BACKGROUND: Stroke remains a leading cause of long-term disability, impairing gait, balance, and mobility, which critically reduces independence and increases fall risks. Wearable biofeedback devices have been developed and widely applied for gait rehabilitation, by providing real-time monitoring and adaptive feedback to enhance motor recovery. This systematic review and meta-analysis aimed to synthesize the existing evidence on effects of wearable real-time biofeedback gait training devices on gait parameters and functional abilities in stroke survivors, to guide future clinical practice and research exploration. METHOD: Databases of PubMed, EMBASE, MEDLINE, Web of Science, Cochrane Library, CINAHL, PsycInfo, PreQuest, and PEDro were searched up to Sep 13th, 2024. Randomized controlled trials (RCTs) investigating and comparing the effects of wearable real-time biofeedback gait training devices, with general rehabilitative gait training or other controls, in stroke survivors were included. The data including subject/participant characteristics, biofeedback device design/set-up, dosage of interventions, and outcome measures were extracted. RESULT: A total of 13 RCTs involving 304 participants were included in this systematic review, and 11 RCTs involving 272 participants were included in the meta-analysis. Seven studies measuring gait speed showed statistically significant differences that favored biofeedback gait training over the controls (SMD = 0.41, P = 0.02, n = 204). Subgroup analyses on the efficacy of pressure sensing technology with auditory feedback showed non-significant results, although the P value was close to reaching statistical significance (SMD = 0.30, P = 0.05, n = 166). The pooled data also showed that biofeedback gait training significantly further improved stroke patients’ balance and functional mobility comparing with controls, as evaluated by the Berg Balance Scale (SMD = 0.44, P = 0.03, n = 95) and Timed Up and Go Test (SMD=-0.36, P = 0.01, n = 190), respectively. The meta-analysis showed that biofeedback training was not significantly better than the control treatment in improving activities of daily living, as measured by the Modified Barthel Index (SMD = 0.21, P = 0.38, n = 74). CONCLUSIONS: This review provides moderate quality evidence that wearable real-time biofeedback gait training can improve balance and functional mobility in post-stroke individuals. While a positive overall trend was observed for gait speed, the most prevalent intervention type (pressure sensing with auditory feedback) did not yield a statistically significant effect. No significant benefit was found for activities of daily living. These findings suggest that biofeedback may serve as a useful adjunct to conventional therapy for improving specific aspects of motor function, including balance, functional mobility, and gait speed. Future research should focus on high-quality implementation trials with larger samples, “sham” conditions, and direct comparisons of feedback modalities.
In the era of Artificial Intelligence (AI), it has become critical for business entities to adopt intelligent production processes that align with green and sustainable economy practices and strategies with sustainability targets. However, G20 economies face a serious challenge in integrating digital intelligence into the manufacturing industry, hindering the prioritization of corporate sustainability responsibilities. Accordingly, this study employed factor analysis to critically examine digital intelligence levels of the manufacturing sector in G20 countries using the CRITIC-entropy combination weight model. The findings reveal significant disparities in digital intelligence manufacturing levels across the G20, with the United States of America and South Korea leading in digital intelligence applications in their manufacturing industry. Countries such as Canada and Italy exhibited slow but relatively uniform adoption of digital intelligence. These results revealed a significant gap in digital intelligence technology innovation application across G20 members, highlighting the need for business-to-business collaboration in manufacturing not just within the country, but across the members. To advance the course of digital intelligence in manufacturing, the study recommends increasing investments in digital intelligence transformation that have the potential to integrate intelligent manufacturing processes with green and sustainable development objectives. These recommendations provide actionable policy insights to foster sustainable industrial growth within the G20.
The escalating global burden of antimicrobial resistance (AMR) necessitates diagnostic strategies that can overcome the limitations of conventional culture-based methods, which often require several days to generate clinically actionable results. Such delays are associated with increased mortality, inappropriate antibiotic use, and continued transmission of resistant pathogens. In this context, microfluidic chip technology has emerged as a promising platform for rapid, miniaturized, and increasingly automated point-of-care diagnostics. Recent advances have enabled integrated lab-on-a-chip systems that combine bacterial isolation, phenotypic antimicrobial susceptibility testing, and genotypic resistance detection within closed and self-contained architectures, thereby reducing contamination risk and operator dependence. In addition, these platforms are increasingly capable of operating at single-cell resolution, allowing the detection of heteroresistance and resistant subpopulations that may be overlooked by conventional bulk assays. A major advantage of microfluidic systems is their ability to bridge phenotypic and genotypic diagnostics by enabling real-time monitoring of bacterial growth, metabolic activity, and morphological responses to antibiotics while simultaneously incorporating on-chip nucleic acid amplification for resistance gene detection. This integrated approach improves the interpretation of discrepancies between genetic determinants and functional resistance. Studies to date have demonstrated high sensitivity and specificity in complex clinical matrices, including blood, urine, and sputum, with turnaround times reduced from days to less than one hour in some applications. Furthermore, the integration of CRISPR-Cas systems, nanomaterial-enhanced biosensing, and machine learning has further improved analytical performance and data interpretation. Nevertheless, important translational challenges remain, including scalable manufacturing, regulatory standardization, and integration into routine clinical workflows. Future microfluidic platforms are expected to support multiplexed, intelligent antimicrobial susceptibility testing capable of simultaneous pathogen identification, resistance profiling, and therapeutic guidance, thereby advancing precision diagnostics for AMR management.
The health care industry is witnessing a rapid proliferation of medical devices. Health care organizations need effective tools to identify devices that best align with their needs, and ensure seamless integration into clinical processes. Existing conceptualizations of expert knowledge remain fragmented, and no comprehensive decision support systems exist to assist stakeholders in evaluating and introducing new medical devices. Ontology-based approaches offer a promising avenue to formalize such complex, multidisciplinary knowledge. This study aims to develop and validate an ontology designed as the backbone of a decision support system to facilitate the informed adoption of medical devices in health care organizations. The ontology was developed using a 5-phase methodology: (1) Elicitation of knowledge through a systematic literature review, a review of existing conceptualizations, and expert interviews; (2) Conceptualization of a preliminary conceptual map; (3) Co-design of a refined map through focus groups with experts; (4) Development of the ontology using the Protégé ontology editor; and (5) Validation of the ontology through interviews with experts. Forty experts from 13 companies across 3 European countries participated, ensuring multidisciplinary coverage. The resulting ontology provides a modular and comprehensive conceptualization of medical devices that balances granularity, conciseness, and practical relevance. It explicitly models key dimensions required for informed adoption decisions, including medical conditions addressed by the device, health services enabled, roles and activities of health care professionals, manufacturer-related information, medical device applications, and structured evidence derived from Health Technology Assessment reports. The ontology's instantiability and practical applicability were validated by populating it with data from 4 Health Technology Assessment reports and by expert assessment, confirming its ability to address stakeholders' core decision-making needs. This study presents a validated ontology to support the informed adoption of medical devices in health care organizations. It addresses a literature gap by providing a comprehensive, structured conceptualization of medical devices that meets stakeholders' key information needs. By formalizing complex expert knowledge, the ontology lays a foundation for future research and the practical development of decision support systems that enable transparent, effective, and efficient medical device adoption.
The reliance of conventional sensors on external power limits the critical need for on-site, rapid antibiotic detection in environmental and food safety. In this study, a triboelectric nanosensor was developed for efficient self-powered detection of tetracycline (TC). Zr/Ce bimetallic UiO-66-NH2 was in-situ grown on the surface of graphene oxide (GO) and uniformly embedded into electrospun polyacrylonitrile nanofibers to form the triboelectric positive layer (PGU). The synergistic effect of Ce doping and amination enabled highly selective recognition and efficient capture of TC molecules. In addition, the GO framework ensured maximal exposure of active sites and provided an efficient charge-transfer pathway for triboelectric signal generation. The PGU-based triboelectric nanosensor (PGU-TENS) exhibits stable output signals of approximately 82.3 V and 8.3 μA. Benefiting from the specific enrichment of TC molecules and the resulting regulation of the electronic structure and dielectric properties of the triboelectric layer, PGU-TENS shows a stable linear response in the concentration range of 0.01-1000 μM and achieves a detection limit as low as 2.66 nM. Excellent selectivity, rapid response, and good operational stability are also obtained. This work presents a novel paradigm for self-powered sensing of antibiotic pollutants, offering a promising route for the development of portable and intelligent sensing devices for environmental and food safety applications.
Background fluorescence-quenching lateral flow immunoassay (BF-LFIA) holds great potential for high-sensitivity detection owing to its unique “turn-on” mechanism. However, its characteristic composite signals, comprising “quenched dark zones” and “fluorescent bright zones,” exhibit high heterogeneity and dynamic evolution, severely restricting the accuracy and universality of automated quantification. To overcome this challenge, a portable analysis system was constructed and a dedicated intelligent image analysis framework is proposed. This framework integrates three core algorithm modules: (1) Synergistic Color-Space Segmentation (SCSS), which leverages the complementary advantages of HSV and CIELAB spaces to achieve robust extraction of fluorescent signal regions; (2) Adaptive Spatial Localization (ASL), which employs pixel density projection and spatial constraint mechanisms to precisely lock onto the regions of interest (ROI) for Test (T) and Control (C) lines; and (3) Adaptive Fusion Quantification (AFQ), which integrates multi-dimensional color features via a dynamic weight allocation mechanism. Validated by a Random Forest model with 5-fold cross-validation, the AFQ algorithm demonstrated robustness in the precise resolution of composite signals. Using folic acid (FA) as the analyte, the system achieved ultrasensitive quantitative detection within the 0–300 ng/mL range. The concentration-response curve, fitted by a four-parameter logistic model, exhibited excellent linearity (R2 = 0.9996). The proposed strategy effectively resolves the quantification challenge of composite signals in BF-LFIA platforms, providing a powerful tool for high-performance point-of-care testing (POCT) and offering a general methodological reference for other biomarker detections based on background fluorescence-quenching mechanisms.
Vitamin B2 (VB2) and Vitamin B6 (VB6) are essential micronutrients for maintaining normal physiological functions. Deficiency and excessive intake of vitamins can lead to significant health issues. Herein, a single-component cerium-based MOF sensor (Ce-MOF), {[Ce2(BTB)2(DMF)2]•2DMF•2.5H2O}n is synthesized via solvothermal methods. The as-prepared Ce-MOF exhibits excellent water stability and distinct ligand-centered emission, which serves as an efficient dual-target sensor, enabling highly selective and sensitive ratiometric and colorimetric detection of VB2, as well as fluorometric detection of VB6. Upon addition of VB2, Ce-MOF exhibits a new emission peak at 527 nm, accompanied by a visible fluorescence color change from blue to yellow-green. The quenching constant is 8.41 × 104 M-1, and the detection limit is as low as 0.052 μM. In the case of VB6, the emission peak gradually red-shifts to 395 nm, with a quenching constant of 2.06 × 104 M-1 and a detection limit of 0.355 μM. Moreover, a Ce-MOF-embedded portable hydrogel combined with smartphone-assisted RGB analysis is fabricated, enabling visual and quantitative detection of VB2 and achieving excellent recovery (92.4-106.6%) in vitamin tablets, banana extracts, and sugar. These results highlight the potential of Ce-MOF as an efficient and practical dual-target sensor for nutritional analysis.
The global sugar industry is facing increasing challenges due to climate variability, sustainability requirements, and the need for improved operational efficiency. These pressures are driving the search for advanced technological solutions to enhance productivity and resource management. Artificial intelligence (AI) has already demonstrated significant potential across various agricultural sectors; however, a comprehensive evaluation of AI applications across the entire sugar industry value chain from crop cultivation to industrial processing and supply chain management remains limited. This review provides a detailed assessment of the current state of AI and internet of things (IoT) implementation in the sugar beet industry. It examines key applications, including precision agriculture for sugarcane and sugar beet cultivation, intelligent monitoring systems for early disease detection, and AI-driven decision support tools for resource optimization. In addition, the study explores the role of AI in sugar manufacturing processes, where machine learning and data-driven models are used to optimize milling operations, improve product quality control, and enable predictive maintenance of industrial equipment. AI technologies are also shown to enhance supply chain efficiency through improved demand forecasting, logistics optimization, and real-time data analytics. Monitoring volatile organic compounds (VOCs) is becoming increasingly important in sugar beet and sugarcane storage. Microbial activity during storage and fermentation can release VOCs such as ethanol, which act as early indicators of crop degradation and spoilage. Detecting these gases using modern gas sensors enables continuous monitoring of storage conditions and crop health. When sensor data is integrated with AI and IoT systems, it can be analyzed in real time to identify early signs of microbial activity, improve storage management, and optimize processing decisions. Such intelligent monitoring systems have the potential to reduce losses and enhance overall efficiency in the sugar production chain.
Non-invasive monitoring of exhaled acetone-a recognized biomarker of diabetes-has offered a promising and convenient diagnostic approach for diabetes. However, conventional optical and metal oxide semiconductor sensors suffer from bulky instrumentation, high power consumption, and poor portability. Metal-organic framework (MOF)-based sensors can overcome these drawbacks but still require improvements in response time and stability. Here, we develop a gate-sensitive field-effect transistor (GS-FET) gas sensor functionalized with a sensitive MOF for ultrafast and noninvasive acetone detection. The MOF serves as a chemical-sensitive gate, modulating the polysilicon channel current, while a solvent-modification strategy promotes the density of edge-unsaturated sites with enhanced adsorption activity, as confirmed by density functional theory. Benefiting from these optimizations, the GS-FET sensor achieves a sub-500 ppb detection limit toward acetone and enables real-time breath analysis when integrated into a portable mobile-linked device. To further improve the practicality and convenience of the gas sensor, we have proposed a data analysis algorithm to predict the concentration of acetone based on the initial response of the sensors within 5 s with high data reliability. This work demonstrates a practical pathway for leveraging MOF-based architectures in ultrafast, noninvasive diabetes diagnosis and provides new insights into the development of high-performance gas sensors.
Allopolyploid species often contain specific genes dedicated to suppressing meiotic homoeologous pairing. In common wheat, TaZIP4-B2 and TaMSH7-3D fulfil this role. Nevertheless, to what extent the loss-of-function of these genes may lead to meiotic breakdown in wheat itself and hence generate karyotypic heterogeneity remains incompletely understood. Here, we show that CRISPR/Cas9-generated loss-of-function mutation of either or both TaZIP4-B2 and TaMSH7-3D leads to disrupted meiosis, triggering widespread karyotypic instability including both numerical and structural chromosomal variations (NCVs and SCVs). NCVs predominantly occurred in the D subgenome, involving preferential gains of 2A/4B/5D and losses of 6A/5B/2D, while frequencies of SCVs among subgenomes followed the order of subgenomes D > A > B, with 6A/5B/2D showing the most rearrangements. Notably, karyotypic variation in Tazip4-B2/Tamsh7-3D double mutants showed initial rapid accumulation followed by gradual stabilization across generations. Karyotypic heterogeneity caused extensive phenotypic diversity, including several key agronomic traits. Notably, Tazip4-B2/Tamsh7-3D double mutant showed more intercalary insertional translocations than the classical ph1b deletion mutant, suggesting its advantage in alien genetic introgression. Moreover, tolerance to strong salinity emerged in progenies of the mutants due to karyotypic variation. Our findings demonstrate that the loss-of-function mutation of TaZIP4-B2/TaMSH7-3D promotes rapid karyotype variability, phenotypic diversity, and environmental adaptability in wheat itself, suggesting a novel possibility for wheat improvement by karyotypic renovation.
To review the research progress on the application of digital orthopedic technology in total hip arthroplasty (THA) for developmental dysplasia of the hip (DDH), thereby providing a reference for clinical decision-making. A comprehensive literature review was conducted to summarize the effectiveness of various emerging digital orthopedic technologies in the context of THA for DDH. Digital orthopedic technologies have significantly enhanced the precision and safety of THA for DDH. Specifically, artificial intelligence-based preoperative planning systems have demonstrated superior accuracy in prosthesis size matching and positioning compared to conventional methods. Additive manufacturing technologies have provided personalized solutions for reconstructing complex bone defects in DDH. Furthermore, robot-assisted and navigation-assisted techniques have effectively improved the accuracy of prosthesis placement and lower limb length restoration in THA. However, each of these digital orthopedic technologies still possesses its own limitations. Through the integration of multiple technologies, digital orthopedics effectively addresses the challenges of precise reconstruction in THA for DDH. Future efforts should focus on further integrating diverse intelligent technologies and equipment to establish a comprehensive digital diagnosis and treatment system, aiming to achieve superior long-term effectiveness. 对数字骨科技术在发育性髋关节发育不良（developmental dysplasia of the hip，DDH）的人工全髋关节置换术（total hip arthroplasty，THA）中应用的研究进展进行综述，为临床诊疗决策提供参考。. 广泛查阅国内外相关文献，对各类新型数字骨科技术在DDH患者THA中应用的疗效进行总结归纳。. 数字骨科技术显著提升了DDH患者关节置换精准性与安全性。其中，人工智能术前规划系统在关节假体型号匹配与安放位置的准确性方面显著优于传统方法；增材制造技术为关节发育不良复杂骨缺损的修复提供了个性化解决方案；机器人及导航辅助关节置换则有效提高了假体安放与下肢长度恢复的精确性。然而，现有各技术也有其局限性。. 数字骨科技术通过多技术融合应用，有效解决了DDH患者THA术中精准重建问题，未来需进一步整合多种智能技术与装备，构建全流程数字化诊疗体系，以实现更优远期疗效。.
Due to the high virulence and lethal effects of Bacillus anthracis spores, the efficient and visualized detection for the primary spore biomarker, 2,6-pyridine dicarboxylic acid (DPA), is of particular importance. In this work, C3-symmetric tris(4-carboxyphenyl)amine (TCA), was selected to construct a water-stable hydrogen-bonded organic framework (HOF-16) with partly-bare carboxyl groups. By the coordination of Eu3+ with the O atoms on carboxyl groups, the as-obtained hybrid material Eu3+@HOF-16 shows a single blue-emissive band derived from TCA. When exposed to DPA, the red characteristic emission of Eu3+ was significantly increased based on the coordication interaction and energy transfer between DPA and Eu3+. Meanwhile, the competitive absorption between DPA and TCA resulted in the decreased luminescence of TCA. Eu3+@HOF-16 was therefore developed as a turn-on and ratiometric fluorescence probe for DPA. The results showed that Eu3+@HOF-16 has a good linear response to DPA in the concentration range of 0-90 μM in aqueous solution, with excellent water stability, high sensitivity (limit of detection = 104 nM) and good selectivity. To conclude, the proposed strategy enables efficient and reliable DPA monitoring, exhibiting remarkable performance in pathogenic agent screening while addressing critical needs in water security and human health management.
Optimising productivity of tightly coupled production lines in, for instance, the production printing or semiconductor industry is difficult due to the diversity of products resulting in different product flows, the variety of constraints, and the precise timing required to coordinate multiple tightly coupled machines. A modular setup provides flexibility and cost reduction through reuse of machinery and schedulers. However, the scheduling of a modular production line is challenging as it leads to a distributed decision process where each module has a limited view of the entire system, while local scheduling decisions have a global impact on schedule feasibility. This work proposes a distributed scheduling method for tightly coupled modular sequential production lines where products cannot overtake each other during production. We develop a multi-agent framework in which a system agent propagates timing constraints of the schedulers of different modules. The system agent aims to reach consensus about the handover times of jobs between modules to converge to a globally feasible schedule. The system agent interacts with local agents that interface with local schedulers, allowing the use of existing local schedulers without modification. We illustrate the approach on production lines with multiple re-entrant flow-shop instances with setup times and due dates, which results in a challenging scheduling problem. The performance of the proposed distributed scheduling method is assessed using a monolithic exact scheduler (implemented as a constraint program, not applicable to modular problems) as a reference. It is found to deliver good quality schedules, having only a 1.15 larger makespan on average than the hypothetical optimum provided by the exact scheduler.
Benefiting from the intrinsic luminescence of levofloxacin (LEV), a ratiometric sensing strategy based on lanthanide metal-organic frameworks (Ln-MOFs) has been developed, enabling dynamic luminescence behavior and superior ratiometric identification and differentiation performance. In this work, a 3D Eu(III)-MOF {[Eu(NBC) (HCOO)]}n (denoted as Eu-NBC, where H2NBC = 2,6-naphthalenedicarboxylic acid) was successfully synthesized as a ratiometric luminescence sensor for LEV identification and differentiation. The Eu-NBC material displays remarkable thermal stability, confirmed by in situ variable temperature PXRD and exceptional luminescence characteristics with good water/pH stability that facilitate visual, quantitative, and ratiometric LEV identification and differentiation. Owing to the intrinsic emission of LEV and competitive absorption effects, Eu-NBC exhibits dual emission bands originating from LEV and Eu3+ ions, which endows Eu-NBC with outstanding on-off ratiometric capabilities-including a high quenching constant of 5.82 × 103 M-1 (0-40 μM in aqueous solution) and 6.05 × 103 M-1 (0-28 μM in HEPES), a low detection limit of 0.2 μM and 0.165 μM, respectively, and a rapid response time within 1 min. Furthermore, a distinct colorimetric transition from red to blue under UV irradiation enables naked-eye identification and differentiation of LEV, supporting the fabrication of portable Eu-NBC hydrogels suitable for real-time monitoring. A smartphone-integrated platform utilizing RGB color analysis was also developed to facilitate on-site quantitative identification and differentiation, while a flexible Eu-NBC@PU film was engineered for practical visual trace analysis. Notably, Eu-NBC achieved excellent recovery rates of 96.2-108.3 % in tap water and 93.1-106.7 % in pasteurized milk for LEV identification, confirming its reliability in real-world applications.
The directional design of multivalent aptamers significantly impacts the development of aptamer-mediated rapid detection methods. In this study, ten kinds of dimeric aptamers were designed using different flexible linkers. The dimeric aptamer of 1AS2 with the "A" base spacer demonstrated the best fluorescence activity and binding activity for patulin when combined with the fluorescence probe of thioflavin T (ThT). A label-free fluorescence aptasensor for patulin detection was developed based on the G-quadruplex/ThT platform using the dimeric aptamer of 1AS2. This fluorescence aptasensor had a wide detection range of 0.5-200.0 ng/mL for patulin within the operation time of 10 min and showed negligible cross-reactions with common mycotoxins. The assay exhibited good recoveries of 85.3 % ∼ 99.3 % in juice samples by the aptasensor, and the results were validated by HPLC analysis with good correlations. This study brings a new strategy for generating multivalent aptamers and label-free aptasensors for the accurate detection of food contaminants.
Simultaneous multi-component analysis of trace-level weakly ionized analytes in complex matrices remains a critical challenge for capillary electrophoresis (CE). To address this, this study developed a novel green analytical strategy by integrating cyclodextrin vortex-assisted dispersive solid-phase extraction (CD-VA-DSPE) with CD-assisted sweeping-field amplified sample stacking (CD-sweeping-FASS) in micellar electrokinetic chromatography (MEKC). The core innovation lies in the dual role of hydroxypropyl-β-cyclodextrin (HP-β-CD) as both an extraction solvent and a pseudostationary phase, enabling a seamless integration of sample preparation and online enrichment. Using the traditional Chinese medicine "Wenyangxiaozheng formula" as a model, the optimized method achieved baseline separation of four target components (lobetyolin, echinacoside, acteoside, calycosin), marking the first demonstration of CD-sweeping-FASS in MEKC for phytochemical analysis. Method validation demonstrated excellent linearity (r ≥ 0.9992) over a concentration range of 1-100 µg mL⁻¹, with limits of detection (LODs) of 0.02-0.05 µg mL⁻¹. The precision was satisfactory (intra- and inter-day RSDs < 2.98 %). Remarkably, compared with conventional CE injection, the method achieved sensitivity enhancements of 106- to 208-fold. With the successful application in quantitative analysis of target components (0.46-9.93 µg mg⁻¹) in both herbal formulations and spiked rat serum, the proposed approach establishes itself as a high-throughput and sustainable solution for the simultaneous determination of trace weakly ionized compounds in complex matrices.
PURPOSE: The objective of this study was to evaluate the predictive value of the peritumoral artery-to-renal aorta flow ratio, derived from digital subtraction angiography, for identifying clear cell renal cell carcinoma at high risk of recurrence after nephrectomy. METHODS: In this prospective study, patients undergoing radical or partial nephrectomy for clear cell renal cell carcinoma between May 2025 and December 2025 were enrolled. Three experienced radiologists calculated the arterial flow ratio from preoperative DSA images using a novel, dedicated software solution. We employed univariate and multivariate logistic regression to determine the association between this hemodynamic parameter and postoperative pathological classification of high-risk clear cell renal cell carcinoma. RESULTS: Among the 100 included patients, a flow ratio cutoff of 0.039 identified 34 patients (34%) with a high ratio. A high flow ratio of peritumoral artery/renal aorta was strongly associated with a pathological diagnosis of high-risk clear cell renal cell carcinoma (66.67% vs. 25.32%, P < 0.001). High-risk tumors exhibiting a high flow ratio were also significantly more likely to present at an advanced pathological T stage (≥ T2) (47.37% vs. 5.26%, P < 0.001). Crucially, a high flow ratio was confirmed as an independent predictor of high-risk clear cell renal cell carcinoma on multivariate analysis (Odds Ratio: 9.809; 95% Confidence Interval: 1.928–49.895; P = 0.006). CONCLUSION: The preoperative flow ratio of peritumoral artery/renal aorta, measured via DSA, is a significant and independent imaging biomarker associated with high-risk clear cell renal cell carcinoma. This novel parameter may aid in preoperative risk stratification.
Avian influenza virus (AIV) poses a global biosecurity threat, highlighting the need for rapid, accurate, and adaptable diagnostic tools to address its genetic diversity and complex transmission environments. Here, we report the development of FIGHTER, a novel ultrafast sample-to-answer microfluidic platform for the highly sensitive on-site detection of H9 and N2 genes to diagnose the H9N2 AIV subtype. The portable FIGHTER platform integrates an all-in-one plastic-aluminum membrane hybrid microfluidic chip with a dual-channel real-time PCR instrument, enabling on-site nucleic acid extraction in 5 min and reverse transcription-quantitative polymerase chain reaction with ultrafast thermal cycling in 18 min. In addition, the limit of detection of the platform is 1 copy/μL for both H9 and N2. The microfluidic chip with preloaded reagents supports simplified manual operation, and the battery-powered instrument features a user-friendly interface, allowing nonspecialists to operate it in resource-limited settings. Moreover, the validation with 60 field clinical samples showed a clinical sensitivity of 97.92% for H9 and 100% for N2, with 100% specificity for both H9 and N2. Hence, FIGHTER offers a simple, affordable, and rapid method for pathogen detection, particularly in complex environments where the conventional laboratory infrastructure is inaccessible.
Two-dimensional conductive MOFs (2Dc-MOFs), with their large specific surface area and pore size, demonstrate great potential as chemiresistive gas sensors for the portable detection of exhaled biomarkers, which is crucial for the early diagnosis and management of respiratory diseases. However, their clinical application remains limited by insufficient sensitivity, poor reversibility, and inadequate environmental/mechanical stability. Here, we report a high-performance sensor based on templated two-dimensional conductive MOF (T-2Dc-MOF) aerogels for the detection of fractional exhaled nitric oxide (FeNO). The sensor was fabricated by in situ conversion of three-dimensional insulating MOF templates into 2Dc-MOFs, followed by integration with carboxylated carbon nanotubes (C-CNTs) to construct heterostructures that regulate hole density in the sensing system, and subsequent embedding of the composite into a polymer aerogel. This design achieves an ultralow detection limit (3.0 ppb), rapid response/recovery (4 s/9 s, over three times faster than current sensors), and outstanding durability-retaining 83.4% of its performance after 500 compression cycles at 70% relative humidity, compared with only 3.2% in the control group. The developed portable FeNO monitor enables real-time tracking of patients' FeNO levels. By combining heterostructure engineering with sensor model construction, this study advances intelligent healthcare and home-based respiratory management systems.
To evaluate the clinical and radiographic results of a novel partial unicondylar arthroplasty (PUCA) using a three-dimensional-printed (3DP) porous tantalum prosthesis for treating focal osteochondral defects (FOCD) of the femoral condyle, in comparison with unicompartmental knee arthroplasty (UKA). This exploratory-retrospective matched-cohort consecutively enrolled study involved 17 patients: 8 in Group A (PUCA with 3DP porous tantalum prosthesis) from a larger trial and 9 in Group B (UKA), matched by age, gender, and BMI. Participants, aged 18-60, had femoral condylar FOCD with complete clinical and imaging data; exclusions included knee instability and incomplete data. Follow-ups were at 6 weeks, 3, 6, 12 months, and annually. The primary outcome was the Hospital for Special Surgery (HSS) knee score, with secondary outcomes including visual analogue scale (VAS), time to full-weight-bearing walking (FWBK), knee injury and osteoarthritis outcome score (KOOS), Lysholm scores and range of motion (ROM). Prosthesis stability and Kellgren-Lawrence (KL) grading were assessed via radiograph, and postoperative complications were compared. Statistical analyses included the Mann-Whitney U test, independent-samples t test, and Fisher's Exact test. All patients averaged 49.6 years old at surgery with a mean follow-up of 49.6 months. No demographic or complication differences were found between groups, and no revisions were needed. Preoperative scores were similar (P > 0.05). Postoperatively, Group A demonstrated significantly greater improvements in KL grades (1.5 ± 0.5 vs. 2.5 ± 0.5, P = 0.006), VAS (1.3 ± 0.5 vs. 2.5 ± 0.5, P = 0.002), HSS (92.3 ± 1.8 vs. 87.4 ± 1.6, P = 0.000), KOOS (90.9 ± 1.6 vs. 88.3 ± 1.9, P = 0.009), Lysholm (91.4 ± 2.4 vs. 88.5 ± 1.9, P = 0.019), and ROM (133.1° ± 6.5° vs. 115.6° ± 4.0°, P = 0.000), except for FWBK (4.9 ± 0.8 vs. 5.3 ± 0.5 weeks, P = 0.189). However, only the difference in ROM met the minimum clinically important difference. All postoperative scores, except for ROM and KL, showed statistically significant improvement compared with preoperative values in both groups radiographs at final follow-up showed stable prostheses in both groups with no signs of loosening. The statistical power for postoperative HSS was 1.0 (G*Power, effect size = 2.89). This initial study is the first to apply personalized PUCA with 3DP porous tantalum prostheses for FOCD, demonstrating promising early outcomes compared with UKA, such as delayed progression of osteoarthritis, effective pain relief, and improved knee function and quality of life. PUCA notably preserves more native tissue and adapts to individual defects, making it clinically feasible by offering a potentially better option for future FOCD management.