ABSTRACT
Liquid self-nano emulsifying drug delivery systems (SNEDDS) of furosemide (FSM) have been explored as a potential solution for enhancing solubility and permeability but are associated with rapid emulsification, spontaneous drug release, and poor in vivo correlation. To overcome the shortcoming, this study aimed to develop liquid and solid self-emulsifying drug delivery systems for FSM, compare formulation dynamics, continue in vivo therapeutic efficacy, and investigate the advantages of solidification. For this purpose, liquid SNEDDS (L-SEDDS-FSM) were formed using oleic acid as an oil, chremophore EL, Tween 80, Tween 20 as a surfactant, and PEG 400 as a co-surfactant containing 53 mg/mL FSM. At the same time, solid SNEDDS (S-SEDDS-FSM) was developed by adsorbing liquid SNEDDS onto microcrystalline cellulose in a 1:1 ratio. Both formulations were evaluated for size, zeta potential, lipase degradation, and drug release. Moreover, in vivo diuretic studies regarding urine volume were carried out in mice to investigate the therapeutic responses of liquid and solid SNEDDS formulations. After dilution, L-SEDDS-FSM showed a mean droplet size of 115 ± 4.5 nm, while S-SEDDS-FSM depicted 116 ± 2.6 nm and zeta potentials of -5.4 ± 0.55 and -6.22 ± 1.2, respectively. S-SEDDS-FSM showed 1.8-fold reduced degradation by lipase enzymes in comparison to L-SEDDS-FSM. S-SEDDS-FSM demonstrated a sustained drug release pattern, releasing 63% of the drug over 180 min, in contrast to L-SEDDS-FSM, exhibiting 90% spontaneous drug release within 30 min. L-SEDDS-FSM exhibited a rapid upsurge in urine output (1550 ± 56 µL) compared to S-SEDDS-FSM, showing gradual urine output (969 ± 29 µL) till the 4th h of the study, providing sustained urine output yet a predictable therapeutic response. The solidification of SNEDDS effectively addresses challenges associated with spontaneous drug release and precipitation observed in liquid SNEDDS, highlighting the potential benefits of solid SNEDDS in improving the therapeutic response of furosemide.
ABSTRACT
This paper presents a novel sound event detection (SED) system for rare events occurring in an open environment. Wavelet multiresolution analysis (MRA) is used to decompose the input audio clip of 30 seconds into five levels. Wavelet denoising is then applied on the third and fifth levels of MRA to filter out the background. Significant transitions, which may represent the onset of a rare event, are then estimated in these two levels by combining the peak-finding algorithm with the K-medoids clustering algorithm. The small portions of one-second duration, called 'chunks' are cropped from the input audio signal corresponding to the estimated locations of the significant transitions. Features from these chunks are extracted by the wavelet scattering network (WSN) and are given as input to a support vector machine (SVM) classifier, which classifies them. The proposed SED framework produces an error rate comparable to the SED systems based on convolutional neural network (CNN) architecture. Also, the proposed algorithm is computationally efficient and lightweight as compared to deep learning models, as it has no learnable parameter. It requires only a single epoch of training, which is 5, 10, 200, and 600 times lesser than the models based on CNNs and deep neural networks (DNNs), CNN with long short-term memory (LSTM) network, convolutional recurrent neural network (CRNN), and CNN respectively. The proposed model neither requires concatenation with previous frames for anomaly detection nor any additional training data creation needed for other comparative deep learning models. It needs to check almost 360 times fewer chunks for the presence of rare events than the other baseline systems used for comparison in this paper. All these characteristics make the proposed system suitable for real-time applications on resource-limited devices.
