Annals of Biomedical Engineering最新文献

Ablation therapy is a type of minimally invasive treatment, utilized for various organs including the brain, heart, and kidneys. The accuracy of the ablation process is critically important to avoid both insufficient and excessive ablation, which may result in compromised efficacy or complications. The thermal ablation is formulated by two theoretical models: the heat transfer (HT) and necrosis formation (NF) models. In modern medical practices, feed-forward (FF) and temperature feedback (TFB) controls are primarily used as ablation control methodologies. FF involves pre-therapy procedure planning based on previous experiences and theoretical knowledge without monitoring the intraoperative tissue response, hence, it can’t compensate for discrepancies in the assumed HT or NF models. These discrepancies can arise due to individual patient’s tissue characteristic differences and specific environmental conditions. Conversely, TFB control is based on the intraoperative temperature profile. It estimates the resulting heat damage based on the monitored temperature distribution and assumed NF model. Therefore, TFB can make necessary adjustments even if there is an error in the assumed HT model. TFB is thus seen as a more robust control method against modeling errors in the HT model. Still, TFB is limited as it assumes a fixed NF model, irrespective of the patient or the ablation technique used. An ideal solution to these limitations would be to actively monitor heat damage to the tissue during the operation and utilize this data to control ablation. This strategy is defined as necrosis feedback (NFB) in this study. Such real-time necrosis monitoring modalities making NFB possible are emerging, however, there is an absence of a generalized study that discusses the integration and quantifies the significance of the real-time necrosis monitor techniques for ablation therapy. Such an investigation is expected to clarify the universal principles of how these techniques would improve ablation therapy. In this study, we examine the potential of NFB in suppressing errors associated with the NF model as NFB is theoretically capable of monitoring and suppressing the errors associated with the NF models in its closed control loop. We simulate and compare the performances of TFB and NFB with artificially generated modeling errors using the finite element method (FEM). The results show that NFB provides more accurate ablation control than TFB when NF-oriented errors are applied, indicating NFB’s potential to improve the ablation control accuracy and highlighting the value of the ongoing research to make real-time necrosis monitoring a clinically viable option.

Background

ChatGPT could be a useful tool in the infectious disease field. However, the application of ChatGPT for the treatment of infectious diseases in vulnerable population has not been determined.

Methods

We designed questions about antibiotic use, including the choice of antibiotics, dose, and treatment duration for prevalent infectious disease in vulnerable populations. Each query was posed to ChatGPT-4, and the answers were independently evaluated by two authors. When there were significant differences in the final scores between the two authors, they discussed the case and answers to obtain results.

Results

Our analysis revealed that 38.1% of responses were comprehensive and correct, with 11.9% containing errors for medication use for patients during pregnancy. For the antibiotic allergy-related questions, 36.1% of responses were comprehensive and correct, and 18.1% contained errors. For older adults, 27.5% of responses were comprehensive and correct, while 25% contained errors. The error rate in patients with kidney disease was 79.2%. For children, 43.8% of answers contained errors.

Conclusion

ChatGPT produced high rates of inaccurate information for treating infectious diseases in special population. Thus, recommendations generated by ChatGPT should be used with caution and checked by healthcare professionals to ensure accuracy and comprehensiveness prior to use.

Objective 

The treatment of mitral valve prolapse involves two distinct repair techniques: chordal replacement (Neochordae technique) and leaflet resection (Resection technique). However, there is still a debate in the literature about which is the optimal one. In this context, we performed an image-based computational fluid dynamic study to evaluate blood dynamics in the two surgical techniques.

Methods 

We considered a healthy subject (H) and two patients (N and R) who underwent surgery for prolapse of the posterior leaflet and were operated with the Neochordae and Resection technique, respectively. Computational Fluid Dynamics (CFD) was employed with prescribed motion of the entire left heart coming from cine-MRI images, with a Large Eddy Simulation model to describe the transition to turbulence and a resistive method for managing valve dynamics. We created three different virtual scenarios where the operated mitral valves were inserted in the same left heart geometry of the healthy subject to study the differences attributed only to the two techniques.

Results 

We compared the three scenarios by quantitatively analyzing ventricular velocity patterns and pressures, transition to turbulence, and the ventricle ability to prevent thrombi formation. From these results, we found that the operative techniques affected the ventricular blood dynamics in different ways, with variations attributed to the reduced mobility of the Resection posterior leaflet. Specifically, the Resection technique resulted in turbulent forces, related with the risk of hemolysis formation, up to 640 Pa, while the other two scenarios exhibited a maximum of 240 Pa. Moreover, in correspondence of the ventricular apex, the Resection technique reduced the areas with low velocity to 15%, whereas the healthy case and the Neochordae case maintained these areas at 30 and 48%, respectively. Our findings suggest that the Neochordae technique developed a more physiological flow with respect to the Resection technique.

Conclusion

Resection technique gives rise to a different direction of the mitral jet during diastole increasing the ability to washout the ventricular apex preventing from thrombi formation, but at the same time it promotes turbulence formation that is associated with ventricular effort and risk of hemolysis.

Purpose

Valve-sparing root replacement (VSRR) is attractive for aortic root dilation as it preserves the native aortic valve (AoV). Low effective height (eH) after reconstruction is a risk factor for repair failure and reoperation. We developed and validated a quantitative AoV repair strategy to reliably restore normal valve proportions to promote long-term function.

Methods

Normal AoV proportions were used to derive geometric relationships for sinotubular junction diameter (D_STJ), free edge length (FEL), free edge angle, and commissure height. These relationships informed two models for predicting eH following VSRR: (1) assuming valve symmetry and (2) accounting for valve asymmetry. Porcine heart (n = 6) ex vivo validation was performed under 4 VSRR scenarios: “Ideal” (tube graft size targeting FEL/D_STJ = 1.28), “Oversized” (one graft size larger than Ideal), “Undersized” (two sizes smaller), and “Undersized + Plicated” (FEL/D_STJ = 1.28 restored with leaflet plication).

Results

Our analytical models predicted eH using preoperative measurements and estimated reconstructed dimensions. The Oversized graft exhibited similar eH to Ideal but higher regurgitation in the ex vivo model, whereas the Undersized graft demonstrated lower eH and regurgitation. Plication in the Undersized graft restored valve function (regurgitation & eH) similar to Ideal in the ex vivo model and above Ideal in the analytical models. Both analytical models predicted ex vivo eH well except in the Oversized and Undersized + Plicated conditions.

Conclusion

Utilizing measurements from preoperative imaging and simple mathematical models, patient-specific operative plans for VSRR can be created by estimating valve dimensions necessary to achieve favorable valve features post-repair. Clinical application of this approach promises to improve consistency in achieving optimal long-term dimensions and durability.

Purpose

To investigate the effect of changing systolic and diastolic blood pressures (SBP and DBP, respectively) on sinus flow and valvular and epicardial coronary flow dynamics after TAVR and SAVR.

Methods

SAPIEN 3 and Magna valves were deployed in an idealized aortic root model as part of a pulse duplicating left heart flow loop simulator. Different combinations of SBP and DBP were applied to the test setup and the resulting change in total coronary flow from baseline (120/60 mmHg), effective orifice area (EOA), and left ventricular (LV) workload, with each combination, was assessed. In addition, particle image velocimetry was used to assess the Laplacian of pressure (({nabla }^{2}text{P})) in the sinus, coronary and main flow velocities, the energy dissipation rate (EDR) in the sinus and the LV workload.

Results

This study shows that under an elevated SBP, there is an increase in the total coronary flow, EOA, LV workload, peak velocities downstream of the valve, ({nabla }^{2}text{P}), and EDR. With an elevated DBP, there was an increase in the total coronary flow and ({nabla }^{2}text{P}). However, EOA and LV workload decreased with an increase in DBP, and EDR increased with a decrease in DBP.

Conclusions

Blood pressure alters the hemodynamics in the sinus and downstream flow following aortic valve replacement, potentially influencing outcomes in some patients.

In the last few years, the microfluidic production of nanoparticles (NPs) is becoming a promising alternative to conventional industrial approaches (e.g., nanoprecipitation, salting out, and emulsification-diffusion) thanks to the production efficiency, low variability, and high controllability of the production parameters. Nevertheless, the development of new formulations and the switching of the production process toward microfluidic platforms requires expensive and time-consuming number of experiments for the tuning of the formulation to obtain NPs with specific morphological and functional characteristics. In this work, we developed a computational fluid dynamic pipeline, validated through an ad hoc experimental strategy, to reproduce the mixing between the solvent and anti-solvent (i.e., acetonitrile and TRIS–HCl, respectively). Moreover, beyond the classical variables able to describe the mixing performances of the microfluidic chip, novel variables were described in order to assess the region of the NPs formation and the changing of the amplitude of the precipitation region according to different hydraulic conditions. The numerical approach proved to be able to capture a progressive reduction of the nanoprecipitation region due to an increment of the flow rate ratio; in parallel, through the experimental production, a progressive increment of the NPs size heterogeneity was observed with the same fluid dynamic conditions. Hence, the preliminary comparison between numerical and experimental evidence proved the effectiveness of the computational strategy to optimize the NPs manufacturing process.

Graphical Abstract

Purpose

Estimating loading of the knee joint may be helpful in managing degenerative joint diseases. Contemporary methods to estimate loading involve calculating knee joint contact forces using musculoskeletal modeling and simulation from motion capture (MOCAP) data, which must be collected in a specialized environment and analyzed by a trained expert. To make the estimation of knee joint loading more accessible, simple input predictors should be used for predicting knee joint loading using artificial neural networks.

Methods

We trained feedforward artificial neural networks (ANNs) to predict knee joint loading peaks from the mass, height, age, sex, walking speed, and knee flexion angle (KFA) of subjects using their existing MOCAP data. We also collected an independent MOCAP dataset while recording walking with a video camera (VC) and inertial measurement units (IMUs). We quantified the prediction accuracy of the ANNs using walking speed and KFA estimates from (1) MOCAP data, (2) VC data, and (3) IMU data separately (i.e., we quantified three sets of prediction accuracy metrics).

Results

Using portable modalities, we achieved prediction accuracies between 0.13 and 0.37 root mean square error normalized to the mean of the musculoskeletal analysis-based reference values. The correlation between the predicted and reference loading peaks varied between 0.65 and 0.91. This was comparable to the prediction accuracies obtained when obtaining predictors from motion capture data.

Discussion

The prediction results show that both VCs and IMUs can be used to estimate predictors that can be used in estimating knee joint loading outside the motion laboratory. Future studies should investigate the usability of the methods in an out-of-laboratory setting.

Instrumented mouthguards (iMGs) are widely applied to measure head acceleration event (HAE) exposure in sports. Despite laboratory validation, on-field factors including potential sensor skull-decoupling and spurious recordings limit data accuracy. Video analysis can provide complementary information to verify sensor data but lacks quantitative kinematics reference information and suffers from subjectivity. The purpose of this study was to develop a rigorous multi-stage screening procedure, combining iMG and video as independent measurements, aimed at improving the quality of on-field HAE exposure measurements. We deployed iMGs and gathered video recordings in a complete university men’s ice hockey varsity season. We developed a four-stage process that involves independent video and sensor data collection (Stage I), general screening (Stage II), cross verification (Stage III), and coupling verification (Stage IV). Stage I yielded 24,596 iMG acceleration events (AEs) and 17,098 potential video HAEs from all games. Approximately 2.5% of iMG AEs were categorized as cross-verified and coupled iMG HAEs after Stage IV, and less than 1/5 of confirmed or probable video HAEs were cross-verified with iMG data during stage III. From Stage I to IV, we observed lower peak kinematics (median peak linear acceleration from 36.0 to 10.9 g; median peak angular acceleration from 3922 to 942 rad/s²) and reduced high-frequency signals, indicative of potential reduction in kinematic noise. Our study proposes a rigorous process for on-field data screening and provides quantitative evidence of data quality improvements using this process. Ensuring data quality is critical in further investigation of potential brain injury risk using HAE exposure data.