Synthesis and characterization of photocatalytic polyurethane and poly(methyl methacrylate) microcapsules for the controlled release of methotrexate.

The aim of this work is to prepare ultraviolet (UV) triggered controlled release of compounds from microcapsule systems (MCs). Polyurethane (PU) and poly(methyl methacrylate) (PMMA) microcapsules were studied with/without chemical functionalization using photocatalytic TiO2 nanoparticles (NPs) on their surface. Once TiO2 nanoparticles are illuminated with UV light (λ = 370 nm), they initiate the rupture of the polymeric bonds of the microcapsule and subsequently initiate the encapsulated compound release, methotrexate (MTX) or rhodamine (Rh), in the present work. The size, polydispersity, charge, and yield of all MCs were measured, being the methotrexate drug release for all systems determined and compared with and without functionalization with TiO2 NPs, under dark, visible light and UV illumination in vitro. Finally, the Rh release was characterized using fluorescence microscopy. The TiO2 NPs size is around 10 nm, as determined by X-ray diffraction experiments. The PU MCs average size is around 60 µm, its electric charge +3.11 mV and yield around 85%. As for the PMMA MCs, the average size is around 280 µm, its electric charge -7.2 mV and yield around 25% and 30% for both MTX and Rh, respectively. In general, adding TiO2 NPs or the encapsulated products to the MCs does not affect the size but functionalization with TiO2 NPs lowers the electric charge. Microcapsules functionalized with TiO2 nanoparticles and irradiated with UV light presented the highest release of MTX and Rh. All other samples showed lower drug release levels when studied under the same conditions.

A Comprehensive Analysis of Liposomal-Based Nanocarriers for Treating Skin and Soft Tissue Infection.

Bacterial skin and soft tissue infections (SSTIs) are widespread microbic invasions of the skin and deeper tissues. Topical drug delivery systems are the most favored administration pathway when treating SSTIs. This is down to their minimal risk of inducing systemic adverse events, reduced development of bacterial resistance, and ease of application. However, they have several drawbacks, including the lack of control over the drug release profile, skin irritations, and the limited permeability of certain compounds through the skin. To address these limitations, several nanocarrier systems were developed, with nanoliposomes standing out as the leading delivery system for the topical management of SSTIs. Despite considerable research into liposomes over the past decade, there remains a gap in detailed knowledge about designing these carriers specifically for SSTIs. Consequently, there is a pressing need for comprehensive research that focuses on the use of nanoliposomes for SSTIs and offers an extensive understanding of both SSTIs and liposomal formulations. This review explores bacterial SSTIs, covering their epidemiology, classification, microbiology, and management. It emphasizes the contribution of liposome-based nanovesicles in enhancing the local administration of antibiotics and natural antibacterial compounds for SSTI management. It also delves into the effects of liposomal formulation changes on the disease therapeutic outcomes. Additionally, it provides a guide for aligning the characteristics of the liposomes with the infection types, depths, properties, and causative agents. This signifies a substantial leap forward in the domains of drug design, development, and delivery.

Seizure classification in EEG signals utilizing Hilbert-Huang transform.

BACKGROUND: Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals. METHOD: Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. RESULTS: The t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use. CONCLUSION: An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.

An alternative respiratory sounds classification system utilizing artificial neural networks.

BACKGROUND: Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. METHODS: This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) toolboxes. The methods have been applied to 10 different respiratory sounds for classification. RESULTS: The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. CONCLUSIONS: The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.

A PC-aided optical foetal heart rate detection system.

Safe monitoring of foetal heart rate is a valuable tool for the healthy evolution and wellbeing of both foetus and mother. This paper presents a non-invasive optical technique that allows for foetal heart rate detection using a photovoltaic infrared (IR) detector placed on the mother's abdomen. The system presented here consists of a photoplethysmography (PPG) circuit, abdomen circuit and a personal computer equipped with MATLAB. A near IR beam having a wavelength of 880 nm is transmitted through the mother's abdomen and foetal tissue. The received abdominal signal that conveys information pertaining to the mother and foetal heart rate is sensed by a low noise photodetector. The PC receives the signal through the National Instrumentation Data Acquisition Card (NIDAQ). After synchronous detection of the abdominal and finger PPG signals, the designed MATLAB-based software saves, analyses and extracts information related to the foetal heart rate. Extraction is carried out using recursive least squares adaptive filtration. Measurements on eight pregnant women with gestational periods ranging from 35-39 weeks were performed using the proposed system and CTG. Results show a correlation coefficient of 0.978 and a correlation confidence interval between 88-99.6%. The t test results in a p value of 0.034, which is less than 0.05. Low power, low cost, high signal-to-noise ratio, reduction of ambient light effect and ease of use are the main characteristics of the proposed system.