The complexity of aortic diseases demands sophisticated modeling approaches to better understand their pathophysiology and optimize treatment strategies [...].
The complexity of aortic diseases demands sophisticated modeling approaches to better understand their pathophysiology and optimize treatment strategies [...].
Liver failure is the 12th leading cause of death worldwide. Protein-bound toxins such as bilirubin are responsible for many complications of the disease. Binder dialysis systems use albumin or another binding molecule in dialysate and detoxifying sorbent columns to remove these toxins. Systems like the molecular adsorbent recirculating system and BioLogic-DT have existed since the 1990s, but survival benefits in randomized controlled trials have not been consistent. New binder dialysis systems, including open albumin dialysis and the Advanced Multi-Organ Replacement system, are being developed. Optimal conditions for binder dialysis have not been established. We developed and validated a computational model of bound solute dialysis. It predicted the impact of changing between two test setups using different polysulfone dialyzers (F3 and F6HPS). We then predicted the impact of varying the dialysate flow rate on toxin removal. We found that bilirubin removal declines with dialysate flow rate. This can be explained through a linear decline in free bilirubin membrane permeability. Our model quantifies this decline through a single parameter (polysulfone dialyzers). Validation for additional dialyzers and flow rates will be needed. This model will benefit clinical trials by predicting optimal dialyzer and flow rate conditions. Accounting for toxin adsorption onto the dialyzer membrane may improve results further.
Since the end of the 20th century, when the first eukaryotic organism was sequenced, genome sequencing as a technique has made incredible progress, and today new methods allow sequencing different genomes and genes at relatively low prices and in a short time [...].
Room reverberation can affect oral/aural communication and is especially critical in computer analysis of voice. High levels of reverberation can distort voice recordings, impacting the accuracy of quantifying voice production quality and vocal health evaluations. This study quantifies the impact of additive simulated reverberation on otherwise clean voice recordings as reflected in voice metrics commonly used for voice quality evaluation. From a larger database of voice recordings collected in a low-noise, low-reverberation environment, voice samples of a sustained [a:] vowel produced at two different speaker intents (comfortable and clear) by five healthy voice college-age female native English speakers were used. Using the reverb effect in Audacity, eight reverberation situations indicating a range of reverberation times (T20 between 0.004 and 1.82 s) were simulated and convolved with the original recordings. All voice samples, both original and reverberation-affected, were analyzed using freely available PRAAT software (version 6.0.13) to calculate five common voice parameters: jitter, shimmer, harmonic-to-noise ratio (HNR), alpha ratio, and smoothed cepstral peak prominence (CPPs). Statistical analyses assessed the sensitivity and variations in voice metrics to a range of simulated room reverberation conditions. Results showed that jitter, HNR, and alpha ratio were stable at simulated reverberation times below T20 of 1 s, with HNR and jitter more stable in the clear vocal style. Shimmer was highly sensitive even at T20 of 0.53 s, which would reflect a common room, while CPPs remained stable across all simulated reverberation conditions. Understanding the sensitivity and stability of these voice metrics to a range of room acoustics effects allows for targeted use of certain metrics even in less controlled environments, enabling selective application of stable measures like CPPs and cautious interpretation of shimmer, ensuring more reliable and accurate voice assessments.
Image-guided mouse irradiation is essential to understand interventions involving radiation prior to human studies. Our objective is to employ Swin UNEt TRansformers (Swin UNETR) to segment native micro-CT and contrast-enhanced micro-CT scans and benchmark the results against 3D no-new-Net (nnU-Net). Swin UNETR reformulates mouse organ segmentation as a sequence-to-sequence prediction task using a hierarchical Swin Transformer encoder to extract features at five resolution levels, and it connects to a Fully Convolutional Neural Network (FCNN)-based decoder via skip connections. The models were trained and evaluated on open datasets, with data separation based on individual mice. Further evaluation on an external mouse dataset acquired on a different micro-CT with lower kVp and higher imaging noise was also employed to assess model robustness and generalizability. The results indicate that Swin UNETR consistently outperforms nnU-Net and AIMOS in terms of the average dice similarity coefficient (DSC) and the Hausdorff distance (HD95p), except in two mice for intestine contouring. This superior performance is especially evident in the external dataset, confirming the model's robustness to variations in imaging conditions, including noise and quality, and thereby positioning Swin UNETR as a highly generalizable and efficient tool for automated contouring in pre-clinical workflows.
Background: Head and neck reconstruction following ablative surgery results in alterations to maxillofacial anatomy and function. These postoperative changes complicate dental rehabilitation. Methods: An innovative modular, stackable guide system for immediate dental rehabilitation during mandibular reconstruction is presented. The virtual surgical planning was performed in Materialise Innovation Suite v26 and Blender 3.6 with the Blenderfordental add-on. The surgical guides and models were designed and manufactured at the point of care. Results: The duration of the surgery was 9 h and 35 min. Good implant stability (>35 Ncm) and a stable occlusion were achieved. After 9 months of follow-up, the occlusion remained stable, and a mouth opening of 25 mm was registered. The dental implants showed no signs of peri-implant bone loss. Superposition of the preoperative planning and postoperative position of the fibula parts resulted in an average difference of 0.70 mm (range: -1.9 mm; 5.4 mm). Conclusions: The in-house developed stackable guide system resulted in a predictive workflow and accurate results. The preoperative virtual surgical planning was time-consuming and required extensive CAD/CAM and surgical expertise. The addition of fully guided implant placement to this stackable guide system would be beneficial. More research with longer follow-ups is necessary to validate these results.
A battery-operated biomedical wearable device gradually assists in clinical tasks to monitor patients' health states regarding early diagnosis and detection. This paper presents the development of a self-powered portable electronic module by integrating an onboard energy-harvesting facility for electrocardiogram (ECG) signal processing and personalized health monitoring. The developed electronic module provides a customizable approach to power the device using a lithium-ion battery, a series of silicon photodiode arrays, and a solar panel. The new architecture and techniques offered by the developed method include an analog front-end unit, a signal processing unit, and a battery management unit for the acquiring and processing of real-time ECG signals. The dynamic multi-level wavelet packet decomposition framework has been used and applied to an ECG signal to extract the desired features by removing overlapped and repeated samples from an ECG signal. Further, a random forest with deep decision tree (RFDDT) architecture has been designed for offline ECG signal classification, and experimental results provide the highest accuracy of 99.72%. One assesses the custom-developed sensor by comparing its data with those of conventional biosensors. The onboard energy-harvesting and battery management circuits are designed with a BQ25505 microprocessor with the support of silicon photodiodes and solar cells which detect the ambient light variations and provide a maximum of 4.2 V supply to enable the continuous operation of an entire module. The measurements conducted on each unit of the proposed method demonstrate that the proposed signal-processing method significantly reduces the overlapping samples from the raw ECG data and the timing requirement criteria for personalized and wearable health monitoring. Also, it improves temporal requirements for ECG data processing while achieving excellent classification performance at a low computing cost.
Anxiety is a widespread mental health issue, and binaural beats have been explored as a potential non-invasive treatment. EEG data reveal changes in neural oscillation and connectivity linked to anxiety reduction; however, harmonics introduced during signal acquisition and processing often distort these findings. Existing methods struggle to effectively reduce harmonics and capture the fine-grained temporal dynamics of EEG signals, leading to inaccurate feature extraction. Hence, a novel Denoised Harmonic Subtraction and Transient Temporal Feature Extraction is proposed to improve the analysis of the impact of binaural beats on anxiety levels. Initially, a novel Wiener Fused Convo Filter is introduced to capture spatial features and eliminate linear noise in EEG signals. Next, an Intrinsic Harmonic Subtraction Network is employed, utilizing the Attentive Weighted Least Mean Square (AW-LMS) algorithm to capture nonlinear summation and resonant coupling effects, effectively eliminating the misinterpretation of brain rhythms. To address the challenge of fine-grained temporal dynamics, an Embedded Transfo XL Recurrent Network is introduced to detect and extract relevant parameters associated with transient events in EEG data. Finally, EEG data undergo harmonic reduction and temporal feature extraction before classification with a cross-correlated Markov Deep Q-Network (DQN). This facilitates anxiety level classification into normal, mild, moderate, and severe categories. The model demonstrated a high accuracy of 95.6%, precision of 90%, sensitivity of 93.2%, and specificity of 96% in classifying anxiety levels, outperforming previous models. This integrated approach enhances EEG signal processing, enabling reliable anxiety classification and offering valuable insights for therapeutic interventions.
Background: Indirect calorimetry is the gold standard field-testing technique for measuring energy expenditure and exercise intensity based on the volume of oxygen consumed (VO2, mL O2/min). Although heart rate is often used as a proxy for VO2, heart rate-based estimates of VO2 may be inaccurate after stroke due to changes in the heart rate-VO2 relationship. Our objective was to evaluate in people post stroke the accuracy of using heart rate to estimate relative walking VO2 (wVO2) and classify exercise intensity. Moreover, we sought to determine if estimation accuracy could be improved by including clinical variables related to patients' function and health in the estimation.
Methods: Sixteen individuals post stroke completed treadmill walking exercises with concurrent indirect calorimetry and heart rate monitoring. Using 70% of the data, forward selection regression with repeated k-fold cross-validation was used to build wVO2 estimation equations that use heart rate alone and together with clinical variables available at the point-of-care (i.e., BMI, age, sex, and comfortable walking speed). The remaining 30% of the data were used to evaluate accuracy by comparing (1) the estimated and actual wVO2 measurements and (2) the exercise intensity classifications based on metabolic equivalents (METs) calculated using the estimated and actual wVO2 measurements.
Results: Heart rate-based wVO2 estimates were inaccurate (MAE = 3.11 mL O2/kg/min) and unreliable (ICC = 0.68). Incorporating BMI, age, and sex in the estimation resulted in improvements in accuracy (MAE Δ: -36.01%, MAE = 1.99 mL O2/kg/min) and reliability (ICC Δ: +20, ICC = 0.88). Improved exercise intensity classifications were also observed, with higher accuracy (Δ: +29.85%, from 0.67 to 0.87), kappa (Δ: +108.33%, from 0.36 to 0.75), sensitivity (Δ: +30.43%, from 0.46 to 0.60), and specificity (Δ: +17.95%, from 0.78 to 0.92).
Conclusions: In people post stroke, heart rate-based wVO2 estimations are inaccurate but can be substantially improved by incorporating clinical variables readily available at the point of care.
This study aimed to evaluate the efficacy of a novel three-dimensional (3D) spinal decompression and correction device in improving the in-brace correction and patient comfort level for adolescents with idiopathic scoliosis (AIS), and to assess the impact of the number of vertebrae involved in the scoliotic curve on the correction's effectiveness. A single-centre, single-blinded randomized controlled trial (RCT) was conducted in 110 AIS patients aged 10-18 years who were randomly allocated into four groups receiving 0-3 days of device intervention. Each session lasted for 30 min and was conducted twice daily. Significant improvements were observed in both the in-brace correction ratio and patient comfort level, particularly in the 2- and 3-day intervention groups (p < 0.001). The number of involved vertebrae for a scoliotic curve was positively correlated with the in-brace correction ratio in the no intervention (or 0-day) and 1-day intervention groups, while this correlation varied in the 2- and 3-day intervention groups. These findings suggested that the prolonged use of the 3D device could improve the correction ratios and patient comfort, while the role of vertebrae involvement in predicting the initial correction may require further exploration to optimize personalized treatment strategies in future clinical practice.