Search Results (1,817)

Lumbar spine disorders often require surgical intervention using medical implants to stabilize or replace damaged structures. As the prevalence of these surgeries increases due to an aging population, rigorous preclinical evaluation is critical. This narrative review aims to summarize current testing methods, identify gaps in clinical translatability, and explore the role of emerging computational technologies. Mechanical testing protocols established by the American Society for Testing and Materials (ASTM) and the International Organization for Standardization (ISO) provide essential standardized data on structural integrity but fail to replicate the complex biological interactions of the human spine. Similarly, animal models offer insights into biological responses like osseointegration but are limited by quadrupedal biomechanics and anatomical differences. Recent advancements in Artificial Intelligence (AI) and Finite Element Analysis (FEA) enable rapid, patient-specific modeling and high-throughput screening, significantly reducing the time and cost of physical testing. Future innovations include 3D-printed personalized implants, bio-responsive materials, and genetically modified animal models to bridge existing translatability gaps. In conclusion, improving the clinical success of lumbar spine implants requires an integrated framework that combines mechanical, biological, and computational approaches. This interdisciplinary collaboration is vital for developing safer and more effective treatments for patients. Full article

Airport stand allocation research has traditionally focused on optimizing assignments within fixed infrastructure configurations, while strategic decisions regarding stand category composition remain underexplored. This study investigates how different proportional distributions of stand categories affect system-level performance under high traffic demand at international airports. A discrete-event simulation model implemented in MATLAB evaluates fifteen infrastructure configurations with varying distributions of small, medium, and large stands, classified according to the ICAO Annex 14. The model employed a first-come–first-served allocation logic to isolate infrastructure-driven effects from algorithmic decision-making. System throughput was measured through acceptance and rejection rates, disaggregated by aircraft stand category. Acceptance rates ranged from 33% to 92% across tested configurations, demonstrating pronounced sensitivity to stand composition. Balanced configurations consistently outperformed asymmetric alternatives. Insufficient stand availability in any single category led to concentrated rejection patterns and non-linear performance degradation; excess capacity in unconstrained categories could not compensate for shortfalls in constrained ones. Proportionality across stand categories is identified as a critical determinant of infrastructure robustness. The proposed simulation framework provides a computationally efficient tool for early-stage (pre-operational planning phase) infrastructure screening, supporting informed strategic capacity decisions prior to detailed operational optimization. Full article

As a key node in the global supply chain, the resilience of ports is crucial for coping with multiple risks such as increasingly frequent climate change, operational accidents, and geopolitics, and ensuring the smooth flow of trade and sustainable development. This paper takes Ningbo-Zhoushan Port, which leads the world in throughput, as the research object, aiming to construct a comprehensive port resilience assessment model. Through the system dynamics method, the smart port system is deconstructed into three interrelated subsystems: meteorology, production, and economic-politics, and a simulation model including a causal relationship diagram and a system flow diagram is established accordingly. The model is verified to be effective and robust through historical data testing and sensitivity analysis. By setting different scenarios, this paper quantitatively analyzes the impact of single and compound risk shocks such as extreme weather, production accidents, and tariff policies on port throughput, and classifies port resilience into three levels: strong, medium, and weak. The research results show that Ningbo-Zhoushan Port shows strong resilience to the above-mentioned single risks. Even when the risk parameters are increased by 100%, the change rate of port throughput is less than the historical average annual change rate by 5.06%. However, in the extreme scenario of multiple risk couplings, the decline in port throughput is more significant, highlighting the importance of coping with compound risks. Further strategy simulation reveals that accelerating the economic development of the hinterland, increasing investment in port infrastructure, increasing the frequency of equipment maintenance, expanding the proportion of high-quality employees, and strengthening public facility management for accurate risk prediction are all effective ways to enhance port resilience. This research provides a scientific decision-making support tool for port managers, and the proposed resilience enhancement strategies have important theoretical and practical significance for ensuring the long-term stable operation of ports and the sustainable development of the regional economy. Full article

Background/Objectives: Epirubicin, Olaparib, and Ribociclib are widely used anticancer agents whose serum concentrations exhibit significant inter-individual variability, supporting the need for reliable and robust analytical methods suitable for pharmacokinetic evaluation and therapeutic exposure assessment. Variations in metabolism, drug–drug interactions, organ function, and treatment regimens may substantially influence systemic exposure, highlighting the importance of accurate quantification in clinical practice. This study describes the development and validation of a solid-phase extraction–liquid chromatography–mass spectrometry (SPE–LC–MS) method for the simultaneous quantification of these drugs in human serum. Methods: Sample preparation was performed using Oasis PRiME HLB^® cartridges to ensure efficient clean-up, optimal recovery, and reduced matrix effects. Chromatographic separation was achieved using gradient elution with 0.1% formic acid and acetonitrile on a reversed-phase column, followed by single-quadrupole mass spectrometric (QDa) detection in the selected ion recording mode. The total run time was 13 min, enabling high-throughput analysis. Results: The method demonstrated good linearity (r > 0.997) over the tested concentration ranges, along with adequate selectivity, precision, accuracy, recovery, and stability, fulfilling the ICH M10 guideline validation criteria. No significant carry-over or interference from endogenous compounds was observed. Conclusions: Application to patient samples confirmed reliable performance in real clinical matrices and consistent quantification across different concentration levels. The proposed approach provides a potentially more accessible alternative in laboratories already equipped with LC-MS systems compared to LC-MS/MS platforms and can be applied in pharmacokinetic studies, representing a proof-of-concept for exposure assessment in oncology. Full article

The human microbiome influences health and disease through diverse biochemical and functional outputs (e.g., enzymes, structural proteins, metabolites, and other cellular components) that affect nearly every aspect of human physiology. Metatranscriptomics (MT), an unbiased RNA sequencing approach, is a high-throughput and high-content method that quantifies both gut microbial taxonomy and active biochemical functions. Because microbial community composition and gene expression are dynamic, understanding temporal variation in the gut metatranscriptome across multiple time scales is essential. Here, we report the temporal dynamics of gut microbiome species and functions using a large cohort (n = 6157) with a clinically validated stool MT test. We quantified microbiome stability from hours to years and assessed taxonomic and functional resilience to major luminal perturbations, such as colonoscopy bowel preparation. Longitudinal analyses of samples collected within the same day, and across days, weeks, months, and years, revealed consistently high stability in both composition and gene expression within a single day and, importantly, across an approximate six-month period. Among individuals reporting stable diets and no antibiotic exposure, taxonomic and functional profiles remained stable for up to three years. Following colonoscopy preparation, our preliminary study of the microbiome demonstrated strong resilience, returning to its pre-procedure state within one week. Overall, these findings demonstrate that the gut microbiome is generally stable over a six-month time frame, with longer-term changes occurring gradually. These findings support the robustness of stool-based MT profiling for species-level and pathway-resolved functional analysis in longitudinal research and health applications. Full article

Background/Objectives: The increasing volume of laboratory testing and the tightening of quality standards have rendered automation tasks in medical laboratories highly relevant. Conventional total laboratory automation (TLA) systems demonstrate high throughput; however, their economic and organizational efficiency is often constrained by their complex integration and substantial implementation costs. In this context, collaborative robots (cobots) are attracting increasing attention due to their ability to perform pre-analytical and logistical tasks in close association with laboratory personnel. The objective of the present study was the systematic integration of commercially available cobots into the pre-analytical workflow of a large centralized laboratory. Methods: The implemented system incorporated a set of specialized modules, including decapping, barcode orientation and verification, analyzer loading, aliquoting, and specimen sorting, with bidirectional integration into the Laboratory Information System (LIS). The architectural design, control algorithms, and primary effects on labor input and operational turnaround time were evaluated. Results: The results demonstrated that the implementation of cobots into laboratory processes led to an 87% reduction in labor input, a 3.4% improvement in liquid aliquoting accuracy, and an overall improvement in nominal throughput, while requiring minimal personnel training. However, human operators performed the aliquoting procedure significantly faster than cobots, with an average speed advantage of 42.5%. Conclusions: The use of collaborative robotic systems in centralized medical laboratories appears promising due to their operational efficiency and flexibility compared to conventional automation platforms and manual workflows. The effect of the use of cobots on the quality/accuracy of the tests needs to be evaluated, and perhaps a larger study of multiple laboratories needs to be conducted to confirm the results are generalizable. Full article

Circulating tumor cells (CTCs) are valuable liquid-biopsy biomarkers, yet their extreme rarity makes high-purity, high-throughput enrichment challenging. In spiral inertial microfluidics, high cell loading induces long-range hydrodynamic interactions that broaden the focused blood-cell stream; consequently, a subpopulation completes the ~0.5 and ~1.0 Dean-cycle migrations with a phase delay, compressing the CTC–blood cell gap and degrading purity. Here we propose a Dean-cycle phase-regulated double-spiral design informed by this phenomenon. This design aims to mitigate the stream-broadening effect by boosting the Dean number during the first half-cycle to promote synchronized blood-cell migration and shifting the CTC equilibrium position near one full cycle to further widen the CTC–blood cell separation. We implement this strategy in a second-generation double-spiral microfluidic chip (SDMC) and scale it to a four-channel parallel array (ASDMC). Under optimized conditions, ASDMC processes diluted whole blood (hematocrit = 4%) without the need for red blood cell (RBC) lysis or antibody labeling, achieving a sample throughput of 1200 μL·min⁻¹. Specifically, it exhibits a mean recovery rate of 98.8% across three spiked tumor cell lines (MCF-7, PC-9, and Mahlavu) and a mean white blood cell (WBC) depletion efficiency of 93.3%. In a pilot clinical testing of 20 patients (NSCLC and HCC), enriched fractions enabled immunofluorescence identification of CK⁺CD45⁻DAPI⁺ CTCs, with an exploratory trend of increasing CTC counts with advanced disease stage (4–34 cells·mL⁻¹). These results describe a scalable, label-free platform, and the observed purification performance aligns with our proposed mechanism: Dean-cycle phase regulation to mitigate blood-cell migration lag. Our findings support further technical validation and clinical assessment in larger cohorts. Full article

This paper presents the design and experimental validation of an innovative robotic test stand for measuring camshaft cam geometry, intended to support preventive quality control in high-volume production. The proposed solution integrates a collaborative robot with a dedicated measurement setup to enable repeatable positioning of the inspected camshaft and automated acquisition of geometric features critical for functional performance. A complete measurement methodology was developed, including the measurement sequence, data acquisition procedure, and processing of the recorded signals to determine key cam geometry parameters. To verify the reliability of the proposed approach, measurement results obtained using the robotic stand were compared with reference data acquired using conventional metrology tools and standard inspection procedures. Experimental studies confirmed that the developed stand provides repeatable measurement results, enabling the stable identification of the examined geometric features across repeated trials. Moreover, a high level of agreement was observed between the measurement data obtained using the proposed method and the reference measurements, demonstrating the suitability of the cobot-based test stand for preventive quality control applications in industrial environments. The concept presented offers a scalable and flexible alternative to manual inspection and dedicated special-purpose gauges, with potential benefits in terms of inspection throughput and standardization of quality control workflows. The novelty of the approach lies in the indirect ultrasonic measurement model combined with a quadrant-based sensor orientation strategy and repeatable 90° camshaft indexing, enabling full-profile acquisition within the robot workspace. Full article

Spiral inertial microfluidic devices provide a simple, high-throughput approach for size-based particle separation; however, translating PDMS-optimized designs into monolithic, fully enclosed 3D-printed channels is often limited by printability and post-print channel clearing. In our previous PDMS study, a $400\times 120\mu \mathrm{m}$ spiral achieved high separation performance after computational optimization and experimental validation. To translate this high-performing PDMS concept into a faster and more cost-effective manufacturing approach, the same separation principle is transferred to a fully 3D-printed, closed-channel spiral device, and the geometry is re-optimized around manufacturability constraints. Printing trials showed that enclosed channels at $400\times 120\mu \mathrm{m}$ and $600\times 180\mu \mathrm{m}$ could not be cleared reliably due to trapped resin and frequent blockage, most often near the inner-outlet region. In contrast, $800\times 240\mu \mathrm{m}$ and $1200\times 360\mu \mathrm{m}$ channels were printed and flushed successfully, and $800\times 240\mu \mathrm{m}$ was selected as the smallest reproducibly functional cross-section. Particle-tracking simulations were then used to re-optimize spiral development length, showing that a 4-turn device provides limited collection for $12\mu \mathrm{m}$ targets (10%), intermediate lengths (5–7 turns) improve collection to 50%, and an 8-turn spiral achieves complete large-particle collection (100%) across tested target sizes (12–$24\mu \mathrm{m}$) while reducing small-particle crossover. Experimental validation of the 8-turn $800\times 240\mu \mathrm{m}$ device at $Q=6\mathrm{m}\mathrm{L} {\min }^{-1}$ using fluorescent polystyrene particles ($18\mu \mathrm{m}$ target; $6\mu \mathrm{m}$ background) yielded an average collection efficiency of 84% and an inner-outlet purity of 92%. Overall, these results demonstrate that spiral inertial separation can be retained in a monolithic 3D-printed format when the design is re-optimized around the smallest reliably clearable enclosed cross-section and sufficient spiral development length. Full article

Precision greenhouse agriculture enhances plant health and crop yields by continuously monitoring key plant parameters. Stem diameter is such a parameter and is monitored to support decisions on plant care. However, traditional contact-based methods induce thigmomorphogenic effects that impact plant growth. Here, we introduce the Optical Caliper (OC), a novel contactless device for precise, non-invasive stem diameter measurement. The OC operates by projecting a collimated light beam to cast a shadow of the stem onto a high-resolution image sensor. The shadow size is a measure for the stem diameter. Controlled laboratory tests show the OC offers an accuracy comparable to that of a Digital Caliper (DC). Field trials on irregular tomato and cucumber stems demonstrate a repeatability of 0.1–0.2 mm. The OC’s non-invasive design and high repeatability exceed the performance of a DC, making it particularly suited for accurately monitoring soft, variable plant structures. Bringing the advantage of avoiding thigmomophogenic effects and thus optimizing crop yield, the OC is a promising tool for high-throughput plant phenotyping and precision agriculture applications. Full article

The proliferation of Internet of Things (IoT) devices has intensified the need for efficient real-time anomaly and intrusion detection, making the selection of an appropriate Complex Event Processing (CEP) engine a critical architectural decision for security-aware data pipelines. Python-based CEP frameworks offer compelling advantages through the seamless integration with data science and machine learning ecosystems; however, rigorous comparative evaluations of such frameworks under realistic IoT security workloads remain absent from the literature. This study presents the first systematic comparative evaluation of Faust and Streamz—two Python-native CEP engines representing fundamentally different architectural philosophies—specifically in the context of IoT network intrusion detection. Faust was selected for its actor-based stateful processing model with native Kafka integration and distributed table support, while Streamz was selected for its reactive, lightweight pipeline design targeting high-throughput stateless processing, making them representative of the two dominant paradigms in Python stream processing. Although both engines target different application niches, their performance characteristics under realistic CEP workloads have never been rigorously compared, leaving practitioners without empirical guidance. The primary evaluation employs an IoT network intrusion dataset comprising 583,485 events from 83 heterogeneous devices. To assess whether the observed performance characteristics are specific to this single dataset or generalize across different workload profiles, a secondary IoT-adjacent benchmark is included: the PaySim financial transaction dataset (6.4 million records), selected because its event schema, fraud-pattern temporal structure, and volume differ substantially from the intrusion dataset, providing a stress test for cross-workload robustness rather than a claim of domain equivalence. We acknowledge the reviewer’s valid point that a second IoT-specific intrusion dataset (such as TON_IoT or Bot-IoT) would constitute a more directly comparable validation; this is identified as a priority for future work. The load levels used in scalability experiments (up to 5000 events per second) intentionally exceed the dataset’s natural rate to stress-test each engine’s architectural ceiling and identify saturation thresholds relevant to large-scale or multi-sensor IoT deployments. We conducted controlled experiments with comprehensive statistical analysis. Our results demonstrate that Streamz achieves superior throughput at 4450 events per second with 89% efficiency and minimal resource consumption (40 MB memory, 12 ms median latency), while Faust provides robust intrusion pattern detection with 93–98% accuracy and stable, predictable resource utilization (1.4% CPU standard deviation). A multi-framework comparison including Apache Kafka Streams and offline scikit-learn baselines confirms that Faust achieves detection quality competitive with JVM-based alternatives (Faust: 96.2%; Kafka Streams: 96.8%; absolute difference of 0.6 percentage points, not statistically significant at $p=0.318$) while retaining the Python ecosystem advantages. Statistical analysis confirms significant performance differences across all metrics ($p<0.001$, Cohen’s $d>0.8$). Critical scalability thresholds are identified: Streamz maintains efficiency above 95% up to 3500 events per second, while Faust degrades beyond 2500 events per second. These findings provide IoT security engineers and system architects with actionable, empirically grounded guidance for CEP engine selection, establish reproducible benchmarking methodology applicable to future Python-based stream processing evaluations, and advance theoretical understanding of the accuracy–throughput trade-off in stateful versus stateless Python CEP architectures. Full article

Based on the follow-up of patients who recovered from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, several reports of people who re-tested positive have been described. This may result from viral reactivation, true reinfection, superinfection, or an initial infection by more than one virus (multiple infection). These scenarios can only be correctly distinguished through viral quasispecies analysis. Herein, 26 cancer patients under extended follow-up for SARS-CoV-2 infection were submitted to multiple longitudinal analyses through nucleic acid isolation, PCR amplification and high-throughput sequencing. SARS-CoV-2 classification and the definition of cases as persistent or repeated infections were based on phylogenetic reconstruction. Supported by their viral complete genomes and intrahost quasispecies over time, the different scenarios were identified. Nine confirmed and 12 plausible persistence cases were identified. Virus evolution dynamics in the intrahost population from patients with persistent infection was shown for the first time. Regarding reinfection, three confirmed and two plausible cases were identified, including one case of multiple infection. Altogether, this is the first study that analyzes the plethora of SARS-CoV-2 within-host minor variants and describes reinfections, multiple infections and viral evolution across time in cancer patients, contributing to the understanding of SARS-CoV-2’s within-host population dynamics in the natural history of COVID-19. Full article

Lead-free double perovskites possess the capabilities of wide bandgap control, excellent photoelectric performance, and environmental friendliness. They are an ideal alternative system for addressing the heavy metal toxicity of lead-based perovskites and promoting their large-scale application. Precise control of their bandgap is key to the green transformation of optoelectronic devices. Bandgap, as a key parameter determining the photoelectric properties of materials, has limitations in traditional experimental determination and DFT calculation methods, such as being time consuming, labour intensive, costly, and difficult to achieve high-throughput screening. Deep learning provides an efficient solution to this problem, but current research has issues such as a single-model architecture and poor interpretability, which cannot effectively support bandgap regulation. This study utilised 2367 valid datasets of lead-free double perovskites sourced from the Materials Project database and relevant literature. Following preprocessing steps, including MinMaxScaler normalisation and Pearson correlation coefficient screening, the dataset was divided into a ratio of 7:1:2. The bandgap prediction capabilities of four models—MLP, deep ensemble learning, PINN, and Transformer—were systematically compared, with feature importance analysed using the SHAP method. The results show that the MLP model performs the best in medium-scale, structured feature prediction. The R² value of the test set is 0.9311, while the MAE, MSE, and RMSE are 0.1915 eV, 0.0975 eV², and 0.3122 eV, respectively. A total of 98% of the test samples have a prediction error of ≤0.4 eV, highlighting the stability of low bandgap systems. The Transformer is more suitable for large-scale, sequential feature prediction, while the MLP has limited generalisation ability for medium-to-high bandgap systems containing elements such as Si and Mg. The SHAP analysis revealed that the five electronic structure descriptors, such as B_HOMO+ and A_LUMO+, are the key influencing factors of the bandgap. The research results are helpful for the high-precision prediction and mechanism explanation of the bandgap of lead-free double perovskites, providing theoretical support for rational material design, performance optimisation, and bandgap-oriented regulation. They also point out the direction for subsequent model improvement. Full article

Rheological data of wood plastic composites (WPCs) are not readily present in many of the common scientific databases. For this reason, designing the processing of WPCs, e.g., extrusion, is difficult or even impossible, and it is often necessary to conduct research on your own to obtain the proper data. In the first part of the paper, studies of WPCs’ rheology have been performed in laboratory and production conditions. Tests in laboratory conditions have been conducted based on High-Pressure Capillary Rheometry (HPCR), using the Melt Flow Index (MFI). Tests in production conditions (on-line) have been performed by measuring the extrusion die pressure and extrusion throughput. The MFI’s viscosity and on-line viscosity results have been assessed against those of HPCR. In the second part of the paper, the viscosity data and models have been used for extrusion process simulations. Experimental studies of the process have been performed, and the experimental results have been used for evaluating the models applied. It was found that the two-point MFI method of determining viscosity and the on-line tests may be a reasonable alternative in the absence of HPCR data. The MFI method using the power-law model is fast and easy to apply and allows for analytical solutions to many processing problems. A significant advantage of on-line tests is that they are performed under real flow conditions of the tested material rather than laboratory conditions that do not take into account the material processing history. Full article

Background/Objectives: Ejiao, a macromolecular protein complex derived from donkey hide, is a traditional Chinese medicine with clinically demonstrated efficacy against ulcerative colitis (UC). Due to its large molecular size and poor absorbability, its therapeutic effects are presumed to depend on gut microbiota. We hypothesized that specific gut bacteria capable of degrading Ejiao might also mediate its biological functions. Methods: To test this hypothesis, a systematic investigation was conducted by integrating culturomics, proteomics, metabolomics, 16S rRNA gene amplicon high-throughput sequencing, and animal disease models. Results: A total of 134 human gut bacterial strains capable of utilizing Ejiao as a nutrient source were isolated. Among them, Ligilactobacillus salivarius D3-8 exhibited the strongest growth in Ejiao-based medium. Genomic analysis identified 63 protease/peptidase genes, and peptidomic profiling confirmed its degradation activity, which released 50 novel peptides. Notably, administration of L. salivarius D3-8 alone significantly alleviated dextran sodium sulfate (DSS)-induced colitis, concurrently increasing the abundance of beneficial bacterium Dubosiella newyorkensis and elevating the anti-inflammatory metabolite indole-3-carbinol via upregulated tryptophan metabolism. Conclusions: Our findings identify L. salivarius D3-8 as both a dedicated Ejiao-degrader and a protective probiotic against colitis. This work provides direct evidence that gut bacteria can utilize Ejiao and proposes a potential novel mechanistic framework in which the biological effects of Ejiao may be mediated through its interaction with specific, functionally potent degraders such as L. salivarius D3-8. Full article