Bioengineering最新文献

Within the healthy human body, cells reside within the physiological environment of a tissue compound. Here, they are subject to constant low levels of mechanical stress that can influence the growth and differentiation of the cells. The liposuction of adipose tissue and the subsequent isolation of mesenchymal stem/stromal cells (MSCs), for example, are procedures that induce a high level of mechanical shear stress. As MSCs play a central role in tissue regeneration by migrating into regenerating areas and driving regeneration through proliferation and tissue-specific differentiation, they are increasingly used in therapeutic applications. Consequently, there is a strong interest in investigating the effects of shear stress on MSCs. In this study, we present a set-up for applying high shear rates to cells based on a rotational rheometer with a small-angle cone-plate configuration. This set-up was used to investigate the effect of various shear stresses on human adipose-derived MSCs in suspension. The results of the study show that the viability of the cells remained unaffected up to 18.38 Pa for an exposure time of 5 min. However, it was observed that intense shear stress damaged the cells, with longer treatment durations increasing the percentage of cell debris.

The segmentation of teeth in 3D dental scans is difficult due to variations in teeth shapes, misalignments, occlusions, or the present dental appliances. Existing methods consistently adhere to geometric representations, omitting the perceptual aspects of the inputs. In addition, current works often lack evaluation on anatomically complex cases due to the unavailability of such datasets. We present a projection-based approach towards accurate teeth segmentation that operates in a detect-and-segment manner locally on each tooth in a multi-view fashion. Information is spatially correlated via recurrent units. We show that a projection-based framework can precisely segment teeth in cases with anatomical anomalies with negligible information loss. It outperforms point-based, edge-based, and Graph Cut-based geometric approaches, achieving an average weighted IoU score of 0.97122±0.038 and a Hausdorff distance at 95 percentile of 0.49012±0.571 mm. We also release Poseidon's Teeth 3D (Poseidon3D), a novel dataset of real orthodontic cases with various dental anomalies like teeth crowding and missing teeth.

Copper nanoparticles (CuNPs) are tiny materials with special features such as high electric conductivity, catalytic activity, antimicrobial activity, and optical activity. Published reports demonstrate their utilization in various fields, including biomedical, agricultural, environmental, wastewater treatment, and sensor fields. CuNPs can be produced utilizing traditional procedures; nevertheless, such procedures have restrictions like excessive consumption of energy, low production yields, and the utilization of detrimental substances. Thus, the adoption of environmentally approachable "green" approaches for copper nanoparticle synthesis is gaining popularity. These approaches involve employing plants, bacteria, and fungi. Nonetheless, there is a scarcity of data regarding the application of microbial bioflocculants in the synthesis of copper NPs. Therefore, this review emphasizes copper NP production using microbial flocculants, which offer economic benefits and are sustainable and harmless. The review also provides a characterization of the synthesized copper nanoparticles, employing numerous analytical tools to determine their compositional, morphological, and topographical features. It focuses on scientific advances from January 2015 to December 2023 and emphasizes the use of synthesized copper NPs in wastewater treatment.

Microfluidic systems offer controlled microenvironments for cell-to-cell and cell-to-stroma interactions, which have precise physiological, biochemical, and mechanical features. The optimization of their conditions to best resemble tumor microenvironments constitutes an experimental modeling challenge, particularly regarding carcinogenesis in the central nervous system (CNS), given the specific features of the blood-brain barrier (BBB). Gel-free 3D microfluidic cell culture systems (gel-free 3D-mFCCSs), including features such as self-production of extracellular matrices, provide significant benefits, including promoting cell-cell communication, interaction, and cell polarity. The proposed microfluidic system consisted of a gel-free culture device inoculated with human brain microvascular endothelial cells (HBEC5i), glioblastoma multiforme cells (U87MG), and astrocytes (ScienCell 1800). The gel-free 3D-mFCCS showed a diffusion coefficient of 4.06 × 10^-9 m²·s^-1, and it reconstructed several features and functional properties that occur at the BBB, such as the vasculogenic ability of HBEC5i and the high duplication rate of U87MG. The optimized conditions of the gel-free 3D-mFCCS allowed for the determination of cellular proliferation, invasion, and migration, with evidence of both physical and biochemical cellular interactions, as well as the production of pro-inflammatory cytokines. In conclusion, the proposed gel-free 3D-mFCCSs represent a versatile and suitable alternative to microfluidic systems, replicating several features that occur within tumor microenvironments in the CNS. This research contributes to the characterization of microfluidic approaches and could lead to a better understanding of tumor biology and the eventual development of personalized therapies.

Primary angle closure glaucoma (PACG) is a major cause of visual impairment, particularly in Asia. Although effective screening tools are necessary, the current gold standard is complex and time-consuming, requiring extensive expertise. Artificial intelligence has introduced new opportunities for innovation in ophthalmic imaging. Anterior chamber depth (ACD) is a key risk factor for angle closure and has been suggested as a quick screening parameter for PACG. This study aims to develop an AI algorithm to quantitatively predict ACD from anterior segment photographs captured using a portable smartphone slit-lamp microscope. We retrospectively collected 204,639 frames from 1586 eyes, with ACD values obtained by anterior-segment OCT. We developed two models, (Model 1) diagnosable frame extraction and (Model 2) ACD estimation, using SWSL ResNet as the machine learning model. Model 1 achieved an accuracy of 0.994. Model 2 achieved an MAE of 0.093 ± 0.082 mm, an MSE of 0.123 ± 0.170 mm, and a correlation of R = 0.953. Furthermore, our model's estimation of the risk for angle closure showed a sensitivity of 0.943, specificity of 0.902, and an area under the curve (AUC) of 0.923 (95%CI: 0.878-0.968). We successfully developed a high-performance ACD estimation model, laying the groundwork for predicting other quantitative measurements relevant to PACG screening.

Prostate cancer is the second most common cancer in men and a leading cause of death worldwide. Early detection is vital, as it often presents with vague symptoms such as nocturia and poor urinary stream. Diagnostic tools like PSA tests, ultrasound, PET-CT, and mpMRI are essential for prostate cancer management. The PI-RADS system helps assess malignancy risk based on imaging. While mpMRI, which includes T1, T2, DWI, and dynamic contrast-enhanced imaging (DCE), is the standard, bpMRI offers a contrast-free alternative using only T2 and DWI. This reduces costs, acquisition time, and the risk of contrast-related side effects but has limitations in detecting higher-risk PI-RADS 3 and 4 lesions. This study compared bpMRI's diagnostic accuracy to mpMRI, focusing on prostate volume and PI-RADS scoring. Both methods showed strong inter-rater agreement for prostate volume (ICC 0.9963), confirming bpMRI's reliability in this aspect. However, mpMRI detected more complex conditions, such as periprostatic fat infiltration and iliac lymphadenopathy, which bpMRI missed. While bpMRI offers advantages like reduced cost and no contrast use, it is less effective for higher-risk lesions, making mpMRI more comprehensive.

Objective: Postoperative pancreatic fistula (POPF) is a common and challenging complication following pancreaticoduodenectomy (PD), occurring in 2% to 46% of cases. Despite various pancreaticojejunostomy techniques, an effective method to prevent POPF has not been established. This study aimed to develop and evaluate a novel 3D-printed biodegradable pancreatic duct stent to simplify the surgical process of pancreaticojejunostomy, reduce anastomotic complexity, and minimize postoperative complications. Methods: Data from 32 patients undergoing total laparoscopic pancreaticoduodenectomy were utilized. Preoperative CT scans were transformed into 3D reconstructions to guide the design and printing of customized stents using polylactic acid (PLA). The stents were assessed for mechanical integrity, surface texture, and thermal stability. Animal experiments were conducted on 16 mini pigs, with the experimental group receiving the novel stent and the control group receiving traditional silicone stents. Results: The 3D-printed stents demonstrated accurate dimensional replication and mechanical reliability. In the animal experiments, the experimental group showed no significant difference in postoperative complications compared to the control group. At 4 weeks post-surgery, CT scans revealed well-healed anastomoses in both groups, with no significant inflammation or other complications. Histological examination and 3D reconstruction models confirmed good healing and device positioning in the experimental group. Conclusion: The 3D-printed biodegradable pancreatic duct stent offers a promising solution for pancreaticojejunostomy, with comparable safety and efficacy to traditional methods. Further research is needed to validate its clinical application.

The development of the finite element method (FEM) combined block polynomial interpolation with the concepts of finite difference formats and the variation principle. Because of this combination, the FEM overcomes the shortcomings of traditional variation methods while maintaining the benefits of current variation methods and the flexibility of the finite difference method. As a result, the FEM is an advancement above the traditional variation methods. The purpose of this study is to experimentally highlight the thermal behavior of two stomatognathic systems, one a control and the other presenting orthodontic treatment by means of a fixed metallic orthodontic appliance, both being subjected to several thermal regimes. In order to sustain this experimental research, we examined the case of a female subject, who was diagnosed with Angle class I malocclusion. The patient underwent a bimaxillary CBCT investigation before initiating the orthodontic treatment. A three-dimensional model with fully closed surfaces was obtained by using the InVesalius and Geomagic programs. Like the tissues examined in the patient, bracket and tube-type elements as well as orthodontic wires can be included to the virtual models. Once it is finished and geometrically accurate, the model is exported to an FEM-using program, such as Ansys Workbench. The intention was to study the behavior of two stomatognathic systems (with and without a fixed metallic orthodontic appliance) subjected to very hot food (70 °C) and very cold food (-18 °C). From the analysis of the obtained data, it was concluded that, following the simulations carried out in the presence of the fixed metallic orthodontic appliance, significantly higher temperatures were generated in the dental pulp.

The purpose of this study was to compare the surface changes and shear bond strength between a resin composite and two zirconia ceramics subjected to sandblasting and forming gas (5% H₂ in N₂) plasma surface treatment. Two types of zirconia ceramic specimens (3Y-TZP and (Y,Nb)-TZP) were divided into groups based on the following surface treatment methods: polishing (Control), sandblasting (SB), sandblasting and plasma (SB-P), and plasma treatment (P). Subsequently, chemical surface modification was performed using Clearfil SE Bond (Kuraray, Tokyo, Japan), and the Filtek Z-250 (3M, Maplewood, MN, USA) resin composite was applied. Shear bond strengths (SBS) and surface characteristics were determined. Plasma treatment was effective in increasing the wettability. For SBS, there were significant differences among the groups, and the (Y,Nb)-TZP and SB-P groups showed the highest bond strength. Similarly, for the 3Y-TZP specimens, the shear bond strength increased with both plasma and sandblasting treatments, although no statistically significant change was observed. In the P group, both (Y,Nb)-TZP and 3Y-TZP showed a significant decrease in shear bond strength with the resin composite compared to the control group.

Periapical radiographs are routinely used in dental practice for diagnosis and treatment planning purposes. However, they often suffer from artifacts, distortions, and superimpositions, which can lead to potential misinterpretations. Thus, an automated detection system is required to overcome these challenges. Artificial intelligence (AI) has been revolutionizing various fields, including medicine and dentistry, by facilitating the development of intelligent systems that can aid in performing complex tasks such as diagnosis and treatment planning. The purpose of the present study was to verify the diagnostic performance of an AI system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on periapical radiographs. A dataset comprising 1000 periapical radiographs collected from 500 adult patients was analyzed by an AI system and compared with annotations provided by two oral and maxillofacial radiologists. A strong correlation (R > 0.5) was observed between AI perception and observers 1 and 2 in carious teeth (0.7-0.73), implants (0.97-0.98), restored teeth (0.85-0.89), teeth with fixed prosthesis (0.92-0.94), and missing teeth (0.82-0.85). The automatic detection by the AI system was comparable to the oral radiologists and may be useful for automatic identification in periapical radiographs.