Quantification Of Parameters Research Articles

Quantitative modeling of lenticulostriate arteries on 7-T TOF-MRA for cerebral small vessel disease.

We developed a framework for segmenting and modeling lenticulostriate arteries (LSAs) on 7-T time-of-flight magnetic resonance angiography and tested its performance on cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) patients and controls. We prospectively included 29 CADASIL patients and 21 controls. The framework includes a small-patch convolutional neural network (SP-CNN) for fine segmentation, a random forest for modeling LSAs, and a screening model for removing wrong branches. The segmentation performance of our SP-CNN was compared to competitive networks. External validation with different resolution was performed on ten patients with aneurysms. Dice similarity coefficient (DSC) and Hausdorff distance (HD) between each network and manual segmentation were calculated. The modeling results of the centerlines, diameters, and lengths of LSAs were compared against manual labeling by four neurologists. The SP-CNN achieved higher DSC (92.741 ± 2.789, mean ± standard deviation) and lower HD (0.610 ± 0.141 mm) in the segmentation of LSAs. It also outperformed competitive networks in the external validation (DSC 82.6 ± 5.5, HD 0.829 ± 0.143 mm). The framework versus manual difference was lower than the manual inter-observer difference for the vessel length of primary branches (median -0.040 mm, interquartile range -0.209 to 0.059 mm) and secondary branches (0.202 mm, 0.016-0.537 mm), as well as for the offset of centerlines of primary branches (0.071 mm, 0.065-0.078 mm) and secondary branches (0.072, 0.064-0.080 mm), with p < 0.001 for all comparisons. Our framework for LSAs modeling/quantification demonstrated high reliability and accuracy when compared to manual labeling. NCT05902039 ( https://clinicaltrials.gov/study/NCT05902039?cond=NCT05902039 ). The proposed automatic segmentation and modeling framework offers precise quantification of the morphological parameters of lenticulostriate arteries. This innovative technology streamlines diagnosis and research of cerebral small vessel disease, eliminating the burden of manual labeling, facilitating cohort studies and clinical diagnosis. The morphology of LSAs is important in the diagnosis of CSVD but difficult to quantify. The proposed algorithm achieved the performance equivalent to manual labeling by neurologists. Our method can provide standardized quantitative results, reducing radiologists' workload in cohort studies.

Optimizing SureQuant for Targeted Peptide Quantification: a Technical Comparison with PRM and SWATH-MS Methods.

Bacterial infections are a major threat to human health worldwide. A better understanding of the properties and physiology of bacterial pathogens in human tissues is required to develop urgently needed novel control strategies. Mass spectrometry-based proteomics could yield such data, but identifying and quantifying scarce bacterial proteins against a preponderance of human proteins is challenging. Here, we explored the recently introduced SureQuant method for highly sensitive targeted mass spectrometry. Using a major human pathogen, the Gram-positive bacteria Staphylococcus aureus, as an example, we evaluated several parameters, including the number of targets and intensity thresholds, for optimal qualitative and quantitative protein analysis. By comparison, we found that SureQuant achieved the same quantitative performance as standard parallel reaction monitoring while allowing accurate and precise quantification of up to 400 targets. SureQuant also surpassed the sensitivity and quantification capabilities of global data-independent acquisition methods. Finally, to facilitate method development, we provide optimized MS parameters for the sensitive quantification of different peptide panel sizes. This study provides a foundation for the broader application of SureQuant in the analysis of clinical specimens containing trace amounts of bacterial proteins as well as other studies requiring ultrasensitive detection of low-abundant proteins.

Review of contemporary fluorescence correlation spectroscopy method in diverse solution studies.

Fluorescence correlation spectroscopy (FCS) is a well-known and established non-invasive method for quantification of physical parameters that preside over molecular mechanisms and dynamics. It combines maximum sensitivity and statistical confidence for the analysis of speed, size, and number of fluorescent molecules and interactions with surrounding molecules by time-averaging fluctuation analysis in a well-defined volume element. The narrow compass of this study is to acquaint the basic principle of diffusion and the FCS method in general regarding variable magnitudes and standardization adjustment. In this review, we give a theoretical introduction, examples of experimental applications, and utensils in solution systems with future perspectives.

Label-free long-term measurements of adipocyte differentiation from patient-driven fibroblasts and quantitative analyses of in situ lipid droplet generation

The visualization and tracking of adipocytes and their lipid droplets (LDs) during differentiation are pivotal in developmental biology and regenerative medicine studies. Traditional staining or labeling methods, however, pose significant challenges due to their labor-intensive sample preparation, potential disruption of intrinsic cellular physiology, and limited observation timeframe. This study introduces a novel method for long-term visualization and quantification of biophysical parameters of LDs in unlabeled adipocytes, utilizing the refractive index (RI) distributions of LDs and cells. We employ low-coherence holotomography (HT) to systematically investigate and quantitatively analyze the 42-day redifferentiation process of fat cells into adipocytes. This technique yields three-dimensional, high-resolution refractive tomograms of adipocytes, enabling precise segmentation of LDs based on their elevated RI values. Subsequent automated analysis quantifies the mean concentration, volume, projected area, and dry mass of individual LDs, revealing a gradual increase corresponding with adipocyte maturation. Our findings demonstrate that HT is a potent tool for non-invasively monitoring live adipocyte differentiation and analyzing LD accumulation. This study, therefore, offers valuable insights into adipogenesis and lipid research, establishing HT and image-based analysis as a promising approach in these fields.

Structural-informed functional MRI analysis of patients with empathy impairment following stroke.

The underlying functional alterations of brain structural changes among patients with empathy impairment following stroke remain unclear. We sought to investigate functional connectivity changes informed by brain structural abnormalities in multimodal magnetic resonance imaging (MRI) among patients with empathy impairment following stroke. We enrolled people who had experienced their first ischemic stroke, along with healthy controls. We assessed empathy 3 months after stroke using the Chinese version of the Empathy Quotient (EQ). During the acute phase, all patients underwent basic magnetic resonance imaging (MRI), followed by multimodal MRI during follow-up. Our MRI analyses encompassed acute infarction segmentation, volumetric brain measurements, regional quantification of diffusion parameters, and both region-of-interest-based and seed-based functional connectivity assessments. We grouped patients based on the severity of their empathy impairment for comparative analysis. We included 84 patients who had stroke and 22 healthy controls. Patients had lower EQ scores than controls. Patients with low empathy had larger left cortical infarcts (odds ratio [OR] 4.082, 95% confidence interval [CI] 1.183-14.088), more pronounced atrophy in the right cingulate cortex (OR 1.248, 95% CI 1.038-1.502), and lower scores on the Montreal Cognitive Assessment (OR 0.873, 95% CI 0.74-0.947). In addition, the cingulate cortex served as the seed in the seed-based analysis, which showed heightened functional connectivity between the anterior cingulate gyrus and the right superior parietal lobule, specifically in the low-empathy group. We did not evaluate the relationship between specific network involvement and empathy impairment among patients following stroke. Among patients with subacute ischemic stroke, reduced empathy was strongly associated with a more severe cognitive profile and atrophy of the right cingulate cortex. Our subsequent structural-informed functional MRI analysis suggests that the enhanced connectivity between the anterior cingulate gyrus and the superior parietal lobule may function as a compensatory mechanism for this atrophy.

Reproducibility of ultrasound-derived fat fraction in measuring hepatic steatosis

PurposeSteatotic liver disease (SLD) has become the most common cause of chronic liver disease. Nevertheless, the non-invasive quantitative diagnosis of steatosis is still lacking in clinical practice. This study aimed to evaluate the reproducibility of the new parameter for steatosis quantification named ultrasound-derived fat fraction (UDFF).Materials and methodsThe UDFF values were independently executed by two operators in two periods. In the process, repeated measurements of the same patient were performed by the same operator under different conditions (liver segments, respiration, positions, and dietary). Finally, the results of some subjects (28) were compared with the MRI-derived proton density fat fraction (PDFF). The concordance analysis was mainly achieved by the intraclass correlation coefficient (ICC) and Bland–Altman.ResultsOne hundred-five participants were included in the study. UDFF had good reliability in measuring the adult liver (ICCintra-observer = 0.96, ICCinter-observer = 0.94). Meanwhile, the ICC of the two operators increased over time. The variable measurement states did not influence the UDFF values on the surface, but they affected the coefficient of variation (Cov) of the results. Segment 8 (S8), end-expiratory, supine, and fasting images had the most minor variability. On the other hand, the UDFF value of S8 displayed satisfied consistency with PDFF (mean difference, −0.24 ± 1.44), and the results of both S5 (mean difference: −0.56 ± 3.95) and S8 (mean difference: 0.73 ± 1.87) agreed well with the whole-liver PDFF.ConclusionUDFF measurements had good reproducibility. Furthermore, the state of S8, end-expiration, supine, and fasting might be the more stable measurement approach.Critical relevance statementUDFF is the quantitative ultrasound parameter of hepatic steatosis and has good reproducibility. It can show more robust performance under specific measurement conditions (S8, end-expiratory, supine, and fasting).Trial registrationThe research protocol was registered at the Chinese Clinical Trial Registry on October 9, 2023 (http://www.chictr.org.cn/). The registration number is ChiCTR 2300076457.Key PointsThere is a lack of non-invasive quantitative measurement options for hepatic steatosis.UDFF demonstrated excellent reproducibility in measuring hepatic steatosis.S8, end-expiratory, supine, and fasting may be the more stable measuring condition.Training could improve the operators’ measurement stability.Variable measurement state affects the repeatability of the UDFF values (Cov).Graphical

Assessment of Urban Resilience to Floods: A Spatial Planning Framework for Cities

Urbanization-led economic growth drives infrastructure investments and population accumulation in cities, hence exploiting natural resources at an extreme rate. In this context, coastal cities have become vulnerable to climate change-induced extreme weather events and human-made disasters in recent history, where effective measures to improve the resilience of cities are pivotal for developing sustainable living environments. This study proposes a framework for assessing urban resilience to natural disasters (floods) using bottom-up spatial interactions among natural, physical, and social systems within cities and regions. It is noted that seminal studies focus on either the mitigation or adaptation strategies within urban environments to assess disaster resilience, where limited multidisciplinary and operational models hinder evaluations at the city scale. Therefore, urban system interactions and quantifiable parameters proposed in this framework are essential for policymakers and disaster management agencies in the timely allocation of resources to optimize the recovery process. Moreover, spatial planning agencies can adopt resilience mapping to identify the potential risk zones and orient sustainable land use management. Urban resilience can be embodied in spatial strategies with the operational framework proposed here, and future urban growth scenarios can be tested in multiple disaster conditions.

Impact of applying different levels of threshold-based artifact correction on the processing of heart rate variability data in individuals with temporomandibular disorder.

Although heart rate variability (HRV) is a valid method to evaluate the behavior of the autonomic nervous system in individuals with temporomandibular disorder (TMD), the measurement can easily be biased by factors involving the analysis methodology, such as the removal of artifacts. Therefore, the objective of this investigation is to evaluate the impact of using different levels of threshold-based artifact correction to process HRV data in individuals with TMD. This cross-sectional observational study. Adults aged 18 to 55 years old with a diagnosis of myogenic TMD, score ≥ 50 on the Fonseca Anamnestic Index (FAI) and pain ≥ 3 on the Numerical Pain Scale (NPS) participated. The HRV was registered in the supine position (short-term) using a Polar S810i. Kubios software was used for HRV analysis using all filters. One-way ANOVA with Tukey-Kramer post-hoc was used to test the differences in HRV using the different Kubios Software artifact correction filters. The effect size was calculated based on the Cohen d. The very strong filter was statistically different (p < 0.05) compared to the no filter in all overview and time domain variables. In the frequency domain, the variables VLF, LF, HF and Total Power showed statistical differences (p < 0.05) when using the very strong filter. The same occurred with the variables SD1, SD2 and DFA α2 of the non-linear analysis (p < 0.05). The most restrictive filter of the Kubios software (very strong) significantly impacts the quantification of HRV parameters in individuals with TMD.

Raman‐Enabled Predictions of Protein Content and Metabolites in Biopharmaceutical Saccharomyces cerevisiae Fermentations

ABSTRACTRaman spectroscopy, a robust and non‐invasive analytical method, has demonstrated significant potential for monitoring biopharmaceutical production processes. Its ability to provide detailed information about molecular vibrations makes it ideal for the detection and quantification of therapeutic proteins and critical control parameters in complex biopharmaceutical mixtures. However, its application in Saccharomyces cerevisiae fermentations has been hindered by the inherent strong fluorescence background from the cells. This fluorescence interferes with Raman signals, compromising spectral data accuracy. In this study, we present an approach that mitigates this issue by deploying Raman spectroscopy on cell‐free media samples, combined with advanced chemometric modeling. This method enables accurate prediction of protein concentration and key process parameters, fundamental for the control and optimization of biopharmaceutical fermentation processes. Utilizing variable importance in projection (VIP) further enhances model robustness, leading to lower relative root mean squared error of prediction (RMSEP) values across the six targets studied. Our findings highlight the potential of Raman spectroscopy for real‐time, on‐line monitoring and control of complex microbial fermentations, thereby significantly enhancing the efficiency and quality of S. cerevisiae‐based biopharmaceutical production.

Inclusive and Accurate Clinical Diagnostics Using Intelligent Computation and Smartphone Imaging.

Smartphone-based colorimetry has been widely applied in clinical analysis, although significant challenges remain in its practical implementation, including the need to consider biases introduced by the ambient imaging environment, which limit its potential within a clinical decision pathway. In addition, most commercial devices demonstrate variability introduced by manufacturer-to-manufacturer differences. Here, we undertake a systematic characterization of the potential imaging interferences that lead to this limited performance in conventional smartphones and, in doing so, provide a comprehensive new understanding of smartphone color imaging. Through derivation of a strongly correlated parameter for sample quantification, we enable real-time imaging, which for the first time, takes the first steps to turning the mobile phone camera into an analytical instrument - irrespective of model, software, and the operating systems used. We demonstrate clinical applicability through the imaging of patients' skin, enabling rapid and convenient diagnosis of cyanosis and measurement of local oxygen concentration to a level that unlocks clinical decision-making for monitoring cardiovascular disease and anemia. Importantly, we show that our solution also accounts for the differences in individuals' skin tones as measured across the Fitzpatrick scale, overcoming potential clinically significant errors in current optical oximetry.

ALS-Based, Automated, Single-Tree 3D Reconstruction and Parameter Extraction Modeling

The 3D reconstruction of point cloud trees and the acquisition of stand factors are key to supporting forestry regulation and urban planning. However, the two are usually independent modules in existing studies. In this work, we extended the AdTree method for 3D modeling of trees by adding a quantitative analysis capability to acquire stand factors. We used unmanned aircraft LiDAR (ALS) data as the raw data for this study. After denoising the data and segmenting the single trees, we obtained the single-tree samples needed for this study and produced our own single-tree sample dataset. The scanned tree point cloud was reconstructed in three dimensions in terms of geometry and topology, and important stand parameters in forestry were extracted. This improvement in the quantification of model parameters significantly improves the utility of the original point cloud tree reconstruction algorithm and increases its ability for quantitative analysis. The tree parameters obtained by this improved model were validated on 82 camphor pine trees sampled from the Northeast Forestry University forest. In a controlled experiment with the same field-measured parameters, the root mean square errors (RMSEs) and coefficients of determination (R2s) for diameters at breast height (DBHs) and crown widths (CWs) were 4.1 cm and 0.63, and 0.61 m and 0.74, and the RMSEs and coefficients of determination (R2s) for heights at tree height (THs) and crown base heights (CBHs) were 0.55 m and 0.85, and 1.02 m and 0.88, respectively. The overall effect of the canopy volume extracted based on the alpha shape is closest to the original point cloud and best estimated when alpha = 0.3.

Performance of Recycled Opaque PET Modified by Reactive Extrusion

A comparative study of the structural integrity of an opaque recycled poly(ethylene terephthalate) (rPET-O) has been carried out with two types of modified rPET-O by applying reactive extrusion techniques, namely (a) using a multi-epoxide reactive agent (REx-rPET-O) and (b) a 90/10 (wt/wt) rPET-O/polycarbonate (PC) blend. The chemical modifications introduced during reactive extrusion were confirmed using differential scanning calorimetry (DSC) and rheological dynamic analysis (RDA). For the quantification of the fracture parameters, an instrumented pendulum impact testing machine was used using specimens in SENB configuration. The structural modifications generated during reactive extrusion promote an increase of between 16 (REx-rPET-O) and 20% (rPET-O/PC) in the stress-intensity factor (KQ) compared to unmodified rPET-O. The most significant differences between both modifications are registered in the “specific work of fracture” (wf) (alternative parameter to the standardized impact strength), where an increase of 61% is reached for the case of rPET-O/PC and only 11% for REx-rPET-O. This trend can be attributed to the type of reactive modification that is generated, namely chain branching (REx-rPET-O) vs. the generation of a random copolymer “in situ” (rPET-O/PC). This copolymer decreases the crystallization capacity and degree of crystalline perfection of rPET-O, promoting an increase in the critical hydrostatic stress conditions for the generation of crazing and crack propagation.

Biomechanics Model to Characterize Atomic Force Microscopy-Based Virus-Host Cell Adhesion Measurements.

We present a model for virus-cell adhesion that can be used for quantitative extraction of adhesive properties from atomic force microscopy (AFM)-based force spectroscopy measurements. We extend a previously reported continuum model of viral cell interactions based on a single parameter representing adhesive energy density by using a cohesive zone model in which adhesion is represented by two parameters, a pull-off stress and associated characteristic displacement. This approach accounts for the deformability of the adhesive receptors, such as the Spike protein and transmembrane immunoglobulin and mucin domain (TIM) family that mediate adhesion of SARS-CoV-2 and Ebola viruses, and the omnipresent glycocalyx. Our model represents receptors as a Winkler foundation and aims to predict the pull-off force needed to break the adhesion between the virus and the cell. By comparing the force-separation curves simulated by the model and experimental data, we found that the model can effectively explain the AFM pull-off force trace, thus allowing quantification of the adhesion parameters. Our model provides a more refined understanding of viral cell adhesion and also establishes a framework for interpreting and predicting AFM force spectroscopy measurements.

Analysis and comparison of retinal vascular parameters under different glucose metabolic status based on deep learning.

To develop a deep learning-based model for automatic retinal vascular segmentation, analyzing and comparing parameters under diverse glucose metabolic status (normal, prediabetes, diabetes) and to assess the potential of artificial intelligence (AI) in image segmentation and retinal vascular parameters for predicting prediabetes and diabetes. Retinal fundus photos from 200 normal individuals, 200 prediabetic patients, and 200 diabetic patients (600 eyes in total) were used. The U-Net network served as the foundational architecture for retinal artery-vein segmentation. An automatic segmentation and evaluation system for retinal vascular parameters was trained, encompassing 26 parameters. Significant differences were found in retinal vascular parameters across normal, prediabetes, and diabetes groups, including artery diameter (P=0.008), fractal dimension (P=0.000), vein curvature (P=0.003), C-zone artery branching vessel count (P=0.049), C-zone vein branching vessel count (P=0.041), artery branching angle (P=0.005), vein branching angle (P=0.001), artery angle asymmetry degree (P=0.003), vessel length density (P=0.000), and vessel area density (P=0.000), totaling 10 parameters. The deep learning-based model facilitates retinal vascular parameter identification and quantification, revealing significant differences. These parameters exhibit potential as biomarkers for prediabetes and diabetes.

A dual-driven fusion model of evaluating the performance and carbon emissions for recycled waste pavement

Solid waste recycling is an essential approach for the sustainable transition of transportation infrastructure development. In this study, a holistic model for recycled waste pavement was developed, achieving a breakthrough in eco-efficiency-based pavement material design. Using this model, we can not only individually assess the technical feasibility of the pavement material and its carbon emissions, but also realize the unified dimensional quantification of multidimensional parameters based on decision-making expectations using a metrics fusion system, thus achieving high-level sustainability decisions for pavement schemes. The proposed model was validated by evaluating the comprehensive properties of steel slag pavements to determine a suitable and durable pavement solution. This study provides a decarbonization strategy for the transportation sector considering waste-recycled pavement design, which may promote the development of more resilient transportation infrastructure and significantly contribute to achieving carbon neutrality and mitigating climate change.•A model for solid waste pavement was established based on synthesized assessing feasibility and carbon emissions.•The field performance and climate system impacts of pavements with solid waste materials were assessed.•It was demonstrated that recycling steel slag for pavement construction can reduce carbon emissions by >50 %.•Our results contribute to achieving sustainable transportation infrastructure systems to mitigate climate issues.•Our methodology can be used not only for road construction but also in the civil engineering field.

Improving standardization and accuracy of in vivo omega plot exchange parameter determination using rotating-frame model-based fitting of quasi-steady-state Z-spectra.

Although Ω-plot-driven quantification of in vivo amide exchange properties has been demonstrated, differences in scan parameters may complicate the fidelity of determination. This work systematically evaluated the use of quasi-steady-state (QUASS) Z-spectra reconstruction to standardize in vivo amide exchange quantification across acquisition conditions and further determined it in vivo. Simulation and in vivo rodent brain chemical exchange saturation transfer (CEST)data at 4.7 T were fit with and without QUASS reconstruction using both multi-Lorentzian and model-based fitting approaches. pH modulation was accomplished both in simulation and in vivo by inducing global ischemia via cardiac arrest. Amide parameters were determined via Ω-plots and compared across methods. Simulation showed that Ω-plots using multi-Lorentzian fitting could underestimate the exchange rate, with error increasing as conditions diverged from the steady state. In comparison, model-based fitting using QUASS estimated the same exchange rate within 2%. These results aligned with in vivo findings where multi-Lorentzian fitting of native Z-spectra resulted in an exchange rate of 64 ± 13 s-1 (38 ± 16 s-1 after cardiac arrest), whereas model-based fitting of QUASS Z-spectra yielded an exchange rate of 126 ± 25 s-1 (49 ± 13 s-1). The model-based fitting of QUASS CEST Z-spectra enables consistent and accurate quantification of exchange parameters through Ω-plot construction by reducing error due to signal overlap and nonequilibrium CEST effects.

Quantification of the Few Parameters and Metallic Elements in the Quaternary Sediments of “Baie Du Repos” and their Interrelation

Mauritania is a fishing country. However, the Mauritanian coast is increasingly exposed to environmental issues mainly due to anthropogenic activities such as the mining, gas, oil, and fishing industries, as well as new agricultural practices that unreasonably use inputs. Environmental monitoring of the Mauritanian coast faces several challenges; thus, improving the fisheries sector begins with enhancing the state of marine ecosystems and implementing environmental monitoring adapted to climatic conditions and local needs. This study aims to evaluate the quality of the sediments of the “Baie du Repos” in the town of Nouadhibou, Mauritania, through the study of organic matter and the quantification of trace metallic elements in the Quaternary sediments of the Bay. Six samples deemed representative of this Bay were taken and transported to the laboratory. The physicochemical analysis of these samples shows that the superficial horizons of 30 cm depth have overall organic matter contents higher than the average threshold value proposed by the literature for 4 out of 6 of the points studied. The contents recorded for the different metallic trace elements indicate that point 1 is the most exposed to contamination, with the highest concentrations of cadmium, lead, copper, iron, and zinc. The ACP (Principal Component Analysis) showed that the metallic trace elements Pb, Cu, Fe, Cd, and Zn are closely related and evolve positively in the same direction. Additionally, it was found that the points studied are divided into three groups: Group 1 contains only point 1, which is the most exposed to contamination by these toxic elements (Pb, Cu, Zn, Fe, and Cd). Group 2 contains points 3, 5, and 6, which are moderately contaminated by metallic elements with a significant dominance of organic matter (OM). Finally, Group 3 is the least contaminated, with a very high content of organic matter (OM).

Decoupling of Photoacoustic Spectroscopy Based on Machine Learning for Bone Chemical Composition Assessment: Simulation Study

Abstract Photoacoustic (PA) spectroscopy technology can be utilized to identification of molecules in biological tissue based on the contrast of optical absorption. However, it is susceptible to the interference of overlapped optical absorption peaks from different chemical components, especially for dense bone tissue which contains both organic and non-organic chemical components. To accurately extract the relative contents of chemical components in bone tissue, this study established a decoupling method based on the PA band ratio and machine learning to quantitatively analyze the proportion of chemical components in bone tissue from the PA spectra. The predicted quantification parameters of different chemical components were consistent with the simulated presets, and could be used to characterize the changes of bone tissue components. Considering that PA technology is non-invasive and radiation-free, this technique shows great potential for the early diagnosis and monitoring of bone disease progression.

Automated External Bone Fixation Robot Design and Orthopedic Analysis

Background: Bone external fixation technology is an important therapeutic approach for limb correction, primarily involving a class of external fixation correction mechanisms. It has achieved good results in limb lengthening and correcting deformities to restore limb function. In clinical practice, existing correction mechanisms face challenges such as high resistance between components, complex installation procedures, and high precision requirements for installation, requiring manual adjustment by physicians, which limits precise control and quantification of correction parameters. Objective: To address these issues, an automated correction bone external fixation robot was designed based on traditional Ilizarov external fixation devices(TIEFD). Methods: By increasing the number of unidirectional hinge connections, the robot can effectively reduce motion resistance. Subsequently, the robot was combined with the tibia for correction motion simulation, obtaining the motion parameters θ of the deformed bones during the correction process based on the trajectory of the tibia's point mass. Finally, correction motion experiments and finite element analysis(FEA) were conducted on the robot combined with a control system. Results: The results showed that the robot could precisely control correction parameters, with the error range for rotational correction not exceeding 0.5 radians and the error range for traction correction not exceeding 0.2 mm. FAE, based on corrective force data, concluded that compared to (TIEFD), the robot could reduce bone stress by 16 MPa during the correction process. Conclusion: The robot effectively reduces patient discomfort, this provides a reliable theoretical basis for bone external fixation technology and holds positive significance for the treatment of skeletal deformities.

Shear-wave elastography as a supplementary tool for axillary staging in patients undergoing breast cancer diagnosis

BackgroundPreoperative evaluation of axillary lymph node status is crucial for the selection of both systemic and surgical treatment in early breast cancer. This study assessed the particular role of additional shear wave elastography (SWE) in axillary staging in patients undergoing initial breast cancer diagnostics.MethodsOne hundred patients undergoing axillary lymph node biopsy due to a sonographically suspicious axillary lymph node were prospectively evaluated with SWE using virtual touch tissue imaging quantification (VTIQ). Mean values of tissue stiffness for axillary tissue and lymph node tissue were measured prior to core-cut biopsy of the lymph node. All lymph nodes were clip-marked during the biopsy. Cut-off values to differentiate between malignant and benign lymph nodes were defined using Youden’s index.ResultsLymph nodes with evidence of malignant tumor cells in the final pathological examination showed a significantly higher velocity as measured by SWE, with a mean velocity of 3.48 ± 1.58 m/s compared to 2.33 ± 0.62 m/s of benign lymph nodes (p < 0.0001). The statistically optimal cutoff to differentiate between malignant and benign lymph nodes was 2.66 m/s with a sensitivity of 69.8% and a specificity of 87.5%.ConclusionsLymph node metastases assessed with SWE showed significantly higher elasticity values compared to benign lymph nodes. Thus, SWE provides an additional useful and quantifiable parameter for the sonographic assessment of suspicious axillary lymph nodes in the context of pre-therapeutic axillary staging in order to differentiate between benign and metastatic processes and support the guidance of definitive biopsy work-up.Critical relevance statementShear-wave elastography provides an additional useful and quantifiable parameter for the assessment of suspicious axillary lymph nodes in the context of pre-therapeutic axillary staging in order to differentiate between benign and metastatic processes and support guiding the definitive biopsy work-up.Key PointsSWE is a quantifiable ultrasound parameter in breast cancer diagnosis.SWE shows a significantly higher velocity in malignant lymph nodes.SWE is useful in improving the sensitivity and specificity of axillary staging.Graphical