Post-myocardial infarction (MI) growth and remodeling (G&R), commonly referred to as fibrosis, involves both geometric deformation and progressive stiffening of infarcted tissue due to collagen accumulation. While zero-dimensional (0D) cardiac G&R models have successfully reproduced organ-level adaptations post-infarction, they often neglect evolving tissue properties associated with collagen turnover. In this study, we address this limitation by incorporating a time-dependent stiffening law into a strain-driven 0D framework, extending the original model by Witzenburg et al. (2018). Collagen turnover (CT) was modeled using a phenomenological exponential function, calibrated against experimental hydroxyproline data. The model was validated against independent canine datasets and benchmarked against both the original reference and a baseline No CT simulation. While full time-course verification was not achieved - due to inconsistencies in baseline reported parameters - control and acute states were accurately reproduced. Critically, the CT-enhanced model reduced the mean standardized z-score (MSZ) by 57.8%, with the most substantial improvements seen in ventricular volume and diastolic pressure predictions. These results confirm the added value of explicitly modeling tissue-level remodeling and highlight the importance of accurate initialization to ensure long-term prediction fidelity in reduced-order frameworks.

Objective Action tremors affect many Parkinson’s disease (PD) patients and can significantly impair daily functioning. These tremors often persist despite dopaminergic therapy, while stereotactic surgery, though effective, is invasive and not suitable for all. Alternative treatment options are therefore needed. The STIL Orthosis is a wearable device developed to reduce forearm tremor. Its effectiveness has been shown in essential tremor, but its impact on PD-related action tremors remains unknown. This pilot study evaluates its effect in a small PD sample.

Methods: Three PD patients with action tremor participated in a sham-controlled, single-blind, crossover pilot study. Tremor severity was assessed in three conditions: baseline, sham, and STIL Orthosis. The primary outcome was the TETRAS score. Secondary outcomes included MDS-UPDRS tremor scores, tremor power (accelerometry and sEMG), and patient satisfaction (PGI-I).

Results: Although effects did not reach statistical significance (for n=3), a consistent trend toward tremor reduction was observed across clinical and accelerometry-based measures when comparing the STIL Orthosis to baseline. TETRAS scores decreased from baseline \(13 \pm 4.0\) and sham \(11 \pm 5.2\) to orthosis \(8.0 \pm 2.1\). MDS-UPDRS tremor scores similarly declined (baseline: \(7.3 \pm 0.9\), sham: \(5.3 \pm 1.7\), orthosis: \(3.3 \pm 0.9\)). Two participants showed reduced tremor power (up to \(55\%\) compared to baseline). EMG tremor power of the ECR and FCR muscles decreased by \(15\%\) and \(58\%\), respectively. All three participants reported slight improvement with the orthosis (PGI-I).

Conclusion: This pilot study indicates that the STIL Orthosis influences action tremor in PD. Larger studies are needed to assess its clinical significance.

Nasal airway obstruction (NAO) is one of the most common symptoms in the human respiratory system and causes a considerable financial burden to both individuals and society. Currently, NAO detection is troublesome to achieve, which can influence the accuracy and effectiveness of clinical surgery. Although several objective measurement techniques are currently available, they are found to be inconsistent with patients’ sensations. In recent years, computational fluid dynamics (CFD) has become a novel technique to objectively assess nasal airflow by simulating the nasal airflow of NAO patients. Existing literature has also shown the potential to correlate relevant CFD parameters with patients’ sensations. Nevertheless, there is still debate on the numerical setup for an accurate solution and the CFD parameter to correlate with the subjective measurement. Based on these existing issues, we mainly investigated the boundary configuration (with and without the external nose), the usage of turbulence/laminar models, the usage of steady-state/transient solvers, and briefly discussed the potential of using unilateral pressure drop ratio as a parameter for NAO detection. 

To begin with, including the external nose in the nasal airflow simulation is recommended in the nasal airflow simulations. The external nose configuration can affect the flow direction through the nostrils and downstream flow distributions. However, the static (and total) pressure drop only shows a 4% difference compared to the commonly-used plane-truncated boundary configuration. Furthermore, using the laminar model is sufficient for the nasal airflow simulations concerning the static pressure drop prediction. The laminar model shows a difference lower than 15% in static pressure drop compared to the experimental values on 3D-printed nasal airway models. We stress the caution of using the 𝑘 − 𝜔 model in the nasal airflow simulations because it tends to overpredict the turbulent viscosity ratio near the inlet unphysically. Moreover, steady-state simulations can also reasonably predict nasal airflow. We observed unsteady effects when comparing the steady-state simulation with the transient simulation with a constant flow rate and the transient simulation with a sinusoidal flow-rate-versus-time profile representing the real-life breathing cycle. Nevertheless, the steady-state simulation achieves an accurate prediction in static pressure drop, with a difference lower than 6% compared to the tested transient simulations. The steady-state simulation can also perfectly match transient simulations in the velocity profile of the recirculation zones and require a much lower computational cost. Last but not least, we also tested the possibility of using the unilateral static pressure drop ratio for NAO detection using CFD. However, we note that future studies should make corrections to account for the nasal cycle effect for NAO detection. 

Overall, we conclude that including the external nose and using the laminar simulations with the steady-state solver can give an acceptable prediction for the nasal airflow, especially concerning the static pressure drop prediction. We also state that applying CFD in the nasal airflow shows the potential for NAO detection, although future studies may consider making some corrections to include the nasal cycle effect.