Compressive Measurements Research Articles

Evaluation of Physical Properties in Carboxymethyl Chitosan Modified Glass Ionomer Cements and the Effect for Dentin Remineralization: SEM/EDX, Compressive Strength, and Ca/P Ratio.

The aim of this article was to evaluate the effects of modifying glass ionomer cement (GIC) with carboxymethyl chitosan (CMC) on surface morphology and remineralization outcomes by examining dentin morphology and calcium ion composition changes. Thirty holes in a cylindrical acrylic mold were filled with three groups of restorative materials: GIC, GIC modified with CMC (GIC-CMC) 5%, and GIC-CMC10%. The surface morphology of each group's materials was observed using scanning electron microscopy (SEM). The compressive strength measurement was performed using a universal testing machine. The dentin remineralization process was performed by applying GIC, GIC-CMC5%, and GIC-CMC10% materials for 14 days on demineralized dentin cavities treated with 17% ethylenediamine tetraacetic acid (EDTA) for 7 days. A morphological evaluation was conducted using SEM. The calcium ion composition and calcium-to-phosphorous (Ca/P) ratio were examined using an energy-dispersive X-ray (EDX). The Kruskal-Wallis and post-hoc Mann-Whitney U tests were performed to compare all four groups of calcium ions (p < 0.05). The modification of GIC with CMC affected the morphological changes in the materials in the form of reduced porosity and increased fractures. A significant difference was found in compressive strength between the GIC-CMC modification materials of GIC-CMC5% and GIC-CMC10% and the GIC control group. The dentin tubule morphology and surface changes were observed after applying GIC, GIC-CMC5%, and GIC-CMC10% materials for 14 days, as evaluated by SEM. The EDX examination showed an increase in calcium ion content and hydroxyapatite formation (Ca/P ratio) after applying the GIC-CMC10% material. The surface porosity of the GIC modification material with the addition of CMC tended to decrease. However, an increase in cracked surfaces that widened, along with the rise in CMC percentage, was found. This modification also reduced the compressive strength of the materials, with the lowest average yield at 10% CMC addition. Therefore, the modification of GIC with CMC affects changes in morphology, calcium ion composition, and Ca/P ratio in demineralized dentin.

Artificial intelligence-based evaluation of carotid artery compressibility via point-of-care ultrasound in determining the return of spontaneous circulation during cardiopulmonary resuscitation

AimThis study introduces RealCAC-Net, an artificial intelligence (AI) system, to quantify carotid artery compressibility (CAC) and determine the return of spontaneous circulation (ROSC) during cardiopulmonary resuscitation. MethodsA prospective study based on data from a South Korean emergency department from 2022 to 2023 investigated carotid artery compressibility in adult patients with cardiac arrest using a novel AI model, RealCAC-Net. The data comprised 11,958 training images from 161 cases and 15,080 test images from 134 cases. RealCAC-Net processes images in three steps: TransUNet-based segmentation, the carotid artery compressibility measurement algorithm for improved segmentation and CAC calculation, and CAC-based classification from 0 (indicating a circular shape) to 1 (indicating high compression). The accuracy of the ROSC classification model was tested using metrics such as the dice similarity coefficient, intersection-over-union, precision, recall, and F1 score. ResultsRealCAC-Net, which applied the carotid artery compressibility measurement algorithm, performed better than the baseline model in cross-validation, with an average dice similarity coefficient of 0.90, an intersection-over-union of 0.84, and a classification accuracy of 0.96. The test set achieved a classification accuracy of 0.96 and an F1 score of 0.97, demonstrating its efficacy in accurately identifying ROSC in cardiac arrest situations. ConclusionsRealCAC-Net enabled precise CAC quantification for ROSC determination during cardiopulmonary resuscitation. Future research should integrate this AI-enhanced ultrasound approach to revolutionize emergency care.

Modeling of thermodynamic processes in internal combustion engine cylinder during cranking in compression measurement tests

The thermodynamics of in-cylinder processes of the internal combustion engine in cranking mode when measuring compression were examined. Analysis of various theoretical models and comparison of known data showed that the problems of constructing mathematical models of the engine operating cycle remain unresolved. The vast majority of practical data and recommendations for measuring cylinder compression are based on empirical knowledge, numerous experiments and tests. In accordance with this, there is a need for computational models of the compression measurement process and their theoretical justification. To solve the identified problems, a mathematical model of the thermodynamic processes in the cylinder was developed. Unlike existing ones, the model describes the processes in the cylinder step by step, taking into account the real nature of the intake-exhaust processes, air leakage through the interfaces of parts and heat exchange with the walls. Using modeling, the main patterns of changes in compression depending on the modes, the nature of damage to the associated parts of the valve mechanism and cylinder-piston group, the amount of leakage and engine temperature were found. Based on the results of the study, the properties of the model which make its use effective in diagnosing and monitoring the technical condition of engines in operation were concluded.

Gypsum-Enhanced Red Mud Composites: A Study on Strength, Durability, and Leaching Characteristics

The strong alkalinity of red mud and the heavy metals it contains pose a serious threat to the environment. This study investigated the possibility of applying red mud as a solid waste material in road construction to mitigate the problem of red mud accumulation. Red mud was modified using titanium gypsum and phosphogypsum as curing agents. The effects of varying gypsum types and mixing ratios on the mechanical properties and heavy metal leaching of the resulting red mud-based materials were assessed using percussion tests, unconfined compressive strength measurements, scanning electron microscopy (SEM), and continuous heavy metal leaching tests. The results showed that the optimal moisture content for titanium gypsum–cement-stabilized red mud (RTC) exceeds that of phosphogypsum–cement-stabilized red mud (RPC), with RTC exhibiting a lower maximum dry density compared to RPC. When the gypsum admixture was within 10%, the strength of the RPC was higher than that of the RTC at the same and curing time. The reticulation in RPC-10 was denser. The cumulative heavy metal releases from both RTC and RPC were within the permissible limits for Class III groundwater discharge standards. Based on the comprehensive test results, RPC is identified as the superior modified red mud material, with an optimal mix ratio of red mud/phosphogypsum/cement of 87:5:8.

Determining peat's vertical hydraulic diffusivity and hydraulic conductivity from naturally occurring hydraulic pressure fluctuations measured at various depths

AbstractThis study presents a method that could be used to assess the in‐situ saturated vertical hydraulic conductivity of deeper and more decomposed peat layers. Deeper peat layers may exhibit very low permeability comparable to clayey sediments, where measurements and sediment sample collection are very difficult. More traditional measurement methods, such as slug‐tests and permeameter tests, have proven difficult to carry out in these conditions, necessitating an alternative method. For surpassing these possible constraints, this paper introduces a field method to quantify vertical hydraulic diffusivity, capitalizing on naturally occurring hydraulic pressure fluctuations measured with buried pressure sensors. The determined diffusivity values are used to calculate vertical hydraulic conductivity of peat using separately measured compressibility and specific storage values. Our research demonstrates the feasibility of using this method to evaluate vertical hydraulic diffusivity in lower peat layers. Calculated diffusivity values, combined with peat compressibility measurements, yield reasonable estimates of vertical hydraulic conductivity for dense fen peat comparable with more widely used in‐situ and laboratory methods. It also shows potential for vertical hydraulic characteristics parametrization in the upper less decomposed and compacted peat layers; however, further research is needed to understand the method's limitations in such conditions. Our approach provides a potential method for measuring in‐situ vertical hydraulic properties of less accessible peat layers, often overlooked, while also providing a way to surpass the scale‐dependency constraints inherent in peatlands' heterogeneous structure. Such information is necessary when studying the hydraulic interactions between groundwater aquifers and peatlands.

A platform for planar dynamic compression of crystalline hydrogen toward the terapascal regime

We describe a method for laser-driven planar compression of crystalline hydrogen that starts with a sample of solid para-hydrogen (even-valued rotational quantum number j) having an entropy of 0.06 kB/molecule at 10 K and 2 atm, with Boltzmann constant kB. Starting with this low-entropy state, the sample is compressed using a small initial shock (&amp;lt;0.2 GPa), followed by a pressure ramp that approaches isentropic loading as the sample is taken to hundreds of GPa. Planar loading allows for quantitative compression measurements; the objective of our low-entropy compression is to keep the sample cold enough to characterize crystalline hydrogen toward the terapascal range.

Joint-learning-based coded aperture compressive temporal imaging

Coded aperture compressive temporal imaging (CACTI) is a recently developed imaging technique based on the theory of compressed sensing. It uses an optical imaging system to sample a high-speed dynamic scene (a set of consecutive video frames), integrates the sampled data in time according to masks (sensing matrix), and thus obtains compressive measurements. Considerable effort has been devoted to the sampling strategy and the ill-posed inverse process of reconstructing a three-dimensional (3D) high-speed dynamic scene from two-dimensional (2D) compressive measurements. The importance of the reconstruction algorithm and the optimization mask is evident. In this paper, a flexible, efficient, and superior quality Landweber iterative method is proposed for video reconstruction through jointly learning the optimal binary mask strategy, relaxation strategy, and regularization strategy. To solve the sparse representation problem in iteration, multiple denoisers are introduced to obtain more regularization prior information. By combining the mathematical structure of the Landweber iterative reconstruction method with deep learning, the challenging parameter selection procedure is successfully tackled. Extensive experimental results demonstrate the superiority of the proposed method.

Adaptive Joint Carrier and DOA Estimations of FHSS Signals Based on Knowledge-Enhanced Compressed Measurements and Deep Learning.

As one of the most widely used spread spectrum techniques, the frequency-hopping spread spectrum (FHSS) has been widely adopted in both civilian and military secure communications. In this technique, the carrier frequency of the signal hops pseudo-randomly over a large range, compared to the baseband. To capture an FHSS signal, conventional non-cooperative receivers without knowledge of the carrier have to operate at a high sampling rate covering the entire FHSS hopping range, according to the Nyquist sampling theorem. In this paper, we propose an adaptive compressed method for joint carrier and direction of arrival (DOA) estimations of FHSS signals, enabling subsequent non-cooperative processing. The compressed measurement kernels (i.e., non-zero entries in the sensing matrix) have been adaptively designed based on the posterior knowledge of the signal and task-specific information optimization. Moreover, a deep neural network has been designed to ensure the efficiency of the measurement kernel design process. Finally, the signal carrier and DOA are estimated based on the measurement data. Through simulations, the performance of the adaptively designed measurement kernels is proved to be improved over the random measurement kernels. In addition, the proposed method is shown to outperform the compressed methods in the literature.

Mathematical and experimental insights into acid-resistant enhancements: Graphene plates' influence on rubber-incorporated cementitious materials

To mitigate the strength loss of rubberised cementitious materials, either concrete or mortar, graphene plates were incorporated. Limited studies have highlighted significant strength enhancement in rubberised cementitious materials incorporating graphene plates under non-acidic conditions. However, a notable gap persists in their performance when exposed to harsh conditions, particularly those induced by acid attack. Therefore, this study aimed to assess the inclusion of graphene plates, alongside Arabic gum as a surfactant for graphene dispersion, on the acid resistance of rubberised mortar exposed to sulphuric acid (H2SO4). One purpose of this study was to experimentally examine their behaviour through visual inspection, compressive strength measurement, and weight loss analysis. The second purpose was to utilise response surface methodology for prediction, optimisation, and developing two quadratic equations for further exploration. The results exhibit a strong alignment between equations and experimental data, with a coefficient of variation below 2.8 % and an impressive R-squared value of 0.99, confirming their reliability and accuracy. Additionally, the proposed mortar incorporating graphene plates and rubber demonstrates superior resistance to acid attacks. Even with up to an 18 % increase in rubber content, it shows reduced strength and weight losses of 47.1 % and 25.2 % after 40 days, respectively, compared to the control mixture's losses of 62.1 % and 33.6 %. It can be concluded that the remarkable results obtained for graphene-based rubberised cementitious material hold promise for real-world applications, particularly where resistance to energy absorption and penetration by aggressive agents is required such concrete pavement.

Mechanical imbalance between normal and transformed cells drives epithelial homeostasis through cell competition.

Cell competition in epithelial tissue eliminates transformed cells expressing activated oncoproteins to maintain epithelial homeostasis. Although the process is now understood to be of mechanochemical origin, direct mechanical characterization and associated biochemical underpinnings are lacking. Here, we employ tissue-scale stress and compressibility measurements and theoretical modeling to unveil a mechanical imbalance between normal and transformed cells, which drives cell competition. In the mouse intestinal epithelium and epithelial monolayer, transformed cells get compacted during competition. Stress microscopy reveals an emergent compressive stress at the transformed loci leading to this compaction. A cell-based self-propelled Voronoi model predicts that this compressive stress originates from a difference in the collective compressibility of the competing populations. A new collective compressibility measurement technique named gel compression microscopy then elucidates a two-fold higher compressibility of the transformed population than the normal population. Mechanistically, weakened cell-cell adhesions due to reduced junctional abundance of E-cadherin in the transformed cells render them collectively more compressible than normal cells. Taken together, our findings unveil a mechanical basis for epithelial homeostasis against oncogenic transformations with implications in epithelial defense against cancer.

Human cytochrome C natural variants: Studying the membrane binding properties of G41S and Y48H by fluorescence energy transfer and molecular dynamics

Cytochrome C (cyt C), the protein involved in oxidative phosphorylation, plays several other crucial roles necessary for both cell life and death. Studying natural variants of cyt C offers the possibility to better characterize the structure-to-function relationship that modulates the different activities of this protein. Naturally mutations in human cyt C (G41S and Y48H) occur in the protein central Ω-loop and cause thrombocytopenia 4. In this study, we have investigated the binding of such variants and of wild type (wt) cyt C to synthetic cardiolipin-containing vesicles. The mutants have a lower propensity in membrane binding, displaying higher dissociation constants with respect to the wt protein. Compressibility measurements reveal that both variants are more flexible than the wt, suggesting that the native central Ω-loop is important for the interaction with membranes. Such hypothesis is supported by molecular dynamics simulations. A minimal distance analysis indicates that in the presence of cardiolipin the central Ω-loop of the mutants is no more in contact with the membrane, as it happens instead in the case of wt cyt C. Such finding might provide a hint for the reduced membrane binding capacity of the variants and their enhanced peroxidase activity in vivo.

Compaction behaviours of 17-4 PH, 316L, and 1.4551 stainless-steel powders during cold pressing

Despite numerous recent technological developments, conventional powder metallurgy (CPM) remains the most commonly applied technique for fabricating stainless-steel products. However, the compressibility of stainless-steel powders is still a critical issue. Martensitic stainless-steel powders (i.e., 17-4 PH) are significantly harder than austenitic stainless-steel powders (i.e., 316L and 1.4551). Herein, three different stainless-steel (DSS) powders (i.e., 17-4 PH, 316L, and 1.4551) were investigated using a high cold pressing pressure (i.e., 1.6 GPa) and a relatively low sintering temperature of 1200 °C during CPM. Dimensional and Archimedes density measurements, volume and radial shrinkage calculations, optical and scanning electron microscopy analyses, and compressive strength measurements were conducted to investigate the physical and mechanical properties. Using 1.6 GPa resulted in a 10 % and 6 % increase in the relative green density of the 17-4 PH and 316L samples, compared with the reported literature. For the first time, relative sintering densities of approximately 97 % and 99 % were obtained for 316L and 1.4551 at a sintering temperature of 1200 °C, respectively. Despite the insufficient sintering process, the relative density of the 17-4 PH sample increased by 2 % compared with the literature values reported for higher sintering temperatures. The shrinkage ratios were much lower than those reported in the literature. Furthermore, the present 316L samples achieved 90 % of the compressive yield strength observed in cast 316L, a 25 % increase compared with that previously reported in the literature. This study extends the CPM procedure for enhancing green densities, employing low sintering temperatures, and reducing the shrinkage values of stainless-steel powders with differing hardness.

An interpretable and flexible fusion prior to boost hyperspectral imaging reconstruction

Hyperspectral image (HSI) reconstruction from the compressed measurement captured by the coded aperture snapshot spectral imager system remains a hot topic. Recently, deep-learning-based methods for HSI reconstruction have become the mainstream due to their high performance and efficiency in the testing inference. However, these learning methods do not fully utilize the abundant spectral information with proper physical spectral priors, resulting in complex architectures and unsatisfactory reconstruction performance. In this paper, we claim that the spectral low-rank property can still help these learning methods and propose a hyperspectral fusion theory, which demonstrates that full HSIs are mathematically equivalent to the closed-form combination of subspace images, mask, and measurements. Based on the above fusion theory, we propose the subspace distillation prior (SP) to efficiently cooperate with existing learning models to enhance the exploration of the spectral low-rank property. In detail, the SP can directly improve the testing inference of existing models (SP1, Section 4.1). Furthermore, SP can also be cooperated with exiting networks to formulate a new framework, which regularizes the existing models to learn the subspace images, and help to reconstruct the full HSIs from subspace images, mask, and measurements (SP2, Section 4.2). We choose six existing representative models for the HSI reconstruction experiments and find that SP1 and SP2 can, respectively, achieve improvements of 0.08 dB∼0.76 dB and 0.36 dB∼1.76 dB on the simulated datasets, demonstrating the advantage of the proposed hyperspectral fusion theory. The source code is available at https://github.com/prowDIY.

Research on compressive strength and thermal conductivity of lightweight phosphogypsum-based composite cementitious materials

Investigation of the mechanical and thermal properties of lightweight phosphogypsum-based cementitious materials can enhance the utilization rate of phosphogypsum-based composites and reduce building energy consumption. In this study, various amounts of hollow glass microspheres (HGMs) are incorporated into phosphogypsum-based composite cementitious materials to investigate their effects on mechanical and thermal insulation properties. Subsequently, comparisons are made between lightweight phosphogypsum-based composite cementitious materials produced by foaming and those with added HGMs. The research findings indicate that HGMs significantly mitigate the loss of compressive strength and notably reduce thermal conductivity, aligning with the development needs of lightweight and high-strength construction materials. Specifically, at a 20 % HGM dosage, the specimen exhibits a thermal conductivity of 0.2512 W·m−1·K−1, marking a 46.0 % decrease compared to specimens without HGMs. Additionally, the compressive strength measures at 25.45 MPa, reflecting a 36.4 % decline relative to specimens without HGMs. However, because of the uncontrolled pore structure and size in lightweight phosphogypsum-based composite cementitious materials produced by direct foaming, the decrease in compressive strength of foamed materials is greater than that of materials with added HGMs when the reduction in the coefficient of thermal conductivity is the same. Moreover, the compressive strength of phosphogypsum-based composite cementitious materials made with HGMs decreases more than that made with the addition of HGMs. This can be attributed to the uniform particle size of HGMs, contributing to a singular and complete pore structure within the phosphogypsum-based cementitious materials, thereby optimizing their performance.

Enhancing data sparsity in spectral signals using wavelet decomposition for improved compression and storage efficiency

Wavelet decomposition (WD) is integrated into the Compressive Sensing (CS) model to enhance data sparsity. This approach aims to ensure efficiency and quality in the received data after compression and reconstruction processes. The proposed WD-CS model is developed for reflected spectra signals from Fiber Bragg Gratings-based extensometers, which were subjected to induced deflections simulating the underground soil movement. WD decomposes the input signal before compressive measurement, followed by transferring and storing the compressed data in the cloud. The spectral data were reconstructed using Orthogonal Matching Pursuit (OMP) followed by wavelet reconstruction. The impact of wavelet decomposition on the quality of compression and reconstruction is assessed and compared against that of the standard CS model. The findings indicate that the WD-CS model can improve data sparsity by a factor of three and compressibility by a factor of ten without degenerating the reconstructed data quality. The error in the Bragg wavelength shift of the reconstructed spectra is minimal extracted using Pseudo-high Resolution Interrogation (PHRI) method. The proposed WD-CS model is useful in improving data compression for FBG spectra as well as storage efficiency, which make it potentially applicable in FBG-based sensor networks for long-term structural health monitoring applications.

E.4 Machine learning based patient classification to predict neurological deterioration in mild Degenerative Cervical Myelopathy

Background: Degenerative Cervical Myelopathy (DCM) is the functional derangement of the spinal cord because of compression from degenerate tissues. Typical neurological symptoms of DCM include gait imbalance and upper extremity paresthesia. While it is thought that greater spinal cord compression leads to increased neurological deterioration, our clinical experience suggests a more complex mechanism involving spinal canal diameter (SCD). Methods: 124 MRI scans from 59 non-operative DCM patients underwent manual scoring of cord compression and SCD measurements. Unsupervised machine learning dimensionality reduction techniques and k-means clustering were used to establish patient groups. These patient groups underwent manual inspection of common compression patterns and SCD similarities to define their unique risk criteria. Results: We found that compression pattern is unimportant at SCD extremes (≤14.5 mm or &gt;15.75 mm). Otherwise, stenosis with clear signs of cord compression at two disc levels and stenosis without clear signs of cord compression at two disc levels result in a relatively higher and lower likelihood of deterioration, respectively. We elucidated five patient groups with unique associated risks for neurological deterioration, according to both SCD range and their cord compression pattern. Conclusions: The specific combination of narrow SCD with focal cord compression increases the likelihood of neurological deterioration in non-operative patients with DCM.

Coded aperture compressive temporal imaging via unsupervised lightweight local-global networks with geometric characteristics

Coded aperture compressive temporal imaging (CACTI) utilizes compressive sensing (CS) theory to compress three dimensional (3D) signals into 2D measurements for sampling in a single snapshot measurement, which in turn acquires high-dimensional (HD) visual signals. To solve the problems of low quality and slow runtime often encountered in reconstruction, deep learning has become the mainstream for signal reconstruction and has shown superior performance. Currently, however, impressive networks are typically supervised networks with large-sized models and require vast training sets that can be difficult to obtain or expensive. This limits their application in real optical imaging systems. In this paper, we propose a lightweight reconstruction network that recovers HD signals only from compressed measurements with noise and design a block consisting of convolution to extract and fuse local and global features, stacking multiple features to form a lightweight architecture. In addition, we also obtain unsupervised loss functions based on the geometric characteristics of the signal to guarantee the powerful generalization capability of the network in order to approximate the reconstruction process of real optical systems. Experimental results show that our proposed network significantly reduces the model size and not only has high performance in recovering dynamic scenes, but the unsupervised video reconstruction network can approximate its supervised version in terms of reconstruction performance.

Microstructure and mechanical properties of cast, eutectic Al-Ce-Ni-Mn-Sc-Zr alloys with multiple strengthening mechanisms

The effects of Ni additions on microstructure and microhardness of near-eutectic Al-9Ce-xNi-0.75Mn-0.18Sc-0.12Zr (x = 2.5, 3.2, and 3.9 wt.%) alloys are investigated for various cooling rates. The as-cast microstructure consists of fine, micron-scale Al11Ce3 and Ni-rich phases formed during eutectic solidification. Two Ni-rich phases are observed: (i) Al27Ce3Ni6 at higher Ni contents and slower solidification rates, and (ii) Al9(Ni,Mn,Fe)2 at lower Ni contents and faster solidification rates. While these Ni-containing alloys have higher microhardness than a Ni-free control alloy, varying the Ni concentration in the 2.5–3.9 wt.% range does not significantly affect the microhardness, indicative of competing strengthening and weakening effects from adding Ni. The alloy with intermediate Ni content (3.2 wt.%) is selected for further microstructural and mechanical characterization. It contains four coarsening-resistant strengthening constituents: (i) micron-scale Al11Ce3 and (ii) Al27Ce3Ni6 or Al9(Ni,Mn,Fe)2 platelets, all formed during eutectic solidification, (iii) L12-Al3(Sc,Zr) nanoprecipitates formed on aging, and (iv) Mn atoms in solid solution in the α-Al matrix. The combined strengthening mechanisms impart high strength at ambient and elevated temperatures, as measured by microhardness (as a function of aging time), by compression and tensile experiments, and by compressive creep measurements. Alloys previously reported in the literature - with various combinations of one, two or three of these four strengthening phases - show lower hardness and creep resistance, indicating that cumulative strengthening can be achieved when combining mechanisms; the present alloy containing all four phases shows a remarkably high creep threshold stress of 62 MPa at 300 °C.

Effect of age on ankle biomechanics and tibial compression during stair descent

BackgroundStress fracture is a concern among older adults, as age-related decrements in ankle neuromuscular function may impair their ability to attenuate tibial compressive forces experienced during daily locomotor tasks, such as stair descent. Yet, it is unknown if older adults exhibit greater tibial compression than their younger counterparts when descending stairs. Research questionDo older adults exhibit differences in ankle biomechanics that alter their tibial compression during stair descent compared to young adults, and is there a relation between tibial compression and specific changes in ankle biomechanics? MethodsThirteen young (18–25 years) and 13 older (> 65 years) adults had ankle joint biomechanics and tibial compression quantified during a stair descent. Discrete ankle biomechanics (peak joint angle and moment, and joint stiffness) and tibial compression (maximum and impulse) measures were submitted to an independent t-test, while ankle joint angle and moment, and tibial compression waveforms were submitted to an independent statistical parametric mapping t-test to determine group differences. Pearson correlation coefficients (r) determined the relation between discrete ankle biomechanics and tibial compression measures for all participants, and each group. ResultsOlder adults exhibited smaller maximum tibial compression (p = 0.004) from decreases in peak ankle joint angle and moment between 17 % and 34 % (p = 0.035), and 20–31 % of stance (p < 0.001) than young adults. Ankle biomechanics exhibited a negligible to weak correlation with tibial compression for all participants, with peak ankle joint moment and maximum tibial compression (r = -0.48 ± 0.32) relation the strongest. Older adults typically exhibited a stronger relation between ankle biomechanics and tibial compression (e.g., r = -0.48 ± 0.47 vs r = -0.27 ± 0.52 between peak ankle joint moment and maximum tibial compression). SignificanceOlder adults altered ankle biomechanics and decreased maximum tibial compression to safely execute the stair descent. Yet, specific alterations in ankle biomechanics could not be identified as a predictor of changes in tibial compression.

Focal compression of the cervical spinal cord alone does not indicate high risk of neurological deterioration in patients with a diagnosis of mild degenerative cervical myelopathy

Degenerative Cervical Myelopathy (DCM) is the functional derangement of the spinal cord resulting from vertebral column spondylotic degeneration. Typical neurological symptoms of DCM include gait imbalance, hand/arm numbness, and upper extremity dexterity loss. Greater spinal cord compression is believed to lead to a higher rate of neurological deterioration, although clinical experience suggests a more complex mechanism involving spinal canal diameter (SCD). In this study, we utilized machine learning clustering to understand the relationship between SCD and different patterns of cord compression (i.e. compression at one disc level, two disc levels, etc.) to identify patient groups at risk of neurological deterioration. 124 MRI scans from 51 non-operative DCM patients were assessed through manual scoring of cord compression and SCD measurements. Dimensionality reduction techniques and k-means clustering established patient groups that were then defined with their unique risk criteria. We found that the compression pattern is unimportant at SCD extremes (≤14.5 mm or > 15.75 mm). Otherwise, severe spinal cord compression at two disc levels increases deterioration likelihood. Notably, if SCD is normal and cord compression is not severe at multiple levels, deterioration likelihood is relatively reduced, even if the spinal cord is experiencing compression. We elucidated five patient groups with their associated risks of deterioration, according to both SCD range and cord compression pattern. Overall, SCD and focal cord compression alone do not reliably predict an increased risk of neurological deterioration. Instead, the specific combination of narrow SCD with multi-level focal cord compression increases the likelihood of neurological deterioration in mild DCM patients.