicine and personalized treatment. References | Related Articles | Metrics

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SHURUI-S System: A Multi-Arm Single-Port Continuum Surgical Robot Configurable for Different Trocar Structures Zhu Chuanxiang, Chen Yuyang, Kuang Haomin, Zhao Jiangran, Xu Kai 2026, 31 (3):  742-758.  doi: 10.1007/s12204-026-2920-2 Abstract ( 27 )   PDF (4825KB) ( 7 )   The emergence of surgical robots has significantly enhanced the efficacy of minimally invasive surgery. Single-port robotic surgery has attracted substantial attention due to its potential to reduce trauma and improve recovery. A single-port surgical robot is typically equipped with a specific trocar, through which the endoscope and surgical instruments are deployed. The trocar design is usually fixed due to the system design. However, it is not always optimal to use one specific trocar to fit all clinical scenarios. This paper therefore, for the first time, proposes a single-port continuum surgical robot with multiple extracorporeal robotic arms, the SHURUI-S system, configurable for different trocar structures to accommodate transabdominal, transumbilical, and transcostal procedures. A unified kinematic framework is derived for the coordinated control of the multiple robotic arms when using different trocars to achieve trocar docking and pose adjustment with collision avoidance. Additionally, a kinematic model incorporating a contact-based, channel-dependent actuation compensation scheme is developed to enhance the accuracy of the continuum surgical instruments. The system overview, design optimization, robotic arm motion capability, and instrument accuracy verification experiments are elaborated. Human clinical trials have been conducted in single-port urologic, gynecologic, and general surgeries, demonstrating the effectiveness of the SHURUI-S system. References | Related Articles | Metrics

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Predicting Fracture Risk in Elderly Patients with Osteoporosis: Advances in Clinical Prediction Tools and Machine Learning Techniques Zhang Xinxin, Yang Rui, Huang Yanli, Fu Hongliang, Jin Lei, Chen Ting, Niu Pei, Cheng Yunlong, Fu Wanting, Yuan Yue, Cheng Yunzhang, Zhang Jian, Xu Ajing 2026, 31 (3):  759-770.  doi: 10.1007/s12204-026-2923-z Abstract ( 32 )   PDF (661KB) ( 10 )   Osteoporotic fractures pose a growing public health burden in aging populations with chronic comorbidities. Current fracture risk tools (e.g., FRAX and QFracture) remain limited by static assessment frameworks and inadequate racial adaptation. Artificial intelligence (AI), particularly multimodal deep learning and temporal modeling, demonstrates superior predictive accuracy in elderly patients with osteoporosis by integrating dynamic physiological, genetic, and clinical variables. This review critically evaluates the clinical applicability of existing prediction tools and synthesizes advances in AI-driven modeling, highlighting four transformative frontiers: multimodal data fusion (imaging, genomics, and real-world monitoring), temporal analysis, meta-learning for cross-population generalization, and interpretable AI for clinical transparency. References | Related Articles | Metrics

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Soft Lower-Limb Exoskeleton for Rehabilitation Training in Older Stroke Patients Bai Jiaxin, Chen Hengyue, Wang Jixian, Yang Guoyuan, Yu Wenwei, Xie Le 2026, 31 (3):  771-782.  doi: 10.1007/s12204-026-2921-1 Abstract ( 34 )   PDF (1564KB) ( 15 )   With the accelerating pace of population aging, the number of older stroke patients continues to rise. Lower-limb motor impairments severely compromise gait stability and quality of life. Conventional rigid exoskeletons are often bulky and provide insufficient comfort and compliance, making it difficult to meet the safety and lightweight requirements of older users. To address these limitations, this study establishes mechanical models of Bowden-cable friction and elasticity, and on this basis proposes a dual-loop assistance strategy that integrates admittance control with PID control to achieve compliant compensation of gait deviations at the knee and ankle joints. Wearable experiments involving two older post-stroke participants demonstrate that the proposed system significantly improves knee flexion and markedly reduces the peak inversion angle in the participant with severe foot inversion, while also decreasing joint angle fluctuations and enhancing postural stability. These findings support the feasibility and application potential of the proposed exoskeleton-assisted strategy for gait rehabilitation in older stroke populations. References | Related Articles | Metrics

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EEG Foundation Models for Brain-Computer Interfaces: Progress and Future Directions Dai Renjie, Dong Shenhua, Lyu Baoliang, Zheng Weilong 2026, 31 (3):  783-799.  doi: 10.1007/s12204-026-2922-0 Abstract ( 37 )   PDF (1278KB) ( 17 )   Electroencephalography (EEG) foundation models are increasingly used as general-purpose backbones for brain-computer interfaces (BCIs) by leveraging large-scale pretraining and task-specific adaptation. This review summarizes recent progress in EEG foundation models from three perspectives: datasets and task coverage, with emphasis on how generalization goals are operationalized by split protocols and concrete evaluation procedures; model design choices, including input construction and tokenization, masked pretraining objectives, and Transformer backbones for spatiotemporal modeling across heterogeneous channel layouts; and downstream adaptation, comparing linear probing, full fine-tuning, and parameter-efficient tuning, while clarifying the conditions under which each setting is most informative. We emphasize that reported gains are often protocol-dependent, as differences in task scope, preprocessing, training budget, and baseline selection can substantially affect comparability and the extent to which conclusions generalize. Finally, we outline future directions for EEG foundation models in BCI, focusing on standardized evaluation infrastructure, EEG-tailored modeling choices, and deployment-aware adaptation under real-world constraints. References | Related Articles | Metrics

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Global Trends in Early-Onset Ischemic Heart Disease Linked to Obesity, 1990–2021: Insights from the Global Burden of Disease Study Yang Liujia, Ji Hongwei, Zhang Jiawen, Huang Honghao, Xu Wei, Wang Donghao, Zhang Zheng, Huang Xinyue, Liu Qi, Zhong Yiming, Chen Yifan, Pu Jun 2026, 31 (3):  800-810.  doi: 10.1007/s12204-026-2942-9 Abstract ( 31 )   PDF (2200KB) ( 9 )   Obesity is a major modifiable risk factor for early-onset ischemic heart disease (IHD), yet its contribution to the global burden across regions and demographics is not fully quantified. This study assesses global trends in obesity-related early-onset IHD, focusing on young adults (aged 25–39 years). We utilized Global Burden of Disease Study 2021 data to track 30-year trends in obesity and early-onset IHD via average annual percentage changes (AAPCs) and socio-demographic index (SDI)-stratified disability-adjusted life year (DALY) counts. Pearson correlation was then applied to evaluate the associations between these variables. Further multi-factor adjustment analysis was conducted using a multivariable linear regression model on 5-year age-stratified data. A significant positive correlation was found between the AAPC in obesity age-standardized prevalence rate (ASPR) and the AAPC in early-onset IHD age-standardized mortality rate (ASMR) (ρ=0.36, P<0.001) and ASPR (ρ=0.29, P<0.001) among young males, with stronger associations than in females. The impact of obesity on early-onset IHD prevalence and mortality strengthened with age, peaking at 35–39 years old for both male and female. Even after having adjusted for dietary, environmental, and metabolic risk factors, obesity remains the top IHD risk factor in almost all subgroups. Clearly, obesity is strongly linked to higher morbidity and mortality in early-onset IHD. References | Related Articles | Metrics

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Medicine–Engineering Integration in General Practice: Current Status and Future Perspectives Xu Zhongqing 2026, 31 (3):  811-818.  doi: 10.1007/s12204-026-2935-8 Abstract ( 25 )   PDF (711KB) ( 9 )   General practice is the cornerstone of primary healthcare, but it faces increasing challenges from workforce shortages and the rising burden of chronic diseases with complex comorbidities. The integration of medicine and engineering offers new opportunities to transform service delivery and enhance the sustainability of primary care systems. This narrative review aims to synthesize recent practices and emerging research on medicine–engineering integration in general practice and proposes strategic directions for building a people-centered intelligent health ecosystem. A comprehensive search was conducted across PubMed, Web of Science, IEEE Xplore, Scopus, and the Cochrane Library using relevant keywords. Sixty-four studies were included in the final review. It was summarized key applications of engineering technologies in general practice, including disease screening, clinical decision support, chronic disease management, healthcare accessibility, and professional education. Major barriers to integration are also analyzed across data infrastructure, clinical workflows, workforce capacity, and ethical and regulatory domains. Technologies such as wearable devices, artificial intelligence, telemedicine platforms, and immersive educational tools have demonstrated substantial potential to enhance diagnostic accuracy, improve chronic disease outcomes, expand access to care, and strengthen workforce training. However, persistent challenges (including data silos, algorithmic bias, limited real-world usability, interdisciplinary talent shortages, and ethical concerns) continue to hinder large-scale implementation. Moving beyond fragmented pilot initiatives towards a coordinated, ecosystem-level approach is essential for the effective integration of intelligent health technologies into general practice. A people-centered intelligent health ecosystem, supported by interdisciplinary collaboration and robust governance, can strengthen primary healthcare delivery and contribute to the achievement of universal health coverage. References | Related Articles | Metrics