查看更多>>摘要：Within the consistent daily rhythm of human life,intervertebral discs endure a variety of complex loads beyond the influences of gravity and muscle forces,leading to significant morphological changes(in terms of volume,area,and height)as well as biomechanical alterations,including an increase in disc stiffness and a decrease in intradiscal pressure.Remarkably,the discs demonstrate an ability to regain their original morphological and biomechanical characteristics after a period of noc-turnal rest.The preservation of normal disc function is critically dependent on this recovery phase,which serves to forestall premature disc degeneration.This phenomenon of disc recovery has been extensively documented through numerous in vivo studies employing advanced clinical techniques such as Magnetic Resonance Imaging(MRI),stadiometry,and intradiscal pressure measurement.However,the findings from in vitro studies present a more complex picture,with reports varying between full recovery and only partial recuperation of the disc properties.Moreover,research focusing on degenerated discs in vitro has shed light on the quantifiable impact of degeneration on the disc ability to recover.Fluid dynamics within the disc are considered a primary factor in recovery,yet the disc intricate multiscale structure and its viscoelastic properties also play key roles.These elements interact in complex ways to influence the recovery mechanism,particularly in relation to the overall health of the disc.The objective of this review is to collate,analyze,and critically evaluate the existing body of in vivo and in vitro research on this topic,providing a comprehensive understanding of disc recovery processes.Such understanding offers a blueprint for future advancements in medical treatments and bionic engineering solutions designed to mimic,support,and enhance the natural recovery processes of intervertebral discs.
查看更多>>摘要：Corneal diseases,the second leading cause of global vision loss affecting over 10.5 million people,underscores the unmet demand for corneal tissue replacements.Given the scarcity of fresh donor corneas and the associated risks of immune rejection,corneal tissue engineering becomes imperative.Developing nanofibrous scaffolds that mimic the natural corneal structure is crucial for creating transparent and mechanically robust corneal equivalents in tissue engineering.Herein,Aloe Vera Extract(AVE)/Polycaprolactone(PCL)nanofibrous scaffolds were primed using electrospinning.The electro-spun AVE/PCL fibers exhibit a smooth,bead-free morphology with a mean diameter of approximately 340±95 nm and appropriate light transparency.Mechanical measurements reveal Young's modulus and ultimate tensile strength values of around 3.34 MPa and 4.58 MPa,respectively,within the range of stromal tissue.In addition,cell viability of AVE/PCL fibers was measured against Human Stromal Keratocyte Cells(HSKCs),and improved cell viability was observed.The cell-fiber interactions were investigated using scanning electron microscopy.In conclusion,the incorporation of Aloe Vera Extract enhances the mechanical,optical,hydrophilic,and biological properties of PCL fibers,positioning PCL/AVE fiber scaffolds as promising candidates for corneal stromal regeneration.
查看更多>>摘要：With an elemental composition similar to bone mineral,and the ability to release phosphorus and calcium that benefit bone regeneration,Calcium Phosphate Glass(CPG)serves as a promising component of bone tissue engineering scaffolds.How-ever,the degradation of CPG composites typically results in increased acidity,and its impact on bone-forming activity is less studied.In this work,we prepared 3D-printed composite scaffolds comprising CPG,Poly-ε-caprolactone(PCL),and various Magnesium Oxide(MgO)contents.Increasing the MgO content effectively suppressed the degradation of CPG,maintaining a physiological pH of the degradation media.While the degradation of CPG/PCL scaffolds resulted in upregulated apoptosis of Rat Bone Marrow-derived Stem Cells(rBMSC),scaffolds containing MgO were free from these negative impacts,and an optimal MgO content of 1 wt％led to the most pronounced osteogenic differentiation of rBMSCs.This work demonstrated that the rapid degradation of CPG impaired the renewability of stem cells through the increased acidity of the surrounding media,and MgO effectively modulated the degradation rate of CPG,thus preventing the negative effects of rapid degrada-tion and supporting the proliferation and osteogenic differentiation of the stem cells.
Shima MahtabianSeyed Mehdi MirhadiNahid Hassanzadeh NematiMelika Sharifi...
1975-1986页
查看更多>>摘要：Immersion of scaffolds in Simulated Body Fluid(10SBF)is a standardized method for evaluating their bioactivity,simu-lating in vivo conditions where apatite deposits can be formed on the surface of scaffold,facilitating bone integration and ensuring their suitability for bone implant purposes,ultimately contributing to long-term implant success.The effect of apatite deposition on bioactivity and cell behavior of TiO2 scaffolds was studied.Scaffolds were soaked in 10SBF for different durations to form HAP layer on their surface.The results proved the development of a hydroxyapatite film resembling the mineral composition of bone Extracellular Matrix(ECM)on the TiO2 scaffolds.The XRD test findings showed the presence of hydroxyapatite layer similar to bone at the depth of 10 nm.A decrease in the specific surface area(18.913 m2g-1),the total pore volume(0.045172 cm3g-1(at p/p0=0.990)),and the mean pore diameter(9.5537 nm),were observed by BET analysis which confirmed the formation of the apatite layer.It was found that titania scaffolds with HAP coating promoted human osteosarcoma bone cell(MG63)cell attachment and growth.It seems that immersing the scaffolds in 10SBF to form HAP coating before utilizing them for bone tissue engineering applications might be a good strategy to promote bioactivity,cell attachment,and implant fixation.
Anbazhagan RajeshVenkatesh Sri HarinyArunachalam Sumathi
1987-1999页
查看更多>>摘要：Sustainable cement-based concrete materials are primarily used for construction,among which vermiculite as lightweight fine aggregate gains more future development prospect.First,a bacterial solution was sprayed over vermiculite and wrapped using calcium sulphoaluminate(CSA)cement to replace with fine aggregate in concrete.Secondly,based on a preliminary test on compressive strength results,10％of Ground Granulated Blast Furnace Slag(GGBS)and a healing solution propor-tion of 9∶1 was selected for preparing self-healing concrete.The fine aggregate was replaced in concrete using vermiculite in 0％,5％,10％and 15％and the findings suggest that bacterial vermiculite replacement should be at most 5％to achieve better results in strength and durable properties.The strength enhancement observed for compressive strength,strength regain,split tensile strength,flexural strength,and ultrasonic pulse velocity were 29.22％,45.5％,34.02％,28.03％and 41.4％respectively.Surface crack healing at 7,14 and 28 days of BIVC was 38.23％,58.82％and 79.41％,which is 3-4％lower than internal crack healing.Microstructural analysis by Scanning Electron Microscopy(SEM),X-Ray Diffractometer(XRD),and Energy Dispersive Spectroscopy(EDS)reveals the existence of calcite,and it was formed due to the bio-mineral action of bacteria with available nutrients in sustainable concrete.
查看更多>>摘要：Feature selection(FS)plays a crucial role in pre-processing machine learning datasets,as it eliminates redundant features to improve classification accuracy and reduce computational costs.This paper presents an enhanced approach to FS for software fault prediction,specifically by enhancing the binary dwarf mongoose optimization(BDMO)algorithm with a crossover mechanism and a modified positioning updating formula.The proposed approach,termed iBDMOcr,aims to fortify exploration capability,promote population diversity,and lastly improve the wrapper-based FS process for software fault prediction tasks.iBDMOcr gained superb performance compared to other well-esteemed optimization methods across 17 benchmark datasets.It ranked first in 11 out of 17 datasets in terms of average classification accuracy.Moreover,iBD-MOcr outperformed other methods in terms of average fitness values and number of selected features across all datasets.The findings demonstrate the effectiveness of iBDMOcr in addressing FS problems in software fault prediction,leading to more accurate and efficient models.
查看更多>>摘要：Feature Selection(FS)is an important data management technique that aims to minimize redundant information in a dataset.This work proposes DENGO,an improved version of the Northern Goshawk Optimization(NGO),to address the FS prob-lem.The NGO is an efficient swarm-based algorithm that takes its inspiration from the predatory actions of the northern goshawk.In order to overcome the disadvantages that NGO is prone to local optimum trap,slow convergence speed and low convergence accuracy,two strategies are introduced in the original NGO to boost the effectiveness of NGO.Firstly,a learning strategy is proposed where search members learn by learning from the information gaps of other members of the population to enhance the algorithm's global search ability while improving the population diversity.Secondly,a hybrid differential strategy is proposed to improve the capability of the algorithm to escape from the trap of the local optimum by perturbing the individuals to improve convergence accuracy and speed.To prove the effectiveness of the suggested DENGO,it is measured against eleven advanced algorithms on the CEC2015 and CEC2017 benchmark functions,and the obtained results demonstrate that the DENGO has a stronger global exploration capability with higher convergence performance and stability.Subsequently,the proposed DENGO is used for FS,and the 29 benchmark datasets from the UCL database prove that the DENGO-based FS method equipped with higher classification accuracy and stability compared with eight other popular FS methods,and therefore,DENGO is considered to be one of the most prospective FS techniques.DENGO's code can be obtained at https://www.mathworks.com/matlabcentral/fileexchange/158811-project1.
查看更多>>摘要：Automatic identification and segmentation of lesions in medical images has become a focus area for researchers.Segmenta-tion for medical image provides professionals with a clearer and more detailed view by accurately identifying and isolating specific tissues,organs,or lesions from complex medical images,which is crucial for early diagnosis of diseases,treatment planning,and efficacy tracking.This paper introduces a deep network based on dendritic learning and missing region detec-tion(DMNet),a new approach to medical image segmentation.DMNet combines a dendritic neuron model(DNM)with an improved SegNet framework to improve segmentation accuracy,especially in challenging tasks such as breast lesion and COVID-19 CT scan analysis.This work provides a new approach to medical image segmentation and confirms its effective-ness.Experiments have demonstrated that DMNet outperforms classic and latest methods in various performance metrics,proving its effectiveness and stability in medical image segmentation tasks.
Amir HamzaMorad GrimesAbdelkrim BoukabouSamira Dib...
2086-2109页
查看更多>>摘要：Medical image segmentation is a powerful and evolving technology in medical diagnosis.In fact,it has been identified as a very effective tool to support and accompany doctors in their fight against the spread of the coronavirus(COVID-19).Various techniques have been utilized for COVID-19 image segmentation,including Multilevel Thresholding(MLT)-based meta-heuristics,which are considered crucial in addressing this issue.However,despite their importance,meta-heuristics have significant limitations.Specifically,the imbalance between exploration and exploitation,as well as premature convergence,can cause the optimization process to become stuck in local optima,resulting in unsatisfactory segmentation results.In this paper,an enhanced War Strategy Chimp Optimization Algorithm(WSChOA)is proposed to address MLT problems.Two strategies are incorporated into the traditional Chimp Optimization Algorithm.Golden update mechanism that provides diversity in the population.Additionally,the attack and defense strategies are incorporated to improve the search space lead-ing to avoiding local optima.The experimental results were conducted by comparing WSChoA with recent and well-known algorithms using various evaluation metrics such as Feature Similarity Index(FSIM),Structural Similarity Index(SSIM),Peak signal-to-Noise Ratio(PSNR),Standard deviation(STD),Freidman Test(FT),and Wilcoxon Sign Rank Test(WSRT).The results obtained by WSChoA surpassed those of other optimization techniques in terms of robustness and accuracy,indicating that it is a powerful tool for image segmentation.
查看更多>>摘要：In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capa-bility and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as GLSDMO.Firstly,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum.In addition,in order to address the prob-lem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in detail.The results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test functions.Finally,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization example.The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.
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