Response Of Biological Tissues Research Articles

Preclinical Evaluation of the Anti-inflammatory Properties of Plants Extracts of Ficus exasperata (Moraceae) on Wistar Rat Models

Introduction: In view of the rising global death rate associated with inflammatory diseases, a cost friendly, more effective and, safer drug with lesser side effects is needed. Inflammation is a part of the complex biological response of vascular tissue to harmful stimuli, such as pathogens, damaged cells, or irritants. It is characterized by redness, swollen joints, joint pain, heat, and loss of joint functions. Chronic inflammatory diseases have been recognized as the most significant cause of death in the world today, with more than 50% of all deaths being attributable to inflammation-related diseases. Non-steroidal anti-inflammatory drugs (NSAIDs) and Steroidal anti-inflammatory (SAIDs) are used throughout the world for the treatment and management of inflammation, pain, and fever. Since many NSAIDs are associated with, side effects such as gastrointestinal bleeding and suppressed function of the immune system, medicinal herbs are new alternatives to search out new chemical substances that would have better therapeutic results with low toxic profile. Recent studies on Ficus exasperata suggest that it might be as effective as some NSAIDs in the treatment of inflammation and related pain. Objectives: To evaluate the anti-inflammatory properties of the different plants extracts of Ficus exasperata in rats of Wistar train. Methods: The powdered material of leaves of Ficus exasperata was extracted by decoction, infusion, aqueous maceration, and hydroethanolic maceration. Phytochemical screening was carried out on the four different extracts: aqueous extract, hydroethanolic extracts, decoction and Infusion. The anti-inflammatory properties of Ficus exasperata were evaluated in vitro by egg albumin and bovine albumin using the heat induced Protein denaturation assay. Results: The leaves of Ficus exasperata was effective in inhibiting albumin denaturation. Varying concentrations of the plant extract significantly, (p < 0.05) inhibited the denaturation of albumin when compared to the control group. Aspirin and Indomethacin showed a similar trend. The inhibition by the extract is concentration-dependent with 32.25 µg/ml having an inhibition of 8.89 for infusion, 9.66% for hydroethanolic, 10.29% for aqueous maceration, and 12.6% for decoction compared to indomethacin and aspirin with inhibition 36.94% and 40.36%, respectively. The highest percentage inhibition was seen at 1000 µg/mL with inhibition of 34.22% for aqueous maceration, 40.77% for infusion, 41.61 % for hydroethanolic and 42.34% for decoction compared to indomethacin and aspirin with 67.06% and 58.45%, respectively. The plants extracts showed anti-inflammatory activity by decreasing the rate of denaturation of protein. Conclusion: The studied extract justifies anti-inflammatory properties, which were confirmed by protecting against protein denaturation.

Thermomechanical response of biological tissues to sudden temperature rise induced by laser beam: insights from three-phase lag theory

Abstract The laser irradiation of living tissues poses a risk of thermal damage, making it a critical factor in medical procedures such as laser surgery and thermal therapies. Effectively predicting and managing this damage, particularly in hyperthermia therapy, is essential for maximizing treatment efficacy while protecting surrounding healthy tissues. In this context, theoretical and computational models of biological heat transfer, especially the enhanced Pennes bioheat transport equation, have attracted significant research interest. This study contributes to the field by providing a novel analytical solution to the refined Pennes bioheat model, incorporating the three-phase lag (TPL) concept. The research examines heat transfer in a one-dimensional region, where the outer surface is exposed to laser heating while the inner surface remains thermally insulated. It explores the mechanical effects of thermal shock induced by laser treatment, focusing on heat generation patterns across different laser intensities in diseased human skin tissues. To validate the model, numerical inverse and Laplace transform techniques were applied, producing results consistent with existing literature. The findings not only advance the theoretical understanding of bioheat transfer but also enhance the safety and effectiveness of laser-based medical therapies.

Contemporary Approach for Traumatic Dental Injuries in the Primary Dentition.

Management of traumatic dental injuries (TDI) is very complex, even under ideal circumstances. Children in the primary dentition have unique needs, and it is important that the diagnosis and treatment choices are offered to parents. The prevalence of TDI in the primary dentition is far greater than for any other age group. Education of parents and caregivers in prevention and emergency management of oral injuries is essential. When injuries occur, the dental professionals must also respond appropriately to establish correct diagnosis of the injuries and assist parents in making decisions for management while communicating realistic expectations for the future. Progress has been made in the diagnosis and management of dental injuries in the primary dentition based on the biological healing response of oral tissues following injury over time. Several factors influence critical decisions that must be made based on basic principles and guidelines to improve successful outcomes for the child. Managing co-operation must be balanced with the benefit of anticipated treatment need. The current evidence challenges previous dental interventions. The IADT guidelines updated the approach to management of TDI in children in the primary dentition. Encouraging a positive attitude to the dental setting early in the life course prepares the child to be a motivated dental attendee throughout their lifetime. This manuscript aimed to outline the critical issues and clinical choices for injuries to the primary dentition and to summarise recent guidance from the International Association of Dental Traumatology (IADT).

A clustering tool for generating biological geometries for computational modeling in radiobiology.

Objective.To develop a computational tool that converts biological images into geometries compatible with computational software dedicated to the Monte Carlo simulation of radiation transport (TOPAS), and subsequent biological tissue responses (CompuCell3D). The depiction of individual biological entities from segmentation images is essential in computational radiobiological modeling for two reasons: image pixels or voxels representing a biological structure, like a cell, should behave as a single entity when simulating biological processes, and the action of radiation in tissues is described by the association of biological endpoints to physical quantities, as radiation dose, scored the entire group of voxels assembling a cell.Approach.The tool is capable of cropping and resizing the images and performing clustering of image voxels to create independent entities (clusters) by assigning a unique identifier to these voxels conforming to the same cluster. The clustering algorithm is based on the adjacency of voxels with image values above an intensity threshold to others already assigned to a cluster. The performance of the tool to generate geometries that reproduced original images was evaluated by the dice similarity coefficient (DSC), and by the number of individual entities in both geometries. A set of tests consisting of segmentation images of cultured neuroblastoma cells, two cell nucleus populations, and the vasculature of a mouse brain were used.Main results.The DSC was 1.0 in all images, indicating that original and generated geometries were identical, and the number of individual entities in both geometries agreed, proving the ability of the tool to cluster voxels effectively following user-defined specifications. The potential of this tool in computational radiobiological modeling, was shown by evaluating the spatial distribution of DNA double-strand-breaks after microbeam irradiation in a segmentation image of a cell culture.Significance.This tool enables the use of realistic biological geometries in computational radiobiological studies.

Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials.

Advanced material models and material characterization of soft biological tissues play an essential role in pre-surgical planning for vascular surgeries and transcatheter interventions. Recent advances in heart valve engineering, medical device and patch design are built upon these models. Furthermore, understanding vascular growth and remodeling in native and tissue-engineered vascular biomaterials, as well as designing and testing drugs on soft tissue, are crucial aspects of predictive regenerative medicine. Traditional nonlinear optimization methods and finite element (FE) simulations have served as biomaterial characterization tools combined with soft tissue mechanics and tensile testing for decades. However, results obtained through nonlinear optimization methods are reliable only to a certain extent due to mathematical limitations, and FE simulations may require substantial computing time and resources, which might not be justified for patient-specific simulations. To a significant extent, machine learning (ML) techniques have gained increasing prominence in the field of soft tissue mechanics in recent years, offering notable advantages over conventional methods. This review article presents an in-depth examination of emerging ML algorithms utilized for estimating the mechanical characteristics of soft biological tissues and biomaterials. These algorithms are employed to analyze crucial properties such as stress-strain curves and pressure-volume loops. The focus of the review is on applications in cardiovascular engineering, and the fundamental mathematical basis of each approach is also discussed. The review effort employed two strategies. First, the recent studies of major research groups actively engaged in cardiovascular soft tissue mechanics are compiled, and research papers utilizing ML and deep learning (DL) techniques were included in our review. The second strategy involved a standard keyword search across major databases. This approach provided 11 relevant ML articles, meticulously selected from reputable sources including ScienceDirect, Springer, PubMed, and Google Scholar. The selection process involved using specific keywords such as "machine learning" or "deep learning" in conjunction with "soft biological tissues", "cardiovascular", "patient-specific," "strain energy", "vascular" or "biomaterials". Initially, a total of 25 articles were selected. However, 14 of these articles were excluded as they did not align with the criteria of focusing on biomaterials specifically employed for soft tissue repair and regeneration. As a result, the remaining 11 articles were categorized based on the ML techniques employed and the training data utilized. ML techniques utilized for assessing the mechanical characteristics of soft biological tissues and biomaterials are broadly classified into two categories: standard ML algorithms and physics-informed ML algorithms. The standard ML models are then organized based on their tasks, being grouped into Regression and Classification subcategories. Within these categories, studies employ various supervised learning models, including support vector machines (SVMs), bagged decision trees (BDTs), artificial neural networks (ANNs) or deep neural networks (DNNs), and convolutional neural networks (CNNs). Additionally, the utilization of unsupervised learning approaches, such as autoencoders incorporating principal component analysis (PCA) and/or low-rank approximation (LRA), is based on the specific characteristics of the training data. The training data predominantly consists of three types: experimental mechanical data, including uniaxial or biaxial stress-strain data; synthetic mechanical data generated through non-linear fitting and/or FE simulations; and image data such as 3D second harmonic generation (SHG) images or computed tomography (CT) images. The evaluation of performance for physics-informed ML models primarily relies on the coefficient of determination . In contrast, various metrics and error measures are utilized to assess the performance of standard ML models. Furthermore, our review includes an extensive examination of prevalent biomaterial models that can serve as physical laws for physics-informed ML models. ML models offer an accurate, fast, and reliable approach for evaluating the mechanical characteristics of diseased soft tissue segments and selecting optimal biomaterials for time-critical soft tissue surgeries. Among the various ML models examined in this review, physics-informed neural network models exhibit the capability to forecast the mechanical response of soft biological tissues accurately, even with limited training samples. These models achieve high values ranging from 0.90 to 1.00. This is particularly significant considering the challenges associated with obtaining a large number of living tissue samples for experimental purposes, which can be time-consuming and impractical. Additionally, the review not only discusses the advantages identified in the current literature but also sheds light on the limitations and offers insights into future perspectives.

Biological Response of the Peri-Implant Mucosa to Different Definitive Implant Rehabilitation Materials.

Sealing the peri-implant tissue is a determining factor for long-term implant survival. In the transmucosal region, the cervical fraction of the prosthetic crown is in contact with these tissues, so mucointegration will also be influenced by the biomaterial used for the prosthetic restoration. This study aims to compare the tissue response generated by definitive restorative materials and polymeric materials from a histological point of view. This study performed an observational prospective cohort study in which biopsies of the peri-implant mucosa were taken after placement of implant-supported prosthetic restorations made of different materials (zirconium oxide, lithium disilicate, and PMMA). A statistically significant difference was observed in the increase in the thickness of the non-keratinized epithelium when comparing the definitive materials (zirconium oxide/lithium disilicate) vs. the provisional material (PMMA) and in the number of collagen fibers when comparing zirconium oxide and lithium disilicate. This study found that zirconia is the material that presents the most adequate biological response of peri-implant tissues. It shows a lower intensity of inflammatory cellular content, a total normality in the number of collagen fibers (the arrangement of the fibers is normal in 90% of the cases), and vascular proliferation of connective tissue in 83% of the cases. These parameters make it a material with a predictable response. Similarly, only the following slight statistically significant differences between the definitive and provisional materials are observed, indicating that the biological response generated by the provisional material (PMMA) is not very different from that obtained with the placement of the definitive restoration.

An investigation of biological tissue responses to thermal shock within the framework of fractional heat transfer theory

An investigation of biological tissue responses to thermal shock within the framework of fractional heat transfer theory

The Complex Interplay of Insulin Resistance and Metabolic Inflammation in Transition Dairy Cows.

During the transition period, dairy cows exhibit heightened energy requirements to sustain fetal growth and lactogenesis. The mammary gland and the growing fetus increase their demand for glucose, leading to the mobilization of lipids to support the function of tissues that can use fatty acids as energy substrates. These physiological adaptations lead to negative energy balance, metabolic inflammation, and transient insulin resistance (IR), processes that are part of the normal homeorhetic adaptations related to parturition and subsequent lactation. Insulin resistance is characterized by a reduced biological response of insulin-sensitive tissues to normal physiological concentrations of insulin. Metabolic inflammation is characterized by a chronic, low-level inflammatory state that is strongly associated with metabolic disorders. The relationship between IR and metabolic inflammation in transitioning cows is intricate and mutually influential. On one hand, IR may play a role in the initiation of metabolic inflammation by promoting lipolysis in adipose tissue and increasing the release of free fatty acids. Metabolic inflammation, conversely, triggers inflammatory signaling pathways by pro-inflammatory cytokines, thereby leading to impaired insulin signaling. The interaction of these factors results in a harmful cycle in which IR and metabolic inflammation mutually reinforce each other. This article offers a comprehensive review of recent advancements in the research on IR, metabolic inflammation, and their intricate interrelationship. The text delves into multiple facets of physiological regulation, pathogenesis, and their consequent impacts.

Additive Manufacturing of Metal Load‐Bearing Implants 2: Surface Modification and Clinical Challenges

AbstractAdditive manufacturing (AM) permits sustainable production of personalized load‐bearing metal implants with complex structures. Regulations prescribe that implants have to meet strict requirements to not harm patients, and production technique should allow the certification of their performance. Process, materials, operating parameters, and customization to patient's needs could limit AM. Layer‐by‐layer material deposition and repeated thermal cycles may make outer surface of AM implants chemically and physically uneven and rough, eliciting biological response of host tissue and hindering therapeutic success. In this paper, we discuss the clinical challenges that AM must overcome to replace traditional techniques in implant and prostheses design.

A Methodological Approach for Interpreting and Comparing the Viscoelastic Behaviors of Soft Biological Tissues and Hydrogels at the Cell-Length Scale

The behavior of a cell is strongly influenced by the physical properties and stimuli in its microenvironment. Furthermore, the activation and modulation of mechanotransduction pathways are involved in tissue development and homeostasis and even pathological processes. Thus, when developing materials aimed at mimicking the extracellular matrixes of healthy or pathological tissues, their mechanical features should be closely considered. In this context, nanoindentation represents a powerful technique for mechanically characterizing biological tissues and hydrogels at the cell-length scale. However, standardized experimental protocols and data analysis techniques are lacking. Here, we proposed a methodological approach based on the nanoindentation technique for quantitatively analyzing and comparing the time-dependent load relaxation responses of soft biological tissues and hydrogels. As this was an explanatory study, stress-relaxation nanoindentation tests were performed on samples of pig and human lung tissues and of a specific gelatin-methacryloyl (GelMA) hydrogel to quantify and compare their viscoelastic properties. The proposed method allowed for identifying the characteristic parameters needed for describing the behavior of each sample, permitting us to quantitatively compare their mechanical behaviors. All samples showed load relaxation at a defined indentation depth because of their intrinsic viscoelastic behaviors, and the GelMA samples showed the highest relaxation capabilities. The distribution of the characterization parameters showed that the biological samples presented similar time-dependent responses, while differences were observed in the GelMA samples. Overall, the proposed methodological approach allows for providing key insights into the time-dependent behaviors of soft biological tissues and hydrogels at the cell-length scale in view of supporting tissue engineering and pathophysiological investigations.

Material Design of Porous Hydroxyapatite Ceramics via Inverse Analysis of an Estimation Model for Bone-Forming Ability Based on Machine Learning and Experimental Validation of Biological Hard Tissue Responses.

Hydroxyapatite and β-tricalcium phosphate have been clinically applied as artificial bone materials due to their high biocompatibility. The development of artificial bones requires the verification of safety and efficacy through animal experiments; however, from the viewpoint of animal welfare, it is necessary to reduce the number of animal experiments. In this study, we utilized machine learning to construct a model that estimates the bone-forming ability of bioceramics from material fabrication conditions, material properties, and in vivo experimental conditions. We succeeded in constructing two models: 'Model 1', which predicts material properties from their fabrication conditions, and 'Model 2', which predicts the bone-formation rate from material properties and in vivo experimental conditions. The inclusion of full width at half maximum (FWHM) in the feature of Model 2 showed an improvement in accuracy. Furthermore, the results of the feature importance showed that the FWHMs were the most important. By an inverse analysis of the two models, we proposed candidates for material fabrication conditions to achieve target values of the bone-formation rate. Under the proposed conditions, the material properties of the fabricated material were consistent with the estimated material properties. Furthermore, a comparison between bone-formation rates after 12 weeks of implantation in the porcine tibia and the estimated bone-formation rate. This result showed that the actual bone-formation rates existed within the error range of the estimated bone-formation rates, indicating that machine learning consistently predicts the results of animal experiments using material fabrication conditions. We believe that these findings will lead to the establishment of alternative animal experiments to replace animal experiments in the development of artificial bones.

Exploring Deep Learning for Estimating the Isoeffective Dose of FLASH Irradiation From Mouse Intestinal Histological Images

PurposeUltra-high dose rate (FLASH) irradiation has been reported to reduce normal tissue damage compared with conventional dose rate (CONV) irradiation without compromising tumor control. This proof-of-concept study aims to develop a deep learning (DL) approach to quantify the FLASH isoeffective dose (dose of CONV that would be required to produce the same effect as the given physical FLASH dose) with post-irradiation mouse intestinal histological images. Methods and materials84 healthy C57BL/6J female mice underwent 16 MeV electron CONV (0.12Gy/s; n=41) or FLASH (200Gy/s; n=43) single fraction whole abdominal irradiation. Physical dose ranged from 12 to 16Gy for FLASH and 11 to 15Gy for CONV in 1Gy increments. 4 days after irradiation, 9 jejunum cross-sections from each mouse were H&E stained and digitized for histological analysis. CONV dataset was randomly split into training (n=33) and testing (n=8) datasets. ResNet101-based DL models were retrained using the CONV training dataset to estimate the dose based on histological features. The classical manual crypt counting (CC) approach was implemented for model comparison. Cross-section-wise mean squared error (CS-MSE) was computed to evaluate the dose estimation accuracy of both approaches. The validated DL model was applied to the FLASH dataset to map the physical FLASH dose into the isoeffective dose. ResultsThe DL model achieved a CS-MSE of 0.20Gy2 on the CONV testing dataset compared with 0.40Gy2 of the CC approach. Isoeffective doses estimated by the DL model for FLASH doses of 12, 13, 14, 15, and 16 Gy were 12.19±0.46, 12.54±0.37, 12.69±0.26, 12.84±0.26, and 13.03±0.28 Gy, respectively. ConclusionsOur proposed DL model achieved accurate CONV dose estimation. The DL model results indicate that in the physical dose range of 13 to 16 Gy, the biological dose response of small intestinal tissue to FLASH irradiation is represented by a lower isoeffective dose compared to the physical dose. Our DL approach can be a tool for studying isoeffective doses of other radiation dose modifying interventions.

Automation in Dentistry with Mechanical Drills and Lasers for Implant Osteotomy: A Narrative-Scoping Review.

The popularity of implants is increasing with the aging population requiring oral-dental rehabilitation. There are several critical steps in the implant workflow, including case selection, implant design, surgical procedure, biological tissue responses, and functional restoration. Among these steps, surgical osteotomy procedures are a crucial determinant of clinical success. This brief review was aimed at outlining the current state of the field in automation-assisted implant surgical osteotomy technologies. A broad search of the literature was performed to identify current literature. The results are outlined in three broad categories: semi-automated static (image-guided) or dynamic (navigation-assisted) systems, and fully-automated robotic systems. As well as the current mechanical rotary approaches, the literature supporting the use of lasers in further refinement of these approaches is reviewed. The advantages and limitations of adopting autonomous technologies in practical clinical dental practices are discussed. In summary, advances in clinical technologies enable improved precision and efficacious clinical outcomes with implant dentistry. Hard-tissue lasers offer further advancements in precision, improved biological responses, and favorable clinical outcomes that require further investigation.

Impact of Phytomolecules with Nanotechnology on the Treatment of Inflammation

Abstract: Inflammation is a part of the biological response of body tissues against harmful stimuli, such as damaged cells, pathogens, irradiations, and toxic compounds. Numerous treatments, including anti-inflammatory drugs that treat the condition of inflammation, are available for its management. Because of the severe adverse effects associated with synthetic medications, phytotherapy may be a promising and effective approach to treating inflammation. The therapeutic potential of herbs is due to their capacity to target a variety of inflammatory mediators, including chemokines, cytokines, nitric oxide, lipoxygenase, nuclear factor kappa-B, and arachidonic acid. Furthermore, nanomedicine may be a valuable and effective formulation approach for overcoming the drawbacks of phytoconstituents, such as their low bioavailability, high first-pass metabolism, and poor stability. The current manuscript provides a thorough description of many phytoconstituents and herbal plants that have great potential for treating inflammation-related diseases, as well as information on their limitations, drug formulations, and regulatory issues.

Possible mechanisms of phlebitis-like abnormal reaction (PLAR) after cyanoacrylate obliteration of varicose veins

Based on their own research and a review of the literature, the authors analyze the possible cellular mechanisms of the development of an inflammatory reaction after the obliteration of varicose veins with cyanoarylate adhesive compounds (CAO), which received the name phlebitis- Like abnormal Reaction (PLAR) in foreign sources. Despite the existing opinion about the “abnormal” nature of the inflammatory reaction, it is noted that the main stages of its development are fully consistent with the currently known molecular and cellular mechanisms of the response of biological tissues to contact with a foreign antigenic substance and are of a natural nature. The cause of the development of acute alterative inflammation in the vein wall is the direct contact of the endothelium with an aggressive environment, which is cyanoacrylate. A specific feature of the development of chronic inflammation in the vein wall is its productive interdaily character, which is replaced by proliferative processes. The main role in the development of successive stages of PLAR development is played by monocytic, mast and giant cells of foreign bodies, as well as the mechanisms underlying the regulation of the functional activity of these cells. During the period of cyanoacrylate biodegradation, its cellular environment corresponds to all morphological features of a phagocytoma, whose activity decreases with the biodegradation of cyanoacrylate with simultaneous connective tissue proliferation. The development of possible chronic granulomatous inflammation is based on a local autoimmune process associated with the formation of giant multinucleated epithelioid cells (Langerhans cells). In conclusion, it is emphasized that today, when using various cyanoacrylate compounds for the purpose of adhesive obliteration of veins, taking into account the accumulated clinical data and morphological studies, the final answers to the existing reasonable objections about the complete safety of the use of cyanoacrylates in clinical practice should be given by fundamental immunohistochemical and genetic studies.

An investigation of how specimen dimensions affect biaxial mechanical characterizations with CellScale BioTester and constitutive modeling of porcine tricuspid valve leaflets

Biaxial mechanical characterizations are the accepted approach to determine the mechanical response of many biological soft tissues. Although several computational and experimental studies have examined how experimental factors (e.g., clamped vs. suture mounting) affect the acquired tissue mechanical behavior, little is known about the role of specimen dimensions in data acquisition and the subsequent modeling. In this study, we combined our established mechanical characterization framework with an iterative size-reduction protocol to test the hypothesis that specimen dimensions affect the observed mechanical behavior of biaxial characterizations. Our findings indicated that there were non-significant differences in the peak equibiaxial stretches of tricuspid valve leaflets across four specimen dimensions ranging from 4.5×4.5mm to 9 × 9mm. Further analyses revealed that there were significant differences in the low-tensile modulus of the circumferential tissue direction. These differences resulted in significantly different constitutive model parameters for the Tong–Fung model between different specimen dimensions of the posterior and septal leaflets. Overall, our findings demonstrate that specimen dimensions play an important role in experimental characterizations, but not necessarily in constitutive modeling of soft tissue mechanical behavior during biaxial testing with the commercial CellScale BioTester.

Simulating the mechanical stimulation of cells on a porous hydrogel scaffold using an FSI model to predict cell differentiation.

3D-structured hydrogel scaffolds are frequently used in tissue engineering applications as they can provide a supportive and biocompatible environment for the growth and regeneration of new tissue. Hydrogel scaffolds seeded with human mesenchymal stem cells (MSCs) can be mechanically stimulated in bioreactors to promote the formation of cartilage or bone tissue. Although in vitro and in vivo experiments are necessary to understand the biological response of cells and tissues to mechanical stimulation, in silico methods are cost-effective and powerful approaches that can support these experimental investigations. In this study, we simulated the fluid-structure interaction (FSI) to predict cell differentiation on the entire surface of a 3D-structured hydrogel scaffold seeded with cells due to dynamic compressive load stimulation. The computational FSI model made it possible to simultaneously investigate the influence of both mechanical deformation and flow of the culture medium on the cells on the scaffold surface during stimulation. The transient one-way FSI model thus opens up significantly more possibilities for predicting cell differentiation in mechanically stimulated scaffolds than previous static microscale computational approaches used in mechanobiology. In a first parameter study, the impact of the amplitude of a sinusoidal compression ranging from 1% to 10% on the phenotype of cells seeded on a porous hydrogel scaffold was analyzed. The simulation results show that the number of cells differentiating into bone tissue gradually decreases with increasing compression amplitude, while differentiation into cartilage cells initially multiplied with increasing compression amplitude in the range of 2% up to 7% and then decreased. Fibrous cell differentiation was predicted from a compression of 5% and increased moderately up to a compression of 10%. At high compression amplitudes of 9% and 10%, negligible areas on the scaffold surface experienced high stimuli where no cell differentiation could occur. In summary, this study shows that simulation of the FSI system is a versatile approach in computational mechanobiology that can be used to study the effects of, for example, different scaffold designs and stimulation parameters on cell differentiation in mechanically stimulated 3D-structured scaffolds.

Poisson’s ratio of fluorapatite crystals

Introduction. It is known that in the pathogenesis of caries and non-carious lesions of teeth, an important role is played by enamel resistance, which is determined by numerous factors, among which the structure and physical and mechanical properties of tooth tissues are considered to be among the most important. This report summarizes the literature data on the Poisson’s ratio of fluorapatite (FAp), a biomaterial and one of the mineral components of dental hard tissues. The literature search was carried out in electronic databases Scopus, Web of Science, PubMed, Medline, Elibrary, MatWeb for the period from January 1970 to March 2023 inclusive. Some of the data are supplemented by our own calculations based on formulas for crystalline hexagonal systems (systems). As is known, Poisson’s ratio determines the volumetric response of materials and biological tissues to mechanical load, but its anisotropic properties have not been studied in detail. Material and methods. Calculations of the Poisson’s ratio of fluorapatite were carried out in the computer algebra system Mathcad 15.0, using the online analysis of elasticity tensors ELATE, and an integrated molecular modeling system was used - the graphic package VESTA (Visualization for Electronic and STructural Analisis). Results and discussion. The Poisson’s ratio values of fluorapatite crystals varied: minimum values 0.057–0.283, maximum values 0.302–0.494, average values obtained by integration in all directions 0.24–0.308. The coefficient of elastic anisotropy of the fluorapatite crystal lattice, calculated based on the values of Poisson’s ratio, is 1.21–5.29. The results obtained were compared with those of hydroxyapatite (HAp) crystals, dentin, enamel and filling materials. The highest values of the parameters μ and μmax are found in dentin (0.312 and 0.54), the lowest μmin are also found in dentin (0.13). Conclusions. Fluorapatite crystals are elastically anisotropic, and Poisson’s ratio μmax/μmin varies over a wide range. The Poisson’s ratio of fluorapatite showed similarity with the transverse strain coefficient of isomorphic hydroxyapatite crystals (0.207–0.374), dentin (0.13–0.54), enamel (0.16–0.47) and dental composite filling materials (0.24–0 ,35). The latter should help improve the quality of restorations using filling materials for replacing hard dental tissues. It has been established that the organic component of dentin increases its Poisson’s ratio as a biocomposite and the anisotropy of elastic properties.

Anti-inflammatory effects of Peucedanum japonicum Thunberg leaves extract in Lipopolysaccharide-stimulated RAW264.7cells.

Peucedanum japonicum Thunberg are perennial herbaceous plants known to be cultivated for food and traditional medicinal purposes. P. japonicum has been used in traditional medicine to soothe coughs and colds, and to treat many other inflammatory diseases. However, there are no studies on the anti-inflammatory effects of the leaves. Inflammation plays an important role in our body as a defense response of biological tissues to certain stimuli. However, the excessive inflammatory response can lead to various diseases. This study aimed to investigate the anti-inflammatory effects of P. japonicum leaves extract (PJLE) in LPS-stimulated RAW 264.7cells. Nitric Oxide (NO) production assay measured by NO assay. Inducible NO synthase (iNOS), COX-2, MAPKs, AKT, NF-κB, HO-1, Nrf-2 were examined by western blotting. PGE2, TNF-α, and IL-6 were analyzed by ELSIA. Nuclear translocation of NF-κB was detected by immunofluorescence staining. PJLE suppressed inducible nitric oxygen synthase (iNOS) and prostaglandin-endoperoxide synthase 2 (cyclooxygenase-2, COX-2) expression, increased heme oxygenase 1 (HO-1) expression, and decreased nitric oxide production. And PJLE inhibited the phosphorylation of AKT, MAPK, and NF-κB. Taken together, PJLE down-regulated inflammatory factors such as iNOS and COX-2 by inhibiting the phosphorylation of AKT, MAPK, and NF-κB. These results suggest that PJLE can be used as a therapeutic material to modulate inflammatory diseases.

Elasticity of whole blood clots measured via Volume Controlled Cavity Expansion

Measuring and understanding the mechanical properties of blood clots can provide insights into disease progression and the effectiveness of potential treatments. However, several limitations hinder the use of standard mechanical testing methods to measure the response of soft biological tissues, like blood clots. These tissues can be difficult to mount, and are inhomogeneous, irregular in shape, scarce, and valuable. To remedy this, we employ in this work Volume Controlled Cavity Expansion (VCCE), a technique that was recently developed, to measure local mechanical properties of soft materials in their natural environment. Through highly controlled volume expansion of a water bubble at the tip of an injection needle, paired with simultaneous measurement of the resisting pressure, we obtain a local signature of whole blood clot mechanical response. Comparing this data with predictive theoretical models, we find that a 1-term Ogden model is sufficient to capture the nonlinear elastic response observed in our experiments and produces shear modulus values that are comparable to values reported in the literature. Moreover, we find that bovine whole blood stored at 4 °C for greater than 2 days exhibits a statistically significant shift in the shear modulus from 2.53 ± 0.44 kPa on day 2 (N = 13) to 1.23 ± 0.18 kPa on day 3 (N = 14). In contrast to previously reported results, our samples did not exhibit viscoelastic rate sensitivity within strain rates ranging from 0.22 – 21.1 s−1. By surveying existing data on whole blood clots for comparison, we show that this technique provides highly repeatable and reliable results, hence we propose the more widespread adoption of VCCE as a path forward to building a better understanding of the mechanics of soft biological materials.