Structural Modeling Research Articles

An Examination of Sleep as a Protective Factor for Depression and Anxiety in the Perinatal Period: Novel Causal Analyses in a Prospective Pregnancy Cohort.

Sleep problems are common in the perinatal period. But the effects of sleep health on long-term postpartum depression and anxiety are underexamined. Using marginal structural models, we estimated the effect of sustained restful sleep quality, or adequate sleep quantity, or both, on clinically significant depression and anxiety at 23- and 32-weeks gestation, and 8-weeks, 8-, and 21-months postpartum. Women (n = 9,211) in the Avon Longitudinal Study of Parents and Children cohort reported on sleep quality (difficulty falling asleep, and early morning awakening), sleep quantity (< or > 5 hours and perceived sleep adequacy), depression (Edinburgh Postnatal Depression Scale), and anxiety (Crown-Crisp Experiential Index). Analyses adjusted for fixed (maternal education, age, body mass index, parity, marital status, smoking) and time varying (alcohol use, psychosocial adversities, depression, anxiety or sleep problems at prior time periods) covariates. Descriptive analyses suggest that sleep alterations persist beyond the immediate postpartum period. Estimates of counterfactual prevalences of outcomes under restful sleep quality and adequate sleep suggest a reduction of the population burden of clinically significant depression between 2.4% - 5.9%, and anxiety by 3.4% - 8.0% for the time points assessed. Interventions for perinatal sleep problems may reduce clinically significant depression and anxiety.

Kinetics of Secoisolariciresinol Glucosyltransferase LuUGT74S1 and Its Mutants.

The lignan secoisolariciresinol (SECO) diglucoside (SDG) is a phytoestrogen with diverse effects. LuUGT74S1 glucosylates SECO to SDG, whereby only small amounts of the monoglucoside SMG are formed intermediately, which exhibit increased activity. To identify critical amino acids that are important for enzymatic activity and the SMG/SDG ratio, 3D structural modeling and docking, as well as site-directed mutation studies, were performed. Enzyme assays with ten mutants revealed that four of them had identical kinetic data to LuUGT74S1, while three showed reduced and one increased catalytic efficiency kcat/Km. S82F and E189L substitutions resulted in the complete absence of activity. A17 and Q136 are crucial for the conversion of SMG to SDG as A17S and Q136F mutants exhibited the highest SMG/SDG ratios of 0.7 and 0.4. Kinetic analyses show that diglucosylation is an essentially irreversible reaction, while monoglycosylation is kinetically favored. The results lay the foundation for the biotechnological production of SMG.

Functionalized Amorphous Carbon Materials via Reactive Molecular Dynamics Simulations.

We derive a database of atomistic structural models of amorphous carbon materials endowed with exohedral functional groups. We start from phases previously derived using the DynReaxMas method for reactive molecular dynamics simulations, which exhibit atomistic and medium-length-scale features in excellent agreement with available experimental data. Given a generic input structure/phase, we develop postprocessing simulation algorithms mimicking experimental preparation protocols aimed at: (1) "curing" the phase to decrease the defect concentration; (2) automatically selecting the most reactive carbon atoms via interaction with a probe molecular species, and (3) stabilizing the phase by saturating the valence of carbon atoms with single-bond functional groups. Although we focus on oxygen-bearing functionalities, they can be replaced with other monovalent groups, such as -H, -COOH, -CHO, so that the protocol is quite general. We finally classify reactive sites in terms of their location within the structural framework and coordination environment (edges, tunnels, rings, aromatic carbons becoming aliphatic) and try to single out descriptors that correlate with tendency to functionalization.

Sex-Specific ADNP/NAP (Davunetide) Regulation of Cocaine-Induced Plasticity

Cocaine use disorder (CUD) is a chronic neuropsychiatric disorder estimated to effect 1–3% of the population. Activity-dependent neuroprotective protein (ADNP) is essential for brain development and functioning, shown to be protective in fetal alcohol syndrome and to regulate alcohol consumption in adult mice. The goal of this study was to characterize the role of ADNP, and its active peptide NAP (NAPVSIPQ), which is also known as davunetide (investigational drug) in mediating cocaine-induced neuroadaptations. Real time PCR was used to test levels of Adnp and Adnp2 in the nucleus accumbens (NAc), ventral tegmental area (VTA), and dorsal hippocampus (DH) of cocaine-treated mice (15 mg/kg). Adnp heterozygous (Adnp+/−)and wild-type (Adnp+/−) mice were further tagged with excitatory neuronal membrane-expressing green fluorescent protein (GFP) that allowed for in vivo synaptic quantification. The mice were treated with cocaine (5 injections; 15 mg/kg once every other day) with or without NAP daily injections (0.4 µg/0.1 ml) and sacrificed following the last treatment. We analyzed hippocampal CA1 pyramidal cells from 3D confocal images using the Imaris x64.8.1.2 (Oxford Instruments) software to measure changes in dendritic spine density and morphology. In silico ADNP/NAP/cocaine structural modeling was performed as before. Cocaine decreased Adnp and Adnp2 expression 2 h after injection in the NAc and VTA of male mice, with mRNA levels returning to baseline levels after 24 h. Cocaine further reduced hippocampal spine density, particularly synaptically weaker immature thin and stubby spines, in male Adnp+/+) mice while increasing synaptically stronger mature (mushroom) spines in Adnp+/−) male mice and thin and stubby spines in females. Lastly, we showed that cocaine interacts with ADNP on a zinc finger domain identical to ketamine and adjacent to a NAP-zinc finger interaction site. Our results implicate ADNP in cocaine abuse, further placing the ADNP gene as a key regulator in neuropsychiatric disorders. Ketamine/cocaine and NAP treatment may be interchangeable to some degree, implicating an interaction with adjacent zinc finger motifs on ADNP and suggestive of a potential sex-dependent, non-addictive NAP treatment for CUD.

Unmasking Fluorinated Moieties on the Surface of Hydride-Terminated Silicon Nanoparticles.

Despite the widespread use of hydrofluoric acid (HF) in the preparation of silicon surfaces, the true nature of fluorinated surface species remains unclear. Here, we employ an array of characterization techniques led by solid-state nuclear magnetic resonance spectroscopy to uncover the nature of fluorinated moieties on the surface of hydride-terminated silicon nanoparticles (H-SiNPs). A structural model that explains the observed trends in 19F and 29Si magnetic shielding is proposed and further supported by quantum chemical computations. Fluorine is incorporated into local oxidation domains on the surface and clustered at the interface of the oxide and surrounding hydride-terminated surface. Silicon sites capped by a single fluorine are also identified by their distinct 19F and 29Si chemical shifts, providing insight into how fluorine termination influences the electronic structure. The extent of fluorine passivation and the effects of fluorine on the optical properties of SiNPs are also discussed. Finally, challenges associated with Teflon contamination are highlighted that future explorations of nanomaterials may have to contend with.

Study of the Mastery of Digital Self-Efficacy through the Learning Process among Students of Universities in the Livestock Sector

Mastery of digital skills is essential for animal husbandry students because digital technology has changed the way of work and innovation in the livestock industry. With digital skills, students can optimize livestock management, track livestock health and reproduction, and predict market developments more efficiently. Perceptions of self-efficacy regarding the ability to use digital technology will encourage increased digital skills for students. Many studies examine digital skills, but limited studies discuss how digital self-efficacy is formed by involving institutional support, instructor influence, peer influence, and digital literacy. Therefore, this study examines a structural model for forming digital self-efficacy involving institutional support, instructor influence, peer influence, and digital literacy. A total of 263 students were involved in filling out the research questionnaire. This study uses structural equation modeling (SEM) analysis with SmartPLS software. The study results reveal that digital self-efficacy is directly influenced by peer influence and digital literacy. Meanwhile, institutional support and instructor influence do not directly affect digital self-efficacy. Another finding, digital literacy mediates the influence of institutional support, instructor influence, and peer influence on student digital self-efficacy. This study discusses in depth how to master digital self-efficacy through the learning process on campus and the implications of this study for universities.

Experimental investigation on the residual strength of concrete under the multi-factor coupling action of chloride penetration, freeze-thaw, and fatigue effect

With the ongoing process of globalization and the rapid expansion of the global economy, the proliferation of coastal constructions and infrastructures has witnessed a notable surge. These structures often encounter a confluence of environmental stressors in practical engineering, including cyclic fatigue loading, chloride salt erosion, and freeze-thaw cycles. These synergistic factors accelerate concrete’s failure, resulting in accelerated deterioration and significantly reduced durability. A comprehensive understanding of the damage characteristics of concrete materials in offshore environments, under the multi-factor coupled action of freeze-thaw phenomena, along with an assessment of their residual strength, is crucial for the development of relevant structural analysis and lifetime prediction models. To address this, twenty-seven dedicated specimens were designed and fabricated for this investigation. They were employed to scrutinize the residual strength of concrete under the combined influences of fatigue, freeze-thaw, and chloride penetration. Nine distinct multi-factor coupling actions were meticulously devised and characterized by different parameters. Following the multi-factor coupling action, tests were conducted to assess the residual flexural and compressive strengths, as well as the chloride ion content near the specimen edges. The findings revealed a substantial negative correlation between the residual flexural and compressive strengths with both the number of freeze-thaw cycles and fatigue loading iterations. In contrast, chloride ion concentration and the stress level of fatigue loading (stress ratio: 0.1–0.3) exhibited a comparatively marginal influence. Despite the relatively short test duration, it was evident that both freeze-thaw cycles and fatigue loading exerted a discernible promotional effect on chloride ion ingress in the concrete of the specimens.

Mechanism of Instantaneous High-Strength Sand–Water Inrush in Steeply Inclined Mining of Soft Coal Seam under Disturbed Rock–Soil Overburden

Abstract Water and sand inrush pose significant threats to underground geotechnical engineering, including shallow buried resource extraction and tunnel construction. To understand the mechanisms behind these phenomena, a mud collapse accident in Chaider Coal Mine was comprehensively investigated through field exploration and laboratory-based testings. Using numerical simulation experiments, we analyzed the failure patterns and seepage characteristics of overlying strata in steeply inclined coal seam mining under various working conditions. We established a structural instability model for water and sand inrush and identified the critical conditions for sand collapse occurrence. Our research indicates that the backfill in the surface mining pit provided a substantial material source for the accident. The overall destabilization of the top coal, due to its insufficient thickness and strength, created a pathway for sand collapse. Furthermore, frequent rainfall during the flood season and the inadequate arrangement of pumping equipment acted as triggers for the sudden water collapse. Preventative measures, such as limiting the mining height, enhancing the shear strength of the top coal, and altering the working face layout, can effectively control the development height of the water-conducting fracture zone. Additionally, timely evacuation and lowering of the aquifer water level, weakening its seepage effect in the top coal area, reducing the moisture content of the bottom soil, and improving its shear strength can mitigate water and sand inrush accidents in the backfill areas of open-pit mines.

Large-scale 3D multiphysics topology optimization of flow–heat–structural models including an islands constraint

This article demonstrates a large-scale 3D topology optimization problem formulation for components in need of internal cooling owing to surrounding hot gas flow. A conjugate heat transfer model is used and the goal of the optimization problem is to maximize the thermal performance subject to a coolant consumption limit. As a model problem, the interior design of a gas turbine guide-vane-like geometry is considered. High-quality finite element meshes are generated automatically by means of a voxelization method. A structural compliance constraint for thermo-mechanical loads, and a constraint for the suppression of free-floating structural parts, are included in the problem formulation. For the latter, an analytical expression for the constraint limit is derived. Several numerical examples indicate that the proposed problem formulation is able to generate interesting conceptual designs for interior-vane-cooling solutions.

Risk-return analysis of clean energy grid project investment based on integrated ISM and Monte Carlo model

To solve the problem of complex and difficult to quantify factors affecting investment returns and risks in clean energy power grids, this study comprehensively applies the interpretive structural model and Monte Carlo model to the analysis of investment risk-returns in clean energy power grid projects. The interpretive structural model is utilized to analyze project investment returns, while the Monte Carlo model is used to analyze project investment risks. The project investment risk is based on the factor analysis of project investment returns, and key risk factors are identified through 1000 simulations, and the impact of these risks on project returns is quantified. By combining the two, the investability of the project is analyzed. The results showed that grid electricity prices, kilowatt hour subsidies, technology learning rates, total annual sunshine hours, and system power generation efficiency were key factors driving investment returns. The average expected value of investment return was about 20%, and the probability of investment return below 6% was close to 0. The overall project is worth investing in. From this, it can be seen that the research designed investment risk-return analysis methods for clean energy grid projects can effectively distinguish the main factors affecting investment returns and risks, and pre simulate the risk situation of returns. This study can provide reference for investor decision-making.

A case study comparing seismic retrofitting techniques for a historically significant masonry building’s minaret

Historical masonry structures are extremely susceptible to earthquakes due to their characteristic features. Seismic performance and corresponding damage patterns vary between these buildings. Even though the main structure was undamaged, many minarets suffered damage or collapsed due to the transmission of large forces from the main mass to the minaret and the abrupt changes in cross-section due to the geometry of the minaret. This study uses an ancient masonry mosque as a case study, whose minaret and main building are constructed as a single structure. The mosque’s minaret under seismic excitation is the focus of this study. The adopted model is called Alaeddin Bey Mosque in Muş, Türkiye. The seismic performance assessment of the minaret, considering various retrofitting options, is mainly based on four critical parameters: base shear, acceleration, displacement, and maximum tensile forces in all three dimensions. The analyzed retrofitting methods include base isolation located in the basement of the mosque, viscous dampers placed only in the upper part of the minaret, Carbon Fiber-Reinforced Polymer fabric fitted to only the minaret, and steel plates applied to only the minaret. Representative structural models of the mosque have been modelled with SAP2000 software. The main novelty of this study is the use of viscous dampers in the minaret. It is the first time a design methodology has been introduced for viscous damper applications in minarets. This methodology aims to prevent local damage to the minaret due to the forces generated by the dampers, while also considering the constraints of limited internal space within the minaret. The finding of this study shows that viscous damper application yields significantly better results compared to the application of Carbon Fiber-Reinforced Polymer fabric and steel plates. However, although base isolation reduces the tensile stress values throughout the entire mosque to levels well below the material’s strength, viscous damper application in the minaret significantly reduces tensile stresses only in the minaret. As a result, viscous dampers are recommended for damage reduction in the minaret only. Otherwise, base isolation should be considered for reducing stress values throughout the entire mosque including the minaret. This study contributes towards the development of new seismic retrofitting methods for historic masonry buildings ‘minaret.

Augmenting supply chain resilience through AI and big data

PurposeNatural calamities like earthquakes, floods and epidemics/pandemics like COVID-19 significantly disrupt almost all the supply networks, ranging from medicines to numerous daily/emergency use items. Supply Chain Resilience is one such option to overcome the impact of the disruption, which is achieved by developing supply chain factors with Artificial Intelligence (AI) and Big Data Analytics (BDA).Design/methodology/approachThis research examines how organizations using AI and BDA can bring resilience to supply chains. To achieve the objective, the authors developed the methodology to gather useful information from the literature studied and developed the Total Interpretive Structural Modeling (TISM) by consulting 44 supply chain professionals. The authors developed a quantitative questionnaire to collect 229 responses and further test the model. With the analysis, a conceptual and comprehensive framework is developed.FindingsA major finding, this research advocates that supply chain resilience is contingent upon utilizing supply chain analytics. An empirical study provides further evidence that the utilization of supply chain analytics has a positive and favorable effect on the flexibility of demand forecasting to inventory management, resulting in increased efficiency.Originality/valueFew studies demonstrate the impact of advanced technology in building resilient supply chains by enhancing their factors. To the best of the authors' knowledge, no earlier researcher has attempted to infuse AI and BDA into supply chain factors to make them resilient.

Structural damage prediction based on Bayesian updating with Monte Carlo sampling

A series of engineering accidents in recent years have caused people to worry about the safety of engineering structures. As buildings continue to age, engineering accidents are increasing, and it is necessary to monitor their health. Bayesian updating can determine the probability of damage to a structure by collecting a posteriori information, and can more accurately estimate the location and extent of damage, thereby predicting the risk of structural damage and repairing the damage in time, significantly improving engineering efficiency. This paper attempts to update the information of the simulated structure based on the Bayesian updating method to obtain a more accurate posterior distribution of the potentially damaged components to demonstrate the feasibility and superiority of the technique. This paper finds that Bayesian updating can be used in conjunction with modeling software such as SMsolver. By constructing a structural model with SMsolver and processing the data with Bayesian updating, it is possible to calculate the damage problems of the structure accurately. At the same time, this paper also uses Monte Carlo sampling technology in combination with the Bayesian updating method to explore the prediction effect of structural damage. Using Monte Carlo sampling methods, the failure probability and damage location of the structure can be effectively estimated under the condition of known prior information, thereby improving production efficiency and the accuracy of structural inspection.

Improved PBPD method for dual steel system considering the global effect of space frames

Performance-based plastic design (PBPD) method has been proposed to design single bay planar frames for twenty years. For a dual steel system, such as an eccentrically braced frame (EBF) structure, there will be both EBF bays and moment resisting frame (MRF) bays. So, the global effect between different frame bays cannot be considered when designing the global structure using the traditional PBPD method. In this paper, a ten-story high-strength steel composite K-shaped eccentrically braced frame (HSS-K-EBF) structure was firstly used for performance design and analysis based on a single bay EBF. The results show that the global structural model obtained by direct performance design of the single bay model cannot achieve the desired performance objectives. To this end, an improved PBPD method for the dual steel system was proposed considering the global effects of space frames. The elastic and elastic–plastic shear distribution relations were calculated for different bays of the frames including EBFs and MRFs. The performance objectives of the global structure were designed and evaluated more accurately by considering the over-strength contribution of shear links and the plastic development rate index ξ of energy dissipation members. The improved PBPD method was used to design the ten-story global HSS-K-EBF structure. The results of nonlinear dynamic time history analysis show that the improved PBPD method can ensure that the plastic damage distribution of energy dissipation members of different bays is more uniform and the plastic damage degree is more consistent with expectations.

Structural model and characteristics of entrepreneurial opportunity recognition abilities among university students in China: a grounded theory approach

The rapid development of mass entrepreneurship and innovation in China has substantially influenced the entrepreneurial environment, particularly among university students who are emerging as a vital entrepreneurial force. This study aimed to explore and identify the key characteristics and structural framework of entrepreneurial talents among university student entrepreneurs in China, specifically concerning their capacity to discover entrepreneurial opportunities. To achieve this, we conducted interviews with 21 exceptional university student entrepreneurs. A structural model was subsequently developed based on grounded theory, with data coded at three levels and tested for theoretical saturation. The results demonstrated that Chinese university students possess implicit and explicit capacities to detect business possibilities. Implicit abilities largely encompass the innate qualities of entrepreneurial drive and environmental intelligence, while explicit abilities comprise the acquired skills of learning, networking, and integration. This model can be used as a benchmark for assessing and enhancing the capacity of Chinese university students who are aspiring entrepreneurs to identify and exploit entrepreneurial opportunities.

The Influence of Financial Literacy, Credit Requirements, and Financial Report Quality on Access to Financing for Micro, Small and Medium Enterprises

This study examines the influence of financial literacy, credit requirements, and the quality of financial reports on access to financing for MSMEs in the micro-trade sector in Yogyakarta City. Using a quantitative approach, data were collected from 80 MSME respondents selected through purposive sampling. Data were gathered via questionnaires with a Likert scale and analyzed using Partial Least Square (PLS) version 4.0 to evaluate the measurement and structural models. The findings reveal that financial literacy significantly influences access to financing, indicated by a T-statistic of 2.234 and a P-value of 0.026. This suggests that higher financial literacy among MSME owners facilitates easier access to financing. Credit requirements also significantly affect access, with a T-statistic of 3.259 and a P-value of 0.001, highlighting the impact of complex credit conditions on MSME financing accessibility. However, the quality of financial reports does not significantly affect access, as shown by a T-statistic of 1.057 and a P-value of 0.290, implying that financial reports are not the primary factor in credit decisions for MSMEs. This study emphasizes the importance of improving financial literacy and addressing credit requirements to enhance financing access for MSMEs.

Structural and Biochemical Characterization of Aminoglycoside Nucleotidyltransferase(6)-Ib From Campylobacter fetus subsp. fetus.

Aminoglycoside antibiotics have played a critical role in the treatment of both Gram-negative and Gram-positive bacterial infections. However, antibiotic resistance has severely compromised the efficacy of aminoglycosides. A leading cause of aminoglycoside resistance is mediated by bacterial enzymes that inactivate these drugs via chemical modification. Aminoglycoside nucleotidyltransferase-6 (ANT(6)) enzymes inactivate streptomycin by transferring an adenyl group from ATP to position 6 on the antibiotic. Despite the clinical significance of this activity, ANT(6) enzymes remain relatively uncharacterized. Here, we report the first high resolution x-ray crystallographic structure of ANT(6)-Ib from Campylobacter fetus subsp. fetus bound with streptomycin. Structural modeling and gel filtration chromatography experiments suggest that the enzyme exists as a dimer in which both subunits contribute to the active site. Moreover, superposition of the ANT(6)-Ib structure with the structurally related enzyme lincosamide nucleotidyltransferase B (LinB) permitted the identification of a putative nucleotide binding site. These data also suggest that residues D44 and D46 coordinate essential divalent metal ions and D102 functions as the catalytic base.

Identification of novel candidate predisposing genes in familial nonmedullary thyroid carcinoma implicating DNA damage repair pathways.

The genetic basis of nonsyndromic familial nonmedullary thyroid carcinoma (FNMTC) is still poorly understood, as the susceptibility genes identified so far only account for a small percentage of the genetic burden. Recently, germline mutations in DNA repair-related genes have been reported in cases with thyroid cancer. In order to clarify the genetic basis of FNMTC, 94 genes involved in hereditary cancer predisposition, including DNA repair genes, were analyzed in 48 probands from FNMTC families, through targeted next-generation sequencing (NGS). Genetic variants were selected upon bioinformatics analysis and in silico studies. Structural modeling and network analysis were also performed. In silico results of NGS data unveiled likely pathogenic germline variants in 15 families with FNMTC, in genes encoding proteins involved in DNA repair (ATM, CHEK2, ERCC2, BRCA2, ERCC4, FANCA, FANCD2, FANCF, and PALB2) and in the DICER1, FLCN, PTCH1, BUB1B, and RHBDF2 genes. Structural modeling predicted that most missense variants resulted in the disruption of networks of interactions between residues, with implications for local secondary and tertiary structure elements. Functional annotation and network analyses showed that the involved DNA repair proteins functionally interact with each other, within the same DNA repair pathway and across different pathways. MAPK activation was a common event in tumor progression. This study supports that rare germline variants in DNA repair genes may be accountable for FNMTC susceptibility, with potential future utility in patients' clinical management, and reinforces the relevance of DICER1 in disease etiology.

Using Multi-Layer Perceptron Driven Diagnosis to Compare Biomarkers for Primary Open Angle Glaucoma.

To use neural network machine learning (ML) models to identify the most relevant ocular biomarkers for the diagnosis of primary open-angle glaucoma (POAG). Neural network models, also known as multi-layer perceptrons (MLPs), were trained on a prospectively collected observational dataset comprised of 93 glaucoma patients confirmed by a glaucoma specialist and 113 control subjects. The base model used only intraocular pressure, blood pressure, heart rate, and visual field (VF) parameters to diagnose glaucoma. The following models were given the base parameters in addition to one of the following biomarkers: structural features (optic nerve parameters, retinal nerve fiber layer [RNFL], ganglion cell complex [GCC] and macular thickness), choroidal thickness, and RNFL and GCC thickness only, by optical coherence tomography (OCT); and vascular features by OCT angiography (OCTA). MLPs of three different structures were evaluated with tenfold cross validation. The testing area under the receiver operating characteristic curve (AUC) of the models were compared with independent samples t-tests. The vascular and structural models both had significantly higher accuracies than the base model, with the hemodynamic AUC (0.819) insignificantly outperforming the structural set AUC (0.816). The GCC + RNFL model and the model containing all structural and vascular features were also significantly more accurate than the base model. Neural network models indicate that OCTA optic nerve head vascular biomarkers are equally useful for ML diagnosis of POAG when compared to OCT structural biomarker features alone.

Mechanical impact of regional structural deterioration and tissue-level compensation on proximal femur trabecular bone

IntroductionOsteoporosis-induced changes in bone structure and composition significantly reduce bone strength, particularly in the human proximal femur. This study examines how these changes affect the mechanical performance of trabecular bone to enhance diagnosis, prevention, and treatment strategies.MethodsA proximal femur sample was scanned using micro-CT at 40 μm resolution. Five regions of interest were selected within the femoral head, femoral neck, and greater trochanter. Structural models simulating various stages of osteoporosis were created using image processing software. Micro-finite element analysis evaluated the mechanical properties of trabecular bone under different conditions of structural deterioration and tissue-level elastic modulus variations. The combined effects of structural deterioration and tissue-level mechanical properties on trabecular bone mechanical performance were further analyzed.ResultsThe mechanical performance of trabecular bone generally follows a power-law relationship with its microstructural characteristics. However, in any specific region, the apparent mechanical properties linearly decrease with structural deterioration. The femoral neck and greater trochanter are more sensitive to structural deterioration than the femoral head. A 5% bone mass loss in the femoral head led to a 7% reduction in mechanical performance, while the femoral neck experienced a 12% loss. Increasing tissue-level elastic modulus improved mechanical performance, partially offsetting bone mass reduction effects.ConclusionTrabecular bone in low bone mass regions is more affected by bone mass loss. Structural deterioration primarily reduces bone strength, but improvements in tissue-level properties can mitigate this effect, especially in early osteoporosis. Targeted assessments and interventions are crucial for effective management. Future research should explore heterogeneous deterioration models to better understand osteoporosis progression.