Enhancing Drug Delivery Precision: Development and Optimization of Nanoparticle-Based Formulations for Targeted Therapy in Preclinical Models

In recent years, the utilization of nanoparticles has proliferated across a wide spectrum of clinical domains. Nanoparticles have been engineered to surmount the constraints associated with free therapeutics and negotiate biological barriers—systemic, microenvironmental, and cellular—that exhibit heterogeneity across diverse patient cohorts and diseases. Mitigating this patient heterogeneity has also been facilitated through precision therapeutics, where tailored interventions have augmented therapeutic effectiveness. Nonetheless, current nanoparticle development predominantly emphasizes the refinement of delivery platforms with a uniform approach. As lipid-based, polymeric, and inorganic nanoparticles undergo increasingly nuanced engineering, there arises the potential for tailoring them to drug delivery in a more personalized manner, ushering in the era of precision medicine. In this Review, we deliberate on sophisticated nanoparticle designs employed in both generalized and precision applications, offering insights into their potential for enhancing precision therapies. We concentrate on advancements in nanoparticle design that surmount heterogeneous barriers to delivery, positing that intelligent nanoparticle design can enhance efficacy in broad delivery applications while facilitating customized designs for precision applications, thereby ultimately enhancing overall patient outcomes

Enhancing Drug Delivery Precision: Development and Optimization of Nanoparticle-Based Formulations for Targeted Therapy in Preclinical Models

In recent years, the utilization of nanoparticles has proliferated across a wide spectrum of clinical domains. Nanoparticles have been engineered to surmount the constraints associated with free therapeutics and negotiate biological barriers—systemic, microenvironmental, and cellular—that exhibit heterogeneity across diverse patient cohorts and diseases. Mitigating this patient heterogeneity has also been facilitated through precision therapeutics, where tailored interventions have augmented therapeutic effectiveness. Nonetheless, current nanoparticle development predominantly emphasizes the refinement of delivery platforms with a uniform approach. As lipid-based, polymeric, and inorganic nanoparticles undergo increasingly nuanced engineering, there arises the potential for tailoring them to drug delivery in a more personalized manner, ushering in the era of precision medicine. In this Review, we deliberate on sophisticated nanoparticle designs employed in both generalized and precision applications, offering insights into their potential for enhancing precision therapies. We concentrate on advancements in nanoparticle design that surmount heterogeneous barriers to delivery, positing that intelligent nanoparticle design can enhance efficacy in broad delivery applications while facilitating customized designs for precision applications, thereby ultimately enhancing overall patient outcomes

Precise detection and localization of R-peaks from ECG signals

Heart rate variability (HRV) is derived from the R-R interval, which depends on the precise localization of R-peaks within an electrocardiogram (ECG) signal. However, current algorithm assessment methods prioritize the R-peak detection’s sensitivity rather than the precision of pinpointing the exact R-peak positions. As a result, it is of great value to develop an R-peak detection algorithm with high-precision R-peak localization. This paper introduces a novel R-peak localization algorithm that involves modifications to the well-established Pan-Tompkins (PT) algorithm. The algorithm was implemented as follows. First, the raw ECG signal X(i) was band-pass filtered (5–35 Hz) to obtain a preprocessed signal Y(i). Second, Y(i) was squared to enhance the QRS complex, followed by a 5 Hz low-pass filter to obtain the QRS envelope, which was transformed into a window signal W(i) by dynamic threshold with a minimum width of 200 ms to mark the QRS complex. Third, Y(i) was used to generate QRS template T(n) automatically, and then the R-peak was identified by a template matching process to find the maximum absolute value of all cross-correlation values between T(n) and Y(i). The proposed algorithm achieved a sensitivity (SE) of 99.78%, a positive prediction value (PPV) of 99.78% and data error rate (DER) of 0.44% in R-peak localization for the MIT-BIH Arrhythmia database. The annotated-detected error (ADE), which represents the error between the annotated R-peak location and the detected R-peak location, was 8.35 ms for the MIT-BIH Arrhythmia database. These results outperformed the results obtained using the classical Pan-Tompkins algorithm which yielded an SE of 98.87%, a PPV of 99.14%, a DER of 1.98% and an ADE of 21.65 ms for the MIT-BIH Arrhythmia database. It can be concluded that the algorithm can precisely detect the location of R-peaks and may have the potential to enhance clinical applications of HRV analysis.</p

Precise detection and localization of R-peaks from ECG signals

Heart rate variability (HRV) is derived from the R-R interval, which depends on the precise localization of R-peaks within an electrocardiogram (ECG) signal. However, current algorithm assessment methods prioritize the R-peak detection’s sensitivity rather than the precision of pinpointing the exact R-peak positions. As a result, it is of great value to develop an R-peak detection algorithm with high-precision R-peak localization. This paper introduces a novel R-peak localization algorithm that involves modifications to the well-established Pan-Tompkins (PT) algorithm. The algorithm was implemented as follows. First, the raw ECG signal X(i) was band-pass filtered (5–35 Hz) to obtain a preprocessed signal Y(i). Second, Y(i) was squared to enhance the QRS complex, followed by a 5 Hz low-pass filter to obtain the QRS envelope, which was transformed into a window signal W(i) by dynamic threshold with a minimum width of 200 ms to mark the QRS complex. Third, Y(i) was used to generate QRS template T(n) automatically, and then the R-peak was identified by a template matching process to find the maximum absolute value of all cross-correlation values between T(n) and Y(i). The proposed algorithm achieved a sensitivity (SE) of 99.78%, a positive prediction value (PPV) of 99.78% and data error rate (DER) of 0.44% in R-peak localization for the MIT-BIH Arrhythmia database. The annotated-detected error (ADE), which represents the error between the annotated R-peak location and the detected R-peak location, was 8.35 ms for the MIT-BIH Arrhythmia database. These results outperformed the results obtained using the classical Pan-Tompkins algorithm which yielded an SE of 98.87%, a PPV of 99.14%, a DER of 1.98% and an ADE of 21.65 ms for the MIT-BIH Arrhythmia database. It can be concluded that the algorithm can precisely detect the location of R-peaks and may have the potential to enhance clinical applications of HRV analysis.</p

Estimation of the Respiratory Rate from Localised ECG at Different Auscultation Sites

The respiratory rate (RR) is a vital physiological parameter in prediagnosis and daily monitoring. It can be obtained indirectly from Electrocardiogram (ECG) signals using ECG-derived respiration (EDR) techniques. As part of the study in designing an early cardiac screening system, this work aimed to study whether the accuracy of ECG derived RR depends on the auscultation sites. Experiments were conducted on 12 healthy subjects to obtain simultaneous ECG (at auscultation sites and Lead I as reference) and respiration signals from a microphone close to the nostril. Four EDR algorithms were tested on the data to estimate RR in both the time and frequency domain. Results reveal that: (1) The location of the ECG electrodes between auscultation sites does not impact the estimation of RR, (2) baseline wander and amplitude modulation algorithms outperformed the frequency modulation and band-pass filter algorithms, (3) using frequency domain features to estimate RR can provide more accurate RR except when using the band-pass filter algorithm. These results pave the way for ECG-based RR estimation in miniaturised integrated cardiac screening device

BSSLAB Localized ECG Data

Localized ECG at PCG Auscultation Sites including Respiration Data description: All the data were saved in ‘.mat’ format, and the sampling frequency was 20kHz. There are 12 folders including 12 subjects’ data. In each folder, there are 4 .mat files, named as 2-A, 1-AM, 1-AP, and 1-AT. ‘2’ and ‘1’ means experimental stage I and stage II. ‘A’, ‘P’, ‘T’, and ‘M’ means the measured auscultation sites. ‘AM’ means both two sites PCG and ECG were measured synchronously. Experimental setting: In stage I, three gel electrodes were positioned over the chest of a subject with standard Lead I as reference. The embroidered electrodes were placed next to the gel ones with Lead I as well. The other two simultaneous ECG were recorded with 10 cm inter-electrode distance (IED) at each auscultation site. Two electronic stethoscopes were put at the centre of the electrodes (auscultation points). The recording duration was 3 mins. In the meantime, a microphone was placed under the subjects’ nose to record the actual respiration as a reference. In stage II, three groups of disposable adhesive ECG sensors were placed at A site with 5 cm, 10 cm, and 15 cm IED. The recording duration was also 3 mins. More details and block diagrams can be found in [1, 2, 3]. Channel description: Stage I channels: 1: time 2: Heart Sound at site A 3: Heart Sound at site M, P, or T 4: Respiratory Signal (raw) 5: ECG Reference (Lead I) 6: ECG embroidered 7: ECG at A 8: ECG at M, P, or T. 9. Respiratory Signal (filtered) Stage II channels: 1: time 2. Null 3. Null 4. Respiratory Signal (raw) 5. ECG 5 cm 6. Null 7. ECG 10 cm 8. ECG 15 cm 9. Respiratory Signal (filtered) Reference: Bao, X., Deng, Y., Gall, N. and Kamavuako, E.N., 2020, February. Analysis of ECG and PCG Time Delay around Auscultation Sites. In BIOSIGNALS (pp. 206-213). Bao, X., Howard, M., Niazi, I.K. and Kamavuako, E.N., 2020, July. Comparison between embroidered and gel electrodes on ECG-derived respiration rate. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC) (pp. 2622-2625). IEEE. Bao, X., Abdala, A.K. and Kamavuako, E.N., 2020. Estimation of the Respiratory Rate from Localised ECG at Different Auscultation Sites. Sensors, 21(1), p.78

The Effect of Signal Duration on the Classification of Heart Sounds:A Deep Learning Approach

Deep learning techniques are the future trend for designing heart sound classification methods, making conventional heart sound segmentation dispensable. However, despite using fixed signal duration for training, no study has assessed its effect on the final performance in detail. Therefore, this study aims at analysing the duration effect on the commonly used deep learning methods to provide insight for future studies in data processing, classifier, and feature selection. The results of this study revealed that (1) very short heart sound signal duration (1 s) weakens the performance of Recurrent Neural Networks (RNNs), whereas no apparent decrease in the tested Convolutional Neural Network (CNN) model was found. (2) RNN outperformed CNN using Mel-frequency cepstrum coefficients (MFCCs) as features. There was no difference between RNN models (LSTM, BiLSTM, GRU, or BiGRU). (3) Adding dynamic information (∆ and ∆²MFCCs) of the heart sound as a feature did not improve the RNNs’ performance, and the improvement on CNN was also minimal (≤2.5% in MAcc). The findings provided a theoretical basis for further heart sound classification using deep learning techniques when selecting the input length

Surface EMG Statistical and Performance Analysis of Targeted-Muscle-Reinnervated (TMR) Transhumeral Prosthesis Users in Home and Laboratory Settings

A pattern-recognition (PR)-based myoelectric control system is the trend of future prostheses development. Compared with conventional prosthetic control systems, PR-based control systems provide high dexterity, with many studies achieving &gt;95% accuracy in the last two decades. However, most research studies have been conducted in the laboratory. There is limited research investigating how EMG signals are acquired when users operate PR-based systems in their home and community environments. This study compares the statistical properties of surface electromyography (sEMG) signals used to calibrate prostheses and quantifies the quality of calibration sEMG data through separability indices, repeatability indices, and correlation coefficients in home and laboratory settings. The results demonstrate no significant differences in classification performance between home and laboratory environments in within-calibration classification error (home: 6.33 &plusmn; 2.13%, laboratory: 7.57 &plusmn; 3.44%). However, between-calibration classification errors (home: 40.61 &plusmn; 9.19%, laboratory: 44.98 &plusmn; 12.15%) were statistically different. Furthermore, the difference in all statistical properties of sEMG signals is significant (p &lt; 0.05). Separability indices reveal that motion classes are more diverse in the home setting. In summary, differences in sEMG signals generated between home and laboratory only affect between-calibration performance