Bioengineering最新文献

Artificial Intelligence (AI) has the potential to revolutionize cytopathology by enhancing diagnostic accuracy, efficiency, and accessibility. However, the implementation of AI in this field presents significant challenges and opportunities. This review paper explores the current landscape of AI applications in cytopathology, highlighting the critical challenges, including data quality and availability, algorithm development, integration and standardization, and clinical validation. We discuss challenges such as the limitation of only one optical section and z-stack scanning, the complexities associated with acquiring high-quality labeled data, the intricacies of developing robust and generalizable AI models, and the difficulties in integrating AI tools into existing laboratory workflows. The review also identifies substantial opportunities that AI brings to cytopathology. These include the potential for improved diagnostic accuracy through enhanced detection capabilities and consistent, reproducible results, which can reduce observer variability. AI-driven automation of routine tasks can significantly increase efficiency, allowing cytopathologists to focus on more complex analyses. Furthermore, AI can serve as a valuable educational tool, augmenting the training of cytopathologists and facilitating global health initiatives by making high-quality diagnostics accessible in resource-limited settings. The review underscores the importance of addressing these challenges to harness the full potential of AI in cytopathology, ultimately improving patient care and outcomes.

On-chip microfluidics are advanced in vitro models that simulate lung tissue's native 3D environment more closely than static 2D models to investigate the complex lung architecture and multifactorial processes that lead to pulmonary disease. Current microfluidic systems can be restrictive in the quantities of biological sample that can be retrieved from a single micro-channel, such as RNA, protein, and supernatant. Here, we describe a newly developed large-scale airway-on-chip model that employs a surface area for a cell culture wider than that in currently available systems. This enables the collection of samples comparable in volume to traditional cell culture systems, making the device applicable to any workflow utilizing these static systems (RNA isolation, ELISA, etc.). With our construction method, this larger culture area allows for easier handling, the potential for a wide range of exposures, as well as the collection of low-quantity samples (e.g., volatiles or mitochondrial RNA). The model consists of two large polydimethylsiloxane (PDMS) cell culture chambers under an independent flow of medium or air, separated by a semi-permeable polyethylene (PET) cell culture membrane (23 μm thick, 0.4 μm pore size). Each chamber carries a 5 × 18 mm, 90 mm² (92 mm² with tapered chamber inlets) surface area that can contain up to 1-2 × 10⁴ adherent structural lung cells and can be utilized for close contact co-culture studies of different lung cell types, including airway epithelial cells, fibroblasts, smooth muscle cells, and endothelial cells. The parallel bi-chambered design of the chip allows for epithelial cells to be cultured at the air-liquid interface (ALI) and differentiation into a dense, multi-layered, pseudostratified epithelium under biological flow rates. This millifluidic airway-on-chip advances the field by providing a readily reproducible, easily adjustable, and cost-effective large-scale fluidic 3D airway cell culture platform.

Background: Regeneration in the case of major burn subjects must involve tissue and structural regeneration, but also functional regeneration, as scars derived from burns often compromise motility. Electromagnetic fields and electrical stimulation may be a possible treatment for these cases, considering they cause a thermal effect and magneto-mechanical transduction first and selective tissue stimulation second.

Methods: A case of a majorly burned woman with severe motor deficits, treated with electromagnetic fields and electrical stimulation in vacuum, associated with a personalized nutritional program, was described. The latter was necessary to favor weight loss with the preservation of the weakened structure. Ultrasonography, Doppler ultrasound, and body composition were measured. Moreover, postural evaluation was performed.

Results: Immediately after the treatment, a restructuring of all tissue was seen. After 6 months, the tissue regeneration was evident, with neo-angiogenesis. From the functional point of view, her motility improved, and she stopped using a walker.

Conclusions: The combined therapy allows her to obtain unthinkable results in a short time. For this reason, it could become the elective treatment for major burn scars.

This study evaluates the influence of conventional versus simplified drilling protocols on bone remodeling after the osteointegration period, marginal bone loss (MBL), and primary implant stability. A randomized, double-blind clinical trial was conducted involving 44 implants in 37 patients over a two-year period. The primary outcome was peri-implant tissue stability, measured as MBL at baseline, 12 months, and 24 months. Secondary outcomes included implant stability, measured via insertion torque, and survival rates. The results indicated no significant differences in initial bone remodeling and MBL between groups after 24 months. Both protocols demonstrated high survival rates, with one implant failure recorded in the simplified protocol group. Although simplified drilling protocols may reduce surgical complexity, concerns about heat generation and reduced adaptability in osteotomy were described in the literature. This study concludes that drilling protocol choice does not significantly impact bone levels during osteointegration, crestal bone maintenance, or implant survival over 24 months, but further research is needed to explore long-term effects and prosthetic factors.

Introduction: After myocardial infarction (MI), the heart undergoes necrosis, inflammation, scar formation, and remodeling. While restoring blood flow is crucial, it can cause ischemia-reperfusion (IR) injury, driven by reactive oxygen species (ROSs), which exacerbate cell death and tissue damage. This study introduces a mathematical model capturing key post-MI dynamics, including inflammatory responses, IR injury, cardiac remodeling, and stem cell therapy. The model uses nonlinear ordinary differential equations to simulate these processes under varying conditions, offering a predictive tool to understand MI pathophysiology better and optimize treatments.

Methods: After myocardial infarction (MI), left ventricular remodeling progresses through three distinct yet interconnected phases. The first phase captures the immediate dynamics following MI, prior to any medical intervention. This stage is mathematically modeled using the system of ordinary differential equations: The second and third stages of the remodeling process account for the system dynamics of medical treatments, including oxygen restoration and subsequent stem cell injection at the injury site.

Results: We simulate heart tissue and immune cell dynamics over 30 days for mild and severe MI using the novel mathematical model under medical treatment. The treatment involves no intervention until 2 h post-MI, followed by oxygen restoration and stem cell injection at day 7, which is shown experimentallyand numerically to be optimal. The simulation incorporates a baseline ROS threshold (Rc) where subcritical ROS levels do not cause cell damage.

Conclusion: This study presents a novel mathematical model that extends a previously published framework by incorporating three clinically relevant parameters: oxygen restoration rate (ω), patient risk factors (γ), and neutrophil recruitment profile (δ). The model accounts for post-MI inflammatory dynamics, ROS-mediated ischemia-reperfusion (IR) injury, cardiac remodeling, and stem cell therapy. The model's sensitivity highlights critical clinical insights: while oxygen restoration is vital, excessive rates may exacerbate ROS-driven IR injury. Additionally, heightened patient risk factors (e.g., smoking, obesity) and immunodeficiency significantly impact tissue damage and recovery. This predictive tool offers valuable insights into MI pathology and aids in optimizing treatment strategies to mitigate IR injury and improve post-MI outcomes.

Peri-prosthetic joint infection (PJI) is a major post-arthroplasty complication that warrants alternative antibacterial approaches to improve prophylaxis and treatment outcomes. Local administration of analgesics post-surgery is common. Recent studies have demonstrated the antimicrobial potential of analgesics and the feasibility of dual drug-eluting ultra-high molecular weight polyethylene (UHMWPE) for local antibacterial applications. However, the antibacterial mechanism of action is poorly understood, and the translational value of antimicrobial dual drug-loaded UHMWPE has not been evaluated. In this study, we utilized the Laurdan assay and gene expression analysis to demonstrate the antibacterial action of bupivacaine hydrochloride (BP) and tolfenamic acid (TA) against Staphylococcus aureus. Furthermore, we incorporated BP and TA into UHMWPE at different weight concentrations and studied their longitudinal drug release and real-time antibacterial properties. The analgesics showed a significant effect on the bacterial membrane properties comparable to known antibiotics and regulated bacterial gene expression. For the dual drug-loaded UHMWPE, the drug release rate from BP/TA combinations was interestingly not a direct function of the loaded drug weight percent, potentially due to the hydrophobicity of TA and the interactions between the two drugs. Combinations of BP and TA at the higher total drug concentration (10 and 20%) showed a prolonged antibacterial effect against S. aureus, with great potential for prophylactic use.

Background: The physicochemical changes of the surface aim to improve cell adhesion, proliferation, and differentiation, that is, better biological interaction with the cells and, consequently, with the peri-implant tissues. In the present study, implants with the same macrogeometry were compared in vitro and in vivo, but with two different surfaces: micro-rough and a new micro-nano-rough surface.

Materials and methods: A total of 90 implants were used, 10 of which were used for in vitro surface characterization (n = 5 per group) through scanning electron microscopy (SEM), atomic force microscopy (AFM), and surface roughness measurements. For in vivo tests, 80 implants (n = 40 per group) were used in 20 rabbits (n = 2 implants per tibia). Two experimental groups were created: a control group, where the implants had a surface treated by sandblasting with titanium oxide microparticles, and a test group, where the implants were sandblasted using the same process as the previous group plus acid conditioned. The implant stability quotient (ISQ) was measured by resonance frequency (initially and at both euthanasia times). Animals were euthanized 3 and 5 weeks after implantation (n = 10 animals per time). Ten samples from each group at each time point were evaluated by removal torque (RTv). Another ten samples from each group were evaluated histologically and histomorphometrically, measuring the percentage of bone-to-implant contact (%BIC) and the bone area fraction occupancy (%BAFO).

Results: In vitro, it was possible to observe a more homogeneous surface for the test group compared to the control group. ISQ values showed statistical differences at both 3 and 5 weeks (test > control). For RTv, the values were: 44.5 ± 4.25 Ncm (control group) and 48.6 ± 3.17 Ncm (test group) for the time of 3 weeks; 64.3 ± 4.50 Ncm (control group) and 76.1 ± 4.18 Ncm (test group) at 5 weeks. The %BIC and %BAFO values measured in both groups and at both times did not show significant differences (p > 0.05).

Conclusions: The higher removal torque and ISQ values presented in the samples from the test group compared to the control group indicate that there was an acceleration in the mineralization process of the newly formed bone matrix.

Proof-of-concept to determine the direct biomechanical effects of cardiac contractility modulation (CCM) on living myocardial slices (LMS) from patients with end-stage heart failure (HF). Left ventricular LMS from patients with end-stage HF were produced and cultured in a biomimetic system with mechanical loading and electrical stimulation. CCM stimulation (80 mA, 40 ms delay, 21 ms duration) enhanced maximum contractile force (CCM: 1229 µN (587-2658) vs. baseline: 1066 µN (529-2128), p = 0.05) and area under the contractile curve (CCM: 297 (151-562) vs. baseline: 243 (129-464), p = 0.05) but did not significantly impact contractile duration, time to peak, or time to relaxation. Increasing CCM stimulation delay, duration, and amplitude resulted in a higher fraction of LMS with a positive inotropic response. Furthermore, CCM attenuated the negative force-frequency relationship in HF-LMS. CCM stimulation enhanced contractile force in HF-LMS. The fraction of LMS exerting a positive inotropic response to CCM increased with increasing delay, duration, and amplitude settings, suggesting that personalizing stimulation parameters could optimize the beneficial effects of CCM. CCM is a novel device-based therapy that may improve contractile function, ejection fraction, functional outcomes, and quality of life in patients with heart failure. However, continuous efforts are needed to identify true responders to CCM therapy, understand the exact mechanisms, and optimize the contractile response to CCM stimulation. The present study revealed that CCM enhanced the contractile force of HF-LMS in a stimulation setting-dependent manner, reaching a larger fraction of the myocardium while increasing delay, duration, and amplitude. This understanding may contribute to the individualization of CCM stimulation settings.

The stability of the tibial component in Total Knee Arthroplasty (TKA) is critical to preventing aseptic loosening, a major cause of implant failure. However, existing tibial stem designs often lead to stress shielding and bone resorption, highlighting the need for further optimization. This study addresses these challenges by employing the Design of Experiments (DOE) methodology, specifically utilizing a full factorial design approach combined with finite element analysis (FEA), to optimize the geometry of the tibial stem. The material properties of the cortical and cancellous bone, as well as the tibial tray, were assigned based on values from the literature, representing their elastic moduli and Poisson's ratios. For boundary conditions, the distal end of the tibia was fully constrained to simulate realistic load transfer, while compressive loads representative of walking and daily activities were applied to the tibial base. Key design parameters, including stem diameter, length, mediolateral ratio (M/L ratio), and wing angle, were systematically analyzed. The results identified stem diameter and length as the most influential factors in improving biomechanical performance, while the wing angle showed minimal impact. The optimized design, featuring a stem diameter of 12 mm, length of 40 mm, M/L ratio of 0.61, and a wing angle of 60°, demonstrated significant reductions in stress shielding and aseptic loosening compared to conventional models. These findings provide valuable insights into enhancing the long-term success of TKA implants by balancing implant stability and minimizing bone resection.

Objective: Named entity recognition (NER) offers a powerful method for automatically extracting key clinical information from text, but current models often lack sufficient support for non-English languages.

Materials and methods: This study investigated a prompt-based NER approach using Google's Gemini 1.5 Pro, a large language model (LLM) with a 1.5-million-token context window. We focused on extracting important clinical entities from Turkish mammography reports, a language with limited available natural language processing (NLP) tools. Our method employed many-shot learning, incorporating 165 examples within a 26,000-token prompt derived from 75 initial reports. We tested the model on a separate set of 85 unannotated reports, concentrating on five key entities: anatomy (ANAT), impression (IMP), observation presence (OBS-P), absence (OBS-A), and uncertainty (OBS-U).

Results: Our approach achieved high accuracy, with a macro-averaged F1 score of 0.99 for relaxed match and 0.84 for exact match. In relaxed matching, the model achieved F1 scores of 0.99 for ANAT, 0.99 for IMP, 1.00 for OBS-P, 1.00 for OBS-A, and 0.99 for OBS-U. For exact match, the F1 scores were 0.88 for ANAT, 0.79 for IMP, 0.78 for OBS-P, 0.94 for OBS-A, and 0.82 for OBS-U.

Discussion: These results indicate that a many-shot prompt engineering approach with large language models provides an effective way to automate clinical information extraction for languages where NLP resources are less developed, and as reported in the literature, generally outperforms zero-shot, five-shot, and other few-shot methods.

Conclusion: This approach has the potential to significantly improve clinical workflows and research efforts in multilingual healthcare environments.